摘要
利用ARCGIS软件,以研究区多年平均年降水量为数据源,开展以下研究:(1)采用反距离权重法、普通(泛)克里金法、样条函数法和趋势面法进行直接内插。(2)利用降水量随海拔递增率,再次利用以上方法进行插值。(3)分析站点数量的变化对插值精度的影响。(4)分析插值空间尺度变化对插值精度的影响。针对插值结果,使用交叉验证法来评估插值方法的优劣,旨在找出研究区年降水量空间插值的最优方法。结果表明:(1)研究区多年降水量与纬度呈现出很好的负相关,与其他各地理环境因子相关性很差或无相关性。(2)考虑降水随海拔的递增率后,研究区南坡西段和研究区海拔<600 m和>800 m的大部分观测站的空间内插精度得到有效提高,其它区域以及其它海拔区间的插值精度反而降低,因此使用单一线性数据作为整个研究区降水随海拔的变化率不够科学和严谨。(3)在缺少研究区不同剖面降水观测资料的情况下,权重为0.001的正规则样条函数法是最优插值方法。(4)像元尺度在50~1 000 m间的变化对降水插值的不确定影响甚微;插值精度与选取的插值点个数无明显相关性,当站点个数为20个时,插值精度最高。
Qinling Mountains are a major east-west mountain range in southem Shaanxi Province, China. It ex- tending about 1 500 kilometers across central China from Gansu-Qinghai border in the west to central Henan Province in the east, form a natural dividing line between China＇s sub-tropical and warm-temperate zones. It also provide a natural boundary between North and South China, and support a huge variety of plant and wildlife, some of which is found nowhere else on the Earth. It plays one of the most important climate boundaries between North and South China. The orographic effects on climate and climatic impacts on hydrological regimes are very important to surrounding areas. Therefore, its precipitation pattern plays an important role on regional and even national vegetation productivity, ecosystem carbon cycle and the ability of water conservation. So far, the studies of optimal interpolation method evaluation are mostly focus on other area, lack of research on complex terrain of Qinling Mountains. In this paper, 22 meteorological stations within Qinling Mountains and 19 nearby stations, the annual precipitation of from 2000 to 2015 combined their geographical information and digital terrain model were analyzed with Arcgis spatial analysis package as follows： （ 1 ） Direct spatial interpolations of precipitation using five different methods which including Inverse Distance Weighting （IDW）, Ordinary Kriging, Universal Kriging, Spline, and Trend Surface Method; （2） Applying the listed interpolation methods again based on the fact that the precipitation increases with increasing elevation; （3） Analyzing the influence of the change of the num- ber of stations on the accuracy of interpolation of precipitation data; （4） Analyzing the influence of interpolation spatial scale on interpolation precision and discussed the optimal interpolation method. In order to find out the op- timal method of spatial interpolation of annual precipitation of Qinling Mountains, the mean absolute error （MAE）, mean
出处
《干旱区地理》
CSCD
北大核心
2017年第3期555-563,共9页
Arid Land Geography
基金
国家自然科学基金面上项目(41575136)
关键词
秦岭
年降水量
空间插值
最优方法
Qin Mountains
Annual Precipitation
Spatial Interpolation
