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Application of Remote Sensing and GIS for Flood Risk Analysis: A C ase Study at Kalu‐Ganga River, Sri Lanka. Participating Agencies. Arthur C Clarke Institute for Modern Technologies Sri Lanka -Dimuth Weliwitiya. University of Peradeniya Sri Lanka -Hemali K. Nandalal.
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Application of Remote Sensing and GIS for Flood Risk Analysis: A Case Study at Kalu‐Ganga River, Sri Lanka
Participating Agencies • Arthur C Clarke Institute for Modern Technologies • Sri Lanka • -Dimuth Weliwitiya University of Peradeniya Sri Lanka -Hemali K. Nandalal • Survey Department • Sri Lanka • -Jayantha Samarasinghe
Objectives • The main objective of this Mini Project is to analyze the flood risk in lower Kalu-Ganga River Basin with respect to the Population and Physical vulnerability in support of flood relief and mitigation. • Specific objectives are to : • Analyze and map the flood hazard in the Kalu-Ganga River basin • Analyze the flood vulnerability in the area
Data Collection Field Visit
Data Processing…… Databasing Topo 10 k sheet Terrain Buildings Land Use Hydro Road Utility Admin Hydro met Data LiDAR • Censes 2001 Demographic • Population • Age • Household ALOS PALSAR
Data Processing…… TIN Model of the Study area Contour Data LIDAR DATA
Methodology……. Data Collection/ Data basing Vulnerability Analysis Hazard Analysis Risk Analysis
Methodology : Hazard Analysis Rainfall –Runoff Modeling
Rainfall-Runoff Modeling Rainfall –Runoff Modeling Basin model developed using HEC-GeoHMS
Rainfall-Runoff Modeling Rainfall –Runoff Modeling Results of the verification runs to compare the simulated flows and the observed flows of the calibrated HEC-HMS model
Flood Modeling Flood Modeling
Results HEC-RAS out put
Results…(Flood Hazard Maps) 100 yr 50 yr 20 yr 10 yr
Comparison: Flood Modeling and Satellite Data Verification of model result by satellite data
Satellite Data • Raw Images (Dry Date, 03 March,2008) ALOS – PALSAR (HH) • Raw Images (Wet Date, 03 June,2008) ALOS – PALSAR(HH)
Satellite Data Verification of model result by satellite data • Filtering – After Applying Lee 3 times with Threshold 0.29,0.14,0.07 • Dry date image Wet date image
Model Result and Satellite Data Verification of model result by satellite data Comparison of the Flood extent derived from HEC-RAS model and Satellite image
Comparison of Model Result and Satellite Data b. Hazard Analysis Verification of model result by satellite data
Vulnerability Analysis Household Vulnerability Analysis… Design of Questionnaire
Vulnerability Analysis HH Sampling Cluster Distribution Household Vulnerability Analysis Sample cluster
Vulnerability Analysis HH Sampling Cluster Distribution Household Vulnerability Analysis Sample cluster
Gender of Residents Vulnerability Analysis Demographic Standing Age Groups Household Vulnerability Analysis Land Characteristics Sensitivity Health Condition Flood Vulnerability analysis VULNERABILITY Rural Standing Water Resources Educational Background Adaptive Capacity Economic Strength& Resilience Exposure Position Relative to River Previous Flood Events Assets
Vulnerability Analysis Household Vulnerability Analysis Sub-Component Index calculation (e.g., Demographic Standing etc.) Major-Component Index calculation (e.g., Sensitivity, Exposure, Adaptive Capacity) Contributing Factor calculation (Hahn a,Riederer & Foster ,2009)
Vulnerability Analysis Household Vulnerability Analysis Contributing Factor calculation Flood vulnerability Index for Each Household (Hahn a,Riederer & Foster ,2009)
Vulnerability Analysis Household Vulnerability Analysis Flood Vulnerability Distribution of the Sample
Vulnerability Analysis Household Vulnerability Analysis Using the vulnerability index values households are categorized into 3 groups as follows
Vulnerability Analysis Household Vulnerability Analysis Flood Vulnerability for 100yr RP Event in Kaluthara District
Vulnerability Analysis Household Vulnerability Analysis Flood Vulnerability Triangle
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