Estimate Soil Erosion Research Articles

Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing

An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources.

Estimation of soil erosion and sediment yield concentration across the Kolleru Lake catchment using GIS

Flat lakes with a large catchment area are especially affected by sediment inputs. The Kolleru Lake catchment (south-eastern India) with a surface area of approximately 6121 km2 belongs to such types of lake basins. The study’s main objective was the assessment of both soil erosion and sediment yield concentration rate of the Kolleru catchment. The study was conducted using the revised universal soil loss equation (RUSLE) model due to its simple and good applicability for soil erosion estimation. Data such as rainfall, soil texture, topography, crop cover management, and support practice factor were integrated into the modeling using RUSLE and ArcGIS. Field data were used both to analyze the soil texture and the slope length factor. The results showed that average annual soil loss was estimated with 13.6 t/ha/year, classifying the Kolleru Lake Basin under a very high erosion rate category. About 38% of the catchment area has encountered slight soil loss. Areas covered with moderate, strong, severe, very severe erosion potential zones are 29%, 17%, 9%, and 5.5%, respectively. This study identified that upland areas with less vegetation cover exported high potential erosion rates. Unlike the soil loss, the sediment delivery ratio values for the catchment were not affected by land use, while it showed a strong relationship with the catchment drainage system. Whereas, the average annual sediment yield was determined with 7.61 t/ha/year, had identified with the same pattern of the soil erosion. Catchment topography, vegetation, drainage system, soil properties, and land use cover played a major role in exporting the highest sedimentation. The outcome of these studies can be used among others to identify critical erosion areas on a pixel basis for the planning of erosion management practices.

تطبيق نموذج RUSLE لتقدير انجراف التربة في قطاع غزة / فلسطين باستخدام نظم المعلومات الجغرافية

The study aimed to calculate the amount of soil erosion in Gaza Strip and identify areas of most exposed to soil erosion risk to take appropriate measures. The study used a Digital Elevation Model of the type of DSM from the Japanese satellite ALSO with a resolution of 30 metre, a satellite image of the satellite Landsat 8 taken on 29/10/2018, and GIS techniques in the calculation of the elements of the Revised Universal Soil Loss Equation (RUSLE). The results showed that the soil drift values in the study area range between 0.11-504 tons/ha/year, an average of 4.7 tons/ha/year, and that the total amount of soil in the Gaza Strip is 18,66658 tons/year, and the areas at risk of a very high drift account for 10% of the Gaza Strip, and are concentrated in Gaza City and the valleys (Gaza Valley, Beit Hanoun Valley, Salqa Valley) and In the coastal area of the cliffs, GIS is an effective and rapid tool in studying the problem of soil erosion. The study recommended using the RUSLE model to integrate with GIS and RS techniques in estimating the risk of soil erosion, and that appropriate measures should be taken to prevent soil erosion in the study area. هدفت الدراسة إلى حساب كميات انجراف التربة في قطاع غزة، وتحديد المناطق الأكثر تعرضاً للانجراف لاتخاذ التدابير المناسبة، واستعانت بنموذج ارتفاع رقمي من نوع DSM من القمر الصناعي الياباني ALSO بدقة 30متر، وبصورة فضائية من القمر Landsat 8 التقطت بتاريخ 29/10/2018م، وبنظم المعلومات الجغرافية في حساب مفردات المعادلة العالمية لانجراف التربة. أظهرت النتائج أن قيم انجراف التربة بقطاع غزة تتراوح ما بين 0.11-504 طن/هكتار/السنة، بمتوسط مقداره 4.7 طن/هكتار/السنة، وأن إجمالي كميات التربة المجروفة تبلغ 1866658 طن/السنة. وأن المناطق المعرضة لخطر انجراف عالي وعالي جداً تشكل ما نسبته 10% من مساحة قطاع غزة، وتتركز في مدينة غزة وأحواض الأودية (وادي غزة، وادي بيت حانون، وادي السلقا)، وفي منطقة الجروف الشاطئية، وأن نظم المعلومات الجغرافية أداة فعالة وسريعة في دراسة مشكلة انجراف التربة. وأوصت الدراسة باستخدام نموذج RUSLE بالتكامل مع تقنيات نظم المعلومات الجغرافية والاستشعار عن بعد في تقدير خطر الانجراف المائي، وبضرورة اتخاذ التدابير المناسبة لمنع انجراف التربة بمنطقة الدراسة.

