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
Related authors
Tanja Denager, Jesper Riis Christiansen, Raphael Johannes Maria Schneider, Peter L. Langen, Thea Quistgaard, and Simon Stisen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2503, https://doi.org/10.5194/egusphere-2025-2503, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
This study demonstrates that incorporating both temperature and temporal variability in water level in emission models significantly influences CO2 emission from peat soil. Especially the co-occurrence of elevated air temperature and low groundwater table significantly influence CO2 emissions under scenarios of rewetting and climate change.
Jun Liu, Julian Koch, Simon Stisen, Lars Troldborg, Anker Lajer Højberg, Hans Thodsen, Mark F. T. Hansen, and Raphael J. M. Schneider
Earth Syst. Sci. Data, 17, 1551–1572, https://doi.org/10.5194/essd-17-1551-2025, https://doi.org/10.5194/essd-17-1551-2025, 2025
Short summary
Short summary
We developed a CAMELS-style dataset in Denmark, which contains hydrometeorological time series and landscape attributes for 3330 catchments (304 gauged). Many catchments in CAMELS-DK are small and at low elevations. The dataset provides information on groundwater characteristics and dynamics, as well as quantities related to the human impact on the hydrological system in Denmark. The dataset is especially relevant for developing data-driven and hybrid physically informed modeling frameworks.
Raoul A. Collenteur, Ezra Haaf, Mark Bakker, Tanja Liesch, Andreas Wunsch, Jenny Soonthornrangsan, Jeremy White, Nick Martin, Rui Hugman, Ed de Sousa, Didier Vanden Berghe, Xinyang Fan, Tim J. Peterson, Jānis Bikše, Antoine Di Ciacca, Xinyue Wang, Yang Zheng, Maximilian Nölscher, Julian Koch, Raphael Schneider, Nikolas Benavides Höglund, Sivarama Krishna Reddy Chidepudi, Abel Henriot, Nicolas Massei, Abderrahim Jardani, Max Gustav Rudolph, Amir Rouhani, J. Jaime Gómez-Hernández, Seifeddine Jomaa, Anna Pölz, Tim Franken, Morteza Behbooei, Jimmy Lin, and Rojin Meysami
Hydrol. Earth Syst. Sci., 28, 5193–5208, https://doi.org/10.5194/hess-28-5193-2024, https://doi.org/10.5194/hess-28-5193-2024, 2024
Short summary
Short summary
We show the results of the 2022 Groundwater Time Series Modelling Challenge; 15 teams applied data-driven models to simulate hydraulic heads, and three model groups were identified: lumped, machine learning, and deep learning. For all wells, reasonable performance was obtained by at least one team from each group. There was not one team that performed best for all wells. In conclusion, the challenge was a successful initiative to compare different models and learn from each other.
Jun Liu, Julian Koch, Simon Stisen, Lars Troldborg, and Raphael J. M. Schneider
Hydrol. Earth Syst. Sci., 28, 2871–2893, https://doi.org/10.5194/hess-28-2871-2024, https://doi.org/10.5194/hess-28-2871-2024, 2024
Short summary
Short summary
We developed hybrid schemes to enhance national-scale streamflow predictions, combining long short-term memory (LSTM) with a physically based hydrological model (PBM). A comprehensive evaluation of hybrid setups across Denmark indicates that LSTM models forced by climate data and catchment attributes perform well in many regions but face challenges in groundwater-dependent basins. The hybrid schemes supported by PBMs perform better in reproducing long-term streamflow behavior and extreme events.
Hafsa Mahmood, Ty P. A. Ferré, Raphael J. M. Schneider, Simon Stisen, Rasmus R. Frederiksen, and Anders V. Christiansen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1872, https://doi.org/10.5194/egusphere-2023-1872, 2023
Preprint withdrawn
Short summary
Short summary
Temporal drain flow dynamics and understanding of their underlying controlling factors are important for water resource management in tile-drained agricultural areas. This study examine whether simpler, more efficient machine learning (ML) models can provide acceptable solutions compared to traditional physics based models. We predicted drain flow time series in multiple catchments subject to a range of climatic and landscape conditions.
