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Advancing snow modelling with machine learning

07 February 2024

Snow modeling is often hampered by the availability of input and calibration data, which can affect the choice of models, their complexity, and transferability. We are pleased to introduce GEMS (Generalizable Empirical Model of Snow Accumulation and Melt), a machine learning based snow model that utilizes parsimonious inputs and demonstrates robust transferability across diverse climatic and topographic conditions. Learn more:

Umirbekov, Atabek ; Essery, Richard;  Müller, Daniel  (2024)  GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic driversGeoscientific Model Development  17(2): 911–929.