Censored data (CD) of floods, that is, the combination of systematic data (SD) and historical data, can help improve the robustness of flood frequency analysis, due to its temporal information expansion. However, in nonstationary flood frequency analysis, the approach to utilize the CD has rarely been investigated. In this study, a covariate‐based nonstationary flood frequency analysis framework based on various likelihood functions using the generalized extreme value (GEV) distribution was built to utilize the CD, with uncertainty considered. This framework was applied to the study of the annual maximum flood frequency of the Yichang gauging station 44 km downstream of the Three Gorges Dam over the period from 1470 to 2017. A summer precipitation anomaly and a reservoir index were used as covariates to explain the variation of the distribution parameters. The results show that for either the SD or CD, the nonstationary models are preferred to the stationary ones by the deviance information criterion, and these nonstationary models may prove to be practical in engineering application, due to the acceptable uncertainty range in flood quantiles derived from covariates. Compared to the stationary or nonstationary models based on the SD, the corresponding model based on the CD results in a higher posterior mean and a smaller posterior standard deviation for the shape parameter of the GEV distribution. It is concluded that the use of historical information under the nonstationary frequency analysis framework may be remarkable in reducing design flood uncertainty, especially for the very small exceedance probability at the tail.

中文翻译：

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删失数据的非平稳频率分析：以1470年至2017年长江洪水为例

洪水的经过审查的数据（CD），即系统数据（SD）和历史数据的结合，由于其时间信息的扩展，可以帮助提高洪水频率分析的鲁棒性。但是，在非平稳洪水频率分析中，很少研究利用CD的方法。在这项研究中，建立了一个基于协变量的非平稳洪水频率分析框架，该框架基于使用通用极值（GEV）分布的各种似然函数来利用CD，并考虑了不确定性。该框架用于研究1470年至2017年三峡大坝下游44公里处宜昌测站的年度最大洪水频率。利用夏季降水异常和储层指数作为协变量来解释分布参数的变化。结果表明，对于SD或CD而言，根据偏差信息准则，非平稳模型优于固定模型，并且由于来自洪水分位数的不确定性范围可以接受，因此这些非平稳模型在工程应用中可能是可行的。协变量 与基于SD的固定模型或非固定模型相比，基于CD的相应模型对GEV分布的形状参数产生较高的后验均值和较小的后验标准差。结论是，在非平稳频率分析框架下使用历史信息可能会显着减少设计洪水的不确定性，