Flood Risk Mapping in the River-Rima Basin, Kebbi State, Nigeria: A Geographic Information System-Based Approach

Abstract

Floods are a growing menace to human life, as well as to infrastructure and agricultural yields in emerging river floodplain regions. The River Rima floodplain is well known in Birnin Kebbi for its agricultural production, especially rice production, which is essential to local livelihood and food security. This study employs an integrated GIS and Analytic Hierarchy Process (AHP) approach to evaluate flood risk zones within the River Rima basin, Nigeria. Six geomorphological and hydrological factors (elevation, slope, drainage density, rainfall, soil type, and land use) were weighted through pairwise comparisons to generate hazard and vulnerability indices. The Flood Hazard Index  was accomplished. Settlement distribution was taken as a susceptibility criterion to create the Flood Vulnerability Index. By linking these maps with indices, a flood risk map was generated, which shows that the study area is divided into high (25.29%), moderate (29.64%), and low (45.07%) flood risk classes, indicating that more than half of the floodplain is at risk of floods. The research identifies six main flood hazard predictors: elevation (38.1%), drainage density (19.6%), slope (17.8%), mean annual rainfall (12.2%), soil type (6.4%), and land use (6.0%). This means that elevation and drainage density, which are geomorphological factors, play a major role in causing floods in the area. Urban expansion poses a great threat to settlements like Ambursa, Birnin Kebbi, and Dagere, whereas settlements like Makerah, Anguwar Kayi, and Maurida are located in the floodplain and are therefore very prone to the effects. Farmland and built-up land are the most at-risk elements, with 64.7% of farmland and 70.2% of built-up areas classified as highly or moderately vulnerable. Based on these findings, the study recommends incorporating land-use zoning, community-based flood preparedness, and relocating critical infrastructure. The implementation of long-term planning strategies demands both climate change projections and machine learning technologies for future flood modelling.