Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-5005-2025
https://doi.org/10.5194/hess-29-5005-2025
Research article
 | 
08 Oct 2025
Research article |  | 08 Oct 2025

How well do process-based and data-driven hydrological models learn from limited discharge data?

Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret

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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 egusphere-2025-1076', Salvatore Manfreda, 18 Apr 2025
    • AC1: 'Reply on RC1', Maria Staudinger, 03 Jun 2025
  • RC2: 'Comment on egusphere-2025-1076', Claudia Brauer, 07 May 2025
    • AC2: 'Reply on RC2', Maria Staudinger, 03 Jun 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (04 Jun 2025) by Nunzio Romano
AR by Maria Staudinger on behalf of the Authors (06 Jun 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (01 Jul 2025) by Nunzio Romano
RR by Salvatore Manfreda (01 Jul 2025)
RR by Claudia Brauer (17 Jul 2025)
ED: Publish as is (27 Jul 2025) by Nunzio Romano
AR by Maria Staudinger on behalf of the Authors (27 Jul 2025)  Author's response   Manuscript 
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Short summary
Three process-based and four data-driven hydrological models are compared using different training data. We found that process-based models perform better with small datasets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
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