COMPOSITE VS. DISTRIBUTED CURVE NUMBERS - EFFECTS ON ESTIMATES OF STORM RUNOFF DEPTHS

Citation
M. Grove et al., COMPOSITE VS. DISTRIBUTED CURVE NUMBERS - EFFECTS ON ESTIMATES OF STORM RUNOFF DEPTHS, Journal of the american water resources association, 34(5), 1998, pp. 1015-1023
Citations number
12
Categorie Soggetti
Geosciences, Interdisciplinary","Water Resources","Engineering, Environmental","Engineering, Civil
ISSN journal
1093474X
Volume
34
Issue
5
Year of publication
1998
Pages
1015 - 1023
Database
ISI
SICI code
1093-474X(1998)34:5<1015:CVDCN->2.0.ZU;2-S
Abstract
The U.S. Department of Agriculture Curve Number (CN) method is one of the most common and widely used techniques for estimating surface runo ff and has been incorporated into a number of popular hydrologic model s. The CN method has traditionally been applied using compositing tech niques in which the area weighted average of all curve numbers is calc ulated for a watershed or a small number of sub-watersheds. CN composi ting was originally developed as a time saving procedure, reducing the number of runoff calculations required. However, with the proliferati on of high speed computers and geographic information systems, it is n ow feasible to use distributed CNs when applying the CN method. Tb det ermine the effect of using composited versus distributed CNs on runoff estimates, two simulations of idealized watersheds were developed to compare runoff depths using composite and distributed CNs. The results of these simulations were compared to the results of similar analyses performed on an urbanizing watershed located in central Indiana and s how that runoff depth estimates using distributed CNs are as much as 1 00 percent higher than when composited CNs are used. Underestimation o f runoff due to CN compositing is a result of the curvilinear relation ship between CN and runoff depth and is most severe for wide CN ranges , low CN values, and low precipitation depths. For larger design storm s, however, the difference in runoff computed using composite and dist ributed CNs is minimal.