Spatiotemporal Patterns and Nonlinear Drivers of Water Yield in Inner Mongolia

Water yield is a key indicator for regional water resource assessment and directly concerns multidimensional socio-ecological sustainability. However, in arid and semi-arid regions, integrated long-term water yield simulation and nonlinear interpretation of driving factors remain insufficient. Therefore, Inner Mongolia was selected to analyze the spatial pattern and nonlinear driving mechanism of water yield depth for sustainable water resource management. Based on the InVEST model, water yield depth during 2001–2024 was simulated, and trend analysis was conducted. Annual XGBoost models with SHAP were used to explain nonlinear driver effects. Results showed a significant east-high and west-low pattern, with significantly increasing and decreasing areas accounting for 12.35% and 4.5%, respectively. Precipitation was the dominant driver, with higher ∣SHAP∣ values in wet years than in dry years. Zonal SHAP showed Pre led in all zones (48.8%, 63.5%, 37.7%), with secondary drivers shifting from forest/topography in the East to temperature in the West. SHAP values increased rapidly after precipitation exceeded thresholds of 200–300 mm in dry years and 400–500 mm in wet years. Under high precipitation, precipitation–non-forest interactions increased rapidly, whereas forest interactions changed little or became negative, showing a scissor-like divergence pattern. XGBoost reproduced the InVEST-simulated water yield depth well (R2 = 0.91 ± 0.03). This workflow provides a reproducible pathway for water resource assessment in arid and semi-arid regions.