自然资源学报 ›› 2015, Vol. 30 ›› Issue (7): 1141-1152.doi: 10.11849/zrzyxb.2015.07.007

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基于优化参数的陕西省气温、降水栅格化方法分析

石志华1, 刘梦云1, 常庆瑞1, 季青2, 刘效栋1, 吴健利1   

  1. 1. 西北农林科技大学资源环境学院, 陕西杨凌 712100;
    2. 武汉大学中国南极测绘研究中心, 武汉 430079
  • 收稿日期:2014-08-18 修回日期:2015-01-12 出版日期:2015-07-20 发布日期:2015-07-14
  • 通讯作者: 刘梦云(1973-),女,副教授,博士,硕士生导师,主要从事土地生态恢复研究。E-mail:lmy471993@163.com E-mail:lmy471993@163.com
  • 作者简介:石志华(1989-),男,湖北阳新人,硕士研究生,主要从事土地资源监测与信息可视化研究。E-mail:zhiqizhonghua@126.com
  • 基金资助:

    国家高技术研究发展计划(863 计划)(2013AA102401); 陕西省自然科学基础研究计划(2013JQ5012);西北农林科技大学博士科研启动基金。

Comparison of Temperature and Precipitation Rasterization Methods Based on Optimized Parameters in Shaanxi Province

SHI Zhi-hua1, LIU Meng-yun1, CHANG Qing-rui1, JI Qing2, LIU Xiao-dong1, WU Jian-li1   

  1. 1.College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China;
    2. Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China
  • Received:2014-08-18 Revised:2015-01-12 Online:2015-07-20 Published:2015-07-14

摘要:

基于统计、地统计基本原理,运用传统插值法、地统计插值法、多元回归法和模拟气象站点法4 大类11 种方法,对陕西省2003—2012 年平均气温、降水量数据进行栅格化。结果表明:① 多元回归法和模拟气象站点法均能大幅提高气温数据的估测准确度,且多元回归法对气温的表示更加精细,其中以“回归+残差IDW(Inverse Distance Weighting)”法精度最高,MAERMSER2分别为0.498、0.775 和0.954 8;② OK(Ordinary Kriging)法对降水数据的估测准确度最高,MAERMSER2为46.934、69.251 和0.947 8;③ 气温、降水栅格化方法具有明显的区域性,不存在绝对普适的最优方法,应根据数据类型、地域特征对原始数据进行探索性分析,从而获得区域最适合的栅格化方法;④ 陕西2003—2012 年多年平均气温为10.925 ℃,标准差2.221 ℃,气温随纬度、海拔的增加而降低,具有鲜明的纬度和垂直地带性;平均降水量为664.446 mm,标准差213.226 mm,降水呈现由南向北逐渐递减的趋势,纬度地带性较强。

关键词: 栅格化, 多元回归, 地统计, 模拟气象站点, 优化参数

Abstract:

According to the basic principles of statistics and geo-statistics, the paper uses 11 different methods of four categories including traditional interpolation method, geo-statistical interpolation method, multiple regression and suppositional meteorological station method, to rasterize the annual average temperature and precipitation in Shaanxi province from 2003 to 2012. The results show that: 1) Multiple regression and suppositional meteorological station methods can greatly improve the temperature estimating accuracy. Furthermore, multiple regression method is more refined in calculating temperature details, especially the“Regression+Residual IDW (Inverse Distance Weighting)”method, whose MAE, RMSE and R2 are 0.498, 0.775 and 0.9548 respectively. 2) OK (Ordinary Kriging) method is the most accurate method in estimating precipitation, whose MAE, RMSE and R2 are 46.934, 69.251 and 0.9478. 3) The rasterisation methods of temperature and precipitation are obviously regionally adapted. There is no a universal optimal rasterization method. So it's necessary to explore and analyze the original data before acquiring the most suitable rasterization method. 4) The perennial average temperature of Shaanxi from 2003 to 2012 is 10.925 ℃, with the standard deviation of 2.221 ℃. The temperature decreases with the increasing of latitude and elevation, having distinct latitudinal and vertical zonality. The average precipitation is 664.446 mm, with the standard deviation of 213.226 mm. The latitudinal zonality of precipitation is strong, showing a decreasing trend from south to north.

Key words: optimized parameters, geo-statistics, multiple regression, rasterization, suppositional meteorological stations

中图分类号: 

  • P468.0