2018
DOI: 10.5194/hess-2018-290
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Stochastic Hydrogeology's Biggest Hurdles Analyzed and Its Big Blind Spot

Abstract: Abstract. This paper considers questions related to the adoption of stochastic methods in hydrogeology. It looks at factors affecting the adoption of stochastic methods including environmental regulations, financial incentives, higher education, and the collective feedback loop involving these factors. We begin by evaluating two previous paper series appearing in the stochastic hydrogeology literature, one in 2004 and one in 2016, and identifying the current thinking on the topic, including the perceived data … Show more

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Cited by 7 publications
(12 citation statements)
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“…In this way we are making the topic of structural uncertainty fully amendable to probabilistic analysis. Second, as pointed out by Rubin et al (2018), the Dempster-Shafer theory can be helpful in accounting for unknown unknowns that result from the interaction of hydrogeological problems with societal developments in general (Walker et al, 2013;Maier et al, 2016). This second benefit is very important but does not necessarily conflict with our notion of probability as a default system for uncertain reasoning.…”
Section: Models For Reasoning With Certainty and The Case For Probabimentioning
confidence: 96%
“…In this way we are making the topic of structural uncertainty fully amendable to probabilistic analysis. Second, as pointed out by Rubin et al (2018), the Dempster-Shafer theory can be helpful in accounting for unknown unknowns that result from the interaction of hydrogeological problems with societal developments in general (Walker et al, 2013;Maier et al, 2016). This second benefit is very important but does not necessarily conflict with our notion of probability as a default system for uncertain reasoning.…”
Section: Models For Reasoning With Certainty and The Case For Probabimentioning
confidence: 96%
“…Extensive discussions on these two categories can found in Beven () and Blöschl et al (). In addition, somewhat more suggestive definitions of uncertainty are provided by Di Baldassarre et al () and Rubin et al (), who discuss differences between “known unknowns” and “unknown unknowns,” standing for aleatory uncertainty and epistemic uncertainty, respectively. Further, in Di Baldassarre et al (), the authors included a third category—“wrong assumptions”—meaning “things we think we know but we actually do not know.” This third category obviously overlaps, to some degree, with the other two, but it is important to mention this third category as we map the universe of uncertainty.…”
Section: On the Limits Of Modeling Uncertaintymentioning
confidence: 99%
“…There is often no direct interplay between hydrogeological considerations and other considerations related to management, regulation, and the general public. However, in some applications, such as when assessing a health risk to a potentially exposed population, hydrogeological characterization and modeling play only one part in the overall risk assessment (De Barros & Rubin, 2008;Maxwell et al, 1999;Rubin et al, 2018). It is thus important to adopt a goal-oriented perspective (Figure 1, arrows B, C, and D), where considerations regarding all aspects revolve around the key management variable-the risk of making a wrong decision-which, in turn, shape the sampling campaign design.…”
Section: Previous Work and Remaining Challengesmentioning
confidence: 99%
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