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ASSESSMENT OF RAINFALL EROSIVITY FACTOR (R) FOR SELECTED LOCATIONS IN IMO STATE, SOUTHEASTERN NIGERIA

Soil erosion in southeastern Nigeria is a major environmental issue of which rainfall is a major contributor to its elevated level(s). This study gives a critical assessment on the rainfall erosivity factor (R) for selected sites in Imo State representing different soil groups geologically by making use of mean monthly rainfall data and GIS techniques. By applying empirical methods the rainfall intensity for all the locations were obtained and was further determined at three different intervals of 30-minutes, 45minutes and 60-minutes respectively. Through the use of the revised universal soil loss equation (RUSLE) the rainfall erosivity factor (R) was obtained for each of the locations and they are as follows; 6054.93 MJmm/ha/hr, 5211.48 MJmm/ha/hr, 5977.29 MJmm/ha/hr, 6171.57 MJmm/ha/hr, 6478.06 MJmm/ha/hr, 5567.54 MJmm/ha/hr, 5350.84 MJmm/ha/hr and 5470.29 MJmm/ha/hr for Ehime Mbano, Ideato North, Ikeduru, Oguta, Ohajiegbema, Orlu and Owerri West respectively. Likewise the rainfall erosivity factor (R) was determined at different rainfall intensities and it was observed that the factor (R) reduced with increase in rainfall intensity, thus conforming to the fact that as rainfall duration increases the soil approaches saturation and erosive force reduces as well. Also, the rainfall erosivity factor (R) for all the locations were subjected to a correlation analysis and they all yielded a strong/perfect correlation with a correlation coefficient of about 0.95 which shows or implies 95% strength. On further subjection to ANOVA the rainfall erosivity factor (R) for all the locations showed statistical significance at 5% value thus showing uniformity of region and non-variation in rainfall characteristics. The values obtained for each of the locations were mapped also to show the varying degree in the factor. Though rainfall is a natural climatic factor which is not amenable to human interference, it is therefore important to enact and adopt proper cover/management practices/measures so as to sustain the environment against further effects.

INTRODUCTION Agricultural soils are the richest supplies of minerals, nutrients, moisture, air, organic materials and microbial activities that support plant growth and production. They provide the pathways through which water and nutrients move to the roots of plants, and they are the matrix of nutrient transformation with the resultant environment for micro-organisms and fauna (Powlson et al., 2011). The availability of soils for agricultural production and food security is limited by the irreparable/destructive effects of erosion. Soil erosion is a serious environmental, economic and social problem, which causes severe land degradation, reduced productivity levels and threatens the stability and health of society in general alongside sustainable development of rural areas (Wang et al., 2013).Soil erosion is the gradual or rapid weathering/disintegration, removal and transportation of soil aggregates through the action of erosive agents such as wind, water, gravity and human disturbance (Gunawan et al., 2013). Soil loss by runoff (overland flow caused by rainfall) is a severe ecological problem occupying 56% of the world wide area and is accelerated by human induced soil degradation (Gelagay and Minale, 2016;Bai et al., 2008). Also the extent of deterioration of soils and water resources due to erosion and pollution has assumed a frightening dimension with its attendant effects on global food security, water quality and hygiene for sustainable livelihoods (Akinbile et al., 2016). The impact of soil erosion is felt in the inability of the soil to meet up with the moisture and nutrient requirements of the plants thereby resulting into soil degradation and low agricultural productivity. According to Michael and Ojha (2003), the potential for erosion is dependent on certain factors which include soil nature/characteristics, slope/topography, presence of vegetative cover and general climatic conditions. The major contributing factor to soil loss and movement is rainfall and its characteristics (intensity, duration, frequency, energy, distribution e.t.c) (Okorafor et al., 2017). The erosive ability of rainfall to initiate soil detachment, movement/transportation and eventual deposition in different locations is regarded as the erosivity of rainfall. Rainfall erosivity factor (R) is one of the six factors of the Universal Soil Loss Equation (Wischmeier and Smith, 1978) and Revised Universal Soil Loss Equation (Renard et al., 1997) which is stated thus; = ………………………………….. (1) Where A= Annual soil Loss R= Rainfall Erosivity Factor K= Soil Erodibility Factor S= Slope Length and Steepness Factor C= Cover Crop Management Factor P= Conservation/Management Practice Factor Rainfall erosivity factor is dependent on the amount, duration, intensity, raindrop size, distribution, frequency and kinetic energy of a rainfall/storm event (Okorafor et al., 2017). This is a natural factor since it is not amenable to human modification and cannot be adjusted or regulated to prevent the resultant damage it may cause. The main objective of this study is to make use of mean monthly rainfall data to determine rainfall erosivity factor (R) for Imo State (different soil groups/locations) and to make use of geographical information system (GIS) to map the study area according to the magnitude of the rainfall erosivity factor (R).

