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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Statistical Modelling of Extreme Rainfall Indices using Multivariate Extreme Value Distributions
https://orcid.org/http://orcid.org/0000-0002-0128-3116
https://orcid.org/http://orcid.org/0000-0003-4379-1546
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. Moreover, 1-day to 5-day indices of rainfall events were constructed to investigate the frequencies and intensities of rainfall events for selected weather stations. Then, the conditional multivariate modelling was fitted to investigate dependence between series of extreme rainfall events. The conditional multivariate modelling has provided all forms of dependence, using Laplace marginal transformations, for which all weather stations are not equally extreme. 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 modelling that was fitted to extreme data. The results obtained from predictions reflected both the marginal and the dependence features, as well as the extremal dependence structure described consistently for indices of rainfall events between weather stations. The modelling framework and results of this study contribute 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 communities.
Statistical Modelling of Extreme Rainfall Indices using Multivariate Extreme Value Distributions
https://orcid.org/http://orcid.org/0000-0002-0128-3116
https://orcid.org/http://orcid.org/0000-0003-4379-1546
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. Moreover, 1-day to 5-day indices of rainfall events were constructed to investigate the frequencies and intensities of rainfall events for selected weather stations. Then, the conditional multivariate modelling was fitted to investigate dependence between series of extreme rainfall events. The conditional multivariate modelling has provided all forms of dependence, using Laplace marginal transformations, for which all weather stations are not equally extreme. 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 modelling that was fitted to extreme data. The results obtained from predictions reflected both the marginal and the dependence features, as well as the extremal dependence structure described consistently for indices of rainfall events between weather stations. The modelling framework and results of this study contribute 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 communities.
Statistical Modelling of Extreme Rainfall Indices using Multivariate Extreme Value Distributions
https://orcid.org/http://orcid.org/0000-0002-0128-3116
https://orcid.org/http://orcid.org/0000-0003-4379-1546
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. Moreover, 1-day to 5-day indices of rainfall events were constructed to investigate the frequencies and intensities of rainfall events for selected weather stations. Then, the conditional multivariate modelling was fitted to investigate dependence between series of extreme rainfall events. The conditional multivariate modelling has provided all forms of dependence, using Laplace marginal transformations, for which all weather stations are not equally extreme. 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 modelling that was fitted to extreme data. The results obtained from predictions reflected both the marginal and the dependence features, as well as the extremal dependence structure described consistently for indices of rainfall events between weather stations. The modelling framework and results of this study contribute 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 communities.
Statistical Modelling of Extreme Rainfall Indices using Multivariate Extreme Value Distributions
Environ Model Assess
Diriba, Tadele Akeba (Autor:in) / Debusho, Legesse Kassa (Autor:in)
Environmental Modeling & Assessment ; 26 ; 543-563
01.08.2021
21 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Conditional dependence modelling , Heffernan and Tawn model , Multivariate extremes , Generalised Pareto distribution , Rainfall indices Environment , Math. Appl. in Environmental Science , Mathematical Modeling and Industrial Mathematics , Operations Research/Decision Theory , Applications of Mathematics , Earth and Environmental Science
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