地理学报 ›› 2012, Vol. 67 ›› Issue (11): 1556-1564.doi: 10.11821/xb201211012

• 生态与环境 • 上一篇    下一篇

山体效应对北半球林线分布的影响分析

赵芳1,2, 张百平1, 庞宇1,2, 姚永慧1, 韩芳3, 张朔1,2, 齐文文1,2   

  1. 1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室, 北京100101;
    2. 中国科学院研究生院, 北京100049;
    3. 滁州学院, 安徽滁州239000
  • 收稿日期:2012-04-20 修回日期:2012-07-17 出版日期:2012-11-20 发布日期:2013-01-07
  • 通讯作者: 张百平(1963-),男,研究员,博士生导师,研究领域为山地生态与GIS应用。E-mail:zhangbp@lreis.ac.cn E-mail:zhangbp@lreis.ac.cn
  • 作者简介:赵芳(1984-),女,河南三门峡人,博士研究生,主要研究方向为山地地理、GIS应用。E-mail:zhaofang@lreis.ac.cn
  • 基金资助:

    国家自然科学基金重点项目(41030528) 和面上项目(40971064)

Mass Elevation Effect and Its Contribution to the Altitude of Timberline in the Northern Hemisphere

ZHAO Fang1,2, ZHANG Baiping1, PANG Yu1,2, YAO Yonghui1, HAN Fang3, ZHANG Shuo1,2, QI Wenwen1,2   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Geographic Information and Tourism College, Chuzhou University, Chuzhou 239000, Anhui, China
  • Received:2012-04-20 Revised:2012-07-17 Online:2012-11-20 Published:2013-01-07
  • Supported by:

    National Natural Science Foundation of China, No.41030528; No.40971064

摘要: 通过搜集整理了北半球516 个林线数据, 结合WorldClim 气象数据计算了林线数据点上的大陆度, 并依据SRTM高程数据提取了林线处的山体基面高度(作为山体效应的代用因子), 然后以纬度、大陆度和山体基面高度为解释变量, 建立三元回归模型。结果表明:线性回归模型的判定系数R2为0.904, 二次回归模型的R2高达0.912。相比先前不考虑基面高度的林线分布模型(R2 = 0.79), 纳入了山体基面高度的林线分布模型能够更加有效的拟合半球尺度的林线分布; 结果还表明, 山体基面高度对北半球林线高度分布的贡献率达到了48.94% (p =0.000), 而纬度和大陆度分别为45.02% (p = 0.000) 和6.04% (p = 0.000)。这揭示了山体效应对半球尺度林线分布具有重要的影响。基面高度在北美洲地区对林线高度的贡献率最大(50.49%, p=0.000), 在欧亚大陆东部地区为48.73% (p = 0.000), 在欧亚大陆西部地区为43.6% (p=0.000)。这一结果说明山体效应对林线分布高度的影响虽有区域差异, 但都有较高的贡献率。

关键词: 北半球, 山体效应, 山体基面高度, 三元回归模型, 林线高度

Abstract: Alpine timberline, as the "ecological transition zone," has long attracted attention of scientists in many fields of study, especially scientists of climatic change in recent years. Many unitary and dibasic fitting models have been developed between timberline and its influencing factors. It has been commonly believed that latitude or temperature is a decisive factor for the altitudinal distribution of timberline, and most of the fitting models involve the relationship between timberline and latitude or temperature. However, these models are usually on regional scale and could not be extended to other regions; on the other hand, hemispherical-scale and continental-scale models usually contain only about 100 timberline data and results in low precision. The present article has collected 516 data points of timberline, and takes latitude, continentality and mass elevation effect as independent variables and timberline elevation as dependent variables to set up a ternary linear regression model. Continentality is calculated using the meteorological data released by WorldClim and mountain base elevation (as alternative factor of the mass elevation effect) is extracted on the basis of SRTM 90-meter resolution elevation data. The results show that the coefficient of determination (R2) of the linear model is as high as 0.904, and that the contribution rate of latitude, continentality and mass elevation effect to timberline elevation is 45.02% (p = 0.000), 6.04% (p = 0.000) and 48.94% (p = 0.000), respectively. This revealed that the influence of mountain mass elevation effect on timberline distribution exceeds that of latitude and continentality put together, and that mass mountain effect is the primary factor in determining the elevation distribution of timberline on continental and hemispherical scale. The contribution rate of the mass elevation effect to the timberlines is, although different in different regions, generally high, e.g., 50.49% (p = 0.000) in North America, 48.73% (p = 0.000) in the eastern Eurasia, and 43.6% (p = 0.000) in the western Eurasia.

Key words: the Northern Hemisphere, the altitudinal distribution of timberline, mass elevation effect, mountain base elevation, linear regression