地理学报 ›› 1988, Vol. 55 ›› Issue (2): 159-168.doi: 10.11821/xb198802008

• 论文 • 上一篇    下一篇

北京城市气温与下垫面结构关系的时相变化

张景哲, 刘启明   

  1. 北京大学地理系
  • 出版日期:1988-04-15 发布日期:1988-04-15

TEMPORAL VARIATIONS 1N THE RELATIONSHIP BETWEEN URBAN TEMPERATURE AND THE STRUCTURE OF URBAN SURFACE IN BEIJING

Zhang Jingzhe, Liu Qiming   

  1. Department of Geography, Peking University
  • Online:1988-04-15 Published:1988-04-15

摘要: 城市气温与城市下垫面结构的关系,是城市气候研究的关键性课题之一。本文利用1982年在北京市区30个观测点上所测得的春、夏、秋、冬四季昼夜八个时相的气温记录和1983年5月航测的北京市区下垫面资料,用多元回归和逐步回归的方法,对北京城市气温与下垫面结构的关系作了分析。分析结果表明:城市气温和城市下垫面结构中绿地、建筑物、水域三要素的相关程度,随着季节和昼夜的变化而变化。绿地的降温作用以夏季白天为最明显,建筑群的增温作用以冬季夜间为最明显。因为所有测点周围 500m范围内都没有面积较大的水体,各时相气温与水域的相关程度都很小,这清楚地说明:城市内的小面积水体对其周围的气温并没有明显的调剂作用。

关键词: 城市气候, 北京, 城市气温, 城市下垫面结构, 时相变化, 多元回归

Abstract: Multiple regression and stepwise regression methods have been used to study the relations beween Beijing urban temperature and the structure of urban surface during daytime and nighttime in different seasons. The data used in this study consisted of day and night temperatures observed on 30 observation sites during spring, summer, autumn and winter of 1982, and the coverage of green areas, building areas and water bodies in the 1000m?1000m area around each observation site. It was found out that Beijing urban temperature had significant positive correlation with the coverage of building areas and negative correlation with the coverage of green areas, but the degree of correlation varies from day to night and from season to season. It was shown that as the coverage of green areas increased, the decrease of temperature was most obvious in summer daytime, and as the coverage of building areas increased, the increase of temperature Was most obvious in winter nighttime. It was also shown that the correlation between temperature and water body was very poor mainly due to the lack of large water body near the observation sites, which means that small water body in urban areas has no significant influence on urban temperature.

Key words: Urban climate, Beijing, Urban temperature, Urban surface structure, Temporal variation, Multiple regression