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Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty with Application to Uranium Transport at the Hanford Site 300 Area (NUREG/CR-6940)
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Publication Information
Manuscript Completed: June 2007
Date Published: July 2007
Prepared by
P.D. Meyer, M. Ye (DRI), M.L. Rockhold, S.P. Neuman (UA)
K.J. Cantrell
Pacific Northwest National Laboratory
P.O. Box 999
Richland, WA 99352
Desert Research Institute
Las Vegas, NV 89119
(Currently at Florida State University, Tallahassee, FL 32306)
University of Arizona
Tucson, AZ 85721
T.J. Nicholson, NRC Project Manager
Prepared for
Division of Fuel, Engineering and Radiological Research
Office of Nuclear Regulatory Research
U.S. Nuclear Regulatory Commission
Washington, DC 20555-0001
NRC Job Code Y6465
Availability
Notice
Abstract
We describe the development and application of a
methodology to systematically and quantitatively assess
predictive uncertainty in groundwater flow and
transport modeling. The methodology considers the
combined impact of hydrogeologic uncertainties associated
with the conceptual-mathematical basis of a
model, model parameters, and the scenario to which
the model is applied. The methodology is based on an
extension of a Maximum Likelihood implementation of
Bayesian Model Averaging. Model uncertainty is represented
by postulating a discrete set of alternative
conceptual models for a site with associated prior
model probabilities. The prior model probabilities reflect
a subjective belief about the relative plausibility
of each model based on its apparent consistency with
available knowledge and data. Posterior model probabilities
are computed and parameter uncertainty is
estimated by calibrating each model to observed system
behavior. Posterior model probabilities are modifications
of the subjective prior values based on an objective
evaluation of each model’s consistency with
available data. Prior parameter estimates are optionally
included. Scenario uncertainty is represented as a discrete
set of alternative future conditions affecting
boundary conditions, source/sink terms, or other aspects
of the models. The associated prior scenario
probabilities reflect a subjective belief about the relative
plausibility of the alternative scenarios. A joint
assessment of uncertainty results from combining
model predictions computed under each scenario using
as weights the posterior model and prior scenario probabilities.
The computed model predictions incorporate
parameter uncertainties using, for example, Monte
Carlo simulation. The uncertainty methodology was
applied to modeling of groundwater flow and uranium
transport at the Hanford Site 300 Area. Eight alternative
models representing uncertainty in the hydrogeologic
and geochemical properties as well as the
temporal variability were considered. Two scenarios
representing alternative future behavior of the Columbia
River adjacent to the site were considered. The scenario
alternatives were implemented in the models
through the boundary conditions. Alternative models
were calibrated using hydraulic head and uranium concentration
observations over a seven-year period. Uranium
concentrations under each scenario were predicted
over a 20-year period. Results demonstrate the
feasibility of applying a comprehensive uncertainty
assessment to large-scale, detailed groundwater flow
and transport modeling. Results also illustrate the ability
of the methodology to provide better estimates of
predictive uncertainty, quantitative results for use in
assessing risk, and an improved understanding of the
system behavior and the limitations of the models.
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