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

Journal of the American Water Resources Association

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Abstract

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, Pu; (2) least-squares regression against Pu; (3) least-squares regression against Pu and additional local variables; and (4) weighted combination of Pu 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.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Improving regional-model estimates of urban-runoff quality using local data
Series title:
Journal of the American Water Resources Association
Volume
32
Issue:
4
Year Published:
1996
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Journal of the American Water Resources Association
First page:
855
Last page:
863
Number of Pages:
9