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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/tc-2018-286
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2018-286
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 23 Jan 2019

Research article | 23 Jan 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal The Cryosphere (TC).

Converting Snow Depth to Snow Water Equivalent Using Climatological Variables

David F. Hill1, Elizabeth A. Burakowski2, Ryan L. Crumley3, Julia Keon4, J. Michelle Hu5, Anthony A. Arendt6, Katreen Wikstrom Jones7, and Gabriel J. Wolken8,9 David F. Hill et al.
  • 1Civil and Constructio n Engineering, Oregon State University, OR, USA
  • 2Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, NH, USA
  • 3Water Resources Graduate Program, Oregon State University, OR, USA
  • 4Civil and Construction Engineering, Oregon State University, OR, USA
  • 5Civil and Environmental Engineering, University of Washington
  • 6Applied Physics Laboratory, University of Washington
  • 7Alaska Division of Geological & Geophysical Surveys, Fairbanks, AK, USA
  • 8Alaska Division of Geological & Geophysical Surveys, Fairbanks, AK, USA
  • 9International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA

Abstract. We present a simple method that allows snow depth measurements to be converted to snow water equivalent (SWE) estimates. These estimates are useful to individuals interested in water resources, ecological function, and avalanche forecasting. They can also be assimilated into models to help improve predictions of total water volumes over large regions. The conversion of depth to SWE is particularly valuable since snow depth measurements are far more numerous than costlier and more complex SWE measurements. Our model regresses SWE against snow depth and climatological (30-year normal) values for mean annual precipitation (MAP) and mean February temperature, producing a power-law relationship. Relying on climatological normals rather than weather data for a given year allows our model to be applied at measurement sites lacking a weather station. Separate equations are obtained for the accumulation and the ablation phases of the snowpack, which introduces day of water year (DOY) as an additional variable. The model is validated against a large database of snow pillow measurements and yields a bias in SWE of less than 0.5 mm and a root-mean-squared-error (RMSE) in SWE of approximately 65 mm. When the errors are investigated on a station-by-station basis, the average RMSE is about 5 % of the MAP at each station. The model is additionally validated against a completely independent set of data from the northeast United States. Finally, the results are compared with other models for bulk density that have varying degrees of complexity and that were built in multiple geographic regions. The results show that the model described in this paper has the best performance for the validation data set.

David F. Hill et al.
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Short summary
We present a new statistical model for converting snow depths to water equivalent. The only variables required are snow depth, day of year, and location. We use the location to look up climatological parameters such as mean annual precipitation and temperature characteristics. The model is simple by design so that it can applied to depth measurements anywhere, anytime. The model is shown to perform better than other widely used approaches.
We present a new statistical model for converting snow depths to water equivalent. The only...
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