Hyojin Kim, Julian Koch, Birgitte Hansen, and Rasmus Jakobsen
Biogeosciences, 22, 4387–4403, https://doi.org/10.5194/bg-22-4387-2025, https://doi.org/10.5194/bg-22-4387-2025, 2025
Short summary
Short summary
Nitrate pollution from farming is a global problem. A natural process called denitrification helps remove nitrate but also releases CO2, which contributes to climate change. Our study shows that CO2 from this process in Danish groundwater may be a major overlooked source – similar to other known agricultural CO2 emissions. This highlights the need to update greenhouse gas reporting to better reflect farming’s full climate impact.
Tanja Denager, Jesper Riis Christiansen, Raphael Johannes Maria Schneider, Peter L. Langen, Thea Quistgaard, and Simon Stisen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2503, https://doi.org/10.5194/egusphere-2025-2503, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
This study demonstrates that incorporating both temperature and temporal variability in water level in emission models significantly influences CO2 emission from peat soil. Especially the co-occurrence of elevated air temperature and low groundwater table significantly influence CO2 emissions under scenarios of rewetting and climate change.
Jun Liu, Julian Koch, Simon Stisen, Lars Troldborg, Anker Lajer Højberg, Hans Thodsen, Mark F. T. Hansen, and Raphael J. M. Schneider
Earth Syst. Sci. Data, 17, 1551–1572, https://doi.org/10.5194/essd-17-1551-2025, https://doi.org/10.5194/essd-17-1551-2025, 2025
Short summary
Short summary
We developed a CAMELS-style dataset in Denmark, which contains hydrometeorological time series and landscape attributes for 3330 catchments (304 gauged). Many catchments in CAMELS-DK are small and at low elevations. The dataset provides information on groundwater characteristics and dynamics, as well as quantities related to the human impact on the hydrological system in Denmark. The dataset is especially relevant for developing data-driven and hybrid physically informed modeling frameworks.
Raoul A. Collenteur, Ezra Haaf, Mark Bakker, Tanja Liesch, Andreas Wunsch, Jenny Soonthornrangsan, Jeremy White, Nick Martin, Rui Hugman, Ed de Sousa, Didier Vanden Berghe, Xinyang Fan, Tim J. Peterson, Jānis Bikše, Antoine Di Ciacca, Xinyue Wang, Yang Zheng, Maximilian Nölscher, Julian Koch, Raphael Schneider, Nikolas Benavides Höglund, Sivarama Krishna Reddy Chidepudi, Abel Henriot, Nicolas Massei, Abderrahim Jardani, Max Gustav Rudolph, Amir Rouhani, J. Jaime Gómez-Hernández, Seifeddine Jomaa, Anna Pölz, Tim Franken, Morteza Behbooei, Jimmy Lin, and Rojin Meysami
Hydrol. Earth Syst. Sci., 28, 5193–5208, https://doi.org/10.5194/hess-28-5193-2024, https://doi.org/10.5194/hess-28-5193-2024, 2024
Short summary
Short summary
We show the results of the 2022 Groundwater Time Series Modelling Challenge; 15 teams applied data-driven models to simulate hydraulic heads, and three model groups were identified: lumped, machine learning, and deep learning. For all wells, reasonable performance was obtained by at least one team from each group. There was not one team that performed best for all wells. In conclusion, the challenge was a successful initiative to compare different models and learn from each other.
