Articles | Volume 28, issue 2
https://doi.org/10.5194/hess-28-357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Jan 2024
Research article |
| 31 Jan 2024
| 31 Jan 2024
Data-driven estimates for the geostatistical characterization of subsurface hydraulic properties
Falk Heße
CORRESPONDING AUTHOR
Institute of Earth and Environmental Sciences, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
Sebastian Müller
Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
Institute of Earth and Environmental Sciences, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
Sabine Attinger
Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
Institute of Earth and Environmental Sciences, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
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Yoram Rubin, Ching-Fu Chang, Jiancong Chen, Karina Cucchi, Bradley Harken, Falk Heße, and Heather Savoy
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Gabriele Baroni, Matthias Zink, Rohini Kumar, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 21, 2301–2320, https://doi.org/10.5194/hess-21-2301-2017, https://doi.org/10.5194/hess-21-2301-2017, 2017
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Falk Heße, Matthias Zink, Rohini Kumar, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 21, 549–570, https://doi.org/10.5194/hess-21-549-2017, https://doi.org/10.5194/hess-21-549-2017, 2017
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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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Alraune Zech and Sabine Attinger
Hydrol. Earth Syst. Sci., 20, 1655–1667, https://doi.org/10.5194/hess-20-1655-2016, https://doi.org/10.5194/hess-20-1655-2016, 2016
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Rohini Kumar, Jude L. Musuuza, Anne F. Van Loon, Adriaan J. Teuling, Roland Barthel, Jurriaan Ten Broek, Juliane Mai, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 20, 1117–1131, https://doi.org/10.5194/hess-20-1117-2016, https://doi.org/10.5194/hess-20-1117-2016, 2016
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In a maiden attempt, we performed a multiscale evaluation of the widely used SPI to characterize local- and regional-scale groundwater (GW) droughts using observations at 2040 groundwater wells in Germany and the Netherlands. From this data-based exploratory analysis, we provide sufficient evidence regarding the inability of the SPI to characterize GW drought events, and stress the need for more GW observations and accounting for regional hydrogeological characteristics in GW drought monitoring.
M. Bechmann, C. Schneider, A. Carminati, D. Vetterlein, S. Attinger, and A. Hildebrandt
Hydrol. Earth Syst. Sci., 18, 4189–4206, https://doi.org/10.5194/hess-18-4189-2014, https://doi.org/10.5194/hess-18-4189-2014, 2014
Related subject area
Subject: Groundwater hydrology | Techniques and Approaches: Uncertainty analysis
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Short summary
We evaluated the uncertainty propagation from the inputs (forcings) and parameters to the predictions of groundwater travel time distributions (TTDs) using a fully distributed numerical model (mHM-OGS) and the StorAge Selection (SAS) function. Through detailed numerical and analytical investigations, we emphasize the key role of recharge estimation in the reliable predictions of TTDs and the good interpretability of the SAS function.
Zexuan Xu, Bill X. Hu, and Ming Ye
Hydrol. Earth Syst. Sci., 22, 221–239, https://doi.org/10.5194/hess-22-221-2018, https://doi.org/10.5194/hess-22-221-2018, 2018
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This study helps hydrologists better understand the parameters in modeling seawater intrusion in a coastal karst aquifer. Local and global sensitivity studies are conducted to evaluate a density-dependent numerical model of seawater intrusion. The sensitivity analysis indicates that karst features are critical for seawater intrusion modeling, and the evaluation of hydraulic conductivity is biased in continuum SEAWAT model. Dispervisity is no longer important in the advection-dominated aquifer.
