A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

Kratzert, Frederik; Klotz, Daniel; Hochreiter, Sepp; Nearing, Grey S.

doi:https://doi.org/10.5194/hess-25-2685-2021

Articles | Volume 25, issue 5

https://doi.org/10.5194/hess-25-2685-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-2685-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 5

Research article

|

20 May 2021

Research article |

| 20 May 2021

A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, and Grey S. Nearing

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Final revised paper (published on 20 May 2021)
Preprint (discussion started on 14 May 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of HESS-2020-221', Anonymous Referee #1, 22 Jun 2020
- AC1: 'Response to reviewer #1', Frederik Kratzert, 22 Jul 2020
RC2: 'Review of hess-2020-221', Anonymous Referee #2, 09 Jul 2020
- AC2: 'Reponse to reviewer #2', Frederik Kratzert, 22 Jul 2020
EC1: 'Final Editor's comment', Dimitri Solomatine, 07 Aug 2020

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 Aug 2020) by Dimitri Solomatine

AR by Frederik Kratzert on behalf of the Authors (07 Sep 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (30 Sep 2020) by Dimitri Solomatine

RR by Anonymous Referee #1 (08 Jan 2021)

RR by Thomas Lees (06 Mar 2021)

ED: Publish subject to minor revisions (review by editor) (10 Mar 2021) by Dimitri Solomatine

AR by Frederik Kratzert on behalf of the Authors (16 Mar 2021) Author's response Author's tracked changes Manuscript

ED: Publish as is (05 Apr 2021) by Dimitri Solomatine

Short summary

We investigate how deep learning models use different meteorological data sets in the task of (regional) rainfall–runoff modeling. We show that performance can be significantly improved when using different data products as input and further show how the model learns to combine those meteorological input differently across time and space. The results are carefully benchmarked against classical approaches, showing the supremacy of the presented approach.