中国流域降水数据的空间插值方法评估
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摘要
降水量的空间分布信息在水资源管理、旱涝灾害预测和可持续发展等研究领域具有重要价值。以中国1 915个气象站在1981～2010年间的平均降水量观测数据为基础,选取了反距离权重法(IDW)、径向基函数法(RBF)、全局多项式法(GPI)、局部多项式法(LPI)、普通克里金法(Ordinary Kriging)、简单克里金法(Simple Kriging)、泛克里金法(Universal Kriging)以及经验贝叶斯克里金法(EBK)8种空间内插方法进行评估。研究依据DEM数据的流域分析结果,对我国三大流域的降水量进行区域插值,同时采用交叉验证方法,对中国范围整体插值精度以及分区后三大流域的插值精度分别进行了验证。结果表明:对全国范围内采用经验贝叶斯克里金插值法取得了较好的效果;三大流域中,对黄河流域采用泛克里金法最优,对长江流域采用普通克里金法最优,珠江流域采用径向基函数法最优。最后以流域内的城市为例进行验证,结果表明各流域的最优空间插值方法具备有效性和指导价值。
The spatial distribution information of precipitation is of great value in the fields of water resource management, drought and flood disaster prediction and sustainable development. Based on precipitation data(1981～2010) and DEM data of 1915 meteorological stations in China, we produce a nice spatial distribution of precipitation of China by using eight interpolation methods, including Inverse Distance Weight method, Radial Basis Function method, Global Polynomial Interpolation method, Local Polynomial Interpolation method, Ordinary Kriging method, Simple Kriging method, Universal Kriging method and Empirical Bayesian Kriging method. By using cross-validation, we find that the Empirical Bayesian Kriging method is optimal for the whole China, and the optimal methods for Yellow River basin, Yangtze River basin and Pearl River basin are Universal Kriging method, Ordinary Kriging method, Radial Basis Function method, respectively. Through verification for cities, it′s proved that the optimal spatial interpolation methods for basins are valid and instructive.
The spatial distribution information of precipitation is of great value in the fields of water resource management, drought and flood disaster prediction and sustainable development. Based on precipitation data(1981～2010) and DEM data of 1915 meteorological stations in China, we produce a nice spatial distribution of precipitation of China by using eight interpolation methods, including Inverse Distance Weight method, Radial Basis Function method, Global Polynomial Interpolation method, Local Polynomial Interpolation method, Ordinary Kriging method, Simple Kriging method, Universal Kriging method and Empirical Bayesian Kriging method. By using cross-validation, we find that the Empirical Bayesian Kriging method is optimal for the whole China, and the optimal methods for Yellow River basin, Yangtze River basin and Pearl River basin are Universal Kriging method, Ordinary Kriging method, Radial Basis Function method, respectively. Through verification for cities, it′s proved that the optimal spatial interpolation methods for basins are valid and instructive.
引文
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[20] 杨奇勇,杨劲松,姚荣江,等.基于GIS和改进灰色关联模型的土壤肥力评价[J].农业工程学报,2010,26(4):100-105.
[21] 李俊晓,李朝奎,殷智慧.基于ArcGIS的克里金插值方法及其应用[J].测绘通报,2013(9):87-90.
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[26] 奚圆圆,黄晓荣,李晶.金沙江流域降水变化特征研究[J].人民长江,2017,48(23):50-55.
[27] 刘爱丽,王培法.地统计学概论[M].北京:科学出版社,2012:360-388.
[2] Teegavarapu R S V,Meskele T,Pathak C S.Geo-spatial grid-based transformations of precipitation estimates using spatial interpolation methods[J].Computers & Geosciences,2012,40(3):28-39.
[3] 赵林,武建军,吕爱锋,等.京津风沙源区植被变化对降水的响应规律研究[J].北京师范大学学报(自然科学版),2010,46(5):610-618.
