A comparative assessment of efficient uncertainty analysis techniques for environmental fate and transport models: Application to the FACT model

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

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

This work presents a comparative assessment of efficient uncertainty modeling techniques, including Stochastic Response Surface Method (SRSM) and High Dimensional Model Representation (HDMR). This assessment considers improvement achieved with respect to conventional techniques of modeling uncertainty (Monte Carlo). Given that traditional methods for characterizing uncertainty are very computationally demanding, when they are applied in conjunction with complex environmental fate and transport models, this study aims to assess how accurately these efficient (and hence viable) techniques for uncertainty propagation can capture complex model output uncertainty. As a part of this effort, the efficacy of HDMR, which has primarily been used in the past as a model reduction tool, is also demonstrated for uncertainty analysis. The application chosen to highlight the accuracy of these new techniques is the steady state analysis of the groundwater flow in the Savannah River Site General Separations Area (GSA) using the subsurface Flow And Contaminant Transport (FACT) code. Uncertain inputs included three-dimensional hydraulic conductivity fields, and a two-dimensional recharge rate field. The output variables under consideration were the simulated stream baseflows and hydraulic head values. Results show that the uncertainty analysis outcomes obtained using SRSM and HDMR are practically indistinguishable from those obtained using the conventional Monte Carlo method, while requiring orders of magnitude fewer model simulations.

Original languageEnglish (US)
Pages (from-to)204-218
Number of pages15
JournalJournal of Hydrology
Volume307
Issue number1-4
DOIs
StatePublished - Jun 9 2005

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

Keywords

  • Computer programs
  • Data processing
  • Hydrology
  • Mathematical methods
  • Statistical analysis

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