Jun Liu, Julian Koch, Simon Stisen, Lars Troldborg, and Raphael J. M. Schneider
Hydrol. Earth Syst. Sci., 28, 2871–2893, https://doi.org/10.5194/hess-28-2871-2024, https://doi.org/10.5194/hess-28-2871-2024, 2024
Short summary
Short summary
We developed hybrid schemes to enhance national-scale streamflow predictions, combining long short-term memory (LSTM) with a physically based hydrological model (PBM). A comprehensive evaluation of hybrid setups across Denmark indicates that LSTM models forced by climate data and catchment attributes perform well in many regions but face challenges in groundwater-dependent basins. The hybrid schemes supported by PBMs perform better in reproducing long-term streamflow behavior and extreme events.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, https://doi.org/10.5194/nhess-24-1897-2024, 2024
Short summary
Short summary
In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
Søren Julsgaard Kragh, Jacopo Dari, Sara Modanesi, Christian Massari, Luca Brocca, Rasmus Fensholt, Simon Stisen, and Julian Koch
Hydrol. Earth Syst. Sci., 28, 441–457, https://doi.org/10.5194/hess-28-441-2024, https://doi.org/10.5194/hess-28-441-2024, 2024
Short summary
Short summary
This study provides a comparison of methodologies to quantify irrigation to enhance regional irrigation estimates. To evaluate the methodologies, we compared various approaches to quantify irrigation using soil moisture, evapotranspiration, or both within a novel baseline framework, together with irrigation estimates from other studies. We show that the synergy from using two equally important components in a joint approach within a baseline framework yields better irrigation estimates.
Hafsa Mahmood, Ty P. A. Ferré, Raphael J. M. Schneider, Simon Stisen, Rasmus R. Frederiksen, and Anders V. Christiansen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1872, https://doi.org/10.5194/egusphere-2023-1872, 2023
Preprint withdrawn
Short summary
Short summary
Temporal drain flow dynamics and understanding of their underlying controlling factors are important for water resource management in tile-drained agricultural areas. This study examine whether simpler, more efficient machine learning (ML) models can provide acceptable solutions compared to traditional physics based models. We predicted drain flow time series in multiple catchments subject to a range of climatic and landscape conditions.
Søren J. Kragh, Rasmus Fensholt, Simon Stisen, and Julian Koch
Hydrol. Earth Syst. Sci., 27, 2463–2478, https://doi.org/10.5194/hess-27-2463-2023, https://doi.org/10.5194/hess-27-2463-2023, 2023
Short summary
Short summary
This study investigates the precision of irrigation estimates from a global hotspot of unsustainable irrigation practice, the Indus and Ganges basins. We show that irrigation water use can be estimated with high precision by comparing satellite and rainfed hydrological model estimates of evapotranspiration. We believe that our work can support sustainable water resource management, as it addresses the uncertainty of a key component of the water balance that remains challenging to quantify.
Julian Koch, Lars Elsgaard, Mogens H. Greve, Steen Gyldenkærne, Cecilie Hermansen, Gregor Levin, Shubiao Wu, and Simon Stisen
Biogeosciences, 20, 2387–2403, https://doi.org/10.5194/bg-20-2387-2023, https://doi.org/10.5194/bg-20-2387-2023, 2023
Short summary
Short summary
Utilizing peatlands for agriculture leads to large emissions of greenhouse gases worldwide. The emissions are triggered by lowering the water table, which is a necessary step in order to make peatlands arable. Many countries aim at reducing their emissions by restoring peatlands, which can be achieved by stopping agricultural activities and thereby raising the water table. We estimate a total emission of 2.6 Mt CO2-eq for organic-rich peatlands in Denmark and a potential reduction of 77 %.
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, https://doi.org/10.5194/hess-26-5605-2022, 2022
Short summary
Short summary
Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.
