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WSP 2428. Hoos, A.B., and Lizarraga, J.S., 1996, Procedures for adjusting regional regression models of urban-runoff quality using local data: U.S. Geological Survey Water-Supply Paper 2428, 33 p.

Statistical operations termed model-adjustment procedures (MAP) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting adjusted regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in a storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP's examined in this study were (1) single-factor regression against the regional model prediction,* P _{u}* (MAP-1F-P); (2) regression against

The procedures were tested using split-sample analysis, with data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-1F-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested.

The MAP's were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP's for the verification data set decreased as the calibration data-set size decreased, but predictive accuracy was not as sensitive for the MAP's as it was for the local regression models.

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