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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Nov 2022
Research article |
| 23 Nov 2022
| 23 Nov 2022
Machine-learning-based downscaling of modelled climate change impacts on groundwater table depth
Department of Hydrology, Geological Survey of Denmark and Greenland
(GEUS), 1350 Copenhagen K, Denmark
Julian Koch
Department of Hydrology, Geological Survey of Denmark and Greenland
(GEUS), 1350 Copenhagen K, Denmark
Lars Troldborg
Department of Hydrology, Geological Survey of Denmark and Greenland
(GEUS), 1350 Copenhagen K, Denmark
Hans Jørgen Henriksen
Department of Hydrology, Geological Survey of Denmark and Greenland
(GEUS), 1350 Copenhagen K, Denmark
Simon Stisen
Department of Hydrology, Geological Survey of Denmark and Greenland
(GEUS), 1350 Copenhagen K, Denmark
Viewed
Total article views: 6,101 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 May 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,265 | 1,734 | 102 | 6,101 | 121 | 141 |
- HTML: 4,265
- PDF: 1,734
- XML: 102
- Total: 6,101
- BibTeX: 121
- EndNote: 141
Total article views: 4,240 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Nov 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,823 | 1,331 | 86 | 4,240 | 110 | 133 |
- HTML: 2,823
- PDF: 1,331
- XML: 86
- Total: 4,240
- BibTeX: 110
- EndNote: 133
Total article views: 1,861 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 May 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,442 | 403 | 16 | 1,861 | 11 | 8 |
- HTML: 1,442
- PDF: 403
- XML: 16
- Total: 1,861
- BibTeX: 11
- EndNote: 8
Viewed (geographical distribution)
Total article views: 6,101 (including HTML, PDF, and XML)
Thereof 5,817 with geography defined
and 284 with unknown origin.
Total article views: 4,240 (including HTML, PDF, and XML)
Thereof 4,055 with geography defined
and 185 with unknown origin.
Total article views: 1,861 (including HTML, PDF, and XML)
Thereof 1,762 with geography defined
and 99 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
16 citations as recorded by crossref.
- Integrating climate resilience and digital twin technologies in housing: A comparative analysis and sustainable design proposal for Jordan and South Africa A. Shehadeh et al. https://doi.org/10.1016/j.rineng.2025.108409
- A New Digital Twin for Climate Change Adaptation, Water Management, and Disaster Risk Reduction (HIP Digital Twin) H. Henriksen et al. https://doi.org/10.3390/w15010025
- CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. https://doi.org/10.5194/essd-17-1551-2025
- Leveraging digital twins for community-driven sustainable WEFE nexus management A. Shehadeh et al. https://doi.org/10.1016/j.sftr.2025.100722
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. https://doi.org/10.5194/hess-28-2871-2024
- Drought dynamics across the hydrological cycle – an extensive validation of the National Hydrological Model of Denmark R. Schneider et al. https://doi.org/10.5194/hess-30-4019-2026
- Modelling of shallow groundwater levels and interaction with drainage system and streams under the impact of climate change S. Thorndahl & R. Nielsen https://doi.org/10.2166/wpt.2024.299
- Modeling groundwater in a changing climate: A review of advanced machine learning solutions N. Chahboune et al. https://doi.org/10.1051/e3sconf/202670801001
- Combined water table and temperature dynamics control CO2 emission estimates from drained peatlands under rewetting and climate change scenarios T. Denager et al. https://doi.org/10.5194/bg-23-441-2026
- The Altered Water Balance: An analysis of urban shallow groundwater level dynamics using a high-density groundwater level observation network for Aalborg, Denmark S. Thorndahl et al. https://doi.org/10.1016/j.ejrh.2026.103652
- Impact of urban geology on model simulations of shallow groundwater levels and flow paths A. LaBianca et al. https://doi.org/10.5194/hess-27-1645-2023
- Machine learning-based spatial downscaling and bias-correction framework for high-resolution temperature forecasting X. Meng et al. https://doi.org/10.1007/s10489-024-05504-z
- Enhancing Urban Sustainability and Resilience: Employing Digital Twin Technologies for Integrated WEFE Nexus Management to Achieve SDGs A. Shehadeh et al. https://doi.org/10.3390/su16177398
- Using random forests to explore the feasibility of groundwater knowledge transfer between the contiguous US and Denmark Y. Ma et al. https://doi.org/10.1088/2515-7620/ad9b08
- Harnessing machine learning for assessing climate change influences on groundwater resources: A comprehensive review A. Bamal et al. https://doi.org/10.1016/j.heliyon.2024.e37073
- High-Resolution Precipitation Modeling in Complex Terrains Using Hybrid Interpolation Techniques: Incorporating Physiographic and MODIS Cloud Cover Influences K. Alsafadi et al. https://doi.org/10.3390/rs15092435
16 citations as recorded by crossref.
