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

Abstract

a b s t r a c t a r t i c l e i n f oThe 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 R 2 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.

Coupling the modified SCS-CN and RUSLE models to simulate hydrological effects of restoring vegetation in the Loess Plateau of China

“…Fu et al (2011) found that the prediction accuracy for λ = 0.05 was greater than that for λ = 0.2 using SCS-CN method to simulate plot runoff of 757 rainfall events in Zizhou and Xifeng cities located in the Loess Plateau of China. Similar results have been obtained from plots or watersheds in USA (Hawkins et al, 2002), semi-arid tropical highlands of northern Ethiopia (Descheemaeker et al, 2008) and the Three Gorges area of China (Shi et al, 2009). In this study, the value of λ is not optimized using the measured rainfall-runoff data, as optimization of parameters cannot adequately examine the applicability of the modified SCS-CN model.…”

“…Firstly, rainfall intensity and rainfall duration have great impact on the quantity of runoff, but they were not considered in the modified SCS-CN model. More efforts are needed to account for the temporal variation of rainfall, such as done in Mishra et al (2008) and Suresh Babu and Mishra (2012). Secondly, it is difficult to independently determine the introduced empirical coefficients in the modified RUSLE model.…”

Including Effects of Watershed Heterogeneity in the Curve Number Method Using Variable Initial Abstraction

“…There have been studies (Shi et al, 2009;Woodward et al, 2003) that 10 tried to determine I a from hydrographs. A problem with this approach is that there can be a time lag between runoff generation in headwaters and its detection at gauging station.…”

“…Conversely, when λ W decreases, storage is transferred from 5 I a to S. This is important because several studies (Baltas et al, 2007;D'Asaro and Grillone, 2012;Shi et al, 2009;Woodward et al, 2003) found that the optimal value of λ W was much less than 0.2, and even close to zero in many watersheds. This shows that there is a positive correlation between a decrease in λ W , storage transfer from I a to S, and a general increase in model performance for the reasons mentioned above.…”

A Web-Based Model to Estimate the Impact of Best Management Practices

“…The initial abstraction coefficient typically varies from 0.0 to 0.2 [19,20]. The CN tables published and currently used typically assume that the initial abstraction coefficient is 0.2.…”