RUSLE model based Annual Soil Loss Quantification for soil erosion protection in Fincha Catchment, Abay River Basin, Ethiopia.

: The quantity of soil loss as a result of soil erosion is dramatically increasing in catchment where land resources management is very weak. In this paper, a RUSLE model-based soil loss quantification technique is presented to estimate the annual soil loss and identify the severity of the erosion in the catchment. This study uses Fincha catchment in Abay river basin as the study area to quantify the annual soil loss by implementing Revised Universal Soil Loss Equation (RUSLE) model developed in ArcGIS version 10.4. Digital Elevation Model (12.5 x 12.5), LANDSAT 8 of Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Annual Rainfall of 10 stations and soil maps of the catchment were used as input parameters to generate the significant factors. Rainfall erosivity factor (R), soil erodibility factor (K), cover and management factor (C), slope length and steepness factor (LS) and support practice factor (P) were used as soil loss quantification significant factors. A model builder for the RUSLE model was developed and raster map calculation algebra was applied in ArcGIS version 10.4 to quantify the total annual soil loss. It was found that the quantified average annual soil loss ranges from 0.0 to 76.5 t ha-1 yr-1 was obtained in the catchment. The area coverage of soil erosion severity with 55%, 35% and 10% as low to moderate, high and very high respectively were identified. The information about the spatial variation of soil loss severity map generated in RUSLE model has a paramount role to alert land resources managers and all stakeholders in controlling the effects via implementation of both structural and non-structural mitigations. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss quantification that can help for protection practices.


INTRODUCTION
Soil erosion is one of the current challenging issues of agriculture causing soil degradation. The severity of soil erosion is very serious in a country where land management is very weak (Thapa, 2020). The decrease in agricultural productivity (Girmay et al., 2020), ecosystem disturbances, and water resources pollutions are some of the major ill impacts of soil erosion that are commonly happening in the world (Borrelli et al., 2020). The topographic conditions, land use land cover, the intensity of the rainfall, and the soil characteristics are major significant factors of soil erosion (Yan et al., 2018). The annual dramatic increment of the depletion of very important soil nutrients (fertility) exposes the residents of this catchment to high expenses of money (Panagos et al., 2018) to use artificial fertilizers to increase the yield. The intrusion of runoff from eroded soil into a water source (Hategekimana et al., 2020) invites harmful pollutants and chemicals to change both the Water Assessment Tool (SWAT), and Water Erosion Prediction Project (WEAP) are commonly used soil loss model (Fayas et al., 2019).
The application of integrated Geographical Information System (GIS) and remote sensing technologies in areas of the earth's surfaces are getting global attention and are widely used (Enea et al., 2018), (Singh & Panda, 2017). The simple empirical function called Universal Soil Loss Equation (USLE) is the most commonly used model for loss assessment and later changed into Revised Universal Soil Loss Equation (RUSLE) was adopted to different catchments in Ethiopia (Dinka, 2020). This paper was conducted in Fincha Catchment where the soil is highly vulnerable to erosion, however, where such studies are not undertaken. Knowing the information about the spatial variation of soil erosion severity is a very important tool for implementing a protective measure in land resource management (Yesuph & Dagnew, 2019). In the Fincha catchment, two man-made reservoirs and a natural lake reservoir are there, and the majority of water sources in this catchment are serving as water supply sources.
As a result of the topographic conditions, the intensity of rainfall, a traditional way of agricultural practices, and the nature of the soil in this catchment; the majority of the agricultural lands are prone to water derived erosion. Therefore, for this particular study area, an integrated GIS and RUSLE model-based soil loss quantification model was used to develop a spatially varied soil erosion severity map which is very important for sustainable land resources management strategies.

Study Area
This study area is conducted in the Fincha catchment ( Fig.1) in Abay River basin, and particularly the study under consideration was delineated fixing the outlet at Fincha River.

