A risk-based decision-making mechanism capable of accounting for uncertainty regarding local conditions is crucial to water resources management, regulation, and policy making. Despite the great potential of hydrogeological models in supporting water resources decisions, challenges remain due to the many sources of uncertainty as well as making and communicating decisions mindful of this uncertainty. This paper presents a framework that utilizes statistical hypothesis testing and an integrated approach to the planning of site characterization, modeling prediction, and decision making. Benefits of this framework include aggregated uncertainty quantification and risk evaluation, simplified communication of risk between stakeholders, and improved defensibility of decisions. The framework acknowledges that obtaining absolute certainty in decision making is impossible; rather, the framework provides a systematic way to make decisions in light of uncertainty and determine the amount of information required. In this manner, quantitative evaluation of a field campaign design is possible before data are collected, beginning from any knowledge state, which can be updated as more information becomes available. We discuss the limitations of this approach by the types of uncertainty that can be recognized and make suggestions for addressing the rest. This paper presents the framework in general and then demonstrates its application in a synthetic case study. Results indicate that the effectiveness of field campaigns depends not only on the environmental performance metric being predicted but also on the threshold value in decision-making processes. The findings also demonstrate that improved parameter estimation does not necessarily lead to better decision making, thus reemphasizing the need for goal-oriented characterization. Environmental Performance Metrics and Water Resources ManagementSustainable groundwater management requires managers to make decisions based on answers to crucial questions regarding the quantity and quality of groundwater resources. For example, a water district manager needs to make decisions on when and where to divert water to storage facilities, so that the district has water that is suitably clean (i.e., that all contaminant concentrations are below the limit of the treatment system) and sufficiently ample (i.e., that there is an acceptable low risk of failure to supply the fluctuating domestic water demand). In these cases, the hydrological/hydrogeological variable(s) involved in these types of questions may be the arrival time of a contaminant plume at a water intake, the groundwater and contaminant flux passing through a specific area over a given period, or the contaminant concentration at a specific location or time. Such variables have been referred to as environmental performance metrics (EPMs; De Barros et al., 2012), the prediction of which helps water managers answer the aforementioned questions.

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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.