the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Advancing measurements and representations of subsurface heterogeneity and dynamic processes: towards 4D hydrogeology
- 1Department of Geology, Ghent University, 9000 Gent, Belgium
- 2Department of Geology and Applied Geology, University of Mons, 7000 Mons, Belgium
- 3UMR 7619 METIS, Sorbonne Université, UPMC Université Paris 06, CCNRS, EPHE, F-75005 Paris, France
- 4Department of Hydrogeology, Helmoltz Centre for Environmental Research, 04318 Leipzig, Germany
- 5Laboratory of Hydrogeological Processes, University of Neuchâtel, 2000 Neuchatel, Switzerland
- 6Urban and Environmental Engineering, Liege University, 4000 Liege, Belgium
- 7Institute of Earth Sciences, University of Lausanne,1015 Lausanne, Switzerland
- 8Agrosphere (IBG 3), Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- 9Geosciences Rennes, UMR 6118, Université de Rennes 1, CNRS, 35000 Rennes, France
- 10Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas (CSIC), Barcelona, 08034, Spain
- 11Géosciences Montpellier, University of Montpellier, CNRS, Univ. des Antilles, 34095 Montpellier, France
- 12Institut d’Optique d’Aquitaine, Muquans, 33400 Talence, France
- 13Department of Environmental Physics and Irrigation, Agricultural Research Organization – Volcani Institute, 7505101 Rishon LeZion, Israel
- anow at: Centro de Geociencas , Universidad Nacional Autonoma de Mexico, 76230 Querétaro, Mexico
- bnow at: Amphos 21 Consulting, 08019 Barcelona, Spain
- cnow at: Federal Institute for Geosciences and Natural Resources (BGR), 13593 Berlin, Germany
- dnow at: Ruden SA, 0349 Oslo, Norway
- enow at: Silixa Ltd, London, UK
- 1Department of Geology, Ghent University, 9000 Gent, Belgium
- 2Department of Geology and Applied Geology, University of Mons, 7000 Mons, Belgium
- 3UMR 7619 METIS, Sorbonne Université, UPMC Université Paris 06, CCNRS, EPHE, F-75005 Paris, France
- 4Department of Hydrogeology, Helmoltz Centre for Environmental Research, 04318 Leipzig, Germany
- 5Laboratory of Hydrogeological Processes, University of Neuchâtel, 2000 Neuchatel, Switzerland
- 6Urban and Environmental Engineering, Liege University, 4000 Liege, Belgium
- 7Institute of Earth Sciences, University of Lausanne,1015 Lausanne, Switzerland
- 8Agrosphere (IBG 3), Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- 9Geosciences Rennes, UMR 6118, Université de Rennes 1, CNRS, 35000 Rennes, France
- 10Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas (CSIC), Barcelona, 08034, Spain
- 11Géosciences Montpellier, University of Montpellier, CNRS, Univ. des Antilles, 34095 Montpellier, France
- 12Institut d’Optique d’Aquitaine, Muquans, 33400 Talence, France
- 13Department of Environmental Physics and Irrigation, Agricultural Research Organization – Volcani Institute, 7505101 Rishon LeZion, Israel
- anow at: Centro de Geociencas , Universidad Nacional Autonoma de Mexico, 76230 Querétaro, Mexico
- bnow at: Amphos 21 Consulting, 08019 Barcelona, Spain
- cnow at: Federal Institute for Geosciences and Natural Resources (BGR), 13593 Berlin, Germany
- dnow at: Ruden SA, 0349 Oslo, Norway
- enow at: Silixa Ltd, London, UK
Abstract. Essentially all hydrogeological processes are strongly influenced by the subsurface spatial heterogeneity and the temporal variation of environmental conditions, hydraulic properties, and solute concentrations. This spatial and temporal variability needs to be considered when studying hydrogeological processes in order to employ adequate mechanistic models or perform upscaling. The scale at which a hydrogeological system should be characterized in terms of its spatial heterogeneity and temporal dynamics depends on the studied process and it is not always necessary to consider the full complexity. In this paper, we identify a series of hydrogeological processes for which an approach coupling the monitoring of spatial and temporal variability, including 4D imaging, is often necessary: (1) groundwater fluxes that control (2) solute transport, mixing and reaction processes, (3) vadose zone dynamics, and (4) surface-subsurface water interaction occurring at the interface between different subsurface compartments. We first identify the main challenges related to the coupling of spatial and temporal fluctuations for these processes. Then, we highlight some recent innovations that have led to significant breakthroughs in this domain. We finally discuss how spatial and temporal fluctuations affect our ability to accurately model them and predict their behavior. We thus advocate a more systematic characterization of the dynamic nature of subsurface processes, and the harmonization of open databases to store hydrogeological data sets in their four-dimensional components, for answering emerging scientific question and addressing key societal issues.
