On Evaluation of Recharge Model Uncertainty: a Priori and a Posteriori
Abstract
Hydrologic environments are open and complex, rendering them prone to multiple interpretations and mathematical descriptions. Hydrologic analyses typically rely on a single conceptualmathematical model, which ignores conceptual model uncertainty and may result in bias in predictions and underestimation of predictive uncertainty. This study is to assess conceptual model uncertainty residing in five recharge models developed to date by different researchers based on different theories for Nevada and Death Valley area, CA. A recently developed statistical method, Maximum Likelihood Bayesian Model Averaging (MLBMA), is utilized for this analysis. In a Bayesian framework, the recharge model uncertainty is assessed, a priori, using expert judgments collected through an expert elicitation in the form of prior probabilities of the models. The uncertainty is then evaluated, a posteriori, by updating the prior probabilities to estimate posterior model probability. The updating is conducted through maximum likelihood inverse modeling by calibrating the Death Valley Regional Flow System (DVRFS) model corresponding to each recharge model against observations of head and flow. Calibration results of DVRFS for the five recharge models are used to estimate three information criteria (AIC, BIC, and KIC) used to rank and discriminate these models. Posterior probabilities of the five recharge models, evaluated using KIC,more »
 Authors:
 Publication Date:
 Research Org.:
 Desert Research Institute, Nevada System of Higher Education
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 875590
 Report Number(s):
 Conf2006001
TRN: US0601151
 DOE Contract Number:
 AC5200NV13609
 Resource Type:
 Conference
 Resource Relation:
 Conference: 2006 International High Level Radioactive Waste Management Conference, Las Vegas, NV, April 30 to May 4, 2006
 Country of Publication:
 United States
 Language:
 English
 Subject:
 12 MANAGEMENT OF RADIOACTIVE WASTES, AND NONRADIOACTIVE WASTES FROM NUCLEAR FACILITIES; CALIBRATION; EVALUATION; PROBABILITY; RADIOACTIVE WASTE MANAGEMENT; SIMULATION
Citation Formats
Ming Ye, Karl Pohlmann, Jenny Chapman, and David Shafer. On Evaluation of Recharge Model Uncertainty: a Priori and a Posteriori. United States: N. p., 2006.
Web.
Ming Ye, Karl Pohlmann, Jenny Chapman, & David Shafer. On Evaluation of Recharge Model Uncertainty: a Priori and a Posteriori. United States.
Ming Ye, Karl Pohlmann, Jenny Chapman, and David Shafer. Mon .
"On Evaluation of Recharge Model Uncertainty: a Priori and a Posteriori". United States.
doi:. https://www.osti.gov/servlets/purl/875590.
@article{osti_875590,
title = {On Evaluation of Recharge Model Uncertainty: a Priori and a Posteriori},
author = {Ming Ye and Karl Pohlmann and Jenny Chapman and David Shafer},
abstractNote = {Hydrologic environments are open and complex, rendering them prone to multiple interpretations and mathematical descriptions. Hydrologic analyses typically rely on a single conceptualmathematical model, which ignores conceptual model uncertainty and may result in bias in predictions and underestimation of predictive uncertainty. This study is to assess conceptual model uncertainty residing in five recharge models developed to date by different researchers based on different theories for Nevada and Death Valley area, CA. A recently developed statistical method, Maximum Likelihood Bayesian Model Averaging (MLBMA), is utilized for this analysis. In a Bayesian framework, the recharge model uncertainty is assessed, a priori, using expert judgments collected through an expert elicitation in the form of prior probabilities of the models. The uncertainty is then evaluated, a posteriori, by updating the prior probabilities to estimate posterior model probability. The updating is conducted through maximum likelihood inverse modeling by calibrating the Death Valley Regional Flow System (DVRFS) model corresponding to each recharge model against observations of head and flow. Calibration results of DVRFS for the five recharge models are used to estimate three information criteria (AIC, BIC, and KIC) used to rank and discriminate these models. Posterior probabilities of the five recharge models, evaluated using KIC, are used as weights to average head predictions, which gives posterior mean and variance. The posterior quantities incorporate both parametric and conceptual model uncertainties.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 30 00:00:00 EST 2006},
month = {Mon Jan 30 00:00:00 EST 2006}
}

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The Coupled Fast Reactivity Measurements Facility (CFRMF), located at the Idaho National Engineering Laboratory, is a zonedcore critical assembly with a fastneutronspectrum zone in the center of an enriched /sup 235/U, watermoderated thermal driver. An accurate knowledge of the central neutron spectrum is important to datatesting analyses which utilize integral reactionrate data measured for samples placed in the CFRMF field. The purpose of this paper is to present the results of a study made with the AMPXII and FORSS code systems to deterine the centralspectrum flux covariance matrix due to uncertainties and correlations in the nuclear data for the materialsmore »