A Calibrated, Watershed-Specific <i>SCS</i>-<i>CN</i> Method: Application to Wangjiaqiao Watershed in the Three Gorges Area, China

The Soil Conservation Service curve number (<inline-formula><math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>C</mi> <mi>S</mi></mrow></semantics></math></inline-formula>-<inline-formula><math display="inline"><semantics><mrow><mi>C</mi> <mi>N</mi></mrow></semantics></math></inline-formula>) 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 <inline-formula> <math display="inline"> <semantics> <mi>&#955;</mi> </semantics> </math> </inline-formula> as 0.20. This paper developed a watershed-specific <inline-formula> <math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>C</mi> <mi>S</mi></mrow></semantics></math></inline-formula>-<inline-formula><math display="inline"><semantics><mrow><mi>C</mi> <mi>N</mi> </mrow> </semantics> </math> </inline-formula> calibration method using non-parametric inferential statistics with rainfall&#8722;runoff data pairs. The proposed method first analyzed the data and generated confidence intervals to determine the optimum values for <inline-formula> <math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>C</mi> <mi>S</mi></mrow></semantics></math></inline-formula>-<inline-formula><math display="inline"> <semantics> <mrow> <mi>C</mi> <mi>N</mi> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>C</mi> <mi>S</mi></mrow></semantics></math></inline-formula>-<inline-formula><math display="inline"><semantics><mrow><mi>C</mi> <mi>N</mi> </mrow> </semantics> </math> </inline-formula> model with the highest Nash&#8722;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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>C</mi> <mi>S</mi></mrow></semantics></math></inline-formula>-<inline-formula><math display="inline"><semantics><mrow><mi>C</mi> <mi>N</mi> </mrow> </semantics> </math> </inline-formula> 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.