Articles | Volume 26, issue 9
https://doi.org/10.5194/hess-26-2405-2022
© 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-26-2405-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Karst spring discharge modeling based on deep learning using spatially distributed input data
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences, Hydrogeology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Tanja Liesch
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences, Hydrogeology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Guillaume Cinkus
HydroSciences Montpellier (HSM), Université de Montpellier, CNRS, IRD, 34090 Montpellier, France
Nataša Ravbar
ZRC SAZU, Karst Research Institute, Titov trg 2, 6230 Postojna, Slovenia
Zhao Chen
Institute of Groundwater Management, Technical University of Dresden, 01062 Dresden
Naomi Mazzilli
UMR 1114 EMMAH (AU-INRAE), Université d'Avignon, 84000 Avignon, France
Hervé Jourde
HydroSciences Montpellier (HSM), Université de Montpellier, CNRS, IRD, 34090 Montpellier, France
Nico Goldscheider
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences, Hydrogeology, Kaiserstr. 12, 76131 Karlsruhe, Germany
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We present a data-driven approach to select optimal locations for groundwater monitoring wells. The applied approach can optimize the number of wells and their location for a network reduction (by ranking wells in order of their information content and reducing redundant) and extension (finding sites with great information gain) or both. It allows us to include a cost function to account for more/less suitable areas for new wells and can help to obtain maximum information content for a budget.
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We present a public dataset of weekly groundwater levels from more than 3,000 wells across Germany, spanning 32 years. It combines weather data and site-specific environmental information to support forecasting groundwater changes. Three benchmark models of varying complexity show how data and modeling approaches influence predictions. This resource promotes open, reproducible research and helps guide future water management decisions.
Vianney Sivelle, Guillaume Cinkus, Naomi Mazzilli, David Labat, Bruno Arfib, Nicolas Massei, Yohann Cousquer, Dominique Bertin, and Hervé Jourde
Hydrol. Earth Syst. Sci., 29, 1259–1276, https://doi.org/10.5194/hess-29-1259-2025, https://doi.org/10.5194/hess-29-1259-2025, 2025
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KarstMod provides a platform for global modelling of the rain level–flow relationship in karstic basins. The platform provides a set of tools to assess the dynamics of the compartments considered in the model and to detect possible flaws in structure and parameterization. This platform is developed as part of the French observatory network on karst hydrology (SNO KARST), which aims to strengthen the sharing of knowledge and promote interdisciplinary research on karst systems at a national level.
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
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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.
Andreas Wunsch, Tanja Liesch, and Nico Goldscheider
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Seasons have a strong influence on groundwater levels, but relationships are complex and partly unknown. Using data from wells in Germany and an explainable machine learning approach, we showed that summer precipitation is the key factor that controls the severeness of a low-water period in fall; high summer temperatures do not per se cause stronger decreases. Preceding winters have only a minor influence on such low-water periods in general.
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Hydrol. Earth Syst. Sci., 27, 2397–2411, https://doi.org/10.5194/hess-27-2397-2023, https://doi.org/10.5194/hess-27-2397-2023, 2023
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The Kling–Gupta Efficiency (KGE) is a performance criterion extensively used to evaluate hydrological models. We conduct a critical study on the KGE and its variant to examine counterbalancing errors. Results show that, when assessing a simulation, concurrent over- and underestimation of discharge can lead to an overall higher criterion score without an associated increase in model relevance. We suggest that one carefully choose performance criteria and use scaling factors.
Guillaume Cinkus, Andreas Wunsch, Naomi Mazzilli, Tanja Liesch, Zhao Chen, Nataša Ravbar, Joanna Doummar, Jaime Fernández-Ortega, Juan Antonio Barberá, Bartolomé Andreo, Nico Goldscheider, and Hervé Jourde
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Numerous modelling approaches can be used for studying karst water resources, which can make it difficult for a stakeholder or researcher to choose the appropriate method. We conduct a comparison of two widely used karst modelling approaches: artificial neural networks (ANNs) and reservoir models. Results show that ANN models are very flexible and seem great for reproducing high flows. Reservoir models can work with relatively short time series and seem to accurately reproduce low flows.
Leïla Serène, Christelle Batiot-Guilhe, Naomi Mazzilli, Christophe Emblanch, Milanka Babic, Julien Dupont, Roland Simler, Matthieu Blanc, and Gérard Massonnat
Hydrol. Earth Syst. Sci., 26, 5035–5049, https://doi.org/10.5194/hess-26-5035-2022, https://doi.org/10.5194/hess-26-5035-2022, 2022
Short summary
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This work aims to develop the Transit Time index (TTi) as a natural tracer of karst groundwater transit time, usable in the 0–6-month range. Based on the fluorescence of organic matter, TTi shows its relevance to detect a small proportion of fast infiltration water within a mix, while other natural transit time tracers provide no or less sensitive information. Comparison of the average TTi of different karst springs also provides consistent results with the expected relative transit times.
Marc Ohmer, Tanja Liesch, and Andreas Wunsch
Hydrol. Earth Syst. Sci., 26, 4033–4053, https://doi.org/10.5194/hess-26-4033-2022, https://doi.org/10.5194/hess-26-4033-2022, 2022
Short summary
Short summary
We present a data-driven approach to select optimal locations for groundwater monitoring wells. The applied approach can optimize the number of wells and their location for a network reduction (by ranking wells in order of their information content and reducing redundant) and extension (finding sites with great information gain) or both. It allows us to include a cost function to account for more/less suitable areas for new wells and can help to obtain maximum information content for a budget.
Markus Merk, Nadine Goeppert, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 25, 3519–3538, https://doi.org/10.5194/hess-25-3519-2021, https://doi.org/10.5194/hess-25-3519-2021, 2021
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Soil moisture levels have decreased significantly over the past 2 decades. This decrease is not uniformly distributed over the observation period. The largest changes occur at tipping points during years of extreme drought, after which soil moisture levels reach significantly different alternate stable states. Not only the overall trend in soil moisture is affected, but also the seasonal dynamics.
Andreas Wunsch, Tanja Liesch, and Stefan Broda
Hydrol. Earth Syst. Sci., 25, 1671–1687, https://doi.org/10.5194/hess-25-1671-2021, https://doi.org/10.5194/hess-25-1671-2021, 2021
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Short summary
Modeling complex karst water resources is difficult enough, but often there are no or too few climate stations available within or close to the catchment to deliver input data for modeling purposes. We apply image recognition algorithms to time-distributed, spatially gridded meteorological data to simulate karst spring discharge. Our models can also learn the approximate catchment location of a spring independently.
Modeling complex karst water resources is difficult enough, but often there are no or too few...