Using monthly precipitation data to estimate the R-factor in the revised USLE

Abstract

The methods used to calculate both the Revised Universal Soil Loss Equation (RUSLE) erosivity factor (R) and the 10 year frequency storm erosion index value (EI10) are presented. As the calculation methods require long-term rainfall intensity data, and such data are not available for all application sites, an approach used to estimate the R-factor is described. Examples illustrating applications of the estimation technique in Africa, Asia, and other parts of the world are summarized. The method, which establishes correlations between measured R-values and more readily available precipitation data, is used to develop relations for estimating R-values in the USA. Correlations based on average monthly precipitation data and the R-factor values for 155 US stations were initially used to develop estimation relations. The 155 stations were segregated based on the annual distribution of monthly precipitation and the correlations improved. Exclusion of 23 stations with both 'winter-type' precipitation distributions and modified Fournier index values greater than 100 mm improved the relations for the remaining 132 stations (r2 = 0.81). An estimation relation for the EI10 is also presented. The R-factor and EI10 estimation relations should facilitate the use of RUSLE for locations with only monthly precipitation data.