Rena Meyer, Wenmin Zhang, Søren Julsgaard Kragh, Mie Andreasen, Karsten Høgh Jensen, Rasmus Fensholt, Simon Stisen, and Majken C. Looms
Hydrol. Earth Syst. Sci., 26, 3337–3357, https://doi.org/10.5194/hess-26-3337-2022, https://doi.org/10.5194/hess-26-3337-2022, 2022
Short summary
Short summary
The amount and spatio-temporal distribution of soil moisture, the water in the upper soil, is of great relevance for agriculture and water management. Here, we investigate whether the established downscaling algorithm combining different satellite products to estimate medium-scale soil moisture is applicable to higher resolutions and whether results can be improved by accounting for land cover types. Original satellite data and downscaled soil moisture are compared with ground observations.
Cited articles
Abbott, M. B., Bathurst, J. C., Cunge, J. A., O'Connell, P. E., and
Rasmussen, J.: An introduction to the European Hydrological System –
Systeme Hydrologique Europeen, “SHE”, 1: History and philosophy of a
physically-based, distributed modelling system, J. Hydrol., 87, 45–59,
https://doi.org/10.1016/0022-1694(86)90114-9, 1986.
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark,
M. P.: A Ranking of Hydrological Signatures Based on Their Predictability in
Space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018.
Anderson, M. C., Norman, J. M., Mecikalski, J. R., Torn, R. D., Kustas, W.
P., and Basara, J. B.: A Multiscale Remote Sensing Model for Disaggregating
Regional Fluxes to Micrometeorological Scales, J. Hydrometeorol., 5,
343–363, https://doi.org/10.1175/1525-7541(2004)005<0343:AMRSMF>2.0.CO;2, 2004.
Anderson, M. C., Yang, Y., Xue, J., Knipper, K. R., Yang, Y., Gao, F., Hain,
C. R., Kustas, W. P., Cawse-Nicholson, K., Hulley, G., Fisher, J. B.,
Alfieri, J. G., Meyers, T. P., Prueger, J., Baldocchi, D. D., and
Rey-Sanchez, C.: Interoperability of ECOSTRESS and Landsat for mapping
evapotranspiration time series at sub-field scales, Remote Sens. Environ.,
252, 112189, https://doi.org/10.1016/j.rse.2020.112189, 2021.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing
area model of basin hydrology, Hydrol. Sci. Sci. Hydrol., 24, 43–69, 1979.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Cheng, J., Kuang, Q., Shen, C., Liu, J., Tan, X., and Liu, W.: ResLap:
Generating High-Resolution Climate Prediction through Image
Super-Resolution, IEEE Access, 8, 39623–39634,
https://doi.org/10.1109/ACCESS.2020.2974785, 2020.
DHI: MIKE SHE – User Guide and Reference Manual,
https://manuals.mikepoweredbydhi.help/2020/Water_Resources/MIKE_SHE_Print.pdf (last access: 17 November 2022), 2020.
Doherty, J.: Calibration and Uncertainty Analysis for Complex Environmental
Models, Watermark Numerical Computing, Brisbane, Australia, ISBN 978-0-9943786-0-6 (electronic), 2015.
Fan, Y., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table Depth, Science, 339, 940–943,
https://doi.org/10.1126/science.1229881, 2013.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019.
Gleeson, T., Befus, K. M., Jasechko, S., Luijendijk, E., and Cardenas, M.
B.: The global volume and distribution of modern groundwater, Nat. Geosci.,
9, 161–164, https://doi.org/10.1038/ngeo2590, 2016.
Gonzalez, R. Q. and Arsanjani, J. J.: Prediction of Groundwater Level
Variations in a Changing Climate: A Danish Case Study, ISPRS Int. J.
Geo-Information, 10, 792, https://doi.org/10.3390/ijgi10110792, 2021.
Guzinski, R. and Nieto, H.: Evaluating the feasibility of using Sentinel-2
and Sentinel-3 satellites for high-resolution evapotranspiration
estimations, Remote Sens. Environ., 221, 157–172,
https://doi.org/10.1016/j.rse.2018.11.019, 2019.
