Articles | Volume 28, issue 19
https://doi.org/10.5194/hess-28-4407-2024
© 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-28-4407-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Oct 2024
Research article |
| 07 Oct 2024
| 07 Oct 2024
Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
Institute of Groundwater Management, TU Dresden, Dresden, Germany
Maximilian Nölscher
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
Andreas Hartmann
Institute of Groundwater Management, TU Dresden, Dresden, Germany
Stefan Broda
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
Related authors
Marc Ohmer, Tanja Liesch, Bastian Habbel, Benedikt Heudorfer, Mariana Gomez, Patrick Clos, Maximilian Nölscher, and Stefan Broda
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-321, https://doi.org/10.5194/essd-2025-321, 2025
Preprint under review for ESSD
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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.
Marc Ohmer, Tanja Liesch, Bastian Habbel, Benedikt Heudorfer, Mariana Gomez, Patrick Clos, Maximilian Nölscher, and Stefan Broda
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-321, https://doi.org/10.5194/essd-2025-321, 2025
Preprint under review for ESSD
Short summary
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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.
Stefan Kunz, Alexander Schulz, Maria Wetzel, Maximilian Nölscher, Teodor Chiaburu, Felix Biessmann, and Stefan Broda
Hydrol. Earth Syst. Sci., 29, 3405–3433, https://doi.org/10.5194/hess-29-3405-2025, https://doi.org/10.5194/hess-29-3405-2025, 2025
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Accurate groundwater level predictions are crucial for sustainable management. This study applies two machine learning models – Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS) and the Temporal Fusion Transformer (TFT) – to forecast seasonal groundwater levels for 5288 wells across Germany. N-HiTS outperformed TFT, with both models performing well in diverse hydrogeological settings, particularly in lowlands with distinct seasonal dynamics.
Markus Giese, Yvan Caballero, Andreas Hartmann, and Jean-Baptiste Charlier
Hydrol. Earth Syst. Sci., 29, 3037–3054, https://doi.org/10.5194/hess-29-3037-2025, https://doi.org/10.5194/hess-29-3037-2025, 2025
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Karst springs respond quickly to environmental changes, making them crucial to understanding climate impacts on groundwater. This study analyses long-term trends in precipitation, temperature, and discharge from more than 50 springs across Europe. Results show that while historical discharge trends align with those of rivers, recent changes are driven by rising temperatures rather than precipitation. These findings highlight climate-driven shifts in groundwater recharge and storage processes.
Pia Ebeling, Andreas Musolff, Rohini Kumar, Andreas Hartmann, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 29, 2925–2950, https://doi.org/10.5194/hess-29-2925-2025, https://doi.org/10.5194/hess-29-2925-2025, 2025
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Groundwater is a crucial resource at risk due to droughts. To understand drought effects on groundwater levels in Germany, we grouped 6626 wells into six regional and two national patterns. Weather explained half of the level variations with varied response times. Shallow groundwater responds fast and is more vulnerable to short droughts (a few months). Dampened deep heads buffer short droughts but suffer from long droughts and recoveries. Two nationwide trend patterns were linked to human water use.
Robert Reinecke, Annemarie Bäthge, Ricarda Dietrich, Sebastian Gnann, Simon N. Gosling, Danielle Grogan, Andreas Hartmann, Stefan Kollet, Rohini Kumar, Richard Lammers, Sida Liu, Yan Liu, Nils Moosdorf, Bibi Naz, Sara Nazari, Chibuike Orazulike, Yadu Pokhrel, Jacob Schewe, Mikhail Smilovic, Maryna Strokal, Yoshihide Wada, Shan Zuidema, and Inge de Graaf
EGUsphere, https://doi.org/10.5194/egusphere-2025-1181, https://doi.org/10.5194/egusphere-2025-1181, 2025
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Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.
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
Hydrol. Earth Syst. Sci., 28, 5193–5208, https://doi.org/10.5194/hess-28-5193-2024, https://doi.org/10.5194/hess-28-5193-2024, 2024
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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.
Benedikt Heudorfer, Tanja Liesch, and Stefan Broda
Hydrol. Earth Syst. Sci., 28, 525–543, https://doi.org/10.5194/hess-28-525-2024, https://doi.org/10.5194/hess-28-525-2024, 2024
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We build a neural network to predict groundwater levels from monitoring wells. We predict all wells at the same time, by learning the differences between wells with static features, making it an entity-aware global model. This works, but we also test different static features and find that the model does not use them to learn exactly how the wells are different, but only to uniquely identify them. As this model class is not actually entity aware, we suggest further steps to make it so.
Andreas Hartmann, Jean-Lionel Payeur-Poirier, and Luisa Hopp
Hydrol. Earth Syst. Sci., 27, 1325–1341, https://doi.org/10.5194/hess-27-1325-2023, https://doi.org/10.5194/hess-27-1325-2023, 2023
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We advance our understanding of including information derived from environmental tracers into hydrological modeling. We present a simple approach that integrates streamflow observations and tracer-derived streamflow contributions for model parameter estimation. We consider multiple observed streamflow components and their variation over time to quantify the impact of their inclusion for streamflow prediction at the catchment scale.
Romane Berthelin, Tunde Olarinoye, Michael Rinderer, Matías Mudarra, Dominic Demand, Mirjam Scheller, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 27, 385–400, https://doi.org/10.5194/hess-27-385-2023, https://doi.org/10.5194/hess-27-385-2023, 2023
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Karstic recharge processes have mainly been explored using discharge analysis despite the high influence of the heterogeneous surface on hydrological processes. In this paper, we introduce an event-based method which allows for recharge estimation from soil moisture measurements. The method was tested at a karst catchment in Germany but can be applied to other karst areas with precipitation and soil moisture data available. It will allow for a better characterization of karst recharge processes.
Tunde Olarinoye, Tom Gleeson, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 26, 5431–5447, https://doi.org/10.5194/hess-26-5431-2022, https://doi.org/10.5194/hess-26-5431-2022, 2022
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Analysis of karst spring recession is essential for management of groundwater. In karst, recession is dominated by slow and fast components; separating these components is by manual and subjective approaches. In our study, we tested the applicability of automated streamflow recession extraction procedures for a karst spring. Results showed that, by simple modification, streamflow extraction methods can identify slow and fast components: derived recession parameters are within reasonable ranges.
Yan Liu, Jaime Fernández-Ortega, Matías Mudarra, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 26, 5341–5355, https://doi.org/10.5194/hess-26-5341-2022, https://doi.org/10.5194/hess-26-5341-2022, 2022
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We adapt the informal Kling–Gupta efficiency (KGE) with a gamma distribution to apply it as an informal likelihood function in the DiffeRential Evolution Adaptive Metropolis DREAM(ZS) method. Our adapted approach performs as well as the formal likelihood function for exploring posterior distributions of model parameters. The adapted KGE is superior to the formal likelihood function for calibrations combining multiple observations with different lengths, frequencies and units.
Yong Chang, Benjamin Mewes, and Andreas Hartmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-77, https://doi.org/10.5194/hess-2022-77, 2022
Revised manuscript not accepted
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This study presents a work to investigate the feasibility of using EC to predict the discharge in a typical karst catchment. We found that the spring discharge can be well predicted by EC in storms using LSTM (Long Short Term Memory) model, while the prediction has relatively large uncertainties in small recharge events. To establish a roust LSTM model for long-term discharge prediction from EC in ungauged catchments, the random or fixed-interval discharge monitoring strategy is recommended.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
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Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Tesfalem Abraham, Yan Liu, Sirak Tekleab, and Andreas Hartmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-271, https://doi.org/10.5194/hess-2021-271, 2021
Preprint withdrawn
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In this study we demonstrate the use of global data products for the regionalization of model parameters. We combine three steps of uncertainty quantification from the parameter sampling, best parameter sets identification, and spatial cross-validation. Our results show the best validation parameters provide the most robust regionalization results, and the uncertainties from the regionalization in the ungauged catchments are higher than those obtained from simulations in the gauged catchments.
