Spatiotemporal dynamics of flood susceptibility under future precipitation variability, population growth, and land cover change

Flood risk in mountainous regions is expected to intensify under the compounding effects of climate change, population growth, and land cover changes. However, there is limited understanding of how these interacting factors will shape future flood risk, particularly in the transboundary and ecologically sensitive Kabul River Basin (KRB). The present study addresses this critical gap by assessing the spatiotemporal patterns of projected flood susceptibility in the KRB from 2020 to 2100 under different future scenarios. Future flood susceptibility was predicted using an eXtreme Gradient Boosting (XGBoost) machine learning model with three dynamic and nine static predictors. The findings indicate a significant shift in flood susceptibility over time. Specifically, the areas classified as “Very Highly” susceptible increased from 11.78% in 2020 to 12.17% in 2040, 14.44% in 2060, 13.32% in 2080, and 13.51% by 2100, while the areas classified as “Very Low” susceptibility steadily declined from 66.17% in 2020 to 56.43% by 2100. The XGBoost model showed strong predictive accuracy (AUC: 0.961–0.962) and high cross-temporal consistency across future scenarios (Correlation: 0.75–0.85), confirming its suitability for flood susceptibility assessment. Bootstrap uncertainty analysis further supported its robustness, with mean AUCs of 0.9817–0.9834, very low standard errors (0.0003), and narrow confidence intervals (0.9719–0.9887). These results underscore the need to integrate dynamic environmental and demographic changes into flood management strategies in KRB. The research offers a transferable outline for flood assessment in climate-sensitive mountainous regions. It provides actionable insights for land use planning and climate adaptation policy aimed at reducing future flood impacts.