Modeling of Soil Loss and Identification of Erosion Hot Spot Areas Using RUSLE Integrated with GIS for Appropriate Conservation Practices in Muga Watershed, Highlands of Ethiopia

Abstract Over cultivation, deforestation and free grazing are major factors facilitating soil erosion. Nowadays; in lower parts of Muga watershed soil erosion become as a continuous environmental problem. In this study an attempt has been made to modeling soil loss and identify the most erosion sensitive areas by using Revised Universal Soil Loss Equation integrated with GIS and remote sensing techniques for planning appropriate conservation measures in Muga watershed. The annual soil loss amount was estimated by using the Revised Universal Soil Loss Equation (RUSLE). Digital Elevation Model, digital soil map, thirty years rainfall records of six stations, and land cover data (Landsat images) were used to develop RUSLE soil loss variables. The annual soil loss rate from the catchment were estimated by integrating RUSLE parameters using raster calculator tool. The annual soil loss rate varies between 0.02 ton/ha/yr and 41.789 ton/ha/yr. The total annual soil loss in the watershed was 59751.41 tones, of these, 12806.15 tons were lost from 371.19 km, 26562.44 tons from 214.30 km, 15300.94 tons from 50.52 km, 4059.05 tons from 4.61 km, and 1022.83 tons from 0.37 km of land per year. The rate of soil eroion was high in the lower part of the watershed. Slope gradient and length factor was the main factor for soil erosion increment followed by Support Practice (P) factor. As result of soil erosion cross tabulation; steep slopes, Rendzic leptosols and dominantly cultivated areas were detected as very severe erosivity. Therefore, the lower parts of the study needs to undertake effective soil and water conservation practices.


Methodology 2.1 Description of the Study Area
Muga watershed is found in Amhara region between 10°6'30''N to 10°43'30''N latitude and 37°49'00''E to 38°16'30''E longitude and it has an area of 641 km 2 (Figure 1). Muga River originates from Choke Mountain at an elevation of 4092 m.a.s.l and flows to Abbay River. The agro-climatic zones of the study area are attributed by wet/moist dega (temperate like climate-highlands) and kola (hot and arid type) zones (NMSA, 2001). The study area characterized as a wet season from June to September and dry season from October to May. The mean annual precipitation of the study area is found 1445 mm, and the minimum and maximum temperature are 5 °C and 25 °C respectively. Soil groups which are found in the study area are Eutric Vertisols (56.68%), Chromic Luvisols (28.55%), Humic Nitosols (5.42%) and Rendizic Leptosols (9.35%). The topography of study area ranges between 1199 m.a.s.l and 4092 m.a.s.l (Demeke and Andualem, 2018).

Sources and Techniques of Data Collection
For this study, both secondary and primary data sources were used. By using Global Positioning System (GPS) instrument, both field survey and observation was conducted for actual primary data collection. The land use land cover map of the study area were developed by using supervised image classification and Ground Control Points (GCPs) for validating the accuracy of image classification. Secondary data were collected from different organizations like meteorological data  from National Meteorology Agency (NMA); soil and Digital Elevation Model (20*20 m) from Ministry of Water, Irrigation and Electricity (MoWIE), land use/cover map of 2013 from Ethiopian mapping agency. The slope length-steepness (LS) factor was derived from digital elevation model.

Soil Loss Estimation
In this study, Revised Universal Soil Loss Equation (RUSLE) integrated with Remote sensing and GIS technique was used to estimate the mean annual soil loss. RUSLE is expressed as equation 1: Where A is the annual soil loss (tons/ha/year); R is the rainfall erosivity factor (MJ mm/ha//h/yr); K is the soil erodibility factor [tons/ha/year]; LS is the slope length-steepness factor (dimensionless); C is the cover and management factor (dimensionless, between 0 and 1); and P is the support practice factor (dimensionless, ranges from zero to one).
To calculate the amount of soil loss rate in the study area, all the erosion factors (R, K, LS, C, and P) were determined based on the previous recommendations to Ethiopian highlands (Hurni, 1985a). The spatial distribution of soil loss was developed by integrating the erosion influencing factors in Arc GIS the raster calculator geoprocessing tool.

Slope Length -Steepness (LS) Factor
The topographic LS factor (Slope length and slope steepness) was determined using equation 3 (Moore and Burch, 1986a and b;Engel, 2005)

---(3)
Where LS is slope steepness-slope length factor; flow accumulation and slope derived from conditioned digital elevation model. Support Practice (P) Factor: Erosion control practice factor (P-factor) is the ratio of soil loss with a specific support practice to the corresponding loss with upslope and downslope cultivation (Wischmeier and Smith, 1978). The P-value for the this study were assigned based on land use class regardless of the slope class they have (Table  3).  Wischmeier and Smith (1978)

Cover and Management(C) Factor
The cover and management (C) factor represent the ratio of soil loss from land with specific vegetation to the corresponding soil loss from continuous fallow (Wischmeier and Smith, 1978;Morgan, 2005). C-factor for this study area has been determined from land use of the area (   Figure  2).

