Estimating Groundwater Recharge in Areas with Little Data and Water Scarcity: A Case Study of Yobe State, Nigeria

Groundwater recharge estimation is critical for sustainable groundwater management, particularly in data-scarce and water-stressed environments. This study applies an observation-constrained Land Surface Model (LSM) approach using NASA’s Global Land Data Assimilation System (GLDAS) Noah product (2004–2024) to estimate groundwater recharge in Yobe State, Nigeria. Spatial and temporal analyses of surface runoff, subsurface runoff, precipitation, and soil moisture were conducted alongside Land Use Land Cover (LULC) assessments (2020 to 2024). Results show that recharge constitutes only 5 to 15% of annual rainfall, with pronounced spatial heterogeneity: higher recharge rates occur in the south eastern cropland and floodplain zones, while the arid northwest experiences minimal recharge. The findings highlight that rainfall alone is not a sufficient predictor of groundwater replenishment; instead, recharge is strongly controlled by soil texture, infiltration, and land cover dynamics. This cost effective approach demonstrates the applicability of satellite-based LSMs in semi-arid regions with limited hydrological data and provides a scalable framework for sustainable water resource management in Nigeria and similar settings.