Articles | Volume 23, issue 6
https://doi.org/10.5194/hess-23-2561-2019
© Author(s) 2019. 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-23-2561-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jun 2019
Research article |
| 13 Jun 2019
| 13 Jun 2019
High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data
Zhenjiao Jiang
CORRESPONDING AUTHOR
Key Laboratory of Groundwater Resources and Environment, Ministry of
Education, College of Environment and Resources, Jilin University,
Changchun, 130021, China
CSIRO Land & Water, Locked Bag 2, Glen Osmond, SA 5064, Australia
Dirk Mallants
CSIRO Land & Water, Locked Bag 2, Glen Osmond, SA 5064, Australia
Luk Peeters
CSIRO Mineral Resources, Locked Bag 2, Glen Osmond, SA 5064, Australia
CSIRO Land & Water, Locked Bag 2, Glen Osmond, SA 5064, Australia
Camilla Soerensen
CSIRO Mineral Resources, Locked Bag 2, Glen Osmond, SA 5064, Australia
Gregoire Mariethoz
University of Lausanne, Faculty of Geosciences and Environment,
Institute of Earth Surface Dynamics, Lausanne, Switzerland
Related authors
Zhenjiao Jiang, Dirk Mallants, Lei Gao, Tim Munday, Gregoire Mariethoz, and Luk Peeters
Geosci. Model Dev., 14, 3421–3435, https://doi.org/10.5194/gmd-14-3421-2021, https://doi.org/10.5194/gmd-14-3421-2021, 2021
Short summary
Short summary
Fast and reliable tools are required to extract hidden information from big geophysical and remote sensing data. A deep-learning model in 3D image construction from 2D image(s) is here developed for paleovalley mapping from globally available digital elevation data. The outstanding performance for 3D subsurface imaging gives confidence that this generic novel tool will make better use of existing geophysical and remote sensing data for improved management of limited earth resources.
Fatemeh Zakeri, Gregoire Mariethoz, and Manuela Girotto
EGUsphere, https://doi.org/10.5194/egusphere-2024-1943, https://doi.org/10.5194/egusphere-2024-1943, 2024
Short summary
Short summary
This study introduces a method for estimating High-Resolution Snow Water Equivalent (HR-SWE) using Low-Resolution Climate Data (LR-CD). By applying a data-driven approach, we utilize historical weather patterns from LR-CD to estimate HR-SWE maps. Our approach uses statistical relationships between LR-CD and HR-SWE data to provide HR-SWE estimates for dates when HR-SWE data is unavailable. This method improves water resource management and climate impact assessments in regions with limited data.
Mathieu Vrac, Denis Allard, Grégoire Mariéthoz, Soulivanh Thao, and Lucas Schmutz
Earth Syst. Dynam., 15, 735–762, https://doi.org/10.5194/esd-15-735-2024, https://doi.org/10.5194/esd-15-735-2024, 2024
Short summary
Short summary
We aim to combine multiple global climate models (GCMs) to enhance the robustness of future projections. We introduce a novel approach, called "α pooling", aggregating the cumulative distribution functions (CDFs) of the models into a CDF more aligned with historical data. The new CDFs allow us to perform bias adjustment of all the raw climate simulations at once. Experiments with European temperature and precipitation demonstrate the superiority of this approach over conventional techniques.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Proc. IAHS, 385, 121–127, https://doi.org/10.5194/piahs-385-121-2024, https://doi.org/10.5194/piahs-385-121-2024, 2024
Short summary
Short summary
This study assesses the impact of climate change on the timing, seasonality and magnitude of mean annual minimum (MAM) flows and annual maximum flows (AMF) in the Volta River basin (VRB). Several climate change projection data are use to simulate river flow under multiple greenhouse gas emission scenarios. Future projections show that AMF could increase with various magnitude but negligible shift in time across the VRB, while MAM could decrease with up to 14 days of delay in occurrence.
