Snow-model parameter identifiability across space and time.

Accurate modeling of Snow Water Equivalent (SWE) and snow depth significantly improves hydrologic forecasts, particularly in mountains area, thus supporting a more sustainable resource management. In balancing the need for detailed process representation with computational efficiency, snow models often rely on ill-defined, or very site specific parameters that hamper upscaling and transferability. Here, we focus on the identifiability and stability of such parameters by comparing a traditional temperature-index modeling approach with a hybrid temperature- and radiation-index method. Analyses were performed across 7 locations around the world. A hybrid temperature -radiation modeling approach reduces calibration variability while improving the simulation accuracy of snow water resources. Although requiring additional parameters, this method ensures higher parameter transferability and stability, making it more suitable for operational applications. The insights derived from this study offer a significant step ahead towards advancing operational snow hydrology modeling.