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

Suhrid Balakrishnan, Amit Roy, Marianthi G. Ierapetritou, Gregory P. Flach, Panos G. Georgopoulos

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)SBH81-SBH813
JournalWater Resources Research
Volume39
Issue number12
DOIs
StatePublished - Dec 2003

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

Keywords

  • Bayesian inference
  • Input distributions
  • Markov chain Monte Carlo (MCMC)
  • Metropolis Hastings
  • SRSM
  • Uncertainty analysis

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