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
Nicolas Massei, Daniel G. Kingston, David M. Hannah, Jean-Philippe Vidal, Bastien Dieppois, Manuel Fossa, Andreas Hartmann, David A. Lavers, and Benoit Laignel
Proc. IAHS, 383, 141–149, https://doi.org/10.5194/piahs-383-141-2020, https://doi.org/10.5194/piahs-383-141-2020, 2020
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This paper presents recent thoughts by members of EURO-FRIEND Water project 3 “Large-scale-variations in hydrological characteristics” about research needed to characterize and understand large-scale hydrology under global changes. Emphasis is put on the necessary efforts to better understand 1 – the impact of low-frequency climate variability on hydrological trends and extremes, 2 – the role of basin properties on modulating the climate signal producing hydrological responses on the basin scale.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-378, https://doi.org/10.5194/hess-2020-378, 2020
Revised manuscript not accepted
Romane Berthelin, Michael Rinderer, Bartolomé Andreo, Andy Baker, Daniela Kilian, Gabriele Leonhardt, Annette Lotz, Kurt Lichtenwoehrer, Matías Mudarra, Ingrid Y. Padilla, Fernando Pantoja Agreda, Rafael Rosolem, Abel Vale, and Andreas Hartmann
Geosci. Instrum. Method. Data Syst., 9, 11–23, https://doi.org/10.5194/gi-9-11-2020, https://doi.org/10.5194/gi-9-11-2020, 2020
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We present the setup of a soil moisture monitoring network, which is implemented at five karstic sites with different climates across the globe. More than 400 soil moisture probes operating at a high spatio-temporal resolution will improve the understanding of groundwater recharge and evapotranspiration processes in karstic areas.
Fanny Sarrazin, Andreas Hartmann, Francesca Pianosi, Rafael Rosolem, and Thorsten Wagener
Geosci. Model Dev., 11, 4933–4964, https://doi.org/10.5194/gmd-11-4933-2018, https://doi.org/10.5194/gmd-11-4933-2018, 2018
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We propose the first large-scale vegetation–recharge model for karst regions (V2Karst), which enables the analysis of the impact of changes in climate and land cover on karst groundwater recharge. We demonstrate the plausibility of V2Karst simulations against observations at FLUXNET sites and of controlling modelled processes using sensitivity analysis. We perform virtual experiments to further test the model and gain insight into its sensitivity to precipitation pattern and vegetation cover.
Zhao Chen, Andreas Hartmann, Thorsten Wagener, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 22, 3807–3823, https://doi.org/10.5194/hess-22-3807-2018, https://doi.org/10.5194/hess-22-3807-2018, 2018
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This paper investigates potential impacts of climate change on mountainous karst systems. Our study highlights the fast groundwater dynamics in mountainous karst catchments, which make them highly vulnerable to future changing-climate conditions. Additionally, this work presents a novel holistic modeling approach, which can be transferred to similar karst systems for studying the impact of climate change on local karst water resources.
Simon Brenner, Gemma Coxon, Nicholas J. K. Howden, Jim Freer, and Andreas Hartmann
Nat. Hazards Earth Syst. Sci., 18, 445–461, https://doi.org/10.5194/nhess-18-445-2018, https://doi.org/10.5194/nhess-18-445-2018, 2018
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In this study we simulate groundwater levels with a semi-distributed karst model. Using a percentile approach we can assess the number of days exceeding or falling below selected groundwater level percentiles. We show that our approach is able to predict groundwater levels across all considered timescales up to the 75th percentile. We then use our approach to assess future changes in groundwater dynamics and show that projected climate changes may lead to generally lower groundwater levels.
Andreas Hartmann, Juan Antonio Barberá, and Bartolomé Andreo
Hydrol. Earth Syst. Sci., 21, 5971–5985, https://doi.org/10.5194/hess-21-5971-2017, https://doi.org/10.5194/hess-21-5971-2017, 2017
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In karst modeling, there is often an imbalance between the complexity of model structures and the data availability for parameterization. We present a new approach to quantify the value of water quality data for improved karst model parameterization. We show that focusing on “informative” time periods, which are time periods with decreased observation uncertainty, allows for further reduction of simulation uncertainty. Our approach is transferable to other sites with limited data availability.
A. Hartmann, J. Kobler, M. Kralik, T. Dirnböck, F. Humer, and M. Weiler
Biogeosciences, 13, 159–174, https://doi.org/10.5194/bg-13-159-2016, https://doi.org/10.5194/bg-13-159-2016, 2016
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We consider the time period before and after a wind disturbance in an Austrian karst system. Using a process-based flow and solute transport simulation model we estimate impacts on DIN and DOC. We show that DIN increases for several years, while DOC remains within its pre-disturbance variability. Simulated transit times indicate that impact passes through the hydrological system within some months but with a small fraction exceeding transit times of even a year.
A. Hartmann, T. Gleeson, R. Rosolem, F. Pianosi, Y. Wada, and T. Wagener
Geosci. Model Dev., 8, 1729–1746, https://doi.org/10.5194/gmd-8-1729-2015, https://doi.org/10.5194/gmd-8-1729-2015, 2015
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We present a new approach to assess karstic groundwater recharge over Europe and the Mediterranean. Cluster analysis is used to subdivide all karst regions into four typical karst landscapes and to simulate karst recharge with a process-based karst model. We estimate its parameters by a combination of a priori information and observations of soil moisture and evapotranspiration. Independent observations of recharge that present large-scale models significantly under-estimate karstic recharge.
A. Hartmann, M. Weiler, T. Wagener, J. Lange, M. Kralik, F. Humer, N. Mizyed, A. Rimmer, J. A. Barberá, B. Andreo, C. Butscher, and P. Huggenberger
Hydrol. Earth Syst. Sci., 17, 3305–3321, https://doi.org/10.5194/hess-17-3305-2013, https://doi.org/10.5194/hess-17-3305-2013, 2013
Related subject area
Subject: Groundwater hydrology | Techniques and Approaches: Modelling approaches
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Hydrol. Earth Syst. Sci., 29, 3405–3433, https://doi.org/10.5194/hess-29-3405-2025, https://doi.org/10.5194/hess-29-3405-2025, 2025
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Bishaw Mihret and Ajebush Wuletaw
Hydrol. Earth Syst. Sci., 29, 2951–2959, https://doi.org/10.5194/hess-29-2951-2025, https://doi.org/10.5194/hess-29-2951-2025, 2025
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This review highlights how geological structures like faults and fractures influence groundwater potential in volcanic rocks of Borena Sayint, northwestern Ethiopian Plateau. These structures enhance secondary porosity, affecting aquifer behaviour. The study stresses integrating geological, geophysical, and hydrological data for sustainable groundwater management in tectonically complex volcanic regions.
Yufei Wang, Daniel Fernàndez-Garcia, and Maarten W. Saaltink
Hydrol. Earth Syst. Sci., 29, 2485–2503, https://doi.org/10.5194/hess-29-2485-2025, https://doi.org/10.5194/hess-29-2485-2025, 2025
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During geological carbon sequestration, the injected supercritical CO2, being less dense, floats above the brine. The dissolution of CO2 into brine helps mitigate the risk of CO2 leakage. As CO2 dissolves into the brine, it increases the density of brine in the upper layer, initiating gravity-driven convection (GDC), which significantly enhances the rate of CO2 dissolution. We derived two empirical formulas to predict the asymptotic dissolution rate driven by GDC in heterogeneous fields.
Elise Verstraeten, Alice Alonso, Louise Collier, and Marnik Vanclooster
Hydrol. Earth Syst. Sci., 29, 1829–1845, https://doi.org/10.5194/hess-29-1829-2025, https://doi.org/10.5194/hess-29-1829-2025, 2025
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This study takes a data-driven approach to evaluate long-term groundwater nitrate concentration trends in Wallonia, Belgium, over ~20 years following the implementation of regional nitrogen management policies. Results highlight a persistently negative impact of agricultural land use, the importance of sustained policies and long-term monitoring to mitigate nitrogen legacy effects, and the need for improved datasets to better capture controlling factors.
