Probabilistic Screening Tool for Ground-Water Contamination Assessment

Abstract

This paper presents a methodology for assessing the effects of source-, chemical-, and aquifer-related parameter uncertainty on the response of a semianalytical transport model using first- and second-order reliability methods. A probabilistic model is developed by coupling the deterministic transport model with a general-purpose probability analysis program. Ground-water contamination risk is addressed by evaluating the probability that a given contaminant exceeds the regulated standards at a well downgradient from a waste source. The general applicability of the methodology is demonstrated on two simple hypothetical case studies of transport of nonreactive and reactive solutes in the subsurface. Sensitivities of the probabilistic outcome to the basic uncertainties in the input random variables are provided through importance factors. The reliability-method results are checked against those obtained using the classical Monte Carlo–simulation method, and the results are in good agreement except for very low probability events in which the reliability methods provide accurate results much more efficiently than the Monte Carlo method. The need for a careful trade-off analysis between accuracy and computer time is highlighted.