Articles | Volume 26, issue 7
Hydrol. Earth Syst. Sci., 26, 1727–1743, 2022
https://doi.org/10.5194/hess-26-1727-2022
Hydrol. Earth Syst. Sci., 26, 1727–1743, 2022
https://doi.org/10.5194/hess-26-1727-2022
Technical note
05 Apr 2022
Technical note | 05 Apr 2022

Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks

Huiying Ren et al.

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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: Reconsider after major revisions (further review by editor and referees) (15 Feb 2020) by Dimitri Solomatine
AR by Xingyuan Chen on behalf of the Authors (25 Jun 2020)  Author's response
ED: Referee Nomination & Report Request started (13 Jul 2020) by Dimitri Solomatine
RR by Anonymous Referee #2 (14 Aug 2020)
RR by Anonymous Referee #1 (25 Oct 2020)
ED: Publish subject to revisions (further review by editor and referees) (04 Nov 2020) by Dimitri Solomatine
AR by Xingyuan Chen on behalf of the Authors (17 Apr 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (25 Apr 2021) by Dimitri Solomatine
RR by Anonymous Referee #2 (08 Jun 2021)
RR by Gerald A Corzo P (24 Jul 2021)
ED: Publish subject to revisions (further review by editor and referees) (28 Jul 2021) by Dimitri Solomatine
AR by Xingyuan Chen on behalf of the Authors (16 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (04 Oct 2021) by Dimitri Solomatine
RR by Anonymous Referee #1 (31 Oct 2021)
RR by Anonymous Referee #3 (01 Dec 2021)
ED: Publish subject to revisions (further review by editor and referees) (10 Dec 2021) by Dimitri Solomatine
AR by Xingyuan Chen on behalf of the Authors (22 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (09 Feb 2022) by Dimitri Solomatine
ED: Publish as is (23 Feb 2022) by Dimitri Solomatine
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Short summary
We used a deep learning method called long short-term memory (LSTM) to fill gaps in data collected by hydrologic monitoring networks. LSTM accounted for correlations in space and time and nonlinear trends in data. Compared to a traditional regression-based time-series method, LSTM performed comparably when filling gaps in data with smooth patterns, while it better captured highly dynamic patterns in data. Capturing such dynamics is critical for understanding dynamic complex system behaviors.