基于ANUSPLIN的降水空间插值方法研究
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摘要
【目的】对黄淮海平原点状降水数据进行空间插值,筛选最优模型,分析插值精度,为该区域水分供给状况分析、农业干旱研究等提供科学依据与技术支撑。【方法】采用基于样条函数插值理论的专业气象插值软件ANUSPLIN,根据黄淮海平原内457个气象站点1981—2010年连续30年的降水数据,分别以分辨率为90 m、1 km的高程数据作为第三变量,对降水数据进行空间插值,根据误差统计选出最优插值模型,分析不同分辨率的数字高程模型与插值精度的关系;为比较ANUSPLIN插值结果,随机选取29个点的降水数据作为验证集,同时将其与克里金插值方法进行比较。【结果】(1)对所选气象站点数据进行交叉验证发现,相比较克里金插值,ANUSPLIN得到的结果精度更高。冬季降水量较少时的插值精度比降水集中的6—8月份的插值精度高,利用ANUSPLIN对冬季的降水数据插值的均方根误差为0.38 mm,夏季为4.19 mm,克里金方法对冬季降水数据插值后对应的RMSE为0.45 mm、夏季为4.31 mm;(2)DEM分辨率越高,对应的插值精度会有所提升,对夏季降水插值较明显,利用90 m分辨率的DEM对夏季降水插值,RMSE为4.19 mm,1 km分辨率的DEM插值后对应的RMSE为4.24 mm。【结论】通过ANUSPLIN对黄淮海平原的降水插值方法研究,探讨插值精度与DEM分辨率的关系,发现提高协变量数据DEM的分辨率可以获得更高精度的降水栅格数据,相比较克里金方法,AUNSPLIN获得的结果更加细致地描绘出地形因素对降雨空间分布的影响,为黄淮海平原干旱分析、指导当地农作物灌溉生产提供重要的决策支持信息。
[Purpose]This study provides an accurate and optimal model of rainfall spatial interpolation for crop water management and agricultural drought monitoring in Huanghuaihai Plain.[Method] In this study,we first apply ANUSPLIN,spline interpolation method,and use 30 years rainfall data of 458 meteorological stations in Huanghuaihai Plain from 1981 to 2010. In addition,we use DEMs(digital elevation model)with resolution of 90 m and 1 km respectively as the third variable for interpolating rainfall data. Then we apply error statistics to select the optimal interpolation model,and analyze the relationship between different DEMs to assess the interpolation accuracy. Finally,we randomly select 30 validation points for comparing the from rainfall data interpolation between Kriging and ANUSPLIN. [Result](1)The ANUSPLIN results are in higher accuracy and smoothness in rainfall results than the ordinary Kriging results. The interpolation accuracy is higher in winter than that in summer,when the rainfall in winter is less than that in summer. The RMSE(root mean squared error)of ANUSPLIN interpolation can be reduced by 0.38 mm in winter and 4.19 mm in summer.Furthermore,the ordinary Kriging interpolation is 0.45 mm in winter and 4.31 mm in summer.(2)The finer DEM resolution(90 m)performed better accuracy in rainfall interpolation with a RMSE of 4.19 mm than the coarser DEM resolution(1 km)with a RMSE of 4.24 mm in summer. [Conclusion] The ANUSPLIN rainfall interpolation method in Huanghuaihai Plain performs better than other ordinary Kriging interpolation method and provides better results with the support of finer resolution DEM.Compared to Kriging method,the results obtained by AUNSPLIN give a more detailed description of the topographic factors on the spatial distribution of precipitation. This study helps to provide important support for drought analysis and guiding local crop irrigation production in Huanghuaihai Plain.