Fabio Oriani, Gregoire Mariethoz, and Manuel Chevalier
Earth Syst. Sci. Data, 16, 731–742, https://doi.org/10.5194/essd-16-731-2024, https://doi.org/10.5194/essd-16-731-2024, 2024
Short summary
Short summary
Modern and fossil pollen data contain precious information for reconstructing the climate and environment of the past. However, these data are only achieved for single locations with no continuity in space. We present here a systematic atlas of 194 digital maps containing the spatial estimation of contemporary pollen presence over Europe. This dataset constitutes a free and ready-to-use tool to study climate, biodiversity, and environment in time and space.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
Short summary
Short summary
Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Nadav Peleg, Herminia Torelló-Sentelles, Grégoire Mariéthoz, Lionel Benoit, João P. Leitão, and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1233–1240, https://doi.org/10.5194/nhess-23-1233-2023, https://doi.org/10.5194/nhess-23-1233-2023, 2023
Short summary
Short summary
Floods in urban areas are one of the most common natural hazards. Due to climate change enhancing extreme rainfall and cities becoming larger and denser, the impacts of these events are expected to increase. A fast and reliable flood warning system should thus be implemented in flood-prone cities to warn the public of upcoming floods. The purpose of this brief communication is to discuss the potential implementation of low-cost acoustic rainfall sensors in short-term flood warning systems.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
Short summary
Short summary
Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
Dirk Mallants, John Phalen, and Hef Griffiths
Saf. Nucl. Waste Disposal, 1, 263–264, https://doi.org/10.5194/sand-1-263-2021, https://doi.org/10.5194/sand-1-263-2021, 2021
Short summary
Short summary
In Australia, long-lived ILW from research reactors and radiopharmaceutical production represents the principal waste stream that requires deep geologic disposal. CSIRO and its partners aim to demonstrate the technical feasibility of the long-term safety of borehole disposal in deep geological formations. We will highlight the main findings from the RD&D undertaken so far.
Zhenjiao Jiang, Dirk Mallants, Lei Gao, Tim Munday, Gregoire Mariethoz, and Luk Peeters
Geosci. Model Dev., 14, 3421–3435, https://doi.org/10.5194/gmd-14-3421-2021, https://doi.org/10.5194/gmd-14-3421-2021, 2021
Short summary
Short summary
Fast and reliable tools are required to extract hidden information from big geophysical and remote sensing data. A deep-learning model in 3D image construction from 2D image(s) is here developed for paleovalley mapping from globally available digital elevation data. The outstanding performance for 3D subsurface imaging gives confidence that this generic novel tool will make better use of existing geophysical and remote sensing data for improved management of limited earth resources.
Moctar Dembélé, Bettina Schaefli, Nick van de Giesen, and Grégoire Mariéthoz
Hydrol. Earth Syst. Sci., 24, 5379–5406, https://doi.org/10.5194/hess-24-5379-2020, https://doi.org/10.5194/hess-24-5379-2020, 2020
Short summary
Short summary
This study evaluates 102 combinations of rainfall and temperature datasets from satellite and reanalysis sources as input to a fully distributed hydrological model. The model is recalibrated for each input dataset, and the outputs are evaluated with streamflow, evaporation, soil moisture and terrestrial water storage data. Results show that no single rainfall or temperature dataset consistently ranks first in reproducing the spatio-temporal variability of all hydrological processes.
Jason Bula, Marc-Henri Derron, and Gregoire Mariethoz
Geosci. Instrum. Method. Data Syst., 9, 385–396, https://doi.org/10.5194/gi-9-385-2020, https://doi.org/10.5194/gi-9-385-2020, 2020
Short summary
Short summary
We developed a method to acquire dense point clouds with a low-cost Velodyne Puck lidar system, without using expensive Global Navigation Satellite System (GNSS) positioning or IMU. We mounted the lidar on a motor to continuously change the scan direction, leading to a significant increase in the point cloud density. The system was compared with a more expensive system based on IMU registration and a SLAM algorithm. The alignment between acquisitions with those two systems is within 2 m.
