Forecasting the Spatio-Temporal Evolution of Groundwater Vulnerability: A Coupled Time-Series and Hydrogeological Modeling Approach

Abstract

Proactive management of groundwater resources is hindered by the static nature of conventional vulnerability assessments, which provide only a single temporal snapshot and lack predictive capability. To address this limitation, we developed a coupled dynamic–spatial modeling framework to forecast the spatio-temporal evolution of groundwater vulnerability. The framework integrates a βSARMA time-series model for precipitation forecasting with an enhanced M-DRASTIC-LAaRd model, which incorporates Land use, Anthropogenic activity, and River network density, weighted via the Analytical Hierarchy Process (AHP) to better capture hydrogeological complexity. The βSARMA model consistently outperformed conventional SARIMA models across the five subregions of Beijing, achieving the lowest RMSE values (0.0832–0.1617) and MAE values (0.0922–0.1372), with an average RMSE reduction of 15.3% relative to the best SARIMA baseline. These results ensure highly reliable dynamic precipitation inputs for the time-varying Net Recharge (R) parameter. Model validation against historical observations yielded a coefficient of determination (R2) of 0.87, confirming the framework’s robustness and predictive accuracy. Applied to the Beijing metropolitan area (1980–2027), the model projects a marked spatial restructuring of groundwater vulnerability: high-vulnerability zones are expected to expand from 38.65% to 46.18%, while low-vulnerability areas will decline from 42.53% to 34.63%. Emerging “hotspots” are concentrated in the southern urban plains, where urbanization and reduced recharge converge. Overall, 27.9% of the region is predicted to experience intensified vulnerability, whereas only 11.5% will show improvement. This study advances groundwater vulnerability assessment from static mapping toward dynamic forecasting, providing a quantitatively validated and spatially explicit framework that supports more informed groundwater management under future environmental change.