Comparison of Estuarine Water Quality Models for Total Maximum Daily Load Development in Neuse River Estuary

The North Carolina Division of Water Quality developed a total maximum daily load (TMDL) to reduce nitrogen inputs into the Neuse River Estuary to address the problem of repeated violations of the ambient chlorophyll a criterion. Three distinct water quality models were applied to support the TMDL:...

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Bibliographic Details
Published in:Journal of water resources planning and management 2003-07, Vol.129 (4), p.307-314
Main Authors: Stow, Craig A, Roessler, Chris, Borsuk, Mark E, Bowen, James D, Reckhow, Kenneth H
Format: Article
Language:English
Subjects:
Brackish
TECHNICAL PAPERS
USA, North Carolina, Neuse R
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Summary:The North Carolina Division of Water Quality developed a total maximum daily load (TMDL) to reduce nitrogen inputs into the Neuse River Estuary to address the problem of repeated violations of the ambient chlorophyll a criterion. Three distinct water quality models were applied to support the TMDL: a two-dimensional laterally averaged model, a three-dimensional model, and a probability (Bayesian network) model. In this paper, we compare the salient features of all three models and present the results of a verification exercise in which each calibrated model was used to predict estuarine chlorophyll a concentrations for the year 2000. We present six summary statistics to relate the model predictions to the observed chlorophyll values: (1) the correlation coefficient; (2) the average error; (3) the average absolute error; (4) the root mean squared error; (5) the reliability index; and (6) the modeling efficiency. Additionally, we examined each model's ability to predict how frequently the 40 g/L chlorophyll a criterion was exceeded. The results indicate that none of the models predicted chlorophyll concentrations particularly well. Predictive accuracy was no better in the more process-oriented, spatially detailed models than in the aggregate probabilistic model. Our relative inability to predict accurately, even in well-studied, data-rich systems underscores the need for adaptive management, in which management actions are recognized as whole-ecosystem experiments providing additional data and information to better understand and predict system behavior.
ISSN:0733-9496
1943-5452
DOI:10.1061/(ASCE)0733-9496(2003)129:4(307)