A method for quantifying the prediction uncertainties associated with water quality models

Abstract Many environmental regulatory agencies depend, to a large extent, upon the use of models to organize, understand, and utilize the information available for regulatory decision making. In light of the extensive use of environmental models, we developed a general analytical protocol to quantify the prediction error associated with commonly used surface water quality models. The methodology is designed in order to compare water quality models configured to represent different levels of spatial, temporal, and mechanistic complexity. This comparison can be accomplished by fitting the models to a benchmark data set. Once the models are successfully fitted to the benchmark data, the prediction errors associated with each application can be quantified using the Monte Carlo simulation techniques. The application of the protocol using these simulation techniques is described in a companion paper in which comparisons among model uncertainty results are made using the Wilcoxon ranked sum test to determine significant differences.