An integrated physics-based–machine learning framework for groundwater head prediction in a highly managed multi-aquifer watershed

Study region Lower Apalachicola–Chattahoochee–Flint (ACF) Basin, Southwestern Georgia. Study focus Predicting hydrogeological processes in complex, heterogeneous multi-aquifer systems remain challenging, especially where intensive agricultural pumping strongly alters groundwater dynamics. This study developed an integrated MODFLOW–machine learning (ML) framework to simulate and forecast spatiotemporal groundwater head variations, drought occurrences under varying climatic and anthropogenic stress conditions. New hydrological insights Among the evaluated ML models, the MODFLOW-XGB exhibited superior performance across 85 observation wells, achieving an RMSE of 0.92 during the testing phase less than one-fourth of the standalone MODFLOW RMSE (3.77). Based on validation metrics, the ML models were ranked in descending order of performance as XGB, RF, GPR, DT, SVM, and ANN. The optimized MODFLOW–XGB model forecasted maximum seasonal groundwater declines ranging from 0.23 to 5.26 m across 30 observation wells, with the largest drops (>2 m) observed in regions of intense irrigation underscoring strong anthropogenic influence. Groundwater drought index revealed spatially variable drought frequency (15–20%) in confined areas with limited recharge, driven by low-conductivity Upper Semi-Confining Layer (USCU). Wells beneath thin USCU experiencing sharp short-term declines during periods of intensive abstraction and limited recharge. Modified Mann-Kendall test and Sen’s slope estimator depicted that no significant long-term trend in groundwater drought but seasonal decline in groundwater was observed. The research reveals the prospective of the coupled framework for real-time groundwater forecasting, early drought warning, and targeted watershed management.