In this study we use a new snow dataset 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 SNOTEL, snow depth and snow water equivalence, and LiDAR-based remote sensing measurements. Our results demonstrate that assimilating citizen science snow depth measurements can improve model performance.
In this study we use a new snow dataset collected by participants in the Community Snow...
Review status: this preprint is currently under review for the journal HESS.
Assimilation of citizen science data in snowpack modeling using a new snow dataset: Community Snow Observations
Ryan L. Crumley1,David F. Hill2,Katreen Wikstrom Jones3,Gabriel J. Wolken3,4,Anthony A. Arendt5,Christina M. Aragon1,Christopher Cosgrove6,and the Community Snow Observations ParticipantsRyan L. Crumley et al.Ryan L. Crumley1,David F. Hill2,Katreen Wikstrom Jones3,Gabriel J. Wolken3,4,Anthony A. Arendt5,Christina M. Aragon1,Christopher Cosgrove6,and the Community Snow Observations Participants
Received: 26 Jun 2020 – Accepted for review: 30 Aug 2020 – Discussion started: 15 Sep 2020
Abstract. In this study, we examine the effectiveness of incorporating citizen science snow depth measurements into the seasonal snow model chain through data assimilation. We also introduce the Community Snow Observations dataset, a citizen science based snow depth measuring campaign. Improvements to model performance are characterized and evaluated using remote sensing datasets, fieldwork, and SNOTEL datasets. After citizen science snow depth measurements were incorporated, improvements to model performance were found in 62 % to 78 % of the simulations, depending on model year. The results suggest that modest measurements from citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling.
Total article views: 333 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
235
92
6
333
9
9
HTML: 235
PDF: 92
XML: 6
Total: 333
BibTeX: 9
EndNote: 9
Views and downloads (calculated since 15 Sep 2020)
Cumulative views and downloads
(calculated since 15 Sep 2020)
Viewed (geographical distribution)
Total article views: 298 (including HTML, PDF, and XML)
Thereof 297 with geography defined
and 1 with unknown origin.
Country
#
Views
%
Total:
0
HTML:
0
PDF:
0
XML:
0
1
1
Latest update: 15 Jan 2021
Search articles
Download
Short summary
In this study we use a new snow dataset 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 SNOTEL, snow depth and snow water equivalence, and LiDAR-based remote sensing measurements. Our results demonstrate that assimilating citizen science snow depth measurements can improve model performance.
In this study we use a new snow dataset collected by participants in the Community Snow...