The erosivity factor (R) in the USLE equation is accounted for by the rainfall-runoff, it is considered as a driver of soil erosion processes. The R factor represent the effect of raindrop impact and also shows the amount and rate of runoff associated with precipitation events, it is defined as the product of kinetic energy and the maximum

30 minute intensity and shows the erosivity of rainfall events [17] .

Rainfall data of 10 years (2004-2014) was obtained from the Loukkos Hydraulic Basin Agency and imported into GIS environment since all the weather stations had geographical coordinates. Annual and monthly rainfall data of Oued El Makhazine watershed was used to calculate the R-factor in this study.

The rainfall erosivity values for the different stations were used to interpolate a rainfall erosivity surface using the Inverse Distance Weighted (IDW) interpolation method in ArcGis environment to generate a raster map for R factor. The IDW interpolation method was selected because rainfall erosivity sample points are weighted during interpolation such that the influence of rainfall erosivity is most significant at the measured point and decreases as distance increases away from the point. The IDW interpolation method is based on the assumption that the estimated value of a point is influenced more by nearby known points than those farther away [18] [19] .

The Equation (2), below developed by Wischmeier and Smith [17] and modified by Arnoldus [20] was used in the computation:

where R is the rainfall erosivity factor (MJ∙mm∙ha⁻¹∙h⁻¹∙y⁻¹), P_i is the monthly rainfall (mm), and P is the annual rainfall (mm).

The mean values of R factor range from 74.63 MJ∙mm∙ha⁻¹∙h⁻¹∙y⁻¹ to 116.11 MJ∙mm∙ha⁻¹∙h⁻¹∙y⁻¹ (Figure 3(a)), approximately 76.82% of the rainfall erosivity factor R ranges between 74.63 MJ∙mm∙ha⁻¹∙h⁻¹∙y⁻¹ and 96.59 MJ∙mm∙ha⁻¹∙h⁻¹∙y⁻¹.

The soil erodibility factor (K) relate to the average long-term soil and soil profile response to the erosive power associated with rainfall and runoff. It is also considered to represent the rate of soil loss per unit of rainfall erosion index for a specific soil. The USLE model utilizes the technique proposed by Wischmeier [21] to measure the K factor of a soil type.

The specific methodology concerns many properties of soil such as gain size (silt, clay and sand), organic matter content, soil structure and soil permeability [21] [22] .

The soil samples collected from Oued El Makhazine watershed were analyzed in soil laboratory of National Institute of Agricultural Research of Settat. For each sample the particle size analysis was performed in five classes (sand, very fine sand, silt, very fine silt, and clay) using Robinson’s pipette method [23] . Organic carbon was measured by the Walkley-Black wet dichromate oxidation [24] and converted to organic matter by multiplying it by 1.724. Soil permeability code was obtained from the National Soils Handbook No. 430 (USDA, 1983) [25] and Soil structure code was determined using Soil Textural Pyramid [26] .

To calculate the soil erodibility, the formula of Wischmeier and Smith [17] was used.

where K is the soil erodibility factor (t∙ha∙h∙ha⁻¹∙MJ⁻¹∙mm⁻¹), M is the particles percentage (% of very fine sand + % of silt x (100% clay)), a is the organic matter content (% C x 1.724), b is the soil structure and c is soil permeability.

The analysis showed that 72 % of samples had clay texture, 18% Sandy clay loam and 10% Clay loam texture.

Erodibility is low for clay rich soils with a low shrink swell capacity, as clay particles mass together into larger aggregates that resist detachment and transportation [27] .

Organic matter content varies from the lowest of 1.37 % up to the highest of 4.57%. The permeability of Oued El Makhazine watershed is very low, a result that was observed in numerous publications [28] -[30] that the presence of clay decreases the level of soil permeability.

The result (Figure 3(b)) shows that Oued El Makhazine watershed has soil erodibility, ranges from 0.24 to 0.85, the watershed has a moderate to severe soil erodibility with 71.26% of the area ranges between 0.68 t∙ha∙h∙ha⁻¹∙MJ⁻¹∙mm⁻¹ and 0.85 t∙ha∙h∙ha⁻¹∙MJ⁻¹∙mm⁻¹.

The L and S factors in USLE reflect the effect of topography on erosion. It has been demonstrated that increases in slope length and slope steepness can produce higher overland flow higher erosion [31] . Moreover, gross soil loss is considerably more sensitive to changes in slope steepness than to changes in slope length [32] .

Therefore one of the key factors in soil loss is topography, especially when the ground slope exceeds a critical angle. Topography factor plays a major role in soil erosion since it dominates the surface run off rate.

The combined LS factor of the watershed was derived from Digital elevation model by means of ArcGis environment. The computation of LS requires factors such as flow accumulation and slope steepness.

The flow accumulation and slope steepness were computed from the DEM using ArcGIS Spatial analyst extension.

The Equation (4), was adopted to calculate LS factor as proposed by Moore and Burch [33] [34] .

where flow accumulation denotes the accumulated upslope contributing area for a given cell, LS = combined slope length and slope steepness factor, cell size = size of grid cell (for this study 30 m) and sin slope = slope degree value in sin.

The LS factor value in the study area varies from 0 to 22 (Figure 4(a)). The Majority of the study area (77.72 %) has LS value ranges between 0 and 6.29.

The coverage factor ranges from 0 (full soil coverage) to 1(no soil coverage) [17] .

C factor represents the effect of soil disturbing activities, plants, crop sequence and productivity level, soil cover and subsurface bio-mass on soil erosion [35] . Natural vegetation plays a predominant role in reducing water erosion [36] .

Vegetation indices such as the NDVI (Normalized Difference Vegetation Index) are quantitative measures, based on vegetation spectral properties that attempt to measure biomass or vegetative vigor [37] . As an indirect estimate of vegetative density, a Normalised Difference Vegetation Index (NDVI), which approximates chlorophyll density, was calculated for the study area using Landsat TM images with a spatial resolution of 30 meter.

Satellite imagery acquired during the rain season are more adapted for this application given that soil erosion is most active and vegetation cover is at its peak during this season.

where NIR is light intensity in the near-infrared, and RED is light intensity in the red band.

This index is an indicator of the energy reflected by the Earth related to various cover type conditions. When the measured spectral response of the earth surface is very similar to both bands, the NDVI values will approach zero. A large difference between the two bands results in NDVI values at the extremes of the data range.

Photosynthetically active vegetation presents a high reflectance in the near IR portion of the spectrum (0.76 - 0.90 μm), in comparison with the visible portion Red band (0.63 - 0.69 μm); therefore, NDVI values for photosynthetically active vegetation will be positive. Areas with or without low vegetative cover (such as bare soil, urban areas), as well as inactive vegetation (unhealthy plants) will usually display NDVI values fluctuating between −0.1 and +0.1. Clouds and water bodies will give negative or zero values.

The following formula was used to generate a C factor surface (Figure 4(b)) from NDVI values [38] -[40] :

where a and b are unitless parameters that determine the shape of the curve relating to NDVI and C factor. According to Van der Knijff an a-value of 1 and a b-value of 2 seem to give reasonable results [38] .

The P factor is the soil-loss ratio with a specific support practice to the corresponding soil loss with up and down slope tillage [41] . Usually, the conservation practice factor corrects the USLE estimation for management and tillage practices that protect the soil from erosion.

In the present study the P factor map was derived from the land use/land cover and support factors. The values of P factor ranges from 0.55 to 1 (Figure 4(c)), in which the high value is assigned to areas with no conservation practices; the minimum values correspond to built-up-land and plantation area with strip and contour cropping.