Impact of Land Use and Land Cover Change on Soil Erosion Using Rusle Model And Gis: A Case of Temeji Watershed, Western Ethiopia

The impact of Land Use/Land Cover (LULC) conversion on soil resources is getting global attention. Soil erosion is one the critical environmental problems worldwide with high severity in developing countries due to land degradation. This study integrates the Revised Universal Soil Loss Equation (RUSLE) model with a Geographic Information Systems (GIS) to estimate the impacts of LU/LC conversion on the mean annual soil loss in Temeji watershed. In this study, LU/LC change of Temeji watershed were assessed from 2000 to 2020 by using 2000 Landsat ETM+ and 2020 Landsat OLI/TIRS images and classied using supervised maximum likelihood classication algorithms. Results indicates that majority of the LU/LC in the study area is vulnerable to soil erosion. Our ndings show that cultivated land had the highest average soil loss rate in Temeji watershed. High soil loss is observed when grass and forest land were converted into cultivated land with mean soil loss of 88.8t/ha/yr and 86.9t/ha/yr in 2020. Results revealed that about 6608.5ha (42.8%) and 8391.8ha (54.4%) were categorized under severe classes in 2000 and 2020, respectively. results


Background
Soil erosion is a critical environmental problem worldwide (Li et al. 2014;Ganasri and Ramesh 2016). At global level, 75 billion tons of soil is removed and 20 million hectares of land lost each year by erosion (Dabral et al. 2008). Soil erosion severity is in uenced by the land use land cover (LULC) type and the cumulative effects of land use and management. For instance, study by Benaud et al. (2020) con rmed that inappropriate land management can enhance soil erosion. Human dominated landscape is more vulnerable to soil erosion than other landscape. Study by Han et al. (2020) indicates that agricultural land experienced more severe erosion than forest and grass land cover. The amount of rainfall, slope and soil types are the fundamental factors determining the severity of soil erosion (Quan et al. 2020;Kiani-Harchegani et al. 2019). These factors are measured using the Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information Systems (GIS). Soil erosion is very high in the highland areas of Ethiopia; characterized by steep slopes, and intensive rainfall (Moges and Taye 2017;Welde 2016;Hailu et al. 2015).
So far, substantial studies have been conducted recently to analyze the impacts of LU/LC on soil erosion in Ethiopia (e.g.; (Alemu and Melesse 2020;Aneseyee et al. 2020;Belihu et al. 2020;Desta and Fetene 2020;Gashaw et al. 2020;Woldemariam and Harka 2020;Kidaneet al. 2019;Kassawmar et al. 2018;Tadesse et al. 2017). However, detail information on soil loss from each land use category is uncertain in several places. Moreover, soil loss estimation during land use conversion from one type to another was not well studied yet. Adequate information on soil loss hazard and LU/LC change is limited for Temeji watershed. Therefore, this study was aimed at analyzing the impact of LU/LC on soil erosion with special emphasis on land conversion though applying Land Use Transfer Matrix (LUTM) method.

Materials And Methods
Description of the study area Temeji watershed is located in the Abay river basin. The study area lies between 9 0 27′30″ and 9 0 36′50″ N and 36 0 57′40″ and 37 0 4′40″ E. Administratively, Temeji watershed is located in Horo district of Horo Guduru Wollega Zone of Oromia National Regional State in Western Ethiopia (Fig.1). The altitude of the study area varies from 1839-3174 above mean sea level. It covers an area of about 15,434ha.

Methods
The integration of RUSLE model and GIS technology was used following (Mohammed et al. 2020;Olorunfemi et al. 2020;Kidaneet al. 2019;Zerihunet al. 2018;Gashaw et al. 2017;Ostovar et al. 2017;Ganasri and Ramesh 2016;Galagay and Minale 2016;Prasannakumar et al. 2012) to determine the impact of LULC on soil erosion in Temeji watershed. The RUSLE model combines various parameters which were acquired from different sources ( Table 1). The overall methodology owchart for this study was indicated in (Fig. 2).

