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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2018-451
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-451
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  24 Oct 2018

24 Oct 2018

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This preprint was under review for the journal HESS but the revision was not accepted.

Technical note: Snow Water Equivalence Estimation (SWEE) Algorithm from Snow Depth Time Series Using a Snow Density Model

Noriaki Ohara1, Siwei He1, Andrew D. Parsekian2, and Thijs Kelleners3 Noriaki Ohara et al.
  • 1Department of Civil and Architectural Engineering, University of Wyoming, 1000 E. University Ave. Laramie, WY 82071
  • 2Department of Geology and Geophysics, University of Wyoming, 1000 E. University Ave. Laramie, WY 82071
  • 3Ecosystem Science and Management, University of Wyoming, 1000 E. University Ave. Laramie, WY 82071

Abstract. Snow water equivalence (SWE) is typically computed from snow weight by the SNOTEL system in the US. However, a snow pillow, the main snow weight sensor used by SNOTEL, requires a large, open, flat area (at least 9 square meters) and substantial maintenance costs. This article presents the snow water equivalence estimation (SWEE) algorithm that estimates the SWE evolution merely from continuous snow depth and temperature measurements using common sensors. The key component is a depth-averaged snow density model that is available in the literature, but is underutilized. Here, we demonstrate that the snow density model can estimate mass exchanges (SWE changes due to snowfall, erosion, deposition, and snowmelt) as well as the SWE. The SWEE algorithm can potentially increase the number of snow monitoring locations because snow depth and temperature sensors are considerably more accessible and economical than snow weighing sensor.

Noriaki Ohara et al.

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Noriaki Ohara et al.

Noriaki Ohara et al.

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