Sand and Dust Storms Compendium.

Sand and
Dust Storms
Compendium
Information and Guidance
on Assessing and Addressing
the Risks

The United Nations Convention to Combat Desertification (UNCCD) is an international agreement on good
land stewardship. It helps people, communities and countries create wealth, grow economies and secure
enough food, clean water and energy by ensuring land users an enabling environment for sustainable land
management. Through partnerships, the Convention’s 197 parties set up robust systems to manage drought
promptly and effectively. Good land stewardship based on sound policy and science helps integrate and
accelerate achievement of the Sustainable Development Goals, builds resilience to climate change and
prevents biodiversity loss.
Compendium supporting and contributing partners
© Maps, photos and illustrations as specified.
Published in 2022 by UNCCD, Bonn, Germany.
Secretariat of the United Nations Convention to Combat Desertification (UNCCD)
Platz der Vereinten Nationen, 53113 Bonn, Germany
Tel: +49-228 / 815-2800
Fax: +49-228 / 815-2898/99
www.unccd.int
secretariat@unccd.int
Recommended citation:
United Nations Convention to Combat Desertification (UNCCD). 2022. Sand and Dust Storms Compendium:
Information and Guidance on Assessing and Addressing the Risks. Bonn, Germany.
National Forestry and
Grassland Administration
of P.R.China

Acknowledgements
The Sand and Dust Storms Compendium is a collaborative effort led by the Secretariat of the United Nations
Convention to Combat Desertification (UNCCD) in collaboration with the UNCCD Science-Policy Interface
(SPI), the World Meteorological Organization (WMO), the World Health Organization (WHO), the United
Nations Environment Programme (UNEP), UN Women, the Food and Agriculture Organization of the
United Nations (FAO), the United Nations Office for Disaster Risk Reduction (UNDRR), the United Nations
Development Programme (UNDP) and external experts and partners. UNCCD would like to thank the authors,
contributors and reviewers for their contributions to this Compendium.
Sand and Dust Storms Compendium team
Coordinator:
Utchang Kang
Co-editors:
Charles Kelly, Utchang Kang
Chapter lead authors:
Chapter 1 Charles Kelly, Utchang Kang
Chapter 2 Sara Basart
Chapter 3 Utchang Kang, Charles Kelly
Chapter 4 Charles Kelly
Chapter 6 Peter Tozer
Chapter 7 Ali Darvishi Boloorani, Alijafar Mousivand
Chapter 8 Ana Vukovic
Chapter 9 Enric Terradellas, Slobodan Nickovic, Alexander Baklanov
Chapter 10 Alexander Baklanov, Utchang Kang, Charles Kelly, Jochen Luther
Chapter 11 Pierpaolo Mudu, Sophie Gumy, Aurelio Tobías, Francesco Forastiere, Michal Krzyzanowski,
Massimo Stafoggia, Xavier Querol
Chapter 12 Utchang Kang, Gemma Shepherd
Graphic designer: Strategic Agenda
Photo editor: Corrina Voigt
Layout and design: Strategic Agenda

Chapter contributors:
• Alexander Baklanov (WMO), Xiao-Ye Zhang (China Meteorological Administration) and Utchang
Kang (UNCCD) contributed in part to Chapter 2.
• Verona Collantes (UN Women), Juan Carlos Villagran de Leon (United Nations Office for Outer
Space Affairs/United Nations Platform for Space-based Information for Disaster Management and
Emergency Response (UNOOSA/UN-SPIDER)) and Corinna Voigt (UNCCD) contributed in part to
Chapter 3.
• Bojan Cvetkovic (Republic Hydrometeorological Service of Serbia) contributed in part to Chapter 8.
• Abdoulaye Harou (WMO), Ata Hussain (WMO), Sang-Sam Lee (Korea Meteorological
Administration), Sang Boom Ryoo (Korea Meteorological Administration), Andrea Sealy (Caribbean
Institute for Meteorology and Hydrology), Robert Stefanski (WMO), Ernest Werner (State
Meteorological Agency of Spain), Chengyi Zhang (China Meteorological Administration) and Xiao-Ye
Zhang (China Meteorological Administration) contributed in part to Chapter 9.
• Miriam Andrioli (WMO), Samuel Muchemi (WMO) and Juan Carlos Villagran de Leon (UNOOSA-
SPIDER) contributed in part to Chapter 10.
• Stephan Baas (FAO), Sophie Charlotte VonLoeben (FAO) and Feras Ziadat (FAO) contributed in part
to Chapter 12.
• Nick Middleton (Oxford University) contributed in part to Chapter 13.
External reviewers
Andrew Goudie (University of Oxford), William Sprigg (University of Arizona), Ali Al-Homood (Kuwait Institute
for Scientific Research), Moutaz Al-Dabbas (Baghdad University) and Guosheng Wang (China National
Desertification Monitoring Centre).
We acknowledge the interactive discussions and valuable inputs from the SDS Technical Guide Writeshop
co-organized by UNCCD and WMO on 1–2 October 2018 in Geneva. Participants included Maliheh Birjandi
(independent), Jose Camacho (WMO), Hossein Fadaei (UN Environment Management Group/UNEP), Cyrille
Honoré (WMO), Maarten Kappelle (UNEP), Jungrack Kim (University of Seoul), Jochen Luther (WMO),
Samuel Muchemi (WMO), Letizia Rossano (United Nations Economic and Social Commission for Asia and
the Pacific/Asian and Pacific Centre for the Development of Disaster Information Management (UNESCAP/
APDIM)), Paolo Ruti (WMO), Joy Shumake-Guillemot (WMO/WHO Climate and Health Joint Office) and
Sanjay Srivastava (UNESCAP).
Throughout the process, technical advice was provided by Sasha Alexander (UNCCD), Louise Baker
(UNCCD), Ismail Binahla (UNCCD), Ali Al-Dousari (Kuwait Institute for Scientific Research), Cihan Dündar
(Turkish State Meteorological Service), Erkan Guler (UNCCD), Xiaoxia Jia (UNCCD), Maarten Kappelle
(UNEP), Qi Lu (Institute of Desertification Studies, China), Miriam Medel (UNCCD), Barron Orr (UNCCD),
Rahul Sengupta (UNDRR) and David Stevens (UNDRR). Verona Collantes (UN Women) and Corinna Voigt
(UNCCD) contributed to a gender-based review of the Compendium.
UNCCD acknowledges that original copyright of Chapter 11 of the Compendium remains vested in WHO and
its permission to publish the chapter in the Compendium.
This Compendium was made possible by the generous financial support provided by the Government of the
Republic of Korea (Korea Forest Service) and the Government of the People’s Republic of China (National
Forestry and Grassland Administration).
As the topic of sand and dust storms is of great significance to the global community, a large number of
individuals have been either directly or indirectly involved in the process.

Disclaimers
The designations employed and the presentation of material in this information product
do not imply the expression of any opinion whatsoever on the part of the United Nations
Convention to Combat Desertification (UNCCD) concerning the legal or development status
of any country, territory, city or area or of its authorities, or concerning the delimitation of its
frontiers and boundaries. The mention of specific companies or products of manufacturers,
whether or not these have been patented, does not imply that these have been endorsed or
recommended by UNCCD in preference to others of a similar nature that are not mentioned.
The views expressed in this information product are those of the authors or contributors
and do not necessarily reflect the views or policies of UNCCD or the authors’ or contributors’
respective affiliated organizations.
UNCCD encourages the use, reproduction and dissemination of material in this information
product. Except where otherwise indicated, material may be copied, downloaded and
printed for private study, research and teaching purposes only, or for use in non-commercial
products or services, provided that appropriate acknowledgement of UNCCD as the source
and copyright holder is given and that UNCCD’s endorsement of users’ views, products
or services is not implied in any way. UNCCD would appreciate receiving a copy of any
publication that uses this publication as a source.
No use of this publication may be made for resale or for any other commercial purpose
whatsoever without prior permission in writing from the United Nations Convention to
Combat Desertification. Applications for such permission, with a statement of the purpose
and extent of the reproduction, should be addressed to the Executive Secretary, UNCCD, UN
Campus Platz der Vereinten Nationen 1, 53113 Bonn, Germany.
Monetary values cited in this document have not been adjusted for inflation or deflation to
2020 values, unless so noted.
Printed on FSC paper.
Cover photo: Alan Stark
ISBN 978-92-95118-10-2 (hard copy)
ISBN 978-92-95118-11-9 (e-copy)

matthieu-joannon-sBXVsMsfoEc-unsplash

Sand and Dust Storms Compendium.

Foreword
Sand and dust storms (SDS) are notoriously unpredictable and difficult to manage. This Compendium is
the first comprehensive publication that draws from the emerging science to offer the latest information
and knowledge on good practice, approaches and frameworks for combating SDS. As addressing the risks
posed by SDS and their impacts is an urgent issue requiring collective action, a collaborative approach has
been taken to developing this Compendium.
SDS are natural phenomena with multiple impacts on both the environment and people. The scale and scope
of these impacts vary from the local to the global, rapid to slow onset, tropics to the Arctic and the land to
oceans. Although some SDS impacts can be positive, unfortunately many are negative and highly damaging.
They include impacts on health, transportation, agriculture, air and water quality, and industrial production
and other sectors. Such impacts disrupt daily life in the affected areas, disregarding political or geographic
boundaries and affecting men and women, young and old alike.
The use of natural ecosystems by people – for example through agricultural and pastoral practices,
water use, soil management, deforestation and urbanization – can make the occurrence and impacts of
SDS worse. Climate change directly and indirectly intensifies these risks. Sustainable natural resource
management therefore has a role to play in addressing SDS.
Concerns about the impact of SDS are growing and the global community urgently needs to find effective
and coordinated solutions. Global efforts under the United Nations are now focused on two approaches.
Firstly, on source mitigation through sustainable land and water management, as encouraged by various
global policies, including land degradation neutrality under Sustainable Development Goal target 15.3. And
secondly, on the mitigation of negative impacts through preparedness and resilience measures, such as
early warning systems, response plans and prepared individuals.
This Compendium adds value to these initiatives by answering two critical questions: what can be done to
manage SDS and how?
For example, large-scale SDS emissions are best managed – indeed may possibly only be reduced – at
source, where risk reduction is a primary goal. This Compendium presents essential options for mitigating
risk and impact, including the management of anthropogenic sources, and its information and guidance is
based on disaster risk-reduction principles.
All stakeholders will find relevant and straightforward information that will help them boost their actions as
they learn more about SDS in this accessible and adaptable Compendium. It is a powerful tool for those who
are looking to make practical and meaningful change.
Ibrahim Thiaw
Executive Secretary, UNCCD

SDS challenges
Sand and dust storms (SDS) are given many local names: examples include the sirocco, haboob, yellow dust, white
storms, or the harmattan. They are a regionally common and seasonal natural phenomenon exacerbated by poor land
and water management, droughts, and climate change. The combination of strong winds and airborne mineral dust
particles can have significant impacts on human health and societies. Fluctuations in intensity, magnitude, or duration can
make SDS unpredictable and dangerous.
In some regions, SDS have increased dramatically in frequency in recent years. Human-induced climate change,
desertification, land degradation, and drought are all thought to play a role. While SDS can fertilize both land and marine
ecosystems, they also present a range of hazards to human health, livelihoods, and the environment. Impacts are observed
in both source regions, and distant areas affected directly and indirectly by surface dust deposits. The hazards associated
with SDS present a formidable challenge to achieving sustainable development.
SDS events do not usually result in extensive or catastrophic damage. However, the accumulation of impacts can be
significant. In source areas, they damage crops, kill livestock, and strip topsoil. In depositional areas atmospheric dust,
especially in combination with local industrial pollution, can cause or worsen human health problems such as respiratory
diseases. Communications, power generation, transport, and supply chains can also be disrupted by low visibility and
dust-induced mechanical failures.
SDS are not new phenomena – some regions of the world have long been exposed to SDS hazards. SDS events typically
originate in low-latitude drylands and subhumid areas where vegetation cover is sparse or absent. They can also occur in
other environments, including agricultural and high-latitude areas in humid regions, when specific wind and atmospheric
conditions coincide.
SDS events can have substantial transboundary impacts, over thousands of kilometres. Unified and coherent global and
regional policy responses are needed, especially to address source mitigation, early warning systems, and monitoring.
SDS impacts are multi-faceted, cross-sectoral and transnational, directly affecting 11 of the 17 Sustainable
Development Goals – yet global recognition of SDS as a hazard is generally low. The complexity and seasonally
cumulative impact of SDS, coupled with limited data, are contributary factors. Insufficient information and assessments
hinder effective decision-making and planning to effectively address SDS sources and impacts.
Key messages

SDS responses
The goal of SDS policy and planning is to reduce societal vulnerability by mitigating the effects of
wind erosion. A multi-sectoral process bolstered by information-sharing involves short- and long-term
interventions, engages multiple stakeholders, and raises awareness of SDS.
Source and impact mitigation activities are part of a comprehensive approach to manage the risks posed
by SDS, from local to regional and global scales. Local communities in source areas are directly affected
and will need to take very different actions to those impacted thousands of kilometres away. Engagement
and participation of all stakeholders is crucial to effective SDS
decision-making and policy, underpinned by up-to-date scientific knowledge.
Source mitigation: Land restoration, using soil and water management practices to protect soils and
increase vegetative cover, can significantly reduce the extent and vulnerability of source areas, and reduce
the intensity of typical SDS events. Such techniques are also vital for land degradation neutrality and when
integrated into sustainable development and land-use priorities, will contribute to food security, poverty
alleviation, gender equality and community cohesion as well as SDS mitigation goals.
Early warning and monitoring: Any effective SDS early warning system demands a whole-of-community
approach. Building on up-to-date risk knowledge, monitoring, and forecasting, all stakeholders (including at-
risk populations) participate to ensure that warnings are provided in a timely and targeted manner, and that
sector-appropriate actions are taken to reduce or avoid impacts.
Impact mitigation: Preparedness reduces vulnerability, increases resilience, and enables a timely and
effective response to SDS events. It involves individuals, communities and organizations as well as industry
and businesses. An effective preparedness strategy includes mitigation measures and protective actions
informed by robust science, vulnerability analyses, and risk assessments.
Cooperation, collaboration and coordination: The United Nations Coalition on Combating Sand and
Dust Storms was launched in September 2019 and has five working groups: adaptation and mitigation;
forecasting and early warning; health and safety; policy and governance; and mediation and regional
collaboration. The United Nations Coalition will help leverage a global response to SDS through collaboration
and cooperation from local to global levels, making the issue more visible, enhancing knowledge-sharing,
and mobilizing resources to upgrade existing efforts.

Death
Valley,
California,
©Marc
Cooper,
2016

Contents
Acknowledgement iv
Disclaimers vi
Foreword ix
Key Messages x
Chapter 1. Introduction 1
1.1 The challenge of sand and dust storms 2
1.2 United Nations System engagement on SDS 3
1.3 Compendium objectives and users 8
1.4 Content of the Compendium 8
1.5 References 10
Chapter 2. The nature of sand and dust storms
12
2.1 SDS definitions 14
2.2 Atmospheric aerosols 14
2.3 Soil-derived mineral dust in the Earth system 17
2.3.1 Dust source areas 17
2.3.2 Dust cycle and associated meteorological processes 21
2.3.3 Meteorological mechanismsinvolved in dust storms 23
2.3.4 Dust seasonality and inter-annual variations 27
2.4 Conclusions 29
2.5 References 30
Chapter 3. Sand and dust storms from a disaster management perspective 40
3.1 SDS as a natural hazard 42
3.2 Low recognition of the disaster potential of SDS 46
3.3 A comprehensive approach to SDS risk management 56
3.3.1 The disaster risk management overview 56
3.3.2 Global approach to SDS risk management 56
3.3.3 Risk knowledge 58
3.3.4 SDS source mapping and monitoring 60
3.3.5 SDS forecasting 60
3.3.6 Communication and dissemination of early warnings 61
3.3.7 Preparedness and response 62
3.3.8 Risk reduction 63
3.3.9 Anthropogenic source mitigation 63

3.4 Comprehensive approach to SDS risk management 64
3.5 Conclusion 68
3.6 References 69
Chapter 4. Assessing the risks posed by sand and dust storms
72
4.1 Assessing SDS disaster risks and impacts 74
4.2 SDS as hazards 76
4.2.1 SDS as composite hazards 76
4.2.2 Spatial coverage, intensity and duration of SDS 79
4.2.3 SDS frequency 80
4.2.4 SDS hazard source and impact areas 80
4.2.5 SDS hazard typology 81
4.3 Vulnerability to SDS 84
4.3.1 Defining vulnerability 84
4.3.2 Vulnerability to SDS 86
4.4 Assessing vulnerability to SDS 88
4.5 Conclusions 94
4.6 Web-based resources 94
4.6 References 97
Chapter 5. Sand and dust storms risk assessment framework 98
5.1 Assessing SDS disaster risks and impacts 100
5.2 Incorporating SDS source-area related risks 101
5.3 Comparing assessment processes 103
5.4 Scaling assessment results 104
5.5 Survey-based SDS assessment process 106
5.6 Expert-based sand and dust storms assessment process 111
5.7 Assigning confidence to results 115
5.8 Using risk assessment results 116
5.9 SDS survey questionnaire 117
5.9.1 Details of the model questionnaire 117
5.9.2 Sample size 118
5.9.3 Modifications to the questionnaire 118
5.9.4 Information on SDS risk management 118
5.10 Conclusions 125
5.11 References 126
Chapter 6. Economic impact assessment framework for sand and dust storms 128
6.1 Damage, costs and benefits of SDS 130
6.1.1 Reviewing the costs and benefits of SDS 130

6.1.2 Previous economic impact studies 131
6.2 Types of costs in the context of SDS 132
6.2.1 Direct and indirect costs 132
6.2.2. Market and nonmarket costs 132
6.2.3. Cost and value 132
6.2.4. On-site (source) and off-site (impact) 133
6.3 Gender, age, disability and economic analysis 133
6.4 Economic impacts of SDS 133
6.4.1 Impacts to consider 133
6.5 Identifying the damage and costs of SDS 135
6.5.1 On-site costs – economic activity 135
6.5.2 Off-site costs – economic activity 135
6.5.3 Off-site benefits 140
6.6 Methods to assess the economic impact of SDS 142
6.6.1 Overview of model types 142
6.6.2 Data requirements 143
6.7 Factors to consider in selecting ways to measure economic impacts of SDS 145
6.7.1 Challenges to be addressed 145
6.7.2 Recommended approach 145
6.8 Benefit-cost framework for analysing dust mitigation or prevention 146
6.8.1 Basic construct of cost-benefit analysis 146
6.8.2 Costs and timing of costs in cost-benefit analysis 147
6.8.3 Discounting and the discount rate 148
6.8.4 On-site benefits of dust mitigation at the source 148
6.8.5 Off-site benefits of dust mitigation at the source 148
6.8.6 Off-site benefits of dust mitigation in the impact region 148
6.9 Non-market valuation methods for inclusion in cost-benefit analysis 149
6.9.1 Hedonic pricing 149
6.9.2 Travel cost method 150
6.9.3 Contingent valuation method 150
6.9.4 Choice modelling 150
6.9.5 Experimental analysis 150
6.10 Examples of costbenefit analysis for dust prevention or mitigation 150
6.10.1 Land/soil surface mitigation 151
6.10.2 Reforestation 152
6.10.3 Off-site mitigation 153
6.10.4 Doing nothing 154

6.11 Issues in cost-basis analysis 156
6.11.1 Distributional efficiency 156
6.11.2 Land tenure issues 156
6.11.3 Transboundary issues – costs, benefits and/ or compensation 157
6.12 Conclusions on costbenefit analysis 158
6.13 Data-collection for assessing the economic impact of SDS 161
6.13.1 The need for good data 161
6.13.2 Types of data required for each sector 161
6.15 References 166
Chapter 7. A geographic information systembased sand and dust
storm vulnerability mapping framework 168
7.1 Damage, costs and benefits of SDS 170
7.2 Approaches to an SDS vulnerability mapping and assessment framework 171
7.3 Key concept of vulnerability assessment and mapping 172
7.3.1 Vulnerability 172
7.3.2 Exposure 172
7.3.3 Sensitivity 172
7.3.4 Adaptive capacity 173
7.4 Impact indicators of SDS for vulnerability mapping 173
7.4.1 Measuring vulnerability 173
7.4.2 Human health 173
7.4.3 Socioeconomic domain 175
7.4.4 Environment domain 176
7.4.5 Agroecosystem domain 177
7.5 Identifying indicators for SDS vulnerability mapping 180
7.6 A geographic information systembased stepwise
procedure for SDS vulnerability mapping 181
7.6.1 SDS vulnerability mapping hypothesis 181
7.6.2 SDS impact assessment 181
7.6.3 Indicator identification 181
7.6.4 SDS data collection 181
7.6.5 Data conversion, standardization, storage and management 182
7.6.6 Weighting of SDS vulnerability mapping elements 182
7.6.7 Integration of indicators to produce a map of components 183
7.6.8 Components map integration to produce SDS vulnerability maps 183
7.7 Conclusion 183
7.8 References 201

Chapter 8. Sand and dust storm source mapping 208
8.1 Overview of the physical sources of SDS 210
8.2 Drivers of SDS source activity 211
8.3 Anthropogenic sources 212
8.4 Distribution of SDS sources 213
8.5 SDS source mapping 214
8.5.1 Two approaches to detecting SDS source areas 214
8.5.2 Sand and dust storm source mapping based on sand and dust storm occurrence 214
8.5.3 SDS source mapping of data on soil surface condition 215
8.5.4 Gridded data on SDS sources 216
8.6 Methodology for high-resolution SDS source mapping 217
8.6.1 Clusters of relevant data 217
8.6.2 Calculating the SDS sources spatial distribution 221
8.6.3 Data sources for sand and dust storm source calculations 223
8.6.4 Use of topographic data for sand and dust storm source mapping 224
8.7 Conclusions 227
8.8 References 229
Chapter 9. Sand and dust storm forecasting and modelling 234
9.1 Impact-based, people-centred SDS forecasting 236
9.2 Components of impact-based forecast and warning 237
9.3 SDS information collection and forecast technology and infrastructure 239
9.3.1 Overview 239
9.3.2 In situ: visibility information from weather reports 239
9.3.3 In situ: air quality monitoring stations 241
9.3.4 Remotely sensed: satellite-derived redgreen- blue (RGB) dust products 244
9.4 The global World Meteorological Organization Sand and Dust
Storm Warning Advisory and Assessment System 246
9.4.1 Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) 246
9.4.2 WMO SDS-WAS regional centre for Northern Africa, the Middle East and Europe 248
9.4.3 WMO SDS-WAS regional centre for Asia 252
9.4.4 SDS-WAS Pan-American regional centre 253
9.4.5 Regional Specialized Meteorological Centres with activity
specialization on Atmospheric Sand and Dust Forecast 255
9.5 National meteorological and hydrometeorological services 257
9.5.1 Government weather services 257
9.5.2 Commercial weather services 259
9.5.3 Voluntary observations 259

9.6 SDS modelling 260
9.6.1 Introduction 260
9.6.2 Development of SDS modelling 260
9.6.3 Overview of numerical dust models 261
9.6.4 Challenges facing SDS models 262
9.6.5 SDS models currently in use 262
9.6.6 Scale of model results 264
9.6.7 Reanalysis products and SDS modelling 264
9.7 Conclusions 266
9.8 References 267
Chapter 10. Sand and dust storms early warning 270
10.1 Introduction 272
10.2 Conceptualizing early warning for SDS 272
10.3 Key components of early warning systems 273
10.4 Impact-based, people-centred forecasting and early warning process 279
10.5 Authority to issue forecasts and warnings 281
10.6 Warning plans and mechanisms 282
10.7 Warning verification 283
10.8 Warning education 283
10.9 Integrating forecasts and warnings into preparedness 284
10.11 References 286
Chapter 11. Sand and dust storms and health: an overview of main findings from
the scientific literature 288
11.2 Health effects of SDS 290
11.3 Exposure to SDS and their health impacts 291
11.4 Estimating health impacts of SDS 293
11.5 Developing a further understanding of health impacts and SDS 294
11.6 Conclusion 294
11.7 References 296
Chapter 12. Sand and dust storms source mitigation 300
12.2 Sources and drivers of SDS 302
12.3 Framing source management in the context of land degradation neutrality 306
12.3.1 Integrated approach for source management of SDS 306
12.3.2 Integrating source management of SDS in the context of
land degradation neutrality 311

12.4 Source mitigation measures – prevention 312
12.4.1 Overview 312
12.4.2 Natural areas and rangelands 313
12.4.3 Croplands 316
12.4.4 Industrial settings 319
12.5 Protective measures 320
12.6 Conclusion 324
12.7 References 325
Chapter 13. Sand and dust storms impact response and mitigation 328
13.2 Overview of SDS preparedness and response 330
13.3 SDS disaster or emergency planning 332
13.4 Sector-specific options to address the impacts of SDS 334
13.4.1 Overview 334
13.4.2 Agriculture 334
13.4.3 Construction 335
13.4.4 Education 336
13.4.5 Electricity 337
13.4.6 Health 337
13.4.7 Hygiene 338
13.4.8 Livestock 339
13.4.9 Manufacturing 339
13.4.10 Public awareness 339
13.4.11 Sport and leisure 340
13.4.12 Transport 340
13.4.13 Water and sanitation 342
13.6 References 344

Figure 1. Links between SDS and SDGs 7
Figure 2. Aerosol optical thickness 15
Figure 3. Annual mean dust emission (a) from ephemeral water bodies and (b) from land use 18
Figure 4. Sources (S1 to S10) and typical depositional areas (D1 and D2) for Asian dust aerosol
associated with spring average dust emission flux (kg km-2 spring-1) between 1960 and 2002 20
Figure 5. Dust cycle processes, their components, controlling factors and impacts on
radiation and clouds 22
Figure 6. Meteosat Second Generation (MSG) RGB Dust Product for 8 March 2006 23
Figure 7. and b. Typical synoptic configurations that can uplift dust over the Middle East 24
Figure 8. Cross section of a haboob 25
Figure 9. Dust whirlwind formation sequence 26
Figure 10. MODIS true colour composite image for 2 January 2007 depicting a dust storm initiated
in the Bodélé Depression, Chad 27
Figure 11. Global seasonal Absorbing Aerosol Index (AAI) based on TOMS satellite imagery 27
Figure 12. The importance of gender in disaster settings 54
Figure 13. A twofold approach to mitigating sand and dust storm hazards for disaster risk reduction 59
Figure 14. Framework for sand and dust storm risk management coordination and cooperation 68
Figure 15. Reported health effects of sand and dust storms 109
Figure 16. Effects of type five SDS on Zira population and subgroups 114
Figure 17. A flowchart of geographic information system vulnerability mapping 170
Figure 18. Major human health impacts of sand and dust storms 174
Figure 19. Major socioeconomic impacts of sand and dust storms 176
Figure 20. Major environmental impacts of sand and dust storms 177
Figure 21. Major impacts of sand and dust storms on agroecosystems 178
Figure 22. Drivers that impact sand and dust storm activity 211
Figure 23. Most relevant human impacts leading to sand and dust storm anthropogenic sources 213
Figure 24. Soil surface parameters necessary for sand and dust storm source mapping 218
Figure 25. United States Department of Agriculture soil texture classification system 219
List of figures

Figure 26. Moderate Resolution Imaging Spectroradiometer Normalized
Difference Vegetation Index (MODIS NDVI) and Enhanced Vegetation Index (EVI) for 2018 220
Figure 27. Different size domains for calculation of S-function 225
Figure 28. Areas (arrows) indicate different domains identified as topographical lows 225
Figure 29. Average S-function values from four different domains
(10°x10°, 5°x5°, 2.5°x2.5°, 1.25°x1.25°) on 0.0083° (30 arcsec) resolution, using
topography data of the same resolution 226
Figure 30. The PM10 and PM2.5 records from Granadilla, Canary Islands,
Spain for August 2012 with Saharan dust outbreaks indicated in peak values 243
Figure 31. EUMETSAT RGBdust product for West Asia on 20 December 2019 245
Figure 32. WMO SDS-WAS regional node operation concept 247
Figure 33. SDS-WAS forecast comparison of dust optical depth
at 550 nm for 4 February 2017 at 12 UTC 249
Figure 34. SDS-WAS multimodel ensemble products for 4 Feb 2017 at 12 UTC:
median and mean (top), standard deviation and range of variation 250
Figure 35. Six-hourly maps of visibility reduced to less than 5 km associated
with airborne sand and dust for 23 February 2016 251
Figure 36. Burkina Faso dust forecast for 3rd January 2018 252
Figure 37. Verification of a dust forecast released by the CUACE34/
dust model with surface SDS observational data from meteorological stations 253
Figure 38. Seven-day surface dust concentration forecast from the Caribbean
Institute for Meteorology and Hydrology WRFChem model 254
Figure 39. Movement of dust from the Sahara Desert to the Amazon Basin 255
Figure 40. Regional WMO SDS-WAS nodes in Barcelona, Beijing and Bridgetown
several key forecasting centres that contribute to global and regional SDS
forecasting, information and guidance 256
Figure 41. Dust aerosol optical depth 36-hour forecast for 26 May 2017
at 12 UTC provided by CAMS 257
Figure 42. Annual mean surface concentration of mineral dust in 2018 calculated
by the SDS-WAS regional centre for Asia, based on NASA MERRA reanalysis 265
Figure 43. Anomaly of the annual mean surface concentration of dust in 2018
relative to mean of 1981–2010, calculated by the SDS-WAS regional centre
for Asia, based on NASA MERRA reanalysis 265

Figure 44. Four elements of end-to-end, people-centred early warning systems 278
Figure 45. Impact-based, people-centred forecast and warning systems
for sand and dust storms 282
Figure 46. Desiccation of ephemeral lakes due to humanmade changes in hydrology 303
Figure 47. Receding shorelines in some inland waterbodies 303
Figure 48. Wind erosion in unprotected croplands – a major source of
dust in dryland agricultural areas 305
Figure 49. Dust Bowl caused by unsustainable dryland agriculture
and prolonged drought periods 305
Figure 50. Damage to infrastructure by moving sand dunes 306
Figure 51. Interlinking steps to support sustainable landuse management 310
Figure 52. Conceptual framework for land degradation neutrality 310
Figure 53. Mobilizing desert dust can be prevented by reducing damage
to protective biological crusts in deserts by confining vehicular traffic 314
Figure 54. Vegetation management in rangelands
protects soil from wind erosion 314
Figure 55. Stabilization of sand dunes in the Kubuqi Desert, northern China 315
Figure 56. Reduced and mulch tillage systems providing
soil protection from wind erosion 317
Figure 57. Windbreak protecting cropland in large field 318
Figure 58. Scattered trees offering protection to cropland and
livestock in a parkland system in Mali 318
Figure 59. Zai pits hold water on the land to improve crop growth in poor or eroded lands 319
Figure 60. Surface stabilization for dust control at an industrial site using soil
binding agents applied by a hydroseeder 320
Figure 61. Trees used to stabilize sand dunes encroaching on an
irrigation scheme on the Nile flood plain 324

Table 1. Factors associated with sand and dust storms 72
Table 2. Sand and dust storm hazard typology 83
Table 3. Comparison of climate change and disaster risk assessment
terminology (Modified from CAMP Alatoo, 2013a) 85
Table 4. Scaling vulnerability to sand and dust storms 93
Table 5. Framing the sand and dust storm risk assessment process 100
Table 6. Sand and dust storm perception survey 120
Table 7. Examples of costs and valuation methods for measuring
impacts on various economic activities 141
Table 8. Summary of methodologies, data requirements and skills required 144
Table 9. Base data 186
Table 10. Demographic and socioeconomic data 188
Table 11. Health and sand and dust storm data 189
Table 12. Meteorological data 191
Table 13. Transport and utility network 192
Table 14. Industrial facilities 194
Table 15. Vegetation data 195
Table 16. Water and precipitation 196
Table 17. Soil and geomorphology 197
Table 18. Advantages and disadvantages of sand and dust storm mapping
using sand and dust storm occurrence 198
Table 19. Advantages and disadvantages of sand and dust
storm mapping based on soil conditions 215
Table 20. WMO synoptic codes associated with airborne sand and dust 216
Table 21. SDS atmospheric models contributing to the WMO SDSWAS
system and regional centres 240
Table 22. Potential agricultural applications of an SDS warning system 263
Table 23. Health outcomes investigated in epidemiological studies 282
Table 24. Preventive measures in rangelands and natural ecosystems 293
Table 25. Measures to minimize wind erosion in cropland 313
Table 26. Measures to protect valuable assets from sand and dust 316
Table 27. Measures to control windblown sand and sand dunes 320
List of tables

Box 1. The UNCCD Policy Advocacy Framework to combat Sand and Dust Storms, 2017 5
Box 2. Local sources of dust 19
Box 3. Women and vulnerability 52
Box 4. SDS and a changing climate 69
Box 5. Impact and risk 75
Box 6. Assessing source areas 102
Box 7. Considering climate, environment and population changes 104
Box 8. Sample simple survey results report-out – health effects 109
Box 9. Including gender and age in the assessment 110
Box 10. Expert-based assessment process overview 111
Box 11. Sample simple expert assessment results report-out – SDS risk 114
Box 12. Integrating gender into the cost-benefit analysis process 159
Box 13. Comparing traditional and impact-based people-centred forecasts 236
Box 14. Copernicus Atmosphere Monitoring Service: a European initiative 257
Box 15. Dust monitoring and forecasting system of the Korea Meteorological Administration 258
Box 16. What is an early warning system? 274
Box 17. Early warning stakeholders 279
Box 18. SDS warning and the Sendai Framework 280
Box 19. Sustainable land management principles 308
Box 20. Integrated landscape management 309
Box 21. Principles of land degradation neutrality 312
Box 22. Sand and dust storms and safe driving guidance 331
Box 23. Gender, preparedness and response 333
List of boxes

Glossary and annexes
Glossary of key disaster-related terms 44
Glossary of key gender terms 52
Annex 1: Potential indicators for SDS vulnerability mapping 53
Annex 2: Data available on the web 198

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UNCCD | Sand and Dust Storms Compendium

UNCCD | Sand and Dust Storms Compendium

UNCCD | Sand and Dust Storms Compendium | Chapter 1 | Introduction
1
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Coffman
–
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23rd
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UNCCD | Sand and Dust Storms Compendium | Chapter 1 | Introduction 1
1. Introduction
Chapter overview
This chapter provides an overview of sand and dust storms (SDS), opening with a review
of the challenges faced in understanding and addressing their negative impacts. The
role of the United Nations System in addressing SDS is summarized and a review of
the UNCCD Policy Advocacy Framework to combat Sand and Dust Storms and its links
with the Sustainable Development Goals (SDGs) is provided. The chapter closes with
the objectives of the Compendium as well as an overview of the content of each of its
chapters.

2
1.1 The challenge of
sand and dust storms
Sand and dust storms (SDS) are natural
phenomena that can affect almost all
sectors of society. An estimated 2,000
million tons of dust are emitted into the
atmosphere annually, of which 75 per cent
is deposited on land and 25 per cent on the
ocean (Shao et al., 2011). The majority of
the sand and dust is emitted due to natural
conditions (UNEP, WMO and UNCCD,
2016). For more on the physics and nature
of SDS, see chapter 2.
As natural phenomena, SDS are a critical
part of the global climate and environment,
with impacts on local and global weather,
nutrient cycles and biomass productivity.
SDS affect a range of sectors, including
health, transport, education, business and
industry, agriculture and farming, and water
and sanitation.
While a comprehensive global assessment
of the economic impact of SDS is yet to be
carried out, the research that is available
indicates that significant economic costs
can be associated with SDS. For instance,
SDS impacts on oil and gas operations
were estimated to cost Kuwait US$ 9.36
million in 2018 (Al-Hemoud et al., 2019).
Meanwhile, the economic impact of
one dust storm on 23 September 2009
affecting Sydney and other parts of eastern
Australia was estimated at between US$
229 and US$ 243 million (Tozer and Leys,
2013).1
Chapter 6 discusses in detail how
to assess the economic impact of SDS.
SDS impact everyone – men, women, boys
and girls – but not all in the same way.
These differences stem from the gender-
based roles in the productive, economic,
family and social spheres that equip
women and men with different skill sets,
capabilities and vulnerabilities. The gender
aspects of SDS are discussed in more
detail in the Special focus section: Gender
and disaster risk reduction in chapter 3.
1 Australian Dollars converted to USD at 2009 exchange rate.
Similarly, SDS affect individuals with a
disability in different ways, with a particular
impact on those with compromised
health. It is crucial that attempts to reduce
the impact of SDS understand these
differences and address them in order to
ensure a fair and equitable approach. More
broadly, the protection of all human rights
should be integral to understanding and
managing SDS.
There are several challenges when
addressing the negative impacts of SDS.
First, effectively managing SDS requires
the wide range of individual negative SDS
impacts on society, including SDS caused
by human action, to be addressed to
ensure that human development continues.
Since addressing a single SDS impact or
contributing factor will not reduce the risk
posed by SDS, a multi-pronged approach is
required.
A second challenge is that SDS impacts
are multi-faceted, cross-sectoral and
often trans-national. For example, in
the agricultural sector, ploughing fields
can lead to local SDS which may impact
the transport sector by contributing
to traffic accidents and fatalities. Dust
from the Sahel of West Africa can reach
the Caribbean. SDS can damage crops
(affecting food security) and increase
the cost of air filtration requirements
for factories producing electronic
components. Global and regional weather
conditions and changes can increase, or
decrease, the intensity and duration of even
local SDS events. Under these conditions,
cross-sectoral and trans-national
approaches and cooperation between
stakeholders, actors and partners outside
their individual normal scope of activity are
required.
A third challenge lies in the diversity of
sectors involved, the scales of intervention
required, and the range of stakeholders
concerned. This challenge involves
assuring that all SDS stakeholders have

access to sufficient information to take
appropriate action to address SDS impacts.
While considerable information on SDS
is available from the chapters of this
Compendium and the materials cited in
the references, no overall packaging of
this information into easily accessible
format focused on managing the diverse
causes and impacts of SDS has yet been
developed.
A fourth challenge is that SDS are not
widely recognized as a natural hazard
that can lead to disaster-level impacts. In
general, SDS rarely result in large-scale
physical damage or a high number of
immediate fatalities: their impacts are
often more hidden, for instance increases
in illnesses and deaths from complications
related to asthma or cardio-vascular
disease. In addition, SDS events, triggered
by the ploughing of fields or Haboob
passage for instance, can lead to fatalities
and damages. However, these events are
usually isolated in time (occurring during
a specific time of the year) and space
(developing from and affecting the same
locations when they do occur).
Despite the dramatic effects of SDS –
such as sand covering crops – the lack of
regular reporting on the full range of SDS
impacts and the limited quantification of
economic impacts (see chapter 6) mean
SDS are a low-profile hazard (Middleton et
al., 2019), with under-recognized disaster
impacts. This low profile has resulted in
less attention being paid to reducing SDS
impacts on vulnerable individuals, at-
risk groups and society in general when
compared with other hazards. Considering
SDS from a disaster risk management
perspective is discussed in more detail in
chapter 3.
Despite these challenges, SDS
management is receiving increasing
attention at the national level. Countries,
including Canada, China, Iran, the Republic
of Korea, United States and others, have
implemented SDS management efforts
(some for decades), with a significant
focus on a natural resource management
approach. National, regional and global
efforts have been implemented to
improve SDS forecasts and warnings,
with significant support from the World
Meteorological Organization (WMO).
1.2 United Nations
System engagement
on SDS
In 2007, the fifteenth World Meteorological
Congress highlighted the importance of
the SDS issue and endorsed the launch of
the Sand and Dust Storm Warning Advisory
and Assessment System (SDS-WAS,
https://public.wmo.int/en/our-mandate/
focus-areas/environment/SDS/warnings)
to facilitate user access to vital information
on SDS. The WMO SDS-WAS is global
federation of partners organized around
regional nodes that integrate research and
user communities (WMO, 2015).
At the global level, the United Nations
General Assembly (UNGA) adopted the
first resolution on SDS, Combating sand
and dust storms (A/RES/70/195), in 2015
(United Nations General Assembly, 2015).
The resolution recognized that SDS pose
a significant challenge to sustainable
development and underscored the need to
promptly undertake measures to address
the impacts and challenges they pose to
society.
The UNGA adopted additional SDS-
relevant resolutions in 2016, 2017, 2018,
2019 and 2020 (United Nations General
Assembly, 2016; United Nations General
Assembly, 2020). These resolutions
acknowledged the role of the United
Nations development system in promoting
international cooperation to combat
SDS and invited relevant institutions,
including the United Nations Environment
Programme (UN Environment), WMO and
the United Nations Convention to Combat
Desertification (UNCCD), to address the
SDS problem.

4
In 2017, the UNCCD 13th session of the
Conference of the Parties (COP) adopted
its first decision on SDS (Decision 31/
COP.13) and invited countries to use the
UNCCD Policy Advocacy Framework to
combat Sand and Dust Storms (UNCCD,
2017. See Box 1) to work on addressing
the impact of SDS. The Policy Framework
presents principles and sets out measures
to minimize the negative impacts of SDS in
three key areas:
• monitoring, prediction and early
warning
• impact mitigation, vulnerability and
resilience, and
• source mitigation
In the same decision, the COP invited
United Nations entities to assist affected
Parties in developing and implementing
SDS policies. Further, it requested that
the UNCCD Secretariat collaborate with
relevant United Nations entities and
specialized organizations to assist Parties
with implementing the Policy Framework
and fostering partnerships to facilitate
capacity development to mitigate SDS
impacts. This Sand and Dust Storms
Compendium: Information and Guidance
on Assessing and Addressing the Risks
is part of efforts by the UNCCD Secretariat,
guided by the COP (Decision 25/COP.14),
working with other United Nations
entities and affected countries, to better
understand and address the impacts of
SDS.
Tsaiga
–July
19th
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Box 1. The UNCCD Policy Advocacy Framework
to combat Sand and Dust Storms, 2017
Goal
The ultimate goal is to reduce societal vulnerability to this recurrent hazard by mitigating
the impacts of wind erosion and SDS. Policy advocacy will focus on efforts under three
headings:
• post-impact crisis management (emergency response procedures)
• pre-impact governance to strengthen resilience, reduce vulnerability and minimize
impacts (mitigation)
• preparedness plans and policies
Objectives
The objectives of the Policy Framework are to:
• develop national SDS policy based on the philosophy of risk reduction, including
legislative and instrumental arrangements, and risk reduction strategies for
resilience and preparedness
• enhance North-South and South-South cooperation on SDS management,
warning and source mitigation
• increase availability of, and access to, robust comprehensive SDS early warning
systems, risk information/communication and risk assessments
• reduce the number of people affected by SDS
• reduce the economic losses and damage caused by SDS
• strengthen resilience and reduce SDS impacts on basic services, including
transport
• reduce erodibility and the extent of anthropogenic SDS source areas in the
context of land degradation neutrality
• enhance scientific understanding of SDS, particularly in areas such as impacts
and monitoring
• enhance coordination/cooperation among stakeholders in SDS action at the
national, regional and global levels for strengthened synergies
• increase financial opportunities for comprehensive SDS early warning and source
mitigation
Principles
The Policy Framework suggests principles for developing and implementing more
proactive SDS policies, in particular resilience building and source mitigation. The SDS
policy should:
• Establish a clear set of principles or operating guidelines to govern the
management of SDS and its impacts. This policy should aim to reduce risk
by developing better awareness and understanding of SDS hazards and the
underlying drivers of societal vulnerability, along with developing a greater
understanding of how being proactive and adopting a wide range of preparedness
measures can increase societal resilience.
• Be consistent and equitable for all regions, population groups (bearing gender
in mind), and economic sectors, and be consistent with the SDGs. Similarly,
achieving sustainable development as set out in these SDGs can help reduce the
occurrence and impact of SDS in affected areas.
• Address dust sources occurring in various environments including drylands,
agricultural fields, coastal areas and high latitudes. Further, because of the
transboundary nature of many SDS events, national SDS policies should be
coordinated in international and regional contexts, as appropriate.

6
• Be driven by prevention rather than by crisis. Reducing the impacts of SDS
requires a policy framework and action on the ground, consistent with the Sendai
Framework for Disaster Risk Reduction 2015–2030.
Priorities for action
The Policy Framework suggests a proactive approach to addressing the negative impact
of SDS in each of the three interrelated principal action areas:
1. Monitoring, prediction and early warning
2. Impact mitigation, vulnerability and resilience, and
3. Source mitigation
Suggested action areas are as follows:
1. Monitoring, prediction, early warning and preparedness
a. Identify and map populations vulnerable to SDS for early warning, including health
advisories.
b. Implement comprehensive early warning systems at national/regional levels.
2. Impact mitigation, vulnerability and resilience
a. Identify and scale up best-practice techniques for physical protection of assets,
including infrastructure and agriculture, against SDS in affected areas.
b. Identify and scale up best-practice strategies to minimize negative impacts of SDS
on key sectors and population groups, including women.
c. Establish and implement coordinated emergency response measures and
strategies across sectors based on systematic impact/vulnerability mapping/
assessment.
3. Source mitigation
a. Identify and monitor SDS source areas.
b. Identify and scale up best-practice techniques for source mitigation.
c. Highlight synergies among the Rio Conventions and related mechanisms and
initiatives for SDS source-area mitigation strategies.
d. Integrate SDS source-area mitigation practices into national efforts towards
achieving SDG target 15.3 on “land degradation neutrality” (LDN). SDS source
mitigation could be linked to LDN target-setting and included as a voluntary sub-
target in source countries.
4. Cross-cutting and integrated actions
a. Identify best-practice policy options and policy failures at the regional, national and
subnational levels.
b. Identify key SDS knowledge gaps for focused research.
c. Mainstream SDS into disaster risk reduction.
d. Build institutional capacity for coordinated and harmonized SDS policy
development and implementation at the regional, national and subnational levels.
e. Explore innovative financing opportunities and other resources needed for SDS
actions.
f. Establish a coordination mechanism and partnership of relevant United Nations
organizations for the consolidation of global policy around SDS in order to
strengthen synergies and cooperation at the global level.
g. Establish an international platform for the dissemination of critical data and the
exchange of experiences.
h. Strengthen regional and subregional cooperation.
The links between SDS management and SDGs are summarized in Figure 1. These efforts
need to ensure that the links between SDS and dependent ecological system continue so
that harm to society from disrupting these systems is avoided.

Reducing air pollution caused by SDS can help families become healthier, save on medical
expenses and improve their productivity.
SDS can cause crop damage, negatively affecting food quality/quantity and food security.
Reducing desertification/land degradation (including soil erosion) in source areas will help
enhance agricultural productivity.
Air pollution caused by SDS poses a serious threat to human health. Many studies link
dust exposure with increases in mortality and hospital admissions due to respiratory and
cardiovascular diseases.
Dust deposition can compromise water quality because desert dust is frequently
contaminated with micro-organisms, salts and/or anthropogenic pollutants.
Mitigating SDS disasters will significantly lower the number of people affected and
economic losses caused, contributing to safer, more sustainable and more disaster-
resilient human settlements.
Improving land/water use and management in SDS source areas contributes to creating
climate-change-resilient landscapes and communities.
Reducing wind erosion in SDS source areas contributes to land degradation neutrality,
thereby enhancing the sustainable use of terrestrial ecosystems.
SDS activities can be part of efforts to strengthen the means of implementation and
revitalize the global partnership for sustainable development.
Source: Adapted from https://sustainabledevelopment.un.org/?menu=1300.
Figure 1.
Links between
SDS and SDGs

8
1.3 Compendium
objective and users
The objective of the Sand and Dust
Storms Compendium: Information and
Guidance on Assessing and Addressing
the Risks is to provide guidance, tools
and methodological frameworks to aid in
the development and implementation of
policies and activities to reduce the impact
of SDS at the national and regional levels.
The Compendium is based on the Policy
Advocacy Framework to combat Sand and
Dust Storms (see Box 1) and focuses on
its three action areas:
• monitoring, prediction and early
warning
• impact mitigation, vulnerability and
resilience, and
• source mitigation
The primary users of the Compendium are
expected to come from two groups:
• officials involved in local and national
government, emergency management,
health, natural resource management,
agriculture, livestock, forestry,
meteorology, transport, etc.
• community and civil society
stakeholders involved in improving
local living conditions, promoting
development and addressing the
needs of groups that are especially
vulnerable to SDS impacts.
The Compendium is expected to increase
awareness among decision makers and
stakeholders about coordinated policies
across sectors in mitigating SDS impacts.
1.4 Content of the
Compendium
The Compendium content is divided into
13 chapters:
• Chapter 1 – “Introduction”, providing
an overview of SDS and the
Compendium.
• Chapter 2 – “The nature of sand and
dust storms”, providing an overview of
the physical nature of SDS.
• Chapter 3 – “Sand and dust storms
from a disaster management
perspective”, providing an overview
of SDS as a hazard and potential
disaster. The chapter reviews how
SDS can be managed and mitigated
and covers the elements that must
be considered in SDS forecasting and
warning.
• Chapter 4 – “Assessing the risks
posed by sand and dust storms”,
discussing the concepts behind
assessing the risks posed by SDS
hazards and disasters.
risk assessment framework”, building
on chapter 3, and providing details of
two methods: one based on expert
opinion and the other based on using
community perceptions of SDS threats
and impacts to assess SDS risk.
• Chapter 6 – “Economic impact
assessment framework for sand
and dust storms”, providing a review
the concepts behind calculating
the economic cost of events and
discussing how this can be applied to
assessing SDS economic impact.

• Chapter 7 – “A geographic information
system-based sand and dust storm
vulnerability mapping framework”,
providing a conceptual review of
vulnerability to SDS. The chapter
describes the technical steps
necessary to assess vulnerability
using geographic information
system (GIS) software. The process
described in chapter 7 provides input
on vulnerability, which can be added
to the expert assessment process
detailed in chapter 5 when sufficient
data are available.
• Chapter 8 – “Sand and dust storm
source mapping”, covering how to
identify and map SDS.
• Chapter 9 – “Sand and dust storm
forecasting and modelling”, covering
efforts at the global to national
weather service levels to anticipate the
development of SDS and where they
will have impacts and examining the
use of models in these efforts.
early warning”, providing an overview
of the structure and operation of SDS
early warning systems.
and health: an overview of main
findings from the scientific literature”,
describing the current state of
research into the health impacts of
SDS.
source mitigation”, providing an
overview of approaches and methods
that can be used to manage SDS
sources and impacts.
impact response and mitigation”,
outlining ways to reduce the impact
of SDS.
Each chapter is prefaced with a short
summary of its content and closes with
a conclusion recapping what has been
covered and implications for addressing
the impacts of SDS.
To facilitate easy use of each chapter,
references and chapter-specific annexes
are included at the end of each chapter,
rather than at the end of the Compendium.
This allows each chapter to be used
as a stand-alone document in practical
application. To ensure that each chapter
can be used as a stand-alone document,
some repetition between chapters has
been necessary.

10
1.5 References
Al-Hemoud A., and others (2019). Economic impact and
risk assessment of sand and dust storms (SDS) on
the oil and gas industry in Kuwait. Sustainability, vol.
11, No. 200. doi:10.3390/su11010200.
Middleton, N., P. Tozer, and B. Tozer (2019). Sand and
dust storms: underrated natural hazards. Disasters,
vol. 43, No. 2. doi:10.1111/disa.12320.
Shao, Yaping, and others (2011). Dust cycle: An
emerging core theme in Earth system science.
Aeolian Research, vol. 2, No. 4, pp. 181–204.
Shao, Y., and others (2003). Northeast Asian dust storms:
Real-time numerical prediction and validation.
Journal of Geophysical Research: Atmospheres, vol.
108, No. D22.
Tozer, P. R., and J. Leys. (2013). Dust Storms – What do
they really cost? The Rangeland Journal, vol. 35, No.
2. DOI: 10.1071/RJ12085
United Nations Convention to Combat Desertification
(2017). Draft advocacy policy frameworks: gender,
drought, and sand and dust storms. Conference of
the Parties. ICCD/COP(13)19.
United Nations Environment Assembly (2016).
Resolution 2/21. Sand and dust storms. United
Nations Environment Programme.
United Nations Environment Programme, World
Meteorological Organization and United Nations
Convention to Combat Desertification (2016).
Global Assessment of Sand and Dust Storms. United
Nations Environment Programme, Nairobi.
United Nations General Assembly (2019). Resolution
74/226. Combating sand and dust storms.
Resolution adopted by the General Assembly on
A/74/381/Add.11.
74/226. Combating sand and dust storms.
Resolution adopted by the General Assembly on
A/75/457/Add.9A
__________ (2018). Resolution 73/237. Combating sand
and dust storms. Resolution adopted by the General
Assembly on A/73/538/Add.10.
Assembly on A/72/420/Add.10.
Assembly on A/71/463.
__________ (2015). Resolution 70/195 Combating sand
Assembly on A/70/472.
World Meteorological Organization (2015).
Sand and Dust Storm Warning Advisory and
Assessment System (SDS-WAS) Science
and Implementation Plan: 2015–2020.
Geneva, Switzerland.

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2018

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2011

UNCCD | Sand and Dust Storms Compendium | Chapter 2 | The nature of sand and dust storms 13
2. The nature of sand
and dust storms
Chapter overview
This chapter provides basic information on sand and dust storms (SDS) as a natural
environmental process. It covers definitions of SDS, their role and interaction within the
Earth system, SDS source areas and their trajectory, and SDS mechanisms and processes
associated with airborne dust. More detailed information on these topics can be found in
the Global Assessment of Sand and Dust Storms (UNEP, WMO and UNCCD, 2016).

UNCCD | Sand and Dust Storms Compendium | Chapter 2 | The nature of sand and dust storms
14
2.1 SDS definitions
There are numerous sources of small
particulate matter in the atmosphere,
including sea salt, volcanic dust, cosmic dust
and industrial pollutants, but this document
refers to mineral particles that originate from
land surfaces. These particles are commonly
graded according to their size, consisting of
clay-sized (<4 microns), silt-sized (4–62.5
microns) or sand-sized (62.5 microns–2mm)
material.
There is no strict distinction in the definitions
of sand storms and dust storms, since there
is a continuum of particle sizes in any storm.
Generally, larger particles tend to return to
the land surface soon after being entrained
and atmospheric concentrations naturally
diminish with distance from source areas
as material in suspension is deposited
downwind by wet and dry processes. Most
of the particles transported more than 100
km from their source are <20 microns in
diameter (Gillette, 1979).
Dust storms are formally defined by the
World Meteorological Organization (WMO)
as the result of surface winds raising large
quantities of dust into the air and reducing
visibility at eye level (1.8 m) to less than
1,000 m (McTainsh and Pitblado, 1987),
although severe events may produce zero
visibility. There is no equivalent formal
definition of sand storms, but storms
dominated by sand tend to have limited areal
extent and hence localized impacts, including
sand dune encroachment.
Dust storms also have local impacts but
their smaller particles can be transported
much farther – over thousands of kilometres
from source, often across international
boundaries – which can bring hazardous
dust haze to distant locations. Large-scale
dust haze events affect areas measured in
tens of thousands and sometimes hundreds
of thousands of square kilometres.
The duration of SDS events varies from a
few hours to several days. Their intensity
is commonly expressed in terms of the
surface atmospheric concentration of
particles and a distinction is typically made
between particles with diameters <10
microns (PM10
) and those with diameter
<2.5 microns (PM2.5
). Atmospheric PM10
dust concentrations exceed 15,000 µg/m3
in severe events (Leys et al., 2011). Hourly
maximum PM2.5
concentrations can exceed
1,000 µg/m3 during intense dust storms
(Jugder et al., 2011).
In chemical terms, the main component
of the particles that make up SDS is silica,
typically in the form of quartz (SiO2). Other
material commonly found in desert dust
includes Al2O3, Fe2O3, CaO, MgO and
K2O, as well as organic matter and a range
of salts, pathogenic microorganisms –
including fungi, bacteria and viruses – and
anthropogenic pollutants.
2.2 Atmospheric aerosols
Atmospheric aerosols are liquid or solid
particles that originate from both natural and
anthropogenic sources and do not distribute
homogeneously in the world (see Figure 2).
Aerosols classify as primary or secondary.
Primary aerosols are directly emitted
as particles into the atmosphere under
mechanical processes from mainly natural
sources such as sea salt from sea spray,
mineral dust from dust storms, sulphate
from volcanoes, and organic aerosols and
black carbon from biomass burning and
anthropogenic industrial emissions.
Secondary aerosols form in the atmosphere
through gas-to-particle conversion processes
from precursor gases (for example H2SO4,
NH3, NOx) – which have both natural
(for example volcanic eruptions) and
anthropogenic origins (for example from
fossil fuel combustion) – to particles by
nucleation processes, and by condensation
and coagulation processes of these particles
(Seinfeld and Pandis, 2016). The most
abundant secondary aerosols are sulphates,
nitrates, ammonium and secondary organic
aerosols, which have increased since the last
century due to rapid growth in population,
urban areas and industrial activities.
Secondary aerosols remain a low contributor
to the total atmospheric aerosol mass in
comparison with primary aerosols (IPCC,
2013).

Note: Aerosol optical thickness of black and organic carbon (green), dust (red-orange), sulphates
(white, outside those regions cover by ice as in the Arctic, Antarctic and high-altitude mountain range
areas in South America) and sea salt (blue) from a 10 km resolution GEOS-5 Nature-Run using the
GOCART model. The screenshot shows the emission and transport of key tropospheric aerosols on
17 August 2006. Source: NASA/GSFC, 2017.
Human exposure to airborne mineral dust
may have an adverse effect on human
health, causing or aggravating allergies,
respiratory diseases and eye infections
(Griffin, 2002; Mallone et al., 2011; Tobias
et al., 2011). Toxicologists refer to aerosols
by their diameter as ultrafine, fine or
coarse matter. Coarse particles have an
aerodynamic diameter ranging from 2.5 to
10µm (PM10
to PM2.5
), which distinguishes
them from the smaller airborne particulate
matter referred to as fine (PM2.5
) and
ultrafine particles (PM1).The WHO
Air Quality Guidelines (World Health
Organization, 2005) provide guidance on
thresholds and limits for key air pollutants
that pose health risks.
Aerosol impacts also extend to climate,
weather, atmospheric chemistry and
air quality, but the largest uncertainties
concern their radiative impacts (IPCC,
2013). Aerosols alter the atmosphere’s
radiative balance by scattering and
absorbing solar and terrestrial radiation
(direct effects) and by changing cloud
microphysics and precipitation processes
through acting as cloud condensation
nuclei/ice nuclei (indirect effects).
Research into the impact of aerosols
in radiative forcing has grown in recent
years because aerosols have been
identified as the largest uncertainty
among other climate-change causes such
as greenhouse gases and changes in
pollution.
Soil-derived mineral dust has emerged
as one of the most studied aerosols in
Earth Sciences. This research reflects the
specific and significant impacts of this
dust on climate, ecosystems, weather, air
quality, human health and socio-economic
activities (Knippertz and Stuut, 2014). Soil-
derived mineral dust is usually considered
natural when wind processes produce it
over arid or semi-arid regions characterized
by sparse vegetation.
Figure 2.
Aerosol optical
thickness

16
The main large dust source regions
correspond with mostly topographically
low and natural dried palaeolakes (Ginoux
et al., 2001, 2012; Prospero et al., 2002). On
the other hand, mineral dust is considered
anthropogenic when human activities
directly lead to dust emission.
There are large uncertainties regarding
the impact of anthropogenic activities on
modulating dust emissions:
• directly, for example by altering the
properties of land, disturbing soils,
desiccating water bodies, removing
vegetation, grazing or ploughing, as
well as from specific types of land use,
for instance, road dust, and
• indirectly, through changes in the
hydrological cycle or changes in dust
generation due to climate change,
including changes in wind and
precipitation patterns that favour
desertification (IPCC, 2013)
Global annual dust emission from natural
and anthropogenic origins are still
uncertain. Based on the global models
participating in the AEROsol model
interCOMparison (AEROCOM) initiative,
emission estimates quantified natural dust
emissions as varying between 1,000 and
4,000 Tg (IPCC, 2013). Moreover, according
to Stanelle et al. (2014), global annual dust
emissions have increased from 729 Tg/
year in the 1880s to 912 Tg/year in the
2000s. About 56 per cent of this change
was attributed to climate change, 40 per
cent to anthropogenic land cover changes
(for example agricultural expansion), with
a 4 per cent natural cycle variability. This
division can vary regionally.
Atmospheric mineral dust strongly
interacts with the Earth system through
direct and indirect impacts (IPCC, 2013).
Mineral dust influences the Earth’s
direct radiative budget by affecting the
processes of absorption and scattering at
solar and infrared wavelengths. Indirect
effects include changes in the number
of cloud condensation nuclei and ice
nuclei (Atkinson et al., 2013; Nickovic et
al., 2016), which in turn affect the optical
properties and the lifetime of clouds. Dust
particles also have effects on atmospheric
chemistry (Krueger et al., 2004).
They can act as a sink for condensable
gases and thus facilitate the formation
of secondary aerosols, which in turn
contribute to PM concentrations.
Dust sedimentation and deposition at
the Earth surface causes changes in the
biogeochemical processes of terrestrial
and marine ecosystems through the
delivery of primary nutrients (Jickells et al.,
2005). Much of this mineral dust emitted
from land surfaces is deposited on the
oceans, where it has significant impacts
on marine biogeochemistry, marine
productivity and deep-sea sedimentation.
Dust deposition provides nutrients to
ocean surface waters and the seabed, thus
boosting primary production, with impacts
on the global nitrogen and carbon cycles.
In coastal waters in particular, nutrients
in desert dust can trigger harmful algal
blooms, with knock-on effects on human
health and economic activity.
Potential links have also been identified
between microorganisms, trace metals
and organic contaminants carried in desert
dust and some of the complex changes
occurring on coral reefs in numerous
parts of the world. Elsewhere, it has been
demonstrated that the Amazon rainforest
is fertilized significantly by Saharan
dust (Yu et al., 2015). At the same time,
SDS have many negative impacts on
the agricultural sector (Stefanski and
Sivakumar, 2009).
Regions of the world in the path of dust-
laden wind record increased ambient air
dust concentrations that are associated
with deteriorations in air quality and the
strong possibility of negative impacts on
human health. Dust events greatly affect
the air quality conditions in Asia (for
example Wang et al., 2016) and Europe
(Pey et al., 2013). Desert dust outbreaks
over southern Europe frequently exceed
daily and annual safety thresholds of
particulate matter set by the European
Union directive on ambient air quality and
cleaner air (for example Basart et al., 2012;
Pey et al., 2013).

As high dust concentrations significantly
reduce visibility through increased
light extinction, they may affect aircraft
operations and ground flights. In addition,
dust and sand can damage aircraft engines
(Clarkson and Simpson, 2017). Airborne
dust is a serious problem for solar energy
power plants (Schroedter-Homscheidt
et al., 2013). The need for accurate dust
observation and prediction products is
of importance for plants built in desert
areas, for instance in Northern Africa (for
example Morocco), West Asia and other
arid areas.
2.3 Soil-derived
mineral dust in
the Earth system
2.3.1. Dust source areas
The world’s major dust sources are located
in the northern hemisphere across an area
called the “dust belt” (i.e. North Africa, the
Middle East and East Asia). In the southern
hemisphere, with less land mass than the
northern hemisphere, dust sources are of
smaller spatial extension and are located
in Australia, South America and Southern
Africa. Significant source areas for SDS are
presented in Figure 3.
Ginoux et al. (2012) present global-
scale high-resolution (0.1º) mapping of
sources based on Moderate Resolution
Imaging Spectroradiometer (MODIS)
Deep Blue estimates of dust optical
depth in conjunction with other data sets,
including land use. The analysis ascribes
dust sources to natural or anthropogenic
(primarily agricultural) origins and
calculates their respective contributions to
emissions.

18
Note: The MODIS Deep Blue emissions are displayed in blue for hydrologic and natural sources and in
red for non-hydrologic and anthropogenic sources.
Source: Image extracted from Ginoux et al., 2012, Figure 16.
North Africa is the largest dust source
in the world (Figure 3). The source zone
comprises the Sahara Desert in the north
and centre and the semi-arid Sahel in the
south. Based on MODIS Deep Blue satellite
observations, North Africa accounts for 55
per cent of global dust emissions, of which
only 8 per cent are anthropogenic, although
it contributes to 20 per cent of global
anthropogenic emissions, mostly from the
semi-arid Sahel (Ginoux et al., 2012).
In North Africa, emission estimates based
on global models widely range from 400 to
2,200 Tg per year (Huneeus et al., 2011).
The great uncertainty in dust emission
estimates is partly due to the lack of
detailed information on dust sources and
accounting for small-scale features that
could potentially be responsible for a large
fraction of global dust emissions (Ginoux
et al., 2012; Knippertz and Todd, 2012).
The single largest dust source in the world
is located in the Bodélé Depression, north
of Lake Chad in North Africa (Ginoux et al.,
2001, 2012; Prospero et al., 2002). With
the other depressions (such as Aoukar
Depression on the Mali-Mauritania border)
and the gaps on the downwind side of
the Saharan mountains (mainly between
15ºN and 20ºN latitude), these sources
combined can contribute about 85 per cent
of all North African dust emissions (Evan et
al., 2015).
In West Asia, the main dust sources are
located in the Arabian Peninsula, such as
the Rub’ Al Khali desert, one of the largest
sand deserts in the world (Ginoux et al.,
2012). Other important dust sources are
located in Iraq, Pakistan, and parts of Iran
and Afghanistan (Goudie and Middleton,
2006; Ginoux et al., 2012; Rezazadeh et al.,
2013).
Figure 3.
Annual mean
dust emission (a)
from ephemeral
water bodies and
(b) from land use

Emission estimates for West Asia vary
from 26 to 526 Tg per year (Huneeus et
al., 2011) and seasonal dust activity varies
depending on the region. Dust activity
peaks in the west of the region during the
winter months and shifts to the east from
spring to summer when the south-west
monsoon is well developed (Prospero et al.,
2002).
The most severe dust storms are
associated with the summer Shamal
(north-westerly winds commonly known as
the “wind of 120 days” (Alizadeh-Choobari
et al., 2014), which can lift large amounts
of dust from their sources and transport
them over considerable distances towards
the Indian Ocean (Li and Ramanathan,
2002). The Sistan Basin located in eastern
Iran and western Afghanistan is the region
with the highest number of dust events in
West Asia. In the winter, dust storms are
mainly caused by the coupling of mid-
latitude cold front systems (with winds
from the north) and the extent of the
southern wind from the Red Sea uplifting
dust from many sources at once (Jiang
et al., 2009; Kalenderski et al., 2013; Jish
Prakash et al., 2015).
A major dust source is located in
southern Iraq. The area is situated within
Al-Muthanna and Thi-Qar provinces
between three major southern Iraqi cities
(Al-Nasriya, Al-Diwaniya and Al-Samawa)
and within the Mesopotamian Basin and
the Samawa and Abu Jir lineaments. The
larger zone extends along the Abu Jir fault
zone that runs down the western side of
the Euphrates River through Karbala, Najaf
and west Kuwait.
The area contains sand dunes and sand
sheets, with an estimated total area of
4,339 km2
and a perimeter of 895 km. Dust
from this source travels through Kuwait,
east Saudi Arabia and reaches as far as
Qatar (more than 1,200 km away).
Based on visibility measurements, Pakistan
is considered a place with a high mean
dust concentration (Rezazadeh et al.,
2013). Dust storms in Pakistan and north-
west India are mainly observed during the
pre-monsoon and monsoon seasons from
April to September, when dry convection
as well as strong downdraft from severe
thunderstorms generate dust storms
(Hussain et al., 2005; Mir et al., 2006; and
Das et al., 2014).
Mesoscale systems, such
as sea breezes across the
coastal areas (for example
the Persian Gulf) and
thunderstorms, make an
important contribution to
dust emissions in West Asia
(Miller et al., 2008).
For Central Asia, Indoitu et al. (2012)
report that the Karakum Desert, northern
lowlands of the Caspian Sea and Kyzylkum
Desert are major historical SDS sources.
In recent decades, desiccated lake beds
due to society’s overuse of water, such as
the Aral Sea in Central Asia (Issanova et
al., 2015), have also become significant
sources of SDS.
Box 2. Local sources of dust
While the dust belt is the major source of dust circulating globally, local sources of dust
can have significant impacts as well.
One typical local source of dust results from ploughing fields, whereby soils can become
entrained in winds. While not contributing to the global dust load, these local sources can
lead to significant negative impacts, including fatalities (NBC 5, 2017).
Other significant local sources of dust include volcanic ash, for instance in Iceland
(Arnalds et al., 2016), and glacial outwash plains (Gisladottir et al., 2005). Identifying SDS
sources is also discussed in chapter 8.

20
In East Asia, the largest natural sources are located in northern China (i.e. Taklamakan
Desert, Badain Jaran Desert, Tengger and Ulan Buh Desert, see Figure 4) and Mongolia
(i.e. Gobi Desert). Dust storms are more frequent and severe in the spring (Zhang et al.,
2003; Ginoux et al., 2012).
Source: Zhang et al., 2003.
In Figure 4, the percentages with standard
deviation in parenthesis denote the average
dust emission from each source and
depositional areas as a proportion of the
total mean emission amount in the last 43
years. The three largest natural sources are
located in Mongolia (S2) with Gobi Desert
as its main part, northern China high dust
region (S6) with Badain Jaran Desert as its
main body, and north-western China high
dust area (S4) with Taklamakan Desert as
its centre. These three main source areas
contribute about 70 per cent of total Asian
dust emission.
Dust particles are mainly carried eastwards
from Central Asia, China and Mongolia to
East Asia, Japan and Korea (Zhang et al.,
1997; Hong et al., 2010), across the North
Pacific Ocean to the western part of North
America (Fairlie et al., 2007), and even to
the Arctic (Fan, 2013).
About 800 Tg yr–1 of Asian dust emissions
are released into the atmosphere annually,
about 30 per cent of which is redeposited
onto the deserts and 20 per cent of which
is transported over regional scales, while
the remaining approximately 50 per cent
is subject to long-range transport to the
Pacific Ocean and beyond (Zhang et al.,
1997).
Asian dust appears to be a continuous
source that dominates background dust
aerosol concentrations on the west
coast of the United States of America
(Thulasiraman et al., 2002; Fischer et
al., 2009). East Asia also contains large
anthropogenic dust sources (25 per cent
of the total), most of which are found in
India and in some regions of China such as
the North China Plain (Ginoux et al., 2012;
Stanelle et al., 2014).
North American dust activity is
concentrated in the south-western United
States (Arizona and California) and north-
western Mexico. The dust events over this
desert area occur most frequently in the
spring and rarely during the rest of the year,
with the minimum dust activity occurring in
winter (Ginoux et al., 2012).
Figure 4.
Sources (S1 to
S10) and typical
depositional
areas (D1 and
D2) for Asian
dust aerosol
associated with
spring average
dust emission flux
(kg km-2
spring-1
)
between 1960
and 2002

Outside the global dust belt, Australia is
the largest dust source in the southern
hemisphere (Ginoux et al., 2012). McTainsh
and Pitblado (1987) identified the five
main high-frequency dust storms regions
in Australia: Lake Eyre basin, Central
Queensland, the Mallee region, the
Nullarbor Plain and the Central Western
Australian coast.
Australian dust is transported across the
continent along two major routes: east,
over the Southern Pacific Ocean and west,
over the Indian Ocean (McTainsh, 1989).
Ginoux et al. (2012) identified that dust
storms mainly occur between September
and February in most of the Australian
source regions.
Based on Ginoux et al. (2012), South
American dust sources can be found
in: the Atacama Desert (Chile), known
as the world’s driest region; Patagonia
(Argentina); and the Bolivian Altiplano
(Bolivia), which contains Salar de Uyuni,
the world’s largest salt flat. The peak
occurrence of dust storms in these regions
is between December and February. Large
anthropogenic dust sources in the region
are predominantly found in Patagonia,
where they are associated with livestock
grazing (Ginoux et al., 2012).
Southern African dust sources are
identified as ephemeral inland lakes,
coastal pans and dry river valleys. Southern
African dust source locations are mainly
found in Namibia (Etosha Basin and Namib
coastal sources), Botswana (Makgadikgadi
Basin) and South Africa (south-western
Kalahari and the Free State).
Dust activity in the region is dominated by
the Makgadikgadi and Etosha pans. Low
activity is detected throughout the year,
but with an increase from the southern
hemisphere in summer and autumn
(Ginoux et al., 2012; Vickery et al., 2013).
Major anthropogenic sources are found
north of Cape Town and Bloemhof Dam,
from agriculture activities, and in southern
Madagascar due to intense deforestation
(Ginoux et al., 2012).
2.3.2. Dust cycle
and associated
meteorological
processes
The dust cycle involves several processes
such as dust emission, transport and
deposition (Figure 5), which occur at
a wide range of spatial and temporal
scales. Based on wind-tunnel experiments
(Bagnold, 1941), dust particles are released
into the atmosphere through three
mechanisms, depending on their size:
• aggregate disintegration for rolling (or
creeping) particles larger than 2 mm
• saltation bombardment for particles
between 60 μm and 2 mm
• aerodynamic entrainment or
suspension of particles finer than
60 μm

22
Emission processes are also affected by
several soil features such as soil moisture,
soil texture, surface crust, roughness
elements and vegetation (see Figure 5).
Once strong winds emit dust particles,
fine dust particles are carried by turbulent
diffusion and convection to higher
tropospheric levels (up to a few kilometres
in height) and then large-scale winds
can transport them over long distances
(Prospero, 1996; Goudie and Middleton,
2006). Dust particles in the atmosphere
scatter and absorb solar radiation and,
acting as cloud condensation nuclei/ice
nuclei, modify clouds and their radiative
and precipitation processes (Figure 5).
Figure 5.
Dust cycle
processes,
their components,
controlling factors
and impacts on
radiation and
clouds
Wind
Turbulent diffusion
Convection
Dry deposition
Transport by
wind and clouds
Impact on radiation
(optical thickness,
backscatter)
Wet deposition
Dust emission
Saltation
Condensation nuclei
Roughness
elements
Trapped particles
Soil texture and surface crust
Creep
CH2 Figure 5.
Source: Shao, 2008.

The lifetime of dust particles in the
troposphere depends on the particle size.
It takes much longer for smaller particles
to deposit back on the surface than larger
particles. Based on observations, the
lifetime of dust particles with a diameter
larger than 20 μm is around 12 hours
(Ryder et al., 2013). Finer particles can
have lifetimes of up to 10 to 15 days,
indicating longer transportation distances
(Ginoux et al., 2001). These particles are
removed from the atmosphere through
dry deposition processes, including
gravitational settling and turbulent transfer,
and wet deposition processes including in-
and below-cloud scavenging.
2.3.3. Meteorological
mechanisms involved
in dust storms
According to WMO, dust storms are
generated by strong surface winds that
raise a large number of dust particles into
the air and reduce visibility to less than
1,000 metres (McTainsh and Pitblado,
1987). There are several meteorological
mechanisms, each with its own diurnal
and seasonal features, occurring at a
wide range of spatiotemporal scales (i.e.
synoptic, mesoscale and microscale) that
may control strong winds and cause dust
storms (Knippertz and Stuut, 2014). These
are discussed below.
Large-scale flows mainly associated with
monsoon circulations (such as with the
Indian and West Africa monsoons, see
Figure 6), shear-lines (observed both near
the ground and in jet streams), and thermal
lows over continents (such as the Saharan
Heat Low, SHL) affect the emission and
transportation of dust by strong large-
scale winds over long distances (Knippertz
and Todd, 2012). Regions affected by the
influence of monsoons are characterized
by a reversal of the mean wind direction
from summer to winter.
Dust storms caused by large-scale trade
winds are typical over the Middle East and
North Africa. In North Africa, the large-
scale north-easterly trade winds called the
Harmattan (see Figure 6) are associated
with the position of the Intertropical
Convergence Zone (ITCZ).
Note: The pink regions show dust mobilization caused by large-scale trade winds such as Harmattan
(black arrows), which also configurate the Intertropical Convergence Zone (white line).
Source: EUMETSAT, https://www.eumetsat.int/website/home/index.html
Figure 6.
Meteosat
Second
Generation
(MSG) RGB Dust
Product for 8
March 2006

24
During summer in West Asia, these winds
blow from the north-west and are called
a summer Shamal or the “wind of 120
days”, given their persistence from June to
September.
Synoptic-scale weather systems (such
as cyclones, anticyclones and their cold
frontal passage, see Figure 6) are the
primary control on episodic, large, intense,
dust events in many source regions. On
the synoptic scale, these are frequently
associated with extratropical cyclonic
disturbances and particularly the trailing
cold fronts with which the latter are
associated.
The passage of a cold front that generates
dust emission is typically associated with
a marked drop in temperature and visibility
and increases in wind and pressure (see,
for example, Knippertz and Fink, 2006).
The dust frontal zone varies significantly
depending on the season and the region
as well as the evolution of the cyclone.
Pre-frontal dust storms (Figure 7a)
occur when low-pressure systems move
towards a stationary anticyclone or a high
topography. Otherwise, post-frontal dust
storms (Figure 7b) occur when a front
passes over the dust source, with the
winds generating dust behind it.
Note: Pre-frontal (Figure 7a) and post-frontal (Figure 7b) associated sand and dust storms. The
Sharqi and Suhaili in yellow in figure 7a are winds in the Middle East. Sharqi comes from the south
and south-east and Suhaili comes from the south-west, as indicated by the white arrows.
Source: The COMET Program, www.meted.ucar.edu.
Figure 7a and b.
Typical synoptic
configurations
that can uplift
dust over the
Middle East
Figure 7a
Figure 7b

Moist convection from cold pool outflows
is the main driver of convective mesoscale
dust storms, called haboobs. Cold pool
outflows are downdrafts caused by the
evaporation and cooling of rain from
thunderstorms which, near the surface,
cause gravity currents where strong winds
can uplift dust. Strong winds (the “head” in
Figure 8) uplift a large amount of dust and
can generate a wall of blowing dust on the
leading edge of the haboob where warm
air is forced upward by the cold air, forming
the “nose” (see Figure 8).
Haboobs may reach 1.5 to 4 km in height
and span hundreds of kilometres over
desert areas. Because of the diurnal cycle
of deep moist convection, they tend to
occur from late afternoon to night, with a
typical lifetime of a few hours (Knippertz
and Todd, 2012; Marsham et al., 2013).
0
2
4
6
8
Height
(kilometres)
Gust front
Head
Wake
Warm air
Outflow boundary
Cool outflow
Strong wind
Dust
Cumulonimbus
cloud
Figure 8. Cross
section of a
haboob
Source: Warner, 2004, Figure 16.10.

26
Microscale dry convection in the daytime
planetary boundary layer (PBL) over
deserts can cause dust whirlwinds and
dust plumes through turbulent circulation.
The most favourable conditions for their
formation are clear skies, strong surface
heating and weak background winds. Dust
whirlwinds have a lifetime from a few
minutes to less than an hour and occur
at spatial scales from a few to several
hundred metres (Knippertz and Todd,
2012).
Figure 9 shows a typical sequence of
a dust whirlwind’s formation caused by
intense surface heating, turbulent winds
and microscale dry convection. The Sun
heats air nearest the ground. Wind causes
the hot air bubble to break through to
the stratified layer. Near-surface cyclonic
circulation is generated around the low-
pressure zone below the newly formed air
bubble. Then, in a tetherball effect, the air
moves faster as it approaches the centre,
then spirals rapidly upward to maintain the
dust whirlwind.
Source: Modified from Ramon Peñas in The National, no date.
Figure 9.
Dust whirlwind
formation sequence
1 Sun heats up the
ground
2 Warm air rises over the
hotspot and pressure
lowers
3 Swirling air picks up
dust, creating the dust
whirlwind
1
2
3
CH2 Figure 9.

Source: NASA Earth Observatory, 2007.
Topographic effects can locally affect
the meteorology of dust emission
and transport processes. This can
occur though gaps in mountain ranges
channelling wind, as in the Bodélé
Depression, the most important dust
emission hotspot at the global scale (see
Figure 10).
Diurnal cycles can also be responsible
for dust mobilization. One example is the
development and subsequent breakdown
of the nocturnal low-level jet (NLLJ).
Daytime heating can also set up land–sea
or mountain–valley circulations that can be
important for the dust emissions in certain
regions.
Inversion downburst storms are
windstorms that occur on sloping coastal
plains with a strong sea breeze. Inversion
downburst storms typically lead to a
very narrow streamer of dust over the
Persian Gulf. As a sea breeze intensifies,
convergence along the sea breeze front
can generate sufficient lift to break a
capping inversion. The resulting instability
leads to the downward mixing of cool air
aloft, which flows downslope and out over
the water. The descending air produces
roll vortices and potentially severe local
dust storms along the coast. Over time, the
inversion is re-established and the event
dies out.
2.3.4. Dust seasonality and
inter-annual variations
Dust emissions and atmospheric transport
from worldwide sources indicate seasonal
and spatial variability (Tegen et al., 2002;
see Figure 11). The data in Figure 11 are
based on Absorbing Aerosol Index (AAI)
averages for 1986–1990, organized by
season:
• winter (DJF) corresponding to
December, January and February
• spring (MAM) corresponding to March,
April and May
• summer (JJA) corresponding to June,
July, August, and
• autumn (SON) corresponding to
September, October and November
Figure 10.
MODIS true
colour composite
image for 2
January 2007
depicting a dust
storm initiated
in the Bodélé
Depression, Chad
Basin

28
The higher (closer to brown) the AAI, the
greater the presence of dust particles.
The variability is mainly characterized by
changes in meteorological conditions
in the low troposphere and by global
circulation patterns.
This includes seasonal displacement of
the Intertropical Convergence Zone (ITCZ)
(Schepanski et al., 2009) and monsoons
(Bou Karam et al., 2008; Cuesta et al., 2010;
Vinoj et al., 2014).
Figure 11. Global
seasonal Absorbing
Aerosol Index (AAI)
based on TOMS
satellite imagery
Source: Tegen et al., 2002.
©Asian
Development
Bank

As shown in Figure 11, dust activity is
associated with a marked seasonality
and shifts throughout the year from
winter, when it is more pronounced in low
latitudes, to summer, when it is observed at
higher latitudes (Tegen et al., 2002, 2013;
Schepanski et al., 2007). North African1
dust is mainly transported along three
main pathways:
• Westward over the North Atlantic
Ocean to the Americas (Prospero
et al., 2002; Marticorena et al.,
2010; Gama et al., 2015). Maximum
occurrence is between June and July
and minimum from December to
February (Prospero, 1996; Basart et al.,
2009; Tsamalis et al., 2013).
• Northward towards the Mediterranean
and Southern Europe. In exceptional
outbreaks, dust particles can be
transported to Scandinavia and
the Baltics (Barkan et al., 2004;
Papayannis et al., 2005; Basart et
al., 2009; Pey et al., 2013; Gkikas et
al., 2016), with a higher occurrence
during spring and summer and lower
occurrence in winter (Basart et al.,
2009; Pey et al., 2013; Gkikas et al.,
2016).
• Eastward (from East Africa), more
frequent in spring and summer
towards the Middle East (Goudie and
Middleton, 2006; Kalenderski and
Stenchikov, 2016), but also possibly
as far as the Himalayas (Carrico et al.,
2003)
Inter-annual variations in dust patterns
also occur. These include differences in
African dust transport linked to drought
conditions in the Sahel and the North
Atlantic Oscillation (NAO) (Prospero and
Lamb, 2003; Chiapello et al., 2005), the
El Niño–Southern Oscillation (ENSO) in
summer (DeFlorio et al., 2016), and surface
temperatures over the Sahara (Wang et al.,
2015). These inter-annual variabilities and
relationships are not yet fully understood
but all reveal the connection between dust
and climate.
1 The use of “North Africa” and “Northern Africa” refer to the area in Africa north of the Equator and not the
area north of the Sahara Desert alone, i.e. the terms encompass parts of what are also called West and East Africa.
2.4 Conclusions
SDS are atmospheric events involving
small particles blown from land surfaces.
They occur when strong, turbulent winds
blow over dry, unconsolidated, fine-grained
surface materials where vegetation cover
is sparse or altogether absent. As these
conditions are most commonly found in
the world’s drylands – deserts and semi-
deserts – this is where SDS events are
most frequent. Sand storms occur within
the first few metres above the ground
surface, but finer dust particles can be
lifted much higher into the atmosphere,
where strong winds frequently transport
them over great distances. SDS play an
integral role in the Earth system, with
numerous and wide-ranging impacts
including on air chemistry and climate
processes, soil characteristics and
water quality, nutrient dynamics and
biogeochemical cycling in both oceanic
and terrestrial environments.

30
2.5 References
Akata, Naofumi, and others (2007). Deposition of 137Cs
in Rokkasho, Japan and its relation to Asian dust.
Journal of Environmental Radioactivity, vol. 95, No.
1, pp. 1–9.
Al-Yahyai, Sultan, and Yassine Charabi (2014). Trajectory
calculation as forecasting support tool for dust
storms. Advances in Meteorology, vol. 2014(Article
ID 698359), pp. 1–6.
Alizadeh-Choobari, Omid Peyman Zawar-Reza and
Andrew Sturman (2014). The “wind of 120 days”
and dust storm activity over the Sistan Basin.
Atmospheric Research, vol. 143, pp. 328–341.
Allen, Christopher, Richard Washington and Sebastian
Engelstaedter (2013). Dust emission and transport
mechanisms in the central Sahara: Fennec ground-
based observations from Bordj Badji Mokhtar,
June 2011. Journal of Geophysical Research:
Atmospheres, vol. 118, No. 12, pp. 6212–6232.
Anderson, James R., and others (1976). A land use
and land cover classification system for use with
remote sensor data. USGS Professional Paper 964.
A revision of the land usekioclassification system
as presented in the USGS Circular 671.
Arakawa, Akio, and Vivian Lamb (1977). Computational
design of the basic dynamical processes of the
UCLA general circulation model. Methods in
Computational Physics: Advances in Research and
Applications, vol. 17, pp. 173–265.
Ashpole, Ian, and Richard Washington (2013).
Intraseasonal variability and atmospheric controls
on daily dust occurrence frequency over the central
and western Sahara during the boreal summer.
118, No.23, pp. 12915–12926.
Atkinson, James D., and others (2013). The importance
of feldspar for ice nucleation by mineral dust in
mixed-phase clouds. Nature, vol. 498, No. 7454, pp.
355–358.
Badia, Alba, and Oriol Jorba (2015). Gas-phase evaluation
in the online NMMB/BSC Chemical Transport
Model over Europe for 2010 in the framework of the
AQMEII-Phase2 project. Atmospheric Environment,
vol. 115, pp. 657–669.
Badia, Alba, and others (2016). Gas-phase chemistry
in the online multiscale NMMB/BSC Chemical
Transport Model: Description and evaluation at
global scale. Geoscientific Model Development
Discussions, vol. 2, pp. 1–47.
Badia, Alba, and others (2017). Description and
evaluation of the Multiscale Online Nonhydrostatic
AtmospheRe CHemistry model (NMMB-
MONARCH) version 1.0: gas-phase chemistry at
global scale. Geoscientific Model Development, vol.
10, No. 2, pp. 609–638.
Bagnold, Ralph (1941). The Physics of Blown Sand and
Desert Dunes. London: Methuen.
Barkan, Joseph, and others (2004). Synoptics of dust
intrusion days from the African continent into the
Atlantic Ocean. Journal of Geophysical Research,
vol.109(D08201), pp. 1–9.
Barnaba, Francesca, and others (2017). Desert dust
contribution to PM10
loads in Italy: Methods
and recommendations addressing the relevant
European Commission Guidelines in support to
the Air Quality Directive 2008/50. Atmospheric
Environment, vol. 161, pp. 288–305.
Basart, Sara, and others (2012). Aerosols in the CALIOPE
air quality modelling system: Evaluation and
analysis of PM levels, optical depths and chemical
composition over Europe. Atmospheric Chemistry
and Physics, vol. 12, No. 7, pp. 3363–3392.
Basart, Sara, and others (2009). Aerosol characterization
in Northern Africa, Northeastern Atlantic,
Mediterranean Basin and Middle East from direct-
sun AERONET observations. Middle East, vol. 9, No.
21, pp. 8265–8282.
Betts, Alan (1986). A new convective adjustment
scheme. Part I: Observational and theoretical
basis. Quarterly Journal of the Royal Meteorological
Society, vol. 112, No. 473, pp. 677–691.
Betts, Alan, and M.J. Miller (1986). A new convective
adjustment scheme. Part II: Single column tests
using GATE wave, BOMEX, ATEX and arctic air-
mass data sets. Quarterly Journal of the Royal
Meteorological Society, vol. 112, No. 473, pp. 693–
709.
Bou Karam, Diana, and others (2008). Dust emissions
over the Sahel associated with the West African
monsoon intertropical discontinuity region: A
representative case-study. Quarterly Journal of the
Royal Meteorological Society, vol 134, No. 632, pp.
621–634.
Bretl, Sebastian, and others (2015). The influence
of absorbed solar radiation by Saharan dust on
hurricane genesis. Journal of Geophysical Research:
Bristow, Charlie S., Karen A. Hudson-Edwards, and
Adrian Chappell (2010). Fertilizing the Amazon
and equatorial Atlantic with West African dust.
Geophysical Research Letters, vol. 37, No. 14, pp.
3–7.
Cakmur, Reha V., and others (2006). Constraining the
magnitude of the global dust cycle by minimizing
the difference between a model and observations.
111, No. 6, pp. 1–24.

Carrico, Christian M., and others (2003). The importance
of carbon and mineral dust to seasonal aerosol
properties in the Nepal Himalaya. Atmospheric
Environment, vol. 37, No. 20, pp. 2811–2824.
Chapman, Elaine G., and others (2009). Coupling aerosol-
cloud-radiative processes in the WRF-Chem model:
investigating the radiative impact of elevated point
sources. Atmospheric Chemistry and Physics
Discussions, vol. 8, No. 4, pp. 14765–14817.
Chiapello, Isabelle, Cyril Moulin and Joseph M. Prospero
(2005). Understanding the long-term variability
of African dust transport across the Atlantic as
recorded in both Barbados surface concentrations
and large-scale Total Ozone Mapping Spectrometer
(TOMS) optical thickness. Journal of Geophysical
Research: Atmospheres, vol. 110, No. D18, pp. 1–9.
Chien, Lung-Chang, and others (2014). Acute increase
of children’s conjunctivitis clinic visits by asian dust
storms exposure - a spatiotemporal study in Taipei,
Taiwan. PLOS ONE, vol. 9, No. 10, pp. 1–10.
Clarkson, Rory, and Harry Simpson (2017). Maximising
airspace use during volcanic eruptions: matching
engine durability against ash cloud occurrence.
In Proceedings of the Science and Technology
Organisation Meeting STO-MP-AVT-272, Vilnius,
Lithuania (pp. 15-17).
Creamean, Jessie M., and others (2013). Dust and
biological aerosols from the Sahara and Asia
influence precipitation in the Western U.S. Science,
vol. 339, No. 6127, p. 1572–1578.
Crouvi, Onn, and others (2017). An Israeli haboob: Sea
breeze activating local anthropogenic dust sources
in the Negev loess. Aeolian Research, vol. 24, pp.
39–52.
Cuesta, Juan, and others (2010). Northward bursts of
the West African monsoon leading to rainfall over
the Hoggar Massif, Algeria. Quarterly Journal of the
Royal Meteorological Society, vol. 136, No. S1, pp.
174–189.
Cuevas, Emilio (2013). Establishing a WMO Sand and
Dust Storm Warning Advisory and Assessment
System Regional Node for West Asia: Current
Capabilities and Needs. Publications Board, World
Meteorological Organization (WMO), WM0-1121.
Cuevas, Emilio, and others (2015). The MACC-II 2007–
2008 reanalysis: atmospheric dust evaluation
and characterization over northern Africa and the
Middle East. Atmospheric Chemistry and Physics,
vol. 15, No. 8, pp. 3991–4024.
Cziczo, Daniel J., and others (2013). Clarifying the
dominant sources and mechanisms of cirrus cloud
formation. Science, vol. 340, No. 6138, pp. 1320–
1324.
d’Almeida, Guillaume A. (1987). On the variability of
desert aerosol radiative characteristics. Journal of
Geophysical Research; Atmospheres, vol. 92, No.
D3, p. 3017.
Das, Someshwar, and others (2014). The SAARC
STORM: a coordinated field experiment on
severe thunderstorm observations and regional
modeling over the South Asian region. Bulletins
of American Meteorological Society, DOI:10.1175/
BAMS-D-12-00237.1, pp. 603-617
Dee, Dick P., and others (2011). The ERA-Interim
reanalysis: Configuration and performance of the
data assimilation system. Quarterly Journal of the
Royal Meteorological Society, vol. 137, No. 656, pp.
553–597.
DeFlorio, Michael J., and others (2016). Interannual
modulation of subtropical Atlantic boreal summer
dust variability by ENSO. Climate Dynamics, vol. 46,
No. 1, pp. 585–599.
Dirmeyer, Paul A., and others (2012). Simulating the
diurnal cycle of rainfall in global climate models:
Resolution versus parameterization. Climate
Dynamics, vol. 39, No. 1–2, pp. 399–418.
Dubovik, Oleg, and others (2002). Variability of
absorption and optical properties of key aerosol
types observed in worldwide locations. Journal of
the Atmospheric Sciences, vol. 59, No. 3, pp. 590–
608.
Dunion, Jason P., and Christopher S. Velden (2004).
The impact of the Saharan Air Layer on Atlantic
tropical cyclone activity. Bulletin of the American
Meteorological Society, vol. 85, No. 3, pp. 353–365.
Ek, Michael B., and others (2003). Implementation of
Noah land surface model advances in the National
Centers for Environmental Prediction operational
mesoscale Eta model. Journal of Geophysical
Research: Atmospheres, vol. 108, No. D22, p. 8851.
Engelstaedter, Sebastian, and others (2015). The
Saharan heat low and moisture transport pathways
in the central Sahara—Multiaircraft observations
and Africa-LAM evaluation. Journal of Geophysical
Research: Atmospheres, vol. 120, No. 10, pp. 1–26.
European Commission (2008). Directive 2008/50/EC
of the European Parliament and of the Council of
21 May 2008 on ambient air quality and cleaner air
for Europe (OJ L 152, 11.6.2008, p. 1–44). Available
at http://eur-lex.europa.eu/LexUriServ/LexUriServ.
do?uri=OJ:L:2008:152:0001:0044:EN:PDF.
Evan, Amato T., and others (2006). New evidence for
a relationship between Atlantic tropical cyclone
activity and African dust outbreaks. Geophysical
Research Letters, vol. 33, No. 19, pp. 1–5.
Evan, Amato T., and others (2011). Influence of African
dust on ocean–atmosphere variability in the
tropical Atlantic. Nature Geoscience, vol. 4, No. 11,
pp. 762–765.
Evan, Amato T., and others (2015). Derivation of an
observation-based map of North African dust
emission. Aeolian Research, vol. 16, pp. 153–162.