Annual soil loss estimation method
The RUSLE model (Renard et al. 1997) was adopted to estimate the annual soil loss on eld slopes. The RUSLE model is highly recommended to soil loss estimation due to its compatibility suitability with GIS technology (Jasrotia and Singh 2006;Prasannakumar et al. 2012) and applicability in limited data conditions (Belayneh et al. 2019). This model was widely used to estimate the mean annual soil loss at worldwide (Woldemariam and Harka 2020;Kidane et al. 2019;Yesuph and Dagnaw 2019;Renard et al. 1997). The total annual soil loss was estimated by raster grid spatial analysis of the six parameters (Renard et al. 1997;Hurni 1985;Wischmeier and Smith 1978). The mean soil loss (A) due to erosion per unit area per year Soil erosion prediction using RUSLE for central Kenyan highland conditions was quanti ed by RUSLE model (Renard et al. 1997) using Eq.1.
Where A is annual soil loss in t/ha/year, R is the rainfall runoff erosivity factor in (MJ/mm/ha/year), LS is the slope length and slop steepness factor, C is the cover and management factor, and P is the conservation practice factor.

Rainfall erosivity (R) factor
Rainfall-runoff erosivity is the primary factor causing soil erosion and accounts for about 85% land degradation in the world (Angima et al. 2003). The R factor quanti es the impact of rainfall on erosion rate (Kayet et al. 2018). Geo-statistical interpolation was used to develop continuous raster grids of the long year average annual rainfall following (Kidane et al. 2019). Mean historical rainfall data (20 years) was collected from Ethiopian National Meteorology Agency (Table 2). Using 20 years precipitation data, the R factor ( Fig. 3) in MJ mm ha -1 h -1 per year was calculated in ArcGIS raster calculator as indicated in Eq.2.
Where (R) is the rainfall erosivity factor, and P is the mean annual rainfall (mm).

Soil erodibility (K) factor
Soil erodibility factor shows the mean long-term soil and soil pro le response to the erosive power associated with rainfall and runoff (Millward and Mersey 1999). K factor indicates the sensitivity of soil to erosion (Kayet et al. 2018). For soil erodibility estimations, soil type and color method were adapted from Hurni (1985) as indicated in (Table 3). Soil types for Temeji watershed were obtained from Oromia Water Work Design and Supervision Enterprise (OWWDSE) to associated soil types and color. For each soil type, K value were assigned and converted to raster grid in ArcGIS environment. A 1:1,000,000 scale map of the soil was used withing ArcGIS environment to determine the erodibility (K) values for each soil type (Fig 4).

Slope length and steepness (LS) factor
The LS factor indicates the impact of topography on soil erosion process. It is the combined effects of slope length (L) factor and the slope steepness (S) factor (Fig 5). There is a direct relationship between slope length and erosion rate (Wchmeier and Smith 1978). As a result, erosion increases as slope length increases. This study used the DEM-ASTER at 30-meter resolution downloaded from US Geological survey. The LS is the ratio of observed soil loss related to the soil loss of standardized plot (22.13) as indicated in (Schmidt et al. 2019). The LS value is considered to have values between 0.02-48 for Ethiopian condition (Hurni 1985) and the study area is ranging from 0-21.32. The length slope (LS) factor was calculated with the support of ArcGIS software spatial analysis using the DEM and slope following equations developed by Moore and Burch (1986) and used by (Mohammed et al. 2020;Kidaneet al. 2019;Ostovari et al. 2017) using Eq.3.
Where LS is the slope length and the slope steepness factor, cell size the size of the grid cell, and the sin slope is the slope degree value in sin.

Cover management (C) factor
In the RUSLE model, the C-factor show the effect of vegetation/crop cover and management practices on soil erosion rate (Ostovar et al. 2017;Millward and Mersey 1999;Renard et al. 1997). The C factor ranges between zero (no susceptibility to soil erosion due to well protected and managed land) to value one (1), which depict high susceptibility to erosion due to lack of protective cover (Mohammed et al. 2020;Olorunfemi et al. 2020;Ganasri and Ramesh 2016). The C values for each LU/LC types were assigned (Table 4). The LU/LC map of the watershed was classi ed using 30*30 m cloud free Landsat7ETM+ and 8 OLI/TIRS satellite images taken in March 2000 and 2020 downloaded from USGS website (http://earthexplorer.usgs.gov), respectively.

Support practices (P) factor
The support practices (P) factor is the ratio of soil loss with speci c support practice to the corresponding soil loss with up and down cultivation (Millward and Mersey 1999;Wischmeier and Smith 1978). Similar to C-values, the P-values ranges from Zero to One, whereby the value zero indicates a good conservation practice and erosion resistance facility and the vale One indicates poor conservation practice and no manmade erosion resistance facility (Olorunfemi et al. 2020;Ganasri and Ramesh 2016;Renard et al. 1997). Because of lack of conservation practices related data in the study watershed the p-factor values were taken from literature review which varies between 0.53 to 0.9 (Fig. 6). The p-values was estimated based on conservation practices, slope and land use land cover types as used by (Kidane et al. 2019).