Soil erosion prediction in Cilebak – Cirasea Micro Watershed, Indonesia

Soil erosion impact on land degradation could threaten soil and water quality. In the mountainous area, soil erosion resulted in losses of topsoil, which containing high nutrients. The study area was located in Cilebak-Cirasea Micro Watershed, Bandung Regency, Indonesia. Landuses in this area were secondary forest (12%), upland agriculture (84%), and paddy field (4%). The objectives of this study were to assess soil erosion and study best management practices (BMP) effect to reduce soil erosion. Universal Soil Loss Equation (USLE) was used to estimate soil erosion with support from Geographic Information System (GIS). USLE parameters, which consist of erosivity, erodibility, slope length, steepness, crop, and soil management, were used to estimate soil erosion. BMP scenarios consist of application raised bed that parallel to contour lines and combination of raised bed that parallel to contour lines and straw mulch addition of 3 and 6 Mg ha−1 year−1. The result indicated that soil erosion rate under current condition was 45.4 Mg ha−1 year−1, which exceeding the tolerable soil loss. All BMP scenarios showed their effectiveness in reducing soil erosion. Proposed BMP to minimize soil erosion rate could probably be able to support government in arranging policies for maintaining or restoring crop productivity for sustainable agriculture.

Water Erosion Risk Assessment in the Kenya Great Rift Valley Region

The Kenya Great Rift Valley (KGRV) region unique landscape comprises of mountainous terrain, large valley-floor lakes, and agricultural lands bordered by extensive Arid and Semi-Arid Lands (ASALs). The East Africa (EA) region has received high amounts of rainfall in the recent past as evidenced by the rising lake levels in the GRV lakes. In Kenya, few studies have quantified soil loss at national scales and erosion rates information on these GRV lakes’ regional basins within the ASALs is lacking. This study used the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates between 1990 and 2015 in the Great Rift Valley region of Kenya which is approximately 84.5% ASAL. The mean erosion rates for both periods was estimated to be tolerable (6.26 t ha−1 yr−1 and 7.14 t ha−1 yr−1 in 1990 and 2015 respectively) resulting in total soil loss of 116 Mt yr−1 and 132 Mt yr−1 in 1990 and 2015 respectively. Approximately 83% and 81% of the erosive lands in KGRV fell under the low risk category (<10 t ha−1 yr−1) in 1990 and 2015 respectively while about 10% were classified under the top three conservation priority levels in 2015. Lake Nakuru basin had the highest erosion rate net change (4.19 t ha−1 yr−1) among the GRV lake basins with Lake Bogoria-Baringo recording annual soil loss rates >10 t ha−1 yr−1 in both years. The mountainous central parts of the KGRV with Andosol/Nitisols soils and high rainfall experienced a large change of land uses to croplands thus had highest soil loss net change (4.34 t ha−1 yr−1). In both years, forests recorded the lowest annual soil loss rates (<3.0 t ha−1 yr−1) while most of the ASAL districts presented erosion rates (<8 t ha−1 yr−1). Only 34% of all the protected areas were found to have erosion rates <10 t ha−1 yr−1 highlighting the need for effective anti-erosive measures.

Sensitivity Assessment of Spatial Resolution Difference in DEM for Soil Erosion Estimation Based on UAV Observations: An Experiment on Agriculture Terraces in the Middle Hill of Nepal

