Assessment of flood vulnerability and identification of flood footprint in Keleghai River basin in India: a geo-spatial approach

Anirban Roy and Srabendu Bikash Dhar ()
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Anirban Roy: Govt. General Degree College
Srabendu Bikash Dhar: Trivenidevi Bhalotia College

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 5, No 35, 4853-4874

Abstract: Abstract The present study attempts to identify the flood footprint of river Keleghai, a tributary of river Haldi, in West Bengal, India. Keleghai basin is known for recurrent flooding that causes severe damage to the socioeconomic infrastructure. So far, no attempt has been made for the identification of flood inundation footprints and flood risk zones in Keleghai river basin through remote sensing and multi-criteria decision-making process. Initially, a flood susceptibility or vulnerability map has been prepared, and secondly, flood footprints have been identified in the said river basin. For the preparation of flood vulnerability map with the help of the analytical hierarchy process (AHP), the elevation, slope, rainfall, normalised difference vegetation index (NDVI), land use and land cover (LULC) and distance from river and topographical wetness index (TWI) of the concerned river basin have been used. To prepare the flood footprints synthetic aperture radar (SAR), data have been processed on Google Earth Engine (GEE) platform. The result shows that more than 50% of the basin area belongs to high risk zone, and the other 40% comes under the moderate risk category. The central, northern and eastern parts of the basin present the highest susceptibility to flood hazard. This area is characterised by moderate-to-low elevation, gentle slope, moderate rainfall and less vegetative cover. This outcome can effectively be utilised in hazard management purpose for Keleghai as well as other river basins. This study will help in identifying the most vulnerable zones of the basin in terms of flood hazard assessment. On the other hand, correlating the empirical model with the real world data will provide excellent opportunity to testify the applicability of the model in decision-making purpose that could lead to a way of resilience.

Keywords: Flood hazard assessment; Topographical wetness index; Google Earth Engine (GEE); Multi-criteria decision-making (MCDM); AHP; Synthetic aperture radar (SAR) data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06411-9

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