32
Fairlie, Thomas Duncan, Daniel J. Jacob and Rokjin J.
Park (2007). The impact of transpacific transport
of mineral dust in the United States. Atmospheric
Fan, Song-Miao (2013). Modeling of observed mineral
dust aerosols in the arctic and the impact on winter
season low-level clouds. Journal of Geophysical
Research: Atmospheres, vol. 118, No. 19, pp.
11,161–11,174.
Farquharson, J. S. (1937). Haboobs and instability in the
Sudan. Quarterly Journal of the Royal Meteorological
Society, vol. 63, No. 271, pp. 393–414.
Fécan, F., Beatrice Marticorena, and Gilles Bergametti
(1999). Parametrization of the increase of the
Aeolian erosion threshold wind friction velocity
due to soil moisture for arid and semi-arid areas.
Annales Geophysicae, vol. 17, No. 1, pp. 149–157.
Fels, Stephen B., and M. Daniel Schwarzkopf (1975).
The simplified exchange approximation: a new
method for radiative transfer calculations. Journal
of the Atmospheric Sciences, vol. 32, No. 7, pp.
1475–1488.
Ferrier, B. S., and others (2002). Implementation of a new
grid-scale cloud and precipitation scheme in the
NCEP Eta Model. In: 19th Conference on Weather
Analysis and Forecasting/15th Conference on
Numerical Weather Prediction. San Antonio, TX:
Amer. Meteor. Soc., pp. 280–283.
Fiedler, Stephanie, and others (2013). Climatology
of nocturnal low-level jets over North Africa and
implications for modeling mineral dust emission.
118, No. 12, pp. 6100–6121.
Fischer, Emily V., and others (2009). A decade of dust:
Asian dust and springtime aerosol load in the U.S.
Pacific Northwest. Geophysical Research Letters,
vol. 36, No. 3.
Foltz, Gregory R., and Michael J. McPhaden (2008).
Trends in Saharan dust and tropical Atlantic climate
during 1980–2006. Geophysical Research Letters,
vol. 35, No. 20, pp. 1–5.
Gama, Carla, and others (2015). Seasonal patterns
of Saharan dust over Cape Verde – a combined
approach using observations and modelling. Tellus
B: Chemical and Physical Meteorology, vol. 67, No.
1, pp. 1–21.
Garrison, Virginia H., and others (2003). African and
Asian dust: from desert soils to coral reefs.
BioScience, vol. 53, No. 5, pp. 469–480.
Gasch, Philipp, and others (2017). An analysis of the
September 2015 severe dust event in the Eastern
Mediterranean. Atmospheric Chemistry and Physics
Discussions (in review), pp. 1–37.
Gillette, Dale A. (1979). Environmental factors affecting
dust emission by wind erosion. In Saharan Dust,
Morales, C., ed. Chichester: Wiley, pp.71–91.
Ginoux, Paul, and others (2001). Sources and
distributions of dust aerosols simulated with the
GOCART model. Journal of Geophysical Research:
Atmospheres, vol. 106, No. D17, pp. 20255–20273.
Ginoux, Paul, and others (2012). Global-scale attribution
of anthropogenic and natural dust sources and
their emission rates based on MODIS Deep Blue
aerosol products. Reviews of Geophysics, vol. 50,
No. RG3005.
Gkikas, Antonis, and others (2016). Mediterranean
intense desert dust outbreaks and their vertical
structure based on remote sensing data.
Atmospheric Chemistry and Physics, vol. 16, No. 13,
pp. 8609–8642.
Gleick, Peter H. (2014). Water, drought, climate change,
and conflict in Syria. Weather, Climate, and Society,
vol. 6, No. 3, pp. 331–340.
Goudie, Andrew S. (2014). Desert dust and human
health disorders. Environment International, vol. 63,
pp. 101–113.
Goudie, Andrew S., and Nicholas J. Middleton (2006).
Desert Dust in the Global System. Berlin: Springer.
Griffin, Dale W., and others (2002). The global
transport of dust: an intercontinental river of dust,
microorganisms and toxic chemicals flows through
the Earth’s atmosphere. American Scientist, vol. 90,
No. 3, pp. 228-235.
Haustein, Karsten (2011). Development of an
Atmospheric Modeling System for Regional and
Global Mineral Dust Prediction: Application to
Northern Africa, Middle East and Europe. Universitat
Politècnica de Catalunya.
Haustein, Karsten, and others (2012). Atmospheric
dust modeling from meso to global scales with
the online NMMB/BSC-Dust model – Part 2:
Experimental campaigns in Northern Africa.
pp. 2933–2958.
Haustein, Karsten, and others (2015). Testing the
performance of state-of-the-art dust emission
schemes using DO4Models field data. Geoscientific
Model Development, vol. 8, No. 2, pp. 341–362.
Heinold, Bernd, and others (2013). The role of deep
convection and nocturnal low-level jets for dust
emission in summertime West Africa: Estimates
from convection-permitting simulations. Journal of
Geophysical Research: Atmospheres, vol. 118, No.
10, pp. 4385–4400.
Heymsfield, Andrew J., and others (2009). Microphysics
of maritime tropical convective updrafts at
temperatures from −20° to −60°. Journal of
the Atmospheric Sciences, vol. 66, No. 12, pp.
3530–3562.
Holben, Brent N., and others (1998). AERONET—A
federated instrument network and data archive
for aerosol characterization. Remote Sensing of

Hong, Yun-Chul, and others (2010). Asian dust storm
and pulmonary function of school children in Seoul.
Science of The Total Environment, vol. 408, No. 4,
pp. 754–759.
Hsu, N. Christina, and others (2004). Aerosol properties
over bright-reflecting source regions. IEEE
Transactions on Geoscience and Remote Sensing,
vol. 42, No. 3, pp. 557–569.
Huneeus, Nicolas, and others (2011). Global dust model
intercomparison in AeroCom phase I, Atmospheric
Chemistry and Physics, vol. 11, No. 15, pp. 7781–
7816.
Huneeus, Nicolas, and others (2016). Forecasting the
northern African dust outbreak towards Europe in
April 2011: A model intercomparison. Atmospheric
4986.
Hussain, Ata, Hazrat Mir, and Muhammad Afzal
(2005). Analysis of dust storms frequency over
Pakistan during 1961–2000. Pakistan Journal of
Meteorology, vol. 2, No. 3, pp. 49–68.
Ibrahim, Mohamed M. and Gehan A. El-Gaely (2012).
Short-term effects of dust storm on physiological
performance of some wild plants in Riyadh, Saudi
Arabia. African Journal of Agricultural Research, vol.
7, No. 47, pp. 6305–6312.
Indoitu, Rodica, Leah Orlovsky and Nikolai Orlovsky
(2012). Dust storms in Central Asia: Spatial and
temporal variations. Journal of Arid Environments,
vol. 85, pp. 62–70.
Intergovernmental Panel on Climate Change (2013).
Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel
on Climate Change. Thomas F. Stocker, Dahe Qin,
Gian-Kasper Plattner, Melinda M.B. Tignor, Simon K.
Allen, Judith Boschung, Alexander Nauels, Yu Xia,
Vincent Bex and Pauline M. Midgley, eds. Cambridge
and New York: Cambridge University Press.
Iversen, James D., and Bruce R. White (1982). Saltation
threshold on Earth, Mars and Venus. Sedimentology,
vol. 29, No. 1, pp. 111–119.
Janjić, Zaviša I. (1994). The step-mountain eta
coordinate model: further developments of the
convection, viscous sublayer, and turbulence
closure schemes. Mon. Wea. Rev, vol. 122, No. 5,
pp. 927–945.
Janjić, Zaviša I. (1996). The surface layer in the NCEP
eta model. 11th Conference on Numerical Weather
Prediction, Norfolk, VA, 19–23 August 1996;
American Meteorological Society, Boston, MA, pp.
354–355.
Janjić, Zaviša I. (2000). Comments on “Development
and evaluation of a convection scheme for use in
climate models”. J. Atmos. Sci., vol. 57, pp. 3686–
3686.
Janjić, Zaviša I. (2001). Nonsingular Implementation of
the Mellor-Yamada Level 2.5 Scheme in the NCEP
Meso Model. NOAA/NWS/NCEP Office Note 437,
p. 61.
Janjić, Zaviša I. (2009). Further development of the
unified multiscale Eulerian model for a broad range
of spatial and temporal scales within the new
National Environmental Modeling System. EGU
General Assembly 2009, 19–24 April 2009, Vienna,
Austria, abstract EGU2009-1587, 11.
Janjić, Zaviša I., and Robert Gall (2012). Scientific
documentation of the NCEP nonhydrostatic
multiscale model on the B grid (NMMB), Part
1 Dynamics, Tech. rep., NCAR/TN-489+STR,
doi:10.5065/D6WH2MZX.
Jemmett-Smith, Bradley C., and others (2015).
Quantifying global dust devil occurrence from
meteorological analyses. Geophysical Research
Letters, vol. 42, No. 4, pp. 1275–1282.
Jiang, Houshuo, and others (2009). Zonal surface wind
jets across the Red Sea due to mountain gap forcing
along both sides of the Red Sea. Geophysical
Jickells, Tim D., and others (2005). Global iron
connections between desert dust, ocean
biogeochemistry, and climate. Science, vol. 308, No.
5718, p. 67–71.
Jish Prakash, P., and others (2015). The impact of dust
storms on the Arabian Peninsula and the Red Sea.
pp. 199–222.
Jorba, Oriol, and others (2012). Potential significance
of photoexcited NO2 on global air quality with the
NMMB/BSC chemical transport model. Journal of
Geophysical Research, vol. 117, No. D13, p. D13301.
Jugder, Dulam, and others (2011). Spatial and temporal
variations of dust concentrations in the Gobi Desert
of Mongolia. Global and Planetary Change, vol. 78,
No. 1, 14–22.
Kalenderski, Stoitchko, and Georgiy Stenchikov (2016).
High-resolution regional modeling of summertime
transport and impact of African dust over the Red
Sea and Arabian Peninsula. Journal of Geophysical
Research: Atmospheres, vol. 121, No. 11, pp. 6435–
6458.
Kalenderski, Stoitchko, Georgiy Stenchikov and Chun
Zhao (2013). Modeling a typical winter-time dust
event over the Arabian Peninsula and the Red Sea.
pp. 1999–2014.
Karami, Sara, and others (2017). A rare case of haboob
in Tehran: Observational and numerical study.
Atmospheric Research, vol. 185, No. 1, pp. 169–185.
Kelley, Colin P., and others (2015). Climate change
in the Fertile Crescent and implications of the
recent Syrian drought. Proceedings of the National
Academy of Sciences, vol. 112, No. 11, pp. 3241–
3246.

34
Kellogg, Christina A., and others (2004). Characterization
of aerosolized bacteria and fungi from desert dust
events in Mali, West Africa. Aerobiologia, vol. 20, No.
2, pp. 99–110.
Kim, Hyunglok, and others (2017). Development and
assessment of the sand dust prediction model by
utilizing microwave-based satellite soil moisture
and reanalysis datasets in East Asian desert areas.
Advances in Meteorology, vol. 2017(Article ID
1917372), p. 13.
Klein, Holger, and others (2010). Saharan dust and ice
nuclei over Central Europe. Atmospheric Chemistry
Klingmüller, Klaus, and others (2016). Aerosol optical
depth trend over the Middle East. Atmospheric
5073.
Klose, Martina, and Yaping Shao (2013). Large-eddy
simulation of turbulent dust emission. Aeolian
Research, vol. 8, pp. 49–58.
Klose, Martina, and Yaping Shao (2016). A numerical
study on dust devils with implications to global
dust budget estimates. Aeolian Research, vol. 22,
pp. 47–58.
Knippertz, Peter, and Jan-Berend W. Stuut, eds. (2014).
Mineral Dust: A Key Player in the Earth System.
Dordrecht: Springer Netherlands.
Knippertz, Peter, and Martin C. Todd (2012). Mineral dust
aerosols over the Sahara: Meteorological controls
on emission and transport and implications for
modeling. Rev. Geophys., vol. 50, No. 1.
Koch, Jacquelin, and Nitron O. Renno (2005). The role
of convective plumes and vortices on the global
aerosol budget. Geophysical Research Letters, vol.
32, No. 18, pp. 1–5.
Kocha, Cécile, and others (2013). The importance of
the diurnal cycle of Aerosol Optical Depth in West
Africa. Geophysical Research Letters, vol. 40, No. 4,
pp. 785–790.
Kok, Jasper F. (2011). A scaling theory for the size
distribution of emitted dust aerosols suggests
climate models underestimate the size of the global
dust cycle. Proceedings of the National Academy of
Sciences, vol. 108, No. 3, pp. 1016–1021.
Krueger, Brenda J., and others (2004). Heterogeneous
chemistry of individual mineral dust particles from
different dust source regions: the importance of
particle mineralogy. Atmospheric Environment, vol.
38, No. 36, pp. 6253–6261.
Kumar, Rajesh, and others (2014). WRF-Chem
simulations of a typical pre-monsoon dust storm
in northern India: Influences on aerosol optical
properties and radiation budget. Atmospheric
2446.
Lacava, Teodosio, and others (2012). A first assessment
of the SMOS soil moisture product with in situ
and modeled data in Italy and Luxembourg. IEEE
Transactions on Geoscience and Remote Sensing,
vol. 50, No. 5, pp. 1612–1622.
Lacis, Andrew A., and James E. Hansen (1974). A
parameterization for absorption of solar radiation in
the Earth’s atmosphere. Journal of the Atmospheric
Sciences, vol. 31, pp. 118–133.
Lavaysse, Christophe, and others (2016). Recent
climatological trend of the Saharan heat low and
its impact on the West African climate. Climate
Dynamics, vol. 47, No. 11, pp. 3479–3498.
Levy, Robert C., and others (2010). Global evaluation
of the Collection 5 MODIS dark-target aerosol
products over land. Atmospheric Chemistry and
Physics, vol. 10, No. 21, pp. 10399–10420.
Levy, Robert C., and others (2013). The Collection 6
MODIS aerosol products over land and ocean.
Atmospheric Measurement Techniques, vol. 6, No.
11, pp. 2989–3034.
Levy, Robert C., and others (2015). MODIS Atmosphere
L2 Aerosol Product. NASA MODIS Adaptive
Processing System, Goddard Space Flight Center,
USA: http://dx.doi.org/10.5067/MODIS/MYD04_
L2.006 (Aqua).
Leys, John F., and others (2011). PM 10 concentrations
and mass transport during “Red Dawn”–Sydney 23
September 2009. Aeolian Research, vol. 3, No. 3, pp.
327–342.
Li, F., and Veerabhadran Ramanathan (2002). Winter
to summer monsoon variation of aerosol optical
depth over the tropical Indian Ocean. Journal of
Geophysical Research, vol. 107, pp. 1–13.
Litvintseva, Anastasia P., and others (2015). Valley fever:
Finding new places for an old disease: Coccidioides
immitis found in Washington state soil associated
with recent human infection. Clinical Infectious
Diseases, vol. 60, No. 1, pp. e1–e3.
Loveland, Thomas R., and Alan S. Belward (1997). The
IGBP-DIS global 1km land cover data set, DISCover:
First results. International Journal of Remote
Sensing, vol. 18, No. 15, pp. 3289–3295.
Mallone, Sandra, and others (2011). Saharan dust and
associations between particulate matter and daily
mortality in Rome, Italy. Environmental Health
Perspectives, vol. 119, No. 10, pp. 1409-1414.
Mamouri, Rodanthi-Elisavet, and others (2016). Extreme
dust storm over the eastern Mediterranean in
September 2015: Satellite, lidar, and surface
observations in the Cyprus region. Atmospheric
13724.
Marcella, Marc P., and Elfaith A. B. Eltahir (2014). The role
of mineral aerosols in shaping the regional climate
of West Africa. Journal of Geophysical Research:

Marinou, Eleni, and others (2017). 3D evolution of
Saharan dust transport towards Europe based on
a 9-year EARLINET-optimized CALIPSO dataset.
Atmospheric Chemistry and Physics Discussions,
vol. 17, No. 9, pp. 1–35.
Marsham, John H., and others (2013). The role of moist
convection in the West African monsoon system:
Insights from continental-scale convection-
permitting simulations. Geophysical Research
Letters, vol. 40, No. 9, pp. 1843–1849.
Marsham, John H., and others (2013). Meteorology
and dust in the central Sahara: Observations from
Fennec supersite-1 during the June 2011 Intensive
Observation Period. Journal of Geophysical
Research: Atmospheres, vol. 118, No. 10, pp. 4069–
4089.
Marticorena, Beatrice, and Gilles Bergametti (1995).
Modeling the atmospheric dust cycle. Part 1:
Design of a soil-derived dust emission scheme.
Journal of Geophysical Research, vol. 100, No. D8,
pp. 16415–16430.
Marticorena, Beatrice, and others (1997). Modeling the
atmospheric dust cycle: 2. Simulation of Saharan
dust sources. Journal of Geophysical Research:
Atmospheres, vol. 102, No. D4, pp. 4387–4404.
Marticorena, Beatrice, and others (2010). Temporal
variability of mineral dust concentrations over West
Africa: Analyses of a pluriannual monitoring from
the AMMA Sahelian Dust Transect. Atmospheric
8915.
McTainsh, Grant H. (1989). Quaternary Aeolian dust
processes and sediments in the Australian region.
Quaternary Science Reviews, vol. 8, No. 3, pp. 235–
253.
McTainsh, Grant H., and Pitblado, J. R. (1987). Dust
storms and related phenomena measured from
meteorological records in Australia. Earth Surface
Processes and Landforms, 12(4), pp. 415–424.
Mellor, George L. and Tetsuji Yamada (1982).
Development of a turbulence closure model for
geophysical fluid problems. Reviews of Geophysics,
vol. 20, No. 4, pp. 851–875.
Meng, Ziqiang, and Bin Lu (2007). Dust events as a
risk factor for daily hospitalization for respiratory
and cardiovascular diseases in Minqin, China.
Atmospheric Environment, vol. 41, No. 33, pp.
7048–7058.
Menut, Laurent, and others (2013). Impact of surface
roughness and soil texture on mineral dust emission
fluxes modeling. Journal of Geophysical Research:
Middleton, Nick, and Utchang Kang (2017). Sand and
dust storms: impact mitigation. Sustainability, vol.
9, No. 6:1053.
Miller, Steven D., and others (2008). Haboob dust storms
of the southern Arabian Peninsula. Journal of
1, pp. 1–26.
Mir, Hazrat, Ata Hussain and Zaheer Ahmed Babar
(2006). Analysis of thunderstorms activity over
Pakistan during (1961–2000). Pakistan Journal of
Mlawer, Eli J., and others (1997). Radiative transfer for
inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave. Journal
of Geophysical Research, vol. 102, No. D14, pp.
16663–16682.
Monin, Andrei S, and Alexander M. Obukhov (1954).
Basic laws of turbulent mixing in the surface layer
of the atmosphere. Contrib. Geophys. Inst. Acad.
Sci. USSR, vol. 24, No. 151, pp. 163–187.
Morcrette, Jean-Jacques, and others (2009). Aerosol
analysis and forecast in the European Centre for
Medium-Range Weather Forecasts Integrated
Forecast System: Forward modeling. Journal of
Geophysical Research, vol. 114, No. D06206.
Morman, Suzette A., and Geoffrey S. Plumlee (2013). The
role of airborne mineral dusts in human disease.
Aeolian Research, vol. 9, pp. 203–212.
Mortier, Augustin, and others (2016). Climatology
of aerosol properties and clear-sky shortwave
radiative effects using Lidar and Sun photometer
observations in the Dakar site. Journal of
11, pp. 6489–6510.
Multi-angle Imaging SpectroRadiometer (MISR) Team
(2015). Terra/MISR Level 3, Component Global
Aerosol Yearly, version 4, Hampton, VA, USA: NASA
Atmospheric Science Data Center (ASDC).
Nadh, Gandhi A., Shyam Vijay and Akshay Gupta (2014).
Investigation on the effects of dust accumulation,
transmittace on glass plates and performance
of mono and poly crystalline silica modules.
International Journal of Renewable Energy Research
(IJRER), vol. 4, No. 3, pp. 628–634.
NASA Earth Observatory (2007). https://
earthobservatory.nasa.gov/
National Aeronautics and Space Administration,
Goddard Space Flight Center (NASA/GSFC) (2017).
Aerosol Transport And Assimilation. Available at
https://gmao.gsfc.nasa.gov/research/aerosol/
Accessed on 31 January 2020.
Nickovic, Slobodan, and others (2001). A model for
prediction of desert dust cycle in the atmosphere.
Journal of Geophysical Research, vol. 106, Np. D16,
pp. 18113–18129.
Nickovic, Slobodan, and others (2016). Cloud ice
caused by atmospheric mineral dust – Part 1:
Parameterization of ice nuclei concentration in the
NMME-DREAM model. Atmospheric Chemistry and
Physics, vol. 16, No. 17, pp. 11367–11378.

36
Notaro, Michael, Yan Yu and Olga V. Kalashnikova
(2015). Regime shift in Arabian dust activity,
triggered by persistent Fertile Crescent drought.
120, No. 19, pp. 10229–10249.
Ortiz-Martínez, Mario G., and others (2015). Linking
endotoxins, African dust PM10
and asthma in
an urban and rural environment of Puerto Rico.
Mediators of Inflammation, vol. 2015, Article ID
784212, p. 14.
Otani, Shinji, and others (2012). The relationship between
skin symptoms and allergic reactions to Asian dust.
International Journal of Environmental Research and
Public Health, vol. 9, No. 12, pp. 4606–4614.
Otto, S., and others (2007). Atmospheric radiative effects
of an in situ measured Saharan dust plume and the
role of large particles. Atmospheric Chemistry and
Physics, vol. 7, No. 18, pp. 4887–4903.
Pandolfi, Marco, and others (2013). Continuous
atmospheric boundary layer observations in the
coastal urban area of Barcelona during SAPUSS.
pp. 4983–4996.
Pantillon,Fantillon,andothers(2015).Aparameterization
ofconvectivedust storms for modelswithmass-flux
convection schemes. Journal of the Atmospheric
Papayannis, Alexandros, and others (2005).
Measurements of Saharan dust aerosols over the
Eastern Mediterranean using elastic backscatter-
Raman lidar, spectrophotometric and satellite
observations in the frame of the EARLINET project.
vol. 5, No. 2, pp. 2075–2110.
Parker, Doug J., and others (2005). The diurnal cycle of
the West African monsoon circulation. Quarterly
Journal of the Royal Meteorological Society, vol. 131,
No. 611, pp. 2839–2860.
Parolari, Anthony J., and others (2016). Climate, not
conflict, explains extreme Middle East dust storm.
Environmental Research Letters, vol. 11, No.
114013, pp. 1–8.
Peñas, Ramon (no date). How dust devils form. The
National.
Pérez, Carlos, and others (2006). Interactive dust-
radiation modeling: A step to improve weather
forecasts. Journal of Geophysical Research:
Atmospheres, vol. 111, No. D16206.
Pérez, Carlos, and others (2011). Atmospheric dust
modeling from meso to global scales with the
online NMMB/BSC-Dust model – Part 1: Model
description, annual simulations and evaluation.
pp. 13001–13027.
Pérez García-Pando, Carlos, and others (2014). Soil
dust aerosols and wind as predictors of seasonal
meningitis incidence in Niger. Environmental Health
Perspectives, vol. 122, No. 7, pp. 679–686.
Pey, Jorge, and others (2013). African dust outbreaks
over the Mediterranean Basin during 2001-2011:
PM10
concentrations, phenomenology and trends,
and its relation with synoptic and mesoscale
meteorology. Atmospheric Chemistry and Physics,
vol. 13, No. 3, pp. 1395–1410.
Pope, Richard J., and others (2016). Identifying errors in
dust models from data assimilation. Geophysical
Prospero, Joseph M. (1996). Saharan dust transport over
the North Atlantic Ocean and Mediterranean: An
overview. In The Impact of Desert Dust Across the
Mediterranean, Stefano Guerzoni and Roy Chester,
eds. Environmental Science and Technology Library,
vol 11. Dordrecht: Springer.
Prospero, Joseph M. (2003). African droughts and
dust transport to the Caribbean: climate change
implications. Science, vol. 302, No. 5647, pp. 1024–
1027.
Prospero, Joseph M., and others (2002). Environmental
characterization of global sources of atmospheric
soil dust identified with the NIMBUS 7 Total Ozone
Mapping Spectrometer (TOMS) absorbing aerosol
product. Reviews of Geophysics, vol. 40, No. 1, pp.
2-1–2-31.
Prospero, Joseph M., and Peter J. Lamb (2003). African
droughts and dust transport to the Caribbean:
Climate change implications. Science, vol. 302, No.
5647, pp. 1024–1027.
Redl, Robert, Andreas H. Fink and Peter Knippertz (2015).
An objective detection method for convective cold
pool events and its application to Northern Africa.
Monthly Weather Review, vol. 143, No. 12, pp. 5055–
5072.
Reinfried, F., and others (2009). Simulations of
convectively-driven density currents in the Atlas
region using a regional model: Impacts on dust
emission and sensitivity to horizontal resolution
and convection schemes. Journal of Geophysical
Research: Atmospheres, vol. 114, No. 8, pp. 1–13.
Remer, Lorraine A., and others (2002). Validation of
MODIS aerosol retrieval over ocean. Geophysical
Rezazadeh, Maryam, Parviz Irannejad and Yapping Shao
(2013). Climatology of the Middle East dust events.
Ridley, D. A., Colette L. Heald and Bonne Ford (2012).
North African dust export and deposition: A satellite
and model perspective. Journal of Geophysical
Research: Atmospheres, vol. 117, No. (D2):D02202,
pp. 1–21.

Rieger, Daniel, and others (2015). ICON-ART 1.0 – A new
online-coupled model system from the global to
regional scale. Geoscientific Model Development,
vol. 8, No. 6, pp. 1659–1676.
Rodríguez, Sergio, and others (2011). Transport of
desert dust mixed with North African industrial
pollutants in the subtropical Saharan Air Layer.
pp. 6663–6685.
Rodríguez, Sergio, and others (2001). Saharan dust
contributions to PM10
and TSP levels in Southern
and Eastern Spain. Atmospheric Environment, vol.
35, No. 14, pp. 2433–2447.
Rüdiger, Christoph, and others (2014). Toward vicarious
calibration of microwave remote-sensing satellites
in arid environments. IEEE Transactions on
Geoscience and Remote Sensing, vol. 52, No. 3, pp.
1749–1760.
Ryder, C. L., and others (2013). Impact of atmospheric
transport on the evolution of microphysical and
optical properties of Saharan dust. Geophysical
Schepanski, Kerstin, and others (2007). A new Saharan
dust source activation frequency map derived from
MSG-SEVIRI IR-channels. Geophysical Research
Letters, vol. 34, No. 18, pp. 1–5.
Schepanski, Kerstin, and others (2009). Meteorological
processes forcing Saharan dust emission inferred
from MSG-SEVIRI observations of subdaily dust
source activation and numerical models. Journal of
Geophysical Research, vol. 114, No. D10, pp. 1–18.
Schepanski, Kerstin, Ina Tegen and Andreas Macke
(2012). Comparison of satellite based observations
of Saharan dust source areas. Remote Sensing of
Schmetz, Johannes, and others (2002). An introduction
to Meteosat Second Generation (MSG). Bulletin of
the American Meteorological Society, vol. 83, No. 7,
pp. 977–992.
Schroedter-Homscheidt, Marion, and others (2013).
Aerosols for concentrating solar electricity
production forecasts: Requirement quantification
and ECMWF/MACC aerosol forecast assessment.
Bulletin of the American Meteorological Society, vol.
94, No. 6, pp. 903–914.
Schulz, Michael, and others (1998). Role of aerosol
size distribution and source location in a three-
dimensional simulation of a Saharan dust episode
tested against satellite-derived optical thickness.
103, No. D9, pp. 10579–10592.
Seinfeld, John H. and Spyros N. Pandis (2016).
Atmospheric Chemistry and Physics: From Air
Pollution to Climate Change. New York: John Wiley
and Sons.
Shao, Yaping (2008). Physics and Modelling of Wind
Erosion. Netherlands: Springer.
Shettle, Eric P. (1986). Optical and radiative properties
of a desert aerosol model. Presented at the
International Radiation Symposium on Current
Problems in Atmospheric Radiation. Perugia, Italy,
21–28 August 1984.
Shirkhani-Ardehjani, Sasan (2012). I.R. of Iran National
Report on Regional Action Plan to combat dust and
sand storm. International Cooperative for Aerosol
Prediction (ICAP) 4th Workshop: Aerosol Emission
and Removal Processes. Frascati, Italy, 14–17 May
2012.
Simmons, Adrian J., and David M. Burridge (1981).
An energy and angular-momentum conserving
vertical finite-difference scheme and hybrid vertical
coordinates. Monthly Weather Review, pp. 758–766.
Slinn, W. George N. (1982). Predictions for particle
deposition to vegetative canopies. Atmospheric
Environment (1967), vol. 16, No. 7, pp. 1785–1794.
Slinn, W. George N. (1984). Precipitation Scavenging.
In Atmospheric Science and Power Production,
D. Randerson, ed. Oak Ridge: US Department of
Energy.
Smirnov, Alexander, and others (2000). Cloud-screening
and quality control algorithms for the AERONET
database. Remote Sensing of Environment, vol. 73,
No. 3, pp. 337–349.
Smith, David J., and others (2011). Microbial survival
in the stratosphere and implications for global
dispersal. Aerobiologia, vol. 27, No. 4, pp. 319–332.
Smith, David J., and others (2013). Intercontinental
dispersal of bacteria and archaea by transpacific
winds. Applied and Environmental Microbiology, vol.
79, No. 4, pp. 1134–1139.
Solomos, Stavros, and others (2012). Density currents
as a desert dust mobilization mechanism.
pp. 11199–11211.
Solomos, Stavros, and others (2017). Remote sensing
and modelling analysis of the extreme dust storm
hitting the Middle East and eastern Mediterranean
in September 2015. Atmospheric Chemistry and
Physics, vol. 17, No. 6, pp. 4063–4079.
Spada, Michele, and others (2013). Modeling and
evaluation of the global sea-salt aerosol distribution:
sensitivity to emission schemes and resolution
effects at coastal/orographic sites. Atmospheric
11755.
Spada, Michele, and others (2015). On the evaluation
of global sea-salt aerosol models at coastal/
orographic sites. Atmospheric Environment, vol.
101, pp. 41–48.
Sprigg, William A., and others (2014). Regional dust
storm modeling for health services: The case of
valley fever. Aeolian Research, vol. 14, pp. 53–73.

38
Stanelle, Tanja, and others (2014). Anthropogenically
induced changes in twentieth century mineral
dust burden and the associated impact on
radiative forcing. Journal of Geophysical Research:
Atmospheres, vol. 119, No. 13, pp. 13,526–13,546.
Stefanski, Robert, and Mannava V. K. Sivakumar (2009).
Impacts of sand and dust storms on agriculture
and potential agricultural applications of a SDSWS.
IOP Conference Series: Earth and Environmental
Science, vol. 7, No. 1.
Stephens, Graeme L., and others (2010). Dreary state
of precipitation in global models. Journal of
D24, pp. 1–14.
Strong, Craig L., and others (2011). Dust transporting
wind systems in the lower Lake Eyre Basin,
Australia: A preliminary study. Aeolian Research, vol.
2, No. 4, pp. 205–214.
Su, Jing, and others (2008). Estimation of Asian dust
aerosol effect on cloud radiation forcing using Fu-
Liou radiative model and CERES measurements.
vol. 8, No. 1, pp. 2061–2084.
Sutton, L. J. (1925). Haboobs. Quarterly Journal of the
Royal Meteorological Society, vol. 51, No. 213, pp.
25–30.
Takemi, Tetsuya (1999). Structure and evolution of a
severe squall line over the arid region in Northwest
China. Monthly Weather Review, vol. 127, No. 6, pp.
1301–1309.
Takemi, Tetsuya (2005). Explicit simulations of
convective-scale transport of mineral dust in severe
convective weather. Journal of the Meteorological
Society of Japan, vol. 83A, pp. 187–203.
Tegen, Ina, and others (2002). Impact of vegetation and
preferential source areas on global dust aerosol:
Results from a model study. Journal of Geophysical
Research: Atmospheres, vol. 107, No. D21, 4576.
Tegen, Ina, and Andrew A. Lacis (1996). Modeling of
particle size distribution and its influence on the
radiative properties of mineral dust aerosol. Journal
of Geophysical Research: Atmospheres, vol. 101,
No. D14, pp. 19237–19244.
Tegen, Ina, Kerstin Schepanski, and Bernd Heinold
(2013). Comparing two years of Saharan dust
source activation obtained by regional modelling
and satellite observations. Atmospheric Chemistry
Teller, Amit, and Zev Levin (2005). The effects of aerosols
on precipitation and dimensions of subtropical
clouds; a sensitivity study using a numerical
cloud model. Atmospheric Chemistry and Physics
Discussions, vol. 5, No. 4, pp. 7211–7245.
Thalib, Lukman, and Abdullah Al-Taiar (2012). Dust
storms and the risk of asthma admissions
to hospitals in Kuwait. Science of The Total
Thomson, Madeleine C., and others (2006). Potential
of environmental models to predict meningitis
epidemics in Africa. Tropical Medicine and
International Health, vol. 11, No. 6, pp. 781–788.
Thulasiraman, S., and others (2002). Sunphotometric
observations of the 2001 Asian dust storm over
Canada and the US. Geophysical Research Letters,
vol. 29, No. 8, pp. 96-1-96-4.
Tobías, A., and others (2011). Short-term effects of
particulate matter on total mortality during Saharan
dust outbreaks: a case-crossover analysis in Madrid
(Spain). Science of the Total Environment, vol. 412,
pp. 386–389.
Todd, Martin C., and Carolina Cavazos-Guerra (2016).
Dust aerosol emission over the Sahara during
summertime from Cloud-Aerosol Lidar with
Orthogonal Polarization (CALIOP) observations.
Atmospheric Environment, vol. 128, pp. 147–157.
Todd, Martin C., and others (2008). Regional model
simulations of the Bodélé low-level jet of Northern
Chad during the Bodélé dust experiment (BoDEx
2005). Journal of Climate, vol. 21, No. 5, pp. 995–
1012.
Di Tomaso, Enza, and others (2017). Assimilation of
MODIS Dark Target and Deep Blue observations in
the dust aerosol component of NMMB-MONARCH
version 1.0. Geoscientific Model Development, vol.
10, No. 3, pp. 1107–1129.
Tsamalis, Christoforos, and others (2013). The seasonal
vertical distribution of the saharan air layer and its
modulation by the wind. Atmospheric Chemistry
(2017). Draft advocacy policy frameworks:
gender, drought, and sand and dust storms. 3
July 2017. ICCD/COP(13)/19. Available at https://
www.unccd.int/sites/default/files/sessions/
documents/2017-08/ICCD_COP%2813%29_19-
1711042E.pdf.
United Nations Office for Disaster Risk Reduction
[formerly UNISDR] (2017). Terminology, Hazards.
Available at http://www.unisdr.org/we/inform/
terminology. Accessed 25 November 2017.
Vickery, Kathryn J., Frank D. Eckardt and Robert G.
Bryant (2013). A sub-basin scale dust plume source
frequency inventory for southern Africa, 2005–
2008. Geophysical Research Letters, vol. 40, No. 19,
pp. 5274–5279.
Vinoj, V., and others (2014). Short-term modulation of
Indian summer monsoon rainfall by West Asian
dust. Nature Geosci. Nature Publishing Group, vol.
7, No. 4, pp. 308–313.
Vukovic, Aleksander, and others (2014). Numerical
simulation of “an American haboob”. Atmospheric
3230.

Vukovic, Aleksander, Borivoj Rajkovic, and Zavisa
Janjic (2010). Land ice sea surface model: short
description and verification. 2010 International
Congress on Environmental Modelling and
Software: Modelling for Environment’s Sake, Fifth
Biennial Meeting, Ottawa, Canada, 5–8 July 2010.
Wang, Rong, and others (2015). Anthropogenic aerosol
deposition reduces the sensitivity of oceanic
productivity to warming. Geophysical Research
Letters, vol. 42, No. 24, pp. 10,745–10,754.
Wang, Ruxing, and others (2016). Influence of dust
storms on atmospheric particulate pollution and
acid rain in northern China. Air Quality, Atmosphere
& Health, volume 10, pp. 297–306.
Wang, Weijie, and others (2015). On the decadal scale
correlation between African dust and Sahel rainfall:
the role of Saharan heat low-forced winds. Science
Advances, 1, No. 9, e1500646, pp. 1–5.
White, Bruce R. (1979). Soil transport by winds on Mars.
Journal of Geophysical Research, vol. 84, No. B9, pp.
4643–4651.
Wijayratne, Upekala C., Sara J. Scoles-Sciulla and
Lesley A. Defalco (2009). Dust deposition effects
on growth and physiology of the endangered
Astragalus jaegerianus (Fabaceae). Madroño, vol.
56, No. 2, pp. 81–88.
World Health Organization (2005). WHO Air Quality
Guidelines Global Update 2005. Report on a Working
Group Meeting. Bonn, Germany, 18–20 October
2005.
Xi, Xin, and Irina N. Sokolik (2016). Quantifying the
anthropogenic dust emission from agricultural land
use and desiccation of the Aral Sea in Central Asia.
121, No. 20, pp. 12270–12281.
Yamauchi, Masatoshi (2012). Secondary wind transport
of radioactive materials after the Fukushima
accident. Earth, Planets and Space, vol. 64, No. 1,
pp. e1–e4.
Yu, Hongbin, and others (2015). The fertilizing role
of African dust in the Amazon rainforest: A first
multiyear assessment based on data from Cloud-
Aerosol Lidar and Infrared Pathfinder Satellite
Observations. Geophysical Research Letters, vol. 42,
No. 6, pp. 1984–1991.
Zender, Charles S., Huisheng Bian and David Newman
(2003). Mineral Dust Entrainment and Deposition
(DEAD) model: Description and 1990s dust
climatology. Journal of Geophysical Research, vol.
108, No. D14, pp. 1–19.
Zhang, Leiming, and others (2001). A size-segregated
particle dry deposition scheme for an atmospheric
aerosol module. Atmospheric Environment, vol. 35,
No. 3, pp. 549–560.
Zhang, X. Y., and others (2003). Sources of Asian dust and
the role of climate change versus desertification in
Asian dust emission. Geophysical Research Letters,
vol. 30, No. 24.
Zhang, Xiao Y., Richard Arimoto and Zhi S. An (1997).
Dust emission from Chinese desert sources linked
to variations in atmospheric circulation. Journal
of Geophysical Research, vol. 102, No. D23, pp.
28041–28047.
Zhang, Xuelei, and others (2016). A systematic review
of global desert dust and associated human health
effects. Atmosphere, vol. 7, No. 158, pp. 1–30.

40
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Stark
on
Flickr,
July
31st,
2011

UNCCD | Sand and Dust Storms Compendium | Chapter 3 | A disaster management perspective 41
3. Sand and dust
storms from a
disaster management
perspective
Chapter overview
This chapter covers how sand and dust storms (SDS) can be considered a hazard and
how hazard and disaster risk management approaches apply to managing their risks and
impacts. Also discussed is a unified approach to SDS management and a framework for
SDS Risk Management Coordination and Cooperation.

UNCCD | Sand and Dust Storms Compendium | Chapter 3 | A disaster management perspective
42
This chapter should be read together with
the following chapters:
• 2 – “The nature of sand and dust
storms”
• 4 – “Assessing the risks posed by
sand and dust storms”
• 6 – “Economic impact assessment
framework for sand and dust storms”
• 7 – “A geographic information
system-based sand and dust storm
vulnerability mapping framework”
• 10 – “Sand and dust storms early
warning”
• 12 – “Sand and dust storms source
mitigation”
• 13 – “Sand and dust storms impact
response and mitigation”
3.1 SDS as a natural
hazard
SDS originate from a combination of
individual elements, principally wind,
sand and dust, but also soil moisture and
other factors (see chapter 2 and Table 1.
Factors associated with sand and dust
storms in chapter 4).
As they are triggered by weather
conditions, SDS can be classified as a
meteorological hazard. However, SDS
only occur if specific geophysical and
geomorphological conditions are met. This
is in contrast with floods, in the sense that
enough rain can lead to flooding despite
the geology or geomorphology on which
the rain falls.
No matter how strong the wind blows,
if the geological and geomorphological
conditions are not right, an SDS event
will not develop. This distinction is not to
belabour the uniqueness of SDS compared
with other hazards, but rather to stress that
assessing and managing the risks from
SDS requires attention to be paid to a range
of environmental conditions and changes
to these conditions over time and space.
Hazards can be classed as rapid/sudden-
onset or slow-onset events. SDS are
generally linked to negative changes in air
quality and land degradation, including soil
erosion, and are considered as slow-onset
hazards (UNEP, 2012). However, there is
a significant question as to whether the
rapid-/slow-onset dichotomy is appropriate
for SDS. Incremental and cumulative
impacts of SDS may be recognized as
long-term and slow-onset. Yet, a single
severe SDS event can develop in a matter
of hours and have significant negative
immediate impacts, for instance dust
storms leading to large-scale traffic
accidents. Understanding slow- and
rapid-onset impacts of SDS helps define
how and when to reduce these impacts,
while paying balanced attention to slow,
cumulative and rapid impacts.
The term “sand and dust storms” itself
groups different events. Seasonal
predominant winds across dry landscapes
can lead to high levels of airborne dust and
low visibility, as in the Harmattan season in
West Africa, with this dust often traveling
thousands of kilometres (Middleton, 2017).
Haboob, the result of a convective frontal
system passing over sand and dust which
is entrained by storm winds, can be part
of seasonal weather patterns or local
changes in weather systems (Roberts and
Knippertz, 2012). SDS also develop locally
due to wind funnelling through or around
mountain ranges for instance, leading to
regular afternoons of sand blowing and
low visibility that lasts several months.
See chapter 2 for more information on the
different types of SDS.
The locations where SDS originate are
often characterized as unvegetated or
sparsely vegetated dry and subhumid
areas. Typical of such areas are the Bodélé
Depression in the West African Sahel
(Middleton, 2017) and arid areas of Central
Asia or Central Australia.

At the same time, SDS can originate from
very local conditions. Fields, industrial
and mining sites and coastal and urban
drylands have all been identified as origins
of SDS (Middleton and Kang, 2017). SDS
have been reported in Iceland due to
high winds blowing across volcanic ash
(Dagsson-Waldhauserova et al., 2015) as
well as sand and dust created by glacial
retreat (Gisladottir et al., 2005). (See
chapters 2 and 8 for more on where SDS
can originate.)
The lower limit of wind speed that can
initiate an SDS event, in the order of 30 km/
hour (NSW Regional Office, 2006), is less
than the 62 km/hour or so that it normally
takes wind alone to cause damage, based
on the Beaufort wind scale (National
Oceanographic and Atmospheric Agency,
n.d.). Understanding how the right wind
speeds and right-sized sand and dust
particles come together, often with other
factors, to create SDS is an essential step
in defining and addressing the impact of
this hazard. See chapter 2 for additional
details on winds and SDS generation.
No strict distinction exists between sand
storms and dust storms. In general, particle
sizes in SDS can range from smaller than
60 micrometres (μm) (classified as dust)
and from 60 μm to 2,000 μm (classified
as sand) (Shao, 2008). The smaller the
particle size, the longer the particle is
likely to remain in the atmosphere and the
further it is likely to travel compared with
larger particles.
A single SDS event can be composed of
a continuum of mineral particle sizes,
although the type of particles at the
source area can lead to an SDS event
with a specific range of particle sizes. For
instance, an SDS event that originates
in very fine loess soils will be composed
of these particles. Similarly, the particle
composition of an SDS event may change
as it travels over different types of soils.
Chapter 2 discusses the relation between
particle size and entrainment in SDS, while
Figure 5 presents the various aspects that
can contribute to a sand or dust storm.
SDS can be triggered by human activity at
local to regional scales. The Dust Bowl of
the United States is one example of human
action that resulted in regional-scale SDS
(Egan, 2006). On the local (subnational)
scale, ploughing fields in the presence of
winds can lead to localized SDS, at times
contributing to fatal accidents (Tobar and
Wilkinson,1991; Associated Press, 1991).
As a hazard affecting health, the particle
size is the main determinant of where
dust comes to rest in the respiratory tract
once inhaled. A distinction is commonly
made between PM10
particles, which can
penetrate into the lungs, and PM2.5
particles
which penetrate into deep lung tissue
(UNEP, WMO and UNCCD, 2016).
SDS source areas and transport pathways
are an important issue given the health
implications of the chemical composition
of sand or dust, and the potential for
contamination through SDS. Atmospheric
pollutants can be mixed into SDS that
move across heavily industrialized and
polluted regions (Chin et al., 2007).
Dust can contain a wide variety of micro-
organisms, including fungi, bacteria
and viruses, that are capable of causing
disease in a range of organisms, including
trees, crops, animals and humans (Kellogg
and Griffin, 2006). Other potential health-
threatening substances that can be found
in SDS include heavy metals and pesticide
residues (Ataniyazova et al., 2001),
polychlorinated biphenyls (Garrison et al.,
2006), pollen (Al–Dousari et al., 2016) and
arsenic (Soukup et al., 2012).

UNCCD | Sand and Dust Storm Compendium | Chapter 3 | A disaster management perspective
44
GLOSSARY OF KEY DISASTER-RELATED TERMS
Disaster: “A serious disruption of the functioning of a community or a society at
any scale due to hazardous events interacting with conditions of exposure, vulnerability
and capacity, leading to one or more of the following: human, material, economic and
environmental losses and impacts” (United Nations Office for Disaster Risk Reduction,
2017).
(Disaster) risk: “The potential loss of life, injury, or destroyed or damaged assets
which could occur to a system, society or a community in a specific period of time,
determined probabilistically as a function of hazard, exposure, vulnerability and capacity”
(United Nations Office for Disaster Risk Reduction, 2017).
(Disaster) risk assessment: “A qualitative or quantitative approach to determine
the nature and extent of disaster risk by analysing potential hazards and evaluating
existing conditions of exposure and vulnerability that together could harm people, property,
services, livelihoods and the environment on which they depend” (United Nations Office for
Disaster Risk Reduction, 2017).
Hazard: an event “…that may cause loss of life, injury or other health impacts,
property damage, social and economic disruption or environmental degradation” (United
Nations Office for Disaster Risk Reduction, 2017).
Mitigation: “… lessening or minimizing of the adverse impacts of a hazardous
event” (United Nations Office for Disaster Risk Reduction, 2017).
Resilience: The “ability of a system, community or society exposed to hazards to
resist, absorb, accommodate, adapt to, transform and recover from the effects of a hazard
in a timely and efficient manner, including through the preservation and restoration of its
essential basic structures and functions through risk management” (United Nations Office
for Disaster Risk Reduction, 2017).
Risk management: The “plans [that] set out the goals and specific objectives
for reducing disaster risks together with related actions to accomplish these objectives”
(United Nations Office for Disaster Risk Reduction, 2017).
Risk reduction: “… preventing new and reducing existing disaster risk and
managing residual risk, all of which contribute to strengthening resilience and therefore
to the achievement of sustainable development” (United Nations Office for Disaster Risk
Reduction, 2017).
Sand and dust storms (SDS): “atmospheric events created when small particles
are blown from land surfaces” (Middleton and Kang, 2017). The UNCCD Policy Advocacy
Framework to combat Sand and Dust Storms refers to mineral sand (particle size 63
microns to 2mm) and dust (particle size range < 1–63 microns) that originates from land
surfaces.
SDS impact mitigation: Reducing the likelihood that sand or dust will have
negative impacts at a location on persons, good, services, infrastructure, animals or the
environment in general (Middleton and Kang, 2017).
Source mitigation: Reducing the likelihood that sand or dust will be emitted from
a location (Middleton and Kang, 2017).
• Vulnerability: “The conditions determined by physical, social, economic and
environmental factors or processes which increase the susceptibility of an individual,
a community, assets or systems to the impacts of hazards” (United Nations Office for
Disaster Risk Reduction, 2017).

UNCCD | Sand and Dust Storm Compendium | Chapter 3 | A disaster management perspective 45
Pierpaolo
Lanfrancott,
©Unsplash,
January
6th,
2017

46
Measures to control the generation of
SDS from human-caused conditions
can be justified as reducing the impact
of SDS triggered by human actions. On
the other hand, interventions to limit
SDS arising from natural (not human-
induced) conditions raise questions as
to whether these efforts could adversely
affect any positive impacts SDS may have
on the environment, in some cases at a
considerable distance from a source area.
Therefore, efforts to control SDS need to
assess the risks arising from the events
(see chapter 4 and 5) and the costs and
benefits involved (see chapter 6).
Major global trajectory of airborne
dust movement and its deposition is
documented using GIS techniques and
satellite imagery (Ginoux et al., 2012;
Shao et al., 2011). Localized and high-
resolution point source information on
SDS development would help develop
appropriate policy measures to reduce
impacts. Source mapping is discussed
further in chapter 8.
SDS can be transboundary hazards
affecting source and destination
areas separated by long distances.
Heavier particles tend to stay in the
vicinity of sources (for example sand
encroachment and blowing sand). Most
dust particles smaller than 20 microns
can be transported hundreds of kilometres
(Gillette, 1979). Smaller particles can move
even further, often thousands of kilometres
from the place of origin (Kutuzov et al.,
2013; Muhs et al., 2007; Prospero, 1999;
McKendry et al., 2011; Grousset et al.,
2003; Uno et al., 2009).
The distinction between source and
destination is an important aspect of
SDS as a hazard as it can dictate the
SDS management strategy in affected
areas. For example, in source areas, policy
priorities are to mitigate the impact of
sand or dust being removed by an SDS
event, building resilience to these impacts
and managing sources, for example
by reducing the potential for winds to
entrain sand or dust. In destination areas,
preparedness and resilience capacity,
coupled with early warning, is the key
policy component (Middleton and Kang,
2017).
Meteorological and atmospheric
dust transport modelling is the key to
understanding the relationship between
source and impact areas (Benedetti et al.,
2014; WMO, 2015). Modelling is discussed
further in chapter 8.
3.2 Low recognition of
the disaster potential
of SDS
SDS are not currently well positioned in
mainstream natural hazard or disaster
research. Middleton et al. (2018) provide
a broad overview of SDS as hazards, with
some detail on the costs of SDS. The
physics (Middleton, 2017; Goudie, 2009)
and transport (Middleton, 2017; Baddock
et al., 2013) and health (Goudie, 2014)
impacts of SDS appear to have been well
researched, although there does not seem
to be the same level of research coverage
for all SDS zones (Pérez and Künzli, 2011).
Much less research appears to have been
conducted into economic impacts (Tozer
and Leys, 2013; Middleton, 2017; and see
chapter 6). Social vulnerability to SDS
appears to have received little attention,
other than in popular literature (Egan, 2006,
for instance).
It seems that great attention is paid to
SDS in North-East Asia, with the Republic
of Korea developing an SDS management
plan (UNEP, WMO and UNCCD, 2016).
SDS have been the subject of long-
term management efforts in the United
States of America (Natural Resources
Conservation Service, 2017) and Canada
(Wang, 2001). At the same time, the
disaster risk management priorities of
Sahelian countries such as The Gambia,
Mali and Niger do not appear to consider
SDS as significant, despite Harmattan and
haboobs being part of the annual weather
cycle of these countries (Gambia, 2017;
Niger, Office of the Prime Minister, 2017;
Chad, 2017).

The absence of SDS in official statements
on hazards facing The Gambia, Niger or
Chad contrasts with the research into
at least one health impact associated
with SDS: the occurrence of meningitis in
the Sahel, which suggests a strong link
between periods of high atmospheric dust
concentrations (and high temperatures)
and outbreaks of this disease (Jusot et al.,
2017).
Several reasons explain why there is little
recognition of SDS. Firstly, SDS usually
cause little major structural damage and
any immediate physical damage that does
occur is relatively minor when compared
with other disasters such as earthquakes
or floods. Fatalities can be associated with
SDS, for instance through traffic accidents
caused by haboobs. However, SDS do not
usually result in large-scale direct human
fatalities or injuries, unlike earthquakes
or hurricanes. While SDS do, in fact,
contribute to morbidity and mortality,
these impacts are often hidden as indirect
causes and buried deep in health statistics
on respiratory or cardio-vascular diseases,
for instance, rather than detailed in
dramatic reports of high death tolls directly
attributed to a single event.
The economic damage from SDS is
often hidden in operating statistics (for
example, a greater need to replace air
filters during the dust season) or indirect
costs of cleaning (see chapter 6 for more
on assessing the economics of SDS.)
Other impacts, such as damage to crops
or dust and sand covering roads or other
infrastructure, are not normally captured in
disaster damage reporting.
The EM-DAT1
Annual Disaster Statistical
Review 2016: The numbers and trends
notes that 100 million persons in China
were affected by SDS in 2002 but does
not report any SDS in 2016 (Guha-Sapir
et al., 2017). EM-DAT classes SDS as a
meteorological disaster, but the publicly
1 http://www.emdat.be/.
2 EM-DAT database accessed on 24 November 2017.
accessible database does not allow the
number or impact of SDS as individual
events to be identified.2
This lack of globally assembled data
makes it difficult to provide evidence as
to the scale or scope of SDS impacts.
National-level data on SDS disaster-related
impacts likely varies on a country-to-
country basis.
Research into SDS, in terms of either
hazards or disasters, is fragmented
spatially and topically. Only limited research
appears to have been carried out in the
Sahel compared with elsewhere, despite
it being a major SDS source. Furthermore,
less research appears to have been done
into the social or economic impacts of
SDS than into the physics or health issues
associated with these events in some parts
of the world.
Reducing the impact of SDS would require
the systematic assessment of SDS as a
hazard and source of impacts, in order
to develop a clearer and evidence-based
understanding of these events from local
to global scales. Such assessments can
provide the knowledge to effectively reduce
the negative impacts of SDS on lives and
well-being.

48
SPECIAL FOCUS SECTION: GENDER
AND DISASTER RISK REDUCTION
3 The Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), http://www.un.org/womenwatch/daw/
cedaw/cedaw.htm.
4 Beijing Declaration and Platform for Action, http://www.un.org/womenwatch/daw/beijing/pdf/BDPfA%20E.pdf.
5 For example: Hyogo Framework for Action 2005–2015: Building the Resilience of Nations and Communities to Disasters, https://
www.unisdr.org/we/inform/publications/1037; Commission on the Status of Women resolution 56/2 and resolution 58/2 on gender equality
and the empowerment of women in disasters, http://www.un.org/ga/search/view_doc.asp?symbol=E/2012/27&Lang=E, http://www.un.org/ga/
search/view_doc.asp?symbol=E/2014/27&Lang=E
“Women and their
participation are critical
to effectively managing
disaster risk and
designing, resourcing
and implementing
gender-sensitive
disaster risk reduction
policies, plans and
programmes; and
adequate capacity-
building measures
need to be taken to
empower women
for preparedness as
well as to build their
capacity to secure
alternate means of
livelihood in post-
disaster situations.”
Paragraph 36 (a)(i)
Sendai Framework
for Disaster Risk
Reduction 2015-
2030 (United Nations,
2015a).
International laws and
agreements are placing gender
equality at the centre of
disaster risk reduction (DRR)
and resilience-building. At the
normative level, the international
community has committed to
focusing on gender equality and
women’s rights in DRR.
These commitments are
grounded in the Convention on
the Elimination of All Forms
of Discrimination against
Women (CEDAW),3
the Beijing
Declaration and Platform for
Action,4
resolutions on gender
equality and the empowerment
of women in natural disasters
by the Commission on the
Status of Women, and other
international agreements.5
The
Sendai Framework for Disaster
Risk Reduction 2015–2030
emphasizes the importance of
engaging women in building
disaster resilience (United
Nations, 2015a).
Despite this focus on gender-
responsive disaster risk
reduction management,
gender perspectives are rarely
incorporated into disaster
preparedness plans and
strategies, vulnerability and risk
assessments, and early warning
systems (United Nations, 2015b)
(see Figure 12). Consequently,
many institutions and
organizations – both national and
local – working on disaster risk
reduction do not engage women,
girls, boys and men equally.
The result is that:
• the impact of hazards on,
and corresponding disaster
risks faced by, women and
girls are not recognized, and
• the needs and capacities
of women and girls are not
considered in planning and
risk reduction and response
activities.
Himanshu
Singh
Gurjaron,
©Unsplash,
June
30,
2016

These results perpetuate
gendered stereotypes and lead to
an increase in women’s and girls’
vulnerability.
There is good reason to conclude
that SDS impact men, women,
boys and girls in different ways.
Evidence from gender-sensitive
disaster research shows that
women and men suffer different
negative health consequences
following extreme events such
as floods, windstorms, droughts
and heatwaves (Plümper and
Neumayer, 2007; IPCC, 2012; Goh,
2013). This effect is strongest in
countries where women have very
low social, economic and political
status.
This highlights the socially
constructed and gender-specific
vulnerability of women to
disasters, which is integral to
everyday socioeconomic patterns
and leads to relatively higher
disaster-related mortality rates
in women compared with men
(Neumayer and Plümper, 2007).
The gender relations between
men and women in disaster risk
reduction have everything to do
with the roles and responsibilities
women and men have at home
and in society.
These roles result in different
identities, social responsibilities,
attitudes and expectations.
Such differences are, on the
whole, unfavourable to women
and lead to gender inequality
that cuts across all levels of
socioeconomic development,
including differences in
vulnerabilities to disasters, and
different capacities to reduce risk
and respond to disasters.
Differences between men and
women exist at multiple levels,
including:
Roles and responsibilities –
Men and women have different
roles and responsibilities
assigned to them (or expected
of them), which can influence
their vulnerability to, as well as
their capacity to cope with, an
SDS event. For example, men
are generally expected to secure
property and infrastructure, which
may lead to them risking their
own lives to do this in precarious
situations. Women, on the other
hand, are expected to prepare the
home and attend to children and
sick family members.
Access to and management
of strategic resources – The
ability to access and manage
information, training, land,
finance, technologies, social
networks, support and other
strategic resources necessary
for well-being and long-term
resilience varies between men
and women. For example, in
some communities, young men
may have greater access than
women to mobile phones and
computers, so they are able to
obtain early warning messages or
can keep track of an SDS event.
Older men and women living
on their own may have limited
mobility and require the support
of others in the community.
People living with disabilities may
also require additional time and
support to be able to respond to
hazards. As women tend to have
less access to resources such as
cash, housing and vehicles, they
have fewer options in responding
to disasters.
Participation and decision-
making – Men and women may
not have the same opportunities
when it comes to economic and
social participation and political
representation. They also have
different decision-making powers
at the household, community
and societal levels. These
differences need to be considered
to ensure men and women can
make choices about their safety,
livelihood options and adaptation
measures.
However, gender issues are often
institutionally marginalized within
organizations that do not have
enough capacity to advance
the issue organization-wide in
a multidisciplinary way. Gender
issues become perfunctorily
treated as “just women’s issues”,
there is a notable absence of
male champions, and gender
expertise is applied in isolation
from processes such as DRR.

50
Box 3. Women and vulnerability
Women are often presented as a “vulnerable group”, with little attention given to the great
variety of ways in which they can actively participate in disasters and their role in fostering
a culture of resilience. This means that the skills and knowledge that women possess and
the powerful role they can play as agents of change within society are often overlooked. In
addition, over-generalizations about the vulnerability of women prevent a deep analysis of
why some people are more vulnerable than others when disaster strikes.
To be clear, it is not always the case that women are more vulnerable than men to SDS
impacts. Some groups of men could also be particularly vulnerable, such as those whose
livelihoods depend on agriculture, or who are unemployed, have a disability, are older
persons or live alone.
Evidence-based assessment and gender analysis can identify the specific needs of
individuals or groups within an affected population. In some circumstances, addressing
the specific needs of women and girls may be best performed by taking gender-
responsive action because in practice, women and girls may need different treatment to
produce equality in outcomes, i.e. to level the playing field so that women can benefit from
equal opportunities.
Gender-responsive actions should not stigmatize or isolate the targeted beneficiaries.
Rather, they should compensate for the consequences of gender-based inequality such
as the long-term deprivation of rights to own property, or of access to financial means,
education or health care.
Gender responsive actions should empower women and build their capacities to be equal
partners with men in working towards solving problems caused by SDS and helping with
reconstruction. Each sector should identify specific actions that could promote gender
equality and strengthen women’s capacities to enjoy their human rights.
©Asian
Development
Bank

Cultural practices regarding gender provide
some of the most fundamental sources of
inequality and exclusion around the world.
The underlying roots of gender injustice
stem from social and cultural dimensions
and manifest themselves through
economic and political consequences,
among many others.
These long-standing inequalities can be
addressed as part of SDS preparedness
work. Sound gender analysis from the
outset is the key to effective SDS response
in the short term and equitable and
empowering societal change in the long
term.
The needs and interests of women, girls,
men and boys vary, as do their resources,
capacities and coping strategies in
crises. The pre-existing and intersecting
inequalities referred to above mean
that women and girls are more likely to
experience adverse consequences in the
event of a sand or dust storm.
In disaster and post-disaster settings,
women often find themselves acting as
the new head of their households due
to separation or loss of male household
members. At the same time, they are
not always able to access resources and
support because there is no assistance
for childcare and tasks such as acquiring
food or water can be dangerous. As men
generally have greater control over income,
land and money, their coping mechanisms
differ.
Thus, different people within a community
may have different vulnerabilities to
disasters. It is critical to understand why
and how different groups of people may
be vulnerable to SDS. Identifying and
assessing the determinants of vulnerability
will pinpoint where to direct the focus and
interventions to reduce vulnerability and
increase people’s capacity to respond and
prepare.
When women and men are included
equally in disaster risk reduction,
their entire communities benefit. A
comprehensive approach to SDS risk
management that integrates gender
is better equipped to ensure that the
particular needs, capacities and priorities
of women, girls, men and boys related to
pre-existing gender roles and inequalities,
along with the specific impacts of the
disaster, are recognized and addressed.
Both men and women bring a range of
skills and talents to disaster risk reduction.
It is vital to identify and leverage all of these
available skills to support the long-term
resilience of individuals and communities
in affected regions.
Mainstreaming gender into SDS risk
management can ensure that these
efforts equitably benefit women and men
while making optimal use of the unique
knowledge and skills of both groups. Such
equitable engagement is essential to
achieving the Sustainable Development
Goals (SDGs), particularly SDG 5 – Gender
Equality and Women’s Empowerment.
Gender equality and women’s
empowerment are crosscutting issues
and prerequisites for achieving many other
SDGs, including SDG 1 – No Poverty, SDG
11 – Sustainable Cities and Communities
and SDG 13 – Climate Action.

52
The following actions, drawn from UNEP
(2013), are key to ensuring a gender-
responsive approach throughout the
integrated SDS risk management planning
process:
• Incorporate gender perspectives into
SDS risk management efforts at the
national, local and community levels,
including in policies, strategies, action
plans and programmes.
• Increase the participation and
representation of women at all levels
of the decision-making process.
• Analyse SDS and climate data from
a gender perspective and collect sex-
disaggregated data.
• Carry out gender analysis as part of
the risk profile by documenting the
different roles that women and men
play in sectors relevant to SDS. For
example:
» How are women and men’s
livelihoods affected by SDS?
» How could gender-based differences
in decision-making power and
ownership of/access to assets lead
to different abilities to respond the
hazard?
» What kinds of information do
women have and need to better
prepare for SDS?
» What does this imply in terms of
differences in vulnerability and
coping capacity between women
and men?
• Ensure that women are being
prominently engaged as agents
of change at all levels of SDS
preparedness, including early warning
systems, education, communication,
information, and networking
opportunities.
• Consider reallocating resources
from the actions planned, in order to
achieve gender equality outcomes.
• Take steps to reduce the negative
impacts of SDS on women, particularly
in relation to their critical roles in rural
areas in the provision of water, food
and energy by offering support, health
services, information and technology.
• Build the capacity of national and
local women’s groups and provide an
adequate platform that presents their
needs and views.
• Include gender-specific indicators and
data disaggregated by sex and age to
monitor and track progress on gender
equality targets.
GENDER INEQUALITIES
EXIST BEFORE DISASTER
STRIKES
Disasters impact women, girls,
men and boys differently due to
their different status and roles in
society. This can be exarcerbated
in times of disaster and limit
their access to the resources and
services they need to be resilient
and to recover.
Integrating gender equality into
disaster risk management
ensures inclusive, effective,
efficient and empowering
responses.
Figure 12. The
importance of
gender in disaster
settings
Source: Adapted from Inter-Agency Standing Committee, 2018.

GLOSSARY OF KEY GENDER TERMS
Gender “refers to the social attributes and opportunities associated with being
male and female and the relationships between women and men and girls and boys,
as well as the relations between women and those between men. These attributes,
opportunities and relationships are socially constructed and are learned through
socialization processes. They are context/ time-specific and changeable. Gender
determines what is expected, allowed and valued in a women or a man in a given context.
In most societies there are differences and inequalities between women and men in
responsibilities assigned, activities undertaken, access to and control over resources, as
well as decision-making opportunities. Gender is part of the broader socio-cultural context.
Other important criteria for socio-cultural analysis include class, race, poverty level, ethnic
group and age.” (UN-Women, OSAGI Gender Mainstreaming - Concepts and definitions)
Gender analysis “is a critical examination of how differences in gender roles,
activities, needs, opportunities and rights/entitlements affect men, women, girls and boys
in certain situation or contexts. Gender analysis examines the relationships between
females and males and their access to and control of resources and the constraints
they face relative to each other. A gender analysis should be integrated into all sector
assessments or situational analyses to ensure that gender-based injustices and
inequalities are not exacerbated by interventions, and that where possible, greater equality
and justice in gender relations are promoted.” (UN-Women Training Centre, Gender Equality
Glossary)
Gender-based evidence (or gender-disaggregated data) “consists of data
that: (i) is collected and disaggregated by sex; (ii) reflects gender issues; and (iii) is based
on concepts that adequately reflect diversity within subgroups (women and men) and
captures all aspects of their lives. This type of data collection takes into account existing
stereotypes, and social and cultural factors that cause gender bias.” (UNDP/UN-Women
(2018), Gender and Disaster Risk Reduction in Europe and Central Asia, Workshop Guide
for Facilitators, p. 132)
Gender equality “refers to the equal rights, responsibilities and opportunities of
women and men and girls and boys. Equality does not mean that women and men will
become the same but that women’s and men’s rights, responsibilities and opportunities
will not depend on whether they are born male or female. Gender equality implies that
the interests, needs and priorities of both women and men are taken into consideration,
recognizing the diversity of different groups of women and men. Gender equality is not
a women’s issue but should concern and fully engage men as well as women. Equality
between women and men is seen both as a human rights issue and as a precondition for,
and indicator of, sustainable people-centered development.” (UN-Women Training Centre,
Gender Equality Glossary)
Gender issue(s) “refers to any issue or concern shaped by gender-based and/ or
sex-based differences between women and men. This may include the status of women
and men in society, the way they interact and relate, differences in their access to, and use
of, resources, and the impact of interventions and policies on women and men.” (UNDP/
UN-Women (2018), Gender and Disaster Risk Reduction in Europe and Central Asia,
Workshop Guide for Facilitators, p. 131)
Gender mainstreaming “is the chosen approach of the United Nations system
and international community toward realizing progress on women’s and girl’s rights,
as a sub-set of human rights to which the United Nations dedicates itself. It is not

54
a goal or objective on its own. It is a strategy for implementing greater equality for
women and girls in relation to men and boys. Mainstreaming a gender perspective is
the process of assessing the implications for women and men of any planned action,
including legislation, policies or programs, in all areas and at all levels. It is a way to
make women’s as well as men’s concerns and experiences an integral dimension of the
design, implementation, monitoring and evaluation of policies and programs in all political,
economic and societal spheres so that women and men benefit equally and inequality is
not perpetuated. The ultimate goal is to achieve gender equality.” (UN-Women Training
Centre, Gender Equality Glossary)
Gender perspective “is a way of seeing or analyzing which looks at the impact of gender
on people’s opportunities, social roles and interactions. This way of seeing is what enables
one to carry out gender analysis and subsequently to mainstream a gender perspective
into any proposed program, policy or organization” (UN-Women Training Centre: Gender
Equality Glossary). “By applying a gender perspective, we can:
• Analyse the causes and consequences of differences between women and men;
• Interpret data according to established sociological (or other) theories about
relationships between women and men;
• Formulate inclusive policies and decisions;
• Design interventions that take into account, and address inequalities and
differences, between women and men.” (UNDP/UN-Women, 2018, Gender
and Disaster Risk Reduction in Europe and Central Asia, Workshop Guide for
Facilitators, p.30.
Gender-responsive approach “means that the particular needs, priorities, power
structures, status and relationships between men and women are recognized and
adequately addressed in the design, implementation and evaluation of activities.
The approach seeks to ensure that women and men are given equal opportunities to
participate in and benefit from an intervention, and promotes targeted measures to
address inequalities and promote the empowerment of women.” (The GEF, 2017, GEF
Policy on Gender Equality)
Gender-sensitive approaches “attempt to redress existing gender inequalities.” (UN-
INSTRAW [now part of UN-Women], Glossary of Gender-related Terms and Concepts,
quoted by Gender Equality Glossary)
Gender stereotypes “Gender stereotypes are simplistic generalizations about the gender
attributes, differences and roles of women and men. Stereotypical characteristics about
men are that they are competitive, acquisitive, autonomous, independent, confrontational,
concerned about private goods. Parallel stereotypes of women hold that they are
cooperative, nurturing, caring, connecting, group-oriented, concerned about public
goods. Stereotypes are often used to justify gender discrimination more broadly and
can be reflected and reinforced by traditional and modern theories, laws and institutional
practices. Messages reinforcing gender stereotypes and the idea that women are inferior
come in a variety of “packages” – from songs and advertising to traditional proverbs.” (UN-
Women Training Centre, Gender Equality Glossary)
Sex-disaggregated data “Sex-disaggregated data is data that is cross-classified by sex,
presenting information separately for men and women, boys and girls. Sex-disaggregated
data reflect roles, real situations, general conditions of women and men, girls and boys
in every aspect of society. For instance, the literacy rate, education levels, business
ownership, employment, wage differences, dependants, house and land ownership, loans

and credit, debts, etc. When data is not disaggregated by sex, it is more difficult to identify
real and potential inequalities. Sex-disaggregated data is necessary for effective gender
analysis.” (UN-Women Training Centre, Gender Equality Glossary)
Women’s and girl’s empowerment “concerns their gaining power and control over their
own lives. It involves awareness-raising, building self-confidence, expansion of choices,
increased access to and control over resources and actions to transform the structures
and institutions which reinforce and perpetuate gender discrimination and inequality.
This implies that to be empowered they must not only have equal capabilities (such as
education and health) and equal access to resources and opportunities (such as land
and employment), but they must also have the agency to use these rights, capabilities,
resources and opportunities to make strategic choices and decisions (such as is provided
through leadership opportunities and participation in political institutions).” (UN-Women
Training Centre, Gender Equality Glossary)
FURTHER READING
Food and Agriculture Organization of the United Nations (FAO) (2016). Gender-responsive
Disaster Risk Reduction in the Agriculture Sector. Guidance for Policy-makers and
Practitioners. Available at http://www.fao.org/3/b-i6096e.pdf.
Food and Agriculture Organization of the United Nations (2018). Guidance Note on Gender-
sensitive Vulnerability Assessments in Agriculture. Available at http://www.fao.org/3/
I7654EN/i7654en.pdf.
Mazurana, Dyan, and others (2011). Sex and Age Matter: Improving Humanitarian
Response in Emergencies. Medford, Massachusetts: Feinstein International Center, Tufts
University. Available at https://fic.tufts.edu/assets/sex-and-age-matter.pdf.
United Nations Development Programme (UNDP) and United Nations Entity for Gender
Equality and the Empowerment of Women (UN-Women) (2018). Gender and Disaster
Risk Reduction in Europe and Central Asia. Workshop Guide for Facilitators. Available at
https://www.undp.org/content/dam/rbec/docs/Gender%20and%20disaster%20risk%20
reduction%20in%20Europe%20and%20Central%20Asia%20-%20Workshop%20guide%20
(English).pdf.
United Nations International Strategy for Disaster Reduction (UNISDR) (2011). 20-Point
Checklist on Making Disaster Risk Reduction Gender Sensitive. Available at https://www.
unisdr.org/we/inform/publications/42360.
United Nations International Strategy for Disaster Reduction (UNISDR), United
Nations Development Programme (UNDP) and International Union for Conservation
of Nature (IUCN) (2009). Making Disaster Risk Reduction Gender-Sensitive. Policy
and Practical Guidelines. Geneva. Available at https://www.unisdr.org/files/9922_
MakingDisasterRiskReductionGenderSe.pdf.

56
3.3 A comprehensive
approach to SDS risk
management
3.3.1. The disaster risk
management overview
Disaster risk management (DRM) is the
“application of disaster risk reduction
policies and strategies to prevent new
disaster risk, reduce existing disaster risk
and manage residual risk, contributing
to the strengthening of resilience and
reduction of disaster losses” (United
Nations Office for Disaster Risk Reduction,
n.d.). In practice DRM involves:
• Preparedness: the actions taken
before a disaster to anticipate the
impacts of a possible disaster and
measures to reduce these impacts.
Preparedness generally covers
planning (incorporating results from
assessing risks), education, training,
stockpiles and ensuring equipment
and human capacities are available
to respond to a disaster. Educating
people identified as “at risk” is a
core preparedness task focused on
enabling these people to reduce this
risk through their own actions.
• Warning: the process of providing
sufficient information in a timely
manner to those at risk and those
who provide assistance following a
disaster, in order to enable actions to
reduce exposure to – or impacts from
– the disaster. Developing warning
systems is part of preparedness.
• Response: the actions immediately
after a disaster that save and sustain
lives.
• Recovery: the set of activities that
begin immediately after a disaster and
continue through the post-disaster
period as people affected by the
disaster seek to return to normal life.
• Risk reduction:6
the measures taken
before a disaster to reduce risks, either
as stand-alone activities or integrated
into development efforts.
6 In some cases, efforts to mitigate hazard impacts are intended to reduce risk.
Disaster risk management is often
presented graphically as a cycle, with
one component following the other, for
example response following warning
following preparedness. However, different
segments of a society faced with the
same hazard may have different levels or
depths of engagement with preparedness,
warning, response, recovery and risk
reduction on account of economic, social
and other factors. The level of engagement
needs to be considered when defining
how each component is achieved and the
degree to which one component is strongly
or weakly linked to the others, for example
warning may be only weakly
linked to response for people living in
informal settlements.
Chapters 4, 5 and 7 cover risk assessment,
the basis for preparedness planning,
warning (who should be warned?),
response (who will need assistance?) and
risk reduction (where is risk reduction
needed?). Chapter 6 provides guidance
on how to assess the costs and benefits
of risk reduction, chapter 12 focuses on
risk reduction from a source mitigation
perspective, while chapter 13 concentrates
on preparedness and response and
chapter 9 covers early warning.
3.3.2. Global approach to
SDS risk management
The Sendai Framework for Disaster Risk
Reduction 2015–2030 (United Nations,
2015a) sets out four priorities for action to
reduce disaster impact:
1. Understanding disaster risk
2. Strengthening disaster risk
governance to manage disaster risk
3. Investing in disaster risk reduction for
resilience, and
4. Enhancing disaster preparedness for
effective response and to “Build Back
Better” in recovery, rehabilitation and
reconstruction.

These priority action areas provide a basis
for conceptualizing comprehensive SDS
risk reduction management.
Drawing on the UNCCD Policy Advocacy
Framework to combat Sand and Dust
Storms (UNCCD, 2017), actions to reduce
damage from SDS fall into two areas:
impact mitigation and source mitigation.
Together, source and impact mitigation
activities provide a comprehensive
approach to managing the potential
disaster risks posed by SDS at local to
global scales.
As indicated by Figure 13:
• Impact mitigation reduces the direct
harm from an SDS event through:
» impact-focused, gender-relevant
education about SDS and their
origins and impacts
» gender-responsive risk and impact
assessment
» gender-responsive vulnerability
mapping of populations and
infrastructure
» comprehensive gender-responsive
early warning and monitoring
» gender-responsive emergency
response and recovery plans
» gender-responsive risk reduction
plans
• Source mitigation reduces the
potential for harm from an SDS event
through:
» gender-responsive sustainable land
management
» gender-responsive integrated
landscape management
» gender-responsive integrated water
management
(See also chapters 11 and 12 for more
information on source and impact
mitigation).
Figure 13.
A twofold
approach to
mitigating sand
and dust storm
hazards for
disaster risk
reduction
Source: Adapted from Middleton and Kang, 2017.

58
Equal attention to both impact and source
mitigation is required for two reasons. First,
the majority of SDS are natural events.
One hundred per cent source mitigation
is unlikely to be practical and could have
other negative impacts. As a result, the
potential for harm from SDS cannot be
avoided.
Second, SDS can arise from very local or
distant sources. For local sources, even
short gaps in mitigation can lead to deadly
SDS events, as in the case of ploughed
fields next to a highway during strong
afternoon winds, where an SDS event can
be generated in a matter of minutes and
last less than an hour.
For distance sources, an SDS event
thousands of kilometres from a location
can have an impact, for instance on
people with breathing problems. Given the
uncertainty as to when and where SDS will
develop and have impacts, prudence calls
for preparedness to mitigate impacts.
For impact mitigation, most of the actions
identified can be integrated into common
practice approaches. In most cases, it
is feasible for existing severe weather
warning systems to include SDS.
Measures to reduce impacts can be
included in existing school and community
disaster awareness education efforts.
Health care system standard operating
procedures and traffic management
protocols can be adjusted to incorporate
measures for managing SDS impacts. This
said, further work on recovery interventions
is likely needed due to the range and
diversity of SDS impacts in contrast to
flooding, for instance, where considerable
infrastructure repair can be required.
Risk reduction in impact areas will
generally overlap with source mitigation
interventions. This is because:
• some impacted locations may also be
sources of SDS particles, and
• sustainable land management-related
interventions are often linked to other
risk reduction measures for floods and
other hazards.
Thus, on the ground, impact mitigation and
source mitigation may take place in the
same location and be linked to other risk
reduction interventions. The advantages of
this situation are that:
• at-risk communities can engage in
both preparing for and reducing the
risk of SDS, and
• single risk reduction measures,
such as tree planting or wetlands
rehabilitation, may reduce the risk
from several hazards at the same time
In terms of SDS source mitigation, it is
worth noting that to be effective these
activities generally have to take place
at scales that are more comparable to
river-basin-wide flood management (for
example a system of flood management
dams and several different types of land-
use interventions). These large-scale
interventions present specific challenges
in terms of funding, engagement of the
population in the target area, and the
lag time between interventions such as
tree planting and dune stabilization and
reduction in SDS intensity.
The following sections review in more
detail the approaches identified in the
UNCCD Policy Advocacy Framework to
combat Sand and Dust Storms (UNCCD,
2017) to reduce the impact of SDS (see
chapter 1). These reviews provide an
introduction to the more detailed technical
materials in the following chapters of the
report.
3.3.3. Risk knowledge
A precise understanding of disaster
risk is a principal step in the disaster
management process and facilitates
appropriate decision-making on risk
mitigation and adaptation strategies.
SDS risk assessment results, based on
a systematic and gender-responsive
analysis, provide results that are useful
throughout the SDS management lifecycle
covering prevention and risk reduction,
preparation and warning, and response and
recovery.

Gender-responsive vulnerability mapping,
as part of the risk assessment process,
identifies the level of impact by SDS on
at-risk populations. These results inform
adaptation and mitigation strategies to
help protect human health and prevent
crop, property and other damage.
Vulnerability maps can be produced using
geographic information system (GIS)
software which combines satellite-derived
Earth observation information with data on
social conditions and status, occupations,
economic conditions, institutions, health
conditions, wealth, culture, and political
conditions, disaggregated by age and
gender, to provide detailed answers to the
following questions:
• Who is vulnerable to SDS, with details
related to sex, age and disability?
• What is the degree of vulnerability?
• What are the reasons for this
vulnerability?
Vulnerability mapping:
• informs decision makers and
policymakers on the severity and
extent of the SDS risks, and who is
most vulnerable, and
• provides information to local
government; emergency, health and
social welfare officials; civil society
and other stakeholders on where to
direct SDS risk management efforts
Risk assessments and vulnerability
assessments are discussed further in
chapters 4, 5 and 7.

60
3.3.4. SDS source mapping
and monitoring
SDS are part of a small group of natural
hazards where the origin of the hazard can
be far away from the impact area. In some
cases, impact areas are located thousands
of kilometres away across country borders.
Precise and up-to-date information on
SDS sources is critical to forecasting
and early warning, as well as to targeting
where source mitigation will be the most
effective.
Global trajectory and deposition of dust
plume movements are relatively well
documented. Major global dust sources
include North Africa and North-East,
East, Central, South and West Asia (Shao
et al, 2011; Ginoux et al., 2012; Goudie
and Middleton, 2006; Prospero et al.,
2002). However, more work is needed to
identify and map local and point sources
with sufficient resolution, accuracy and
local data and information to justify
source mitigation efforts. The potential
contamination of dust with pathogens and
pollutants at source and in transportation
also make the precise mapping of SDS
dust sources and trajectories important in
reducing the SDS risk to human health.
GIS software and models can bring
together multiple data sets on precipitation,
evaporation, drought, soil moisture,
temperature, land and soil degradation,
vegetation and land use to improve source
area monitoring (Gerivani et al., 2011; Kim
et al., 2013; Cao et al., 2015; Borelli et al.,
2016). To this process can be added data
and analysis from vulnerability mapping to
provide a clearer picture of who might be
more or less vulnerable during specific SDS
events associated with specific weather
and socioeconomic conditions. Source
area and vulnerability mapping results can
also be used in identifying which source
mitigation measures can be used to reduce
vulnerability. (See chapters 2 and 8 for
more information on source mapping.)
3.3.5. SDS forecasting
Combining SDS source mapping
and monitoring, the detection of SDS
occurrence and monitoring dust plumes
movement and near- and long-term
forecasting is core to comprehensive SDS
management. Dust raising and transport
is monitored using a combination of data
from satellites, networks of light detection
and ranging (LIDAR) and radiometers, air-
quality monitoring and weather stations.
Ground-based observations from weather
stations provide a powerful, lengthy,
standardized data set that extends in
some parts of the world continuously for
more than 50 years. Chapter 9 discusses
in detail the current global SDS monitoring
and forecasting system.
©Gary
sauer-thompson
on
Flickr

The drawbacks of using dust
weather data include the
relatively sparse distribution
of meteorological stations in
key source regions, including
the Sahara, parts of Arabia,
the Gobi and Taklamakan
Deserts and central Australia,
as well as the low and often
variable frequencies of
observations.
However, there is the potential for
establishing a citizen science approach
to SDS monitoring and warning based
on the nature of some SDS genesis in
low pressure zones, their movement,
knowledge about seasonal or diurnal wind
conditions that can generate SDS, and
access to weather satellite imagery and
forecasts. See chapter 9 for an example
of citizen science SDS monitoring from
Australia.
Using citizen science to monitor SDS does
not displace official monitoring, forecasting
and warning systems, but empowers
at-risk populations to be more engaged
in the management of the risks they face.
This citizen science approach reflects the
concept that risk management best starts
at the individual level, rather than placing a
reliance on top-down communication and
on official directives before taking action.
3.3.6. Communication
and dissemination
of early warnings
For SDS early warning systems to have the
desired results, early warning information
needs to reach women, girls, men and
boys. Equally, the effectiveness of modes
of communication and information
dissemination is critical to ensuring that
vulnerable population groups are aware of,
and able to prepare for, a hazard. Gender
roles, social status, culture and traditions
affect the processing and dissemination
of information that people receive through
community warning systems. Information
flows often fail to reach women, especially
those living in remote areas (UNISDR,
UNDP and IUCN, 2009).
Disseminating warnings and other SDS-
related information can use a range
of communication channels, including
mobile phone text messages, free-to-air
and paid broadcast networks, website
updates, emails, word-of-mouth, and
open-air warning signals where appropriate
(Harriman, 2014). However, care is needed
to ensure that messages are clear, have
practical value and address the social
preference for confirming warnings with
other information. Education before actual
warnings are sent about the content of
warning messages and what to do when a
message is received is critical to success
when actual warnings are issued.
Technologies such as SMS (short
messaging service), WhatsApp, Twitter®,
Instagram® or other commercial
messaging services can be used in
warnings. For instance, in South Korea,
warnings of dust events are issued by
the Korea Meteorological Administration
using local media and SMS text alerts for
users who register on their air-quality alert
website (KMA, 2019).
However, evidently not all messages
sent via SMS or similar technologies are
received, or read, immediately and the
content of these messages can be very
limited. Further, these technologies rely on
phone or Internet service, which may not
be available in all at-risk locations, or may
not be operational due to other factors
when warnings need to be issued. SDS
early warning is discussed in more detail in
chapter 10.

62
3.3.7. Preparedness
and response
Preparedness for SDS events is based on
asking:
• What is the likely type, frequency and
timing of an SDS event?
• Who will be affected, considering
gender, age and disability?
• Which measures should be
implemented before the event (prior to
a warning) and regarding warnings to
reduce the impact of an SDS event?
This process uses information from the
SDS risk and vulnerability assessments,
modelling and past disasters to develop
scenarios of expected events. Risk
assessment and vulnerability data are
used to identify the location of at-risk
populations, and why specific groups may
be more or less vulnerable, for instance due
to health, occupation, housing conditions,
gender or wealth.
Preparedness plans generally include
warning procedures, specific measures to
be taken once a warning has been received
as well as when the SDS event is taking
place, and education and simulation plans.
In general, plans are based on integrating
government and civil society activities
into the response to a potential disaster.
For instance, a preparedness plan may
identify that a health centre will call on
Red Crescent or Red Cross volunteers
to provide support when the number of
people coming to the clinic for treatment
following the SDS exceeds the human
resources available to the clinic.
In many cases, a general preparedness
plan for a community, region or nation is
complimented by sector-specific plans with
additional details for the expected user.
For instance, a national preparedness plan
would detail the sectoral responsibilities
of different departments and services in
the event of a sand or dust storm, while
each of these parties would have more
detailed plans based on the delegated
responsibilities.
Globally, some level of disaster
preparedness plan exists (whether formal
or informal) for almost all towns or
similar settlements. It is also common for
disaster preparedness plans to exist at
the regional and national levels. Given the
likely existence of a disaster preparedness
plan, the initial steps in preparing for
SDS response is to integrate risk and
vulnerability information into the plan,
followed by developing SDS scenarios
and identifying response options. The
effectiveness of response options can
be tested through a scenario-based
simulation, with the whole SDS component
complemented by a public education plan
using schools, community events and
other opportunities.
Actual response to SDS can vary
considerably depending on the scale
and impact of the SDS event, the level
of preparedness and the timeliness of
warnings and whether they were followed.
As with other disasters, response to SDS is
an adaptive process. Critical tasks are to:
1. Assess and document the impacts of
the SDS.
2. Establish a response coordinating
system (defined in advance in the
preparedness plan).
3. Focus initial response on those
groups that risk and vulnerability
assessments have identified as at
high risk (for example older persons,
very young children, individuals with
compromised health) and consider
gender roles and vulnerabilities.
4. Allocate resources to those parties
involved in the response that face the
greatest need.
5. Initiate discussions and planning on
recovery, which should be integrated
into the initial response as far as
possible. (Information for recovery
planning should come from the first
task of assessing impacts.)
The Sphere Handbook, especially page 11,
provides further guidance on responding
to disasters (Sphere Association, 2018).
Preparedness and impact mitigation
(response) are discussed further in
chapter 13.

3.3.8. Risk reduction
Under the Policy Advocacy Framework
(UNCCD, 2017), risk reduction takes place
through source mitigation and impact
mitigation (see Figure 13). Broadly, risk
reduction focuses on two areas:
• Physical measures that can reduce or
prevent the impact of an SDS event.
These measures are often based
on improved land-use planning and
land-use management, as discussed
further in chapter 13, but they can also
include improvements to air supplies
in buildings or improvements to roads
to reduce SDS impacts.
• Socioeconomic measures that:
» reduce the level of damage that
an SDS event can cause at the
individual or household level
» improve the ability of at-risk
individuals or groups to address the
impacts of the SDS event
The socioeconomic measures include
a wide range of possible interventions
targeted at addressing a specific impact
of an SDS event. For instance, less
wealthy families can be provided grants or
materials to improve windows and doors
to reduce dust infiltration. Individuals with
respiratory problems can be provided
with breathers and appropriate power
supplies at no or low cost. Families
identified as more at risk can be offered
economic opportunities to generate
additional income to self-finance measures
for reducing SDS impacts. A significant
element in defining and choosing
appropriate socioeconomic measures is
understanding risk and vulnerability, with
education about SDS and risk reduction
measures important in enabling a specific
at-risk individual or group to select the best
options for their needs.
3.3.9. Anthropogenic
source mitigation
There are numerous technical measures
for mitigating SDS at source (see chapter
12), including a wide array of techniques
that are used for wind erosion control,
most of which were developed to protect
cultivated fields from soil loss (Skidmore,
1986; Nordstrom and Hotta, 2004).
At any particular location, a range of
measures is typically employed. Riksen et
al. (2003) distinguish between techniques
designed to minimize actual risk (short-
term: for example cultivation practices
such as minimum tillage) and those that
minimize potential risk (long-term: for
example planting windbreaks).
Most of the technical measures are usually
applied in places where wind erosion is
predominantly an anthropogenic land-use
issue. The main exceptions are in desert
areas where naturally occurring mobile
sand dunes and blowing sand present
challenges to human activities.
Action taken to mitigate anthropogenic
sources of SDS contributes towards
the global aspiration to halt and reverse
land degradation by 2030 (Sustainable
Development Goal target 15.3 https://
sdgs.un.org/goals/goal15) and is in line
with the concept of land degradation
neutrality (LDN). Sustainable land
use management (SLM), in particular,
contributes towards resolving issues
surrounding the need to achieve social,
economic and environmental objectives in
areas where productive land uses compete
with environmental and biodiversity goals
(Sayer et al., 2013).

64
3.4 Comprehensive approach to SDS risk management
Given the diverse spatial and temporal nature of SDS, impact and source management
require a unified, coordinated cross-sectoral approach. As summarized in Figure 14, this
approach involves three main groups:
1. The agencies, institutions
and authorities responsible
for setting SDS risk
management policies
and implementing plans
covering risk reduction,
preparedness, warning and
response. Key members of
this group include:
» land and water
management authorities,
including land
reclamation authorities
» agriculture and livestock
ministries
» health authorities
» finance authorities
» meteorology and
hydrology services
» disaster management
authorities
» transport authorities
» public safety authorities
» gender/women’s
ministries/committees
Jared
Verdi,
©Unsplash,
October
21st,
2017

2. The scientific research and
academic communities
responsible for:
» understanding the social
and physical nature
of SDS, including risk
and vulnerability and
the physical mechanics
behind the origins of and
causes of SDS impacts
» identifying the ways in
which source and impact
mitigation policy and
practice can be effective
and
» monitoring SDS-related
policies and practices to
assess effectiveness and
define improvements to
reduce risk
3. The at-risk communities
impacted by SDS and
who should be directly
empowered to reduce SDS
risk through:
» comprehensive risk
management plans
covering risk reduction,
preparedness, warning
and response
» a solid understanding of
the origins and impacts
of SDS and measures to
mitigate SDS
» involvement in impact-
based warning systems
that reflect specific
threats and in the means
to mitigate these threats
» involvement in land and
water use plans and
programmes that can
reduce the generation of
SDS

66
In general, at-risk communities include
the private sector as well as non-
governmental organizations (NGOs) that
are involved in risk reduction, preparedness
and response. NGOs can vary widely in
their nature and focus, from women-led
mutual credit groups to international
organizations involved in the environment
and development. Efforts should be made
to involve as many NGOs as possible in
addressing the impacts of SDS on at-risk
populations.
The process, as indicated in Figure 14,
is iterative, with a constant exchange
between the three groups in an attempt to
find better policies and activities to reduce
SDS impacts.
This process is also gender-responsive,
recognizing that women, boys, girls and
men are affected differently by SDS and are
presented with different ways of reducing
SDS impacts based on their social or
cultural roles and expectations. Similar
attention is given to young children and
older persons as well as those individuals
with compromised health, all of whom
may be impacted more severely by an SDS
event than the general population.
Figure 14.
Framework
for sand and
dust storm risk
management
coordination and
cooperation

Box 4. SDS and a changing climate
SDS are clearly affected by climate conditions, both in terms of climate variability and
climate change. Chapter 3 on climate change and desertification in Climate Change and
Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation,
Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in
Terrestrial Ecosystems (Mirzabaev et al., in press) reports:
• The loss of vegetation or drying of soil “due to intense land use and/or climate change
can be expected to cause an increase in sand and dust storms (high confidence)”.
• There is “high confidence that there is a negative relationship between vegetation
green-up and the occurrence of dust storms”.
• “By decreasing the amount of green cover and hence increasing the occurrence of
sand and dust storms, desertification will increase the amount of shortwave cooling
associated with the direct effect (high confidence)”.
• “There is medium confidence that the semi-direct and indirect effects of this dust
would tend to decrease precipitation and hence provide a positive feedback to deser-
tification”. However, the “overall combined effect of dust aerosols on desertification
remains uncertain”.
(All quoted text from p. 268, Mirzabaev et al., in press).
Note that these conclusions relate more directly to desertification than to SDS. Changes
to the climate may also affect other factors linked to SDS generation. These include longer
periods where seasonal lakes are dry, thus contributing to longer periods of SDS genera-
tion, and changes to river flooding duration, where longer low-water periods can provide
more source sediment for SDS entrainment.
One of the challenges around understanding the impact of a changing climate on SDS is
the lack of extensive weather data collection and observations systems, which limits the
understanding of climatic conditions. This same situation also impacts the understanding
of SDS, as well as the implementation of warning systems and evaluation of the effective-
ness of risk reduction.
Specific approaches to addressing the impact of a changing climate are not included
in the Compendium. However, SDS source mitigation approaches incorporating land
degradation neutrality, sustainable land management, integrated land management and
integrated water use management described in chapter 12 are all core to addressing
the impact of climate on SDS generation and management. Improving the collection and
understanding of weather data, at global to local levels, will also contribute to better under-
standing the links between a changing climate and SDS.
Source: Mirzabaev, A., and others (2019). Desertification. In Climate change and land:
an IPCC special report on climate change, desertification, land degradation, sustainable
land management, food security, and greenhouse gas fluxes in terrestrial ecosystems,
Priyadarshi R. Shukla, Jim Skea, Eduardo Calvo Buendía, Valérie Masson-Delmotte, Hans-
Otto Pörtner, Debra C. Roberts, Panmao Zhai, Raphael Slade, Sarah Connors, Renée van
Diemen, Marion Ferrat, Eamon Haughey, Sigourney Luz, Suvadip Neogi, Minal Pathak,
Jan Petzold, Joana Portugal Pereira, Purvi Vyas, Elizabeth Huntley, Katie Kissick, Malek
Belkacemi and Juliette Malley, eds. In press.

68
3.5 Conclusion
SDS are a significant natural process, but
also a natural hazard that is receiving
increasing attention. This increased
attention is highlighting not only the
human, social and economic impact of
SDS, but also the ways in which the risks
posed by SDS can be addressed.
The efforts to address the impacts of SDS
focus on two areas:
• impact mitigation, to reduce the direct
harm from SDS, and
• source mitigation, to reduce the
potential for harm from sand and dust
These efforts involve authorities and
agencies, scientific research and academic
communities and, most importantly, the
communities, households and individuals
at risk from SDS. The combined effort is
iterative and, to be effective and support
all those at risk, must consider gender, age
and health status.
The following chapters of the Compendium
provide more details on how SDS impacts
and sources can be managed, how risks
and vulnerability can be assessed and how
research and data collection can support
preparedness, warning and the response to
SDS. As indicated by Figure 14, this effort
is collaborative insofar as it requires the
cooperation of many sectors and actors
working together in a way that builds on
experience and continually improves work
to reduce the impact of SDS.