[Purpose]This study provides an accurate and optimal model of rainfall spatial interpolation for crop water management and agricultural drought monitoring in Huanghuaihai Plain.[Method] In this study,we first apply ANUSPLIN,spline interpolation method,and use 30 years rainfall data of 458 meteorological stations in Huanghuaihai Plain from 1981 to 2010. In addition,we use DEMs(digital elevation model)with resolution of 90 m and 1 km respectively as the third variable for interpolating rainfall data. Then we apply error statistics to select the optimal interpolation model,and analyze the relationship between different DEMs to assess the interpolation accuracy. Finally,we randomly select 30 validation points for comparing the from rainfall data interpolation between Kriging and ANUSPLIN. [Result](1)The ANUSPLIN results are in higher accuracy and smoothness in rainfall results than the ordinary Kriging results. The interpolation accuracy is higher in winter than that in summer,when the rainfall in winter is less than that in summer. The RMSE(root mean squared error)of ANUSPLIN interpolation can be reduced by 0.38 mm in winter and 4.19 mm in summer.Furthermore,the ordinary Kriging interpolation is 0.45 mm in winter and 4.31 mm in summer.(2)The finer DEM resolution(90 m)performed better accuracy in rainfall interpolation with a RMSE of 4.19 mm than the coarser DEM resolution(1 km)with a RMSE of 4.24 mm in summer. [Conclusion] The ANUSPLIN rainfall interpolation method in Huanghuaihai Plain performs better than other ordinary Kriging interpolation method and provides better results with the support of finer resolution DEM.Compared to Kriging method,the results obtained by AUNSPLIN give a more detailed description of the topographic factors on the spatial distribution of precipitation. This study helps to provide important support for drought analysis and guiding local crop irrigation production in Huanghuaihai Plain.
引文
[1]何红艳,郭志华,肖文发.降水空间插值技术的研究进展.生态学杂志,2005,24(10):1187~1191.
[2]周汝良,丁琨,石雷.稀疏观测数据的空间内插方法的分析与比较.云南地理环境研究,2008,20(4):1~4.
[3]孙然好,刘清丽,陈利顶.基于地统计学方法的降水空间插值研究.水文,2010,30(1):14~17,58.
[4]秦伟良,刘悦.空间插值法在降水分布中的应用.南京信息工程大学学报,2010,2(2):162~165.
[5]朱会义,贾绍凤.降雨信息空间插值的不确定性分析.地理科学进展,2004,23(2):34~42.
[6]Hutchinson M.F.Interpolating mean rainfall using thin plate smoothing splines.International Journal of Geographical Information Science,1995,9(4):385~403.
[7]Hutchinson,M.F.Interpolation of rainfall data with thin plate smoothing splines-part i:two dimensional smoothing of data with short range correlation.Journal of Geographic Information and Decision Analysis.1998,2:153~167.
[8]Atkinson P.M.,Lloyd C.D.Mapping precipitation in Switzerland with ordinary and indicator kriging.Special issue:Spatial Interpolation Comparison 97.Journal of Geographic Information and Decision Analysis,1998,2(1/2):72~86.
[9]吴娴,黄伟,陈发虎.1951-2012年中国大陆0.025°×0.025°高分辨率月气温和降水量格点数据集的建立及其初步应用.兰州大学学报,2014,50(2):213~220.
[10]张仁平,张云玲,郭靖,等.新疆地区降水分布的空间插值方法比较.草业科学,2018,35(3):521~529.
[11]钱永兰,吕厚荃,张艳红.基于ANUSPLIN软件的逐日气象要素插值方法应用与评估.气象与环境学报,2010,26(2):7~15.
[12]刘正佳,于兴修,王丝丝,等.薄盘光滑样条插值中三种协变量方法的降水量插值精度比较.地理科学进展,2012,31(1):56~62.
[13]谭剑波,李爱农,雷光斌.青藏高原东南缘气象要素Anusplin和Cokriging空间插值对比分析.高原气象,2016,35(4):875~886.
[14]徐翔,许瑶,孙青青,等.复杂山地环境下气候要素的空间插值方法比较研究.华中师范大学学报,2018,52(1):122~129.
[15]王江,乐章燕,廖荣伟,等.中国区域温度和降水不同空间插值方法精度对比.气象与环境学报,2016,32(6):85~93.
[16]杨建莹,陈志峰,严昌荣,等.近50年黄淮海平原气温变化趋势和突变特征.中国农业气象,2013,34(1):1~7.
[17]Hutchinson,M.F.ANUSPLIN version 4.3 user guide.The Australian National University,Centre for Resource and Environmental Studies,2004,Canberra.http://cres.anu.edu.au/outputs/software.php.
[18]赵煜飞,朱江,许艳.近50年中国降水格点数据集的建立及质量评估.气象科学,2014,34(4):414~420.
[19]刘志红,Mcvicar Tim-R.,Van niel T.-G,等.专用气候数据空间插值软件ANUSPLIN及其应用.气象,2008,398(2):92~100.
[20]刘志红,TimR.McVicar,Tom G.Van Niel,等.基于ANUSPLIN的时间序列气象要素空间插值.西北农林科技大学学报,2008(10):227~234.
