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 uncertainty with risk 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.

Constraining the Magnitude and Uncertainty of Specific Yield for Use in the Water Table Fluctuation Method of Estimating Recharge

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“…Stochastic hydrogeology operates on the concept of probability, which is inherently subjective and reflects the level of understanding or confidence in the actual state of affairs within a system that exhibits randomness [411][412][413][414]. Hence, stochastic hydrogeological models must address the probable distribution of input parameters and their associated uncertainties: theoretical uncertainties arising from limited knowledge about the processes impacting model outcomes; measurement uncertainties stemming from instrument accuracy; and uncertainties attributed to spatial and temporal non-uniformity or missing data [415,416].…”

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