Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3555-2021
© Author(s) 2021. 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-25-3555-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jun 2021
Research article |
| 23 Jun 2021
| 23 Jun 2021
Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe
Institute of Bio and Geosciences, Agrosphere (IBG-3),
Forschungszentrum Jülich, 52428 Jülich, Germany
Center for High-Performance Scientific Computing in Terrestrial
Systems, Geoverbund ABC/J, 52428 Jülich, Germany
Carsten Montzka
Institute of Bio and Geosciences, Agrosphere (IBG-3),
Forschungszentrum Jülich, 52428 Jülich, Germany
Bagher Bayat
Institute of Bio and Geosciences, Agrosphere (IBG-3),
Forschungszentrum Jülich, 52428 Jülich, Germany
Stefan Kollet
Institute of Bio and Geosciences, Agrosphere (IBG-3),
Forschungszentrum Jülich, 52428 Jülich, Germany
Center for High-Performance Scientific Computing in Terrestrial
Systems, Geoverbund ABC/J, 52428 Jülich, Germany
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Mehdi Rahmati, Lutz Weihermüller, Jan Vanderborght, Yakov A. Pachepsky, Lili Mao, Seyed Hamidreza Sadeghi, Niloofar Moosavi, Hossein Kheirfam, Carsten Montzka, Kris Van Looy, Brigitta Toth, Zeinab Hazbavi, Wafa Al Yamani, Ammar A. Albalasmeh, Ma'in Z. Alghzawi, Rafael Angulo-Jaramillo, Antônio Celso Dantas Antonino, George Arampatzis, Robson André Armindo, Hossein Asadi, Yazidhi Bamutaze, Jordi Batlle-Aguilar, Béatrice Béchet, Fabian Becker, Günter Blöschl, Klaus Bohne, Isabelle Braud, Clara Castellano, Artemi Cerdà, Maha Chalhoub, Rogerio Cichota, Milena Císlerová, Brent Clothier, Yves Coquet, Wim Cornelis, Corrado Corradini, Artur Paiva Coutinho, Muriel Bastista de Oliveira, José Ronaldo de Macedo, Matheus Fonseca Durães, Hojat Emami, Iraj Eskandari, Asghar Farajnia, Alessia Flammini, Nándor Fodor, Mamoun Gharaibeh, Mohamad Hossein Ghavimipanah, Teamrat A. Ghezzehei, Simone Giertz, Evangelos G. Hatzigiannakis, Rainer Horn, Juan José Jiménez, Diederik Jacques, Saskia Deborah Keesstra, Hamid Kelishadi, Mahboobeh Kiani-Harchegani, Mehdi Kouselou, Madan Kumar Jha, Laurent Lassabatere, Xiaoyan Li, Mark A. Liebig, Lubomír Lichner, María Victoria López, Deepesh Machiwal, Dirk Mallants, Micael Stolben Mallmann, Jean Dalmo de Oliveira Marques, Miles R. Marshall, Jan Mertens, Félicien Meunier, Mohammad Hossein Mohammadi, Binayak P. Mohanty, Mansonia Pulido-Moncada, Suzana Montenegro, Renato Morbidelli, David Moret-Fernández, Ali Akbar Moosavi, Mohammad Reza Mosaddeghi, Seyed Bahman Mousavi, Hasan Mozaffari, Kamal Nabiollahi, Mohammad Reza Neyshabouri, Marta Vasconcelos Ottoni, Theophilo Benedicto Ottoni Filho, Mohammad Reza Pahlavan-Rad, Andreas Panagopoulos, Stephan Peth, Pierre-Emmanuel Peyneau, Tommaso Picciafuoco, Jean Poesen, Manuel Pulido, Dalvan José Reinert, Sabine Reinsch, Meisam Rezaei, Francis Parry Roberts, David Robinson, Jesús Rodrigo-Comino, Otto Corrêa Rotunno Filho, Tadaomi Saito, Hideki Suganuma, Carla Saltalippi, Renáta Sándor, Brigitta Schütt, Manuel Seeger, Nasrollah Sepehrnia, Ehsan Sharifi Moghaddam, Manoj Shukla, Shiraki Shutaro, Ricardo Sorando, Ajayi Asishana Stanley, Peter Strauss, Zhongbo Su, Ruhollah Taghizadeh-Mehrjardi, Encarnación Taguas, Wenceslau Geraldes Teixeira, Ali Reza Vaezi, Mehdi Vafakhah, Tomas Vogel, Iris Vogeler, Jana Votrubova, Steffen Werner, Thierry Winarski, Deniz Yilmaz, Michael H. Young, Steffen Zacharias, Yijian Zeng, Ying Zhao, Hong Zhao, and Harry Vereecken
Earth Syst. Sci. Data, 10, 1237–1263, https://doi.org/10.5194/essd-10-1237-2018, https://doi.org/10.5194/essd-10-1237-2018, 2018
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This paper presents and analyzes a global database of soil infiltration data, the SWIG database, for the first time. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists or they were digitized from published articles. We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models.
