Uncertainty reduction and characterization for complex environmental fate and transport models: An empirical Bayesian framework incorporating the stochastic response surface method

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Read Full Article (file size: 823726 bytes) Cited by WATER RESOURCES RESEARCH, VOL. 39, NO. 12, 1350, doi:10.1029/2002WR001810, 2003 Uncertainty reduction and characterization for complex environmental fate and transport models: An empirical Bayesian framework incorporating the stochastic response surface method Suhrid Balakrishnan Environmental and Occupational Health Sciences Institute, University of Medicine and Dentistry of New Jersey-R.W. Johnson Medical School and Rutgers University, Piscataway, New Jersey, USA Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey, USA Amit Roy Environmental and Occupational Health Sciences Institute, University of Medicine and Dentistry of New Jersey-R.W. Johnson Medical School and Rutgers University, Piscataway, New Jersey, USA Marianthi G. Ierapetritou Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey, USA Gregory P. Flach Savannah River Technology Center, Savannah River Site, Aiken, South Carolina, USA Panos G. Georgopoulos Environmental and Occupational Health Sciences Institute, University of Medicine and Dentistry of New Jersey-R.W. Johnson Medical School and Rutgers University, Piscataway, New Jersey, USA Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey, USA Abstract In this work, a computationally efficient Bayesian framework for the reduction and characterization of parametric uncertainty in computationally demanding environmental 3-D numerical models has been developed. The framework is based on the combined application of the Stochastic Response Surface Method (SRSM, which generates accurate and computationally efficient statistically equivalent reduced models) and the Markov chain Monte Carlo method. The application selected to demonstrate this framework involves steady state groundwater flow at the U.S. Department of Energy Savannah River Site General Separations Area, modeled using the Subsurface Flow And Contaminant Transport (FACT) code. Input parameter uncertainty, based initially on expert opinion, was found to decrease in all variables of the posterior distribution. The joint posterior distribution obtained was then further used for the final uncertainty analysis of the stream base flows and well location hydraulic head values. Received 30 October 2002; accepted 24 July 2003; published 17 December 2003. Index Terms: 6309 Policy Sciences: Decision making under uncertainty; 1832 Hydrology: Groundwater transport; 1869 Hydrology: Stochastic processes; 3210 Mathematical Geophysics: Modeling. Read Full Article (file size: 823726 bytes) Cited by Citation: Balakrishnan, S., A. Roy, M. G. Ierapetritou, G. P. Flach, and P. G. Georgopoulos (2003), Uncertainty reduction and characterization for complex environmental fate and transport models: An empirical Bayesian framework incorporating the stochastic response surface method, Water Resour. Res., 39(12), 1350, doi:10.1029/2002WR001810. Copyright 2003 by the American Geophysical Union.

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