Markov Chain Monte Carlo Approach for Parameter Uncertainty Quantification and Its Impact on Groundwater Mass Transport Modeling: Influence of Prior Distribution

作者：Shen, Sheng; Zeng, Guangming^*; Liang, Jie; Li, Xiaodong; Tan, Yong; Li, Zhongwu; Li, Jianbing

来源：Environmental Engineering Science, 2014, 31(9): 487-495.

DOI：10.1089/ees.2012.0236

• groundwater
• mass transport
• MCMC
• prior distribution
• uncertainty

Markov Chain Monte Carlo (MCMC) theory and stochastic simulation techniques were incorporated to analyze the effect of different prior knowledge on quantifying parameter uncertainty and its impact on mass transport in heterogeneous aquifer. The MCMC algorithm employing the Metropolis-Hastings rule (MH-MCMC) was used to obtain the posterior distribution of log-hydraulic conductivity. Random simulation technology, Sequential Gaussian Simulation, was used to generate a spatial stochastic hydraulic conductivity field. We investigated two different assumptive prior knowledge scenarios, a uniform prior distribution and a Gaussian prior distribution. Results showed that the prior knowledge could affect the posterior distributions of parameters. When the Gaussian prior distribution was adopted, there was a better convergence of parametric posterior distribution and a decrease in the zone of uncertainty influence and the area of confidence interval on groundwater mass transport modeling. However, it was difficult to draw the conclusion that the Gaussian prior distribution was preferred because the relative influence of parameter prior distribution depended on the location, number of measurements, and methods to reflect the heterogeneity of hydraulic conductivity. Therefore, the prior distribution is a sensitive input parameter and should be defined based upon best available data.

• 出版日期2014-9
• 单位湖南大学