Global groundwater recharge estimation through hybrid modeling

Groundwater provides drinking water for billions and supports nearly half of irrigated agriculture, yet global renewable groundwater availability—quantified as groundwater recharge—remain highly uncertain. Here, we simulate global groundwater recharge using a hybrid model that seamlessly integrates machine learning with physical processes. The hybrid model substitutes machine learning for poorly represented hydrological processes while retaining established physical equations, such as water balance. By leveraging diverse Earth system observations—including streamflow-derived groundwater discharge, satellite-retrieved terrestrial water storage anomalies, and flux tower evapotranspiration—the hybrid model effectively integrates process knowledge with multi-source data constraints to improve the accuracy of global groundwater recharge simulations. Such integration may also deepen our process understanding of groundwater recharge.