Title: Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models

Abstract

While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. Our study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. Moreover, these reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achievemore » more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Finally, limitations of applying MLBMA to the synthetic study and future real-world modeling are discussed.« less

Authors:

Lu, Dan ^[1]; Ye, Ming ^[2]; Curtis, Gary P. ^[3]

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+ Show Author Affiliations

1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. Florida State Univ., Tallahassee, FL (United States)
3. U.S. Geological Survey, Menlo Park, CA (United States)

Publication Date:

Sat Aug 01 00:00:00 EDT 2015

Research Org.:

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE

OSTI Identifier:

1265806

Alternate Identifier(s):

OSTI ID: 1248433

Grant/Contract Number:  

AC05-00OR22725; SC0002687; SC0000801; SC0008272; 0911074; 51328902; SC0003681

Resource Type:

Accepted Manuscript

Journal Name:

Journal of Hydrology

Additional Journal Information:

Journal Volume: 529; Journal Issue: P3; Journal ID: ISSN 0022-1694

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

58 GEOSCIENCES; Uncertainty analysis; Reactive transport; Maximum likelihood Bayesian model averaging