Haegyeong Lee, Manuel Gossler, Kai Zosseder, Philipp Blum, Peter Bayer, and Gabriel C. Rau
Hydrol. Earth Syst. Sci., 29, 1359–1378, https://doi.org/10.5194/hess-29-1359-2025, https://doi.org/10.5194/hess-29-1359-2025, 2025
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A systematic laboratory experiment elucidates two-phase heat transport due to water flow in saturated porous media to understand thermal propagation in aquifers. Results reveal delayed thermal arrival in the solid phase, depending on grain size and flow velocity. Analytical modeling using standard local thermal equilibrium (LTE) and advanced local thermal non-equilibrium (LTNE) theory fails to describe temperature breakthrough curves, highlighting the need for more advanced numerical approaches.
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.
Attila Galsa, Márk Szijártó, Ádám Tóth, and Judit Mádl-Szőnyi
EGUsphere, https://doi.org/10.5194/egusphere-2025-559, https://doi.org/10.5194/egusphere-2025-559, 2025
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Understanding groundwater flow is crucial for both environmental and economic reasons. In our study, the dynamic interaction of the forces driving groundwater flow is presented, partly in synthetic models and partly in a real deep geothermal reservoir. We point out that ignoring certain driving forces can lead to oversimplification of groundwater flow and even misinterpretation of the phenomena, causing environmental problems or economic losses.
Sivarama Krishna Reddy Chidepudi, Nicolas Massei, Abderrahim Jardani, Bastien Dieppois, Abel Henriot, and Matthieu Fournier
Hydrol. Earth Syst. Sci., 29, 841–861, https://doi.org/10.5194/hess-29-841-2025, https://doi.org/10.5194/hess-29-841-2025, 2025
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This study explores how deep learning can improve our understanding of groundwater levels, using an approach that combines climate data and physical characteristics of aquifers. By focusing on different types of groundwater levels and employing techniques like clustering and wavelet transform, the study highlights the importance of targeting relevant information. This research not only advances groundwater simulation but also emphasizes the benefits of different modelling approaches.
Han Cao, Jinlong Qian, Huanliang Chen, Chunwei Liu, Minghui Lyu, Shuai Gao, Weihong Dong, and Caiping Hu
EGUsphere, https://doi.org/10.5194/egusphere-2025-281, https://doi.org/10.5194/egusphere-2025-281, 2025
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This research employed multi-methods to quantitatively analyze the impacts of managed aquifer recharge (MAR) and exploitation on the dynamics of groundwater level and quality in a typical northern China karst area, the Baotu Spring area in Jinan City. The complementary techniques enhance the accuracy of quantitative research. The results indicate that the MAR and exploitation in Baotu Spring area significantly impact karst groundwater level and quality.
Igor Karlović, Mitja Janža, Edmundo Placencia-Gómez, and Tamara Marković
EGUsphere, https://doi.org/10.5194/egusphere-2025-327, https://doi.org/10.5194/egusphere-2025-327, 2025
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This study presents a methodology for understanding heterogeneity in alluvial aquifers by combining borehole data, electrical resistivity tomography, and stochastic modeling. The approach uses hydrofacies to incorporate spatial variability into groundwater models. The hydrofacies distribution helps define hydraulic conductivity fields, essential for groundwater flow and contaminant transport simulations, providing a valuable tool for sustainable aquifer management in sedimentary environments.
Friederike Currle, René Therrien, and Oliver S. Schilling
EGUsphere, https://doi.org/10.5194/egusphere-2025-372, https://doi.org/10.5194/egusphere-2025-372, 2025
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We present a new approach to simulate the transport of microbes in river-aquifer systems in the integrated hydrological model HydroGeoSphere. Compared to existing models, the advantage of the new implementation lies in the consideration of all relevant parts of the water budget and the flexibility to simulate in parallel the reactive transport of several microbial species and solutes. The new developed tool enables to improve our understanding of pathogen transport in river-groundwater systems.
Aohan Jin, Wenguang Shi, Renjie Zhou, Hongbin Zhan, Quanrong Wang, and Xuan Gu
EGUsphere, https://doi.org/10.5194/egusphere-2024-4145, https://doi.org/10.5194/egusphere-2024-4145, 2025
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This study developed a novel deep transfer learning (DTL) approach, which integrates the physical mechanisms from an analytical model using a transfer learning technique. Results indicate that the DTL model maintains satisfactory performance even in heterogeneous conditions, with uncertainties in observations and sparse training data compared to the DNN model.
Etienne Marti, Sarah Leray, and Clément Roques
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-381, https://doi.org/10.5194/hess-2024-381, 2024
Revised manuscript accepted for HESS
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We show that the response of groundwater-dependent wetlands to recharge changes can be predicted based on landform properties, providing a practical approach for wetland vulnerability assessment. We reveal that mountain catchments are less sensitive to recharge changes than lowland catchments. It offers insights for evaluating the vulnerability of catchments to climate change impacts and has direct implications for water resource management and conservation planning in diverse landscapes.
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
Hydrol. Earth Syst. Sci., 28, 5193–5208, https://doi.org/10.5194/hess-28-5193-2024, https://doi.org/10.5194/hess-28-5193-2024, 2024
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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.
Steven Reinaldo Rusli, Victor F. Bense, Syed M. T. Mustafa, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci., 28, 5107–5131, https://doi.org/10.5194/hess-28-5107-2024, https://doi.org/10.5194/hess-28-5107-2024, 2024
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In this paper, we investigate the impact of climatic and anthropogenic factors on future groundwater availability. The changes are simulated using hydrological and groundwater flow models. We find that future groundwater status is influenced more by anthropogenic factors than climatic factors. The results are beneficial for informing responsible parties in operational water management about achieving future (ground)water governance.
Nariman Mahmoodi, Ulrich Struck, Michael Schneider, and Christoph Merz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-214, https://doi.org/10.5194/hess-2024-214, 2024
Revised manuscript accepted for HESS
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Understanding water balance in lakes is complex. We studied Lake Gross Glienicke in Germany, using an innovative method that combines isotope measurements and a hydrological model to improve estimates of water inflow and evaporation. Our findings show a high correlation between the two approaches, leading to better predictions of lake water dynamics. This research offers a reliable way to evaluate the model outputs.
Laurent Gourdol, Michael K. Stewart, Uwe Morgenstern, and Laurent Pfister
Hydrol. Earth Syst. Sci., 28, 3519–3547, https://doi.org/10.5194/hess-28-3519-2024, https://doi.org/10.5194/hess-28-3519-2024, 2024
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Determining water transit times in aquifers is key to a better understanding of groundwater resources and their sustainable management. For our research, we used high-accuracy tritium data from 35 springs draining the Luxembourg Sandstone aquifer. We assessed the mean transit times of groundwater and found that water moves on average more than 10 times more slowly vertically in the vadose zone of the aquifer (~12 m yr-1) than horizontally in its saturated zone (~170 m yr-1).
Anna Pazola, Mohammad Shamsudduha, Jon French, Alan M. MacDonald, Tamiru Abiye, Ibrahim Baba Goni, and Richard G. Taylor
Hydrol. Earth Syst. Sci., 28, 2949–2967, https://doi.org/10.5194/hess-28-2949-2024, https://doi.org/10.5194/hess-28-2949-2024, 2024
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This study advances groundwater research using a high-resolution random forest model, revealing new recharge areas and spatial variability, mainly in humid regions. Limited data in rainy zones is a constraint for the model. Our findings underscore the promise of machine learning for large-scale groundwater modelling while further emphasizing the importance of data collection for robust results.
Andreas Wunsch, Tanja Liesch, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 28, 2167–2178, https://doi.org/10.5194/hess-28-2167-2024, https://doi.org/10.5194/hess-28-2167-2024, 2024
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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.