Brady A. Flinchum, Eddie Banks, Michael Hatch, Okke Batelaan, Luk J. M. Peeters, and Sylvain Pasquet
Hydrol. Earth Syst. Sci., 24, 4353–4368, https://doi.org/10.5194/hess-24-4353-2020, https://doi.org/10.5194/hess-24-4353-2020, 2020
Short summary
Short summary
Identifying and quantifying recharge processes linked to ephemeral surface water features is challenging due to their episodic nature. We use a unique combination of well-established near-surface geophysical methods to provide evidence of a surface and groundwater connection in a flat, semi-arid region north of Adelaide, Australia. We show that a combined geophysical approach can provide a unique perspective that can help shape the hydrogeological conceptualization.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 13, 2611–2630, https://doi.org/10.5194/gmd-13-2611-2020, https://doi.org/10.5194/gmd-13-2611-2020, 2020
Short summary
Short summary
Stochastic simulations are key tools to generate complex spatial structures uses as input in geoscientific models. In this paper, we present a new open-source tool that enables to simulate complex structures in a straightforward and efficient manner, based on analogues. The method is tested on a variety of use cases to demonstrate the generality of the framework.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 24, 2841–2854, https://doi.org/10.5194/hess-24-2841-2020, https://doi.org/10.5194/hess-24-2841-2020, 2020
Short summary
Short summary
At subdaily resolution, rain intensity exhibits a strong variability in space and time due to the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection). In this paper we explore a new method to simulate rain type time series conditional to meteorological covariates. Afterwards, we apply stochastic rain type simulation to the downscaling of precipitation of a regional climate model.
Shaoqing Dai, Xiaoman Zheng, Lei Gao, Chengdong Xu, Shudi Zuo, Qi Chen, Xiaohua Wei, and Yin Ren
Biogeosciences Discuss., https://doi.org/10.5194/bg-2020-36, https://doi.org/10.5194/bg-2020-36, 2020
Preprint withdrawn
Short summary
Short summary
This paper proposes a method to integrate the advantages of machine learning and spatial statistics, different datasets, and multiple environmental covariates to improve the accuracy of aboveground biomass estimation models, which provides a useful reference for climate change mitigation. This combined method can make full use of data from different sources, and realize the complementary advantages of machine learning and spatial statistics, which has important implications for other fields.
James M. Thornton, Gregoire Mariethoz, Tristan J. Brauchli, and Philip Brunner
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-181, https://doi.org/10.5194/tc-2019-181, 2019
Manuscript not accepted for further review
Short summary
Short summary
Meltwater runoff from steep mountainous terrain holds great societal and ecological importance. Predicting snow dynamics in unmonitored areas and/or under changed climate requires computer simulations. Yet variability in alpine snow patterns poses a considerable challenge. Here we combine existing tools with high-resolution observations to both constrain and quantify the uncertainty in historical simulations. Snowpack evolution was satisfactorily reproduced and uncertainty substantially reduced.
Shaoqing Dai, Xiaoman Zheng, Lei Gao, Shudi Zuo, Qi Chen, Xiaohua Wei, and Yin Ren
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-202, https://doi.org/10.5194/bg-2019-202, 2019
Preprint withdrawn
Short summary
Short summary
We propose a low-cost approach that combines machine learning with spatial statistics to construct a regional forest C sequestration map from non-representative sample units. The experimental results demonstrate that the combined methods can improve the accuracy of the C sequestration map. This work provides a useful reference for climate change mitigation and other cases that used non-representative sample units.
Lionel Benoit, Aurelie Gourdon, Raphaël Vallat, Inigo Irarrazaval, Mathieu Gravey, Benjamin Lehmann, Günther Prasicek, Dominik Gräff, Frederic Herman, and Gregoire Mariethoz
Earth Syst. Sci. Data, 11, 579–588, https://doi.org/10.5194/essd-11-579-2019, https://doi.org/10.5194/essd-11-579-2019, 2019
Short summary
Short summary
This dataset provides a collection of 10 cm resolution orthomosaics and digital elevation models of the Gornergletscher glacial system (Switzerland). Raw data have been acquired every 2 weeks by intensive UAV surveys and cover the summer 2017. A careful photogrammetric processing ensures the geometrical coherence of the whole dataset.
Pierre-Olivier Bruna, Julien Straubhaar, Rahul Prabhakaran, Giovanni Bertotti, Kevin Bisdom, Grégoire Mariethoz, and Marco Meda
Solid Earth, 10, 537–559, https://doi.org/10.5194/se-10-537-2019, https://doi.org/10.5194/se-10-537-2019, 2019
Short summary
Short summary
Natural fractures influence fluid flow in subsurface reservoirs. Our research presents a new methodology to predict the arrangement of these fractures in rocks. Contrary to the commonly used statistical models, our approach integrates more geology into the simulation process. The method is simply based on the drawing of images, can be applied to any type of rocks in various geological contexts, and is suited for fracture network prediction in water, geothermal, or hydrocarbon reservoirs.
