Articles | Volume 24, issue 10
Hydrol. Earth Syst. Sci., 24, 4887–4902, 2020
https://doi.org/10.5194/hess-24-4887-2020
Hydrol. Earth Syst. Sci., 24, 4887–4902, 2020
https://doi.org/10.5194/hess-24-4887-2020

Research article 14 Oct 2020

Research article | 14 Oct 2020

Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada

Fraser King et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (11 May 2020) by Kerstin Stahl
AR by Fraser King on behalf of the Authors (20 May 2020)  Author's response    Manuscript
ED: Publish subject to minor revisions (further review by editor) (27 Jul 2020) by Kerstin Stahl
AR by Fraser King on behalf of the Authors (04 Aug 2020)  Author's response    Manuscript
ED: Publish as is (28 Aug 2020) by Kerstin Stahl
AR by Fraser King on behalf of the Authors (02 Sep 2020)  Author's response    Manuscript
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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.