Musa, Auwal Ahmad ， Mourad, Khaldoon A.
2026-06-02 INTERNATIONAL JOURNAL OF RIVER BASIN MANAGEMENT 2026 null(卷), null(期), (null页)
Water scarcity is a major challenge in semi-arid regions where limited water availability and increasing demand threaten populations and ecosystems. This study assessed seasonal water scarcity at the sub-watershed level in the Gongola River catchment, northeast Nigeria, using a Water Scarcity Index (WSI) based on naturalised water availability and sectoral water use for domestic, agricultural, and industrial activities. Seasonal WSI values were combined with population data to estimate exposure, while sensitivity tests evaluated uncertainty in hydrological and demand parameters. Three machine-learning models (Random Forest, XGBoost, and GBM) were applied to identify key scarcity drivers. Results show strong seasonal variability, with agricultural withdrawals causing acute deficits in SW 5, 6, 8, and 9 where water use exceeds availability. More than one million residents are exposed to moderate-to-severe scarcity, particularly during the Transition season. Machine-learning results identify irrigated area, precipitation, evapotranspiration, and soil texture as dominant controls. Overall, water scarcity in the Gongola catchment is demand-driven, spatially heterogeneous, and seasonally amplified.