Data Collection
To generate soil erosion significant factors namely; R-factor, P-factor, K-factor, LS-factor, and C-factor, annual average precipitation of 11 stations (Table 1 and

Soil Loss Models
Researches have been widely undertaking for developing a reliable soil loss estimation.

RUSLE model
Of soil loss models, Revised Universal Soil Equation (RUSLE) is a popular and commonly used reliable model for annual soil loss quantification(Birhanu Iticha & Takele, 2019). In this study, RUSLE was used to generate the spatially varied soil erosion severity map by combining five factors. The thematic maps of the raw input parameters of the significant factors (R-factor, P-factor, K-factor, LS-factor, and C-factor) were prepared with 12.5 x 12.5 m spatial resolution. A map algebra algorithm in spatial tool analyst and a model builder (Fig.4) developed in ArcGIS were implemented to combine the significant factors using the RUSLE equation as shown below. The general flowchart showing the detailed procedures and data needed is summarized in (Fig. 3).
Where A The total annual soil loss (t/ha per year), t is the thickness of lost soil R Rainfall erosivity factor (MJmm ha -1 h -1 year -1 ) K Soil erodibility factor (t haMJ -1 mm -1 ) LS Slope length and steepness factor (dimensionless) C Over and management factor (dimensionless) P Support practice factor (dimensionless)

Rainfall erosivity factor (R)
The rainfall erosivity factor (R) describes the relationship between the rainfall intensity and the soil responses to it (Prasannakumar et al., 2012) (Abdulkadir et al., 2019). In this catchment, the spatial variability of the intensity of the rainfall varies from 1353.65 mm to 2030.93mm.
There is a positive relationship between the intensity of the precipitation and the soil characteristics in such a way that if the intensity is very high, there is the probability of severe soil erosion (Yesuph & Dagnew, 2019). Ten years of historical mean annual precipitation of 11 stations contributing to the catchment were considered in this study to generate information about erosivity in the area. An aerial raster geodatabase of rainfall was generated from the point rainfall historical data using Inverse Distance Weight (IDW) interpolation technique (Dessalegn et al., 2017) with 12.5 x 12.5 m spatial resolution using the regression equation developed by (Eq.2) = 1.24 * 1.36 … … … … … … (2) Where R Rainfall erosivity factor (MJmm ha -1 h -1 year -1 ) P Annual mean precipitation (mm)

Soil Erodibility factor (K)
The properties of soil and the degree of erodibility are interconnected parameters. When the drop of rainfall hits the soil particles, there is a high probability of disintegration in soil particles when the soil hitting external force exceeds the cohesion forces between soil particles (Kayet et al., 2018). The ability of soil particles in persisting against rainfall is different in different soil types and this property is expressed in terms of erodibility factor (Ayenew et al., 2018). In this catchment there are more than 10 soil types are available and reclassified into 6 dominant types ( Fig. 5 and Table 2) and k-values were assigned.

Topographic Factor (LS)
The severity of the spatial variability of soil erosion highly relies on the topographic conditions of an area. The steepness or the flatness of an agricultural land governs the degree of the erodibility of soil particles. The speed of the water flowing over soil and the slope of the topography are dependent parameters (Kayet et al., 2018). The length of the slope and slope steepness of the area in the study area was generated using Digital Elevation Model (DEM) of 12.5 x 12.5m spatial resolution and LS-factor was generated Where LS Slope length and steepness factor (dimensionless)

Cover and Management factor (C)
The types of cover and land use in agricultural land is highly interrelated factors. Raw input DEM was corrected by applying fill and flow direction in ArcGIS using spatial tool analyst. Slope (%) and flow accumulation were generated for the study area and reclassified based on the c-values in spatial tool analyst. The types of land use land cover (LULC) in the study area (Fig.7) and the corresponding c-values (Table 3) were assigned.
In the same fashion, the values for support practice factor (P) were generated from the land use land cover map.