Evgeny Shavelzon and Yaniv Edery
Hydrol. Earth Syst. Sci., 28, 1803–1826, https://doi.org/10.5194/hess-28-1803-2024, https://doi.org/10.5194/hess-28-1803-2024, 2024
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We investigate the interaction of transport with dissolution–precipitation reactions in porous media using the concepts of entropy and work to quantify the emergence of preferential flow paths. We show that the preferential-flow-path phenomenon and the hydraulic power required to maintain the driving pressure drop intensify over time along with the heterogeneity due to the interaction between the transport and the reactive processes. This is more pronounced in diffusion-dominated flows.
Stephen Lee, Dylan J. Irvine, Clément Duvert, Gabriel C. Rau, and Ian Cartwright
Hydrol. Earth Syst. Sci., 28, 1771–1790, https://doi.org/10.5194/hess-28-1771-2024, https://doi.org/10.5194/hess-28-1771-2024, 2024
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Global groundwater recharge studies collate recharge values estimated using different methods that apply to different timescales. We develop a recharge prediction model, based solely on chloride, to produce a recharge map for Australia. We reveal that climate and vegetation have the most significant influence on recharge variability in Australia. Our recharge rates were lower than other models due to the long timescale of chloride in groundwater. Our method can similarly be applied globally.
Yiming Li, Uwe Schneidewind, Zhang Wen, Stefan Krause, and Hui Liu
Hydrol. Earth Syst. Sci., 28, 1751–1769, https://doi.org/10.5194/hess-28-1751-2024, https://doi.org/10.5194/hess-28-1751-2024, 2024
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Meandering rivers are an integral part of many landscapes around the world. Here we used a new modeling approach to look at how the slope of riverbanks influences water flow and solute transport from a meandering river channel through its bank and into/out of the connected groundwater compartment (aquifer). We found that the bank slope can be a significant factor to be considered, especially when bank slope angles are small, and riverbank and aquifer conditions only allow for slow water flow.
Yiqun Gan and Quanrong Wang
Hydrol. Earth Syst. Sci., 28, 1317–1323, https://doi.org/10.5194/hess-28-1317-2024, https://doi.org/10.5194/hess-28-1317-2024, 2024
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1. A revised 3D model of solute transport is developed in the well–aquifer system. 2. The accuracy of the new model is tested against benchmark analytical solutions. 3. Previous models overestimate the concentration of solute in both aquifers and wellbores in the injection well test case. 4. Previous models underestimate the concentration in the extraction well test case.
Annika Nolte, Ezra Haaf, Benedikt Heudorfer, Steffen Bender, and Jens Hartmann
Hydrol. Earth Syst. Sci., 28, 1215–1249, https://doi.org/10.5194/hess-28-1215-2024, https://doi.org/10.5194/hess-28-1215-2024, 2024
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This study examines about 8000 groundwater level (GWL) time series from five continents to explore similarities in groundwater systems at different scales. Statistical metrics and machine learning techniques are applied to identify common GWL dynamics patterns and analyze their controlling factors. The study also highlights the potential and limitations of this data-driven approach to improve our understanding of groundwater recharge and discharge processes.
Tom Müller, Matteo Roncoroni, Davide Mancini, Stuart N. Lane, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 28, 735–759, https://doi.org/10.5194/hess-28-735-2024, https://doi.org/10.5194/hess-28-735-2024, 2024
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We investigate the role of a newly formed floodplain in an alpine glaciated catchment to store and release water. Based on field measurements, we built a numerical model to simulate the water fluxes and show that recharge occurs mainly due to the ice-melt-fed river. We identify three future floodplains, which could emerge from glacier retreat, and show that their combined storage leads to some additional groundwater storage but contributes little additional baseflow for the downstream river.
Benedikt Heudorfer, Tanja Liesch, and Stefan Broda
Hydrol. Earth Syst. Sci., 28, 525–543, https://doi.org/10.5194/hess-28-525-2024, https://doi.org/10.5194/hess-28-525-2024, 2024
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We build a neural network to predict groundwater levels from monitoring wells. We predict all wells at the same time, by learning the differences between wells with static features, making it an entity-aware global model. This works, but we also test different static features and find that the model does not use them to learn exactly how the wells are different, but only to uniquely identify them. As this model class is not actually entity aware, we suggest further steps to make it so.
Trine Enemark, Rasmus Bødker Madsen, Torben O. Sonnenborg, Lærke Therese Andersen, Peter B. E. Sandersen, Jacob Kidmose, Ingelise Møller, Thomas Mejer Hansen, Karsten Høgh Jensen, and Anne-Sophie Høyer
Hydrol. Earth Syst. Sci., 28, 505–523, https://doi.org/10.5194/hess-28-505-2024, https://doi.org/10.5194/hess-28-505-2024, 2024
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In this study, we demonstrate an approach to evaluate the interpretation uncertainty within a manually interpreted geological model in a groundwater model. Using qualitative estimates of uncertainties, several geological realizations are developed and implemented in groundwater models. We confirm existing evidence that if the conceptual model is well defined, interpretation uncertainties within the conceptual model have limited impact on groundwater model predictions.
Yong Zhang, Graham E. Fogg, HongGuang Sun, Donald M. Reeves, Roseanna M. Neupauer, and Wei Wei
Hydrol. Earth Syst. Sci., 28, 179–203, https://doi.org/10.5194/hess-28-179-2024, https://doi.org/10.5194/hess-28-179-2024, 2024
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Pollutant release history and source identification are helpful for managing water resources, but it remains a challenge to reliably identify such information for real-world, complex transport processes in rivers and aquifers. In this study, we filled this knowledge gap by deriving a general backward governing equation and developing the efficient solver. Field applications showed that this model and solver are applicable for a broad range of flow systems, dimensions, and spatiotemporal scales.
Mennatullah T. Elrashidy, Andrew M. Ireson, and Saman Razavi
Hydrol. Earth Syst. Sci., 27, 4595–4608, https://doi.org/10.5194/hess-27-4595-2023, https://doi.org/10.5194/hess-27-4595-2023, 2023
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Wetlands are important ecosystems that store carbon and play a vital role in the water cycle. However, hydrological computer models do not always represent wetlands and their interaction with groundwater accurately. We tested different possible ways to include groundwater–wetland interactions in these models. We found that the optimal method to include wetlands and groundwater in the models is reliant on the intended use of the models and the characteristics of the land and soil being studied.
Conny Tschritter, Christopher J. Daughney, Sapthala Karalliyadda, Brioch Hemmings, Uwe Morgenstern, and Catherine Moore
Hydrol. Earth Syst. Sci., 27, 4295–4316, https://doi.org/10.5194/hess-27-4295-2023, https://doi.org/10.5194/hess-27-4295-2023, 2023
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Understanding groundwater travel time (groundwater age) is crucial for tracking flow and contaminants. While groundwater age is usually inferred from age tracers, this study utilised two machine learning techniques with common groundwater chemistry data. The results of both methods correspond to traditional approaches. They are useful where hydrochemistry data exist but age tracer data are limited. These methods could help enhance our knowledge, aiding in sustainable freshwater management.
Jose M. Bastias Espejo, Chris Turnadge, Russell S. Crosbie, Philipp Blum, and Gabriel C. Rau
Hydrol. Earth Syst. Sci., 27, 3447–3462, https://doi.org/10.5194/hess-27-3447-2023, https://doi.org/10.5194/hess-27-3447-2023, 2023
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Analytical models estimate subsurface properties from subsurface–tidal load interactions. However, they have limited accuracy in representing subsurface physics and parameter estimation. We derived a new analytical solution which models flow to wells due to atmospheric tides. We applied it to field data and compared our findings with subsurface knowledge. Our results enhance understanding of subsurface systems, providing valuable information on their behavior.