3.6 References
Al–Dousari, Ali M., and others (2016). Temporal and
spatial assessment of pollen, radionuclides,
minerals and trace elements in deposited dust
within Kuwait. Arabian Journal of Geosciences,
vol. 9.
Ataniyazova, O.A., and others (2001). Levels of certain
metals, organochlorine pesticides and dioxins in
cord blood, maternal blood, human milk and some
commonly used nutrients in the surroundings of the
Aral Sea (Karakalpakstan, Republic of Uzbekistan.
Acta Paediatrica, vol. 90, No. 7.
Baddock, Matthew C., and others (2013). Aeolian dust
as a transport hazard. Atmospheric Environment,
vol. 71.
Borelli, Pasquale, Emanuele Lugato and Panos Panagos
(2016). A pan-European quantitative assessment of
soil loss by wind. Geophysical Research Abstracts,
vol. 18.
Cao, Hui, and others (2015). Identification of sand and
dust storm source areas in Iran. Journal of Arid
Land, vol. 7, No. 5.
Chad (2017). Declaration officielle du Tchad a la plate-
forme globale pour la reduction des risques de
catastrophes (RRC) du 22 au 26 mai 2017 a Cancun
au Mexique [Official declaration of Chad at the
Global Platform for Disaster Risk Reduction on 22–
26 May 2017 in Cancun, Mexico]. Available at www.
unisdr.org/conferences/2017/globalplatform/en/
programme/statements.
Chin, Mian, and others (2007). Intercontinental transport
of pollution and dust aerosols: implications for
regional air quality. Atmospheric Chemistry and
Physics, vol, 7.
Dagsson-Waldhauserova, Pavla, and others (2015).
Snow–dust storm: unique case study from Iceland,
March 6–7, 2013. Aeolian Research, vol. 16.
Egan, Timothy (2006). The Worst Hard Time: The Untold
Story of Those Who Survived the Great American
Dust Bowl. New York: Houghton Mifflin Company.
Gambia (2017). Government statement – Republic
of The Gambia at the UNISDR Global Platform
Conference on DRR, Cancun Mexico, 22–26
May 2017. Available at http://www.unisdr.org/
conferences/2017/globalplatform/en/programme/
statements. Accessed 3 December 2017.
Garrison, Virginia H., and others (2003). African and
Asian dust: from desert soils to coral reefs.
BioScience, vol. 53, No. 5.
Gerivani, Hadi, and others (2011). The source of dust
storm in Iran: a case study based on geological
information and rainfall data. Carpathian Journal of
Earth and Environmental Sciences, vol. 6, No. 1.
Gillette, Dale A. (1979). Environmental factors affecting
dust emission by wind. In SaharanDust:Mobilization,
Transport, Deposition, Christer Morales, ed. John
Wiley & Sons, Ltd.
of anthropogenic and natural dust sources and their
emission rates based on MODIS Deep Blue aerosol
products. Reviews of Geophysics, vol. 50, No. 3.
Gisladottir, Fanney Osk, Olafur Arnalds and Guðrún
Gisladottir (2005). The effect of landscape and
retreating glaciers on wind erosion in South Iceland.
Land Degradation & Development, vol. 16, No. 2.
Goh, Amelia (2013). A literature review of the gender-
differentiated impacts of climate change on
women’s and men’s assets and well-being in
developing countries, CAPRi Working Paper No.
106. Washington, D.C.: CGIAR Systemwide Program
on Collective Action and Property Rights (CAPRi).
health disorders. Environment International, vol. 63.
Goudie, Andrew S, and Nicholas J. Middleton (2006).
Desert Dust in the Global System. Springer.
Grousset, Francis E., and others (2003). Case study of
a Chinese dust plume reaching the French Alps.
Geophysical Research Letters, vol. 30.
Guha-Sapir, Debarati, and others (2017). Annual Disaster
Statistical Review 2016: The Numbers and Trends.
Brussels: Centre for Research on the Epidemiology
of Disasters (CRED).
Harriman, Lindsey M. (2014), Climate change
implications and use of early warning systems
for global dust storms. In Reducing Disaster: Early
Warning Systems for Climate Change, Zommmers,
Zinter, and Ashbindu Singh, eds. Dordrecht: Springer
Science+Business Media.
Inter-Agency Standing Committee (IASC) (2018). The
Gender Handbook for Humanitarian Action.
Intergovernmental Panel on Climate Change (IPCC)
(2012). Managing the risks of extreme events and
disasters to advance climate change adaptation.
A Special report of Working Groups I and II of the
Intergovernmental Panel on Climate Change,
Christopher B. Field, Vicente Barros, Thomas F.
Stocker, Qin Dahe, David Jon Dokken, Kristie L. Ebi,
Michael D. Mastrandrea, Katharine J. Mach, Gian-
Kasper Plattner, Simon K. Allen, Melinda Tignor
and Pauline M. Midgley, eds. New York: Cambridge
University Press.
Jusot, Jean-François, and others (2017). Airborne dust
and high temperatures are risk factors for invasive
bacterial disease. Journal of Allergy and Clinical
Immunology, vol. 139, No. 3.

70
Kellogg, Christina A., and Dale W. Griffin (2006).
Aerobiology and the global transport of desert dust.
Trends in Ecology & Evolution, vol. 21, No. 11.
Khaniabadi, Yusuf Omidi, and others (2017). Impact
of Middle Eastern dust storms on human health.
Atmospheric Pollution Research, vol. 8.
Kim, Dongchul, and others (2013). The effect of the
dynamic surface bareness to dust source function,
emission, and distribution. Journal of Geophysical
Research Atmospheres, vol. 118, No. 2.
Kok, Jasper, and others (2012). The physics of wind-
blown sand and dust. Reports on Progress in
Physics, vol. 75, No. 10.
Korea Meteorological Service (KMA) (n.d.). Asian dust.
Available at https://web.kma.go.kr/eng/weather/
asiandust/intro.jsp.
Kutuzov, Stanislav, and others (2013). High-resolution
provenance of desert dust deposited on Mt. Elbrus,
Caucasus in 2009-2012 using snow pit and firn core
records. The Cryosphere, vol. 7. No. 5.
Manyena, Siambabala Bernard (2006). The concept of
resilience revisited. Disasters, vol. 30, No. 4.
McGrath, Matt (2017). Lack of dust makes China’s
air pollution much worse, 12 May. Available
at http://www.bbc.com/news/science-
environment-39895558.
McKendry, I.G., and others (2011). Long-range transport
of Asian dust to the Lower Fraser Valley, British
Columbia, Canada. Journal of Geophysical Research
Middleton, Nicholas J. (2017). Desert dust hazards: a
global review. Aeolian Research, vol. 24.
Middleton, Nicholas, and Utchang Kang (2017). Sand
and dust storms: impact mitigation. Sustainability,
vol. 9, No. 6.
Middleton, Nicholas, Peter Tozer, and Brenton Tozer
(2018). Sand and dust storms: underrated natural
hazards. Disasters, vol. 43, No. 1.
Muhs, D.R., and others (2007). Geochemical evidence
for African dust inputs to soils of western Atlantic
islands: Barbados, the Bahamas, and Florida.
Journal of Geophysical Research Atmospheres, vol.
112, No. F2.
National Oceanographic and Atmospheric Agency
(NOAA) (n.d.). Beaufort Wind Scale. Available at
http://www.spc.noaa.gov/faq/tornado/beaufort.
html.
Natural Resources Conservation Service (2017). More
than 80 years helping people help the land: a
brief history of NRCS. Available at https://www.
nrcs.usda.gov/wps/portal/nrcs/detail/national/
about/history/?cid=nrcs143_021392. Accessed 3
December 2017.
New York Times (1991). 12 are killed as dust shrouds
highway, 30 November. Available at http://www.
nytimes.com/1991/11/30/us/12-are-killed-as-
dust-shrouds-highway.html. Accessed 19 August
2017.
Niger, Office of the Prime Minister (2017). Declaration
du Niger a l’occasion de la cinquieme plate-forme
mondiale [Declaration of Niger on the occasion of
the fifth Global Platform]. Available at http://www.
unisdr.org/conferences/2017/globalplatform/en/
programme/statements. Accessed 3 December
2017.
Nordstrom, Karl F., and Shintaro Hotta (2004). Wind
erosion from cropland in the USA: a review of
problems, solutions, and prospects. Geoderma vol.
121, No. 3–4.
NSW Regional Office (2006). About dust – test, May.
Available at http://www.bom.gov.au/nsw/sevwx/
facts/dust.shtml. Accessed 28 January 2018.
Pérez, Laura, and Nino Künzli (2011). Saharan dust:
no reason to exempt from science or policy.
Occupational and Environmental Medicine, vol. 68,
No. 6.
Plümper, Thomas, and Eric Neumayer (2007). The
gendered nature of natural disasters: the impact
of catastrophic events on the gender gap in life
expectancy, 1981–2002. Annals of the American
Association of Geographers, vol. 97, No. 3.
Prospero, Joseph M. (1999). Long-term measurements
of the transport of African mineral dust to the
southeastern United States: implications for
regional air quality. Journal of Geophysical
Research. Atmospheres, vol. 104, No. D13.
soil dust identified with the Nimbus 7 Total Ozone
Mapping Spectrometer (TOMS) absorbing aerosol
product. Reviews of Geophysics, vol. 40, No. 1.
Roberts, Alex, and Peter Knippertz (2012). Haboobs:
convectively generated dust storms in West Africa.
Weather, vol. 67, No. 12.
Riksen Michael, Floor Brouwer, and Jan de Graaff.
(2003). Soil conservation policy measures to
control wind erosion in northwestern Europe.
Catena, vol. 52, No. 3.
Sayer, Jeffrey, and others (2013). Ten principles for a
landscape approach to reconciling agriculture,
conservation, and other competing land uses.
Proceedings of the National Academy of Sciences,
vol. 110.
Shao, Yaping (2008). Physics and Modelling of Wind
Erosion. Second edition. Springer.
Shao, Yaping, and others (2011). Dust cycle: an
Aeolian Research, vol. 2, No. 4.

Skidmore, E.L. (1986). Wind erosion control. Climate
Change, vol. 9, No. 1–2.
Soukup, Deborah, and others (2012). Arsenic
concentrations in dust emissions from wind
erosion and off-road vehicles in the Nellis Dunes
Recreational Area, Nevada, USA. Aeolian Research,
vol. 5.
Sphere Association (2018). The Sphere Handbook.
Available at https://handbook.spherestandards.
org/en/sphere/#ch001.
Tobar, Hector and Tracy Wilkinson (1991). Dust storm
causes I-5 pileup; 6 die, 50 hurt: traffic: wreckage
north of Coalinga involves up to 100 cars and big-rig
trucks. CHP closes 150 miles of road, 30 November.
Available at http://articles.latimes.com/1991-
11-30/news/mn-70_1_dust-storm. Accessed 3
December 2017.
Tozer, Peter (in press). An Economic Impact Assessment
Model for Measuring the Impact of Sand and Dust
Storms: And a Framework for the Cost Benefit
Analysis of Dust Mitigation Strategies. United Nation
Convention to Combat Desertification (UNCCD).
Tozer, Peter, and John Leys (2013). Dust storms – what
do they really cost? The Rangeland Journal, vol. 35,
No. 2.
United Nations (2015a). Sendai Framework for Disaster
Risk Reduction 2015–2030. Available at https://
www.undrr.org/publication/sendai-framework-
disaster-risk-reduction-2015-2030.
United Nations (2015b). Gender Responsive Disaster
Risk Reduction. A Contribution by the United Nations
to the Consultation Leading to the Third UN World
Conference on Disaster Risk Reduction. Version
2. Available at https://www.preventionweb.net/
files/40425_gender.pdf.
(UNCCD) (2017). Draft advocacy policy frameworks:
Gender, Drought, and Sand and Dust Storms. 3 July.
ICCD/COP(13)/19.
United Nations Development Programme (2013). Gender
and disaster risk reduction. Policy Brief 3. Available
at http://www.undp.org/content/dam/undp/library/
gender/Gender%20and%20Environment/PB3-AP-
Gender-and-disaster-risk-reduction.pdf.
United Nations Environment Assembly (2016).
Nations Environment Programme (UNEP).
United Nations Environment Programme (UNEP)
(2012). Land Health Surveillance: An Evidence-
based Approach to Land Ecosystem Management.
Illustrated with a Case Study in the West Africa
Sahel. Nairobi.
United Nations Environment Programme (UNEP), World
Meteorological Organization (WMO), and United
Nations Convention to Combat Desertification
(UNCCD) (2016). Global Assessment of Sand and
Dust Storms. Nairobi: UNEP.
United Nations International Strategy for Disaster
Reduction (UNISDR), United Nations Development
Programme (UNDP), and International Union for
Conservation of Nature (IUCN). (2009). Making
Disaster Risk Reduction Gender-Sensitive. Policy
and Practical Guidelines. Geneva.
[formerly UNISDR] (n.d.) https://www.unisdr.org/
we/inform/terminology#letter-d.
Uno, Itsushi, and others (2009). Asian dust transported
one full circuit around the globe. Nature Geoscience
Letters.
Wang, Sen (2001), Fighting dust storms: the case of
Canada’s Prairie region. In Global Alarm: Dust and
Sandstorms from the World’s Drylands, Yang Youlin,
Victor Squires, and Lu Qi, eds.

72

UNCCD | Sand and Dust Storms Compendium | Chapter 4 | Assessing the risks 73
4. Assessing the risks
posed by sand and dust
storms
Chapter overview
This chapter discusses the nature of sand and dust storms (SDS) as a hazard and
summarizes the differences between risks and impacts. Factors associated with SDS are
identified, an SDS typology is proposed and the issue of vulnerability to SDS is explored.

UNCCD | Sand and Dust Storms Compendium | Chapter 4 | Assessing the risks
74
4.1 Assessing SDS
disaster risks
and impacts
A definition of disaster risk can be found in
the Glossary of key disaster-related terms
(Chapter 3). Risk can be understood as the
combination of:
• a hazard of a specific magnitude,
intensity, spatial extent and frequency
(a hazard event)
• exposure of society directly or
indirectly to this hazard event
• the level of social and physical
vulnerability to this hazard event and
• the capacity to deal with the impact of
the specific hazard event
Where there is no exposure to a hazard,
there is no risk, and therefore no need for a
risk assessment.
Capacity is considered to be the practical
opposite of vulnerability. Assessing
vulnerability can incorporate any capacity
to not experience damage (i.e. reduce
vulnerability) from a hazard event. Further
background on disaster risk assessment
can be found in European Commission
(2010) and Schneiderbauer and Herlich
(2004).
Box 5 discusses the link between impact
and risk assessment. Understanding the
potential impact from, or risk posed by,
SDS, requires answers to the following
three questions:
• What is the physical and spatial
nature of the SDS hazard, at different
intensities and frequencies?
• How do SDS hazard events (such as
Harmattan, haboob and dust storms)
affect humans, society and nature, or
what is the nature of vulnerability to
SDS?
• How can risks from different
combinations of SDS intensity and
vulnerabilities be compared to identify
the optimum points of intervention for
reducing these risks?
In general, risk is seen as a negative factor
– something that threatens lives and
well-being. However, in the case of SDS (as
with other hazards), not all of its impacts
are negative. For instance, flooding can
bring nutrients to flooded fields and SDS
can have positive impacts on forestry and
the ocean food chain (as cited in Goudie,
2009), or contribute to a dampening effect
on hurricane development (University of
Wisconsin-Madison, 2008).
At the same time, defining and quantifying
trade-offs between positive and negative
impacts is complicated; even more so
in the case of SDS due to the lack of a
full understanding of the links between
possible positive impacts and related
possible negative impacts. As a result,
SDS risk assessment focuses on negative
impacts of SDS events, examining
how these events interact with human
vulnerabilities to cause harm. Once
identified, these risks can become the
object of efforts to reduce negative
impacts on lives and well-being.
Finally, it is critical to understand that
risk assessments present a trade-off
between accuracy, cost and timely results.
Extremely accurate assessments are
costly and time-consuming, while rapid
inexpensive assessments can deliver
contestable or unusable results. The two
assessment procedures presented in this
chapter can provide usable, and verifiable,
results at reasonable costs.

Box 5. Impact and risk
Impact is how an event, real or conjectured, could affect something (for example a river)
or someone (for example people living near a river). Post disaster impact assessments
document what has happened during and after a disaster. For SDS, such assessments
can be used to define future SDS impacts for the same or similar events.
However, information from post disaster impact assessments is not easily used to project
the impacts of events that have not yet been experienced, or where there have been
significant changes to the environment. Nonetheless, post disaster impact assessments
can provide information that is useful in considering the impacts of SDS and they should
be conducted whenever possible.
Environmental impact assessments (EIA) take a different approach to assessing impact.
An EIA focuses on assessing the impact of a proposed action (for example a road project)
and at least one alternative (for example no road) to generate a comparison of impacts
and provide input into the best option for achieving a stated goal (such as improving
access to a community) (International Association for Impact Assessment and Institute of
Environmental Assessment, UK, 1999). The challenge with an EIA-type impact assessment
is that its focus on a defined product (such as the construction of a road) and alternatives
is difficult to reconcile with understanding the impact of a range of SDS hazard events with
varying intensity, duration, recurrence and impacts.
The alternative to the post disaster and the environmental impact assessment approaches
is to look at SDS from the perspective of the future risk of impacts on humans, society
and the environment in general. These risks, or future impacts, are defined by different
combinations of SDS hazard frequency, spatial extent and intensity and the levels of
vulnerability of a population threatened by different combinations of these characteristics.
This is usually done through disaster risk assessment, where a variety of methods can be
used to develop an understanding of SDS impacts under a variety of conditions.
Gennadiy
Ratushenko
©World
Bank

76
4.2 SDS as hazards
4.2.1. SDS as composite
hazards
SDS as a hazard is broadly defined as
where blowing sand or dust causes
visibility to drop below 1,000 metres
(WMO, 2014). The US Air Force recognizes
two classes of SDS: one where visibility
is between 1,000 and 500 metres and
the second where visibility is below 500
metres (Secretary of the Air Force, 2003).
These two classes allow for a better
differentiation of SDS intensity.
The World Health Organization (WHO) has
indicated that, for particulate matter, “no
threshold has been identified below which
no damage to health is observed” (World
Health Organization, 2016). While WHO
sets guidelines for small particulate matter,
the general finding means that any level
of particulate matter found in SDS needs
to be considered an active hazard, i.e. a
potential source of harm.
To understand what makes an SDS event
a hazard, the range of factors that must
come together to create it must be defined.
The term “sand and dust storms” highlights
the composite nature of the hazard,
involving sand, dust, storm and a range of
other factors.
A single hazard event can be defined by
the factors that contribute to (or mitigate
against) an SDS event and its spatial
coverage (size) or magnitude, intensity,
duration and frequency. Also important
are the impact and source areas of the
event, given how these can affect the other
four factors. Nevertheless, even when the
factors that normally contribute to an SDS
event are present, it is not guaranteed that
an SDS event will occur (Middleton, 2017a).
Table 1 sets out the factors that can
contribute to, or mitigate against, the
development of an SDS event. Each
factor is briefly described, together with
parameters for measuring it (useful in SDS
warning systems) and notes providing
additional information on the factor.
The table supports the SDS risk
assessment process by identifying
what contributes to (and what can
reduce) the likelihood of an SDS event.
Considering these factors as part of the
risk assessment process will improve the
accuracy and focus of an assessment.
It should be noted that while SDS events
release dust, sand, spores, pollen and other
small particulate matter (aerosols) into
the atmosphere, not all of these elements
in the atmosphere are linked to SDS. A
range of aerosols exist in the atmosphere
independent of SDS, including particles
from fire and other forms of combustion,
volcanic ash, pollen and spores (Boucher,
2015). Individually, these atmospheric
aerosols can pose significant health and
other risks but they are not covered in the
assessment apart from their involvement
in SDS. (See chapter 2 for more on what an
SDS event comprises.)

Factor Description Parameters Notes
Wind Wind speed above
a specific level can
mobilize sand or dust.
• Speed
• Direction
• Duration of gusts
• Turbulence
Wind speeds needed
to create a storm differ
under different land-use,
land-cover and land-form
conditions. Surface level
effects, turbulence and
fluid dynamics can affect
the point or location
at which sand or dust
become mobile. See
Kok et al. (2012) for a
detailed discussion of
the interactions between
wind, sand and dust.
Precipitation (rain and
snow)
Rainfall reduces the
development of SDS,
while periods of reduced
precipitation (normal,
seasonal or abnormal)
can lead to increased
likelihood of SDS.
Snow-covered land is
not expected to be a
source of sand or dust,
but patchworks of snow-
covered and non-covered
land may enable SDS
generation.
• Cumulative
precipitation
compared to
average
• Period of
days without
precipitation
(seasonal
precipitation may
be average but
with extended dry
periods)
• Snow cover
Humidity levels may be
an alternative indicator if
high humidity is linked to
a lack of SDS.
Seasonal snow cover
may define seasonality
of SDS development.
Precipitation can also
enhance soil moisture
and cohesion (Middleton,
2019).
Drought The absence of normal
levels of rainfall
(drought) can lead to dry
soils, which are more
likely to contribute to
SDS. Drought can also
cause the reduction or
loss of vegetation that
provides soil cover or
disrupts wind speeds to
reduce the generation
of SDS.
• Negative change
in precipitation
compared to
short- to long-term
averages
Long-term drought can
change vegetation and
land cover, increasing the
likelihood of SDS.
Soil moisture Soil moisture can affect
the looseness of surface
soil and its ability to be
transported by wind.
• Level of soil
moisture
Soil moisture can change
with daily heating. Wind
can have a drying effect.
Soil moisture can be high
in the morning following
frost or condensed
moisture and low in the
afternoon/evening due to
solar heating and wind.
Ground temperature Whether the ground is
above or below freezing.
Freezing temperatures
make sand and dust
mobilization less
likely. High ground
temperatures can
contribute to convention-
related wind speed and
dust whirlwinds and can
reduce soil moisture and
dry the soil.
• Ground
temperature
Frozen sand or dust is
unlikely to be mobilized
by wind. Daily changes
from a frozen to unfrozen
state may define periods
when sand or dust can
be mobilized.
Table 1.
Factors
associated
with sand and
dust storms

78
Sand Sand-sized material
can be mobilized by
wind of a specific speed
under specific ground
conditions.
• Presence of sand
and in what form:
dunes, sheets,
alluvial deposits?
• Grain size more
than 63 microns
• Quantity of sand
available to be
mobilized
• Type of land cover
• Type of land use
Sand often moves
relatively short distances
when compared to
dust. Wind-blown sand
can do damage from
pitting as well as filling,
covering or piling against
infrastructure, or burying
vegetation.
Dust Dust-sized material can
be mobilized in an SDS
event.
• Grain size less than
63 microns
• Quantity of dust to
be mobilized
• Type of land cover
• Type of land use
Dust can usually travel
very long distances,
particularly if lofted to
higher altitudes. Dust
clouds are often higher
in altitude than blowing
sand.
Land cover Substances and natural
and unnatural structures
that cover land can
protect sand or dust
from wind action, either
partially or totally.
• Standard
land-cover
characteristics
likely to
contribute to
sand mobilization
should be noted.
Land roughness should
be considered as this
may disrupt or augment
wind movement.
Changes in land
cover (for instance
seasonal ploughing
and deterioration
in vegetation) can
significantly change the
potential for sand or dust
movement, if only for a
short period.
Former or occasional
lake beds and other
areas usually covered by
water1
Dry or former lake beds,
glacial outwash planes,
seasonally dried rivers
or flood zones can all
become sources of sand
or dust when dry.
• Presence of sand
or dust in formerly
water-covered
locations
These source areas
can change seasonally
or not be active for
years, depending on
water levels or glacial
activity. Some locations
can also be relatively
inactive when covered by
vegetation but activated
following ploughing or
other human activities.
Land use How land is being used
(impacted by humans)
• Standard
land-cover
characteristics
likely to
contribute to
sand mobilization
should be noted
• Soil conservation
measures
How land is used (for
example ploughing,
grazing) can create
seasonal or long-term
conditions that make
sand and dust available
for the wind to move.
Soil conservation
measures (such as no-till
ploughing or windbreaks)
can affect the availability
of sand or dust for
movement and wind
speeds.
1 Added based on comments by Goudie, 2019.

Chemicals or minerals The presence of
potentially harmful
natural or manufactured
chemicals or minerals in
source locations
• Antecedent land
use
• Areas known to
contain harmful
chemicals or
minerals
• Chemical analysis
of source areas
and presence in
deposited sand
or dust
Research suggests
that some minerals
and chemicals in sand
or dust have positive
impacts (Goudie, 2009).
Some chemicals present
in sand and dust may
not be natural but the
result of manufactured
processes (for example
pesticides and residues)
or other human-
generated processes
(for example nuclear
explosions).
Pollen and natural
organic compounds
Carried by storms in the
same way as sand and
dust, but with different
impacts
• Organic
composition
of airborne
substances
A factor when carried
in SDS but not when
present due to other
weather conditions.
These compounds have
a variety of impacts
through a variety of
pathways.
Disease agents Communicable diseases
transmitted together with
or on sand or dust
• Presence of
disease agents
that can be
transmitted by
wind and sand or
dust particles
Whether disease agents
can be transported is
separate from whether
they have an impact.
Other non-pathological
organisms
Micro-organisms,
including fungi,
transported by wind
directly or on sand or
dust
• Presence of micro-
organisms
Organisms may not be
pathological but may
contribute to or establish
a presence in the local
ecology.
4.2.2. Spatial coverage,
intensity and duration
of SDS
The area covered by a specific type of SDS
event is important in assessing the overall
impact of the event, with intensity and
duration also crucial factors. The general
assumption is that an SDS event in a larger
area will have a greater impact compared
with an event of the same intensity and
duration covering a smaller area. At the
same time, the greater the duration or
intensity of an SDS event, the greater the
impact it will have when compared with
less lengthy or less intense events with the
same spatial coverage.
These general assumptions need to be
conditioned by possible variations within
an SDS event. For instance, wind speed in
one part of an SDS event may drop due to
local conditions, leading to a reduction in
the quantity of dust or sand being moved
– or the opposite may occur. Meanwhile,
sand or dust size, or the inclusion of
chemical contamination or disease agents,
in an SDS event may affect the severity of
SDS impacts on the environment.
Therefore, within an SDS event, actual
intensity and duration need to be assessed
at the locations where impact is being
assessed. This reflects the weather
observation process, whereby observers
report on the conditions they observe
and not on conditions reported from
other sources. While remote sensing may
provide improvements in understanding
the areal coverage, intensity and duration
of SDS, the results would need to be
calibrated to the level of individual on-the-
ground observers in order to be useful in
assessing local impacts.

80
4.2.3. SDS frequency
Hazard frequency is computed based on
the expected return period for an event of a
specific intensity and duration at a specific
location. It would be useful if return periods
were defined on locally based frequency
curves, but this may make comparing
results across locations difficult if these
periods were different.
For the purposes of the assessment, the
recommended return periods are 1:1, 1:10,
1:25 and 1:50.2
As more than one SDS can
occur in any one year, and the intensity of
SDS conditions can vary within a season,
an additional, more frequent, return period
can be set at 5:1, or an event once every
two months. A risk assessment matrix
based on the frequency and intensity of
SDS has been suggested and applied to
assess SDS events in Kuwait (Al-Hemoud
et al., 2019).
Since intensity can vary within an SDS
event, and may be less intense at the
start and end than during the midpoint,
or more intense at the start than the end,
the return period should be based on the
most intense point of the storm, based on
the 1,000 metre visibility threshold. Also
note that these return periods are for SDS
that can be grouped into specific event
typologies (see Table 2).
4.2.4. SDS hazard source
and impact areas
Global SDS mapping efforts (see UNEP
et al., 2016; Huimin et al., 2015) provide a
good overview of where SDS originate and
where they impact. The global and regional
mapping of SDS source and impact areas
is important in understanding the global
extent of the hazard and how source
and impact areas are linked even when a
considerable distance apart (for example
Sahelian dust in Barbados or Brazil).
2 While a 1:100 return period is commonly used in risk assessment, it is unclear whether sufficient data are availa-
ble globally for an assessment at this return period to be possible in most cases.
3 A challenge with assessing chemical or disease components of SDS is that this information often needs to be
collected during an SDS event.
However, mapping from a global
perspective likely understates the local
generation and impact of SDS at the
national and subnational scales. This local
generation and impact can occur through,
for instance, the ploughing of multiple
fields over a short period of time during a
windy week in the spring, or can arise from
winds that move sand on a daily basis but
over relatively short distances each day for
several months a year, for instance, leading
to local sand storms and the movement
of dunes across roads or fields, but over a
fairly small area.
SDS can actively collect sand and dust
during movement, as is the case with
SDS associated with convective frontal
weather systems (for example a haboob).
Observations suggest that this ongoing
collection of sand and dust can be a
significant contributor to the overall sand
and dust load of an SDS event.
Nonetheless, all SDS impacts are local.
The assessment of the risks associated
with these impacts needs to focus on
where the impacts occur. Information on
the origin of the sand or dust and factors
such as disease or chemical contamination
are helpful in understanding impact and
risks, and should constitute part of the
information collected and reviewed in an
assessment, if possible.3
It is likely that many SDS source areas are
also impact areas. Exceptions, such as
Sahelian dust in Barbados, or dust in Korea
or Japan, are relatively well documented
and can be identified as part of the
assessment process. As a result, the SDS
assessment process does not need to
differentiate between source and impact
zones except by noting that both sourcing
and impacts are occurring in the same
location, if this is the case.

The source-impact overlap could pose
a challenge in locations where the
physical process of sourcing sand
or dust leads to significant negative
impacts on the environment, for instance
erosion damaging vegetation or crop
production. Where local source area
impacts are considered significant, they
can be integrated into the overall SDS risk
assessment process by expanding the
survey process to consider the impacts
of concern (see chapter 5 on collecting
information on SDS impacts).
If specific hazards such as wind erosion or
chemical contamination are of significant
concern, these hazards should be subject
to their own risk assessment. A separate
assessment of risks from hazards in a
source area can be useful in designing
location-specific mitigation measures, for
instance to control wind erosion.
4.2.5. SDS hazard typology
A significant range of combinations of
winds, sand, dust, land cover and other
factors can lead to SDS. The fact that they
can move across thousands of kilometres
or affect a single small valley adds to the
challenge of classifying each SDS event
reported.
In reality, SDS risk assessments cannot
undertake long-term extensive scientific
research to create a detailed classification
of SDS events for each location to be
assessed. In addition, weather station data,
which can be very scarce in a number of
the SDS regions, may miss SDS events
(for example, a haboob may pass between
observations) or a reporting station may
be located where localized SDS events
occur, such as downwind from a gap in
mountains causing localized blowing sand,
leading to limited reliability of records of
SDS events. (See O’Loingsigh, 2014, for
a discussion on using weather station
observations to understand SDS events.)
This challenge can be addressed by using
a typology of SDS that captures their main
4 “Region” and “regional” are used here to refer to regions of the globe, not political divisions.
characteristics in a uniform and clearly
understandable manner. An SDS hazard
typology is provided in Table 2.
The typology is not intended to present a
new scientific definition of SDS, but rather
to provide a practical framing of SDS that
enables an assessment of relative SDS
impacts and risks. Similar typologies are
used for earthquakes (Modified Mercalli
Intensity Scale, USGS, n.d.) and wind
(Beaufort Wind Scale, NOAA, n.d.).
The typology is based on two broad
factors:
1. Intensity, defined by the distance
of objects visible at eye-level to
an observer during an SDS event.
This definition of intensity draws
on the visibility-less-than-1,000
metres definition (Secretary of the
Air Force, 2003), but recognizes the
WHO reference to no acceptable
minimum level of dust (World Health
Organization, 2016). Visibility is used
because it is (1) employed as part
of the official reporting on weather
conditions, (2) easily measured
through reference to known objects
(for example, is the smoke stack
visible?), (3) can easily be included in
an assessment questionnaire, and (4)
results are relatively less likely to be
disputed.
2. Scale, defined by the area covered by
an SDS event. Three areal classes are
used:
• Small (local) – sand and dust
transported over tens of kilometres,
generally occurring within part of one
country
• Large – sand and dust transported
over hundreds of kilometres, generally
affecting several countries, or
occurring at a regional4
scale
• Very large – sand and dust
transported over thousands of
kilometres, generally crossing several
countries and often several regions

82
Note that the scale of the event and the
scale of the assessment are different. An
assessment within a country may consider
one or more small-scale events, such as
SDS triggered by ploughing, or a very large
event, such as dust transported over a
great distance, for instance from the Sahel
to Brazil. The typology is impact-location-
based, in the sense that it is applied where
an SDS event is occurring. A small, high-
intensity SDS event in one location may be
part of a very large, low-intensity SDS event
in another location.
Not every SDS event will fit exactly into a
grouping in the typology, but any SDS event
is expected to fit primarily into one of the
six groupings. Outliers can be assigned
to groups to which they have the greatest
number of common major characteristics.
The typology incorporates:
• the most relevant World
Meteorological Organization (WMO)
description of SDS characteristics
taken from the Manual on the
Observation of Clouds and Other
Meteors (Secretariat, 1975),5
noted in
the table as “WMO” and
• the WMO system for standardized
coding of observed weather
conditions at the time of observation
(see https://www.nodc.noaa.gov/
archive/arc0021/0002199/1.1/data/0-
data/HTML/WMO-CODE/WMO4677.
HTM), noted in the table as “Obs.”.
5 The WMO definitions are also available at https://cloudatlas.wmo.int/lithometeors-other-than-clouds.html, with
pictures, for reference.
Individual countries also have their
own SDS classification systems. For
instance, China is reported to use a
five-level classification system based
on a combination of visibility and wind
speed, while the Republic of Korea uses
the duration of the presence of sand
and dust particle size in the atmosphere
(Kang, 2018). These national classification
systems can be integrated into the
narratives for each type of SDS shown
in the table, as part of the background
preparation for the assessment procedures
detailed in
chapter 5.
It should be kept in mind that the typology
is for use among individuals who are
not weather experts. The objective is to
establish a common understanding of the
hazard being assessed by those being
interviewed about it.
In the case of the survey-based
assessment (chapter 5.5), the typology
is used to classify perception-based
information about SDS affecting those
being surveyed. For the expert-based
assessment (chapter 5.6), the typology
aids assessment team members in
understanding the hazard being assessed
and helps with framing the different types
of impacts from different types of events.
Rod
Longko
©Unsplash,
January
2,
2018

High intensity, large area
(Type One)
Frontal generation of dust wall through convection;
source and impact areas overlap; can include local
movement of sand; high dust density (visibility can
drop below tens of metres); hundreds of kilometres
long but not very deep; national or subregional; high
wind speed (tens of kilometres per hour); often short
duration and not persistent; at times with precipitation
following; very seasonal (specific months). Example:
haboob. WMO: “Dust storm or sandstorm” and
Obs.: “Thunderstorm combined with duststorm or
sandstorm at the time of observation”.
Low or moderate intensity, large area
(Type Two)
Frontal generation of dust; limited source generation
in impact area; variable density (visibility rarely
down to 1 km, and infrequently lower); hundreds
of kilometres long and deep, extending over large
areas; long-distance transport possible (thousands
of kilometres), national to regional in scale; moderate
to no frontal speed, diurnal movement and persistent
over days to months; without precipitation; seasonal
(range of specific months). Example: Harmattan.
WMO: “dust haze” to “dust storm or sandstorm”
depending on intensity.
High intensity, small area
(Type Three)
Windblown sand or dust carried over short distances
(tens of kilometres) with prevailing winds (not haboob
or Harmattan); source and impact areas can overlap;
high speed (tens of kilometres per hour); generally
local; often locally significant reduction of visibility;
often limited spatial scale but can be frequent and
persistent (for example diurnal winds). Example:
afternoon sand storms in areas with numerous sand
dunes. WMO: “Blowing dust or blowing sand”.
Low to moderate intensity, small area
(Type Four)
Windblown sand or dust carried over short distances
(tens of kilometres) with prevailing winds (not haboob
or Harmattan); source and impact areas can overlap;
limited reduction of visibility; limited source or impact
areas but can be persistent (for example diurnal
winds) over weeks to months; seasonal; without
precipitation. Example: blowing dust or sand due
to land forms (for example passing between two
mountains) that channel and increase wind speed
over source areas such as river beds, dryland or dry
lake beds. WMO: ““Blowing dust or blowing sand” to
“Drifting dust or drifting sand”.
High intensity, very small area
(Type Five)
Windblown sand or dust carried over very short
distances (tens of kilometres) due to high speed
(tens of kilometres per hour); source and impact
areas overlap, very local; often locally significant
reduction of visibility; frequent and persistent (for
example diurnal winds) or triggered by changes in
local conditions. Example: dust from ploughed fields
obscuring highways. WMO: “Blowing dust or blowing
sand”.
Low intensity, very large area
(Type Six)
Regional movement of dust at low density (dust
visible but not disruptive to normal activities); source
and impact areas different; often at mid-to-high
altitude, over large areas; persistent over days or
months, but with variable density; seasonal. Example:
Dust from the Sahel in Barbados. WMO: “haze” or
“dust haze” and Obs.: “Widespread dust in suspension
in the air, not raised by wind at or near the station at
the time of observation”.
Table 2.
Sand and dust
storm hazard
typology

84
4.3 Vulnerability to SDS
4.3.1. Defining vulnerability
For this report, vulnerability is understood
to be “The conditions determined
by physical, social, economic and
environmental factors or processes which
increase the susceptibility of an individual,
a community, assets or systems to the
impacts of hazards” (United Nations Office
for Disaster Risk Reduction, 2017).
Attention to vulnerability, or the potential
impact of SDS, broadly focuses on:
• human health impacts, including
illness and fatalities associated with
SDS
• economy and industry, including
economic and financial impacts and
livelihoods
• social impacts, generally related to
how SDS affect a person, a family or
society, for instance changes in social
and gender-based roles as a result of
SDS impacts
• political system impacts, including
the governance of SDS vulnerabilities
and the allocation of power within a
society, and
• environmental impacts, including
impacts on the ecology and nature
resources
Capacity is often used as a counterweight
to vulnerabilities, such as in the
Vulnerability and Capacity Assessment
process (International Federation of
Red Cross and Red Crescent Societies,
2006). For practical reasons, the focus of
assessing vulnerability is on what can be
considered “net vulnerability”, that is, taking
into account any capacities that may
reduce vulnerability.
6 The situation described in Manyena (2006) continues today.
The concept of resilience is also being
increasingly used in association with
vulnerability. While the concept has
attracted considerable attention, definitions
are still in a state of flux, making it hard
to apply consistently when assessing
vulnerability.6
Resilience is considered to be something
that occurs after a hazard event has had
an impact and has revealed vulnerability.
As resilience does not relate directly to
the level of impact, but rather the ability
to rebound from this impact, it is not
incorporated into assessing vulnerability.
This report uses a disaster risk
assessment concept for assessing
vulnerability to SDS. An alternate approach
to defining vulnerability draws on the
process of assessing the impact of climate
change. In this approach, vulnerability is
“… the propensity of human and ecological
systems to suffer harm and their ability to
respond to stresses imposed as a result of
climate change effects” (Parry et al., 2007).
Table 3 provides a more detailed
explanation of how the climate change
assessment of vulnerability and the
disaster risk assessment terminology
compare. Per the comparisons in the
table, the climate change definition of
vulnerability is close to the one used in
disaster risk assessment. As a result,
the climate change-based assessments
of vulnerability (see chapter 7) can be
integrated into the vulnerability analysis
process described in the table.

Table 3.
Comparison of
climate change
and disaster
risk assessment
terminology
(Modified from
CAMP Alatoo,
2013a)
Term As applied to climate
change assessment
As applied to disaster risk
assessment
Exposure “…background climate conditions
against which a system operates,
and any changes in those
conditions…”
Whether someone or something is
in a location that can be affected
by a hazard.
Sensitivity “…the responsiveness of a system
to climatic influences, and the
degree to which changes in
climate might affect it in its current
form...”
Incorporated as part of
vulnerability.
Potential outcome Exposure and sensitivity Incorporated as part of
vulnerability.
Adaptive capacity “Adaptation reflects the ability of
a system to change in a way that
makes it better equipped to deal
with external influences.”
Incorporated as part of
vulnerability, but only to potential
damage and not to risk reduction.
Vulnerability Exposure, sensitivity, potential
outcome and adaptive capacity,
as defined in climate change
assessment.
The damage that can be done
by a hazard event of a specific
magnitude, frequency and timing.
Hazard The change between the current
and future climate (e.g. increase in
average temperature).
An event that can lead to negative
consequences on humans.
Hazard event Incorporated in Exposure – “…any
changes in those conditions”.
An occurrence of a hazard of a
specific magnitude, timing and
frequency.
Frequency Incorporated in Exposure – “…any
How often a hazard of a specific
magnitude will occur.
Magnitude Incorporated in Exposure – “…any
The physical scale of a hazard
event, measured in a standard
metric (e.g. mm of precipitation).
Resilience Similar to Adaptive capacity but
only in relation of a hazard event,
not reducing the likelihood of
future hazard events.
The means that reduce the initial
outcome of a hazard event on
six capitals; the means to reduce
vulnerability.
The “As applied to climate change assessment” column contains quotes from the Australian Green-
house Office (Allen Consulting Group, 2005). The use of “vulnerability” in climate change assessments
is broader than the use of the word in disaster risk assessment. For more on this difference, see
Jones et al. (n.d.).

86
4.3.2. Vulnerability to SDS
Since SDS can vary in size, duration, intensity and so forth, as indicated in Table 2. Sand
and dust storm hazard typology, assessing vulnerability to SDS must consider the full
range of possible impacts (i.e. vulnerabilities) from these events. Middleton and Kang
(2017) developed a list of impacts, arranged by sand and dust entrainment, transport
and deposition. This list is expanded on below to provide a broad base for considering
vulnerabilities as part of the risk assessment process.
Conflict – SDS may take place in ongoing or post-
conflict zones. The conflict may induce conditions
that increase the likelihood of SDS events (see
Tharoor, 2015), or post-conflict recovery may lead
to measures to reduce SDS vulnerability, such as
re-filling marshes in the Khuzestan Province of
south-western Iran.7
Economic – These impacts can be associated
with disrupted transportation, but also reduced
agriculture and animal production (Stefanski
and Sivakumar, 2009), and can cause significant
loses (as cited in Jugder et al., 2011), as well
as contamination of production facilities (for
example semiconductor manufacture) and
increased operating costs (Kang, 2018). SDS can
also cause damage to electrical transmission
and communications systems and increase
operating costs in the form of higher cleaning and
maintenance costs (for example air conditioner
filters), and household and business cleaning
following the passage of an SDS event (Middleton,
2017b).
SDS events can also affect major national
economies, such as the oil and gas operations and
oil transport in Kuwait (Al-Hemoud et al., 2019), or
flight operations (Al-Hemoud et al., 2017). They can
also impact tourism (Tulinius, 2013), with these
impacts also shared across transport (for example
diverted aircraft) and livelihoods (for example
reduced income due to dusty weather reducing
tourist excursions).8
See chapter 6 for more on the
economic impacts of SDS.
7 As viewed during a field trip organized as part off the International Conference on Combating Sand and Dust Storms, Tehran, Iran.
3–5 July 2017.
8 Tourists can also intentionally visit SDS-impacted areas, such as the Dust Bowl in the United States.
9 SDS are often associated with low humidity. While entrained dust and sand does affect air density, the lack of heat-retaining mois-
ture in the air can lead to a pattern of warm days due to direct heating from the sun and cool nights since the dry air retains little heat.
Environmental – Apart from location-specific
environmental impacts, SDS can also have broad
environmental impacts by affecting weather
patterns (University of Wisconsin-Madison, 2008),
albedo and atmospheric clarity (for example
affecting photosynthesis).9
These impacts are
often so broad as to be difficult to assess on an
SDS-event-specific basis.
The movement and removal of sand and dust over
short or long distances is due to a combination
of winds and ground conditions. This movement
can reduce soil depth and fertility, cover vegetation
and create hard-pan surfaces that do not support
vegetation normally found in the local environment.
These impacts are to the source area environment,
but source areas can also experience the other
impacts summarized below, as sand and dust
may move over very short distances, making the
source-destination distinction less relevant when
SDS occur.
Financial – All the aforementioned impacts have
direct or indirect impacts on finances, whether
from loss of employment due to damage irrigation
systems, loss of production for the same reason,
increased operating costs due to a need to clean
up after an SDS event, or increased operating and
maintenance costs for infrastructure. Under ideal
conditions, all the financial impacts of SDS would
be translated into clearly defined cost data, leading
to a clear costing of these impacts. Middleton
(2017b) and Tozer and Leys (2013) provide
overviews of SDS cost issues.

However, this is likely possible in only a few cases
where good quality reporting on the range of
impacts is available (Tozer and Leys, 2013). See
chapter 6 for more on the financial aspects of
SDS.
Governance – These impacts are generally
associated with the extent to which a governance
system (including political systems and politics)
respond to SDS, as single events or as a type
of hazard. Disaster risk governance systems
that have strong capacity to address SDS will
reduce the impacts noted above, with weak
governance having the opposite impact. For SDS
as a transboundary hazard, governance impacts
include consideration of national as well as
transnational capacities, generally in the form of
cooperation and collaboration, as well as the role
that regional and international organizations are
engaged in to assist governments with managing
SDS. More on risk governance can be found in
Gall et al. (2014), while Hemachandraa et al.
(2017) discuss the role of women in disaster risk
governance.
Health – Entrained dust, in particular where
particles are smaller than 10 microns, can enter
lungs and smaller 2.5 microns can reach deep into
lung tissue (UNEP et al., 2016). The result can be
severe breathing problems for at-risk populations
(for example people with chronic lung problems),
as well as the potential for disease transmission
(Goodyear, 2014) or the transportation of toxic
chemicals or radiation, for instance reported
for the Aral Sea region (Columbia University,
2008). Other direct health impacts include eye
and circulation problems, as well as illnesses
from contaminated water supplies. Vulnerability
to health impacts appears to first impact those
with pre-existing health conditions (for example
asthma) and then, as SDS conditions become
more severe, the larger population in an SDS-
impacted location. (See Goudie, 2014; Khaniabadi
et al., 2017; Al-Hemoud et al., 2018; and Middleton,
2017b.) See chapter 11 for more details on health
and SDS.
Infrastructure – SDS events can close roads with
blowing sand or, under the right conditions, shift
the ballast of roads. Blowing sand and moving
sand dunes (often associated in space and time)
can cover buildings and other infrastructure and
incur recurrent costs for regular sand clearance.
The movement of sand and large quantities of
dust can fill irrigation and water supply channels,
reducing effectiveness and requiring increased
maintenance costs and also affecting water
quality (which can lead to health issues, as well).
Dust can impact solar panel efficiency (Al-Dousari
et al., 2019) and microwave and radio transmission
effectiveness. Blowing sand can pit glass on solar
panels and other surfaces, leading to reduced
effectiveness and higher operating costs. (See
Middleton, 2017b and Baddock et al., 2013.)
Livelihoods – Livelihoods impacts are a broad
category that can encompass economic, health,
infrastructure and financial impacts but generally
focus predominantly on SDS impacts at the
individual and household levels. These impacts
include lost or reduced income due to SDS
damage to crops or reduced work opportunities,
reduced food security due to these and other
impacts, SDS-related health cost burdens on
individuals and families and other impacts that
may be noted at the individual or household levels,
but not well captured elsewhere.
Social – Health and other impacts can have a
knock-on effect on individuals, extended families
and society in general. These impacts can range
from the stress of dusty conditions or blowing
sand to caring for family members who experience
health problems during an SDS event. Social
systems are important in reducing or mitigating
impacts and the severity of impacts often reflects
how well social systems deal with potential
disasters.
Transportation – SDS can lead to reduced
visibility, leading to transport accidents (Tobar
and Wilkinson, 1991; Associated Press, 1991).
Even relatively low densities of atmospheric dust
have contributed to aircraft accidents. Note that
transport impacts can be very local (blowing
dust due to the ploughing of fields) or regional
(dusty conditions leading to airport closures).
(See Baddock et al., 2013, for a more detailed
discussion of SDS and the transport sector.)

88
4.4 Assessing
vulnerability to SDS
Defining a process for assessing
vulnerability to SDS needs to firstly
consider the availability and reliability of
data on weather conditions (including air
quality), health status, economic impacts
and environmental conditions, and whether
the data are consistent spatially and over
time. Where SDS-affected locations have
good data, in the sense of reliability and
consistency, a range of statistical methods
can be used to assess impacts and
differentiate impacts by levels of exposure
to a single SDS event, or the cumulative
impact of several events. Chapter 7
provides an SDS-focused process to
assess vulnerability where data availability
or quality is not a critical issue.
It is also possible, and preferred as a
decision-making tool, to define SDS
impacts in terms of value lost. Such
economic impact assessments are often
used after a disaster to define the cost of
the disaster. As part of a risk assessment,
projecting economic loss from future
events can be very useful in identifying
where investments in risk reduction will be
most effective. Economic-loss-based risk
assessment and updates can be extremely
useful in measuring progress in reducing
losses and the changing nature of risk over
time.
Chapter 6 provides a process for
assessing the economic impact of SDS.
Where data are available, economic
damage and loss assessment procedures
can be used, with such assessments often
being carried out, in one form or another,
post disaster (see Global Facility for
Disaster Reduction and Recovery, n.d.).
However, a challenge arises when the
assessment of SDS vulnerability includes
locations where data are not considered
fully reliable or consistent for all the
impacted areas and populations. This
situation, in addition to missing data
sets for some locations covered in an
assessment, will yield results that over- or
understate vulnerabilities, or miss them
altogether. Such results limit the utility of
an assessment in defining and prioritizing
actions to reduce individual and societal
vulnerability to SDS.
Clearly, some SDS-affected locations have
access to reliable and consistent data.
However, to compare SDS impacts at a
regional scale, between nations or between
adjoining parts of neighbouring nations,
the least reliable or consistent sources of
data need to be considered the norm upon
which the assessment process is based.
Issues with data reliability and consistency
and the availability of sex- and age-
disaggregated data are noted for several
large parts of the SDS-affected areas
globally.
A common approach to the need for
reliable and consistent data is to create
proxy indicators of vulnerability using
the best available data. One example
is associating the level of poverty
with increased vulnerability under the
assumption that poorer people will have
fewer means to manage a hazard.
While such logical justifications for
selecting indicators from limited data sets
may appear sound, the process faces three
problems:
• The underlying data, for instance on
poverty, may have the same reliability-
consistency issues as for data more
directly related to SDS vulnerability.
• There may be no clear evidence to
back the logical justification, in part
because of the lack of reliable or
consistent data.
• The process of combining different
indicators may not address the issue
that the indicators themselves may
not be comparable. For instance, does
it make sense to combine poverty
levels and urban environmental
conditions and poverty levels and rural
environmental conditions, given that
urban and rural environments are very
different?
Working through these problems, for
an assessment process that needs
to consider local to aggregate global
SDS vulnerability, presents significant
challenges that are unlikely to be resolved

in the near future. (See chapter 7 on data
used for a GIS-based system to assess
vulnerability.)
The alternative is to turn to research on
the sociology of hazards and use the
perception of vulnerability to measure and
compare vulnerability.
The use of perceptions in understanding
vulnerability and risk is well established
(see Slovic,1987, and Pidgeon et al., 2003).
In practice, using perceptions to assess
vulnerability is reasonable because:
• data can be collected in ways that are
reliable and consistent spatially and
over time
• these data can be analysed using
normal quantitative methods, and
• the process can incorporate general
perceptions of SDS vulnerability from
those at risk and potentially more
informed perceptions from topical
experts
Evidence indicates that individuals
act to address hazards based on their
perceptions of the significance (threat) of
a hazard. Knowing how individuals, and
groups of individuals in a location, perceive
a hazard, and how these perceptions differ
due to gender, age, social status and so
on, is important to understanding how
individuals will act to address the hazard.
This, in turn, helps define the needs for
education about the hazard before people
will be being willing to act to reduce
vulnerability.
Data on respective perceptions of SDS
vulnerability are most easily collected
through a questionnaire administered to
individuals or groups. Recent advances in
data collection have significantly reduced
the difficulty and time needed to collect
and analyse questionnaire-generated
data.10
10 The KoBoToolbox is a commonly used software package for the collection and analysis of data collected
through questionnaires. See https://www.kobotoolbox.org/.
As noted, individuals use their perceptions
as a way of defining their vulnerability
to hazards. Meanwhile, an expert’s
understanding of vulnerability is based
on research and data, but also on their
professional experience – their perceptions
– gained over time. Thus, a doctor
treating breathing problems will base their
assessment of vulnerability not only on
research results and recorded health data
from patients, but also on their experience
in treating patients with similar conditions.
This combination of data-based analysis
and experience significantly expands an
expert’s ability to understand and define
vulnerability.
Using expert understanding of vulnerability
presents two challenges:
• No single expert will have a full
understanding of all aspects of
vulnerability.
• Individual experts may frame their
understanding in ways that are
different from other experts in the
same field.
The first challenge is addressed by
involving a range of experts from different
fields (for example health, weather,
agriculture, social services, economics,
emergency management, transport,
gender) in the assessment process. Within
reason, the more – and the more diverse
– the experts involved, the broader and
deeper the common understanding of
vulnerability to SDS that will develop.
The selection of experts should reflect
the scale of the assessment. For
example, experts with a knowledge
of vulnerability due to changes in
environmental conditions within one part
of a country may not be appropriate for an
assessment with a transnational focus on
vulnerabilities.

90
The second challenge is addressed by
providing those involved in the assessment
with a structured set of definitions of
levels of vulnerability. This serves to frame
discussions and decisions by experts
so that, to a significant degree, expert
understanding of vulnerability generates
similar assessment results across different
locations and scales of assessment. This
allows assessment results to be compared
across space and scale – a significant
advantage given the global nature of SDS
events.
The use of expert understanding in a
structured assessment framework is an
adaptation of the Delphi method, with a
focus on gaining a consensus of experts
on levels of vulnerability.
Background on the Delphi method, and
its more complex applications, can be
found in Cuhls (n.d.). A similar method for
climate hazards is described in the CAMP
Alatoo and UNDP Central Asia Climate Risk
Management Program (2013a, and 2013b).
Framing vulnerability
The analytical framework to be used
by experts in assessing vulnerability is
drawn from the Sustainable Livelihoods
Framework (SLF) (United Kingdom of
Great Britain and Northern Ireland, 1999)
and the identification of types of capital
that can be affected by a hazard. An
advantage of using the SLF is that it
covers a broad range of factors which
can define vulnerability and so provides

a broad base for understanding the
nature of vulnerability and where actions
to reduce vulnerability can be targeted.
The Sustainable Livelihoods Framework
encompasses the categories of impacts
already set out in chapter 4.3.2.
The six types of capital used to assess
vulnerability are:
1. human, principally human health in
recognition of the health impacts of
SDS, including fatalities due to SDS-
related transport or other accidents
2. natural, broadly, the natural
environment (for example ecology,
natural resources) which can be
affected by, but also contribute to, SDS
in the case of locations that are both
sources of SDS and impacted by SDS
3. physical, including infrastructure
(such as roads and irrigation, power,
communications and other lifeline
systems) and assets needed for work
or employment, including seeds, tools
and equipment that can be affected
by SDS
4. financial, covering the income, credit
and savings available to places
vulnerable to SDS to pursue normal
activities and cover extraordinary
costs, where these assets can be lost
or reduced by an SDS event. Note that
the cost of addressing SDS impacts
can reduce savings even as income
remains unaffected.
5. social, covering the personal
connections (for example extended
family, associations, and other support
mechanisms) that play a significant
role in reducing or exacerbating
vulnerability to SDS
6. political, the governance systems that
can reduce or increase vulnerability to
SDS
The first five types of capital are adapted
from the Department for International
Development (United Kingdom of Great
Britain and Northern Ireland, 1999) and
Twigg (2001). Political capital is not
included in the standard SLF but it is
included in the SDS assessment process
to capture government engagement in
addressing vulnerability. These six capitals
largely cover the focus of the SDS risk
assessment on the environment, economy
and industry, human health and socio-
politics.

92
Table 4. Scaling vulnerability to sand and
dust storms provides descriptive indicators
for various levels of SDS vulnerability for
each of the six capitals, ranging from
insignificant to extreme.
While the expert-based assessment draws
primarily on the participating experts’
understanding of the impacts of SDS,
reference should be made, where possible,
to existing reliable and consistent data
sets. This reference to available data
supports a deeper understanding of the
nature of vulnerability and can make the
selection of one descriptor of vulnerability
over another easier and clearer.
Elaborating on what is covered under each
capital in terms of vulnerability to SDS
based on local conditions, for instance
including solar panels under the physical
capital group, can help with developing the
expert consensus on levels of vulnerability.
In other words, the more information to
inform expert decision-making, the better.
SDS impacts are not consistent across
all age groups and physical conditions.
As a result, the expert-based assessment
process should first cover the general
population vulnerable to SDS within an
area to be assessed. Moreover, on the
surface, the SLF framework does not
differentiate between women, men, boys or
girls, age or disability. As a result, gender,
age and disability analysis should be used
as part of the scaling of vulnerability to
better understand the vulnerabilities and
capacities.
Consequently, the assessment process
should then be redone for specific groups
considered to have specific or heightened
vulnerabilities to SDS, such as girls,
women, children, older persons or those
with lung or circulation-related health
conditions, for example. This leads to
results that help understand the depth
and breadth of vulnerability to SDS
across the at-risk population.
©Kevin
Gessner
on
Flickr
March
17th,
2014

Type of
capital
Level of vulnerability
Insignificant Low Medium High Extreme
Human,
focused on
human health
No negative
short- or long-
term outcomes
for health
indicated.
Temporary
negative short-
term health
outcomes for
part of general
population; no
deaths.
Limited,
short-term
negative health
conditions
for majority
of the target
population; one
or more deaths
attributed
directly to dust
or sand.
Large numbers
of target
population
experiencing
negative short-
to long-term
health impacts,
with several
deaths directly
attributed to
sand or dust.
Widespread
health
impacts and
fatalities above
1:10,000/day
in affected
population.*
Physical,
focused on
infrastructure
and physical
assets needed
for work or
other purposes
No vulnerability
of physical
capital noted.
Limited, local,
short-term
damage
to limited
segments
of physical
capital.
Broad but
short-term
(less than a
week) damage
to physical
capital.
General, lasting
(more than
a month)
damage
to physical
capital.
Destruction
of physical
capital, limiting
the use of
infrastructure
and buildings
and the
operations
of irrigation
systems and
affecting
resources for
crop production
or animal
husbandry.
Financial,
focused
on income,
savings or
access to credit
No loss of
income or
financial
resources.
Temporary
loss of
income due to
unemployment
or other
reasons (for
example no
rental income),
reduction
in savings,
increased
reliance on
credit, or a
combination of
all three.
Loss of
income due to
unemployment
or other
reasons (for
example no
rental income)
beyond
a month,
reduction of
savings for
more than a
month, reliance
on credit or a
combination of
all three.
Loss of work
for more than
six months
and reliance
on savings or
credit to meet
needs.
Near-total loss
of income and
savings and
no access to
credit.
Social, focused
on support
available from
family, friends
and other
social networks
Support from
social network
not needed.
Limited
support from
social network
required.
Significant
support from
social network
required, but
for only a
limited period
(months).
Significant
support from
social network
required for
an extended
period (beyond
several
months).
Total reliance
on social
network to
meet needs.
Natural,
focused on
the state of
the natural
environment
and natural
resources
No damage
beyond levels
normally
experienced.
Short-term
reduced use
of natural
resources to
meet basic
needs.
Reduced use
of (access
to) natural
resources
needed to
meet normal
needs for 3–4
months.
Extended
reduced access
to natural
resources
needed to meet
normal needs.
No access
to natural
resources due
to damage
to natural
systems.
Political,
focused on
capacity of
governance
systems to
address threats
from SDS
Government
response
addresses
threat.
Government
response
effective but
with limited
gaps.
Government
engagement
with SDS, but
significant
gaps.
Very limited
government
engagement
with SDS.
No government
engagement
with SDS.
Note: The 1:10,000 fatalities to population threshold is generally used as the marker for a transition
from a normal level of fatalities to those indicating a disaster. For more details on disaster-related
fatality rates, see Checchi and Roberts, 2005.
Table 4.
Scaling
vulnerability
to sand and
dust storms

94
4.5 Conclusions
This chapter has reviewed the nature
of SDS as a hazard and defined SDS
characteristics that should be considered
when defining the scale and impact of
these events. A typology of SDS events has
been provided based on the characteristics
of different SDS events. The typology is
intended to make SDS classification clearer
for SDS risk assessment, considering that
those performing the assessments will not
be SDS experts.
The chapter has reviewed the nature of
vulnerability and how it is affected by SDS.
A table for Scaling vulnerability to sand
and dust storms has been developed
based on a modification of the Sustainable
Livelihoods Framework (SLF) (United
Kingdom of Great Britain and Northern
Ireland, 1999). This vulnerability scaling
provides those conducting SDS risk
assessments with a way of assessing
vulnerability in data-poor conditions, or
where data are inconsistent between
locations. The vulnerability assessment
process is also linked to the
GIS Vulnerability Mapping process found
in chapter 7.
The materials covered in the chapter,
and the typology and vulnerability scaling
information, provide a straightforward
foundation for assessing the risks posed
by SDS. Specific approaches to risk
assessment are covered in chapter 5.
4.6 Web-based resources
• Environment and Disaster
Management – http://envirodm.org/
• Environmental Emergencies Centre –
http://www.eecentre.org/
• Environmental Peacebuilding – https://
postconflict.unep.ch/publications/
UNEP_ECP_PBR01_highvalue.pdf
• The Health and Environment Linkages
Initiative (HELI) – http://www.who.int/
heli/impacts/hiabrief/en/
• ReliefWeb – https://reliefweb.int/
• WMO, Environment web page
– https://public.wmo.int/en/our-
mandate/focus-areas/environment/
sand-and-dust-storm/sand-and-dust-
storm-warnings
• WMO Sand and Dust Storm Warning
Advisory and Assessment System
(SDS-WAS) – https://www.wmo.int/
pages/prog/arep/wwrp/new/SDS_
WAS_background.html
• Convention on Biological Diversity,
What is impact assessment? – https://
www.cbd.int/impact/whatis.shtml

4.7 References
Adger,W.N.,andothers(2007).Assessmentofadaptation
practices, options, constraints and capacity.
Concepts and methods. In Climate Change 2007:
Impacts, Adaptation and Vulnerability. Contribution
of Working Group II to the Fourth Assessment
Report of the Intergovernmental Panel on Climate
Change, Martin Parry, Osvaldo. Canziani, Jean
Palutikof, Paul van der Linden, and Clair Hanson,
eds. New York: Cambridge University Press.
Al-Dousari, Ali, and others (2019). Solar and wind energy:
challenges and solutions in desert regions. Energy,
vol. 176.
Al-Hemoud, Ali, and others (2017). Socioeconomic
effect of dust storms in Kuwait. Arabian Journal of
Geosciences, vol. 10.
Al-Hemoud, Ali, and others (2018). Health impact
assessment associated with exposure to PM10
and
dust storms in Kuwait. Atmosphere, vol. 9, No. 6.
Al-Hemoud, Ali, and others (2019). Economic impact and
11, No. 1.
Allen Consulting Group (2005). Climate Change Risk
and Vulnerability: Promoting an Efficient Adaptation
Response in Australia. Canberra: Australian
Greenhouse Office.
vol. 71.
Boucher, Olivier. (2015). Atmospheric aerosols. In
Atmospheric Aerosols: Properties and Climate
Impacts. New York and London: Springer.
CAMP Alatoo, United Nations Development Programme
(UNDP) Central Asia Climate Risk Management
Program (2013a). Climate Risk Assessment Guide
– Central Asia.
__________ (2013b). Kyrgyzstan Climate Risk Profile.
Checchi, Francesco, and Les Roberts (2005). Interpreting
and using mortality data in humanitarian
emergencies: a primer for non-epidemiologists.
Network Paper 52. London: Humanitarian Practice
Network.
Columbia University (2008). The Aral Sea Crisis:
Environmental Impacts. Available at http://www.
columbia.edu/~tmt2120/environmental%20
impacts.htm
Cuhls, Kerstin (n.d.). Delphi method. Available
at https://www.scribd.com/
document/66338733/16959-DelphiMethod
European Commission (2010). Risk assessment and
mapping guidelines for disaster management.
Commission Staff Working Paper. Brussels.
Gall, M., S. Cutter, and K. Nguyen (2014). Governance in
Disaster Risk Management. AIRDR Project Report
No. 3. Beijing: Integrated Research on Disaster Risk
(IRDR).
Global Facility for Disaster Reduction and Recovery
(n.d.). Disaster, Damage, Loss and Needs
Assessment: Training Guidelines. World Bank.
Goodyear, Dana (2014). Death dust, 13 January.
Available at https://www.newyorker.com/
magazine/2014/01/20/death-dust. Accessed 19
August 2017.
Goudie, Andrew (2009). Dust storms: recent
developments. Journal of Environmental
Management, vol. 90, No. 1.
__________ (2014). Desert dust and human health
disorders. Environment International, vol. 63.
__________ (2019). Personal communication (review of
this chapter).
Hemachandra, Kinkini, Dilanthi Amaratunga, and
Richard Haigh (2017). Role of women in disaster
risk governance. Procedia Engineering, vol. 212.
Huimin, Yang, and others (2015). Mapping sand-dust
storm risk of the world. In World Atlas of Natural
Disaster Risk, Peijun Shi, and Roger Kasperson,
eds. Springer-Verlag and Beijing Normal University
Press.
International Association for Impact Assessment
(IAIA), and Institute of Environmental Assessment
(IEA) (1999). Principles of Environmental Impact
Assessment Best Practice. Available at http://
www.iaia.org/uploads/pdf/principlesEA_1.
pdf.
International Federation of Red Cross and Red
Crescent Societies (IFRC) (2006). What Is VCA?
An Introduction to Vulnerability and Capacity
Assessment. Geneva.
Jones, Roger, and others (n.d.). Assessing current
climate risks. In Adaptation Policy Frameworks for
Climate Change: Developing Strategies, Policies
and Measures, Bo Lim, and Erika Spanger-Siegfried,
eds. Cambridge: Cambridge University Press.
Jugder, Dulam, and others (2011). Spatial and temporal
variations of dust concentrations in the Gobi Desert
of Mongolia. Global and Planetary Change, vol. 78,
No. 1–2.
Kang, Utchang (2018). Interview by C. Kelly. Bonn, 5
February.

96
Khaniabadi, Yusef Omidi, and others (2017). Impact
of Middle Eastern dust storms on human health.
Kok, Jasper, and others (2012). The physics of wind-blown
sand and dust. Reports on Progress in Physics, vol.
75, No. 10.
Manyena, Siambabala Bernard (2006). The concept of
resilience revisited. Disasters, vol. 30, No. 4.
McGrath, Matt (2017). Lack of dust makes China’s air
pollutionmuchworse,12May.Availableathttp://www.
bbc.com/news/science-environment-39895558.
Middleton, Nicholas. (2017a). Phone interview by C. Kelly.
Kuwait, 10 May.
__________ (2017b). Desert dust hazards: a global review.
Aeolian Research, vol. 24.
__________ (2019). Personal communication (review of this
chapter).
Middleton, Nicholas, and Utchang Kang (2017). Sand and
dust storms: impact mitigation. Sustainability, vol. 9,
No. 6.
National Oceanic and Atmospheric Administration (NOAA)
(n.d.). Beaufort Wind Scale. Available at http://www.
spc.noaa.gov/faq/tornado/beaufort.html.
New York Times (1991). 12 are killed as dust shrouds
highway, 30 November. Available at http://www.
nytimes.com/1991/11/30/us/12-are-killed-as-dust-
shrouds-highway.html. Accessed 19 August 2017.
O’Loingsigh, T., and others (2014). The Dust Storm
Index (DSI): a method for monitoring broadscale
wind erosion using meteorological records. Aeolian
Research, vol.12.
Pidgeon, Nick, Roger Kasperson, and Paul Slovic, eds.
(2003). The Social Amplification of Risk. Cambridge:
Cambridge University Press.
Schneiderbauer, Stefan, and Daniele Herlich (2004). Risk,
Hazard and People’s Vulnerability to Natural Hazards:
A Review of Definitions, Concepts and Data. European
Commission Directorate-General, Joint Research
Centre.
Secretary of the Air Force (2003). Surface Weather
Observations. Air Force Manual 15-111. Department
of the Air Force, Government of the United States of
America.
Slovic, Paul (1987). Perception of risk. Science, vol. 236.
Stefanski, R. and Sivakumar, M. V. K. (2009). Impacts of
sand and dust storms on agriculture and potential
agricultural applications of a SDSWS. IOP Conference
Series: Earth and Environmental Science, vol. 7, No. 1.
Tharoor, Ishaan (2015). Syria’s war helped create an
epic dust storm, scientists say, 7 October. Available
at https://www.washingtonpost.com/news/
worldviews/wp/2015/10/07/syrias-war-helped-
create-an-epic-dust-storm-scientists-say/?utm_
term=.08580dee7dda.
Tobar, Hector, and Tracy Wilkinson (1991) Dust storm
causes I-5 pileup; 6 Die, 50 Hurt: traffic: wreckage
north of Coalinga involves up to 100 cars and big-rig
trucks. CHP closes 150 miles of road, 30 November.
Available at http://articles.latimes.com/1991-11-30/
news/mn-70_1_dust-storm.
Tozer Peter, and John Leys (2013). Dust storms – what
do they really cost? The Rangeland Journal, vol. 35,
No. 2.
Tulinius, K. (2013). So what are these sandstorms I
keep hearing about? 2 October. Available at https://
grapevine.is/mag/articles/2013/10/02/so-what-are-
these-sandstorms-i-keep-hearing-about/.
Twigg, John (2001). Sustainable livelihoods and
vulnerability to disasters. Disaster Management
Working Paper 2/2001. Benfield Greig Hazard
Research Centre.
United Kingdom of Great Britain and Northern Ireland,
Department for International Development (1999).
Sustainable Livelihoods Guidance Sheets. London.
United Nations Development Programme (UNDP) (2013).
Issue Brief: Disaster Risk Governance. Bureau for
Crisis Prevention and Recovery. Available at http://
www.undp.org/content/dam/undp/library/crisis%20
prevention/disaster/Issue_brief_disaster_risk_
reduction_governance_11012013.pdf.
Meteorological Organization (WMO) and United
United Nations Office for Disaster Risk Reduction (UNDRR)
(2017). Hazard. Available at https://www.undrr.org/
terminology/hazard.
United States Geological Survey (USGS) (n.d.). The
Modified Mercalli Intensity Scale. Available at
https://www.usgs.gov/natural-hazards/earthquake-
hazards/science/modified-mercalli-intensity-
scale?qt-science_center_objects=0#qt-science_
center_objects.
University of Wisconsin-Madison (2008). African dust
storms may cool Atlantic, lessen hurricanes, 20
February. Available at https://www.sciencedaily.com/
releases/2008/02/080215191428.htm.
World Health Organization (WMO) (2014). Guide to
Meteorological Instruments and Methods of
Observation. WMO-No. 8. Geneva.
__________ (2016). Ambient (outdoor) air pollution. http://
www.who.int/mediacentre/factsheets/fs313/en/.
World Meteorological Organization (WMO) (1975). Manual
on the Observation of Clouds and Other Meteors
(Partly Annex I to WMO Technical Regulations).
International Cloud Atlas, Volume I. No. 407. World
Meteorological Organization.

98
©CDC
Global
on
Flickr,
February
13,
2020

UNCCD | Sand and Dust Storms Compendium | Chapter 5 | Risk assessment framework 99
5. Sand and dust
storms risk assessment
framework
Chapter overview
This chapter reviews the conceptual approach to assessing SDS risk and provides two methods
for assessing this risk: one using expert opinions and the second using the perceptions of those
who are at risk from SDS. Each of these methods is described in a step-by-step process (includ-
ing assessment forms and questionnaires) and includes samples of assessment outputs. Also
discussed are how to assign confidence to results; the consideration of climate, environment and
population changes; and assessing impacts in source areas.

UNCCD | Sand and Dust Storms Compendium | Chapter 5 | Risk assessment framework
100
5.1 Framing the SDS risk
assessment process
The risk assessment process, as described
in chapter 4, brings information on SDS
hazards and vulnerabilities to this hazard
together to define risk for different return
periods for different types of SDS events.
The generalized process for an SDS
risk assessment is set out in Table 5,
with specific procedures for survey and
expert-based assessments covered in this
chapter.
Any assessment report should include
a summary of the SDS situation being
assessed as part of Task 2, alongside
background information on the
assessment area, typical types of SDS
experienced and other types of hazards
or disasters that may occur. The report
should note whether the assessment
location is a major source area for SDS.
# Task Notes
1 Identify and document a reason for the assessment. If possible, the assessment should be
linked to SDS risk mitigation in a specific
area or location.
2 Define the spatial area of the assessment and whether the
assessment focuses on a source area, an impact area or
both, for combined source/impact locations.
Note that for some SDS, source and impact
areas can overlap, and local sourcing may
be significant (for example Type One).
In general, the smaller the assessment
area, the more precise the risk assessment.
If the source area is some distance from
the impact area, a short description of the
origin and movement of the SDS should be
included.
Identify whether the sand and dust is
expected to have any contamination or be
a transmission mode for a disease.
3 Identify the SDS types from Table 2 to be covered in the
assessment.
For areas affected by more than one type
of SDS, the risk assessment process
treats each type of SDS separately, with
comparable results.
4 Assign return periods to the SDS being assessed. See chapter 4.2.3 on return periods. Return
periods can be defined using weather
data from one or more stations in the
assessment area, and the more data the
better.
5 Collect data on vulnerability to SDS and other factors. Choose whether to use the questionnaire
or expert approaches to assess
vulnerability (see chapters 5.5 and 5.6).
The assessment should include the
analysis of existing vulnerabilities and
capacities specific to girls, women, boys
and men and consider age and disability
factors.
6 Repeat steps 2 to 4 for each type of SDS that can affect
the spatial area covered by the assessment.
7 Analyse results by SDS type and return period. Results can be compared by return period
across type, but most likely by type for
return periods.
Location, gender, age, disability, health
conditions, social status and economic
factors should form part of the analysis,
with these factors included in the reporting
of results.
Table 5.
Framing the sand
and dust storm
risk assessment
process

8 Develop a report covering the assessment results. The report should explain the reason for
the assessment and the assessment
process and should detail results and their
implications for, for instance, risk reduction.
9 Validate the results. The assessment results should be shared
with, and validated by, at the least a
representative group of the populations
covered by the risk assessment.
Comments from the validation should be
incorporated into any report and used to
improve the assessment process, and in
particular, the vulnerability assessment.
5.2 Incorporating
SDS source-area
related risks
Many, but not all, locations impacted
by SDS also contribute sand and dust
that circulates in an SDS event. Both
assessment methods described in this
chapter can incorporate SDS source area
risks (for example erosion associated with
dust generation or movement of sand due
to wind) into the assessment results.
For the survey-based assessment, source
area risks are included by asking about
the perceived and observed impacts of
SDS events on the local environment. For
instance, do SDS events remove topsoil,
reducing locations where crops can be
grown, or does blowing sand and dust
during SDS events fill in irrigation canals?
In the questionnaire in Table 6, questions
31 and 33 touch on source area impacts.
Additional questions can be added to
expand on specific source area concerns
noted for where the assessment is taking
place.
For the expert assessment, conditions
related to source area risks can be included
within the background information and
location-specific questions can be posed
to the experts as part of the assessment
process. The extent to which source area
risks are incorporated into the expert
assessment will depend on the level of
pre-assessment research available.
Where no sand or dust is taken up in an
SDS event (for example in Barbados),
the source of sand and dust would be
considered only if this sand or dust had
an impact on the population and locations
being assessed. This would be the case,
or instance, for dust containing chemical
contaminates that put human health at
risk.
Information on sand and dust source areas
may be very useful in an assessment, and
in identifying ways to reduce risk. However,
tracking the source of sand and dust, and
its chemical or biological characteristics,
can be complicated. The costs and
time involved in developing a detailed
assessment of source area and sand or
dust characteristics may not be feasible
with the resources typically available for
risk assessments. If this information is to
be used, it needs to be collected before
an assessment and to feed into the
formulation of SDS characteristics that are
used in defining the scope and questions
used in the survey assessment or as input
for experts in the expert assessment
process. See Box 6 for more information
on assessing source areas.

102
Box 6. Assessing source areas
Identifying source areas can be important to determining the impact that sand or dust
may have on the at-risk population. A challenge exists in that SDS source areas are quite
diverse, ranging from large dry lake beds to a few square kilometres of ploughed land. As a
result, the assessment design should consider both (1) the nature of the source area as a
contributor of hazards (for example disease agents or radiation in dust) and (2) the extent
to which some or all of the sand and dust in a storm comes from a local or distant source.
Where some or all of the sand and dust in a storm comes from a source at the location
being assessed, this factor should be included in the risk assessment.
A somewhat differently focused assessment would involve looking at the impact of sand
or dust coming from a specific area on that area alone. In this case, either the survey or
expert procedures could be used, but the focus of questions and discussions would be
directed towards the impact of wind and other factors on the physical, social and econom-
ic environment where these factors are present.
For instance, if SDS events cause a loss of top soil affecting crop production, then the
assessment would focus on these impacts to understand the nature of the hazard, vulner-
abilities and resulting risks.
In most cases, these source area impacts would be part of the overall risk assessment.
However, in some locations the source area impacts may be greater or more significant
than other impacts or may be more significant in terms of overall or specific risk reduction.
In these cases, a risk assessment focusing on source area impacts alone may be justified.
©UN
Photo,
John
Isaac

5.3 Comparing
assessment
processes
Ideally, both the survey and expert
assessments discussed below are
conducted for the same locations. This
provides a basis for comparing results and
gaining a deeper understanding of SDS
risk.
Advantages of the survey approach
include obtaining more direct information
on impacts from those affected by
SDS, a clearer understanding of how
these may differ across age, gender and
social groupings, and results that can
be presented on a per capita basis (for
example “x per cent of the total population
indicated y impact”).
The survey approach also identifies the
most significant concerns about SDS
among the surveyed populations; an
important consideration when selecting
risk reduction options. At the same time,
surveys can be expensive, require time
(weeks to months depending on their
scale) and may yield variable (and possibly
inconsistent) results for different locations
surveyed, reflecting localized SDS impacts
and risks.
Advantages of the expert approach
include time (for example a two-day
assessment workshop with 15 experts),
cost and results that are based, in part,
on research and synergized from expert
opinions developed over years and across
disciplines. In general, expert assessment
results carry greater weight with decision
makers and can consider multiple hazard
and impact interactions across medium- to
large-scale SDS situations.
Challenges with the expert assessment
include that the results can be general in
nature and not applicable to each location
within an impact area. Results can also
be strongly influenced by the technical
expertise of experts involved, for example
a preponderance of health experts
participating in an assessment will skew
results towards SDS health issues.
Broadly speaking:
• field survey-based assessments are
most useful in identifying SDS risk
issues that can be addressed at the
project level
• expert assessment results focus more
on policy outcomes
However, field surveys can also be used
to frame policy, particularly when used
to explain the impacts of SDS on at-risk
individuals and as input into the expert
assessment process.
Either assessment procedure, when used
in the same way for different locations, can
be used to compare SDS impacts and risks
between assessed locations. To ensure
that these comparisons are appropriate,
the scale (number of persons covered by
surveys, or spatial area covered by expert
assessments) should be similar.

104
Box 7. Considering climate, environment and population
changes
Risk assessments are used as inputs into future actions to reduce the risk of negative im-
pacts. It is important to consider whether changes to the climate, the overall environment
(both prime elements in the generation of SDS) or at-risk populations will change the risk.
With changes to the climate, the issue to be researched is whether the projected changes
will change weather and weather patterns in such a way as to increase or decrease the
likelihood or intensity of SDS events. Similarly, will changes to the environment, related to
climate change, changing land use or other factors, affect the likelihood and frequency
of SDS events? For at-risk populations, will the change in the number, composition (for
example increased numbers of older persons) or other factors change the impact of SDS
events? Unfortunately, how these factors combine and affect – or are affected by – SDS
are not global or uniform.
In the case of the expert-based assessment (see chapter 5.6), background information
collected as part of the assessment work can be used to summarize projected impacts
of changes to the climate, environment and at-risk populations. These expected changes
can be incorporated into the assessment process. For instance, once the rating process is
complete, the experts can be asked how projected changes in the climate, environment or
at-risk populations could change the results.
Incorporating possible changes to the climate, environment or at-risk populations into
the survey-based assessment (see chapter 5.5) is problematic as a respondent’s recall
of long-term changes is often limited. In this case, the team conducting the assessment
should add a research-based prospective analysis of how the survey results may change
based on projected changes to the climate, environment or at-risk populations.
5.4 Scaling assessment
results
The survey assessment process uses
statistical methods to compare the data
collected with the overall population in the
assessment target area. This is particularly
useful in determining the number of
persons affected by a certain aspect
of an SDS event. In turn, this scaling of
impact can identify where the most severe
impacts occur and identify specific target
populations and impacts for risk reduction.
This is why survey-based assessments are
useful for project-level interventions.
The expert assessment process is more
specific to the impact and risks for a
spatial area affected and is less specific to
affected populations, and thus, as noted,
for policy-level considerations. However,
because the expert assessment process
considers impacts on, and risks to, specific
populations (for example children and
women), it is possible to broadly project
the number of persons at risk from a
specific aspect of an SDS event based
on the general demographics of the area
being assessed.
When comparing the same SDS risks for
two different populations, the population
with the greatest number of persons at risk
is considered to be at greater overall risk.
In other words, risks being equal, the more
people affected, the greater the overall risk.
It is possible to use statistical methods
to compare the relative significance of
different SDS risks, within or between
populations, for survey assessments.
For the expert assessment process,
the comparison of risks is possible by
comparing the risk ratings. However, as
the expert process does not incorporate
demographic data in the same way that the
survey process does, comparison between
risks and populations are indicative based
on the agreed judgements of the experts
involved. In this case, an assessment of
confidence in the results is needed (see
chapter 5.7).

©John
Panell
on
Flickr,
July
12,
2005

106
5.5 Survey-based SDS
assessment process
This section describes the steps to
develop, implement and analyse results
from an assessment of perceptions of risk
posed by SDS based on the survey process
framed in chapter 5.1 and Table 5, which
is generally based on a questionnaire or
question guide. Note that the assessment
process first considers perceptions of
vulnerability, before combining these
perceptions with hazard information to
generate a risk assessment.
This process involves a trade-off
between precision on return periods
(explained below) and local knowledge
of vulnerabilities to SDS. The results are
most appropriate for considering the risk
posed by more frequent events, but they
can capture vulnerability to a less frequent,
but more severe event, if the assessment is
conducted soon after this event.
The survey process is relatively quick and
simple and can be repeated at regular
intervals to develop a more detailed overall
longitudinal understanding of SDS risk.
As the same procedure would be used for
each survey, results would be comparable
over time and across locations.
Step one – Define why the assessment is
needed
An assessment of SDS risk should have a
clear purpose and, preferably, a role in SDS
risk reduction.
Step two – Define the location for the
assessment
The selected geographic location for the
assessment should be well defined to
avoid later confusion as to where actual
surveys will take place.
Step three – Collect background data
These data should include demographic
and socio-economic information that
can be used to describe the assessed
populations, the economy and
infrastructure. Data on past SDS events
and other hazards and disasters should be
collected for reference.
The SDS data will provide the basis for
defining SDS types and return periods (see
chapter 2). Key informant interviews and
an analysis of gender, age, disability and
other factors defining the at-risk group
should also be used to understand the
physical, social and economic nature of the
survey locations.
Step four – Design the survey
Normal procedures for using field survey
questionnaires should be used to design
the survey work, including the sample
frame, confidence levels and survey
procedures. Decide whether the survey will
be conducted on an individual basis or with
focus groups or key informants or using a
combination of methods.
A commercial company can be hired to
design and undertake the survey and
conduct the analysis. It is also possible to
work with NGOs or other segments of civil
society to develop and conduct the SDS
survey. Finally, government institutions,
for instance statistics offices, may have
the capacity to undertake the survey work
using their own resources or they may be
able to commission it.
In general, the larger a survey (larger
sample size), the greater the cost. The
cost–results trade-off is a core part of
the design process. Surveys at the level
of villages in an assessment area of 100
villages will be more expensive and time-
consuming than surveys at the district
level for 10 districts. The total population
covered may be the same (the 100 villages
are located in the 10 districts), but the
results will be less specific if the scale
of the assessment focuses on the 10
districts.
Assessment scale is important when
comparing results across assessments.
An assessment at the level of 10 districts
cannot be compared to an assessment
covering 100 villages within a district until
the results from the latter are aggregated
to the district level. This aggregation
process will lead to a reduction in spatial
specificity in terms of vulnerabilities and
results.

Survey design should ensure that sampling
covers all segments of a society and that
results can be disaggregated by gender,
age and physical capacities.
Deciding who will conduct the survey
and how they will do so will define the
organization and size of the survey team
and the level of management and support
required. Work on survey design would
cover survey methods, team composition,
logistics, etc. These details are not
covered here as they are standard for
questionnaire-based surveys.
Step five – Develop a questionnaire and
plan the field survey
A model questionnaire for an assessment
of perceived vulnerabilities to SDS is
provided in chapter 5.9. This questionnaire
would need to be adapted for each
area being assessed to reflect local
environmental or social issues, but the core
questions and scaling of answers should
remain the same to enable comparison
of survey results across assessments.
As a matter of normal practice, any
questionnaire should be tested before
general use.
The field survey work should be planned
out in detail once the questionnaire has
been developed. The planning builds on the
survey design process and should include
staffing and job descriptions, training of
surveyors, written procedures for selecting
those to be interviewed, printing or
otherwise providing questionnaires, quality
control and logistics, at a minimum. Online
resources or the services of a professional
field survey expert or company can be
used in the planning process. As a general
rule, academic standards should be
incorporated into the field survey plan.
In some cases, survey data can be
collected using software that uses the
Internet to automatically report the data
collected into a database for analysis.1
The
use of data-collection software should be
integrated into the questionnaire and field
survey design process.
1 The KoBoToolbox is a commonly used software package for the collection and analysis of data collected
through questionnaires. See https://www.kobotoolbox.org/.
Step six – Secure authorization to
conduct the survey
Countries and organizations generally have
protocols or review panels that should
approve a survey or other public data
collection process.
Step seven – Conduct the survey
This step involves implementing the plan
developed in Step four.
Step eight – Analyse and report on the
data
Basic analysis of the survey results should
be carried out using standard statistical
packages to compile and present simple
results (for example frequency, number
of responses) for each question. The
questions on SDS experienced by those
interviewed should be linked to the six
types of SDS set out in Table 2. Sand and
dust storm hazard typology, which should
be included in the analysis process by
totalling the number of each type of SDS.
Different types of analysis can then be
performed. First, responses by the whole
surveyed population can be presented in
terms of the perceived severity of each
type of SDS reported. This analysis can be
presented as percentages of total number
of respondents.
Second, analysis can compare the
severity responses by type of SDS using
disaggregated data on gender, age,
occupation, economic group or other
criteria collected through the questionnaire.
In each case, the analysis should be done
by category, for instance perceived impact
on health, agriculture, travel, infrastructure,
social connections, or warning, as set out
in the questionnaire. The result provides
an impact category-by-category analysis
identifying the impacts that are perceived
as most severe for each type of SDS.

108
Results should be reported as text, with
the use of charts and maps to facilitate
understanding. See Box 8 for a sample
chapter of a simple report-out example.2
Normal academic-level procedures for
presenting data and reporting results
should be followed, including reporting on
the validity of statistical results.3
Step nine – Disseminate and validate
results
As per Task 9 of the Framework (Table 5),
results should be validated by sharing them
with those affected by SDS and living in the
assessment area. Dissemination products
include reports, press releases, journal
articles and public events.
Additional considerations
In general, perception surveys will not
allow for an assessment of multiple return
periods but they can cover different types
of SDS. In most cases, the survey will
capture perceptions based on the most
recent events, which may be more severe
than average events. By dating these most
recent events, it is possible to link them to
observed weather data and classify them
in terms of statistical return periods.
2 The text provided is a snippet and would be longer in a real report.
3 The level of confidence in results should be based on standard statistical analysis and not on the process set out
in chapter 5.7.
Perception surveys can face difficulties
in trying to align participant descriptions
of an SDS event with standard names or
the typology (Table 2). To address this
challenge, pictures of different types of
SDS can be prepared in advance and
used by the participants to select the
type of SDS most like the one that they
describe. This process can improve the
accuracy of the assessment process and
the link between SDS recorded at weather
stations and SDS reported by the survey
participants.
It is also important to consider when to
conduct a survey. A survey during the
normal SDS season may yield perceptions
skewed by an ongoing or most recent
SDS event. Thus, where possible, surveys
should be conducted outside normal SDS
periods. The selected area should be well
defined to avoid later confusion as to
where actual surveys will take place.
Reporting on results should include
a description of the SDS issue being
assessed and other background on the
assessed location.

Box 8. Sample simple survey results report-out – health
effects
A survey of 240 respondents (46 per cent male) was conducted in Zira Department (pop-
ulation 5,632; 52 per cent female) to assess the perceived impact of SDS on health. The
data are presented in the chart below. The median per capita income for the district is US$
3,760, the main occupation is semi-mechanized farming (wheat, maize) and the poverty
rate is 15 per cent.
For Type Five SDS (high intensity-very small area), 83 per cent of respondents (56 per
cent female) reported important or very severe health effects. Note that the survey area is
subject to Type Five SDS due to the ploughing of loess-type soils during the spring windy
period. For Type One SDS (high intensity-large area), 52 per cent (62 per cent female)
reported important or severe effects. Few respondents indicated more than limited effects
from Type Two or Six SDS (low or moderate intensity-large area and low intensity-very
large area).
The Type Five important and severe health effects reported during the survey included:
• asthma (mentioned 46 times)
• fever following SDS events (mentioned 142 times)
• breathing problems requiring hospitalization (mentioned 74 times)
• high blood pressure and circulation problems (mentioned 73 times)
• eye irritations (mentioned 153 times)
• general difficulty in breathing, not requiring hospitalization (65 times)
Older persons and young children were reported to be the most affected. No fatalities
were reported among the survey population.
Based on weather data from Zira airport, Type One storms have a return period of twice
a year, Type Two events twice a year, Type Five events three times a year and Type Six
events once a year. Type Three and Four events were not reported by respondents or iden-
tified based on airport data.
Figure 15.
Reported health
effects of sand
and dust storms
0
20
40
60
80
100
120
140
160
Number
of
Persons
Reporting
None Very Limited Some Important Very Severe
Reported Level of Effect of SDS on Health
REPORTED HEALTH EFFECTS OF SDS
Type 1
Type 5
Type 2
Type 6

110
Box 9. Including gender and age in the assessment
Good practice for conducting and reporting on assessments calls for gender and age
to be an integral part of both processes. Including age as a factor in data collection and
analysis helps with understanding the differential impact that sand and dust can have
on young children and older persons. Incorporating gender assists in understanding how
impacts can differ within a population where different gender groups may live and operate
in different physical and social conditions.
For survey-based assessments (see chapter 5.5):
Gender is included by:
1. Ensuring that assessment teams and field assessment teams are gender-balanced,
as far as possible
2. Collecting data on gender – of the individuals contacted, focus group meeting
members and the general population – as part of the assessment process
3. Analysing data from a gender perspective to identify practical and strategic gender
impacts
4. Disaggregating data analysis, results and conclusions
Age is included by:
1. Collecting information on the age of respondents. This information is usually divided
into three groups: young children (younger than 60 months), older persons (at or over
the local age of retirement, usually between 60 and 65) and the remaining age group
(between 6 and 60 years). The 6 to 60 age group can be further segmented if justified
by expected SDS impacts or other factors. The basis for segmenting people into
specific age groups should be provided as part of the assessment reporting.
2. Disaggregating data analysis, results and conclusions by designated age group.
Common good practice is to also disaggregate impacts by age groups and gender, for
example, SDS impacts on older women.
For the expert-based assessment (see chapter 5.6), gender and age are included by
repeating the assessment process and asking how the assessment results would change
for specific age groups, by gender, or by a combination of both (for example girls). As with
the survey-based assessment:
• Expert teams should be gender-balanced as far as possible, and supported by
dedicated gender expertise where available.
• Results should be disaggregated by age, gender and, where relevant, age/gender
combinations.

5.6 Expert-based sand and dust storms assessment
process
Box 10. Expert-based assessment process overview
The expert-based process involves:
1. Selecting an SDS type from Table 2. Sand and dust storm hazard typology, with
reference to background materials on SDS for the locations being assessed.
2. Having the experts review Table 4. Scaling vulnerability to sand and dust storms and
agree on a score for each type of capital that most accurately reflects the effect of
the SDS event on the overall population covered in the assessment. The Insignificant,
Low, Medium, High and Extreme scores can be converted into numbers (1 to 5) for
ease of reference. If relevant, notations can be added to the scoring to reflect specific
details that may be relevant to the overall assessment results.
3. Repeating the process for population subgroups, most often women and girls, older
persons (over 64 years), children under 5 years and people with a physical disability.
4. Assigning confidence levels to each assessment. This can be done at the time of an
individual assessment (preferred) or after a round of assessments for an SDS type.
5. Repeating the process for each SDS type relevant for the area being assessed.
This section describes a process for using
expert understanding of SDS vulnerability,
together with data collected on SDS types
and frequencies, to develop a comparable
understanding of SDS risk. The process
uses Table 4. Scaling vulnerability to sand
and dust storms.
Step one – Define why the assessment is
needed
A clear purpose and justification for
assessing SDS risk should be developed,
preferably linked to SDS risk reduction.
Step two – Define the location for the
assessment
A well-defined assessment area should
be selected to reduce confusion over
the applicability of results and facilitate
the collection of background data and
planning.
Step three – Design the assessment
workshop
An expert-based assessment will normally
take place in a workshop format, generally
for one day. The design of the workshop
should involve:
• Identifying between 7 and 12 experts
who will participate (the number
depends on their experience). They
should be experts in one of the areas
related to SDS or knowledgeable about
the population in the assessment
area. These experts can include
meteorologists, geographers,
sociologists, agriculturalists,
community development experts,
experts on gender, age and disability,
health officials (doctors as well as
public health specialists), engineers
responsible for infrastructure at risk
from SDS and government officials
involved in disaster risk management.
• Identifying a location for the workshop
that provides sufficient meeting space
and facilities for a one-day workshop.
• Selecting one or more workshop
moderators experienced in the
methods used to develop consensus
when dealing with diverse information
and potential ambiguity. Although
the moderators do not need to be
knowledgeable about SDS before
a workshop, they should be fully
cognisant of the workshop briefing
materials before the workshop. Where
moderators knowledgeable on SDS
are available, they should be used.
• Identifying any specific information
or materials (for example maps)
that should be assembled before the
workshop.

