洪水频率分析的区域贝叶斯POT模型
A regional Bayesian POT model for flood frequency analysis
摘要：
洪水频率分析通常基于极值分布对当地径流序列的拟合。然而，当局部数据序列较短时，频率分析结果变得不可靠。区域频率分析是降低估计不确定性的一种简便方法。在这项工作中，我们提出了一个区域贝叶斯模型的短记录长度的网站。该模型比指数洪水模型的限制性更小，同时保留了“均匀区域”的形式主义。所提出的模型的性能进行评估，在法国的一组计量站。分位数估计的准确性作为合并组的同质性程度的函数也进行了分析。结果表明，区域贝叶斯模型优于指数洪水模型和局部估计。此外，似乎与较小且高度均匀的区域相比，使用相对较大且均匀的区域可能会导致更准确的结果。
英文摘要：
Flood frequency analysis is usually based on the fitting of an extreme value distribution to the local streamflow series. However, when the local data series is short, frequency analysis results become unreliable. Regional frequency analysis is a convenient way to reduce the estimation uncertainty. In this work, we propose a regional Bayesian model for short record length sites. This model is less restrictive than the index flood model while preserving the formalism of "homogeneous regions". The performance of the proposed model is assessed on a set of gauging stations in France. The accuracy of quantile estimates as a function of the degree of homogeneity of the pooling group is also analysed. The results indicate that the regional Bayesian model outperforms the index flood model and local estimators. Furthermore, it seems that working with relatively large and homogeneous regions may lead to more accurate results than working with smaller and highly homogeneous regions.
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