Ronan Abhervé, Clément Roques, Alexandre Gauvain, Laurent Longuevergne, Stéphane Louaisil, Luc Aquilina, and Jean-Raynald de Dreuzy
Hydrol. Earth Syst. Sci., 27, 3221–3239, https://doi.org/10.5194/hess-27-3221-2023, https://doi.org/10.5194/hess-27-3221-2023, 2023
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We propose a model calibration method constraining groundwater seepage in the hydrographic network. The method assesses the hydraulic properties of aquifers in regions where perennial streams are directly fed by groundwater. The estimated hydraulic conductivity appear to be highly sensitive to the spatial extent and density of streams. Such an approach improving subsurface characterization from surface information is particularly interesting for ungauged basins.
Amanda Triplett and Laura E. Condon
Hydrol. Earth Syst. Sci., 27, 2763–2785, https://doi.org/10.5194/hess-27-2763-2023, https://doi.org/10.5194/hess-27-2763-2023, 2023
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Accelerated melting in mountains is a global phenomenon. The Heihe River basin depends on upstream mountains for its water supply. We built a hydrologic model to examine how shifts in streamflow and warming will impact ground and surface water interactions. The results indicate that degrading permafrost has a larger effect than melting glaciers. Additionally, warming temperatures tend to have more impact than changes to streamflow. These results can inform other mountain–valley system studies.
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
Hydrol. Earth Syst. Sci., 27, 1961–1985, https://doi.org/10.5194/hess-27-1961-2023, https://doi.org/10.5194/hess-27-1961-2023, 2023
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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.
Wenguang Shi, Quanrong Wang, Hongbin Zhan, Renjie Zhou, and Haitao Yan
Hydrol. Earth Syst. Sci., 27, 1891–1908, https://doi.org/10.5194/hess-27-1891-2023, https://doi.org/10.5194/hess-27-1891-2023, 2023
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The mechanism of radial dispersion is important for understanding reactive transport in the subsurface and for estimating aquifer parameters required in the optimization design of remediation strategies. A general model and associated analytical solutions are developed in this study. The new model represents the most recent advancement on radial dispersion studies and incorporates a host of important processes that are not taken into consideration in previous investigations.
Vanja Travaš, Luka Zaharija, Davor Stipanić, and Siniša Družeta
Hydrol. Earth Syst. Sci., 27, 1343–1359, https://doi.org/10.5194/hess-27-1343-2023, https://doi.org/10.5194/hess-27-1343-2023, 2023
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In order to model groundwater flow in karst aquifers, it is necessary to approximate the influence of the unknown and irregular structure of the karst conduits. For this purpose, a procedure based on inverse modeling is adopted. Moreover, in order to reconstruct the functional dependencies related to groundwater flow, the particle swarm method was used, through which the optimal solution of unknown functions is found by imitating the movement of ants in search of food.
Shouchuan Zhang, Zheming Shi, Guangcai Wang, Zuochen Zhang, and Huaming Guo
Hydrol. Earth Syst. Sci., 27, 401–415, https://doi.org/10.5194/hess-27-401-2023, https://doi.org/10.5194/hess-27-401-2023, 2023
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We documented the step-like increases of water level, flow rate, and water temperatures in a confined aquifer following multiple earthquakes. By employing tidal analysis and a coupled temperature and flow rate model, we find that post-seismic vertical permeability changes and recharge model could explain the co-seismic response. And co-seismic temperature changes are caused by mixing of different volumes of water, with the mixing ratio varying according to each earthquake.
Xiaoying Zhang, Fan Dong, Guangquan Chen, and Zhenxue Dai
Hydrol. Earth Syst. Sci., 27, 83–96, https://doi.org/10.5194/hess-27-83-2023, https://doi.org/10.5194/hess-27-83-2023, 2023
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In a data-driven framework, groundwater levels can generally only be calculated 1 time step ahead. We discuss the advance prediction with longer forecast periods rather than single time steps by constructing a model based on a temporal convolutional network. Model accuracy and efficiency were further compared with an LSTM-based model. The two models derived in this study can help people cope with the uncertainty of what might occur in hydrological scenarios under the threat of climate change.
Dimitri Rambourg, Raphaël Di Chiara, and Philippe Ackerer
Hydrol. Earth Syst. Sci., 26, 6147–6162, https://doi.org/10.5194/hess-26-6147-2022, https://doi.org/10.5194/hess-26-6147-2022, 2022
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The reproduction of flows and contaminations underground requires a good estimation of the parameters of the geological environment (mainly permeability and porosity), in three dimensions. While most researchers rely on geophysical methods, which are costly and difficult to implement in the field, this study proposes an alternative using data that are already widely available: piezometric records (monitoring of the water table) and the lithological description of the piezometric wells.
Raphael Schneider, Julian Koch, Lars Troldborg, Hans Jørgen Henriksen, and Simon Stisen
Hydrol. Earth Syst. Sci., 26, 5859–5877, https://doi.org/10.5194/hess-26-5859-2022, https://doi.org/10.5194/hess-26-5859-2022, 2022
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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.
Luca Guillaumot, Laurent Longuevergne, Jean Marçais, Nicolas Lavenant, and Olivier Bour
Hydrol. Earth Syst. Sci., 26, 5697–5720, https://doi.org/10.5194/hess-26-5697-2022, https://doi.org/10.5194/hess-26-5697-2022, 2022
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Recharge, defining the renewal rate of groundwater resources, is difficult to estimate at basin scale. Here, recharge variations are inferred from water table variations recorded in boreholes. First, results show that aquifer-scale properties controlling these variations can be inferred from boreholes. Second, groundwater is recharged by both intense and seasonal rainfall. Third, the short-term contribution appears overestimated in recharge models and depends on the unsaturated zone thickness.
Alberto Casillas-Trasvina, Bart Rogiers, Koen Beerten, Laurent Wouters, and Kristine Walraevens
Hydrol. Earth Syst. Sci., 26, 5577–5604, https://doi.org/10.5194/hess-26-5577-2022, https://doi.org/10.5194/hess-26-5577-2022, 2022
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Heat in the subsurface can be used to characterize aquifer flow behaviour. The temperature data obtained can be useful for understanding the groundwater flow, which is of particular importance in waste disposal studies. Satellite images of surface temperature and a temperature–time curve were implemented in a heat transport model. Results indicate that conduction plays a major role in the aquifer and support the usefulness of temperature measurements.
Tunde Olarinoye, Tom Gleeson, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 26, 5431–5447, https://doi.org/10.5194/hess-26-5431-2022, https://doi.org/10.5194/hess-26-5431-2022, 2022
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Analysis of karst spring recession is essential for management of groundwater. In karst, recession is dominated by slow and fast components; separating these components is by manual and subjective approaches. In our study, we tested the applicability of automated streamflow recession extraction procedures for a karst spring. Results showed that, by simple modification, streamflow extraction methods can identify slow and fast components: derived recession parameters are within reasonable ranges.
Min Lu, Bart Rogiers, Koen Beerten, Matej Gedeon, and Marijke Huysmans
Hydrol. Earth Syst. Sci., 26, 3629–3649, https://doi.org/10.5194/hess-26-3629-2022, https://doi.org/10.5194/hess-26-3629-2022, 2022
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Lowland rivers and shallow aquifers are closely coupled. We study their interactions here using a combination of impulse response modeling and hydrological data analysis. The results show that the lowland catchments are groundwater dominated and that the hydrological system from precipitation impulse to groundwater inflow response is a very fast response regime. This study also provides an alternative method to estimate groundwater inflow to rivers from the perspective of groundwater level.
Zhongxia Li, Junwei Wan, Tao Xiong, Hongbin Zhan, Linqing He, and Kun Huang
Hydrol. Earth Syst. Sci., 26, 3359–3375, https://doi.org/10.5194/hess-26-3359-2022, https://doi.org/10.5194/hess-26-3359-2022, 2022
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Four permeable rocks with different pore sizes were considered to provide experimental evidence of Forchheimer flow and the transition between different flow regimes. The mercury injection technique was used to measure the pore size distribution, which is an essential factor for determining the flow regime, for four permeable stones. Finally, the influences of porosity and particle size on the Forchheimer coefficients were discussed.
