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 needs of stochastic methods, the attitude in regulations and the court system regarding stochastic methods, education of the workforce and the availability of software tools needed for implementing stochastic methods in practice. Comparing the state of adoption in hydrogeology to petroleum reservoir engineering allowed us to identify quantitative metrics on which to base our analysis. For impediments to the adoption of stochastic hydrology, we identified external factors as well as self-inflicted wounds. What emerges is a picture much broader than current views. Financial incentives and regulations play a major role in stalling adoption. Stochastic Hydrology's blind spot is in confusing between risk and uncertainty and ignoring uncertainty. We show that stochastic hydrogeology comfortably focused on risk while ignoring uncertainty, to its own detriment and to the detriment of its potential clients. The imbalance between the treatment on risk on one hand and uncertainty on the other is shown to be common to multiple disciplines in hydrology that interface with risk and uncertainty.

What We Talk About When We Talk About Uncertainty. Toward a Unified, Data-Driven Framework for Uncertainty Characterization in Hydrogeology

“…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.…”

Hydrogeological Modeling and Water Resources Management: Improving the Link Between Data, Prediction, and Decision Making

“…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.…”

“…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.…”

“…As illustrated in Figure 1, the main feature of goal-oriented design is the inclusion of considerations from management, regulation, and the general public. Under the framework proposed by Nowak et al (2012), any constraints or considerations must be quantified and codified into an optimization algorithm, which can be challenging or impossible, due to the elaborate relationship that exists between industry, society, government, and the general public (Brulle & Pellow, 2006;Rubin et al, 2018). After arriving at the optimal design, it must be then checked for conformity to all the applicable noncodifiable constraints and considerations on an ex post facto basis.…”

“…In reality, however, uncertainty may also exist in the model structure, the model parameters, and the forcing and response data (Beven, 2016). Furthermore, the interfaces between hydrogeology and related disciplines (such as climate science and sociology and political science) are additional sources of uncertainties that can be difficult to integrate into the modeling process (Rubin et al, 2018). In practice, the effort one could make toward this ideal case is to condition the model and the its parameters on the best available data, which has the potential to significantly reduce both the conceptual model uncertainty and the model parameter uncertainty.…”