Qiyu Chen, Gregoire Mariethoz, Gang Liu, Alessandro Comunian, and Xiaogang Ma
Hydrol. Earth Syst. Sci., 22, 6547–6566, https://doi.org/10.5194/hess-22-6547-2018, https://doi.org/10.5194/hess-22-6547-2018, 2018
Short summary
Short summary
One of the critical issues in MPS simulation is the difficulty in obtaining a credible 3-D training image. We propose an MPS-based 3-D reconstruction method on the basis of 2-D cross sections, making 3-D training images unnecessary. The main advantages of this approach are the high computational efficiency and a relaxation of the stationarity assumption. The results, in comparison with previous MPS methods, show better performance in portraying anisotropy characteristics and in CPU cost.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 22, 5919–5933, https://doi.org/10.5194/hess-22-5919-2018, https://doi.org/10.5194/hess-22-5919-2018, 2018
Short summary
Short summary
We propose a method for unsupervised classification of the space–time–intensity structure of weather radar images. The resulting classes are interpreted as rain types, i.e. pools of rain fields with homogeneous statistical properties. Rain types can in turn be used to define stationary periods for further stochastic rainfall modelling. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm.
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
Short summary
Short summary
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.
Kashif Mahmud, Gregoire Mariethoz, Andy Baker, and Pauline C. Treble
Hydrol. Earth Syst. Sci., 22, 977–988, https://doi.org/10.5194/hess-22-977-2018, https://doi.org/10.5194/hess-22-977-2018, 2018
Short summary
Short summary
This study explores the relationship between drip water and rainfall in a SW Australian karst, where both intra- and interannual hydrological variations are strongly controlled by seasonal variations in recharge. The hydrological behavior of cave drips is examined at daily resolution with respect to mean discharge and the flow variation. We demonstrate that the analysis of the time series produced by cave drip loggers generates useful hydrogeological information that can be applied generally.
K. Mahmud, G. Mariethoz, A. Baker, P. C. Treble, M. Markowska, and E. McGuire
Hydrol. Earth Syst. Sci., 20, 359–373, https://doi.org/10.5194/hess-20-359-2016, https://doi.org/10.5194/hess-20-359-2016, 2016
Short summary
Short summary
Caves offer a natural inception point to observe both the long-term groundwater recharge and the preferential movement of water through the unsaturated zone of such limestone. In this study, we develop a method that combines automated drip rate logging systems and remote sensing techniques to quantify the infiltration processes within a cave.
L. J. M. Peeters, G. M. Podger, T. Smith, T. Pickett, R. H. Bark, and S. M. Cuddy
Hydrol. Earth Syst. Sci., 18, 3777–3785, https://doi.org/10.5194/hess-18-3777-2014, https://doi.org/10.5194/hess-18-3777-2014, 2014
B. Rogiers, K. Beerten, T. Smeekens, D. Mallants, M. Gedeon, M. Huysmans, O. Batelaan, and A. Dassargues
Hydrol. Earth Syst. Sci., 17, 5155–5166, https://doi.org/10.5194/hess-17-5155-2013, https://doi.org/10.5194/hess-17-5155-2013, 2013
Related subject area
Subject: Groundwater hydrology | Techniques and Approaches: Modelling approaches
Multivariate and long-term time series analysis to assess the effect of nitrogen management policy on groundwater quality in Wallonia, BE
Laboratory heat transport experiments reveal grain-size- and flow-velocity-dependent local thermal non-equilibrium effects
Improvement of the KarstMod modelling platform for a better assessment of karst groundwater resources
Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information?
