自然资源学报

自然资源学报 ›› 2016, Vol. 31 ›› Issue (11): 1906-1917.doi: 10.11849/zrzyxb.20151336

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基于平稳与非平稳GEV模型的鄱阳湖流域极值降水模拟

尹义星1a,1b, 陈海山1a, 许崇育2, 陈莹3, 赵君1b, 孙善磊1a   

  1. 1. 南京信息工程大学 a. 气象灾害教育部重点实验室,b.水文气象学院,南京 210044;
    2. 奥斯陆大学地学系,奥斯陆 挪威;
    3. 福建师范大学地理科学学院,福州 350007
  • 收稿日期:2015-12-04 修回日期:2016-04-18 出版日期:2016-11-20 发布日期:2016-11-20
  • 作者简介:尹义星（1974- ）,男,博士,安徽和县人,副教授,主要从事水文气象学方面的研究。E-mail:yyxrosby@126.com
  • 基金资助:
    国家自然科学基金项目（41671022）; 江苏省普通高校自然科学研究资助项目（15KJB170014）; 中国博士后科学基金（2013M531384）; 江苏省博士后科研资助计划（1301136C）

Modeling Extreme Precipitation in the Poyang Lake Basin Based on Stationary and Non-stationary GEV Models

YIN Yi-xing1a,1b, CHEN Hai-shan1a, XU Chong-yu2, CHEN Ying3, ZHAO Jun1b, SUN Shan-lei1a   

  1. 1. a. China Key Laboratory of Meteorological Disaster of Ministry of Education, b. College of Hydrometeorology, Nanjing University of Information Science and Technology, Nanjing 210044, China;
    2. Department of Geosciences, University of Oslo, Oslo, Norway;
    3. College of Geographic Sciences, Fujian Normal University, Fuzhou 350007, China
  • Received:2015-12-04 Revised:2016-04-18 Online:2016-11-20 Published:2016-11-20
  • Supported by:
    National Natural Science Foundation of China, No.41671022; Natural Science Foundation of the Jiangsu Higher Education Institutions,No.15KJB170014; China Postdoctoral Science Foundation Funded Project, No.2013M531384; Jiangsu Planned Projects for Postdoctoral Research Funds, No.1301136C

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摘要： 论文基于鄱阳湖流域降水数据,采用平稳和非平稳GEV模型进行极值降水的模拟和分析。检测各站年最大1 d降水量序列（AMS1）的非平稳特征,将时间作为位置参数的协变量进行非平稳AMS1序列的GEV模拟。结果表明：1）鄱阳湖流域AMS1序列的形状参数基本均大于0,服从Fréchet分布;位置和尺度参数的空间分布较一致,形状参数则有差异。2）在较高重现期下由轮廓似然方法估计的置信区间比Delta方法更准确;重现水平的轮廓似然函数曲线在较高重现期之下呈较显著不对称性。3）不同重现期下的鄱阳湖流域极值降水等值线图的空间分布特征,与位置和尺度参数的分布图更为接近,与形状参数的差别则较大。4）基于非平稳GEV模型得到赣县站随时间变化的极值降水设计值,其在1951年的100 a一遇设计值到2010年下降为接近50 a一遇,预示着未来发生极值降水和洪灾的风险加大。

关键词: GEV模型, 非平稳, 极值降水, 鄱阳湖流域

Abstract: This paper chooses precipitation data of 14 meteorological stations in the Poyang Lake Basin to model the changes of extreme precipitation from 1951 to 2010 based on both stationary and non-stationary Generalized Extreme Value (GEV) models. The non-stationary characteristics of the annual maximum 1-day series (AMS1) were detected, and the non-stationary GEV model which used the time as a covariate to the location parameter is utilized for non-stationary AMS1. The results showed that: 1) The shape parameters of the AMS1 are all bigger than 0, and follow the Fréchet distribution; the spatial distribution of the GEV location and scale parameters are quite consistent with each other, but the spatial distribution of shape parameters is not consistent with them. 2) The confidence interval given by Profile methods are more accurate for longer return periods in comparison to Delta method, and evident asymmetry appears in the return level’s Profile log-likelihood curve for longer rerun periods. 3) The spatial pattern of the extreme precipitation for different return periods are obtained, and the patterns are in line with the patterns of the location and scale parameters, but are different from the pattern of shape parameters. 4) The time-varying return levels (effective return level) for different return periods are obtained from the non-stationary GEV model of Ganxian Station. The return level of 100 years in 1951 will decrease to 50 years in 2010 for AMS1, which indicates greater risk for extreme precipitation and flood disasters in the future.

Key words: extreme precipitation, GEV model, non-stationary, Poyang Lake Basin

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  • TV125