Uncertainty quantification of contaminant transport and risk assessment with conditional stochastic collocation method

Liangsheng Shi, Lingzao Zeng, Yunqing Tang, Cheng Chen, Jinzhong Yang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Solute transport prediction is always subject to uncertainty due to the scarcity of observation data. The data worth of limited measurements can be explored by conditional simulation. This paper presents an efficient approach for the conditional simulation of solute transport in a randomly heterogeneous aquifer. The conditioning conductivity field is parameterized by the Karhunen-Loève (KL) expansion, and the concentration field is represented by Lagrange polynomials of random variables in the KL expansion. After employing the stochastic collocation method (SCM), stochastic governing advection-dispersion equations are reduced to a series of uncoupled deterministic equations. The concentration realizations can be obtained by sampling the established Lagrange polynomials instead of solving governing equations repeatedly. We assess the accuracy and computational efficiency of this method in comparison to the conditional Monte Carlo simulation. The influence of conditioning to hydraulic conductivity measurements on transport is analyzed. Numerical results demonstrate that the SCM can efficiently derive the conditional statistics of concentration as well as the probability of the aquifer to be contaminated. It is shown that the contamination risk is significantly influenced by measurements conditioning.

Original languageEnglish
Pages (from-to)1453-1464
Number of pages12
JournalStochastic Environmental Research and Risk Assessment
Volume27
Issue number6
DOIs
StatePublished - Aug 2013

Keywords

  • Conditional simulation
  • Random field
  • Solute transport
  • Stochastic collocation method

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