The Impact of Geological Structures on Groundwater Potential Assessment in Volcanic Rocks of the Northwestern Ethiopian Plateau: A Review
Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge
The impact of future changes in climate variables and groundwater abstraction on basin-scale groundwater availability
Numerical Analysis of the Effect of Heterogeneity on CO2 Dissolution Enhanced by Gravity Driven Convection
Towards a Global Spatial Machine Learning Model for Seasonal Groundwater Level Predictions in Germany
Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features
Short high-accuracy tritium data time series for assessing groundwater mean transit times in the vadose and saturated zones of the Luxembourg Sandstone aquifer
High-resolution long-term average groundwater recharge in Africa estimated using random forest regression and residual interpolation
Towards understanding the influence of seasons on low-groundwater periods based on explainable machine learning
Shannon entropy of transport self-organization due to dissolution–precipitation reaction at varying Peclet numbers in initially homogeneous porous media
A high-resolution map of diffuse groundwater recharge rates for Australia
Influence of bank slope on sinuosity-driven hyporheic exchange flow and residence time distribution during a dynamic flood event
Technical note: A model of chemical transport in a wellbore–aquifer system
Disentangling coastal groundwater level dynamics in a global dataset
Current and future roles of meltwater–groundwater dynamics in a proglacial Alpine outwash plain
On the challenges of global entity-aware deep learning models for groundwater level prediction
Incorporating interpretation uncertainties from deterministic 3D hydrostratigraphic models in groundwater models
Adjoint subordination to calculate backward travel time probability of pollutants in water with various velocity resolutions
On the optimal level of complexity for the representation of groundwater-dependent wetland systems in land surface models
Estimation of groundwater age distributions from hydrochemistry: comparison of two metamodelling algorithms in the Heretaunga Plains aquifer system, New Zealand
Technical note: Novel analytical solution for groundwater response to atmospheric tides
Calibration of groundwater seepage against the spatial distribution of the stream network to assess catchment-scale hydraulic properties
Climate-warming-driven changes in the cryosphere and their impact on groundwater–surface-water interactions in the Heihe River basin
Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions
A general model of radial dispersion with wellbore mixing and skin effects
Estimation of hydraulic conductivity functions in karst regions by particle swarm optimization with application to Lake Vrana, Croatia
The origin of hydrological responses following earthquakes in a confined aquifer: insight from water level, flow rate, and temperature observations
Advance prediction of coastal groundwater levels with temporal convolutional and long short-term memory networks
Three-dimensional hydrogeological parametrization using sparse piezometric data
Machine-learning-based downscaling of modelled climate change impacts on groundwater table depth
Frequency domain water table fluctuations reveal impacts of intense rainfall and vadose zone thickness on groundwater recharge
Characterizing groundwater heat transport in a complex lowland aquifer using paleo-temperature reconstruction, satellite data, temperature–depth profiles, and numerical models
Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components
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
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Bishaw Mihret and Ajebush Wuletaw
EGUsphere, https://doi.org/10.5194/egusphere-2024-4201, https://doi.org/10.5194/egusphere-2024-4201, 2025
Short summary
Short summary
Geological structures, including faults, fractures, and lithological variations, play a crucial role in determining groundwater dynamics in the volcanic rocks of the Northwestern Ethiopian Plateau. Faults act as both conduits and barriers, while fractures are key to groundwater flow in hard-rock terrains. Advanced techniques like geophysics, remote sensing, and GIS improve assessments of fault-controlled and fracture-controlled aquifers.
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
Short summary
Short summary
We show the results of the 2022 Groundwater Time Series Modelling Challenge; 15 teams applied data-driven models to simulate hydraulic heads, and three model groups were identified: lumped, machine learning, and deep learning. For all wells, reasonable performance was obtained by at least one team from each group. There was not one team that performed best for all wells. In conclusion, the challenge was a successful initiative to compare different models and learn from each other.
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
Short summary
Short summary
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.
Yufei Wang, Daniel Fernandez Garcia, and Maarten W. Saaltink
EGUsphere, https://doi.org/10.22541/au.170709021.19680723/v1, https://doi.org/10.22541/au.170709021.19680723/v1, 2024
Short summary
Short summary
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.
Stefan Kunz, Alexander Schulz, Maria Wetzel, Maximilian Nölscher, Teodor Chiaburu, Felix Biessmann, and Stefan Broda
EGUsphere, https://doi.org/10.5194/egusphere-2024-3484, https://doi.org/10.5194/egusphere-2024-3484, 2024
Short summary
Short summary
Accurate groundwater level predictions are essential for a sustainable groundwater management. This study applies two machine learning (ML) models—N-HiTS and TFT—to seasonally predict groundwater levels for 5,288 monitoring wells across Germany. Both approaches provided good predictions across diverse hydrogeological conditions, whereby N-HiTS outperformed the TFT. Both models showed better perforance in areas with high data density, in lowlands, and when distinct seasonal dynamics occurred.
