Statistical Modelling of Extreme Rainfall in Taiwan

In this paper, the annual maximum daily rainfall data from 1961 to 2010 are modelled for 18 stations in Taiwan. We fit the rainfall data with stationary and non-stationary generalized extreme value distributions (GEV), and estimate their future behaviour based on the best fitting model. The non-stat...

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Detalles Bibliográficos
Autores: Chu, Lan-Fen, McAleer, Michael, Chang, Ching-Chung
Tipo de recurso: informe técnico
Fecha de publicación:2012
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/49127
Acceso en línea:https://hdl.handle.net/20.500.14352/49127
Access Level:acceso abierto
Palabra clave:Extreme theory
Extreme rainfall
Return level
Typhoon.
Econometría (Economía)
5302 Econometría
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spelling Statistical Modelling of Extreme Rainfall in TaiwanChu, Lan-FenMcAleer, MichaelChang, Ching-ChungExtreme theoryExtreme rainfallReturn levelTyphoon.Econometría (Economía)5302 EconometríaIn this paper, the annual maximum daily rainfall data from 1961 to 2010 are modelled for 18 stations in Taiwan. We fit the rainfall data with stationary and non-stationary generalized extreme value distributions (GEV), and estimate their future behaviour based on the best fitting model. The non-stationary model means that the parameter of location of the GEV distribution is formulated as linear and quadratic functions of time to detect temporal trends in the maximum rainfall. Future behavior refers to the return level and the return period of the extreme rainfall. The 10, 20, 50 and 100-years return levels and their 95% confidence intervals of the return levels stationary models are provided. The return period is calculated based on the record-high (ranked 1st) extreme rainfall brought by the top 10 typhoons for each station in Taiwan. The estimates show that non-stationary model with increasing trend is suitable for the Kaohsiung, Hengchun, Taitung and Dawu stations. The Kaohsing and Hengchun stations have greater trends than the other two stations, showing that the positive trend extreme rainfall in the southern region is greater than in the eastern region of Taiwan. In addition, the Keelung, Anbu, Zhuzihu, Tamsui, Yilan, Taipei, Hsinchu, Taichung, Alishan, Yushan and Tainan stations are fitted well with the Gumbel distribution, while the Sun Moon Lake, Hualien and Chenggong stations are fitted well with the GEV distribution.Universidad Complutense de Madrid20122012-12-0120122012-12-01technical reporthttp://purl.org/coar/resource_type/c_18ghinfo:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/20.500.14352/49127reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial 3.0 Españahttps://creativecommons.org/licenses/by-nc/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/491272026-06-02T12:44:21Z
dc.title.none.fl_str_mv Statistical Modelling of Extreme Rainfall in Taiwan
title Statistical Modelling of Extreme Rainfall in Taiwan
spellingShingle Statistical Modelling of Extreme Rainfall in Taiwan
Chu, Lan-Fen
Extreme theory
Extreme rainfall
Return level
Typhoon.
Econometría (Economía)
5302 Econometría
title_short Statistical Modelling of Extreme Rainfall in Taiwan
title_full Statistical Modelling of Extreme Rainfall in Taiwan
title_fullStr Statistical Modelling of Extreme Rainfall in Taiwan
title_full_unstemmed Statistical Modelling of Extreme Rainfall in Taiwan
title_sort Statistical Modelling of Extreme Rainfall in Taiwan
dc.creator.none.fl_str_mv Chu, Lan-Fen
McAleer, Michael
Chang, Ching-Chung
author Chu, Lan-Fen
author_facet Chu, Lan-Fen
McAleer, Michael
Chang, Ching-Chung
author_role author
author2 McAleer, Michael
Chang, Ching-Chung
author2_role author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Extreme theory
Extreme rainfall
Return level
Typhoon.
Econometría (Economía)
5302 Econometría
topic Extreme theory
Extreme rainfall
Return level
Typhoon.
Econometría (Economía)
5302 Econometría
description In this paper, the annual maximum daily rainfall data from 1961 to 2010 are modelled for 18 stations in Taiwan. We fit the rainfall data with stationary and non-stationary generalized extreme value distributions (GEV), and estimate their future behaviour based on the best fitting model. The non-stationary model means that the parameter of location of the GEV distribution is formulated as linear and quadratic functions of time to detect temporal trends in the maximum rainfall. Future behavior refers to the return level and the return period of the extreme rainfall. The 10, 20, 50 and 100-years return levels and their 95% confidence intervals of the return levels stationary models are provided. The return period is calculated based on the record-high (ranked 1st) extreme rainfall brought by the top 10 typhoons for each station in Taiwan. The estimates show that non-stationary model with increasing trend is suitable for the Kaohsiung, Hengchun, Taitung and Dawu stations. The Kaohsing and Hengchun stations have greater trends than the other two stations, showing that the positive trend extreme rainfall in the southern region is greater than in the eastern region of Taiwan. In addition, the Keelung, Anbu, Zhuzihu, Tamsui, Yilan, Taipei, Hsinchu, Taichung, Alishan, Yushan and Tainan stations are fitted well with the Gumbel distribution, while the Sun Moon Lake, Hualien and Chenggong stations are fitted well with the GEV distribution.
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-12-01
2012
2012-12-01
dc.type.none.fl_str_mv technical report
http://purl.org/coar/resource_type/c_18gh
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/49127
url https://hdl.handle.net/20.500.14352/49127
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial 3.0 España
https://creativecommons.org/licenses/by-nc/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial 3.0 España
https://creativecommons.org/licenses/by-nc/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,223283

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