Estimation of RUSLE EI<inf>30</inf> based on 10 min interval rainfall data and GIS-based development of rainfall erosivity maps for Hitotsuse basin in Kyushu Japan

Abstract

Land erosion is regarded as one of the most important phenomenon causes land degradation. In revised universal soil loss equation (RUSLE) erosion model, rainfall erosivity factor (R) is one of the important parameters. Ideally, the calculation of EI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</sub> (R factor) uses breakpoint rainfall intensity data which is calculated manually from graphical charts that are generated by continuously recording rain gauges. However, due to limited availability of breakpoint rainfall data, many simple methods for estimating EI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</sub> have been developed by using yearly, monthly and daily rainfall data. In this research, due to limited data availability, pluviograph data at 10 minute interval from eight stations in Hitotsuse basin were used to compute EI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</sub> (R factor) for RUSLE. The approach used in this research is based on storm rainfall and duration data from 1990 to 2009. This method is based on the calculation of rainfall energy per unit depth of rainfall, total storm kinetic energy (E), rainfall intensity for a particular increment of a rainfall, and maximum 30 minute rainfall intensity. Furthermore, EI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</sub> were computed, and then GIS method was used to create rainfall erosivity maps. The annual rainfall erosivity values prediction model was developed based on MFI values.