陈浩铭, 庞博, 任汉承, 杨芳, 郑自琪, 周斯聪. 基于Copula函数的昆明市降水特征组合风险空间分布研究[J]. 北京师范大学学报(自然科学版). DOI: 10.12202/j.0476-0301.2024083
引用本文: 陈浩铭, 庞博, 任汉承, 杨芳, 郑自琪, 周斯聪. 基于Copula函数的昆明市降水特征组合风险空间分布研究[J]. 北京师范大学学报(自然科学版). DOI: 10.12202/j.0476-0301.2024083
CHEN Haoming, PANG Bo, REN Hancheng, YANG Fang, ZHENG Ziqi, ZHOU Sicong. Study on the spatial distribution of combined risk of precipitation characteristics in Kunming city based on copula functions[J]. Journal of Beijing Normal University(Natural Science). DOI: 10.12202/j.0476-0301.2024083
Citation: CHEN Haoming, PANG Bo, REN Hancheng, YANG Fang, ZHENG Ziqi, ZHOU Sicong. Study on the spatial distribution of combined risk of precipitation characteristics in Kunming city based on copula functions[J]. Journal of Beijing Normal University(Natural Science). DOI: 10.12202/j.0476-0301.2024083

基于Copula函数的昆明市降水特征组合风险空间分布研究

Study on the spatial distribution of combined risk of precipitation characteristics in Kunming city based on copula functions

  • 摘要: 气候变化导致极端降雨事件发生频率增加,成为城市水安全的重要威胁.基于不同降水特征,评估降水特征的组合风险空间分布,能够为极端降雨灾害的防治和城市防洪减灾提供重要的科学依据.本文以昆明市城区2019—2021年56个雨量站逐5分钟降雨量数据为基础,采用超定量法进行降水事件选样,计算了降雨历时D、峰值雨量P和总降雨量V.通过优选边缘分布函数及Copula函数构建了降水特征的组合风险概率分析模型,定量评估了不同代表情况下的降水特征组合风险概率及其空间分布特征.结果表明:昆明市各站点D大多服从皮尔逊Ⅲ型(PE3)分布,V大多服从对数正态(Lognorm)分布,P多服从广义极值(GEV)分布、广义logistic(GLO)分布和广义帕累托(GPO)分布;所有站点的峰值雨量P和降雨历时D呈负相关或相互独立,均服从Frank Copula和Independent Copula函数,Clayton Copula和Frank Copula函数可以更好地描述大部分站点的(PV)和(DV)变量组合的联合分布;昆明市主城区东部的山区,容易发生降雨历时、峰值雨量或总降雨量较大的降雨,主城区中部则更易发生降雨历时长、峰值雨量高、总降雨量大的降雨.

     

    Abstract: Climate change has led to an increased frequency of extreme rainfall events, posing a significant threat to urban water security. Assessing the spatial distribution of combined risk based on different precipitation characteristics can provide important scientific evidence for the prevention and mitigation of extreme rainfall disasters and urban flood control. This paper uses the 5-minute rainfall data from 56 rain gauges in the urban area of Kunming city from 2019 to 2021 as the basis, adopts the overdetermined method for precipitation event sampling, and calculates the rainfall duration D, peak rainfall P, and total rainfall V. A combined risk probability analysis model of precipitation characteristics is constructed by selecting optimal marginal distribution functions and Copula functions, which quantitatively assesses the combined risk probability of precipitation characteristics under different representative conditions and their spatial distribution characteristics. The results show that the variable D of each station in Kunming City mostly follows the Pearson Type III (PE3) distribution, variable V mostly follows the Log-Normal (Lognorm) distribution, and variable P mostly follows the Generalized Extreme Value (GEV) distribution, Generalized Logistic (GLO) distribution and Generalized Pareto (GPO) distribution; the peak rainfall P and rainfall duration D of all stations are negatively correlated or independent, all following the Frank Copula and Independent Copula functions. The Clayton Copula and Frank Copula functions can better describe the joint distribution of the variable combinations (P, V) and (D, V) for most stations. In the mountainous areas in the eastern part of Kunming’s main urban area, rainfall events with longer duration, higher peak rainfall, or greater total rainfall are likely to occur, while the central part of the main urban area is more prone to rainfall events with long duration, high peak rainfall, and large total rainfall.

     

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