112
• Developing an assessment workshop
agenda covering the purpose of the
workshop, methods, ground rules and
expected results (see Step six)
• Defining how the workshop results will
be disseminated and validated.
Step four – Collect background data
Background data should include physical,
demographic (for example gender, age,
disabilities), economic, social and other
information that describes the population
to be assessed. Specific details (for
example frequency, intensity, duration) of
past SDS events should be collected and
compiled into a narrative summary based
on the typology set out in chapter 3 and
Table 2. Sand and dust storm hazard
typology.
Step five – Sharing information before the
workshop
An information package should be shared
with workshop participants before the
event. The package should include (1) The
background and reason for the workshop,
(2) Information on SDS in the assessment
area (for example SDS types and return
times) and other background information
collected in Step four, (3) Logistics
arrangements, (4) Ground rules and (5)
A reasonably detailed description of the
process to be used in the workshop.
In general, most participants will not (or
at least not fully) read the information
package but any improvement in
knowledge about the workshop process or
SDS gained before the workshop will help
the workshop process operate with fewer
problems.
Step six – Conduct the workshop
The workshop should be led by one or
more moderators and generally follow
these agenda points:
• opening, introductions and objectives
of the workshop
• background to SDS in the assessment
area, including handing out of SDS
typology and return period information
• review of background information
on the assessment area, including
handing out of background
information
• review of the assessment process
(see Box 10. Expert-based
assessment process overview)
• conduct the assessment process
in as many rounds as needed to
cover the SDS types identified for the
assessment area
• summarize results
• describe how the results will be used
• conduct a short workshop
assessment covering the workshop
process and facilities and services
• closing
As appropriate, there can be opening and
closing speeches as well as certificates
provided indicating that participants
assisted in conducting the SDS
assessment.
Step seven – Document, disseminate and
validate results
As per Tasks 8 and 9 of the Framework
(Table 5), workshop results should be
compiled into a report and validated by
sharing with those affected by SDS and
living in the assessment area. A level of
confidence in the survey results should be
included in the final report. See chapter 5.7
on setting confidence levels.
An expert-group assessment report can
report results for specific vulnerabilities to
specific types of SDS. An example of such
reporting out is provided in Box 11. Sample
simple expert assessment results report-
out – SDS risk.
A second approach is to calculate
a number that indicates the relative
importance (size) of the overall vulnerability
assessment and to present it in a spider
diagram for each group covered by the
assessment, and for each SDS type. This

is done by calculating the area of each
triangle that makes up the spider for each
group/type combination covered by an
assessment.
The resulting number indicates the
relative importance (size) of each of the
six vulnerability factors (capitals) when
compared to a scoring of “extreme”
(vulnerability) and “insignificant”
(vulnerability) for all six factors considered.
The resulting numbers can be used to
compare vulnerability across locations and
across groups. They can also be used, in
an X/Y plot, to indicate comparative levels
of risk, as described above.
The use of the area calculation avoids,
in large measure, the issues related to
attempting to compare very different
characteristics of vulnerability in the
absence of a standard metric for all
characteristics, such as economic value
or a research-based way of comparing
different types of vulnerability. Procedures
for calculating spider diagram area and
further discussion on this approach can be
found in CAMP Alatoo and UNDP Central
Asia Climate Risk Management Program
(2013). The calculation process can be
set as a formula in Excel® or similar
software, so that the results are generated
automatically once vulnerability scores
have been entered.
Normal (academic) good practice should
be used in writing the assessment report.
The procedures used should be clearly
described and the results understandable
so that the same process can be used
elsewhere and results can be compared.
©Ricardo
Liberato
on
Flickr,December
22,
2005

114
Box 11. Sample simple expert assessment results
report-out – SDS risk
An expert assessment of SDS impacts on people living in Zira District was conducted by
a team of experts from the fields of meteorology, geography, social sciences, agriculture,
community development, health and engineering. Zira District has a population of 5,632
(52 per cent female), with a median per capita income of US$ 3,760. The main occupation
is semi-mechanized farming (wheat, maize) and the poverty rate is 15 per cent.
Based on weather data from Zira airport, Type One storms have a return period of twice
a year, Type Two events twice a year, Type Five events three times a year and Type Six
events once a year. Type Three and Four events were not reported based on airport data.
The assessment covered the general population, women and girls and older persons. The
results presented in the following graph for Type Five SDS (high intensity-very small area)
indicate that this SDS has:
• a large impact on the health of older persons, with effects (albeit less severe) on
women and girls and the general population
• a large impact on financial capital for women and girls, possibly due to increased
costs of cleaning following SDS
• a medium impact on the financial capital of older persons, likely due to the need
for medical care
Note: Vulnerability effects scores where Extreme = 5; High = 4, Medium = 3, Low = 2 and
Insignificant = 1.
Figure 16. Effects
of type five SDS
on Zira population
and subgroups
Financial Capital
Social Capital Physical Capital
4
3
2
1 General
Elderly
Natural Capital
Health
EFFECTS OF TYPE FIVE SDS ON ZIRA POPULATION AND SUBGROUPS
Women and Girls

5.7 Assigning confidence
to results
There is a need to indicate the level of
confidence in assessment results. The
challenge is that the information used to
generate results may not be uniform for all
locations covered, for all relevant data sets
used, or for the same data sets used in
different assessments.
Clearly stating the level of confidence that
assessors have in the results of their work
is professionally appropriate. It also allows
those using the assessment results to
factor any limitations into their decision-
making process.
For a questionnaire-based assessment, the
statement of confidence can be developed
based on the results of statistical
analysis and reference to operational
challenges faced in conducting a survey.
These challenges will typically include
no access to some of the assessment
areas, large numbers of refusals to
participate, confusion as to the types of
SDS discussed, unwillingness to answer
specific questions and difficulty in ensuring
gender-balanced surveys.
For the expert-based process, one option
for assessing confidence is through
external reviews. This is good practice but,
in the case of SDS assessments, presents
three challenges. First, there may not be
sufficient experts not involved in a specific
assessment to conduct a robust external
review, or there may be an insufficient
number of experts to review numerous
local or regional scale assessments.
Second, the external reviewers may
disagree between themselves, and with
the initial assessors, on the substance
and rigour of the data used, leading to
disagreements about the data even before
they review the results.
Finally, there may not be agreed metrics
by which to define substance and
rigour for individual pieces of or groups
of data, which makes understanding
these parameters – as part of the initial
assessment and as part of the review
process – a challenge.
Another option, used in the Managing the
Risks of Extreme Events and Disasters
to Advance Climate Change Adaptation
report (Intergovernmental Panel on
Climate Change, 2012), is to establish a
set of terms that define the assessors’
confidence in the (1) quality of the data
used, and (2) the accuracy of the results.
Adapting this approach to SDS
assessments, the quality of data used can
be rated as having:
• poor representation of the spatial or
temporal scope of the assessment
• fair representation of the spatial or
temporal scope of the assessment, or
• good representation of the spatial or
temporal scope of the assessment
In each case, the definition of spatial or
temporal scope would depend on the scale
of the assessment. A data set may be
spatially and temporally good for a specific
location when assessing a specific type
of SDS, but spatially and temporally poor
when used as part of a continent-level
assessment for all types of SDS.
Confidence in the assessment results can
be assessed as being:
Low, where
• a considerable part of the data needed
for the assessment is not available or
• the data used may have a weak
connection to the issue of concern or
• the actual understanding of the
physical or social processes involved
is weak.
Medium, where
• the required data are generally
available and
• there is a reasonable connection with
the issue of concern and
• there is a basic understanding of the
physical or social processes involved.
High, where
• all the necessary data to conduct a
robust assessment are available and
• there are clear linkages between the
data and the issue of concern and
• the physical and social processes
involved are well understood.

116
To avoid overstating confidence, an
assessment is rated by the lowest
descriptor. For instance, where data are
available but weakly connected to the
issue of concern, the rating would be “low
confidence”.
Where the assessment of the impact on
one type of capital is considered to have
greater or less quality or confidence than
for other information used, this should be
stated as part of the overall statement of
confidence. The more specific statements
of confidence and data quality are for
data sets under the assessment, the more
transparent and credible the assessment
results.
Ideally, confidence in results should
be stated for each segment of the
assessment process, for instance, for
health and the general population; for
health and women and girls; and for health
and older persons. If this cannot be done,
the experts involved in the assessment
should set overall confidence levels for
each of the major sources of vulnerability
covered. In addition, confidence in the SDS
typologies used should also be indicated.
All confidence statements should be
consensus-based. If there is an inability to
agree on specific confidence levels, then
a majority and minority statement can be
made, accompanied by short justifications.
5.8 Using risk
assessment results
(This section should be read in conjunction
with chapters 3, 5.5, 5.6, 9, 10, 12 and 13).
The purpose of a risk assessment is to
identify risks so that they can be reduced.
For disaster risk reduction to be effective
and efficient, the most salient risks need to
be prioritized for mitigation or reduction to
acceptable levels.
Both assessment methods provide results
that identify risk salience and can guide
risk reduction interventions. The potential
uses of SDS risk assessment results for
risk reduction can be summarized as
follows:
• SDS risk management policy:
Results from either assessment
process can frame SDS risk reduction
policy by providing evidence-based
identification of the importance of
risks from SDS. As the expert process
can be quicker and cover larger areas
than the survey process, its use in
policy development (for instance
a national SDS risk management
strategy) can be more direct.
The survey process provides stronger
evidence-based results (due to the use
of statistical analysis), but can take more
time and be more costly. At the policy
level, these results can be used to refine
strategies for more specific interventions
addressing the range of risks identified as
salient for the at-risk population.
• SDS warning: Warning of SDS events
is based on research into the hazards
and the identification and monitoring
of triggers. The survey process can
help identify which triggers are most
relevant to at-risk populations (as
people respond best to warnings
based on triggers they know and
understand), and their receptivity to
specific actions that can be taken to
reduce SDS impacts, depending on the
type of SDS event for which warnings
are provided.
• SDS response: In general, specific
disaster relief and recovery operations
are not undertaken for most SDS.
The expert process can help identify
and raise the profile of SDS response
options by identifying where specific
responses can be most effective in
reducing SDS impact. An example
would be linking SDS health
vulnerability and risk to specific
subpopulations and identifying the
effectiveness of response efforts for
this subpopulation. Survey-based
results can also identify local SDS
coping or adaptation measures
that can be formalized into SDS
response plans. This input is very
useful in ensuring that response
measures match local capacities and
preferences.

• Risk reduction: Both assessment
procedures can identify where risk
reduction efforts should be targeted,
with the expert process more focused
on strategic interventions and the
survey process more focused on
on-the-ground interventions. Both
procedures can be used to assess
the costs-to-benefit decision points
for specific SDS risk reduction
interventions or for packages of
interventions.
The survey process can be used to
identify the salience of specific SDS
impacts for at-risk groups, which can
then be used to define preferences
for specific risk reduction options. As
noted, survey results are likely more
useful than the expert process in
planning specific SDS risk reduction
interventions. Initial surveys can
be used to define baselines and
subsequent surveys (often using
reduced sampling) can be used to
assess progress in reducing perceived
SDS impacts and levels of risk.
These uses of assessment results
to address SDS risks need to be
matched by a good understanding of
the physical processes and impacts
related to different types of SDS in
different locations. Results from both
assessments can be used, in part,
to guide where research into local
SDS causes and impacts should be
targeted, by type of impact, location or
at-risk group.
Finally, results from both assessments
of risks from specific hazards can
feed into larger assessments and
strategies related to the management
of other hazards and risks, such
as from flooding, severe weather,
or drought. In this sense, SDS risk
assessments further the integration
of SDS into mainstream disaster risk
management.
5.9 SDS survey
questionnaire
5.9.1. Details of the model
questionnaire
Table 6 provides a model for the field-
level SDS risk assessment questionnaire
which is presented in table format to
include instructions and guidance. This
information should be removed from the
actual questionnaire but can be provided
to the teams conducting surveys to
assist their work. To ensure that results
are comparable across surveys and
assessments, the scaling of the response
to questions should not be changed.
The questionnaire is designed to
be administered to one person, but
questions and responses are based on
the assumption that it will take place in
a household. The questionnaire wording
should be modified if it is clearly only being
administered to a single person or is being
carried out with a focus group or through
a key informant interview (The latter is not
preferred as the scope of coverage would
be limited).
Use of the questionnaire should follow
normal good practice for data collection.
Anyone with whom the questionnaire
is used should be provided with an
explanation of the purpose of the
survey, how the results will be used, and
particulars of the survey process and
organizations involved.

118
5.9.2. Sample size
Questionnaire-based surveys have no
defined limit regarding the maximum
number of people, households or other
groups that can be included in the survey.
The maximum target population is
generally defined through a combination
of time to conduct the survey, funding
and staffing. Setting the statistical
confidence level and indicator for a survey
can determine practical maximum and
minimum limits for the sample size.4
5.9.3. Modifications to the
questionnaire
The model questionnaire should be
revised to reflect local conditions and
the focus of the survey work. Additional
questions can be added to the survey
form, for instance to include perceptions
of other hazards besides SDS. However, a
field-tested survey should not take more
than 30 minutes to administer, including
introductions, completing the form and any
other formalities.
If the survey is carried out on a one-to-
one basis, gender and age information is
already collected in the form. Using this
information to disaggregate responses
would be a normal part of the analysis and
report-out process.
If the questionnaire is used to collect
household responses (i.e. not one-to-
one with an individual), the number of
questions needs to be increased to allow
for information to be collected on effects
that may be different for males and
females (generally men and women but
also, where appropriate, boys and girls).
This can be done for each of the “effect”
question sets (items 27 to 41), by adding
additional questions following the format
of Are these effects the same for men and
women or boys and girls? If not, is there
1 - no effect, 2 – very limited effect, 3 –
some effect, 4 – important effect, 5 – very
severe effect, and recording the answers
separately for each group covered.
4 Confidence level and confidence indicators can be calculated at https://surveysystem.com/sscalc.htm or
similar sites. (Reference to a commercial website does not indicate a recommendation or support for the company
involved.)
The different responses, if any, are then
used in the analysis and report-out of the
survey to differentiate SDS impacts by the
groups covered.
Item 25 of the model questionnaire
provides for collecting a statement from
the person or group being interviewed
describing the characteristics of an SDS
event, and then estimating the reduction
in visibility to match the description as
closely as possible to one of the SDS types
described in Table 2. This process could be
time-consuming and the respondent may
have difficulty in accurately and quickly
determining visibility distance.
The alternative is to prepare pictures
of each type of SDS in advance with
descriptive text covering the key points
from Table 2. These pictures would
be shown to the respondents, who
would choose one or more pictures
as the basis for covering items 25 to
40 in the questionnaire. This use of a
visual reference makes it clearer to the
respondent what the survey questions are
about and makes the classification of the
response by SDS type clearer and more
credible.
5.9.4. Information on SDS
risk management
The model survey in Table 6 is focused
on collecting information on SDS impacts.
Additional questions can be added to
collect information on SDS preparedness,
response plans, warning systems,
information dissemination and ongoing
mitigation activities.
The challenge with adding questions is
that they can make the survey overly long,
thereby reducing the number of surveys
that a team can complete in a designated
time, and taking excessive time from those
who are being questioned. Testing of the
questionnaire can assess whether its
length is excessive or whether questions
on SDS risk management are appropriate.

An alternative is to use key informants
to explore how SDS risks are managed,
particularly as statistics on risk
management options are not needed. Key
informants include officials, individuals,
households, businesses and academics.
A strategy of diversifying sources of
information can assist with developing
a broad understanding of SDS risk
management practices.
©Véronique
Mergaux
on
Flickr,
February
24,
2017

120
Sequence number Information/question Information to be
entered
Notes
1 Date
2 Surveyor 1 Name One surveyor should be
male and one female.
3 Surveyor 2 Name
4 Sequence number Number indicating the
sequence of the survey,
starting from 1
The sequence number
can include a letter
or additional number
indicating the team that
conducted the survey.
5 Location Town or other location
where the survey is
taking place
6 GPS reference Global Positioning
System reference for the
place of the interview
7 Gender of the
respondent
Male or female
8 Agreement to conduct
survey
Yes or no The person surveyed
should agree to the
survey. If not, the survey
is ended.
9 Age In years Age can also be
collected using a range
of ages, for example 10
to 19, 20 to 29, etc.
10 Is the respondent the
head of the household?
Yes or no
11 If the respondent is
not the head of the
household, what is the
gender of the head of the
household?
Male or female
12 What is the profession
of the head of the
household?
Select from list. A list of typical
professions should
be added before the
questionnaire is used.
13 How many persons are
resident in the household
at the time of the survey?
Number The number should not
include persons who are
not currently sleeping
in the household (i.e.
people who are traveling
or working somewhere
else temporarily).
14 Of these persons, how
many are female?
Number
many are under five
years of age?
Number
many are over 64 years
of age and what is their
gender?
Number and gender
17 Are there any persons
with disabilities resident
in the household and
what is their gender?
Yes or no, with gender
indicated
18 If yes, list the types of
disabilities.
Select from list. Prepare the list in
advance.
19 Does the household rent
or own the place where
they live?
Renters or owners
Table 6. Sand
and dust storm
perception survey

entered
Notes
20 Does the household have
electricity?
Yes or no
21 Does the household have
running water?
Yes or no
22 What type of sanitation
facility does the
household use?
Select from list. Prepare list in advance.
23 Does the household own
any of the following:
car, TV, radio, computer,
tractor or truck, boat?
Yes or no for each item Update the list based on
likely local ownership of
assets.
24 Has the household
experienced a sand or
dust storm?
Yes or no If no, end the survey.
25 If yes, ask for a
description of the most
recent event. Prompt for:
when the SDS occurred
(month, year)
time of day
how long it lasted
how much visibility was
reduced at the worst
point in the storm.
Use a reference point,
for instance a tree or
building that was not
visible during the storm.
Write down the response. After the question,
estimate the distance to
the structure or reference
point not visible during
the storm.
26 With reference to the
storm described, ask
how frequently per year
these events take place.
Indicate per year If less than once a year,
indicate how often over
a number of years, for
instance, once in five
years.
27 Ask whether the storm
described had an effect
on the health of anyone
in the household.
Answer scale:
1 – no effect
2 – very limited effect
3 – some effect
4 – important effect
5 – very severe effect
28 For answers 2 to 5
on the scale, ask for
a description of what
happened.
Write down the response. Detail for each affected
individual.
Note gender, age and
disability status (if
appropriate) for each
respondent or person
discussed.
described had any
effect on buildings,
roads or other
infrastructure (water
systems, irrigation,
electrical systems,
communications) where
the household is located.
Answer scale:
1 – no effect
3 – some effect
happened.
Write down the response. Include as much detail
as possible.
discussed.

122
entered
Notes
described had any effect
on the household’s
fields, crops or garden
production.
Answer scale:
1 – no effect
3 – some effect
happened.
as possible.
discussed.
caused soil loss or other
erosion.
Answer scale:
1 – no effect
3 – some effect
This question focuses on
the impact of a location
contributing sand or dust
to an SDS event through
wind erosion.
happened.
as possible.
discussed.
caused the household
to lose income (i.e.
someone could not work
or their business could
not function due to the
storm).
Answer scale:
1 – no effect
3 – some effect
happened.
as possible.
discussed.
37 Ask whether the
storm described had
any effect on land,
pasture, forests or other
natural resources that
are available to the
household.
Answer scale:
1 – no effect
3 – some effect
happened.
as possible.
discussed.
described led the
household to use their
social connections to
deal with the effects of
the storm.
Answer scale:
1 – no
2 – very limited use
3 – some use
4 – important use
5 – very significant use
Note that “social
connections” can be
reworded to reflect
kinship ties, extended
family or other social
connections that are
common in the location
where the survey is
taking place.

entered
Notes
40 For answers 2 to 5 on
the scale, ask for a
description of which
connections were used
and for which purposes.
as possible.
discussed.
41 Ask whether the effects
of the storm had, in their
opinion, been reduced
by warnings or any other
actions taken by the
Government.
Answer scale:
5 – no
4 – very limited reduction
3 – some reduction
2 – important reduction
1 – very significant
reduction
Note that the answer
scale is the inverse for
the other responses,
making “very significant
reduction” the opposite
of “very severe effect”.
42 For all answers, ask for a
description of the actions
taken.
as possible. The impacts
of warnings should be
linked to one or more of
the capitals if possible.
discussed.
43 Ask the household
whether they have
experienced any other
types of sand and dust
storms in the past.
Yes or no
44 If yes, repeat items 25 to
41 for this event.
After the second round
with items 25 to 41,
ask again if there are
any other sand or
dust storms that the
household remembers.
If yes, repeat the
process until all storms
mentioned by the
household are covered
per items 25 to 41.
45 If no other storms
are reported, ask the
household to rate the
significance of the
storms they described
against the effects of
floods.
Rating
1. Not significant
2. Much less significant
3. As significant
4. More significant
5. Much more significant
This item and the next
should include the
most significant natural
hazards identified for the
assessment area.
discussed.
Seek input from men,
women, girls and boys.

124
entered
Notes
46 If no other storms
are reported, ask the
household to rate the
significance of the
storms they described
against the effects of
drought.
Rating
1. Not significant
2. Much less significant
3. As significant
4. More significant
5. Much more significant
Additional items can be
added to cover additional
hazards.
discussed.
Seek input from men,
women, girls and boys.
47 Close by thanking the
respondent and telling
them when a report
based on the survey will
be available.
©Maria
Olson,
EU
ECHO

5.10 Conclusions
This chapter has covered practical ways of
assessing the risks posed by SDS to at-risk
populations. Two approaches have been
defined based on (1) expectations of data
reliability and spatial consistency across
all SDS-affected locations and (2) a need
to deliver practical results that can help
reduce SDS impacts.
One assessment approach uses
questionnaire-based surveys of
populations at risk of SDS to combine
perceptions of SDS vulnerability with a
typology of SDS events and generate
results that are comparable across
locations and scales. The second
assessment approach uses expert
knowledge and the SDS typology to define
vulnerability levels and risks, which are also
comparable across scales.
Either approach can be used at very
local, national or regional scales. If either
approach is used consistently between
locations, the results from each approach
can be compared and, when appropriate,
aggregated to increase understanding of
SDS impacts and risks.
The survey approach can be used to cover
a wide geographic area and uses random
or selective sampling to collect information
on a wide range of affected populations.
These results can then be shared as part
of the expert approach to aid experts in
developing a common understanding
of the SDS hazard and impacts and in
framing the decision-making process.
This process uses the strengths of a
perception-based understanding of
SDS risk and the strengths of an expert
understanding of the physical, economic
and social consequences of SDS.
The cost of the survey process depends
on the scale of the survey: the larger the
at-risk population covered, the greater
the expected cost for an individual
survey. Surveys are likely best done
at subnational scales defined by SDS
source and impact locations and then
aggregated to national and subglobal
results. The survey approach can be
implemented by commercial survey firms,
non-governmental organizations, civil
society groups, academic institutions or
government statistical offices and can be
part of larger assessments of hazards or
socioeconomic or health conditions.
The cost of the expert process is
considered relatively low per workshop.
Each assessment workshop can cover the
subnational to national level in scale, again
defined by the types of SDS of concern.
These workshops can be organized by
governments, academic institutions or
international organizations.
The two approaches set out are based
on current practice for assessing disaster
risk, hazards and vulnerabilities, but have
not been tested or validated in the field.
Validation may yield changes to both
approaches and the underlying procedures
and supporting materials. Where these
changes are necessary, they should be
applied consistently within each approach
to ensure that assessment results are
comparable.
To date SDS, as hazards and potential
disasters, have not gained significant
attention within the disaster risk
management community. Providing
practical assessment procedures
will enable this community to better
understand the threat posed by SDS and
to develop effective measures to reduce
these risks.

126
5.11 References
CAMP Alatoo and United Nations Development
Programme (UNDP) Central Asia Climate Risk
Management Program (2013). Climate Risk
Assessment Guide – Central Asia.
Managing the risks of extreme events and
A Special report of Working Groups I and II of
the Intergovernmental Panel on Climate Change,
Stocker, Qin Dahe, David Jon Dokken, Kristie L. Ebi,
Michael D. Mastrandrea, Katharine J. Mach, Gian-
Kasper Plattner, Simon K. Allen, Melinda Tignor
and Pauline M. Midgley, eds. New York: Cambridge
University Press. p. 582.

1
2
8
©Wikimedia
Commons,
ESA,
September
11th,
2018

UNCCD | Sand and Dust Storms Compendium | Chapter 6 | Economic impact assessment framework 129
6. Economic impact
assessment frame-
work for sand and dust
storms
Chapter overview
This chapter discusses different approaches to assessing the economic impact of sand
and dust storms (SDS). The chapter begins with a review of research into SDS, followed
by an extensive discussion of the different types of costs which need to be considered
when assessing the economic impacts of SDS. This is followed by a review of the different
methods which can be used to assess economic impacts and an extensive discussion of
the cost-benefit (or benefit-cost) method as applied to SDS. The chapter concludes with
a review of the data sources which should be used in the cost-benefit method and in the
overall assessment of the economic impact of SDS.

UNCCD | Sand and Dust Storms Compendium | Chapter 6 | Economic impact assessment framework
130
6.1 Damage, costs and
benefits of SDS
6.1.1. Reviewing the costs
and benefits of SDS
Sand and dust storms (SDS) differ from
many other disasters in that there is
usually very little major structural damage.
The physical damage caused by SDS is
relatively minor when compared to other
disasters such as earthquakes or floods.
SDS do not usually result in directly
attributable fatalities or injuries, with
most health-related impacts associated
with other health conditions such as
respiratory diseases, eye problems or
cardiovascular diseases. However, SDS can
be the proximate cause of fatalities and
injuries due to transport accidents, most
commonly road accidents in conditions of
high sand and dust.
The most evident damage caused
by SDS is impacts on the natural
environment due to, for instance, dust
and sand accumulation or inundation on
croplands. Sand and dust can also affect
infrastructure operations by entering
commercial, manufacturing or residential
structures, leading to productivity- or
production-related issues, as well as the
need for cleanups, removals or limiting
economic activity. Neither the human
nor the financial impacts of SDS are
well captured in international disaster
databases, such as those maintained
by the Centre for Research on the
Epidemiology of Disasters (see chapter 3).
The economic impact of SDS is somewhat
unique, in that there is a cost at the source
of the sand or dust emission through
losses in soil and/or sand and associated
losses in productivity or income. In
areas where there is no direct economic
activity, indirect costs will still be incurred
through loss of soil nutrients or carbon,
and perhaps ecosystem services. There
are also costs imposed on the region
downwind of the emission region, due
to economic disruption caused by the
event(s), such as closure of transportation
services and cleaning of roads, houses
and business premises (Huszar and Piper,
1986; Tozer and Leys, 2013).
The impact of SDS can be mitigated at
the source with investments in soil and
land management practices, such as
using forestry or cover crops to reduce
soil losses or movement of sand when
weather conditions could lead to an SDS
event. Furthermore, the effect on the
downwind region can be reduced with
mitigation practices such as installation
of air filtration systems or early warning
systems to ensure that members of high
risk populations remain indoors.
However, the net benefits and/or costs of
mitigation, either at the source or in the
impact region, need to be considered in
the context of the overall cost of SDS to
an economy. This consideration needs
to take place either in a region within a
country (such as a province, state or set of
states), country or global region (including
several countries), such that the benefits of
mitigation outweigh the costs.
Measuring the impact of SDS for
each country is critical as it allows the
government of a country to determine if the
costs of SDS can be moderated through an
investment in mitigation projects within the
country in the source area. The key aspect
here is that the benefits of dust mitigation
outweigh the costs of the mitigation
measures, recognizing that the control of
all SDS impacts may be not feasible from a
financial perspective.
It is important to recognize that most
benefits of mitigation will accrue to
individuals, but most of the costs are
incurred by the government or government
agencies. Thus, even though there may
be a net benefit, the funding agency may
not have sufficient funds to finance the
mitigation programme. What must also
be remembered here is that the objective
is to reduce the effects of dust on the
population in the impact region, not to
eradicate SDS completely, as SDS are
part of the natural cycles of the world
and therefore total removal of SDS is
undesirable from a total environmental
perspective.

Dust mitigation projects may also be
undertaken in source regions outside
the national boundaries of a country, as
airborne dust particles have been shown to
travel long distances, hence there can be
a significant distance between the source
region and the impact region. As a result,
the benefits and costs of a mitigation
programme may fall on, or be incurred by,
different countries or regional government
instrumentalities. However, the major
decision criterion is that the net benefits
of the programme (the sum of benefits
in both the impact and source regions)
exceed the costs.
There are numerous approaches to
measure the economic impact of SDS
and to measure the costs and benefits of
mitigation programmes. To that end, this
chapter presents a method of measuring
the costs of SDS on the impact region
and provides a framework to measure the
costs and benefits of various mitigation
strategies in either the source or the
impact regions.
6.1.2. Previous economic
impact studies
Given the prevalence of SDS around the
world, the number of economic impact
assessments is very limited. In one
of the first attempts at measuring the
economic impact of SDS, Huszar and
Piper (1986) used surveys of businesses
and households to quantify the off-site
costs of sand and dust storms in New
Mexico, in the United States of America
(USA). Huszar and Piper (1986) estimated
the costs of SDS in New Mexico alone
were approximately $857 million (in 1985
dollars). This is only the cost to households
and businesses and does not include
other costs such as the removal of sand
and dust from roads by city, county and
state transportation authorities, nor does
it include defence force costs for cleaning
airbases located in the state.
In a study of the costs of wind erosion,
or SDS, in South Australia, Williams and
Young (1999) estimated the annual
average costs of SDS events to the
population of that state was $A 23 million
(in 1999 Australian dollars). Most of the
cost ($A 20 million) was health related. The
range of costs estimated by Williams and
Young (1999) was from
$A11 to $A56 million.
Ai and Polenske (2008) used Input-Output
(I-O) modelling to estimate the costs
of SDS in Beijing in 2000. The authors
concluded that the delayed impacts of
SDS exceeded the immediate effects.
Delayed impacts are those that do not
occur on the day(s) of a dust event but
are consequences of the dust storm.
Immediate effects occurred in the
construction, trade and household sectors,
and totalled $US 66 million. Delayed effects
on the agricultural and manufacturing
sectors totalled US $198 million. Together,
the total economic cost of SDS was
$US264 million (in 2003 dollars).
Miri et al. (2009) estimated that SDS cost
the Sistan region of eastern Iran US$ 125
million from 2000 to 2004. Most of the
costs – 61 per cent – were reportedly
related to household cleaning and reduced
electronic equipment life. A further 25
per cent were associated with the cost of
health-related issues, including hospital
admissions.
Measuring the economic impact of one
significant dust storm in New South Wales,
Australia, Tozer and Leys (2013) estimated
the costs to be $A299 million (range of
$A293 to $A313 million in 2012 Australian
dollars) in that state alone, without
measuring the impacts on other states
which experienced the dust storm. Most of
the impact was on the household sector,
with 85 per cent of the costs. The next two
most impacted sectors were transport
(principally air traffic) and commercial
activity.
SDS economic impact was studied in
Kuwait, with the impact on the oil and gas
operations estimated to cost $US 9.36
million in 2018 (Al-Hemoud et al., 2019).
Also, oil export losses due to closeout
of marine terminals were estimated at
US $1.03 million per ship (Al-Hemoud et
al., 2017). Airline delays due to airport
operations shutdown were also estimated.

132
6.2 Types of costs in the
context of SDS
6.2.1. Direct and indirect
costs
Several researchers define two types of
costs associated with disasters – direct
and indirect (see, for example, Hallegatte
and Pyzyluski, 2010). Direct costs are those
associated with the immediate impact of a
disaster. In the context of SDS, most costs
are direct costs, as the impacts of SDS do
not typically have long-term effects on an
economy in the same way as damage and
reconstruction caused by hurricanes and
earthquakes does, requiring rebuilding of
damaged structures and functions within
the economy of the affected area.
Indirect costs are those that are imposed
on an economy due to business disruptions
or other similar impacts brought on by
a disaster. As noted, SDS do not have a
long-term impact on most of the economy.
A thorough review of the economic impact
studies related to SDS events is presented in
Al-Hemoud et al. (2019).
One set of indirect costs that SDS may
impose on an economy is due to the long-
term loss of income for landowners in the
SDS source region(s). Depending on the
level of loss, indirect costs may exceed
direct costs in some regions. From a
socioeconomic perspective, this can have
long-term impacts, particularly if the costs
push a vulnerable population past a critical
threshold.
6.2.2. Market and non-market
costs
Market costs are those costs that can be
directly estimated due to a market for a
product or that can be estimated using a
market valuation technique. In the context
of SDS, many of the damage costs can be
estimated using market cost, in that there
are established markets for the products or
services affected.
Non-market costs or values are for
damage or products for which there is no
direct market. Examples of products or
damages that fit into this category include
damage to cultural icons or historic sites,
environmental or ecosystem services, or
human lives.
There are some ways to measure the
economic impact of events on human
life, such as disability-adjusted life years
(DALY) or quality-adjusted life years
(QALY) (World Health Organization [WHO],
2016). However, these are used as an
index for the value of all lives and do not
take into account many social, cultural
and economic factors (Arnesen and
Nord, 1999). There are also accepted
methods to estimate non-market values for
environmental services or loss in revenue
from cultural or tourism events, such as
contingent valuation (willingness to pay)
or travel costs (Hanley and Spash, 1993;
Harris, 2006; Ninan, 2014).
6.2.3. Cost and value
One important distinction to make is the
difference between cost and value. A ‘cost’
is how much a person has to pay for a
product, or the price of that product, which
is usually reasonably easy to observe
in a marketplace. In contrast, ‘value’ is
somewhat subjective, and is a measure of
what a person would be willing to pay for a
product or service that may not have a fully
functioning market.
The key difference here
is what a person has to
pay against what they are
willing to pay. In the context
of SDS, much of what is
discussed in the following
sections will be a cost-
based analysis. However,
when discussing effects of
SDS, such as damage to
cultural icons or reduction
in ecosystem services,
methods of assigning value
to these types of services
will also be discussed.

6.2.4. On-site (source) and
off-site (impact)
SDS create damage in two locations, the
source location and impact region. The
economic impact in either location will
depend on many things, such as the level
and types of economic activity in either
region, the activities undertaken in the
source region that may contribute to SDS
events, such as farming or cropping, the
relative wealth of the population in each
location and damage to the environment
or ecosystems in either location. Other
factors that need to be considered include
damage to environmental or ecosystem
services in either region, or the human
aspects, such as health and income
distribution in the source and/or impact
region.
6.3 Gender, age,
disability and
economic analysis
Gender, age and disabilities are important
to consider in assessing the economic cost
of SDS. Specific impacts may be greater
for men, women, boys or girls due to their
social or economic situations. For instance,
if men are obliged to work outside in areas
where SDS are common, then impacts on
their health could be significant and could
have an impact on how long or how often
then can work.
Similarly, age and disability are factors in
some of the health impacts of SDS. For
instance, older persons are potentially
more vulnerable to respiratory or
cardiovascular conditions which can be
exacerbated by SDS. These SDS impacts
may increase health care costs, require
other family members or hired help
to assist the affected or take affected
persons away from productive activities.
To the extent that disaggregated data are
available, economic assessment results
should identify the extent to which SDS
impacts affect different gender, age and
disability groups in terms of participation
in, and benefiting from, the economy. This
type of analysis can be useful in tying
statistical analyses used in economic
impact assessments to real challenges
faced by SDS-affected groups.
6.4 Economic impacts
of SDS
6.4.1. Impacts to consider
Research on the economic impact of
SDS has focused on the direct impacts
on the main drivers of an economy, such
as transport, manufacturing or the costs
of cleaning incurred by households and
industry (Huszar and Piper, 1986; Tozer and
Leys, 2013).
However, two other major components
in a society can be significantly affected
by SDS in either the source or the impact
region. These are (i) the environment or
ecosystem within a region or country and
(ii) the human dimension, beyond losses
of income due to lower production or
sales. However, the key concept here is
that the three components; economic,
environmental and human, are all tightly
interlinked, meaning that they cannot be
easily separated when measuring the
overall economic impact of SDS.
The impacts of SDS on the economic
activity within a source or impact country
or region are relatively easily measured
and in most cases are direct costs, with
some minor indirect costs. Environmental
or ecosystems services can be severely
affected by SDS in either the source or
the impact region, depending on the
environment or ecosystems in each region.
In the source region, soil erosion,
damage to waterways and/or habitat or
ecosystem loss or damage are some of
the consequences of SDS emissions. Air
quality, waterways siltation and ecosystem
damage are some of the environmental
consequences in the impact region of SDS.
The human side of the impacts of SDS are
a little more complex to disentangle due
to differences across regions or countries
from which SDS are sourced and/or
impacted. The reasons for this are due

134
to (i) the complexity of economic welfare
and equality in the source and/or impact
regions and (ii) how erosion of the soil –
the source of material for SDS – affects
the livelihoods of those relying on it as a
source of food and/or income.
Another reason for this complexity is that
soil erosion is a dynamic factor affecting
production and productivity of land in
the source region. Incomes are not only
affected in one year by soil erosion. If
erosion continues, then production – and,
by extension, incomes or wealth of the
population in the source area – will be
continually reduced until the soil is unable
to sustain any cropping activities at all,
hence reducing food supply or landowner
income on the affected land to zero.
Another aspect of the human side
of the impact of SDS is the health of
the population, at the source or, more
commonly, in the impact region, in that
dust has been shown to negatively affect
certain segments of the population. This
is a somewhat complex situation. An SDS
event may trigger a health crisis leading to
a fatality, but attributing this fatality to SDS
may be difficult, for several reasons. The
person may have had a history of health
problems before the SDS event, such as
cardio-pulmonary issues. This places them
in a high-risk category. There may have
been a significant timespan between the
SDS event and the health effect.
Similar issues exist in the case of non-
fatal health events, such as an acute
case of difficulty breathing which required
hospitalization (and thus lead to costs).
However, SDS may not be the only factor
in the hospitalization and therefore
untangling the costs that can be attributed
to SDS becomes difficult.
Another human impact of SDS is the loss
of life or increased care for people injured
in transport accidents, most often air- or
land-based in nature. Calculating the
economic impact is challenging, as there
is a need to consider the health impacts
(fatalities, injuries) as well as the loss of
goods and services due to the accident.
While an accident itself may be very
location-specific – for instance, closing a
section of a major highway – the knock-on
effects on changes in traffic patterns (for
example, redirecting commercial trucks
onto alternate routes) can be hard to
capture using available data.
Finally, the health conditions triggered by
SDS events will vary across populations,
due to factors such as gender, age,
income and wealth, nutrition access and
availability, as well as the ability to avoid
dust events through housing and/or
ventilation. Distinguishing these variations
in conditions of SDS-affected individuals
can be difficult when the data available is
limited in coverage or detail.
©Nasa
Earth
Observatory

6.5 Identifying the
damage and
costs of SDS
6.5.1. On-site costs –
economic activity
On-site damage is usually in the form
of loss of soil and sand, which leads to
scalding1
of the site. Associated with
the loss of soil or sand is the loss of soil
nutrients and organic matter including soil
carbon (Leys and McTainsh, 1994; Leys,
2002). This loss of soil or soil nutrients
reduces the productive capacity of the
soil, and thereby potentially reduces the
income for landowners or land users, with
the impact varying based on the location,
economic and political context of the
region (Economics of Land Degradation
[ELD] Initiative and United Nations
Environment Programme [UNEP], 2015).
Further costs are incurred in the source
region due to damage to infrastructure
such as irrigation or water systems,
destruction of fences, loss of livestock and
forage for livestock, sandblasting of crops
and road cleaning. Dust can also contain
soil carbon, which could have a value to
the landowner, particularly if in the future
carbon sequestration and carbon markets
become more functional.
Huszar and Piper (1986) suggest that an
approximation of the immediate on-site
costs of wind erosion, such as damage to
infrastructure, can be obtained from the
off-site costs. Using the method proposed
by Huszar and Piper (1986), a value of 2
per cent of the costs of household cleaning
can be used as the basis for determining
on-site costs based on the calculations.
Using this method, Tozer and Leys (2013)
estimated on-site costs of approximately
$A 5.1 million for a single severe dust
storm that affected eastern Australia in
2009. The estimated cost was consistent
with the Natural Disaster Relief Assistance
request of $ A4.5 million to compensate
landowners for costs and losses due to the
event (Kelly, 2009).
1 See https://www.qld.gov.au/environment/land/management/soil/erosion/types for a definition.
However, the method used by Huszar and
Piper (1986) does not account for the long-
term loss in productivity or income due to
soil erosion and soil nutrient loss, and may
only be appropriate in situations where
productive land is the source area, such
as in remote grazing regions, like central
Australia or the southwest of the USA.
ELD Initiative and UNEP (2015) provide
an approach that can measure the loss in
production and income due to soil erosion
in general, but the methodology can be
applied to countries where SDS originate,
as some of the losses in soil and/or sand
are due to anthropogenic activities, such as
agriculture or deforestation.
6.5.2. Off-site costs –
economic activity
Off-site costs of SDS will depend on many
factors, with the principal factor being the
level of economic activity in the impact
region. For example, SDS that affects
mainly agricultural or pastoral regions
may not have as much economic impact
as SDS that affects a major metropolitan
area. The main reason for the difference
in impact across different regions can
be attributed to the level of infrastructure
in the different regions and the relative
populations.
Major urban centres are more affected
by SDS than less populated rural areas.
This is simply due to the higher amount of
the population that are subject to health
impacts, the level of wholesale, retainment
of commercial and industrial activities,
and disruptions caused by SDS impacts
on traffic or the provision of education
due to school closure or restriction of
outside activities in these urban areas.
Implicitly included in the costs incurred
within many sectors, including commerce,
manufacturing, transport and the public
sector, is the cost of cleaning or removal of
sand and/or dust from impacted locations.

136
Transport
Major cities tend to have more key transport infrastructure than regional centres, including
airports and airline hubs with significantly higher aircraft movements, rivers, seaports and
road transport systems. Any factor that limits capacity or vehicular movement can cause
substantial economic losses.
Costs to the various transportation subsectors vary due to the types of impacts. The airline
industry is affected as SDS typically reduces visibility, making landing and taking off difficult.
This can lead to aircrafts being grounded, leading to flight delays, cancellations or diversions.
An SDS event can have several impacts. Airlines will lose income through reduced passenger
numbers, with some passengers receiving fare refunds. Aeroplanes will need to be diverted if
they cannot land at an affected airport. Following diversions and delays, aeroplanes will need
to be repositioned to ensure the schedule returns to normal after the SDS event.
In some cases, airlines will provide food and/or accommodation for passengers that are
affected by delays or cancellations or provide alternative means of transport to their final
destination (Williams and Young, 1999; Tozer and Leys, 2013).
Although water transport may not be as severely affected by reduced visibility as the airline
industry, it may cause port and ferry services to be reduced (Tozer, 2012). Also, port services
may be affected through increased loading or unloading times due to worker health and
safety issues. For instance, dust may cover surfaces, making them unsafe to work on. A
reduction in port processing time could add costs such as demurrage to the total costs for a
ship owner or charterer.
The impact on the road
system can be a significant
cost. The effects of SDS on
road transport are:
» road closures due to
either visibility or dust or
sand on the road surface
» traffic accidents due
to surface or visibility
conditions
» reduced transport
requirements as a knock-
on effect from reduced
activity in other sectors,
such as the construction
industry
Dust storms have been
shown to directly lead to
traffic accidents in, at least,
Australia, Iran and the USA
(Williams and Young, 1999;
Burritt and Hyers, 1981; Miri
et al., 2009).
Two aspects that can affect
the costs of road transport
are:
» travel speed during SDS
» the number of vehicles on
the road during an event
These two aspects affect
travel time for road users.
Travel speed may be reduced
due to poor visibility during
a dust storm, but if some
employees or parents remain
at home during the event, the
number of vehicles on the
road system may be reduced
(Tozer and Leys, 2013). As a
result, the impact on travel
speed and transport costs
may be difficult to estimate.
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Health
The health impacts of SDS are difficult to measure and to assign a cost to, due to the
differences in reporting across countries or regions and differences in analyses of data. In
a review of 50 papers reporting health effects due to dust or poor air quality, de Longueville
et. al. (2013) found mixed results as to whether health was impacted by atmospheric dust
or poor air quality.
One issue that arises in much research related to the health impacts of dust is attribution
of effect. For example, an at-risk portion of the population, especially those with pre-
existing cardiopulmonary issues, may have a higher mortality or morbidity rate during a
dust storm due to the atmospheric dust exacerbating the pre-existing condition.
The issue then becomes whether the dust is the cause of the mortality or morbidity or
simply the final contributor that leads to the death (de Longueville et al., 2013). Huszar and
Piper (1986) estimated that the health costs to households of a series of SDS events were
approximately US$ 19 million out of the total household cost budget of US$ 458 million.
Tozer and Leys (2013) did not find any significant health effects of the Red Dawn event in
Australia in 2009, but this may be at least partially attributed to an early warning system in
place for at-risk populations. However, the health costs estimated are only the direct costs
to households and do not capture the effects on society of reduced health due to SDS.
Household cleaning
Previous research has shown that households face the highest direct costs of SDS due
to interior and exterior cleaning, as well as repairs and maintenance of structures and
vehicles (Huzsar and Piper, 1986; Tozer and Leys, 2013). Miri et al. (2009) found that
household cleaning costs accounted for over 85 per cent of the total costs estimated for
dust storms in the Sistan region of Iran. In assessing household cleaning costs, the value
of time and resources used, as well as income opportunities lost or deferred, need to be
understood in terms relative to the economy and level of income where these actions are
taking place.
In many cultures, household cleaning is a task allocated to women and girls. The
additional work needed to clean up after an SDS event could increase overall workload
for women and girls and reduce opportunities to otherwise gain income or non-monetary
assets (for example, from the collection of natural resources).
Commerce and manufacturing
Measuring the effect of SDS on the commercial sector is fraught with challenges. Some
expenditure that is not made during an SDS event may be made after, meaning that there
is no loss in income for some commercial operators. This is especially true for food and
essential items purchases made by households, as the purchases are simply delayed
rather than not made, and only delayed for the duration of the event.
However, time-sensitive purchases, such as newspapers and perishable or fresh foods like
bread or fruit, may not occur during the SDS event. The absence of these purchases will
cause retailers to lose revenue and the product(s) to be discarded. Similarly, discretionary
purchases by consumers, such as takeaway coffee, may not be made, again reducing
retailer income (Tozer and Leys, 2013). Other indirect costs may be incurred in the
commercial sector due to delays in delivery of goods required for production or movement
of goods out of production facilities.

138
The manufacturing sector may be affected by SDS if the particulate matter enters the
manufacturing facility, or through delays in material required for production being held
up in transit. For example, electronics component manufacturers in Korea noted that on
days of high particulate matter, more faulty products or faults in final components were
observed (Kim, 2009).
Another cost of SDS in the commercial sector is that of absenteeism, or employees being
absent to care for children (if schools are closed during an SDS event) or others in need
of care. Absenteeism has been shown to reduce productivity, and as a consequence
of the SDS event, must be added to the cost. A point to consider is that only the loss of
productivity should count towards costs incurred as a result of the SDS event, as costs of
production should include costs of workers taking leave for various reasons (Tozer and
Leys, 2013).
Agriculture
SDS can impose costs on the agricultural sector through:
1. Crop destruction or reduced yield
2. Reduced animal production due to animal death or lower yields of milk or meat
Ai and Polenske (2008) estimated that the impact of SDS on the agricultural sector in
the Beijing region in 2000 was the second highest only to the manufacturing sector and
constituted about 36 per cent of the total cost in that year.
For annual crops, losses are due to sand or wind blasting and can lead to complete loss
of crops in a particular region or a reduction in yield due to partial losses. The impact on
perennial crops could be similar to annual crops in that the current year crop could be lost
or reduced. However, there may also be a longer-term effect on some perennial crops due
to tree or crop damage (for example, Lucerne/alfalfa crowns being damaged), leading to
reduced production in future years.
Animal production can also be affected in several ways. There may be a reduction in milk
produced during the SDS event, thus costing the producer income with no compensatory
reduction in costs. The SDS may lead to the loss of animals, either through death
(particularly through suffocation in severe events) or through producers being unable
to locate them after they fled the SDS event. An animal producer may also face lost,
destroyed or damaged feed stocks, pasture or forage crops, requiring the producer to
purchase feed that they would otherwise not have done.
Other costs
Other costs of SDS in the impact region include:
1. Reduction in construction and mining activity, due to health and safety issues at the
construction or mine site
2. Increased emergency service activity, due to road or traffic accidents or ambulance
traffic transporting patients to hospitals due to dust-related health problems
3. Damage to utility infrastructure such as electricity transmission lines or pylons
In some cases, SDS may lead to damage, but there may already be pre-existing conditions
that contributed to the final damage caused by SDS.

SDS can also impact cultural, leisure and sporting activities and the cost to the economy
will depend on the type of event affected. Estimating these costs can sometimes be
difficult, particularly if the event is a one-off event such as an outdoor music concert.
The closure of schools and educational establishments due to health concerns can also
impose costs on the economy. However, many of the costs will be captured in other
estimations.
The costs of carers remaining at home because of SDS events will be captured in the
absentee estimation and reduced transactions at commercial establishments will be
gathered in the retail/wholesale sector calculation.
As noted earlier in chapter 6.2, there are different costs associated with SDS, and there
are also different or more appropriate valuation methods for some of these costs –
market or non-market valuation. Table 7 presents a brief overview of some of the costs
covered earlier in this section and appropriate methods of estimating or valuing these
costs. For some costs, such as health or water resources, the total impact of costs may
be estimated using a combination of methods, due to the differing impacts across sectors
and the population.
©Bertknot
on
Flickr,
September
23rd,
2009

140
6.5.3. Off-site benefits
Typically, there are few immediate benefits
offered by SDS events, and in the context
of the overall costs and benefits of SDS,
off-site benefits are usually relatively small
when compared to off-site costs. Benefits
of SDS arise from two main sources –
nutrient deposition on land and nutrient
and mineral deposition in water, particularly
ocean bodies.
SDS dust content can contain soil
nutrients, such as nitrogen, phosphorus
and potassium, as well as organic carbon.
When deposited, these can provide
nutrients to crops or pasture downwind
of the source area. Leys (2002) estimated
that dust deposited after a dust event
contained 0.0034 g/m2
of total nitrogen
and 0.0008 g/m2
of total phosphorus.
Nutrient and mineral deposition in
ocean bodies can provide nutrients to
phytoplankton, which in turn can increase
fish stocks, as phytoplankton are in the
lower levels of the ocean food chain
(Cropp et al., 2005).
The benefits of soil carbon deposition
are more difficult to estimate due to the
need for a value for carbon in the system
where the deposition occurs. The challenge
in terms of estimating the benefits is
determining the overall dynamics of the
food chain and the time for any increase
in phytoplankton to flow through to the
upper levels of the food chain where
economically viable populations of fish are
located. Iron contained in dust can also
lead to increased carbon sequestration by
phytoplankton as well (Blain et al., 2007).
Again, the amount and value of carbon
sequestered is difficult to estimate and
beyond the scope of the current study.
One point to note here is that some
degree of dust movement is an integral
and natural part of the earth system.
This deposition brings benefits as well as
hazards to human communities (Middleton
and Goudie, 2001). Total removal of dust
movement is undesirable and probably
extremely costly in terms of ecosystem
losses.
©Marc
Cooper

Economic activity Cost type: Valuation type:
Direct Indirect Market Non-market
Transport Airline delays and
cancellations
Rail or road delays
due to closures
Usually market-
based
Health Hospital
admissions
Decrease in health
of individuals over
time
Direct expenditures
on health-related
costs
Mortality or
morbidity costs on
society – can use
disability-adjusted
life years or other
measures but not
market-based
measures
Cleaning –
Household and
commercial
Direct cost NA Market costs of
product and time
NA
Commercial or
manufacturing
Loss of sales or
production during
dust event
Reduced, or loss
of, sales due to
inability to get
product to market
or get inputs into
manufacturing
plants
Market costs of
lost sales in both
direct and indirect
cases
NA
Agriculture Loss of marketable
product; delays
in harvesting at
optimal time
Delayed regrowth
of perennial crops,
or loss of product
due to delays in
planting at optimal
time
Market costs of
lost production in
current or future
crops
NA
Water resources NA Dust deposition
in water ways, i.e.
rivers, canals etc.
Cost of dredging or
dust/mud build-up
Losses in services
in the future, such
as water access
or availability;
effect on fish or
other populations
affected by build-
up
Ecosystem
services
Loss of use during
event
Dust deposition in
ecosystem, i.e. on
plants
Loss of income by
service providers
Most costs will be
valued using non-
market techniques,
such as travel
cost or contingent
valuation methods
Table 7.
Examples
of costs and
valuation
methods for
measuring
impacts
on various
economic
activities

142
6.6 Methods to assess
the economic impact
of SDS
6.6.1. Overview of model
types
The assessment of the economic
impact of SDS can be undertaken using
a variety of methods, from relatively
simple accounting-type methods to more
complex econometric or mathematical
programming models (Cochrane, 2004).2
The methods can be categorized as
follows:
• combined econometric and
optimization models – computable
general equilibrium (CGE), partial
equilibrium (PE), or other generic
econometric and simulation models
• linear programming models – Input-
Output (I-O) models
• survey methods and analysis
• accounting-type models
• hybrid models
These models have been used to
measure the economic impact of SDS
or other disasters. Their applicability or
usefulness in assessing the economic
impact of SDS depends on available data,
the type(s) of event, and assumptions
made. Table 8 briefly summarizes each
methodology, data requirements and
analytical skills required to undertake an
economic assessment of SDS using each
methodology.
Computable general equilibrium (CGE)
models have been used to analyse the
impact of disasters on economies but
have not been used to study SDS impacts
(see, for example, Rose and Lim, 2002;
Rose and Liao, 2005). As the name implies,
CGE models are models of a whole
economy, including households, firms and
government (through taxation submodels).
The model is based on the social
accounting matrix (SAM) for that economy.
2 A full comparison and motivation for any one type of model is beyond the scope of this chapter.
Readers interested in a more complete discussion should consult the references provided at the end of the chapter.
A SAM captures all the interactions
between the various industry sectors
within an economy, including households,
firms or businesses, and where necessary,
governments through the impact of
taxation on costs of production and
incomes.
These types of models rely heavily on
the parameterization of the models and
price changes to measure the impacts
of perturbations to the economy, such as
disasters or changes in taxation policy,
and how they affect the whole economy.
However, the impact of SDS on prices or
changes in interactions between sectors,
as measured by the SAM, is difficult
to do given the frequent nature of SDS
events, within a year and over many years
(Cochrane, 2004).
Input-Output (I-O) models, which are
similar to the CGE models, rely on the
SAM to measure the interactions between
industry sectors. As a result, they have
very limited flexibility to deal with changes
that occur within a year – changes which
may not significantly impact interactions
between sectors.
Another problem with CGE or I-O models
in the analysis of SDS is that to measure
the impact, the SAM or parameters of
the model rely on changes from a base
scenario which is perturbation-free.
However, as noted earlier, because SDS
occur frequently within and across years,
identifying a counterfactual base is very
difficult.
One aspect that the SAM – and therefore
I-O or CGE models – does not capture
due to the non-market valuation is the
value of the environment or ecosystem
services, except through transactions
such as cleaning costs or travel costs to
an environmentally sensitive destination.
These types of models do not typically
have the ability to capture the impact on
humans of SDS, either through mortality or
morbidity or changes in the distribution of
wealth or equality.

If measuring the impact of SDS across a
region, either within a country or across
several countries, a model of the region or
each country in the region is required. In
some countries these types of models are
available, for example, studying the effects
of SDS on Beijing, Ai and Polenske (2008)
used a regional I-O model to estimate the
impact of SDS.
Surveys have been used in previous
analysis of the impact of SDS (Huszar and
Piper 1986). Surveys are typically limited
to certain segments of the economy, such
as households or businesses, and may
not capture the interrelationships between
industry sectors.
However, surveys are useful in identifying
specific costs or types of costs, as
shown by Huszar and Piper (ibid.), who
surveyed households and businesses
in the state of New Mexico in the USA
and identified household costs down
to specific categories, such as exterior
painting, landscaping, interior cleaning and
laundry, and automotive damage. However,
Huszar and Piper (ibid.) did not survey
transportation agencies or firms, or public
agencies such as the state Department
of Transport or the emergency services.
Therefore, the costs of the dust storms
may be underestimated.
Tozer and Leys (2013) and Williams and
Young (1999) used an accounting-type
framework to estimate the costs of dust
storms in two Australian states. The
studies utilized the survey data of Huszar
and Piper (1986), adjusted for the situation
and differences in frequency of SDS and
exchange rates, to measure some of the
impacts of SDS.
This approach requires complete
identification of all costs and the ability
to source the required data to enable
full costs of SDS to be measured. Also,
this type of analysis needs to ensure
that interactions between sectors of an
economy are captured. Care needs to be
taken to ensure double counting of costs is
avoided (Cochrane, 2004).
Cochrane (ibid.) identifies one other type
of tool to analyse the impact of natural
disasters, and this is what he terms
“hybrid models”. These types of models
are usually disaster-type, case, country or
region-specific and are criticized for being
somewhat ad hoc.
An example of this type of model provided
by Cochrane (ibid.) is the HAZUS model
that is used to simulate indirect economic
losses from natural disasters such as
floods or earthquakes in the United States.
Cochrane (ibid.) indicates that hybrid
models can also include combinations of
two of the model types discussed earlier,
providing they are well constructed and
allow for sound loss accounting, and
that they are reasonable models to use
in calculating economic costs of natural
disasters.
6.6.2. Data requirements
One crucial aspect of selecting a tool to
analyse the economic impact of SDS in a
country or region is the availability of the
required data. Where possible, the data
used should enable a disaggregation of
impacts by gender, age and disability.
Techniques such as CGE or I-O require
sufficient data to construct the SAM,
therefore data that shows all the
interactions between segments in the
economy is needed. This implies that
significant industry level data are required
as being able to measure the interactions
between sectors and measuring the
substitutability of production across
sectors is a requirement for the SAM. Other
methods, such as the cost accounting or
survey method, do not require as much
data as CGE or I-O, but do still require
significant amounts of data, some of which
can be difficult to identify and collect, such
as household costs, reductions in retail
sales, or consumer willingness to pay for
environmental damage.

144
For the accounting method that uses
survey data – or the survey method
itself – it is necessary to identify the
survey population, and from within that
population, the survey sample. This should
inform the design of the survey, which also
requires a pilot test. Then, the data must be
collected and analysed.
A similar approach is required for the non-
market valuation studies, in that surveys
or other similar research tools need to be
developed to collect the required data to
value environmental or ecosystems loss or
damage.
Impact
methodology
Data
requirements
Analyst skills Strengths of
method
Weaknesses of
method
Applications to
sand and dust
storm impact
analysis
Computable
general
equilibrium
(CGE)
Very high –
need data set
including the
entire economy.
Very high
– need to
be able to
construct
a social
accounting
matrix.
Good for single
event analysis.
Need a control
year.
No applications
to sand and
dust storms.
Has been
applied in
single event
disasters: Rose
and Lim (2002),
California
earthquake;
Horridge,
Madden and
Wittwer (2005),
Australia
drought.
Input-Output
(I-O)
Very high –
need data set
including the
entire economy.
Very high
– need to
be able to
construct
a social
accounting
matrix.
Good for single
event analysis.
Need a control
year.
Ai and
Polenske
(2008), impact
of sand and
dust storms on
Beijing.
Surveys Medium – need
a good response
rate to surveys.
Medium, but
high with
respect to
survey design
and sample
selection.
Simple; easy
for low-skilled
analysts. Can
extrapolate
single events
to multiple
events.
May be costly
to gather
sufficient
quality and
quantity of data
for complete
analysis.
Huszar and
Piper (1986),
impact on
New Mexico of
multiple sand
and dust storm
events.
Hybrid Medium–high. Medium–high
– need skill
to identify
data and data
gaps.
Relatively
simple; can
capture
whole impact,
providing there
are no data
gaps. Can
extrapolate
single events
to multiple
events.
If there are
data gaps or
poor data-
collection, very
poor results.
Tozer and Leys
(2013), Single
event sand and
dust storms in
Australia;
Miri et al.
(2009), multiple
events in
Sistan region
of Iran.
Table 8. Summary
of methodologies,
data requirements
and skills required

6.7 Factors to consider
in selecting ways to
measure economic
impacts of SDS
6.7.1. Challenges to be
addressed
The principal challenge in measuring
the economic impact of SDS is not the
physical, that is, not the type of SDS event
or the geography or geology of a region or
country. The main challenge is ensuring all
relevant and consistent data are identified
and collected, and that the economic
impact is measured relatively accurately.
It must be remembered that any measure
of economic impact will be an estimate.
Any measures of impact will have some
degree of error due simply to the data-
collection and analysis process, and
the time delay for some impacts to flow
through an economy.
The more differentiated economic activity
is within a country, the more data are
required to fully measure the impact of
SDS. One point to consider here is that
SDS may not impact all economic sectors
in an economy or a country due to the
geographic concentration of SDS, thus
reducing the need for a full set of economic
measures or data for the whole economy,
only those sectors impacted.
Another limitation to identifying an
appropriate method of impact analysis
is the available skill set of analysts within
a region and existing economic models.
If a country has the capacity to collect
sufficient data or the skill set to construct
a SAM and therefore a CGE or I-O model –
which would be ideal for a single-event SDS
– then a simpler method, such as surveys
or a hybrid model, is required.
Another determining factor in the selection
of an appropriate method for impact
assessment is the budget available for the
analysis. Undertaking a comprehensive
survey of an economy is an expensive
operation. The amount of data that can be
collected using a survey or set of surveys
may be a limiting factor.
6.7.2. Recommended
approach
Given the diversity of resources to collect
and analyse SDS economic impact data
across countries, the recommendation
here is that a relatively simple approach
be taken. The preferred method is a
hybrid of cost accounting and surveys,
where surveys are used to identify costs
that may not be readily available, such as
household cleaning costs. Another reason
for recommending this method is that it
will allow cross-country comparisons, as all
countries or regions will be using the same
framework.
As noted throughout the preceding
discussion, availability and consistency of
data can be problematic when undertaking
impact analysis, and also when comparing
across events within a country or across
countries. Another issue that arises
with data-collection, and indeed impact
assessment, is that of timescale and
estimating the impact of multiple events
from single-event data.

146
It is recommended that a consistent
method of data-collection be utilized to
ensure valid and relatively accurate data
are collected. This will also allow valid
comparison across countries or regions. A
comprehensive set of guidelines for data-
collection and data sources are provided in
chapter 6.13.
One significant issue with respect to
impact of SDS is related to the effects of
SDS on human health and the attribution
of an SDS event to mortality and morbidity
in the impact region. It is recommended
that research be undertaken to accurately
measure the impact of SDS events on
human health, and that this research
properly identifies the true impact of
SDS on human health. This implies that
research must be comprehensive, beyond
simple correlation analysis of hospital
admissions and SDS events, that prior
health status must be identified and that
demographic variables such as gender,
income, age, household location and
construction must be fully captured in the
data-collection and analysis.
6.8 Benefit-cost
framework for
analysing dust
mitigation or
prevention
6.8.1. Basic construct of
cost-benefit analysis
Benefit-cost analysis (BCA) or cost-benefit
analysis (CBA) is a method of analysis
that is used to compare the investment
value of different projects.3
Cost-benefit
analysis is a form of investment analysis
that takes into account current and future
costs and benefits associated with a
project to estimate the net present value
(NPV) of the project. Using NPV as a basis
of comparison allows decision makers to
evaluate projects that may have different
income or cost flows throughout the life of
a project.
3 A full description of the basics of ‘cost-benefit analysis’ and ‘net present value’ is beyond the scope of this
chapter. Interested readers are referred to Harris (2006), Hanley and Spash (1993) or Robison and Barry (1996) as
starting points for descriptions of the two methods.
An NPV model for a proposed dust-
mitigation programme could take the
following form:
Where:
• C0 is the initial cost of the mitigation
investment
• Rt and Ct are the revenues and
costs generated from the mitigation
programme
• t = 1 to T are the number of time
periods in which the investment is
measured.
• p is the discount rate and measures
the time value of money
For example, NPV can be used to compare
two projects:
• one with a high initial cost and a long
period before income is received, such
as planting a forest
• one with smoother income and cost
flows, such as an annual cropping
programme
The main difference between CBA and NPV
investment analysis is that CBA extends
NPV by adding non-market information to
more extensively capture the true value
or full costs and benefits of a project
(Hanley and Spash, 1993). This extension
allows policy and decision makers to
understand the implications of including
non-market information, such as the value
that environmental or ecosystems services
have, on a project’s total benefits and
costs. One aspect that is not captured in
CBA is the equality or distribution of wealth
in different socioeconomic classes and
how proposed investments affect these
different groups (Wegner and Pascual,
2011).
CBA proceeds in a series of stages in a
process which is fairly linear, although all
stages may be overlapping in some sense.

Stage 1 is simply identification of the
project, where:
• the first component is the resources
that will be reallocated in the project
– this includes financial and physical
resources
• the second component is
identification of the impacted
populations, including both positive
and negative impacts
Stage 2 is to identify the impacts of the
project on reallocated resources. These
impacts can be physical or financial – the
reduction in dust emission and the costs
of this reduction at the emission source, as
well as the changes in the environmental
services that occur because of the project.
Stage 3 involves identification of the
economically relevant impacts. This
may sound redundant, as most costs or
benefits from a project will be economically
relevant, but a major discussion in the
economics literature concerns the
inclusion of transfer or compensation
payments. This will be discussed in more
detail in a later section, but a brief précis on
the context of SDS and compensation may
be helpful.
For example, if a dust-mitigation project
generates a net-positive benefit across
a region, this indicates that the project
is feasible, even if one of the impacted
populations is negatively affected and
another population is positively affected.
The positively affected population could
compensate the negatively affected
population to balance impacts. However,
in the context of CBA, this is considered a
transfer payment and is not included in the
“benefits” of the project.
Stage 4 is the physical quantification
of impacts. This stage is critical, as
quantifying the timing of these impacts is
also measured. At this stage, if necessary,
uncertainty can be included in the
calculation of impacts, either physical or
financial.
Stage 5 is the valuation of the impacts. At
this stage, valuation includes taking into
account the time value of money from
Stage 4 when impacts occur. “Time value
of money” takes into account the fact that,
in theory, money loses value over time, so
a dollar today is worth more than a dollar
tomorrow. As a result, investors or project
managers prefer higher returns earlier in a
project than later.
6.8.2. Costs and timing of
costs in cost-benefit
analysis
Costs incurred and timing of costs
depend on selected practice. For
example, undertaking an annual cropping
programme to provide some surface
cover to reduce soil erosion will incur
annual costs for seed, fertilizer, chemicals
and pesticides (if used), some form of
mechanization (machinery or draught
animal) for ploughing, sowing and – if
necessary – harvesting and labour
required for all activities including sowing,
harvesting, storage and transport. All these
costs will be incurred each and every year
of the farming programme.
If the preferred choice is to use some form
of forestry for dust mitigation, a large
investment is required in the first year
for land preparation and tree planting. A
lower cost may be incurred in the year
immediately after planting the trees
for activities such as weed control or
irrigation of the young trees to ensure their
survival. In subsequent years, very few
costs will be incurred, as the trees require
little maintenance, assuming long-term
irrigation is not necessary. The level of
maintenance costs incurred will depend on
whether the forest project is a permanent
forest or a harvested forest.
If the forest is to be permanent (not
harvested), little maintenance is needed
beyond the initial year or two. If the forest
is to be harvested and replanted, then
regular maintenance will be required for
activities such as trimming and thinning to
ensure a profitable crop can be harvested.

148
6.8.3. Discounting and the
discount rate
When analysing investments over time,
it is necessary to convert future costs
and/or benefits to current values so that
comparison of investments is undertaken
in a standard value. To undertake the
conversion, future costs or benefits
are “discounted” by the discount rate,
The discount rate is a measure of the time
value of money. Higher discount rates
imply that the time value of money is high,
so income is preferred earlier rather than
later in the life of an investment. A discount
rate of zero implies that there is no time
preference for income.
Selection of the discount rate depends
on the risks involved, the current inflation
rate, cost of money (the interest rate),
and whether there is an additional
consideration of the social rate of time
preference (Harris, 2006). The selection of
an appropriate discount rate for analysing
a mitigation project is a critical decision
and should not be made lightly. Selecting
an inappropriate discount rate for project
comparison can make a project appear to
be more or less preferable, as the discount
rate affects the current value of costs
and benefits over the life of a project, and
the current values change with different
discount rates.
6.8.4. On-site benefits of
dust mitigation at the
source
On-site benefits can come from several
sources. The first is relatively simple – the
crop or timber can generate income, if that
is the practice selected. However, timing
of the income will differ depending on the
practice chosen.
For an annual cropping programme,
income will be received every year, where
income will be a function of price and yield.
For a forest, the majority of income will be
received when the forest is harvested, with
potentially some income in years when the
forest is thinned.
The second source of benefit is through
costs saved in the cropping programme
through reduced soil erosion that can
maintain or even increase crop yields, and
loss of soil nutrients through dust emission
to the atmosphere. In some cases, there
may appear to be no obvious on-site
benefits, but there may be some less
obvious benefits. For example, a sand dune
stabilization project may appear to have
no on-site benefits, but if the stabilization
project reduces dune encroachment on a
road, then there is an on-site benefit.
6.8.5. Off-site benefits of
dust mitigation at the
source
Off-site benefits of dust mitigation are
numerous, with the benefits contingent
on the impact region, economic and
environmental infrastructure and activity
within that region, and the level and type
of dust mitigation achieved in the source
region. As discussed in earlier chapters,
SDS affects many sectors of the impact
region, thus any reduction in either
frequency or severity of SDS or the amount
of dust deposited during SDS may be
beneficial. However, measuring the benefits
can be difficult. Unless SDS are completely
eliminated, there will still be some negative
effects on the impact region.
6.8.6. Off-site benefits of
dust mitigation in the
impact region
Different types of mitigation processes
can be undertaken in the impact region to
reduce the effects of SDS. These include
early warning systems or mechanical aids
such as air filtration systems or improved
building construction to reduce dust
entering buildings.
Again, it may be difficult to measure
impact, as only those segments of the
population that are affected by SDS may
take advantage of the early warning
system or improve the construction of their
home so as to reduce the impact of dust
on their family. However, there is some
indication that early warning systems for
vulnerable segments of the population can
reduce the effects of SDS.

Tozer and Leys (2013) report that during
the Red Dawn event in 2009, affecting
Sydney and other parts of eastern
Australia, there was no significant increase
in hospital admissions. They attributed this
to a health warning system that sent SMS
messages to those in the population with
respiratory problems who had subscribed
to the system. One point to remember here
is that mitigation programmes or early
warning systems in the impact region do
not reduce the amount of dust impacting
the region, they simply reduce the impact
of dust on the region.
6.9 Non-market valuation
methods for inclusion
in cost-benefit
analysis
The major challenge in CBA is estimating
costs and/or benefits for attributes that
may be impacted by SDS but have no
identifiable market value or method
to value them using market-based
techniques, such as environmental
benefits or ecosystem services. There
are two classes of non-market valuation
techniques: (i) revealed preferences and (ii)
stated preferences.
• Revealed preferences, as the name
implies, are modelled on actual
behaviour, typically purchase or
demand behaviour, that is, how and on
what consumers spend their money
(Just, Hueth and Schmitz, 2004).
• Stated preferences are based on what
consumers say they are going to do,
usually shown by survey responses
(ibid.).
Within these two classes are different
methods for revealed preferences. There is
hedonic pricing and the travel cost method,
and for stated preferences, contingent
valuation and choice modelling.
A final category of valuation is to use some
form of experimental analysis to identify
the “value” for the “service” provided. Each
of these different methods can be applied
to various non-market issues arising in
CBA of SDS mitigation strategies.
The literature on valuing ecosystem
services – or for other system attributes
which have no discernible market – is vast
and comprehensive. See, for example,
Ninan (2014) and the references and
examples contained therein. From
the perspective of CBA, the following
techniques are presented as potential
methods of valuation. As a full explanation
of the techniques is beyond the scope of
this chapter, readers are directed to the
references section as a starting point for
further information on methods discussed
herein.
6.9.1. Hedonic pricing
Hedonic price analysis treats a “product”
not as a single product but as a collection
of attributes, qualities and characteristics
which consumers desire and for which they
are willing to pay. The price a consumer
pays for a product reflects how they “value”
each attribute of that product (Costanigro
and McCluskey, 2011).
When a consumer purchases a car,
they are purchasing the set of attributes
contained within the car – safety features,
colour, engine capacity, number of seats,
brand and reputation, among other
attributes. Some car brands are more
expensive, such as Lamborghini®, and
some are relatively inexpensive, such as
Nissan®.
Consumers will pay more for the
Lamborghini® because they are willing
to pay more for the set of attributes
associated with that brand rather than the
Nissan®, even if the primary rationale for
a car as a means of transport is the same
for both brands. The application of hedonic
pricing in CBA of SDS is relatively limited
as there are few “products” involved in SDS
mitigation that could be analysed in this
way.

150
6.9.2. Travel cost method
The travel cost method uses consumer
behaviour to measure the value consumers
place on “goods” such as environmentally
or culturally significant sites (Hanley and
Spash, 1993). The method measures
how much consumers are willing to pay
to “travel” to a site, where paying includes
travel costs, such as flying or driving,
entry fees, accommodation costs, capital
equipment (for example, camping gear),
and on-site expenses such as food and
drink. By summing the travel costs across
the expected number of visitors to a site,
the “value” of the site can be estimated.
6.9.3. Contingent valuation
method
The contingent valuation method (CVM)
uses surveys of consumers, usually in
some form of controlled experiment. They
are asked how much they would be willing
to pay for a particular product or service
with specific attributes. In ecosystem or
environmental analysis, “consumers” are
asked how much they would be willing
to pay for the services provided by the
ecosystem or environmentally sensitive
area, or alternatively, they are asked how
much they would be willing to accept for
the loss of the services provided (Ninan,
2014).
6.9.4. Choice modelling
Choice modelling is similar to CVM, except
that instead of valuing the service provided
by the ecosystem or environmentally
sensitive area, “consumers” are asked to
value the specific environmental attributes
of the area, then to choose between the
alternatives that provide varying levels of
the attributes (Ninan, 2014).
6.9.5. Experimental analysis
This method is used to address some of
the shortcomings of the stated preference
methods, such as the differences
between what people say in the surveys,
to determine willingness to pay and
their actual behaviour, referred to as the
“hypothetical bias”. In some experimental
analyses, consumers use real money to
determine a more accurate WTP. This can
remove some of the hypothetical bias
that may be apparent in survey responses
in which there are no consequences for
decisions made.
6.10 Examples of cost-
benefit analysis for
dust prevention or
mitigation
There are numerous examples of SDS
mitigation practices or restoration
projects which are intended to address
anthropogenic causes of SDS. The
following are examples to demonstrate the
application of CBA in measuring the costs,
benefits, timing and location (on-site or off-
site) of these costs or benefits, and other
implications of the mitigation practice. The
examples do not provide a comprehensive
set of potential solutions.
©Thomas
Wanhoff
on
Flickr,
September
23rd,
2009

Any mitigation or restoration project needs
to take into account local conditions such
as soil type, water availability, aspect or
topography on which to base the project
design and the CBA process.
The four different scenarios are:
1. Land/soil surface mitigation through
planting crops, re-establishing pasture
or creating new pasture
2. Reforestation, including planning
perennial tree crop
3. Off-site mitigation in the impact
region.
4. Doing nothing
Note that “doing nothing” provides a
comparison against the other three
measures listed.
Each scenario will have a unique set of
incomes and costs throughout the life of
the project, which will affect the NPV of
the project. Each scenario will also have
different sets of non-market issues and
income distributions.
One point to note here is that some of
the following practices could generate
benefits through carbon sequestration.
However, due to a lack of well-established
markets, these benefits may not currently
be able to be measured, although they can
be considered when markets are more
established.
6.10.1. Land/soil surface
mitigation
Pasture – No livestock grazing
If pasture or grasses are sown and no
livestock are to be grazed then the on-site
costs will be for the pasture seed and
fertilizer, and any associated machinery
or labour costs. The total costs of the
pasture sowing project will depend on the
area sown but will typically be incurred
in the first year of the project, then some
maintenance applications of fertilizer may
be necessary in later years, and possibly
permanent fencing to keep grazing animals
out, if desired. There will be no on-site
benefits except for the reduction in soil
erosion over time.
Off-site benefits, which include the
reduction of costs due to SDS, will depend
on the area sown and the reduction in dust
emissions from the source area (we also
assume that there are no other mitigation
practices undertaken in the impact region,
thus there are no additional costs incurred
in the impact region). One point to consider
is that the full potential for reduction in
dust emission may not occur in the first
year of pasture development, as the
pasture may take some time to establish
and cover all exposed soil surfaces.
Pasture – Livestock grazing
The second option to allow grazing of the
pasture once established. This will have a
benefit in the source region, with income
being generated by herders that use the
land. If the “right” mix of pasture species
is sown, soil erosion may be reduced and,
in some cases, reversed. Similar to the “no
grazing” approach, the benefits or reduced
costs will depend on reduction in the
amount of dust emitted from the source
region.
In this scenario, pasture costs will be
incurred in the initial year, and costs
to purchase livestock – if not already
owned – will also be incurred. Pasture
maintenance and animal-related costs will
be incurred in all years subsequent to the
establishment year. Benefits will occur in
each year that SDS impacts are reduced.
Annual cropping
An annual rain-fed planning system
of one or several crops to provide soil
surface cover or reduce the amount of soil
lost through wind erosion can increase
incomes in the source region and reduce
costs in the impact region. In this scenario,
the on-site costs may include crop seed
and fertilizers, herbicides or pesticides, if
needed, as well as labour (for sowing, crop
maintenance and harvesting), machinery
costs (if machinery is used) and costs of
transport for taking a crop to market.

152
Assuming some or all of the crop is
marketed, crop producers in the source
region will benefit from the income. Both
costs and income will be incurred and
received in every year of the project. Similar
to the pasture systems, benefits in the
impact region will be due to the reduction
in dust affecting the impact region. This
reduction will be dependent on the amount
of dust mitigated.
6.10.2. Reforestation
Non-harvested permanent forest
An alternative to annual cropping or animal
enterprises is to establish some form of
perennial crop, such as an agroforestry
activity, or a perennial tree crop, such as an
orchard or other plantation-type operation.
The costs and benefits in these types of
enterprises are very different to annual
systems, in that a high establishment
cost is incurred in the first year of the
project, with no or very limited income in
early years, while the perennials become
established.
For a non-harvested forest, a very large
investment cost is incurred in the first
year of the project with the purchase
and planting of trees, land preparation,
and, if necessary, irrigation or some
other form of water application system
to ensure trees will grow. One significant
cost in this operation will be labour for
land preparation, tree planting and forest
maintenance. Some costs will also be
incurred in the years immediately after
establishment to ensure the forest grows
as desired and trees grow towards
maturity. Given that the forest is not to
be harvested, there will be no income
derived from the forest itself, but other
income may be generated if the forest is
open to recreational activities, such as
camping, hunting, walking, or harvesting
mushrooms or wild plants.
The dust mitigation benefits of this type of
practice will vary over the period until the
forest becomes fully established. In the
early years of the forest, dust mitigation
may be relatively low as the trees will not
provide sufficient wind speed reduction to
significantly lower dust emission. As the
forest matures, the reduction in wind speed
will reduce erosion and subsequently
reduce dust, which may be deposited in the
impact region. In other words, the off-site
benefits will be low in early years then
steadily improve until the forest reaches
maturity. Again, the scale of benefits will be
contingent on the level of dust reduction
due to the forest.
Commercial harvested forest
The initial costs of a commercially
harvested forest will be similar to the
non-harvested forest, as land needs to be
prepared and trees planted. However, more
costs will be incurred in subsequent years,
as forest maintenance is required to ensure
the harvested lumber generates higher
income.
The other main difference is that income
will be generated when the forest is
harvested, and there is also potential for
a small income to be generated from
either sales of trees thinned to ensure high
quality trees will be harvested at the end
of the forest’s lifespan or from charging
for access to the forest to harvest wild
plants. For the forest to continue providing
a dust mitigation benefit, land preparation
after the forest is harvested needs to
incorporate dust-reduction measures and
the associated costs.
The dust mitigation benefits in the impact
region will also be slightly different than
for the permanent forest. There may be
periods during the forest establishment
period when mitigation benefits are
reduced while the new forest grows to a
size in which dust emission reductions can
be observed in the impact region. However,
as with all dust mitigation strategies, the
level of dust reduction in the impact region
will depend on the scale of the forest
project.
Commercial perennial fruit or nut
orchards
In this scenario, the orchard is a
commercial operation producing fruit
or nuts. A higher initial cost would be

expected as more infrastructure, such as a
more extensive irrigation system, may be
required, and fruit trees would be expected
to be more expensive than forest species.
Depending on the fruit, nut or mix of fruit
and nut trees, the income flow will vary
somewhat, but it would be expected
that the orchard would begin to provide
economic levels of production within
three to four years of establishment. This
income would grow until the trees reach
a mature size and steady production level
by about year six or seven after planting.
The cost structure for an orchard will also
be different, as costs will be incurred in all
years after establishment, even in years
when the trees are not producing a crop,
as they still need care and maintenance to
ensure maximum possible crop production
when they do mature.
An orchard will mitigate dust through
reduced wind speed and soil erosion.
However, similar to the forest options, the
level of mitigation will be low in the years
before the orchard reaches maturity. Again,
the level of dust mitigation in the impact
region will depend on how much dust
emission reduction occurs in the source
area due to the orchard.
One point to consider here is that it is
possible to combine any of the options
listed above to reduce dust emission from
the source region. This may be a preferable
option in regions where livestock raising is
a main source of income, as trees, in the
form of wind breaks or small forests, can
be used to reduce wind speed across the
soil surface and allow the establishment of
pastures or annual crops. If developed with
appropriate tree species, forests can also
provide wood for fuel and dust mitigation
if the trees can be coppiced for wood
supply. Also, forests or crops can provide
non-timber or non-food products such as
medicinal products or raw material for
further processing, such as tree saps.
6.10.3. Off-site mitigation
Governments, occupants or businesses in
the impact region of SDS can undertake
practices to reduce the impact of sand or
dust on their region, lives or businesses.
However, the key point here is that any
practice will not reduce the level of dust
deposited in the region, as the dust
originates at the point of origin.
Examples of dust mitigation practices
include early warning systems. Warnings
enable vulnerable segments of the
population and important sectors of the
economy to take action to reduce the
impact of SDS on that segment or sector.
In anticipation of warnings, building
improvements, such as air filtration
systems or installing tighter fitting
windows and doors, can be used to reduce
dust penetration into buildings or houses.
Early warning systems, in some form,
can reduce the impact on important
sectors. For example, in the transport
sector, airlines can initiate programmes
to reschedule or cancel flights before
passengers arrive at the airport to board
their aeroplane, thus reducing the costs of
cancellation or incurring accommodation
and other costs due to flight cancellation.
Similarly, for road transportation, early
warnings can be provided to those people
planning on driving, and this may reduce
road accidents due to the poor visibility
caused by dust. These warning systems
ensure that vulnerable segments of the
population – such as those with respiratory
or cardiovascular problems – remain
indoors or in locations where dust levels
in the air are relatively lower to reduce the
probability of more serious health issues
arising.
Construction or modification of buildings
to reduce dust penetration is an option
that has been used successfully in
some regions of the world to reduce the
impact of dust on processes or people.
For example, Samsung® in South
Korea modified their buildings’ housing
manufacturing processes to reduce
the number of faults in components
manufactured during SDS events (Kim,
2009).
The costs of the mitigation process will
depend on the type of process. In the

154
case of early warning systems, it would
be expected that governments would
bear most of the cost, and the costs
would depend on the type of system
designed and implemented and the types
of warnings given to the population. When
individuals or firms choose to construct
or modify buildings, then it would be
expected that individuals or firms would be
responsible for the costs.
As for the benefits of these practices, these
would depend on the reduction in problems
caused, such as reduced mortality and/
or morbidity, road accidents or flight
cancellations. Through reduced costs,
the benefits could also flow to private
corporations, such as airlines, as the
costs of flight cancellations or aeroplane
repositioning may be reduced, due to the
early warning systems developed and
implemented. As noted above, the benefits
of these types of approaches will be to the
segments of the economy mostly at risk.
There may be no reduction in other areas,
such as road cleaning, due to there being
no reduction in dust emission from the
source area.
6.10.4. Doing nothing
While this may seem a trivial option, it is
still an option in some regions or countries,
simply because they may not perceive any
benefits from incurring costs to reduce
SDS, or they may think that the costs of
reducing SDS far outweigh the benefits.
Another issue that arises here – and which
will be discussed in more detail in a later
section – is that of transboundary issues,
with respect to the distances dust travels
from source region to impact region.
In the above discussion, most mitigation
projects were in the context of
anthropogenic sources of dust and can
include water management projects.
However, they may also include natural
sources of dust that may be causing
significant off-site costs, although these
types of projects would need to consider
natural cycles and what the implications
would be if the source was mitigated.

©Thomas
Wanhoff
on
Flickr,
September
23rd,
2009

156
6.11 Issues in cost-basis
analysis
There are several issues within the context
of dust emission and mitigation practices
that also need to be addressed in a broader
context than the confines of the practices
discussed in chapter 6.10. These include
the:
• distributional effects of costs and
benefits and the distribution efficiency
of wealth and income of the proposed
practices
• transboundary issues, particularly with
respect to costs, benefits and potential
compensation in the source and
impact countries or regions
• land tenure issues, with respect
to land being accessed or used in
mitigation practices
6.11.1. Distributional
efficiency
When analysing the results of a CBA for a
proposed mitigation strategy, the benefits
may outweigh the costs. Therefore,
the strategy – from a purely economic
perspective – is worthwhile (as the project
is allocatively efficient). However, from a
wealth distribution efficiency perspective,
this may not be the case. For example, if
the practice requires that previous users
of the land be displaced, and their sources
of income or wealth are reduced, then
they may suffer losses of either income or
wealth.
Even if there are sufficient excess benefits
to compensate for this loss in the
project, there may be no compensation
forthcoming from within the project. This
argument also holds if the “wealth” of the
society is increased by the project, yet
more landholders who are displaced have
their wealth reduced after the project than
those in the impact region who may have
their wealth increased due to the project,
resulting in a redistribution of wealth to the
detriment of those in the source region.
6.11.2. Land tenure issues
One important factor contributing to the
success or failure of a mitigation project
relates to land tenure, and this is also
related to the previous point regarding
wealth distribution. Land tenure is
important, as it has implications for the
incentives to be provided to landowners
to undertake any dust mitigation project
proposed. For example, if a project requires
that land be taken out of some form of
production for a number of years, and that
land is privately owned, then the landowner
would need to be compensated in some
form for the loss in income.
One type of land ownership that
could create some issues in terms of
desertification and dust mitigation is
that of “commons”, or common property,
where land may be owned by government
but access is unrestricted. Commons
and access to commons can lead to the
problem identified as “the tragedy of the
commons” (Hardin, 1968).
In this research, Hardin (1968) discusses
the implications for unlimited or
unrestricted access to common property
using a grazing common as an example.
Without restricting access to the common,
individuals will graze their own livestock
without consideration of the behaviour of
others, which in turn leads to overgrazing
and degradation of the commons, which
in the long run has a detrimental effect on
everyone.
In terms of desertification and dust
mitigation, commons may be a source area
of dust emission due to overgrazing or the
removal of tree cover for wood for fuel.
Reducing access to users of the land may
lead to reduced income or reduced wealth,
as farmers may have to reduce stock
numbers due to limited access to grazing.
In terms of dust mitigation projects, if
part of the commons is to be utilized in a
dust mitigation project, the question then
arises as to what happens to those who
were accessing the commons. Will they be
compensated? If the area of the commons

is reduced, will access also be reduced to
ensure overuse does not occur?
Using a simple example may help in
understanding the problem. Assume there
is a commons of 1,000 hectares and that
1,000 sheep – owned by many farmers
– graze on the commons, therefore the
stocking rate is one sheep per hectare.
If a dust mitigation project reduces the
commons area to, say, 900 hectares,
there are one of two potential outcomes
for the farmers grazing their sheep on the
commons: (i) the same number of sheep
graze the reduced area, increasing the
stocking rate to 1.1 sheep per hectare or (ii)
the number of sheep is reduced to 900,
to maintain the stocking rate at one
sheep per hectare.
The questions that arise here are:
• How do policymakers reduce the
number of sheep by 100?
• How do they do that equitably?
• Do policymakers then allow the extra
stocking rate and potential overgrazing
in the commons?
6.11.3. Transboundary issues
– costs, benefits and/
or compensation
As noted earlier, transboundary issues are
a common problem with SDS events, as
dust can travel vast distances, crossing
many national borders from the source
to the final deposition region. Addressing
or considering transboundary concerns
in determining both the impact of SDS
to begin with – and what the process
may be for determining the process to be
employed in developing and implementing
dust mitigation strategies – is critical to the
success of any mitigation practice.
For example, if dust is emitted from one
region, without affecting that region except
through the loss of soil and soil nutrients
(as discussed earlier), then that region
may not be willing to undertake mitigation,
due to the costs of the proposed work,
with potentially little benefit to that region.
However, the countries in the impact
region may offer to fund dust mitigation
programmes, as there is a benefit to the
countries providing the funds through a
reduction of the cost of dust impacts.
One important issue with respect to
transboundary issues is that of national
sovereignty and how costs, benefits and
compensation may affect sovereignty.
For example, one nation that may be
affected by dust may offer to help pay
for dust mitigation in another, with the
aim to reduce the effects of dust on the
population of the donor country. This
may need to be done in a manner which
achieves the desired goal for the donor
country but does not impinge on the
recipient country’s sovereignty. These
types of issues could be addressed with
tools such as debt-for-nature swaps
(United Nations, 1997), whereby a country
(or countries) in the impact region could
reduce a source country’s debt in exchange
for that country undertaking a sand or dust
mitigation strategy to reduce emissions.

158
6.12 Conclusions on cost-
benefit analysis
The basis of CBA for measuring
dust mitigation projects is relatively
straightforward. However, there are several
issues that need some structure. The most
significant of these is non-market valuation
– and selecting the most appropriate
method of non-market valuation to
measure costs and/or benefits in dust
mitigation projects. In the case of the
different costs and benefits of a mitigation
project, there will more than likely be one
more appropriate method of non-market
valuation, but the most appropriate method
will vary with the type of non-market
problem being measured.
For example, ecosystem services can be
measured using different methods, such
as the travel cost or contingent valuation
methods. The selection of method is
somewhat determined by the main “user”
of the ecosystem service. Thus, there is
no definitive recommendation as to the
“most appropriate” method of measuring
non-market valuations across all types
of costs and benefits, but researchers
are encouraged to consult the extensive
literature on non-market valuation
techniques applicable to the type of cost or
benefit being measured.
Also, as noted above, the selection of an
appropriate discount rate is critical to
measuring the net value of any mitigation
project. The key recommendation here
is that the discount rate should include
investment costs and societal values
– attached aspects of the mitigation
projects. This is particularly important
when measuring the costs and benefits
of projects that impact or are impacted by
non-market factors, such as ecosystem
services or cultural locations.
The other main issue with respect to CBA
is that of compensation and distributional
efficiency. However, again, there is no
definitive recommendation, as most of
these issues are dependent on the affected
population and on country policies. It would
be preferable for distributional efficiency
be taken into account when determining
compensation or other effects of dust
mitigation projects on the populations
of the source or impact regions.
Recommendations on transboundary
costs, benefits and compensation are
also not made due to factors such as
national sovereignty and determination of
appropriate methods for estimating costs
or payments in dust mitigation projects.
©Rajiv
Bhuttan
on
Flickr,
August
18th,
2013

Box 12. Integrating gender into the cost-benefit analysis
process
Gender considerations: A cost-benefit analysis can disaggregate costs and benefits
according to different groups, including men, women, youth and people with disabilities,
to better understand who incurs the costs and who enjoys the benefits from specific
measures. A good gender analysis that identifies expected costs and benefits to men and
women is a prerequisite for being able to value them on a disaggregated basis.
Why do it?
A cost-benefit analysis can help inform decisions about whether to proceed with an
activity, decision or project and/or choose which option to implement. It can be particularly
valuable for advocacy and communication to involve decision makers in finance and
planning to demonstrate the expected social and economic returns associated with a
project (i.e. for every $1 invested, how much society will benefit). A good cost-benefit
analysis can expose the real (and sometimes hidden) costs facing women (for example,
in terms of time spent working), and by demonstrating the economic return on these
initiatives to society, support arguments for investing in capacity-building and support to
women. Consideration of distributional issues within a cost-benefit analysis framework
is also vital in terms of assessing the feasibility of options. If one particular group is
disadvantaged by a proposed option, they are unlikely to support the initiative, which will
undermine the achievement of results. Consideration of distributional issues therefore
provides invaluable information on how project design should be adjusted to account for
these factors.
When to do it?
A cost-benefit analysis can be used at various stages during the programme or project
cycle:
• During the solution analysis and design phases, it can help inform the design of
the project proposal and appraise the worth and feasibility (or otherwise) of the
proposal(s).
• During implementation, it can check that the project is on track and inform any
project design refinements and adjustments for the remainder of the project
period.
• As part of an evaluation at the end of the project period, it can evaluate its
performance or success. This can support transparency and accountability
in reporting on how well funds have been spent and learning about whether a
project
(or that type of project) is worthwhile and should be replicated.
Entry points for gender analysis
At the heart of the consideration of gender within a cost-benefit analysis framework is
the treatment of equity and distributional impacts. The basic measure of overall benefits
in a cost-benefit analysis reflects economic efficiency: $10 of benefits accruing to a
poor farmer are treated the same as $10 of benefits to a wealthy hotel owner. In reality,
societies commonly give greater weight to gains by disadvantaged groups. Consideration
of how gains and losses are distributed is vital to ensuring that social equity is considered
alongside economic efficiency.
In a cost-benefit analysis, the value of costs and benefits is determined by people’s
willingness to pay for (or how much they would pay to avoid) a good or service.

160
In reality, the willingness to pay is affected by the ability to pay. This means that the
valuation of costs and benefits is based on the current ability of society to pay, or in other
words, the current distribution of wealth in society, including existing inequalities in that
wealth distribution.
A cost-benefit analysis is one tool that can feed into the decision-making process. Its
results should be considered alongside other tools that examine equity and distributional
issues in more detail.
Steps to incorporate gender into the cost-benefit analysis process
1. Determine the objectives of the cost-benefit analysis
Ensure that all relevant stakeholders (including men, women, elders, youth, children and
people with disabilities) have fed into the decision-making process on which options to
assess. Whose priorities are represented?
2. Identify costs and benefits – with and without analysis
When identifying the different costs and benefits and based on a good understanding of
the underlying situation and problems, ensure that information on the distribution of those
costs and benefits is captured and documented.
3. Measure, value and aggregate costs and benefits
When measuring, valuing and aggregating costs and benefits, ensure that no detail
relating to the distribution of costs and benefits is lost.
4. Conduct sensitivity analysis
A sensitivity analysis tests the results of a cost-benefit analysis for changes in key
parameters about which we are uncertain (for example, rainfall). If a sensitivity analysis
alters the distribution of costs and benefits significantly, ensure that this information is
captured.
5. Consider equity and distributional implications
This section should expose any equity or distributional issues related to the costs and
benefits of different options and how they might affect the feasibility of the project.
Possible approaches for maximizing benefits accruing to particular groups, including
women, and measures for addressing any groups that are disadvantaged by the proposed
options should be discussed.

Adapted from Vunisea, Aliti and others (2015).
The Pacific gender & climate change
toolkit. Secretariat of the Pacific Community.
Available at https://www.pacificclimatechange.
net/document/pacific-gender-climate-change-
toolkit-complete-toolkit. Accessed on 17 July
2019.
6.13 Data-collection
for assessing the
economic impact
of SDS
6.13.1. The need for good
data
Good data are the key to assessing the
economic impact of SDS. This data needs
to include gender, age and health status of
the individuals covered in any assessment.
The challenge for gathering good data
is that some of the impacts of SDS are
difficult to measure directly, such as
household cleaning or impacts of mortality
and morbidity in the population. Another
challenge that arises is that of duration
and frequency of SDS events, which makes
estimating costs more difficult, as some
costs are ongoing, and it is sometimes
hard to clearly define costs incurred for
each event. There are numerous sectors
impacted by SDS events and the timing of
some events can be especially costly, such
as an event that occurs during flowering
of a perennial tree crop or annual crop,
reducing fruit set or total yield of a crop.
One of the challenges for data-collection
for the purpose of measuring the impact of
SDS is the timescale of data measurement.
For example, given the infrequency of
major dust storms in Australia, Tozer and
Leys (2013) reported the impacts of a
single major dust storm. However, as noted
above, SDS events in other regions of the
world occur on a more frequent basis,
thus possibly making data-collection more
difficult.
The other challenge for measuring impact
is the determination of the effect of
frequency of SDS in any one year on the
overall economic impact. For example,
data collected for one year in which there
were few SDS events may underestimate
the average economic impact across
time and overestimate the impact if the
data are collected in a year in which there
were more frequent events. Thus, the
challenge of scaling up or down due to
timescale and frequency of events needs
to be considered when analysing data to
measure the economic impact of SDS.
The number of sectors impacted by SDS
throughout a year will depend on the
major economic sectors in each country,
where and when SDS events occur, and
the geography and location of major
infrastructure throughout a country. For
example, a landlocked country will not have
a port sector, thus, sea transportation will
not be affected. Also, many countries that
have major industries – such as oil and gas
exploration and extraction or electronics
manufacturing – could face significant
costs of SDS if these industries have to
cease production due to SDS events.
6.13.2. Types of data
required for each
sector
Agriculture
Annual crops – Crop losses due to sand
or wind blasting can be a complete loss of
crops in a particular region or a reduction
in yield due to partial losses. To measure
these types of impacts, ascertaining
areas of all crops – or the most significant
crops – in a region or country is necessary.
Also necessary is a method to compare
yield losses in the cases where yield was
affected.
Perennial crops – Similar to annual crops,
but there may also be a longer-run effect
on some perennial crops if trees or plants,
such as Lucerne/alfalfa crowns, are
damaged.
Animal production – This can be affected
in several ways. If the system is using
animals for milk production, there may be a
reduction in milk produced during the SDS
event, thus costing the producer income
with no compensatory reduction in costs.

162
The SDS event may lead to the loss, either
through death or animals fleeing the SDS
and the producer not being able to locate
them afterwards, so there may be a loss
in terms of a reduced number of animals.
The final loss for animal producers would
be through lost, destroyed or damaged
feed stocks, either pasture or forage crops.
Measuring these types of variables will
be difficult, but if we can capture animal
losses, that will at least be a start.
Transport
The transport sector is one of the
economic sectors most affected by an
SDS event and depending on the transport
infrastructure in a country, the costs can be
substantial.
Air – The airline industry is most affected
due to airport closures leading to
cancellation, delay or diversion of aircrafts.
This translates into costs for airlines and
passengers. The minimum data needed
for this are the number of aircrafts delayed,
diverted or cancelled at each location.
These may be sourced from the national
department that handles air traffic or from
the airlines themselves. If possible, the
number of passengers affected would be
really useful, and if possible – but highly
unlikely – the costs incurred by the airlines
due to the SDS event(s). Also, if possible,
the costs of cleaning airport facilities,
especially runways and taxiways, would
be useful data. One good source of data
for estimating the cost of aircraft delay is
Cook and Tanner (2011). This research is
focused on air traffic control delays but
contains numerous estimations of costs
for aircraft delay and for passenger and
crew costs.
Sea/water – The impacts and costs in
this sector will be due to different factors,
depending on the aspect considered.
For port operations, such as loading
and unloading of ships, there could be
delays caused by the SDS event(s), and
in this case it will be necessary to know,
if possible, what the costs of delayed
loading/unloading are. For ferry operations,
it is necessary to know the number of
ferries delayed or cancelled and the
number of travellers affected. If possible,
finding out how travellers pay for their ferry
fare would be useful.
Land – The costs incurred in the land
transport sector are due to three separate
impacts: road closures, road cleaning
and road accidents. Road closures and
traffic reduction data may be sourced
from the department responsible for road
or transport. The impact of closures and
similar impacts will usually be relatively
small unless a major highway is closed for
a significant amount of time.
Road cleaning costs will depend on
whether this is undertaken and where road
closures are the source of data, the case
may be the same.
Traffic accident data are necessary to
estimate the costs of injury or death due
to accidents. However, it is important to
make sure that the accidents occurred
during a period of SDS or as a result of
low visibility caused by SDS. The source
of data for traffic accidents may be the
emergency services that attend accidents,
or a transport-related agency that collects
data on these types of events.
Another cost incurred by the transport
sector is reduced income due to loss of
business on the day(s) of an SDS event.
Some measure of reduced income or
number of loads carried would be useful.
Again, this may come from a government
agency, or even a private transport agency
that represents the transport industry, as
they may collect data on this.
Infrastructure
Infrastructural impacts of SDS are
usually on the physical aspects of the
infrastructure, either damage or cleaning
of infrastructure. Sometimes, there is no
damage or cleaning, depending on the
severity of the SDS event. Also, some
types of damage cannot be measured
and therefore cannot be costed. This
is particularly the case with siltation of
waterways or dams.

