Wenhai Shi

and 1 more

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.

Wenhai Shi

and 1 more

Soil Conservation Service Curve Number (SCS-CN) is one of the widely used methods to estimate surface runoff because of its simplicity, convenience and widespread acceptance. However, the method still has several limitations such as ignorance of storm duration, lack of guidance on antecedent condition and absence of slope factor. In this study, an equation of the CN value combining with the original CN2 value and three introduced factors of slope, soil moisture and storm duration was developed to improve the SCS-CN method. The proposed method was calibrated and validated using a dataset of three experimental plots in the a watershed on the Loess Plateau. The results indicated that the proposed method, which boosted the model efficiencies to 80.58% and 80.44% in calibration and validation cases, respectively, performed better than the original SCS-CN, Huang et al. (2006) and Huang et al.(2007) methods which considered the single factor of slope and soil moisture in the SCS-CN method, respectively. Using the parameters derived from the initial three experimental plots, the proposed method was used to predict runoff from the remaining three experimental plots in another watershed.The root mean square error between the measured and predicted runoff values was improved from 5.53 mm to 2.01 mm. Furthermore, a sensitivity analysis of the parameters in the proposed method indicated that the parameters of soil moisture (b1 and b2) and storm duration equations (c) are more sensitive than those parameters of slope equation (a1 and a2) and λ. It can be concluded that the proposed method incorporating the three factors, may predict surface runoff more accurately in the Loess Plateau of China.