Global Journal of Researches in Engineering, J: General Engineering, Volume 1 9 Issue 2

Estimation of Rainfall Erosivity Index for Auchi, Edo State, Using Lombardi’s Method Ajayi A. S. α , Ehiomogue P. σ , Kayong A. E. ρ & Duweni E. C. Ѡ Abstract - The erosivity factor in the universal soil loss equation (USLE) provides an effective means of evaluating the erosivity power of rainfall. This study evaluated the erosivity factor based on monthly and annual precipitation rainfall data of Auchi, Edo State covering a period of 2005 – 2014 using Lombardi method (EI =1.03V d 1.51 ). It was discovered that higher rainfall values resulted in high erosivity index values which was in line with other tropical climates. The average annual erosivity index for the city during the period of study was 587.32 MJ mm/hr. The R 2 of 0.651 shows that precipitation alone contributed 65.1% of the erosion risk within the study period. The knowledge of impact of rainfall on erosivity is essential in soil erosion risk assessment and for soil and water conservation planning. Keywords: erosivity, kinetic energy, erosion, rainfall intensity, lombardi, erodibility, auchi, soil loss. I. I ntroduction oil loss is closely related to rainfall partly through the detaching power of raindrops striking the soil surface and partly through the contribution of rain to runoff. This applies particularly to erosion by overland flow and rills, for which intensity is generally considered to be the most important rainfall characteristic (Morgan, 1942). Soil degradation resulting from erosion by storm water is perceived as one of the main climate-related problems worldwide since it has large environmental and economic impacts, especially in agricultural areas (Isikwe et al., 2015; Angulo-Martínez and Beguería, 2009). One of the most important factors in soil erosion by water is the erosive potential of raindrop impact. The rainfall erosivity factor (R) in the Universal Soil Loss Equation (USLE) is generally recognized as one of the best parameters for the prediction of the erosive potential of raindrop impact (Loureiro and Coutinho 2001). Various properties of raindrops, such as intensity, velocity, size, and kinetic energy, are among the most frequently used parameters to develop erosivity indices. The A r I m (rainfall amount × maximum intensity), EI 30 (rainfall energy× maximum 30-min intensity), and KE > 1(total kinetic energy of all of the rain falling at more than 25 mm h -1 ) are the most important rainfall erosivity indices. These 3 indices were developed by Lal, Wischmeier and Smith, and Hudson (Isikwe et al., 2015; Yu, 1998). A direct computation of rainfall erosivity factors requires long-term data for both the amount and intensity of rainfall. In such a situation, more readily available types of parameters (rainfall amount-based indices) such as monthly or annual rainfall data could be utilized to predict rainfall erosivity indices. This makes it possible to adopt the correct strategies for soil conservation. Factors affecting the rate of soil erosion are rainfall, runoff, wind, soil, slope, plant cover and the presence or absence of conservation measures (Morgan, 1979). Rainfall erosivity is the potential ability of rainfall to cause soil loss (Silva, 2004). The rainfall erosivity index represents the climate influence on water related soil erosion (Isikwe et al., 2015). Erosion is seen as a multiplier of rainfall erosivity (the R factor, which equals the potential energy); this multiplies the resistance of the environment, which comprises K (soil erodibility), SL (the topographical factor), C (plant cover and farming techniques) and P (erosion control practices). Since it is a multiplier, if one factor tends toward zero, erosion will tend toward zero. This erosion prediction equation is composed of five sub-equations, and is given as: A = R. K. L. S. C. (1) Where, A is the average annual soil loss (Mg ha - 1 yr -1 ); R is the rainfall erosivity index; K is the soil erodibility factor; L is the slope length factor; S is the slope gradient factor; C is the vegetation cover factor, and P Is the conservation protection factor. Each intensity has a corresponding kinetic energy, according to the Eq. 2, (Wichmeier and Smith, 1978). KE = 11.87 + 8.73log 10 I (2) Wischmeier’s index, EI 30 = KE x I 30 , KE = kinetic energy of rainfall expressed in metric tons × m/ha/cm of rainfall. I 30 = is 30 minutes rainfall intensity in mm/hr. The intensity of rainfall is determined from the rainfall amount and duration using Eq. 3 below; = ℎ (3) S © 2019 Global Journals Global Journal of Researches in Engineering 35 Year 2019 ( ) Volume XIxX Issue II Version I J Author : Department of Agricultural and Bio-Environmental Engineering Technology, Auchi Polytechnic Auchi. Author : Department of Agricultural Engineering, Michael Okpara University of Agriculture, Umudike, Abia State Nigeria. Author : Department of Agricultural and Bio-Environmental Engineering, Samaru-Kataf Campus Nuhu Bamalli Polytechnic Zaria- Nigeria. Author : Department of Civil Engineering, University of Ibadan, Oyo State Nigeria. e-mail: ajayistan@gmail.com α σ ρ Ѡ