Andreas Wunsch, Tanja Liesch, Guillaume Cinkus, Nataša Ravbar, Zhao Chen, Naomi Mazzilli, Hervé Jourde, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 26, 2405–2430, https://doi.org/10.5194/hess-26-2405-2022, https://doi.org/10.5194/hess-26-2405-2022, 2022
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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.
Brian Berkowitz
Hydrol. Earth Syst. Sci., 26, 2161–2180, https://doi.org/10.5194/hess-26-2161-2022, https://doi.org/10.5194/hess-26-2161-2022, 2022
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Extensive efforts have focused on quantifying conservative chemical transport in geological formations. We assert that an explicit accounting of temporal information, under uncertainty, in addition to spatial information, is fundamental to an effective modeling formulation. We further assert that efforts to apply chemical transport equations at large length scales, based on measurements and model parameter values relevant to significantly smaller length scales, are an unattainable
holy grail.
Guilherme E. H. Nogueira, Christian Schmidt, Daniel Partington, Philip Brunner, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 26, 1883–1905, https://doi.org/10.5194/hess-26-1883-2022, https://doi.org/10.5194/hess-26-1883-2022, 2022
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In near-stream aquifers, mixing between stream water and ambient groundwater can lead to dilution and the removal of substances that can be harmful to the water ecosystem at high concentrations. We used a numerical model to track the spatiotemporal evolution of different water sources and their mixing around a stream, which are rather difficult in the field. Results show that mixing mainly develops as narrow spots, varying In time and space, and is affected by magnitudes of discharge events.
Jacques Bodin, Gilles Porel, Benoît Nauleau, and Denis Paquet
Hydrol. Earth Syst. Sci., 26, 1713–1726, https://doi.org/10.5194/hess-26-1713-2022, https://doi.org/10.5194/hess-26-1713-2022, 2022
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Assessment of the karst network geometry is an important challenge in the accurate modeling of karst aquifers. In this study, we propose an approach for the identification of effective three-dimensional discrete karst conduit networks conditioned on tracer tests and geophysical data. The applicability of the proposed approach is illustrated through a case study at the Hydrogeological Experimental Site in Poitiers, France.
Zexuan Xu, Rebecca Serata, Haruko Wainwright, Miles Denham, Sergi Molins, Hansell Gonzalez-Raymat, Konstantin Lipnikov, J. David Moulton, and Carol Eddy-Dilek
Hydrol. Earth Syst. Sci., 26, 755–773, https://doi.org/10.5194/hess-26-755-2022, https://doi.org/10.5194/hess-26-755-2022, 2022
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Climate change could change the groundwater system and threaten water supply. To quantitatively evaluate its impact on water quality, numerical simulations with chemical and reaction processes are required. With the climate projection dataset, we used the newly developed hydrological and chemical model to investigate the movement of contaminants and assist the management of contamination sites.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., and Davis, A.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, Zenodo [code], https://doi.org/10.5281/zenodo.4724125, 2015. a
Ahmadi, A., Olyaei, M., Heydari, Z., Emami, M., Zeynolabedin, A., Ghomlaghi, A., Daccache, A., Fogg, G. E., and Sadegh, M.: Groundwater Level Modeling with Machine Learning: A Systematic Review and Meta-Analysis, Water, 14, 949, https://doi.org/10.3390/w14060949, 2022. a, b
Alibrahim, H., and Ludwig, S. A.: Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization, in: 2021 IEEE Congress on Evolutionary Computation (CEC), Kraków, Poland, 28 June–1 July 2021, 1551–1559, https://doi.org/10.1109/CEC45853.2021.9504761, 2021. a
Armstrong, R. A.: Should Pearson's correlation coefficient be avoided?, Ophthal. Physl. Opt., 39, 316–327, https://doi.org/10.1111/opo.12636, 2019. a
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l'hydrologie du bassin versant, Hydrol. Sci. B., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979. a
BGR: Geomorphographische Einheiten von Deutschland, Bundesanstalt für Geowissenschaften und Rohstoffe, Hannover, https://geoportal.bgr.de/mapapps/resources/apps/geoportal/index .html?lang=de#/datasets/portal/60ab5e4e-9493-44b0-9cae-d9ce603de742 (last access: 15 June 2024), 2006. a, b
BGR: Geologische Übersichtskarte der Bundesrepublik Deutschland 1:250.000 (GÜK250), Bundesanstalt für Geowissenschaften und Rohstoffe, Hannover, https://produktcenter.bgr.de/terraCatalog/DetailResult.do?fileIde ntifier=0f2e1b5b-fc02-4491-a12b-2178473f5c84 (last access: 15 June 2024), 2019a. a
BGR: Hydrogeologische Übersichtskarte 1:250.000 von Deutschland (HÜK250), Bundesanstalt für Geowissenschaften und Rohstoffe, Hannover, https://geoportal.bgr.de/mapapps/resources/apps/geoportal/index .html?lang=de#/datasets/portal/61ac4628-6b62-48c6-89b8-46270819f0d6 (last access: 15 June 2024), 2019b. a
BKG: Digitales Geländemodell Gitterweite 1000 m (DGM1000), Bundesamt für Kartographie und Geodäsie, Frankfurt am Main, https://gdz.bkg.bund.de/index.php/default/digitale-geodaten/digitale-gelandemodelle/digitales-gelandemodell-gitterweite-1000-m-dgm1000.html (last access: 15 June 2024), 2021. a, b, c
Chakraborty, D., Başağaoğlu, H., Gutierrez, L., and Mirchi, A.: Explainable AI reveals new hydroclimatic insights for ecosystem-centric groundwater management, Environ. Res. Lett., 16, 114024, https://doi.org/10.1088/1748-9326/ac2fde, 2021. a, b
Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr, A. W.: Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package), Neurocomputing, 307, 72–77, https://doi.org/10.1016/j.neucom.2018.03.067, 2018. a
Copernicus: CORINE Land Cover 2018 (vector/raster 100 m), Europe, 6-yearly, European Environment Agency, https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac, 2018. a, b
Copernicus: Index Corine Land Cover (CLC), European Environment Agency, https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/ (last access: 15 June 2024), 2022. a
Daliakopoulos, I. N., Coulibaly, P., and Tsanis, I. K.: Groundwater level forecasting using artificial neural networks, J. Hydrol., 309, 229–240, https://doi.org/10.1016/j.jhydrol.2004.12.001, 2005. a
de Graaf, I., Gleeson, T., Rens van Beek, L., Sutanudjaja, E., and Bierkens, M.: Environmental flow limits to global groundwater pumping, Nature, 574, 90–94, https://doi.org/10.1038/s41586-019-1594-4, 2019. a
DWD: Climate Data Center – Grids Germany- HYRAS dataset, https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/hyras_de/, last access: 15 June 2024. a
Earman, S. and Dettinger, M.: Potential impacts of climate change on groundwater resources – A global review, J. Water Clim. Change, 2, 213–229, https://doi.org/10.2166/wcc.2011.034, 2011. a
