Articles | Volume 17, issue 2
Model description paper
02 Feb 2024
Model description paper |  | 02 Feb 2024

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

Atabek Umirbekov, Richard Essery, and Daniel Müller

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Cited articles

Armstrong, R. L., Rittger, K., Brodzik, M. J., Racoviteanu, A., Barrett, A. P., Khalsa, S.-J. S., Raup, B., Hill, A. F., Khan, A. L., Wilson, A. M., Kayastha, R. B., Fetterer, F., and Armstrong, B.: Runoff from glacier ice and seasonal snow in High Asia: separating melt water sources in river flow, Reg. Environ. Change, 19, 1249–1261,, 2019. 
Aschauer, J., Michel, A., Jonas, T., and Marty, C.: An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0, Geosci. Model Dev., 16, 4063–4081,, 2023. 
Awad, M. and Khanna, R.: Support Vector Regression, in: Efficient Learning Machines, Apress, Berkeley, CA, 67–80,, 2015. 
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309,, 2005. 
Bavera, D., Bavay, M., Jonas, T., Lehning, M., and De Michele, C.: A comparison between two statistical and a physically-based model in snow water equivalent mapping, Adv. Water Resour., 63, 167–178,, 2014. 
Short summary
We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.