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

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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,, 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,, 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,<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,, 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. 
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.