Soil Loss Quantification in Fincha catchment
The quantified annual soil loss values are generally ranging from 0 to 76.5 in thickness  [9] was also repeated in this study. The area covered in the percentage of soil erosion severity of 10%, 45%, 30%, and 15% as low, moderate, high, and very high respectively were identified (Fig. 5). As revealed in the severity map generated using the RUSLE model (Fig. 13), the agricultural lands which cover 65% (13.96 ha) of the total area are highly vulnerable to erosion and the qualitative classification of the area is between high to very high to the soil loss risk.
The effect of the erosion is very visible in this catchment when compared to estimated soil loss in other catchments [4]. In terms of the significant factors; rainfall erosivity (Rfactor), cover and management(C-factor), and support and conservation practice (Pfactor) factors revealed high significance while the other factors are relatively low significant for the initiation of soil erosion, and the values of the corresponding factors were shown in Fig.6-8 respectively. The spatial variability of mean annual rainfall shown in (Fig.6) showed that the majority of croplands and soil types in the lower part of the catchment is very sensitive to soil loss and this fact is observed in the severity map ( Fig.13). The rate of soil loss seen in the catchment is higher than the total annual soil formation rate ranges from 2 to 22 t/ha per year for the different land uses units of Ethiopia [8] and special attention should be given to minimize the rate of soil loss in the catchment by implementing soil formation strategies or soil and resources management strategies. Support and conservation practices factor (P-factors) values were assigned based on the soil types in the study area. The soil map was reclassified into six dominant soil types and the corresponding p-factor values were given (Fig. 11).

Figure 4: Rainfall erosivity factor (R-factor) values in Fincha catchment
The low-land agricultural areas in this catchment are less vulnerable to soil erosion and relatively less soil loss is visible[16] due to the low velocity of runoff water. The traditional way of agricultural systems and soil conservation practices is very weak in the lower part of the study area, therefore, the support and conservation practices factor (p-factor) reveals that the croplands and bare land are very exposed and sensitive to erosion due to the incoming runoff water from the highland areas[16]; Kayet et al., 2018). The slope length and steepness (LS) is also another factor that describes the sensitivity of soil to erosion that governs the velocity of runoff water exerting high pushing forces on the soil particles and causing detachment of soil particles, which in turn lead to erosion. In this catchment, the slope ranges from 0 to 79.9 %, and due to the steepness of the slope, the soil loss is very visible especially for the slope values of more than 11% [6]. The ranges of slope in degree (Fig.9) and the corresponding LS-factor values were generated (Fig.10) according to the studies conducted by (Dinka, 2020;Thapa, 2020). A model builder for the RUSLE model was developed and raster map algebra was applied in ArcGIS version 10.4 to quantify the total annual soil loss. It was found that the quantified average annual soil loss (Fig.13) which ranges from 0.0 to 76.5 t ha-1 yr-1 was obtained in the catchment. In this study, qualitative classification based five erosion severity classes as very high (> 45 t ha -1 year -1), high (30-45 t ha -1 year -1), moderate (15-30 t ha -1 year -1-), low (0-15 t ha -1 year -1-) were identified ( Fig.12 and

Conclusion
The quantification of soil loss using an integrated RUSLE model and GIS is successfully provided a qualitative classification-based identification of soil loss severity understandings in Fincha catchment of Abay river basin in Ethiopia. In general, five erosion severity classes as very high (> 45 t ha -1 year -1), high (30-45 t ha -1 year -1), moderate (15-30 t ha -1 year -1-), low (0-15 t ha -1 year -1-). The soil erosion-prone areas map generated in this catchment provides necessary information for soil and land resources management practices for the implementation of either structural or nonstructural soil conservation measures. From this study, it was found that the upper and the low-lying areas are highly vulnerable to soil erosion and a soil conservation strategy should be implemented to control the loss of top fertile soil in the catchment.
Additionally, capacity building training should be given for the farmers and soil conservation experts to minimize the man-made soil loss driving factors such as