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TC | Volume 12, issue 3
The Cryosphere, 12, 891–905, 2018
https://doi.org/10.5194/tc-12-891-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 12, 891–905, 2018
https://doi.org/10.5194/tc-12-891-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Mar 2018

Research article | 12 Mar 2018

Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models

Andrew M. Snauffer et al.
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Cited articles  
Anderton, S., White, S., and Alvera, B.: Evaluation of spatial variability in snow water equivalent for a high mountain catchment, Hydrol. Process., 18, 435–453, 2004. a
Aschbacher, J.: Land surface studies and atmospheric effects by satellite microwave radiometry, PhD thesis, University of Innsbruck, Innsbruck, Austria, 1989. a, b
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.: ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, 2015. a
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Estimating winter snowpack throughout British Columbia is challenging due to the complex terrain, thick forests, and high snow accumulations present. This paper describes a way to make better snow estimates by combining publicly available data using machine learning, a branch of artificial intelligence research. These improved estimates will help water resources managers better plan for changes in rivers and lakes fed by spring snowmelt and will aid other research that supports such planning.
Estimating winter snowpack throughout British Columbia is challenging due to the complex...
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