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Title: 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 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 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):
PNNL-SA-47117
401001060; TRN: US200905%%239
DOE Contract Number:
AC05-76RL01830
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 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 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}
}

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  • 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 conceptual-mathematical 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 » 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 weight the posterior model and prior scenario probabilities. 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 represent 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. Results demonstrate the feasibility of applying a comprehensive uncertainty assessment to large-scale, detailed groundwater flow and transport modeling and illustrate the benefits of the methodology I providing better estimates of predictive uncertiay8, quantitative results for use in assessing risk, and an improved understanding of the system behavior and the limitations of the models.« less
  • 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 site-specific data the method can be carried out with prior parameter estimates basedmore » on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four projections, and associated kriging variances, were averaged using the posterior model probabilities as weights. Finally, cross-validation was conducted by eliminating from consideration all data from one borehole at a time, repeating the above process, and comparing the predictive capability of the model-averaged result with that of each individual model. Using two quantitative measures of comparison, the model-averaged result was superior to any individual geostatistical model of log permeability considered.« less
  • 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 » generic site characterization data as well as site-specific characterization and monitoring data to evaluate parameter uncertainty in each of the alternative conceptual models. Propagation of parameter uncertainty will be carried out through implementation of a general stochastic model of groundwater flow and transport in the saturated and unsaturated zones. Evaluation of prediction uncertainty will make use of Bayesian model averaging and visualization of model results. The goal of this study is to develop a practical tool to quantify uncertainties in the conceptual model and parameters identified in performance assessments.« less
  • 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 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 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 » 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. An application of the uncertainty methodology to modeling of groundwater flow and uranium transport at the Hanford Site 300 Area is presented.« less
  • 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 non-linear 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 inverse-sensitivity approach, directlymore » evaluates the sensitivity of inverse estimates of model parameters with respect to the calibration targets. Further, we can also evaduate the sensitivity of model prediction based on inverse model parameter estimates with respect to the calibration targets. The proposed methodology can be applied to problems such as estimation of predictive uncertainty, optimization of data collection strategies, and design of monitoring networks. Its implementation can be performed efficiently through parallelization. Results based on a simple groundwater flow inverse problem are presented to illustrate the basis for the method.« less