Halsnæs, K., Larsen, M. A. D., and Drenck, K. L.: Samfundsøkonomiske
konsekvenser af oversvømmelser og investeringer i klimatilpasning, 56
pp., DTU, Department of Management Engineering, Kgs. Lyngby, Denmark, https://backend.orbit.dtu.dk/ws/portalfiles/portal/268507361/Samfunds_konomiske_konsekvenser_af_oversv_mmelser_og_investeringer_i_klimatilpasning_final_reduced.pdf, last access: 17 November 2022.
Henriksen, H. J., Troldborg, L., Nyegaard, P., Sonnenborg, T. O., Refsgaard,
J. C., and Madsen, B.: Methodology for construction, calibration and
validation of a national hydrological model for Denmark, J. Hydrol., 280,
52–71, https://doi.org/10.1016/S0022-1694(03)00186-0, 2003.
Henriksen, H. J., Kragh, S. J., Gotfredsen, J., Ondracek, M., van Til, M.,
Jakobsen, A., Schneider, R. J. M., Koch, J., Troldborg, L., Rasmussen, P.,
Pasten-Zapata, E., and Stisen, S.: Dokumentationsrapport vedr.
modelleverancer til Hydrologisk Informations- og Prognosesystem, GEUS, https://doi.org/10.22008/gpub/38113, 2020a.
Henriksen, H. J., Kragh, S. J., Gotfredsen, J., Ondracek, M., van Til, M.,
Jakobsen, A., Schneider, R. J. M., Koch, J., Troldborg, L., Rasmussen, P.,
Pasten-Zapata, E., and Stisen, S.: Sammenfatningsrapport vedr.
modelleverancer til Hydrologisk Informations- og Prognosesystem, GEUS, https://doi.org/10.22008/gpub/38112, 2020b.
Højberg, A. L., Troldborg, L., Stisen, S., Christensen, B. B. S., and
Henriksen, H. J.: Stakeholder driven update and improvement of a national
water resources model, Environ. Model. Softw., 40, 202–213,
https://doi.org/10.1016/j.envsoft.2012.09.010, 2013.
Im, J., Park, S., Rhee, J., Baik, J., and Choi, M.: Downscaling of AMSR-E
soil moisture with MODIS products using machine learning approaches,
Environ. Earth Sci., 75, 1120, https://doi.org/10.1007/s12665-016-5917-6,
2016.
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer,
L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G.,
Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A.,
Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N.,
Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C.,
Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D.,
Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J. F., Teichmann, C.,
Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: New
high-resolution climate change projections for European impact research,
Reg. Environ. Chang., 14, 563–578,
https://doi.org/10.1007/s10113-013-0499-2, 2014.
Jakobsen, P. R., Hermansen, B., and Tougaard, L.: Danmarks digitale
jordartskort 1:25000 – Version 4.0, GEUS, https://doi.org/10.22008/gpub/30680, 2015.
Koch, J. and Schneider, R.: Long short-term memory networks enhance
rainfall-runoff modelling at the national scale of Denmark, GEUS Bull., 49,
8292, https://doi.org/10.34194/geusb.v49.8292, 2022.
Koch, J., Stisen, S., Refsgaard, J. C., Ernstsen, V., Jakobsen, P. R., and
Højberg, A. L.: Modeling Depth of the Redox Interface at High Resolution
at National Scale Using Random Forest and Residual Gaussian Simulation,
Water Resour. Res., 55, 1451–1469, https://doi.org/10.1029/2018WR023939,
2019a.
Koch, J., Berger, H., Henriksen, H. J., and Sonnenborg, T. O.: Modelling of the shallow water table at high spatial resolution using random forests, Hydrol. Earth Syst. Sci., 23, 4603–4619, https://doi.org/10.5194/hess-23-4603-2019, 2019b.
Koch, J., Gotfredsen, J., Schneider, R., Troldborg, L., Stisen, S., and
Henriksen, H. J.: High Resolution Water Table Modeling of the Shallow
Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model,
Front. Water, 3, 701726, https://doi.org/10.3389/frwa.2021.701726, 2021.
