There are various methods to estimate the curve number (CN) for flood studies. In the ungauged basins, hydrologists rely on the use of an NRCS-CN table called (CN_design). The CN_design, in this study, is estimated using remote sensing techniques and geographical information systems based on alluvium-rock-vegetation classification of natural basins. However, in gauged basins, it is common to use rainfall-runoff data through the application of the least-squares method (LSM) to get the best CN value (CN_obs), or the asymptotic fitting method (AFM) to obtain asymptotic CN (CN_∞). A comparison between these methods is made under the effect of changing both the coefficient of abstraction ratio, λ, and the effect of data sorting techniques to find out the best estimation of CN for reliable prediction of floods. A methodology has been developed to convert the NRCS-CN table values at λ = 0.2 to λ = 0.01 for arid basins. The relationship between the observed CN and the NRCS-CN table shows that estimating runoff using λ = 0.2 is best made by CN of the impervious area (CN_imp = 90) instead of 98 (for urban areas) used in the literature. The highest value of CN between the methods is the CN_design, then CN_obs. CN_∞ shows the lowest value. Therefore, for a safe design of the hydraulic structures, it is recommended to use CN_design. However, for the simulation of the rainfall (P)-runoff (Q) process in the natural basins, it is recommended to use CN_obs at the natural sorting of data pairs (P: Q). The root mean square error (RMSE) of CN is reduced from 11 at CN_imp = 98 to 7 at CN_imp = 90. This value reflects the infiltration process in the rocks due to the high density of fractures and fissures in the mountains in the area. The developed NRCS-CN table at λ = 0.01 reduces the RMSE of the estimated runoff depth by 57%.

有多种方法可以估算洪水研究的曲线数（CN）。在未充填的盆地中，水文学家依靠使用称为（CN _design）的NRCS-CN表来使用。在本研究中，基于自然盆地的冲积层-岩石-植被分类，使用遥感技术和地理信息系统估算了CN的_设计。但是，在规范盆地中，通常通过应用最小二乘法（LSM）来获得最佳的CN值（CN _obs），或者使用渐近拟合法（AFM）来获得渐近的CN来使用降雨径流数据。（_CN∞）。在改变抽象系数比λ的影响下，对这两种方法进行了比较。，以及数据分类技术的效果，以便找出CN的最佳估计值，从而对洪水进行可靠的预测。已经开发出一种方法将 干旱盆地的NRCS-CN表值从λ  = 0.2转换为λ = 0.01。观测到的CN与NRCS-CN表之间的关系表明，使用λ  = 0.2估算径流量最好是由不透水区的CN（CN _imp  = 90）而不是文献中使用的98（对于城市地区）。方法之间的CN最高值是CN_设计，然后是CN _obs。CN _∞显示的最低值。因此，为安全设计水工结构，建议使用CN_设计。但是，为了模拟天然流域的降雨（P）-径流（Q）过程，建议在对数据对（P：Q）进行自然排序时使用CN _obs。CN的均方根误差（RMSE）从CN _imp  = 98时的11减小到CN _imp  = 90时的7。该值反映了由于山地中高密度的裂缝和裂隙而导致的岩石入渗过程。区。在λ= 0.01时开发的NRCS-CN表将估计径流深度的RMSE降低了57％。