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HESS | Articles | Volume 22, issue 11
Hydrol. Earth Syst. Sci., 22, 5675–5695, 2018
https://doi.org/10.5194/hess-22-5675-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 22, 5675–5695, 2018
https://doi.org/10.5194/hess-22-5675-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Nov 2018

Research article | 02 Nov 2018

Stochastic hydrogeology's biggest hurdles analyzed and its big blind spot

Yoram Rubin et al.

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Cited articles

Aquaveo LLC: GMS User Manual: The Groundwater Modeling System, 558 pp., 2012. 
Baron, J.: Blackwell Handbook of Judgment and Decision Making, edited by: Koehler, D. J. and Harvey, N., Blackwell Publishing Ltd., Oxford, UK, 19–37,2004. 
Bellin, A. and Rubin, Y.: HYDRO_GEN: A spatially distributed random field generator for correlated properties, Stoch. Hydrol. Hydraul., 10, 253–278, https://doi.org/10.1007/BF01581869, 1996. 
Bellin, A. and Rubin, Y.: On the use of peak concentration arrival times for the inference of hydrogeological parameters, Water Resour. Res., 40, W07401, https://doi.org/10.1029/2003WR002179, 2004. 
Berger, J.: Robust Bayesian analysis: sensitivity to the prior, J. Stat. Plan. Infer., 25, 303–328, 1990. 
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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,...
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