The popular Natural Resources Conservation Service Curve Number (NRCS-CN) (earlier known as Soil Conservation Service Curve Number (SCS-CN) method of rainfall-runoff modeling has often faced the criticism of exhibiting quantum jumps in runoff computations because of the sudden jumps appearing in CN-values derived from NEH-4 tables for three antecedent moisture conditions (AMC), viz., AMC-I, AMC-II, and AMC-III valid for dry, normal, and wet conditions, respectively. The variability of antecedent soil moisture within an AMC category is responsible for the abrupt jump and other deficiencies in the CN method for runoff estimation. This paper suggests a novel procedure to account for the antecedent moisture (M), preventing quantum jumps and eliminating deficiencies in determination of CN and, in turn, estimation of direct runoff. Its validity was verified utilizing the observed rainfall (P)-runoff (Q) events from 36 US watersheds, four sub-catchments of the Godavari basin, and small agricultural plots at Roorkee, India. The performance of the proposed model (M5) for runoff prediction was compared with the existing NRCS-CN (M1), Mishra and Singh (2002) (M2), Singh et al. (2015) (M3), and Verma et al. (2021) (M4) model using various performance indices. Using the CNs derived from observed events, model M5 was seen to have performed better than M1-M4 in terms of Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Percent Bias (PBIAS) for the data of US watersheds, and CN-P correlation improved as the coefficient of determination (R²) enhanced. Similarly, using the RS & GIS-based CNs on natural watersheds of the Godavari basin and considering AMC-I, the performance of M5 was again better than M1-M4 in terms of RMSE, Mean Bias Error (mBIAS), Mean Absolute Error (MAE), and Normalized-Nash Sutcliffe Efficiency (NNSE). Interestingly, there existed a significant (p < 0.05) relationship between the in-situ water content (w) measured for the experimental plots of Roorkee and the model input variable antecedent moisture (M), offering a physical touch to the conceptual model.

利用美国 36 个流域、戈达瓦里盆地的四个子集水区和印度鲁尔基的小块农田观测到的降雨 (P)-径流 (Q) 事件验证了其有效性。提出的径流预测模型 (M5) 的性能与现有的 NRCS-CN (M1)、Mishra 和 Singh (2002) (M2)、Singh 等人进行了比较。(2015) (M3) 和 Verma 等人。(2021) (M4) 模型使用各种性能指标。使用源自观察到的事件的 CN，模型 M5 在美国流域数据的 Nash Sutcliffe 效率 (NSE)、均方根误差 (RMSE) 和百分比偏差 (PBIAS) 方面表现优于 M1-M4 , CN-P 相关性随着决定系数 (R 和印度 Roorkee 的小块农田。提出的径流预测模型 (M5) 的性能与现有的 NRCS-CN (M1)、Mishra 和 Singh (2002) (M2)、Singh 等人进行了比较。(2015) (M3) 和 Verma 等人。(2021) (M4) 模型使用各种性能指标。使用源自观察到的事件的 CN，模型 M5 在美国流域数据的 Nash Sutcliffe 效率 (NSE)、均方根误差 (RMSE) 和百分比偏差 (PBIAS) 方面表现优于 M1-M4 , CN-P 相关性随着决定系数 (R 和印度 Roorkee 的小块农田。提出的径流预测模型 (M5) 的性能与现有的 NRCS-CN (M1)、Mishra 和 Singh (2002) (M2)、Singh 等人进行了比较。(2015) (M3) 和 Verma 等人。(2021) (M4) 模型使用各种性能指标。使用源自观察到的事件的 CN，模型 M5 在美国流域数据的 Nash Sutcliffe 效率 (NSE)、均方根误差 (RMSE) 和百分比偏差 (PBIAS) 方面表现优于 M1-M4 , CN-P 相关性随着决定系数 (R²）增强。同样，在戈达瓦里盆地的自然流域上使用基于 RS 和 GIS 的 CN，并考虑 AMC-I，M5 在 RMSE、平均偏差误差 (mBIAS)、平均绝对误差 ( MAE）和归一化纳什萨特克利夫效率（NNSE）。有趣的是，在 Roorkee 实验地块测量的原位含水量 (w) 与模型输入变量先行水分 (M) 之间存在显着的 (p < 0.05) 关系，为概念模型提供了物理接触。