哈尔滨工业大学学报
哈爾濱工業大學學報
합이빈공업대학학보
JOURNAL OF HARBIN INSTITUTE OF TECHNOLOGY
2015年
4期
87-92
,共6页
刘成林%周玉文%隋军%高琳
劉成林%週玉文%隋軍%高琳
류성림%주옥문%수군%고림
降雨特征%3维Copula函数%多变量分析%水文
降雨特徵%3維Copula函數%多變量分析%水文
강우특정%3유Copula함수%다변량분석%수문
rainfall character%3-copula function%3-variate analysis%hydrology
鉴于径流数据缺乏且难以长期监测而降雨数据相对完整,通常假定降雨和径流同频率,采用设计降雨进行水文分析计算,但此方法很难真实全面地反映降雨变化特征。为此,提出一种基于3维Copula函数的降雨特征多变量频率分析方法。首先利用降雨强度法将连续的降雨时间序列分割成若干个降雨事件,采用年最大值法取样,统计出表征雨量的特征变量,然后引入3维Copula 函数构建降雨特征3变量联合概率模型,并以广州1961~2012年历史降雨数据为例进行分析。结果表明,基于3维Copula函数的多变量分析方法计算简单、可靠性高,可以进行3种不同降雨特征变量的组合分析,得到各种不同量级变量的遭遇概率和条件概率,能够更全面地反映降雨特征并更好地满足水文分析计算需求。
鑒于徑流數據缺乏且難以長期鑑測而降雨數據相對完整,通常假定降雨和徑流同頻率,採用設計降雨進行水文分析計算,但此方法很難真實全麵地反映降雨變化特徵。為此,提齣一種基于3維Copula函數的降雨特徵多變量頻率分析方法。首先利用降雨彊度法將連續的降雨時間序列分割成若榦箇降雨事件,採用年最大值法取樣,統計齣錶徵雨量的特徵變量,然後引入3維Copula 函數構建降雨特徵3變量聯閤概率模型,併以廣州1961~2012年歷史降雨數據為例進行分析。結果錶明,基于3維Copula函數的多變量分析方法計算簡單、可靠性高,可以進行3種不同降雨特徵變量的組閤分析,得到各種不同量級變量的遭遇概率和條件概率,能夠更全麵地反映降雨特徵併更好地滿足水文分析計算需求。
감우경류수거결핍차난이장기감측이강우수거상대완정,통상가정강우화경류동빈솔,채용설계강우진행수문분석계산,단차방법흔난진실전면지반영강우변화특정。위차,제출일충기우3유Copula함수적강우특정다변량빈솔분석방법。수선이용강우강도법장련속적강우시간서렬분할성약간개강우사건,채용년최대치법취양,통계출표정우량적특정변량,연후인입3유Copula 함수구건강우특정3변량연합개솔모형,병이엄주1961~2012년역사강우수거위례진행분석。결과표명,기우3유Copula함수적다변량분석방법계산간단、가고성고,가이진행3충불동강우특정변량적조합분석,득도각충불동량급변량적조우개솔화조건개솔,능구경전면지반영강우특정병경호지만족수문분석계산수구。
There is an underlying assumption that run?off and rainfall in a given urban catchment are equivalent and, further, to use design rainfall depth as a proxy for run?off in hydrological analyses and calculations. However, when employing this approach, it is difficult to accurately and fully reflect the variability in rainfall characteristics. To address this issue, a method for the copula?based multivariate frequency analysis of rainfall characteristics was proposed by using historical rainfall data (1961-2012) from Guangzhou city. First, continuous rainfall time series were divided into individual rainfall events using the rainfall intensity method. Then the characteristic variables of rainfall were calculated by sampling using the annual maximum method. Finally, a three?dimensional copula was introduced to build a multivariate joint probability distribution model of rainfall characteristics. The results show that the copula?based multivariate analysis is easy to implement and provides reliable results. This approach can be used to analyse the conditional probabilities of variables for different orders of magnitude. It can fully reflect rainfall characteristics, which serve an important reference for urban flood control and drainage planning.