Fine‐Scale Characterization of Groundwater Recharge Efficacy Under Ecological Water Replenishment: An AI‐Enhanced Learning Framework Benchmarked Against Traditional Geostatistics

Ecological Water Replenishment (EWR) is critical for restoring depleted aquifers, yet quantifying its spatiotemporal impacts remains challenging. Leveraging multi‐source data sets and Light Gradient Boosting Machine (LightGBM), this study reconstructs high‐resolution (250 m) groundwater level dynamics in the Yongding River basin, Beijing, and quantifies feature importance. We find that the LightGBM simulations significantly outperforms traditional Kriging interpolation. While dynamic hydrological variables dominate model construction, static geographic factors are crucial for maintaining spatial consistency, effectively mitigating the limitations of purely data‐driven approaches. Results show that while EWR drives groundwater recovery, its efficiency exhibits distinct diminishing marginal returns. This study elucidates the spatiotemporal impacts of EWR, offering scientific insights to optimize water allocation strategies in water‐scarce regions.