Hanna Post, Harrie-Jan Hendricks Franssen, Xujun Han, Roland Baatz, Carsten Montzka, Marius Schmidt, and Harry Vereecken
Biogeosciences, 15, 187–208, https://doi.org/10.5194/bg-15-187-2018, https://doi.org/10.5194/bg-15-187-2018, 2018
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Estimated values of selected key CLM4.5-BGC parameters obtained with the Markov chain Monte Carlo (MCMC) approach DREAM(zs) strongly altered catchment-scale NEE predictions in comparison to global default parameter values. The effect of perturbed meteorological input data on the uncertainty of the predicted carbon fluxes was notably higher for C3-grass and C3-crop than for coniferous and deciduous forest. A future distinction of different crop types including management is considered essential.
Dominik Rains, Xujun Han, Hans Lievens, Carsten Montzka, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 21, 5929–5951, https://doi.org/10.5194/hess-21-5929-2017, https://doi.org/10.5194/hess-21-5929-2017, 2017
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We have assimilated 6 years of satellite-observed passive microwave data into a state-of-the-art land surface model to improve surface soil moisture as well as root-zone soil moisture simulations. Long-term assimilation effects/biases are identified, and they are especially dependent on model perturbations, applied to simulate model uncertainty. The implications are put into context of using such assimilation-improved data for classifying extremes within hydrological monitoring systems.
Carsten Montzka, Michael Herbst, Lutz Weihermüller, Anne Verhoef, and Harry Vereecken
Earth Syst. Sci. Data, 9, 529–543, https://doi.org/10.5194/essd-9-529-2017, https://doi.org/10.5194/essd-9-529-2017, 2017
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Global climate models require adequate parameterization of soil hydraulic properties, but typical resampling to the model grid introduces uncertainties. Here we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the problems. It preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters that enables modellers to perturb hydraulic parameters for model ensemble generation.
Bernd Schalge, Jehan Rihani, Gabriele Baroni, Daniel Erdal, Gernot Geppert, Vincent Haefliger, Barbara Haese, Pablo Saavedra, Insa Neuweiler, Harrie-Jan Hendricks Franssen, Felix Ament, Sabine Attinger, Olaf A. Cirpka, Stefan Kollet, Harald Kunstmann, Harry Vereecken, and Clemens Simmer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-557, https://doi.org/10.5194/hess-2016-557, 2016
Manuscript not accepted for further review
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In this work we show how we used a coupled atmosphere-land surface-subsurface model at highest possible resolution to create a testbed for data assimilation. The model was able to capture all important processes and interactions between the compartments as well as showing realistic statistical behavior. This proves that using a model as a virtual truth is possible and it will enable us to develop data assimilation methods where states and parameters are updated across compartment.
Stefan J. Kollet
Hydrol. Earth Syst. Sci., 20, 2801–2809, https://doi.org/10.5194/hess-20-2801-2016, https://doi.org/10.5194/hess-20-2801-2016, 2016
Wolfgang Kurtz, Guowei He, Stefan J. Kollet, Reed M. Maxwell, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 9, 1341–1360, https://doi.org/10.5194/gmd-9-1341-2016, https://doi.org/10.5194/gmd-9-1341-2016, 2016
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This paper describes the development of a modular data assimilation (DA) system for the integrated Earth system model TerrSysMP with the help of the PDAF data assimilation library.
Currently, pressure and soil moisture data can be used to update model states and parameters in the subsurface compartment of TerrSysMP.
Results from an idealized twin experiment show that the developed DA system provides a good parallel performance and is also applicable for high-resolution modelling problems.
P. Shrestha, M. Sulis, C. Simmer, and S. Kollet
Hydrol. Earth Syst. Sci., 19, 4317–4326, https://doi.org/10.5194/hess-19-4317-2015, https://doi.org/10.5194/hess-19-4317-2015, 2015
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This study highlights the grid resolution dependence of energy and water balance of the 3-D physically based integrated surface-groundwater model. The non-local controls of soil moisture were found to be highly grid resolution dependent, but the local vegetation control strongly modulates the scaling behavior of surface energy fluxes. For coupled runs, variability in patterns of surface fluxes due to this scale dependence can affect the simulated atmospheric boundary layer and local circulation.
X. Han, X. Li, G. He, P. Kumbhar, C. Montzka, S. Kollet, T. Miyoshi, R. Rosolem, Y. Zhang, H. Vereecken, and H.-J. H. Franssen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-8-7395-2015, https://doi.org/10.5194/gmdd-8-7395-2015, 2015
Revised manuscript not accepted
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DasPy is a ready to use open source parallel multivariate land data assimilation framework with joint state and parameter estimation using Local Ensemble Transform Kalman Filter. The Community Land Model (4.5) was integrated as model operator. The Community Microwave Emission Modelling platform, COsmic-ray Soil Moisture Interaction Code and the Two-Source Formulation were integrated as observation operators for the multivariate assimilation of soil moisture and soil temperature, respectively.
