Articles | Volume 12, issue 5
The Cryosphere, 12, 1579–1594, 2018
https://doi.org/10.5194/tc-12-1579-2018
The Cryosphere, 12, 1579–1594, 2018
https://doi.org/10.5194/tc-12-1579-2018

Research article 03 May 2018

Research article | 03 May 2018

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

Edward H. Bair 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
AR by Edward Bair on behalf of the Authors (24 Feb 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (01 Mar 2018) by Marie Dumont
RR by Anonymous Referee #3 (23 Mar 2018)
ED: Publish subject to minor revisions (review by editor) (23 Mar 2018) by Marie Dumont
AR by Edward Bair on behalf of the Authors (29 Mar 2018)  Author's response    Manuscript
ED: Publish as is (03 Apr 2018) by Marie Dumont
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Short summary
In Afghanistan, almost no snow measurements exist. Operational estimates use measurements from satellites, but all have limitations. We have developed a satellite-based technique called reconstruction that accurately estimates the snowpack retrospectively. To solve the problem of estimating today's snowpack, we used machine learning, trained on our reconstructed snow estimates, using predictors that are available today. Our results show low errors, demonstrating the utility of this approach.