Abstract
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.
Highlights
The ability to make surface runoff estimations has become an essential part of development strategies in water resources management, flood control, and water and soil conservation [1]
The popularity of the Soil Conservation Service Curve Number (SCS-CN) method is due to its simplicity, convenience, widespread acceptance, applicability to ungauged watersheds, and requirement of only one parameter (CN), which is determined by four readily grasped watershed characteristics (soil group, land cover, surface condition, and antecedent moisture condition (AMC)) [12,13]
The overall performance of all tested models based on statistical indicators is compared in
Summary
The ability to make surface runoff estimations has become an essential part of development strategies in water resources management, flood control, and water and soil conservation [1].A multitude of hydrologic models have been developed to predict direct runoff. The method was originally developed for surface runoff prediction, but has been applied to several other areas such as the infiltration, sediment yield, and transport of pollutants [3,4] It has been extensively integrated into many hydrological and ecological models [5], including AnnAGNPS (Annualized Agricultural Nonpoint Source Pollution Model) [6], CREAMS (Chemicals, Runoff, and Erosion from Agricultural Management Systems) [7], EPIC The SCS-CN has some limitations such as lack of a land slope factor, discounting the storm duration, and the absence of guidance on antecedent moisture conditions [14–19]. These advantages and disadvantages made the SCS-CN method a perennial topic of discussion over the last four decades [20–25]
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