Investigating the spatiotemporal behavior of VIC model parameters over the Tibetan plateau via global sensitivity analysis and machine learning

The Tibetan Plateau (TP), known as the ‘Asian Water Tower’, poses significant challenges for hydrological modeling due to its complex cryospheric processes and parametric uncertainties. To address these challenges, we developed an integrated evaluation framework that combines spatiotemporal performance metrics with three global sensitivity analysis methods, based on the Variable Infiltration Capacity (VIC) model. Four alpine river basins were used as case studies to assess the impact of 33 parameters on nine hydro-energy variables across daily, seasonal, and spatial scales. Key drivers of spatial heterogeneity in parameter sensitivities were identified. The results indicate that snow albedo, leaf area index, and the soil drainage parameter broadly influence multiple processes. Runoff and baseflow sensitivities vary spatiotemporally. Random Forest-based SHapley Additive Explanations analysis reveals an east-west gradient in parameter sensitivity, driven by temperature, precipitation, and radiation. Two-step parameter optimization improves the average daily simulation efficiency and spatial consistency for land surface temperature and snow cover fraction by 33% and 30%, respectively, without compromising runoff accuracy. Transferring parameter sensitivities to similar basins confirms the framework's robustness and generalizability. This study underscores the importance of non-runoff parameters, enhances simulation performance, and provides insights into seasonal hydrological variability for more robust model applications across the TP.