地理科学进展 ›› 2011, Vol. 30 ›› Issue (11): 1387-1394.doi: 10.11820/dlkxjz.2011.11.008

• 气候与环境变化 • 上一篇    下一篇

基于温度作为辅助变量的中国降水δ18O空间分布特征

李亚举1, 张明军1,2, 王圣杰1, 李忠勤1,2, 李小飞1   

  1. 1. 西北师范大学地理与环境科学学院,兰州 730070;
    2. 中国科学院寒区旱区环境与工程研究所冰冻圈科学国家重点实验室/天山冰川观测试验站,兰州 730000
  • 收稿日期:2011-03-01 修回日期:2011-06-01 出版日期:2011-11-25 发布日期:2011-11-25
  • 通讯作者: 张明军,E-mail: mjzhang2004@163.com E-mail:mjzhang2004@163.com
  • 作者简介:李亚举(1988-),男,河南平顶山人,硕士研究生,主要研究方向为同位素地球化学。E-mail: liyaju100@126.com
  • 基金资助:

    国家自然科学基金项目(41161012,40701035,40631001,40571033,40701034);教育部新世纪优秀人才支持计划项目(NCET-10-0019);陇原青年创新人才扶持计划项目;国家重点基础研究发展规划(973)项目(2010CB951003,2007CB411501);中国科学院知识创新工程重要方向项目(KZCX2-YW-127);冰冻圈科学国家重点实验室自主研究项目资助;西北师范大学知识与科技创新工程创新团队项目(NWNU-KJCXGC-03-66);西北师范大学科研骨干培育项目(NWNU-KJCXGC-03-78)。

Spatial Distribution of δ18O in China's Precipitation Based on a Secondary Variable of Temperature

LI Yaju1, ZHANG Mingjun1,2, WANG Shengjie1, LI Zhongqin1,2, LI Xiaofei1   

  1. 1. College of Geography and Environment Sciences, Northwest Normal University, Lanzhou 730070, China;
    2. State Key Laboratory of Cryospheric Sciences/Tianshan Glaciological Station, Cold and Arid Regions Environmental and Engineering Research Institute, CAS, Lanzhou 730000, China
  • Received:2011-03-01 Revised:2011-06-01 Online:2011-11-25 Published:2011-11-25

摘要: 本研究利用从WorldClim 获得的高分辨率气象数据分析稳定同位素温度效应,研究发现最热季度的平均气温与中国62 个站点同位素年平均值之间表现出很好的相关性(R2=0.79)。在此基础上利用地统计学方法,也就是采用温度作为辅助变量的空间插值算法——局部平均的简单克里金法(Simple Kriging with locally varying mean,SKlm),对中国降水中稳定同位素年平均值空间分布进行了模拟,得到了较高分辨率的中国降水中年均δ18O空间分布图,该图能够较好地表现区域同位素变化模式,能够反映如降水汽团的来源、性质等的气候环境背景,也能在一定程度上反映包括当地的纬度、地形、海拔高度等在内的局部地理因素。

关键词: δ18O, 辅助变量, 克里金插值, 空间分布, 中国

Abstract: To acquire knowledge of temperature effect on stable isotope, the relation between various temperature related variables and isotopic composition of modern precipitation was explored, based on the high-resolution meteorological data and the isotope values from WorldClim and China's stable isotope observation sites. We used a linear model to fit the relation between the different temperature variables and the isotopic composition. Although the annual mean temperature does well to explain the annual mean isotope signal, the better correlation between the mean temperature in the hottest quarter and the annual mean isotope of 63 sites in China is found (R2=0.79). The temperature during the coldest quarter is used as an ancillary variable in simple kriging with varying local means (SKlm). In SKlm, the residual isotope values from the regression with mean temperature in the hottest quarter are kriging interpolated, which are then added to the high-resolution spatial distribution of stable isotope (δ18O) in China's precipitation. So more local isotope effects are accounted for by the spatial interpolation of the residual isotope values. With the good correlation between mean temperature in the hottest quarter and annual mean isotope values, the spatial distribution map can well present the pattern of variability of isotope in China. The low prediction error and a symmetrical distribution of the differences between the true and predicted values demonstrate the successful application of the SKlm approach. In summary, using surface temperature as a factor does improve the prediction of the China's isotope variation in precipitation compared to a combination of latitude and altitude, and also indicates the environmental background of regional climate and local geographic factor.

Key words: China, Kriging interpolation, oxygen isotope, secondary variable, spatial distribution