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

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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. 
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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.