Estimation of Soil Loss and identification of hotspot areas
In this study, RUSLE equation was integrated with Arc GIS 10.1 technique to conduct cell by cell calculation of mean annual soil loss rate (ton/ha/year) and to identify high erosion hot spot areas in Muga Watershed. Raster map of each RUSLE parameters was derived from different data sources and discussed as follows.

Rain Fall Erosivity(R) Factor
The values of R-factor (Table 5) in Muga watershed ranges from 664.53 (Mota station) to 772.74 (Bichena station). The R-factor values for each station was assigned with color ( Figure 3a).

Soil Erodibility (K) Factor
The K-value for each major soil class (Figure 3b) was assigned with special reference to their color . Chromic Luvisols have high k-value (0.158) which are highly affected by erosion. Humic Nitisols and Rendzic Leptosols have low soil erodibility factor with k-value of 0.128 and 0.127 respectively. These soils are less susceptible to detachment and erosion. An area of 182.99 km 2 was highly erodible while 59.92 km 2 was less erodible to soil erosion (   Figure 3. (a) R-factor and (b) K-factor Slope Length and Steepness (LS) factor LS-factor was computed using equation (3) suggested by Moore and Bruch (1985); Mitasova and Mitas (1999); and Simms et al., (2003). According to the analysis, the LS factors of the watershed were found in the range of 0.275 to 21.786. Upstream and downstream parts of the watershed had the highest value of LS and middle part of the watershed had the lowest value of LS which was distributed in gently flat to undulating terrain. This indicated that the upstream and downstream parts of the study area are highly sensitive to soil erosion ( Figure 4a).

Support Practice (P) Factor
The values of P-factor were found between 0.8 and 1.0 (Table 8). The highest P-value was found in the upstream part of the study area, while the smallest values were observed in the downstream and middle parts of the watershed (Figure 4b).

Cover and Management (C) Factor
As shown in table 9, the value of C-factor was found between 0.01 and 0.15. The dominantly cultivated land has a large value of c-factor (0.15) which is highly vulnerable to erosion. Next to dominantly cultivated land, moderately cultivated land (0.1) is moderately vulnerable to soil erosion. Afro-alpine and urban having the c-factor values of 0.01 and 0.05 respectively are less vulnerable to erosion (Figure 4c).

Estimation of Soil Loss
RUSLE is an empirically based model that has the ability to predict long term average annual rate of soil erosion on slopes using data on rainfall pattern, soil type, and topography, conservation, and management practices. Potential annual soil loss was estimated from the product of factors (R, K, LS, C, and P) for the Muga watershed ranges from 0.02 -41.489 ton/ha/yr. The estimated level of soil loss rate was classified into five classes as shown in figure 5. The classified soil loss map showed that 99.22% of the total area falls under the tolerable rate followed by 0.78% of the total area comes under critical soil. Table 10 showed that there are 12806.15 tons of soil loss from 371.19 km 2 area, 26562.44 tons soil loss from 214.30 km 2 area, 15300.94 tons soil loss from 50.52 km 2 area, 4059.05 tons soil loss from 4.61 km 2 area, 1022.83 tons soil loss from 0.37 km 2 per year. Results revealed that severe to extreme severe erosion risk areas found in the downstream parts of the watershed due to the presence of steep lands, over cultivation, high rainfall and overgrazing. In the middle part of the watershed, it was observed the low erosion damage area.  Figure 5 showed the spatial distribution of erosion hotspot area and it revealed that the potential soil loss was greater along the steep slope areas. In downstream parts of the watershed over cultivation has been practiced without implementing any soil and water conservation measures which leads to the major soil erosion risks.
Generally, removal of vegetation cover through deforestation, cultivation of steep slope, overgrazing, ploughing the land several times, the practice of removing plant residues, poor physical soil and water conservation measures can cause for high soil loss in the study areas (Demeke and Andualem, 2018).

Conclusions
Using RUSLE parameters by GIS and Remote sensing techniques guarantee the handling of spatially variable data and inaccessible area easily and efficiently. The findings of this study incorporates spatially distributed soil loss rate of Muga watershed. The annual soil loss of the watershed extends from 0.02 to 41.789 t/ha/year. Very high Journal of Environment and Earth Science www.iiste.org ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online) Vol.9, No.10, 2019 soil loss was observed in the lower parts of the watershed at a rate exceeding the tolerable soil loss limit. This was due to the steepness of the slope and low management practices /absence of supporting the practice. This sever soil loss area could threaten agricultural productivity and extends its offsite effect of sedimentation on Abay River. In this study, slope length and gradient (LS) factor was the primary influential RUSLE parameter followed by Support Practice (P) Factor.
Substantial investment on soil and water conservation measure could be done in the lower part of the watershed irrespective of the watershed logic (conservation measures should be started at the upper part of the watershed). RUSLE does not consider gully erosion which is now seriously dissected and fragments grazing and farmlands. Thus, further study on gully erosion estimation and sedimentation is recommended.