Snow density is an important measure in hydrology used to convert snow depth to the snow water equivalent (SWE). A model developed by Sturm, Tara and Liston predicts the snow density by using snow depth, the snow age and a snow class defined by the location. In this work this model is extended to include location and seasonal weather-specific variables. The model is named Weather Snow Density Model (Weather SDM). A Bayesian framework is chosen, and the model is fitted to and tested for 4,040 Norwegian snow depth and densities measurements between 1998 and 2011. The final model improved the snow density predictions for the Norwegian data compared to the model of Sturm by up to 50%. Further, the Weather SDM is extended to utilize local year-specific snow density observations (Weather&ObsDensity SDM). This reduced the prediction error an additional 16%, indicating a significant improvement when utilizing information provided by annual snow density measurements.
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Research Article|February 02 2015
Weather SDM: estimating snow density with high precision using snow depth and local climate
Oddbjørn Bruland
Oddbjørn Bruland
1Department of Hydraulic and Environmental Engineering, Norwegian University of Science and Technology, Trondheim N-7490, Norway2Statkraft, Trondheim 7037, Norway
E-mail: Oddbjorn.Bruland@ntnu.no
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Åshild Færevåg
Åshild Færevåg
3Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim N-7490, Norway
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Ingelin Steinsland
Ingelin Steinsland
3Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim N-7490, Norway
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Glen E. Liston
Glen E. Liston
4Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
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Knut Sand
Knut Sand
2Statkraft, Trondheim 7037, Norway
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Hydrology Research (2015) 46 (4): 494-506.
Article history
Received:
March 19 2014
Accepted:
December 22 2014
Citation
Oddbjørn Bruland, Åshild Færevåg, Ingelin Steinsland, Glen E. Liston, Knut Sand; Weather SDM: estimating snow density with high precision using snow depth and local climate. Hydrology Research 1 August 2015; 46 (4): 494–506. doi: https://doi.org/10.2166/nh.2015.059
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