DEM-based Methods for Flood Risk Mapping at Large Scale
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The document discusses methods for large-scale flood risk mapping using geomorphic-based techniques, emphasizing the need for simplified procedures due to challenges in data availability and quality in many regions. It presents the Geomorphic Flood Index (GFI) as an effective tool for identifying flood-prone areas, alongside the development of a QGIS plugin for practical application. Limitations and advantages of the method are also outlined, including its cost-effectiveness and utility in data-poor environments.
DEM-based Methods for Flood Risk Mapping at Large Scale
- 1. Metodi speditivi per la mappatura del rischio idraulico su larga scala DEM-based Methods for Flood Risk Mapping at Large Scale Salvatore Manfreda 27 giugno 2019
- 2. Flood events recorded during the period 1985-2010 G.R.Brakenridge, "Global Active Archive of Large Flood Events", Dartmouth Flood Observatory, University of Colorado, http://floodobservatory.colorado.edu/Archives/index.html.
- 3. https://www.researchgate.net/lab/Centre-for-Research-on-the- Epidemiology-of-Disasters-Debarati-Guha-Sapir Impact of Natural Hazards at global scale
- 4. Distribution of Natural Hazards (1994-2013) Floods have also become more frequent, rising from 123 per year on average between 1994 and 2003 to an annual average of 171 per year in the 2004-2013 period.
- 7. Motivation: Why Simplified Procedures? ü Cost; ü Time; ü Data and parameters not available for all areas. Poor density of gauging stations in some regions (Asia, Africa, Australia). (Herold and Mouton, 2011) Flood risk assessment in data poor environments poses a great deal of challenge. Hydrological and hydraulic models are the best approach for deriving detailed flood hazard maps, but they require large amounts of:
- 8. Geomorphic Procedures Basin morphology contains an extraordinary amount of information about flood exposure Does exist a physical attribute of the surface able to reveal if a portion of a river basin is exposed to flooding?
- 9. Best performing geomorphic descriptor: GFI Geomorphic Flood Index (A) hr ≈ aAr nH (B) Location under exam Nearest element of the river network along the flow path Flow path Ar (A) river basin representation; (B) cross-sectional view of the channel and floodplain.
- 10. Linear Binary Classification based on the GFI
- 11. Measures of Performances MODEL PREDICTION (Morphological method) EVENT NO EVENT GOLD STANDARD TRUTH (Standard map) EVENT TRUE POSITIVE FALSE NEGATIVE NO EVENT FALSE POSITIVE TRUE NEGATIVE
- 12. 1 basin in Ethiopia, AFRICA (Samela et al., 2016) 8 river basin in ITALY: (Manfreda et al.,2014,2015) Testing the reliability in different contexts Full continental U.S.A. (Samela et al., 2017a,b) Pan-European region (Tavares Da Costa et al., 2019)
- 13. 100-yr flood-prone areas for the continental U.S. according to the linear binary classifier based on the GFI. The large-scale map allows to see that the index produces a realistic description of the flood prone areas, with the possibility to extend the flood hazard information where the Federal Emergency Management Agency (FEMA) maps are lacking (grey areas). EXAMPLE OF APPLICATION The method has been implemented in a QGIS plugin called Geomorphic Flood Area Tool that can be downloaded from the repository: https://plugins.qgis.org/plugins/GeomorphicFloodIndex/
- 15. The plugin can bee downloaded for free from the QGIS repository and also from: https://github.com/HydroLAB-UNIBAS/GFA- Geomorphic-Flood-Area. QGIS plugin: Geomorphic Flood Area (GFA) tool
- 16. GFA TOOL
- 17. GFA TOOL
- 18. GFA TOOL
- 19. GFA TOOL
- 20. GFA TOOL
- 21. GFA TOOL
- 22. GFA TOOL
- 23. GFA TOOL
- 24. GFA TOOL
- 25. Geomorphic flood Index map
- 26. Calibration map
- 27. Geomorphic flood Areas map
- 28. Water Depth Estimate In addition to flood extent, the inundation depth is a key factor in many riverine settings for estimation of flood induced damages. Therefore, recently the GFI method has been further exploited to derive in a simple way an approximate, but immediate, estimate of the water surface elevation in a river and surrounding areas. Nepal, July 2018 Iran, April 2019
- 29. Water Depth Estimate (*e.g. Samela et al., 2017; Nardi et al., 2006; Engelund and Hansen, 1967; Ibbitt, 1997; Ibbitt et al., 1998; Knighton, 2014; Leopold et al., 1965; Leopold and Maddock, 1953; Li, 1974; McKerchar et al., 1998; Park, 1977; Rodriguez-Iturbe and Rinaldo, 1997; Smith, 1974; Whiting et al., 1999)
- 31. Water Depth Estimate (A) hr ≈ aAr n H WD (B) Location under exam Nearest element of the river network along the flow path Flow path Schematic description of the parameters used to derive the GFI and the water level depth estimated in a hypothetical cross-section:
- 32. Case study: Bradano River, Italy (outlet) Geomorphic Flood IndexDigital Elevation Model
- 33. Case study: Bradano River, Italy (outlet) Hydraulic model VS GFI method
- 34. Hydraulic model VS Geomorphic approach
- 35. Case Study: Bradano River (Italy) FLOOD EXTENT PERFORMANCES Calibrated threshold, τ Scale factor, a SENSITIVITY: True Positive Rate, RTP ERROR Type II: False Negative Rate, RFN SPECIFICITY: True Negative Rate, RTN ERROR Type I: False Positive Rate, RFP Objective function: RFP + RFN 1.014 0.3627 89% 11% 90% 10% 21% Table 1. Performances of the linear binary classification based on the GFI during Calibration. INUNDATION DEPTH PERFORMANCES Comparison over the 1D- domain of the hydraulic simulation Comparison within the 2D- domain of the hydraulic simulation Linear correlation coefficient, r 0.917 0.906 Root Mean Square Error, RMSE (m) 0.620 0.335 Table 2. Performance measures calculated comparing the water depths estimated using the Geomorphic Flood Index method and the FLORA-2D hydraulic model.