Soil erosion in the agricultural area of a hill slope is a fundamental issue for crop productivity and environmental sustainability. Building terrace is a very popular way to control soil erosion, and accurate assessment of the soil erosion rate is important for sustainable agriculture and environmental management. Currently, many soil erosion estimations are mainly based on the freely available medium or coarse resolution digital elevation model (DEM) data that neglect micro topographic modification of the agriculture terraces. The development of unmanned aerial vehicle (UAV) technology enables the development of high-resolution (centimeter level) DEM to present accurate topographic features. To demonstrate the sensitivity of soil erosion estimates to DEM resolution at this high-resolution level, this study tries to evaluate soil erosion estimation in the Middle Hill agriculture terraces in Nepal based on UAV derived high-resolution (5 × 5 cm) DEM data and make a comparative study for the estimates by using the DEM data aggregated into different spatial resolutions (5 × 5 cm to 10 × 10 m). Firstly, slope gradient, slope length, and topographic factors were calculated at different resolutions. Then, the revised universal soil loss estimation (RUSLE) model was applied to estimate soil erosion rates with the derived LS factor at different resolutions. The results indicated that there was higher change rate in slope gradient, slope length, LS factor, and soil erosion rate when using DEM data with resolution from 5 × 5 cm to 2 × 2 m than using coarser DEM data. A power trend line was effectively used to present the relationship between soil erosion rate and DEM resolution. The findings indicated that soil erosion estimates are highly sensitive to DEM resolution (from 5 × 5 cm to 2 × 2 m), and the changes become relatively stable from 2 × 2 m. The use of DEM data with pixel size larger than 2 × 2 m cannot detect the micro topography. With the insights about the influencing mechanism of DEM resolution on soil erosion estimates, this study provides important suggestions for appropriate DEM data selection that should be investigated first for accurate soil erosion estimation.

Torrential Flood Water Management: Rainwater Harvesting through Relation Based Dam Suitability Analysis and Quantification of Erosion Potential

In this study, a relation-based dam suitability analysis (RDSA) technique is developed to identify the most suitable sites for dams. The methodology focused on a group of the most important parameters/indicators (stream order, terrain roughness index, slope, multiresolution valley bottom flatness index, closed depression, valley depth, and downslope gradient difference) and their relation to the dam wall and reservoir suitability. Quantitative assessment results in an elevation-area-capacity (EAC) curve substantiating the capacity determination of selected sites. The methodology also incorporates the estimation of soil erosion (SE) using the Revised Universal Soil Loss Equation (RUSLE) model and sediment yield at the selected dam sites. The RDSA technique identifies two suitable dam sites (A and B) with a maximum collective capacity of approximately 1202 million m3. The RDSA technique was validated with the existing dam, Gomal-Zam, in the north of Sanghar catchment, where RDSA classified the Gomal-Zam Dam in a very high suitability class. The SE estimates show an average of 75 t-ha−1y−1 of soil loss occurs in the study area. The result shows approximately 298,073 and 318,000 tons of annual average sediment yield (SY) will feed the dam A and B respectively. The SE-based sediment yield substantiates the approximate life of Dam-A and Dam-B to be 87 and 90 years, respectively. The approach is dynamic and can be applied for any other location globally for dam site selection and SE estimation.

Agricultural erosion modelling: Evaluating USLE and WEPP field-scale erosion estimates using UAV time-series data

Soil erosion models, typically applied at basin and watershed scales, are rarely evaluated at agricultural field scales due to the lack of spatially-distributed time series data. A novel unmanned aerial vehicle (UAV) methodology was used to quantify farm-field soil erosion from nine UAV surveys and structure-from-motion (SfM). Using a semi-distributed approach, we evaluated soil erosion estimates from the Universal Soil Loss Equation (USLE) and Water Erosion Prediction Project (WEPP). The annual erosion rate, measured with the UAV methodology, was 18.83 t ha−1 yr−1, with USLE and WEPP predictions of 26.23 t ha−1 yr−1 and 16.41 t ha−1 yr−1, respectively. Modelled annual and sub-annual erosion rates with WEPP were within the upper-limit of predictive accuracy, while the USLE tended to systematically overestimate soil erosion rates. These outcomes have implications on the efficacy of conservation efforts, which is highlighted through a discussion and comparison of different best-management practice applications.

A framework for testing large-scale distributed soil erosion and sediment delivery models: Dealing with uncertainty in models and the observational data

Evaluating distributed soil erosion models is challenging because of the uncertainty in models and measurements of system responses. Here, we present an approach to evaluate soil erosion and sediment delivery models, which incorporates sediment source fingerprinting and sediment-rating curve uncertainty into model testing. We applied the Generalized Likelihood Uncertainty Estimation (GLUE) methodology to the Sediment Delivery Distributed model (SEDD) for a large catchment in Southeast Brazil. The model was not rejected, as 23.4% of model realizations were considered behavioral. Fingerprinting results and SEDD simulations showed a partial agreement regarding the identification of the main sediment sources in the catchment. However, grid-based estimates of soil erosion and sediment delivery rates were highly uncertain, which restricted the model's usefulness for quantifying sediment dynamics. Although our results are case-specific, similar levels of error might be expected in erosion models elsewhere. The representation of such errors should be standard practice.

Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds

The universal soil loss equation (USLE) is a widely used empirical model for estimating soil loss. Among the USLE model factors, the cover management factor (C-factor) is a critical factor that substantially impacts the estimation result. Assigning C-factor values according to a land-use/land-cover (LULC) map from field surveys is a typical traditional approach. However, this approach may have limitations caused by the difficulty and cost in conducting field surveys and updating the LULC map regularly, thus significantly affecting the feasibility of multi-temporal analysis of soil erosion. To address this issue, this study uses data mining to build a random forest (RF) model between eight geospatial factors and the C-factor for the Shihmen Reservoir watershed in northern Taiwan for multi-temporal estimation of soil loss. The eight geospatial factors were collected or derived from remotely sensed images taken in 2004, a digital elevation model, and related digital maps. Due to the memory size limitation of the R software, only 4% of the total data points (population dataset) in each C-factor class were selected as the sample dataset (input dataset) for analysis using the stratified random sampling method. Seventy percent of the input dataset was used to train the RF model, and the other 30% was used to test the model. The results show that the RF model could capture the trend of vegetation recovery and soil loss reduction after the destructive event of Typhoon Aere in 2004 for multi-temporal analysis. Although the RF model was biased by the majority class’s large sample size (C = 0.01 class), the estimated soil erosion rate was close to the measurement obtained by the erosion pins installed in the watershed (90.6 t/ha-year). After the model’s completion, we furthered our aim to address the input dataset’s imbalanced data problem to improve the model’s classification performance. An ad-hoc down-sampling of the majority class technique was used to reduce the majority class’s sampling rate to 2%, 1%, and 0.5% while keeping the other minority classes at a 4% sample rate. The results show an improvement of the Kappa coefficient from 0.574 to 0.732, the AUC from 0.780 to 0.891, and the true positive rate of all minority classes combined from 0.43 to 0.70. However, the overall accuracy decreases from 0.952 to 0.846, and the true positive rate of the majority class declines from 0.99 to 0.94. The best average C-factor was achieved when the sampling rate of the majority class was 1%. On the other hand, the best soil erosion estimate was obtained when the sampling rate was 2%.

Influence of DEM resolution on the RUSLE model: Case of abandoned quarries in Settat province (Morocco)

Topographic slope information is one of the critical variables, which governs soil erosion. This topographic slope can be derived from the Digital Elevation Model (DEM). Significant discrepancies are found in the estimation of soil erosion using different DEMs of different resolutions. In the present study, the Revised Universal Soil Loss Equation (RUSLE) was used for soils in the Settat province (Morocco) to assess the risk of water erosion caused by abandoned quarries. The soil erosion rate was divided into five classes to illustrate the erosion rate variability using two DEMs (30m and 90m). The impact of topography on erosion was determined by calculating the value of the LS factors. In this case, the values obtained vary between 0 - 120.623 for ASTER DEM (30m) and 0 - 10.225 for DEM SRTM (90m). The results also show that most quarries have a soil loss rate that varies between 0 t/ha/year and 8.1 t/ha/year for ASTER DEM (30 m). However, for DEM SRTM (90 m), the soil loss rate is zero. This suggests that RUSLE model users should use high-resolution input data for a close representation of reality and capture the maximum results with reasonable accuracy.

Comparison of the erosion prediction models– A case study of coastal region

Water erosion is one of the most serious problems of soil degradation in the world, the Konkan region of Maharashtra state is particularly exposed to this phenomenon. In recent times, the estimation of soil erosion using empirical models has been a promising research topic as their application over a large and ungauged areas remains a real challenge due to the limited availability and quality of the required data. Using the geospatial technologies, this study aims to estimate and compare the soil erosion rates by the two models of Universal Soil Loss Equation (USLE) and Modiûed Universal Soil Loss Equation (MUSLE) for Ratnagiri district in Konkan region of India. Soil loss of Ratnagiri district by USLE model was found to be43.61 t/ha/yrand sediment yield by MUSLE model was 33.45 t/ha/yr. Sediment yield was more than 70% of total soil loss, which is on higher side by 20% for Konkan region but follows the runoff percentage trend. So, MUSLE model can be used with caution of overestimation in data scare situation in heavy rainfall, hilly region of lateritic soil, wherever runoff gauging stations are not available.

