A New Global Deep Learning Framework to Generalize Groundwater Simulation Across Hydrogeological Diversity and Anthropogenic Influence

Groundwater levels integrate the combined effects of climate variability, surface–subsurface interactions, and anthropogenic activities. Capturing their temporal dynamics across diverse hydrogeological settings and varying degrees of human influence remains a major scientific challenge, particularly in regions where physical descriptors and anthropogenic forcing data are scarce or uncertain. This study investigates whether a single deep learning model can generalize groundwater level simulations across a large number of hydrogeologically contrasted monitoring stations in metropolitan France. The proposed framework relies on long-term time series of groundwater levels and meteorological forcings (precipitation and temperature), collected from the French groundwater  monitoring network and meteo-france SAFRAN reanalysis. Climate-driven groundwater dynamics is first learned from meteorological inputs only, and the architecture of the Deep Learning model is subsequently extended to account for anthropogenic influences by incorporating groundwater pumping data where available, despite their sparse and uneven spatial coverage. This strategy enables the integration of human-induced forcing while maintaining consistency with climate-driven groundwater behavior under heterogeneous spatio-temporal water abstraction data availability. The results show the ability of the proposed framework to reproduce temporal groundwater dynamics across a wide range of hydrogeological contexts and degrees of anthropogenic influence. They also highlight the relevance of the approach for developing scenarios of regional-scale groundwater evolution under changes in climate conditions and water uses.