当前位置: X-MOL 学术Environ. Ecol. Stat. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Conditional modelling approach to multivariate extreme value distributions: application to extreme rainfall events in South Africa
Environmental and Ecological Statistics ( IF 3.8 ) Pub Date : 2021-04-27 , DOI: 10.1007/s10651-021-00498-0
Legesse Kassa Debusho , Tadele Akeba Diriba

Multivariate extreme value models are used to investigate the combined behaviour of several weather variables. To investigate joint dependence of extreme rainfall events, a multivariate conditional modelling approach was considered to analyse the behaviour of joint extremes of rainfall events at selected weather stations in South Africa. The results showed that the multivariate conditional modelling fitted to daily maximum rainfall events provided apparent benefits in terms of improved precision in the estimation of the marginal parameters of generalised Pareto distribution. The conditional modelling approach provided all forms of dependence using Laplace marginal transformations, for which all weather stations are not extreme equally. Bootstrap sampling was also employed to account for models uncertainty in computing the prediction standard errors, and compared with the prediction obtained from the conditional model fitted to extreme data. The results obtained from predictions reflected both the marginal and the dependence features, and the extremal dependence structure described consistently for extreme daily maximum rainfall events between weather stations. The current study contributes towards understanding the salient features on the extremal dependence of rainfall extremes which are associated with e.g., flash floods and landslides. This knowledge has practical applications in disaster risk preparedness by responsible authorities.



中文翻译:

多元极值分布的条件建模方法:在南非的极端降雨事件中的应用

多变量极值模型用于研究几个天气变量的组合行为。为了研究极端降雨事件的联合依赖性,考虑采用多元条件建模方法来分析南非选定气象站降雨事件的联合极端事件的行为。结果表明,针对每日最大降雨事件进行拟合的多元条件模型为提高广义帕累托分布边际参数的估计精度提供了明显的好处。条件建模方法使用拉普拉斯边际变换提供了所有形式的依赖关系,对于这些依赖关系,并非所有气象站都是极端极端的。在计算预测标准误差时,还采用Bootstrap采样来考虑模型的不确定性,并将其与从拟合至极端数据的条件模型获得的预测进行比较。从预测中获得的结果既反映了边际特征和依存关系特征,也描述了极端依赖结构,一致描述了气象站之间每日极端的最大降雨事件。当前的研究有助于理解与极端降雨极端相关的显着特征,极端极端降雨与例如洪水和山体滑坡有关。这些知识在主管当局的灾难风险准备中具有实际应用。从预测中获得的结果既反映了边际特征和依存关系特征,也描述了极端依赖结构,一致描述了气象站之间每日极端的最大降雨事件。当前的研究有助于理解与极端降雨极端相关的显着特征,极端极端降雨与例如洪水和山体滑坡有关。这些知识在主管当局的灾难风险准备中具有实际应用。从预测中获得的结果既反映了边际特征和依存关系特征,也描述了极端依赖结构,一致描述了气象站之间每日极端的最大降雨事件。当前的研究有助于理解与极端降雨极端相关的显着特征,极端极端降雨与例如洪水和山体滑坡有关。这些知识在主管当局的灾难风险准备中具有实际应用。山洪暴发和山体滑坡。这些知识在主管当局的灾难风险准备中具有实际应用。山洪暴发和山体滑坡。这些知识在主管当局的灾难风险准备中具有实际应用。

更新日期:2021-04-28
down
wechat
bug