Integrated Approach to Total Maximum Daily Load Development for Neuse River Estuary using Bayesian Probability Network Model (Neu-BERN)
Publication: Journal of Water Resources Planning and Management
Volume 129, Issue 4
Abstract
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.
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Published In
Journal of Water Resources Planning and Management
Volume 129 • Issue 4 • July 2003
Pages: 271 - 282
Copyright
Copyright © 2003 American Society of Civil Engineers.
History
Received: Jun 11, 2002
Accepted: Feb 10, 2003
Published online: Jun 13, 2003
Published in print: Jul 2003
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