Electricity – The main costs here are
damage to pylons or transmission lines,
and the main consideration here is that
the damage is due to SDS. In some cases,
SDS may lead to damage, but there may
already be pre-existing conditions that
contributed to the final damage caused by
SDS. Cleaning of transmission lines and/
or insulators may also be undertaken to
reduce the potential for electrical short
circuits and fires. The costs of damage
and/or cleaning could/should be available
from the electrical transmission company.
Also, in some countries or regions where
electricity is generated by solar plants,
the costs of cleaning of solar panels may
be available from the plants or electricity
generation company.
Water and gas
These utilities are not usually affected by
SDS, as they are typically underground.
However, if there are reports of damage,
please gather any data you can.
Construction
The construction industry costs are due
to delays in construction. Thus, we need
to know how much construction activity is
going on in an economy, and how the SDS
event(s) impact construction activity, such
as how many worksites were closed down
and for how long.
Oil and mineral exploration and
production
Similar to the construction industry, costs
are due to delays in exploration. Therefore,
we need to know how much exploration
activity is going on in an economy, and how
the SDS event(s) impact on exploration
activity, such as how many worksites were
closed down and for how long. A second
impact on the oil and mineral extraction
industries is reduced revenue when oil
wells or mines are not operating. Therefore,
we need to identify if these facilities are
impacted by SDS. Some mines, such as
underground mines, may be less affected
than others, such as open-pit mines.
Commercial activity – Retail/wholesale
Commercial activity is probably the
hardest sector to measure, as there are
no observable impacts other than the
possibility of fewer people purchasing
goods. The best way to measure this is
through survey data, but in most cases,
this is not feasible. To measure the impact,
we use a scale of sales activity based on
national retail/wholesale sales data, which
should be obtainable from one of the
economic agencies within a country, such
as the central bank or a department of
treasury or finance.
Manufacturing
Manufacturing will only be impacted by
SDS if the particulate matter enters the
manufacturing facility, or if materials
required for production are held up in
transit, causing delays. For example,
electronics component manufacturers
in Korea noted that on days of high
particulate matter, there were more faulty
products or faults in final components.
Collecting data on this will be difficult
due to facility-specific issues, but may be
possible through survey work at a later
date, as shown by Kim (2009).
Emergency services
Calls and requests for emergency
services, such as police, ambulance or fire,
may increase during SDS due to health
incidents, fire or road accidents that may
be a result of the events. Data for this
type of service can come directly from
the police, fire or ambulance services,
or indirectly through the agency that
manages these services. To ensure that
there is indeed an increase in service
requirements, it is necessary to gather
data from comparable periods with no SDS
activity.

164
Health
The impacts on health can usually be
measured in either admissions through
accident and emergency rooms or some
other proxy such as ambulance activity.
The best source is through the health
department or the agency that manages
hospitals in the region impacted by the
SDS. Again, it is necessary to have a
comparative set of data for periods when
there is no SDS activity.
Absenteeism
Absenteeism is the absence from
work of employees due to family or
caring responsibilities. Some costs of
absenteeism are already captured in costs
of production, but research has shown
that there is a reduction in productivity
as well as production. The problem
with measuring absenteeism is putting
a number on the percentage of people
absent from work due to an SDS event, as
well as ascertaining the typical absentee
rate for a particular country or region with
which to compare it.
Households
Many household costs are captured under
other headings, such as absenteeism or
health, but the major cost for households
due to an SDS event is cleaning, which
includes cars, internal and external
cleaning, and repairs and maintenance
of vehicles and structures if necessary.
It may be possible to assign some value
to these costs if, for example, we know
the replacement rate for air conditioners
and other types of filters, the duration of
the SDS event and how much matter was
deposited.
The other costs households incur are
for dust mitigation. These can include
investments in new doors and windows
that seal out dust more effectively, or
air-filtration or conditioning systems, so
some measure of these would be helpful.
However, identifying which investments
were made for dust mitigation as
opposed to lifestyle improvement may be
problematic.
Arts, sports and leisure
Many events and activities in the arts,
sports and leisure sector can be limited or
cancelled due to health concerns or lack
of attendees. Therefore, if it is possible to
identify which events may be cancelled
and the potential loss of income for this
sector, many events that are cancelled are
not replaced and ticket holders usually get
their money back, again the loss in income
is due to the costs incurred in organization
and preparation.
Schools and education facilities
School and other education facilities may
be closed due to an SDS event, but in many
cases, there is no direct loss in income or
increased costs, as teachers and other
workers in this sector are paid regardless.
The main cost in this sector would be
parents and carers having to stay at home
to care for children and other dependents,
and these costs would be captured in the
absenteeism chapter.
Concluding comments
In some cases, it may not be possible
to directly obtain the data required, but
other sources such as media reports,
insurance companies or other similar
agencies, as well as secondary data, can
be used to validate and/or verify estimated
or assumed values. In other cases, the
sector is not a major sector in the region or
country’s economy, so it is not critical that
the data be collected.
©Paul
O’Rear
on
Flickr,
February
24th,
2007

6.14 Conclusions
This chapter covered an assessment
framework for the economic impact of
SDS. Different approaches have been
discussed and the data requirements
for these approaches presented. Types
of costs, direct and indirect, market and
non-market, and on-site and off-site,
were defined. One key point here is the
difference between value and cost, which is
critical in estimating the economic impact
of SDS. SDS impact many sectors of an
economy. These sectors were identified
and the types of impacts SDS may have on
these sectors were discussed.
The challenge with any economic analysis,
particularly for natural disasters, is that of
data requirements and availability, and this
will drive the “ideal” method of analysis.
Input-output (I-O) modelling is difficult in
the context of SDS, as I-O requires a base-
year without SDS as the comparison year
for measuring impact. Computable general
equilibrium (CGE) models have been
used to measure the impacts of natural
disasters, but require significant amounts
of data, and are reliant on parameters
to measure economic impact. Surveys
and accounting methods have also been
used and do capture the impacts of SDS
but require full identification of impacts
and assumptions regarding costs and
measurement of these costs.
The key aspect of the successful
construction of an SDS economic impact
assessment is the availability of good data,
meaning data that accurately measures
the impact of SDS events. Data-collection
also needs to be comprehensive to cover
all affected sectors of the economy.

166
6.15 References
Ai, Ning, and Polenske, Karen R. (2008). Socioeconomic
impact analysis of yellow dust storms: An approach
and case study for Beijing. Economic Systems
Research, vol. 20, No. 2, pp. 187–203.
Al-Hemoud, Ali, and others (2019). Economic impact and
11, p. 200. doi.org/10.3390/su11010200.
Al-Hemoud, Ali, and others (2017). Socioeconomic
effect of dust storms in Kuwait. Arabian Journal
of Geosciences, vol. 10, No. 18. Available at doi.
org/10.1007/s12517-016-2816-9.
Amanollahi, Jamil, and others (2012). Assessment of
vegetation variation on primarily creation zones of
the dust storms around the Euphrates using remote
sensing images. EnvironmentAsia, vol. 5, No. 2, pp.
76–81.
Arnesen, Trude Margrete, and Nord, Erik (1999). The
value of DALY life: problems with ethics and validity
of disability-adjusted life years. British Medical
Journal, vol. 319, pp. 1423–1425.
Blain, Stéphane, and others (2007). Effect of natural iron
fertilization on carbon sequestration in the Southern
Ocean. Nature, vol. 446 (26 April), pp. 1070–1074.
Burritt, Benjamin E., and Hyers, Albert (1981). Evaluation
of Arizona’s highway dust warning system. In
Desert dust: origin, characteristics and effects
on man, Troy L. Péwé, ed. Geological Society of
America Special Papers, vol. 186, pp. 281–292.
Cochrane, Hal (2004). Economic loss: myth and
measurement. Disaster Prevention and
Management, vol. 13, pp. 290–296.
Cook, Andrew J., and Tanner, Graham (2011). European
airline delay cost reference values. Department
of Transport Studies, London: University of
Westminster.
Costanigro, Marco, and McCluskey, Jill J. (2011).
Hedonic price analysis in food markets. In The
Oxford Handbook of the Economics of Food
Consumption and Policy. Jayson L. Lusk, Jutta
Roosen and Jason F. Shogren, eds. Oxford: Oxford
University Press.
Cropp, Roger A., and others (2005). Coupling
between ocean biota and atmospheric aerosols:
Dust, dimethylsulphide, or artefact? Global
Biogeochemical Cycles, vol. 19, pp. 1–16.
de Longueville, Florence, and others (2013). Desert dust
impacts on human health: an alarming worldwide
reality and a need for studies in West Africa.
International Journal of Biometeorology, vol. 57, pp.
1–19. Available at doi.org/10.1007/s00484-012-
0541-y.
Economics of Land Degradation Initiative and United
Nations Environment Programme (2015). The
economics of land degradation in Africa: benefits of
action outweigh the costs; a complementary report
to the ELD Initiative. Available at www.eld-initiative.
org. Accessed on 7 September 2016.
Hallegatte, Stephane, and Przyluski, Valentin (2010).
The economics of natural disasters: concepts and
methods. Policy Research Working Paper 5507.
Washington, D.C.: World Bank.
Hanley, Nick, and Spash, Clive L. (1993). Cost-benefit
analysis and the environment. Cheltenham: Edward
Elgar Publishing.
Hardin, Garrett (1968). The tragedy of the commons.
Science, vol. 162, pp. 1243-1248.
Harris, Jonathan M. (2006). Environmental and natural
resource economics: a contemporary approach –
2nd ed. Boston: Houghton Mifflin Company.
Horridge, Mark J., Madden, John, and Wittwer, Glyn
(2005). The impact of the 2002-2003 drought on
Australia. Journal of Policy Modelling, vol. 27, No. 3,
pp. 285–308.
Husar, Rudolf B., and others. (2001). Asian dust events
of April 1998. Journal of Geophysical Research
Atmospheres, vol. 106, No. D16, pp. 18,317–18,330.
Huszar, Paul C., and Piper, Steven L. (1986). Estimating
the off-site costs of wind erosion in New Mexico.
Journal of Soil and Water Conservation, vol. 41, No.
6, pp. 414–416.
Just, Richard E., Hueth, Darrell L., and Schmitz, Andrew
(2004). The welfare economics of public policy.
Cheltenham: Edward Elgar Publishing.
Kaskaoutis, Dimitris G., and others (2008). Study on
an intense dust storm over Greece. Atmospheric
Kelly, Tyler (2009). Natural Disaster Relief Assistance
– Far West New South Wales dust storm. Media
release. New South Wales Department of Lands,
Sydney.
Kim, J-I. (2009). Socioeconomic impact assessment
study of dust. Seoul: Chung-Ang University.
Leys, John F. (2002). Wind erosion effects on nutrient
redistribution and soil productivity. In Encyclopaedia
of Soil Science, Rattan Lal, ed. New York: Marcel
Dekker, pp. 499–502.
Leys, John F., and McTainsh, Grant Harvey (1994). Soil
loss and nutrient decline by wind erosion – cause
for concern. Australian Journal of Soil and Water
Conservation, vol. 7, No. 3, pp. 30–40.
Leys, John F., and others (2011). PM10 concentrations
and mass transport during “Red Dawn” – Sydney
September 2009. Aeolian Research, vol. 3, pp.
327–342.

Menut, Laurent, Masson, Olivier, and Bessagnet, Bertrand
(2009).ContributionofSaharandustonradionuclide
aerosol activity levels in Europe? The 21–22
February 2004 case study. Journal of Geophysical
Research, vol. 114, No. D16202. Available at https://
doi.org/10.1029/2009JD011767.
Middleton, Nicholas J., and Goudie, Andrew S.
(2001). Saharan dust: sources and trajectories.
Transactions of the Institute of British Geographers,
vol. 26, pp. 165–181.
Miri, Abbas, and others (2009). Environmental and
socio-economic impacts of dust storms in Sistan
Region, Iran. International Journal of Environmental
Studies, vol. 66, No. 3, pp. 343–355.
Ninan, Karachepone N. (2014). Valuing ecosystem
services: methodological issues and case studies.
Cheltenham: Edward Elgar Publishing.
Robison, Lindon J., and Barry, Peter J. (1996). Present
Value Models and Investment Analysis. Northport:
Alabama. The Academic Page.
Rose, Adam, and Lim, Dongsoon (2002). Business
interruption losses from natural hazards:
conceptual and methodological issues in the
case of the Northridge earthquake. Environmental
Hazards, vol. 4, pp. 1–14. Available at doi.
org/10.3763/ehaz.2002.0401.
Rose, Adam, and Liao, Shu-Yi (2005). Modelling regional
economic resilience to disasters: a computable
general equilibrium analysis of water service
disruptions. Journal of Regional Science, vol. 45, pp.
75–112.
Sohn, Keon Tae (2013). Statistical guidance on seasonal
forecast of Korean dust days over South Korea in
the springtime. Advances in Atmospheric Sciences,
vol. 30, pp. 1343–1352.
Tozer, Peter R. (2012). Economic impact of off-site wind
erosion. Final report project - MD250.11. Buronga:
Lower Murray Darling Catchment Management
Authority.
Tozer, Peter R., and Leys, John F. (2013). Dust Storms –
What do they really cost? The Rangeland Journal,
vol. 35, pp. 131–142.
United Nations (1997). Glossary of Environment
Statistics. Studies in Methods, Series F, No. 67. New
York: United Nations.
Vanderstraeten, Peter, and others (2008) Dust storm
originate from Sahara covering Western Europe: A
case study. Atmospheric Environment, vol. 42, pp.
5489–5493.
Wegner, Giulia, and Pascual, Unai (2011). Cost-benefit
analysis in the context of ecosystem services for
human well-being: a multidisciplinary critique.
Global Environmental Change, vol. 21, pp. 492–504.
Williams, Peter, and Young, Mike (1999). Costing dust:
How much does wind erosion cost the people of
South Australia? Adelaide: CSIRO Land and Water,
Policy and Economic Research Unit.
Wing, Ian Sue (2004). Computable general equilibrium
models and their use in economy-wide policy
analysis: everything you ever wanted to know
(but were afraid to ask). Technical Note No. 6.
Cambridge, MA: Massachusetts Institute of
Technology Joint Program on the Science and
Policy of Global Change.
World Health Organization (2016). Health statistics and
information systems. Metrics: Disability-Adjusted
Life Year (DALY). Available at http://www.who.int/
healthinfo/global_burden_disease/metrics_daly/
en/. Accessed 22 September, 2016.

1
6
8
Michael
Tuszynski,
©Unsplash,
May
8,
2019

UNCCD | Sand and Dust Storms Compendium | Chapter 7 | GIS-based vulnerability mapping framework 169
7. A geographic infor-
mation system-based
sand and dust storm
vulnerability mapping
framework
Chapter overview
This chapter provides a sand and dust storms (SDS)-focused process to assess
vulnerability using geographic information system (GIS) procedures where data
availability or quality is not a critical issue. The chapter provides a flow chart for GIS-
based vulnerability assessment and conceptual models of how SDS affect the health,
socio-economic, environmental and agro-ecological domains of a vulnerable area (from
local to global). Detailed attention is paid to the selection of vulnerability indicators
(including tables of possible indicators). The chapter includes specific formula to produce
vulnerability maps using a GIS platform.
This chapter should be read in conjunction with chapters 3, 4, 5 and 6.

UNCCD | Sand and Dust Storms Compendium | Chapter 7 | GIS-based vulnerability mapping framework
170
7.1 Overview
This chapter describes a procedure for
a geographic information system (GIS)-
based mapping of vulnerability to sand and
dust storms (SDS). The goal is to elaborate
this procedure in detail and strengthen the
users’ ability in understanding practical
considerations on data-collection and
analysis using a GIS for SDS vulnerability
mapping.
As noted elsewhere, data availability
and access differ among countries and
stakeholders of SDS. The proposed
procedure is intended to be applicable
and adaptable in different circumstances.
This assures that even with limited
data accessibility, a basic map of SDS
vulnerability can be achieved.
Vulnerability, its components and the
relevant indicators exhibit such a large and
complex spatial-temporal variability that an
interactive GIS-based platform is required
to handle them. Accordingly, stakeholders
having uneven profiles of data, skills and
abilities will be able to adapt this mapping
procedure.
This adaptation is closely linked to the level
of integration of relevant data, such as
GIS layers, remote sensing data, available
web data and non-GIS information, into
the procedure. To do so, limitations and
shortcomings of implementing GIS for
sand and dust storm vulnerability mapping
(SDS-VM) should be well understood.
These limitations include a lack of data,
available data not being in GIS format and
GIS data having no uniform data model
and structure, among other scenarios.
Mapping SDS vulnerability can be
subjective if the experiences and opinions
of experts and stakeholders are inserted
into the procedure (for example, by
selecting different sets of indicators) and
can create different vulnerability maps for
the same SDS phenomenon. It is therefore
necessary to propose a general procedure
that can provide objective estimates of
vulnerability, unbiased towards different
users or environmental conditions. The
stepwise procedure and the order of
specific steps required to implement the
procedure are illustrated in Figure 17.
Figure 17. A
flowchart of
geographic
information
system
vulnerability
mapping
Literature review, expert
knowledge, consultants, and
stakeholders meeting
Investigate:
Data accessibility
Data availability
Data model and structure
Data-collection planning
GIS-available layers
Remote sensing data
Analog data and maps
Web-available data and
maps
Ground-based data
Non-spatial data
Data model and structure
Data resolution/scale
Classification
SDS vulnerability mapping
hypothesization
Impact assessments
Indicator identification
Data collection
Data coversion,
standardization, storage and
management
SDS-VM elements
(components and
indicators) weighting
Data integration to produce
SDS vulnerability map
Exposure, sensitivity and adaptive capacity
components
Direct and indirect impacts on different
domains (health, socioeconomic,
environmental and agroecosystem)
List of influential indicators
SDS-VAM GeoDataBase
SDS vulnerability map
CH7 Figure 17.
Activity
Input
• Process
Outcome
Output

7.2 Approaches to an
SDS vulnerability
mapping and
assessment
framework
The complex and multidimensional nature
of vulnerability makes any mapping
methodology framework arbitrary,
overlapping and contentious to a degree,
depending on disciplinary differences
in how to formulate vulnerability
(Intergovernmental Panel on Climate
Change [IPCC], 2012a). The majority of
vulnerability assessments and mapping
developed over the past decades involve
statistical analysis, designing vulnerability
indices and the use of GIS (United Nations
Environment Programme [UNEP], 2003).
The empirical-statistical approach is
based on the statistical analyses of
observed damage data and distributions
from past hazard events. Statistical data
and techniques (for example, regression,
correlation, normalization and statistical
indices) are commonly used to identify
vulnerable communities and develop
composite indices. Such indices combine
several particular indicators and deliver
simple and usable results from a vast
amount of diverse information.
The indicator-based approach estimates
the overall vulnerability from a set of
indicators representing interactions
between hazard and system elements.
Indicator-based vulnerability is flexible and
applicable to different hazards and it can
be easily adapted to user needs (Kappes et
al., 2012). However, there is a need to base
indicators on evidence or proven models,
otherwise they should be used cautiously
as a tool for decision-making. Most
vulnerability assessment and mapping
indicators are model driven and not data
driven, making them susceptible to a
degree of uncertainty.
Composite indices make the information
easily usable by potential users, including
governments and public sectors. For
instance, the Committee for Development
Policy (2000) and the Caribbean Group for
Cooperation in Economic Development and
the World Bank (2002) used statistically
normalized variables with equal weights to
construct composite vulnerability indices.
However, despite their simplicity and
directness, composite indices are prone to
delivering poor outcomes in the absence
of evidence or evidence-based models.
While commonly used, composite indices
are often flawed by linking and combining
different indicators into one resulting value.
On the other hand, GIS offers a flexible and
useful tool to show the spatial distribution
of vulnerable regions and communities.
By relying on analytical frameworks and
proven models, such an approach leads
to accurate results. GIS can accept data
derived from a variety of sources such as
satellite imagery, aerial photography and
spatially referenced maps and associated
tabular attribute data. This is critical, since
data might be collected in different ways
and integrated in different forms.
Furthermore, GIS provides a powerful
platform for geostatistical/geospatial
analysis, as well as visualization and
mapping tools. Examples of GIS-based
vulnerability mapping are the:
• Climate Change Vulnerability Map,
an interactive online GIS platform
(http://maps.massgis.state.ma.us/
map_ol/cc_vuln.php) provided by the
Massachusetts Department of Public
Health, Bureau of Environmental
Health
• Interactive map of Central America
presenting vulnerability to different
natural hazards prepared by UNEP/
GRID Sioux Falls (1999)
• Food Insecurity and Vulnerability
Information and Mapping Systems
(FIVIMS), developed by Food and
Agriculture Organization (FAO) (1998)

172
7.3 Key concept
of vulnerability
assessment and
mapping
7.3.1. Vulnerability
The word “vulnerability” has different
applications and interpretations in
different disciplines. “Vulnerability” may
refer to “biophysical vulnerability” that
is closely aligned with the concepts of
“hazard”, “exposure” or “risk”, or it may
highlight the socioeconomic and cultural
processes that are more in line with the
concepts of “resilience”, “coping capacity”,
and/or “adaptive capacity” (Preston
and Stafford-Smith, 2009). There might
also be integrated conceptualization of
vulnerability, considering both biophysical
and socioeconomic factors that collectively
create the potential for harm. Considering
only components of biophysical
vulnerability (that is, only exposure
and sensitivity), regardless of adaptive
capacity, can lead to biased estimates
of vulnerability and, consequently, an
erroneous policy implication (Piya et al.,
2016).
Given the multidimensional and complex
nature of SDS, it is necessary to consider
vulnerability as a function of three
interactive components: (i) exposure to
change; (ii) associated sensitivities and (iii)
related adaptive capacities (Polsky et al.,
2007). The first two components directly
influence vulnerability so that the more
the exposure or sensitivity, the greater the
vulnerability.
On the other hand, adaptive capacity is
inversely related to vulnerability, thus, an
increase in the adaptive capacity will result
in lower vulnerability. Multiple definitions
exist for the components in different
disciplines and the distinctions between
them are not always clear. All components,
however, are site- and system-specific
and vary over time. The three components
of vulnerability are explained in the next
sections.
7.3.2. Exposure
“Exposure” refers to the nature and
degree to which elements of a system
are at risk of a natural or human-induced
hazard (IPCC, 2012b). Elements at risk
could include individuals, livelihoods,
ecosystems and resources, infrastructure,
environmental, agricultural, economic,
and social assets (IPCC, 2014b). Gender,
age and health status should also be
considered in establishing exposure.
Exposure can be considered geographically
by identifying the location, characteristics,
number and type of elements exposed to
hazard or harm. Although sometimes used
interchangeably in the literature, there is
a distinct difference between vulnerability
and exposure.
Exposure can be regarded as a necessary,
but not sufficient, determinant of
vulnerability (IPCC, 2014a). This means
that there might be elements exposed
to hazards but that are not vulnerable,
while to be vulnerable, it is necessary
to be exposed to hazard. Information
on exposure is of vital importance for
vulnerability assessment to address how
at-risk elements of a given system act
when subjected to hazard. In the case of
SDS, frequency, intensity and duration of
exposure to the events are also critical, as
they will increase the likelihood of risk for
the given elements.
7.3.3. Sensitivity
Sensitivity is another concept related to
vulnerability, defined as the degree to
which a system is modified or affected by
hazard stimuli (IPCC, 2014a). Depending
on their characteristics, various systems
react differently to the same hazard event.
For example, a system might be vulnerable
to flood, but not to drought. Sensitivity
determines how different elements in a
given system respond when hazard events
occur.
For a given system, sensitivity can either be
limited to identifying whether the system
is sensitive to a hazard/perturbation or, in

a more comprehensive way, to measure
the degree of sensitivity. Sensitivity can
also be used to rank different elements
of the system based on their sensitivity
to hazard/perturbation. Exposure
and sensitivity are closely connected
determinants of the vulnerability of a
system and dependent on the interaction
between the characteristics of the system
and the attributes of the hazard stimulus
(Cutter et al., 2009).
7.3.4. Adaptive capacity
While exposure and sensitivity determine
the scale and nature of likely impacts
caused by hazards/perturbations, the
adaptive capacity of the system quantifies
its ability to cope with, manage, recover,
and adapt to the potential adverse impacts
(IPCC, 2014a). Adaptive capacity, in
general, can be expressed as the process,
action or state in a system (individual,
community, sector and country) to better
cope with, recover and adjust to changing
conditions and risks.
In the context of SDS, adaptive capacity
of a system is seen as adjustments in
ecological and socioeconomic behaviours
in response to potential or actual SDS
events to reduce society’s vulnerability.
Due to the variability in SDS impacts and
consequences, adaptive capacity tends
to be context-specific, meaning that it
varies from situation to situation, among
societies and individuals presenting
temporal and spatial variation. Gender, age
and health status need to be considered in
defining adaptive capacity.
7.4 Impact indicators of
SDS for vulnerability
mapping
7.4.1. Measuring vulnerability
Vulnerability is not an intrinsic property
of a system to be directly observed or
measured. Instead, it has to be deduced
through a set of variables (indicators)
estimating exposure, sensitivity and
adaptive capacity. A common practice to
estimate vulnerability is to use surrogate
measures of vulnerability components and
then aggregate them to yield the overall
vulnerability “score”.
Different vulnerability assessments can be
classified based on the vulnerability factors
that they consider (Füssel, 2007). Human
and natural systems are fundamentally
interlinked and risks to one would
eventually translate into risks to the other
(UNEP, 2003).
This means that the measure of
vulnerability should include factors from
both humans and the environment, plus
the associated risks to both.
The United Nations Inter-Agency
Secretariat of the International Strategy
for Disaster Reduction (UN/ISDR) (2005),
for instance, classified four groups of
vulnerability factors associated with hazard
reduction: physical, economic, social and
environmental. Vulnerability factors, in
turn, can be inferred from the impacts of
hazard on different aspects of system.
Accordingly, in this document, the impacts
of SDS are grouped into four main domains
including human health, socio-economy,
environment and agroecosystem.
Each domain has a number of
subdivisions, which map out the major
elements of interest. These impacts are
then used to select the ultimate set of
indicators to assess SDS vulnerability and
produce maps. The mapping process also
needs to discern how vulnerability may
differ by the gender, age or health status of
the individuals being assessed.
7.4.2. Human health
SDS threatens human health and safety in
many ways, by affecting the environment
that provides us with clean air, food, water
and security (Goudie, 2014). Assuming
that the impacts of SDS are projected
to increase over the coming decades,
current health threats will likely persist and
intensify. The health impacts of SDS are
dependent on:

174
• the location of human populations with
respect to the emission sources of SDS
and the downwind direction of dust
transport and deposition
• the amount of suspended materials
that SDS contain
• particle sizes and chemical
compositions (ibid.) and the health
status of the vulnerable population
There are generally three types of health
impacts:
• Type 1. Medical and physical health
• Type 2. Mental health and well-being
• Type 3. Community health
Type 1 considers human health impacts of
air pollution and contamination pathways
caused by SDS. Depending on their origins
and pathways, SDS may transport heavy
metal, residue of chemicals including
plant fertilizer, pesticides and herbicides,
dioxins, toxic hydrocarbons, radionuclide
contaminants and radioactive isotopes
(ibid.).
The fine dust particles, bacteria, pollen and
fungi carried by dust storms are reported
to have important effects on human health
(Péwé, 1981). Suspended materials in the air
can be inhaled and cause serious disorders
if they accumulate in the respiratory system.
Although reporting inconsistent results
across different studies and geographical
locations, the literature includes several
studies reporting health impacts associated
with SDS (e.g. Nativ et al.,1997; Choi et al.,
2011; Tam et al., 2012; Baddock et al., 2013;
Martinelli, Olivieri and Girelli, 2013; Deroubaix
et al., 2013; Sprigg, 2016; Middleton, 2017).
Among them, four reviews (de Longueville et
al., 2013; Hashizume et al., 2010; Karanasiou
et al., 2012; Zhang et al., 2016) have noted
similar results, suggesting that potential
health effects associated with SDS may
increase cardiovascular mortality and
respiratory hospital admissions.
Type 2 refers to mental health and well-
being effects of SDS that may cause stress,
anxiety, depression, grief, sense of loss,
strains on social relationship and post-
traumatic stress disorder. These kinds of
effects are integral parts of the overall SDS-
related human health impacts. Although
these effects may rarely occur in isolation,
they often interact with other socioeconomic
and environmental stressors.
Type 3 considers the overall SDS-related
impacts on the health of groups and
communities. The community health
effects can lead to increased interpersonal
aggression, increased social instability and
decreased community cohesion. The main
pathways and types of health impact of SDS
are shown in Figure 18.
Figure 18.
Major human
health impacts
of sand and
dust storms
SAND AND DUST STORM
HEALTH IMPACTS
01 MORTALITY
● All-natural cause mortality
● Cardiovascular diseases
● Respiratory diseases
03 OTHER
● Pregnancy outcomes
02 MORBIDITY
● Cardiovascular diseases
● Respiratory diseases (including asthma, COPD
and pneumonia)
● Coccidiodomycosis
● Dermatological disorders
● Conjunctivitis
● Meningococcal meningitis
● Allergic rhinitis

The direct impacts on health are mostly
caused by changes in exposure to SDS.
Communities and individuals differ in their
vulnerability to certain health outcomes.
A community’s health vulnerability is a
function of health outcome sensitivity and
its capacity to adapt to new conditions.
Several factors such as environmental
conditions, population size, growth, age,
sex, density, food availability, education
level, income level, pre-existing health
status and the availability and quality of
public health care affect a community’s
health vulnerability.
It is likely that poor populations, and
particularly older persons, due to their
lower immunological capacity and the
very young, due to their not fully developed
lungs and airways, are at greatest health
risk to SDS. The vulnerability of the poor
may endanger the well-being of other
members of the same community and
hence increase the overall vulnerability of
the population.
7.4.3. Socioeconomic
domain
SDS have profound impacts on
socioeconomic systems of different scales,
from local up to the global economy. The
immediate impacts can be remarkable.
China’s economic losses due to dust
storms and desertification is estimated to
amount to US$ 6.5 billion per year (Youlin,
Squires and Qi, 2002).
Nonetheless, it is believed that the
actual socioeconomic impacts of SDS
are difficult to measure because of the
long-term consequences and implications
they have on the society and economy
(United Nations Convention to Combat
Desertification [UNCCD], 2016). SDS
socioeconomic impacts are more severe
as the storms cross populated areas
and industrial zones such as big cities
and towns. They cause significant harm,
both at their sources and through their
deposition in downwind areas by reducing
air quality and depositing particles (Chan
et al., 2005). These impacts encompass
a relatively broad range of effects across
many sectors of the economy and society.
In general, socioeconomic costs will likely
escalate as a result of dust storms (Jeong,
2008; Meibodi et al, 2015). For example,
the loss of topsoil, resulting in the loss of
soil nutrients, carbon and organic matter,
is among on-site costly damages of SDS
(Leys, 2002). Sand and dust deposition
can harm vegetation by covering them
and reducing the photosynthesis process
through blocking sunlight or even
burying vegetation cover in some areas.
Infrastructure can also be sandblasted or
buried by SDS. Deposited dust increases
cleaning costs, such as for telegraph
poles, fencing, walls, railway sleepers and
roads (Middleton, 1986; 2017), buildings
and streets (Huszar and Piper, 1986).
As an example, Huszar and Piper (ibid.)
summarized that the major off-site
impact of dust storms in the USA was on
households, mainly because of cleaning
costs of interior spaces and domestic
landscapes.
SDS can also cause major damages to
utility systems such as power distribution
grids (Maliszewski, Larson and Perrings,
2012), solar power plants (Sarver,
Al-Qaraghuli and Kazmerski, 2013),
radio/microwave satellite and ground
communications (Abuhdima and Saleh,
2010) and rail networks (Cheng et al.,
2015).
Human activities can be limited, including
closure of transport networks and road
traffic during SDS (Deetz et al., 2016;
Goudie and Middleton, 2006), air trafficking
problems (Holyoak, Aitken and Elcock,
2011), flight cancellations and delays
(Kang, 2004), and other air transport
effects (Tozer and Leys, 2013). SDS
can also impose considerable costs on
individuals and business owners in both
urban and rural areas (Anderson, van
Klinken and Shepherd, 2008).
Continued SDS over several years would
cause forced migration by destruction of
farmlands and facilities (Gregory, 1991).
For example, hundreds of thousands of
people were forced to leave their homes
and migrate because of the Dust Bowl in
the 1930s (Hurt, 1981).

176
Figure 19.
Major
socioeconomic
impacts of
sand and dust
storms
Tourism and recreational facilities, markets
and shopping centres, public facilities
and governmental offices, cultural and
religious facilities can also be drastically
affected by SDS events. Water resources
back-up facilities such as dams, reservoirs,
catchments and flood-control installations
may be filled up with sand. Figure 19
depicts the major socioeconomic impact
of SDS.
7.4.4. Environment domain
There is significant concern about the
impacts of SDS on the environment.
SDS have most of their impact within the
atmosphere and significantly contribute
to atmospheric aerosol loads and pollution
(Xie et al., 2005; Xin et al., 2007; Zakey et
al., 2006).
The reduction of planetary insolation
caused by suspended particles in the
atmosphere can have a cooling influence
on climate (Seinfeld et al., 2004) and
alter Earth’s radiative balance (Highwood
and Ryder, 2014). This cooling influence,
along with varying aerosol loads in the
atmosphere, change the atmospheric
dynamic structure and modify the
atmospheric circulation pattern, with
implications for climate change (Shao et
al., 2007; Won et al., 2004).
SDS events can impose direct effects on
climate processes and air chemistry (Kim
et al., 2003), atmospheric geochemical
cycles (Shao et al., 2011) and influence
oceans and land biogeochemical cycling
(Gabric et al., 2010). Dust storms can also
have important impacts on tropical storm
and cyclone intensities (Evan et al., 2006).
SDS transport huge quantities of mineral
dust particles from deserts and farmlands
and therefore affect the global mineral and
geochemical dust budget of atmosphere
(Knippertz, 2014; Zender et al., 2004).
Moreover, dust particles in the atmosphere
can absorb other anthropogenic
atmospheric pollutants (Onishi et al., 2012)
and transport them to other areas.
Another major impact of SDS on the
environment is the reduction of ecosystem
services (Lal, 2014) including the four
categories: provisioning, regulating,
supporting and cultural services.
Ecosystem services are contributions of
ecosystems to both directly and indirectly
support human survival and well-being.
Negative impacts on these systems
influence the quality of human life. The
main environmental impacts of SDS are
shown in Figure 20.
SAND AND DUST STORM
SOCIO-ECONOMIC IMPACTS
02 DAMAGING ESSENTIAL FACILITIES / SERVICES
● Tourism and recreational facilities
● Markets and shopping centres
● Public facilities and governmental offices
● Cultural and religious facilities
03 INCREASING CLEANING COSTS
● Telegraph poles, fencing, walls
● Railway sleepers and roads
● Buildings and streets
06INCREASING MIGRATION
01 DAMAGING UTILITY SYSTEMS
● Power distribution grids
● Solar power plants
● Radio/microwaves satellite & ground communication
& rail communications
05 IMPOSING COSTS ON INDIVIDU-
ALS AND BUSINESS OWNERS
04 LIMITING HUMAN ACTIVITIES
● Closure of transport networks and road traffic
● Air trafficking problems, air flight cancellations and
delay, and air transport effects

7.4.5. Agroecosystem
domain
SDS can have several negative impacts on
agroecosystems through soil erodibility,
sediment deposition and photosynthesis
reduction on agricultural lands (Sivakumar,
2005; Stefanski and Sivakumar, 2009). The
worst impact of SDS on agroecosystems
is the stripping of topsoil from farmlands
that accelerates soil erosion and land
degradation and lessens soil productivity
(Zobeck, Fryrear and Pettit, 1989).
Topsoil is the most fertile fraction of the
soil, made up of minerals and decomposed
organic matter that can be removed and
transported over long distances. In the
long term, SDS can change the nature of
soils (Menéndez et al., 2007), as well as
their chemical, physical and biological
characteristics (Huszar and Piper, 1986).
They can also impact contribution of
micronutrients to ecosystems (Boy and
Wilcke, 2008), cause soil loss (Riksen and
De Graaff, 2001) and reduce its water-
holding capacity.
A further significant impact of SDS on
agroecosystems is through either direct or
indirect loss of crop yield and livestock.
Direct impacts include physical damage to
crops, animals and trees caused by SDS.
Crop yield reduction can be triggered by
carrying seeds (Larney et al., 1998), total
or partial burial of seedlings under sand
and dust deposits, loss of plant leaves as
a result of sandblasting and delaying plant
development.
Plants exposed to sandblasting (or buried
under sand and dust deposits) may
lose their leaves, resulting in reduced
photosynthetic activity (Sharifi, Gibson
and Rundel, 1997) and consequently
reduced plant dry matter production that
is necessary for plant growth and the
development of grain or fruit (Stefanski
and Sivakumar, 2009). Direct impacts
can be considered in terms of short-term,
temporary damage at a particular crop
stage (for example, early season, maturity
or before harvest) during the growth
season to complete crop loss.
SDS may also change the physical and
chemical characteristics of a plant’s leaves
(Farmer, 1993) and reduce plant’s biomass
(Burkhardt, 2010). Livestock not properly
sheltered during the storms could suffer
directly (Mu et al., 2013).
Figure 20.
Major
environmental
impacts of sand
and dust storms SAND AND DUST STORM
ENVIRONMENTAL IMPACTS
02 ATMOSPHERIC IMPACTS
● Atmospheric pollution
● Absorbing anthropogenic atmospheric pollutants
● Changing the global mineral and geochemical dust budget
of atmosphere
03 REDUCING ECOSYSTEM SERVICES
● Provisioning
● Regulating
● Supporting
● Cultural
01 EFFECTS ON CLIMATE PROCESSES
● Effects on climate processes and air chemistry
● Effects on tropical storm and cyclone intensities
● Effects on geochemical cycle and atmospheric conditions
05 EFFECTS ON OCEANS & LAND
BIOGEOCHEMICAL CYCLING
04 CHANGING EARTH’S RADIATIVE BALANCE

178
For instance, during two dust storms that
occurred in China in May 1993 and April
1998, 120,000 and 110,000 livestock were
killed, respectively (Shao and Dong, 2006).
Environmental stresses caused by SDS
can also reduce livestock productivity and
growth (Starr, 1988).
SDS can also cause indirect damages
such as loss of potential production due
to disturbed access to goods and services
and increased costs of production. These
indirect impacts are the expected result
of low incomes, production decline,
environmental degradation and other
associated factors (Das et al., 2003).
Besides, SDS can increase disease risk
of organisms, such as trees, crop plants
and animals (Kellogg and Griffin, 2006),
threatening food production by affecting
rangeland and agricultural productivity
(Issanova et al., 2015). They can intensify
drought (Han et al., 2008) and even change
precipitation regimes (Knippertz and Stuut,
2014) and such changes could eventually
negatively affect agroecosystems. The
major impact of SDS on agroecosystems
are shown in Figure 21.
Figure 21.
Major impacts
of sand and
dust storms on
agroecosystems
SAND AND DUST STORM
AGRO-ECOSYSTEM IMPACTS
01 LOSS OF LIVESTOCK
● Direct livestock damage
● Decrease livestock productivity and growth
02 SOIL ERODIBILITY & LAND DEGRADATION
● Change nature of soils
● Change soil chemical/physical and biological
● Contribution of micro-nutrients to ecosystems
● Soil lost
03 INCREASE DISEASE RISK & THREATEN FOOD
PRODUCTION
● Increase disease risk of organisms, such as trees, crop plants
and animals
● Threaten food production by affecting rangeland and
04 LOSS OF CROP YIELD
● Carrying seeds by SDS
● Burial of seedlings under sand deposits
● Loss of plant leaves as a result of sandblasting
● Delaying plant development
● Physical and chemical characteristic of plant’s leaves
● Reduce plant’s biomass
05 INTENSIFY DROUGHT
06 CHANGE PRECIPITATION REGIME
characteristics
agricultural productivity

©Asian
Development
Bank

180
7.5 Identifying indicators
for SDS vulnerability
mapping
In order to include an indicator in the
analysis of SDS-VM, the following
questions should be considered:
Question 1: How do the given indicators
(GIS data layer) contribute to vulnerability
to SDS?
Question 2: To which vulnerability
component(s) (exposure, sensitivity or
adaptive capacity) does the given indicator
belong?
Question 3: To which level of analysis
(local, sectoral, national or international)
does the given indicator belong?
Answers to these questions will determine
whether a particular indicator should be
included in the analysis.
Annex 1 includes the answers to these
three questions for each indicator. The
potential source of data collection for each
indicator is also provided. Alternative web-
based data (the majority of which are freely
available) are also provided in the tables.
Detailed descriptions and web addresses
of these sources are given in Annex 2.
Where no appropriate data are available,
(indicated with “NA”), guidance on how to
measure, calculate or extract the given
indicator is outlined. Data provided by
these sources often vary in scale, quality
and content. Therefore, different users
must decide which data among all the
given sources best suits their needs. The
data format of each indicator has also
been provided in Tables 9 to 17 (indicated
with “DF” in the tables). However, in some
cases, data might be provided in different
formats that require conversion.
In summary, the list of the main indicators
is provided based on expert assessment
and literature review, including all
necessary information on data acquisition,
data necessity and data sources for
SDS-VM. It is then up to the end users at
different levels (residential, ecosystem
and political levels) to decide how the
associated indicators should be valued and
weighted, and how vulnerability should be
acted upon.
An ideal SDS-VM would require precise
measurements of all the impacts of
SDS as input indicators to estimate the
vulnerability. However, in practice, several
impacts are either not measurable or
very difficult to measure. Besides, all
the impacts are not equally important;
some are only influential under particular
circumstances. It is therefore reasonable
to restrict them to a set of quantifiable
(measurable) indicators.
The relevant indicators for mapping SDS
vulnerability are listed in Tables 9 to 17
(Annex 1). Attempts are made to include
a large number of indicators associated
with SDS vulnerability components, but
availability and accessibility of data pose
practical limitations on the number finally
included in the methodology. These
indicators are selected based on the
existing literature, experts’ knowledge and
their contribution to different components
of SDS-VM.
The inclusion of some indicators into
different components is relatively
subjective. Indicators determining the
extent and intensity of SDS are assigned to
the exposure component. Those indicators
reflecting the system’s susceptibility to
perturbation are included in the sensitivity
component. Indicators that are rather more
responsive to policy development and
prevention strategies are considered as
adaptive capacity.
Nevertheless, there are indicators
that might be shared among different
components. To summarize, this chapter
provides an expert assessment of key
impacts and indicators to SDS-VM.
However, it is then up to the end users at
different levels (residential, ecosystem and
political) to decide how the associated
indicators should be valued and weighted,
and how vulnerability should be acted
upon.

7.6 A geographic
information system-
based stepwise
procedure for SDS
7.6.1. SDS vulnerability
mapping hypothesis
SDS vulnerability mapping can be
hypothesized based on the relationship
between the system’s exposure, its
sensitivity and the adaptive capacity.
Hence, in order to formulate an appropriate
mathematical relationship for vulnerability
mapping, extensive literature review and
expert consultation is specifically required.
The estimation of SDS-VM components
requires measurable indicators which are
often affected by a number of limiting
factors including data availability and
applicability, mapping objectives, precision
and accuracy of vulnerability maps,
the SDS characteristics (for example,
spatial-temporal behaviour, chemical and
mineralogical compositions, SDS impacts
and the different stages of SDS events
(emission, transport and deposition)).
Therefore, careful considerations of these
factors must be provided in the hypothesis.
7.6.2. SDS impact
assessment
The SDS vulnerability components have
to be measurably expressed in the form
of direct and indirect impacts on different
scales and in different categories.
Therefore, a careful and thorough
literature review on impact assessment
for directing the SDS vulnerability mapping
to measurable indicators is conducted
and a wide-ranging and comprehensive
methodology for assessing the impacts of
SDS is adapted.
Accordingly, four main domains of
impacts (human health, socio-economy,
environment and agroecosystems) are
categorized. These four categories need
to be measurably transformable into
indicators (for example, GIS layers) for
the GIS-SDS-VM. This depends on the
level of economic, social or technological
developments, as well as some influential
parameters such as distance from
SDS sources and physical-chemical
characteristics of SDS particles. SDS
impacts can vary over different areas and
levels, requiring critical care in the SDS
impact assessment.
7.6.3. Indicator identification
“Indicator identification” describes how to
transform assessed impacts of SDS into
quantifiable indicators (GIS layers) to which
associated variables are categorized.
Different stakeholders (users) may choose
a set of indicators from those provided
in Annex 1, depending on their needs,
or follow similar criteria and add new
indicators to the list.
7.6.4. SDS data collection
There do not appear to be specific
protocols for required GIS data types,
models and structures for a GIS-based
SDS-VM. This document mainly focuses
on activities to provide basic data
requirements for GIS analysis to target the
needs of SDS-VM. Data collection is the
most expensive activity of any GIS-based
analysis, as well as vulnerability mapping.
SDS-related data are very heterogeneous,
based on their many diverse sources and
the data-capturing processes.
Data collection, including primary (direct
measurement) and secondary (derived
from other data sources) data, is carried
out in different spatial scales and for
different purposes in both raster and vector
data models. Several different sources
to collect data on the relevant indicators
are provided and alternative sources are
listed in Tables 9 to 17 (Annex 1). The
same sources might be used for different
indicators and afford the user the freedom
to select sources for data collection.

182
7.6.5. Data conversion,
standardization,
storage and
management
Data always differ according to certain
applications and data acquisition
techniques. Data models, for example,
vector (point, line, area) and raster
(pixel, grid), are two different spatial
representations with different advantages
and disadvantages to be compared with
each other for GIS analysis. Other non-
spatial data sources also need to be
converted into spatial representations and
all data must be transformed into the same
data model and structure (for example,
map projection, spatial scale and data
format).
Unification of different measurement
scales (such as nominal, ordinal, interval
and ratio) of the indicators is a prerequisite
step in GIS analysis. Thus, scaling or
standardization must be applied to
convert the inconsistent data to unique
scale and units. There is a number of
methods for standardizing for different
purposes (Hwang and Yoon, 2012;
Massam, 1988). In the GIS-based SDS-VM,
the fuzzy membership functions (Jiang
and Eastman, 2000) and the score range
procedure (Malczewski, 1999; Malczewski
and Rinner, 2015) are more adaptable to
standardize the available data.
Different techniques for GIS data storage
and management are available to organize
spatial and tabular data to be retrievable
for updating, querying and analysis. There
are several geodatabase management
systems applicable for the SDS-GIS-VM. As
an example, ARCGIS® geodatabases1
can
be used to store and manage data sets in
three levels:
1. File geodatabases: stored as folders in
a file system, each data set is held as
a file that can scale up to 1 TB in size.
The file geodatabase is recommended
over personal geodatabases.
1 http://www.esri.com, https://desktop.arcgis.com/en/arcmap/10.3/manage-data/geodatabases/
types-of-geodatabases.htm
2. Personal geodatabases: all data sets
are stored within a Microsoft Access
data file, which is limited to 2 GB.
3. Enterprise geodatabases: also known
as multi-user geodatabases, they can
be unlimited in size and numbers of
users. Stored in a relational database
using Oracle®, Microsoft SQL®
Server, IBM DB2®, IBM Informix®, or
PostgreSQL®.
7.6.6. Weighting of SDS
elements
Due to the complex nature of the SDS
phenomenon and the status of the SDS-VM
elements (the components and indicators)
with unequal influences, the weighting
methodology is a prerequisite for data
integration to produce an SDS vulnerability
map. Therefore, in order to express the
importance of each VM’s element relative
to others, the weighting functions are
required.
A number of methods are developed for
weight allocation in different disciplines
that are mostly based on ranking from the
experts, literature reviews and previous
studies (Choo et al., 2012). The GIS-
based weightings are mainly carried out
in global and local approaches. Global
methods assume the spatial homogeneity
of measured variables and consequently
a single weight will be assigned to each
indicator (GIS layer). Ranking, rating and
pairwise comparison approaches are
common global weighting approaches
(Malczewski, 2006).
Unlike global methods, the local
approaches allocate weights based on
measuring spatial heterogeneity within
each indicator (Malczewski and Rinner,
2015). The proximity-adjusted criterion
weights, range-based local weighting, and
entropy-based local weighting methods
are commonly used as local weighting
approaches (Malczewski and Rinner, 2015).
©Ketih
Fulton

In any scoring/weighting process, the
greater the number allocated to an
indicator or component, the more that
indicator or component will influence the
final vulnerability map of the GIS analysis.
Although different weighting methods can
be used in SDS-VM, the weighting method
of the analytic hierarchy process (AHP), a
pairwise comparison method introduced
by Saaty (1980), is recommended for
GIS-based SDS vulnerability mapping, due
to its applicability and simplicity in weight
allocation.
7.6.7. Integration of
indicators to produce a
map of components
Another critical step in SDS-VM, after
selecting indicators of exposure, sensitivity
and adaptive capacity and their relative
weights, is finding out how to integrate
these indicators to construct component
maps. Once the weight for each indicator
(as well as weights of the components)
is obtained, the spatial data integration
will be carried out through a raster
overlay process to produce exposure,
sensitivity and adaptive capacity maps.
The component maps are created through
Equation 7.1.
(Equation 7.1)
7.6.8. Components map
integration to produce
SDS vulnerability maps
For the creation of a final vulnerability map,
the literature includes three main equations
(IPCC, 2012a; UNEP, 2003):
Vulnerability map=Exposure+Sensitivity -
Adaptive Capacity
(Equation 7.2)
Vulnerability map=(Exposure*Sensitivity)
/Adaptive Capacity
(Equation 7.3)
Vulnerability map= Exposure*Sensitivity -
Adaptive Capacity
(Equation 7.4)
These equations show profound
differences between the ways that
the ultimate vulnerability map can be
calculated.
Depending on which equation is used for
the calculation, the outcome vulnerability
map is expected to be inevitably different.
Deciding whether adding, multiplying or
dividing the indicators should be selected
is therefore a significant issue. A practical
solution to test the equations is to run
the data for a single location, applying
each equation and using knowledge from
experts’ reviews, the results most closely
matched reality. This, however, is not a
trivial task and requires both knowledge
experts and suitable data sets.
In the context of SDS-VM, different
components should not be equally
considered, since they do not share a
linear relationship, as increasing exposure
is not linearly linked with the increase
in sensitivity. Thus, Equation 7.2 giving
equal weights and importance to all the
components is not recommended to
calculate SDS-VM. Moreover, expressing
the SDS-VM equation as a ratio with
adaptive capacity as the denominator
(as in Equation 7.3) may bias the output
vulnerability for marginal values of adaptive
capacity. In this case, very low (or high)
adaptive capacity will force the vulnerability
to be very high (or low). It is hence
recommended to create vulnerability maps
using Equation 7.4.
7.7 Conclusion
Many arid and semi-arid areas worldwide
are currently experiencing an increase in
the occurrence, distribution and severity of
SDS that seem likely to intensify in future.
Understanding the expected damage or
harm resulting from these events, that
is, the level of vulnerability of a society
exposed to SDS, is vital, to formulate well-
targeted adaptation and mitigation policies
and strategies.

184
Vulnerability is a multidimensional and
complex concept, generally expressed as
“the capacity to be wounded”. Vulnerability
to SDS, as a multi-cause and multi-faceted
phenomenon, is contextual and dynamic
and encompasses temporal and spatial
considerations. It depends on a variety of
factors from different domains including
health, socio-economy, environment and
agroecosystems.
Any vulnerability mapping will necessarily
include some assumptions on its three
main components of (1) exposure, (2)
sensitivity and (3) adaptive capacity.
Assumptions can be made in selecting
the appropriate indicators to express the
components to the measurement and
weighting of a given indicator. These
assumptions introduce uncertainty into
the calculation of each component and
will inevitably be aggregated into the
ultimate vulnerability map. The inherent
complexity and assumptions make any
SDS vulnerability mapping methodology
subjective, overlapping and contentious to
a degree.
Several approaches are available
to quantify vulnerability to different
environmental hazards. Statistical tools,
composite vulnerability indices and
GIS-based mapping are among the most
prevalent approaches in the literature.
This chapter has presented a conceptual
GIS-based framework to produce SDS
vulnerability map.
Comprehensive consideration is given to
the selection of appropriate indicators to
measure three vulnerability components
based on a careful study of identifying
SDS hazardous impacts on different
dimensions of human life and the
environment. A broad range of indicators
are included according to the existing
literature, expert’s knowledge and their
contribution to different components of
SDS vulnerability.
However, data availability and accessibility
posed practical limitations to the final
number of relevant indicators included.
Major indicators are listed in tables where
necessary information on data acquisition,
potential data sources, alternative
web-based data and relevancy for SDS
vulnerability are given. Attempts are made
to provide a general methodological
framework so that it can easily be adapted
by different stakeholders according to their
necessities and challenges.
In this sense, end users will decide how to
incorporate different indicators and how to
value and weight them in the calculation
of SDS vulnerability. This guarantees
that even with limited data availability
and accessibility, a basic map of SDS
vulnerability is achievable.
Moreover, a stepwise GIS-based procedure
including specific steps required to
implement SDS vulnerability mapping
is provided to avoid ambiguity for the
users. These steps involve a hypothesis,
impact assessment, identifying indicators,
data collection, data standardization,
weighting, indicator integration to produce
a component map and finally components
map integration to produce an SDS
vulnerability map. Each step is elaborated
in detail and practical considerations on
various procedures are discussed.
This document is the first effort in
developing a methodology framework to
assess and map SDS vulnerability as no
such methodology exists in the literature.
The aim was to present an integrated
methodology framework to provide a
picture of society’s vulnerability to SDS on
local to global scales, enabling planners
and policy/decision makers to compare
the relative overall human vulnerability due
to SDS at different levels. The proposed
methodology has to be implemented
and evaluated through case studies in
different sectors, as well as different
countries. Further research is required to
study driving forces of SDS, its different
impacts, indicator identification and three
vulnerability components, as illustrated in
Figures 18, 19, 20 and 21.

186
Annex 1: Potential indicators for SDS vulnerability mapping
Category Indicator
(GIS data layer)
Possible source Alternative web-
based data
Questions
(chapter 7.5)
Base
maps
Administrative
unit (national,
provincial/state,
city, town, district
and village
boundaries)
DF: polygon
National map
services
DIVA-GIS; Database of
Global Administrative
Areas (GADM);
OpenStreetMap®;
Global Land-Use
Dataset; Google
Maps services;
GEONETWORK;
Socioeconomic Data
and Applications
Center (SEDAC)
NA: Can be extracted
from remotely-sensed
imageries and web-
based map services.
Q1: Administrative
units serve as
the basis and
starting point
for vulnerability
mapping, upon
which all the other
spatial data are
based.
Q2: –
Q3: All levels.
Elevation, slope
and aspect
DF: point/raster
National
topographic
services
Advanced Spaceborne
Thermal Emission
and Reflection
Radiometer Global
Digital Elevation
Model (ASTER
GDEM); Shuttle Radar
Topography Mission
(SRTM); Natural Earth;
DIVA-GIS; Consultative
Group on International
Agricultural Research -
Consortium for Spatial
Information (CGIAR-
CSI); GEONETWORK;
SEDAC
NA: Topographic data
can be estimated
from satellite (radar,
LiDAR and stereo
images) data.
Q1: Topographic
risk is an integral
part of most
vulnerability
mapping, in
particular SDS-
VAM.
Q2: Exposure.
Q3: All levels.
Land-use/land
cover
DF: raster/polygon
National map
services
DIVA-GIS; SEDAC;
OpenStreetMap;
Global Land-
Use Dataset;
GEONETWORK;
United States
Geological Survey
(USGS) Land
Cover; Moderate
Resolution Imaging
Spectroradiometer
(MODIS) products;
SEDAC
NA: Vegetation
maps (forest and
agriculture) can
replace this layer if
no data are available.
Such maps can also
be estimated from
satellite imageries.
Q1: Land cover
and/or land-use
influence the
occurrence,
intensity and
duration of SDS
both at the source
and deposition
areas.
Q2: Sensitivity.
Q3: All levels.
Table 9.
Base data

Category Indicator
(GIS data layer)
based data
Questions
(chapter 7.5)
Watersheds
DF: polygon
National statistical
services, national
hydrological
organizations
HydroSHEDS;
GEONETWORK
NA: Vegetation
maps (forest and
agriculture) can
replace this layer if no
data are available.
Q1: Information
on watersheds
is important for
combating sources
of SDS and
provides a basis
for studies on the
scale of basins.
Q2: Sensitivity.
Q3: Watershed
level.
Matt
Artz,
©Unsplash,
November
19,
2017

188
Population
distribution
map
(demographic
data)
Age, gender, ethnic
groups
DF: point/polygon
Census data World Bank
Geodata; SEDAC
NA: Regional and
global estimations
can be considered.
Q1: Characteristics
like age, gender
and ethnicity
can influence
vulnerability.
Q2: Sensitivity and
adaptive capacity.
Q3: All levels.
Population density
DF: point/polygon Census data
DIVA-GIS; SEDAC;
WorldPop; Global
Land-Use Dataset;
GEONETWORK;
World Bank Geodata
NA: Regional and
global estimates
can be considered.
Q1: Higher
population density
and growth cause
congestion and
dense infrastructure
and hence increase
vulnerability.
Q2: Sensitivity.
Q3: All levels.
Population growth
rate
DF: point/polygon
Census data WorldPop; Atlas of
the Biosphere; World
Bank Geodata;
SEDAC
NA: Regional and
global estimates
can be considered.
Socioeconomic
and
sociopolitical
map
Household wealth
and income
DF: point
Census data World Bank
Geodata; SEDAC
NA: Regional and
global estimates
can be considered.
Q1: Socioeconomic
and sociopolitical
circumstances
are among the
main drivers of
adaptive capacity
and influence
vulnerability.
Q2: Sensitivity and
adaptive capacity.
Q3: All levels.
Infant mortality rate
DF: polygon/point
Census data SEDAC; World Bank
Geodata
NA: Regional and
global estimates
can be considered.
Poverty index
DF: polygon/point
Census data SEDAC;
GEONETWORK;
World Bank Geodata
NA: Regional and
global estimates
can be considered.
Education level
DF: point/polygon
Census data OpenStreetMap;
GEONETWORK
NA: Regional and
global estimates
can be considered.
Conflict events/
political violence
DF: polygon
National and
international reports
provided by different
organizations
Uppsala Conflict
Data Program
(UCDP); Armed
Conflict Location &
Event Data Project
(ACLED)
NA: Regional and
global estimates
can be considered.
Table 10.
Demographic
and
socioeconomic
data

Category Indicator
(GIS data layer)
Possible source Alternative web-based
data
Questions
(chapter 7.5)
Health
Health
infrastructure
index
DF: polygon
Census data GEONETWORK; World
Bank Geodata
NA: Regional and
global estimates can be
considered.
Q1: Health
infrastructure
index can lower
vulnerability by
promoting adaptive
capacity.
Q2: Adaptive
capacity.
Q3: All levels.
Emergency
response
facilities
DF: point
National map
services; thematic
maps
OpenStreetMap
NA: Regional and
considered.
Human health
index
DF: polygon
Census data GEONETWORK; World
Bank Geodata
NA: Regional and
considered.
Q1: Health
status is among
immediate impacts
of SDS and can
significantly
influence
vulnerability.
Q2: Sensitivity.
Q3: All levels.
Livestock
DF: point
Agriculture census
data
GEONETWORK; SEDAC
NA: Regional and
considered.
Wildlife
DF: point
Wildlife census data SEDAC; UN Environment
Programme World
Conservation Monitoring
Centre (UNEP WCMC)
NA: Regional and
considered.
SDS
data
SDS
DF: raster
SDS content map;
spatial-temporal
expansion map;
concentration map
MODIS products
from satellite data
(optical and LiDAR data).
Q1: SDS-related
data are used to
map vulnerability
through exposure
component,
as the higher
the exposure,
the higher the
vulnerability.
Q2: Exposure.
Q3: All levels.
Aerosol optical
depth (AOD)
DF: raster/point
AOD map; ground
stations data
MODIS products;
AERONET
NA: Can be calculated
using a range of satellite
data.
Visibility
DF: raster/point
Meteorological data;
Synoptic weather
stations data
Calculated from MODIS
products and AERONET
NA: Can be calculated
using AOD data.
SDS numerical
model outputs
(e.g. Weather
Research and
Forecasting
Model (WRF),
WRF-Chem and
DREAM)
DF: raster
Numerical
models output
data (e.g. World
Meteorological
Organization Sand
and Dust Storm
Warning Advisory
and Assessment
System (WMO-SDS-
WAS))
Barcelona
Supercomputing Centre
NA: Regional dust
models.
Table 11.
Health and sand
and dust storm
data

190
©Kyle
Taylor
on
Flickr,
December
4th,
2009

Category Indicator
(GIS data layer)
based data
Questions (chapter
7.5)
Meteorological
and
climate
data
Precipitation
DF: raster/point
Meteorological data
(stations)
WorldClim;
GEONETWORK;
Climate Research
Unit (CRU) Climate
Datasets; GCM
Downscaled Data
Portal
NA: Can be derived
from remote
sensing satellites
(e.g. Tropical
Rainfall Measuring
Mission (TRMM))
Q1: Meteorological
factors directly
influence SDS
formation and
spatial-temporal
expansion and
hence affect
vulnerability.
Q2: Sensitivity.
Q3: All levels.
Aridity Index Aridity map Global Aridity Index
NA: Can be
extracted based on
meteorological data
and remote sensing.
Natural disaster
hotspots (drought
and dust storm)
Disaster hotspot
map
Natural Disaster
Hotspots
NA: Can be
extracted based on
meteorological data
and remote sensing.
Temperature (time
series)
DF: raster/point
Meteorological data
(stations)
SEDAC;
GEONETWORK;
CRU Climate
Datasets; GCM
Downscaled Data
Portal
NA: Can be derived
from remote
sensing satellites
(e.g. MODIS).
Wind speed and
direction
DF: polyline
Meteorological data
(stations)
CRU Climate
Datasets; MODIS
products; GCM
Downscaled Data
Portal; Hysplit
model; WMO data
portal
NA: Can be derived
from remote
sensing satellites
(e.g. CALIPSO,
CloudSat).
Table 12.
Meteorological
data

192
Category Indicator
(GIS data layer)
based data
Questions (chapter
7.5)
Air pressure
DF: raster/polyline
Meteorological data
(stations)
CRU Climate
Datasets; GCM
Downscaled Data
Portal
NA: Can be derived
from remote
sensing satellites
(e.g. CALIPSO,
CloudSat).
Albedo
DF: raster
Reflectance data NASA Earth
Observations (NEO);
MODIS products
NA: Can be retrieved
from remote
sensing satellites
(e.g. Landsat,
Sentinel).
Q1: Shows the
ability of the
surface to reflect
solar light, has a
significant impact
on soil moisture
and regulates
meteorological
variables.
Q2: Exposure.
Q3: All levels.
Category Indicator
(GIS data layer)
Potential source Alternative web-based
data
Questions (chapter
7.5)
Transport
Railway
DF: polyline
National map
services,
organizational
thematic maps
SEDAC; OpenRailwayMap
imageries and web-based
map services.
Q1: Communication
routes and networks
are vulnerable and
will be affected
by SDS through
accidents and
cancellations. On
the other hand, they
can help people to
communicate for
better adaptation
and mitigation.
Q2: Sensitivity and
adaptive capacity.
Q3: All levels, mainly
sectoral.
Road
DF: polyline
National map
services,
organizational
thematic maps
OpenStreetMap; SEDAC;
gROADS
map services.
Table 13.
Transport and
utility network

Category Indicator
(GIS data layer)
Potential source Alternative web-based
data
Questions (chapter
7.5)
Airline routes
DF: polyline
IATA airline map
and national airway
maps
OpenFlights
NA:--
Marine
DF: polyline
National map
services and
organizational
thematic maps
World Port Index
map services.
Q1: These
infrastructures
will experience the
reduction of their
desired efficiency as
SDS is increased.
Q2: Sensitivity and
adaptive capacity.
sectoral.
Airport, harbours,
bus terminals, train
stations
DF: point
National map
services and
organizational
thematic maps
OpenStreetMap;
OpenFlights;
World Port Index
map services.
Utility
network
and
facilities
Communication
stations, electricity
and gas stations
National map
services and
organizational
thematic maps
OpenStreetMap;
GEONETWORK
map services. (e.g. Google
Maps services, Bing
maps).
Q1: Utility networks
will be negatively
affected by SDS
and influence
vulnerability.
Q2: Sensitivity.
Q3: Local and
sectoral.
Power plants,
electric power
facilities and
distribution lines
National map
services and
organizational
thematic maps
OpenStreetMap;
GEONETWORK
Maps services, Bing
maps).
Telecommunication
facilities and
distribution lines
(cables, networks)
National map
services and
organizational
thematic maps
OpenStreetMap;
GEONETWORK
Maps services, Bing
maps).

194
Category Indicator
(GIS data layer)
Possible source Alternative web-based data Questions (chapter
7.5)
Essential
facilities
Tourism and
recreational
facilities
DF: point/polygon
National map
services and
organizational
thematic maps
OpenStreetMap;
GEONETWORK
NA: Can be extracted from
remotely-sensed imageries
and web-based map
services. (e.g. Google Maps
services, Bing maps).
Q1: They will
be negatively
affected by SDS
and influence
vulnerability.
Q2: Sensitivity.
Q3: Local and
sectoral.
Cultural and
religious facilities
DF: point/polygon
National map
services and
organizational
thematic maps
OpenStreetMap;
GEONETWORK; United
Nations Educational,
Scientific and Cultural
Organization (UNESCO)
reports
and web-based map
Q1: They provide
essential facilities
for adaptation and
mitigation to SDS.
Q2: Adaptive
capacity.
Q3: Up to national
level.
Public facilities
and governmental
offices
DF: point/polygon
National map
services and
organizational
thematic maps
OpenStreetMap;
GEONETWORK
and web-based map
Markets and
shopping centres
DF: point/polygon
National map
services and
organizational
thematic maps
OpenStreetMap;
GEONETWORK
and web-based map
Industrial
facilities
Factories
DF: point
National map
services and
organizational
thematic maps
OpenStreetMap;
GEONETWORK
and web-based map
Q1: As SDS
frequency
increases, the
industrial sector
will experience
the reduction of
the labour-force
efficiency, reducing
product quality and
increasing costs of
cleaning.
Q2: Sensitivity.
sectoral.
Food industry
DF: point
National map
services and
organizational
thematic maps
OpenStreetMap;
GEONETWORK
and web-based map
Table 14.
Industrial
facilities

Category Indicator
(GIS data
layer)
Possible
source
Alternative web-based data Questions (chapter 7.5)
Vegetation
Agriculture
DF: raster/
polygon
National map
services,
cadastral
and land-use
maps
EarthStat; GIAM; Global Land-Use
Dataset
remotely-sensed imageries (e.g.
Landsat and Sentinel).
Q1: Distinguish the
different types of agro-
economic activities
which are sensitive to
dust particles. They also
have positive roles in
reducing vulnerability
by increasing adaptive
capacity from the
viewpoint of local
community’s economy.
Q2: Sensitivity and
adaptive capacity.
sectoral.
Horticulture
and orchard
DF: raster/
polygon
National map
services,
cadastral
and land-use
map
EarthStat; GIAM; USGS Land
Cover
Rangeland
DF: raster/
polygon
National map
services,
natural
resources
and land
cover map
Global Land-Use Dataset; SEDAC;
USGS Land Cover
Q1: Distinguish the
different types of
green coverage which
are sensitive to dust
particles. They also
have positive roles in
reducing vulnerability by
absorbing suspended
particles.
Q2: Sensitivity.
Q3: All levels.
Forest
DF: raster/
polygon
National map
services,
natural
resource
maps
Atlas of the Biosphere;
GEONETWORK; UNEP WCMC;
USGS Land Cover; Phased Array
type L-band Synthetic Aperture
Radar (PALSAR) forest/non-forest
map, MODIS products
Table 15.
Vegetation
data

196
Category Indicator
(GIS data
layer)
Possible source Alternative web-based data Questions
(chapter 7.5)
Water
Lakes, dams
and water
reservoirs
DF: polygon
National map
services,
hydrological
maps,
organizational
thematic maps
SEDAC; OpenStreetMap;
Global Reservoir and
Dam Database (GRanD);
Global Lakes and Wetlands
Database (GLWD)
(e.g. MODIS and Landsat).
Q1: Distinguish
the surface water
bodies that need
protection against
dust pollutants
deposition. In
addition, they play
a positive role in
air humidity, wet
deposition and air
cooling.
Q2: Sensitivity.
Q3: All levels.
Rivers and
drainage
network and
canals
DF: polyline
National map
services,
hydrological
maps,
organizational
thematic maps
HydroSHEDS
NA: Can be extracted based
on topographic data (e.g.
SRTM).
Wetlands
DF: raster/
polygon
National map
services
UNEP WCMC; GLWD
(e.g. MODIS and Landsat).
Q1: Distinguish the
different wetland
ecosystems and
the exposed flora
and fauna. They
have positive
impacts on air
humidity, wet
deposition and air
cooling.
Q2: Sensitivity.
Q3: All levels.
Groundwater
level
DF: raster/
polygon
Groundwater
maps
Global groundwater maps
(e.g. GRACE).
Q1: The lower
the groundwater
level, the more
vulnerable the land
for SDS emission
and the higher the
vulnerability.
Q2: Sensitivity.
Q3: All levels.
Snow
cover
map
Average snow
depth and
snow cover
DF: polygon/
raster
Snow depth and
snow cover maps
MODIS products
(e.g. Landsat and Sentinel)
Q1: Snow depth
and snow cover
have impacts on
vulnerability by
absorbing SDS
pollutant particles.
Q2: Sensitivity.
Q3: All levels.
Table 16.
Water and
precipitation

Category Indicator
(GIS data layer)
Possible
source
Alternative web-based
data
Questions (chapter 7.5)
Soil
Soil erodibility
DF: raster/
polygon
Soil erodibility
map
GEONETWORK; Atlas of
the Biosphere; FAO soil
maps;
NA: can be calculated by
soil erosion models (e.g.
European Soil Erosion
Model (EUROSEM))
Q1: The higher the soil
erodibility, the higher the
vulnerability to SDS.
Q2: Sensitivity.
Q3: All levels.
Soil moisture
DF: raster/
polygon
Soil moisture
maps
Satellite-derived products
such as Soil Moisture and
Ocean Salinity (SMOS)
and Soil Moisture Active
Passive (SMAP) satellite
maps
imageries.
Q1: Soil moisture and
texture affect soil
sensitivity to erosion and
influence vulnerability.
Q2: Sensitivity.
Q3: All levels.
Soil texture
DF: raster/
polygon
Soil physical
properties
maps
GEONETWORK; World
Soil Information; FAO soil
maps
imageries.
Forest
DF: raster/
polygon
National map
services,
natural
resource maps
GEONETWORK; UNEP
WCMC; USGS Land
Cover; PALSAR Forest/
Non-Forest map, MODIS
products
imageries (e.g. Landsat
and Sentinel).
Geology
and
Geomorphology
Geological
maps
DF: raster/
polygon
National map
services,
organizational
thematic maps
GEONETWORK;
OneGeology Portal
imageries.
Q1: Provide information
for SDS-VAM by
contributing to soil
erodibility map generation.
Q2: Sensitivity.
Q3: All levels.
Geomorphology
and Landforms
DF: raster/
polygon
National map
services,
organizational
thematic maps
OneGeology Portal
from GIS modelling
by remotely-sensed
imageries.
Note: NA: No appropriate data are available.
DF: Data format.
Table 17.
Soil and
geomorphology

198
Annex 2: Data available on the web
ACLED (http://www.acleddata.com/data) is a database
that codes the dates and locations of all reported
political violence and protest events in over 60
developing countries. Political violence includes
events that occur within civil wars and periods of
instability.
AERONET (https://aeronet.gsfc.nasa.gov/) provides
globally distributed observations of spectral
aerosol optical depth (AOD), inversion products and
perceptible water in diverse aerosol regimes.
ASTER GDEM (https://asterweb.jpl.nasa.gov/gdem.
asp) provide 30m resolution global elevation data
derived from ASTER satellite images. ASTER GDEM
coverage spans from 83 degrees north latitude to
83 degrees south, encompassing 99 per cent of
Earth’s landmass.
Atlas of the Biosphere (https://nelson.wisc.edu/sage/
data-and-models/atlas/) provides information
about the environment and human interactions
with the environment including per capita oil usage,
literacy rate, population growth rate, cropland and
built-up land, soil pH, snow depth, snow coverage
and more.
Barcelona Supercomputing Centre (https://ess.bsc.es/
bsc-dust-daily-forecast) demonstrates the ongoing
value of climate services, air quality services and
dust services to society and the economy.
CGIAR-CSI (http://srtm.csi.cgiar.org/) is a geoportal
that provides Shuttle Radar Topographic Mission
(SRTM) 90m (and resampled 250m) digital elevation
data (DEM) for the entire world. The SRTM DEM are
originally produced by NASA and are considered
among the most valuable elevation data worldwide.
CRU Climate Datasets (http://www.cru.uea.ac.uk/
data/) provides a variety of available high- and
low- resolution data sets including precipitation,
temperature, pressure, drought.
DIVA-GIS (http://www.diva-gis.org/gdata/) contains
a collection of spatial data worldwide, including
administrative areas, inland water, roads, railways,
elevation, land cover, population and climate. Spatial
data have been collected from different sources and
are available for any country in the world.
EarthStat (http://www.earthstat.org/) provides
geographic data sets of the distribution of particular
crops, water depletion and natural vegetation,
among other data sets.
GADM (http://www.gadm.org/) is a spatial database of
the location of the world’s administrative boundaries
including countries and lower level subdivisions.
GCM Downscaled Data Portal (http://www.ccafs-
climate.org/data/) includes a wide range
downscaled (higher-resolution) data created from
theoutputsofawiderangeofglobalclimatemodels.
It contains the majority of important climate
variables with a better spatial resolution.
GEONETWORK (http://www.fao.org/geonetwork/srv/
en/main.home) A geographic information system
(GIS) aggregation website including administrative
and political boundaries, agriculture and livestock,
applied ecology, base maps, remote sensing,
biological and ecological resources, watersheds
(river basins), climate, fisheries and aquaculture,
forestry, human health, hydrology and water
resources, infrastructures, land cover and land-use,
population and socioeconomic indicators, soils and
soil resources and topography.
GIAM (http://waterdata.iwmi.org/) contains information
on global irrigated and rain-fed croplands, irrigation
water sources (surface, groundwater), cropping
intensity (single, double, continuous) and dominant
crop types.
Global Aridity Index (https://cgiarcsi.community/
data/global-aridity-and-pet-database/) provides
global indices of aridity data and at 30 arc-second
resolution in raster format.
Global groundwater maps (https://www.whymap.
org/whymap/EN/Maps_Data/maps_data_node_
en.html) is a spatial portal to provide data and
information about the major groundwater resources
of the world.
Global Lakes and Wetlands Database (GLWD) (https://
www.worldwildlife.org/pages/global-lakes-and-
wetlands-database)isaportalincludingglobalmaps
of lakes, reservoirs, wetlands, swamps, and other
environments.
Global Land Use Dataset (http://nelson.wisc.edu/
sage/data-and-models/global-land-use/grid.php)
includes a number of data sets showing population,
land area, cropland area, land cover, land suitability
for cultivation, grazing land area and built-up area at
0.5 degree resolution.
Global Reservoir and Dam (GRanD) Database (http://
atlas.gwsp.org/index.php) is an online data set that
compiles reservoirs with a storage capacity of more
than 0.1 km.³ The data includes spatially explicit
records of dams and reservoirs at high spatial
resolution with extensive metadata.
Global Roads Open Access Data Set (gROADS) (http://
sedac.ciesin.columbia.edu/data/set/groads-
global-roads-open-access-v1/data-download) is a
data set of roads worldwide hosted by the Center

for International Earth Science Information Network
(CIESIN).
HydroSHEDS (https://www.hydrosheds.org/) contains
hydrological data and maps extracted from
the Shuttle Radar Topography Mission (STRM)
elevation data including global river networks,
watershed boundaries, drainage directions and flow
accumulations.
MODIS products (https://modis.gsfc.nasa.gov/data/
dataprod/) provides a rich data set of global
atmosphere, land, cryosphere and ocean products.
A great number of products are included, for
instance, snow cover, aerosol products, cloud
product, land cover, albedo and many more.
NASA Earth Observations (NEO) (https://neo.sci.gsfc.
nasa.gov/view.php?datasetId=MCD43C3_M_BSA)
provides Albedo data retrieved from satellite
imageries.
NaturalDisasterHotspots(http://sedac.ciesin.columbia.
edu/data/collection/ndh#) is a geoportal including
a range of geographic data on natural disasters
(including volcanoes, earthquakes, landslide, flood
and ‘multihazards’) with hazard frequency and
economic loss, among other indicators.
Natural Earth (http://www.naturalearthdata.com/)
provides a convenient resource for making custom
maps. It contains free vector and raster map data at
1:10m, 1:50m, and 1:110m scales.The data includes
country borders, administrative maps, populated
places, urban areas, water bodies and boundaries,
islands, coastline, glaciated areas, land cover and
shaded relief. Bear in mind that some data are only
available for particular countries/continents.
OneGeology Portal (http://portal.onegeology.org/
OnegeologyGlobal/) is a spatial portal including
combined geological data from many geological
organizations across the world. Basic geological
data are available for many countries.
OpenFlights (https://openflights.org/data.html)
contains airports, airline routes, train stations and
ferry terminals spanning the globe.
OpenRailwayMap (http://www.openrailwaymap.org/) is
adetailedonlinemapofglobalrailwayinfrastructure,
built on OpenStreetMap data.
OpenSeaMap (http://openseamap.org/index.
php?id=openseamapandno_cache=1) provides
online map of global marine ways, built on
OpenStreetMap data.
OpenStreetMap (http://www.geofabrik.de/data/
download.html) is a crowdsourced database
including a number of GIS-ready shapefiles such
as urban extent, administrative boundaries, roads,
points of interest, buildings and ferry routes.
PALSAR Forest/Non-Forest map (http://www.eorc.jaxa.
jp/ALOS/en/palsar_fnf/fnf_index.htm) Global 25m
resolution PALSAR-2/PALSAR Mosaic and Forest/
Non-forestmapofafreelyavailabledatasetgenerated
by applying Japan Aerospace Exploration Agency
(JAXA)’s powerful processing and sophisticated
analysis method/techniques to several images
obtained with Japanese Phased Array type L-band
Synthetic Aperture Radars (PALSAR and PALSAR-2)
on Advanced Land Observing Satellite (ALOS) and
Advanced Land Observing Satellite-2 (ALOS-2).
Protected Planet (https://www.protectedplanet.net/) is
a publicly available online platform where terrestrial
and marine protected areas and access-related
statistics can be explored and downloaded.
Socioeconomic Data and Applications Center
(SEDAC) (http://sedac.ciesin.columbia.edu/) is
a data centre in NASA’s Earth Observing System
Data and Information System (EOSDIS) hosted
by CIESIN at Columbia University. It provides a range
of socioeconomic spatial data, including settlement
points, urban areas, environmental indicators
(annual maps of PM2.5
, urban heat islands, land
surface temperature, NO2
concentrations), spatial
economic data, population density, population,
global anthropogenic biomes, roads, agricultural
lands, water bodies, poverty maps (for 28 countries)
and many more regional and local data (log in
required).
UNEP GEOdata (http://geodata.grid.unep.ch/) is the
authoritative source for data sets used by United
Nations Environment Programme (UNEP) and
its partners in the Global Environment Outlook
(GEO) report and other integrated environment
assessments. Its online database holds more
than500differentvariables,asnational, subregional,
regional and global statistics or as geospatial data
sets (maps), covering themes like fresh water,
population, forests, emissions, climate, disasters,
health and GDP.
UNEP WCMC (http://datadownload.unep-wcmc.org/
datasets) includes a wide range of data sets from
the United Nations Environment Programme
(UNEP) World Conservation Monitoring Centre
such as global wetlands, global distribution of coral
reefs, mangrove distributions, tropical dry forests,
wilderness, global distribution of saltmarshes and
more.
Uppsala Conflict Data Program (UCDP) (http://ucdp.
uu.se/) is an online map presenting the location
and statistics of instances of political violence in
different parts of the world.
USGS Land Cover (https://www.usgs.gov/core-science-
systems/science-analytics-and-synthesis/gap/
science/land-cover-data-download?qt-science_

200
center_objects=0#qt-science_center_objects) is a
very useful web page providing a great number of
links to many land cover, forestry, albedo, agriculture,
river observations and many more data sets.
WMO GAWSIS (https://gawsis.meteoswiss.ch/
GAWSIS/#/).TheWorldMeteorologicalOrganization
(WMO) Global Atmosphere Watch Station
Information System.
WMO OSCAR (https://www.wmo-sat.info/oscar/). The
Observing Systems Capability Analysis and Review
Tool (OSCAR) is the WMO’s official repository of
WIGOS metadata for all surface-based observing
stations and platforms.
WMO SDS-WAS (https://sds-was.aemet.es/) and
(https://www.wmo.int/pages/prog/arep/wwrp/
new/Sand_and_Dust_Storm.html). WMO Sand and
System.
WMO WIGOS (https://www.wmo.int/pages/prog/www/
wigos/index_en.html). WMO Integrated Global
Observing System.
World Bank Geodata (http://databank.worldbank.org/
data/home.aspx) includes a wide range of global
data such as population, financial data, education
statistics and indicators, gender statistics, health
nutrition and population statistics and many more
data sets.
World Port Index (http://msi.nga.mil/NGAPortal/MSI.
portal?_nfpb=trueand_pageLabel=msi_portal_
page_62andpubCode=0015) is a database that
contains the location and physical characteristics
of, and the facilities and services offered by, major
ports and terminals worldwide.
World Soil Information (https://www.isric.org/) is a
geoportal that provides soil information on national
and/or local levels. Gridded datasets covering the
world’s soils at a maximum resolution of 5 arc-
minutes with 22 attributes for each cell including
organic carbon content, clay content, silt content,
sand content and water capacity.
WorldClim (http://www.worldclim.org/) is a set of global
climate data (temperature (min, max, mean) and
precipitation)withaspatialresolutionofabout1km2
.
Climate data are available from the past, present and
predicted data for future conditions.
WorldPop (http://www.worldpop.org.uk/) is an open
access database of high spatial resolution,
contemporary data on human population
distributions for most parts of the world.

7.8 References
Abuhdima, Esmaeil M., and Saleh, Ibrahim M. (2010).
Effect of sand and dust storms on microwave
propagation signals in Southern Libya. Paper
presented at the Melecon 2010–2010 15th IEEE
Mediterranean Electrotechnical Conference.
ArcGIS (n.d.). Types of geodatabases. Available at http://
desktop.arcgis.com/en/arcmap/10.3/manage-
data/geodatabases/types-of-geodatabases.htm.
Adger, W. Neil (1999). Social vulnerability to climate
change and extremes in coastal Vietnam. World
Development, vol. 27, No. 2, pp. 249–269.
Adger, W. Neil, and Kelly, P. Mick (1999). Social
vulnerability to climate change and the architecture
ofentitlements.MitigationandAdaptationStrategies
for Global Change, vol. 4, No. 3, pp. 253–266.
Adger, W. Neil, and others (2004). New indicators
of vulnerability and adaptive capacity, vol. 122.
Norwich: Tyndall Centre for Climate Change
Research.
Alwang, Jeffrey, and others (2001). Vulnerability: a view
from different disciplines. In Social Protection
discussion paper series, No. SP 0115. Washington,
D.C.: World Bank.
Anderson, Linda, van Klinken, R.D., and Shepherd,
Damien (2008). Aerially surveying Mesquite
(Prosopis spp.) in the Pilbara. In A climate of
change in the rangelands. Proceedings of the 15th
Australian rangeland society biennial conference. D.
Orr, ed. Australia: Australian Rangeland Society.
Armbrust, D.V, and Lyles, Leon (1975). Soil stabilizers
to control wind erosion. In Soil Conditioners, W.R
Gardner and W.C. Moldenhauer, ed., pp 77–82.
vol. 71, 7-14.
Boy, Jens, and Wilcke, Wolfgang (2008). Tropical Andean
forest derives calcium and magnesium from
Saharan dust. Global Biogeochemical Cycles, vol.
22, No. 1.
Brooks, Nick (2003). Vulnerability, risk and adaptation:
A conceptual framework, vol. 38, 1–16. Norwich:
Tyndall Centre for Climate Change Research.
Burkhardt, Juergen (2010). Hygroscopic particles
on leaves: nutrients or desiccants? Ecological
Monographs, vol. 80, No. 3, pp. 369–399.
Caribbean Group for Cooperation in Economic
Development and World Bank (2002). Caribbean
Economic Overview 2002: Macroeconomic Volatility,
Household Vulnerability, and Institutional and Policy
Responses. Available at https://app.overton.io/
document.php?policy_document_id=worldbank-bd
5ad86bc47f2579337bdddf4e456d37
Chan, Yiu-Chang, and others (2005). Influence of the 23
October 2002 dust storm on the air quality of four
Australian cities. Water, Air, and Soil Pollution, vol.
164, No. 1–4, pp. 329–348.
Chaudhuri, Shubham, Jalan, Jyotsna, and Suryahadi,
Asep (2002). Assessing household vulnerability to
poverty from cross-sectional data: A methodology
and estimates from Indonesia. New York:
Department of Economics, Columbia University.
Chen, Jen-Ping, and others (2004). Simulations of Asian
yellow dust incursion over Taiwan for the spring of
2002 and 2003. Terrestrial Atmospheric Oceanic
Sciences, vol. 15, No. 949, e981.
Cheng, Jian-jun, and others (2015). Characteristics of the
disastrous wind-sand environment along railways
in the Gobi area of Xinjiang, China. Atmospheric
Choi, Hyun, and others (2011). Asian dust storm particles
induce a broad toxicological transcriptional
program in human epidermal keratinocytes.
Toxicology letters, vol. 200, No. 1, pp. 92–99.
Choo, Eng U., Schoner, Bertram, and Wedley, William
C. (1999). Interpretation of criteria weights in
multicriteria decision making. Computers and
Industrial Engineering, vol. 37, No. 3, pp. 527–541.
Cinner, Joshua E., and others (2012). Vulnerability of
coastal communities to key impacts of climate
change on coral reef fisheries. Global Environmental
Change, vol. 22, No.1, pp. 12–20.
Committee for Development Policy (2000). Economic
Vulnerability Index Explanatory Note. CDP2000/
PLEN/21. http://www.un.org/esa/analysis/
devplan/cdp00p21.pdf Accessed on 19 October
2012.
Cook, Benjamin I., Miller, Ron L., and Seager, Richard
(2008). Dust and sea surface temperature forcing
of the 1930s “Dust Bowl” drought. Geophysical
Research Letters, vol. 35, No. 8.

202
Cutter, Susan L. (1996). Vulnerability to environmental
hazards. Progress in human geography, vol. 20, No.
4, pp. 529–539.
Cutter, Susan L., Boruff, Bryan J., and Shirley, W. Lynn
(2003). Social vulnerability to environmental
hazards. Social Science Quarterly, vol. 84, 2, pp.
242–261.
Cutter, Susan L., and others (2009). Social vulnerability to
climate variability hazards: a review of the literature.
Final Report to Oxfam America. University of South
Carolina: Hazards and Vulnerability Research
Institute.
Darvishi Boloorani, Ali, and others (2014). Dust storms
in the West Asia region. Available at https://www.
researchgate.net/publication/272564183_Dust_
Storms_in_the_West_Asia_Region
Darvishi Boloorani, Ali, and others (2014). Investigation
of dust storms entering Western Iran using remotely
sensed data and synoptic analysis. Journal of
Environmental Health Science and Engineering, vol.
12, No. 1, p. 124.
Das, Haripada P., and others (2003). Agrometeorology
related to extreme events, Technical Note No. 201,
WMO No. 943. Geneva: World Meteorological
Organization.
De Longueville, Florence, and others (2013). Desert dust
International Journal of Biometeorology, vol. 57, No.
1, pp. 1–19.
Deetz, Konrad, and others (2016). Numerical simulation
of a dust event in northeastern Germany with a new
dust emission scheme in COSMO-ART. Atmospheric
Deroubaix, Adrien, and others (2013). Suitability of
OMI aerosol index to reflect mineral dust surface
conditions: preliminary application for studying the
link with meningitis epidemics in the Sahel. Remote
Sensing of Environment, vol. 133, pp. 116–127.
Dow, Kirstin (1992). Exploring differences in our
common future(s): the meaning of vulnerability to
global environmental change. Geoforum, vol. 23,
No. 3, pp. 417–436.
Downing, Thomas E., and Patwardhan, Anand (2003).
Vulnerability Assessment for Climate Adaptation:
APF Technical Paper 3. New York: United Nations
Development Programme.
Downing, Thomas E., and others (2005). Assessing
vulnerability for climate adaptation. In Adaptation
policy frameworks for climate change: Developing
strategies, policies and measures, Bo Lim, Erica
Spanger-Siegfried, Ian Burton, Elizabeth Malone,
Saleemul Huq, eds. Cambridge: Cambridge
University Press, pp. 61–90.
Esri (n.d.). Homepage. Available at http://www.esri.com.
Evan, Amato and others (2006). New evidence for a
relationship between Atlantic tropical cyclone
activity and African dust outbreaks. Geophysical
Farmer, Andrew M. (1993). The effects of dust on
vegetation—a review. Environmental Pollution, vol.
79, No. 1, pp. 63–75.
Fellmann, Thomas (2012). The assessment of climate
change related vulnerability in the agricultural
sector: reviewing conceptual frameworks. FAO/
OECD Workshop Building Resilience for Adaptation
to Climate Change in the Agriculture Sector, p.
23–24.
Ford, James (2002). Vulnerability: concepts and issues.
PhD Scholarly Field Paper, Guelph: University of
Guelph.
Füssel, Hans-Martin (2005). Vulnerability in climate
change research: A comprehensive conceptual
framework. Oakland: University of California
International and Area Studies.
Füssel, Hans-Martin (2007). Vulnerability: a generally
applicable conceptual framework for climate
change research. Global Environmental Change, vol.
17, No. 2, 155–167.
Gabric, Albert J., and others (2010). Australian dust
storms in 2002–2003 and their impact on Southern
Ocean biogeochemistry. Global Biogeochemical
Cycles, vol. 24, No. 2.
Gillett, Dale A. (1979). Environmental factors affecting
dust emission by wind erosion. Saharan Dust, pp.
71–94.
Godwin, William (1876). Lives of the Necromancers.
London: Frederick J. Mason.
health disorders. Environment International, vol. 63,
pp. 101–113.
Goudie, Andrew S., and Middleton, Nicholas J. (2006).
Desert dust in the global system: Berlin: Springer-
Verlag Berlin Heidelberg.
Green, Francis H.Y., and others (1990). Characterization
of airborne mineral dusts associated with farming
activities in rural Alberta, Canada. International
Archives of Occupational and Environmental Health,
vol. 62, No. 6, pp. 423–430.
Gregory, James N. (1991). American exodus: the Dust
Bowl migration and Okie culture in California. New
York: Oxford University Press.
Hagen, L., and Woodruff, N. (1973). Air pollution from
duststorms in the Great Plains. Atmospheric
Environment (1967), vol. 7, No. 3, pp. 323–332.
Han, Yongxiang, and others (2008). Dust aerosols: a
possible accelerant for an increasingly arid climate
in North China. Journal of Arid Environments, vol.
72, No. 8, pp. 1476–1489.
Handisyde, Neil, and others (2006). The effects of

climate change on world aquaculture: a global perspective.
Aquaculture and Fish Genetics Research Programme, Stirling
Institute of Aquaculture. Final Technical Report, DFID. Stirling:
University of Stirling.
Highwood, Eleanor J., and Ryder, Claire L. (2014). Radiative effects
of dust. In Mineral Dust: A key player in the Earth system, 1st
edition, Peter Knippertz and Jan Berend Stuut, eds. Dordrecht:
Springer.
Hobbs, Benjamin F., and Meier, Peter (2012). Energy decisions and
the environment: a guide to the use of multicriteria methods, vol.
28. New York: Springer Science and Business Media.
Holyoak, Adam L., Aitken, Peter J., and Elcock, Mark S. (2011).
Australian dust storm: impact on a statewide air medical
retrieval service. Air Medical Journal, vol. 30, No. 6, pp. 322–
327.
Hurt, R. Douglas (1981). The Dust Bowl: An agricultural and social
history. Chicago: Nelson-Hall Publications.
Huszar, Paul C., and Piper, Steven L. (1986). Estimating the off-site
costs of wind erosion in New Mexico. Journal of Soil and Water
Hwang, Ching-Lai, and Yoon, Kwangsun (2012). Multiple attribute
decision making: methods and applications, a state-of-the-art
survey, vol. 186. Berlin: Springer-Verlag Berlin Heidelberg.
Intergovernmental Panel on Climate Change (2012a). Determinants
of risk: exposure and vulnerability. In Managing the risks of
extreme events and disasters to advance climate change
adaptation. A Special report of Working Groups I and II of the
Intergovernmental Panel on Climate Change, Christopher B.
Field, Vicente Barros, Thomas F. Stocker, Qin Dahe, David Jon
Dokken, Kristie L. Ebi, Michael D. Mastrandrea, Katharine J.
Mach, Gian-Kasper Plattner, Simon K. Allen, Melinda Tignor
and Pauline M. Midgley, eds. New York: Cambridge University
Press.
––––––––––––––––––––––––––––––––––– (2012b).
Managing the risks of extreme events and disasters to advance
climate change adaptation. A special report of Working
Groups I and II of the Intergovernmental Panel on Climate
Change, Christopher B. Field, Vicente Barros, Thomas F.
Stocker, Qin Dahe, David Jon Dokken, Kristie L. Ebi, Michael D.
Mastrandrea, Katharine J. Mach, Gian-Kasper Plattner, Simon
K. Allen, Melinda Tignor and Pauline M. Midgley, eds. New York:
––––––––––––––––––––––––––––––––––– (2014a).
Climate Change 2014: Impacts, Adaptation, and Vulnerability.
Part A: Global and Sectoral Aspects. In Contribution of
Working Groups I, II and III to the fifth assessment report of
the Intergovernmental Panel on Climate Change, Christopher
B. Field, Vicente Barros, Katharine J. Mach, Michael D.
Mastrandrea, T. Eren Bilir, Monalisa Chatterjee, Kristie L. Ebi,
Yuka Otsuki Estrada, Robert C. Genova, Betelhem Girma, Eric
S. Kissel, Andrew N. Levy, Sandy MacCracken, Patricia R.
Mastrandrea and Leslie L. White, eds. Cambridge: Cambridge
University Press.
––––––––––––––––––––––––––––––––––– (2014b).
Contribution of Working Groups I, II and III to the fifth
assessment report of the Intergovernmental Panel on Climate
Change, Christopher B. Field, Vicente Barros, Katharine
J. Mach, Michael D. Mastrandrea, T. Eren Bilir, Monalisa
Chatterjee, Kristie L. Ebi, Yuka Otsuki Estrada, Robert C.
Genova, Betelhem Girma, Eric S. Kissel, Andrew N. Levy, Sandy
MacCracken, Patricia R. Mastrandrea and Leslie L. White, eds.
Cambridge: Cambridge University Press.
Issanova, Gulnura, and others (2015). Aeolian transportation of
sand and dust in the Aral Sea region. International Journal of
Environmental Science and Technology, vol. 12, No. 10, pp.
3213–3224.
Jeong, Dai-Yeun (2008). Socio-economic costs from yellow dust
damages in South Korea. Transborder Environmental and
Natural Resource Management, pp. 185-198.
Jiang, Hong, and Eastman, J. Ronald (2000). Application of fuzzy
measures in multi-criteria evaluation in GIS. International
Journal of Geographical Information Science, vol. 14, No. 2, pp.
173–184.
Kang, Kwang-Kyu (2004). A study on the analysis of damages of
Northeast Asian dust and sand storm and of the regional
cooperation strategies. Korea Environment Institute, vol. 2,
No. 1, pp. 112–118. Available at https://www.kei.re.kr/elibList.
es?mid=a20402040000&elibName=researchreport&class_
i d = & a c t = v i e w & c
id=667800&rn=1068&nPage=107&keyField=&keyWord=
Kappes, Melanie S., Papathoma-Koehle, Maria, and Keiler, Margreth
(2012). Assessing physical vulnerability for multi-hazards
using an indicator-based methodology. Applied Geography, vol.
32, No. 2, pp. 577–590.
Kellogg, Christina A., and Griffin, Dale W. (2006). Aerobiology and
the global transport of desert dust. Trends in Ecology and
Evolution, vol. 21, No. 11, pp. 638–644.
Kelly, P. Mick, and Adger, W. Neil (2000). Theory and practice in
assessing vulnerability to climate change and facilitating
adaptation. Climatic Change, vol. 47, No. 4, pp. 325–352.
Kim, Ki-Hyun, and others (2003). The chemical composition of fine
and coarse particles in relation with the Asian Dust events.
Atmospheric Environment, vol. 37, No. 6, pp. 753–765.
Knippertz, Peter (2014). Meteorological aspects of dust storms. In
Mineral Dust: A key player in the Earth system, 1st edition, Peter
Knippertz and Jan Berend Stuut, eds. Dordrecht: Springer, pp.
121–147.
Knippertz, Peter., and Stuut, Jan Berend (2014). Introduction. In
Mineral Dust: A key player in the Earth system, 1st edition, Peter
Knippertz and Jan Berend Stuut, eds. Dordrecht: Springer, pp.
1–14.
Koven, Charles Dunbar (2006). On the sources, composition, and
climatic effects of mineral dust in the atmosphere. ProQuest
Dissertations and Theses; Thesis (Ph.D.), University of
California, Berkeley. Publication Number AAI3253934.
Dissertation Abstracts International, vol. 68-02, section B,
page: 0850.; 151 p. Available at https://ui.adsabs.harvard.edu/
abs/2006PhDT.......204K/abstract
Lal, Rattan (2014). Soil conservation and ecosystem services.

