降水量空间插值方法在小样本区域的比较研究

针对降水量观测点数量少的小样本区域，寻求适合本区域的降水量空间插值方法，采用已知观测点降水量数据对待估点降水量进行预测，得到可信度较高的降水量预测结果，对指导当地生产生活具有十分重要的意义。在分析美国Upper Sangamon流域9个站点的72个月月降水量数据的基础上，比较了基于ArcGIS的普通克里金法、反距离权重法、样条函数法、趋势面法4种空间插值方法的插值结果与观测数据，分析了降水量空间插值方法对内插点和外推点的插值精度及应用不同插值方法时观测点数量对插值精度的影响。结果表明:反距离权重法较优于其他3种空间插值方法。空间插值精度随观测点数量变化而变化，对于按照距待估点距离由远及近减少观测点的选点方法，反距离权重法插值精度较高，受观测点数量影响较小，样条函数法与趋势面法受观测点数量影响较大，普通克里金法处于两者之间。

Comparison Research on Rainfall Interpolation Methods for Small Sample Areas

For small sample areas with few precipitation observation stations, it is necessary to seek spatial interpolation method to forecast the precipitation of certain point by using the existing precipitation from the observation stations. The reliable precipitation forecast data are significant for the local production and life. Based on analyzing the average precipitation data of nine stations in seventy-two months in Upper Sangamon watershed in Illinois, USA, the interpolation results of precipitation and the observed data were compared on the basis of ArcGIS, where four methods were used, Ordinary Kriging method (Kriging), Inverse Distance Weighting method (IDW), Spline function method (Spline) and Trend surface method (Trend). The precision of the four methods was analyzed on inner insert and the outer insert, and the influence of the number of the observation station on the precision of interpolation method was analyzed. The results show that IDW is better than the other three spatial interpolation methods. The precision of spatial interpolation methods varies with different numbers of observation station. The precision of IDW interpolation is higher when the number of the observation station decreases according to the distance from the observation to the certain point. And IDW is little affected by the number of observation station. Spline and Trend are greatly affected by the number of observation station, and performance of Kriging is between Spline and Trend.