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Groundwater Modeling with Nonlinear Uncertainty Analyses to Enhance Remediation Design Confidence
Author(s) -
Brouwers Martinus,
Martin Paul J.,
Abbey Daron G.,
White Jeremy
Publication year - 2018
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/gwat.12669
Subject(s) - environmental remediation , context (archaeology) , stakeholder , risk analysis (engineering) , environmental science , risk assessment , groundwater , risk management , computer science , civil engineering , environmental planning , engineering , contamination , business , geography , geotechnical engineering , ecology , public relations , computer security , archaeology , finance , political science , biology
Soil and groundwater contamination are often managed by establishing on‐site cleanup targets within the context of risk assessment or risk management measures. Decision‐makers rely on modeling tools to provide insight; however, it is recognized that all models are subject to uncertainty. This case study compares suggested remediation requirements using a site‐specific numerical model and a standardized analytical tool to evaluate risk to a downgradient wetland receptor posed by on‐site chloride impacts. The base case model, calibrated to observed non‐pumping and pumping conditions, predicts a peak concentration well above regulatory criteria. Remediation scenarios are iteratively evaluated to determine a remediation design that adheres to practical site constraints, while minimizing the potential for risk to the downgradient receptor. A nonlinear uncertainty analysis is applied to each remediation scenario to stochastically evaluate the risk and find a solution that meets the site‐owner risk tolerance, which in this case required a risk‐averse solution. This approach, which couples nonlinear uncertainty analysis with a site‐specific numerical model provides an enhanced level of knowledge to foster informed decision‐making (i.e., risk‐of‐success) and also increases stakeholder confidence in the remediation design.