滨海城市雨潮遭遇联合分布模拟与设计

滨海城市河流常常遭受暴雨和潮汐顶托双重影响导致洪涝灾害，需要重视雨潮遭遇联合分布模拟与设计。以深圳市西乡河为例，采用年最大值法（AM）和超定量序列法（POT）两种选样方法，基于Copula方法模拟24h暴雨遭遇日高潮位的联合分布特征，对比雨潮遭遇传统重现期和二次重现期差异，根据同频法和权函数法反推计算雨潮设计组合值。结果表明：雨潮边缘分布最优模型均为广义正态分布（GNO），不同选样方法雨量分布模型参数差异明显。雨潮之间呈现较弱的正相依性，Archimedean Copulas均能较好地模拟雨潮遭遇联合分布特征，最优模型为Gumbel—Hougaard Copula。同频法反推雨潮设计组合值，二...

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Bibliographic Details
Published in水科学进展 Vol. 28; no. 1; pp. 49 - 58
Main Author 涂新军 杜奕良 陈晓宏 柴苑苑 卿颖
Format Journal Article
LanguageChinese
Published 中山大学水资源与环境研究中心,广东广州510275 2017
广东省华南地区水安全调控工程技术研究中心,广东广州510275%深圳市水务规划设计院有限公司,广东深圳,518036%湖南省水利电力勘测设计研究总院,湖南长沙,410000
Subjects
Copula函数
二次重现期
滨海城市
设计组合值
超定量序列法
雨潮遭遇
设计组合值
滨海城市
encounter of heavy rain and high tidal level
a pair of designed values
二次重现期
peaks over threshold
Copula function
second return period
coastal city
Copula函数
雨潮遭遇
超定量序列法
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ISSN1001-6791
DOI10.14042/j.cnki.32.1309.2017.01.006

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Summary:滨海城市河流常常遭受暴雨和潮汐顶托双重影响导致洪涝灾害，需要重视雨潮遭遇联合分布模拟与设计。以深圳市西乡河为例，采用年最大值法（AM）和超定量序列法（POT）两种选样方法，基于Copula方法模拟24h暴雨遭遇日高潮位的联合分布特征，对比雨潮遭遇传统重现期和二次重现期差异，根据同频法和权函数法反推计算雨潮设计组合值。结果表明：雨潮边缘分布最优模型均为广义正态分布（GNO），不同选样方法雨量分布模型参数差异明显。雨潮之间呈现较弱的正相依性，Archimedean Copulas均能较好地模拟雨潮遭遇联合分布特征，最优模型为Gumbel—Hougaard Copula。同频法反推雨潮设计组合值，二次重现期雨量和潮位均大于传统联合重现期，POT选样的潮位大于AM。权函数法选出的雨潮设计组合值，偏重于较高的潮位，雨量设计值较小。当明确了选样方法、联合分布模型和重现期类型，给定联合重现期的雨潮设计组合值是个此消彼长的过程，若选择较大的雨量设计值，则潮位值变小，反之亦然。从防洪潮设计安全角度考虑，POT选样方法及二次重现期设计更为安全。
Bibliography:Instream flood in a coastal city usually occurs under the influence of heavy rain and high tidal level. Thus, modeling and design of joint distribution of precipitation and tide require increased attention. With Xixianghe River basin of Shenzhen city, Southern China, used as a case, 24-hour data of heavy rain and comparative daily high tidal level are used for two sampling methods, namely, annual maximum （AM） and peaks over threshold （POT）. The joint distribution model of precipitation and tide is established by using Copula functions. In this model, the difference between the traditional and second return periods of joint distribution of precipitation and tide is analyzed. The pair values of precipitation and tide are investigated according to two optimally designed methods, namely, equalized frequency method and most-likely weight function. Results show that the generalized normal distribution （GNO） is optimally selected to model the marginal distribution of precipitation and tide, but the differences of mo
ISSN:1001-6791
DOI:10.14042/j.cnki.32.1309.2017.01.006