Shifts in the Dominance of Climatic, Anthropogenic, and Landscape Drivers Explain the Spatial Variation in the Budyko-type Equation Parameter

Abstract. Hydroclimatic transition zones are critical hotspots of global environmental change, yet the spatial heterogeneity of their hydrological functioning remains poorly understood because of the complex interplay of natural and anthropogenic factors. In this study, we propose a machine learning-driven diagnostic framework to better understand the spatially divergent drivers of the Budyko parameter (ω) across 12 representative catchments in the semi-arid to semi-humid transition zone. By integrating principal component analysis with hierarchical clustering, we objectively identified three distinct hydrological functional zones. Four machine learning algorithms (XGBoost, RF, ANN, and SVM) were subsequently systematically benchmarked for each zone to select the optimal model, and Shapley Additive exPlanations (SHAP) analysis was performed to quantify the driving mechanisms. The results reveal a fundamental spatial shift in the dominant drivers of ω: C1 is dominated by climatic factors (53.52 %), C3 is dominated by anthropogenic factors (68.73 %), and C2 is jointly driven by climatic (37.24 %), anthropogenic (31.85 %), and landscape (30.91 %) factors. Specifically, the primary drivers for ω are temperature (T, 32.42 %) in C1, leaf area index (LAI, 24.59 %) in C2, and GDP (25.09 %) in C3. The critical thresholds for shifting the directional contribution of these factors were 8.17 °C, 1.16, and 25.8×10⁴ USD, respectively. Furthermore, the directional impacts of climatic, anthropogenic, and landscape drivers vary significantly across zones, with pairwise interactions exhibiting distinct patterns of synergy and trade-offs. This study demonstrates that local landscape characteristics and human activity patterns can override macroclimatic controls, providing support for spatially differentiated water resource management in climatic transition zones.