Articles | Volume 18, issue 12
https://doi.org/10.5194/tc-18-5753-2024
https://doi.org/10.5194/tc-18-5753-2024
Research article
 | 
11 Dec 2024
Research article |  | 11 Dec 2024

Exploring how Sentinel-1 wet-snow maps can inform fully distributed physically based snowpack models

Bertrand Cluzet, Jan Magnusson, Louis Quéno, Giulia Mazzotti, Rebecca Mott, and Tobias Jonas

Viewed

Total article views: 3,063 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,585 414 64 3,063 60 118
  • HTML: 2,585
  • PDF: 414
  • XML: 64
  • Total: 3,063
  • BibTeX: 60
  • EndNote: 118
Views and downloads (calculated since 12 Feb 2024)
Cumulative views and downloads (calculated since 12 Feb 2024)

Viewed (geographical distribution)

Total article views: 3,063 (including HTML, PDF, and XML) Thereof 3,029 with geography defined and 34 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Nov 2025
Download
Short summary
We use novel wet-snow maps from Sentinel-1 to evaluate simulations of a snow-hydrological model over Switzerland. These data are complementary to available in situ snow depth observations as they capture a broad diversity of topographic conditions. Wet-snow maps allow us to detect a delayed melt onset in the model, which we resolve thanks to an improved parametrization. This paves the way to further evaluation, calibration, and data assimilation using wet-snow maps.
Share