ABSTRACT
Climate-driven water scarcity in Nigeria's Benue River Basin remains an urgent challenge amid increasing hydro-climatic variability and anthropogenic pressures. This study advances the regional understanding by integrating high-resolution hydro-climatic datasets (1990–2023) with a hybrid modeling framework that couples nonparametric trend analysis (Mann-Kendall, Sen’s slope) and machine learning diagnostics. Unlike previous studies, which predominantly relied on single-method analyses or generic baselines, our methodology employs nonlinear elasticity metrics and cross-basin statistical comparisons to uncover spatio-temporal patterns in water availability and deficit. Model robustness is validated through ablation studies, effect size estimates, and uncertainty quantification, providing statistically rigorous insights into the complex feedbacks between land use change, precipitation extremes, and river discharge. The findings contribute actionable knowledge for adaptive water management and policy formulation in data-limited regions, emphasizing the limitations and uncertainties inherent in hydro-climatic modeling for semi-arid West Africa.
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