The spatial interpolation of meteorological data have important applications in ecological environment monitoring, such as soil erosion, ecological vulnerability evaluation. However, there are significant differences in the interpolation accuracy of climatic elements under different topographic and geomorphic conditions. Based on data of 810 meteorological stations across the country, five typical interpolation methods, namely ordinary Kriging method, inverse distance weight method, spline function method, natural neighborhood method and trend surface method, were utilized in this paper to analyze and compare the interpolation accuracy of five climate factors, namely temperature, precipitation, accumulated temperature(>10°),wind speed and sunshine hours, under different topographic and geomorphological conditions. The results showed that: (1) Ordinary kriging method of temperature had better applicability in plain, hill and medium-large undulating mountain areas for temperature while the inverse distance weight method and spline function method had higher interpolation accuracy in the platform area and the small undulating mountain area, respectively. (2) The optimal interpolation of the precipitation in plain, platform and medium-large undulating mountain areas was ordinary kriging method while the inverse distance weight method and spline function method had better applicability in small undulating mountain area. (3) For accumulated temperature (>10 °C), the spline function method had higher interpolation accuracy in plain and platform areas, while ordinary kriging method had better applicability in hilly, small and medium-large undulating mountain area al (4) The optimal spatial interpolation of wind speed in plain and hilly areas was the inverse distance weight method. The natural neighborhood method and the spline function method had the best applicability in plateau, medium-large undulating mountainous areas and small undulating mountain areas, respectively. (5) For sunshine hours, the optimal spatial interpolations in plain and hilly areas were natural neighborhood method and spline function method, respectively, while the ordinary kriging method and the inverse distance weighting method had better applicability in platform, large undulating mountainous areas and small undulating mountainous areas, respectively .

气象数据的空间插值在生态环境监测中具有重要的应用价值，如水土流失，生态脆弱性评价等。但是，在不同地形和地貌条件下，气候元素的插值精度存在显着差异。本文基于全国810个气象站的数据，采用普通克里格法，反距离权重法，样条函数法，自然邻域法和趋势面法五种典型插值方法，对插值精度进行了分析和比较。不同地形和地貌条件下的五个气候因素，即温度，降水，累积温度（> 10°），风速和日照时数。结果表明：（1）普通温度克里格法在平原，丘陵和中大型起伏山区对温度的适用性较好，而反距离权重法和样条函数法分别在平台区域和小起伏山区具有较高的插补精度。 。（2）平原，平台和中大型起伏山区降水的最佳插值方法是普通克里格法，反距离权重法和样条函数法在小起伏山区的适用性更好。（3）对于积温（> 10°C），样条函数法在平原和平台区域具有较高的插值精度，而普通克里金法在丘陵地区具有更好的适用性；中小起伏的山区al（4）平原和丘陵地区风速的最佳空间插值是反距离权重法。自然邻域法和样条函数法分别在高原地区，中大型起伏山区和小型起伏山区具有最佳的适用性。（5）在日照时间，平原和丘陵地区的最佳空间插值分别是自然邻域法和样条函数法，而普通克里金法和反距离权重法在平台，起伏较大的山区和较小的地区具有较好的适用性。起伏的山区。自然邻域法和样条函数法分别在高原地区，中大型起伏山区和小型起伏山区具有最佳的适用性。（5）在日照时间，平原和丘陵地区的最佳空间插值分别是自然邻域法和样条函数法，而普通克里金法和反距离权重法在平台，起伏较大的山区和较小的地区具有较好的适用性。起伏的山区。自然邻域法和样条函数法分别在高原地区，中大型起伏山区和小型起伏山区具有最佳的适用性。（5）在日照时间，平原和丘陵地区的最佳空间插值分别是自然邻域法和样条函数法，而普通克里金法和反距离权重法在平台，起伏较大的山区和较小的地区具有较好的适用性。起伏的山区。