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

Umirbekov, Atabek; Essery, Richard; Müller, Daniel

doi:https://doi.org/10.5194/gmd-17-911-2024

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

Data sets

SM-SnowMIP meteorological and evaluation datasets at ten reference sites (in situ and bias corrected reanalysis data) Cecile Menard and Richard Essery https://doi.org/10.1594/PANGAEA.897575

CHELSA-W5E5 v1.0: W5E5 v1.0 downscaled with CHELSA v2.0 Dirk N. Karger et al. https://doi.org/10.48364/ISIMIP.836809.3

Model code and software

GEMS: Generalizable empirical model of snow accumulation and melt (version 1.0) A. Umirbekov et al. https://doi.org/10.5281/zenodo.10161423

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.