地理科学 ›› 2010, Vol. 30 ›› Issue (3): 355-362.doi: 10.13249/j.cnki.sgs.2010.03.355

• 论文 • 上一篇    下一篇

基于IBIS模型的1955~2006年中国土壤温度模拟及时空演变分析

朱求安1, 江洪1,2, 刘金勋3, 方秀琴4, 余树全2   

  1. 1. 南京大学国际地球系统科学研究所, 江苏 南京 210093;
    2. 浙江林学院国际空间生态与生态 系统生态研究中心, 浙江 杭州 311300;
    3. Stinger Ghaffarian Technologies (SGT, Inc.), contractor to the USGS EROS Center, Sioux Falls 57198, USA;
    4. 河海大学水文水资源与水利工程科学国家重点实验室, 江苏 南京 210098
  • 收稿日期:2009-07-14 修回日期:2009-11-05 出版日期:2010-05-20 发布日期:2010-05-20
  • 基金资助:
    本研究得到科技部973项目(2005CB422207,2005CB422208)、科技部数据共享平台建设项目(2006DKA32308)、科技部国际合作项目(20073819)资助。

Simulation and Trend Analysis of Soil Temperature in China from 1955 to 2006 Using IBIS Model

ZHU Qiu-an1, JIANG Hong1,2, LIU Jin-xun3, FANG Xiu-qin4, YU Shu-quan2   

  1. 1. International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210093;
    2. International Research Center of Spatial Ecology and Ecosystem Ecology, Zhejiang Forestry University, Hangzhou, Zhejiang 311300;
    3. Stinger Ghaffarian Technologies (SGT, Inc.), contractor to the USGS EROS Center, Sioux Falls 57198, USA;
    4. State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Hohai University, Nanjing, Jiangsu 210098
  • Received:2009-07-14 Revised:2009-11-05 Online:2010-05-20 Published:2010-05-20

摘要: 基于IBIS模型对中国1955~2006年的土壤上层1m的年平均与月平均土壤温度进行模拟,并利用全国气象站点土壤温度观测数据对模拟结果进行验证,结果显示中国南方区的模拟效果优于北方及青藏高原区,春、夏、秋三季模拟效果优于冬季,总体而言取得了较满意的效果。基于模拟结果,利用Mann-Kendall方法对中国1955~2006年年平均和月平均土壤温度进行趋势分析的结果表明,年平均土壤温度,中国北方呈显著上升趋势,南方呈非显著上升趋势,四川盆地、贵州中部、藏东南及天山地区等小部分区域呈现显著或非显著下降趋势;月平均土壤温度,北方基本保持显著上升趋势,南方地区7~9月份总体呈现出下降的趋势,8月份最为显著。

Abstract: The Integrated Biosphere Simulator (IBIS) is used to evaluate the spatial and temporal patterns of annual and monthly top 1 m soil temperature across China for the period 1955-2006. Observed soil temperature data collected from 650 national meteorological stations were used to validate the simulated soil temperature in spatial and temporal scales. Mean error (ME), root mean square error (RMSE) and coefficient of determination were used as the performance criterion. The validation results presented that IBIS model performed better in the southern China than in the northern China and the Tibetan plateau region. The mean error and root mean square error are less than 2℃ in the southern China and more than 3℃ in some area of the northern China and the Tibetan plateau region for annual soil temperature. Monthly validation results showed that the model performed better in spring, summer and autumn than in winter. The mean error was bigger than 3℃ in winter and it was between -1.4℃ and 2℃ in other three seasons. The root mean square error was larger than 3.5℃ in winter and it was between 2℃ and 3℃ in other three seasons. The root mean square error and coefficient of determination also indicated better performance in the southern China than in the northern China and the Tibetan plateau region for monthly soil temperature simulation. The coefficient of determination showed that the model captured the spatial variance of soil temperature well. Based on the simulated soil temperature, trend analysis was applied for annual and monthly soil temperature from 1955 to 2006 using Mann-Kendall method. Annual soil temperature presented significant increasing trends in the northern China and slight increasing trends in the southern China. Small areas of Sichun Basin, center part of Guizhou Province, southeast part of Tibet, and Tianshan Mountains region presented significant or slight decreasing trends for soil temperature. Monthly soil temperature trends analysis presented significant increasing trends in most areas of the northern China and in the southern China the spatial pattern of trends for each month was different. From July to September, the soil temperature presented decreasing trends in most area of the southern China. In August, the soil temperature presented significant decreasing trends in the southern China. Uncertainty should be considered in soil temperature simulation. Uncertainty in observed data used to validate the simulated results, in input data such as soil texture, atmospheric boundary condition including solar radiation, air temperature and precipitation required by the models, and in spatial scale matching will make the soil temperature estimation diverge from the true state because of gradually errors accumulating.

中图分类号: 

  • S152.8