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<br />001351 <br /> <br />21 <br /> <br />Before selecting a model, the urbanizing community must thoroughly <br />analyze the model documentation to insure against violatillg model <br />limitations. A goou starting reference is Brandstetter (8). He has <br />reviewed a number of the more popular rainfall-runoff models, and has <br />compiled a table that lists the features and capabilities of each. <br />A number of rainfall-runoff models from the Conceptual and <br />Physically-based categories llill satisfy the first criteria. Which <br />of these models will be the most cost effective prediction tool for <br />the community? This is a tough question. <br />It hds been suggested that physically-based rainfall-runoff models <br />yield more accurate results (17). This accuracy translates into better <br />utili2ation of funds due to the better information available for <br />project analysis. However, these same mouels are more expensive to <br />initiate than the conceptual rainfall-runoff models. The higher cost <br />stems frOlil the longer initiation time, the higher expertise requirements, <br />and the greater data requirements associated with the physically-based <br />models. The cost effectiveness question must, therefore, be addressed <br />in blo parts: First, "To what degree are physically-based models <br />more accurate than conceptual models?" and second, "How sensitive is <br />cost effectiveness to increased accuracy?" This section will address <br />the first part and the next section will address the economic sensitivity <br />question. <br />Data used for comparison - The wri ters compared predictive accuracy <br />of the two model categories by examining the published results for <br />models within each category. Using the results published in scientific <br />journals is justified since they presumably represent results that an <br />urbaniziny community could expect from its staff or from an engineering <br />