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<br />OJ1351 <br /> <br />22 <br /> <br />consultant. One weaKness of this approach is that it presumes the <br />published work to be relatively unbiased. This presumption may not <br />always be justified since some of the published studies compare <br />existing models with a model developed by the author of the study. <br />The writers found eight published papers with sufficient prediction <br />information to examine. Table 1I-2 lists the authors of the eight <br />papers, the rainfall-runoff models tested, and information on the <br />basins used in the tests. These basins represent a full range of typi- <br />cal urbanizing sub-basins. They vary from 13 acres to 2 square miles <br />in area with 20% to 55% of that area being developed. <br />The writers grouped the tested models as follows: <br />1) Physically-based models. <br />a) Stormwater Management Model (SWMM). <br />b) University of Cincinnati Urban Runoff Model (UCUR). <br />2) Conceptual Models. <br />a) Colorado Urban Hydrograph Procedure (CUHP). <br />b) Soil Conservation Service Hydrograph (SCS). <br />c) Road Research Laboratory Method (RRLM). <br />d) Queens University Urban Runoff Model (QUURM). <br />e) Battelle Urban Wastewater Management Model (BNW). <br />f) Unit Hydrograph (UH). <br />This model classification scheme is not absolute but generally <br />separates the models into groups with: <br />1) Similar data requirements. <br />2) Similar basin segmentation requirements. <br />3) Similar algorithm complexity. <br />4) Equal numbers of calibration parameters. <br />