Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 22
Articles | Volume 26, issue 22
https://doi.org/10.5194/hess-26-5859-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5859-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 4,790 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,644 1,075 71 4,790 80 95
  • HTML: 3,644
  • PDF: 1,075
  • XML: 71
  • Total: 4,790
  • BibTeX: 80
  • EndNote: 95
Views and downloads (calculated since 30 May 2022)
Cumulative views and downloads (calculated since 30 May 2022)

Viewed (geographical distribution)

Total article views: 4,790 (including HTML, PDF, and XML) Thereof 4,565 with geography defined and 225 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Nov 2025
Download
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.
Read more
Share