Characterizing the Influence of Multiple Uncertainties on Predictions of Contaminant Discharge in Groundwater Within a Lagrangian Stochastic Formulation

Abstract

Assessing the risks associated with transport of contaminants in hydrogeological systems requires the characterization of multiple sources of uncertainty. This paper examines the impact of the uncertainty in the source zone mass release rate, aquifer recharge, and the spatial structure of the hydraulic conductivity on transport predictions. Through the use of the Lagrangian framework, we develop semianalytical solutions for the first two moments of the total solute discharge through a control plane while accounting for source zone release conditions and recharge. We employ global sensitivity analysis (GSA) to investigate how the predictive uncertainty of the mass discharge is affected by uncertainty in source zone mass release rate, recharge, and the variance of the log-conductivity field. The semianalytical solutions are employed with the polynomial chaos expansion technique to perform a GSA. Our results reveal the relative influence of each source of uncertainty on the robustness of model predictions, which is critical for site managers to allocate resources and design mitigation strategies.

Technical note: Displacement variance of a solute particle in heterogeneous confined aquifers with random aquifer thickness fields

“…That is, solute dispersion occurs in situations where advection dominates and solute particles do not transfer across streamlines. There are numerous studies in the literature on solute transport under advection-dominated conditions, e.g., Dagan (1984), Rubin and Bellin (1994), Butera et al (2009), Cvetkovic (2016), Ciriello and Barros (2020), etc.…”

Reduced‐Order Models Unravel the Joint Impact of Aperture Heterogeneity and Shear‐Thinning Rheology on Fracture‐Scale Flow Metrics

“…The key advantage of using PCE is that this technique provides an approximation of the response surface of the QoIs in the form of a polynomial series in the space of variability of the selected governing parameters (Ghanem & Spanos, 1991; Wiener, 1938). To be determined, the PCE requires a negligible number of model runs, thus being advantageous over onerous MCs for mapping out the governing parameter space (Ciriello & Barros, 2020).…”

“…The polynomial chaos expansion (PCE) technique is used for model reduction since it has demonstrated its effectiveness in different hydrogeological settings. For example, PCE has been used to accelerate variance-based global sensitivity analysis and uncertainty quantification (otherwise prohibitive from a computational point of view) in transport problems in subsurface hydrology (Ciriello & Barros, 2020;Ciriello et al, 2012Ciriello et al, , 2017Kim et al, 2022;Oladyshkin et al, 2012) and for a sensitivity-based transport model calibration (Ciriello et al, 2013(Ciriello et al, , 2015. PCE has also been used to compute distribution-based global sensitivity analysis (Ciriello et al, 2019), and to predict the future behavior of water resources under climate change (Focaccia et al, 2021).…”

Assessing Contaminant Mass Discharge Uncertainty With Application of Hydraulic Conductivities Derived From Geoelectrical Cross‐Borehole Induced Polarization and Other Methods

“…In groundwater management, contaminant mass discharge (CMD) is becoming an important and commonly applied metric for evaluating the risk that contaminants pose to downgradient receptors such as groundwater resources, abstraction wells for water supply and surface water (Anderson, 2021; Balbarini et al., 2018; Ciriello & de Barros, 2020; Koch & Nowak, 2015; Pollicino et al., 2021; Rønde et al., 2017). The approach has also shown benefits for monitoring the effect and long term performance of remedial action at contaminated sites (Annable et al., 2008; Fjordboge et al., 2012; Haluska et al., 2019; ITRC, 2017; Mateas et al., 2017).…”