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
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Modeling complex karst water resources is difficult enough, but often there are no or too few climate stations available within or close to the catchment to deliver input data for modeling purposes. We apply image recognition algorithms to time-distributed, spatially gridded meteorological data to simulate karst spring discharge. Our models can also learn the approximate catchment location of a spring independently.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Ahl, A.: Automatic 1D inversion of multifrequency airborne electromagnetic
data with artificial neural networks: discussion and a case study,
Geophys. Prospect., 51, 89–98, 2003.
Amit, S. N. K. B., Shiraishi, S., Inoshita, T., and Aoki, Y.: Analysis of
satellite images for disaster detection, Geoscience and Remote Sensing
Symposium (IGARSS), 2016 IEEE International, 5189–5192, 2016.
Archie, G. E.: The electrical resistivity log as an aid in determining some
reservoir characteristics, T. AIME, 146, 54–62, 1942.
Auken, E., Christiansen, A. V., Westergaard, J. H., Kirkegaard, C., Foged,
N., and Viezzoli, A.: An integrated processing scheme for high-resolution
airborne electromagnetic surveys, the SkyTEM system, Explor. Geophys.,
40, 184–192, 2009.
Auken, E., Christiansen, A. V., Kirkegaard, C., Fiandaca, G., Schamper, C.,
Behroozmand, A. A., Binley, A., Nielsen, E., Effersø, F., and
Christensen, N. B.: An overview of a highly versatile forward and stable
inverse algorithm for airborne, ground-based and borehole electromagnetic
and electric data, Explor. Geophys., 46, 223–235, 2014.
Bishop, C. M.: Neural networks for pattern recognition, Oxford University
Press, 1996.
Christensen, N. K., Minsley, B. J., and Christensen, S.: Generation of 3-D
hydrostratigraphic zones from dense airborne electromagnetic data to assess
groundwater model prediction error, Water Resour. Res., 53, 1019–1038,
2017.
Dong, C., Loy, C. C., He, K., and Tang, X.: Image super-resolution using
deep convolutional networks, IEEE T. Pattern Anal., 38, 295–307, 2016.
Drexel, J. F. and Preiss, W. V.: The Geology of South Australia, Geological Survey of South Australia Bulletin, 54, 300–632, 1995.
English, P., Lewis, S., Bell, J., Wischusen, J., Woodgate, M., Bastrakov,
E., Macphail, M., and Kilgour, P.: Water for Australia's arid
zone – Identifying and assessing Australia's palaeovalley groundwater
resources: Summary report, Waterlines Report Series, 2012.
Fitterman, D. V., Menges, C. M., Al Kamali, A. M., and Jama, F. E.:
Electromagnetic mapping of buried paleochannels in eastern Abu Dhabi
Emirate, UAE, Geoexploration, 27, 111–133, 1991.
Gallant, J. C. and Dowling, T. I.: A multiresolution index of valley bottom
flatness for mapping depositional areas, Water Resour. Res., 39, WR001426, https://doi.org/10.1029/2002WR001426, 2003.
Granek, J.: Application of machine learning algorithms to mineral
prospectivity mapping, University of British Columbia, 2016.
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang,
X., Wang, G., and Cai, J.: Recent advances in convolutional neural networks,
Pattern Recognition, 2017.
Gunnink, J. L., Bosch, J. H. A., Siemon, B., Roth, B., and Auken, E.: Combining ground-based
and airborne EM through Artificial Neural Networks for modelling glacial till under saline
groundwater conditions, Hydrol. Earth Syst. Sci., 16, 3061–3074, https://doi.org/10.5194/hess-16-3061-2012, 2012.
Hao, S., Wang, W., Ye, Y., Li, E., and Bruzzone, L.: A Deep Network
Architecture for Super-Resolution-Aided Hyperspectral Image Classification
With Classwise Loss, IEEE T. Geosci. Remote, 56,
4650–4663, 2018.
Holzschuh, J.: Low-cost geophysical investigations of a paleochannel aquifer
in the Eastern Goldfields, Western Australia, Geophysics, 67, 690–700, 2002.
Hou, B., Frakes, L., Sandiford, M., Worrall, L., Keeling, J., and Alley, N.:
Cenozoic Eucla Basin and associated palaeovalleys, southern
Australia – climatic and tectonic influences on landscape evolution,
sedimentation and heavy mineral accumulation, Sediment. Geol., 203,
112–130, 2008.
