We develop a probability network model to characterize eutrophication in the Neuse River Estuary, North Carolina, and support the estimation of a total maximum daily load (TMDL) for nitrogen. Unlike conventional simulation models, probability networks describe probabilistic dependencies among system variables rather than substance mass balances. Full networks are decomposable into smaller submodels, with structure and quantification that reflect relevant theory, judgment, and/or observation. Model predictions are expressed probabilistically, which supports consideration of frequency-based water quality standards and explicit estimation of the TMDL margin of safety. For the Neuse Estuary TMDL application, the probability network can be used to predict compliance with the dissolved oxygen and chlorophyll a regulatory criteria as a function of riverine nitrogen load. In addition, the model includes ecological endpoints, such as fishkills and shellfish survival, that are typically more meaningful to stakeholders than conventional water quality characteristics. Incorporating these unregulated attributes into TMDL decisions will require explicit consideration of costs, benefits, and relative likelihoods of various possible outcomes under alternate loading scenarios.
Integrated approach to total maximum daily load development for Neuse River Estuary using Bayesian probability network model (Neu-BERN)