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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-103', Matthieu Lafaysse, 10 Aug 2023
    • AC1: 'Reply on RC1', Atabek Umirbekov, 24 Oct 2023
  • RC2: 'Comment on gmd-2023-103', Anonymous Referee #2, 21 Sep 2023
    • AC2: 'Reply on RC2', Atabek Umirbekov, 24 Oct 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Atabek Umirbekov on behalf of the Authors (22 Nov 2023)  Author's response   Author's tracked changes 
EF by Polina Shvedko (24 Nov 2023)  Manuscript 
ED: Publish as is (19 Dec 2023) by Fabien Maussion
AR by Atabek Umirbekov on behalf of the Authors (27 Dec 2023)  Author's response   Manuscript 
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.