204
International Soil and Water Conservation Research,
vol. 2, No.3, pp. 36–47.
Larney, Francis J., and others (1998). Wind erosion
effects on nutrient redistribution and soil
productivity. Journal of Soil and Water Conservation,
vol. 53, No. 2, pp. 133–140.
Leys, John F. (2002). Wind erosion effects on nutrient
redistribution and soil productivity. In Encyclopaedia
of Soil Science, Rattan Lal, ed. New York: Marcel
Dekker, pp. 499–502.
Ligon, Ethan, and Schechter, Laura (2003). Measuring
vulnerability. The Economic Journal, vol. 113, No.
486, pp. C95–C102.
Malczewski, Jacek (1999). GIS and multicriteria decision
analysis. Hoboken: John Wiley and Sons.
Malczewski, Jacek (2006). GIS based multicriteria
decision analysis: a survey of the literature.
International Journal of Geographical Information
Science, vol. 20, No. 7, pp. 703–726.
Malczewski, Jacek, and Rinner, Claus (2015).
Multicriteria decision analysis in geographic
information science. Berlin: Springer-Verlag Berlin
Heidelberg.
Maliszewski, Paul J., Larson, Elisabeth K., and Perrings,
Charles (2012). Environmental determinants of
unscheduled residential outages in the electrical
power distribution of Phoenix, Arizona. Reliability
Engineering & System Safety, vol. 99, pp. 161–171.
Martinelli, N., Olivieri, Oliviero, and Girelli, Domenico
(2013). Air particulate matter and cardiovascular
disease: a narrative review. European Journal of
Internal Medicine, vol. 24, No. 4, pp. 295–302.
Massam, Bryan H. (1988). Multi-criteria decision making
(MCDM) techniques in planning. Progress in
Planning, vol. 30, pp. 1–84.
Meibodi, Ali Emami, and others (2015). Economic
modeling of the regional polices to combat dust
phenomenon by using game theory. Procedia
Economics and Finance, vol. 24, pp. 409–418.
Menéndez, Inmaculada, and others (2007). Airborne
dust accumulation and soil development in the
North-East sector of Gran Canaria (Canary Islands,
Spain). Journal of Arid Environments, vol. 71, No. 1,
pp. 57–81.
Middleton, Nicholas J. (1986). A geography of dust
storms in South‐West Asia. Journal of Climatology,
vol. 6, No. 2, pp. 183–196.
Middleton, Nicholas J. (2017). Desert dust hazards:
a global review. Aeolian Research, vol. 24, pp.
53–63. Available at http://dx.doi.org/10.1016/j.
aeolia.2016.12.001.
Moreno, Alvaro, and Becken, Susanne (2009). A climate
change vulnerability assessment methodology for
coastal tourism. Journal of Sustainable Tourism,
vol. 17, No.4, pp. 473–488.
Mu, Haosheng, and others (2013). Long-term effects of
livestock loss caused by dust storm on Mongolian
inhabitants: a survey 1 year after the dust storm.
Yonago Acta Medica, vol. 56, No. 1, p. 39.
Nativ, Ronit, and others (1997). Water salinization in
arid regions – observations from the Negev desert,
Israel. Journal of Hydrology, vol. 196, No. 1, pp.
271–296.
Naval Research Laboratory (2009). Description of
NAAPS (Navy Aerosol Analysis and Prediction
System) Global Aerosol Model, 14 May. Available at
http://www.nrlmry.navy.mil/aerosol/Docs/globaer_
model.html.
O’Brien, Karen, and others (2004). What’s in a word?
Conflicting interpretations of vulnerability in climate
change research. CICERO Working Paper. Oslo:
Center for International Climate and Environmental
Research.
O’Keefe, Phil, Westgate, Ken, and Wisner, Ben (1976).
Taking the naturalness out of natural disasters.
Nature, vol. 260, No. 5552, pp. 566–567.
Olmos, Santiago (2001). Vulnerability and adaptation
to climate change: concepts, issues, assessment
methods. London: Climate Change Knowledge
Network.
Onishi, Kazunari, and others (2012). Atmospheric
transport route determines components of Asian
dust and health effects in Japan. Atmospheric
Pearson, Leonie J., and others (2011). Interpretive review
of conceptual frameworks and research models
that inform Australia’s agricultural vulnerability
to climate change. Environmental Modelling and
Software, vol. 26, No. 2, pp. 113–123.
People’s Republic of China, State Forestry and Grassland
Administration (2013). China National Action Plan
to Combat Desertification (2010–2020).
Péwé, Troy L. (1981). Desert dust: origin, characteristics,
and effect on man, vol. 186. McLean: Geological
Society of America.
Piya, Luni, Joshi, Niraj Prakash, and Maharjan, Keshav
Lall (2016). Vulnerability of Chepang households
to climate change and extremes in the Mid-Hills
of Nepal. Climatic Change, vol. 135, No. 3-4, pp.
521–537.
Polsky, Colin D., Neff, Rob, and Yarnal, Brent (2007).
Building comparable global change vulnerability
assessments: the vulnerability scoping diagram.
Global Environmental Change, vol. 17, No. 3, pp.
472–485.
Preston, Benjamin L., and Stafford-Smith, Mark (2009).
Framing vulnerability and adaptive capacity
assessment: Discussion paper: Aspendale: CSIRO
Climate Adaptation National Research Flagship.
Pritchett, Lant, Suryahadi, Asep, and Sumarto, Sudamo

(2000). Quantifying vulnerability to poverty:
a proposed measure, applied to Indonesia.
Washington, D.C.: World Bank.
ProgrammeofResearchonClimateChangeVulnerability,
Impacts and Adaptation (2013). Research priorities
on vulnerability, impacts and adaptation: responding
to the climate change challenge. Nairobi: United
Riksen, Michel, and de Graaff, Jan (2001). On‐site and
off‐site effects of wind erosion on European light
soils. Land Degradation & Development, vol. 12, No.
1, pp. 1–11.
Saaty, Thomas L. (1980). The analytic hierarchy process.
New York: McGrawHill.
Sarver, Travis, Al-Qaraghuli, Ali, and Kazmerski,
Lawrence L. (2013). A comprehensive review of the
impact of dust on the use of solar energy: history,
investigations, results, literature, and mitigation
approaches. Renewable and Sustainable Energy
Reviews, vol. 22, pp. 698–733.
Schröter, Dagmar, Polsky, Colin, and Patt, Anthony G.
(2005). Assessing vulnerabilities to the effects of
global change: an eight step approach. Mitigation
and Adaptation Strategies for Global Change, vol. 10,
No. 4, 573–595.
Seinfeld, John H., and others (2004). ACE-ASIA: Regional
climatic and atmospheric chemical effects of
Asian dust and pollution. Bulletin of the American
Meteorological Society, vol. 85, No. 3, pp. 367–380.
Shao, Longyi, and others (2007). Mineralogical
characteristics of airborne particles collected in
Beijing during a severe Asian dust storm period
in spring 2002. Science in China Series D: Earth
Shao, Yaping, and Dong, Chaohua (2006). A review
on East Asian dust storm climate, modelling and
monitoring. Global and Planetary Change, vol. 52,
No.1, pp. 1–22.
Sharifi, M. Rasoul, Gibson, Arthur C., and Rundel, Philip
W. (1997). Surface dust impacts on gas exchange
in Mojave Desert shrubs. Journal of Applied Ecology,
vol. 34, No. 4, pp. 837–846.
Sivakumar, Mannava V.K. (2005). Impacts of sand
storms/dust storms on agriculture. In Natural
Disasters and Extreme Events in Agriculture, M.V.
Sivakumar, R.P. Motha, Haripada P. Das, eds. Berlin:
Springer, pp. 159–177.
Sprigg, William A. (2016). Dust storms, human health
and a global early warning system. In Extreme
Weather, Health, and Communities: Interdisciplinary
Engagement Strategies, Sheila Lakshmi Steinberg,
William A. Sprigg, eds. Switzerland: Springer, pp.
59–87.
Starr, J.R. (1988). Weather, climate and animal
performance. Geneva: Commission for Agricultural
Meteorology and World Meteorological
Organization.
Stefanski, Robert, and Sivakumar, Mannava V.K. (2009).
and potential agricultural applications of a SDSWS.
IOP Conference Series: Earth and Environmental
Science, vol. 7, No. 1.
Tam, Wilson W.S., and others (2012). Effect of dust
storm events on daily emergency admissions for
respiratory diseases. Respirology, vol. 17, No. 1, pp.
143–148.
Terradellas, Enric, Baldasano, Jose M., and Cuevas,
Emilio (2014). Regional Centre for Northern Africa,
Middle East and Europe of the WMO Sand and Dust
Storm Warning Advisory and Assessment System.
Geneva: World Meteorological Organization.
Tiller, K.G., Smith, Lester, Frank, and Merry, R.H.
(1987). Accessions of atmospheric dust east of
Adelaide, South Australia, and the implications for
pedogenesis. Journal of Soil Research, vol. 25, No.
1, pp. 43–54.
Tozer, Peter R., and Leys, John F. (2013). Dust Storms
– What do they really cost? The Rangeland Journal,
vol. 35, pp. 131–142.
Turner, Billie Lee, and others (2003). A framework for
vulnerability analysis in sustainability science.
vol. 100, No. 14, pp. 8074–8079.
(2005). Regional master plan for the prevention
and control of dust and sandstorms in north east
Asia. Available at https://knowledge.unccd.int/
publication/northeast-asia.
–––––––––––––––––––––––––––––––––––
–––– (2013). Sub–regional action programme to
combat desertification in West Africa. Available at
https://www.unccd.int/sites/default/files/relevant-
links/2017-07/Economic%20Community%20
o f % 2 0 W e s t % 2 0 A f r i c a n % 2 0 S t a t e s % 2 0
%28ECOWAS%29.pdf.

206
––––––––––––––––––––––––––––––––––
––––– (2015). Desertification land degradation
and drought (DLDD) – some global facts and
figures. Available at http://www.unccd.int/Lists/
SiteDocumentLibrary/WDCD/DLDD%20Facts.pdf
–––––––––––––––––––––––––––––––––––
–––– (2016). Land Degradation Neutrality Fund:
An innovative investment fund project. United
Nations Convention to Combat Desertification,
the Global Mechanism and Mirova. Available
at http://188.165.241.85/gm/website/file/358/
download?token=fgcn4I8-.
United States, Government Printing Office (1938).
Monthly Catalog, United States Public Documents.
Washington, D.C.: United States Government
Publishing Office.
United Nations Economic and Social Council (2016).
Resolution adopted by the Economic and Social
Commission for Asia and the Pacific on 24 May
2016. No. E/ESCAP/RES/72/7.
United Nations Environment Programme (2003).
Assessing human vulnerability to environmental
change: concepts, issues, methods and case studies.
Available at https://www.unenvironment.org/
resources/report/assessing-human-vulnerability-
environmental-change-concepts-issues-methods-
and.
––––––––––––––––––––––––––––––––
(2012). Land health surveillance: an evidence-
based approach to land ecosystem management
– illustrated with a case study in the West Africa
Sahel. Nairobi.
–––––––––––––––––––––––––––––––– (2016).
United Nations, Environment Programme, World
Global assessment of sand and dust storms.
Nairobi: United Nations Environment Programme.
70/195. Combating sand and dust storms. United
Nations General Assembly.
United Nations Inter-Agency Secretariat of the
International Strategy for Disaster Reduction
(UN/ISDR) (2005). Hyogo Framework for Action
2005–2015: Building the Resilience of Nations
and Communities to Disasters. World Conference
on Disaster Reduction, 18–22 January 2005,
Kobe, Japan, https://www.unisdr.org/2005/wcdr/
intergover/official-doc/L-docs/Hyogo-framework-
for-action-english.pdf
United Nations Office for Disaster Risk Reduction (2015).
Sendai Framework for Disaster Risk Reduction.
Available at https://www.undrr.org/implementing-
sendai-framework/what-sf.
Westphal, Douglas L., Toon, Owen B., and Carlson,
Toby N. (1987). A two‐dimensional numerical
investigation of the dynamics and microphysics
of Saharan dust storms. Journal of Geophysical
Research: Atmospheres, vol. 92, No. D3, pp. 3027–
3049.
Won, Jae-Gwang, and others (2004). Estimation of direct
radiative forcing of Asian dust aerosols with sun/
sky radiometer and lidar measurements at Gosan,
Korea. Journal of the Meteorological Society of
Japan, vol. 82, No. 1, pp. 115–130.
Xie, Shaodong, Li Qi, Yuanhang Zhang, and Tang,
Xiaoyan (2005). Characteristics of air pollution in
Beijing during sand-dust storm periods. Water, Air,
and Soil Pollution: Focus, vol. 5, No. 3, pp. 217–229.
Xin, Jinyuan, and others (2007). Aerosol optical depth
(AOD) and Ångström exponent of aerosols
observed by the Chinese Sun Hazemeter Network
from August 2004 to September 2005. Journal of
D5.
Youlin, Yang, Squires, Victor, and Qi, Lu (2002). Global
alarm: dust and sandstorms from the world’s
drylands. Bangkok: United Nations Convention to
Combat Desertification.
Young,Anthony(1989).Agroforestryforsoilconservation,
vol. 4. Exeter: CAB international Wallingford and
Wheatons Ltd.
Zakey, Ashraf Sahar, Solmon, Fabien, and Giorgi, Filippo
(2006). Development and testing of a desert dust
module in a regional climate model. Atmospheric
Chemistry and Physics Discussions, vol. 6, No. 2, pp.
1749–1792.
Zender, Charles S., Miller, Ron L., and Tegen, Ina
(2004). Quantifying mineral dust mass budgets:
terminology, constraints, and current estimates.
Eos Transactions American Geophysical Union, vol.
85, No. 48, pp. 509–512.
Zhang, Xiao-Yin, and others (2003). Sources of
Asian dust and role of climate change versus
desertification in Asian dust emission. Geophysical
Zobeck, Ted M., Fryrear, Donald W., and Pettit, R. (1989).
Management effects on wind-eroded sediment
and plant nutrients. Journal of Soil and Water

©NASA/MODIS
Rapid
Response
Team,
August
7th,
2017

UNCCD | Sand and Dust Storms Compendium | Chapter 8 | Sand and dust storm source mapping 209
8. Sand and dust storm
source mapping
Chapter overview
This chapter provides extensive details on how to map potential sand and dust storm
(SDS) source areas based on the nature of the soil. The chapter covers drivers of
SDS source activity, anthropogenic sources, the distribution of SDS sources and two
approaches to SDS source mapping. The chapter includes a process for high-resolution
SDS source mapping based on soil and surface data, provides formulae for this type of
analysis and includes a list describing data sources which can be used in the SDS source
mapping process. This chapter is to be read in conjunction with chapter 2.

UNCCD | Sand and Dust Storms Compendium | Chapter 8 | Sand and dust storm source mapping
210
8.1. Overview of
the physical
sources of SDS
Based on the information compiled from
Lu and Shao (2001), Shao (2008) and
United Nations Environment Programme
(UNEP), World Meteorological Organization
(WMO) and United Nations Convention to
Combat Desertification (UNCCD) (2016),
the primary source of sand and dust storms
(SDS) can be defined as “a bare topsoil
surface susceptible to wind erosion or any
surface capable of emitting soil particles in
favourable wind conditions”. “Bare topsoil” is
a soil surface fraction without vegetation or
snow/ice cover or that is covered by a water
body (for example, a lake, river or wetland).
A soil surface is susceptible to wind erosion
when it contains smaller soil particles,
generally clay and silt size particles up to
about 50–60μm in diameter, depending
on the classification system (Schaetzl
and Anderson, 2009). In case of high
surface wind velocity, sand size particles
(predominantly very fine sand of up to about
100 μm in diameter) may be emitted from
a surface and carried away, but over much
shorter distances than finer particles.
The likelihood of soil becoming part
of an SDS event is increased if the soil
structure is disturbed and loose, leading
to particles being free for uptake by wind.
Other conditions that can contribute to soil
becoming part of an SDS event include:
• low topsoil moisture
• the soil not being frozen
• surface wind velocity above a certain
threshold closely related to particle
size distribution in topsoil and topsoil
moisture (see chapter 2)
SDS source locations and conditions are
distinguished by the nature of the source:
• Permanent SDS sources are mostly
located in desert areas and are
constantly susceptible to wind erosion
given their fine (small μm) topsoil
content, permanent warm and arid
climate, no or limited vegetation cover
and the general absence of water
bodies.
• Dynamic SDS sources can change
in the level of SDS-related activity
depending on the season, weather
conditions and human impacts.
The dynamics of SDS sources are related to
seasonal changes in the vegetation cover,
snow cover, the existence of or changes
in the extent of water bodies and whether
the soil is frozen. These variations create
notable changes in SDS source geographic
distribution.
Dynamic SDS sources range from “seasonal”
to “occasional”. “Seasonal” sources are
usually controlled by climatological
seasonality in weather conditions and
“occasional” sources are the ones not
necessarily active during favourable
seasonal conditions, but which require an
additional driver to trigger their activity,
usually extreme weather and/or direct
human impacts. SDS sources may evolve
into sources with different temporal activity,
meaning they may change from occasional
to seasonal or permanent, or vice versa,
depending on the impacts of drivers of SDS
source activity. Determining the likelihood of
such behaviour requires regular monitoring
of SDS sources.
Drought, as an extreme seasonal or multi-
season weather condition, may lead to
SDS or an increase in SDS activity. Heat
waves may prevent freezing of topsoil
and contribute to increased SDS activity.
For additional details on permanent and
dynamic sources, see Kim et al. (2013),
Vukovic et al. (2014), Tegen (2016), WMO
and UNEP (2013) and UNEP, WMO and
UNCCD (2016).
Human interventions can have positive or
negative impacts on SDS source activity.
Sustainable land management practices,
such as afforestation and climate smart
agriculture (Sanz et al. 2017), may reduce the
likelihood of SDS (see chapter 12 and 8.3).
On the other hand, anthropogenic impacts
that can induce and increase vulnerability of
topsoil to wind erosion come from different
sectors of the economy and include direct
and indirect impacts. This is discussed
further in chapter 8.3.

Identifying and mapping SDS sources,
and understanding why these locations
produce SDS, provides information for
SDS risk and impact assessment, SDS
mitigation planning, SDS forecasting
and establishment of SDS early warning
systems (WMO and UNEP, 2013) (see
chapters 5, 6, 7, 9, 10, 11
and 12). Mapping the spatial and
temporal distribution of SDS sources
requires:
• understanding what causes
the formation and activation of SDS
sources (see chapter 8.2)
• defining parameters for SDS
productive areas (see chapter 8.2).
• understanding ways to adjust SDS
mapping procedures to provide useful
information
A proposed methodology to detect the
surface potential for SDS formation is
described in chapter 8.5.
8.2. Drivers of SDS
source activity
Four drivers impact the existence of SDS
sources, as summarized in Figure 22 and
discussed herein. Each driver interacts with
each of the other drivers. This interaction
can vary in time and space and may lead to
an increase or decrease in SDS generation.
Climate conditions: Climate is one of the
main drivers of the formation of permanent
SDS sources in desert areas (Shao 2008;
Shao et al., 2011).
Figure 22.
Drivers that
impact sand
and dust storm
activity
SDS
SOURCES
climate
conditions
surface
conditions
human
activities
weather
conditions
Extreme aridity, together with high winds
in desert areas with insufficient vegetation
and long-term exposure to erosion, can
lead to the formation of SDS sources.
Climate conditions also affect seasonal
activity of SDS sources, which is related to
seasonal change of surface conditions –
mainly of vegetation cover – and seasonal
winds (Kim et al., 2013; Tegen, 2016).
Weather conditions: Weather conditions
can induce additional SDS source activity
and lead to the formation of new SDS
sources. Consistent or repetitive dry
weather conditions with seasonal wind
patterns is distinguished as a separate
driver from climate conditions. At the same
time, changes from usual SDS source
behaviour can be the result of extreme
weather conditions, which become more
common in a world where the climate is
constantly changing (Intergovernmental
Panel on Climate Change [IPCC], 2012;
2014a). Meteorological drought is an
example of extreme weather and can
cause increased SDS source activity.

212
However, the true effect of drought also
depends on other drivers (Figure 22)
which can amplify or reduce the impact
of drought. In mid- and higher latitudes
heat waves may trigger the activity of
SDS sources during the season when the
surface is usually frozen or covered by
snow. This effect is expected to increase
in the future under the changing climate
conditions.
Wind speeds which vary from usual
seasonal atmospheric circulation are
also an element in the weather driver
package. For example, during extreme
surface heating or intense cold frontal
movement, formation of strong convective
activity is possible. This can produce cold
downdrafts from clouds and, consequently,
high surface winds that increase SDS
source activity in the event of low humidity
conditions (Knippertz et al., 2009; Knippertz
and Todd, 2012; Vukovic et al., 2014).
Terms associated with such events are
“haboob”, “line of instability”, “cold pool” and
“density currents”.
Surface conditions: Surface conditions
are soil characteristics (most importantly
soil texture and structure), soil condition
(moisture and temperature), and land cover
(bare soil fraction). Soil texture with a fine
particle content is a precondition for a
location becoming an SDS source. If soil
structure is disturbed, topsoil particles are
more susceptible to wind erosion where
soil moisture is low and soil temperature
is above freezing (Kok, 2011; Kim et al.,
2013; Wu et al., 2018). Bare soil surface
is a precondition for the existence of
active SDS sources, which means there is
no vegetation, snow/ice or water on the
topsoil. Areas that include fractions of bare
soil surface, like sparsely vegetated area,
are considered as SDS sources, with less
possibility of dust emission compared to
fully bare land areas.
Due to the complexity of the ways
surface conditions and soil surfaces
respond to other drivers, and their large
spatial and temporal variability (including
many unknown processes), it is better
to distinguish surface conditions as a
separate driver. Expanding knowledge of
soil composition can strongly contribute
to understanding of these interactions, as
well as the understanding of SDS impacts
on humans and the environment (Nickovic
et al., 2012; 2013; Sprigg et al., 2014).
Human activities: Interaction of humans
with natural processes can lead to
amplification or suppression of other
drivers.
Direct impacts of human activities include
change of surface conditions. Water
scarcity, tillage, grazing and deforestation
can have a direct impact on soil
degradation (Orr et al., 2017) and thereby
result in the amplification of SDS source
activity. Sustainable land management
practices (Sanz et al., 2017; Orr et al., 2017)
can reduce SDS activity. Indirect impacts
of humans on SDS activity include the
anthropogenic impact on the climate which
affects the other drivers of SDS source
activity.
Human activities are a significant driver
for changes in the whole climate system,
with increasing world population and
global warming currently the two largest
stressors for the environment. The human
impact is measured as a planetary-scale
geological force (Diffenbaugh and Field,
2013; Steffen et al., 2015; Cherlet et al.,
2018). This is the reason for separate
analysis of SDS sources, which exist
mainly as a consequence of human
activities, as described in chapter 8.3.
8.3. Anthropogenic
sources
Human activities have a significant impact
on the climate system (IPCC, 2014b) and
especially on land surface characteristics
by transforming them to surfaces suitable
for food production and other economy
benefits (IPCC, 2019).
These activities can impact SDS source
formation and increase the activity
of dynamic SDS sources, possibly
transforming them into permanent source
areas (UNEP, WMO and UNCCD 2016;
United Nations Economic and Social
Commission for Asia and the Pacific [UN
ESCAP], 2018). Enhanced emissions can
cause severe negative impacts on the

environment, human health and safety
(Pauley, Baker and Barker et al., 1996;
Arizona Department of Environmental
Quality, 2012; Sprigg et al., 2014; Irfan et al.,
2017).
When human activities are the
predominant driver of SDS source
activity, these SDS sources are
called “anthropogenic sources”. The
human activities which contribute to
anthropogenic sources occur in multiple
sectors, including agriculture, water,
forestry, energy and transport.
Anthropogenic sources can result in
“direct” and “indirect” impacts. Factors with
“direct impacts” that have the most effect
on SDS source activity are:
• land cover changes, disturbance of
the topsoil and loss of soil structure,
which are mostly the consequence
of agriculture practices (tillage and
livestock breeding).
• use of water for irrigation, hygienic
needs (especially for large urban
● Tillage
● Water scarcity
● Livestock
● Other
● Climate change
DIRECT IMPACTS INDIRECT IMPACTS
ANTHROPOGENIC
SOURCES
areas) and industry.
• other factors that can dominate
impact in some regions, such as
deforestation, fires, mining.
Human activity-related climate change
has an impact on an increased frequency
and intensity of severe weather events,
like drought, fires and high winds, and
thereby can have “indirect impact” on SDS
source activity (IPCC 2012; 2014a). The
most important impacts which lead to the
formation of anthropogenic sources are
shown in Figure 23.
Recognizing and acknowledging the
human impact on SDS source activity
and understanding the impact of SDS
generated from anthropogenic sources
is important for SDS source mitigation
planning and implementation. Prioritizing
mitigation of anthropogenic sources
considers restoration of the natural dust
cycle in the climate system and achieving
land degradation neutrality. Assessment
of climate change impact on SDS source
activity contributes to adaptation planning
in areas vulnerable to SDS.
8.4. Distribution of
SDS sources
Knowledge on SDS source distribution is
an initial step for assessment of risk and
impact of SDS and implementation of SDS
source mitigation measures. Distribution
and patterns of dust sources are complex
and have high spatial and temporal
variability, which is the consequence of the
high spatial variability of topsoil texture
and structure, land-use, socioeconomic
impacts and variability of climate and
weather conditions.
Figure 23.
Most relevant
human impacts
leading to sand
and dust storm
anthropogenic
sources

214
Spatial scales of SDS sources range from
large-scale erodible areas in desert regions
to point-like sources usually sensitive to
agriculture practice and water scarcity
(Shao et al., 2011; Lee et al., 2009 ; Ginoux
et al., 2012; Vukovic et al., 2014), as well
as the retreat of glaciers and occurrence
of high-latitude SDS events (Bullard et al.,
2016; Arnalds, Dagsson-Waldhauserova
and Ólafsson, 2016). A dense pattern of
point-like sources may individually emit
dust plumes that merge into a larger-
scale SDS event, which may reach the
significance of emissions from large-scale
sources.
Areas and locations that have the best
conditions (drivers) for SDS generation
and that produce a major share of airborne
sand and dust concentrations are called
“hotspots” (Engelstaedter and Washington,
2007). This type of source is usually:
• small in scale and situated in larger-
scale SDS productive areas (Lary et
al., 2015; Feuerstein and Schepanski,
2019), or
• distributed as individual sources
outside desert areas (Lee et al., 2003;
Arnalds, Dagsson-Waldhauserova and
Ólafsson, 2016).
The global and regional distribution of
major SDS source areas has been covered
in detail in several reports, including WMO
and UNEP (2013) and UNEP, WMO and
UNCCD (2016). The main SDS productive
source areas are situated in the desert
belt in the northern hemisphere (Central
Asia, the Middle East, North Africa). Other
notable SDS productive areas are in south-
west part of the United States of America
(USA), the southern part of South America,
south Africa and Australia. See chapter 2
for more information on SDS source areas.
8.5. SDS source mapping
8.5.1. Two approaches to
detecting SDS source
areas
Understanding where to implement SDS
source reduction actions requires knowing
where SDS can originate and how sand
and dust can be entrained into SDS events
(Middleton and Kang, 2017). Two major
factors that influence the generation of
SDS are high surface winds and a free-soil
surface.
High surface wind velocity can be a
consequence of seasonal patterns of
large-scale atmospheric circulation and/
or extreme local weather conditions
(see chapter 8.2). A “free-soil surface” is
relatively dry, unprotected topsoil (free of
vegetation, snow, ice or water), which is not
frozen, the soil particles of which are free
to be emitted under windy conditions. As
surface winds of sufficient velocity for soil
particle emission are common in all parts
of the world, SDS generation is determined
in a significant way by the existence of a
free-soil surface.
SDS source mapping can be divided into
two approaches:
1. SDS source mapping from data on
SDS occurrence
2. SDS source mapping from data on
surface conditions
These two approaches are discussed as
follows.
8.5.2. Sand and dust storm
source mapping based
on sand and dust
storm occurrence
SDS source mapping based on SDS
occurrence uses data on SDS occurrence,
such as satellite data, ground PM
measurements and visibility data (Wang,
2015). Results are better if longer periods
of data are included in the analysis.
Global distribution of SDS sources
obtained using this approach can be found
in Shao (2008), Shao et al. (2011) and
Ginoux et al. (2012). Remotely-sensed
data and machine learning can generate
relatively high-resolution point-like sources
(Lary et al., 2015). The advantages and
disadvantages of mapping based on data
on SDS occurrence are listed in Table 18.

Advantages Disadvantages
• Good representation (high confidence) of
synoptic overview of major and frequently active
sand and dust storm (SDS) sources (permanent
and seasonal).
• Recognize global and regional sources that are
dominant in SDS generation.
• It represents mapping of SDS activity (or
occurrence), not SDS sources.
• Spatial and temporal coverage of SDS
observations is not continuous.
• Resolution is lower than mapping resolutions of
other soil surface related parameters.
• Unable to recognize/delineate many of small-
scale and, occasionally, active SDS events.
• Climatological approach (averaging of long-term
data) gives advantage to natural (permanent
and seasonal) and/or larger scale SDS sources.
• Underestimates SDS sources which are small
scale and/or not frequently active.
Table 18.
Advantages and
disadvantages
of sand and dust
storm mapping
using sand
and dust storm
occurrence
8.5.3. SDS source mapping
of data on soil surface
condition
This approach to SDS source mapping
uses a combination of data on the potential
for the soil surface to emit soil particles
which can be carried away from source in
favourable wind conditions, that is, the soil
surface’s susceptibility to wind erosion.
The approach is based on use of soil and
surface data to estimate (parameterize)
information on soil surface potential to
produce SDS, rather than to detect SDS
occurrence.
The SDS source mapping based on soil
conditions is used, for example, in mapping
SDS sources in numerical modelling of
dust transport (Nickovic et al., 2001; Kim
et al., 2013; Vukovic et al., 2014), and in
studies that investigate the level of land
degradation and desertification (UNCCD,
2017; Cherlet et al., 2018). This approach to
SDS source mapping is less used due to its
complexity.
However, the approach can significantly
contribute towards the better definition
of SDS source patterns, including their
small-scale features, which is necessary
in planning actions related to SDS source
mitigation. Advantages and disadvantages
of mapping based on data on soil surface
conditions are listed in Table 19.
©ESA,
CC
BY-SA
3.0
IGO,
July
11th,
2008

216
Advantages Disadvantages
• Contains data on soil characteristics and land-
use.
• Can provide high-resolution SDS source
patterns.
• Can detect/delineate small-scale sources and
distinguish SDS source hotspots.
• Can detect surfaces with high potential for SDS
generation in extreme weather conditions, even
if they are not frequently active.
• Requires a relatively complex combination of
information from different sources of data.
• Due to high spatial variability and insufficient soil
sampling, the quality of soil information may be
low, which requires implementation of additional
information.
• Does not include information on frequency of
SDS generation.
Table 19.
Advantages and
disadvantages
of sand and dust
storm mapping
based on soil
conditions
Information on SDS sources based on
SDS observations can be used to verify the
reliability of data obtained from surface
observations over larger SDS source
regions. A good – and relatively simple
– example of this methodology is SDS
source mapping using topography data
which is verified using satellite data, found
in Ginoux et al. (2001), and later improved
with seasonal SDS source change, found in
Kim et al. (2013).
Overcoming the disadvantages of this
approach involves:
• acquiring more accurate national data
• additional national observations and
data sets
• methodologies that enable even higher
resolution mapping.
A basic methodology for SDS source
mapping using surface data, with possible
map upgrades depending on data
availability and quality, is discussed in more
detail in chapter 8.6.
8.5.4. Gridded data on
SDS sources
“SDS source mapping” means
representation of geo-referenced data on
SDS sources on a regular grid with certain
resolution, where one number represents
information about the SDS source in a grid
box with dimensions that depend on the
map resolution. Usually, information on the
SDS source is scaled to have values from
0 to 1 (where 0 is no SDS source in the grid
box and 1 is the whole area in the grid box
being fully SDS-productive and/or have
highest potential for SDS generation) or in
percentage terms (0–100%).
Depending on the approach used for SDS
source mapping, the data obtained can
have different meanings.
1. When SDS source mapping is done
using data on SDS occurrence
(Prospero et al., 2002; Walker et al.,
2009; Ginoux et al., 2012; Akhlaq et
al., 2012; Shao et al., 2013; Division of
Earth & Ecosystem Sciences, 2013;
Sinclair and Jones, 2017), gridded
information on SDS sources is usually
derived from the frequency of SDS
detection. Thereby, this kind of map
represents frequency of SDS activity,
assuming that areas with the highest
frequency are the strongest sources
of SDS, which corresponds to close
to one in the SDS source map. In this
case, SDS source hotspots are areas
with the highest frequency of SDS
occurrences.
2. When SDS source mapping is carried
out using data on soil surface
conditions, gridded information on
SDS sources represent the potential
of the soil surface in the grid box to
emit particles in the event of high wind
conditions. Thereby, this kind of map
represents a fraction of the free-soil
surface in the grid box. Values closer
to one represent areas that are highly
susceptible to wind erosion in cases
of high surface velocity winds. In this
case, SDS source hotspots are the
surfaces with higher potential for
emission of particles.
On climate scales, areas with the most
frequent SDS occurrences will coincide, in
a large part of the world, with areas with
the highest potential for SDS generation.
Because of their dynamic component
caused by the change in SDS source
drivers (see chapter 8.2), over larger
timescales, SDS source map patterns can
be significantly different, especially during
extreme weather events that can trigger
the activation of SDS source hotspots.

Such SDS sources can have low frequency
of activity and are could possibly not be
recognized as hotspots in the mapping
approach that uses data on SDS
occurrence, but must be recognized as
having a high potential for SDS generation
in mapping approaches that use data on
surface conditions. For this reason, and
due to direct and indirect human impacts
on SDS formation (see chapter 8.3),
mapping of SDS sources for the purpose of
mitigation planning, forecasting of SDS and
early warning systems, should consider
applying a methodology based on soil
surface data.
8.6. Methodology for
high-resolution SDS source
mapping
This section explains a methodology
that enables high-resolution SDS source
mapping, which relies on the approach
discussed in chapter 8.5.2. It is based
on available global data, which may be
supplemented or replaced with national
data of higher accuracy and resolution, if
available, or may be supplemented with
additional information available on national
level, like SDS source hotspots.
8.6.1. Clusters of
relevant data
Implementation of a methodology based
on soil surface data analysis is necessary
to achieve high-resolution SDS source
mapping (at a 1 km or higher level of
detail) which includes all areas that have
the potential to generate SDS in favourable
wind conditions.
A list of basic (most important) parameters
that are required in SDS source mapping is
presented in Figure 24. These parameters
represent clusters of data sets, which are
combined using certain criteria, mainly
based on setting threshold values that
serve the purpose of eliminating non-
productive areas from the global land
surface.
Therefore, this approach to SDS source
mapping may be understood as an
elimination method – excluding areas
that are certainly not SDS-productive. The
remaining areas represent potentially SDS-
productive surfaces, which should include
all permanent and dynamic (seasonal and
occasional) sources.
©ESA,
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BY-SA
3.0
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September
26th,
2008

218
An initial cluster of parameters that
are necessary for SDS source mapping
(Figure 24) includes:
• data on soil characteristics
• data on land cover
• data on soil condition
Here are separated soil characteristic and
soil condition data, where:
• “characteristics” describes soil as a
material (texture, composition, etc.),
and
• “condition” describes the soil
properties which change according to
seasonal and weather conditions.
Both can be impacted by human activities
(see chapter 8.2 and 8.3).
Data that can provide information about
listed parameters are universally available,
but quality may differ from region to region.
To further increase the quality of SDS
source maps, implementation of national
data and information is necessary.
1 See https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/survey/.
Soil characteristics
The most important information regarding
soil characteristics is the soil texture and
soil structure. Surface soil texture will
provide information on soil particle size
distribution, such as whether the soil
contains particles that are small enough
to be uplifted from the surface and carried
away from the source (Lu and Shao 2001;
Shao, 2008).
Such soil texture classes, based on the
United States Department for Agriculture
soil classification system,1
are presented
in Figure 25. Soil texture classes should
include clay and silt size particles, but
classes that have major part of sand size
particles will not be ignored, just will be
considered as less productive, because of
their significant role in emission processes
(Shao 2008; Sweeney et al., 2016). The
most SDS productive soils, considering
soil texture, are marked in red in Figure
25, medium productive in green and least
productive in blue.
Figure 24.
Soil surface
parameters
necessary for
sand and dust
storm source
mapping
● Texture
● Structure
SOIL CHARACTERISTICS
● Vegetation
● Water
LAND COVER
● Moisture
● Temperature
SOIL CONDITION
SDS SOURCE MAP
National data
Note: Use of national data, if available, can improve the result of SDS source mapping at subnational
and national scales, based on global data sets.

Key: Red – soil texture classes with higher content of fine soil particles. Green – soil texture classes
with medium to low fine soil particles content, Blue – dominant coarse soil texture.
Note: Adapted from Natural Resources Conservation Service (n.d.).
Figure 25.
United States
Department
of Agriculture
soil texture
classification
system
Information on the surface soil structure
provides information on whether a soil
surface is disturbed or loose. Aggregate
stability is related to organic matter con-
tent (Chaney and Swift, 1984). Soil that has
low structural stability is found to have very
low content of soil organic carbon (SOC).
Desert areas have values of about 0.2 per
cent and other areas in arid climates about
0.5 per cent (Fan Yang et al., 2018).
Soil organic carbon is one of the indicators
used to assess land degradation and
monitor land degradation neutrality (Cowie
et al., 2018). Degraded soils are vulnerable
to wind erosion, a land degradation
process linked to SDS source formation.
Usually, fine soil texture is related to
richer SOC content (Meliyo et al., 2016;
Johannes et al., 2017), but where there
is a fine structure and low SOC, surface
soil particles can be loose where other
parameters show favourable conditions
for the activation of SDS sources. Setting
upper SOC thresholds can exclude
surfaces that have good surface structure
and where soil particles are in stable
condition. Low values or decreasing SOC
values can serve to identify areas with
increasing exposure to wind erosion, and
which can become SDS sources.
The depth to bedrock can be one more
limiting parameter categorized under soil
characteristics (Shangguan et al., 2016). If
the soils are shallow, they are most likely
not significant SDS sources. Other soil
characteristics that are indicative of its
mineral and biochemical composition are
important for understanding the interaction
of particles with the environment, and their
impact on climate system and humans.
However, such information is very scarce.
Only a few data sets on soil characteristics
related to SDS generation are available
on a global level (Nickovic et al., 2012;
Journet, Balkanski and Harrison, 2014;
Perlwitz, Pérez García-Pando and Miller,
2015), and the available information can be
improved. Soil data in global data sets can
be of low quality and not regularly updated.
Improving soil data sets can be done using
national-level data, which are, however,
usually not publicly available.
Land cover
Land cover data can be used to identify
surfaces that are bare or sparsely/partially
vegetated, and without snow/ice cover or
water bodies (Tegen et al., 2002; Kim et al.,
2013; Vukovic et al., 2014).

220
This information can be derived from
regularly updated satellite data to detect
changes in the activity of SDS sources.
Parameters that can provide this kind of
information are Normalized Difference
Vegetation Index (NDVI) or Enhanced
Vegetation Index (EVI) data.
Land cover or land-use data are usually
updated annually and can provide
information about the type of surface
(forest, grassland, cropland, bare, urban).
Land cover types that can be considered
potentially dust-productive are (i) bare land
or (ii) sparsely vegetated land, grassland,
scrubland and cropland. Other land cover
types that can also be impacted by human
impact drivers (see chapter 8.3.) can
become anthropogenic sources due to the
loss of ground cover, due, for example, to
melting ice, fire or deforestation.
Land cover data can be
used to detect bare regions
but are insufficient for
detecting dynamic SDS
sources. As a result, land
cover data can be used
together with NDVI/EVI
data to detect types of SDS
source.
A priority in SDS source mapping, related
to land cover analysis, is to use NDVI or
EVI data and land cover data in a more
diagnostic manner to recognize types of
the SDS sources. NDVI data are commonly
used for SDS source mapping, but EVI
can correct some distortions arising from
atmospheric haze and ground cover below
vegetation (Heute et al. 2002).
Figure 26 presents an example of NDVI
and EVI data for 2018 for Mongolia where
differences between these two indices are
clearly visible. Red values represent areas
covered with vegetation and blue values
areas without vegetation. Updated SDS
source maps at a national level based on
NDVI/EVA data can be used to identify
different types of the SDS source (pasture,
mining, among others).
Soil condition
The most important parameters related
to soil condition, which are mainly related
to weather conditions but can also be
impacted by human activities, are (i) soil
moisture and (ii) soil temperature. These
parameters are discussed as follows.
If topsoil with favourable soil
characteristics is dry enough and not
frozen, emission from the surface is
possible in favourable windy conditions.
If topsoil is drier, the wind velocity
threshold for emission of particles is lower
(Bagnold, 1941; Fécan, Marticorena and
Bergametti; Nickovic et al., 2001; Pérez
García-Pando et al., 2011). Soil temperature
needs to be well below 0°C to be frozen,
and the threshold may depend on soil
composition (Kim et al., 2013). Soil freezing
temperature also depends on moisture
content, because low-moisture soils need
lower temperatures to freeze, and in soil
saturated with water, will most likely freeze
at temperatures near 0°C.
Figure 26. Moderate
Resolution Imaging
Spectroradiometer
Normalized
Difference Vegetation
Index (MODIS NDVI)
and Enhanced
Vegetation Index
(EVI) for 2018
Note: Values are multiplied by 104.
Source: Personal communication, courtesy of
Jungrack Kim

Setting an upper threshold for moisture
data and a lower threshold for temperature
data will distinguish areas that can
generate SDS if other parameters allow
classification of these areas as SDS
sources. More about data sources and
data manipulation can be found in the next
section.
Other data and improvements of sand and
dust storm source mapping
Necessary data for SDS source mapping
described in the previous section are
available on a global level or can be derived
from global data sets. At regional and
national levels, further improvements of
data quality and resolution are possible
for most of the listed parameters, using
regional and national data sets (Figure
24), such as soil types and composition,
soil condition data, weather and climate
data and information on human activities
(Gerivani et al., 2011; Cao et al., 2015;
Borrelli et al., 2016). Better diagnostics
on SDS source types are also possible,
especially of anthropogenic sources,
for example, mining sites, conventional
agricultural production sites, glacier retreat
zones or loss of vegetation due to fires.
Mapping of SDS sources at the national
level, including spatial and temporal
resolution improvements, can be done
by implementing SDS source monitoring
using remote sensing and high-resolution
topographic and geomorphological
information (Bullard et al., 2011; Parajuli
and Zender, 2017; Feuerstein and
Schepanski, 2019; Iwahashi et al., 2018).
Improvements of SDS source mapping by
implementation of topographic data are
discussed in more detail in chapter 8.6.4.
8.6.2. Calculating the
SDS sources spatial
distribution
Calculations can be used to identify the
likelihood of SDS source development
based on a range of factors, including
soil texture, soil structure, bare soil
surface, soil moisture and frozen soil.
Calculation processes described below
focus on extracting and processing
data to develop SDS source maps. The
calculations detailed below are based on
an assumption that land surface can be
SDS-productive (land is SOURCE=1) and
continues with filtering using values for
the soil surface parameters explained as
follows.
Soil texture
Data on soil texture provides the fraction
(percentage) of clay and silt content.
Higher clay and silt content mean higher
potential for SDS formation. The United
States Department of Agriculture (USDA)
soil texture types that have fine particle
contents sufficient for blowing dust and
SDS formation, can have total clay and
silt content mainly above 50 %. Surfaces
with sand-dominant content should not
be excluded but rather scaled as less
productive than surfaces with higher
content of clay and silt, because heavier
particles less contribute to emission rates
during high wind events and require higher
wind velocities to carry them away from
sources.
Setting up the lower threshold on total clay
and silt content will exclude surfaces that
are not significantly active because of the
very high, coarse fraction content. Scaling
soil texture potential for SDS formation is
directly related to finer particle content:
SOURCE = FTX , if FTX < FTXmin then set
FTX = 0
where FTX is a fine soil texture fraction
with values 0 to 1. Threshold FTXmin is not
necessary, as lower FTX values will reduce
SOURCE function. However, adjusting
threshold value may exclude surfaces that
are insignificant, for example, for transport
far from the source and long-range
transport.
Soil structure
To distinguish soils with a loose surface,
meaning that particles on the surface are
more susceptible to wind erosion, values
of SOC can be used. Arid and desert
surfaces have low SOC content, well

222
below 1 per cent (0.2–0.5 per cent), but for
vulnerable areas that are experiencing soil
degradation and can transform into SDS
sources, SOC can be up to 1 per cent. SOC
information is implemented in SDS source
mapping by defining the upper threshold,
and all soil surfaces with lower values can
be considered to have unstable or low
structure, and thereby susceptible to wind
erosion:
SOURCE = FTX x STR, if SOC < SOCmax
then STR=1, if SOC ≥ SOCmax then STR = 0
where STR is the soil structure parameter
and SOCmax is a defined threshold value,
which depends on the interest in SDS
source mapping, that is, only desert areas
or areas that include surfaces vulnerable
to wind erosion under extreme drought
and negative human impacts. However,
relations between wind erosion impact
and SOC content is poorly known, and
thresholds should be carefully chosen
in order not to exclude potential dust
emission areas.
Bare soil surface
The bare soil surface fraction in the grid
box can be detected using NDVI (or EVI)
values above zero to exclude water bodies,
snow and ice cover. Values up to 0.1 fully
distinguish bare surfaces, but areas with
higher values can also include a fraction of
bare soil surface.
The relation of NDVI values with a fraction
of vegetation has not yet been determined,
but according to the literature (which is
mainly related to NDVI rather than EVI
for this purpose), the upper boundary
of 0.15 can include a major part of fully
bare and sparsely vegetated surfaces.
Water, snow and vegetation cover may
change depending on the SDS source
drivers. A regular update of the values
of this parameter is recommended.
Implementation of data on bare soil surface
fraction (BSF) can be done as follows:
SOURCE= FTX x STR x BSF, if NDVI >
NDVImax and NDVI ≤ 0 then BSF=0, if
0<NDVI≤0.1 BSF=1, and if 0.1 < NDVI ≤
NDVImax then 1 ≥ BSF ≥ 0 or also can be
set to BSF=1 where BSF is the bare soil
fraction with values from 0 to 1, depending
on the NDVI (EVI) values, and NDVImax
is the threshold for NDVI. This threshold
value may be adjusted to different land
cover types.
The relation of BSF and NDVI values, when
the soil surface in the grid box is partially
covered with vegetation, can be improved
with the use of higher-resolution soil
surface observations. Due to less noise in
the EVI data compared to NDVI, the use of
EVI should be considered.
Land cover or land-use data can be used
to identify types of SDS sources, by
overlaying this information with SOURCE
data, and to double check exclusion of
irrelevant surfaces. Land cover types that
can be potential SDS source areas include
bare land, grassland (pastures), cropland,
scrubland (open scrubland). These data
are updated annually.
Soil moisture
Soil moisture usually depends on the
climate zone. However, as soil moisture
varies seasonally and is dependent on
weather conditions, a process of looking
at soil moisture for all areas with possible
low soil moisture permits the detection
of seasonal and occasional SDS sources.
This is particularly true at the beginning of
the growing season.
Soil moisture measurements are usually
very sparse and/or not available to the
public. A few global data sets are available,
from the European Centre for Medium-
Range Weather Forecast (ECMWF)
or National Oceanic and Atmospheric
Administration (NOAA) analysis and
satellite data. Data are updated every 6 to
12 hours, or daily. Relatively new ERA5-
Land database provides data on higher
spatial and temporal resolution, generated
by surface scheme which is a part of the
ECMWF forecast system, with available
data at 1 hour interval.
If soil moisture (SM) is below a certain
threshold, emission is possible:
SOURCE= FTX x STR x BSF x DSF, if SM ≤
SMmax than DSF = 1, if SM > SMmax DSF = 0

where DSF is dryness of soil surface and
permits SDS source activity if SM is below
threshold SMmax.
Determining a threshold is not easy for two
reasons:
1. Water capacity is different for different
soil compositions.
2. Moisture thresholds where emission
stops can change with wind velocity
(higher value where there is higher
wind velocity).
Adjusting SMmax can be done using
information on drought, aridity, national
data on soil types and their characteristics
and values of SM that coincide with dry
periods.
Frozen soil
Soil temperature (ST) is important for
excluding frozen soil surface areas. This
is especially important during winter and
early spring seasons, when areas are
without vegetation and strong winds are
possible (usually in continental climates).
Temperature thresholds for frozen soil
are below -10°C in case of lower soil
moisture and depend on soil composition.
If the soil moisture is higher soil freezing
temperature is increasing.
Temperature data can be derived as soil
moisture data, from EMWF or NOAA
reanalysis and satellite data, and are also
updated in 6 to 12-hour cycles, or daily. It
can be obtained from ERA5-Land database
on higher spatial and temporal resolution.
If soil temperature (surface air temperature
can also be used) is above some threshold
value, emission is possible:
SOURCE= FTX x STR x BSF x DSF x NFS ,
if ST ≥ STmin than NFS = 1, if ST < STmin
NFS = 0
where NFS is not a frozen soil surface
and permits SDS source activity if ST is
above threshold STmin. Issues related to
determining this threshold are similar to
those of SMmax but related to conditions
favourable for soil freezing.
8.6.3. Data sources for sand
and dust storm source
calculations
The data sets described as follows can be
used for SDS source mapping. The data
sets are geo-referenced, in standard grid
presentations and regularly distributed
globally. However, a user should investigate
possible sources of relevant data for their
region which can improve SDS source
mapping accuracy.
Soil texture (clay and silt content) and
SOC data:
The International Soil Reference and
Information Centre (ISRIC) world soil
information database provides SoilGrids
(soil global gridded information) which
enables users to manipulate data online
and to download data sets (Hengl et al.,
2014; Hengl et al., 2017). Data sets are
1km resolution and higher, available in TIFF
format and in WGS84 latitude-longitude
projection. Another extensive source on
soil data are FAO databases. The relevant
links are:
• http://www.fao.org/soils-portal/data-
hub/soil-maps-and-databases/en/
• http://www.isric.org
• https://soilgrids.org
• https://www.isric.org/explore/soilgrids
• https://files.isric.org/soilgrids/
Bare surface and land cover data:
NDVI and EVI data are Moderate Resolution
Imaging Spectroradiometer (MODIS) Terra
and Aqua products. The global MOD13A3
data set is recommended, as it is updated
every month and has been available
since the year 2000, in 1km resolution in
Sinusoidal projection. A more frequent 16-
day product, available in higher resolution,
is MOD13A2. The file format is HDF-EOS.
The relevant links are:
• https://modis.gsfc.nasa.gov/about/
• https://ladsweb.modaps.eosdis.nasa.
gov/missions-and-measurements/
products/MOD13A3/
• https://e4ftl01.cr.usgs.gov/

224
The recommended MODIS Land Cover
Type product is MCD12Q1 Version 6
(variable LC-Type1 – IGBP classification
scheme for land cover). It is updated
annually and has been available since 2001
in 500m resolution in Sinusoidal projection.
The file format is HDF-EOS. The relevant
links are:
• https://lpdaac.usgs.gov/products/
mcd12q1v006/
• https://e4ftl01.cr.usgs.gov/MOTA/
MCD12Q1.006/
One tool that can be used for decoding
the MODIS data and for data manipulation
is R studio, with the following libraries:
MODISTools, raster, gdal and gdalUtils.” R
studio may be commercial software (see
https://www.rstudio.com/).
More information about NASA products
and Earth data can be found here:
• https://earthdata.nasa.gov .
Another option for land cover data are
provided by the European Space Agency
Climate Change Initiative (ESA CCI) (Wei et
al., 2018). Data sets are annual, available
for the period 1992–2015, with a resolution
of 300m. The file types are GeoTIFF
and NetCDF. Registration is required to
download data. The relevant links are:
• http://www.esa-landcover-cci.org
• http://maps.elie.ucl.ac.be/CCI/viewer/
index.php
Soil moisture and temperature data:
For soil surface moisture and temperature
data, it is recommended to use data sets
from the European Centre for Medium-
Range Weather Forecast ERA5 product,
available for public use. Data are in 30km
(0.25° x 0.25°) resolution, featuring hourly
and monthly averages since 1979. Data
projection is WGS84 latitude-longitude
and the file format is GRIB. The decoding
software is wgrib. Soil data are available
for four depths. The relevant link is:
• https://www.ecmwf.int/en/forecasts/
datasets/reanalysis-datasets/era5
Another global reanalysis product is
the National Centers for Environmental
Prediction/National Center for Atmospheric
Research (NCEP/NCAR) Reanalysis
1 Project, which provides data sets in
much coarser resolution. Data is for the
period from 1948, at 2.5°x2.5° resolution,
with a 6-hour temporal resolution and
daily averages (Kalnay et al. 1996). The
file format is netCDF and the decoding
software is NCL, Python and Fortran.
Soil moisture data is also available from
the ESA CCI: ESA CCI SM version 04.2 ESA
– CCI Surface Soil Moisture merged with
the ACTIVE Product. Data sets are daily
(reference time 00 UTC), in 0.25°x0.25°
resolution, with two versions covering the
period 1978 to 2016. The relevant link is:
• https://www.esa-soilmoisture-cci.org
Soil moisture and temperature data are
available on higher spatial (0.1o) and
temporal resolution (1h) in ERA5-Land
database:
• https://www.ecmwf.int/en/era5-land
All data should be adjusted to the same
projection, resolution and grid position for
easy data manipulation.
8.6.4. Use of topographic
data for sand and dust
storm source mapping
Data on soil characteristics in global
data sets are constantly improving.
However, the quality of these data is likely
inadequate for most parts of the world.
This is due to the high spatial variability of
soil composition, the limited areas sampled
compared with the total Earth land
surface and the lack of international data
exchange. The most reliable parameter is
soil texture. To further distinguish areas
with finer particles from coarser topsoil,
information on topography can be used.
Under the assumption that alluvial
deposits of fine soil particles are dominant
in areas of dried river- and lake beds,
and retreating glaciers, that is, in places
exposed to increased erosion during the
topsoil formation, SDS source mapping
can be improved. Such areas are placed
in topographical lows (pits), which can
be derived from data on topography.
Topographical lows can have large scales

(such as the Taklamakan desert) due to
very small areas – “hotspots” (for example,
Iceland sources).
The simple approach described in Ginoux
et al. (2001) and used for global dust
forecast purposes in Kim et al. (2013) can
be used to detect topographical lows. The
function they used to estimate the fraction
of alluvium available for wind erosion, at a
point, scaled to values from 0 to 1 (lower
values mean a low alluvium fraction is
available, and higher values mean higher
alluvium content), is now recognized as
S-function. The S-function is calculated
using maximum, minimum and in point
altitudes, searching the values within the
box 10°x10° around the point for which S
is calculated. Simple modification of this
approach is possible to include smaller-
scale features (hotspots).
Figure 27 presents several domains for
calculation of the value of the S-function
in the middle (blue x). Applying this
calculation in high-resolution and with
different domains, large- and small-scale
features of topographical lows can be
recognized.
Figure 28 presents a vertical cross section
of areas that S-function values recognize
as topographical lows (pits), indicating
the calculation of the S value for different
domains (arrows). If high S values are
recognized in all domains for the point (grid
box) where the S-function is calculated, it
is highly probable that the grid box is an
SDS source hotspot, if allowed by other
soil surface parameters. If values obtained
for smaller domains have low values,
it means that a large region is flat and
most probably much less SDS-productive,
but individual SDS source hotspots are
possible.
Source: Ginoux et al., 2001.
Figure 27.
Different size
domains for
calculation of
S-function
Large domain
Medium domain
Small
y
x
Figure 28.
Areas (arrows)
indicate different
domains identified
as topographical
lows
Large domain Medium domain Small
y
x

226
Figure 29 provides an example of a global
calculation of the average S-function
at 0.0083° resolution (30 arcsec, about
1km on the equator) using an ensemble
of values obtained for four different
size domains (10°x10°, 5°x5°, 2.5°x2.5°,
1.25°x1.25°). Values are obtained as the
average of S-function results for different
domains.
From the assumption based on S-function
meaning, lower values contain a lower
fraction of alluvium (which is considered
SDS productive), and higher values
most probably contain a higher content
of SDS productive soils. Improving the
identification of hotspots associated with
alluvial deposits – which are of smaller
spatial scale – is done by giving greater
weight to results of S-function calculations
using smaller domains or using higher-
resolution topography data with a smaller
domain for S-function calculation.
To identify the most SDS-productive
regions globally, identification of bigger
pits is improved by giving greater weight
to results of S-function calculations
obtained with a larger domain. However,
this results in a loss of fine high-resolution
spatial source identification. The results
of this process coincide with global
SDS-productive regions (Ginoux et al.,
2001). Note that Figure 29 is an additional
component for SDS source mapping
and is not a map of SDS sources itself.
S-function values are sensitive to i) the
domain chosen for the calculation and ii)
the resolution of topographic data.
Adding this kind of information to an SDS
source map can help to distinguish more
SDS-productive areas and exclude less
significant areas:
PSOURCE = PSF x SOURCE
where PSF is preferential SDS-productive
surface, with values 0 to 1. It can be
derived using the approach provided by
Ginoux et al. (2001) from an ensemble
of S-function values derived for different
domains. It is possible to obtain ensemble
values that give more weight to the
small-scale features, but that also provide
information on larger impact areas, which
may prove useful. Another way for using
information obtained from S-function is to
apply some adjustments (corrections) of
soil texture data to enhance the content of
fine soil particles content in areas where
higher probability for higher alluvium
content (higher S-function values).
Figure 29. Average
S-function values
from four different
domains (10°x10°,
5°x5°, 2.5°x2.5°,
1.25°x1.25°) on
0.0083° (30 arcsec)
resolution, using
topography data of
the same resolution
Source: Ana Vukovic and Bojan Cvetkovic.

Besides using topographic data to
distinguish more productive areas, other
data sets may be employed (Zender et
al., 2003). Geomorphology data sets may
provide information regarding the location
of alluvium (Bullard et al., 2011; Iwahashi
et al., 2018), and PFS can be derived from
such information.
Another example for implementation of
topographic data in SDS source mapping
is using watershed flow accumulation
data (Feuerstein and Schepanski, 2019). If
possible, monitoring and implementation
of very high-resolution topographic data
and local surface roughness using remote-
sensing techniques may provide additional
information for SDS source monitoring and
higher-quality SDS source mapping (Menut
et al., 2013; Yun et al., 2015; Demura et al.,
2016; Kim 2017; Lin et al., 2018).
8.7. Conclusions
Choosing the methodology for SDS source
mapping requires having a clear purpose
for which the SDS source map will serve.
If the purpose of the SDS source map is to
estimate global distribution of major and
most active global (or continental) SDS
sources, without the need for a relatively
precisely defined spatial pattern of most
SDS-productive hotspots, mapping can
be done using observations on SDS
occurrence. This will serve to better
understand aspects such as the global
airborne dust cycle, regional dust transport
and the seasonality of major sources.
If the purpose of SDS source mapping is
to estimate the potential of soil surfaces
to produce SDS in favourable weather
conditions, a more complex cluster of
data is required, as explained in the
methodology for high-resolution SDS
source mapping.
This approach enables a spatial SDS
source pattern to be distinguished at
high resolution, including most critical
hotspots. This approach is recommended
for vulnerability and risk assessments,
especially for local SDS events, which
are usually not very visible in SDS
observations, as well as for planning SDS
source mitigation and improving warning
and alert systems.
Understanding the spatial and temporal
variability of soil surface conditions and
activity of SDS source areas depends on
many factors. However, the use of national
data sets and field observations can
significantly increase the accuracy of SDS
source mapping.

228
©Copernicus
Sentinel
data
(2015)/ESA,
CC
BY-SA
3.0
IGO,
July
10th,
2015

8.8. References
Arizona Department of Environmental Quality (2012).
State of Arizona exceptional event documentation
for the events of July 2nd through July 8th 2011,
for the Phoenix PM10 nonattainment area. Report.
Maricopa: Arizona Department of Environmental
Quality, Maricopa County Air Quality Department,
Maricopa and Maricopa Association of
Governments.
Arnalds, Olafur, Dagsson-Waldhauserova, Pavla, and
Ólafsson, Haraldur (2016). The Icelandic volcanic
aeolian environment: processes and impacts
— a review. Aeolian Research, vol. 20, pp. 176–
195. Available at http://dx.doi.org/10.1016/j.
aeolia.2016.01.004.
Akhlaq, Muhammad, Sheltami, Tarek R., and Mouftah,
Hussain T. (2012). A review of techniques and
technologies for sand and dust storm detection.
Reviews in Environmental Science and Bio/
Technology, vol. 11, No. 3, pp. 305–322. Available
at http://dx.doi.org/10.1007/s11157-012-9282-y.
Bagnold, Ralph A. (1941). The Physics of Blown Sand and
Desert Dunes. Dordrecht: Springer Science and
Business Media.
Borrelli, Pasquale, and others (2016). Towards a Pan-
European assessment of land susceptibility to
wind erosion. Land Degradation and Development,
vol. 27, pp. 1093–1105. Available at https://
dx.doi.org/10.1002/ldr.2318.
Bullard, Joanna E., and others (2011). Preferential dust
sources: a geomorphological classification
designed for use in global dust-cycle
models. Journal of Geophysical Research,
vol. 116, F04034. Available at https://dx.doi.
org/10.1029/2011JF002061.
________________________ (2016). High-latitude dust in the
Earth system. Reviews of Geophysics, vol. 54, pp.
447–485.
Cao, Hui, and others (2015). Identification of sand and
dust storm source areas in Iran. Journal of Arid
Land, vol. 7, No. 5, pp. 567–578. Available at
https://dx.doi.org/10.1007/s40333-015-0127-8.
Chaney, Keith, and Swift, Roger S. (1984). The influence
of organic matter on aggregate stability in some
British soils. European Journal of Soil Science, vol.
35, No. 2, pp. 223–230. Available at http://dx.doi.
org/10.1111/j.1365-2389.1984.tb00278.
Cherlet, Michael, and others (2018). World atlas of
desertification. Rethinking land degradation and
sustainable land management. Luxembourg:
Publication Office of the European Union.
Cowie, Annette L., and others (2018). Land in balance:
The scientific conceptual framework for Land
Degradation Neutrality. Environmental Science
and Policy, vol. 79, p. 25–35. Available at http://
dx.doi.org/10.1016/j.envsci.2017.10.011.
Demura, Yuta, and others (2016). Estimates of
critical ground surface condition for Asian dust
storm outbreak in Gobi desert region based
on remotely sensed data, 2nd International
Conference on Atmospheric Dust (DUST2016),
ProScience, vol. 3 (2016), pp. 21–30. Available at
http://dx.doi.org/10.14644/dust.2016.004.
Diffenbaugh, Noah S., and Field, Christopher B. (2013).
Changes in ecologically critical terrestrial climate
conditions. Science, vol. 341, No. 6145, pp.
486–492. Available at http://dx.doi.org/10.1126/
science.1237123.
Division of Earth & Ecosystem Sciences (2013).
Integrated Desert Terrain Forecasting for
Military Operations. Earth and Ecosystem
Sciences. Available at https://www.dri.edu/earth-
ecosystem-sciences/earth-eco-research/1782-
integrated-desert-terrain-forecasting-for-military-
operations-dod-desert-terrain.
Engelstaedter, Sebastian, and Washington,
Richard (2007). Temporal controls on
global dust emissions: the role of surface
gustiness. Geophysical Research Letters,
vol. 34, No. 15. Available at https://doi.
org/10.1029/2007GL029971.
Fan Yang, Laiming Huang, and others (2018). Vertical
distribution and storage of soil organic and
inorganic carbon in a typical inland river basin,
Northwest China. Journal of Arid Land, vol. 10,
No. 2. Available at http://dx.doi.org/10.1007/
s40333-018-0051-9, 2018.
Fécan, Francis, Marticorena, Beatrice, and Bergametti,
Gilles (1999). Parameterization of the increase
of the aeolian erosion threshold wind friction
velocity due to soil moisture for arid and semi-
arid areas, Annales Geophysicae, vol. 17, pp.
194–157.
Feuerstein, Stefanie, and Schepanski, Kerstin (2018).
Identification of dust sources in a Saharan
dust hot-spot and their Implementation in a
dust-emission model. Remote Sensing, vol. 11,
No. 4. Available at: http://dx.doi.org/10.3390/
rs11010004.

230
Gerivani, Hadi, and others (2011). The source of dust
storm in Iran: a case study based on geological
information and rainfall data. Carpathian Journal
of Earth and Environmental Sciences, vol. 6, No.
1, pp. 297–308.
Ginoux, Paul, and others (2001). Sources and
distributions of dust aerosols simulated with
the GOCART model. Journal of Geophysical
Research, vol. 106, pp. 20255–20273.
____________________ (2012). Global-scale attribution
of anthropogenic and natural dust sources
and their emission rates based on MODIS
Deep Blue aerosol products. Reviews of
Geophysics, vol. 50, No. 3. Available at https://doi.
org/10.1029/2012RG000388.
Hengl, Tomislav, and others (2014). SoilGrids1km —
Global Soil Information Based on Automated
Mapping. PLoS ONE, vol. 9, No. 8. Available
at https://dx.doi.org/10.1371/journal.
pone.0105992.
______________________ (2017). SoilGrids250m: Global
Gridded Soil Information Based on Machine
Learning. PLoS ONE, vol. 12, No. 2. Available at
http://dx.doi.org/10.1371/journal.pone.0169748.
Heute, Alfredo, and others (2002). Overview of the
radiometric and biophysical performance of the
MODIS vegetation indices. Remote Sensing of
Environment, vol. 83, Issue 1–2, pp. 195–213.
Available at http://dx.doi.org/10.1016/S0034-
4257(02)00096-2.
Managing the risks of extreme events and
A Special report of Working Groups I and II of
the Intergovernmental Panel on Climate Change,
Stocker, Qin Dahe, David Jon Dokken, Kristie L.
Ebi, Michael D. Mastrandrea, Katharine J. Mach,
Gian-Kasper Plattner, Simon K. Allen, Melinda
Tignor and Pauline M. Midgley, eds. New York:
Cambridge University Press. p. 582.
––––––––––––––––––––––––––––––––––
(2014a). Impacts, Adaptation, and Vulnerability.
Part A: Global and Sectoral Aspects. In
Contribution of Working Groups I, II and III to the
fifth assessment report of the Intergovernmental
Panel on Climate Change, Christopher B. Field,
Vicente Barros, Katharine J. Mach, Michael D.
Mastrandrea, T. Eren Bilir, Monalisa Chatterjee,
Kristie L. Ebi, Yuka Otsuki Estrada, Robert C.
Genova, Betelhem Girma, Eric S. Kissel, Andrew N.
Levy, Sandy MacCracken, Patricia R. Mastrandrea
and Leslie L. White, eds. Cambridge: Cambridge
University Press, p. 1132.
–––––––––––––––––––––––––––––––––
(2014b). Synthesis Report. Contribution of
Working Groups I, II and III to the fifth assessment
report of the Intergovernmental Panel on Climate
Change, The Core Writing Team, Rajendra
K. Pachauri and Leo A. Meyer, eds. Geneva:
Intergovernmental Panel on Climate Change, 151
pp.
–––––––––––––––––––––––––––––––––––––
(2019). Summary for Policymakers. In: Climate Change
and Land: an IPCC special report on climate
change, desertification, land degradation,
sustainable land management, food security, and
greenhouse gas fluxes in terrestrial ecosystems
[P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-
Delmotte, H.- O. Pörtner, D. C. Roberts, P. Zhai, R.
Slade, S. Connors, R. van Diemen, M. Ferrat, E.
Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold,
J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick,
M. Belkacemi, J. Malley, (eds.)].
Irfan, Furqan B., and others (2017). Health system
response and adaptation to the largest
sandstorm in the Middle East. Disaster Medicine
and Public Health Preparedness, vol. 11, No. 2, pp.
227–238. Available at https://doi.org/10.1017/
dmp.2016.111.
Iwahashi, Junko, and others (2018). Global terrain
classification using 280 m DEMs: segmentation,
clustering, and reclassification, Progress in Earth
and Planetary Science, vol. 5, No. 1. Available at:
http://dx.doi.org/10.1186/s40645-017-0157-2.
Johannes, Alice, and others (2017). Optimal organic
carbon values for soil structure quality of arable
soils. Does clay content matter? Geoderma,
vol. 302, p.14–21. Available at http://dx.doi.
org/10.1016/j.geoderma.2017.04.021.
Journet, Emilie, Balkanski, Yves, and Harrison, Sandy
(2014). A new data set of soil mineralogy for
dust-cycle modelling. Atmospheric Chemistry and
Physics, vol. 14, No. 8, pp. 3801–3816. Available
at http://dx.doi.org/10.5194/acp-14-3801-2014.
Kalnay, and others (1996). The NCEP/NCAR 40-Year
Reanalysis Project. Bulletin of the American
Meteorological Society, vol. 77, No. 3., pp. 437–472.
Kim, Dongchul, and others (2013). The effect of the
dynamic surface bareness on dust source
function, emission, and distribution. Journal
of Geophysical Research Atmospheres,
vol. 118, No. 2. Available at https://doi.
org/10.1029/2012JD017907.

Kim, Jung-Rack (2017). Estimation of aeolian dune
migration over Martian surface employing high
precision photogrammetric measurements. The
International Archives of the Photogrammetry,
Remote Sensing and Spatial Information Sciences,
vol. XLII-3/W1. International Symposium on
Planetary Remote Sensing and Mapping, 13–16
August, Hong Kong.
Knippertz, Peter, Trentmann, Jorg, and Seifert,
Axel (2009). High-resolution simulations of
convective cold pools over the northwestern
Sahara. Journal of Geophysical Research
Atmospheres, vol. 114. Available at https://doi.
org/10.1029/2008JD011271, 2009.
Knippertz, Peter, and Todd, Martin C. (2012). Mineral
dust aerosols over the Sahara: meteorological
controls on emission and transport and
implications for modeling. Reviews of
Geophysics, vol. 50, No. 1. Available at http://
dx.doi.org/10.1029/2011RG000362.
Kok, Jasper F. (2011). Does the size distribution of
mineral dust aerosols depend on the wind speed
at emission? Atmospheric Chemistry and Physics,
vol. 11, No. 7, pp. 10149–10156.
Lary, David John, and others (2015). Machine learning
in geosciences and remote sensing. Geoscience
Frontiers, vol. 7, No. 1. Available at http://dx.doi.
org/10.1016/j.gsf.2015.07.003.
Lee, Jeffrey A., and others (2009). Land use/land cover
and point sources of the 15 December 2003
dust storm in southwestern North America.
Geomorphology, vol. 105, No. 1–2. pp. 18–27, doi:
10.1016/j.geomorph.2007.12.016.
__________________________ (2011). Geomorphic and
land cover characteristics of aeolian dust
sources in West Texas and eastern New Mexico,
USA. Aeolian Research, vol. 3, No.4, pp. 459–
466. Available at http://dx.doi.org/10.1016/j.
aeolia.2011.08.001.
Lin, Cheng-Wei, and others (2018). A new paradigm for
aeolian process monitoring employing UAV and
satellite sensors: Application case in Kubuqi
desert, China. 20th EGU General Assembly, 4–13
April, Vienna, Austria, p.12235.
Lu, Hua, and Shao, Yaping (2001). Toward quantitative
prediction of dust storms: an integrated wind
erosion modeling system and its applications,
Environmental Modelling & Software, vol. 16, No.
3, pp. 233–249.
Meliyo, Joel Loitu, and others (2016). Variability of soil
organic carbon with landforms and land use in
the Usambara Mountains of Tanzania. Journal of
Soil Science and Environmental Management, vol.
7, No. 9, pp. 123–132. Available at http://dx.doi.
org/ 10.5897/JSSEM2016.0557.
Menut, Laurent, and others (2013). Impact of surface
roughness and soil texture on mineral dust
emission fluxes modeling. Journal of Geophysical
Research: Atmospheres, vol. 118, pp. 6505–6520.
Available at http://dx.doi.org/10.1002/jgrd.50313.
Middleton, Nick, and Kang, Utchang (2017). Sand and
dust storms: impact mitigation. Sustainability,
vol. 9, No. 6, p. 1053. Available at http://dx.doi.
org/10.3390/su9061053.
Natural Resources Conservation Service (n.d.).
Soil Textural Triangle. Available at https://
www.nrcs.usda.gov/Internet/FSE_MEDIA/
nrcs142p2_050242.jpg.
Journal of Geophysical Research: Atmospheres,
vol. 106 (D16), pp. 18113–18129.
_________________________ (2012). Technical Note:
High-resolution mineralogical database of dust-
productive soils for atmospheric dust modeling.
Atmospheric Chemistry and Physics, vol.12, pp.
845–855. Available at https://doi.org/10.5194/
acp-12-845-2012.
Nickovic, Slobodan, Vukovic, Ana, and Vujadinovic,
Mirjam (2013). Atmospheric processing of iron
carried by mineral dust. Atmospheric Chemistry
and Physics, vol. 13, pp. 9169–9181. Available at
http://dx.doi.org/10.5194/acp-13-9169-2013.
Orr, Barron J., and others (2017). Scientific conceptual
framework for land degradation neutrality. A report
of the Science-Policy Interface. Bonn: United
Nations Convention to Combat Desertification.
Parajuli, Sagar Prasad, and Zender, Charles S. (2017).
Connecting geomorphology to dust emission
through high-resolution mapping of global land
cover and sediment supply. Aeolian Research,
vol. 27, pp. 47–65.
Pauley, Patricia M., Baker, Nancy L., and Barker, Edward
H. (1996). An observational study of the
“Interstate 5” dust storm case. Bulletin of the
American Meteorological Society, vol. 77, No. 4,
pp. 693–720.

232
Pérez García-Pando, Carlos, and others (2011).
Atmospheric dust modeling from meso to global
scales with the online NMMB/BSC-Dust model –
Part 1: Model description, annual simulations and
evaluation, Atmospheric Chemistry and Physics,
vol. 11, pp. 13001–13027. Available at https://
dx.doi.org/10.5194/acp-11-13001-2011.
Perlwitz, Jan P., Pérez García-Pando, Carlos, and Miller,
Ron L. (2015). Predicting the mineral composition
of dust aerosols – Part 2: Model evaluation and
identification of key processes with observations.
Atmospheric Chemistry and Physics, vol. 15,
pp. 11629–11652. Available at http://dx.doi.
org/10.5194/acp-15-11629-2015.
characterization of global sources of
atmospheric soil dust identified with the
NIMBUS 7 Total Ozone Mapping Spectrometer
(TOMS) absorbing aerosol product. Reviews of
Geophysics, vol. 40, No. 1. Available at http://
dx.doi.org/10.1029/2000RG000095.
Sanz, María José, and others (2017). Sustainable land
management contribution to successful land-
based climate change adaptation and mitigation.
A report of the Science-Policy Interface.
Bonn: United Nations Convention to Combat
Desertification.
Schaetzl, Randall J., and Anderson, Sharon (2009).
Soils: genesis and geomorphology. Cambridge:
Shangguan, Wei, and others (2016). Mapping the
global depth to bedrock for land surface
modeling. Journal of Advances in Modeling
Earth Systems. Available at http://dx.doi.
org/10.1002/2016MS000686.
Shao, Yaping (2008). Physics and modelling of wind
erosion. Dordrecht: Springer Netherlands.
Shao, Yaping, and others (2011). Dust cycle: an emerging
core theme in Earth system science, Aeolian
Research, vol. 2, No. 4, pp. 181–204. Available at
http://dx.doi.org/10.1016/j.aeolia.2011.02.001.
Shao, Yaping, Klose, Martina, and Wyrwoll, Karl-Heinz
(2013). Recent global dust trend and connections
to climate forcing. Journal of Geophysical
Research Atmospheres, vol. 118, pp. 11107–
11118.
Sprigg William A., and others (2014). Regional dust
storm modeling for health services: the case of
valley fever. Journal of Aeolian Research, vol. 14,
pp. 53–73.
Sinclair, Samantha N., and Jones, Sandra L. (2017).
Subjective Mapping of Dust-Emission Sources
by Using MODIS Imagery, Report ERDC/CRREL
TR-17-8. Hanover: Cold Regions Research and
Engineering Laboratory.
Steffen, Will, and others (2015). The trajectory of
the Antropocene: The Great Acceleration.
The Antropocene Review, vol. 2, No. 1,
pp. 81–98. Available at http://dx.doi.
org/10.1177/2053019614564785.
Sweeney, Mark, and others (2016). Sand dunes as
potential sources of dust in northern China.
Science China Earth Science, vol. 59, pp. 760–
769. Available at http:dx.doi.org/10.1007/
s11430-015-5246-8.
Tegen, Ina (2016). Interannual variability and decadal
trends in mineral dust aerosol, Technical Report,
SDS-WAS-2016-001. Sand and Dust Storm
Warning Advisory and Assessment System
Regional Center for Northern Africa-Middle East-
Europe.
Tegen, Ina, and others (2002). Impact of vegetation
and preferential source areas on global dust
aerosol: Results from a model study. Journal of
Geophysical Research, vol. 107, No. D21. Available
at http://dx.doi.org/10.1029/2001JD000963.
United Nations Economic and Social Commission for
Asia and the Pacific (2018). Sand and dust storms
in Asia and the Pacific: opportunities for regional
cooperation and action, No. ST/ESCAP/2837.
Bangkok: United Nations, Economic and Social
Commission for Asia and the Pacific.
United Nations Environment Programme, World
Global assessment of sand and dust storms.
Nairobi: United Nations Environment Programme.
Vukovic, Ana, and others (2014). Numerical simulation
of “an American haboob”. Atmospheric Chemistry
Walker, Annette L., and others (2009). Development of a
dust source database for mesoscale forecasting
in southwest Asia. Journal of Geophysical
Research, vol. 114, Issue D18, p. 207. Available at
http://dx.doi.org/10.1029/2008JD011541.
Wang, Julian X.L (2015). Mapping the global dust
storm records: review of dust data sources in
supporting modeling/climate study. Current
Pollution Reports, vol. 1, No. 2, pp. 82. Available
at http://dx.doi.org/10.1007/s40726-015-0008-y.

Wei, Li, and others (2018). Gross and net land cover
changes in the main plant functional types
derived from the annual ESA CCI land cover
maps (1992–2015). Earth System Science Data,
vol. 10, pp. 219–234. Available at http://dx.doi.
org/10.5194/essd-10-219-2018.
World Meteorological Organization and United Nations
Environment Programme (2013). Establishing a
WMO sand and dust storm warning advisory and
assessment system regional node for West Asia:
current capabilities and needs. Technical report.
Geneva: United Nations Environment Programme
and World Meteorological Organization.
Wu, Wei, and others (2018). Wind tunnel experiments
on dust emissions from different landform types.
Journal of Arid Land, vol. 10, No. 4, pp. 548–560.
Available at http://dx.doi.org/10.1007/s40333-
018-0100-4.
Zender, Charles S., Newman, David, and Torres, Omar
(2003). Spatial heterogeneity in aeolian erodibility:
Uniform, topographic, geomorphic, and
hydrologic hypotheses. Journal of Geophysical
Research, vol. 108, pp. 4543. Available at https://
dx.doi.org/ 10.1029/2002JD003039.
Yun, Hye-Won, and others (2015). Long-Term
Observations of Dust Storms in Sandy Desert
Environments. EGU General Assembly ,12-17
April, Vienna, id.8287.

UNCCD | Sand and Dust Storms Compendium | Chapter 9 | Sand and dust storm forecasting and modelling 235
9. Sand and dust storm
forecasting and modelling
Chapter overview
This chapter covers the concept of impact-based, people-centred forecasting and
summarizes the procedures used in the approach. The chapter includes an extensive
discussion of the technologies and infrastructure used to collect data on sand and dust
storms (SDS), including in situ and remote sensing options. An extensive discussion is
provided on the global World Meteorological Organization Sand and Dust Storm Warning
Advisory and Assessment System (WMO SDS-WAS), with an example of how this system
can be linked to national-level forecasting. Information is provided on national-level SDS data
collections, including on national meteorological and hydrometeorological services, private
weather services and citizen science engagement in SDS.
This chapter is based on the experience of the WMO SDS-WAS and national SDS forecasting
systems and also addresses SDS modelling. It should be read in conjunction with chapter 10
on SDS early warning, as well as chapter 2, which provides an overview of SDS.

UNCCD | Sand and Dust Storms Compendium | Chapter 9 | Sand and dust storm forecasting and modelling
236
9.1 Impact-based,
people-centred
SDS forecasting
Impact-based forecasting provides
information on the impacts of forecasted
weather on the individuals who will
experience it (i.e. people-oriented). Impact-
based forecasts are provided to disaster
management, health, transport and other
stakeholders and also, importantly, to the
public, through impact-based forecasting
and warning services (IBFWS).
The outreach to the public recognizes that
those individuals who can be affected
by forecasted weather have the first, and
often best, opportunities to mitigate or
avoid the impact of this weather (see
chapter 10 on SDS early warning). Impact-
based forecasts are therefore intentionally
people-centred (see Box 13).
Impact-based (people-centred) forecasting
is an integral part of the SDS warning
process. This chapter focuses on
forecasting and public outreach elements
of the SDS forecasting process. Chapter
10 focuses on the warning process and
provides an overview of the combined
forecast and warning process.
Box 13. Comparing traditional and impact-based
people-centred forecasts
A traditional SDS weather forecast can state that sand and dust conditions are expected
during a certain period over a general area, for example: There will be a dust storm in the
next few days affecting the country.
An impact-based forecast is more precise, for example: There will be a high-intensity dust
storm over the next two days affecting the four northern states of the country. The storm
will pose difficulties for individuals with breathing problems. These individuals should take
steps to protect against the dust, including staying inside and using air conditioners where
possible. Schools may also limit outside time for students to reduce the impact of the dust.
In other words, impact-based, people-centred SDS forecasting:
• focuses on the impacts of an SDS event on specific groups, based on SDS type
and level of risk
• indicates the locations that will be affected
• indicates the expected duration of the impacts of the SDS event and
• provides information to reduce the impacts of the SDS event

9.2 Components
of impact-based
forecast and warning
Impact-based forecast and warning
services are based on:
• A very good, near real-time
understanding of evolving weather
conditions, based on weather models
incorporating accurate and timely
weather data from ground, ocean and
space-based observing systems.
• A clear classification of weather
hazard categories that affect
a particular location and their
corresponding types and levels of
impact.
• A risk assessment matrix developed
through consultations between
a national meteorological and
hydrological service (NMHS) and
stakeholders (for example, national
disaster management authority and
the transport and education sectors).
The risk matrix enables a forecaster
to assign a level of impact for specific
locations and on specific groups and
assets when issuing an impact-based
forecast.
The risk assessment structure used in
impact-based forecasting “is defined as
the probability and magnitude of harm
attendant on human beings, and their
livelihoods and assets because of their
exposure and vulnerability to a hazard.
The magnitude of harm may change
due to response actions to either reduce
exposure during the course of the event
or reduce vulnerability to relevant hazard
types in general” (World Meteorological
Organization [WMO], 2015). This definition
is sufficiently close to the definition
used in chapters 5 and 7, meaning that
information collected through the risk
assessment and vulnerability procedures
throughout those chapters can be used to
support impact-based forecasting.
WMO sets out a mathematical formula
to calculate impact risk. The formula
incorporates the uncertainty associated
with forecasts (WMO, 2015). Uncertainty
is able to be included because predictive
models include information on expected
accuracy.
In practice, mathematical calculations of
impact risk may not always be practical.
Most often, this is due to a lack of sufficient
or appropriate data on exposure and
vulnerability. In these cases, the forecaster,
in consultation with other experts, would
need to make the best-fit assessment
of the impacts of an SDS event and
incorporate any caveats on the forecast
into the formal forecast statement.
Three decision-making procedures can
contribute to an impact-based forecasting
approach (WMO, 2015):
• The forecaster would provide a simple
link between the nature (such as
intensity, duration, location) of the SDS
event and its expected impacts. For
instance, if a dense area of dust was
identified as approaching a city, the
forecast would reflect that the dust
would be dense. The forecast would
not describe the impact of this dense
dust on vulnerable groups or services
(for example, transport) in the city. It
would be expected that, on learning
of the forecast, people would take the
necessary action based on previous
experience or advice from others.
• The forecaster uses their experience,
based on past SDS events and
information on the forecast event, to
identify likely impacts. For instance,
with the SDS approaching a city, the
forecast would indicate the expected
time of arrival and state that people
with health problems may be affected
and should stay indoors, thus
addressing a common SDS impact
and providing relevant advice. While
impacts would be identified, they
would not be highly specific and only
a general mention of measures to
reduce impacts would be made.

238
• The forecaster would draw directly
from models setting out likely
(uncertainty-defined) magnitudes
of the SDS event as well as risk
assessments and would identify:
• who, specifically, could be
impacted
• how, specifically, they would be
impacted and
• where, specifically, these impacts
would take place
The resulting forecast would:
• include more specific information on
impacts on vulnerable groups (for
example, older persons, children)
• be more precise about when the SDS
event was expected to arrive and end
• indicate if some locations may be
more or less impacted
• identify how the SDS event could
affect services and commercial and
other activities, such as delaying air
travel and slowing traffic during rush
hour
Clearly, the third, model-driven, approach
is the most complicated. It is based on
good models (or ensembles of models), an
understanding of who and where could be
impacted based on risk and vulnerability
assessments, and what these impacts
could be over time. Developing this depth
of knowledge about SDS requires an NMHS
to work in partnership with other sectors to
develop a comprehensive understanding of
SDS and their diverse impacts (WMO, 2015;
WMO, 2020).
The second process, which relies less on
modelling and more on experience, can
be effective if technical means are limited.
The forecaster’s use of their experience to
identify impacts can be strengthened by:
• Using a consensus-approach to
identify impacts, where several
forecasters agree as to expected
impacts.
• Incorporating input from stakeholders,
including the national disaster
management authority, on impacts
and at-risk groups. This can be
done through the risk assessment
methods set out in chapters 5 and
7, as well as consultations with key
sectors that are affected by SDS
(for example, health, education,
disaster management offices). (Box
17 in chapter 10 identifies SDS early
warning stakeholders, which overlap
with forecast stakeholders.)
The consultations can use a retrospective
approach, whereby the NMHS collects
impacts from stakeholders following an
SDS event and accumulates a list of types
of events linked to specific impacts over
time. This event-to-impact information can
be used to develop a reference table which
can be incorporated into the forecast
process. A process to collect information
on past SDS is provided in chapter 5.
The process of establishing impact-based
forecasting involves developing standard
criteria for classifying different levels of
SDS events. The SDS hazard typology in
chapter 4.2.5 provides a general grouping
of SDS events into similar categories.
However, more detailed classifications,
based on standard criteria to define the
meteorological magnitude of a specific
SDS, are useful for the impact-based
forecasting process.
An example for a haboob would be
setting standard criteria for different
magnitudes of a haboob based on wind
speed, dust content, presence or absence
of precipitation after the passage of a
haboob, and so on. These characteristics
are then grouped to identify haboobs of
different intensities, such as class one,
class two, class three, class four. These
groupings, or classes, of haboobs are then
linked to anticipated impacts based on
impacts during past haboobs. For instance,
a class two haboob would cause changes
in aircraft landing patterns, while a class
three haboob would close an airport to
all landings and take-offs. (Chapter 4
describes a preliminary typology for SDS
which uses a similar approach.)
While the process of defining and
assigning impacts may seem complicated,
the link between an SDS event of a specific

intensity and its expected impacts on
humans and society must be understood
if the forecasting process is to work.
Similar classification systems are used for
cyclones, hurricanes and typhoons.
In developing impact-based forecasts, it
is also necessary to revisit the issue of
who has the authority to issue warnings
(see chapter 10). While an NMHS may
develop impact-based forecast procedures
(including criteria and standards for
classifying SDS) and can generate
forecasts which specify impact and
measures to address this impact, the
authority to release this information may
not rest with the NMHS.
The actual difference between a prognostic
forecast of weather conditions and an
impact-based forecast may not be that
great, but prognostic forecast would be
considered the regular and routine work of
the NMHS. Moving into identifying impact
and steps to take to address this impact
may move an NMHS into a new area of
work and responsibilities.
WMO suggests that this shift is necessary
to ensure weather information reduces
negative impacts (WMO, 2015), but this
process needs to be coordinated with
other stakeholders. Chapter 5 in WMO
Guidelines on Multi-hazard Impact-
based Forecast and Warning Services
provides a road map for how impact-based
forecasting can be integrated into the work
of an NMHS and its partners (WMO, 2015).
9.3 SDS information
collection and forecast
technology and
infrastructure
9.3.1. Overview
This section reviews the technology
and physical infrastructure that collects
and processes information on SDS in
support of forecasting and warning. This
infrastructure ranges from ground stations
to satellites and incorporates model-based
and other analysis to deliver information
which can be used to provide an impact-
based warning to those who may be
affected by an SDS event.
Observations of dust transport and
concentrations in the atmosphere are
very important to early warning and risk
reduction in many sectors, including
health, transport, education and industry.
There are two approaches to collecting
information on sand and dust:
• In situ data from synoptic or
aeronautical meteorological stations
providing information on horizontal
visibility, dust particulate concentration
(for example, PM10
) and weather at
the time of the report. These reports
can be near real-time from automatic
weather stations or several times a
day from human reports.
• Remotely sensed, including ground-
and space-based instruments, with
data often collected on a near real-
time basis, although processing may
be completed at regular intervals, for
instance, every six or 12 hours.
In situ measurements of particulate matter
concentration are systematic and have
high spatial density in developed countries.
However, they can be very sparse,
discontinuous and rarely near real-time
close to the main global sources of dust.
Satellite products present global coverage.
However, they usually integrate the bulk
aerosol content over the vertical column
and do not provide information close to the
ground.
9.3.2. In situ: visibility
information from
weather reports
Where weather records have excellent
spatial and temporal coverage, visibility
data included in meteorological
observations can be used as an alternative
way of monitoring dust events. Visibility is
mainly affected by the presence of aerosol
and water in the atmosphere.

240
The use of visibility data has to be
complemented with information on
present weather to discard those cases
where visibility is reduced by the presence
of hydrometeors (such as fog or rain) or
particles of a different nature (such as
smoke, ash or anthropic pollution).
Description WMO code Associated with sand and
dust
Haze 05 Unclear
Widespread dust in suspension not raised by wind 06 Yes
Dust or sand raised by wind 07 Yes
Well-developed dust or sand whirls 08 Yes
Dust or sandstorm within sight but not at station 09 Yes
Slight to moderate dust storm, decreasing in intensity 30 Yes
Slight to moderate dust storm, no change 31 Yes
Slight to moderate dust storm, increasing in intensity 32 Yes
Severe dust storm, decreasing in intensity 33 Yes
Severe dust storm, no change 34 Yes
Severe dust storm, increasing in intensity 35 Yes
Heavy thunderstorm with dust storm 98 Yes
1 The WMO definitions are also available at https://cloudatlas.wmo.int/lithometeors-other-than-clouds.html, with
pictures for reference.
Table 20 shows the WMO synoptic codes
of present weather that can be associated
with airborne sand and dust (Secretariat
of the World Meteorological Organization,
1975).1
Table 20.
WMO synoptic
codes associated
with airborne
sand and dust
Human weather observations are made on
a fixed schedule and, in some locations,
without a full (360 degree) view of the
sky. In general, the start and end times
of weather events (including SDS events)
are also recorded at, and reported by,
meteorological observatories.
However, the WMO coding may not
indicate that an SDS event has occurred
if the event takes place between reporting
times or does not take place within the
viewing area of an observation station. See
O’Loingsigh et al. (2014) on weather station
data and identifying SDS events.
Horizontal visibility is an indication of the
intensity of attenuation of solar radiation
by the suspended particles including dust.
Several empirical equations relating to
surface dust concentrations and visibility
have been proposed. However, there
is not a universal relationship between
both magnitudes, as visibility reduction
is strongly influenced by particle size
distribution and has a clear dependence on
ambient humidity. In turn, size distribution
can be highly variable depending on source
soil characteristics, wind erosivity and
the observation point’s distance from the
eroding source.

Empirical calculations relating to surface
dust concentrations and visibility include:
• North America: Chepil and Woodruff
(1957), Patterson and Gillette (1977)
• West Africa: D’Almeida (1986),
Mohamed et al. (1992), Camino et al.
(2015)
• North-East Asia: Shao et al. (2003)
• East Asia: Wang et al. (2008)
• West Asia: Dayan et al. (2008)
• North-East Asia: Jugder et al. (2014)
• Australia: Baddock et al. (2014)
9.3.3. In situ: air quality
monitoring stations
Air quality monitoring stations regularly
collect data on the presence of particulate
matter in the sampled air. This matter can
include mineral dust from SDS events,
as well as background levels of airborne
particles from, for instance, industrial
pollution or mining.
Various international and regional
organizations and national governments
have established guidelines,
recommendations, directives or
legislation on the maximum permissible
concentration levels of atmospheric
constituents considered as pollutants.
None of these regulations specifically refer
to mineral dust.
The main air quality limits are associated
with World Health Organization (WHO)
guidelines on air quality related to human
health. Presently, only PM10
, PM2.5
and
PM1
are considered, as these variables
are the references for the epidemiological
studies. There is no evidence about how
the chemical composition of aerosols and
specifically sand or dust can affect human
health.
At the same time, regulations have been
set for concentrations of suspended
particles in the air, including:
• The European Union 2008/50/EC
Directive (European Commission,
2008) sets 50 µg/m3
as the 24-hour-
mean limit value for PM10
, with 35 µg/
m3
permitted. The WHO guidelines for
particulate matter exceedances each
year set 40 µg/m3
as the annual-mean
limit value for PM10
, compared with 25
µg/m3
for PM2.5
.
• Guidance on ozone, nitrogen dioxide
and sulphur dioxide to reduce the
health impacts of air pollution
recommends a maximum 24-hour-
mean value of 50 µg/m3
and an
annual-mean value of 20 µg/m3
for particles with aerodynamical
diameter less than 10 µm (PM10
), with
a maximum 24-hour-mean value of
10 µg/m3
and an annual-mean value
of 25 µg/m3
for PM2.5
(European
Commission, 2008).

