Futuristic flood risks assessment, in the Upper Vellar Basin, integrating AHP and bivariate analysis

Abstract

Flood susceptibility maps provide invaluable information for assessing and managing flood-prone areas, aiding in proactive planning, risk reduction strategies, and safeguarding vulnerable communities. The current research concentrates on advancing sustainable development practices by undertaking a comprehensive assessment of flood susceptibility in the Upper Vellar basin, with a projection for 2050. Employing an integrative methodology, this study utilizes an Analytical Hierarchy Process (AHP) and Bivariate Analysis. Nine critical parameters were used: elevation, distance from the river, distance from the road, drainage density, predicted LULC, projected precipitation, slope, soil type, and Topographic Wetness Index (TWI). The Modules of Land Use Change Evaluation (MOLUSE) plugin, which uses Cellular Automata-Artificial Neural Network (CA-ANN), was employed to predict the LULC map for the year 2050. Furthermore, bias-corrected Coupled Model Intercomparison Project 6 (CMIP 6) EC EARTH 3 Model (GCM) RCP 4.5 and 8.5 projected precipitation data were employed. The resulting flood susceptibility zones are classified into three categories: low, moderate, and high, with proportions of 32.64%, 55.52%, and 11.84% for RCP 4.5, and 34.63%, 53.46%, and 11.91% for RCP 8.5, respectively, concerning the total area. In both scenarios, nearly 38% of the settlement area is at high flood risk. This study provides essential insights for policymakers and stakeholders, facilitating the formulation of sustainable strategies to address projected changes in land use, precipitation patterns, and flood susceptibility in the Upper Vellar region up to 2050.