关键词: 气候变化风险, 聚类分析, 自组织特征映射网络, 青藏高原

Abstract: To indicate the comprehensive features of climatic change risk sources in Qinghai-Tibet Plateau, the Self-Organizing Feature Map, an artificial neural network model, was established. This research obtained the comprehensive analysis results of 91 sites of the Qinghai-Tibet Plateau area from 1971 to 2008, using the relative intensity of annual rainfall, surface dryness index, annual snow depth, annual snow days, and annual average wind speed as clustering variables and Self-Organizing Feature Map with number of categories of 4. The conclusion can be described as four types, namely snowing leading, elements equilibrium, rainstorm leading, and dryness and average wind speed leading. Further analysis indicated that the space distribution characteristics of these types corresponded with the high value areas of single indicator analyses indeed; however, comprehensive analysis contained rich and full-scale information to a higher degree and was also closer to the real conditions.

Key words: risk of climate change, clustering analysis, self-organizing feature map network, Qinghai-Tibet Plateau