R. M. Maxwell, L. E. Condon, and S. J. Kollet
Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, https://doi.org/10.5194/gmd-8-923-2015, 2015
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A model that simulates groundwater and surface water flow has been developed for the major river basins of the continental United States. Fundamental data sets provide input to the model resulting in a natural organization of stream networks and groundwater flow that is compared to observations of surface water and groundwater. Model results show relationships between flow and area that are moderated by aridity and represent an important step toward integrated hydrological prediction.
F. Gasper, K. Goergen, P. Shrestha, M. Sulis, J. Rihani, M. Geimer, and S. Kollet
Geosci. Model Dev., 7, 2531–2543, https://doi.org/10.5194/gmd-7-2531-2014, https://doi.org/10.5194/gmd-7-2531-2014, 2014
Related subject area
Subject: Groundwater hydrology | Techniques and Approaches: Modelling approaches
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Exploring river–aquifer interactions and hydrological system response using baseflow separation, impulse response modeling, and time series analysis in three temperate lowland catchments
Experimental study of non-Darcy flow characteristics in permeable stones
Karst spring discharge modeling based on deep learning using spatially distributed input data
HESS Opinions: Chemical transport modeling in subsurface hydrological systems – space, time, and the “holy grail” of “upscaling”
Spatiotemporal variations in water sources and mixing spots in a riparian zone
Delineation of discrete conduit networks in karst aquifers via combined analysis of tracer tests and geophysical data
Reactive transport modeling for supporting climate resilience at groundwater contamination sites
Improved understanding of regional groundwater drought development through time series modelling: the 2018–2019 drought in the Netherlands
Simulation of long-term spatiotemporal variations in regional-scale groundwater recharge: contributions of a water budget approach in cold and humid climates
Feedback mechanisms between precipitation and dissolution reactions across randomly heterogeneous conductivity fields
Taking theory to the field: streamflow generation mechanisms in an intermittent Mediterranean catchment
Coupling saturated and unsaturated flow: comparing the iterative and the non-iterative approach
Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape
Estimation of groundwater recharge from groundwater levels using nonlinear transfer function noise models and comparison to lysimeter data
Early hypogenic carbonic acid speleogenesis in unconfined limestone aquifers by upwelling deep-seated waters with high CO2 concentration: a modelling approach
Impacts of climate change on groundwater flooding and ecohydrology in lowland karst
How daily groundwater table drawdown affects the diel rhythm of hyporheic exchange
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)
Groundwater and baseflow drought responses to synthetic recharge stress tests
Determination of vadose zone and saturated zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning
Mariana Gomez, Maximilian Nölscher, Andreas Hartmann, and Stefan Broda
Hydrol. Earth Syst. Sci., 28, 4407–4425, https://doi.org/10.5194/hess-28-4407-2024, https://doi.org/10.5194/hess-28-4407-2024, 2024
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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.
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.
Raoul Alexander Collenteur, Ezra Haaf, Mark Bakker, Tanja Liesch, Andreas Wunsch, Jenny Soonthornrangsan, Jeremy White, Nick Martin, Rui Hugman, Michael Fienen, Ed de Sousa, Didier Vanden Berghe, Xinyang Fan, Tim Peterson, Janis 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, Jaime Gómez-Hernández, Seifeddine Jomaa, Anna Pölz, Tim Franken, Morteza Behbooei, Jimmy Lin, Bryan Tolson, and Rojin Meysami
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-111, https://doi.org/10.5194/hess-2024-111, 2024
Revised manuscript accepted for HESS
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We present the results of the 2022 groundwater modeling challenge, where 15 teams applied data-driven models to simulate hydraulic heads. 3 groups of models were identified: lumped models, machine learning models, and deep learning models. For all wells, reasonable performance was obtained by at least 1 team from group. There was not 1 team that performed best for all wells. In conclusion, the challenge was a successful initiative to compare different models and learn from each other.
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.
Steven Reinaldo Rusli, Victor F. Bense, Syed M. T. Mustafa, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-26, https://doi.org/10.5194/hess-2024-26, 2024
Revised manuscript accepted for HESS
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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 found out that the future groundwater status is influenced more so by anthropogenic factors compared to climatic factors. The results are beneficial to inform the responsible parties in operational water management to achieve future (ground)water governance.
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.
