2018 Volume 47 Issue 2-3 Pages 51-70
The frequent occurrences of natural disasters in recent years has led to the importanceof areal statistical modeling of natural disasters in the field of environmental statistics. Forexample, understanding the trend of extreme precipitation using areal statistical modelingenables taking preventive measures to avoid damage from flooding. To analyze such data,we need to consider the tail behavior of distributions, and one such approach is extremevalue analysis.
In this paper, we consider multivariate extreme value modeling to flexibly analyze maximaobserved at plural sites. We focus especially on the spatial domain and adopt max-stableprocesses derived from underlying Gaussian random fields to build multivariate extremevalue models and apply them to annual maximum daily precipitation datasets in Japan.For flexible modeling, the models we propose have a generalized extreme value marginaldistribution with covariates. The results of the analysis revealed the relationship of latitudeand altitude to precipitation in the study region. Confidence intervals for 30-year returnlevel calculated using our model became shorter than those using the single site model formost of the sites. The findings also revealed the existence of a temporal trend of annualmaximum daily precipitation, but climate factors such as temperature and El Ni?no do nothave a significant influence. We also computed the current and 2050’s 30-year return levelpredictions for the region, including the sites with no observation. Finally, we comparedthe proposed method with the regional frequency analysis method, which has been usedcommonly in extreme rainfall data analysis in Japan.