A Comparison of Various Uncertainty Propagation Methods: An Example of Subsurface Contaminant Transport

Abstract

Groundwater resources are under increasing threat of contamination and wasteful use in many parts of the world. Groundwater flow and integrated contaminant transport models are commonly used to predict the fate of contaminants in the subsurface environments. However, the lack of reliable data and complexity of the natural environmental systems, the predictions are subjected to large uncertainties. For reliable decision-making, these contaminant transport models are required to explicitly consider associated uncertainties in their parameters. This paper aimed to compare the results of three common uncertainty propagation methods using an example of contaminant transport in groundwater. The research employed an advection–dispersion equation (ADE) to describe the transport of a contaminant in groundwater. For simplicity, two parameters - dispersivity and velocity - were considered in the uncertainty analysis. Fuzzy set theory, Monte Carlo simulation (MCS), and Probability Box (P-Box) method were investigated. The cumulative distribution functions generated from these analyses were compared to evaluate the capabilities of these methods. The comparison showed that P-Box method provided a more comprehensive analysis with lesser assumptions as compared to other methods, and also found to be more pragmatic way to describe and propagate uncertainties in complex environmental systems. Furthermore, execution time required to perform uncertainty analysis using P-Box method was comparatively much less than MCS .