Articles | Volume 26, issue 13
Research article
05 Jul 2022
Research article |  | 05 Jul 2022

Deep learning rainfall–runoff predictions of extreme events

Jonathan M. Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shalev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, and Grey S. Nearing


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on hess-2021-423', John Ding, 20 Aug 2021
    • CC2: 'Clarify on CC1', John Ding, 30 Oct 2021
      • AC1: 'Reply on CC2', Jonathan Frame, 29 Dec 2021
  • RC1: 'Comment on hess-2021-423', Anonymous Referee #1, 05 Sep 2021
    • AC2: 'Reply on RC1', Jonathan Frame, 29 Dec 2021
  • RC2: 'Comment on hess-2021-423', Anonymous Referee #2, 23 Sep 2021
    • AC3: 'Reply on RC2', Jonathan Frame, 29 Dec 2021
  • RC3: 'Comment on hess-2021-423', Anonymous Referee #3, 26 Nov 2021
    • AC4: 'Reply on RC3', Jonathan Frame, 29 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) (06 Jan 2022) by Nadav Peleg
AR by Jonathan Frame on behalf of the Authors (18 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (22 Feb 2022) by Nadav Peleg
RR by Anonymous Referee #1 (12 Mar 2022)
RR by Anonymous Referee #2 (28 Mar 2022)
ED: Publish subject to technical corrections (01 May 2022) by Nadav Peleg

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that deep learning models may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis. The deep learning models remained relatively accurate in predicting extreme events compared with traditional models, even when extreme events were not included in the training set.