242
• The United States of America National
Ambient Air Quality Standards
(https://www.epa.gov/criteria-air-
pollutants/naaqs-table) set 150 µg/
m3
as the 24-hour-mean limit value for
PM10
, not to be exceeded more than
once per year on average over three
years. They also set an annual-mean
limit value (averaged over three years)
of 12 µg/m3
for PM2.5
and a 24-hour-
mean (ninety-eighth percentile,
averaged over three years) limit value
of 35 µg/m3 for PM2.5
.
Based on these guidelines and standards,
air quality measurement stations usually
assess total suspended particle (TSP)
levels at PM10
or PM2.5
concentrations.
These measurements integrate the
contribution of the various elements in the
air and are not exclusively characteristic
of dust particles. They are, however, very
useful for monitoring mineral dust events
because of the episodic nature of SDS
events.
It is important to understand how the
location of a measurement station may
affect data on TSP or PMx levels. For
example, an abundance of anthropogenic
particulates close to cities, large industrial
parks or roads can mask the presence
of mineral dust. On the other hand, bulk
aerosol mass measurements from
stations that usually record a low aerosol
background and are sited in places where
it is known that high aerosol mass events
are caused by dust episodes represent a
relatively cheap approximate method for
long-term dust observation.
Gravimetry (weighing) of sampling filters
is the reference method used to measure
the concentration of particulate matter.
The ambient air is passed through a filter,
where particles are collected. Filters are
weighted before and after sampling at
a controlled temperature and relative
humidity.
2 Available for purchase at https://shop.bsigroup.com/ProductDetail?pid=000000000030260964.
Mass concentrations are determined by
dividing the increase in the filter mass (due
to sample collection) by the volume of
sampled air.
Reference gravimetric methods used in
air quality networks (for example, DIN
EN 12341:2014, Ambient air – Standard
gravimetric measurement method for the
determination of the PM10
or PM2.5
mass
concentration of suspended particulate
matter,2
or its United States of America
equivalent) facilitate data comparability
between different stations.
However, filter-based sampling is labour
intensive. Filters must be conditioned,
weighed before sampling, installed
and removed from the instrument, and
reconditioned and weighed again at
a special facility. Results may not be
available for days or weeks. Furthermore,
filter-based techniques integrate samples
over a long period of time, usually 24 hours,
to obtain the required minimum mass for
analysis.
With the increasing concerns about
the effect of particulate matter (PM)
on human health, the limitations of
the time-integrated filter approach are
becoming apparent, while the delay
involved in sampling and determining PM
concentration is also a concern.
Continuously operating sampling methods
such as tapered element oscillating
microbalance (TEOM) or beta attenuation
monitoring can detect suspended matter
almost in situ, but these methods require
operating conditions that differ from
the environmental situation or are not
completely specific to mass. It is, therefore,
necessary to introduce correction factors
in these measurements.
In TEOM devices, the mass of the particles
collected on a substrate that vibrates
at constant amplitude is determined as
a function of the decreasing frequency
prompted by an increase in particle

mass through time. Alternatively, in the
beat-attenuation devices, the number
of beta particles transmitted across a
filter decreases when the sample load
increases.
Figure 30 shows the monthly record of
PM10
and PM2.5
from the TEOM station set
in Granadilla, Canary Islands, Spain. Three
dust episodes can be clearly identified as
the peaks of mass concentration for PM10
.
Chemical analysis is required to determine
the proportion of mineral dust present
in filter samples. The most common
method is based on determining the mean
content of selected tracers present in soil.
Silicon (Si) and aluminium (Al) account
respectively for 33 per cent and 8 per cent
of mean soil composition.
Figure 30.
The PM10 and
PM2.5 records
from Granadilla,
Canary Islands,
Spain for
August 2012
with Saharan
dust outbreaks
indicated in peak
values
Source: Gobierno de Canarias [Data provided by the Government of the Canary Islands].
Detailed information on the methods used
for dust monitoring and characterization
(including size distribution, bulk
composition and optical properties) can
be found in the review paper by Rodríguez
et al. (2012) and references therein. As
a synthesis, tracer analysis is the most
accurate procedure, but the filter ash
method is a less expensive alternative.
Air quality networks performing systematic
measurements with high spatial density
are well established in developed countries.
However, these measurements can be
3 See https://community.wmo.int/activity-areas/gaw
very sparse, discontinuous and rarely near
real-time close to the main dust source
areas. Furthermore, there is no protocol for
routine international exchange of air quality
data, so their use is often limited to the
national level.
The WMO Global Atmosphere Watch
(GAW) Programme3
is working to cover
this gap. Its main goals are to “ensure
long-term measurements in order to detect
trends in global distributions of chemical
constituents in air and the reasons for
them.

244
With respect to aerosols, the objective of
GAW is to determine the spatio-temporal
distribution of aerosol properties related
to climate forcing and air quality on
multi-decadal timescales and on regional,
hemispheric and global spatial scales”
(Global Atmosphere Watch, World Data
Centre for Aerosols, n.d.).
The GAW Programme envisions the
comprehensive, integrated and sustained
observation of aerosols on a global scale
through a consortium of existing research
aerosol networks that complement
aircraft, satellite and environmental
agency networks (WMO, 2009). According
to GAWSIS,4
the GAW aerosol network
consists of 28 global stations and over 200
fully operational regional and contributing
stations.
9.3.4. Remotely sensed:
satellite-derived red-
green-blue (RGB)
dust products
Satellite products offer large spatial
coverage (regional to global) and regular
observations and are available to weather
centres and other institutions in near real-
time. However, using satellite products
to monitor dust events faces several
problems:
• The high integration of satellite products
over the atmospheric column makes
it difficult to ascertain the elevation of
dust particles, i.e. whether they are close
to the ground or at altitude.
• Low aerosol detectability over bright
surfaces, such as deserts, affects
instruments operating in the visible or
near-infrared part of the spectrum. In
addition, products from these spectral
bands are not available at night.
• The high-resolution instruments flying
on board polar-orbiting satellite
platforms have the potential to provide
good quality dust information, but this
information is not frequent enough for
SDS forecasting.
• There is no information about dust
layers under clouds.
4 See https://gawsis.meteoswiss.ch/GAWSIS/#/
Operational meteorologists typically use
multi-spectral product measurements by
instruments on geostationary satellites for
dust monitoring and nowcasting. The latest
generation of geostationary satellites are a
vital tool for atmospheric monitoring, since
they combine the specific advantages of
geosynchronous orbits (high-frequency
coverage over a vast geographic domain)
with the capabilities of high-resolution
radiometers.
Multi-spectral products are based on
several monochrome images of the
same view that are captured by different
sensors. By providing extra information
that highlights specific features that are
not perceptible in the original images, these
products make it easier to detect and track
dust clouds.
The European Organisation for the
Exploitation of Meteorological Satellites
(EUMETSAT) RGB-dust product is part of a
collection referred to as “RGB imagery” or
“RGB products”, which are implemented to
address several forecasting challenges for
both daytime and night-time applications.
In these products, brightness temperatures
or paired band differences are used to set
the red, green and blue intensities of each
pixel in the final image, resulting in a false-
colour composite (European Organisation
for the Exploitation of Meteorological
Satellites [EUMETSAT], 2009).
The EUMETSAT Meteosat Second
Generation (MSG) dust product is based
upon three infrared channels of the
Spinning Enhanced Visible and Infrared
Imager (SEVIRI) on board the MSG
satellite.
It is designed to monitor the evolution of
dust storms over deserts during both day
and night.

The RGB combination exploits the
difference in emissivity between desert
surfaces and dust.
In addition, during the daytime, the RGB
combination exploits the temperature
difference between the hot desert surface
and the cooler dust cloud (Figure 31).
Dust appears pink or magenta in this RGB
combination. Dry land appears from pale
blue (daytime) to pale green (night-time).
Thick, high-level clouds have red-brown
tones while thin, high-level clouds appear
very dark (almost black).
Emissions and subsequent transport
in individual dust events can be very
well observed and followed in the RGB
composite pictures, especially using
temporal loops. The full disc view includes
the whole of Europe, all of Africa and the
Middle East and allows frequent sampling
(every 15 minutes) with a spatial resolution
of 3 km in the nadir. This enables rapidly
evolving events to be monitored, which in
turn helps the weather forecaster swiftly
recognize and predict hazardous dust
events.
The RGB-dust product has some important
limitations. Firstly, high cloud cover can
obscure dust plumes beneath clouds
and make spatial analysis of the dust
more difficult. Secondly, the magenta/
pink variations are not indicators of dust
thickness.
Finally, the product provides little or no
information on the height of the dust cloud.
In particular, it is almost impossible to
determine from the images whether there
is substantial dust concentration near the
ground surface.
More recently, similar products have
been developed for other platforms. The
Japanese Himawari-8/Advanced Himawari
Imager (AHI) allows forecasters to use
an RGB-dust product to monitor airborne
dust over the Western Pacific region. In
2016, EUMETSAT relocated Meteosat-8,
the first of the MSG satellites, to 41.5°E to
enable data coverage of the Indian Ocean
to continue. It allows the EUMETSAT RGB-
dust product to be generated for West Asia,
a region where the coverage was deficient.
Figure 31.
EUMETSAT RGB-
dust product for
West Asia on 20
December 2019
Source: Image provided by EUMETSAT.

246
An RGB-dust product has been made
available from the Advanced Baseline
Imager (ABI) instrument on board GOES-
16 to monitor dust events over America
and its surrounding oceans. GOES-16 is
the first spacecraft in the National Oceanic
and Atmospheric Administration’s (NOAA)
new generation of geostationary satellites.
As part of NOAA’s efforts to prepare users
for the new geostationary era, RGB-dust
products for America have been under
development since 2011, with images
from the Moderate Resolution Imaging
Spectroradiometer (MODIS) and the Visible
Infrared Imaging Radiometer Suite (VIIRS)
instruments.
9.4 The global
World Meteorological
Organization Sand and Dust
Storm Warning Advisory and
Assessment System
9.4.1. Sand and Dust Storm
Warning Advisory and
Assessment System
(SDS-WAS)
The earliest prototype of the WMO SDS-
WAS was the SDS RDP (Sand and Dust
Storm Research and Development Project),
which was established in 2004 in Beijing
under the framework of the WMO World
Weather Research Programme (WWRP)
and its GAW Programme (WMO, 2012,
2014; Nickovic et al., 2015). At the third
International Conference for Early Warning
held in Bonn in 2006, WMO proposed the
establishment of an SDS early warning
system. In 2007, an SDS-WAS kick-off
meeting was held in Barcelona and the
fifteenth World Meteorological Congress
endorsed the launch of the WMO SDS-
WAS.
This system is tasked with enhancing
countries’ ability to deliver timely and
quality SDS forecasts, observations,
information and knowledge to users
through an international partnership of
research and operational communities
(Nickovic et al., 2015; Terradellas et al.,
2015; Basart et al., 2019; WMO, 2020).
The WMO SDS-WAS works as an
international hub of research, operational
centres and end users, which is currently
organized through three regional nodes:
• a regional node for Northern Africa,
the Middle East and Europe (NAMEE),
coordinated by a regional centre
in Barcelona, Spain, hosted by the
State Meteorological Agency of
Spain (AEMET) and the Barcelona
Supercomputing Center (BSC)
• a regional node for Asia, coordinated
by a regional centre in Beijing, China,
hosted by the China Meteorological
Administration
• a regional node for Pan America,
coordinated by a regional centre in
Bridgetown, Barbados, hosted by the
Caribbean Institute for Meteorology
and Hydrology
These three regional WMO SDS-WAS
nodes are described in more detail in the
following sections.
The conceptual operation of an WMO
SDS-WAS node is summarized in
Figure 32. Each WMO SDS-WAS node
shares observations and, in some
cases, modelling input with partner
organizations. A quality assurance
control and standardization procedure
(i.e. calibration and validation) is applied
to produce long-term and near real-time
data from observations, followed by dust
forecasts. The results are used to analyse,
monitor and forecast SDS. These outputs
are provided to the NMHS and other
stakeholders on a daily basis.
Note that the WMO SDS-WAS centres
operate in support of the NMHS, providing
them with the best available analysis
and forecasts. In turn, each NMHS is
responsible for issuing specific forecasts
within their respective countries. WMO
SDS-WAS products are also available on
the respective WMO SDS-WAS centre
websites.
Source: Adapted from WMO, 2012.

Figure 32.
WMO SDS-WAS
regional node
operation concept
Observations
Valid
assimilation
Modelling
CAL/VAL QA
Users
National
meteorological
and
hydrological
services and
other
stakeholders
Capacity-
building
Analysis
Monitoring
Forecasting
Long-term data
archives
Partner A
Partner C
Partner E
Partner B
Partner D
Near real-time
data archives
CH9 Figure 32.
©United
Nations,
Martine
Perret

248
9.4.2. WMO SDS-WAS
regional centre for
Northern Africa, the
Middle East and
Europe
The WMO SDS-WAS regional centre for
NAMEE based in Barcelona collects and
distributes forecast products based on
different numerical models on a daily
basis through its web page.5
In addition to
specialists in observations and modelling,
the node also has geographers, social
scientists and communication experts.
This initiative has grown significantly
with the incorporation of more and more
partners.
At present, 12 modelling groups provide
forecasts every three hours of dust surface
concentration (DSC) and dust optical depth
(DOD) at 550 nm for a reference area
extending from 25°W to 60°E in longitude
and from 0° to 65°N in latitude.
The reference area is intended to cover the
main source areas in Northern Africa and
West Asia, as well as the main transport
routes and deposition zones from the
equator to the Scandinavian Peninsula.
5 See https://sds-was.aemet.es/
Forecasts of up to 72 hours are updated
every three hours (Terradellas et al., 2016).
Ensemble multi-model products are
generated daily by the NAMEE regional
centre after bilinearly interpolating all
forecasts to a common grid mesh of 0.5º
x 0.5º. Multi-model forecasting intends to
alleviate the shortcomings of individual
models while offering an insight into the
uncertainties associated with a single-
model forecast. Centrality products
(median and mean) aim to improve the
accuracy of the single-model approach to
forecasting.
Spread products (standard deviation
and range of variation) indicate whether
forecast fields are consistent within
multiple models, in which case there
is greater confidence in the forecast.
Graphic examples of forecast outputs are
presented in Figures 33 and 34.
©Tony
Webster
on
Flickr
June
10th,
2017

Figure 33.
SDS-WAS
forecast
comparison of
dust optical depth
at 550 nm for 4
February 2017 at
12 UTC
Note: An dust optical thickness (DOD) of less
0,2 (pale green) indicates low content of aerosol
in the atmosphere (i.e. a clean sky condition),
whereas a value of above 3 (dark brown) indi-
cates high content of aerosol (i.e. extreme and
intense sand and dust storms).
Source: WMO SDS-WAS NAMEE regional centre,
2017: https://sds-was.aemet.es/forecast-products/
dust-forecasts/compared-dust-forecasts

250
An important step in forecasting is
evaluating the results that have been
generated. The dust optical depth (DOD)
forecasts are first compared with the
aerosol optical depth (AOD) provided by
the Aerosol Robotic Network (AERONET)
(Holben et al., 1998; Dubovik and King,
2000) for a set of selected dust-prone
stations located in Northern Africa,
the Middle East and Southern Europe
(Terradellas et al., 2016; Basart et al.,
2017).
A system to evaluate the performance
of the different models has been
implemented. Different evaluation scores
are computed in order to quantify the
agreement between predictions and
observations for individual stations, for
three regions (Sahara-Sahel, West Asia
and the Mediterranean) and for the whole
reference area, as well as for different
timescales (monthly, seasonal and annual).
An evaluation system based on satellite
products has also been implemented.
Specifically, it uses two different
aerosol retrievals based on the MODIS
spectrometer travelling on board the
Terra and Aqua satellites operated by
the National Aeronautics and Space
Administration (NASA).
Since October 2015, the WMO SDS-WAS
NAMEE regional centre has released maps
covering a six-hour period that indicate
the weather stations in its geographical
domain that report visibility reduced to less
than 5 km associated with the presence of
airborne sand and dust. Figure 35 shows
the maps of 23 February 2016, where dust
activity is evident in the Sahel, the Maghreb
and West Asia.
Figure 34.
SDS-WAS multi-
model ensemble
products for 4 Feb
2017 at 12 UTC:
median and mean
(top), standard
deviation and
range of variation
(bottom)
Source: SDS-WAS NAMEE regional centre, 2017: https://sds-was.aemet.es/forecast-products/dust-fore-
casts/multimodel-products

Figure 35.
Six-hourly maps
of visibility
reduced to
less than 5 km
associated with
airborne sand
and dust for 23
February 2016
Source: SDS-WAS NAMEE regional centre, 2016: https://sds-was.aemet.es/forecast-products/dust-obser-
vations/visibility
Since October 2018, a warning advisory
system for airborne dust has been
available in Burkina Faso. Every day, two
colour-coded maps with the warning levels
for the next two days (D+1 and D+2) are
produced. This clear, concise information
helps with planning any activities
vulnerable to airborne dust and can
activate services and procedures aimed at
mitigating damages caused to agriculture,
public health or any other vulnerable
sector. The warning advisory levels are
based on the multi-model median forecast
and are set according to the highest
concentration value expected for the day.
The warning advisory thresholds have been
calculated based on a percentile-based
approach calculated from the time series
of the multi-model median between 2013
and 2017 (Terradellas et al., 2018).
Each of Burkina Faso’s 13 administrative
regions is colour-coded on the map (see
Figure 36) to represent one of four levels
of warning advisory:
• red to indicate extremely high
concentrations of airborne dust
(corresponding to values above the
97.5th percentile)
• orange to indicate very high
concentrations (corresponding to
values above the 90th percentile)
• yellow to indicate high concentrations
(corresponding to values above the
80th percentile)
• green to indicate normal dust
concentration

252
Figure 36.
Burkina Faso dust
forecast for 3rd
January 2018
Source: SDS-WAS NAMEE regional centre, 2018:
https://sds-was.aemet.es/forecast-products/burki-
na-faso-warning-advisory-system?date=
9.4.3. WMO SDS-WAS
regional centre for Asia
The WMO SDS-WAS regional centre for
Asia was launched in 2008, hosted by the
China Meteorological Administration in
Beijing.6
The Asia SDS-WAS node’s regional
steering group includes representatives of
China, Japan, the Republic of Korea, India,
Mongolia and Kazakhstan.7
In 2017, the
WMO Executive Council also approved
the operational status of the Beijing SDS-
WAS regional centre for Asia as the WMO
Regional Specialized Meteorological Centre
with activity specialization on Atmospheric
Sand and Dust Forecast (RSMC-ASDF
Beijing), which is hosted by China. It has
Central and Eastern Asia and some parts
of Western Asia as its geographic domain.
6 See http://eng.nmc.cn/sds_was.asian_rc/
7 See http://www.wmo.int/pages/prog/arep/wwrp/new/documents/Asian_Node_RSG_member_updated_
Sept_2016.pdf
Two regional models and four global
models provide forecasts every three hours
of DSC and DOD at 550 nm, operationally,
at the RSMC-ASDF Beijing. Information on
sand and dust is collected daily and used
in six numerical models to produce regular
reports.
The RSMC-ASDF Beijing covers the primary
dust sources in the Asian region, and
transport routes and deposition zones up
to the Central Pacific. It covers DSC and
DOD with a three-hour frequency and a
lead time of up to 72 hours. The initiative
is aimed at facilitating the development of
the forecasting techniques and improving
the forecast accuracy within the SDS-WAS
regional node for Asia.

Dust forecasts are evaluated using an
approach that differs from that used by the
NAMEE regional centre, mainly because
Asian dust is affected by relatively more
substantial anthropogenic activities, even
in the source area, while the AOD used
in the NAMEE regional centre does not
entirely represent the dust aerosol in Asia.
A thread scoring system based on different
observational sources has been integrated
into a geographical information system.
The observational data set consists of
regular surface weather reports, PM mass
concentration data, AOD retrievals from the
China Aerosol Remote Sensing Network
(CARSNET), retrievals from the Fēngyún
(FY) satellites and lidar data.
Four categories of dust event have been
defined:
1. Suspended dust: horizontal visibility
less than 10 km and very low wind
speed
2. Blowing dust: visibility between 1 and
10 km
3. Sand and dust storm: visibility less
than 1 km and
4. Severe sand and dust storm: visibility
less than 500 m (Wang et al., 2008).
Figure 37 shows an SDS verification
system that was developed based on
ground-based SDS observational data and
supplemented with SDS data retrieval from
the FY-2C satellite (Wang et al., 2008).
Figure 37.
Verification of a
dust forecast
released by
the CUACE34
/
dust model with
surface SDS
observational
data from
meteorological
stations
Notes: The S-like symbol denotes the routine observed SDS event by surface meteorological stations.
Source: SDS-WAS regional centre for Asia, 2017: http://eng.nmc.cn/sds_was.asian_rc/
9.4.4. SDS-WAS Pan-
American regional
centre8
The SDS-WAS Pan-American regional
centre,9
based at the Caribbean Institute for
Meteorology and Hydrology in Barbados,
conducts an exercise that is similar to the
8 Chinese Unified Atmospheric Chemistry Environment for Dust
9 See http://sds-was.cimh.edu.bb/
other two regional centres. This institute
provides seven-day regional forecasts of
surface dust, PM2.5
, PM10
and ozone (O3
)
concentration for the Caribbean using
the advanced Weather Research and
Forecasting model coupled with Chemistry
(WRF-Chem) (Figure 38).
Dust concentration – microgram per cubic meter

254
However, in addition to the regional
focus, the Barbados centre will provide
information for, and links to, global SDS-
WAS forecasts based on three US global
models run by NOAA, NASA and the US
Navy, as well as the ensemble of global
research
models of the International Cooperative for
Aerosol Prediction (ICAP).
In accordance with the aims of the SDS-
WAS, the Barbados centre is a node for
collaboration across the Americas, working
with other SDS-WAS centres to:
• develop, refine and distribute to the
global community products that are
useful in reducing the adverse impacts
of SDS, and
• assess the impacts of SDS on society
and nature
The centre’s highest priority is addressing
the adverse health implications of airborne
dust in the region, which experiences
both local-source dusts, such as from the
Mojave, Sonoran and Atacama deserts,
and imported dusts from arid lands of
other continents, such as from the deserts
of Asia and Africa (Figures 38 and 39).
Every year, storms in Africa transport 40
million tons of dust from the Sahara Desert
to the Amazon Basin over 8,000 km away.
Dust is carried to the Caribbean in spring/
summer and to the south-eastern United
States of America in summer.
High-latitude dust in places such as
Greenland is also a concern for this region,
but is an aspect of SDS that is sometimes
overlooked.
Figure 38.
Seven-day
surface dust
concentration
forecast from
the Caribbean
Institute for
Meteorology and
Hydrology WRF-
Chem model
Source: http://sds-was.cimh.edu.bb/
Dust concentration – microgram per cubic meter

Source: J. Schmaltz and R. Lindsey, MODIS Rapid Response Team, NASA (2017).
Figure 39.
Movement of dust
from the Sahara
Desert (right) to
the Amazon Basin
(left)
9.4.5. Regional Specialized
Meteorological
Centres with activity
specialization on
Atmospheric Sand and
Dust Forecast
In 2013, the positive results obtained by
the WMO SDS-WAS demonstrated the
feasibility of the SDS forecast approach
and identified the need to start developing
operational services beyond the scope of
research and development (Terradellas
et al., 2016). This resulted in WMO
establishing the designation process
and the mandatory functions of Regional
Specialized Meteorological Centres with
activity specialization on Atmospheric
Sand and Dust Forecast, otherwise known
as RSMC-ASDF (WMO, 2015).
The basic mandatory functions of RSMC-
ASDF are to:
• Prepare regional forecast fields using
a dust forecast model continuously
throughout the year, on a daily basis.
The model shall consist of a numerical
weather prediction (NWP) model
incorporating online parametrizations
of all the major phases of the
atmospheric dust cycle.
• Generate forecasts, with an
appropriate uncertainty information
statement, of the following minimum
set of variables: dust load (kgm-2
),
dust concentration at the surface
(μgm–3
), DOD at 550 nm, and three-
hour accumulated dry and wet
deposition (kgm–2
). Forecasts shall
cover the period from the forecast
starting time (00 and/or 12 UTC) up
to a forecast time of at least 72 hours,
with an output frequency of at least
three hours. They shall cover the
whole designated area. The horizontal
resolution shall be finer than about
0.5x0.5ºº.
• Disseminate through the Global
Telecommunication System – WMO
Information System (GTS-WIS) and
provide on its web portal the forecast
products in pictorial form not later
than 12 hours after the forecast
starting time.
• Issue an explanatory note on the web
portal when operations are stopped
due to technical problems.
There are currently two RSMC-ASDF:
• RSMC-ASDF Barcelona (Barcelona
Dust Forecast Centre, https://dust.
aemet.es), which started operations
in 2014. The Barcelona Dust Forecast
Centre is a joint initiative of the
State Meteorological Agency of
Spain (AEMET) and the Barcelona
Supercomputing Center (BSC). It
provides daily dust forecasts for
Northern Africa (north of the equator),
the Middle East and Europe, based

256
on the in-house BSC Multiscale
Nonhydrostatic AtmospheRe
CHemistry model (NMMB-
MONARCH).
• RSMC-ASDF Beijing (Beijing Dust
Forecast Centre, http://eng.nmc.cn/
sds_was.asian_rc/) started operations
in 2016. It is managed by the China
Meteorological Administration and
provides dust forecasts for Asia using
six numerical models.
Additional details on the operations of the
two RSMC-ASDF can be found by clicking
on the web links in the descriptions above.
Figure 40 identifies the location of regional
WMO SDS-WAS nodes in Barcelona,
Beijing and Bridgetown as well as several
key forecasting centres that contribute to
global and regional SDS-WAS forecasting,
information and guidance. The regional
nodes are denoted by red boxes.
In addition to national centres, research
groups and the SDS-WAS centre, the
European Centre for Medium-Range
Weather Forecasts (ECMWF) provides
global daily aerosol forecasts including
dust forecasts. See Box 14 for more
details.
Source: WMO SDS-WAS: www.wmo.int/sdswas
Figure 40.
Regional WMO
SDS-WAS nodes
in Barcelona,
Beijing and
Bridgetown
several key
forecasting
centres that
contribute to
global and
regional SDS
forecasting,
information and
guidance

Box 14. Copernicus Atmosphere Monitoring Service: a
European initiative
Since 2008, the ECMWF has been providing daily aerosol forecasts (including dust
forecasts) as part of successive European Union-funded projects. A detailed description of
the forecast and analysis model, including aerosol processes, is provided in Morcrette et
al. (2009) and Benedetti et al. (2009).
These efforts have made it possible to incorporate dust forecasts into the operational
Copernicus Atmosphere Monitoring Service (CAMS), which provides daily global dust
forecasts up to five days in advance and contributes to the WMO SDS-WAS. All data
are publicly available online at https://atmosphere.copernicus.eu/ and on the SDS-WAS
centres’ websites. An example is shown below.
Source: CAMS, 2017: https://atmosphere.copernicus.eu/
Figure 41.
Dust aerosol
optical depth
36-hour forecast
for 26 May
2017 at 12 UTC
provided by
CAMS
9.5 National
meteorological and
hydrometeorological
services
9.5.1. Government
weather services
National meteorological and
hydrometeorological services (NMHS)
are responsible for formulating SDS
forecasts and issuing warnings at the
national level. For more on SDS early
warning, see chapter 10.
NMHS can access guidance on SDS-WAS
forecasting from the SDS-WAS centres and
via the WMO website (https://www.wmo.
int/pages/prog/arep/sdswas/). These
outputs, together with any modelling done
by NMHS, can be used in daily and near-
term (up to three days) forecasting for SDS.
The capacity of NMHS to manage the SDS
data analysis and forecasting process can
vary considerably. Box 15 summarizes how
the Korea Meteorological Administration
manages this process.
Depending on the size of a country and its
NMHS capacities, forecasts and warnings
may be developed at the subnational
(provincial or state) level.

258
Box 15. Dust monitoring and forecasting system of the
Korea Meteorological Administration
The Republic of Korea Meteorological Administration (KMA) monitors and forecasts Asian
dust in four stages:
First, the KMA uses Asian dust observations made by the naked eye as well as PM10
concentrations from the China-KMA Joint SDS Monitoring Network located in the SDS
source regions and along the pathways to Korea.
Second, the KMA also uses international meteorological information from the Global
Telecommunication System (GTS) at three-hour intervals and satellite images from the
Communication, Ocean and Meteorological Satellite (COMS), NOAA, Himawari-8 and Aqua
& Terra/MODIS to identify the location and intensity of Asian dust.
Third, the supercomputer-simulated Asian Dust Aerosol Model (ADAM) results are fed to
the KMA intranet to be utilized for Asian dust forecasting and to the WMO SDS-WAS Asian
centre to be included in its regional ensemble.
Finally, PM10
concentrations from 29 sites and particle counter data from seven sites are
utilized to identify the path and intensity of Asian dust.
The KMA’s Asian Dust Warning System uses the results of the monitoring and forecasting
system to issue warnings when the hourly average dust (PM10
) concentration is expected
to exceed 800 μg/m3
for over two hours. When the KMA issues a warning, the information
is shared with the public and broadcasting companies online, including through social
networking services.

These forecasts and the associated
warning information need to be linked to
subnational (provincial, state) disaster
management authorities, as well as other
organizations and actors involved in
dealing with SDS.
The issuance of impact-based SDS
forecasts and warnings at the national
and subnational levels requires strong
collaboration among the NMHS, national
disaster management authorities and other
national stakeholder organizations that hold
data on SDS vulnerability and exposure,
which may be necessary in order to assess
the impact of SDS. Where NMHS modelling
and forecasting capacities may be limited,
SDS-WAS products can be used to directly
support NMHS with local forecasting.
For example, the WMO SDS-WAS NAMEE
regional centre supports the Burkina Faso
National Meteorological Agency regarding
the aforementioned warning advisory
system for airborne dust in the country.
9.5.2. Commercial weather
services
Commercial weather services can also
provide SDS warnings to the general public.
For instance, the Weatherzone® website
provided forecasts and information on a
dust storm affecting Sydney in November
2018.10
These services can also inform
the public about SDS more generally,
for example the AccuWeather® website
explains Saharan dust.11
Significantly, non-
NMHS sources may disagree with official
sources on SDS forecasts: although many
commercial weather reports are derived
from official NMHS reports or information,
they can also be developed from modelling
and information systems that operate in
parallel to government or WMO systems.
Commercial forecasts are significant for
the SDS warning process insofar as, in
some cases, SDS information may be
10 http://www.weatherzone.com.au/news/dust-storm-begins-to-impact-sydney-as-nsw-government-issues-air-
quality-warning/528801
11 https://www.accuweather.com/en/health-wellness/everything-you-need-to-know-about-saharan-dust/764481
made quickly and widely available to the
general population through commercial
forecasts on public media such as
commercial radio, TV or mobile phones
(where people may be able to purchase
a service providing weather forecasts).
This requires that NMHS and commercial
forecasters collaborate to ensure warning
messages are accurate, recognizing that
more accurate information, disseminated
through more channels, is generally
preferable to the opposite.
To ensure that SDS forecasts are
consistent and SDS warnings are timely,
accurate and coordinated, NMHS and
commercial forecasters working in a
country should develop a coordinated
forecast and warning dissemination plan
(see chapter 10). This plan may also need
to include forecasting coming from outside
a country when warnings are commonly
provided from these sources, for example
through global media.
9.5.3. Voluntary
observations
Voluntary observations are used to
develop both NMHS and commercial
weather services’ forecast and warnings
products. One example of a voluntary SDS
observation system is the Community
DustWatch network in Australia, which
uses a citizen science approach, involving
the use of trained volunteers to collect
scientific data. This provides a cost-
effective method to address gaps in data
collection and reporting on SDS.
The Community DustWatch network
provides instruments and observer reports
on SDS which complement information
collected through the Australian Bureau of
Meteorology’s system. Observer reports
can be provided in near real-time or as
after-the-fact reports. The former can be
used for SDS forecasting and warning,
while the latter can be used to support
research into SDS.

260
Additional details are available from the
Community DustWatch website.
9.6 SDS modelling
9.6.1. Introduction
The sections below provide an overview
of the use of models for SDS forecasting.
Regional SDS forecast centres (for
example, Barcelona and Beijing) use
models to develop their forecasts of SDS
activity. Models considering global climate
conditions also need to incorporate sand
and dust to understand how SDS can
affect climate, and how the climate is
changing.
As discussed in Benedetti et al. (2014),
several reasons have motivated the
development of dust modelling/forecasting
capabilities for short-term forecasts and
for long-term impact assessments:
• Decision makers have long desired the
ability to forecast severe dust events
in order to issue early warnings and
mitigate their impacts.
• There is a pressing need to monitor
the Earth’s environment to better
understand changes and adapt to
them, especially in the context of
climate.
• While the importance of dust–climate
interactions has long been recognized
(Intergovernmental Panel on Climate
Change [IPCC], 2007; 2013), it is only
more recently that the importance
of feedback mechanisms between
dust and atmosphere for weather
forecasting has been highlighted
(Pérez et al., 2006; Nickovic et al.,
2016).
SDS observations have only a limited
capacity to monitor SDS, as they help
assess SDS evolution only several hours
in advance using simplified spatial and
temporal extrapolation of their features.
The short nature of this approach is too
limiting to provide complete and effective
SDS warnings.
To extend the time validity of SDS early
warnings to short-term (up to three days)
and medium-term (up to 10 days in
advance) periods, the natural response
was to extend the capabilities of the
NWP models so that they are able to
predict concentrations of atmospheric
constituents such as mineral dust.
9.6.2. Development of
SDS modelling
Over the last decade, a dozen numerical
modelling systems for sand and dust
forecasting have been developed. Most
models use atmospheric weather
prediction models as an online driver.
Dust particle distribution is introduced in
the models as a common component.
The dust mass conservation equation is
embedded as one of the model governing
equations (Nickovic et al., 2001; Tegen
and Schulz, 2014). To simulate the
SDS processes, advanced numerical
parameterization methods are used.
Monitoring the process of SDS, obtaining
the relevant parameters of its occurrence,
development and change, providing the
observational basis for describing the
weather process of SDS, carrying out
numerical dust forecasts and providing
corresponding SDS early warnings are
urgently required if we are to effectively
mitigate the impact of SDS and prevent
and reduce disasters. These activities
are also of great significance to national
decision-making on how the impact of SDS
can be addressed.
The first dust forecasting systems with
regional (Nickovic, 1996; Nickovic and
Dobricic, 1996) and global (Westphal et
al., 2009) model domains were introduced
in the 1990s. Since then, numerical
model-based dust forecasts have become
available in many national meteorological
services and research centres around the
world (Benedetti et al., 2014).
Due to the progressive increase in available
computing power, models are run every
day with greater horizontal and vertical
resolutions in order to better describe
small-scale processes, such as the effect
of cold outflows from thunderstorms on
dust emission. Some forecasting systems

also assimilate satellite and ground-based
observations so that they have a much
better description of the dust content in the
initial state and can therefore predict its
evolution more accurately.
Despite extensive efforts in recent years,
dust predictions still lack the accuracy
of ordinary weather forecasts. Besides,
the prediction of surface concentration
– which is the key parameter for most
applications – is much less accurate than
that of columnar parameters, such as dust
load or optical thickness.
One of the methods being worked on
to improve forecast skills is ensemble
prediction, which aims to describe the
future state of the atmosphere from
a probabilistic point of view. Multiple
simulations are run to account for the
uncertainty of the initial state and/or
for the inaccuracy of the model and the
mathematical methods used to solve its
equations (Palmer et al., 1993).
Two dust multi-model ensemble systems
are currently in operation:
1. The WMO SDS-WAS multi-model
ensemble, operated by the SDS-
WAS NAMEE regional centre, based
on 12 regional and global models
(Terradellas et al., 2016; Basart et al.,
2019).
2. The International Cooperative for
Aerosol Prediction’s multi-model
ensemble (ICAP MME). This is a
consensus-style forecast generated
from eight global NWP models that
include mineral dust as well as other
aerosol species (Sessions et al., 2014).
9.6.3. Overview of
numerical dust models
The impacts of dust on the Earth’s
radiation balance, atmospheric
dynamics, biogeochemical processes
and atmospheric chemistry are only
partly understood and remain largely
unquantified. An assessment of the various
effects and interactions of dust and
climate requires quantification of global
atmospheric dust loads and their optical
and microphysical properties.
Dust distributions that are used in
assessments of dust effects on climate
usually rely on results from large-scale
numerical models that include dust as a
tracer. Over the last few years, numerical
prediction of dust concentration has
become prominent at several research
and operational weather centres due to
growing interest from diverse stakeholders,
such as solar energy plant managers,
health professionals, aviation and military
authorities, and policymakers. Including
dust transport interaction with the
atmosphere in numerical models can
improve the accuracy of weather forecasts
and climate simulations and help improve
understanding of the environmental
processes caused by mineral dust
(Knippertz and Stuut, 2014).
To estimate the impact of dust on the
Earth system, knowledge of atmospheric
dust’s life cycle (including dust source
activation and subsequent dust emission,
dust transport routes, and dust deposition)
is crucial. In order to correctly describe
and quantify the dust cycle, one needs
to understand equally well local-scale
processes such as saltation and
entrainment of individual dust particles,
as well as large-scale phenomena such as
mid- and long-range transport.
NWP and research on atmospheric
dynamics models with an embedded dust
component can be used to:
• study and predict processes that
influence dust distribution (for
example, haboobs) and
• assess the dust global budget,
including the contribution of the
different dust storms
Typically, dust mass concentration is
added as a prognostic parameter and
equations mathematically describe the
most significant processes over time, such
as dust emission, vertical turbulent mixing,
long-range transport of dust in the free
atmosphere, and wet and dry deposition to
the Earth surface.

262
These complex mathematical models can
predict the SDS process with reasonable
accuracy and thus help to reduce
hazardous impacts of SDS. The same kind
of dust models are also used for climatic-
scale projections and assessment and to
investigate dust at large scale and for long-
term changes (such as desertification).
9.6.4. Challenges facing
SDS models
Dust models face a number of challenges
owing to the complexity of the system,
including:
• The physical processes involved in
the dust cycle, particularly for dust
emission, are not yet fully understood
(also see chapter 2).
• The need for accurate, frequent and
detailed weather forecasts.
• The vast range of scales required to
fully account for all of the physical
processes related to dust emission,
transport and deposition (i.e.
timescales ranging from seconds to
weeks).
• The paucity of suitable dust
observations available for model
development, evaluation and
assimilation, particularly for desert
dust sources.
• The wide range of scales required to
fully account for all processes related
to SDS development. Dust production
is a function of surface wind stress
and soil conditions, but the wind is an
extremely variable parameter in both
space and time and soil properties are
highly heterogeneous and not always
well characterized.
• Soil conditions, which heavily impact
dust emission, are not always well
known in potential source areas (see
chapter 8).
9.6.5. SDS models
currently in use
There has been a considerable increase
in the number and complexity of dust
atmospheric models used for research
and operational purposes (Nickovic at al.,
2015). Table 21 sets out the main global
and regional SDS models used by different
meteorological or research centres.
Outputs from these models provide inputs
for the WMO SDS-WAS system and its
regional centres, as described elsewhere in
this chapter.
©White
Sands
National
Park,
November
5th,
2016

Model Institution Domain Data assimilation
BSC-DREAM8b_c2 Barcelona
Supercomputing Center
Regional NO
CAMS-ECMWF ECMWF Global MODIS-AOD
DREAM8-NMME-CAMS South East European
Virtual Climate Change
Center (SEEVCCC)
Regional YES (ECMWF
dust-analysis)
NMMB/MONARCH Barcelona
Supercomputing Center
Regional NO
MetUM Met Office Global MODIS/Aqua
GEOS-5 NASA Global MODIS
NGAC NOAA National Centers
for Environmental
Prediction (NCEP)
Global NO
EMA REG CM4 Egyptian Meteorological
Authority (EMA)
Regional NO
WRF-Chem National Observatory of
Athens (NOA)
Regional NO
SILAM Finnish Meteorological
Institute (FMI)
Global NO
LOTOS-EUROS TNO Regional NO
ICON-ART Deutscher Wetterdienst
(DWD)
Regional YES (data assimilation
cycle for dust, currently
no AOD/dust obs. used)
CUACE China Meteorological
Administration (CMA)
Regional three-dimensional
variational (3D-VAR)
ADAM3 National Institute
of Meteorological
Sciences of the
Korea Meteorological
Administration (NIMS/
KMA)
Regional Optimal interpolation (OI)
MASINGAR Meteorological Research
Institute of the Japan
Meteorological Agency
(MRI/JMA)
Global two-dimensional
variational (2D-VAR)
NAAPS and ICAP
ensemble
U.S. Naval Research
Laboratory (NRL)
Global YES
WRF-Chem Caribbean Institute
for Meteorology and
Hydrology (CIMH)
Regional NO
Source: Adapted from the WMO SDS-WAS website: www.wmo.int/sdswas
Table 21.
SDS atmospheric
models
contributing to
the WMO SDS-
WAS system and
regional centres

264
9.6.6. Scale of model results
Due to increased computing power, these
models can be run at greater spatial
resolutions to allow for more detailed
investigations of smaller area processes,
such as the effects of cold outflows from
thunderstorms on dust emission (Heinold
et al., 2013; Vukovic et al., 2014; Solomos
et al., 2017). At the same time, there have
been some new approaches to treating
emission processes in the models at high
resolution (Kok, 2011; Klose and Shao,
2016).
At global scales, models can reproduce the
main dust transport pathways driven by
large-scale flows (mainly associated with
monsoon winds and frontal passages),
showing that these storms are the main
contributor to the dust global budget
(Cakmur et al., 2006; Huneeus et al., 2011).
However, the contribution of smaller-scale
dust storms (such as those associated
with convection in haboobs or dust
whirlwinds) to overall dust flows is still
uncertain (Knippertz and Todd, 2012).
In West Africa, both haboobs and the
breakdown of nocturnal low-level jets
(NLLJs) appear to account for 30 to 50 per
cent of dust emissions in summer (Allen
et al., 2013; Fiedler et al., 2013; Heinold
et al., 2013; Marsham et al., 2013; Pope
et al., 2016 Miller et al. (2008) estimated
that the haboob activity in the Middle East
in summertime could be responsible for
30 per cent of its dust emissions.
Dust whirlwinds (see chapter 2) are not
easily identified in operational dust models
and are still linked to large uncertainty
in the modelling process (Knippertz and
Todd, 2012; Jemmett-Smith et al., 2015;
Klose and Shao, 2016). According to global
estimates, microscale dust whirlwinds
could contribute by ~26 per cent ± 18 per
cent to total dust emissions (Koch and
Renno, 2005). Recent studies (including
Jemmett-Smith et al., 2015) estimate
their global contribution at ~3.4 per cent
(uncertainty range 0.9–31 per cent).
Technogenic smaller-scale dust storms
(< 1 km) are usually local-scale phenomena
and require high-resolution meso-scale
computer fluid dynamics (CFD) type
models for such SDS assessments (see,
for example, Amosov et al., 2014).
9.6.7. Reanalysis products
and SDS modelling
Reanalysis products are also used for
long-term impact assessments of SDS
and are increasingly being used for climate
monitoring and assessment.
Reanalysis is the process whereby an
unchanging data assimilation system is
used to provide a consistent reprocessing
of meteorological and atmospheric
composition observations, typically
spanning an extended segment of the
historical data record.
The process relies on an underlying
forecast model to combine disparate
observations in a physically consistent
manner, enabling the production of gridded
data sets for a broad range of variables,
including ones that are sparsely or not
directly observed (Gelaro
et al., 2017). Two global reanalyses that
include dust content are:
• NASA’s Modern-Era Retrospective
analysis for Research and
Applications, Version 2 (MERRA-2),
which provides data beginning in 1980,
is the latest atmospheric reanalysis
version for the modern satellite era
produced by NASA’s Global Modeling
and Assimilation Office (GMAO)
(Gelaro et al., 2017), (see Figures 42
and 43), and the
• Copernicus Atmosphere Monitoring
Service (CAMS) reanalysis, which
started in 2003.

Figure 42.
Annual
mean surface
concentration of
mineral dust in
2018 calculated
by the SDS-WAS
regional centre
for Asia, based
on NASA MERRA
reanalysis
Figure 43.
Anomaly of
the annual
mean surface
concentration
of dust in 2018
relative to mean
of 1981–2010,
calculated by
the SDS-WAS
regional centre
for Asia, based
on NASA MERRA
reanalysis
Source: WMO Airborne Dust Bulletin, 2019.
Source: WMO Airborne Dust Bulletin, 2019.

266
9.7 Conclusions
SDS forecasting focuses on the impacts
of weather on people, framed as impact-
based, human centred forecasting. This
approach provides individuals at risk from
SDS with information on emerging SDS
as well as on actions that can be taken to
address the expected impacts of SDS.
There is a range of in situ and remote
options to collect data on SDS events,
each with specific advantages. Three WMO
SDS-WAS regional centres (in Barcelona,
Beijing and Barbados) collect and process
data from in situ and remotely sensed
sources to develop products that support
SDS forecasting at a regional level. They
also support countries with national-
level forecasting and issuing their own
warnings.
While some countries are capable of
developing their own forecasts, a majority
use SDS-WAS products to improve impact-
based, people-centred forecasting and
reduce the impacts of SDS on lives and
well-being.
SDS modelling has made rapid progress
and involves a number of models and
reanalysis as part of efforts to improve the
understanding of SDS and provide useful
forecasts which feed into effective warning
results. A number of challenges with the
modelling process remain, particularly
linked to small SDS events (such as dust
whirlwinds) and accounting for soil and
local weather (particularly wind) conditions.
However, current model outputs provide
a significant contribution of SDS forecast
and monitoring outputs through the WMO
SDS-WAS system and for some NMHS.
McDobbie
Hu,
©Unsplash,
March
27th,
2015

9.8 References
Allen, Christopher J. T., Richard Washington, and
Sebastian Engelstaedter (2013). Dust emission
and transport mechanisms in the central Sahara:
Fennec ground‐based observations from Bordj
Badji Mokhtar, June 2011. Journal of Geophysical
Research: Atmospheres, vol. 118, No. 12, 6212-
6232.
Amosov, Pavel, Alexander Baklanov, and Olga Rigina
(2014). Numerical modelling of tailings’ dusting
processes. Lambert Academic Publishing.
Baddock, Matthew C., and others (2014). A visibility and
total suspended dust relationship. Atmospheric
Basart, Sara (2017). Forecast Evaluation: AERONET
vs. SDS-WAS Multi-model Forecast for 2016.
Barcelona: WMO SDS-WAS. SDSWAS-
NAMEE-2017-001.
Basart, Sara, and others (2019). The WMO SDS-WAS
Regional Center for Northern Africa, Middle East
and Europe. E3S Web Conf., 99 (2019) 04008, doi:
https://doi.org/10.1051/e3sconf/20199904008.
Benedetti, Angela, and others (2014). Operational
dust prediction. In Mineral Dust: A Key Player
in the Earth System, P. Knippertz and J.-B.W.
Stuut eds., Dordrecht: Springer, pp. 223-265,
doi:10.1007/978-94-017-8978-3_10.
Cakmur, R. V., and others (2006). Constraining the
magnitude of the global dust cycle by minimizing
the difference between a model and observations.
vol. 111, No. 6, pp. 1–24.
Camino, C., and others (2015). An empirical equation
to estimate mineral dust concentrations from
visibility observations in Northern Africa. Aeolian
Chepil, W. S., and N.P. Woodruff (1957). Sedimentary
characteristics of dust storms; Part II, Visibility
and dust concentration. American Journal of
Science, vol. 255, No. 2, pp. 104–114.
d’Almeida, Guillaume A. (1986). A model for Saharan
dust transport. Journal of Climate and Applied
Dayan, Uri, and others (2008). Suspended dust over
southeastern Mediterranean and its relation to
atmospheric circulations. International Journal of
Climatology, vol. 28, No. 7, pp. 915–924.
Di Tomaso, Enza, and others (2017). Assimilation of
MODIS Dark Target and Deep Blue observations
in the dust aerosol component of NMMB-
MONARCH version 1.0. Geoscientific Model
Development, vol. 10, pp. 1107–1129.
Dubovik, Oleg, and Michael D. King (2000). A flexible
inversion algorithm for retrieval of aerosol
optical properties from Sun and sky radiance
measurements. Journal of Geophysical Research:
Atmospheres, vol. 105, No. D16, pp. 20673–
20696.
European Commission (2008). Directive 2008/50/EC of
the European Parliament and of the Council of
21 May 2008 on ambient air quality and cleaner
air for Europe (OJ L 152, 11.6.2008, p. 1–44).
Available at https://www.eea.europa.eu/policy-
documents/directive-2008-50-ec-of.
European Organisation for the Exploitation of
Meteorological Satellites (EUMETSAT) (2009).
Best Practices for RGB Compositing of Multi-
Spectral Imagery. User Service Division. Available
at http://oiswww.eumetsat.int/~idds/html/doc/
best_practices.pdf Accessed 25 May 2017.
Fiedler, S., and others (2013). Climatology of nocturnal
low‐level jets over North Africa and implications
for modeling mineral dust emission. Journal of
12, 6100-6121.
Gelaro, Ronald, and others (2017). The Modern-
Era Retrospective Analysis for Research and
Applications, Version 2 (MERRA-2). Journal of
Climate, vol. 30, No. 14.
Global Atmosphere Watch, World Data Centre for
Aerosols (no date). Available at https://www.gaw-
wdca.org/.
Heinold, B., and others (2013). The role of deep
convection and nocturnal low‐level jets for dust
emission in summertime West Africa: Estimates
from convection‐permitting simulations. Journal
of Geophysical Research: Atmospheres, vol. 18,
No. 10, 4385-4400.
Holben, Brent N., and others (1998). AERONET—A
federated instrument network and data archive
for aerosol characterization. Remote Sensing of
Huneeus, N., and others (2011). Global dust model
intercomparison in AeroCom phase I.
Atmospheric Chemistry and Physics, vol. 11, No.
15, pp. 7781–7816.
(2007). AR4 Climate Change 2007: the Physical
Science Basis: Working Group I Contribution
to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change (Vol.
4). Cambridge University Press.

268
__________________________________________ (2013).
AR5 Climate Change 2013: the Physical Science
Basis: Working Group I Contribution to the Fifth
Assessment Report of the Intergovernmental
Panel on Climate Change. Cambridge University
Press.
Jemmett-Smith, Bradley C., and others (2015).
Quantifying global dust devil occurrence from
meteorological analyses. Geophysical Research
Letters, vol. 42, No. 4, pp. 1275–1282.
Jugder, Dulam, and others (2014). Quantitative analysis
on windblown dust concentrations of PM10
(PM2.5) during dust events in Mongolia. Aeolian
Klose, Martina, and Yaping Shao (2016). A numerical
study on dust devils with implications to global
dust budget estimates. Aeolian Research, vol. 22,
pp. 47–58.
Knippertz, Peter, and Jan-Berend W. Stuut (2014).
Mineral Dust: A Key Player in the Earth System.
Dordrecht: Springer Science+Business Media,
https://doi.org/10.1007/978-94-017-8978-3, 10,
978-94.
Knippertz, Peter, and M.C. Todd (2012). Mineral dust
aerosols over the Sahara: Meteorological controls
on emission and transport and implications for
modeling. Rev. Geophys., vol. 50 (RG1007).
Koch, Jacquelin, and Nilton O. Renno (2005). The role
of convective plumes and vortices on the global
aerosol budget. Geophysical Research Letters,
vol. 32, No. 18, pp. 1–5.
Kok, Jasper F. (2011). A scaling theory for the size
distribution of emitted dust aerosols suggests
climate models underestimate the size of the
global dust cycle. Proceedings of the National
Academy of Sciences of the United States of
America, vol. 108, No. 3, pp. 1016–1021.
Marsham, John H., and others (2013). Meteorology
and dust in the central Sahara: Observations
from Fennec supersite‐1 during the June
2011 Intensive Observation Period. Journal of
10, 4069–4089.
Miller, Steven D., and others (2008). Haboob dust storms
of the southern Arabian Peninsula. Journal of
Geophysical Research: Atmospheres, vol. 113,
No. D1.
Mohamed, A. Ben, and others (1992). Spatial and
temporal variations of atmospheric turbidity and
related parameters in Niger. Journal of Applied
Morcrette, Jean-Jacques, and others (2009). Aerosol
analysis and forecast in the European Centre for
Medium‐Range Weather Forecasts integrated
forecast system: Forward modeling. Journal of
Geophysical Research: Atmospheres, vol. 114,
No. D6.
Nickovic, Slobodan (1996). Modeling of dust process
for the Saharan and Mediterranean area. In The
Impact of Desert Dust across the Mediterranean,
Stefano Guerzoni and Roy Chester, eds.
Dordrecht: Springer.
vol. 106, No. D16, pp. 18113–18129.
Nickovic, Slobodan, and others (2015). WMO
Assessment System (SDS-WAS). Science
and Implementation Plan 2015–2020. World
Meteorological Organization: WWRP 2015-5
Report. Available at https://www.researchgate.
net/publication/323384367_WMO_Sand_
and_Dust_Storm_Warning_Advisory_and_
Assessment_System_SDS-WAS_Science_and_
Implementation_Plan_2015-2020
Nickovic, Slobodan, and others (2016). Cloud ice
caused by atmospheric mineral dust – Part 1:
Parameterization of ice nuclei concentration
in the NMME-DREAM model. Atmospheric
Chemistry and Physics, vol. 16, No. 17, pp.
11367–11378.
Nickovic, Slobodan, and Srdjan Dobricic (1996). A model
for long-range transport of desert dust. Monthly
Weather Review, vol. 124, No. 11, 2537-2544.
O’Loingsigh, Tadhg, and others (2014). The Dust Storm
Index (DSI): A method for monitoring broadscale
wind erosion using meteorological records.
Palmer, T. N., and others (1993). Ensemble prediction. In
Proceedings of the ECMWF Seminar on Validation
of Models over Europe, vol. 1, pp. 21–66.
Patterson, Edward M., and Dale A. Gillette (1977).
Measurements of visibility vs mass-
concentration for airborne soil particles.
Atmospheric Environment (1967), vol. 11, No. 2,
pp. 193–196.
Pérez, Carlos, and others (2006). Interactive dust‐
radiation modeling: A step to improve weather
forecasts. Journal of Geophysical Research:

Pope, Richard J., and others (2016). Identifying errors in
dust models from data assimilation. Geophysical
Research Letters, vol. 43, No. 17, 9270–9279.
Rodríguez, Sergio, Andres Alastuey, and Xavier Querol
(2012). A review of methods for long term in
situ characterization of aerosol dust. Aeolian
Research, vol. 6, pp. 55-74.
Secretariat of the World Meteorological Organization
(1975). Manual on the Observation of Clouds and
Other Meteors, (Partly Annex I to WMO Technical
Regulations). International Cloud Atlas, volume I.,
No. 407. World Meteorological Organization.
Sessions, Walter R., and others (2014). Development
towards a global operational aerosol consensus:
basic climatological characteristics of the
International Cooperative for Aerosol Prediction
Multi-Model Ensemble (ICAP-MME). ACPD, vol.
14, No. 10, 14933–14998.
Shao, Yaping, and others (2003). Northeast Asian dust
storms: Real‐time numerical prediction and
validation. Journal of Geophysical Research:
Schmaltz. J. and R. Lindsey (2017). MODIS Rapid
Response Team. NASA, USA.
Solomos, Stavros, and others (2017). Remote sensing
and modelling analysis of the extreme dust storm
hitting the Middle East and eastern Mediterranean
in September 2015. Atmospheric Chemistry and
Physics, vol. 17, No. 6, pp. 4063–4079.
Tegen, Ina, and Michael Schulz (2014). Numerical dust
models. In Mineral Dust: A Key Player in the Earth
System, P. Knippertz and J.-B.W. Stuut eds.,
Dordrecht: Springer, pp. 201–222.
Terradellas, Enric, and others (2018). Warning Advisory
System for Sand and Dust Storm in Burkina Faso.
Barcelona: WMO SDS-WAS. SDS-WAS-2018-001.
Available at https://sds-was.aemet.es/materials/
technical-reports/SDSWASNAMEE2018001.pdf.
Terradellas, Enric, Sara Basart, and Emilio Cuevas Agulló
(2016). Airborne Dust: from R&D to Operational
Forecast. 2013–2015 Activity Report of the SDS-
WAS Regional Center for Northern Africa, Middle
East and Europe. Joint publication of State
Meteorological Agency (AEMET) and World
Meteorological Organization (WMO).
Terradellas, Enric, Slobodan Nickovic, and Xiao-Ye
Zhang (2015). Airborne dust: a hazard to human
health, environment and society. WMO Bulletin,
vol. 64, No. 2, 44-48.
doi:10.5194/acp-14-3211-2014
Wang, Y. Q., and others (2008). Surface observation
of sand and dust storm in East Asia and its
application in CUACE/dust. Atmospheric
Chemistry and Physics, vol. 8, No. 3, pp. 545–553.
Westphal D. L., and others (2009). Operational aerosol
and dust storm forecasting. IOP Conference
Series: Earth and Environmental Science, vol. 7,
No. 012007 doi:10.1088/1755-1307/7/1/012007.
WMO Airborne Dust Bulletin (2019). WMO Airborne Dust
Bulletin No. 3 [for the year 2018]. SDS-WAS, WMO.
Available at https://library.wmo.int/doc_num.
php?explnum_id=6268
World Meteorological Organization (WMO) (2009).
Recommendations for a Composite Surface-
Based Aerosol Network. GAW Report No. 207.
Available at https://www.wmo-gaw-wcc-aerosol-
physics.org/files/gaw-207.pdf.
__________________________________ (2012). Sand and Dust
Storm Warning Advisory and Assessment System
(SDS-WAS) Science and Implementation Plan for
2011-2015. World Meteorological Organization
(WMO) Secretariat, Research Department,
Atmospheric Research and Environment Branch,
Geneva, Switzerland, SDS-WAS Report. February
2012.
__________________________________ (2014). Seventh
Session of the Scientific Steering Committee
(SSC) for the World Weather Research Programme
(WWRP). Boulder, USA. Available at https://library.
wmo.int/doc_num.php?explnum_id=9748
__________________________________ (2015). WMO
Guidelines on Multi-hazard Impact-based
Forecast and Warning Services. WMO-No. 1150.
__________________________________ (2020). Sand and
System. Science Progress Report. Prof. Alexander
Baklanov (WMO) and Prof. Xiaoye Zhang (CMA)
eds. World Meteorological Organization (WMO),
GAW Report No. 254 & WWRP Report 2020-4.
Geneva, Switzerland, June 2020, 45pp.

UNCCD | Sand and Dust Storms Compendium | Chapter 10 | Sand and dust storms early warning 271
10. Sand and dust
storms early warning
Chapter overview
The chapter provides a general overview of requirements for a sand and dust storms (SDS)
warning system involving national meteorological and hydrological services (NMHS), national
disaster management authorities (NDMAs) and a wide range of other stakeholders. The
effectiveness of a warning system is demonstrated by how well individuals and other parties
at risk take preventive actions to mitigate risks once a warning is received. The chapter
discusses responsibilities for forecasts and warnings, warning dissemination, people-centred,
impact-based warning, warning verification and the process by which individuals take action
once a warning has been received. While the chapter content is general, it provides core
guidance on developing SDS warning systems at the national or subnational levels.

UNCCD | Sand and Dust Storms Compendium | Chapter 10 | Sand and dust storms early warning
272
10.1. Introduction
Warnings are a core part of disaster risk
management processes, provided they
are disseminated early enough to permit
actions to reduce or avoid the impacts of
hazards. This chapter provides an overview
of early warning approaches to sand and
dust storms (SDS) based on generally
accepted practices.
SDS warning systems are complex and can
operate in different ways and with different
actors, depending on the country involved.
As a result, individual users and countries
are expected to adopt the overall early
warning system concept described below
to best meet their needs and capacities.
This chapter should be read together
with chapters 3, 9, 12 and 13, as well as
World Meteorological Organization (WMO)
(2018), WMO (2017) and WMO (2015a;
2015b), which provide additional details on
developing a multi-hazard early warning
system (MHEWS). Reference should also
be made to the WMO Sand and Dust Storm
Warning Advisory and Assessment System
(WMO SDS-WAS) and its operational
centres within the WMO Global Data-
processing and Forecasting System
(GDPFS) (see chapter 9).
10.2. Conceptualizing
early warning for SDS
The core concept applied in early warning
is that the individual at risk is the starting
point for the warning process. The timing,
content, reception and understanding of
warnings should enable individuals, com-
munities, businesses and organizations at
immediate risk to take actions to reduce or
avoid impacts from the risks they face (see
Box 16).
While it can be difficult to ensure good
and timely dissemination of warnings,
individuals with a good understanding of
warning factors can often initiate actions
on their own to reduce or avoid SDS
impacts.
As a result, at-risk individuals, communities,
businesses and organizations should be
empowered to understand warning signals
and to take action to avoid or mitigate the
impact of SDS. Educating individuals about
SDS risks and warning signs, as discussed
in this chapter, is an essential part of an
effective early warning system.
Box 16. What is an early warning system?
An early warning system is “an integrated system of hazard monitoring, forecasting and
prediction, disaster risk assessment, communication and preparedness activities systems
and processes that enables individuals, communities, governments, businesses and oth-
ers to take timely action to reduce disaster risks in advance of hazardous events”.
Source: United Nations Office for Disaster Risk Reduction (UNDRR), 2018.

Traditional knowledge can also play a
significant role in triggering warnings and
taking action. This knowledge should be
part of any warning system and should
be used to integrate the overall warning
process into the culture of individuals and
societies that are the targets of a warning
process.
The content of a warning message is
dependent on (1) the knowledge (data and
analysis) about weather events available to
a forecaster, (2) the time available to take
action, and (3) the nature of the actions to
be taken.
Warnings of near-term events (minutes
to days in advance) provide immediate
guidance to at-risk populations to take
action to address the expected impact of
SDS. Such short-term (up to several days)
warnings can be based on operational
forecasts of dust concentrations (WMO,
2015a, also see chapter 9).
SDS warnings can also be based on
medium- to long-term situations (months
or longer). For instance, if data indicate
a wetter than normal monsoon with
expected early seasonal storms, a
warning could be issued anticipating the
development of more or more powerful
haboobs at the beginning of the monsoon.
Based on seasonal warnings, individuals
and institutions may take appropriate
action, such as replacing filters or resealing
windows to limit from entering buildings
(see chapter 13 for more on SDS
preparedness and impact mitigation). This
seasonal forecast would be followed by
warnings when forecasts indicated actual
haboob development or arrival at a location
is expected.
An effective warning process is people-
centred and impact-focused (WMO, 2018).
The people-centred aspect recognizes that
it is at-risk individuals who turn warning
into action and that it is therefore the
people who need to be involved in the
design and operation of early warning
systems from the start, making the last
mile the first mile. The impact aspect of
the warning system identifies how SDS can
affect individuals, communities or assets,
and what actions can be taken to reduce
their threat.
10.3. Key components of
early warning systems
An effective people-centred and impact-
based early warning system has four
components (United Nations General
Assembly, 2016):
• detection, monitoring, analysis and
forecasting, as discussed in chapters
2, 3, 8 and 9
• disaster risk and hazard knowledge,
as discussed in chapters 3, 4, 5, 7, 12
and 13
• preparedness and response capacities
as discussed in chapter 13
• warning dissemination and
communication, as discussed in this
chapter.
Figure 44 provides a set of core questions
for each component as presented in
Multi-hazard Early Warning Systems: A
Checklist (WMO, 2018). The document
was developed by several international
organizations with a key role in early
warning under the International Network
for Multi-Hazard Early Warning Systems
(IN-MHEWS) as an update of Developing
Early Warning Systems: A Checklist
(United Nations International Strategy for
Disaster Reduction [UNISDR], 2006). The
key questions for warning dissemination
and communication are summarized in the
lower left box of the figure.
An MHEWS addresses several hazards
and impacts of similar or different types
in contexts where hazardous events may
occur alone, simultaneously, cascadingly
or cumulatively over time, and takes into
account the potential interrelated effects.
The ability of an MHEWS to warn of one
or more hazards increases the efficiency
and consistency of warnings through
coordinated and compatible mechanisms
and capacities, involving various disciplines
to ensure updated and accurate hazards
identification and monitoring for multiple
hazards (United Nations General Assembly,
2016).

276
Source: United Nations International Strategy for Disaster Reduction (UNISDR) (2006).
Figure 44.
Four elements
of end-to-end,
people-centred
early warning
systems
DISASTER RISK KNOWLEDGE
• Are key hazards and related threats
identified?
• Are exposure, vulnerabilities, capacities
and risks assessed?
• Are roles and responsibilities of
stakeholders identified?
• Is risk information consolidated?
DETECTION, MONITORING,
ANALYSIS AND FORECASTING
OF THE HAZARDS AND
POSSIBLE CONSEQUENCES
• Are there monitoring systems in place?
• Are there forecasting and warming
systems in place?
• Are there institutional mechanisms in
place?
WARNING DISSEMINATION AND
COMMUNICATION
• Are organizational and decision-making
processes in place and operational?
• Are communication systems and
equiptment in place and operational?
• Are impact-based early warnings
communicated effectively to promt action
by target groups?
PREPAREDNESS AND RESPONSE
CAPABILITIES
• Are disaster preparedness measures,
including response plans, developed and
operational?
• Are public awareness and education
campaigns conducted?
• Are public awareness and response tested
and evaluated?
CH10 Figure 44.
In terms of disaster risk management good
practice, an effective SDS early warning
system uses a whole of community
approach (National Weather Service,
2018). In this approach, the actions by
all stakeholders, especially at-risk and
otherwise affected populations, are
incorporated into a single approach to
ensure that warnings are provided in a
timely manner and that appropriate actions
are taken to reduce or avoid negative
impacts. An integrated process for
defining, establishing and managing early
warning systems requires the involvement
of a wide range of stakeholders (see
Box 17).

Box 17. Early warning stakeholders
A range of stakeholders in the forecast and warning process have important roles in devel-
oping, disseminating and using the SDS warning information. These include:
• specific at-risk groups that could experience significant negative health or other
impacts from SDS
• regional forecast centres, including SDS forecasters, modellers and researchers
• national meteorological and hydrological services (NMHS), including forecasters,
modellers and weather education specialists
• geological services or surveys, environment authorities and other national
technical agencies and national alerting authorities
• national disaster management authorities (NDMA) and subnational counterparts,
including planners, early warning system managers, response managers and
trainers
• telecommunications officials, including technicians focused on system reliability
and message management (including targeting messages to specific locations or
audiences)
• health authorities and hospitals, including health specialists, facility managers,
patient managers and emergency health care providers
• transport system management authorities (air, land, sea), including planners,
maintenance crews and police (this should be separate under public safety or
similar grouping) to ensure safety during SDS
• the media, including radio, TV and the Internet, as well as those working through
these systems (for example, news readers, presenters, bloggers, etc.)
• agricultural and livestock producers, including agronomists, livestock specialists
and infrastructure managers, to minimize SDS-related losses
• the private sector (businesses, industry and services, etc.), including those that
can be affected by high airborne sand or dust loads, including high precision or
low contamination production facilities and food preparation and sales
• education providers, including teachers providing education on SDS and school
directors taking action to ensure student safety during SDS
• community welfare or care groups, which focus on assisting those more likely
to be affected by SDS, including civil society organizations, non-governmental
organizations and volunteers
• international (regional and global, inter-governmental and non-governmental)
organizations.

278
Operationally, an SDS early warning system
is based on an overall warning plan,
which includes sources of information
and analysis, dissemination methods and
standard operating procedures (SOPs) to
ensure warnings are received in a timely
manner. Such plans are complemented
by subplans for specific sectors (for
example, health) and specific facilities
(such as clinics) or specific purposes
(such as road safety). The planning and
overall coordination of the warning process
is usually led by the national disaster
management authority (NDMA) or similar
agency, with some countries decentralizing
part of these responsibilities to the
subnational level.
In some countries, the national
meteorological and hydrological service
(NMHS) may be involved in warning
dissemination in coordination with the
NDMA.
These NMHS-generated warnings can be
issued by local offices based on local near-
real-time assessments of warning needs.
The effectiveness of SDS early warning
systems and plans is judged not only by
the sophistication of the SDS forecast and
modelling. Rather, success is also based
on how well individuals at risk from SDS
take action to avoid or reduce the impact
of the SDS. The people-centred, impact-
focused approach takes forecast and
warning data and combines these with
vulnerability and exposure data in order
to assess potential impacts and yield
practical actions to reduce the impact of
SDS on individuals, livelihoods and society
as a whole.
Box 18. SDS warning and the Sendai Framework
The overall people-centred, impact-focused concept of early warning systems is reflected
in three priorities for action of the Sendai Framework for Disaster Risk Reduction
2015–2030 (United Nations, 2015):
• Priority 1: Understanding disaster risk, which is addressed through the work on
disaster risk knowledge (upper left box in Figure 44).
• Priority 2: Strengthening disaster risk governance to manage disaster risk, which is
addressed by focusing on coordination and partnerships, improving the effectiveness
of the overall early warning system at all levels and across stakeholders, and having
feedback mechanisms in place to allow for the system to improve over time.
• Priority 4: Enhancing disaster preparedness for effective response and to “Build Back
Better” in recovery, rehabilitation and reconstruction, which is addressed through
building, maintaining and strengthening “people-centred multi-hazard, multisectoral
forecasting and early warning systems” (Ibid, p. 21), especially elements three
(warning dissemination and communication) and four (preparedness and response
capabilities) (see Figure 44).
In addition, improving SDS early warning systems contributes to achieving global target G
“Substantially increase the availability of and access to multi-hazard early warning
systems and disaster risk information and assessments to the people by 2030” of the
Sendai Framework (UNDRR, 2018, p. 155), to be reflected through respective monitoring
and reporting within the Sendai Framework Monitor tool (see https://sendaimonitor.
unisdr.org/).

10.4. Impact-based,
people-centred forecasting
and early warning process
As discussed in chapter 9, SDS should
be addressed through an impact-based,
people-centred forecast and warning
process. Figure 45 graphically presents
this process.
In the impact-based, people-centred
forecast process:
• The NDMA leads the development and
updating of SDS risk assessments
(see chapters 4, 5, 7, and 6 for
economic impacts).
• The NMHS integrates the risk
assessment outputs into the
forecasting and warning process.
• Results of assessments are integrated
into the SDS modelling, monitoring
and forecasting process, which also
incorporates inputs from the WMO
SDS-WAS modelling, monitoring and
forecast process, as well as inputs
from the NMHS observation system
and voluntary SDS observations
(see chapter 9 on the Community
DustWatch network).1
• The NMHS, or subnational branches,
monitor SDS development on a
near-real-time basis (over the next 12
hours).
• The NMHS, or subnational branches,
issue specific SDS (impact-based,
if possible) forecasts focusing on
specific locations where SDS are
expected.
1 See https://www.environment.nsw.gov.au/topics/land-and-soil/soil-degradation/wind-erosion/community-dust-
watch
• Depending on policies, the NDMA or
NMHS issues warnings when justified
by the available modelling, monitoring
and observations.
• At-risk individuals take action based
on the warnings and an understanding
of the SDS impacts in order to avoid or
reduce the expected impacts.
• After SDS events, the NMHS,
together with the NDMA and other
stakeholders, assesses the impact of
the forecast and warning messages
on whether at-risk individuals took
action to avoid or mitigate SDS
impacts. These assessments feed
back to the system to improve the
forecasting and warning process and
product.
As described in chapter 9, if an NMHS
does not have access to risk or
vulnerability assessments, a pragmatic
approach is recommended through which
the NMHS and NDMA agree on impact
matrices for SDS events and classify them
in terms of the severity of the impact for
various user groups.

280
Table 22 provides an example of how the warning process can be integrated into tactical,
strategic and research aspects of managing the impacts of SDS on specific sectors, in
this case, agriculture. This type of planning can be integrated into SDS source mitigation
(chapter 12) and impact mitigation and response (chapter 13) plans and procedures.
Tactical (short term) Strategic (long term) Research
• Near-term warnings for
agricultural communities to
take preventive action:
• harvesting maturing crops
• sheltering livestock
• strengthening infrastructure
(houses, roads, crop
storage).
• Improved SDS climatology
for long-term planning for
agricultural communities:
• planning windbreaks and
shelterbelts (direction, size,
etc.)
• planning for infrastructure
and crops
• post-storm crop damage
assessments.
• Forecasting locust
movement.
• Improving soil/wind erosion
and land degradation
models.
• Forecasting plant and animal
pathogen movement and
the relationship of SDS to
disease outbreaks.
• Archiving SDS warning
system products for forensic
use.
Source: Stefanski and Sivakumar, 2009
Figure 45.
Impact-based,
people-centred
forecast and
warning systems
for sand and dust
storms
Table 22.
Potential
agricultural
applications of
an SDS warning
system
NDMA leads SDS risk
assessment
World Weather Watch
information related to
SDS
SDS-WAS modelling,
monitoring and
forecast outputs
NMHS incorporates risk
information into an
impact-based forecast
process
National SDS modelling,
monitoring and
forecasting support for
medium (10 day) and
near-term (3 day) SDS
forecasting*
NMHS weather observation
system information on
SDS**
NMHS issues forecast
of possible SDS, with
specific locations and
impacts identified
NHMS monitoring of near
real-time (>12 hours)
potential for SDS
Voluntary observation
system information on
SDS
NDMA or NMHS (depending
on country policy) issues
warnings to populations at
risk of SDS impacts (includes
specific impact-based
warnings for specific
subgroups)
At-risk populations take
action based on prepared-
ness plans (which vary by the
nature of the risk and who is
at risk)
NMHS assesses the impact
of forecast and warning
messaging (with NDMA)
* Level of capacity varies between countries.
** National data provided to SDS-WAS via World Weather Watch.
CH10 Figure 45.
©Scott
Robinson
on
Flickr
,
March
13th,
2017

10.5. Authority to
issue forecasts
and warnings
There is a significant distinction between:
• forecasts, which include details
of weather and atmospheric dust
conditions and how they may change,
and
• warnings, which are issued by a
mandated authority and intended
to trigger specific (compulsory
or voluntary) actions and legal
authorities, for example, requiring that
facilities close or traffic be stopped.
It is important to note that forecasts may
include alerts or watches and may be
issued by the same authority (such as an
NDMA) that issues warnings.
Due to the difference between forecasts
and warnings, clarity is needed. In terms
of plans and procedures, there should be a
policy defining who has the authority to:
• issue forecasts, alerts and watches
• issue warnings
• order actions based on these
warnings, such as closing facilities,
restricting travel or implementing
emergency contingency plans.
How forecast or warning information is
provided to the public can vary between
countries. In some cases, written-text
watches and warnings are the norm,
while in other countries, colours or
numbers may be used to indicate the
significance of information about hazard
events. Understanding the warning
mechanisms and terminology used by
authorities and how it relates to decisions
taken when a warning is received is an
important component of an SDS early
warning system.
While forecasts are normally provided by
an NMHS, the authority to issue official
warnings may rest, for example, with the:
• NMHS, based on established
protocols, SOP and warning plans,
with additional information on actions
to be taken
• NDMA, which receives forecasts and
warnings from the NMHS and then
retransmits these with or without
additional information, based on
emergency response plans
• Office of the Prime Minister or
President, when authority to initiate
the legal authorities associated with
warnings rests with these officials
• state commissions charged with
emergency management, having the
statutory authority to provide warnings
and manage disasters.
It should also be noted that in many
countries, disaster risk management is
delegated to the subnational (province,
state) level, with the NDMA playing a
supporting role. In these cases, it may
be the head of the state or province, the
head of the provincial or state disaster
management office or another official,
such as a senior police officer, who has the
authority to issue warnings. Subnational
warnings may be based on information
from subnational NMHS offices with
a capacity to generate forecasts or on
information provided by a centrally located
forecast office, usually the national NMHS
office.
In addition, disaster management
authorities at the national, provincial/state
or county/city administrative levels may
use commercial forecasting services and
other services (such as social media) for
additional localized information on which
to base localized warnings. The use of
commercial services does not replace the
NMHS, but should provide a level of local
detail which may not be available from a
NMHS.

282
In addition to NMDA and NMHS warnings
being issued, specific sectoral warnings
may also be issued by various authorities,
including aviation, road transport, health
and education, based on forecasts of the
NHMS or other technical agencies. Public
authorities and the private sector can
also use commercial forecast services to
anticipate and prepare for hazard events,
issue internal warnings and alter standard
practices based on warning and response
plans.
To summarize, because the SDS warning
process can vary considerably between
countries, the following questions need
clear answers:
• Who has the mandate and authority
to issue forecasts, alerts, warnings or
watches?
• Who has the legal authority to issue
warnings?
• Who ensures that a warning is acted
upon? The parties responsible for
ensuring that warnings are followed
can be different from the party which
issues the warning. For instance,
while it may be the NMHS that issues
a warning, the police may have
the authority to take action, such
as restricting traffic, based on the
warning.
• To whom does the NMHS or
subnational offices provide forecasts
and warnings and how?
• How can the NMHS and NDMA ensure
that warnings are issued in a timely
manner?
10.6. Warning plans and
mechanisms
The need for clarity on the roles and
responsibilities for forecasting and
issuing warnings is usually addressed
through detailed planning, resulting in
plans and procedures for forecasts and
warnings. In general, forecast plans are
developed internally by the NMHS, with the
development of warning plans led by the
NDMA (if there are separate forecast and
warning authorities in the country).
However, due to the end-to-end and
overlapping nature of these plans and a
need for forecast and warning authorities
to work collaboratively, a single severe
weather forecast and warning plan can be
considered good practice. Such forecast
and warning plans also need to involve
other stakeholders, as summarized in
Box 17.
Warning plans need to specifically consider
the mechanisms that will be used to
disseminate warnings. The general concept
is that every at-risk individual who should
receive an SDS warning is to be contacted
through at least two warning mechanisms
(see chapter 10.8 on the process by which
people react to warnings).
Common mechanisms for warning
dissemination include print media,
radio, TV, the Internet (including emails,
social media and warning websites) and
mobile phone messaging. Sirens and
traditional face-to-face communication
are also still important mechanisms.
WMO provides guidance on disseminating
and communicating SDS warnings. See
https://public.wmo.int/en/our-mandate/
focus-areas/natural-hazards-and-disaster-
risk-reduction/mhews-checklist/warning-
dissemination-and-communication. Which
includes information that can be adapted
for use by a NMHS or another authority
that disseminates and communicates SDS
warnings.
Redundancy should be built into early
warning systems to address the risk that
any warning mechanism may fail. This
redundancy is both for the mechanisms
used to warn (for example, sirens and radio
both being used to issue warnings) and
for the communication systems which
link those issuing the warnings to specific
warning mechanisms (for example, two
ways to trigger a warning siren).
Under a multi-hazard warning approach,
SDS warnings would generally be sent out
through the same warning systems used
for other hazards. This would increase the
frequency with which warning systems
are used and allow for more frequent
verification that a multi-hazard warning
system is working as expected.

10.7. Warning verification
Once warning messages and systems are
developed and functioning it is necessary
to verify both the accuracy and usefulness
of the messages being delivered as well as
the effectiveness of the system. This can
be done in two ways:
• Message and system testing: This
process involves testing messages
with possible target audiences to
verify that the messages result in the
intended actions. This verification
can be done through focus groups,
simulation exercises or surveys
(including commercial survey or
feedback services). The feedback on
the messages and their dissemination
allows for the content of messages
to be adjusted to improve the
mechanisms’ results.
• Post-event review: This process is
carried out after an actual SDS event
and involves asking those who should
have received warning messages
to review the usefulness and
effectiveness of the messages they
received (if they were received). This is
usually conducted through some form
of survey, the results of which helps
to improve the forecast and warning
system, including the formulation and
dissemination of alert and warning
mechanisms.
The importance of verifying warnings
should not be underestimated. Without
this feedback, an NMHS, NDMA or other
parties involved in the warning process
could find it hard to know whether the
warnings issued helped people to avoid
or mitigate the impact of SDS. Identifying
whether, how and why warnings resulted
in protective actions can improve warning
messaging and dissemination, which
in turn should increase the likelihood of
individuals receiving warnings to take
protective actions.
10.8. Warning education
For warnings to be successful, it is
crucial that those receiving the warning
understand the information provided and
the corresponding actions to be taken
to reduce SDS impacts in both the short
term and long term, acting and adapting
their general response to warnings as
necessary. Warning education processes
involve two aspects:
• understanding how and why warnings
are or are not acted upon by those
who receive them, and
• implementing a campaign to increase
and sustain the knowledge of those
receiving warnings so that they can
take the appropriate action when
warnings are received, thereby
triggering longer-term and systematic
behavioural changes.
The first point is of critical importance.
If a warning is issued and not used,
then it has no value. As summarized in
Emergency Alert and Warning Systems:
Current Knowledge and Future Research
Directions (National Academies of Science,
Engineering, and Medicine, 2018, p. 20),
individuals who receive a warning message
go through a process of:
• understanding whether the message
is relevant to the person receiving it
• determining whether the warning is
real or not
• personalizing the message as
something for which action is needed
• deciding whether action needs to be
taken
• confirming whether the information is
correct and actions should be taken.
Unless warning messages and work to
prepare people for warning messages take
these points into account, it is unlikely that
warning messages will be fully effective.
The role of a continual education campaign
is twofold:
• Educating those receiving a warning
helps them move through the five
aforementioned steps. If an individual
is aware of the types of warnings that
may be issued, the typical content
of the messages, the expected or
recommended action to be taken
following a warning and how to
confirm the veracity of messages and

284
actions (if they are needed), then they
will complete the five-step process
quicker and with more certainty.
• Building the knowledge of
populations at risk from SDS about
these phenomenon and how they
can impact society, along with
the measures that can be taken
to address their impacts. This
knowledge-building needs to be an
ongoing process for three reasons:
1. A knowledgeable population is a
prepared population.
2. At-risk populations are constantly
changing in terms of numbers,
the composition of vulnerable
groups and location.
3. The means that a population may
have to address SDS impacts can
change over time. An ongoing
education process can influence
individuals, families, government
services, businesses and others
to improve the level of protection
from and resilience to SDS.
People and society need to know
how to reduce the impacts of
SDS before they can take action.
Some risk reduction measures
should be taken long before
warnings are received.
10.9. Integrating forecasts
and warnings into
preparedness
Chapter 13 discusses preparing for and
mitigating SDS. Within the preparedness
process, SDS forecasting and warning have
four key roles. First, understanding the
nature of SDS – which involves developing
data sets, modelling and analysis needed
to make the forecasts – creates the
basis for understanding SDS as a hazard
for which preparedness is needed. This
understanding provides input into SDS
management plans and procedures,
including source and impact mitigation.
Second, the technical process and
procedures for transforming information
on SDS into a forecast lead to a result
which does, or does not, trigger a warning.
In other words, the content of a forecast
can tell individuals to be prepared for SDS
or can inform them that there is no need
for concern.
Third, forecasts can trigger warnings,
based on established warning criteria/
thresholds, plans and procedures. While
a forecast can indicate a possible need
to prepare for SDS (or not), the warning
generated by a forecast triggers a set of
actions to reduce the impact of SDS (see
chapter 13). This triggering process is at
the core of the impact-based forecasting
and warning concept and is what activates
short-term plans to reduce SDS impacts
and hasten recovery.
Finally, the process of educating those
at risk about SDS so that warnings can
be effective (chapter 10.8) not only
improves capacities to respond once
the warning has been received, but also
improves the level of individual and
societal preparedness for SDS. This
preparedness is important when SDS
threats are imminent, but can also result
in those at risk taking additional actions
before a warning is issued or received in
order to reduce the actual impact of SDS.
The development of an effective warning
system therefore improves preparedness
and also reduces risk.

10.10. Conclusions
SDS forecasts and warnings are important
to reduce the impact of these hazards on
individuals, communities, organizations
and society as a whole. For effective
warnings that lead to protective actions,
an SDS warning system needs plans that
bring together the forecast capacities of
an NMHS and the warning and response
capabilities of an NDMA into a common
plan.
These plans need to be clear on who is
responsible for issuing warnings, how
these warnings are to be issued and what
information the warnings should contain.
In general, following the people-centred,
impact-based forecasting approach,
warnings should include information about
specific expected impacts of forecasted
SDS, along with specific actions to address
these impacts which also detail specific
locations if possible.
©manypeanuts
on
Flickr
,
August
31st,
2007

286
10.11. References
National Academies of Sciences, Engineering, and
Medicine (2018). Emergency Alert and Warning
Systems: Current Knowledge and Future Research
Directions. Washington, D.C.: The National
Academies Press.
National Weather Service (2018). National Weather
Service (NWS) Service Description Document
(SDD). Impact-Based Decision Support Services
for NWS Core Partners.
Stefanski, Robert, and Mannava Sivakumar (2009).
and potential agricultural applications of a
SDSWS. WMO/GEO Expert Meeting on an
International Sand and Dust Storm Warning
System. IOP Conference Series: Earth and
Environmental Science, vol. 7.
United Nations (2015). Sendai Framework for Disaster
Risk Reduction 2015–2030.
United Nations General Assembly (2016). Report of the
open-ended intergovernmental expert working
group on indicators and terminology relating to
disaster risk reduction. 1 December. A/71/644.
United Nations International Strategy for Disaster
Reduction (UNISDR) (2006). Developing Early
Warning Systems: A Checklist. Available at https://
www.unisdr.org/2006/ppew/info-resources/
ewc3/checklist/English.pdf.
(UNDRR) (2018). Technical Guidance for
Monitoring and Reporting on Progress in Achieving
the Global Targets of the Sendai Framework for
Disaster Risk Reduction. Collection of Technical
Notes on Data and Methodology (new edition).
World Meteorological Organization (WMO) (2015a).
Assessment System (SDS-WAS). Science and
Implementation Plan 2015–2020. Geneva.
__________ (2015b). WMO Guidelines on Multi-hazard
Impact-based Forecast and Warning Services.
Geneva.
__________ (2017). Manual on the Global Data-processing
and Forecasting System. Annex IV to the WMO
Technical Regulations. WMO Report 485.
__________ (2018). Multi-hazard Early Warning Systems: A
Checklist. Geneva.

Photo
by
Alex
Jones
on
Unsplash,
Photo
by
CDC
on
Unsplash

UNCCD | Sand and Dust Storms Compendium | Chapter 11 | Sand and dust storms and health 289
11. Sand and dust
storms and health:
an overview of main
findings from the
scientific literature
Chapter overview
The chapter provides an overview of research into the health impacts of sand and dust
storms (SDS). Most studies of SDS and health linkages have been conducted in Asia,
Europe and the Middle East, with studies severely lacking in West Africa. Important issues
in understanding SDS health impacts include: (1) the characterization of dust exposure
of individuals and populations, which can be done in different ways; (2) the availability of
health data is a challenge in many areas affected by SDS; and (3) even if exposure and
health data are available, the method used to distinguish between dust storms and days
affected by dust, along with the design of epidemiological studies, vary greatly, making it
difficult to compare results from different studies.
Many health outcomes, both for mortality and morbidity, mainly focus on the short-term
effects of SDS and have identified an increased risk of cardiovascular mortality and
respiratory morbidity, including asthma. There is a lack of studies on the long-term effects
of SDS, which means that estimates of the impact and burden of SDS are yet to be fully
developed.

UNCCD | Sand and Dust Storms Compendium | Chapter 11 | Sand and dust storms and health
290
11.1 Introduction
This chapter will briefly discuss issues
related to exposure to sand and dust
storms (SDS), along with their health
effects and impacts.
It should be read together with
chapters 12 and 13.
Arid and semi-arid regions are the main
global source areas for airborne mineral
dust. These source areas comprise a third
of the Earth’s land surface, with some
2 billion people exposed daily (Safriel et
al., 2005). At the same time, SDS have a
significant impact over areas thousands
of kilometres away from source areas
(Ginoux et al., 2012; Prospero et al., 2002),
carrying anthropogenic pollutants (Mori,
2003; Rodríguez et al., 2011) as well
as microorganisms and toxic biogenic
allergens (Goudi, 2014; Griffin et al., 2001;
Ho et al., 2005).
According to the Intergovernmental
Panel on Climate Change (IPCC), SDS will
have potentially harmful health effects
in the future (Intergovernmental Panel
on Climate Chane [IPCC], 2019). SDS
are therefore a challenge for the health
system (Allahbakhshi et al., 2019) and have
received increasing attention in recent
years in terms of their impact on human
health.
To date, there are no studies on the long-
term health effects of SDS, which are
needed to inform on the overall impact
of such events on health. This chapter
therefore presents the current evidence
available, derived from existing short-term
epidemiological studies from affected
areas which suggest potential health
effects of SDS (de Longueville et al., 2013;
Hashizume et al., 2010; Karanasiou et al.,
2012; Zhang
et al., 2016).
The impacts of SDS and desertification are
also related to well-being and social issues,
though there are few available studies
on this aspect (Adeel et al., 2005; World
Health Organization [WHO], 2006), which is
considered to be outside of the scope of
this chapter.
11.2 Health effects of SDS
The health effects of SDS depend on
where human populations are located in
relation to SDS source areas, the downwind
direction of dust transported from them
and the length of exposure (Goudie, 2014).
The populations most susceptible to
suffering from the short-term effects of
suspended particulates are considered to
be older persons, individuals with chronic
cardiopulmonary disorders and children
(Goudie, 2014).
Previously published reviews, systematic
or not, reported inconsistent results
across studies and geographical regions
(de Longueville et al., 2013; Hashizume et
al., 2010; Karanasiou et al., 2012; Zhang
et al., 2016). These reviews identified
and summarized evidence from at least
45 epidemiological studies published
between 1999 and 2014, predominantly
on the short-term health effects of SDS. A
potential limitation in the literature is the
lack of studies conducted on the long-term
health effects of SDS.
The health outcomes more frequently
studied include: (a) daily mortality by
all-natural causes and specific causes;
(b) cardiovascular and respiratory issues;
and (c) morbidity as documented in
hospital admissions and emergency
room admissions/visits, mainly for
cardiovascular and respiratory issues,
including asthma and chronic obstructive
pulmonary disease (COPD) (see Table 23).
Overall, the four reviews (de Longueville
et al., 2013; Hashizume et al., 2010;
Karanasiou et al., 2012; Zhang et al., 2016)
had similar conclusions, suggesting that
potential health effects linked to SDS may
increase cardiovascular mortality and
respiratory hospital admissions.

Mortality • All-natural cause mortality
• Cardiovascular diseases
• Respiratory diseases
Morbidity • Cardiovascular diseases
• Respiratory diseases (including asthma, COPD and pneumonia)
• Coccidioidomycosis
• Dermatological disorders
• Conjunctivitis
• Meningococcal meningitis
• Allergic rhinitis
Other • Pregnancy outcomes
Source: Adapted from Goudie, 2014 and Querol et al., 2019.
Table 23.
Health outcomes
investigated in
epidemiological
studies
Other more specific morbidity outcomes
have also been considered, although to a
lesser extent, including: (a) cardiovascular-
related outcomes (stroke, ischaemic
heart disease, heart failure, myocardial
infarction); (b) acute coronary syndrome
and out-of-hospital cardiac arrest; and (c)
respiratory-related conditions (pneumonia
and upper respiratory tract infection).
Allergy (daily clinical visits for allergic
rhinitis) and infectious diseases outcomes
(daily clinical visits for conjunctivitis
and diagnosed cases of meningococcal
disease) have been studied, but only
occasionally.
Furthermore, just a few individual case-
series (panel) studies have evaluated daily
respiratory symptoms and peak expiratory
flow of patients with asthma. None of the
published studies considered deaths or
injuries resulting from transport accidents
occurring during SDS.
The published studies differed in terms
of settings, assessment methods for SDS
exposure, lagged exposures examined,
and epidemiological study designs applied.
Moreover, none of the previous reviews,
systematic or not, attempted to assess
the quality of the evidence across the
published studies.
For this reason, the World Health
Organization (WHO) decided to
systematically synthesize the evidence
on the health effects of SDS, accounting
for the relevant desert dust patterns from
source areas and emissions, transport and
composition (Tobías et al., 2019a; Tobías
et al., 2019b). This systematic review will
be the first one to retrieve and evaluate
published studies on the health effects
of desert dust following a standardized
protocol for data collection and reporting
of findings. The results of this systematic
review will provide evidence to fill the
knowledge gap of the health effects
of desert dust and may help develop
appropriate preventive measures for dust
episodes (WHO, in preparation).
11.3 Exposure to SDS and
their health impacts
Desert dust can be transported for
hundreds of kilometres and its natural
composition can be affected by several
human sources (Mori, 2003; Rodríguez et
al., 2011), making the distinction between
natural and anthropogenic particulate
matter (PM) sources difficult to assess
for the health effects of SDS. Recently,
Querol et al. (2019) critically reviewed the
exposure metrics for SDS commonly used
in epidemiological studies.
Desert dust can be defined as a binary
exposure, comparing the occurrence of
the health outcome between days with
and without a desert dust event. This
exposure metric for SDS has mainly been
used in studies conducted in eastern Asia
(Hashizume et al., 2010; Tobias et al.,
2019b).

