黄河流域积温数据栅格化方法优选
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国家“十一五”“863”资助项目(2006AA100220);国家科技支撑计划资助项目(2006BAD29B01)


Methodology for rasterizing accumulated temperature data in the Yellow River Basion
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    摘要:

    在获得黄河流域109个气象站点45a的逐日平均气温、各气象站的经纬度以及海拔高度数据和中国数字高程模型的基础上,采用Cokriging、积温垂直递减和“回归分析计算+残差插值”3种方法对黄河流域≥0℃的积温栅格化进行了探讨,结果表明:相关性为“回归分析计算+残差插值”>积温垂直递减>Cokriging,T检验的双尾显著性概率Sig.:“回归分析计算+残差插值”<积温垂直递减<0.05<Cokriging,Cokriging方法结果差异不显著,“回归分析计算+残差插值” 方法比积温垂直递减差异性更显著,比较分析可知“回归分析计算+残差插值”是最适合的\.

    Abstract:

    Based on the average temperature from 1961 to 2005, longitude and latitude, height above sea level of 109 meteorological stations in the Yellow River Basin (YRB), and China DEM, accumulated temperature data were rasterized using different methods, viz, Cokriging, accumulated temperature vertically descending and “model based computation result plus spatialized residues”. The results show that the magnitudes of the correlation coefficients can be ranked in the following order, “model based computation result plus spatialized residues”> accumulated temperature vertically descending>Cokriging. But also, the magnitudes of the T\|test Sig. can be ranked in the following order, “model based computation result plus spatialized residues”<“model based computation result plus spatialized residues” <0.05<Cokriging. Therefore, “model based computation result plus spatialized residues” has the highest accuracy.

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张燕卿,刘勤*,严昌荣,何文清,刘爽.黄河流域积温数据栅格化方法优选.生态学报,2009,29(10):5580~5585

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