Mai, J., Tolson, B. A., Shen, H., Gaborit, É., Fortin, V., Gasset, N.,
Awoye, H., Stadnyk, T. A., Fry, L. M., Bradley, E. A., Seglenieks, F.,
Temgoua, A. G. T., Princz, D. G., Gharari, S., Haghnegahdar, A., Elshamy, M.
E., Razavi, S., Gauch, M., Lin, J., Ni, X., Yuan, Y., McLeod, M., Basu, N.
B., Kumar, R., Rakovec, O., Samaniego, L., Attinger, S., Shrestha, N. K.,
Daggupati, P., Roy, T., Wi, S., Hunter, T., Craig, J. R., and Pietroniro,
A.: Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E),
J. Hydrol. Eng., 26, 05021020,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0002097, 2021.
Meyer, H. and Pebesma, E.: Predicting into unknown space? Estimating the
area of applicability of spatial prediction models, Methods Ecol. Evol., 12,
1620–1633, https://doi.org/10.1111/2041-210X.13650, 2021.
Møller, A. B., Beucher, A., Iversen, B. V., and Greve, M. H.: Predicting
artificially drained areas by means of a selective model ensemble, Geoderma,
320, 30–42, https://doi.org/10.1016/j.geoderma.2018.01.018, 2018.
Motarjemi, S. K., Møller, A. B., Plauborg, F., and Iversen, B. V.:
Predicting national-scale tile drainage discharge in Denmark using machine
learning algorithms, J. Hydrol. Reg. Stud., 36, 100839,
https://doi.org/10.1016/j.ejrh.2021.100839, 2021.
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D.,
Frame, J. M., Prieto, C., and Gupta, H. V.: What Role Does Hydrological
Science Play in the Age of Machine Learning?, Water Resour. Res., 57, e2020WR028091, https://doi.org/10.1029/2020WR028091, 2021.
Nijzink, R. C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H. H. G., and Hrachowitz, M.: The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models, Hydrol. Earth Syst. Sci., 20, 1151–1176, https://doi.org/10.5194/hess-20-1151-2016, 2016.
Noorduijn, S. L., Refsgaard, J. C., Petersen, R. J., and Højberg, A. L.:
Downscaling a national hydrological model to subgrid scale, J. Hydrol., 603, 126796, https://doi.org/10.1016/j.jhydrol.2021.126796, 2021.
Olesen, S. E.: Kortlægning af potentielt dræningsbehov på
landbrugsarealer opdelt efter landskabselement, geologi, jordklasse,
geologisk region samt høj/lavbund, 30 pp., Aarhus Universitet, Det Jordbrugsvidenskabelige Fakultet, https://pure.au.dk/portal/en/publications/kortlaegning-af-potentielt-draeningsbehov-paa-landbrugsarealer-opdelt-efter-landskabselement-geologi-jordklasse-geologisk-region-samt-hoejlavbund(db364720-183e-11de-a378-000ea68e967b).html (last access: 17 November 2022), 2009.
Pasten-Zapata, E., Sonnenborg, T. O., and Refsgaard, J. C.: Climate change:
Sources of uncertainty in precipitation and temperature projections for
Denmark, GEUS Bull., 43, e2019430102-01,
https://doi.org/10.34194/GEUSB-201943-01-02, 2019.
Read, J. S., Jia, X., Willard, J., Appling, A. P., Zwart, J. A., Oliver, S.
K., Karpatne, A., Hansen, G. J. A., Hanson, P. C., Watkins, W., Steinbach,
M., and Kumar, V.: Process-Guided Deep Learning Predictions of Lake Water
Temperature, Water Resour. Res., 55, 9173–9190,
https://doi.org/10.1029/2019WR024922, 2019.
