Capturing Spatiotemporal Hydraulic Connectivity for Groundwater Level Prediction in Over-Exploited Aquifers: A Multi-Source Fusion Graph Learning Approach (MF-STGCN)

Accurate prediction of shallow groundwater levels is crucial for water resource management in over-exploited regions like the North China Plain, where intensive pumping has created non-steady flow fields with strong spatial hydraulic interactions. Traditional approaches—whether physical models constrained by parameter equifinality or machine learning methods assuming spatial independence—fail to explicitly characterize aquifer hydraulic connectivity and effectively integrate multi-source monitoring data. This study proposes a Multi-source Fusion Spatiotemporal Graph Convolutional Network (MF-STGCN) that represents the monitoring well network as a hydraulic connectivity graph, employing graph convolutions to capture spatial water level propagation patterns while integrating temporal dynamics through LSTM modules. An adaptive fusion mechanism quantifies contributions of natural drivers (precipitation, evaporation) and anthropogenic extraction to water level responses. Validation using 518 monitoring stations (2018–2022) demonstrates that MF-STGCN reduces RMSE compared to traditional time series models, with improvement primarily attributed to explicit modeling of spatial hydraulic dependencies. Interpretability analysis identifies Hebi and Shijiazhuang as severe over-exploitation zones and reveals significant response lag effects in the Handan-Xingtai corridor. This study demonstrates that spatial propagation patterns, rather than single-point temporal features, are key to improving prediction accuracy in over-exploited aquifers, providing a new data-driven paradigm for regional groundwater dynamics assessment and targeted management strategies.