—This paper has two primary purposes. First, we fit the annual maximum daily rainfall data for 6 rainfall stations, both with stationary and non-stationary generalized extreme value (GEV) distributions for the periods 1911-2010 and 1960-2010 in Taiwan, and detect changes between the two phases for extreme rainfall. The non-stationary model means that the location parameter in the GEV distribution is a linear function of time to detect temporal trends in maximum rainfall. Second, we compute the future behavior of stationary models for the return levels of 10, 20, 50 and 100-years based on the period 1960-2010. In addition, the 95% confidence intervals of the return levels are provided. This is the first investigation to use generalized extreme value distributions to model extreme rainfall in Taiwan.
—Extreme rainfall, generalized extreme value, return level, statistical modeling.
Lan-Fen Chu is with the National Science and Technology Center for Disaster Reduction (NCDR) (e-mail: firstname.lastname@example.org).
Michael McAleer is with Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, The Netherlands.
Szu-Hua Wang is with the Department of Urban Affairs and Environmental Planning at Chinese Culture University, Taiwan.
Cite:Lan-Fen Chu, Michael McAleer, and Szu-Hua Wang, "Statistical Modelling of Recent Changes in Extreme Rainfall in Taiwan," International Journal of Environmental Science and Development vol. 4, no. 1, pp. 52-55, 2013.