Description of Study Area
Imo State is located within latitude 4°45ʹN and 7°15ʹN and longitude 6°50ʹE and 7°25ʹE and covers an area of about 5100 km 2 and falls within the rain forest belt of Nigeria with two distinct seasons (i.e. wet and dry) which exists within the region (Okoro et al., 2014;Okorie, 2015). The study area experiences high rainfall amounts of about 1600-2900 mm (Selemo et al., 2012;Okorafor et al., 2018). The relative humidity ranges from 75-90% (Udokporo et al., 2015) with mean minimum temperature of 23.5°C and mean maximum temperature of 32.9°C (Okorie, 2015). According to Nkheloane et al., 2012;Ernest and Onweremadu, 2016), the soils of Imo State have a characterized brown colour ranging from strong dark brown to yellowish red thus indicating the strong presence of iron oxide; also soils of Imo State are characterized by high sand percentage, low organic matter content and low water storage capacity thus placing the zone as an area of high susceptibility to accelerated erosion and land degradation (Ufot et al., 2016: Ibeje, 2016. The soils are predominantly ultisols derived from coastal plain sand (benin formation) and have low pH with exchangeable bases (Onwudike, 2010). The high temperature and humidity experienced within the region favors luxuriant plant growths which produce the climax vegetation of the tropical rainforest zone (Udokporo et al., 2015). Furthermore the watershed is covered by depleted rainforest shrubs having thick under brushes, creeping vines and deep green vegetation which is as a result of heavy decay of plant droppings, foliage and litter. The map of Imo State is as shown in Figure 1.

Selection of Locations
The study area was subdivided into eight soil groups based on geological classification of base materials found within each zone. The various soil groups with their major towns and percentage area of coverage are as summarized in Table 1. The soil map also indicating the various soil groups are as shown in Figure 2.

Data Collection and Computation
In order to determine the rainfall erosivity factor (R) of the selected locations rainfall data (Mean monthly rainfall amount and rainfall days) were obtained from NIMET 0.5h = 30 minutes duration The results obtained for the rainfall erosivity factor (R) for all the locations representing different soil groups was interfaced with ArcGis 10.1 and under raster distribution the study area was mapped to reveal the variation of rainfall erosivity factor (R) within the various soil groups.

III. RESULTS
Based on the 31year rainfall data used rainfall intensity for different time intervals (30minutes, 45minutes and 60minutes) was obtained and based on the varying time intervals the kinetic energy and rainfall erosivity factor (R) were also obtained for all the various locations representing different soil groups as well. The results for rainfall intensity, kinetic energy and rainfall erosivity factor (R) for each location are as shown in Tables 3.1-3.9. The summary of the mean rainfall erosivity factor (R) i.e EI30 for all the locations (different soil groups) are also as shown in Table 3.10.

5470.29
The mean annual rainfall erosivity factors (R) as shown in Table  3.10 for all the locations was transposed into an ArcGIS 10.1 interface and the map of rainfall erosivity was obtained using raster distribution and high aspect supervision. The rainfall erosivity map is as shown in Figure 2.