Fabio, D. N., Abba, S. I., Pham, B. Q., Towfiqul Islam, A. R. M., Talukdar, S., and Francesco, G.: Groundwater level forecasting in Northern Bangladesh using nonlinear autoregressive exogenous (NARX) and extreme learning machine (ELM) neural networks, Arab. J. Geosci., 15, 647, https://doi.org/10.1007/s12517-022-09906-6, 2022. a
Famiglietti, J. S.: The global groundwater crisis, Nat. Clim. Change, 4, 945–948, https://doi.org/10.1038/nclimate2425, 2014. a
Frick, C., Steiner, H., Mazurkiewicz, A., Riediger, U., Rauthe, M., Reich, T., and Gratzki, A.: Central European high-resolution gridded daily data sets (HYRAS): Mean temperature and relative humidity, Meteorol. Z., 23, 15–32, https://doi.org/10.1127/0941-2948/2014/0560, 2014. a, b
Fritsch, F. N. and Butland, J.: A Method for Constructing Local Monotone Piecewise Cubic Interpolants, SIAM J. Sci. Stat. Comp., 5, 300–304, https://doi.org/10.1137/0905021, 1984. a
Gholizadeh, H., Zhang, Y., Frame, J., Gu, X., and Green, C. T.: Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama, Sci. Total Environ., 901, 165884, https://doi.org/10.1016/j.scitotenv.2023.165884, 2023. a
Goderniaux, P., Brouyère, S., Wildemeersch, S., Therrien, R., and Dassargues, A.: Uncertainty of climate change impact on groundwater reserves – Application to a chalk aquifer, J. Hydrol., 528, 108–121, https://doi.org/10.1016/j.jhydrol.2015.06.018, 2015. a
Gomez, M.: mgomezo12/Performance_CNN_v3: Assessing Groundwater Level Modelling using a 1D-CNN: Linking Model Performances to Geospatial and Time Series Features (Version 3), Zenodo [code and data set], https://doi.org/10.5281/zenodo.12531372, 2024. a, b
Gong, Y., Zhang, Y., Lan, S., and Wang, H.: A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida, Water Resour. Manag., 30, 375–391, https://doi.org/10.1007/s11269-015-1167-8, 2016. a, b
Guzman, S. M., Paz, J. O., and Tagert, M. L. M.: The Use of NARX Neural Networks to Forecast Daily Groundwater Levels, Water Resour. Manag., 31, 1591–1603, https://doi.org/10.1007/s11269-017-1598-5, 2017. a
Heudorfer, B., Liesch, T., and Broda, S.: On the challenges of global entity-aware deep learning models for groundwater level prediction, Hydrol. Earth Syst. Sci., 28, 525–543, https://doi.org/10.5194/hess-28-525-2024, 2024. a
Hunt, E. D., Hubbard, K. G., Wilhite, D. A., Arkebauer, T. J., and Dutcher, A. L.: The development and evaluation of a soil moisture index, Int. J. Climatol., 29, 747–759, https://doi.org/10.1002/joc.1749, 2009. a
Hunter, J. D.: Matplotlib: A 2D Graphics Environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Jordahl, K., Van den Bossche, J., Fleischmann, M., Wasserman, J., McBride, J., and Gerard, J.: geopandas/geopandas: v0.8.1, Zenodo [code], https://doi.org/10.5281/zenodo.3946761, 2020. a
Kløve, B., Ala-Aho, P., Bertrand, G., Gurdak, J. J., Kupfersberger, H., Kværner, J., Muotka, T., Mykrä, H., Preda, E., Rossi, P., Uvo, C. B., Velasco, E., and Pulido-Velazquez, M.: Climate change impacts on groundwater and dependent ecosystems, J. Hydrol., 518, 250–266, https://doi.org/10.1016/j.jhydrol.2013.06.037, 2013. a, b, c, d, e
Knoll, L., Breuer, L., and Bach, M.: Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning, Sci. Total Environ., 668, 1317–1327, https://doi.org/10.1016/j.scitotenv.2019.03.045, 2019. a
Kratzert, F., Gauch, M., Klotz, D., and Nearing, G.: HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin, Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, 2024. a
LBEG: Hydrogeologische Räume und Teilräume in Niedersachsen, Landesamt für Bergbau, Energie und Geologie, Hannover, https://www.umwelt.niedersachsen.de/startseite/themen/wasser/grundwasser/grundwasserbericht_niedersachsen/nutzung_schutz_und_uberwachung/hydrogeologischer_uberblick/ (last access: 15 June 2024), 2016. a, b, c
LeCun, Y., Hinton, G., and Bengio, Y.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015. a
Lee, S., Lee, K. K., and Yoon, H.: Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors, Hydrogeol. J., 27, 567–579, https://doi.org/10.1007/s10040-018-1866-3, 2019. a, b
Lerner, D. N. and Harris, B.: The relationship between land use and groundwater resources and quality, Land Use Policy, 26, 265–273, https://doi.org/10.1016/j.landusepol.2009.09.005, 2009. a
Linke, C.: Leitlinien zur Interpretation regionaler Klimamodelldaten des Bund-Länder-Fachgespräches “Interpretation regionaler Klimamodelldaten”, Landesamt für Umwelt Brandenburg, Potsdam, https://lfu.brandenburg.de/cms/media.php/lbm1.a.3310.de/blfg_leitlinie_klima.pdf (last access: 15 June 2024), 2017. a
Liu, Q., Gui, D., Zhang, L., Niu, J., Dai, H., Wei, G., and Hu, B. X.: Simulation of regional groundwater levels in arid regions using interpretable machine learning models, Sci. Total Environ., 831, 154902, https://doi.org/10.1016/j.scitotenv.2022.154902, 2022. a
LSN: Öffentliche Wasserversorgung und Abwasserbeseitigung, Tech. rep., Landesamt für Statistik Niedersachsen, Hannover, https://www.statistik. niedersachsen.de/startseite/themen/umwelt_und_energie/umwel t-und-energie-in-niedersachsen-statistische-berichte-q-i-2-178924.html (last access: 15 June 2024), 2016. a, b
Malik, A. and Bhagwat, A.: Modelling groundwater level fluctuations in urban areas using artificial neural network, Groundwater for Sustainable Development, 12, 100484, https://doi.org/10.1016/j.gsd.2020.100484, 2021. a
Mohanty, S., Jha, M. K., and Raul, S. K.: Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites, Water Resour. Manag., 29, 5521–5532, https://doi.org/10.1007/s11269-015-1132-6, 2015. a
Moriasi, D. N., Gitau, M. W., Pai, N., and Daggupati, P.: Hydrologic and water quality models: Performance measures and evaluation criteria, T. ASABE, 58, 1763–1785, https://doi.org/10.13031/trans.58.10715, 2015. a
NMUEK: Lower Saxony contribution to the management plans 2015 to 2021 for the Elbe, Weser, Ems, and Rhine river basins, Niedersächsisches Ministerium für Umwelt, Energie und Klimaschutz, Hannover, https://www.nlwkn.niedersachsen.de/download/109179/Management_plans_2015_to_2021.pdf (last access: 15 June 2024), 2015. a, b, c
Pan, Y., Gong, H., Zhou, D., Li, X., and Nakagoshi, N.: Impact of land use change on groundwater recharge in Guishui River Basin, China, Chinese Geogr. Sci., 21, 734–743, https://doi.org/10.1007/s11769-011-0508-7, 2011. a
Pistocchi, A.: Leaf Area Index (MAPPE model), Tech. rep., European Commission, Joint Research Centre (JRC), https://data.jrc.ec.europa.eu/dataset/jrc-mappe-europe-setup-d-18-lai (last access: 15 June 2024), 2015. a
Post, V. E. and von Asmuth, J. R.: Revue : Mesure du niveau piézométrique-nouvelles technologies, pièges classiques, Hydrogeol. J., 21, 737–750, https://doi.org/10.1007/s10040-013-0969-0, 2013. a
Rajaveni, S. P., Brindha, K., and Elango, L.: Geological and geomorphological controls on groundwater occurrence in a hard rock region, Applied Water Science, 7, 1377–1389, https://doi.org/10.1007/s13201-015-0327-6, 2017. a
Rauthe, M., Steiner, H., Riediger, U., Mazurkiewicz, A., and Gratzki, A.: A Central European precipitation climatology – Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS), Meteorol. Z., 22, 235–256, https://doi.org/10.1127/0941-2948/2013/0436, 2013. a, b, c
