%0 Journal Article
%T 基于空间插值方法的重庆降水信息展布
%A 张守平
%A 杨清伟
%A 江志航
%A 王国泰
%A 魏 佳
%J 南水北调与水利科技
%D 2018
%X 不同空间插值方法在不同地区的插值精度不同。为确定重庆市降雨量的空间分布, 采用重庆市 12 个气象站 1960- 2014 年降水数据, 运用系数为 2、3、4 的反距离权重法、普通克里金法、考虑高程的协同克里金法及考虑温度的协同克里金法, 按多年平均、最大 3 个月及最小 3 个月不同降水量指标进行分析, 并以均方根误差和纳什效率系数进行验证。结果表明, 对于三种指标, 不同插值方法的优劣从好到差均为考虑高程的协同克里金法、考虑温度的协同克里金法、普通克里金法、系数分别为 4、3、2 的反距离权重法。对于反距离权重法, 系数越大则误差越小。三 种降水量指标以多年平均降水量为输入数据的插值结果更加准确。考虑温度的协同克里金法在降水量较小或降水与温度相关性较强时有良好的插值精度。在重庆地表变化幅度较大的地区, 考虑高程的协同克里金法更能体现高程变化对降水量的影响。  Different spatial interpolation methods have different interpolation accuracy in different regions. In order to determine the spatial distribution of rainfall in Chongqing , we used the precipitation data of 12 meteorological stations in Chongqing from 1960 to 2014, and used the IDW method with a coefficient of 2, 3, and 4, the Ordinary Kriging method, the elevation Co-Kriging method, and the temperature Co-Kriging to analyze the precipitation in terms of different indexes, which were the multi-year average, largest 3-month, and smallest 3-month. The results were validated with RMSE and Nash efficiency coefficients. The results showed that in Chongqing, for the three indexes, the interpolation methods from more accurate to less accurate were the elevation Co-Kriging method, temperature Co-Kriging method, OK, IDW4, IDW3, IDW2. For the IDW method, the larger the co-efficient, the smaller the error. Among the three indexes, using the multi-year average precipitation as the input data could produce a more accurate interpolated result . The temperature CK method had good interpolation accuracy when the precipitation was small or the correlation between precipitation and temperature was strong. In the areas with large surface changes in Chongqing , the Co-Kriging method considering elevation can better reflect the impact of elevation changes on precipitation.  国家重点基础研究发展计划(2015CB452701) ; 国家重点研发计划(2016YFC0401301) ; 开放基金项目(SLK2015B05) ; 开放研究基金项目( IWHR-SKL-201712 ) ; 重庆市教委科学技术研究项目 (KJ1600533) ; 重庆市基础与前沿研究计划重点项目 (cstc2015jcyjBX0041)
%K 降水
%K 空间插值
%K 反距离权重法
%K 普通克里金法
%K 协同克里金法
%K 气温
%K 重庆
%K precipitation
%K spatial interpolation
%K IDW
%K ordinary Kriging
%K Co-Kriging
%K temperature
%K Chongqing
%U http://www.nsbdqk.net/nsbdyslkj/article/abstract/20180303?st=article_issue