Articles | Volume 29, issue 15
https://doi.org/10.5194/hess-29-3405-2025
https://doi.org/10.5194/hess-29-3405-2025
Research article
 | 
01 Aug 2025
Research article |  | 01 Aug 2025

Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany

Stefan Kunz, Alexander Schulz, Maria Wetzel, Maximilian Nölscher, Teodor Chiaburu, Felix Biessmann, and Stefan Broda

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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-2024-3484', Anonymous Referee #1, 05 Dec 2024
    • AC1: 'Reply on RC1', Stefan Kunz, 17 Jan 2025
  • RC2: 'Comment on egusphere-2024-3484', Abel Henriot, 15 Dec 2024
    • AC3: 'Reply on RC2', Stefan Kunz, 17 Jan 2025
  • RC3: 'Comment on egusphere-2024-3484', Anonymous Referee #3, 17 Dec 2024
    • AC2: 'Reply on RC3', Stefan Kunz, 17 Jan 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) (12 Feb 2025) by Alberto Guadagnini
AR by Stefan Kunz on behalf of the Authors (18 Mar 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (18 Mar 2025) by Alberto Guadagnini
RR by Leonardo Sandoval (28 Mar 2025)
ED: Publish as is (29 Mar 2025) by Alberto Guadagnini
AR by Stefan Kunz on behalf of the Authors (31 Mar 2025)
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Short summary
Accurate groundwater level predictions are crucial for sustainable management. This study applies two machine learning models – Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS) and the Temporal Fusion Transformer (TFT) – to forecast seasonal groundwater levels for 5288 wells across Germany. N-HiTS outperformed TFT, with both models performing well in diverse hydrogeological settings, particularly in lowlands with distinct seasonal dynamics.
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