292
These studies consistently found excess
risks on desert dust days, especially for
cardiovascular mortality (1.6 per cent) and
respiratory morbidity (6.8 per cent) (Tobías
et al., 2019b). Despite the intuitive design,
these studies are highly dependent on
the methodology to identify dust events
and do not provide information on the
dose-response relationship between SDS
exposure and the health outcome.
The studies conducted in southern
Europe have mostly considered daily PM
concentrations as the main exposure,
evaluating whether the health effects of PM
differed between days with and without dust
events (Karanasiou et al., 2012; Tobias et
al., 2019b) by considering the dust binary
exposure as an effect modifier of the link
between PM and health.
The hypothesis underlying this approach is
that it is not only PM from anthropogenic
sources that is related to adverse health
effects, but also particles originating from
natural sources, especially desert dust
advection from arid regions. Most of the
studies found consistent evidence of larger
effects of PM with a diameter of less than
10 μm (PM10
) and a coarse fraction (PM10-2.5
)
on cardiovascular mortality during days with
dust (increasing the risk of mortality by 9.0
per cent for a rise of 10 mg/m3
) than without
dust events (2.1 per cent), and similarly for
respiratory morbidity (13.8 per cent and
-2.4 per cent, respectively). However, no
difference was found for PM with a diameter
of less than 2.5 μm (PM2.5
) (Tobías et al.,
2019b).
Photo
by
Paul
Szewczyk
on
Unsplash

The limitation of this approach is that PM
is a mixture of natural and anthropogenic
sources, even on dust days, which makes
it difficult to attribute health effects to
a specific source by classifying days
according to the presence of a dust event.
Some studies have attempted to attribute
daily PM exposure by separating desert
and anthropogenic sources, showing that
both sources were minimally correlated to
each other and could be jointly analysed
as independent risk factors for human
health (Stafoggia et al., 2016). Under this
approach, a multicentre study conducted
in 11 cities of the Mediterranean region
reported similar risk estimates for the
anthropogenic and natural dust loads
of PM10
on daily mortality and morbidity
(Stafoggia et al. 2016).
A separate study conducted in the
city of Barcelona, which considered
anthropogenic loads of PM10
on days with
and without dust events, reported that
there was a larger risk of cardiovascular
mortality for PM10
from anthropogenic
contributions on dust days than non-dust
days and that natural dust loads had a
non-significant effect (Pérez et al., 2012).
This approach is suitable to estimate
concentration–response functions
between desert and anthropogenic PM
sources and health outcomes to assess
the health impact of SDS.
However, studies conducted in East Asia,
especially Japan, showed larger effects
of Asian dust than suspended particulate
matter on specific cardiovascular mortality
outcomes (Kashima et al., 2012; Kashima
et al., 2016) and ambulance calls for
respiratory issues (Kashima et al., 2014).
Moreover, a relevant issue here is the
difference between geographical regions,
such as the Middle East, which has huge
SDS events, and others such as southern
Europe, where there are many small-scale
dust episodes. In the former, it would
not be particularly useful to investigate
the independent effects of desert and
anthropogenic sources, while in the
later, this would be the most informative
approach.
11.4 Estimating
health impacts
of SDS
Health impacts of air pollution are
assessed by calculating their attributable
proportion, which is the fraction of health
outcomes resulting from air pollution
of a population exposed to specific
concentration levels. This attributed
proportion is calculated using relative
risks, or exposure–response function
(ERF), from epidemiological studies. Other
input data used to carry out an impact
analysis include (1) the level of air pollution
concentrations, (2) the population exposed,
and (3) the baseline incidence of the health
outcomes under consideration.
All epidemiological studies currently
available only consider the short-term
effects of SDS and provide estimates of
the relative risk, or ERF, associated with PM
mass concentration and not specifically
with sand or dust exposure levels (for
example, Stafoggia, 2016).
Unfortunately, epidemiological studies
on the long-term effects of SDS are not
available and ERFs related to any type
of PM are used to assess long-term
health impacts in populations exposed
to SDS. This may potentially lead to very
different results to those obtained had the
ERFs been gathered using local data on
exposure and health outcomes from SDS-
affected regions.
To date, ERFs based on PM2.5
studies
carried out in the United States of America
or Europe, which are locations with lower
PM2.5
concentrations and that likely have
different PM2.5
compositions, have been
applied in SDS health impact assessments
(Khaniabadi et al., 2017). Current estimates
of the impacts should therefore be taken
with caution as the use of these functions
cannot be automatically generalized.
The quantification of desert dust-related
health impacts has been published in few
studies for short-terms effects (Khaniabadi
et al., 2017; Renzi et al., 2018; Shahsavani
et al., 2019; Viel et al., 2019) but rarely for

294
long-term effects. Long-term exposure to
desert dust, for example, was estimated
to have generated 402,000 deaths in 2005
(Giannadaki et al., 2014).
The global fraction of cardiopulmonary
deaths caused by atmospheric desert dust
amounts to about 1.8 per cent, though in
the 20 countries most affected by dust, in
the so-called ‘dust belt’, this is estimated to
be much higher at about 15–50 per cent
(Giannadaki et al., 2014).
While in the city of Ilam in the West of
Iran (172,213 inhabitants), the annual
average and maximum PM10
value were
78 μg/m3
and 769 μg/m3
respectively, the
maximum person-days of exposure were
on days with concentrations between 40
μg/m3
and 49 μg/m3
(Khaniabadi et al.,
2017). Considering a baseline of 1,250
and 48 for COPD and respiratory mortality
respectively, about 338 and 26 cases were
estimated as excess cases per year in Ilam
(Khaniabadi et al., 2017).
Health impact estimates of SDS pose
several challenges, including that:
• Exposure has to be thoroughly
determined.
• Relative risks at very high levels of
air pollution are to be extrapolated
from risks measured for
populations exposed to low-medium
concentrations levels.
• Health data are often not available in
the areas affected by SDS – for short-
term exposures, health impacts should
be designed by calculating impacts
for dust days separately if the number
of such days and corresponding
concentrations are known.
In the case of long-term effects, yearly
concentrations must be considered,
though the share of PM due to desert
dust compared with the total PM is only
known approximately. Extrapolating the
risk at very high levels of air pollution (for
example, more than 50 μg/m3
for PM2.5
)
is difficult, as most of the epidemiological
studies have been conducted in areas with
lower PM2.5
concentrations.
Available ERF extrapolation methods,
such as integrated exposure risk
functions (Burnett et al., 2014) have been
developed for long-term exposures due to
combustion-related PM2.5
. Their application
for SDS might be questioned.
11.5 Developing a further
understanding of health
impacts
and SDS
Although studies on SDS and human
health are producing evidence on various
health effects, there remain gaps in more
clearly understanding how SDS and health
impacts are linked.
To address these gaps, further study is
needed in the following areas:
1. The design of studies on the
effects of SDS on health should be
improved, as most of the studies
have used an ecological time-series
approach, which cannot demonstrate
causality. Dominici and Zigler
(2017) proposed criteria to evaluate
evidence of causality in environmental
epidemiology that should be
considered carefully for SDS studies,
based on: (a) what actions or exposure
levels are being compared; (b) whether
an adequate comparison group was
constructed; and (c) how closely these
design decisions approximate an
idealized randomized study.
2. PM exposure features should be
better explored in epidemiological
studies (Querol et al., 2019). For
example, available modelling and
meteorological tools, surface PM
concentrations and PM2.5
/PM10
ratios
could be used to define desert dust
events and to quantify desert and
anthropogenic sources of PM. The
nature of major sources of dust
and PM compositions also needs
to be investigated in more detail,
allowing for an assessment of the
anthropogenic load of PM during SDS,
and, if relevant, of the bio-aerosol load.

3. Studies of the health effects of SDS in
and near hotspots, especially in West
Africa and the Middle East, should be
increased due to a lack of studies in
these areas of significant SDS sources
and impacts.
4. Surveillance and health data collection
for populations in cities, regions and
countries mainly affected by SDS need
to be developed and/or improved,
in particular for cardiovascular and
respiratory diseases.
Health impact assessments of SDS should
be further discussed and developed to
tackle existing questions and challenges.
There is a need to develop estimates for
the long-term effects of SDS on human
health. There is also a need to develop and
explore appropriate methods (and/or ERF)
to identify the fraction of diseases that can
be attributed, based on causality, to SDS,
to estimate the health impact and global
disease burden associated with SDS.
SDS mitigation measures are essential
to prevent negative health effects.
Behavioural and technological
interventions can mitigate the occurrence
of SDS and exposure to desert dust. WHO
will provide, within the current revision of
the WHO Air Quality Guidelines (the main
product for air pollution and public health),
good practice statements on SDS.
Reducing exposure is usually achieved
through informing the population about
a forthcoming event, minimizing outdoor
activities that would have otherwise
been carried out and cleaning streets
after intense episodes to reduce urban
resuspension of deposited dust. In the
last decade, face masks and air filters
have been the prominent technology to
emerge, though their promotion for public
health purposes is questionable (Rice and
Mittleman, 2017). See chapters 12 and 13
for further discussion.
LEO
RAMIREZ—AFP/Getty
Image

296
11.6 Conclusion
Epidemiological studies have mainly
investigated the short-term health
effects of SDS, suggesting that such
phenomena have harmful effects leading
to cardiovascular mortality and respiratory
morbidity. However, a harmonized protocol
for epidemiological studies on the short-
term effects of SDS is needed, as this will
allow for comparable results that could
enable robust meta-analyses to be carried
out along with the application of results in
SDS health risk assessments. Furthermore,
long-term studies on the effects of SDS
are also needed in order to strengthen the
assessment of the health burden of SDS.
In any case, SDS needs to be recognized
as a public health issue. Stakeholders,
citizens and policymakers should consider
appropriate measures when dealing
with this hazard. Exposure abatement
(mitigation) strategies, including reducing
emissions of local pollutants, alerting
the population, abating resuspension of
deposited dust after intensive SDS or
reducing hydrological and agricultural
human-driven dust emissions, are
necessary to protect the population.

NOEL
CELIS—AFP/Getty
Image

298
11.7 References
Adeel, Zafar, and others (2005). Ecosystems and Human
Well-being: Desertification Synthesis. Washington,
D.C.: World Resources Institute.
Allahbakhshi, Kiyoumars, and others (2019).
Preparedness components of health systems in
the Eastern Mediterranean Region for effective
responses to dust and sand storms: a systematic
review [version 1; peer review: 2 approved].
F1000Research.
Burnett, Richard T., and others (2014). An integrated
risk function for estimating the global burden of
disease attributable to ambient fine particulate
matter exposure. Environmental Health
Perspectives, vol. 122, No. 4.
de Longueville, Florence, and others (2013). Desert dust
International Journal of Biometeorology, vol. 57,
No. 1.
Dominici, Francesca, and Corwin Zigler (2017). Best
practices for gauging evidence of causality in
air pollution epidemiology. American Journal of
Epidemiology, vol. 186.
Giannadaki, Despina, A. Pozzer, and J. Lelieveld (2014).
Modeled global effects of airborne desert dust on
air quality and premature mortality. Atmospheric
Chemistry and Physics, vol. 14.
No. 3.
Goudie, Andrew S. (2014). Desert dust and human health
disorders. Environment International, vol. 63.
Griffin, Dale, and others (2001). African desert dust in the
Caribbean atmosphere: Microbiology and public
health. Aerobiologia, vol. 17.
Hashizume, Masahiro, and others (2010). Health effects
of Asian dust events: a review of the literature.
Nihon Eiseigaku Zasshi [Japanese Journal of
Hygiene], vol. 65.
Ho, Hsiao-Man., and others (2005). Characteristics
and determinants of ambient fungal spores in
Hualien, Taiwan. Atmospheric Environment, vol.
39, No. 32.
(2019). Climate Change and Land: An IPCC Special
Report on Climate Change, Desertification, Land
Degradation, Sustainable Land Management, Food
Security, and Greenhouse Gas Fluxes in Terrestrial
Ecosystems, Valérie Masson-Delmotte, Hans-
Otto Pörtner, Jim Skea, Eduardo Calvo Buendía,
Panmao Zhai, Debra Roberts, Priyadarshi, R.
Shukla, Raphael Slade, Sarah Connors, Renée
van Diemen, Marion Ferrat, Eamon Haughey,
Sigourney Luz, Suvadip Neogi, Minal Pathak,
Jan Petzold, Joana Portugal Pereira, Purvi Vyas,
Elizabeth Huntley, Katie Kissick, Malek Belkacemi,
and Juliette Malley, eds. In press.
Karanasiou, Angeliki, and others (2012). Health effects
from Sahara dust episodes in Europe: literature
review and research gaps. Environment
International, vol. 47.
Kashima, Saori, and others (2012). Asian dust and daily
all-cause or cause-specific mortality in western
Japan. Occup Envron Med., vol 69, No. 12.
______________________ (2016). Asian dust effect on
cause-specific mortality in five cities across South
Korea and Japan. Atmospheric Environment, vol.
128.
Kashima, Saori, Takashi Yorifuji, and Etsuji Suzuki (2014).
Asian dust and daily emergency ambulance calls
among elderly people in Japan: An analysis of
its double role as a direct cause and as an effect
modifier. Occup Envron Med., vol. 56, No. 12.
Khaniabadi, Yusef Omidi, and others (2017). Impact of
Middle Eastern dust storms on human health.
Mori, Ikuko (2003). Change in size distribution and
chemical composition of kosa (Asian dust)
aerosol during long-range transport. Atmospheric
Environment, vol. 37.
Pérez, Laura, and others. (2012). Effects of local and
Saharan particles on cardiovascular disease
mortality. Epidemiology, vol. 23.
soil dust identified with the NIMBUS 7 Total
Ozone Mapping Spectrometer (TOMS) absorbing
aerosol product. Reviews of Geophysics, vol. 40,
No. 1.

Querol, Xavier, and others (2019). Monitoring the impact
of desert dust outbreaks for air quality for health
studies. Environment International, vol. 130.
Renzi, Matteo, and others (2018). Short-term effects
of desert and non-desert PM10 on mortality in
Sicily, Italy. Environment International, vol. 120.
Rice, Mary B., and Murray A. Mittleman (2017). Dust
storms, heart attacks, and protecting those at
risk. European Heart Journal, vol. 38, No. 43.
Rodríguez, Sergio, and others (2011). Transport of
desert dust mixed with North African industrial
pollutants in the subtropical Saharan Air Layer.
Atmospheric Chemistry and Physics, vol. 11,
No. 3.
Safriel, Uriel, and others (2005). Dryland systems. In
Ecosystems and Human Well-being. Current State
and Trends, Volume 1, Millennium Ecosystem
Assessment. Washington, D.C.: Island Press.
Shahsavani, Abbas, and others (2019). Short-term
effects of particulate matter during desert
and non-desert dust days on mortality in Iran.
Environment International, vol. 134.
Stafoggia, Massimo, and others (2016). Desert dust
outbreaks in Southern Europe: contribution
to daily PM10 concentrations and short-
term associations with mortality and hospital
admissions. Environmental Health Perspectives,
vol. 124, No. 4.
Tobías, Aurelio, and others (2019a). WHO Global Air
Quality Guidelines: systematic review of health
effects of dust and sand storms. Geneva: World
Health Organization (WHO) (submitted).
______________________ (2019b). Health effects of desert
dust and sand storms: a systematic review and
meta-analysis protocol. BMJ Open.
Viel, Jean-Francois, and others (2019). Impact of
Saharan dust episodes on preterm births in
Guadeloupe (French West Indies). Occupational
and Environmental Medicine, vol. 76, No. 5.
World Health Organization (WHO) (2006). Ecosystems
and Human Well-being: Health Synthesis. Geneva.
_____________________ (in preparation). Health Effects of
Sand and Desert Dust. Geneva.
Zhang, Xuelei, and others (2016). A systematic review of
global desert dust and associated human health
effects. Atmosphere, vol. 7, No. 12.

UNCCD | Sand and Dust Storms Compendium | Chapter 12 | Sand and dust storms source mitigation 301
12. Sand and dust
storms source mitigation
Chapter overview
This chapter reviews conceptual approaches and practical options to mitigate the sources of
sand and dust storms (SDS) based on land degradation neutrality, sustainable land man-
agement, integrated land management and integrated water use management. Examples of
SDS source mitigation measures are provided.

UNCCD | Sand and Dust Storms Compendium | Chapter 12 | Sand and dust storms source mitigation
302
12.1 Introduction
This chapter explains how mitigating the
source of sand and dust storms (SDS)
can be integrated into national and/or
regional planning, in line with global goals
and initiatives, such as land degradation
neutrality (LDN) targets, taking into account
sustainable land management (SLM),
integrated landscape management (ILM)
and integrated water use management.
The focus of the chapter is on reducing,
to the greatest degree possible and
particularly from anthropogenic sources,
dust emissions and sand movements
through measures focusing on:
• natural ecosystems
• rangelands
• croplands
• industrial settings, including mining,
roads and construction.
The measures covered in this chapter can
be divided into two groups, those which:
• reduce the generation of SDS at their
source
• protect the environment, physical
infrastructure and social and
economic activities from sand and
dust once they are in a state of
movement.
These measures focus on:
• reducing wind speed in natural areas,
rangelands and croplands
• controlling windblown sand and
moving sand dunes
• implementing SLM, land-use
planning and integrated landscape
management approaches to integrate
control measures into overall efforts
to improve land use, sustainability and
economic and social development.
Chapter 13 more closely considers
measures that can be taken to minimize
the impact of SDS as hazard events across
different segments of society.
This chapter addresses the United Nations
Convention to Combat Desertification
(UNCCD) Policy Advocacy Framework
to combat Sand and Dust Storms
(United Nations Convention to Combat
Desertification [UNCCD], 2017), focusing
on source and impact mitigation, while
providing avenues for monitoring,
prediction and early warning, vulnerability
reduction and resilience strengthening.
12.2 Sources and
drivers of SDS
This section should be read together with
chapters 2, 3, 8 and 13.
Although there is much uncertainty on the
exact numbers, about 75 per cent of global
dust emissions are derived from natural
sources (Ginoux et al., 2012). Major dust
sources are dominated by inland drainage
basins or depressions in arid areas due to
the wind-erodible nature of their surface
materials and geomorphic dynamics
(Bullard et al., 2011). However, natural
ecosystems are increasingly subject to
human pressures due to climate change
and land-use and land-cover changes,
which may intensify their importance as
source areas in the future (Millennium
Ecosystem Assessment, 2005).
Meteorological variables, such as wind
velocity and low-level turbulence, are
direct or indirect causing factors of SDS.
Another causing factor of SDS includes
soil-related factors, such as soil texture,
soil moisture, soil temperature and
vegetation cover, which at least in part
is subjected to climate-related factors,
including precipitation level and drought, as
well as land degradation, both directly and
indirectly.
There are strong reinforcing cycles,
whereby removal of vegetation and
unsustainable land management practices
increase soil exposure to wind and
increase soil susceptibility to erosion (Lal,
2001). Threats to natural areas include
human intervention in hydrological cycles
around ephemeral lakes, rivers or streams,
as well as alluvial fans, playas and saline
lakes in arid areas.
Such disturbances may accelerate
desiccation, lower water tables, reduce
soil moisture and reduce vegetation cover,
thus exposing susceptible sediments

to wind erosion (Gill, 1996). Hydrology
disturbances around ephemeral lakes and
playas are often due to demand for water
resources for urban areas or irrigation.
Another contributor to playa desiccation
is the development of roads and
communication linear infrastructures
that block or divert the inflow of drainage
waters (Gill, 1996). Other causes resulting
in accelerated wind erosion and dust
mobilization include the removal of
vegetation, loss of biodiversity and
destruction of protective biological
crusts in deserts due to vehicular traffic,
tillage operations, loss in ecological
connectivity and changes in animal
migration patterns or exposure of erodible
subsurface sediments.
Agricultural areas are a potential dust
source. Unsustainable practices in the
crop, livestock and forestry subsectors,
such as the overuse of water or
diversion of rivers for irrigation purposes,
deforestation and forest degradation and
intensive tilling or overgrazing, among
many others, can lead to land degradation
and directly contribute to higher risks of
SDS.
A failure to consider the potential for
sensitive soil types to become a source of
dust has been missed in the development
of farming and livestock production.
Figure 46.
Desiccation of
ephemeral lakes
due to human-
made changes in
hydrology
Figure 47.
Receding
shorelines in
some inland
waterbodies
Source: San Antonio Express-News.
Aral Sea, Central Asia
Source: Krapivin et al., 2019.
https://www.mdpi.com/2306-5338/6/4/91/htm
Salton Sea, California, USA
Source: Johnston et al., 2019.
https://www.sciencedirect.com/science/article/abs/
pii/S0048969719304164 and https://ars.els-cdn.
com/content/image/1-s2.0-S0048969719304164-
ga1_lrg.jpg

304
Examples include:
• the use of farming methods that
led to a loss of vegetation (which
previously reduced the potential for
dust generation) and contributed to
the Dust Bowl in the western plains of
the United States
• overstocking of rangelands in the
south-western United States, which
caused region-wide transitions of
grasslands to shrublands with low
forage value (Finch, 2004)
• the East African groundnut scheme,
which attempted to convert
rangelands to land for mechanized
peanut production agriculture (Herrick
et al., 2016).
In Europe, wind erosion is a common
process in the agricultural lands of most
countries (Borrelli et al., 2016). The major
risk factor for wind erosion and SDS in
croplands, rangelands and forest areas is
a decrease in vegetation cover, primarily
because it increases wind velocity, exposes
surfaces, usually makes surfaces less
stable and enhances the risk of dust
whirlwinds and reduces the trapping of
sand and dust particles (Middleton, 2011).
Vegetation also provides a natural
mechanical barrier, controlling wind flows
and reducing surface shear stress at the
ground surface. Decrease in vegetation
cover and any other management practices
that remove or disturb organic layers at the
soil surface (for example, ploughing) also
increase surface exposure to wind. Organic
inputs to soil are important for maintaining
soil structure and biological activity, which
increase effective particle size through
aggregation as well as resistance to the
detachment of soil particles by wind.
When individual land degradation
processes occurring at the local level
combine to affect large areas of drylands,
it results in desertification. UNCCD defines
desertification as land degradation in
arid, semi-arid and dry subhumid areas
due to various factors, including climatic
variations and human activities (UNCCD,
2017).
Desertification is among the strongest
large-scale drivers of SDS, as it reinforces
wind erosion due to the development of
degraded and exposed dry surfaces over
large dryland areas with a long wind fetch.
The combination of vegetation removal
and unsustainable land management
practices increases soil exposure to wind
and therefore soil susceptibility to erosion.
Marshes of Mesopotamia
(NASA Earth Observatory)
Source: https://earthobservatory.nasa.gov/imag-
es/1716/vanishing-marshes-of-mesopotamia
Lake Urmia, Iran
(NASA Earth Observatory)
Source: https://earthobservatory.nasa.gov/imag-
es/76327/lake-orumiyeh-iran
1998
2011
2000
1973 - 1976

Figure 49.
Dust Bowl caused
by unsustainable
dryland
agriculture
and prolonged
drought periods
Figure 48.
Wind erosion
in unprotected
croplands – a
major source of
dust in dryland
agricultural areas
Windblown sand and moving sand dunes
can occur at wind speeds below those
required to generate SDS. Despite this, they
are considered in this chapter as they pose
a hazard to:
• road and irrigation infrastructure, for
example, covering roads, filling canals
• ground transport, by reducing visibility
and damaging vehicles
• buildings and walls, through covering
or banking up against structures
• fields, through covering or reducing
the size of cultivatable areas.
Active, young or small sand dunes with a
relatively rapid turnover of sand are unlikely
to be major or persistent sources of dust
because they contain little fine material
(Bullard et al., 2011). The resulting dunes
can, however, pose a risk to infrastructure,
particularly roads and buildings, but also
agricultural lands and gardens.
The disturbance of older dunes, on the
other hand, will increase the risk of dust
emissions. Any reduction in vegetation
cover as a result of unsustainable
harvesting, cultivation, grazing, burning
or even drought, may lead to dune
destabilization (Middleton, 2011).
Source: Canada, Ministry of Agriculture, Food and Rural Affairs.
Source: Pinterest.

306
Figure 50.
Damage to
infrastructure
by moving sand
dunes
Source: David Thomas.
Climate change can exacerbate the
frequency and intensity of SDS as a result
of changes in several drivers of these
storms, including wind velocity, prolonged
dry spells and reduced rainfall in source
areas, which decreases soil moisture and
vegetation cover. Dust generation and sand
dune movement often increase in areas
affected by periodic drought.
At the same time, land degradation also
contributes to climate change (IPCC,
2019), due to the production of additional
greenhouse gases, changes in surface
energy balances and direct contributions of
dust to the atmosphere, all of which are the
result of changes in the condition of land in
an SDS-vulnerable area (Arimoto, 2001).
Human-induced climate change is
considered a driver in both natural and
anthropogenic SDS generation. Climate
change mitigation measures can help
reduce dust emissions. Available options
to address the impact of human-induced
contributors to SDS are described in the
2014 report of the Intergovernmental Panel
on Climate Change (IPCC).
12.3 Framing source
management in
the context of
land degradation
neutrality
12.3.1. Integrated
approach for source
management
of SDS
The Global Assessment of Sand and
Dust Storms (United Nations Environment
Programme [UNEP], World Meteorological
Organization [WMO] and UNCCD, 2016)
identifies integrated approaches for
SDS control in large areas, combining
measures to cover different components
of the landscape, including cropland,
rangeland and deserts. An integrated
approach is needed in potential source
areas, in particular combining integrated
landscape management with sustainable
management of all landscape elements,
while implementing proper land-use
management including integrated land and
water management and dust reduction
from industrial sites, depending on the
complexity of SDS drivers, factors and
sources. Integrated landscape-level
measures, including water resources,
are especially important, given the
transboundary impacts of SDS.
Protective and rehabilitative measures

in natural land, cropland and industrial
settings for SDS mitigation should form
part of integrated strategies for SDS
source management using SLM and ILM.
SLM (Box 19) can be defined as “the use
of land resources, including soils, water,
animals and plants, for the production
of goods to meet changing human
needs, while simultaneously ensuring the
long-term productive potential of these
resources and the maintenance of their
environmental functions” (Liniger et al.,
2008). SLM practices reduce soil and land
degradation by different driving factors,
such as wind and run-off.
Best SLM practices are rather well
documented, with many recorded,
for example, in the World Overview of
Conservation Approach and Technologies
(WOCAT) database, which was created
in the mid-1990s.1
WOCAT continues
to upload information, particularly on
SLM technologies and adaption, through
collaboration In this regard, the UNCCD
Science-Policy Interface (SPI) technical
report (Sanz et al., 2017) provides
scientifically sound practical guidance
for selecting SLM practices that help
address desertification, land degradation
and drought, climate change adaptation
and mitigation, and for creating an
enabling environment for their large-scale
implementation considering local realities.
Improved SLM requires a better
understanding of the interrelationships
and coordination mechanisms linking
ecological, social, cultural, political and
economic dimensions by all stakeholders
from local to international levels.
Participatory planning approaches at the
community levels and a cross-sectoral
coordination development framework will
also play a role towards managing land
in a sustainable way (Alemu, 2016). Land
suitability analysis and participatory land-
use planning are necessary to choose the
optimum practices for any given set of
biophysical and socioeconomic conditions.
1 See https://qcat.wocat.net/en/wocat/.
The greatest attention needs to be paid to
ILM in potential source areas, combining
sustainable management of all landscape
elements, including integrated water
management and the reduction of dust
from industrial sites. ILM (Box 20) refers
to long-term collaboration among different
groups of stakeholders to achieve the
multiple objectives required from the
landscape, such as agricultural production,
the delivery of ecosystem services, cultural
heritage and values and rural livelihoods,
among others (Scherr et al., 2012).
ILM supports integration across sectors
and scales, increases coordination and
ensures that planning, implementation and
monitoring processes are harmonized at
the landscape, subnational and national
levels. Integrated water resources
management is an important component
of ILM and is especially relevant to SDS
preventive measures.
By coordinating strategies and approaches
and maximizing synergies between
different levels of government, ILM can
create cost efficiencies at multiple levels,
including SDS mitigation. Given that
ILM supports an inclusive, participatory
process that engages all stakeholders
in collaborative decision-making and
management, it can also help empower
communities. As a natural resource
management strategy, ILM can enhance
regional and transnational cooperation
across ecological, economic and political
boundaries.

308
Box 19. Sustainable land management principles
The TerraAfrica Partnership (https://www.wocat.net/library/media/26/) presents three
principles of SLM as well as principles for upscaling SLM:
SLM principle 1: increased land productivity
• Increase water-use efficiency and water productivity (reduce losses, increase
storage, upgrade irrigation)
• Increase soil fertility and improve nutrient and organic matter cycles
• Improve plant material and plant management, including integrated pest
management
• Improve microclimatic conditions
SLM principle 2: improved livelihoods and human well-being
• Support small-scale land users with initial investments, where there are often high
initial costs and no immediate benefits
• Ensure maintenance through land users’ ownership of SLM activities
• Consider cultural values and norms
SLM principle 3: improved ecosystems
• Prevent, mitigate and rehabilitate land degradation
• Conserve and improve biodiversity
• Mitigate and adapt to climate change (increase carbon stock above and below
ground, for example, through improved plant cover and soil organic matter)
Principles for upscaling SLM
1. Create an enabling environment: institutional, policy and legal framework
2. Ensure local participation combined with regional planning
3. Build capacities and train people
4. Monitor and assess SLM practices and their impacts
5. Provide decision support at the local and regional levels to:
• identify, document and assess SLM practices
• select and adapt SLM practices
• select priority areas for interventions

Box 20. Integrated landscape management
Five key elements characterize ILM, all of which facilitate participatory development
processes. These are:
1. Shared or agreed upon management objectives that encompass multiple benefits
from the landscape.
2. Field practices that are designed to contribute to multiple objectives.
3. Management of ecological, social and economic interactions for the realization of
positive synergies and the mitigation of negative trade-offs.
4. Collaborative, community-engaged planning, management and monitoring processes.
5. The reconfiguration of markets and public policies to achieve diverse landscape
objectives (Scherr et al., 2012).
Sayer et al. (2013) proposed 10 principles for ILM. A landscape approach seeks to provide
tools and concepts for allocating and managing land to achieve social, economic and
environmental objectives in areas where agriculture, mining and other productive land
uses compete with environmental and biodiversity goals. These principles emphasize
adaptive management, stakeholder involvement and multiple objectives:
1. Continual learning and adaptive management.
2. Common concern entry point.
3. Multiple scales of intervention.
4. Multifunctionality.
5. Multiple stakeholders.
6. Negotiated and transparent change logic.
7. Clarification of rights and responsibilities.
8. Participatory and user-friendly monitoring.
9. Resilience.
10. Strengthened stakeholder capacity.

310
Planning
‘biophysical’ and ‘human’
dimensions in
participatory land-use
planning process
Assessment
Land resources status
and trends
Degradation
Conservation
Restoration
LAND EVALUATION
PRIORITIZATION
People-centred
negotiation process
Land resource
planning tools
Governance and
gender
Enabling
environment
Partnership
SLM scaling-up WOCAT
UNCCD K-hub
Farmer field schools
LADA
Collect Earth
SHARP/RAPTA
LADA
Collect Earth
Ex-ACT
LADA
Collect Earth
Ex-ACT
LOCAL
PROVINCIAL
NATIONAL
MULTI-STAKEHOLDER
MULTI-SCALE
MULTI-SECTOR
LAND USE /
LANDSCAPE UNITS
*Sustainable food and agriculture
SFA* multiple benefits: biodiversity and ecosystem services, climate
resilience, food security and poverty alleviation
Four interlinked steps to support sustainable management of land resources
CH12 Figure 51.
Monitoring
Assessing impact
Informing decision
makers
LDN TARGETS
Landscape management
Implementing and
scaling up SLM practices
ACHIEVING LDN
Figure 51.
Interlinking
steps to support
sustainable land-
use management
Source: Food and Agriculture Organization of the
United Nations (FAO), 2018.
HEALTHY ECOSYST
E
M
S
FOOD SE
C
U
R
I
T
Y
H
U
M
A
N
W
E
L
L
-
B
E
ING
LDN
Land-based natural capital
and ecosystem services
for each land type
REVERSED PAST
DEGRADATION
A level balance = neutrality = no net loss
Avoid or reduce new
degradation via sustainable
land management (SLM)
Reverse past degradation via
restoration & rehabilitation
Reverse
Reduce
Avoid
NEW
DEGRADATION
HEALTHY ECOSYS
T
E
M
S
FOOD S
E
C
U
R
I
T
Y
H
U
M
A
N
W
E
L
L
-
B
E
I
NG
LDN
Land-based natural capital
and ecosystem services
for each land type
Losses Gains
Losses Gains
REVERSED PAST
DEGRADATION
A level balance = neutrality = no net loss
Avoid or reduce new
degradation via sustainable
land management (SLM)
Reverse past degradation via
restoration & rehabilitation
Reverse
Reduce
Avoid
NEW
DEGRADATION
Anticipate and plan
Interpret and adjust
CH12 Figure 52.
Figure 52.
Conceptual
framework for
land degradation
neutrality
Source: UNCCD, 2016.

Four interlinked steps are promoted to support
sustainable land management: assessment,
planning, landscape management through SLM
implementation, and monitoring (Figure 51).
These are indispensable components to scaling
up SLM practices, which generate tangible
positive impacts and support the achievement
of sustainable management of natural
resources and combating land degradation.
12.3.2. Integrating source
management of SDS
in the context of land
degradation neutrality
LDN is adopted as SDG target 15.3.2
The
concept of LDN is designed to develop and
implement policies promoting the rehabilitation
and restoration of degraded land. It can
provide a practical framework to develop and
implement SDS source management strategies
that take into consideration existing measures
and approaches.
LDN can be achieved by avoiding land
degradation and upscaling SLM and ILM
practices. Restoration and rehabilitation
measures of degraded land
can greatly contribute to SDS source mitigation
at the national and regional levels.
Measures to achieve LDN targets in SDS source
areas can reduce the susceptibility of land to
wind erosion, thus reducing the frequency and
intensity of SDS. Reducing dust emissions and
the impacts of SDS can better be achieved
through the successful implementation
of sustainable water use. SDS source
management is directly and/or indirectly linked
to the LDN indicators, namely land productivity,
land cover/land-use change and soil organic
matter.
Figure 52 illustrates the interrelationships
among the major elements of the scientific
conceptual framework for LDN.
• The target at the top of the diagram
expresses the vision of LDN, emphasizing
the link between human prosperity and the
natural capital of land – the stock of natural
resources that provides flows of valuable
goods and services.
2 See https://sustainabledevelopment.un.org/?menu=1300.
• The balance scale in the centre illustrates
the mechanism for achieving neutrality,
ensuring that future land degradation
(losses) are counterbalanced through
planned positive actions elsewhere
(gains) within the same land type (same
ecosystem and land potential).
• The fulcrum of the scale depicts the
hierarchy of responses. Avoiding
degradation is the highest priority, followed
by reducing degradation and finally
reversing past degradation.
• The arrow at the bottom of the diagram
illustrates that neutrality is assessed by
monitoring the LDN indicators relative to a
fixed baseline. The arrow also shows that
neutrality needs to be maintained over time
through land-use planning that anticipates
losses, plans gains and applies adaptive
learning, where tracking allows for mid-
course adjustments to help ensure that
neutrality is maintained in the future.
The LDN conceptual framework (Box 21)
emphasizes that the goal of LDN is to
maintain or enhance the land resource base,
in other words, the stocks of natural capital
associated with land resources, in order to
sustain the ecosystem services that flow from
them, including food production and other
livelihood benefits. The conceptual framework
creates a common understanding of the LDN
objective and consistency in approaches to
achieving LDN. It has been designed to create
a bridge between the vision and practical
implementation of LDN through national action
programmes by defining LDN in operational
terms (UNCCD, 2016).
The conceptual framework applies to all types
of land degradation so that it can be used
by countries according to their individual
circumstances. The framework provides a
scientifically-sound basis to understand LDN
in order to inform the development of practical
guidance to pursue it and to monitor progress
towards related targets.

312
12.4 Source mitigation
measures –
prevention
12.4.1. Overview
Measures to prevent SDS focus
on reducing risks posed by the
aforementioned drivers.
The protection of natural areas and the
sustainable management of dryland
forests, rangelands and croplands are
critical preventive measures to counteract
SDS, especially in areas where sediments
or soils are sensitive to wind erosion.
Integrated landscape management is the
optimal strategy, combining sustainable
management of all the above landscape
elements, including integrated water
management.
Mapping sensitive source areas will
help with the prioritization of areas for
preventive action, using the criteria
developed by Bullard et al. (2011), for
example, for determining susceptibility
to erosion based on geomorphology (see
chapters 2, 6 and 8).
Box 21. Principles of land degradation neutrality
The LDN conceptual framework presents principles to be followed by all countries that
choose to pursue LDN. The principles govern the application of the framework and help
prevent unintended outcomes during the implementaton and monitoring of LDN. There is
flexibility in applying many principles, but the fundamental structure and approach of the
framework are fixed in order to ensure consistency and scientific rigour.
1. Maintain or enhance land-based natural capital.
2. Protect the rights of land users.
3. Respect national sovereignty.
4. For neutrality, the LDN target equals (is the same as) the baseline.
5. Neutrality is the minimum objective: countries may elect to set a more ambitious
target.
6. Integrate planning and implementation of LDN into existing land-use planning
processes.
7. Counterbalance anticipated losses in land-based natural capital with interventions to
reverse degradation in order to achieve neutrality.
8. Manage counterbalancing at the same scale as land-use planning.
9. Counterbalance like-for-like (Counterbalance within the same land type).
10. Balance economic, social and environmental sustainability.
11. Base land-use decisions on multivariable assessments, considering land potential, land
condition, resilience and social, cultural and economic factors.
12. Apply the response hierarchy in devising interventions for LDN: avoid–reduce–reverse
land degradation.
13. Apply a participatory process: include stakeholders, especially land users, in designing,
implementing and monitoring interventions to achieve LDN.
14. Reinforce responsible governance: protect human rights, including tenure rights,
develop a review mechanism and ensure accountability and transparency.
15. Monitor using the three UNCCD land-based global indicators: land cover, land
productivity and carbon stocks.
16. Use the one-out, all-out approach to interpret the result of these three global indicators.
17. Use additional national and subnational indicators to aid interpretation and to fill gaps
for ecosystem services not covered by the three global indicators.
18. Apply local knowledge and data to validate and interpret monitoring data.
19. Apply a continuous learning approach: anticipate, plan, track, interpret, review, adjust
and create the next plan.
Source: UNCCD, 2016.

Objective Control measures
Sustainable land and water-use planning around
ephemeral lakes, rivers or streams, and alluvial fans,
playas and saline lakes in arid areas
Prevent diversion of water
Prevent devegetation of surrounding catchments
Avoid/reduce disturbance of natural crusts (algal,
lichens)
Manage vegetation in rangelands Avoid overgrazing through reduced stocking rates or
rotational and controlled grazing
Avoid over-exploitation of trees and shrubs
Reduce burning of grasses and plant litter
Maintain perennial grasses
Protect vegetation in natural steppe, desert areas, and
dune fields
Retain diverse vegetation cover
Reduce fire risk
Avoid/reduce disturbance of natural crusts
Fix sand dunes Plant dead fences, grasses and shrubs
Source: Adapted from UNEP, WMO and UNCCD, 2016.
Table 24.
Preventive
measures in
rangelands
and natural
ecosystems
12.4.2. Natural areas
and rangelands
Preventive measures in natural
ecosystems and rangelands focus on
vegetation and water management, as
well as the sustainable management of
livestock
(Table 24).
In natural ecosystems, protection
measures should aim to retain diverse
vegetation, reduce fire risk and minimize
disturbances of natural crusts by vehicular
traffic. For example, disturbances of
deserts can disrupt the natural vegetation
patchiness, resulting in more connected
pathways between bare soil patches,
which provide channels for wind and water
erosion as well as transport, thus leading
to desertification (Okin et al., 2009).
Methods for controlling wind erosion and
soil degradation in rangelands are often
designed to reduce the pressure of grazing
by excluding livestock from pastures
either for short periods to allow the plants
to mature and shed their seeds or for a
certain number of years to allow degraded
rangelands to fully recover.
Alternatively, reduced stocking rates
could be introduced by placing a limit
on livestock densities through the
establishment of prescribed carrying
capacities per hectare in areas where
grazing is allowed (Middleton and Kang,
2017).
However, these types of rangeland
management measures need to consider
and ensure secure user rights as well as
adequate incentives for rangeland users,
supporting them in building organizational
capacities and collective actions. There is
increasing recognition that for sustainable
rangeland management in drylands,
location-specific, biophysical, social,
cultural and economic factors at various
temporal and spatial scales need to be
taken into consideration (Vetters, 2004).
Various strategies can be implemented
to manage the socioeconomic impacts
caused due to the drying lakes and
waterbodies, including SDS. For example,
re-wetting or re-charging of waterbodies
and establishing vegetation covers in
dried lake beds can be considered in
the context of SDS source mitigation,
taking into consideration the specificity of
local situations (Tussupova et al., 2020;
Robinson 2018).

314
Source: Jennifer Lalley, University of Johannesburg.
Figure 53.
Mobilizing
desert dust can
be prevented by
reducing damage
to protective
biological crusts
in deserts
by confining
vehicular traffic
Figure 54.
Vegetation
management
in rangelands
protects soil from
wind erosion
Source: Conservation International.
Remedial measures are generally too
expensive to be practical except in
situations where high-value assets are
at risk. Such measures include returning
stream flows to re-flood old lake beds,
applying chemical surfactants, spreading
gravels, irrigating to dampen the soil
surface, implementing mechanical
compaction and paving roads (Gill and
Cahill, 1992).

Figure 55.
Stabilization of
sand dunes in the
Kubuqi Desert,
northern China
Source: UNEP, 2015.
However, there have been remarkable
instances of degraded desert land being
reclaimed and sand dunes being stabilized
through revegetation (where water
resources allow), despite the high labour
requirements involved. One stabilization
method involves laying out fences of straw
and bundled shrub stems in a grid pattern
across the land, before planting drought-
resistant indigenous shrubs which are
established using a water-jetting technique.
After 25 years, this results in a protection
belt, as seen in Figure 55, thus stabilizing
sand dunes and preventing their impacts to
roads, for example (UNEP, 2015).

316
12.4.3. Croplands
Strategies for controlling SDS in cultivated areas aim to reduce soil exposure to wind,
decrease wind speed or minimize soil movement (Table 25). All wind erosion control
measures (Mann, 1985; Yang et al., 2001) are relevant in controlling SDS.
Reduce periods with little or no soil cover* Adjusting the time of planting
Relay cropping
Crop rotation
Reduced or no tillage
Reduce area with little or no soil cover Inter-cropping
Cover cropping/nurse crops
Mixed cropping
Strip cropping
Surface mulching
Reduced or no tillage
Multi-strata systems
Good crop management
Increase soil resistance to wind erosion Increased input of organic residues through increased
crop productivity, organic mulches, manures
Reduced soil disturbance through limited or no tillage
Reduce wind speed within and between fields Ridging
Strip cropping
Crop rotation
Hedgerows
Dead fencing (crop or tree residues)
Linear planting of trees
Scattered planting of trees
Reduce soil movement Tillage practices that increase surface roughness
Note: * Soil cover is the degree to which soil is protected by vegetation, organic litter layers or mulch.
Source: UNEP, WMO and UNCCD, 2016.
The most fundamental measure is reducing soil exposure to wind by:
• protecting the soil with live or dead vegetation
• minimizing the time and area that soil has little or no cover, especially during dry
periods or wind erosion seasons.
Table 25.
Measures to
minimize wind
erosion in
cropland

Various cropping, residue management
and reduced tillage practices can help
achieve this objective. In addition, roots
of live vegetation act as a soil binding
mechanism. Crop management practices
that increase above-ground or below-
ground inputs of organic residues to the
soil, either through improved productivity
or by returning a larger fraction of residues,
will improve soil stability and resistance
to detachment and erosion, by increasing
the threshold velocity required for soil
movement or by increasing surface
roughness.
Conservation agriculture, for example,
is recognized as an efficient method for
reducing wind erosion losses. It aims to
achieve minimal soil disturbance through
reduced or no tillage, maximize residue
cover on the soil surface and improve
water use and soil fertility through inter-
cropping.
Some agronomic management practices,
such as mulching, for example, that
increase crop vigour also reduce the
time that soil is bare during the cropping
season. Vegetation cover and soils
can also be increased and stabilized
respectively through various traditional soil
and water conservation measures, which
include water harvesting techniques, soil
conservation bunds and organic manures
(Biazin et al., 2012, Schwilch et al., 2014).
Other good management includes factors
such as the use of quality planting material,
optimal plant density, appropriate soil and
crop nutrient management and adequate
pest and disease control.
Figure 56.
Reduced and
mulch tillage
systems providing
soil protection
from wind erosion
Source: Paul Jasa - Extension Engineer, May 2018.

318
Figure 57.
Windbreak
protecting
cropland in large
field
Figure 58.
Scattered
trees offering
protection to
cropland and
livestock in a
parkland system
in Mali
Source: NRCS, 2012 - Field windbreak in northwest Iowa, by Lynn Betts
Adoption of agroforestry and silvopastoral
systems, in which trees are integrated with
agricultural land use, pasture and livestock,
can also reduce the risk of SDS. Trees
and shrubs can be planted around fields
and homesteads, along roadsides, on soil
conservation contours within fields and in
riparian areas.
In dryland areas, scattered trees can play
a significant role in protecting croplands.
The use of biodegradable material and
by-products, for example, from the cotton
industry, provides opportunities to protect
large areas (Young, 1989).
Source: Gemma Shepherd, UNEP 2012

Note: Zai pits can help crops or other vegetation to grow in otherwise barren or unvegetated soils, for
example, denuded dunes.
Source: CGIAR.
Figure 59.
Zai pits hold
water on the land
to improve crop
growth in poor or
eroded lands
12.4.4. Industrial settings
Industrial sources of dust, such as mining
operations, have specific options for
preventing dust from being generated or
leaving the site. These include various
types of dust collection systems, water
application (hydraulic dust control) to dry
materials, physicochemical control of
surfaces and cultivation of tailing dumps
(Cecala et al., 2012).
Physicochemical methods may be used
to stabilize tailing dumps using both
natural materials and synthetic polymeric
materials with structure-forming properties
(Masloboev et al., 2016). Solutions of
inorganic and organic natural cementitious
polymeric materials and multi-component
binding materials (polyacrylamide, liquid
rubber, bitumen, etc.) are used as binding
reagents.
Several studies (for example, Baklanov and
Rigina, 1998; Amosov et al., 2014) have
examined the effects of different factors
and conditions on dust production from
tailing dumps, including wind velocity,
humidity and other meteorological
parameters, material moisture content,
the size and shape of particles, the
efficiency of dust catching and the height
and geometry of tailing dumps, as well as
specific measures to reduce dusting, such
as protective barriers. Numerical modelling
studies have indicated that two-metre
high protective barriers located on the
leeward side of tailing dumps is effective
in reducing levels of atmospheric pollution
downwind (Melnikov et al., 2013).

320
Source: Bender GmbH and Co.KG.
12.5 Protective measures
Physical protection of valuable assets, such as towns, infrastructure and irrigation
schemes, are given in Table 26.
Restrict movement of sand and dust around valuable
assets
Stabilize sand dunes
Windbreaks around urban areas, along roads and
other infrastructure
Sand dune fixation with vegetation or chemical
substances
Agroforestry
Prevent sand accumulation Aerodynamic methods, such as alignment of roads,
removal of obstacles to wind and land shaping
Figure 60.
Surface
stabilization
for dust control
at an industrial
site using soil
binding agents
applied by a
hydroseeder
Table 26.
Measures to
protect valuable
assets from sand
and dust

A major challenge is to protect areas
and infrastructure from unwanted dust
and sand deposits from SDS. Reducing
wind speed through tree planting, such
as shelterbelts, around urban areas and
infrastructure helps to trap dust and
deposit sand outside these areas (Bird et
al., 1992). However, impacts on lighter dust
particles carried above tree height may be
limited.
Wind erosion can blow sand and mobile
sand dunes at wind speeds that are too
low to generate SDS, but which pose an
aeolian hazard (Wiggs, 2011). Measures to
protect against this type of sand and dune
movement are therefore relevant. Such
measures tend to be associated with active
dune fields and sand transport corridors in
drylands where topographic depressions
accumulate sand-sized material.
Urban areas and infrastructure, as well as
farms established on the edges of such
areas, become susceptible to windblown
sand and moving sand dunes (Wiggs,
2011). Active dunes can migrate more than
15 metres per year, causing significant
hazards to human activities (Al-Harthi,
2002).
There are various measures for controlling
windblown sand and moving sand dunes,
as summarized in Table 27. Examples of
various types of fences used to protect the
Qinghai-Tibet railway in China are given
by Zhang et al. (2010). Various measures
implemented to protect infrastructure in
Kuwait are summarized by Al-Awadhi and
Misak (2000).
Control measures Examples
Windblown sand
Enhance deposition Ditches, fences, tree belts
Enhance transport Streamlining techniques; creating a smooth texture
over the land surface; erecting panels to deflect the
air flow
Reduce the supply of sand upwind Surface stabilizing techniques; fences; vegetation
Deflect moving sand Fences, tree belts
Moving dunes
Mechanical removal Bulldozing
Dissipation Reshaping; trenching; surface stabilization techniques
Immobilization through altering aerodynamic form Surface stabilization techniques; fences
Table 27.
Measures
to control
windblown
sand and
sand dunes
Source: Watson, 1985.

Stabilizing sand dunes usually involves
some form of primary temporary
protection to reduce sand movement
and aid the establishment of vegetation
(FAO, 2010). Primary stabilization can be
accomplished by stone mulching, wetting,
chemical stabilizers, biological crusting
or covering the ground with any other
material, such as plastic sheets, nets and
geotextiles, among others.
Fences of materials such as straw and tree
branches are also frequently used, either
in chequerboard or linear arrangements.
More capital-intensive methods using
sprays of petroleum emulsion products
have been tested in Egypt, Kuwait and
Libya for stabilizing sand dunes prior to
establishing vegetation (Grainger, 1990;
Ramadan et al., 2010) and are used to
stabilize surfaces in some industrial
settings.
The mitigation of SDS using a hybrid
biological–mechanical system was shown
to be cost-effective with an equivalent
saving of 4.6 years of sand encroachment.
The integrated biological–mechanical
control system comprises two impounding
fences (two-metre high, chain-link and slats
fencing) situated 90–100 metres apart
with three rows of drought-resistant trees
(Prosopis juliflora and Acacia etbaica) in the
middle section between the two fences.
The total effectiveness of this integrated
system is between 25 and 30 years, with
the system’s unit cost totalling around US$
198,000 per 1 km, including chain-linked
fences, trees and irrigation for one year (Al-
Hemoud et al., 2019).
Vegetative techniques may involve
either protecting existing vegetation
as a preventive measure or planting
adapted grasses, shrubs or trees. Careful
attention must be paid to the selection
of species that are well-adapted to the
harsh conditions. Different species may be
adapted to various parts of dunes.
Reducing wind speed within and between
fields is a critical control measure. Tall
vegetation or structures are most effective
in reducing wind speed over large areas
(Figure 61).
Windbreaks can reduce wind speeds by
50–80 per cent in open fields for up to
15–20 times the distance of the height
of the windbreak (Burke, 1998; Skidmore,
1986).
The distance of the wind reduction effect
is directly proportional to the height of
the windbreak. Windbreak porosity also
affects the pattern of wind velocity within
the shelter zone, with porosity of 20 per
cent having been found to maximize
the protection distance (Burke, 1998).
However, as wind velocity increases and
the direction stops moving perpendicular
to the barrier, the fully protected zone will
start to diminish (Tatarko, 2016).
Nursery operations therefore need careful
planning, particularly if the production of
large quantities of seedings is anticipated,
such as the adoption of drought-resistant
species in the dry areas, including for
example, Atriplex spp. and Salsola spp.
Careful attention also needs to be paid to
the sustainability of water use, especially
when planting trees, which may grow well
in the first few years but later deplete water
tables and die off.
Temporary irrigation is often required
to ensure that plants survive during the
establishment phase. Efficient methods
for irrigation during planting have been
established, such as water jetting (UNEP,
2015). Options for planting include
seedlings planting, mechanized contour
planting (semicircular bunds using the
Vallerani system), direct sowing and aerial
seeding. Sustainable management and
harvesting of vegetation are essential
for preventing dune destabilization (FAO,
2010). Only 15 per cent vegetation cover
may be sufficient to stabilize sand surfaces
(Lancaster, 2011).

324
Source: UNEP.
Figure 61.
Trees used
to stabilize
sand dunes
encroaching on an
irrigation scheme
on the Nile flood
plain
Aerodynamic methods to harness wind
to remove sand from urban or other
areas have also been used (FAO, 2010).
Such methods aim to increase wind
speed without introducing turbulence so
that deposits are transported away. For
example, streets in some Sahelian towns
are orientated parallel to the prevailing
wind.
Obstacles placed in the path of sand-
laden wind can be used to increase wind
speed through a compression effect, such
as placing stones at a certain distance
from one another along the crest of a
dune. The removal of obstacles from
strips along roads, known as transverse
streamlining, has been used to reduce
sand accumulation, such as in Mauritania
along the Road of Hope, though this needs
constant maintenance (FAO, 2010). It is
worth noting that protective measures
that are not green infrastructures or
nature-based need to be considered with
a precautionary approach. This approach
must take into account all aspects of
ecological connectivity in order to avoid
unintended negative impacts on other
ecological processes such as animal
migration.
12.6 Conclusion
Policies for SLM and ILM can best be
deployed in the context of the LDN target
to address SDS sources. In the LDN target-
setting process, there is an opportunity to
collectively consider options to mitigate
SDS sources, particularly anthropogenic
sources, including the assessment and
trend of land degradation and identification
of land degradation drivers, with the
participation of relevant stakeholders
linked to land and water resources. An
integrated and holistic approach of SLM,
land-use planning and ILM can be an
integral part of and maximize synergies
among various actions to reduce
anthropogenic dust emissions at larger
scales in the long term.
Regional cooperation is crucial for the
management of anthropogenic dust
emissions at landscape levels, including
water management. Regional mechanisms
based on strong political commitment
are therefore needed to coordinate policy
between source and deposit areas. SDS
source management can be integrated
into regional processes, where appropriate,
and LDN target-setting can be included in
policy- and decision-making processes and
implemented as a priority in SDS prone
areas, with pertinent financial investment
and technical assistance provided.

12.7 References
Al-Awadhi, Jasem M., and Raafat Misak (2000). Field
assessment of aeolian sand processes and sand
control measures in Kuwait. Kuwait Journal of
Science and Engineering, vol. 27, No. 1.
Al-Harthi, Abbas (2002). Geohazard assessment of
sand dunes between Jeddah and Al-Lith, western
Saudi Arabia. Environmental Geology, vol. 42,
No. 4.
Al-Hemoud, Ali, and others (2019). Economic impact
and risk assessment of sand and dust storms
(SDS) on the oil and gas industry in Kuwait.
Sustainability, vol. 11, No. 1.
Alemu, Molla M. (2016). Sustainable land management.
Journal of Environmental Protection, vol. 7, No. 4.
Amosov, Pavel, Alexander Baklanov, and Olga Rigina
(2014). Numerical modelling of tailings’ dusting
processes. Lambert Academic Publishing.
Arimoto, Richard (2001). Eolian dust and climate:
relationships to sources, tropospheric chemistry,
transport and deposition. Earth-Science Reviews,
vol. 54, No.1–3.
Baklanov, Alexander, and Olga Rigina (1998).
Environmental modelling of dusting from mining
and concentration sites in the Kola Peninsula,
Northwest Russia. Proceedings of the XI World
Clear Air and Environment Congress, vol. 1, No.
4F–3. 14–18 September, Durban.
Biazin, Birhanu, and others (2012). Rainwater harvesting
and management in rainfed agricultural systems
in sub-Saharan Africa – a review. Physics and
Chemistry of the Earth, vol. 47–48.
Bird, P.R., and others (1992). The role of shelter in
Australia for protecting soils, plants and livestock.
Agroforestry Systems, vol. 20, No. 1.
Borrelli, Pasquale, and others (2016). Towards a Pan‐
European assessment of land susceptibility to
wind erosion. Land Degradation & Development.
Bullard, Joanna E., and others (2011). Preferential dust
sources: a geomorphological classification
designed for use in global dust-cycle models.
Journal of Geophysical Research. Earth Surface,
vol. 116, No. F4.
Burke, Steven (1998). Windbreaks. Victoria: Inkata press.
Cecala, A.B., and others (2012). Dust Control Handbook
for Industrial Minerals Mining and Processing.
Report of Investigations 9689. Pittsburgh and
Spokane: Department of Health and Human
Services.
Finch, Deborah M. (2004). Assessment of Grassland
Ecosystem Conditions in the Southwestern United
States. Volume 1. Fort Collins, Colorado: United
States Department of Agriculture, Forest Service.
Available at https://www.fs.fed.us/rm/pubs/
rmrs_gtr135_1.pdf.
Food and Agriculture Organization of the United Nations
(FAO) (2010). Fighting Sand Encroachment.
Lessons from Mauritania. FAO Forestry Paper
158. Rome.
__________ (2018). Land resources planning (LRP)
to address land degradation and promote
sustainable land management (SLM). Available
at http://www.fao.org/3/I9922EN/i9922en.pdf.
Gill, Thomas E. (1996). Eolian sediments generated by
anthropogenic disturbance of playas: human
impacts on the geomorphic system and
geomorphic impacts on the human system.
Geomorphology, vol. 17, No. 1–3.
Gill, Thomas E., and Thomas A. Cahill (1992). Playa–
generated dust storms from Owens Lake. In The
History of Water: Eastern Sierra Nevada, Owens
Valley, White–Inyo Mountains, Clarence A. Hall,
Victoria Doyle-Jones, and Barbara Widawski, eds.
Los Angeles, California: University of California.
No. 3.
Grainger, Alan (1990). TheThreatening Desert: Controlling
Desertification. London: Earthscan Ltd.
Herrick, J.E., and others (2016). The Land–Potential
Knowledge System (LandPKS): mobile apps
and collaboration for optimizing climate
change investments. Ecosystem Health and
Sustainability, vol. 2, No. 3.
(2014). Climate Change 2014: Mitigation of
Climate Change. Contribution of Working
Group III to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change,
Ottmar Edenhofer, Ramón Pichs-Madruga, Youba
Sokona, Jan C. Minx, Ellie Farahani, Susanne
Kadner, Kristin Seyboth, Anna Adler, Ina Baum,
Steffen Brunner, Patrick Eickemeier, Benjamin
Kriemann, Jussi Savolainen, Steffen Schlömer,
Christoph von Stechow, and Timm Zwickel, eds.
Cambridge and New York: Cambridge University
Press.

326
__________ (2019). Summary for Policymakers. In:
Climate Change and Land: an IPCC special
report on climate change, desertification, land
degradation, sustainable land management, food
security, and greenhouse gas fluxes in terrestrial
ecosystems P.R. Shukla, J. Skea, E. Calvo
Buendia, V. Masson-Delmotte, H.- O. Pörtner, D.
C. Roberts, P. Zhai, R. Slade, S. Connors, R. van
Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M.
Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E.
Huntley, K. Kissick, M. Belkacemi, J. Malley, eds.
Johnston, Jill, and others (2019). The disappearing
Salton Sea: A critical reflection on the emerging
environmental threat of disappearing saline
lakes and potential impacts on children’s health.
Science of The Total Environment, vol. 663, 804-
817.
Krapivin, Vladimir, Ferdenant A. Mkrtchyan and Gilbert
L. Rochon (2019). Hydrological model for
sustainable development in the Aral Sea region.
Hydrology, vol. 6, No. 4.
Lal, Rattan (2001). Soil degradation by erosion. Land
Degradation & Development, vol. 12, No. 6.
Lancaster, Nicholas (2011). Desert dune processes and
dynamics. In Arid Zone Geomorphology: Process,
Form and Change in Drylands. Third Edition, David
S.G. Thomas, ed. West Sussex: John Wiley &
Sons, Ltd.
Liniger, Hanspeter, and others, eds. (2008). A
Questionnaire for Mapping Land Degradation
and Sustainable Land Management. Bern: World
Overview of Conservation Approaches and
Technologies (WOCAT). Available at https://
www.wocat.net/documents/209/MapQuest_
version_1.pdf.
Mann, H.S. (1985). Wind erosion and its control. In Sand
Dune Stabilization, Shelterbelts and Afforestation
in Dry Zones. FAO Conservation Guide No. 10.
Rome: Food and Agriculture Organization of the
United Nations (FAO).
Masloboev, Vladimir A., and others (2016). Methods to
reduce the environmental hazards of mining and
processing of minerals in the Arctic regions. IMPC
2016: XXVIII International Mineral Processing
Congress Proceedings. Canadian Institute of
Mining, Metallurgy and Petroleum.
Melnikov, N.N., Alexander Baklanov, and P.V. Amosov
(2013). Influence of a height of protection barrier
of dustforming surface of tailing dump on
atmosphere pollution. Gornyi Zhurnal.
Middleton, Nicholas (2011). The human impact. In Arid
Zone Geomorphology: Process, Form and Change
in Drylands. Third Edition, David S.G. Thomas, ed.
West Sussex: John Wiley & Sons, Ltd.
Middleton, Nicholas, and Utchang Kang (2017). Sand and
dust storms: impact mitigation. Sustainability,
vol. 9, No. 6.
Millennium Ecosystem Assessment (2005). Ecosystems
and Human Well-being: Synthesis. Washington,
D.C.: Island Press.
Okin, Gregory S., and others. (2009). Do changes in
connectivity explain desertification? BioScience
vol. 59.
Ramadan, Ashraf, and others (2010). The effectiveness
of two polymer-based stabilisers offering an
alternative to conventional sand stabilisation
methods. In Land Degradation and Desertification:
Assessment Mitigation and Remediation, Pandi
Zdruli, Marcello Pagliai, Selim Kapur, and Angel
Faz Cano, eds. Springer.
Robinson, Alexander (2018). The Spoils of Dust:
Reinventing the Lake that Made Los Angeles.
Applied Research & Design.
Sanz, M.J., and others (2017) Sustainable Land
Management contribution to successful land-
based climate change adaptation and mitigation.
A Report of the Science-Policy Interface. United
(UNCCD), Bonn, Germany.
Sayer, Jeffrey, and others (2013). Ten principles for a
landscape approach to reconciling agriculture,
conservation, and other competing land uses.
vol. 110, No. 21.
Scherr, Sara J., Seth Shames, and Rachel Friedman
(2012). From climate-smart agriculture to
climate-smart landscapes. Agriculture & Food
Security, vol. 1.
Schwilch, Gudrun, Hanspeter Liniger, and Hans
Hurni (2014). Sustainable land management
(SLM) practices in drylands: how do they
address desertification threats? Environmental
Management, vol. 54, No. 5.
Skidmore, E.L. (1986). Wind erosion control. Climatic
Change, vol. 9, No. 1–2.
Tatarko, John (2016). Wind Erosion: Problem,
Processes, and Control. Kansas: United States
Department of Agriculture. Available at http://
www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/
nrcs142p2_019407.pdf.
Tussupova, Kamshat, Anchita, Peder Hjorth and
Mojtaba Moravej (2020). Drying lakes: A review
on the applied restoration strategies and health
conditions in contiguous areas. Water, vol. 12,
No. 3, 749; https://doi.org/10.3390/w12030749

(UNCCD) (2016). Land in balance. The scientific
conceptual framework for land degradation
neutrality. Science-Policy Brief 02. September
2016.Bonn.Availableathttps://knowledge.unccd.
int/sites/default/files/2018-09/18102016_Spi_
pb_multipage_ENG_1.pdf.
__________ (2017). Draft advocacy policy frameworks:
Gender, Drought, and Sand and Dust Storms. 3
July. ICCD/COP(13)/19.
United Nations Environment Programme (UNEP) (2012).
Land Health Surveillance: An Evidence-based
Approach to Land Ecosystem Management.
Illustrated with a Case Study in the West Africa
Sahel. Nairobi.
__________ (2015). Review of the Kubuqi Ecological
Restoration Project: A Desert Green Economy Pilot
Initiative. Nairobi.
Meteorological Organization (WMO), and United
Vetter, Susanne (2004). Rangelands at equilibrium and
non-equilibrium: recent developments in the
debate. Journal of Arid Environments, vol. 62, No.
2.
Watson, A. (1985). The control of wind blown sand and
moving dunes: a review of the methods of sand
control in deserts, with observations from Saudi
Arabia. Quarterly Journal of Engineering Geology
and Hydrology, vol. 18.
Wiggs, Giles F.S. (2011). Geomorphological hazards in
drylands. In Arid Zone Geomorphology: Process,
Form and Change in Drylands. Third Edition, David
S.G. Thomas, ed. West Sussex: John Wiley &
Sons, Ltd.
Yang, Youlin, Victor Squires, and Lu Qi, eds. (2001).
Global Alarm: Dust and Sandstorms from the
World’s Drylands. Bangkok: Asia Regional
Coordinating Unit, United Nations Convention to
Combat Desertification (UNCCD).
Young, Anthony (1989). Agroforestry for Soil
Conservation. Wallingford, United Kingdom: CAB
International.
Zhang, Ke-cun, and others (2010). Damage by wind-
blown sand and its control along Qinghai-Tibet
Railway in China. Aeolian Research, vol. 1,
No. 3.

UNCCD | Sand and Dust Storms Compendium | Chapter 13 | Sand and dust storms impact response and mitigation 329
13. Sand and dust
storms impact response
and mitigation
Chapter overview
This chapter reviews approaches to address and mitigate the impacts of sand and dust
storms (SDS) on humans and the economy. After an overview of SDS preparedness
and emergency response procedures, the chapter identifies sector-specific measures to
address SDS impacts.

UNCCD | Sand and Dust Storms Compendium | Chapter 13 | Sand and dust storms impact response and mitigation
330
13.1 Introduction
This section of the Compendium looks at
ways to mitigate the impact of sand and
dust storms (SDS) through preparedness
and emergency response procedures
(see chapter 3 for overview of disaster
risk management.) To date, most efforts
to manage the risks posed by SDS
have focused on understanding the
mechanisms and origins of SDS (chapter
2), monitoring, forecasting and warning of
SDS (chapters 9 and 10) and mitigation of
SDS development at their source (chapter
12).
Less attention has been paid, as part of
the disaster risk management process, to
mitigating the impacts of SDS either as
they occur or once they have occurred.
This is likely due to the low profile of
SDS (see Middleton et al., 2018) and the
diverse impacts of SDS across sectors,
which together make developing a unified
approach complicated. It is expected
that, over time, additional examples of
responses to SDS will become available
and can be integrated into a more
comprehensive approach to SDS risk
management.
The identification of specific measures
for response and impact mitigation
should be based on risk and vulnerability
assessments (see chapters 4, 5 and 7).
The economic effectiveness and cost-
to-benefit justification of each of these
measures needs to be assessed based
on local conditions (see chapter 6). In
some cases, mitigation measures that are
technically possible cannot be justified
based on their expected benefits.
Following an overview of SDS
preparedness (chapter 13.2) and response
and SDS disaster planning (chapter 13.3),
chapter 13.4 provides an overview of SDS
preparedness and response options and
specific actions which have been identified
to reduce SDS impacts through impact-
based warnings, both during and in the
immediate aftermath of SDS.
Chapter 13.4 should be read in conjunction
with chapter 12 as there can be
considerable overlap between impact and
source mitigation in practice.
13.2 Overview of
SDS preparedness
and response
Preparedness and emergency or disaster
response play critical roles in mitigating
disaster risk and minimizing impacts.
Preparedness for and emergency response
to SDS events take place at the individual,
family, community and organizational
(factory, school, etc.) levels.
As Ejeta et al. (2015) point out,
preparedness strategies are developed
through identification and mapping of the
hazard in question, a vulnerability analysis
and a risk assessment (see chapters
5 and 7 on SDS risk and vulnerability
assessments). Knowledge gained in
these ways can then be used to develop
protective actions.
Effective preparedness
reduces vulnerability,
increases mitigation levels
and enables timely and
effective response to a
disaster event. These
actions will shorten
the recovery period
from a disaster, while
simultaneously increasing
community resilience.
Preparedness, apart from building
operational capacities and reserves,
focuses on educating those at risk to adopt
behaviours which reduce risk and increase
coping capacities. An interesting example
of using education to change behaviour
is from the state of Arizona of the United
States of America.

Box 22. Sand and dust storms and safe driving guidance
• Avoid driving into or through a dust storm.
• If you encounter a dust storm, immediately check the traffic around your vehicle
(front, back and to the side) and begin slowing down.
• Do not wait until poor visibility makes it difficult to safely pull off the roadway – do
it as soon as possible. Completely exit the highway if you can.
• Do not stop in a travel lane or in the emergency lane. Look for a safe place to pull
completely off the paved portion of the roadway.
• Turn off all vehicle lights, including emergency lights. You do not want other
vehicles approaching from behind to use your lights as a guide, possibly crashing
into your parked vehicle.
• Set your emergency brake and take your foot off the brake.
• Stay in the vehicle with your seatbelts buckled and wait for the storm to pass.
• Drivers of high-profile vehicles should be especially aware of changing weather
conditions and travel at reduced speeds.
Source: Arizona Department of Transport, n.d.
The Arizona state government and
National Weather Service website Pull
Aside, Stay Alive1
provides information to
drivers on how to respond to the very rapid
deterioration in visibility during the sudden
onset of dust walls typically associated
with a haboob, which is a common cause
of dust-related accidents on the Interstate
10 (I-10) highway (see Box 22; Day, 1993).
Monitoring, prediction, forecasting and
early warning (see chapters 9 and 10)
facilitate preparedness and emergency
response. The development of SDS is
monitored using data from satellites,
networks of Lidar2
stations and
radiometers, air-quality monitoring and
meteorological stations (Akhlaq et al.,
2012). All of these sources contribute data
to modelling efforts, which enhance our
understanding of the processes involved
and are used to produce predictions and
early warnings (see chapter 10).
Operational dust forecasts have been
developed at several WMO SDS Warning
Advisory and Assessment System
(SDS-WAS) centres (see chapter 9), as
well as by national meteorological and
hydrological services (NMHS). However,
NMHS capacities to develop and issue SDS
warnings vary considerably and warning
procedures
1 See www.pullasidestayalive.org.
2 Light detection and ranging.
can vary between countries. Forecasts
and warnings can be communicated to
the public via a range of media, including
television, radio, short message service
(SMS) text alerts and smartphone
applications, as discussed in chapter 10.
Intrusive warnings can be provided via
messages which break into radio or TV
transmissions or send out blanket SMS.
Detailed SDS forecasts are not always
needed for warning systems. Forecasting
for localized haboobs, which occur at
spatial scales of a few kilometres, is
under development (Vukovic et al., 2014).
However, systems designed to warn
drivers of dusty conditions on susceptible
highways have been used in the southwest
of the United States of America for several
decades (Burritt and Hyers, 1981). More
recently, remotely-controlled signs are
being replaced with systems linked to
in situ sensors that detect poor-visibility
conditions and alert motorists via overhead
electronic signs.
There is evidence to suggest that
media alerts of poor air-quality result in
behavioural changes that tend to lower
exposure to air pollutants (Wen et al.,
2009).

332
A similar finding was reached in
assessments of the health impacts
associated with a severe dust storm in
Australia by Tozer and Leys (2013), which
highlighted the importance of health alert
SMS and emails sent to people advising of
a high-pollution event. Further investigation
of the Australian event by Merrifield
et al. (2013) concluded that because
the dust storm and consequent public
health messages had widespread media
coverage, the health consequences from
this particular dust event were likely to
represent the optimal health outcomes that
could be hoped for in similar future events.
Nonetheless, significant challenges
remain with the reception and uptake of
SDS warnings. Research indicates that
those receiving warning messages can
be expected to follow a “milling” process
before taking action to respond to a
warning, which involves:
• understanding the warning
• believing the warning
• personalizing the warning
• deciding whether to take action based
on the warning
• searching and confirming the warning.
The last step can involve visual verification
of an SDS approach, which may provide
limited time to take protective actions.
Furthermore, an individual receiving a
warning may not act until they are sure that
their family members will be safe (National
Academies of Sciences, Engineering, and
Medicine, 2018).
While advanced technologies (mobile
phones, satellites, etc.) are useful in
disseminating warnings, it is not certain
that these technologies will always reach
all those at risk. In many parts of the
world where SDS are common, these
technologies are not available or have
limited coverage, for example, only major
urban centres. As a result, locally managed
SDS warning systems are often required.
In many cases, source mitigation
measures, as described in chapter 12,
can be effective in reducing SDS impacts
and should be included in preparedness
measures.
For instance, increasing vegetation cover in
urban landscapes, particularly with trees to
slow wind speeds, may reduce the health
problems associated with atmospheric
PM10
and PM2.5
concentrations, as well
as biological and chemical aspects of
pollution (see Janhäll, 2015). In both rural
and urban areas, increased vegetation has
the potential to reduce pollutants through
filtration (see Hwang et al., 2011) and to
regulate microclimatic conditions in a way
that offers at least perceived benefits and
well-being (Lafortezza et al., 2009).
13.3 SDS disaster or
emergency planning
Current general good practice is for
disaster or emergency plans to be
developed at the individual, family, village,
town, city, county, province or state and
national levels, as well as for industry and
business. These plans generally follow a
similar model, with individual and family
plans focusing on immediate survival after
a disaster (for example, stocking food,
water, medicine, etc.) and each higher level
of plan focusing on providing support to
the next lower group, for example, county
plans defining support to cities, towns
and villages, and state or provincial plans
defining support to counties within the
state or province.
Hall (2017) identifies four objectives of
emergency and disaster planning:
• prevent injuries and fatalities
• reduce damage to buildings and
materials
• protect the surrounding community
and environment
• facilitate the continuation of normal
operations.
Disaster plans can be developed for
individuals, communities, public and
private facilities, such as airports and
hospitals, manufacturing and business
units. Given the generally low profile of
SDS as a hazard, only a few examples of
SDS integration into the different levels
of disaster or emergency planning are
widely available. An example of guidance
on family-level planning is provided in the
Be Prepared, Take Action, Be Informed

video3
and web page4
developed by the
state of Arizona Department of Emergency
and Military Affairs of the United States of
America.
An example of state (province) level SDS
management planning is contained within
the Oregon Natural Hazards Mitigation
3 https://youtu.be/X3qw5kr51eE.
4 https://ein.az.gov/hazards/dust-storms.
Plan 2015 for the state of Oregon of the
United States of America (State of Oregon,
2015).
The plan includes an assessment of
SDS and historical examples of impacts,
references to warnings and impacts, and
source mitigation measures.
Box 23. Gender, preparedness and response
The Compendium’s special focus section on gender and disaster risk reduction (see
chapter 3) provides an overview of why including gender is important in addressing
SDS and identified gender-related considerations across types of SDS risk management
interventions. As a general rule, all public consultations should collect inputs using a
gender-based perspective and from vulnerable individuals and groups, carrying out
planning based on these perspectives.
In developing preparedness measures, gender, as well as factors defining vulnerability and
vulnerable groups, should be incorporated in analysis and actions. Disaster response plans
should also incorporate this type of analysis and should define specific impact mitigation
measures and approaches which respond to the vulnerabilities identified.
Good practice is to include a gender specialist and disaster risk management
in preparedness and response planning and during operations. Staff involved in
preparedness or response should be trained on gender and disaster risk management in
the normal course of their work.
Photo: Tsubasa Enomoto, UNDP. Drill for emergency evacuation plan

334
In general, an SDS disaster plan for a
specific location or activity (city, school,
factory, etc.) should follow the outline of
other disaster plans for the same location
or activity. Based on current good practice,
an SDS disaster plan above the family level
could be expected to include the following
elements:
• Authorities for the plan (may be
included in the overall plan for all
disasters).
• An overview of SDS as a hazard in the
area covered by the plan.
• A risk assessment (see chapters 4,
5 and 7).
• Specific source and impact mitigation
measures based on the risk
assessment. This section may include
references to subsidiary plans specific
to individual sectors, for example, for
a hospital or road transport (source
mitigation measures would apply if the
location is also a source of SDS).
• Warning, information dissemination
and public awareness procedures.
Warning procedures may include
standard operating procedures to
effectively disseminate warnings
based on the impact-based forecasting
approach (World Meteorological
Organization [WMO], 2015).
• Operational details or examples of
impact mitigation measures, where
appropriate (see chapter 13.4 and
chapter 12). Providing details or
examples can facilitate practising
of plans before a disaster and
implementation once a warning has
been issued.
• Links to other programmes (such as
soil conservation), which could play a
role in SDS mitigation.
• Sources of information and contacts.
As appropriate, annexes to the plan can
include specific procedures for source and
impact mitigation and the identification
of who takes primary and supporting
responsibilities for implementing such
procedures. In general, SDS disaster or
emergency plans should include sufficient
information to allow necessary actions to
be taken, ensuring that no excessive details
are added that may hinder the use of the
plan.
13.4 Sector-specific
options to address
the impacts of SDS
13.4.1. Overview
The following sections provide summaries
of possible impacts of SDS, as well as
preparedness and mitigation measures
which can be implemented for specific
sectors. Source mitigation measures
(chapter 11) are often also appropriate
for impact mitigation, particularly
where impacted locations may be
also contributing to the overall load of
atmospheric sand and dust load.
13.4.2. Agriculture
For sandstorms (for example, blowing
sand and moving sand dunes), impact
mitigation measures can include:
• installing sand fences near agriculture
areas (Al-Hemoud et al., 2019)
• planting trees or shrubs to block the
movement of sand and dust (Al-
Hemoud et al., 2019)
• deploying equipment and personnel to
clear irrigation and drainage channels
from sand
• changing harvesting or planting
procedures and timing to avoid the
impact of moving sand.

In most cases, applying source mitigation
measures to reduce the movement of sand
before sandstorm conditions develop are
more effective than large-scale impact
mitigation. However, both may need to be
applied in areas where sandstorms are
common and threaten large areas.
For dust storms, impact mitigation
measures can include:
• wetting crops after SDS to remove
dust from plants (dust on plant leaves
may affect development)
• closing vents in greenhouses to
prevent dust entry
• removing or protecting machinery
which may be affected by dust
• reducing the use of farm equipment
which could need additional
maintenance if used in high-dust
conditions (for example, replacement
of air filters, cleaning, etc.).
The use of agricultural machinery during
SDS also needs to address the impacts
of SDS on safe driving and operation, for
example, ensuring that workers can be
seen by equipment operators.
13.4.3. Construction
For road construction, consideration
should be given to:
• safe operation of equipment during
limited visibility
• safety of workers around equipment
during limited visibility
• stabilization of road terracing and
roadbed development so that the
winds associated with SDS do not
move the material.
Note that assuring good worker visibility is
a normal method to improve safety when
working near equipment. The nature of
SDS may require additional measures to
improve worker visibility, including:
• verifying that standard visibility vests
work in high-dust environments
• assessing whether goggles and
dust masks impact visibility and
communication
• ensuring that equipment operators
located in cabs have good visibility
of work areas (for example, frequent
window cleaning may be required).
Photo
by
REUTERS/Thomas
Peter.

336
These measures are in addition to the
health measures that may be needed when
working in the hot and dry environments
where SDS are common (hydration,
protection from solar radiation, etc.).
For building construction, consideration
should be given to:
• erecting physical cloth or plastic
sheet curtains to limit dust entry into
working areas (but with adequate air
conditioning when needed)
• using water sprays or misters to
reduce dust load in work areas
• assessing and addressing any
limitations in worker visibility or
ability to be seen or heard when using
goggles and dust masks
• initiating the operation of air-
conditioning systems early in a
building’s construction, along with
permanent or temporary (for example,
plastic sheeting) closure of openings
to the outside of buildings or within
them to reduce dust entry and remove
dust from work areas (these measures
need to take into account fire safety).
These measures can also improve overall
working conditions within buildings.
In addition, for both road and building
construction, source mitigation measures
should be in place to limit the generation of
dust during normal times and SDS events.
13.4.4. Education
In education facilities:
• procedures can be initiated before
SDS events to reduce dust entry, by
closing and sealing windows
• dust rooms5
can be constructed onto
entry ways
• misters can be used to reduce dust
load at entry ways and within large
open areas
5 A dust room would serve as an area where outside air would be physically isolated from inside air to limit dust
from entry though doorways.
• air-conditioning systems can be
operated in a way to increase filtering
(though filters would need to be
cleaned or replaced more frequently)
• in-room air filter units can be used as
needed to reduce dust loads
• schedules for collecting and returning
students using buses or other means
of transport can be modified to limit
their exposure to SDS outside the
education facility
• special procedures should be
developed to assist students and staff
with health conditions that can be
affected during SDS (such as asthma,
impaired vision, etc.).
For education institutions with dormitories,
implementing an SDS response will need
to include the participation of dormitory
residents. Models for engaging students in
SDS response addressing transport-related
issues can be taken from procedures
for dealing with severe weather, such as
thunderstorms and tornadoes.
These measures can be integrated into
school emergency plans and, with the
exception of dust rooms, be put in place
when an SDS warning is received.
Knowledge about SDS, their causes and
impacts, can be integrated into school
curriculum. Most curriculum include
natural science and increasingly include
core or supplemental topics on natural
hazards and disaster management into
which SDS management can be integrated.
In addition, education on SDS can be
undertaken by interest groups in schools,
such as an environment club, community
organizations, including scouts and girls’ or
boys’ clubs or other such organizations.
Note that these measures apply to all levels
of the education system, from preschool
to university. Facilities at each level in the
education system should have disaster
management plans, with this being a legal
requirement in many countries.

These plans should include SDS early
warning and impact mitigation.
13.4.5. Electricity
Interventions to address the impact of SDS
on electricity generation, transmission and
use are most likely in the following areas:
• Generation – Clean solar panels of dust
and protect equipment from short- and
long-term impacts of dust by improving
the filtration of air taken in directly
by equipment, (for example, diesel
generators), and in the environment
where the equipment operates (for
example, generator rooms), based on
forecasts6
and warnings.
• Transmission – Ensure that winds
associated with SDS do not damage
transmission lines or equipment,
including measures taken before any
severe weather to limit damage.
6 Electricity generation planning can use weather forecasts to anticipate SDS and identify impacts several hours to
several days in advance, incorporating this into operational plans.
• Demand – Anticipate, based on
previous SDS events, increases in
electricity demand from cleaning
activities after the event and during
the event from increased use of air
conditioners and other equipment.
13.4.6. Health
The two immediate threats to the health
sector come from:
• the movement of dust into health
facilities, which impacts hygiene in
the facility, the operation of equipment
and testing, and the health of patients
• an increase in the caseload of
individuals with health conditions
that are aggravated by sand or dust
conditions.
Photo:
UNDP
Indonesia

338
Measures to reduce the impact of sand
and dust on a health facility include:
• sealing windows and other openings
before SDS to reduce air entry from
outside
• using dust rooms at entry ways to
physically isolate dust from inside
air and limit it from entering though
doorways
• using misters to reduce dust load at
entry ways and within large open areas
• using air-conditioning systems to
increase air filtering (filters would
need to be cleaned or replaced more
frequently)
• using in-room air filter units to reduce
dust loads
• frequent use of wet mopping to remove
dust from floors and other surfaces
• washing clothes exposed to sand and
dust to reduce secondary entrapment,
specifically inside areas that have
been isolated from SDS events (such
as rooms with sealed windows)
• modifying opening and closing
schedules to limit exposure to SDS
• reducing movement into spaces where
sensitive equipment is located or tests
take place
• increasing the use of breathing
apparatus designed to reduce air intake
from ambient air, for example, using a
face mask instead of a cannula.
Measures to reduce the impact of
increased caseloads associated with an
SDS event include:
• increasing staff based on an SDS
warning
• increasing supplies of treatment drugs
and equipment
• separating triage and treatment
facilities from the main health facility,
incorporating the aforementioned
methods, such as dust rooms, misters
and air conditioning
• increasing potential patients’
knowledge of ways to reduce or
avoid the impacts of SDS, which
can involve long-term education for
SDS-vulnerable patients, as well as
messaging as part of SDS warnings
on how to reduce SDS impacts.
13.4.7. Hygiene
Living facilities (houses, apartments, care
facilities, public offices and commercial
markets and places of assembly) can
take actions similar to those for education
facilities:
• sealing windows and other openings
outside
• using dust rooms at entry ways to
physically isolate dust from inside
air and limit it from entering though
doorways
• using misters to reduce dust load
at entry ways and within large open
areas
• using air-conditioning systems to
frequently)
• using in-room air filter units to reduce
dust loads
• wet mopping frequently to remove
dust from floors and other surfaces
• washing clothes exposed to sand and
dust to reduce secondary entrapment,
specifically inside areas that have
been isolated from SDS events (such
as rooms with sealed windows)
• modifying opening and closing
schedules to limit exposure to SDS.
For some public facilities, including
shopping malls and closed markets,
expanding hygiene efforts can be part of
activities to provide safer places as refuge
from SDS for those who may be outside
when the event developed (such as a
haboob). This activity would be similar
to the establishment of warming spaces,
such as tents, during extreme cold events,
or to cooling spaces during extreme heat
events. In some situations, cooling spaces
will be needed at the same time as SDS
events.

13.4.8. Livestock
SDS impacts on livestock, including
cattle and other ruminants, horses, goats,
sheep, ducks, geese and other animals
kept in controlled situations (for example,
not ranging without human intervention)
include:
1. respiratory problems
2. difficulty accessing food if pastureland
is covered in dust or sand
3. entering into traffic or water sources in
an effort to avoid the dust or sand, or
because of poor visibility.
Livestock owners or managers should
develop a plan for managing SDS based
on local conditions and also seek expert
advice from specialists and veterinarians
on animal health impacts and normal
reactions to SDS by the animals of
concern. Specific measures that can be
considered to reduce impacts include:
• moving animals to enclosed areas
before SDS events
• moving animals inside before SDS,
but considering the need for adequate
ventilation, water and food for the
duration of the event
• providing additional food stocks if
normal food supplies (for example,
pasture) is covered by sand or dust
• allowing animals to move to open
rangelands to reduce excitement that
may be due to SDS, such as haboobs,
and associated with thunder or heavy
winds and rains (though care should
be taken to ensure that moving
animals does not put them at risk of
lightning strikes)
• moving animals away from roads
and waterways to avoid unplanned
movements into these areas.
If animals are being kept inside a building, it
is important to consider the environmental
conditions (heat and humidity) within the
building if a large number of animals are
present and normal ventilation has been
shut down because of the SDS. This could
lead to hot and humid conditions which
contribute to animal health issues.
If SDS are common, developing an
understanding of common local practice
is important as these animals may have
adapted to this hazard from experience.
Measures such as misters may be tested
to reduce temperatures and dust loading.
Masks are unlikely to be effective.
13.4.9. Manufacturing
Impact mitigation for manufacturing is
likely to fall into three areas:
• reducing the entry of dust into
facilities through closing and sealing
windows and other openings,
improving filtering and using air locks
and positive pressure to block inward
air movement
• reducing the dust load carried by
employees and others entering
facilities by requiring a change of
clothes or the use of overalls
• increasing the cleaning of raw
materials, parts supplied and items
manufactured to reduce the presence
of dust.
Although these measures are likely to be
common practice during non-SDS periods,
they can be expanded and upgraded
through, for example, additional washing
or resealing of openings, based on SDS
forecasts and warnings.
13.4.10. Public awareness
Improving public awareness of SDS
impacts can improve the uptake of warning
messages (see chapter 9) and the overall
adoption of impact mitigation measures.
Awareness can be raised through:
• the education system (see chapter
13.4.4)
• information campaigns before and
during expected SDS periods
• site-specific SDS information,
usually integrated into early warning
messages (see chapter 10).

340
Raising public awareness about
hazards, potential disasters and impact
mitigation is a major task of national
and subnational disaster management
offices, with considerable experience
and documentation on these types of
efforts available. See the document Public
Awareness and Public Education for
Disaster Risk Reduction: Key Messages
(International Federation of Red Cross and
Red Crescent Societies [IFRC], 2013) for
a starting point on public awareness and
impact mitigation.
13.4.11. Sport and leisure
In most cases, outdoor sports and leisure
activities would be cancelled based on SDS
forecasts and warnings. Due to the short
lead time and short duration for haboobs,
it can be useful to set up temporary
refuges (for example, in a sports hall)
so that people can avoid driving during
the immediate passage of a storm (see
chapter 13.4.12 on transport).
In any case, the organizers of outdoor
sports and leisure events during periods of
possible SDS should:
• be in contact with weather and
disaster management services to
get timely forecast and warning
information
• have plans on managing SDS events,
coordinated with local authorities as
needed
• have assessed and be prepared for
the immediate health impacts of SDS
on health-compromised individuals,
including training immediate
responders, stockpiling emergency
supplies, planning evacuations to
health facilities with local health
authorities and providing warnings
specifically for these individuals when
SDS are expected.
Indoor events are less likely to be directly
affected by SDS. However, plans should be
developed to:
• seal windows and other openings
outside
• open dust rooms at entry ways to
physically isolate dust from inside air
and to limit it from entering though
doorways
• use misters to reduce dust load at
entry ways and within large open
areas
• use air-conditioning systems to
frequently)
• use in-room air filter units to reduce
dust loads
• wet mop frequently to remove dust
from floors and other surfaces
• modify opening and closing schedules
to limit exposure to SDS
• identify how to adjust participants’
road transport plans to limit driving
in severe dust conditions, including
driving at night when dust can have
the same impact as fog on visibility.
13.4.12. Transport
The transport sector has received
considerable attention with respect
to reducing the impact of SDS. For air
transport, civil aviation regulations,
company operation procedures, advances
in technology and improved SDS
forecasting and modelling have been
generally effective in reducing the risk
posed by SDS in their various forms (see
Baddock et al., 2013, for an example from
Australia).
The greatest risk to air transport
likely comes from aircraft flying into
unanticipated SDS conditions (such
as haboobs or the Harmattan front)
and attempting to land with limited
visibility. This seems less likely to occur
with scheduled air services, which
are supported by dedicated weather

services, and more likely with private or
small commercial operations, based on
experiences in the Sahel.
Specific measures to reduce the impact of
SDS on aircraft (and their users) include:
• using forecasts to identify whether
SDS are possible at the destination or
on-route
• deciding not to fly to a destination
where SDS may occur during the flight
or close to the expected landing
• landing in advance of forecasted SDS
or at an alternative airport where SDS
conditions are severe at the intended
destination
• plugging or covering vents, intakes
and tubes to prevent dust from
entering and sealing windows and
doors, if possible
• ensuring that all intakes are clear of
dust, plugs and covers before starting
the aircraft
• vacuuming the inside of the aircraft
after SDS to improve hygiene, limit
secondary dust entrapment, reduce the
need to replace air filters and reduce
impacts on sensors and instruments
(adapted from SKYbrary, 2019).
Conditions similar to those found in SDS
can also develop for helicopters in the final
stages of landing or on taking off from
unimproved landing sites (for example,
no pavement). These “brown-out” events
are the result of the helicopter blades
causing dust, sand and other small items
to become airborne when the aircraft is
very close to the ground. These events can
cause pilot disorientation and difficulty in
landing (Rash, 2006).
Ways to address this problem include:
• pilots being ready to use instrument
landing procedures when brown-out is
expected
• covering the landing area with a
chemical treatment to prevent dust,
sand and debris
• watering the area where an aircraft will
land to remove conditions that allow
dust and sand to be entrained in the
downdraft from the aircraft (adapted
from Rash, 2006).
Overall, the challenge in reducing the
impact of SDS on road transport is
significant. The greatest risk to this
transport likely comes from haboobs
or locally-blowing dust associated with
agriculture (for example, ploughing fields).
Impact mitigation for road transport
includes the following:
• risk assessments and the
identification of specific SDS source
areas and times of year (this applies
to both haboobs and dust from
agricultural activities, which can be
time- and location-specific)
• public awareness (see chapter
13.4.10), including posting signs in
possible SDS locations
• planning, including annual awareness
campaigns, site mitigation measures
(such as sand fences) and response to
forecasts and warnings
• information collection, research and
source mitigation plans to reduce
long-term risk and improve the
understanding of local conditions that
can generate SDS
• site-specific warning messages,
safety patrols and traffic controls (for
example, warning lights or changes to
speed limits when SDS are forecast).
An example of these steps comes from
Arizona in the United States of America,
where the National Weather Service and
state and local authorities have developed
a programme to collect research on SDS,
disseminate the information to at-risk
populations, use the information in impact
and source mitigation and develop public
awareness on how to manage SDS while
driving. Information on the Arizona effort
can be found at:
• Arizona Emergency Information
Network, Dust Storms: https://ein.
az.gov/hazards/dust-storms
• National Weather Service, Dust Storm
Workshops: https://www.weather.gov/
psr/DustWorkshops
• City of Phoenix, Storms and
Monsoons: https://www.phoenix.gov/
emergencysite/Pages/Storms-and-
Monsoons.aspx

342
• Monsoon Safety, Thunderstorms
and Dust Storms: http://www.
monsoonsafety.org/facts/dust-storms.
htm.
The Arizona programme also includes a
public information video titled Pull Aside,
Stay Alive.7
In addition, the Arizona State Department
of Emergency and Military Affairs has
developed an SDS video on the theme of
preparedness, taking action and being
informed, which includes specific guidance
on what to do when driving near or into
SDS, as well as other impact mitigation
advice.8
13.4.13. Water and sanitation
SDS impacts on water quality are expected
to primarily result in an increased sediment
load as dust settles on water supplies. The
impact is expected to be larger the greater
the surface area of water covered by dust.
Reducing the impact of dust will
require water filtration both at the
water supply systems level and the
individual (household) level for water
storage containers. The need to filter
SDS-contaminated water may reduce
the throughput of large-scale treatment
operations and increase the quantity and
cost of deflocculating (pre-filtering removal
of impurities) from the water. Filtering SDS-
contaminated water at the household level
may not be needed (for example, if the level
of contamination is small) or can be done
using normal water filters.
Efforts to remove dust from water supplies
may be justified based on chemical or
biological contaminants transported on
or with dust. This risk should be assessed
before SDS events.
7 Available at http://www.pullasidestayalive.org/.
8 The video is available at https://youtu.be/X3qw5kr51eE and is presented in sign language as well as spoken
word with images.
If needed, measures for cleaning large and
small water supplies can be developed,
with public education on the need to clean
household water supplies incorporated into
the SDS public awareness process.
Some of the sanitation-related impacts
of SDS are likely to be addressed through
the measures described under the chapter
on hygiene (chapter 13.4.7). However,
based on actual SDS impacts and time and
resources available, SDS-related sanitation
measures will likely focus on:
• washing streets, sidewalks and public
areas to remove dust
• clearing accumulated sand from
drains and drainage systems (in urban
areas)
• increasing sewage treatment plant
operations to deal with additional
greywater produced from hygiene-
related activities (such as increased
washing of clothes, floor cleaning,
etc.).
13.5 Conclusions
There are a range of measures that can
be taken to prepare for and mitigate the
impacts of SDS. The selection of specific
measures needs to consider the type of
SDS that may occur, the extent to which a
warning is possible, and the nature of the
activities being undertaken when SDS may
occur. Where not yet already in existence,
SDS preparedness and response plans
ranging from the individual to national
levels should be developed as a normal
part of disaster risk management, based on
standard approaches to disaster planning.
In all cases, education about SDS and
impact mitigation measures should be
provided to anyone at risk, even if for a
short time, and should be supported by
warning and preparedness plans.

344
13.6 References
Akhlaq, Muhammad, Tarek R. Sheltami, and Hussein
T. Mouftah (2012). A review of techniques and
technologies for sand and dust storm detection.
Reviews in Environmental Science and Bio/
Technology, vol. 11, No. 3.
Al-Hemoud, Ali, and others (2019). Economic impact
and risk assessment of sand and dust storms
(SDS) on the oil and gas industry in Kuwait.
Sustainability, vol. 11, No.1.
Arizona Department of Transport (ADOT) (n.d.)
Pull Aside, Stay Alive. Available at http://
pullasidestayalive.org.
vol. 71.
Burritt, Benjamin E., and Albert Hyers (1981). Evaluation
of Arizona’s highway dust warning system.
Geological Society of America, vol. 186.
Day, Robert W. (1993). Accidents on interstate highways
caused by blowing dust. Journal of Performance
of Constructed Facilities, vol. 7, No. 2.
Ejeta, Luche Tadesse, Ali Ardalan, and Douglas Paton
(2015). Application of behavioral theories to
disaster and emergency health preparedness: a
systematic review. PLoS Currents, July 2015.
Hall, Kimberlee K. (2017). Emergency response planning.
In Planning and Managing the Safety System, Ted
S. Ferry, and Mark A. Friend, eds. London: Bernan
Press.
Hwang, Hee-Jae., Se-Jin Yook, and Kang-Ho Ahn (2011).
Experimental investigation of submicron and
ultrafine soot particle removal by tree leaves.
Atmospheric Environment, vol. 45, No. 38.
International Federation of Red Cross and Red Crescent
Societies (IFRC) (2013). Public Awareness and
Public Education for Disaster Risk Reduction: Key
Messages. Geneva. Available at https://www.
ifrc.org/PageFiles/103320/Key-messages-for-
Public-awareness-guide-EN.pdf.
Janhäll, Sara (2015). Review on urban vegetation and
particle air pollution – deposition and dispersion.
Atmospheric Environment, vol. 105.
Lafortezza, Raffaele, and others (2009). Benefits and
well-being perceived by people visiting green
spaces in periods of heat stress. Urban Forestry
& Urban Greening, vol. 8, No. 2.
Merrifield, Alistair, and others (2013). Health effects
of the September 2009 dust storm in Sydney,
Australia: did emergency department visits and
hospital admissions increase? Environmental
Health, vol. 12, No. 1.
Middleton, Nicholas, Peter Tozer, and Brenton Tozer
(2018). Sand and dust storms: underrated natural
hazards. Disasters, vol. 43, No. 1.
National Academies of Sciences, Engineering, and
Medicine (2018). Emergency Alert and Warning
Systems: Current Knowledge and Future Research
Directions. Washington, D.C.: The National
Academies Press.
Rash, Clarence E. (2006). Flying blind. Aviation Safety
World. December 2006. Alexandria, Virginia:
Flight Safety Foundation.
SKYbrary, 2019. Sand storm, 4 April. Available at https://
www.skybrary.aero/index.php/Sand_Storm.
State of Oregon (2015). Oregon Natural Hazards
Mitigation Plan 2015.
Tozer, Peter, and John Leys (2013). Dust storms – what
do they really cost? The Rangeland Journal, vol.
35, No. 2.
and Physics, vol. 14, No. 7.
Wen, Xiao-Jun, Lina Balluz, and Ali Mokdad (2009).
Association between media alerts of air quality
index and change of outdoor activity among
adult asthma in six states, BRFSS, 2005. Journal
of Community Health, vol. 34, No. 1.
World Meteorological Organization (WMO) (2015).
WMO Guidelines on Multi-hazard Impact-based
Forecast and Warning Services. Geneva.

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