Results And Discussion
Land use/land cover (LU/LC) change The spatial extents of different LU/LC are presented in Fig 7 (2000 and 2020). The LU/LC of the study area was classi ed into ve major classes: Bare land, cultivated land, forest, grass land, and settlement.
Among the existing land use, cultivated land constituted the largest coverage, which is about 8805.9ha (57.1%) and 11181.0ha (72.4%) in 2000 and 2020 respectively. The LU/LC analysis show that the cultivated land spatial coverage is increasing overtime. Similar results are obtained by Negassa et al. (2020), which reports that cultivated land is increased by 50.8% around Komto protected forest priority in East wollega zone. The cultivated land increase with rate of 118.75ha/year. The agricultural land expansions were at expense of forest and grasslands. This nding is supported by other studies (Belihu et al. 2020;Shang et al. 2019). The forest and the grass land cover are the 2 nd and 3 rd coverage both in the year 2000 and 2020 (Table 5).
The declining trends of forest and grassland in the study resulted in land degradation predominantly soil erosion. Reduction of forest and grass land area resulted in an increase in surface runoff (Shang et al. 2019). Deforested lands are exposed to the potential impacts of rain drops, which accelerate the detachment, removal and transportation of soil particles (Kidane et al. 2019). Additionally, rapid population growth enhances the over-exploitation forest resources for agricultural activities that contributes land degradation particularly on steep slopes. The use of forest products for energy consumptions and house construction are another factor that accelerates the declining of forest coverage in the study area.

Land use transfer matrix (LUTM) analysis
In this study, LUTM (post classi cation) method was used to detect LU/LC change from 2000 to 2020.The LUTM method is derived from the quantitative description of state transition system analysis ( Fig. 8). The LU/LC matrix was produced by overlaying two LU/LC maps of the same area to shows probability that one particular LU/LC category changed into other land cover category. From the ve LU/LC classes, cultivated land is the most vulnerable, while the forest land use class is the least vulnerable to soil erosion (Table 6). Soil is highly eroded especially, when other LU/LC is converted to farm land. The result is in line with ndings of (Negassa et al. 2020).

Analysis of soil erosion using RUSLE model
The estimated mean annual soil loss of Temeji watershed is presented in Table 6. The mean annual soil loss was determined by a cell by cell analysis of the soil loss surface by multiplying the RUSLE factors. In this study, we evaluated the impact of LU/LC change on soil erosion for the year 2000 and 2020.The result of soil erosion map of each LU/LC for the two periods was presented (Fig. 9).
More than 50% of the total area of the watershed is grouped under severe category i.e., majority of the LU/LC of the study area is highly vulnerable for soil erosion (Table 7). This result has a reasonable agreement with (Haregeweyn et al. 2017;Belayneh et al. 2019). The high vulnerability of Temeji watershed to soil erosion is associated with agricultural encroachment to forest and grass land. Similar research nding was reported by (Kidane et al. 2019) in west Shewa zone of Oromia National Regional state in Ethiopia, which report that the local communities continue to expand their cultivated land to more erosion prone areas. The conversions of the original forest cover into farmlands and grass land caused a decline in forest cover. Similarly, reduction in grassland covers was largely caused by the conversion of its initial extent into farmlands (Esa et al. 2018). The result indicated that the conversions of various LU/LC classes to cultivated land was the most detrimental to soil erosion, while forest was the most effective barrier to soil loss (Sharma et al. 2010).

Conclusions
This paper reveals the application of empirical soil erosion model such as RUSLE integrated with GIS to assess the impact of LU/LC on soil erosion in Temeji Watershed, Western Ethiopia. An effort has been made to analyze the impact of change in LU/LC on soil erosion. The quantitative indication obtained through interpretation of satellite images indicated that majority of the LU/LC of the study area is highly vulnerable for soil erosion particularly; cultivated land is the most susceptible land use/land cover for soil erosion. Soil is highly eroded especially, when other land use /land cover is converted in to farm land.
Thus, soil and water conservation measures should be undertaken in the study area to minimize the loss of soil through erosion.