MSc Defence ppt_presnetation at ITC

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a case study ofa case study of Nakhon Ratchasima, Thailand
Spatial Modeling and Timely Prediction of SalinizationSpatial Modeling and Timely Prediction of Salinization
usingusing SAHYSMODSAHYSMOD in GIS environmentin GIS environment
Thesis Assessment Board
Prof. Dr.V. G.Jetten Chairman
Dr.T.W.J. Van Asch External Examiner
Dr.A. (Abbas) Farshad First Supervisor
Dr.D. B. (Dhruba) Pikha Shrestha Second Supervisor
Tsegay Fithanegest Desta
Applied Earth Sciences: GEOHAZARDS

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Presentation outlinePresentation outline
1.1. IntroductionIntroduction
Research ObjectiveResearch Objective
Research questionsResearch questions
1.1. The Study AreaThe Study Area
Salient featuresSalient features
1.1. Materials and MethodsMaterials and Methods
MaterialsMaterials
MethodsMethods
4.4. Result discussionResult discussion
Model calibrationModel calibration
Model validationModel validation
Salinity predictionSalinity prediction
4.4. Conclusion and RecommendationConclusion and Recommendation

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1.0 IntroductionIntroduction
1. Salt and Salinization
def. 1.salt is presences of excessive soluble salts in the root zone, while
2. salinization is process of its accumulation in root zone area,
Together threaten
 Biophysical ecology
 Crop physiological processes
 Biogeochemical processes of agricultural soils
 National socio-economy
2. General objective
To detect soil salinity change both in time and spaces dimension and to model
salinization as a process.
3. General research questions
Could soil salinity change be detected in spaces & time dimension? and
salinization be modelled as a process? Applied Earth Sciences: GEOHAZARDS

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2.0 The Study Area
Study area salient features
Name: Nong sung
1. Geographical
• 795858 – 821786 E
• 1659635 –1688797 N
• Elevation: 114 – 209 Meter amsl.
2. Administrative
• Province: Nakhon Ratchasima
• Region: Northeast
• Country: Thailand
3. Area size:816 km2
4. Climatic features
• Aver. Rainfall=1030mm/yr
• Aver. Temperature = 27o
c
5. Dominant soil and Rocks
• Sandy loam
• Mahasarakham formation

2.0 Layout of the study Area
5

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3.0 Literature review
The millstone literature reviews of the study:-
1. Greiner (1997) Saline soils are identified by their:-
• EC, SAR, ESP, and degree of acidity (pH) soil extract @ 250
C
• ECe > 4 mS/m, threshold for deleterious effects to occur
2. The findings of Slavich and Petterson (1993),
• EC1:X soil-water solutions, is > 2 to 3*higher than the FC H2
O
content
3. Oosterbaan (2005) SAHYSMOD model works based on electrical
conductive at filed capacity (ECfc) so needs conversions.
f = texture dependant conversion factor
ECe = standardized electrical conductivity(EC of saturation extract)
EC1:5 = Electrical conductivity of 1gm soil to 5 ml of distilled water.
ECe = f * EC1:5 ECFc = ECe*2

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4.0 Materials and Methods
1. Materials
Hardware
Gramin GPS 12X, pH meter, EC meter, Topographic map,
Geopedological map, DEM map and Aerial photo, lab
chemicals and buffer solutions and user manuals.
Software
ArcInfo Map, Erdas, ILWIS, ENVI,
SPSS, MS-Excel, MS-Word, and
SAHYSMOD
2. Breakdown of methods followed
Pre field work
At field work
Post field work

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4.1 Methods followed
Extrapolated

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0
20
40
60
80
100
120
140
160
180
200
0.908 1.816 7.264 175.75 0.454 2.27 11.21 3.99
Observed_ECFc[dS/m]
Simulated_ECFc[dS/m]
observed_ECFc[dS/m] Simulated_ECFc[dS/m]
y = 0.833x - 0.3642
R2
= 0.9999
0
20
40
60
80
100
120
140
160
0 50 100 150 200
Series1
Linear (Series1)
5.0 Model calibration and evaluation

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Determining of leaching efficiency
0
20
40
60
80
100
120
0.01 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
percentage of leaching efficency
ECFC(dS/m)
ECFC_0.1
ECFC_0.2
ECFC_0.3
ECFC_0.4
ECFC_0.5
ECFC_0.6
ECFC_0.7
ECFC_0.8
ECFC_0.9
ECFC_1
6.0 Sensitivity analysis
Model sensitivity analysis for drainage installation
0.00
1.00
2.00
3.00
4.00
5.00
6.00
1
3
5
7
9
11
13
15
17
19
21
Time
ECFC(dS/m)
Dd_1m Dd_1.5m Dd_2m calibrated line

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0
20
40
60
80
100
120
0.9 1.7 5.9 97.7 0.6 1.7 15.2 4.1
Observ_ECFc(dS/M)
Simulated_ECFc(dS/M)
Obse_Validation Simul_Validation
6.0 Validation analysis