Jackson, J. A. (Ed.): Glossary of geology, Berlin, Springer, 1057 pp., ISBN 3-540-27951-2, 2005.
Jones, D. A., Wang, W., and Fawcett, R.: High-quality spatial climate
data-sets for Australia, Australian Meteorological and Oceanographic
Journal, 58, 233–248, 2009.
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv
preprint arXiv:1412.6980, 2014.
Knight, R., Smith, R., Asch, T., Abraham, J., Cannia, J., Viezzoli, A., and
Fogg, G.: Mapping aquifer systems with airborne electromagnetics in the
Central Valley of California, Groundwater, 56,
893–908, 2018.
Lane, R.: Ground and airborne electromagnetic methods, Geophysical and
Remote Sensing Methods for Regolith Exploration, CRCLEME Open File Report,
144, 2002.
Längkvist, M., Kiselev, A., Alirezaie, M., and Loutfi, A.:
Classification and segmentation of satellite orthoimagery using
convolutional neural networks, Remote Sens., 8, 329–350, 2016.
Ley-Cooper, A. and Munday, T.: Groundwater Assessment and Aquifer
Characterization in the Musgrave Province, South Australia: Interpretation
of SPECTREM Airborne Electromagnetic Data, Goyder Institute for Water
Research Technical Report Series, 2013.
Luo, Y., Zhou, L., Wang, S., and Wang, Z.: Video Satellite Imagery Super
Resolution via Convolutional Neural Networks, IEEE Geosci. Remote S., 14, 2398–2402, 2017.
Magee, J. W.: Palaeovalley groundwater resources in arid and semi-arid
Australia: A literature review, Geoscience Australia, 249 pp., 2009.
Maidment, D. R. and Morehouse, S.: Arc Hydro: GIS for water resources,
ESRI, Inc., 2002.
Marcais, J. and de Dreuzy, J. R.: Prospective Interest of Deep Learning for
Hydrological Inference, Groundwater, 55, 688–692, https://doi.org/10.1111/gwat.12557, 2017.
Marker, P. A., Foged, N., He, X., Christiansen, A. V., Refsgaard, J. C., Auken, E., and Bauer-Gottwein, P.: Performance evaluation of groundwater model hydrostratigraphy from airborne electromagnetic data and lithological borehole logs, Hydrol. Earth Syst. Sci., 19, 3875–3890, https://doi.org/10.5194/hess-19-3875-2015, 2015.
Meller, C., Genter, A., and Kohl, T.: The application of a neural network to
map clay zones in crystalline rock, Geophys. J. Int., 196,
837–849, 2013.
Moysey, S., Caers, J., Knight, R., and Allen-King, R. M.: Stochastic
estimation of facies using ground penetrating radar data, Stoch.
Env. Res. Risk A., 17, 306–318, https://doi.org/10.1007/s00477-003-0152-6, 2003.
Mulligan, A. E., Evans, R. L., and Lizarralde, D.: The role of paleochannels
in groundwater/seawater exchange, J. Hydrol., 335, 313–329, 2007.
Munday, T., Abdat, T., Ley-Cooper, Y., and Gilfedder, M.: Facilitating Long-term Outback Water Solutions,
Goyder Institute for Water Research, 40 pp.,
2013.
Olhoeft, G. R.: Electrical properties of granite with implications for the
lower crust, J. Geophys. Res.-Sol. Ea., 86, 931–936,
1981.
Pardo-Igúzquiza, E. and Dowd, P. A.: CONNEC3D: a computer program for
connectivity analysis of 3D random set models, Comput. Geosci.,
29, 775–785, 2003.
Parkhomenko, E. I.: Electrical properties of rocks, Springer Science &
Business Media, 2012.
Perol, T., Gharbi, M., and Denolle, M.: Convolutional neural network for
earthquake detection and location, Science Advances, 4, E1700578, https://doi.org/10.1126/sciadv.1700578, 2018.
Pollock, D. and Cirpka, O. A.: Fully coupled hydrogeophysical inversion of
synthetic salt tracer experiments, Water Resour. Res., 46, W07501, https://doi.org/10.1029/2009WR008575, 2010.
