Articles | Volume 18, issue 7
https://doi.org/10.5194/tc-18-3253-2024
https://doi.org/10.5194/tc-18-3253-2024
Research article
 | 
22 Jul 2024
Research article |  | 22 Jul 2024

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Tate G. Meehan, Ahmad Hojatimalekshah, Hans-Peter Marshall, Elias J. Deeb, Shad O'Neel, Daniel McGrath, Ryan W. Webb, Randall Bonnell, Mark S. Raleigh, Christopher Hiemstra, and Kelly Elder

Viewed

Total article views: 2,102 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,635 405 62 2,102 33 79 64
  • HTML: 1,635
  • PDF: 405
  • XML: 62
  • Total: 2,102
  • Supplement: 33
  • BibTeX: 79
  • EndNote: 64
Views and downloads (calculated since 20 Sep 2023)
Cumulative views and downloads (calculated since 20 Sep 2023)

Viewed (geographical distribution)

Total article views: 2,102 (including HTML, PDF, and XML) Thereof 2,036 with geography defined and 66 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 08 Mar 2025
Download
Short summary
Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. We combined high-spatial-resolution snow depth information with ground-based radar measurements to solve for snow density. Extrapolated density estimates over our study area resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation and were utilized in the calculation of SWE.
Share