Comparison of Estuarine Water Quality Models for Total Maximum Daily Load Development in Neuse River Estuary
by Craig A. Stow, (Duke Univ., Nicholas School of the Environment and Earth Sciences, Envir. Sci. and Policy Div., Durham, NC 27708; Addresses as of August 2003, Dept. of Envir. Health Sci., Arnold School of Public Health, Univ. of South Carolina, Columbia, SC 29208. E-mail: cstow@duke.edu), Chris Roessler, (North Carolina Dept. of Environment and Natural Resources, Div. of Water Quality, Raleigh, NC 27699), Mark E. Borsuk, (Duke Univ., Nicholas School of the Envir. and Earth Sciences, Envir. Sci. and Policy Div., Durham, NC 27708; presently, Dept. of Systems Analysis, Integrated Assessment, and Modeling (SIAM) Swiss Federal Inst. for Envir. Sci. and Technol. (EAWAG), 8600 Dübendorf, Switzerland), James D. Bowen, (Civ. Engrg. Dept., Univ. of North Carolina at Charlotte, Charlotte, NC 28223), and Kenneth H. Reckhow, (Duke Univ., Nicholas School of the Environment and Earth Sci., Envir. Sci. and Policy Div., Durham, NC 27708)
Journal of Water Resources Planning and Management, Vol. 129, No. 4, July/August 2003, pp. 307-314, (doi 10.1061/(ASCE)0733-9496(2003)129:4(307))
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| Document type: |
Journal Paper |
| Abstract: |
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 μg/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. |
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