Articles | Volume 25, issue 6
Hydrol. Earth Syst. Sci., 25, 3017–3040, 2021
https://doi.org/10.5194/hess-25-3017-2021
Hydrol. Earth Syst. Sci., 25, 3017–3040, 2021
https://doi.org/10.5194/hess-25-3017-2021

Research article 04 Jun 2021

Research article | 04 Jun 2021

Investigating ANN architectures and training to estimate snow water equivalent from snow depth

Konstantin F. F. Ntokas 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) (02 Feb 2021) by Ryan Teuling
AR by Konstantin Ntokas on behalf of the Authors (09 Mar 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (10 Mar 2021) by Ryan Teuling
RR by Anonymous Referee #2 (24 Mar 2021)
RR by Anonymous Referee #1 (12 Apr 2021)
ED: Publish subject to minor revisions (review by editor) (12 Apr 2021) by Ryan Teuling
AR by Konstantin Ntokas on behalf of the Authors (22 Apr 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (27 Apr 2021) by Ryan Teuling
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Short summary
This article shows a conversion model of snow depth into snow water equivalent (SWE) using an ensemble of artificial neural networks. The novelty is a direct estimation of SWE and the improvement of the estimation by in-depth analysis of network structures. The usage of an ensemble allows a probabilistic estimation and, therefore, a deeper insight. It is a follow-up study of a similar study over Quebec but extends it to the whole area of Canada and improves it further.