Articles | Volume 26, issue 22
Research article
23 Nov 2022
Research article |  | 23 Nov 2022

Machine-learning-based downscaling of modelled climate change impacts on groundwater table depth

Raphael Schneider, Julian Koch, Lars Troldborg, Hans Jørgen Henriksen, and Simon Stisen


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-122', Anonymous Referee #1, 13 Jul 2022
    • AC1: 'Reply on RC1', Raphael Schneider, 18 Jul 2022
  • RC2: 'Comment on hess-2022-122', Anonymous Referee #2, 19 Jul 2022
    • AC2: 'Reply on RC2', Raphael Schneider, 21 Jul 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (29 Aug 2022) by Harrie-Jan Hendricks Franssen
AR by Raphael Schneider on behalf of the Authors (06 Oct 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (06 Oct 2022) by Harrie-Jan Hendricks Franssen
RR by Anonymous Referee #1 (02 Nov 2022)
RR by Anonymous Referee #2 (03 Nov 2022)
ED: Publish as is (05 Nov 2022) by Harrie-Jan Hendricks Franssen
Short summary
Hydrological models at high spatial resolution are computationally expensive. However, outputs from such models, such as the depth of the groundwater table, are often desired in high resolution. We developed a downscaling algorithm based on machine learning that allows us to increase spatial resolution of hydrological model outputs, alleviating computational burden. We successfully applied the downscaling algorithm to the climate-change-induced impacts on the groundwater table across Denmark.