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

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (25 Jan 2021) by Jan Seibert
AR by Ryan Crumley on behalf of the Authors (05 Mar 2021)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (09 Mar 2021) by Jan Seibert
RR by Anonymous Referee #3 (07 Apr 2021)
RR by Anonymous Referee #4 (17 Jun 2021)
ED: Publish subject to minor revisions (review by editor) (27 Jun 2021) by Jan Seibert
AR by Ryan Crumley on behalf of the Authors (08 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (10 Jul 2021) by Jan Seibert
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.