Joint Report of Peer Review Panel for Numeric Nutrient Criteria for the Great Bay Estuary New Hampshire Department of Environmental Services June, 2009

This peer review was authorized through a collaborative agreement sponsored by the New Hampshire Department of Environmental Services (DES) and the Cities of Dover, Rochester and Portsmouth, New Hampshire. The purpose was to conduct an independent scientific peer review of the document entitled, “Numeric Nutrient Criteria for the Great Bay Estuary,” dated June, 2009 (DES 2009 Report)

Water Quality Prediction and Probability Network Models

It is a common strategy in surface water quality modeling to attempt to remedy predictive inadequacies by incorporating additional mechanistic detail into the model. This approach reflects the reasonable belief that enhanced scientific understanding of basic processes can be used to improve predictive modeling. However, nature is complex, and even the most detailed simulation model is extremely simple in comparison. At some point, additional detail exceeds our ability to simulate and predict with reasonable error levels. In those situations, an attractive alternative may be to express the complex behavior probabilistically, as in statistical mechanics, for example. This viewpoint is the basis for consideration of Bayesian probability networks for surface water quality assessment and prediction. To begin this examination of Bayes nets, some simple water quality examples are used for the illustration of basic ideas. This is followed by discussion of a set of proposed probability network ..

Water Quality Prediction, Mechanism, and Probability Network Models

this paper which is an examination of the usefulness of probability (or Bayesian) networks for water quality modeling. To provide a framework for this study, the analysis begins in the next section with a brief discussion of detailed, mechanistic water quality models. The identification of problems with uncertainty analysis using mechanistic models then leads to consideration of probability networks. The probability network model is described, an example is presented to illustrate this approach, and the potential for probability models to support decision making is discussed. Mechanistic Models and Predictio