Flood Susceptibility and Potential Flood Risk Assessment in Afghanistan Using Morphometric and Socioeconomic Indicators

Afghanistan is highly vulnerable to climate-driven extremes because of its combination of rugged geography and socio-political instability. Frequent events of extreme precipitation, floods, and droughts pose severe socio-economic and environmental challenges. Floods are particularly destructive, yet national-scale potential flood risk in Afghanistan has not been systematically assessed, largely due to limited data and field access. This study addresses this gap by mapping flood susceptibility, vulnerability, and risk using remote sensing (RS) and geographic information systems (GIS) at both subbasin and provincial scales. We apply a hybrid approach that combines Principal Component Analysis (PCA) to identify key environmental, climatic, and socio-economic indicators with the Analytic Hierarchy Process (AHP) to derive consistent weights and reduce subjectivity in decision-making. The results show that the eastern and northeastern ssubbasins especially within the Panj-Amu and Kabul River basins, have the highest flood susceptibility due to intense precipitation, steep terrain, and efficient drainage. Vulnerability increases in the densely populated northern and northeastern provinces, where land-use change and socio-economic constraints elevate flood-related impacts. Overall, 31% and 20% of study areas are classified as Very High and High vulnerability zones, respectively. The composite potential flood-risk index identifies that approximately 24% and 22% of Afghanistan fall within Very High and High flood risk zones, concentrated in the northern and eastern provinces. Model performance, evaluated using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC), indicates strong agreement between mapped Very High/High risk zones and frequently flooded provinces, with the upper-threshold scenario yielding an AUC of 0.913. These findings support targeted resource allocation, mitigation planning, and disaster-risk reduction in data-scarce and conflict-affected mountain regions.