Improving regional-model estimates of urban-runoff quality using local data

Urban water-quality managers need load estimates of storm-runoff pollutants to design effective remedial programs. Estimates are commonly made using published models calibrated to large regions of the country. This paper presents statistical methods, termed model-adjustment procedures (MAPs), which use a combination of local data and published regional models to improve estimates of urban-runoff quality. Each MAP is a form of regression analysis that uses a local data base as a calibration data set to adjust the regional model, in effect increasing the size of the local data base without additional, expensive data collection. The adjusted regional model can then be used to estimate storm-runoff quality at unmonitored sites and storms in the locality. The four MAPs presented in this study are (1) single-factor regression against the regional model prediction, P_u; (2) least-squares regression against P_u; (3) least-squares regression against P_u and additional local variables; and (4) weighted combination of P_u and a local-regression prediction. Identification of the statistically most valid method among these four depends upon characteristics of the local data base. A MAP-selection scheme based on statistical analysis of the calibration data set is presented and tested.