Abstract
The Soil Conservation Service curve number ( S C S-C N) method is one of the most popular methods used to compute runoff amount due to its few input parameters. However, recent studies challenged the inconsistent runoff results obtained by the method which set the initial abstraction ratio λ as 0.20. This paper developed a watershed-specific S C S-C N calibration method using non-parametric inferential statistics with rainfall–runoff data pairs. The proposed method first analyzed the data and generated confidence intervals to determine the optimum values for S C S- C N model calibration. Subsequently, the runoff depth and curve number were calculated. The proposed method outperformed the runoff prediction accuracy of the asymptotic curve number fitting method, linear regression model and the conventional S C S-C N model with the highest Nash–Sutcliffe index value of 0.825, the lowest residual sum of squares value of 133.04 and the lowest prediction error. It reduced the residual sum of squares by 66% and the model prediction errors by 96% when compared to the conventional S C S-C N model. The estimated curve number was 72.28, with the confidence interval ranging from 62.06 to 78.00 at a 0.01 confidence interval level for the Wangjiaqiao watershed in China.
Highlights
Accurate direct surface runoff is essential for water resources’ planning and development to reduce the occurrence of sedimentation and flooding at their downstream areas [1,2,3]
Since λ varies from location to location, a watershed-specific Soil Conservation Service curve number (SCS-curve number (CN)) calibration method was proposed by using non-parametric inferential statistics based on rainfall and runoff data pairs
This paper presents the use of inferential statistics to calibrate the primary SCS-CN rainfall–runoff model
Summary
Accurate direct surface runoff is essential for water resources’ planning and development to reduce the occurrence of sedimentation and flooding at their downstream areas [1,2,3]. Based on the study results of a group of researchers from the United States of America, λ = 0.05 was reported as the best value to represent watersheds in the United States of America and they proposed Equation (5) for runoff prediction while the S correlation equation between S0.05 and. Since λ varies from location to location, a watershed-specific SCS-CN calibration method was proposed by using non-parametric inferential statistics based on rainfall and runoff data pairs. SCS-CN rainfall–runoff model, a watershed-specific S correlation equation, λ value and the CN of Wangjiaqiao watershed to further improve their runoff prediction accuracy [13]. A calibrated, watershed-specific SCS-CN rainfall–runoff model was developed while an equation was derived to correct the runoff prediction of the conventional SCS-CN model. No other published work has incorporated inferential statistics to calibrate the SCS-CN model
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