气候变化领域集成服务门户(CCPortal): Nonstationary bayesian modeling of precipitation extremes in the Beijing-Tianjin-Hebei Region; China

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DOI	10.1016/j.atmosres.2020.105006
	Nonstationary bayesian modeling of precipitation extremes in the Beijing-Tianjin-Hebei Region; China
	Song X.; Zou X.; Mo Y.; Zhang J.; Zhang C.; Tian Y.
发表日期	2020
ISSN	0169-8095
卷号	242
英文摘要	This paper investigates the nonstationarity of precipitation extremes by incorporating time-varying and physical-based explanatory covariates, using daily precipitation data across the Beijing-Tianjin-Hebei (BTH) region, China. We perform the stationary and nonstationary generalized extreme value (GEV) models based on the Bayesian framework to estimate the expected return levels of precipitation extremes with the 90% credible intervals. Results reveal that the nonstationarity of precipitation extremes is not prominently visible for the majority of sites in BTH. However, the nonstationary GEV models exhibit better performance to capture the variations of precipitation extremes by comparison to the stationary models based on four evaluation criteria. Further, this work attempts to determine the best covariate to illustrate the possible effects of environmental changes on the frequency analysis. Results indicate that the El Nino-Southern Oscillation (ENSO) is the top of the best covariates, followed by the East Asian summer monsoon, North Atlantic Oscillation (NAO) and local temperature anomaly. Moreover, the best covariates are dominated by the physical-based covariates, and the best models with nonlinear functions of covariates are found in the majority of sites. Finally, the best-fitted models are used to estimate the design values of return levels in precipitation extremes. Results illustrate that the differences between the stationary modeling and nonstationary modeling in the median condition of covariates are not significant for most of the sites. But the discrepancies will be enhanced if the covariates locate in a high (95-percentile) or low (5-percentile) value. Our findings suggest that the nonstationary modeling of precipitation extremes might prove more useful and reliable, especially in the uncommon conditions of physical-based covariates. © 2020 Elsevier B.V.
英文关键词	Bayesian framework; Beijing-Tianjin-Hebei region; Frequency analysis; Nonstationarity; Precipitation extremes
语种	英语
scopus关键词	Atmospheric pressure; Bayesian networks; Beijing-tianjin-hebei regions; East Asian summer monsoon; El Nino southern oscillation; Environmental change; Generalized extreme value; Non-stationary model; North Atlantic oscillations; Precipitation extremes; Climatology; Bayesian analysis; El Nino-Southern Oscillation; extreme event; monsoon; North Atlantic Oscillation; numerical model; precipitation (climatology); temperature anomaly; Beijing [China]; China; Hebei; Tianjin
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141891
作者单位	School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, 221116, China; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, 210029, China; Research Center for Climate Change, Ministry of Water Resources, Nanjing, 210029, China
推荐引用方式 GB/T 7714	Song X.,Zou X.,Mo Y.,et al. Nonstationary bayesian modeling of precipitation extremes in the Beijing-Tianjin-Hebei Region; China[J],2020,242.
APA	Song X.,Zou X.,Mo Y.,Zhang J.,Zhang C.,&Tian Y..(2020).Nonstationary bayesian modeling of precipitation extremes in the Beijing-Tianjin-Hebei Region; China.Atmospheric Research,242.
MLA	Song X.,et al."Nonstationary bayesian modeling of precipitation extremes in the Beijing-Tianjin-Hebei Region; China".Atmospheric Research 242(2020).