Thomas Hermans et al.
Status: final response (author comments only)
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RC1: 'Comment on hess-2022-95', Ty P. A. Ferre, 01 Apr 2022
This is a very useful 'flag in the sand' to indicate the current state of geophysical methods and to put them in context with hydrologic challenges for which they may be relevant. The authors' list is a veritable who's who of hydrogeophysics. Impressive. The only minor addition that I would have liked to see is some mention of the potential role of geophysics in the growing applicaitons of machine learning in hydrogeology. This seems a natural fit that may well alleviate some of the problems of both fields - hydrology and geophysics. The use of ML in hydrology is limited by a lack of data that can be collected by direct means - hydrogeophysics can help to address that. Geophysics is limited because we apply very limited, simplistic petrophysical models and geophysical forward models. Perhaps a less-model-dependent interpretation with ML could alleviate that. I'll leave it to the authors to decide whether they want to open this door. But, it seems to me that a paper that is marking what the field sees as the near future might want to offer an opinion on this!
Nice work!
Ty Ferre
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CC1: 'Reply on RC1', Thomas Hermans, 07 Apr 2022
Dear Ty,
Thank you for reviewing the paper and for your constructive comments. Your suggestion is highly relevant, and machine learning is indeed a promising way to integrate geophyscal data in hydrological studies. So far we are only discussing learning approach for prediction purposes and spatial heterogeneity representation. There are some recent papers that investigated the use of machine learning for petrophysical relationship (e.g. Gottschalk and Knight, 2022) for airborne data, which opens the possibility to inform the spatial variability of aquifer at the ctachment (or even beyond) scale. We will see how we can best include those innovative approaches in the manuscript during the revision.
Cheers,
Thomas
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CC1: 'Reply on RC1', Thomas Hermans, 07 Apr 2022
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RC2: 'Comment on hess-2022-95', Anonymous Referee #2, 27 Apr 2022
I was quite excited when I started to read Hermans et al especially with their stated objective “to identify and discuss when, why, and for which processes and applications the characterization of dynamic hydrogeological processes is crucial.”
The authors provide a massive amount of detail on groundwater fluxes, transport, mixing and reactions processes, soil moisture dynamics in the vadose zone and surface-subsurface water interactions which was interesting and useful but very little synthesis of simplified messages. I think a third to a half of the details could be striped away without losing much. They promise to provide opinions on where and when these groundwater processes can reasonably be simplified or not but fail to deliver on this. I suggest significantly expanding this with a table that suggests criteria for when, why and what scale each of these processes can be simplified or not.
Instead of such useful synthesis in the modeling section they just suggest more computing power in this text:
Cloud computing combined with increasing computational power should allow to model the subsurface at a higher 4D resolution for an increasing number of applications in the future, including for (small) consulting companies and field practitioners (Hayley, 2017; Kurtz et al., 2017).
Another overall observation and critique is that scale is of outmost importance but not clearly and consistently defined and discussed in this manuscript. I think every element of the mansucirpt should clarify how scale plays in and I would suggest putting the processes and methods on a time-space graph like this: https://www.researchgate.net/figure/Spatial-and-temporal-scales-of-measurement-black-and-modeling-red-methods-GCM_fig1_255586996
In sum I think this manuscript would benefit from significant re-think and re-write as a major revision.
Thomas Hermans et al.
Thomas Hermans et al.
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