A software tool for translating deterministic model results into probabilistic assessments of water quality standard compliance

Most assessments of whether a water body will comply with pollutant standards after modification of land use, loading, or climate change are based on the results of deterministic simulation models. These models, including those used to support the United States Environmental Protection Agency (USEPA) total maximum daily load (TMDL) program, typically do not account for common sources of assessment uncertainty. Instead, model results are typically represented by a time series of predicted pollutant concentration values or the parameters of a frequency-based distribution of these values over a specified time period. The rate of exceedance of relevant pollutant limits is then assessed directly from this time series or distribution to determine standard compliance. In this way, sampling and analysis-based variability and model uncertainty are typically ignored, although they may substantially influence the probability of non-compliance. To help address this problem, we introduce ProVAsT (Probabilistic Water Quality Standard Violation Assessment Tool), a software tool encoded in the graphical model-based package Analytica®. Here, we present a version of ProVAsT which translates model-predicted in situ fecal indicator bacteria (FIB) pollutant concentrations into the expected frequency of water quality standard violations and provides a Bayesian measure of the degree of confidence in this assessment. We call this version ProVAsT-FIB. Along with inputting their own simulation model results, users can specify the particular water quality analysis methods employed (e.g. the analytic procedure used and the number and volume of sample aliquots) as well as the numeric limits pertaining to local water quality standards. It is our hope that ProVAsT will encourage the rational consideration of uncertainty and variability in water quality assessments by reducing the burden of complex statistical calculations. © 2009 Elsevier Ltd. All rights reserved.