Articles | Volume 26, issue 21
Hydrol. Earth Syst. Sci., 26, 5493–5513, 2022
https://doi.org/10.5194/hess-26-5493-2022
Hydrol. Earth Syst. Sci., 26, 5493–5513, 2022
https://doi.org/10.5194/hess-26-5493-2022
Technical note
04 Nov 2022
Technical note | 04 Nov 2022

Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks

Grey S. Nearing 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-2021-515', Ralf Loritz, 25 Nov 2021
  • RC2: 'review of hess-2021-515', Anonymous Referee #2, 02 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (07 Feb 2022) by Erwin Zehe
AR by Grey Nearing on behalf of the Authors (20 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (20 Jun 2022) by Erwin Zehe
RR by Anonymous Referee #2 (15 Aug 2022)
ED: Publish subject to technical corrections (17 Aug 2022) by Erwin Zehe
AR by Grey Nearing on behalf of the Authors (09 Sep 2022)  Author's response    Manuscript
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Short summary
When designing flood forecasting models, it is necessary to use all available data to achieve the most accurate predictions possible. This manuscript explores two basic ways of ingesting near-real-time streamflow data into machine learning streamflow models. The point we want to make is that when working in the context of machine learning (instead of traditional hydrology models that are based on bio-geophysics), it is not necessary to use complex statistical methods for injecting sparse data.