How can SPARROW models be used to guide the planning of future monitoring programs?
|Back to Top| SPARROW models are statistical in nature and their uncertainty is often a function of both the quality and number of data available for calibration. For SPARROW models, calibration data consist of load estimates at monitoring sites. Inaccurate or imprecise load measurements at monitoring sites will create uncertainty in the models as will fewer monitoring sites. Where uncertainty is associated with large prediction errors, additional or refined monitoring can potentially be implemented to reduce the uncertainty. In a more general sense, compiling data for a SPARROW calibration may reveal limitations in the available monitoring load data. This information could help agencies make their monitoring programs more efficient. SPARROW models are atypical in the realm of water-quality modeling in that they are capable of assisting with the interpretation of the data collected at a network of monitoring sites (Smith and others, 1997). Once a SPARROW model has been constructed for
