PDF Download

[1]LI Yan,ZHU Jun,HU Ya,et al.Comparison Analysis on Different Spatial Interpolation Methods to Simulate Monthly Precipitation in Sichuan Province[J].Research of Soil and Water Conservation,2017,24(01):151-154,160.

Copy

Comparison Analysis on Different Spatial Interpolation Methods to Simulate Monthly Precipitation in Sichuan Province

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 24 Number of periods: 2017 01 Page number: 151-154,160 Column: Public date: 2017-02-28

Title:: Comparison Analysis on Different Spatial Interpolation Methods to Simulate Monthly Precipitation in Sichuan Province

Author(s):: LI Yan¹, ZHU Jun^1,2, HU Ya^1,2, ZHANG Heng^1,2; 1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;
2. State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China

Keywords:: spatial interpolation; precipitation simulation; cross validation; error analysis

CLC:: P208;P332

DOI:: -

Abstract:: The precipitation information can’t be obtained directly in some areas because the meteorological observation points are few in the western region of China. Using the spatial data interpolation methods to estimate the precipitation in the adjacent areas is one of the important means. For the less observation points in Sichuan Province, we combine with the digital elevation model (DEM) of the spatial resolution of 90 m×90 m, and use the ordinary of inverse distance weighted interpolation (IDW), the inverse distance weighted interpolation of considering each point elevation (IDW), local polynomial interpolation (LPI), the ordinary Kriging interpolation method (OK) and the collaborative Kriging interpolation method (CK) to interpolate the every monthly and annual average precipitation in Sichuan Province. The cross checking method was used to verify the results of interpolation, and the average error (MAE) and the root mean square error (RMS) were used as the criteria for evaluating the five interpolation methods. The results show that the IDW interpolation of considering the points elevation is more precise than the ordinary IDW, can significantly improve the accuracy of interpolation, Kriging average error and root mean square error is smaller than IDW and local polynomial interpolation, the collaborative Kriging has better interpolation results because of considering the influence of digital elevation model on rainfall. Therefore, collaborative Kriging is more suitable for spatial interpolation of rainfall data in mountain area.

References:: [1] 王常森,陶月赞,方必和.淮北平原年降水量空间插值模型的比选[J].水文,2012,32(2):49-53.
[2] 虎雄岗,谈树成,金艳珠,等.基于地形高程的云南省降雨量空间插值方法研究[J].热带地理,2012,32(4):370-377.
[3] 刘光孟,汪云甲,王允.反距离权重插值因子对插值误差影响分析[J].中国科技论文在线,2010,5(11):879-884.
[4] 刘刚,赵荣,刘纪平,等.澜沧江流域降水量空间分布的克里格插值分析[J].测绘科学,2007,32(3):104-105.
[5] 王智,吴友均,梁凤超,等.新疆地区年降水量的空间插值方法研究[J].中国农业气象,2011,32(3):331-337.
[6] 姜晓剑,刘小军,黄芬,等.逐日气象要素空间插值方法的比较[J].应用生态学报,2010,21(3):624-630.
[7] 房林东,廖卫红,王明元,等.考虑高程的雨量反距离权重插值法研究[J].人民黄河,2015,37(9):38-41.
[8] 庄立伟,王石立.东北地区逐日气象要素的空间插值方法应用研究[J].应用气象学报,2003,14(5):605-615.
[9] 孔令娜,向南平.基于ArcGIS的降水量空间插值方法研究[J].测绘与空间地理信息,2012,35(3):123-126.
[10] 宋丽琼,田原,邬伦,等.日降水量的空间插值方法与应用对比分析:以深圳市为例[J].地球信息科学,2008,10(5):566-572.
[11] 汤国安,杨昕.ArcGIS地理信息系统空间分析试验教程[M].北京:科学出版社,2006.
[12] 邵晓梅,严昌荣,魏红兵.基于Kriging插值的黄河流域降水时空分布格局[J].中国农业气象,2006,27(2):65-69.
[13] 曾怀恩,黄声享.基于Kriging方法的空间数据插值研究[J].测绘工程,2007,16(5):5-8.
[14] 邓羽,刘盛和,姚峰峰,等.基于协同克里格的基准地价评估及空间结构分析[J].地理科学进展,2010,28(3):403-408.
[15] 徐天献,王玉宽,傅斌.四川省降水空间分布的插值分析[J].人民长江,2010,41(10):9-12.
[16] 焦永清,李斌,张坤.基于空间可视化的气温插值方法比较[J].测绘工程,2013,22(5):62-64.

Similar References:

Memo

Last Update: 1900-01-01