Esther Brakkee, Marjolein H. J. van Huijgevoort, and Ruud P. Bartholomeus
Hydrol. Earth Syst. Sci., 26, 551–569, https://doi.org/10.5194/hess-26-551-2022, https://doi.org/10.5194/hess-26-551-2022, 2022
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Periods of drought often lead to groundwater shortages in large regions, which cause damage to nature and the economy. To take measures, we need a good understanding of where and when groundwater shortage occurs. In this study, we have tested a method that can combine large amounts of groundwater measurements in an automated way and provide detailed maps of how groundwater shortages develop during a drought period. This information can help water managers to limit future groundwater shortages.
Emmanuel Dubois, Marie Larocque, Sylvain Gagné, and Guillaume Meyzonnat
Hydrol. Earth Syst. Sci., 25, 6567–6589, https://doi.org/10.5194/hess-25-6567-2021, https://doi.org/10.5194/hess-25-6567-2021, 2021
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This work demonstrates the relevance of using a water budget model to understand long-term transient and regional-scale groundwater recharge (GWR) in cold and humid climates where groundwater observations are scarce. Monthly GWR is simulated for 57 years on 500 m x 500 m cells in Canada (36 000 km2 area) with limited uncertainty due to a robust automatic calibration method. The increases in precipitation and temperature since the 1960s have not yet produced significant changes in annual GWR.
Yaniv Edery, Martin Stolar, Giovanni Porta, and Alberto Guadagnini
Hydrol. Earth Syst. Sci., 25, 5905–5915, https://doi.org/10.5194/hess-25-5905-2021, https://doi.org/10.5194/hess-25-5905-2021, 2021
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The interplay between dissolution, precipitation and transport is widely encountered in porous media, from CO2 storage to cave formation in carbonate rocks. We show that dissolution occurs along preferential flow paths with high hydraulic conductivity, while precipitation occurs at locations close to yet separated from these flow paths, thus further funneling the flow and changing the probability density function of the transport, as measured on the altered conductivity field at various times.
Karina Y. Gutierrez-Jurado, Daniel Partington, and Margaret Shanafield
Hydrol. Earth Syst. Sci., 25, 4299–4317, https://doi.org/10.5194/hess-25-4299-2021, https://doi.org/10.5194/hess-25-4299-2021, 2021
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Understanding the hydrologic cycle in semi-arid landscapes includes knowing the physical processes that govern where and why rivers flow and dry within a given catchment. To gain this understanding, we put together a conceptual model of what processes we think are important and then tested that model with numerical analysis. The results broadly confirmed our hypothesis that there are three distinct regions in our study catchment that contribute to streamflow generation in quite different ways.
Natascha Brandhorst, Daniel Erdal, and Insa Neuweiler
Hydrol. Earth Syst. Sci., 25, 4041–4059, https://doi.org/10.5194/hess-25-4041-2021, https://doi.org/10.5194/hess-25-4041-2021, 2021
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We compare two approaches for coupling a 2D groundwater model with multiple 1D models for the unsaturated zone. One is non-iterative and very fast. The other one is iterative and involves a new way of treating the specific yield, which is crucial for obtaining a consistent solution in both model compartments. Tested on different scenarios, this new method turns out to be slower than the non-iterative approach but more accurate and still very efficient compared to fully integrated 3D model runs.
Vince P. Kaandorp, Hans Peter Broers, Ype van der Velde, Joachim Rozemeijer, and Perry G. B. de Louw
Hydrol. Earth Syst. Sci., 25, 3691–3711, https://doi.org/10.5194/hess-25-3691-2021, https://doi.org/10.5194/hess-25-3691-2021, 2021
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We reconstructed historical and present-day tritium, chloride, and nitrate concentrations in stream water of a catchment using
land-use-based input curves and calculated travel times of groundwater. Parameters such as the unsaturated zone thickness, mean travel time, and input patterns determine time lags between inputs and in-stream concentrations. The timescale of the breakthrough of pollutants in streams is dependent on the location of pollution in a catchment.
Raoul A. Collenteur, Mark Bakker, Gernot Klammler, and Steffen Birk
Hydrol. Earth Syst. Sci., 25, 2931–2949, https://doi.org/10.5194/hess-25-2931-2021, https://doi.org/10.5194/hess-25-2931-2021, 2021
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This study explores the use of nonlinear transfer function noise (TFN) models to simulate groundwater levels and estimate groundwater recharge from observed groundwater levels. A nonlinear recharge model is implemented in a TFN model to compute the recharge. The estimated recharge rates are shown to be in good agreement with the recharge observed with a lysimeter present at the case study site in Austria. The method can be used to obtain groundwater recharge rates at
sub-yearly timescales.
Franci Gabrovšek and Wolfgang Dreybrodt
Hydrol. Earth Syst. Sci., 25, 2895–2913, https://doi.org/10.5194/hess-25-2895-2021, https://doi.org/10.5194/hess-25-2895-2021, 2021
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The evolution of karst aquifers is often governed by solutions gaining their aggressiveness in depth. Although the principles of
hypogene speleogenesisare known, modelling studies based on reactive flow in fracture networks are missing. We present a model where dissolution at depth is triggered by the mixing of waters of different origin and chemistry. We show how the initial position of the mixing zone and flow instabilities therein determine the position and shape of the final conduits.
