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