Mohamad E. Gharamti, Johan Valstar, Gijs Janssen, Annemieke Marsman, and Ibrahim Hoteit
Hydrol. Earth Syst. Sci., 20, 4561–4583, https://doi.org/10.5194/hess-20-4561-2016, https://doi.org/10.5194/hess-20-4561-2016, 2016
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The paper addresses the issue of sampling errors when using the ensemble Kalman filter, in particular its hybrid and second-order formulations. The presented work is aimed at estimating concentration and biodegradation rates of subsurface contaminants at the port of Rotterdam in the Netherlands. Overall, we found that accounting for both forecast and observation sampling errors in the joint data assimilation system helps recover more accurate state and parameter estimates.
Nikolaj Kruse Christensen, Steen Christensen, and Ty Paul A. Ferre
Hydrol. Earth Syst. Sci., 20, 1925–1946, https://doi.org/10.5194/hess-20-1925-2016, https://doi.org/10.5194/hess-20-1925-2016, 2016
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Our primary objective in this study is to provide a virtual environment that allows users to determine the value of geophysical data and, furthermore, to investigate how best to use those data to develop groundwater models and to reduce their prediction errors. When this has been carried through for alternative data sampling, parameterization and inversion approaches, the best alternative can be chosen by comparison of prediction results between the alternatives.
A. Hernández-Antonio, J. Mahlknecht, C. Tamez-Meléndez, J. Ramos-Leal, A. Ramírez-Orozco, R. Parra, N. Ornelas-Soto, and C. J. Eastoe
Hydrol. Earth Syst. Sci., 19, 3937–3950, https://doi.org/10.5194/hess-19-3937-2015, https://doi.org/10.5194/hess-19-3937-2015, 2015
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A conceptual model of groundwater flow processes and mixing was developed using a combination of hydrogeochemistry, isotopes and multivariate analysis. The implementation to the case of Guadalajara showed that groundwater was classified into four groups: cold groundwater, hydrothermal water, polluted groundwater and mixed groundwater. A multivariate mixing model was used to calculate the proportion of different fluids in sampled well water. The result helps authorities in decision making.
X. Y. Liang and Y.-K. Zhang
Hydrol. Earth Syst. Sci., 19, 2971–2979, https://doi.org/10.5194/hess-19-2971-2015, https://doi.org/10.5194/hess-19-2971-2015, 2015
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The error or uncertainty in head, obtained with an analytical or numerical solution, at an early time is mainly caused by the random initial condition. The error reduces with time, later reaching a constant error. The constant error at a later time is mainly due to the effects of the uncertain source/sink. The error caused by the uncertain boundary is limited to a narrow zone. Temporal scaling of head exists in most parts of a low permeable aquifer, mainly caused by recharge fluctuation.
X. He, T. O. Sonnenborg, F. Jørgensen, A.-S. Høyer, R. R. Møller, and K. H. Jensen
Hydrol. Earth Syst. Sci., 17, 3245–3260, https://doi.org/10.5194/hess-17-3245-2013, https://doi.org/10.5194/hess-17-3245-2013, 2013
A. Bárdossy
Hydrol. Earth Syst. Sci., 15, 2763–2775, https://doi.org/10.5194/hess-15-2763-2011, https://doi.org/10.5194/hess-15-2763-2011, 2011
E. Vázquez-Suñé, J. Carrera, I. Tubau, X. Sánchez-Vila, and A. Soler
Hydrol. Earth Syst. Sci., 14, 2085–2097, https://doi.org/10.5194/hess-14-2085-2010, https://doi.org/10.5194/hess-14-2085-2010, 2010
R. Rojas, O. Batelaan, L. Feyen, and A. Dassargues
Hydrol. Earth Syst. Sci., 14, 171–192, https://doi.org/10.5194/hess-14-171-2010, https://doi.org/10.5194/hess-14-171-2010, 2010
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Short summary
In this study, we have presented two different advances for the field of subsurface geostatistics. First, we present data of variogram functions from a variety of different locations around the world. Second, we present a series of geostatistical analyses aimed at examining some of the statistical properties of such variogram functions and their relationship to a number of widely used variogram model functions.
In this study, we have presented two different advances for the field of subsurface...