Patrick Morrissey, Paul Nolan, Ted McCormack, Paul Johnston, Owen Naughton, Saheba Bhatnagar, and Laurence Gill
Hydrol. Earth Syst. Sci., 25, 1923–1941, https://doi.org/10.5194/hess-25-1923-2021, https://doi.org/10.5194/hess-25-1923-2021, 2021
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Lowland karst aquifers provide important wetland habitat resulting from seasonal flooding on the land surface. This flooding is controlled by surcharging of the karst system, which is very sensitive to changes in rainfall. This study investigates the predicted impacts of climate change on a lowland karst catchment in Ireland and highlights the relative vulnerability to future changing climate conditions of karst systems and any associated wetland habitats.
Liwen Wu, Jesus D. Gomez-Velez, Stefan Krause, Anders Wörman, Tanu Singh, Gunnar Nützmann, and Jörg Lewandowski
Hydrol. Earth Syst. Sci., 25, 1905–1921, https://doi.org/10.5194/hess-25-1905-2021, https://doi.org/10.5194/hess-25-1905-2021, 2021
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With a physically based model that couples flow and heat transport in hyporheic zones, the present study provides the first insights into the dynamics of hyporheic responses to the impacts of daily groundwater withdrawal and river temperature fluctuations, allowing for a better understanding of transient hyporheic exchange processes and hence an improved pumping operational scheme.
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
Jost Hellwig, Michael Stoelzle, and Kerstin Stahl
Hydrol. Earth Syst. Sci., 25, 1053–1068, https://doi.org/10.5194/hess-25-1053-2021, https://doi.org/10.5194/hess-25-1053-2021, 2021
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Potential future groundwater and baseflow drought hazards depend on systems' sensitivity to altered recharge conditions. With three generic scenarios, we found different sensitivities across Germany driven by hydrogeology. While changes in drought hazard due to seasonal recharge shifts will be rather low, a lengthening of dry spells could cause stronger responses in regions with slow groundwater response to precipitation, urging local water management to prepare for more severe droughts.
Martin J. Wells, Troy E. Gilmore, Natalie Nelson, Aaron Mittelstet, and John K. Böhlke
Hydrol. Earth Syst. Sci., 25, 811–829, https://doi.org/10.5194/hess-25-811-2021, https://doi.org/10.5194/hess-25-811-2021, 2021
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Groundwater in many agricultural areas contains high levels of nitrate, which is a concern for drinking water supplies. The rate at which nitrate moves through the subsurface is a critical piece of information for predicting how quickly groundwater nitrate levels may improve after agricultural producers change their approach to managing crop water and fertilizers. In this study, we explored a new statistical modeling approach to determine rates at which nitrate moves into and through an aquifer.
Cited articles
Adamowski, J. and Chan, H. F.: A wavelet neural network conjunction model for groundwater level forecasting, J. Hydrol., 407, 28–40,
https://doi.org/10.1016/j.jhydrol.2011.06.013, 2011.
Adamowski, J. F.: River flow forecasting using wavelet and cross-wavelet transform models, Hydrol. Process., 22, 4877–4891, https://doi.org/10.1002/hyp.7107, 2008.
Andersen, O. B., Seneviratne, S. I., Hinderer, J., and Viterbo, P.: GRACE-derived terrestrial water storage depletion associated with the 2003 European heat wave, Geophys. Res. Lett., 32, 1–4,
https://doi.org/10.1029/2005GL023574, 2005.
Banerjee, S. and Mitra, M.: Application of cross wavelet transform for ECG
pattern analysis and classification, IEEE Trans. Instrum. Meas., 63, 326–333, https://doi.org/10.1109/TIM.2013.2279001, 2014.
Bloomfield, J., Brauns, B., Hannah, D. M., Jackson, C., Marchant, B., and Van Loon, A. F.: The Groundwater Drought Initiative (GDI): analysing and
understanding groundwater drought across Europe, EGU General Assembly,
8–13 April 2018, Vienna, Austria, EGU2018-4540, 2018.
Brownlee, J.: How to Develop an Ensemble of Deep Learning Models in Keras,
available at:
https://machinelearningmastery.com/model-averaging-ensemble-for-deep-learning-neural-networks/
(last access: November 2019), 2018.
Christensen, J. H. and Christensen, O. B.: A summary of the PRUDENCE model
projections of changes in European climate by the end of this century, Climatic Change, 81, 7–30, https://doi.org/10.1007/s10584-006-9210-7, 2007.
