Statistical Modelling of Extreme Rainfall in Taiwan
AbstractIn 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.
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Bibliographic InfoPaper provided by Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico in its series Documentos de Trabajo del ICAE with number 2012-27.
Length: 20 pages
Date of creation: Dec 2012
Date of revision:
Note: For financial support, the first and third authors are grateful to the Taiwan Climate Change Projection and Information Platform Project (NSC 100-2621-M-492-001), and the second author wishes to acknowledge the Australian research Council, National science Council, Taiwan, and the Japan Society for the promotion of Science.
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Other versions of this item:
- Lan-Fen Chu & Michael McAleer & Ching-Chung Chang, 2013. "Statistical Modelling of Extreme Rainfall in Taiwan," Tinbergen Institute Discussion Papers 13-006/III, Tinbergen Institute.
- Lan-Fen Chu & Michael McAleer & Ching-Chung Chang, 2012. "Statistical Modelling of Extreme Rainfall in Taiwan," KIER Working Papers 835, Kyoto University, Institute of Economic Research.
- Lan-Fen Chu & Michael McAleer & Ching-Chung Chang, 2013. "Statistical Modelling of Extreme Rainfall in Taiwan," Working Papers in Economics, University of Canterbury, Department of Economics and Finance 13/09, University of Canterbury, Department of Economics and Finance.
- Chu, L-F. & McAleer, M.J. & Chang, C-C., 2012. "Statistical Modelling of Extreme Rainfall in Taiwan," Econometric Institute Research Papers EI 2012-35, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters
- Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
- Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-01-26 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Demetris Koutsoyiannis & George Baloutsos, 2000. "Analysis of a Long Record of Annual Maximum Rainfall in Athens, Greece, and Design Rainfall Inferences," Natural Hazards, International Society for the Prevention and Mitigation of Natural Hazards, International Society for the Prevention and Mitigation of Natural Hazards, vol. 22(1), pages 29-48, July.
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