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Uncertainty Analysis of Stage-Discharge Curves by Generalized Likelihood Uncertainty Estimation (GLUE) Method

机译:广义似然性不确定性估计(胶水)方法对舞台放电曲线的不确定性分析

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摘要

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 (a(1), a(2), and a(3)) 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 a(1) 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.
机译:额定曲线的校准是由于参数的不确定性导致的挑战。该问题增加了一个具有相当季节性植被多样性的区域。在该研究中,广义似然不确定性估计(胶水)方法与所提出的阶段 - 放电模型相结合,由Maghrebi等人引入,计算英国和科罗拉多河主要河流中的额定曲线的参数不确定性在阿根廷。在胶水方法中,通过比较所提出的方法的有效参数的前后分布来研究每个参数的灵敏度和不确定性分析。应当注意,在所提出的模型中研究的所有参数,例如功率函数参数(a(1),a(2)和a(3))和曼宁的粗糙度系数可以使用最佳参数域使用校准方法。结果表明,与其他参数相比,粗糙度参数的粗糙度参数和功率函数参数A(1)的高灵敏度。为了评估不确定性结果,使用了两个平均相对间隔长度(ArIL(CI))和由95CI(P-CI95%)因子置换为95%置信水平的百分比的观察百分比。结果表明,如果从中级中选择了观察数据,则可以获得更有利的结果,并在模型的输出范围内逐步展示不确定性。

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