Articles | Volume 26, issue 21
Hydrol. Earth Syst. Sci., 26, 5449–5472, 2022
https://doi.org/10.5194/hess-26-5449-2022
Hydrol. Earth Syst. Sci., 26, 5449–5472, 2022
https://doi.org/10.5194/hess-26-5449-2022
Research article
01 Nov 2022
Research article | 01 Nov 2022

Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States

Kieran M. R. Hunt et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-53', Lennart Schmidt, 17 Mar 2022
  • RC2: 'Comment on hess-2022-53', Frederik Kratzert, 28 Mar 2022
  • RC3: 'Comment on hess-2022-53', Anonymous Referee #3, 02 Apr 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (10 May 2022) by Rohini Kumar
AR by Kieran Hunt on behalf of the Authors (17 Aug 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (22 Aug 2022) by Rohini Kumar
RR by Frederik Kratzert (05 Sep 2022)
RR by Lennart Schmidt (06 Oct 2022)
ED: Publish subject to minor revisions (review by editor) (06 Oct 2022) by Rohini Kumar
AR by Kieran Hunt on behalf of the Authors (13 Oct 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (19 Oct 2022) by Rohini Kumar
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Short summary
In this study, we use three models to forecast river streamflow operationally for 13 months (September 2020 to October 2021) at 10 gauges in the western US. The first model is a state-of-the-art physics-based streamflow model (GloFAS). The second applies a bias-correction technique to GloFAS. The third is a type of neural network (an LSTM). We find that all three are capable of producing skilful forecasts but that the LSTM performs the best, with skilful 5 d forecasts at nine stations.