Purvance, D. T. and Andricevic, R.: On the electrical-hydraulic
conductivity correlation in aquifers, Water Resour. Res., 36,
2905–2913, 2000.
Renard, P. and Allard, D.: Connectivity metrics for subsurface flow and
transport, Adv. Water Resour., 51, 168–196, 2013.
Rhoades, J., Raats, P., and Prather, R.: Effects of liquid-phase electrical
conductivity, water content, and surface conductivity on bulk soil
electrical conductivity, Soil Sci. Soc. Am. J., 40,
651–655, 1976.
Robinson, D., Binley, A., Crook, N., Day-Lewis, F., Ferré, T., Grauch,
V., Knight, R., Knoll, M., Lakshmi, V., and Miller, R.: Advancing
process-based watershed hydrological research using near-surface geophysics:
A vision for, and review of, electrical and magnetic geophysical methods,
Hydrol. Process., 22, 3604–3635, 2008.
Samadder, R. K., Kumar, S., and Gupta, R. P.: Paleochannels and their
potential for artificial groundwater recharge in the western Ganga plains,
J. Hydrol., 400, 154–164, 2011.
Simpson, G. G.: Uniformitarianism. An inquiry into principle, theory, and
method in geohistory and biohistory, in: Essays in evolution and genetics in
honor of Theodosius Dobzhansky, Springer, 43–96, 1970.
Soerensen, C. C., Munday, T. J., Ibrahimi, T., Cahill, K., and Gilfedder,
M.: Musgrave Province,
South Australia: processing and inversion of airborne electromagnetic (AEM) data, Goyder Institute for
Water Research, 57 pp., 2016.
Spies, B. R.: Depth of investigation in electromagnetic sounding methods,
Geophysics, 54, 872–888, 1989.
Taylor, A., Pichler, M., Olifent, V., Thompson, J., Bestland, E., Davies,
P., Lamontagne, S., Suckow, A., Robinson, N., and Love, A.: Groundwater Flow
Systems of North-eastern Eyre Peninsula (G-FLOWS Stage-2): Hydrogeology,
geophysics and environmental tracers, Goyder Institute for Water Research
Technical Report Series, 2015.
Tu, J. V.: Advantages and disadvantages of using artificial neural networks
versus logistic regression for predicting medical outcomes, J. Clin. Epidemiol., 49, 1225–1231, 1996.
Tuna, C., Unal, G., and Sertel, E.: Single-frame super resolution of
remote-sensing images by convolutional neural networks, Int. J. Remote Sens., 39, 2463–2479, 2018.
Varma, S.: Hydrogeological review of the Musgrave Province, South Australia,
Goyder Institute for Water Research Technical Report Series, 2012.
Viezzoli, A., Christiansen, A. V., Auken, E., and Sørensen, K.: Quasi-3D
modeling of airborne TEM data by spatially constrained inversion,
Geophysics, 73, F105–F113, 2008.
Vilhelmsen, T. N., Behroozmand, A. A., Christensen, S., and Nielsen, T. H.:
Joint inversion of aquifer test, MRS, and TEM data, Water Resour. Res., 50, 3956–3975, 2014.
Wang, Z. and Bovik, A. C.: A universal image quality index, IEEE Signal Proc. Let., 9, 81–84, 2002.
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P.: Image quality
assessment: from error visibility to structural similarity, IEEE T. Image Process., 13, 600–612, 2004.
Worthington, P. F.: The uses and abuses of the Archie equations, 1: The
formation factor-porosity relationship, J. Appl. Geophys., 30,
215–228, 1993.
Yang, D., Oldenburg, D. W., and Haber, E.: 3-D inversion of airborne
electromagnetic data parallelized and accelerated by local mesh and adaptive
soundings, Geophys. J. Int., 196, 1492–1507, 2013.
Short summary
Paleovalleys often form productive aquifers in the semiarid and arid areas. A methodology based on deep learning is introduced to automatically generate high-resolution 3-D paleovalley maps from low-resolution electrical conductivity data derived from airborne geophysical surveys. It is validated by borehole logs and the surface valley indices that the proposed method in this study provides an effective tool for regional-scale paleovalley mapping and groundwater exploration.
Paleovalleys often form productive aquifers in the semiarid and arid areas. A methodology based...
