Modeling Spatial Pattern of Precipitation with GIS and Multivariate Geostatistical Methods in Chongqing Tobacco Planting Region, China
Precipitation is important factor affecting vegetation and controlling key ecological processes. In order to quantify spatial patterns of precipitation in Chongqing tobacco planting region, China, under ArcGIS platform, three mul-tivariate geostatistical methods including cokriging, small grid and regression kriging, coupled with auxiliary topographic factors extracted from a 1:100000 DEM were applied to predict spatial distribution of precipitation for January (the least month), June (the richest month) and the whole year. The results showed that cokriging was the best for prediction precipitation of January, which could explain 58％ of the total variation. Small grid simulation with IDW interpolation exhibited higher accuracy for both June precipitation and annual precipitation, which explained 72％ and 61％ of the total variation respectively. Generally, multivariate geostatistics accounted for most of the spatial variability in mean precipitation and especially could exhibit great improvement for esti-mating precipitation in areas where topography has a major influence on the precipitation.
Modeling spatial prediction GIS Cokriging Small grid simulation Regression Kriging
Xuan Wang Jiake Lv Chaofu Wei Deti Xie
College of Computer and Information Science, Southwest University,Chongqing, 400715, P.R. China Chon Chongqing Key Laboratory of Digital Agriculture, Chongqing, 400716, P.R. China
国际会议
南昌
英文
512-524
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）