Abstract
The Soil Conservation Service Curve Number (SCS-CN), one of the most
commonly used methods for surface runoff prediction, was developed by
the United States Department of Agriculture (USDA). For many years, the
direct application of the CN look-up table derived from USDA in regions
elsewhere with different characteristics was questionable, because it
could lead to a large error in runoff prediction. To eliminate this
error, some studies suggested that CN entries should be revised based on
measured data, whereas others indicated that major factors affecting
runoff should be considered for application in specified regions. In
this study, the above-mentioned CN revision approaches were compared to
adjust CN values using a large amount of rainfall-runoff observation
data for 43 study sites across the Loess Plateau region. The results
showed that the average CN values of each watershed obtained from the
measured rainfall-runoff data are quite different from the tabulated CN2
values. However, the calculated average CN values produce little
improvement in runoff estimation with the SCS-CN method, due to large CN
value variation. Therefore, three factors—soil moisture, rainfall
depth, and intensity—were identified as influencing the CN values
under field conditions in the Loess Plateau, and a new CN value with a
CN2 value in the conventional SCS-CN method was developed. The
reliability of the proposed method was tested with data from three
watersheds on the Loess Plateau. High Nash–Sutcliffe efficiency (NSE =
74.70%) and low root mean square error (RMSE = 3.08 mm) indicated that
the proposed method could accurately estimate runoff and was more
reliable than the standard SCS-CN method (NSE = 19.26%; RMSE = 5.51
mm). Moreover, the factors incorporated in the proposed method seem to
more effectively reflect the large CN value variations than the revised
CN2 value based on measured dataset in the Loess Plateau region.