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

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 hydrau...

Full description

Saved in:
Bibliographic Details
Published in:Water resources research 2020-10, Vol.56 (10), p.n/a
Main Authors: Ciriello, Valentina, Barros, Felipe P. J.
Format: Article
Language:English
Subjects:
Aquifer recharge
Aquifers
Chaos
contaminant source zone
Contaminants
Discharge
Geology
global sensitivity analysis
Groundwater
Groundwater discharge
Groundwater recharge
Hydraulic conductivity
Hydrogeology
Mass
Mitigation
Pollution transport
polynomial chaos expansion
Polynomials
Predictions
probabilistic risk analysis
Risk assessment
Sensitivity analysis
Solutes
stochastic hydrology
Stochasticity
Transport
Uncertainty
Variance analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. Key Points We develop semianalytical solutions for the low‐order moments of solute discharge in groundwater We investigate relative importance of source release rate, aquifer recharge, and log‐conductivity variance on the moments of solute discharge Global sensitivity analysis is employed with surrogate models to characterize predictive uncertainty
ISSN:0043-1397
1944-7973
DOI:10.1029/2020WR027867