摘要翻译：
洪水频率分析通常基于对局部径流序列的极值分布的拟合。然而，当局部数据序列较短时，频率分析结果变得不可靠。区域频率分析是减少估计不确定性的一种方便方法。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网站。该模型在保留“均质区域”形式的同时，比指数洪水模型限制更少。在法国的一组测量站上对所提模型的性能进行了评估。本文还分析了分位点估计的精度与池群的齐次程度的关系。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，研究相对较大且均匀的区域似乎比研究较小且高度均匀的区域可能导致更准确的结果。
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英文标题：
《A regional Bayesian POT model for flood frequency analysis》
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作者：
Mathieu Ribatet (UR HHLY, INRS), Eric Sauquet (UR HHLY), Jean-Michel
  Gr\'esillon (UR HHLY), Taha B.M.J. Ouarda (INRS)
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最新提交年份：
2008
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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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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PDF链接：
https://arxiv.org/pdf/802.0433