skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: 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 conceptual-mathematical model, which ignores conceptual model uncertainty and may result in bias in predictions and under-estimation 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 » are used as weights to average head predictions, which gives posterior mean and variance. The posterior quantities incorporate both parametric and conceptual model uncertainties.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Desert Research Institute, Nevada System of Higher Education
Sponsoring Org.:
USDOE
OSTI Identifier:
875590
Report Number(s):
Conf-2006-001
TRN: US0601151
DOE Contract Number:
AC52-00NV13609
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 NON-RADIOACTIVE 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 conceptual-mathematical model, which ignores conceptual model uncertainty and may result in bias in predictions and under-estimation 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}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • Hydrologic analyses are commonly based on a single conceptual-mathematical model. Yet hydrologic environments are open and complex, rendering them prone to multiple interpretations and mathematical descriptions. Considering conceptual model uncertainty is a critical process in hydrologic uncertainty assessment. This study assesses recharge and geologic model uncertainty for the Climax mine area of the Nevada Test Site, Nevada. Five alternative recharge models have been independently developed for Nevada and the Death Valley area of California. These models are (1) the Maxey-Eakin model, (2 and 3) a distributed parameter watershed model with and without a runon-runoff component, and (4 and 5) amore » chloride mass-balance model with two zero-recharge masks, one for alluvium and one for both alluvium and elevation. Similarly, five geological models have been developed based on different interpretations of available geologic information. One of them was developed by the U.S. Geological Survey for the Death Valley Regional Flow System (DVRFS) model; the other four were developed by Bechtel Nevada for the Yucca Flat Corrective Action Unit (CAU). The Climax mine area is in the northern part of the Yucca Flat CAU, which is within the DVRFS. A total of 25 conceptual models are thus formulated based on the five recharge and five geologic models. The objective of our work is to evaluate the conceptual model uncertainty, and quantify its propagation through the groundwater modeling process. A model averaging method is applied that formally incorporates prior information and field measurements into our evaluation. The DVRFS model developed by the U.S. Geological Survey is used as the modeling framework, into which the 25 models are incorporated. Conceptual model uncertainty is first evaluated through expert elicitation based on prior information possessed by two expert panels. Their perceptions of model plausibility are quantified as prior model probabilities, which are then updated by the site measurements of head and flux through inverse modeling. Posterior model probabilities of the models are then evaluated after the updating process, and used as weights in the summation of each model's mean predictions and associated predictive uncertainty. Deterministic simulation results using calibrated parameters are examined to investigate different model predictions of each alternative model. Parametric uncertainty of each model is assessed using Monte Carlo simulation, and the uncertainty is compared for each model to evaluate uncertainty bounds. Finally, the uncertainty bounds of model averaging, incorporating both parametric and conceptual model uncertainty, are evaluated and compared with those of individual models. It is shown that model averaging provides larger uncertainty bounds, indicating that more uncertainty is incorporated, rendering model predictions more scientifically defensible.« less
  • The Coupled Fast Reactivity Measurements Facility (CFRMF), located at the Idaho National Engineering Laboratory, is a zoned-core critical assembly with a fast-neutron-spectrum zone in the center of an enriched /sup 235/U, water-moderated thermal driver. An accurate knowledge of the central neutron spectrum is important to data-testing analyses which utilize integral reaction-rate 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 AMPX-II and FORSS code systems to deterine the central-spectrum flux covariance matrix due to uncertainties and correlations in the nuclear data for the materialsmore » which comprise the facility.« less