TY - JOUR
T1 - Uncertainty quantification of contaminant transport and risk assessment with conditional stochastic collocation method
AU - Shi, Liangsheng
AU - Zeng, Lingzao
AU - Tang, Yunqing
AU - Chen, Cheng
AU - Yang, Jinzhong
PY - 2013/8
Y1 - 2013/8
N2 - 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.
AB - 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.
KW - Conditional simulation
KW - Random field
KW - Solute transport
KW - Stochastic collocation method
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U2 - 10.1007/s00477-012-0682-x
DO - 10.1007/s00477-012-0682-x
M3 - Article
AN - SCOPUS:84880054942
SN - 1436-3240
VL - 27
SP - 1453
EP - 1464
JO - Stochastic Environmental Research and Risk Assessment
JF - Stochastic Environmental Research and Risk Assessment
IS - 6
ER -