Dawson, C. W. and Wilby, R. L.: Hydrological modelling using artificial neural networks, Prog. Phys. Geogr., 25, 80–108, https://doi.org/10.1177/030913330102500104, 2001.
Duveiller, G., Fasbender, D., and Meroni, M.: Revisiting the concept of a
symmetric index of agreement for continuous datasets, Sci. Rep., 6, 1–14, https://doi.org/10.1038/srep19401, 2016.
EEA: Amount of groundwater abstraction, in: Groundwater quality and quantity in Europe, Office for Official Publications of the European Communities,
Luxembourg, 1999.
EEA: Meteorological and hydrological droughts, available at:
https://www.eea.europa.eu/data-and-maps/indicators/river-flow-drought-2/assessment
(last access: January 2019), 2016.
Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J., and
Kollet, S.: Pan-European groundwater to atmosphere terrestrial systems
climatology from a physically consistent simulation, Sci. Data, 6, 320, https://doi.org/10.1038/s41597-019-0328-7, 2019.
Gasper, F., Goergen, K., Shrestha, P., Sulis, M., Rihani, J., Geimer, M., and
Kollet, S.: Implementation and scaling of the fully coupled Terrestrial
Systems Modeling Platform (TerrSysMP v1.0) in a massively parallel supercomputing environment – A case study on JUQUEEN (IBM Blue Gene/Q),
Geosci. Model Dev., 7, 2531–2543, https://doi.org/10.5194/gmd-7-2531-2014, 2014.
Gers, F. A., Schmidhuber, J., and Cummins, F.: Learning to Forget: Continual
Prediction with LSTM, Neural Comput., 12, 2451–2471, https://doi.org/10.1162/089976600300015015, 2000.
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. Manage., 30, 375–391, https://doi.org/10.1007/s11269-015-1167-8, 2016.
Goodfellow, I., Bengio, Y., and Courville, A.: Bagging and Other Ensemble
Methods, in: Deep learning, MIT Press, Cambridge, Massachusetts, 250–251, 2017.
Govindaraju, R.: Artificial Neural Networks in Hydrology. I: Preliminary
Concepts, J. Hydrol. Eng., 5 115–123, https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(115), 2000.
Grinsted, A., Moore, J. C., and Jevrejeva, S.: Application of the cross
wavelet transform and wavelet coherence to geophysical time series, Nonlin. Processes Geophys., 11, 561–566, https://doi.org/10.5194/npg-11-561-2004, 2004.
Hartick, C., Furusho-Percot, C., Goergen, K., and Kollet, S.: An Interannual
Probabilistic Assessment of Subsurface Water Storage Over Europe Using a Fully Coupled Terrestrial Model, Water Resour. Res., 57, e2020WR027828, https://doi.org/10.1029/2020WR027828, 2021.
Haykin, S.: What Is A Neural Network?, in: Neural Networks and Learning Machines, Prentice Hall, New York, 1–2, 2009.
Hinton, G., Srivastava, N., and Swersky, K.: Overview of mini-batch gradient
descent, available at: https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf,
last access: January 2020.
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural Comput.,
9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Karim, M. N. and Rivera, S. L.: Comparison of feed-forward and recurrent neural networks for bioprocess state estimation, Comput. Chem. Eng., 16,
S369–S377, https://doi.org/10.1016/S0098-1354(09)80044-6, 1992.
Kenda, K., Čerin, M., Bogataj, M., Senožetnik, M., Klemen, K., Pergar, P., Laspidou, C., and Mladenić, D.: Groundwater Modeling with
Machine Learning Techniques: Ljubljana polje Aquifer, Proceedings, 2, 697, https://doi.org/10.3390/proceedings2110697, 2018.
Keune, J., Gasper, F., Goergen, K., Hense, A., Shrestha, P., Sulis, M., and
Kollet, S.: Studying the influence of groundwater representations on land
surface-atmosphere feedbacks during the European heat wave in 2003, J. Geophys. Res., 121, 13301–13325, https://doi.org/10.1002/2016JD025426, 2016.
Keune, J., Sulis, M., and Kollet, S. J.: Potential Added Value of Incorporating Human Water Use on the Simulation of Evapotranspiration and
Precipitation in a Continental-Scale Bedrock-to-Atmosphere Modeling System:
A Validation Study Considering Observational Uncertainty, J. Adv. Model. Earth Syst., 11, 1959–1980, https://doi.org/10.1029/2019MS001657, 2019.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.:
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks,
Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018,
2018.
Kurtz, W., He, G., Kollet, S. J., Maxwell, R. M., Vereecken, H., and Franssen, H. J. H.: TerrSysMP-PDAF (version 1.0): A modular high-performance data assimilation framework for an integrated land surface-subsurface model,
Geosci. Model Dev., 9, 1341–1360, https://doi.org/10.5194/gmd-9-1341-2016, 2016.
