Analysis of rainfall erosivity factor (R) on prediction of erosion yield using USLE and RUSLE Model’s; A case study in Mayang Watershed, Jember Regency, Indonesia

Abstract

The Rainfall erosivity has a relatively high effect on soil erosion, in addition to being very difficult to predict and control. The Universal Soil Loss Equation (USLE) and The Revised Universal Soil Loss Equation (RUSLE) model are commonly used to predict erosion yield in Indonesia. However, these models have several erosivity formulations that give different results. In this sense, identifying the sensitivity of different erosivity formulations in both models above is important. The aim of this study is to analyze soil erosion yield prediction influenced by the difference in erosivity equation on the same rainfall data used in the models while other parameters used are the same. The monthly rainfall and annual rainfall data were tested using the erosivity formulas. The (1) Bols and (2) Utomo equations were tested using monthly rainfall data, while the (3) Bols and (4) Hurni equations were tested using annual rainfall data. The results show that the prediction of soil erosion yields estimates using monthly rainfall data in both models have no significant differences. On the other hand, soil erosion estimates using annual rainfall data in the models have significant differences, whereas the USLE model estimation results in 63% erosion yield on low classification (0-15 ton ha-1 year-1). Meanwhile, the RUSLE model estimates only 59% erosion yield on low classifications. Another result is that the USLE model estimates lower erosion yield than the RUSLE model when the models use annual rainfall data, which may give significantly different recommendations for soil conservation in Indonesia, especially in reducing erosion yield at the Watershed level.

Keywords

• Erodibility
• GIS integration
• Sensitivity analysis
• Soil erosion
• Watershed management