Razafimaharo, C., Krähenmann, S., Höpp, S., Rauthe, M., and Deutschländer, T.: New high-resolution gridded dataset of daily mean, minimum, and maximum temperature and relative humidity for Central Europe (HYRAS), Theor. Appl. Climatol., Springer, Berlin, https://doi.org/10.1007/s00704-020-03388-w, 2020. a, b
Reback, J., McKinney, W., Jbrockmendel, Van den Bossche, J., Augspurger, T., Cloud, P., Hawkins, S., Tratner, J., She, C., Ayd, W., Hoefler, P., Klein, A., Petersen, T., Roeschke, M., Schendel, J., Seabold, S., Sinhrks, and Waucomont, F.: pandas-dev/pandas: Pandas 1.4.2, Zenodo [code], https://doi.org//10.5281/zenodo.6702671, 2020. a
Reichenau, T. G., Korres, W., Montzka, C., Fiener, P., Wilken, F., Stadler, A., Waldhoff, G., and Schneider, K.: Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA), PLOS ONE, 11, 158451, https://doi.org/10.1371/journal.pone.0158451, 2016. a
Retike, I., Bikše, J., Kalvāns, A., Dēliņa, A., Avotniece, Z., Zaadnoordijk, W. J., Jemeljanova, M., Popovs, K., Babre, A., Zelenkevičs, A., and Baikovs, A.: Rescue of groundwater level time series: How to visually identify and treat errors, J. Hydrol., 605, 127294, https://doi.org/10.1016/j.jhydrol.2021.127294, 2022. a
Roshni, T., Jha, M. K., and Drisya, J.: Neural network modeling for groundwater-level forecasting in coastal aquifers, Neural Comput. Appl., 32, 12737–12754, https://doi.org/10.1007/s00521-020-04722-z, 2020. a
Rust, W., Holman, I., Corstanje, R., Bloomfield, J., and Cuthbert, M.: A conceptual model for climatic teleconnection signal control on groundwater variability in Europe, Earth-Sci. Rev., 177, 164–174, https://doi.org/10.1016/j.earscirev.2017.09.017, 2018. a
Snoek, J., Larochelle, H., and Adams, R. P.: Practical Bayesian optimization of machine learning algorithms, in: Advances in Neural Information Processing Systems, vol. 4, edited by: Pereira, F., Burges, C. J. C., Bottou, L., and Weinberger, K. Q., Curran Associates, Inc., ISBN 978-1627480031, 2012. a, b, c
Takafuji, E. H. de M., da Rocha, M. M., and Manzione, R. L.: Groundwater Level Prediction/Forecasting and Assessment of Uncertainty Using SGS and ARIMA Models: A Case Study in the Bauru Aquifer System (Brazil), Nat. Resour. Res., 28, 487–503, https://doi.org/10.1007/s11053-018-9403-6, 2019. a
Tao, H., Hameed, M. M., Marhoon, H. A., Zounemat-Kermani, M., Heddam, S., Sungwon, K., Sulaiman, S. O., Tan, M. L., Sa'adi, Z., Mehr, A. D., Allawi, M. F., Abba, S. I., Zain, J. M., Falah, M. W., Jamei, M., Bokde, N. D., Bayatvarkeshi, M., Al-Mukhtar, M., Bhagat, S. K., Tiyasha, T., Khedher, K. M., Al-Ansari, N., Shahid, S., and Yaseen, Z. M.: Groundwater level prediction using machine learning models: A comprehensive review, Neurocomputing, 489, 271–308, https://doi.org/10.1016/j.neucom.2022.03.014, 2022. a
Tarasova, L., Gnann, S., Yang, S., Hartmann, A., and Wagener, T.: Catchment characterization: Current descriptors, knowledge gaps and future opportunities, Earth-Sci. Rev., 241, 104739, https://doi.org/10.1016/j.earscirev.2024.104739, 2024. a
Treidel, H., Martin-Bordes, J. L., and Gurdak, J. J.: Climate change effects on groundwater resources: A global synthesis of findings and recommendations, International Association of Hydrogeologists, Taylor & Francis Group, Boca Raton, FL, ISBN 978-0-415-63152-2, 2011. a
Van Der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy array: A structure for efficient numerical computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., Vijaykumar, A., Bardelli, A. P., Rothberg, A., Hilboll, A., Kloeckner, A., Scopatz, A., Lee, A., Rokem, A., Woods, C. N., Fulton, C., Masson, C., Häggström, C., Fitzgerald, C., Nicholson, D. A., Hagen, D. R., Pasechnik, D. V., Olivetti, E., Martin, E., Wieser, E., Silva, F., Lenders, F., Wilhelm, F., Young, G., Price, G. A., Ingold, G.-L., Allen, G. E., Lee, G. R., Audren, H., Probst, I., Dietrich, J. P., Silterra, J., Webber, J. T., Slavič, J., Nothman, J., Buchner, J., Kulick, J., Schönberger, J. L., de Miranda Cardoso, J. V., Reimer, J., Harrington, J., Rodríguez, J. L. C., Nunez-Iglesias, J., Kuczynski, J., Tritz, K., Thoma, M., Newville, M., Kümmerer, M., Bolingbroke, M., Tartre, M., Pak, M., Smith, N. J., Nowaczyk, N., Shebanov, N., Pavlyk, O., Brodtkorb, P. A., Lee, P., McGibbon, R. T., Feldbauer, R., Lewis, S., Tygier, S., Sievert, S., Vigna, S., Peterson, S., More, S., Pudlik, T., Oshima, T., Pingel, T. J., Robitaille, T. P., Spura, T., Jones, T. R., Cera, T., Leslie, T., Zito, T., Krauss, T., Upadhyay, U., Halchenko, Y. O., and Vázquez-Baeza, Y.: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nature Meth., 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a, b
Wendt, D. E., Van Loon, A. F., Bloomfield, J. P., and Hannah, D. M.: Asymmetric impact of groundwater use on groundwater droughts, Hydrol. Earth Syst. Sci., 24, 4853–4868, https://doi.org/10.5194/hess-24-4853-2020, 2020. a
Wriedt, G., and NLWKN: Grundwasser Grundwasserbericht Niedersachsen Sonderausgabe zur Grundwasserstandssituation in den Trockenjahren 2018 und 2019, Niedersächsischer Landesbetrieb für Wasserwirtschaft, Küsten- und Naturschutz, Hannover, https://www.nlwkn.niedersachsen.de/download/156169/NLWKN_2020_Grundwasserbericht_Niedersachsen_Sonderausgabe_zur_Grundwasserstandssituation_in_den_Trockenjahren_2018_und_2019_Band_41_.pdf (last access: 15 June 2024), 2020. a
Wunsch, A., Liesch, T., and Broda, S.: Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX), Hydrol. Earth Syst. Sci., 25, 1671–1687, https://doi.org/10.5194/hess-25-1671-2021, 2021. a, b, c, d, e
Wunsch, A., Liesch, T., and Broda, S.: Deep learning shows declining groundwater levels in Germany until 2100 due to climate change, Nat. Commun., 13, 1206, https://doi.org/10.1038/s41467-022-28770-2, 2022. a, b, c, d
Xu, Y. S., Shen, S. L., Cai, Z. Y., and Zhou, G. Y.: The state of land subsidence and prediction approaches due to groundwater withdrawal in China, Nat. Hazards, 45, 123–135, https://doi.org/10.1007/s11069-007-9168-4, 2008. a
Zanotti, C., Rotiroti, M., Sterlacchini, S., Cappellini, G., Fumagalli, L., Stefania, G. A., Nannucci, M. S., Leoni, B., and Bonomi, T.: Choosing between linear and nonlinear models and avoiding overfitting for short and long term groundwater level forecasting in a linear system, J. Hydrol., 578, 124015, https://doi.org/10.1016/j.jhydrol.2019.124015, 2019. a
Zhang, Q., Li, P., Ren, X., Ning, J., Li, J., Liu, C., Wang, Y., and Wang, G.: A new real-time groundwater level forecasting strategy: Coupling hybrid data-driven models with remote sensing data, J. Hydrol., 625, 129962, https://doi.org/10.1016/j.jhydrol.2023.129962, 2023. a
Short summary
To understand the impact of external factors on groundwater level modelling using a 1-D convolutional neural network (CNN) model, we train, validate, and tune individual CNN models for 505 wells distributed across Lower Saxony, Germany. We then evaluate the performance of these models against available geospatial and time series features. This study provides new insights into the relationship between these factors and the accuracy of groundwater modelling.
To understand the impact of external factors on groundwater level modelling using a 1-D...
