Earth Sciences 2017; 6(5-1): 87-92 http://www.sciencepublishinggroup.com/j/earth doi: 10.11648/j.earth.s.2017060501.23 ISSN: 2328-5974 (Print); ISSN: 2328-5982 (Online) Monthly Variations of Rainfall Erosivity (R factor) in Shida Kartli, Georgia Mariam Tsitsagi 1, * , Ana Berdzenishvili 2 , Ketevan Gogidze 1 1 Department of Geomorphology and Geoecology, Vakhushti Bagrationi Institute of Geography, Ivane Javakhishvili Tbilisi State University, Tbilisi, Georgia 2 Faculty of Exact and Natural Sciences, Ivane Javakhishvili Tbilisi State University, Tbilisi, Georgia Email address: mariam.tsitsagi@tsu.ge (M. Tsitsagi), anaberdzenishvili3@gmail.com (A. Berdzenishvili), ketevan.gogidze@tsu.ge (K. Gogidze) * Corresponding author To cite this article: Mariam Tsitsagi, Ana Berdzenishvili, Ketevan Gogidze. Monthly Variations of Rainfall Erosivity (R factor) in Shida Kartli, Georgia. Earth Sciences. Special Issue: New Challenge for Geography: Landscape Dimensions of Sustainable Development. Vol. 6, No. 5-1, 2017, pp. 87-92. doi: 10.11648/j.earth.s.2017060501.23 Received: June 28, 2017; Accepted: June 29, 2017; Published: August 21, 2017 Abstract: Soil erosion is a global problem that tends to become more extreme on the background of climate change. Rainfall is one the main drivers of soil erosion. One of the best indicators of the potential erosion risks is the rainfall-runoff erosivity factor (R) of the revised universal soil loss equation (RUSLE). Shida Kartli is one of the main agrarian regions in the country and research on soil erosion has the great importance. The purpose of this study is to assess monthly variations of rainfall erosivity in Shida Kartli region from the RUSLE R-factor, based on the best available datasets. The rainfall erosivity index for a rainfall event, EI 30 , is calculated from the total kinetic energy and maximum 30 min intensity of individual events. However, these data are unavailable in study region since 1990. Alternative approaches are used for the calculation of EI 30 in this paper. Soil erosion rate is sufficiently high in eastern Georgia. According to the results of previous studies, two maximums of R- factor are calibrated in May and July in Shida Kartli. A set of equations is presented for calculating monthly and annual R factor values based on daily precipitation data for Shida Kartli in the current study. Data have been collected from 2 meteorological stations for the period from January 1990 through December 2016. Precipitation time series for both stations included 27 years. Rainfall-runoff factor (R) for each month (R month ) of study period has been determined and seasons with high rainfall erosivity were established for both stations. Keywords: Soil Erosion, Precipitation, Rainfall, Erosivity, Monthly Time Step 1. Introduction Soil is one of the vital components of the natural environment that is non-renewable on a human time-scale [1]. One of the most harmful natural processes is soil erosion. Soil erosion is a global problem that tends to become more extreme with the extreme variations in weather [2]. Soil erosion by water affects soil quality and productivity by reducing infiltration rates, water-holding capacity, nutrients, organic matter, soil biota and soil depth [3]. Soil erosion in agricultural areas has been studied intensively throughout the last decades and rates have been measured at continuous and event scales [4]. Soil erosion rate is sufficiently high in eastern Georgia, for instance in Alazani and Iori river basins. 28 t soil is lost every year by erosion in Alazani river basin and 20 t in Iori river basin respectively [5]. Soil erosion is difficult to measure at large scales, soil erosion models are crucial estimation tools at regional, national and European levels [3]. Soil loss prediction is important to assess the risks of soil erosion and to determine appropriate soil use and management [6]. Erosional soil degradation by stormwater is perceived as one of the main problems worldwide since it has large environmental and economic impacts, especially in agricultural areas [7]. Moreover, it is assumed that rainfall erosivity will potentially increase due to climate change because of the associated change in precipitation characteristics [8]. The underlying assumption is that rainfall is getting more variable and hence more extreme rainfall events could be expected resulting in