Leveraging GIS-based multi-criteria analysis for flood-prone area detection in Nsanje District, Malawi.

Flooding remains one of the most severe and recurrent natural hazards in sub-Saharan Africa, posing persistent threats to rural livelihoods, infrastructure, and environmental stability. In Malawi, the Lower Shire River Valley, particularly Nsanje district, is highly vulnerable to recurrent flooding due to climatic variability, land-use change, and geomorphological characteristics. Despite the growing availability of geospatial data, localized flood vulnerability assessments remain limited, constraining effective disaster preparedness and land-use planning. This study applied a Geographic Information System-based multi-criteria analysis using the Analytical Hierarchy Process to identify flood-prone areas in Nsanje district, Malawi. Five key spatial parameters were integrated: land cover, precipitation, slope, proximity to water bodies, and elevation. The datasets included Sentinel-2 imagery, CHIRPS precipitation records, Shuttle Radar Topography Mission digital elevation data, and official hydrological data. The weighted overlay analysis revealed that land cover and precipitation were the dominant determinants of flood susceptibility, accounting for 59.4 percent and 21.0 percent of the total influence, respectively. The resulting flood susceptibility map showed a pronounced north-south gradient in vulnerability, with the southern corridor exhibiting the highest exposure to flooding. Traditional Authority Nyachikadza emerged as the most critically affected area, with more than 1,500 residents situated in high-risk zones, whereas Traditional Authorities Malemia and Tengani displayed moderate levels of vulnerability. The findings indicate that the combined effects of land-use practices, rainfall intensity, and terrain configuration strongly influence flood susceptibility in Nsanje. This study provides a cost-effective and replicable framework for flood risk assessment in data-scarce environments. By leveraging freely available satellite data and open-source geospatial tools, it offers actionable intelligence to support disaster management, spatial planning, and community resilience in flood-prone areas. This approach contributes to the broader sustainability agenda by informing climate adaptation strategies that are aligned with national and global commitments to disaster risk reduction and Sustainable Development Goals. The five-factor model used here should be read as a first-order, operationally feasible approximation of flood susceptibility; geology, soil permeability, and drainage density were not incorporated and are discussed as an explicit limitation, and the precipitation criterion reflects cumulative seasonal totals rather than storm-scale rainfall intensity.