Articles | Volume 20, issue 5
https://doi.org/10.5194/hess-20-1925-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-1925-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
13 May 2016
Research article |
| 13 May 2016
Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models
Nikolaj Kruse Christensen
CORRESPONDING AUTHOR
Department of Geoscience, Aarhus University,
Aarhus, Denmark
Steen Christensen
Department of Geoscience, Aarhus University,
Aarhus, Denmark
Ty Paul A. Ferre
Department of Hydrology and Water Resources, University of
Arizona, Tucson, USA
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Cited
13 citations as recorded by crossref.
- A model of transmissivity and hydraulic conductivity from electrical resistivity distribution derived from airborne electromagnetic surveys of the Mississippi River Valley Alluvial Aquifer, Midwest USA S. Ikard et al. https://doi.org/10.1007/s10040-022-02590-6
- Generation of 3‐D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error N. Christensen et al. https://doi.org/10.1002/2016WR019141
- Assessing the Impact of Fractured Zones Imaged by ERT on Groundwater Model Prediction: A Case Study in a Chalk Aquifer in Voort (Belgium) B. Van Riet et al. https://doi.org/10.3389/frwa.2021.783983
- Review of integrating thermal, electrical, and seismic geophysical methods for shallow groundwater modeling in the critical zone A. Rivière et al. https://doi.org/10.5802/crgeos.330
- Combining hydrologic, chemical, and geophysical deep learning-based inversion for heterogeneous aquifer structure identification Y. Xia et al. https://doi.org/10.1016/j.jhydrol.2025.134701
- Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy N. Christensen et al. https://doi.org/10.5194/hess-21-1321-2017
- TEMPORARY REMOVAL: Computational electromagnetic geophysics for groundwater system studies: A review on established practices and recent advances P. Rulff et al. https://doi.org/10.1016/j.jhydrol.2026.135542
- Remote sensing and hydrogeophysics give a new impetus to integrated hydrological models: A review M. Lubczynski et al. https://doi.org/10.1016/j.jhydrol.2024.130901
- Direct prediction of spatially and temporally varying physical properties from time‐lapse electrical resistance data T. Hermans et al. https://doi.org/10.1002/2016WR019126
- Magnetic resonance sounding measurements as posterior information to condition hydrological model parameters: Application to a hard-rock headwater catchment N. Lesparre et al. https://doi.org/10.1016/j.jhydrol.2020.124941
- Hydrofacies simulation based on transition probability geostatistics using electrical resistivity tomography and borehole data L. Ma et al. https://doi.org/10.1007/s10040-022-02539-9
- Optimization of Aquifer Monitoring through Time-Lapse Electrical Resistivity Tomography Integrated with Machine-Learning and Predictive Algorithms V. Giampaolo et al. https://doi.org/10.3390/app12189121
- Hydrogeophysical inversion using a physics-based catchment model with hydrological and electromagnetic induction data M. Pleasants et al. https://doi.org/10.1016/j.jhydrol.2024.132376
13 citations as recorded by crossref.
- A model of transmissivity and hydraulic conductivity from electrical resistivity distribution derived from airborne electromagnetic surveys of the Mississippi River Valley Alluvial Aquifer, Midwest USA S. Ikard et al. https://doi.org/10.1007/s10040-022-02590-6
- Generation of 3‐D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error N. Christensen et al. https://doi.org/10.1002/2016WR019141
- Assessing the Impact of Fractured Zones Imaged by ERT on Groundwater Model Prediction: A Case Study in a Chalk Aquifer in Voort (Belgium) B. Van Riet et al. https://doi.org/10.3389/frwa.2021.783983
- Review of integrating thermal, electrical, and seismic geophysical methods for shallow groundwater modeling in the critical zone A. Rivière et al. https://doi.org/10.5802/crgeos.330
- Combining hydrologic, chemical, and geophysical deep learning-based inversion for heterogeneous aquifer structure identification Y. Xia et al. https://doi.org/10.1016/j.jhydrol.2025.134701
- Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy N. Christensen et al. https://doi.org/10.5194/hess-21-1321-2017
- TEMPORARY REMOVAL: Computational electromagnetic geophysics for groundwater system studies: A review on established practices and recent advances P. Rulff et al. https://doi.org/10.1016/j.jhydrol.2026.135542
- Remote sensing and hydrogeophysics give a new impetus to integrated hydrological models: A review M. Lubczynski et al. https://doi.org/10.1016/j.jhydrol.2024.130901
- Direct prediction of spatially and temporally varying physical properties from time‐lapse electrical resistance data T. Hermans et al. https://doi.org/10.1002/2016WR019126
- Magnetic resonance sounding measurements as posterior information to condition hydrological model parameters: Application to a hard-rock headwater catchment N. Lesparre et al. https://doi.org/10.1016/j.jhydrol.2020.124941
- Hydrofacies simulation based on transition probability geostatistics using electrical resistivity tomography and borehole data L. Ma et al. https://doi.org/10.1007/s10040-022-02539-9
- Optimization of Aquifer Monitoring through Time-Lapse Electrical Resistivity Tomography Integrated with Machine-Learning and Predictive Algorithms V. Giampaolo et al. https://doi.org/10.3390/app12189121
- Hydrogeophysical inversion using a physics-based catchment model with hydrological and electromagnetic induction data M. Pleasants et al. https://doi.org/10.1016/j.jhydrol.2024.132376
Saved (final revised paper)
Latest update: 04 Jun 2026
Short summary
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.
Our primary objective in this study is to provide a virtual environment that allows users to...