《生产建设项目土壤流失量测算导则》在仁沐新高速公路中应用的合理性探讨

《生产建设项目土壤流失量测算导则》在仁沐新高速公路中应用的合理性探讨

Comparison of Two Different Calculation Methods of Fractional Vegetation Cover

Fractional Vegetation Cover (FVC) is an important parameter for soil erosion equation. The Fractional Vegetation Cover of typical soil erosion county in red soil region of South China was calculated by parameter revision method (PRM) and fusion method (FM).The results of the two methods were compared from two parts of FVC level and 24 half-months FVC. The results show that the FVC level calculated by PRM was concentrated in high level, and that calculated by FM is mainly in medium-high level and high level. The time variation of FVC in 24 half-months calculated by the two methods was the same, but the majority of FVC value calculated by PRM was higher than that calculated by FM. It is better to choose FM method to calculate FVC in estimating soil erosion.

Soil erosion estimation using remote sensing techniques in the shirvan plain of the republic of Azerbaijan

Soil erosion estimation using remote sensing techniques in the shirvan plain of the republic of Azerbaijan

Potential Soil Loss Estimation and Erosion-Prone Area Prioritization Using RUSLE, GIS, and Remote Sensing in Chereti Watershed, Northeastern Ethiopia

Soil erosion by water is the major form of land degradation in Chereti watershed, Northeastern Ethiopia. This problem is exacerbated by high rainfall after a long period of dry seasons, undulating topography, intensive cultivation, and lack of proper soil and water conservation measures. Hence, this study aimed to estimate the 23 years (1995-2018) average soil erosion rate of the watershed and to identify and prioritize erosion-vulnerable subwatersheds for conservation planning. The integration of the revised universal soil loss equation (RUSLE), geographic information system, and remote sensing was applied to estimate the long-term soil loss of the watershed. The RUSLE factors such as rainfall erosivity ( R), soil erodibility ( K), topography ( LS), cover and management ( C), and support and conservation practices ( P) factors were computed and overlayed to estimate the soil loss. The result showed that the annual soil loss rate of the watershed ranged up to 187.47 t ha−1 year−1 in steep slope areas with a mean annual soil loss of 38.7 t ha−1 year−1, and the entire watershed lost a total of about 487 057.7 tons of soil annually. About 57.9% of the annual watershed soil loss was generated from 5 subwatersheds which need prior intervention for the planning and implementation of soil conservation measures. The integrated use of RUSLE with GIS and remote sensing was found to be indispensable, less costly, and effective for the estimation of soil erosion, and prioritization of vulnerable subwatersheds for conservation planning.

A New Method for Computing the Sediment Delivery Ratio for the Hyper-Concentrated Flow Areas of the Loess Plateau, China

The sediment delivery ratio (SDR) is an important index for understanding sediment erosion, transportation and deposition features in a river basin. Based on the commonly accepted definition of SDR and the characteristics of the sediment delivery process in hyper-concentrated flow areas of the Loess Plateau, China, a new model for computing the SDR is proposed. The model is a functional relation of fractional form, in which the denominator is the surface runoff of the basin, and the numerator is the water volume needed for saturated sediment discharge in the controlling hydrological station at the exit of the basin when the saturated sediment load is equal to the measured sediment load. Using the proposed SDR equation, the long-term series of SDRs in the Yanhe River basin, a typical hyper-concentrated flow basin in the Loess Plateau, were calculated from 1952 to 2010. The results show that the long-term annual average SDR in the Yanhe River basin was 0.92, which is consistent with the results of previous studies; this finding confirms the validity and effectiveness of the method. The proposed method only requires gauge data of sediment concentration, sediment delivery rate, sediment delivery volume, and runoff ratio from hydrological stations, which makes it easy to use, and it can be used to easily estimate soil erosion in the basin.

