Unravelling flood risk in the Rel River watershed, Gujarat using coupled earth observations, multi criteria decision making and Google Earth Engine - Jodhani, Keval H.; Patel, Dhruvesh; Madhavan, N.; Gupta, Nitesh; Singh, Sudhir Kumar; Rathnayake, Upaka (Results in engineering, 2024)
Socioeconomic developments, ineffective drainage systems, and insufficient river control, all contribute to significant loss of property and life due to the constant threat of floods. Therefore, controlling flood threats across Rel River, Dhanera, Gujarat has become even more crucial due to floods causing strain throughout the area during monsoon season. The different 52 micro-watersheds were formed across the study region using earth observations for the estimation of flood hazards, vulnerability, and risk. The AHP-MCDM was employed to assign priority rank, weightage, and risk category for each micro watershed. The flood hazard zone was mapped and its vulnerability was characterized in different categories varying from very low to very high. The normalized weights of each factor i.e, hazard indicator (soil, elevation, slope, flow accumulation, rainfall) and vulnerability indicator (LULC, distance from the hospital, population density map) were estimated employing the AHP-MCDM technique whereas LULC along with most of other factors were derived from GEE. The integration of vulnerability and hazard indicators, provides insights into understanding the flood sensitivity, facilitating the preparation of the flood risk map. 20 micro-watersheds were susceptible to high to very high risk and covered an area of 213.15 km2 whereas 32 micro-watersheds were in the range of very low to moderate category which covers the area of 228.41 km2. Therefore, the integration of GEE and spectral indices for obtaining various hazard & vulnerability indicators, which were prioritized and ranked using AHP is a unique methodology, facilitating a robust evaluation of flood risk mapping. •Flood risk map using hazard and vulnerability indicators.•LULC map prepared using GEE and RF with kappa coefficient 0.95.•AHP-MCDM was employed to each indicator.•48 % of watershed area was in high to very high flood risk.