- 36. Flood risk as the conjunction of hazard and vulnerability As next step, we are working with stage – damage functions, which relate inundation depth to damage level.
- 37. Case Study: Romania INPUT DATA: üReference flood hazard study: pan-European 100-years flood hazard maps at 100m resolution derived by the Joint Research Centre (JRC) for river basins with an upstream area greater than 500 km2 (Alfieri et al., 2014); üDEM: 30-m resolution SRTM 1 Arc-Second Global elevation data. OUTPUT: Geomorphic flood-prone areas and expected inundation depth for Romania (30m resolution and delineation also in basins with a drainage area lower than 500 km2).
- 38. Flood Risk Map derived for Romania
- 39. Limitations Ø The method does not perform a hydrologic analysis (no use of precipitation); it is calibrated using a flood hazard map derived for a specific return period, and this is the only link to hydrology. Ø It does not solve the fluid hydrodynamic equations: it cannot describe the flood wave propagation; Ø It is based on two input data and is therefore dependent from the accuracy of those data: DEM (quality vertical accuracy, and resolution, pre-processing conditioning procedure) and flood hazard map (accuracy, size of the calibration area); Ø It is not able to consider the presence of transversal structures (e.g. bridges and culverts) and other man-made features, especially sub-grid scale features, for example embankments; Ø This simplified method produces errors, with a general tendency to overestimate both extent and water depth.
- 40. Advantages and Potentials Ø Low cost and simple data requirement: use of data freely available; Ø Computationally efficient method; Ø Ability to map at high resolutions large geographic areas both in data-poor and data- rich environments. Possibility to start from detailed flood studies existing for limited areas and extend and downscale the delineation over large undetermined areas, enhancing both spatial detail and coverage of flood hazard information. Suitable for applications in undetermined/unstudied areas, over large-scale basins, or minor tributaries, data-scarce regions and across political borders, providing information that, although approximate, may be of practical utility for preliminary assessment of expected flood damage, flood management and mitigation at regional/national/continental/global scale.
- 41. Related Publication Manfreda & Samela (2019). A DEM-based Method for a Rapid Estimation of Flood Inundation Depth. Journal of Flood Risk Management. Samela, Albano, Sole, & Manfreda (2018). A GIS tool for cost-effective delineation of flood-prone areas. Computers, Environment and Urban Systems. Tavares da Costa, Manfreda, Luzzi, Samela, Mazzoli, Castellarin, Bagli (2019). A web application for hydrogeomorphic flood hazard mapping. Environmental Modelling and Software. Manfreda, Samela, Troy, (2018). The use of DEM-based approaches to derive a priori information on flood-prone areas, in Flood monitoring through remote sensing, Springer Remote Sensing/Photogrammetry, 61-79. Samela, Troy, & Manfreda, (2017a). Geomorphic classifiers for flood-prone areas delineation for data-scarce environments. Advances in Water Resources. Samela, Manfreda, Troy, (2017b). Dataset of 100-year flood susceptibility maps for the continental U.S. derived with a geomorphic method. Data in Brief. Samela, Manfreda, De Paola, Giugni, Sole, & Fiorentino, (2016). Dem-based approaches for the delineation of flood prone areas in an ungauged basin in Africa, Journal of Hydrologic Engineering. Manfreda, Samela, Gioia, Consoli, Iacobellis, Giuzio, Cantisani, & Sole, (2015). Flood-Prone Areas Assessment Using Linear Binary Classifiers based on flood maps obtained from 1D and 2D hydraulic models, Natural Hazards. Manfreda, Nardi, Samela, Grimaldi, Taramasso, Roth, Sole (2014). Investigation on the Use of Geomorphic Approaches for the Delineation of Flood Prone Areas, Journal of Hydrology. Manfreda, Samela, Sole & Fiorentino (2014). Flood-Prone Areas Assessment Using Linear Binary Classifiers based on Morphological Indices, ASCE- ICVRAM-ISUMA 2014. Di Leo, Manfreda, Fiorentino, (2011). An automated procedure for the detection of flood prone areas: r.hazard.flood, Geomatics Workbooks n. 10 - "FOSS4G-it: Trento 2011". Manfreda, Di Leo, & Sole, (2011). Detection of Flood Prone Areas using Digital Elevation Models. Journal of Hydrologic Engineering. Thanks for your attention
- 42. Acknowledgements Caterina Samela Attilio Castellarin Ricardo Tavares da Costa Paolo Mazzoli Stefano Bagli Fernando Nardi