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Root Zone Salinity
(dS/m)
Poly_ID
Observed Simulated
ME MAE RMS
1 0.91 0.15 0.76 0.76 0.76
2 1.66 1.38 0.28 0.28 0.28
3 5.90 4.90 1.00 1.00 1.00
4 97.70 81.10 16.60 16.60 16.60
5 0.55 0.09 0.46 0.46 0.46
6 1.66 0.27 1.39 1.39 1.39
7 15.18 12.60 2.58 2.58 2.58
8 4.14 3.44 0.70 0.70 0.70
Mean of Errors 2.97 2.97 1.72
Table 4 Mean error of measured Vs simulated root zone salinity of validation
7.1 Validation analysis …Mean of errors

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8.0 Model prediction Groundwater table-Salinity
Correlation between root zone salinity[Cr4] and Excess of
groundwater inflow overoutflow[Gaq]
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
25.00
30.00
0 2 4 6 8
Polygon nr.
Cr4(dS/m)andGaq(cm/season)
Cr4
Gaq

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8.1 Prediction cont…. Numerical Salinity result
Appendix 1 Predicted root zone salinity (dS/m)
Poly_ID Obsereved Year_0 Year_5 Year_10 Year_15 Year_20
(dS/m) average average average average average
1 0.91 0.15 1.42 5.47 10.75 16.70
2 1.82 1.51 3.28 6.58 10.00 13.40
3 7.26 6.03 1.48 0.42 0.19 0.13
4 175.75 146.00 40.35 30.05 27.85 26.65
5 0.45 0.07 4.58 4.55 4.44 4.32
6 2.27 0.36 0.17 0.13 0.13 0.12
7 11.21 9.30 20.50 19.70 18.75 17.85
8 3.99 3.31 10.45 9.82 9.24 8.71
Appendix 1 Predicted transtion zone salinity (dS/m)
Poly_ID Obse Year_0 Year_5 Year_10 Year_15 Year_20
(dS/m) average average average average average
1 0.91 0.91 1.34 1.87 2.19 2.41
2 1.82 1.82 1.70 1.59 1.56 1.57
3 2.27 2.27 2.53 1.90 1.31 0.89
4 154.47 154.00 37.30 29.45 27.60 26.45
5 0.91 0.91 4.60 4.53 4.41 4.29
6 3.18 3.18 2.13 1.44 1.00 0.72
7 19.95 20.00 20.40 19.55 18.55 17.65
8 6.46 6.46 10.35 9.70 9.12 8.61
Appendix 1 Predicted aquifer zone salinity (dS/m)
Polygon Obsereved Year_0 Year_5 Year_10 Year_15 Year_20
ID (dS/m) average average average average average
1 1.82 1.82 1.99 2.15 2.30 2.43
2 1.36 1.36 1.39 1.44 1.49 1.54
3 6.36 6.36 6.38 6.43 6.50 6.55
4 31.35 31.40 29.90 28.65 27.65 26.60
5 4.99 4.99 4.73 4.58 4.44 4.32
6 2.72 2.72 2.73 2.74 2.75 2.75
7 21.66 21.70 20.55 19.45 18.55 17.65
8 11.40 11.40 10.45 9.81 9.23 8.71

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8.3 Prediction spatial salinity distribution based on three aspects

Applied Earth Sciences: GEOHAZARDS16
8.4 DSS for extrapolation model prediction results
Table 6 Designed decision supporting system for extrapolation of model predicted attributes
DEM
< 192m
Slope
< 0.5%
LUS
2
VSS
Expert decision

Applied Earth Sciences: GEOHAZARDS17
8.5 Interim Conclusion
With successful model
• Calibration, sensitivity analysis,
• validation and evaluation works
 Able to predicate soil salinity perfectly and identify/model
1. Saline geopedologic units
2. Salinity rate per geopedologic unit per year.
3. Change of salinization in space and in time.
4. Main factor of salinization, Rise of saline GWT.
5. Biophysical factors that aggravate salinization :-
I. Hot climate condition, high ETO
II. Deforestation.
III. Uncontrolled irrigation practise
IV. Traditional salt making remnants

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8.6 Prediction spatial salinity distribution map for the three profiles

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9.0 Recommendation
1. salinity related
Reafforestation indigenous trees.
Agronomic packages:
 Adding organic matter
 Using resistance Varity
 Crop rotation
Installation of drainage network => 4 to 5 times
1. Model related
Area selection should be done with care.
Grid alignment limitation should be noted before use.
Output data reduction should be noted before use.
Integrating different factors of salinization is advisable.

I recommend a change detection research to be carried
1. The study area is large enough for bigger grid size
creation and alignment.
2. There are enough resources references materials
3. Salinization rate of the area is fast & destructive
 so it needs monitoring
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10.0 Concluding remark

MSc Defence ppt_presnetation at ITC

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