Refsgaard, J. C., Sonnenborg, T. O., Butts, M. B., Christensen, J. H.,
Christensen, S., Drews, M., Jensen, K. H., Jørgensen, F., Jørgensen,
L. F., Larsen, M. A. D., Rasmussen, S. H., Seaby, L. P., Seifert, D., and
Vilhelmsen, T. N.: Climate change impacts on groundwater hydrology – where
are the main uncertainties and can they be reduced?, Hydrol. Sci. J., 61,
2312–2324, https://doi.org/10.1080/02626667.2015.1131899, 2016.
Rodell, M., Famiglietti, J. S., Wiese, D. N., Reager, J. T., Beaudoing, H.
K., Landerer, F. W., and Lo, M. H.: Emerging trends in global freshwater
availability, Nature, 557, 651–659,
https://doi.org/10.1038/s41586-018-0123-1, 2018.
Rodriguez-Galiano, V., Mendes, M. P., Garcia-Soldado, M. J., Chica-Olmo, M.,
and Ribeiro, L.: Predictive modeling of groundwater nitrate pollution using
Random Forest and multisource variables related to intrinsic and specific
vulnerability: A case study in an agricultural setting (Southern Spain),
Sci. Total Environ., 476–477, 189–206,
https://doi.org/10.1016/j.scitotenv.2014.01.001, 2014.
Samaniego, L., Thober, S., Wanders, N., Pan, M., Rakovec, O., Sheffield, J.,
Wood, E. F., Prudhomme, C., Rees, G., Houghton-Carr, H., Fry, M., Smith, K.,
Watts, G., Hisdal, H., Estrela, T., Buontempo, C., Marx, A., and Kumar, R.:
Hydrological Forecasts and Projections for Improved Decision-Making in the
Water Sector in Europe, B. Am. Meteorol. Soc., 100, 2451–2472,
https://doi.org/10.1175/BAMS-D-17-0274.1, 2019.
Scharling, M.: Klimagrid Danmark – Nedbør, lufttemperatur og potentiel
fordampning 20X20 & 40 × 40 km – Metodebeskrivelse, Danish Meteorological
Institute, ISSN 1399-1388 (Technical Report 99-12), https://www.dmi.dk/fileadmin/user_upload/Rapporter/TR/1999/tr99-12.pdf (last access: 17 November 2022), 1999a.
Scharling, M.: Klimagrid Danmark Nedbør 10 × 10 km (ver. 2) –
Metodebeskrivelse, Danish Meteorological Institute, ISSN 1399-1388 (Technical Report 99-15), https://www.dmi.dk/fileadmin/user_upload/Rapporter/TR/1999/tr99-15.pdf (last access: 17 November 2022), 1999b.
Schneider, R., Stisen, S., and Højberg, A. L.: Hunting for Information in
Streamflow Signatures to Improve Modelled Drainage, 14, 110, https://doi.org/10.3390/w14010110, 2022.
Soltani, M., Bjerre, E., Koch, J., and Stisen, S.: Integrating remote
sensing data in optimization of a national water resources model to improve
the spatial pattern performance of evapotranspiration, J. Hydrol., 603,
127026, https://doi.org/10.1016/j.jhydrol.2021.127026, 2021.
Soylu, M. E. and Bras, R. L.: Global Shallow Groundwater Patterns from Soil
Moisture Satellite Retrievals, IEEE J. Sel. Top. Appl. Earth Obs. Remote
Sens., 15, 89–101, https://doi.org/10.1109/JSTARS.2021.3124892, 2022.
Stisen, S., Sonnenborg, T. O., Højberg, A. L., Troldborg, L., and
Refsgaard, J. C.: Evaluation of Climate Input Biases and Water Balance
Issues Using a Coupled Surface-Subsurface Model, Vadose Zo. J., 10, 37–53,
https://doi.org/10.2136/vzj2010.0001, 2011.
