Dynamic Assessment of Water Ecosystem Service Value in the North China Plain and Study of Its Multidimensional Driving Mechanisms

This study investigates the spatiotemporal dynamics and driving mechanisms of Water Supply Ecosystem Service Value (ESV) in the North China Plain from 2002 to 2022. Addressing the critical challenges of water scarcity and ecological degradation in this densely populated and agriculturally intensive region, the research develops an integrated framework to quantify the relative contributions of multi-dimensional drivers to the water supply service (quantified by biophysical supply, W). A Particle Swarm Optimization (PSO) algorithm was employed to automate hyperparameter tuning for XGBoost and Random Forest models, with model interpretability enhanced via SHAP (SHapley Additive exPlanations) to elucidate non-linear feature importance and directional impacts. Results demonstrate that the PSO-XGBoost model outperforms PSO-Random Forest in predictive performance (R2 = 0.8013 vs. 0.7443). The total water supply exhibited a significant annual decline of 1.98 billion m3 (p < 0.05), with 53.4% of the study area showing significant pixel-level temporal trends. The supply structure is dominated by soil moisture (80–90%), while externally transferred water, despite increasing rapidly, exhibits high interannual variability. SHAP analysis identifies vegetation cover (NDVI), clay content, GDP, and population density as the predominant drivers. Notably, GDP shows a strong negative correlation with water supply, reflecting a trade-off where intensive socio-economic expansion increases water consumption at the expense of ecosystem supply capacity. Methodologically, the PSO-XGBoost-SHAP framework enables both high predictive accuracy and detailed attribution of driving factors. These findings highlight the strategic importance of soil water (“Green Water”) conservation and offer actionable insights for adaptive water resource management, providing a replicable analytical approach for other regions facing similar hydrological challenges.