GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers

Abstract

Abstract. Snow modelling is often hampered by the availability of input and calibration data, which can affect the choice of models, their complexity, and transferability. To address the trade-off between model parsimony and transferability, we present the Generalizable Empirical Model of Snow Accumulation and Melt (GEMS), a machine-learning-based model, which requires only daily precipitation, temperature or its daily diurnal cycle, and basic topographic features to simulate snow water equivalent (SWE). The model embeds a support vector regression pretrained on a large dataset of daily observations from a diverse set of the SNOwpack TELemetry Network (SNOTEL) stations in the United States. GEMS does not require any user calibration, except for the option to adjust the temperature threshold for rain–snow partitioning, though the model achieves robust simulation results with the default value. We validated the model with long-term daily observations from numerous independent SNOTEL stations not included in the training and with data from reference stations of the Earth System Model–Snow Model Intercomparison Project. We demonstrate how the model advances large-scale SWE modelling in regions with complex terrain that lack in situ snow mass observations for calibration, such as the Pamir and Andes mountains, by assessing the model's ability to reproduce daily snow cover dynamics. Future model improvements should consider the effects of vegetation, improve simulation accuracy for shallow snow in warm locations at lower elevations, and possibly address wind-induced snow redistribution. Overall, GEMS provides a new approach for snow modelling that can be useful for hydroclimatic research and operational monitoring in regions where in situ snow observations are scarce.