Parameter uncertainty analysis is one of the hot issues in hydrology studies, and the Generalized Likelihood Uncertainty Estimation (GLUE) is one of the most widely used methods. However, the scale of the existing research is relatively small, which results from computational complexity and limited computing resources. In this study, a parallel GLUE method based on a Message-Passing Interface (MPI) was proposed and implemented on a supercomputer system. The research focused on the computational efficiency of the parallel algorithm and the parameter uncertainty of the Xinanjiang model affected by different threshold likelihood function values and sampling sizes. The results demonstrated that the parallel GLUE method showed high computational efficiency and scalability. Through the large-scale parameter uncertainty analysis, it was found that within an interval of less than 0.1%, the proportion of behavioral parameter sets and the threshold value had an exponential relationship. A large sampling scale is more likely than a small sampling scale to obtain behavioral parameter sets at high threshold values. High threshold values may derive more concentrated posterior distributions of the sensitivity parameters than low threshold values.

中文翻译：

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基于消息传递接口的并行水文模型参数不确定性分析

参数不确定性分析是水文学研究的热点问题之一，广义似然不确定性估计（GLUE）是应用最广泛的方法之一。然而，现有研究的规模相对较小，这是由于计算复杂和计算资源有限造成的。在这项研究中，提出了一种基于消息传递接口 (MPI) 的并行 GLUE 方法，并在超级计算机系统上实现了该方法。研究重点是并行算法的计算效率和新安江模型的参数不确定性受不同阈值似然函数值和采样大小的影响。结果表明，并行 GLUE 方法显示出较高的计算效率和可扩展性。通过大规模参数不确定性分析，发现在小于0.1%的区间内，行为参数集的比例与阈值呈指数关系。大抽样规模比小抽样规模更有可能在高阈值下获得行为参数集。与低阈值相比，高阈值可以推导出更集中的灵敏度参数后验分布。