Abstract Reliable estimation of groundwater recharge is essential for sustainable water resource management, as it underpins assessments of water availability, wellhead protection, contaminant transport, groundwater–surface water interactions, urbanization impacts, and aquifer vulnerability—yet national-scale assessments are particularly challenging in geologically complex regions like Ethiopia. This study presents a 250-m distributed recharge estimation by integrating three distinct methods—WetSpass-M, Baseflow Separation (BFS), and Chloride Mass Balance (CMB)—each offering unique insights due to differences in conceptual frameworks, assumptions, and input data requirements. The results revealed average annual groundwater recharge estimates of 73 mm (81 billion cubic meters) from WetSpass-M, 85 mm (51 billion cubic meters) from BFS, and 65 mm (72 billion cubic meters) from the CMB method across Ethiopia—each associated with a 95% confidence interval of ± 14 mm (16 BCM) for WetSpass-M, ± 13 mm (8 BCM) for BFS, and ± 13 mm (14 BCM) for CMB—derived through Monte Carlo-based uncertainty simulations. Spatial and temporal patterns of recharge were found to be strongly controlled by rainfall distribution, a conclusion further supported by the Cramér’s V correlation analysis. Hence, high rainfall basins (Abbay, Baro, and Tekeze) showed higher estimates—135–172 mm by WetsSpass-M and 139–182 mm by BFS; as well as rainy months (June–September)—10.5–18 mm by WetSpass-M and 3–14.2 mm by BFS. Interestingly, rainfall distribution had a dominant influence on recharge dynamics, outweighing soil texture effects—fine-grained soils exhibited higher recharge than coarse-textured ones due to preferential rainfall patterns. As Ethiopia’s first national-scale study combining multi-method approaches, high-resolution spatiotemporal analysis, and comprehensive datasets, this work significantly improves recharge estimation accuracy.
Degu et al. (Sun,) studied this question.