Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 2
Articles | Volume 17, issue 2
https://doi.org/10.5194/gmd-17-911-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-911-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
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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Latest update: 08 May 2024
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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.
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