Bridging the gap: An interpretable coupled model (SWAT-ELM-SHAP) for blue-green water simulation in data-scarce basins

Blue water (BW) and green water (GW) are crucial components of the hydrological cycle, but their accurate simulation and interpretation remain challenging in data-scarce basins. We propose the SWAT-ELM-SHAP model, coupling the Soil and Water Assessment Tool (SWAT), Ensemble Learning Model (ELM), and Shapley Additive Explanations (SHAP) method. This novel approach bridges the gap between a physically-based hydrological model, a data-driven machine learning (ML) model, and a holistically-interpreted SHAP method, offering accurate blue-green water simulation and holistic result interpretation for improved water resources management in data-scarce basins. We took the transfer simulation of blue-green water from the Xiangjiang River Basin (source basin) to the Zishui River Basin (target basin) as a case study to test and evaluate the feasibility of the coupled model during 1991–2022. The model performance results indicate that the simulation accuracy of our new coupled model is improved in data-scarce basins. In combination with hydrological response features generated by SWAT and meteorological features as the ELM input, our model enhances the daily blue-green water simulation. The Nash-Sutcliffe Efficiency coefficient (NSE) for BW, Green water flow (GWF), and Green water storage (GWS) consistently exceeds 0.77 during the calibration period (1991–2010) and exceeds 0.8 during the testing period (2011–2022). The interpretation results of coupled model demonstrate that SHAP holistic interpretation provides good interpretability for blue-green water simulation results in data-scarce basins. In general, the SWAT-ELM-SHAP offers a referenced approach that can reliably and efficiently simulate blue-green water in data-scarce basins, but more importantly, can further our understanding of the potential causal relationships, influence mechanisms, and variation mechanisms of blue-green water under changing environmental conditions.