宜蘭地面降雨之統計降尺度推估：以IPCC-SRA2情境為例

Abstract

The purpose of this study is to develop a statistical downscaling model to downscale the monthly and weekly rainfall in Yi-Lan region, which located at northeastern Taiwan. This study included two parts, single point downscaling and regional downscaling, monthly rainfall observation from only Yi-Lan weather station was used for the former, and weekly rainfall observation from 24 rainfall stations located at Yi-Lan region were used for the latter. The space-time variations of climate variables and monsoon features among the season lead to distinct local rainfall patterns in study area. Hence, this study combined EOF method and K-means clustering to classify a year into four seasons, which were mei-yu season (April to June), typhoon season (July to August), transition season (September to November), and northeastern monsoon season (December to March). Downscaling model was then established base on this classification. To establish the nonlinear relationship between large scale climate variables and the local regional rainfall, Support Vector Machine (SVM) and Empirical Orthogonal Function (EOF) method were mainly used. The EOF method was used not only to reduce the data dimension of the space-time climate variables and local region rainfall observation, but to identify the most important spatial patterns of each climate variables and local rainfall during the study period. This approach is expected to add some real physical meaning in statistical downscaling method. In results, the model performed well in only mei-yu season, but not quite well in typhoon and transition seasons. Also, in the GCM future scenario, it shows that the rainfall significant increase in mei-yu and transition season, decrease in typhoon and northeastern monsoon season, but not significant. Though the model performance in transition season is not quite well, the model could still indicate that trend of rainfall in this season will apparently increase.