Rainfall erosivity of the Revised Universal Soil Loss Equation (RUSLE) is influenced by the type, amount, and intensity of storm. In this research, rainfall data from 18 recording rain gauge stations were collected and analyzed. | A. KARAMI, M. HOMAEE, M. R. NEYSHABOURI, S. AFZALINIA, S. BASIRAT Research Article Turk J Agric For 36 (2012) 207-216 © TÜBİTAK doi: Large scale evaluation of single storm and short/long term erosivity index models Alidad KARAMI1,*, Mehdi HOMAEE1, Mohammad Reza NEYSHABOURI2, Sadegh AFZALINIA3, Sanaz BASIRAT4 1 Department of Soil Science, Faculty of Agriculture, Tarbiat Modares University, . Box: 14115-336, Tehran - IRAN 2 Department of Soil Science, Faculty of Agriculture, University of Tabriz, . Box: 51666-16471, Tabriz - IRAN 3 Department of Agricultural Engineering Research, Fars Research Center for Agriculture and Natural Resources, . Box: 73415-111, Shiraz - IRAN 4 Department of Horticulture Science of Shiraz Branch, Islamic Azad University, . Box: 71397-77669, Shiraz - IRAN Received: Abstract: Rainfall erosivity of the Revised Universal Soil Loss Equation (RUSLE) is influenced by the type, amount, and intensity of storm. In this research, rainfall data from 18 recording rain gauge stations were collected and analyzed. Further, their single storm, daily, monthly, and annual erosion indices were calculated and estimated by different models. Duration of each rainfall was divided into 15 min intervals. Intensity and energy of each interval, maximum rainfall intensity of 30 min, total energy of each rainfall, and erosivity index of every single storm were calculated. Furthermore, Cooley’s model for single storm was evaluated and its coefficients were estimated. For daily rainfall erosion index prediction, Richardson’s model was assessed and its coefficients were also estimated. A new power model based on monthly rainfall was proposed in order to predict monthly rainfall erosion index. For the estimation of the annual rainfall erosion index, the Arnoldus model was evaluated and its coefficients were estimated. The coefficients for all equations were also determined using multiple regression. According to the .