Le, X. H., Ho, H. V., Lee, G., and Jung, S.: Application of Long Short-Term
Memory (LSTM) neural network for flood forecasting, Water, 11, 1387, https://doi.org/10.3390/w11071387, 2019.
Liong, S.-Y., Lim, W.-H., and Paudyal, G. N.: River Stage Forecasting in
Bangladesh: Neural Network Approach, J. Comput. Civ. Eng., 14, 1–8,
https://doi.org/10.1061/(ASCE)0887-3801(2000)14:1(1), 2000.
Ma, Y., Matta, E., Meißner, D., Schellenberg, H., and Hinkelmann, R.: Can
machine learning improve the accuracy of water level forecasts for inland
navigation? Case study: Rhine River Basin, Germany, in: 38th IAHR World Congr. 2019, Water – Connect. world, Panama City, https://doi.org/10.3850/38WC092019-0274, 2019.
Ma, Y., Montzka, C., Bayat, B., and Kollet, S.: Using Long Short-Term Memory
networks to connect water table depth anomalies to precipitation anomalies
over Europe, Jülich DATA [data set], https://doi.org/10.26165/JUELICH-DATA/WPRA1F, 2021.
Maxwell, R. M.: Infiltration in Arid Environments: Spatial Patterns between
Subsurface Heterogeneity and Water-Energy Balances, Vadose Zone J., 9, 970–983, https://doi.org/10.2136/vzj2010.0014, 2010.
McKee, T. B., Doesken, N. J., and Kleist, J.: The Relationship OF Drought Frequency And Duration To Time Scales, in: Proceedings of the 8th Conference
on Applied Climatology, American Meteorological Society, Anaheim, 179–184, 1993.
Mohanty, S., Jha, M. K., Raul, S. K., Panda, R. K., and Sudheer, K. P.: Using
Artificial Neural Network Approach for Simultaneous Forecasting of Weekly
Groundwater Levels at Multiple Sites, Water Resour. Managw., 29, 5521–5532, https://doi.org/10.1007/s11269-015-1132-6, 2015.
Müller, A. C. and Guido, S.: Generalization, Overfitting, and Underfitting, in Introduction to machine learning with Python: A Guide For Data Scientists, O'Reilly Media, Inc., Sebastopol, 28–31, 2017.
Naghibi, S. A., Pourghasemi, H. R., and Dixon, B.: GIS-based groundwater
potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran, Environ.
Monit. Assess., 188, 1–27, https://doi.org/10.1007/s10661-015-5049-6, 2016.
Nayak, P. C., Satyaji Rao, Y. R., and Sudheer, K. P.: Groundwater level
forecasting in a shallow aquifer using artificial neural network approach,
Water Resour. Manage., 20, 77–90, https://doi.org/10.1007/s11269-006-4007-z, 2006.
Norris, B.: July drought in Europe to cost at least € 3.5bn, Aon –
Commercial Risk, available at:
https://www.commercialriskonline.com/july-drought-to-cost-at-least-e3-5bn-aon/
(last access: January 2019), 2018.
Olah, C.: Understanding LSTM Networks – colah's blog, available at:
http://colah.github.io/posts/2015-08-Understanding-LSTMs/ (last access: June 2018), 2015.
Perlman, H.: Where is Earth's water? USGS Water-Science School, available at:
https://web.archive.org/web/20131214091601/http://ga.water.usgs.gov/edu/earthwherewater.html
(last access: August 2019), 2013.
Prokoph, A. and El Bilali, H.: Cross-wavelet analysis: A tool for detection
of relationships between paleoclimate proxy records, Math. Geosci., 40, 575–586, https://doi.org/10.1007/s11004-008-9170-8, 2008.
Sahoo, B. B., Jha, R., Singh, A., and Kumar, D.: Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series
forecasting, Acta Geophys., 67, 1471–1481, https://doi.org/10.1007/s11600-019-00330-1, 2019.
Shen, C.: A Transdisciplinary Review of Deep Learning Research and Its
Relevance for Water Resources Scientists, Water Resour. Res., 54, 8558–8593, https://doi.org/10.1029/2018WR022643, 2018.
Shrestha, P., Sulis, M., Masbou, M., Kollet, S., and Simmer, C.: A scale-consistent terrestrial systems modeling platform based on COSMO, CLM,
and ParFlow, Mon. Weather Rev., 142, 3466–3483, https://doi.org/10.1175/MWR-D-14-00029.1, 2014.
Sulis, M., Keune, J., Shrestha, P., Simmer, C., and Kollet, S. J.: Quantifying the Impact of Subsurface-Land Surface Physical Processes on the
Predictive Skill of Subseasonal Mesoscale Atmospheric Simulations, J. Geophys. Res.-Atmos., 123, 9131–9151, https://doi.org/10.1029/2017JD028187, 2018.
