Lake and watershed management strategies and recent environmental legislation dictate that nonpoht nutrient sources associated with storm water runoff must be assessed. Accordingly, a nutrient flu assessment for phosphorus and nitrogen is conducted through an extensive literature review of nutrient export studies. These studies are reevaluated. The nutrient export coefficients are screened according to sampling design criteria and compiled according to land use. The ecological mechanisms within each land use influencing the magnitude of nutrient flux are also discussed

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[1] A Bayesian nonlinear regression modeling method is introduced and compared with the least squares method for modeling nutrient loads in stream networks. The objective of the study is to better model spatial correlation in river basin hydrology and land use for improving the model as a forecasting tool. The Bayesian modeling approach is introduced in three steps, each with a more complicated model and data error structure. The approach is illustrated using a data set from three large river basins in eastern North Carolina. Results indicate that the Bayesian model better accounts for model and data uncertainties than does the conventional least squares approach. Applications of the Bayesian models for ambient water quality standards compliance and TMDL assessment are discussed.

Abstract. This paper illustrates the advantages of a multilevel/hierarchical approach for predictive modeling, including flexibility of model formulation, explicitly accounting for hierarchical structure in the data, and the ability to predict the outcome of new cases. As a generalization of the classical approach, the multilevel modeling approach explicitly models the hierarchical structure in the data by considering both the within-and between-group variances leading to a partial pooling of data across all levels in the hierarchy. The modeling framework provides means for incorporating variables at different spatiotemporal scales. The examples used in this paper illustrate the iterative process of model fitting and evaluation, a process that can lead to improved understanding of the system being studied.

The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. Ecological risk and environmental analysis often involve scientific assessments that are highly uncertain. Still, environmental management decisions are being made, often with the support of a mathematical simulation model. In the area of pollutant transport and fate in surface waters, few of the extant simulation models have been rigorously evaluated. Limited observational data and limited scientific knowledge are often incompatible with the highly-detailed model structures of the large pollutant transport and fate models. Two examples are presented to illustrate data and knowledge weaknesses that are likely to undermine these large models for decision support. An alternative to comprehensive structured simulation models is proposed as a flexible approach to introduce science into the environmental risk assessment and decision making process.

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Abstract:The North Carolina Division of Water Quality developed a total maximum daily load ͑TMDL͒ 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 network͒ 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: ͑1͒ the correlation coefficient; ͑2͒ the average error; ͑3͒ the average absolute error; ͑4͒ the root mean squared error; ͑5͒ the reliability index; and ͑6͒ 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.