Nonstationary Frequency Analysis of Censored Data: A Case Study of the Floods in the Yangtze River From 1470 to 2017

Abstract

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.

A River Network‐Based Hierarchical Model for Deriving Flood Frequency Distributions and Its Application to the Upper Yangtze Basin

“…The rest reduction induced by the reservoirs above Wulong is 2,045 m 3 /s, making a relative contribution of 14% to the total reduction. This finding generally agrees with the previous studies, in which the TGR was also found to have the largest contribution in declining the flood magnitude of the whole Upper Yangtze basin (Jiang, Zhang, & Luo, 2019; B. Xiong et al., 2020).…”

“…10.1029/2020WR029374 13 of 21 found to have the largest contribution in declining the flood magnitude of the whole Upper Yangtze basin (Jiang, Zhang, & Luo, 2019;B. Xiong et al, 2020).…”

“…The FFD estimation relying only on at-site flood samples is often limited by available flood observations (Merz & Blöschl, 2008a;B. Xiong et al, 2020).…”

Design flood estimation with varying record lengths in Norway under stationarity and nonstationarity scenarios

“…Nonstationary FFA (NFFA) is proposed to address the nonstationary (NS) issue of extreme flood events. As evidenced from recent review articles, there are several different methods for NFFA (Khaliq et al 2006;Hall et al 2013;Madsen et al 2014;Bayazit 2015;Hao & Singh 2016;Yan et al 2017b;Salas et al 2018;Jiang et al 2019;Qu et al 2020;Xiong et al 2020;Yan et al 2021), among which the time-varying moments (TVM) method is the most popular approach. In the TVM method, the statistical parameters of flood distribution are no longer invariant but change with time or other covariates to capture the trend of flood frequency (Rigby & Stasinopoulos 2005).…”

Nonstationary Analyses of the Maximum and Minimum Streamflow in Tamsui River Basin, Taiwan

“…Impacts of climate change and anthropogenic activities on streamflow regime have drawn considerable attention recently [18][19][20][21][22][23]. A vast literature has thus been devoted to exploring non-stationarity in streamflow series worldwide [24][25][26][27][28][29][30][31]. However, most studies focus on extreme streamflow such as flood or low flow, while few studies have investigated them simultaneously.…”