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干旱区地理 ›› 2015, Vol. 38 ›› Issue (5): 948-959.

• 气候与水文 • 上一篇    下一篇

TRMM数据在中国降雨侵蚀力计算中的应用

王凯, 陈璐, 马金辉, 刘飞   

  1. 兰州大学资源环境学院, 甘肃兰州 730000
  • 收稿日期:2015-01-18 修回日期:2015-03-22 出版日期:2015-09-25
  • 通讯作者: 马金辉(1964-)男,甘肃天水人,副教授,博士,研究方向GIS环境建模.Email:majh@lzu.edu.cn E-mail:majh@lzu.edu.cn
  • 作者简介:王凯(1990-)男,湖北黄冈人,研究生,硕士,研究方向环境定量遥感.Email:350698431@qq.com
  • 基金资助:

    基于干涉测量和物联网技术的甘肃南部地质灾害监测预警(编号:052500003);国家自然科学面上项目"黄土阶地斜坡的稳定性分析研究——以兰州地区为例"(编号:41172328)

Calculation of rainfall erosivity in China with TRMM Data

WANG Kai, CHEN Lu, MA Jinhui, LIU Fei   

  1. WANG Kai, CHEN Lu, MA Jinhui, LIU Fei
  • Received:2015-01-18 Revised:2015-03-22 Online:2015-09-25

摘要: 长时间序列降雨过程资料的获取一直是降雨侵蚀力计算中的一个难题。尝试利用地面实测站点数据分别对TRMM(Tropical Rainfall Measuring Mission)卫星的3B43和3B42数据进行回归建模和订正,并采用订正后的3 h平均降雨强度代替30 min最大降雨强度,同时基于TRMM数据的EI180的降雨侵蚀力算法,计算出了全国南北纬50°范围(TRMM覆盖区)内2013年月、季和年降雨侵蚀力;最后分别计算了省域和区域尺度下的降雨侵蚀力对全国尺度下的结果进行对比验证。结果表明:(1) TRMM降水数据比地面站点观测的降水量略大,但与实测站点数据具有很好的线性回归关系,其季尺度决定系数R2均较高,由此也说明了TRMM数据可以很好地反映全国范围内降雨的季节性变化。(2)利用订正后的TRMM3B42数据计算出研究区内的年均降雨侵蚀力为536.02 MJ·mm·hm-2·h-1·a-1,其降雨侵蚀主要集中在5~8月份。(3) 2013年全国降雨侵蚀变化趋势由东南向西北方向逐渐降低,且沿海省份较内陆省份降雨侵蚀较高。(4)通过对年降雨侵蚀力结果与实测站点降雨量以及订正的TRMM降水数据分析表明,降雨侵蚀力与降水之间存在着紧密的二次非线性关系。(5)通过尺度验证,其中省域尺度验证误差为8.34%,区域尺度误差仅为0.24%,由此说明了TRMM数据在不同尺度下均具有良好的适应性,同时也验证了方法在不同尺度下的有效性。该方法为有效解决土壤侵蚀中降雨强度计算资料缺乏的瓶颈,同时也为降雨侵蚀力的计算提供了一条有效的途径。

关键词: TRMM, 降雨侵蚀力, 回归建模, 降雨强度, 季节性变化, 二次非线性关系

Abstract: Long time series rainfall data had always been a difficult problem in the calculation of rainfall erosivity. This paper attempted to make regression modeling and correction on the TRMM(tropical rainfall measuring mission)3B42 and 3B43 data by using the measured rainfall data of meteorological stations, and then took the corrected 3 hours average rainfall intensity instead of the maximum rainfall intensity of 30 minutes, and calculated the rainfall erosivity on the month, season and year scales within the extent of 50 degrees of latitude from south to north of China(TRMM covered area)in 2013 by the method of EI180. At last, this paper chose the provincial and sub-regional scale to validate the results of national scale. These results showed as follows:(1) The TRMM data is a little lager than the measured rainfall data in China, however, it is a good linear regression relation between the measured and TRMM rainfall data, and the seasonal determination coefficient(R2)are above 0.6, and the highest level is as much as above 0.87, which verified that the seasonal variation of precipitation can be well reflected with TRMM data of China, and also can be applied to rainfall erosivity;(2) This paper calculated that the average annual rainfall erosivity is 536.02 MJ·mm·hm-2·h-1·a-1 by using the corrected TRMM 3B42 data in 2013, and the rainfall erosivity concentrated in the month of May to August;(3) The calculated rainfall erosivity trend is reduced gradually from southeast to northeast, and also the rainfall erosivity of coastal provinces is higher than that of the inland provinces, and the order of rainfall erosivity from high to low is south China, east China, central China, northeast, southwest, north and northwest in turn;(4) By analysis the relations between the rainfall erosivity and the rainfall of meteorological stations as well as the corrected TRMM data show that there exist a quadratic nonlinear relation between them closely;(5) By validation with different scales, the error of provincial scale is 8.34% and that of sub-regional scale is 0.2%, which validates that TRMM data is well of applicability in different scales, and the method of this paper is effective. However, under the background of large scale, this paper would ignore some regions inevitably where TRMM data is poor of applicability, and the TRMM data would have much gap with the measured rainfall data of meteorological stations, which leads some error of the rainfall erosivity results to some extent, therefore, it is important that these regions be considered separately in the future researches. In a word, the method in this paper can provide certain reference basis for solving the bottleneck of lacking calculated rainfall intensity data in the soil erosion effectively as well as provide an effective way for calculating rainfall erovisity.

Key words: Tropical Rainfall Measuring Mission, Rainfall Erosivity, Regression Modeling, Rainfall Intensity, Seasonal Variation

中图分类号: 

  • S157