[4] 曾红伟,李丽娟,张永萱,等.大样本降水空间插值研究:以2009年中国年降水为例[J].地理科学进展,2011,30(7):811-818.
[5] 刘元波,傅巧妮,宋平,等.卫星遥感反演降水研究综述[J].地球科学进展,2011,26(11):1162-1172.
[6] 陈超辉,李崇银,谭言科,等.基于交叉验证的多模式超级集合预报方法研究[J].气象学报,2010,68(4):464-476.
[7] 于洋,卫伟,陈利顶,等.黄土高原年均降水量空间插值及其方法比较[J].应用生态学报,2015,26(4):999-1006.
[8] 许娈,董美莹,陈锋.基于逐时降水站点资料空间插值方法对比研究[J].气象与环境学报,2017,33(1):34-43.
[9] 殷嘉霖,屈创.湖南省年均降水量空间插值模拟方法比较研究[J].甘肃科技,2013,29(1):38-40.
[10] Piazza A D,Conti F L,Noto L V,et al.Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily,Italy[J].International Journal of Applied Earth Observation & Geoinformation,2011,13(3):396-408.
[11] Teegavarapu R S V.Estimation of missing precipitation records integrating surface interpolation techniques and spatio-temporal association rules[J].Journal of Hydroinformatics,2009,11(2):13246.
[12] Teegavarapu R S V.Use of universal function approximation in variance-dependent surface interpolation method:An application in hydrology[J].Journal of Hydrology,2007,332(1):16-29.
[13] Eischeid J K,Pasteris P A,Diaz H F,et al.Creating a serially complete,national daily time series of temperature and precipitation for the Western United States[J].Journal of Applied Meteorology,2010,39(9):1580-1591.
[14] 何红艳,郭志华,肖文发.降水空间插值技术的研究进展[J].生态学杂志,2005,24(10):1187-1191.
[15] 朱子明,祁新华.基于Moran'I的闽南三角洲空间发展研究[J].经济地理,2009,29(12):1977-1980.
[16] 张余庆,陈昌春.江西多年平均降水量空间插值模型的选取与比较[J].水土保持研究,2013,20(4):8-13.
[17] 于晓艳,马劲松.基于地统计学的江西省年降水量插值研究[J].测绘科学,2011,36(4):83-85.
[18] Wang S,Huang G H,Lin Q G,et al.Comparison of interpolation methods for estimating spatial distribution of precipitation in Ontario,Canada[J].International Journal of Climatology,2015,34(14):3745-3751.
[19] Chen Y,Shan X,Jin X,et al.A comparative study of spatial interpolation methods for determining fishery resources density in the Yellow Sea[J].Acta Oceanologica Sinica,2016,35(12):65-72.
[20] 杨奇勇,杨劲松,姚荣江,等.基于GIS和改进灰色关联模型的土壤肥力评价[J].农业工程学报,2010,26(4):100-105.
[21] 李俊晓,李朝奎,殷智慧.基于ArcGIS的克里金插值方法及其应用[J].测绘通报,2013(9):87-90.
[22] Goovaerts P.Geostatistics for Natural Resource Evaluation[J].Environmental Science,1997,42(4):437-438.
[23] Krivoruchko K.Empirical bayesian kriging[M].Esri:Redlands,CA,USA,2012.
[24] Olea R A.Optimal contour mapping using universal kriging[J].Journal of Geophysical Research,1974,79(5):695-702.
[25] Guo R,Li F,He W,et al.Spatial and Temporal Variability of Annual Precipitation during 1958-2007 in Loess Plateau,China[C]//Computer and Computing Technologies in Agriculture IV.Springer Berlin Heidelberg,2010.
[26] 奚圆圆,黄晓荣,李晶.金沙江流域降水变化特征研究[J].人民长江,2017,48(23):50-55.
[27] 刘爱丽,王培法.地统计学概论[M].北京:科学出版社,2012:360-388.