Articles | Volume 24, issue 10
https://doi.org/10.5194/hess-24-4887-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-4887-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Oct 2020
Research article |
| 14 Oct 2020
| 14 Oct 2020
Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada
Deptartment of Geography & Environmental Management, University of Waterloo, Ontario, Canada
Andre R. Erler
Aquanty, Waterloo, Ontario, Canada
Steven K. Frey
Aquanty, Waterloo, Ontario, Canada
Deptartment of Earth & Environmental Sciences, University of Waterloo, Ontario, Canada
Christopher G. Fletcher
Deptartment of Geography & Environmental Management, University of Waterloo, Ontario, Canada
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Short summary
Snow is a critical contributor to our water and energy budget, with impacts on flooding and water resource management. Measuring the amount of snow on the ground each year is an expensive and time-consuming task. Snow models and gridded products help to fill these gaps, yet there exist considerable uncertainties associated with their estimates. We demonstrate that machine learning techniques are able to reduce biases in these products to provide more realistic snow estimates across Ontario.
Snow is a critical contributor to our water and energy budget, with impacts on flooding and...