- Integrating climate resilience and digital twin technologies in housing: A comparative analysis and sustainable design proposal for Jordan and South Africa A. Shehadeh et al. https://doi.org/10.1016/j.rineng.2025.108409
- A New Digital Twin for Climate Change Adaptation, Water Management, and Disaster Risk Reduction (HIP Digital Twin) H. Henriksen et al. https://doi.org/10.3390/w15010025
- CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. https://doi.org/10.5194/essd-17-1551-2025
- Leveraging digital twins for community-driven sustainable WEFE nexus management A. Shehadeh et al. https://doi.org/10.1016/j.sftr.2025.100722
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. https://doi.org/10.5194/hess-28-2871-2024
- Drought dynamics across the hydrological cycle – an extensive validation of the National Hydrological Model of Denmark R. Schneider et al. https://doi.org/10.5194/hess-30-4019-2026
- Modelling of shallow groundwater levels and interaction with drainage system and streams under the impact of climate change S. Thorndahl & R. Nielsen https://doi.org/10.2166/wpt.2024.299
- Modeling groundwater in a changing climate: A review of advanced machine learning solutions N. Chahboune et al. https://doi.org/10.1051/e3sconf/202670801001
- Combined water table and temperature dynamics control CO2 emission estimates from drained peatlands under rewetting and climate change scenarios T. Denager et al. https://doi.org/10.5194/bg-23-441-2026
- The Altered Water Balance: An analysis of urban shallow groundwater level dynamics using a high-density groundwater level observation network for Aalborg, Denmark S. Thorndahl et al. https://doi.org/10.1016/j.ejrh.2026.103652
- Impact of urban geology on model simulations of shallow groundwater levels and flow paths A. LaBianca et al. https://doi.org/10.5194/hess-27-1645-2023
- Machine learning-based spatial downscaling and bias-correction framework for high-resolution temperature forecasting X. Meng et al. https://doi.org/10.1007/s10489-024-05504-z
- Enhancing Urban Sustainability and Resilience: Employing Digital Twin Technologies for Integrated WEFE Nexus Management to Achieve SDGs A. Shehadeh et al. https://doi.org/10.3390/su16177398
- Using random forests to explore the feasibility of groundwater knowledge transfer between the contiguous US and Denmark Y. Ma et al. https://doi.org/10.1088/2515-7620/ad9b08
- Harnessing machine learning for assessing climate change influences on groundwater resources: A comprehensive review A. Bamal et al. https://doi.org/10.1016/j.heliyon.2024.e37073
- High-Resolution Precipitation Modeling in Complex Terrains Using Hybrid Interpolation Techniques: Incorporating Physiographic and MODIS Cloud Cover Influences K. Alsafadi et al. https://doi.org/10.3390/rs15092435
Saved (final revised paper)
Latest update: 08 Jul 2026
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.
Hydrological models at high spatial resolution are computationally expensive. However, outputs...
