Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-4651-2021
https://doi.org/10.5194/hess-25-4651-2021
Research article
 | 
31 Aug 2021
Research article |  | 31 Aug 2021

Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations

Ryan L. Crumley, David F. Hill, Katreen Wikstrom Jones, Gabriel J. Wolken, Anthony A. Arendt, Christina M. Aragon, Christopher Cosgrove, and Community Snow Observations Participants

Viewed

Total article views: 2,412 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,705 667 40 2,412 41 39
  • HTML: 1,705
  • PDF: 667
  • XML: 40
  • Total: 2,412
  • BibTeX: 41
  • EndNote: 39
Views and downloads (calculated since 15 Sep 2020)
Cumulative views and downloads (calculated since 15 Sep 2020)

Viewed (geographical distribution)

Total article views: 2,412 (including HTML, PDF, and XML) Thereof 2,267 with geography defined and 145 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Apr 2024
Download
Short summary
In this study, we use a new snow data set collected by participants in the Community Snow Observations project in coastal Alaska to improve snow depth and snow water equivalence simulations from a snow process model. We validate our simulations with multiple datasets, taking advantage of snow telemetry (SNOTEL), snow depth and snow water equivalence, and remote sensing measurements. Our results demonstrate that assimilating citizen science snow depth measurements can improve model performance.