Articles | Volume 22, issue 11
https://doi.org/10.5194/hess-22-5675-2018
© Author(s) 2018. 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-22-5675-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Stochastic hydrogeology's biggest hurdles analyzed and its big blind spot
Civil and Environmental Engineering, University of California, Berkeley, 94720, USA
Ching-Fu Chang
Civil and Environmental Engineering, University of California, Berkeley, 94720, USA
Jiancong Chen
Civil and Environmental Engineering, University of California, Berkeley, 94720, USA
Karina Cucchi
Civil and Environmental Engineering, University of California, Berkeley, 94720, USA
Bradley Harken
Civil and Environmental Engineering, University of California, Berkeley, 94720, USA
Falk Heße
Computational Hydrosystems, Helmholtz Centre for Environmental Research (UFZ), 04318 Leipzig, Germany
Heather Savoy
Civil and Environmental Engineering, University of California, Berkeley, 94720, USA
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Cited
14 citations as recorded by crossref.
- A Snapshot of the World's Groundwater Challenges U. Lall et al. https://doi.org/10.1146/annurev-environ-102017-025800
- An effective multi-objective optimization approach for groundwater remediation considering the coexisting uncertainties of aquifer parameters Y. Yang et al. https://doi.org/10.1016/j.jhydrol.2022.127677
- GSTools v1.3: a toolbox for geostatistical modelling in Python S. Müller et al. https://doi.org/10.5194/gmd-15-3161-2022
- Ensemble modeling of the two-dimensional stochastic confined groundwater flow through the evolution of the hydraulic head’s probability density function J. Meza & M. Levent Kavvas https://doi.org/10.1016/j.jhydrol.2025.132689
- MODFLOW Application for Exploitable Groundwater Resource Assessment of the Zhem Artesian Basin Aquifer Complex, Kazakhstan D. Sapargaliyev et al. https://doi.org/10.3390/app15105443
- Hydrological design in the HELPING decade – inspiring the community to innovate the hydrological design concept S. Fischer et al. https://doi.org/10.1080/02626667.2024.2436634
- Representing driver-response complexity in ecosystems using an improved conceptual model C. Bentley & A. Anandhi https://doi.org/10.1016/j.ecolmodel.2020.109320
- Uncertainties in physical and tracer methods in actual groundwater recharge estimation in the thick loess deposits of China W. Wang et al. https://doi.org/10.1016/j.jhydrol.2024.131127
- Regionalization with hierarchical hydrologic similarity and ex situ data in the context of groundwater recharge estimation at ungauged watersheds C. Chang & Y. Rubin https://doi.org/10.5194/hess-23-2417-2019
- Application of upscaling methods for fluid flow and mass transport in multi-scale heterogeneous media: A critical review X. Zhang et al. https://doi.org/10.1016/j.apenergy.2021.117603
- Conceptual framework addressing timescale mismatch uncertainty: Nitrous-oxide (N2O) modeled and measured, Kansas, USA M. Arango et al. https://doi.org/10.1016/j.ecolmodel.2023.110536
- A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants K. Samborska-Goik & M. Pogrzeba https://doi.org/10.3390/app14093675
- Ensemble-based stochastic permeability and flow simulation of a sparsely sampled hard-rock aquifer supported by high performance computing J. Bruckmann & C. Clauser https://doi.org/10.1007/s10040-020-02163-5
- Pumping Optimization for Groundwater Remediation Under Uncertainty Using an Improved Hiking Optimization Algorithm X. Xia et al. https://doi.org/10.1007/s11270-026-09419-y
14 citations as recorded by crossref.
- A Snapshot of the World's Groundwater Challenges U. Lall et al. https://doi.org/10.1146/annurev-environ-102017-025800
- An effective multi-objective optimization approach for groundwater remediation considering the coexisting uncertainties of aquifer parameters Y. Yang et al. https://doi.org/10.1016/j.jhydrol.2022.127677
- GSTools v1.3: a toolbox for geostatistical modelling in Python S. Müller et al. https://doi.org/10.5194/gmd-15-3161-2022
- Ensemble modeling of the two-dimensional stochastic confined groundwater flow through the evolution of the hydraulic head’s probability density function J. Meza & M. Levent Kavvas https://doi.org/10.1016/j.jhydrol.2025.132689
- MODFLOW Application for Exploitable Groundwater Resource Assessment of the Zhem Artesian Basin Aquifer Complex, Kazakhstan D. Sapargaliyev et al. https://doi.org/10.3390/app15105443
- Hydrological design in the HELPING decade – inspiring the community to innovate the hydrological design concept S. Fischer et al. https://doi.org/10.1080/02626667.2024.2436634
- Representing driver-response complexity in ecosystems using an improved conceptual model C. Bentley & A. Anandhi https://doi.org/10.1016/j.ecolmodel.2020.109320
- Uncertainties in physical and tracer methods in actual groundwater recharge estimation in the thick loess deposits of China W. Wang et al. https://doi.org/10.1016/j.jhydrol.2024.131127
- Regionalization with hierarchical hydrologic similarity and ex situ data in the context of groundwater recharge estimation at ungauged watersheds C. Chang & Y. Rubin https://doi.org/10.5194/hess-23-2417-2019
- Application of upscaling methods for fluid flow and mass transport in multi-scale heterogeneous media: A critical review X. Zhang et al. https://doi.org/10.1016/j.apenergy.2021.117603
- Conceptual framework addressing timescale mismatch uncertainty: Nitrous-oxide (N2O) modeled and measured, Kansas, USA M. Arango et al. https://doi.org/10.1016/j.ecolmodel.2023.110536
- A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants K. Samborska-Goik & M. Pogrzeba https://doi.org/10.3390/app14093675
- Ensemble-based stochastic permeability and flow simulation of a sparsely sampled hard-rock aquifer supported by high performance computing J. Bruckmann & C. Clauser https://doi.org/10.1007/s10040-020-02163-5
- Pumping Optimization for Groundwater Remediation Under Uncertainty Using an Improved Hiking Optimization Algorithm X. Xia et al. https://doi.org/10.1007/s11270-026-09419-y
Saved (final revised paper)
Latest update: 21 Jun 2026
Short summary
This paper addresses questions related to the adoption of stochastic methods in hydrogeology, looking at factors such as environmental regulations, financial incentives, higher education, and the collective feedback loop involving these factors. We show that stochastic hydrogeology's blind spot is in focusing on risk while ignoring uncertainty, to the detriment of its potential clients. The imbalance between the treatments of risk and uncertainty is shown to be common to multiple disciplines.
This paper addresses questions related to the adoption of stochastic methods in hydrogeology,...