Stisen, S., Schneider, R., Ondracek, M., and Henriksen, H. J.: Modellering
af terrænnært grundvand, vandstand i vandløb og vand på
terræn for Storå og Odense Å. Slutrapport (FODS 6.1 Fasttrack
metodeudvikling), 1–170 pp., https://doi.org/10.22008/gpub/32582, 2018.
Stisen, S., Ondracek, M., Troldborg, L., Schneider, R. J. M., and van Til,
M. J.: National Vandressource Model – Modelopstilling og kalibrering af
DK-model 2019, https://doi.org/10.22008/gpub/32631, 2019.
Sun, A. Y. and Tang, G.: Downscaling Satellite and Reanalysis Precipitation
Products Using Attention-Based Deep Convolutional Neural Nets, Front. Water,
2, 536743, https://doi.org/10.3389/frwa.2020.536743, 2020.
Tesoriero, A. J., Gronberg, J. A., Juckem, P. F., Miller, M. P., and Austin,
B. P.: Predicting redox-sensitive contaminant concentrations in groundwater
using random forest classification, Water Resour. Res., 53, 7316–7331,
https://doi.org/10.1002/2016WR020197, 2017.
The Danish Agency for Data Supply and Infrastructure (SDFI): HIP – Hydrologisk Informations- og Prognosesystem, https://hip.dataforsyningen.dk/ (last access: 17 November 2022), 2021.
Thejll, P., Boberg, F., Schmith, T., Christiansen, B., Christensen, O. B.,
Madsen, M. S., Su, J., Andree, E., Olsen, S., Langen, P. L., Skovgaard, K.
M., Olesen, M., Pedersen, R. A., and Payne, M. R.: Methods used in the
Danish Climate Atlas, 67 pp., Danish Meteorological Institute, ISBN 978-87-7478-690-0, 2021.
Tran, H., Leonarduzzi, E., De la Fuente, L., Hull, R. B., Bansal, V.,
Chennault, C., Gentine, P., Melchior, P., Condon, L. E., and Maxwell, R. M.:
Development of a Deep Learning Emulator for a Distributed
Groundwater–Surface Water Model: ParFlow-ML, Water, 13, 3393,
https://doi.org/10.3390/w13233393, 2021.
Tyralis, H., Papacharalampous, G., and Langousis, A.: A Brief Review of
Random Forests for Water Scientists and Practitioners and Their Recent
History in Water Resources, Water, 11, 910, https://doi.org/10.3390/w11050910,
2019.
van Roosmalen, L., Christensen, B. S. B., and Sonnenborg, T. O.: Regional
Differences in Climate Change Impacts on Groundwater and Stream Discharge in
Denmark, Vadose Zo. J., 6, 554–571, https://doi.org/10.2136/vzj2006.0093,
2007.
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A.,
Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T.,
Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: an overview, Clim. Change, 109,
5–31, https://doi.org/10.1007/s10584-011-0148-z, 2011.
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F.
P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis,
D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B.,
Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade,
A., and Whitehead, P.: Hyperresolution global land surface modeling: Meeting
a grand challenge for monitoring Earth's terrestrial water, Water Resour.
Res., 47, 1–10, https://doi.org/10.1029/2010WR010090, 2011.
Wunsch, A., Liesch, T., and Broda, S.: Deep learning shows declining
groundwater levels in Germany until 2100 due to climate change, Nat.
Commun., 13, 1–13, https://doi.org/10.1038/s41467-022-28770-2, 2022.
Yang, Y., Cao, C., Pan, X., Li, X., and Zhu, X.: Downscaling Land Surface
Temperature in an Arid Area by Using Multiple Remote Sensing Indices with
Random Forest Regression, Remote Sens., 9, 789, https://doi.org/10.3390/rs9080789,
2017.
Zhang, J., Liu, K., and Wang, M.: Downscaling Groundwater Storage Data in
China to a 1-km Resolution Using Machine Learning Methods, Remote Sens., 13,
523, https://doi.org/10.3390/rs13030523, 2021.
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...
