地理学报 ›› 2013, Vol. 68 ›› Issue (1): 95-107.doi: 10.11821/xb201301011

• 气候变化 • 上一篇    下一篇

基于MODIS数据的青藏高原气温与增温效应估算

姚永慧, 张百平   

  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
  • 收稿日期:2012-07-15 修回日期:2012-08-25 出版日期:2013-01-20 发布日期:2013-03-19
  • 通讯作者: 张百平(1963-),男,研究员,博士生导师,中国地理学会会员(S110001706M)。E-mail:zhangbp@lreis.ac.cn E-mail:zhangbp@lreis.ac.cn
  • 作者简介:姚永慧(1975-),女,湖北安陆人,博士,中国地理学会会员(S110007303M),主要从事GIS、RS应用与山地环境研究。E-mail:yaoyh@lreis.ac.cn
  • 基金资助:

    国家自然科学基金重点项目(41030528); 国家自然科学基金项目(41001278) 资助

MODIS-based estimation of air temperature and heating-up effect of the Tibetan Plateau

YAO Yonghui, ZHANG Baiping   

  1. State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2012-07-15 Revised:2012-08-25 Online:2013-01-20 Published:2013-03-19
  • Supported by:

    National Natural Science Foundation of China, No.41030528; No.41001278

摘要: 利用2001-2007 年MODIS地表温度数据、137 个气象观测台站数据和ASTERGDEM数据, 采用普通线性回归分析方法(OLS)及地理加权回归分析方法(GWR), 研究了高原月均地表温度与气温的相关关系, 最终选择精度较高的GWR分析方法, 建立了高原气温与地表温度、海拔高度的回归模型。各月气温GWR回归模型的决定系数(Adjusted R2) 都达到了0.91 以上(0.91~0.95), 标准误差(RMSE) 介于1.16~1.58℃;约70%以上的台站各月残差介于-1.5~1.5℃之间, 80%以上的台站的残差介于-2~2℃之间。根据该模型, 估算了青藏高原气温, 并在此基础上, 将高原及周边地区7 月份月均气温转换到4500 m和5000 m海拔高度上, 对比分析高原内部相对于外围地区的增温效应。研究结果表明:(1) 利用GWR方法, 结合地面台站的观测数据和MODIS Ts、DEM等, 对高原气温估算的精度高于以往普通回归分析模型估算的精度(RMSE=2~3℃), 精度可以提高到1.58℃;(2) 高原夏半年海拔5000 m左右的高山区气温能达到0℃以上, 尤其是7 月份, 海拔4000~5500 m的高山区的气温仍能达到10℃左右, 为山地森林的发育提供了温度条件, 使高原成为北半球林线分布最高的地方;(3) 高原的增温效应非常突出, 初步估算, 在相同的海拔高度上高原内部气温要比外围地区高6~10℃。

关键词: 青藏高原, 气温估算, MODIS地表温度, GWR方法, 增温效应

Abstract: Time series of MODIS land surface temperature (LST) data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, were used to estimate and map the spatial distribution of monthly mean air temperatures of the Tibetan Plateau and neighboring areas. Time series and regression analyses of monthly mean land surface temperature (Ts) and monthly mean air temperature (Ta) were conducted using both ordinary linear regression (OLS) and geographical weighted regression (GWR) methods. Analysis shows that recorded Ta is rather closely related to Ts, and that the GWR method has a much better result (adjusted R2 > 0.91, root mean square error (RMSE)=1.16-1.58℃) for estimating Ta than OLS. The GWR model, with MODIS Ts and altitude as independent variables, was thus used to estimate Ta for the Tibetan Plateau. For more than 80% of the stations, the Ta retrieved from Ts had residuals lower than 2℃. Analysis of the spatial pattern of retrieved Ta data showed that the mean Ta of the summer half year was higher than 0℃ even at high altitudes of 5000±600 m of the plateau, especially in the warmest month (July) the Ta in high mountain areas with altitudes of 4000-5500 m could reach as high as 10 ℃. This may help explain why the highest timber line in the northern hemisphere is located on the Tibetan Plateau. According to our results, Ta in July was probably 6-10℃ warmer in the inner plateau than in the outer plateau at any given elevation which resulted from the heating up effect of the Plateau.

Key words: Tibetan Plateau, air temperature estimation, MODIS land surface temperature, geographical weighted regression, heating-up effect