在区域农田生态系统生产力模拟模型研究中,空间插值可以提供每个计算栅格的气象要素资料。然而,在众多的气象要素空间插值方法中,并没有一种适合每一个气象要素的普适的最佳插值方法。本文以全国725站1951~1990年整编资料中的旬平均温度和计算得来的675站的月平均光合有效辐射日总量 (PAR) 为数据源,选用了距离平方反比法 (IDS)、梯度距离平方反比法 (GIDS) 和普通克立格法 (OK) 等3种插值方法,进行了方法选取的探讨。交叉验证结果表明：3种方法中,温度插值的平均绝对误差 (MAE) 的排序为IDS>OK>GIDS,其值分别为2.15 ^oC、1.90 ^oC和1.32 ^oC;在作物生长季节 (4~10月),MAE分别2.0 ^oC、1.9 ^oC和1.2 ^oC,表明GIDS法在温度插值方面更具实用价值;对于PAR,MAE的排序为OK>GIDS>IDS,其值分别为0.83MJ/m²、071MJ/ m²和0.46MJ/ m²,说明复杂的方法并不必然具有更好的效果。对这2个气象要素的空间分布特征分析表明：温度和PAR的经、纬向梯度和高度梯度均具有明显的季节性变化特征;温度的纬向梯度有近似正弦曲线的较强的季节变化,表现为夏季高,而冬、春季低;温度的高度梯度年内变化范围为- 0.0033~- 0.0048 ^oC/m,GIDS法能较细致地反映温度随海拔高度的变化;PAR也具有明显的纬向梯度,其季节变化特征与温度类似。最后,对中国陆地区域以1'×1'进行插值,生成了中国陆地区域的温度和PAR的空间分布栅格图。

Spatial interpolation of climate data is frequently required to provide input for regional plant growth models. As no single method among so many available ones to spatial interpolation of climate variables is optimal for all regions and all variables, it is very important to compare the results obtained using alternative methods applied to each set of data. For estimating 30-year of 10-day mean air temperature and monthly photosynthetic active radiation (PAR) fluxes at specific sites in China, we examined ordinary Kriging (OK) and other two relatively simple methods, one is inverse distance squared (IDS) technique and the other is gradient plus inverse-distance-squared (GIDS) technique. Based on the mean absolute errors from cross-validation tests, the methods were ranked as GIDS>OK>IDS for interpolating 10-day mean temperature, and IDS>GIDS>OK for interpolating monthly mean PAR fluxes. GIDS gives the lowest errors which averaged 1.3 oC for 10-day mean temperature. IDS gives the lowest errors averaged 0.46 MJ/(m²·d) for monthly PAR fluxes. Although OK errors were more consistent for temperatures of different seasons, yet GIDS had lower errors during crop growth seasons. Compared with OK, GIDS was simple to apply for interpolating 10-day mean temperature. Since PAR was affected by so many factors, the performance of GIDS and OK were not as good as we expected. It also implied that the complex methods could not ensure the best results. The multiple linear regressions carried out for GIDS revealed strong gradients in temperature and PAR fluxes varied by month in a fairly consistent way. For all 10-days, r was above 0.94 for temperature, and temperatures decreased with increasing latitude, longitude and elevation. The elevation coefficient for temperature during the year ranged from -0.0033~-0.0048 oC/m. For PAR, r was lower than that for temperature. The coefficients of PAR for latitude, longitude and elevation ranged from positive values to negative values during the year. Using a 1 km Digital Elevation Model (DEM) for China, the first 10-day mean temperature and monthly mean PAR flux of July were estimated for each pixel to assess the performance of the three interpolation techniques. From temperature maps, we can see that GIDS gives more reasonable estimates in valleys and mountainous area than the other two techniques. The temperature map generated by GIDS shows the elevation effects of temperature in West China especially in valleys and Tibet Plateau. The strong temperature trends towards the northeast in East China and PAR trends towards the northwest were also shown in the maps.