[21]Zhang X,Shao J,Luo H.Spatial interpolation of air temperature with ANUSPLIN in Three Gorges Reservoir Area.International Conference on Remote Sensing.IEEE,2011.
[22]沈艳,熊安元,施晓晖,等.中国55年来地面水汽压网格数据集的建立及精度评价.气象学报,2008(2):283~291.
[23]刘志红,R.mcvicar Tim,Niel Tom G.Van,等.基于5变量局部薄盘光滑样条函数的蒸发空间插值.中国水土保持科学,2006,4(6):23~30.
[2]周汝良,丁琨,石雷.稀疏观测数据的空间内插方法的分析与比较.云南地理环境研究,2008,20(4):1~4.
[3]孙然好,刘清丽,陈利顶.基于地统计学方法的降水空间插值研究.水文,2010,30(1):14~17,58.
[4]秦伟良,刘悦.空间插值法在降水分布中的应用.南京信息工程大学学报,2010,2(2):162~165.
[5]朱会义,贾绍凤.降雨信息空间插值的不确定性分析.地理科学进展,2004,23(2):34~42.
[6]Hutchinson M.F.Interpolating mean rainfall using thin plate smoothing splines.International Journal of Geographical Information Science,1995,9(4):385~403.
[7]Hutchinson,M.F.Interpolation of rainfall data with thin plate smoothing splines-part i:two dimensional smoothing of data with short range correlation.Journal of Geographic Information and Decision Analysis.1998,2:153~167.
[8]Atkinson P.M.,Lloyd C.D.Mapping precipitation in Switzerland with ordinary and indicator kriging.Special issue:Spatial Interpolation Comparison 97.Journal of Geographic Information and Decision Analysis,1998,2(1/2):72~86.
[9]吴娴,黄伟,陈发虎.1951-2012年中国大陆0.025°×0.025°高分辨率月气温和降水量格点数据集的建立及其初步应用.兰州大学学报,2014,50(2):213~220.
[10]张仁平,张云玲,郭靖,等.新疆地区降水分布的空间插值方法比较.草业科学,2018,35(3):521~529.
[11]钱永兰,吕厚荃,张艳红.基于ANUSPLIN软件的逐日气象要素插值方法应用与评估.气象与环境学报,2010,26(2):7~15.
[12]刘正佳,于兴修,王丝丝,等.薄盘光滑样条插值中三种协变量方法的降水量插值精度比较.地理科学进展,2012,31(1):56~62.
[13]谭剑波,李爱农,雷光斌.青藏高原东南缘气象要素Anusplin和Cokriging空间插值对比分析.高原气象,2016,35(4):875~886.
[14]徐翔,许瑶,孙青青,等.复杂山地环境下气候要素的空间插值方法比较研究.华中师范大学学报,2018,52(1):122~129.
[15]王江,乐章燕,廖荣伟,等.中国区域温度和降水不同空间插值方法精度对比.气象与环境学报,2016,32(6):85~93.
[16]杨建莹,陈志峰,严昌荣,等.近50年黄淮海平原气温变化趋势和突变特征.中国农业气象,2013,34(1):1~7.
[17]Hutchinson,M.F.ANUSPLIN version 4.3 user guide.The Australian National University,Centre for Resource and Environmental Studies,2004,Canberra.http://cres.anu.edu.au/outputs/software.php.
[18]赵煜飞,朱江,许艳.近50年中国降水格点数据集的建立及质量评估.气象科学,2014,34(4):414~420.
[19]刘志红,Mcvicar Tim-R.,Van niel T.-G,等.专用气候数据空间插值软件ANUSPLIN及其应用.气象,2008,398(2):92~100.
[20]刘志红,TimR.McVicar,Tom G.Van Niel,等.基于ANUSPLIN的时间序列气象要素空间插值.西北农林科技大学学报,2008(10):227~234.
[21]Zhang X,Shao J,Luo H.Spatial interpolation of air temperature with ANUSPLIN in Three Gorges Reservoir Area.International Conference on Remote Sensing.IEEE,2011.
[22]沈艳,熊安元,施晓晖,等.中国55年来地面水汽压网格数据集的建立及精度评价.气象学报,2008(2):283~291.
[23]刘志红,R.mcvicar Tim,Niel Tom G.Van,等.基于5变量局部薄盘光滑样条函数的蒸发空间插值.中国水土保持科学,2006,4(6):23~30.