Uncertainty Segregation and Comparative Evaluation in Groundwater Remediation Designs: A Chance-Constrained Hierarchical Bayesian Model Averaging Approach

Abstract

Groundwater remediation designs rely on simulation models that are subjected to various sources of uncertainty. This study introduces a hierarchical Bayesian model averaging (HBMA) method to segregate sources of uncertainty in a hierarchical order and conduct comparative evaluation of BMA models for remediation designs. A BMA tree of models is developed to understand the impact of individual sources of uncertainty in the remediation design. The HBMA method is applied to chance-constrained (CC) formulation for an aquifer remediation design that aims to reduce concentration at a selected control point using scavenger wells. Thirty-six (36) flow and transport models for concentration prediction are developed to analyze the impact of three sources of uncertainty on the remediation design. An essential step in HBMA is to calculate posterior model probabilities as model weights. The best simulation model has the highest model weight. The results show that although using the best simulation model requires the least pumping rate for the scavenger well, it underestimates prediction variances of concentration. The scavenger well pumping rate increases as more sources of uncertainty are considered. The HBMA enables the chance-constrained formulation to consider both model parameter and model structure uncertainties for aquifer remediation designs. The contributions of prediction variance from individual sources of uncertainty to the total prediction variance can be evaluated, which provides an understanding of the impact of individual sources of uncertainty and their corresponding propositions on remediation designs.