Calibration of the rating curve is a challenge due to uncertainty in the parameters. This problem increases in an area with considerable seasonal vegetation diversity. In this study, the generalized likelihood uncertainty estimation (GLUE) method was combined with the proposed stage-discharge model, introduced by Maghrebi et al., to compute the parameter uncertainty of the rating curve in the Main River in the UK and the Colorado River in Argentina. In GLUE methodology, the sensitivity and uncertainty analysis of each parameter were investigated by comparing the prior and posterior distribution of the effective parameters in the proposed method. It should be noted that all the parameters studied in the proposed model, such as power function parameters (a₁, a₂, and a₃) and Manning’s roughness coefficient can be acquired with an optimal parameter domain using the calibration method. The results demonstrated low sensitivity of roughness parameters and high sensitivity of power function parameter a₁ in comparison with other parameters. In order to evaluate the uncertainty results, two average relative interval length (ARIL_CI) and the percent of observations bracketed by the 95CI (P_CI95%) factors at 95% confidence level were used. The results showed that if the observational data were selected from the middle level, a more favorable result would be obtained and demonstrated less uncertainty in the output range of the model.

由于参数的不确定性，定标曲线的校准是一个挑战。在具有大量季节性植被多样性的地区，这一问题更加严重。在这项研究中，将广义似然不确定性估计（GLUE）方法与Maghrebi等人介绍的拟议的阶段流量模型相结合，以计算英国主河和科罗拉多河的等级曲线的参数不确定性在阿根廷。在GLUE方法中，通过比较所提出方法中有效参数的前后分布，研究了每个参数的敏感性和不确定性分析。应该注意的是，在建议的模型中研究的所有参数，例如幂函数参数（a ₁，a ₂，和一个₃）和曼宁粗糙系数可以使用的校准方法的最佳参数域来获取。结果表明，与其他参数相比，粗糙度参数的灵敏度较低，而幂函数参数a _1的灵敏度较高。为了评估不确定性结果，使用了95％置信水平下的两个平均相对间隔长度（ARIL _CI）和由95CI括起来的观察百分比（P _CI95％）。结果表明，如果从中间水平选择观测数据，则可获得更好的结果，并且模型输出范围的不确定性较小。