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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-321
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-321
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Sep 2020

15 Sep 2020

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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 Participants Ryan L. Crumley et al.
  • 1Water Resources Science, Oregon State University, Corvallis, OR 97331, USA
  • 2School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USA
  • 3Alaska Division of Geological and Geophysical Surveys, Fairbanks, AK 99709, USA
  • 4International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
  • 5University of Washington, Applied Physics Laboratory, WA 98105, USA
  • 6Geography Department, Oregon State University, Corvallis, OR 97331, USA
  • Citizen scientists participating in the project Community Snow Observations (CSO)

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.

Ryan L. Crumley et al.

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Ryan L. Crumley et al.

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Latest update: 28 Sep 2020
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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...
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