Nonstationary modeling of extreme precipitation in China

doi:10.1016/j.atmosres.2016.07.014

YIC-IR > 中国科学院海岸带环境过程与生态修复重点实验室 > 海岸带信息集成与战略规划研究中心

	Nonstationary modeling of extreme precipitation in China
	Gao, M; Mo, DY; Wu, XQ; Gao, M (reprint author), Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China. Email:[email protected]
发表期刊	ATMOSPHERIC RESEARCH
ISSN	0169-8095
	2016-12-15
卷号	182 页码:1-9
关键词	Precipitation Extreme Stationarity Trend Gev Distribution Return Level
DOI	10.1016/j.atmosres.2016.07.014
产权排序	[Gao, Meng; Mo, Dingyuan; Wu, Xiaoqing] Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China; [Mo, Dingyuan] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
作者部门	海岸带信息集成与综合管理实验室
英文摘要	The statistical methods based on extreme value theory have been traditionally used in meteorology and hydrology for a long time. Due to climate change and variability, the hypothesis of stationarity in meteorological or hydrological time series was usually not satisfied. In this paper, a nonstationary extreme value analysis was conducted for annual maximum daily precipitation (AMP) at 631 meteorological stations over China for the period 1951-2013. Stationarity of all 631 AMP time series was firstly tested using KPSS test method, and only 48 AMP time series showed non-stationarity at 5% significance level. The trends of these 48 nonstationary AMP time series were further tested using M-K test method. There were 25 nonstationary AMP time series mainly distributed in southern and western China showing significant positive trend at 5% level. Another 5 nonstationary AMP time series with significant negative trends were near northern urban agglomeration, Sichuan Basin, and central China. For these nonstationary AMP time series with significant positive or negative trends, the location parameter in generalized extreme value (GEV) distribution was assumed to be time-varying, and the trends were successfully characterized by the nonstationary GEV models. For the remaining 18 nonstationary AMP time series mainly in the eastern portion of China, no significant trend was detected. The correlation analysis showed that only 5 nonstationary AMP time series were significantly correlated with one or two of the four climate indices EASMI, WPI, SOI, and PDO. Then, the location and scale parameters in the GEV distribution were modeled as functions of the significantly correlated climate indices. The modeling results in this study showed that the nonstationary GEV distributions performed better than their stationary equivalents. Finally, 20-year and 50-year return levels of precipitation extremes at all 631 stations were estimated using the best fitting distribution for the year 1961 and 2013, respectively. (C) 2016 Elsevier B.V. All rights reserved.
文章类型	Article
资助机构	Youth Innovation Promotion Association of the Chinese Academy of Sciences(2016195) ; CAS Knowledge Innovation Project(KZCX2-EW-QN209) ; S&T Service Network Initiative(KFJ-EW-STS-127-2) ; National Natural Science Foundation of China(31570423)
收录类别	SCI
语种	英语
关键词[WOS]	RIVER-BASIN ; PROBABILITY-DISTRIBUTION ; SUMMER MONSOON ; CLIMATE ; EVENTS ; TEMPERATURE ; RAINFALL ; WEATHER ; TRENDS ; INDEX
研究领域[WOS]	Meteorology & Atmospheric Sciences
WOS记录号	WOS:000384868900001
引用统计	被引频次：68[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/17479
专题	中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心中国科学院烟台海岸带研究所中国科学院海岸带环境过程与生态修复重点实验室
通讯作者	Gao, M (reprint author), Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China. Email:[email protected]
推荐引用方式 GB/T 7714	Gao, M,Mo, DY,Wu, XQ,et al. Nonstationary modeling of extreme precipitation in China[J]. ATMOSPHERIC RESEARCH,2016,182:1-9.
APA	Gao, M,Mo, DY,Wu, XQ,&Gao, M .(2016).Nonstationary modeling of extreme precipitation in China.ATMOSPHERIC RESEARCH,182,1-9.
MLA	Gao, M,et al."Nonstationary modeling of extreme precipitation in China".ATMOSPHERIC RESEARCH 182(2016):1-9.

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