This study presents a procedure to estimate more accurate direct surface runoff from large rainfall (>25.4 mm)-runoff events. Improved models (M5-M7) are derived by coupling two concepts: (i) initial abstraction as 2% of the total rainfall and (ii) runoff coefficient = degree of saturation. Performance of ten different models including the original SCS-CN method (M1), Mishra and Singh 2002 (M2), Mishra et al. 2006 (M3), Ajmal et al. 2016 (M4), improved models (M5-M7) and their simplified forms (M8-M10) is evaluated using large (7687) number of rainfall events derived from 98 watersheds of USDA-ARS to assess the accuracy of runoff estimation. Quantitatively, it is assessed using seven performance indices, viz., R², NSE, PBIAS, RMSE, NRMSE, RSR, and MAE; categories; and Ranking and Grading System (RGS). The resulting high values of R², RSR, RGS, and lowest values of NSE, PBIAS, RMSE, NRMSE, and MAE for the improved models (M5-M7) reveal that improved models performed better than the existing models (M1-M4). Similarly, based on different performance categories, all improved models exhibited superior performance in most of the watersheds than did the existing models. Sensitivity analysis indicated CN to be the most sensitive parameter of the improved model. The proposed model is seen to have overcome the limitations of the original and its previous versions intended for large events and can thus be used for estimating runoff more accurately.

这项研究提出了一种程序，可以从大降雨（> 25.4 mm）径流事件中估算出更准确的直接地表径流。改进的模型（M5-M7）是通过结合两个概念得出的：（i）初始抽象占总降雨量的2％，以及（ii）径流系数=饱和度。十种不同模型的性能，包括原始的SCS-CN方法（M1），Mishra和Singh 2002（M2），Mishra等。2006（M3），Ajmal等人。2016年（M4），改良模型（M5-M7）及其简化形式（M8-M10）使用来自美国农业部ARS的98个流域的大量降雨事件（7687）进行评估，以评估径流估算的准确性。使用7个性能指标（即R ²）进行定量评估，NSE，PBIAS，RMSE，NRMSE，RSR和MAE；类别；和排名与评分系统（RGS）。对于改进模型（M5-M7），R ²，RSR，RGS的高值以及NSE，PBIAS，RMSE，NRMSE和MAE的最低值表明，改进模型的性能优于现有模型（M1-M4） 。同样，基于不同的性能类别，所有改进的模型在大多数分水岭上都表现出比现有模型更好的性能。敏感性分析表明，CN是改进模型的最敏感参数。所提出的模型被认为已经克服了用于大型事件的原始模型及其先前版本的局限性，因此可以用于更准确地估算径流。