Elsevier

CATENA

Volume 77, Issue 1, 15 April 2009, Pages 1-7
CATENA

Research on the SCS-CN initial abstraction ratio using rainfall-runoff event analysis in the Three Gorges Area, China

https://doi.org/10.1016/j.catena.2008.11.006Get rights and content

Abstract

The Soil Conservation Service Curve Number (SCS-CN) method is widely used for predicting direct runoff from rainfall. The ratio of initial abstraction (Ia) to maximum potential retention (S) was assumed in its original development to be equal to 0.2 in SCS-CN method. The constant initial abstraction ratio is the most ambiguous assumption and requires considerable refinement. The objectives of this study were (1) to determine the initial abstraction ratio, in an experimental watershed in the Three Gorges Area of China, by analyzing measured rainfall-runoff events; (2) to compare the performance of the traditional and modified Ia/S values with observed rainfall-runoff data. The dataset consisted of 6 years of rainfall and runoff measurements from the experimental watershed. The results indicated that the Ia/S values, using event rainfall-runoff data, varied from 0.010 to 0.154, with a median of 0.048. The average initial abstraction ratio of the watershed was equal to 0.053. The standard SCS-CN method underestimates large runoff events, yielded a slope of the regression line of 0.559 and an intercept of 0.301. The modified Ia/S value was about 0.05 that better predicted runoff depths with an R2 of 0.804 and a linear regression slope of 0.834. It also improved model efficiency coefficient (E) to 0.768 compared with 0.482 for traditional Ia/S value. This Ia/S-adjusted SCS-CN method appears to be better appropriate for runoff prediction in the Three Gorges Area of China.

Introduction

Estimation of surface runoff is essential for the assessment of water yield potential of a watershed, planning of soil and water conservation measures, reducing the sedimentation and flooding hazards downstream. Although many hydrologic models are available for the estimation of direct runoff from storm rainfall, most models are limited because of their intensive input data and calibration requirements. Thus, models used for management decisions should be simple and unpretentious, with few data requirements and clearly stated assumptions (Grayson et al., 1992). The Soil Conservation Service Curve Number (SCS-CN) method, developed by the USDA-Soil Conservation Service (SCS, 1972), is widely used for the estimation of direct runoff for a given rainfall event from small agricultural watersheds. Due to its low input data requirements and its simplicity, many watershed models such as CREAMS (Knisel, 1980), AGNPS (Young et al., 1989), EPIC (Sharpley and Williams, 1990), and SWAT (Arnold et al., 1996) use this method to determine runoff. It has, however, been recently extended for other applications, including sediment yield or soil moisture modeling and the feedback on CN (Mishra et al., 2006a, Mishra et al., 2006b, Reshmidevia et al., 2008, Singh et al., 2008).

The SCS-CN (SCS, 1972) method is based on a water balance and two fundamental hypotheses which can be expressed, respectively, asP=Ia+F+QQPIa=FSIa=λSwhere P is the precipitation (mm), Ia is the initial abstraction (mm), F is the cumulative infiltration excluding Ia, Q is the direct runoff (mm), S is the potential maximum retention after beginning of the runoff (mm), and λ is the initial abstraction ratio. Combining Eqs. (1), (2), (3) gives an expression for Q:Q=(PIa)2P+SIa

Eq. (4) is valid for P > Ia, otherwise, Q = 0. The parameter S in Eq. (4) is defined asS=25400CN254where S is in mm and CN (curve number) varies based on one of three antecedent soil moisture conditions: curve number I-dry, curve number II-average, and curve number III-wet.

Traditionally, Ia is often set equal to 0.2S in Eq. (3). Since the history and documentation of this relationship are obscure, the assumption of Ia = 0.2S has been frequently questioned for its validity and applicability, invoking a critical examination of the IaS relationship for pragmatic applications. Mishra et al., 2006a, Mishra et al., 2006b, employing a large dataset of 84 small watersheds (area = 0.17 to 71.99 ha) of USA, investigated a number of initial abstraction (Ia)-potential maximum retention (S) relations incorporating antecedent moisture as a function of antecedent precipitation. Jain et al. (2006) reviewed the IaS relationship, and proposed a new non-linear relation incorporating storm rainfall (P) and S. Ponce and Hawkins (1996) suggest that the fixing of the initial abstraction ratio at 0.2 may not be the most appropriate number, and that it should be interpreted as a regional parameter.

The Three Gorges Area (TGA) covers 21 counties and cities in central China's Hubei Province and Chongqing Municipality with a total area of 58,800 km2. Due to long-term human activities including overuse and inappropriate development, soil erosion has become a serious issue. It was reported that annual soil loss in the TGA is about 157 million t, of which 40 million t are delivered to the Yangtze River by 700 t km 2 yr 1 (Shi et al., 1992, Lu and Higgitt, 1998). In addition, the Three Gorges Dam being built to harness power from the Yangtze River is also a matter of hot debate. In order to decrease soil erosion, maintain land productivity and improve environmental quality, a series of soil conservation practices are being implemented, including tree plantations, establishment of pasturelands, and construction of terraces. In the TGA, most agricultural watersheds are ungauged, having no record whatsoever of rainfall-runoff processes. An appropriate method to predict surface runoff from watershed is, therefore, essential for the design of these soil conservation works. The SCS-CN method has been widely used in China (Huang et al., 2007, Shi et al., 2007), although its precision has been argued. The selection of a proper Ia/S value is crucial to accurate estimation of direct runoff from the SCS-CN method (Jain et al., 2006). Thus, the objectives of this study were: (1) to determine the initial abstraction ratio (Ia/S), in an experimental watershed in the TGA, by analyzing measured rainfall-runoff events; (2) to compare the performance of the traditional and modified Ia/S values with observed rainfall-runoff data. We improved upon the SCS-CN method, rather than developing a new approach, because this method is widely used, and modifications to that method could be readily implemented in China (Huang et al., 2006).

Section snippets

The study area

The study was conducted in the Wangjiaqiao watershed (31°5′N–31°9′N, 110°40′E–110°43′E) which lies in Zigui County of Hubei Province, China. It is about 50 km northwest of the Three-Gorge Dam (TGD) and covers an area of 1670 ha (Fig. 1). Elevations within the watershed range from 184 m to 1180 m with slopes ranging from 2° to 58° and average of 23°. The length of the main channel is 6 496 m, and streams have a trellis drainage pattern. The average annual runoff is 390 mm. The baseflow reaches a

Initial abstraction ratio based on rainfall-runoff event analysis

Twenty-nine storm events were analyzed according to rainfall-runoff processes, and it was found that Ia/S ratios varied greatly between storms within watershed. The calculated Ia/S values varied from 0.010 to 0.154, with a median of 0.048. The average initial abstraction ratio of the watershed was equal to 0.052 (Table 3). Fig. 6 shows that the (Ia/S) ratio is predominantly around 0.05 and is not related to rainfall depth P. It is obvious that a more appropriate value of Ia/S would be in about

Conclusions

The initial abstraction ratio (Ia/S) in SCS-CN equation was determined using rainfall-runoff event analysis from an agricultural watershed in the TGA of China. The results indicated that the Ia/S values varied from 0.010 to 0.154, with a median of 0.048. The average initial abstraction ratio of the watershed was equal to 0.052. This is mainly attributed to the landscape and geological characteristics in the study watershed. A comparison between standard and modified Ia/S values showed that

Acknowledgements

Financial support for this research was provided by the National Natural Science Foundation of China (No. 40671178), the Key Project of Chinese Academy of Sciences (No. KZCX2-YW-421) and the Key Project of Chinese Ministry of Education (No. 108165).

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