ALBERT

Description: 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.

Description: 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.

Description: Located in the critical zone at the intersection between surface water and groundwater, hyporheic zones (HZ) host a variety of hydrological, biological and biogeochemical processes regulating water availability and quality and sustaining riverine ecosystems. However, difficulty in quantifying water fluxes along this interface has limited our understanding of these processes, in particular under dynamic flow conditions where rapid variations can impact large-scale HZ biogeochemical function. In this study, we introduce an innovative measurement assimilation chain for determining uncertainty-quantified hydraulic and thermal HZ properties, as well as associated uncertainty-quantified high-frequency water fluxes. The chain consists in the assimilation of data collected with the LOMOS-mini geophysical device with a process-based, Bayesian approach. The application of this approach on a synthetic case study shows that hydraulic and thermal HZ properties can be estimated from LOMOS-mini measurements, their identifiability depending on the Peclet number – summarizing the hydrological and thermal regime. Hydraulic conductivity values can be estimated with precision when greater than ~10−5m · s−1 when other HZ properties are unknown, with decreasing uncertainty when other HZ properties are known prior to starting the LOMOS-mini measurement assimilation procedure. Water fluxes can be estimated in all regimes with varying accuracy, highest accuracy is reached for fluxes greater than ~10−6m · s−1, except under highly conductive exfiltration regimes. We apply the methodology on in situ datasets by deriving uncertainty-quantified HZ properties and water fluxes for 2 data points collected during field campaigns. This study demonstrates that the LOMOS-mini monitoring technology can be used as complete and stand-alone sampling solution for quantifying water and heat exchanges under dynamic exchange conditions (time resolution 〈 15 min).