基于Copula函数的暴雨要素三维联合分布——以宽甸县为例
The Three-dimensional Joint Distributions of Rainstorm Factors Based on Copula Function: A Case in Kuandian County, Liaoning Province
查看参考文献21篇
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文摘
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以辽宁省宽甸县为例,利用1955~2012年逐日降水数据,提取年暴雨日数(D50)、年暴雨量(P50)、年均暴雨强度(I)和暴雨比(R)共4个暴雨要素,运用K-S法确定各单要素最优概率分布函数;针对暴雨要素多面性,通过引入Copula函数,构建三维联合分布并进行AIC和RMSE优度检验,确定适合暴雨要素的最优Copula函数,分析多要素联合后暴雨的概率和重现期特征。研究表明:①单变量拟合仅反映暴雨单个要素本身的信息,无法涉及要素间的联系;三维Copula联合可从3方面呈现暴雨要素间的内在信息,更贴近实际;暴雨本身的多要素性,为Copula函数在暴雨分析上提供了广阔前景;②年暴雨日数、年暴雨量和年均暴雨强度的联合适合反映宽甸县暴雨重现期;宽甸县暴雨联合重现期短,多为0~2 a,同现重现期较长,集中于200 a左右;2种重现期变化趋势一致,存在同步效应,反映了暴雨要素的不可分割性。 |
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其他语种文摘
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Taking Kuandian County in Liaoning Province as an example, the author extract four rainstorm factors: the annual rainstorm days, annual of rainstorm depth, annual average rainstorm intensity and rainstorm contribution, through the daily precipitation data from 1955 to 2012 and use the Kolmogorov-Smimov method to determine optimal probability distribution for each single factor. For the multifaceted rainstorm factor, we use AIC and RMSE test to confirm the best fitted copulas connect function suitable for rainstorm factor by introducing the copula function and building three-dimensional joint distribution, and analyze the probability of rainstorm and characteristics of return period with many combined factors. Research shows that: 1) The joint of annual rainstorm days, annual of rainstorm depth and annual average rainstorm intensity is suitable for reaction joint return period of rainstorm factor in Kuandian County; In Kuandian County, joint return period is short and distribute on 0-2 years, co-occurrence return period is longer, concentrated in around 200 years; the change trend of two kinds of return period is consistent, it has synchronization effect, this reflect inseparable of rainstorm factor. 2) The univariate reflect just one factor of information in rainstorm and doesn't involved in the relationship between factors; Three-dimensional copulas joint can present the internal information between heavy elements from three aspects and closer to the actual; Multiple factors of rainstorm, as copulas function on the rainstorm analysis provides a broad prospects. |
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来源
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地理科学
,2017,37(4):603-610 【核心库】
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DOI
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10.13249/j.cnki.sgs.2017.04.014
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关键词
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Copula函数
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三维联合
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暴雨要素
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重现期
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地址
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1.
东北师范大学地理科学学院, 吉林, 长春, 130024
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哈尔滨师范大学地理科学学院, 黑龙江, 哈尔滨, 150025
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北京师范大学/民政部/教育部减灾与应急管理研究院, 北京, 100875
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-0690 |
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学科
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自然地理学 |
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基金
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哈尔滨科技局科技创新人才研究专项基金
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齐齐哈尔软科学项目
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齐齐哈尔大学青年教师科研启动支持计划项目
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文献收藏号
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CSCD:5976424
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参考文献 共
21
共2页
|
|
1.
中国气象局.
中国气象灾害年鉴,2012:17-22
|
被引
1
次
|
|
|
|
|
2.
Sun Zhongyi. Integrated risk zoning of drought and waterlogging disasters based on fuzzy comprehensive evaluation in Anhui Province, China.
Natural Hazards,2014,71(3):1639-1657
|
被引
3
次
|
|
|
|
|
3.
王朋岭. 2012年4月华南地区降水异常事件及成因诊断分析.
地理科学,2015,35(3):352-357
|
被引
4
次
|
|
|
|
|
4.
孙凤华. 东北地区降水日数、强度和持续时间的年代际变化.
应用气象学报,2007,18(5):610-618
|
被引
59
次
|
|
|
|
|
5.
姚莉. 我国中东部逐时雨强时空分布及重现期的估算.
地理学报,2010,65(3):293-300
|
被引
16
次
|
|
|
|
|
6.
Yin Z. Community-based scenario modelling and disaster risk assessment of urban rainstorm waterlogging.
Journal of Geographical Sciences,2011,21(2):274-284
|
被引
4
次
|
|
|
|
|
7.
Fu Guangtao. Copula-based frequency analysis of overflow and flooding in urban drainage systems.
Journal of Hydrology,2014,510:49-58
|
被引
4
次
|
|
|
|
|
8.
Renard B. Use of a gaussian copula for multivariate extreme value analysis: Some case studies in hydrology.
Advances in Water Resources,2007,30(4):897-912
|
被引
8
次
|
|
|
|
|
9.
Hao Zengchao. Multivariate standardized drought index: a parametric multi-index model.
Advances in Water Resources,2013,57:12-18
|
被引
2
次
|
|
|
|
|
10.
Kao S C. A copula-based joint deficit index for droughts.
Journal of Hydrology,2010,380(1/2):121-134
|
被引
47
次
|
|
|
|
|
11.
Mcmillan H. Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models.
Journal of Hydrology,2011,400(1/2):83-94
|
被引
3
次
|
|
|
|
|
12.
Moazami S. Uncertainty analysis of bias from satellite rainfall estimates using copula method.
Atmospheric Research,2014,137:145-166
|
被引
4
次
|
|
|
|
|
13.
Zhang Q. Spatio-temporal variations of precipitation extremes in Xinjiang, China.
Journal of Hydrology,2012,434/435:7-18
|
被引
18
次
|
|
|
|
|
14.
Li N. The return period analysis of natural disasters with statistical modeling of bivariate joint probability distribution.
Risk Analysis: an Official Publication of the Society for Risk Analysis,2013,33(1):134-145
|
被引
9
次
|
|
|
|
|
15.
Li Jianfeng. Future joint probability behaviors of precipitation extremes across China: Spatiotemporal patterns and implications for flood and drought hazards.
Global and Planetary Change,2015,124:107-122
|
被引
3
次
|
|
|
|
|
16.
Liu Xueqin. The joint return period analysis of natural disasters based on monitoring and statistical modeling of multidimensional hazard factors.
The Science of the Total Environment,2015,538:724-732
|
被引
5
次
|
|
|
|
|
17.
程纯枢.
中国农业百科全书,1986
|
被引
2
次
|
|
|
|
|
18.
Sklar A. VFonctions de repartition a n dimensions et leurs marges, V Publ.
Inst. Statist. Univ. Paris,1959:8
|
被引
1
次
|
|
|
|
|
19.
Shahin M.
Erosion and Sedimentation in Drainage Basins and in Storage Reservoirs,2007:333-367
|
被引
1
次
|
|
|
|
|
20.
王金玲. 近51年宽甸旱涝年气候分析和预报.
辽宁气象,2005(4):20-21
|
被引
1
次
|
|
|
|
|
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