3.1: Correlation Analysis of Data
The rainfall amount, rainfall intensity (I), rainfall energy (E) and rainfall erosivity factor (R) for the different rainfall intensities for each of the locations (different soil groups) were subjected to correlation analysis to obtain the coefficient of correlation between the parameters. Tables 3.12-3.20 presents the values of the coefficient of correlation for each parameters and the location  all show a strong/perfect correlation with the others location within the regions since all had a correlation coefficient of above 0.9 and the results were all statistically significant at 5%.  Table 3.14 above shows the correlation of rainfall intensity (I), rainfall energy (E) and rainfall erosivity factor (R) for Ideato North (Sandstones). From the result it was observed that rainfall intensity (I), rainfall energy (E) and rainfall erosivity factor (R) for Ideato North (Sandstone) all show a strong/perfect correlation with the others at Ideato North since all had a correlation coefficient of above 0.95 which is about 95% strength and the results were all statistically significant at 5%.
*. Correlation is significant at the 0.05 level (2-tailed). Table 3.17 above shows the correlation of rainfall intensity (I), rainfall energy (E) and rainfall erosivity factor (R) for Ohajiegbema (Sands, Gravel and Clays). It was observed that rainfall intensity (I) and rainfall erosivity factor (R) for Ohajiegbema (Sands, Gravel and Clays) all showed a strong/perfect correlation of above 0.95 which is 95% strength while rainfall energy (E) showed a fairly strong correlation of about 0.63 with the others at Ohajiegbema , but all the results were statistically significant at 5%. 1.000 ** 1.000 ** 1.000 ** .982 ** 1.000 ** 1 EI60 1.000 ** 1.000 ** 1.000 ** .983 ** 1.000 ** 1.000 ** 1 **. Correlation is significant at the 0.01 level (2-tailed). Table 3.18 above shows the correlation of rainfall intensity (I), rainfall energy (E) and rainfall erosivity factor (R) for Okigwe (Sandstones, Limestones and Coal). From the result it was observed that rainfall intensity (I), rainfall energy (E) and rainfall erosivity factor (R) for Okigwe (Sandstones, Limestones and Coal) all showed a strong/perfect correlation with the others at Okigwe since all had a correlation coefficient of above 0.95 which is about 95% strength and the results were all statistically significant at 5%.   Table 3.20 above shows the correlation of rainfall intensity (I), rainfall energy (E) and rainfall erosivity factor (R) for Owerri (Sands and Clay). It was observed from the result that rainfall intensity (I), rainfall energy (E) and rainfall erosivity factor (R) for Owerri (Sands and Clay) all showed a strong/perfect correlation with the others at Owerri since all had a correlation coefficient of above 0.95 which is about 95% strength and the results were all statistically significant at 5%. Furthermore an ANOVA was carried out on rainfall erosivity factor for all the locations and it was observed that they did not show any significance since p-value > 0.05, which also signifies no variation in rainfall properties since all the locations considered fall within the same region.

IV. DISCUSSIONS
From the values displayed from Tables 3.1-3.10, it was observed that as rainfall duration increased the ratio of rainfall amount to duration (rainfall intensity) decreased accordingly. Likewise the rainfall erosivity factor (R) at different rainfall intensities also decreased with increase in rainfall duration. This relationship is dependent on the fact that as rainfall duration increases the soil undergoes saturation and pore spaces are filled thus exceeding infiltration capacity at this point the soil particles no longer undergo detachment or splash but movement so therefore as rainfall duration increases rainfall intensity keeps decreasing on consistent distribution (Ojha and Michael, 2003). According to Salako, (2010) and Salako (2006) rainfall erosivity factor (R) decreased with increase in rainfall duration, and it further stated that rainfall erosivity (R) at 15minutes rainfall intensity gives a better and more precise range of values for erosion prediction and management when compared to higher intensities. For all the locations the rainfall erosivity factor (R) for each of the months varied between 1668.38 -9776.58 MJmmha -1 hr -1 , the highest rainfall erosivity values were observed in June (rainy season) and the lowest rainfall erosivity values were observed in January (dry season). The mean annual rainfall erosivity (R) for all the soil texture groups/locations was found to be between 5211.48 -6478.06 MJmmha -1 hr -1 . It was observed that the location having the highest mean rainfall erosivity factor was Ohajiegbema and the least mean rainfall erosivity factor was observed in Ideato North. Ezemonye and Emeribe (2012) estimated that rainfall erosivity factor for humid regions are between 5000-6700 MJmmha -1 hr -1 . Also according to Salako et al., (1995); Obi and Salako (1995)  The mean annual rainfall erosivity (R) for all the soil texture groups/locations was found to be between 5211.48 -6478.06 MJmmha -1 hr -1 .
The study area falls within the range of medium to high rainfall erosivity with a value range of 5211.48-6478.06 MJmmha -1 hr -1 .
Coefficient of correlation for all the soil group zones/locations considered is 0.95 and above except for Orlu and Ohajiegbema which showed correlation coefficients of 0.40 and 0.60 at EI45 and E respectively.
ANOVA showed that al the locations were statistically significant at 5% thus reflecting uniform/similar rainfall characteristics VI.