Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty
Abstract
We describe the development and application of a methodology to systematically and quantitatively assess predictive uncertainty in groundwater flow and transport modeling that considers the combined impact of hydrogeologic uncertainties associated with the conceptualmathematical 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 that reflect a 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; 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, with associated prior scenario probabilities. A joint assessment of uncertainty results from combining model predictions computed under each scenario using as weights the posterior model and prior scenario probabilities.
 Authors:
 Publication Date:
 Research Org.:
 Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 947942
 Report Number(s):
 PNNLSA47117
401001060; TRN: US200905%%239
 DOE Contract Number:
 AC0576RL01830
 Resource Type:
 Conference
 Resource Relation:
 Conference: Proceedings of the 3rd Federal Interagency Hydrologic Modeling Conference
 Country of Publication:
 United States
 Language:
 English
 Subject:
 58 GEOSCIENCES; HYDROLOGY; GEOLOGIC MODELS; GROUND WATER; FLOW MODELS; DATA COVARIANCES; BOUNDARY CONDITIONS; ENVIRONMENTAL TRANSPORT; modeling; hydrogeologic; uncertainty; parameter uncertainty; model uncertainty; scenario
Citation Formats
Meyer, Philip D., Ye, Ming, Neuman, Shlomo P., Rockhold, Mark L., Cantrell, Kirk J., and Nicholson, Thomas J. Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty. United States: N. p., 2006.
Web.
Meyer, Philip D., Ye, Ming, Neuman, Shlomo P., Rockhold, Mark L., Cantrell, Kirk J., & Nicholson, Thomas J. Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty. United States.
Meyer, Philip D., Ye, Ming, Neuman, Shlomo P., Rockhold, Mark L., Cantrell, Kirk J., and Nicholson, Thomas J. Mon .
"Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty". United States.
doi:.
@article{osti_947942,
title = {Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty},
author = {Meyer, Philip D. and Ye, Ming and Neuman, Shlomo P. and Rockhold, Mark L. and Cantrell, Kirk J. and Nicholson, Thomas J.},
abstractNote = {We describe the development and application of a methodology to systematically and quantitatively assess predictive uncertainty in groundwater flow and transport modeling that considers the combined impact of hydrogeologic uncertainties associated with the conceptualmathematical 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 that reflect a 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; 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, with associated prior scenario probabilities. A joint assessment of uncertainty results from combining model predictions computed under each scenario using as weights the posterior model and prior scenario probabilities.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2006},
month = {Mon May 01 00:00:00 EDT 2006}
}

This report to the Nuclear Regulatory Commission (NRC) describes the development and application of a methodology to systematically and quantitatively assess predictive uncertainty in groundwater flow and transport modeling that considers the combined impact of hydrogeologic uncertainties associated with the conceptualmathematical basis of a model, model parameters, and the scenario to which the model is applied. The methodology is based on a n 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 that reflect a belief about themore »

Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty
The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal sitespecific data the method can be carried out with prior parameter estimates basedmore » 
Analysis of Hydrogeologic Conceptual Model and Parameter Uncertainty
A systematic methodology for assessing hydrogeologic conceptual model, parameter, and scenario uncertainties is being developed to support technical reviews of environmental assessments related to decommissioning of nuclear facilities. The first major task being undertaken is to produce a coupled parameter and conceptual model uncertainty assessment methodology. This task is based on previous studies that have primarily dealt individually with these two types of uncertainties. Conceptual model uncertainty analysis is based on the existence of alternative conceptual models that are generated using a set of clearly stated guidelines targeted at the needs of NRC staff. Parameter uncertainty analysis makes use ofmore » 
Incorporating Scenario Uncertainty within a Hydrogeologic Uncertainty Assessment Methodology
The development and application of a methodology to systematically and quantitatively assess predictive uncertainty in groundwater flow and transport modeling is described. The methodology considers the combined impact of hydrogeologic uncertainties associated with the conceptualmathematical 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 to include the impact of uncertainty in the future hydrologic scenario to which the models are applied. Scenario uncertainty is represented as a discrete set of alternative future conditions affecting boundary conditions, source/sink terms,more » 
Estimation of parameter uncertainty using inverse model sensitivites
Forward model sensitivities are commonly applied to evaluate the uncertainty in model parameter estimates obtained through inverse analysis. In this case, the forward sensitivity (Jacobian) matrix is applied to compute an approximate representation of the covariance matrix of inverse parameter estimates. However, this approach can produce biased estimates of the covariance matrix because it does not account accurately for correlations between uncertainty of calibration targets and estimates. Typically, these correlations are nonlinear and depend on the spatial and temporal structure of inverse targets and estimated parameters. A better but much more computationally intensive method, which we call inversesensitivity approach, directlymore »