Articles | Volume 28, issue 23
https://doi.org/10.5194/hess-28-5193-2024
© Author(s) 2024. 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-28-5193-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Dec 2024
Research article |
| 04 Dec 2024
| 04 Dec 2024
Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge
Raoul A. Collenteur
CORRESPONDING AUTHOR
Department Water Resources and Drinking Water (W+T), Eawag, Duebendorf, Switzerland
Ezra Haaf
Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden
Mark Bakker
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Tanja Liesch
Institute of Applied Geosciences, Division of Hydrogeology, Karlsruhe Institute of Technology, Karlsruhe, Germany
Andreas Wunsch
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, Germany
Jenny Soonthornrangsan
Department of Geoscience & Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Jeremy White
Intera, Fort Collins, Colorado, USA
Nick Martin
Southwest Research Institute (SWRI), San Antonio, Texas, USA
Rui Hugman
Intera, Fort Collins, Colorado, USA
Ed de Sousa
Intera, Fort Collins, Colorado, USA
Didier Vanden Berghe
Burgeap, Ginger Group, Lyon, France
Xinyang Fan
Department of Geography and Geosciences, GeoZentrum Nordbayern, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Erlangen, Germany
Institute of Geography & Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
Tim J. Peterson
Department of Civil Engineering, Monash University, Clayton, Australia
Jānis Bikše
Department of Geology, University of Latvia, Riga, Latvia
Antoine Di Ciacca
Environmental Research, Lincoln Agritech Ltd, Lincoln, New Zealand
Xinyue Wang
Data Science Institute (DSI), Brown University, Providence, Rhode Island, USA
Yang Zheng
Data Science Institute (DSI), Brown University, Providence, Rhode Island, USA
Maximilian Nölscher
German Federal Institute for Geoscience and Resources (BGR), Berlin, Germany
Julian Koch
Department of Hydrology, Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark
Raphael Schneider
Department of Hydrology, Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark
Nikolas Benavides Höglund
Department of Geology, Lund University, Lund, Sweden
Sivarama Krishna Reddy Chidepudi
Morphodynamique Continentale et Côtière, Univ. Rouen Normandie, UNICAEN, CNRS, M2C UMR 6143, 76000 Rouen, France
BRGM, 3 av. C. Guillemin, 45060 Orleans CEDEX 02, France
Abel Henriot
BRGM, 3 av. C. Guillemin, 45060 Orleans CEDEX 02, France
Nicolas Massei
Morphodynamique Continentale et Côtière, Univ. Rouen Normandie, UNICAEN, CNRS, M2C UMR 6143, 76000 Rouen, France
Abderrahim Jardani
Morphodynamique Continentale et Côtière, Univ. Rouen Normandie, UNICAEN, CNRS, M2C UMR 6143, 76000 Rouen, France
Max Gustav Rudolph
Institute of Groundwater Management, Dresden University of Technology, Dresden, Germany
Amir Rouhani
Department of Aquatic Ecosystem Analysis and Management, Helmholtz Centre for Environmental Research – UFZ, Magdeburg, Germany
J. Jaime Gómez-Hernández
Institute for Water and Environmental Engineering, Universitat Politècnica de València, Valencia, Spain
Seifeddine Jomaa
Department of Aquatic Ecosystem Analysis and Management, Helmholtz Centre for Environmental Research – UFZ, Magdeburg, Germany
Anna Pölz
Institute of Hydraulic Engineering and Water Resources Management, TU Wien, Vienna, Austria
Interuniversity Cooperation Centre Water and Health, Vienna, Austria
Tim Franken
Sumaqua, Louvain, Belgium
Morteza Behbooei
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada
Jimmy Lin
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada
Rojin Meysami
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada
Data sets
Data and Code from the 2022 Groundwater time series Modeling Challenge R. Collenteur et al. https://doi.org/10.5281/zenodo.10438290
Model code and software
Data and Code from the 2022 Groundwater time series Modeling Challenge R. Collenteur et al. https://doi.org/10.5281/zenodo.10438290
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.
We show the results of the 2022 Groundwater Time Series Modelling Challenge; 15 teams applied...
