摘要
在山地和高原区域,地形对降水影响比较显著。常规空间插值方法通常不考虑地形要素,插值精度有限。考虑到降水量与高程存在较强的相关关系,采用局部线性加权回归模型预测山地和高原区域的降水分布。推导了回归计算公式,并在ArcGIS 9.0中编程实现算法。选取美国德克萨斯州西北部地区进行局部线性加权回归空间插值,并与普通Kriging、倒距离加权法比较。误差分析表明:在地形复杂的地区,线性加权回归模型比传统方法有优势。
Precipitation is evidently influenced by the terrain in the altiplano and mountain areas, in which the common methods, such as Inverse Distance Weighted （IDW） , Kriging Statistics and Polynomial Approximation, can＇t effectively estimate the actual spatial distribution of precipitation. Elevation is a significant factor in precipitation and, on a given mountain slope, precipitation typically increases with elevation. Accordingly, a local weighted linear regression model （WLR） is introduced attempting to accurately interpolate precipitation in the altiplano and mountain areas. The linear regression of precipitation versus elevation for spatial interpolation method is implemented in ArcGIS 9.0 software using VBA programming. The weight of each precipitation observation is calculated by the distance between the estimated point and the observation point. Case study of precipitation interpolation in northwestern Texas shows that： （1） WLR model is better than the common methods such as Kriging and IDW in terms of MAE and RMSE of cross validation in altiplano and mountain areas for specific precipitation periods. （2） Due to the seasonal characteristics of the precipitation distribution, the precision of WLR interpolation varies in different periods of precipitation ; compared with the common methods, the WLR model is better than IDW and Kriging methods for August precipitation data and has no evident difference for January data. （3） In the complex terrain area, the WLR model has evident advantages over the common approaches, and in the relatively flat area the model matches the IDW method. Considering that precipitation is influenced by more geographic factors such as mountain slope, aspect and wind direction, it is expected to develop a multiple linear regression model for precipitation interpolation in the future studies.
出处
《地球信息科学》
CSCD
2008年第1期14-19,共6页
Geo-information Science
基金
河南省高等学校创新人才基金(2004-2009年度)
关键词
降水量
空间插值
线性加权回归模型
precipitation
spatial interpolation
weighted linear regression model
