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It is typical for decisions on the management of surface water quality to impact several environmental, social, and economic factors or attributes important to the public. In theory, this can lead to a difficult and complex problem analysis, but in practice many factors are virtually ignored during analysis and decision making. Further, it is typical for the predictions of the impact of proposed management actions on these attributes to be highly uncertain. Often it is possible to reduce that uncertainty through additional research and analysis, but resources for this are scarce and decision makers may not be inclined to wait for these results as they seek action and quick problem solution.

In part to provide the scientific basis for problem solution and action, a variety of simulation models have been developed, yet increasingly it is being recognized that these models are not very reliable. As a consequence, whether it is the initial intent or not, successful management often involves judgment-based decision, followed by implementation, feedback, and readjustment. This “learning by doing” approach is a pragmatic attempt to deal with growth, change, new information, and imprecise forecasting.

Learning by doing, or adaptive management, is a strategy that treats management as a continually ongoing process utilizing observation and feedback. Management actions may change, or adapt, to the observational feedback; rather than create an elaborate model a priori and base all subsequent decisions on predictions from that model, the adaptive approach emphasizes updating of a model based on observation and learning as time passes. Updating the model may be done informally or formally through the use of statistical methods, and management actions can then be adapted to be consistent with the predictions of the revised model.

This adaptive approach may be particularly appealing in situations where population growth, land use change, and variability in climatic forcing functions exceed the limited realm of current observation and experience. Such systems involve complex and often highly nonlinear relationships among various elements; prediction in these chaotic environments can be difficult in the short term and useless in the long term. Even state-of-the art models of such systems require periodic observation, evaluation, and revision in order to improve predictive validity.

Among the analytic challenges for successful adaptive management is the design of a postimplementation monitoring program. For a TMDL, monitoring is useful to assess compliance with the standard, to assess the effectiveness of implemented pollution control measures, and to help guide modifications of the TMDL if standards compliance is not achieved. In most cases, formal integration of the post-implementation monitoring data with the initial TMDL forecasting model, perhaps using Bayesian analysis, provides the most efficient use of all relevant information. In addition, when assessment of an adaptive TMDL is expressed in terms of an update of the original TMDL forecasting model, other learning opportunities should be considered. For example, research might be conducted that could reduce the uncertainty in important functional relationships in the model.

In a general sense, the post-implementation design problem is equivalent to a value-of-information assessment in decision analysis. Using the TMDL forecasting model and decision analysis, the modeler could quantify the expected improvement in TMDL forecasting due to proposed: (1) ambient water quality monitoring, (2) research to improve the model (e.g., better characterization of denitrification), and/or (3) assessment of the effectiveness of particular BMPs. Then, those information-gathering activities that were expected to result in the greatest reduction (for the resources invested) in TMDL forecast uncertainty could be selected. This analysis is examined in the context of a recent nitrogen TMDL on the Neuse Estuary in North Carolina using a proposed scheme for post-implementation TMDL monitoring/research design.
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Document Type: Research Article

Publication date: 2003-01-01

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