Articles | Volume 26, issue 11
https://doi.org/10.5194/hess-26-2829-2022
© 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-26-2829-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Influence of low-frequency variability on high and low groundwater levels: example of aquifers in the Paris Basin
Lisa Baulon
CORRESPONDING AUTHOR
Normandie University, UNIROUEN, UNICAEAN, CNRS, M2C, 76000 Rouen, France
BRGM, 3 av. C. Guillemin, 45060 Orleans CEDEX 02, France
Nicolas Massei
Normandie University, UNIROUEN, UNICAEAN, CNRS, M2C, 76000 Rouen, France
Delphine Allier
BRGM, 3 av. C. Guillemin, 45060 Orleans CEDEX 02, France
Matthieu Fournier
Normandie University, UNIROUEN, UNICAEAN, CNRS, M2C, 76000 Rouen, France
Hélène Bessiere
BRGM, 3 av. C. Guillemin, 45060 Orleans CEDEX 02, France
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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.
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Hydro-climate observations (such as precipitation, temperature, and river discharge time series) reveal very complex behavior inherited from complex interactions among the physical processes that drive hydro-climate viability. This study shows how even small perturbations of a physical process can have large consequences on some others. Those interactions vary spatially, thus showing the importance of both temporal and spatial dimensions in better understanding hydro-climate variability.
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Short summary
Aquifers often act as low-pass filters, dampening high-frequency (intra-annual) and amplifying low-frequency (LFV, multi-annual to multidecadal) variabilities originating from climate variability. By processing groundwater level signals, we show the key role of LFV in the occurrence of groundwater extremes (GWEs). Results highlight how changes in LFV may impact future GWEs as well as the importance of correct representation of LFV in general circulation model outputs for GWE projection.
Aquifers often act as low-pass filters, dampening high-frequency (intra-annual) and amplifying...