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

Bair, Edward H.; Abreu Calfa, Andre; Rittger, Karl; Dozier, Jeff

doi:https://doi.org/10.5194/tc-12-1579-2018

Articles | Volume 12, issue 5

https://doi.org/10.5194/tc-12-1579-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-12-1579-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 12, issue 5

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, Andre Abreu Calfa, Karl Rittger, and Jeff Dozier

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Final revised paper (published on 03 May 2018)
Supplement to the final revised paper
Preprint (discussion started on 11 Oct 2017)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'Review Comment', Jessica Lundquist, 15 Dec 2017
- AC1: 'Response to Referee #1', Edward Bair, 24 Feb 2018
RC2: 'review of Bair et al. “Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan” (tc-2017-196)', Steven Fassnacht, 31 Dec 2017
- AC2: 'Response to Referee #2', Edward Bair, 24 Feb 2018

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

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.