Various empirical models have been developed to estimate the rainfall erosivity factor (R factor) in soil loss equation. However, estimating the R factor requires high-resolution rainfall data and the empirical models must be validated prior to local application. This study reviewed three major R factor models and evaluated the applicability of these models using hourly rainfall data (1973~2007) archived for four cities (Seoul, Chuncheon, Namwon, and Jeju) in Korea. The monthly R factor was calculated using the EI30 method based on hourly rainfall data. Monthly effective rainfall amounts and the number of effective rainy days were extracted, and model parameters were estimated using subset data from 1973 to 2002. Subsequent validation test conducted using subset data from 2003 to 2007. Stepwise regression analysis identified the monthly rainfall amount and the number of rainy days with daily rainfall exceeding 10 mm as the most significant variables of the R factor. The validation result showed acceptable predictive capability (R² ³ 0.7) but the accuracy reduced with increasing R factor. Use of effective rainfall variables was found to simplify the process for R factor prediction, compared to the EI30 method. However, the existing R factor models do not fully incorporate the interaction between rainfall amount and intensity and the nonlinearity of R factor with rainfall data. Therefore, improving R factor estimation models is necessary to enhance soil erosion predictions under future climate conditions where extreme rainfall events are expected to occur.

Keywords: Rainfall erosivity factor, Soil erosion, Rainfall amount, Rainfall intensity, Regression analysis