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Modeling Extreme Rainfall Events in Taiwan Using a Spatial-Temporal Hierarchical Framework

Tzu-Han Peng Co-Author
Graduate Institute of Statistics, National Central University, Taiwan
 
Cheng-Ching Lin Co-Author
Institute of Statistics and Data Science, National Tsing-Hua University, Taiwan
 
Nan-Jung Hsu Co-Author
Institute of Statistics and Data Science, National Tsing Hua University
 
Chun-Shu Chen First Author
National Central University
 
Chun-Shu Chen Presenting Author
National Central University
 
Monday, Aug 4: 9:35 AM - 9:50 AM
1145 
Contributed Papers 
Music City Center 
The PoT-GEV model, which integrates the generalized extreme value distribution with the peaks-over-threshold method, is a robust tool for extreme value analysis. Originally developed by Olafsdottir et al. (2021) for fitting block maxima data, it offers the capability to simultaneously analyze trends in the frequency and intensity of extreme events. In this study, we advance the PoT-GEV framework by introducing a spatial hierarchical structure combined with temporal effects. Spatial dependencies are captured through a latent spatial Gaussian process applied to the PoT-GEV parameters, while temporal covariates are incorporated to model time-varying effects. To address computational challenges, we replace traditional Markov Chain Monte Carlo methods with the Laplace approximation, significantly improving efficiency. The proposed methodology is validated through extensive simulation studies across diverse scenarios. Furthermore, its practical utility is demonstrated by applying the model to analyze extreme rainfall events in Taiwan.

Keywords

Block maximum series data

Climate data analysis

Generalized extreme value distribution

Laplace approximation

Latent spatial Gaussian process 

Main Sponsor

Section on Statistics and the Environment