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Multivariate drought frequency estimation using copula method in Southwest China

机译:基于copula方法的西南地区多元干旱频率估算。

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Drought over Southwest China occurs frequently and has an obvious seasonal characteristic. Proper management of regional droughts requires knowledge of the expected frequency or probability of specific climate information. This study utilized k-means classification and copulas to demonstrate the regional drought occurrence probability and return period based on trivariate drought properties, i.e., drought duration, severity, and peak. A drought event in this study was defined when 3-month Standardized Precipitation Evapotranspiration Index (SPEI) was less than -0.99 according to the regional climate characteristic. Then, the next step was to classify the region into six clusters by k-means method based on annual and seasonal precipitation and temperature and to establish marginal probabilistic distributions for each drought property in each sub-region. Several copula types were selected to test the best fit distribution, and Student t copula was recognized as the best one to integrate drought duration, severity, and peak. The results indicated that a proper classification was important for a regional drought frequency analysis, and copulas were useful tools in exploring the associations of the correlated drought variables and analyzing drought frequency. Student t copula was a robust and proper function for drought joint probability and return period analysis, which is important for analyzing and predicting the regional drought risks.
机译:中国西南地区的干旱频发,具有明显的季节性特征。正确管理区域干旱需要了解特定气候信息的预期频率或概率。这项研究利用k均值分类和copulas基于三元干旱特征(即干旱持续时间,严重程度和峰值)来证明区域干旱发生概率和恢复期。根据区域气候特征,当3个月标准降水蒸发蒸腾指数(SPEI)小于-0.99时,便定义为干旱事件。然后,下一步是根据年降水量和季节降水量和温度,通过k均值方法将区域划分为六个聚类,并为每个子区域的每种干旱性质建立边际概率分布。选择了几种copula类型以测试最佳拟合分布,而Student t copula被公认为是综合干旱持续时间,严重程度和峰值的最佳类型。结果表明,适当的分类对于区域干旱频率分析很重要,而copulas是探索相关干旱变量的关联和分析干旱频率的有用工具。学生t copula是干旱联合概率和回归期分析的强大而适当的功能,对于分析和预测区域干旱风险具有重要意义。

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  • 来源
    《Theoretical and applied climatology》 |2017年第4期|977-991|共15页
  • 作者单位

    Beijing Municipal Weather Forecast Ctr, Beijing 100089, Peoples R China|Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China|Yangtze Univ, Sch Geosci, Wuhan 430100, Peoples R China;

    Univ Chinese Acad Sci, Key Lab Computat Geodynam, Beijing 100049, Peoples R China;

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