Sun, A. Y. and Scanlon, B. R.: How can Big Data and machine learning benefit
environment and water management: A survey of methods, applications, and
future directions, Environ. Res. Lett., 14, 073001, https://doi.org/10.1088/1748-9326/ab1b7d, 2019.
Sun, Y., Wendi, D., Kim, D. E., and Liong, S. Y.: Technical note: Application
of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest, Hydrol. Earth Syst. Sci., 20, 1405–1412,
https://doi.org/10.5194/hess-20-1405-2016, 2016.
Sung, J. Y., Lee, J., Chung, I. M., and Heo, J. H.: Hourly water level
forecasting at tributary affecteby main river condition, Water, 9, 1–17, https://doi.org/10.3390/w9090644, 2017.
Supreetha, B. S., Shenoy, N., and Nayak, P.: Lion Algorithm-Optimized Long
Short-Term Memory Network for Groundwater Level Forecasting in Udupi
District, India, Appl. Comput. Intel. Soft Comput., 2020, 8685724,
https://doi.org/10.1155/2020/8685724, 2020.
Thomas, T., Jaiswal, R. K., Nayak, P. C., and Ghosh, N. C.: Comprehensive
evaluation of the changing drought characteristics in Bundelkhand region of
Central India, Meteorol. Atmos. Phys., 127, 163–182, https://doi.org/10.1007/s00703-014-0361-1, 2015.
Tian, J., Li, C., Liu, J., Yu, F., Cheng, S., Zhao, N., and Wan Jaafar, W. Z.: Groundwater depth prediction using data-driven models with the assistance of gamma test, Sustainability, 8, 1–17, https://doi.org/10.3390/su8111076, 2016.
Torrence, C. and Compo, G. P.: A Practical Guide to Wavelet Analysis, B. Am. Meteorol. Soc., 79, 61–78, https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2, 1998.
Van Loon, A. F., Kumar, R., and Mishra, V.: Testing the use of standardised
indices and GRACE satellite data to estimate the European 2015 groundwater
drought in near-real time, Hydrol. Earth Syst. Sci., 21, 1947–1971,
https://doi.org/10.5194/hess-21-1947-2017, 2017.
Veleda, D., Montagne, R., and Araujo, M.: Cross-wavelet bias corrected by
normalizing scales, J. Atmos. Ocean. Tech., 29, 1401–1408,
https://doi.org/10.1175/JTECH-D-11-00140.1, 2012.
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A
multiscalar drought index sensitive to global warming: The standardized
precipitation evapotranspiration index, J. Climate, 23, 1696–1718,
https://doi.org/10.1175/2009JCLI2909.1, 2010.
Wilhite, D. A.: Drought as a natural hazard: Concepts and definitions, in:
Drought: A Global Assessment, vol. 1, Routledge, London, 3–18, 2000.
Yang, C.-C., Prasher, S. O., Lacroix, R., Sreekanth, S., Patni, N. K., and
Masse, L.: Artificial Neural Network Model for Subsurface-Drained Farmlands,
J. Irrig. Drain. Eng., 123, 285–292, https://doi.org/10.1061/(ASCE)0733-9437(1997)123:4(285), 1997.
Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O., and Lee, K. K.: A comparative
study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, J. Hydrol., 396, 128–138, https://doi.org/10.1016/j.jhydrol.2010.11.002, 2011.
Zhang, D., Lindholm, G., and Ratnaweera, H.: Use long short-term memory to
enhance Internet of Things for combined sewer overflow monitoring, J. Hydrol., 556, 409–418, https://doi.org/10.1016/j.jhydrol.2017.11.018, 2018.
Zhang, J., Zhu, Y., Zhang, X., Ye, M., and Yang, J.: Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in
agricultural areas, J. Hydrol., 561, 918–929, https://doi.org/10.1016/j.jhydrol.2018.04.065, 2018.
Zhang, X., Peng, Y., Zhang, C., and Wang, B.: Are hybrid models integrated
with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences, J. Hydrol., 530, 137–152,
https://doi.org/10.1016/j.jhydrol.2015.09.047, 2015.
Zhao, T., Zhu, Y., Ye, M., Mao, W., Zhang, X., Yang, J., and Wu, J.:
Machine-Learning Methods for Water Table Depth Prediction in Seasonal
Freezing-Thawing Areas, Groundwater, 58, 419–431, https://doi.org/10.1111/gwat.12913, 2020.
Short summary
This study utilized spatiotemporally continuous precipitation anomaly (pra) and water table depth anomaly (wtda) data from integrated hydrologic simulation results over Europe in combination with Long Short-Term Memory (LSTM) networks to capture the time-varying and time-lagged relationship between pra and wtda in order to obtain reliable models to estimate wtda at the individual pixel level.
This study utilized spatiotemporally continuous precipitation anomaly (pra) and water table...
