Flash flood-induced vulnerability and need assessment of wetlands using remote sensing, GIS, and econometric models

Abstract

Flash flood vulnerability mapping and livelihood vulnerability assessment are often considered essential elements of risk reduction strategies and act as indispensable initiatives. In line with the contention, an effort has been made to restrict flash flood vulnerability mapping and need assessment based on vulnerability classification at haor regions in Bangladesh using satellite remote sensing, geographic information system (GIS), and econometric models. First, flash flood vulnerability mapping was performed using a bivariate statistical regression-based frequency ratio (FR) model. The vulnerability mapping results revealed that wetlands from land use land cover (LULC), soil adjusted vegetation index (SAVI) (>0.65–0.94), drainage density (>0.0166–0.0219 m/m2), flooding depth (2.61–3.40 m), and rainfall (2243–2619 mm/6 months) have significant roles in flash floods occurring with selected factor weight values of 2.241, 1.935, 1.576, 1.391 and 1.032, respectively. Moreover, the outputs of vulnerability mapping were classified into very high (7.57%), high (22.56%), moderate (56.96%), low (12.34%), and very low (0.56%), covering areas of 1303.0, 3882.0, 9801.0, 2124.0 and 96.0 km2, respectively. Second, 40 sampled respondents from each vulnerable groups were interviewed to assess their vulnerability and coping strategies against flash floods using field survey data through the composite livelihood vulnerability index (CLVI). CLVI findings showed that the vulnerability differences among the five groups of respondents were relevant to vulnerability mapping and classification done in the remote sensing and GIS platform. The logit estimates explained that the age of the household head, household size, farming experience, educational status, occupation of the household head, farm size, proximity to the marketplace, and no. of earning members affect farmers' attitudes toward coping strategies at different levels significantly. Identifying the risk of a flash flood in providing farmers with accurate information, developing advanced risk management strategies, and providing agricultural credit and service provision, policymakers and research institutions may benefit from mapping and evaluating their livelihood vulnerability on a single platform.