HR: 1340h
AN: H13A-1322    [Abstracts]
TI: Analysis of Model Uncertainties to Support Risk-Based Decisions Regarding Groundwater Contamination
AU: * Birdsell, K H
EM: khb@lanl.gov
AF: Earth and Environmental Sciences Division, Los Alamos National Laboratory, MS T003, Los Alamos, NM 87545 United States
AU: Vesselinov, V V
EM: vvv@lanl.gov
AF: Earth and Environmental Sciences Division, Los Alamos National Laboratory, MS T003, Los Alamos, NM 87545 United States
AU: Davis, P
EM: p_davis@EnviroLogicInc.com
AF: EnviroLogic Inc., 12127B, Suite 4 State Highway 14 North, Cedar Crest, NM 87008 United States
AU: Hollis, D
EM: dhollis@lanl.gov
AF: Security and Safeguards Division, Los Alamos National Laboratory, MS F674, Los Alamos, NM 87545 United States
AU: Newman, B D
EM: bnewman@lanl.gov
AF: Earth and Environmental Sciences Division, Los Alamos National Laboratory, MS T003, Los Alamos, NM 87545 United States
AU: Echohawk, J C
EM: echohawk@lanl.gov
AF: Earth and Environmental Sciences Division, Los Alamos National Laboratory, MS T003, Los Alamos, NM 87545 United States
AB: Model simulations are widely used in environmental management decision processes. However, there are various sources of uncertainty that commonly impact the model results. Consequently, it is crucial to account for all the possible model uncertainties that impact the model results so that they are adequately considered in the management decision process. Here we discuss an uncertainty analysis of model simulations related to a contamination site located within Los Alamos National Laboratory, NM. We describe how uncertainties are quantified and propagated through a series of coupled groundwater models and then used in a risk-based decision analysis to identify and rank alternative actions to protect the environment and water users from potential impacts of groundwater contamination from former liquid-effluent discharges. Uncertainties in the contaminant source, infiltration distribution, and transport through the unsaturated and saturated zones are analyzed using a series of alternative conceptual models and stochastic model parameters. Alternative conceptual models and uncertain model parameters are defined to encompass a large range of possible uncertainties associated with potential groundwater flow and transport based on existing data and expert knowledge about the system. In all, eight alternative conceptual models using 38 uncertain parameters were analyzed. For each conceptual model and related stochastic parameter realization, we simulate contaminant transport from the contaminant outfall to water-supply wells over the next 1000 years. Based on the simulated contaminant concentrations in the groundwater pumped by water-supply wells, we evaluate health risk for the receptors. Based on the model results, sensitivity analysis is applied to identify the parameters and conceptual model elements causing high concentrations at the water-supply wells. Decision analysis is applied to define the optimal course(s) of action, which may include clean-up, stabilization, additional characterization, and monitoring. If additional characterization is identified as an action that can reduce risk, the sensitivity analysis yields information not only about which parameters should be better characterized (and which should not), but also to what degree the uncertainty or variability in a specific parameter should be reduced. If the uncertainty is reduced to within the defined limits through characterization, then an updated risk assessment would calculate reduced risk. The results of our analysis demonstrate that due to dilution of the contaminants in the regional aquifer and within the water-supply wells, all of the alternative conceptual models yield low, calculated risk for the receptors. The uncertainty in model predictions is affected principally by the uncertainty in conceptualization. Currently, field exploration and study at the site continues, and these new data will allow us to test whether the uncertainties included in the risk assessment are broad enough so that the obtained conclusions will not change.
DE: 1829 Groundwater hydrology
DE: 1847 Modeling
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: Fall Meeting 2005