The Role of Land Use Management on Land Degradation: The Case Study of Kashidar Basin in Golestan Province, Iran

Land resources are increasingly under pressure and there are many competing interests. However, unsustainable agricultural expansion and urbanization remain the main driving forces in land resource depletion. Suitable land use management on steep slope farmlands can decrease the destructive effects of farming. The main aim of this paper is showing the role of land use management on soil erosion. The EPM empirical model which developed in former Yugoslavia is one of the applicable models for estimating soil erosion and sediment delivery. With the help of this model, changes in the rate of erosion due to the application of different land management methods can be examined. In order to achieve the objectives of this study, Kashidar watershed in Golestan province -Iran was selected. The required maps and parameters of EPM model were determined in the desired area and with the help of Arc-Gis software, the relations and equations presented in this model, the amount of soil erosion in Kashidar watershed was calculated. The rate of soil erosion was calculated under two different management scenarios of rain fed agriculture and agroforestry system. The results of this study shows that changing the land use from dry farming on steep slope to agroforestry can decrease the soil erosion up to 70 percent. Sustainable land management practices are urgently required because of widespread resource degradation from poor land use practices. Climate change impacts are expected to further aggravate the situation.

Comparison and Applicability of Selected Soil Erosion Estimation Models

Soil erosion is a globally challenging issue that hinders agricultural productivity by enhancing land degradation and loss to the top fertile soil. Although it is a global issue, its effect is adverse on farmers dwell in developing countries. Hence, providing information on soil loss is crucial to plan and implement appropriate soil and water conservation measures. Accordingly, erosion estimation models were developed and grouped as empirical, conceptual, and physical-based broad umbrella. This review paper primarily is intended to compare the opportunities and limitations of widely implemented soil erosion estimation models and review their applicability by selecting widely used models such as: USLE, RUSLE, SLEMSA, and WEPP. The result of this review revealed that the so reviewed erosion models have been designed to predict soil loss from sheet and rill erosion. Evidence from studies indicated that R/USLE models can be universally used by calibrating to the local environmental conditions. They are simple, requires less data and computational time, however; they are not event responsive and measure soil loss from gully and stream-bank erosion. But, RUSLE model has different parameter calculation procedure than the USLE. This study also depicts the SLEMSA model treats soil erosion factors as a separate entities and is highly influenced by LS factors. The WEPP model has capability to estimate soil loss in a short time scale and out-of-place erosion rates, but; it only works for individual hillslope. Thus, based on the result of this review the following recommendations are forwarded for further study to fill the gaps; upgrading of R/USLE parameters, modification of topographic sub-model of SLEMSA, and revision of essential parameters in WEPP model to estimate erosion from large catchments.

Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia

BackgroundSoil erosion is one of the major environmental challenges and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. Quantifying and identifying the spatial patterns of soil erosion is important for management. The present study aims to estimate soil erosion by water in the Northern catchment of Lake Tana basin in the NW highlands of Ethiopia. The estimations are based on available data through the application of the Universal Soil Loss Equation integrated with Geographic Information System and remote sensing technologies. The study further explored the effects of land use and land cover, topography, soil erodibility, and drainage density on soil erosion rate in the catchment.ResultsThe total estimated soil loss in the catchment was 1,705,370 tons per year and the mean erosion rate was 37.89 t ha−1 year−1, with a standard deviation of 59.2 t ha−1 year−1. The average annual soil erosion rare for the sub-catchments Derma, Megech, Gumara, Garno, and Gabi Kura were estimated at 46.8, 40.9, 30.9, 30.0, and 29.7 t ha−1 year−1, respectively. Based on estimated erosion rates in the catchment, the grid cells were divided into five different erosion severity classes: very low, low, moderate, high and extreme. The soil erosion severity map showed about 58.9% of the area was in very low erosion potential (0–1 t ha−1 year−1) that contributes only 1.1% of the total soil loss, while 12.4% of the areas (36,617 ha) were in high and extreme erosion potential with erosion rates of 10 t ha−1 year−1 or more that contributed about 82.1% of the total soil loss in the catchment which should be a high priority. Areas with high to extreme erosion severity classes were mostly found in Megech, Gumero and Garno sub-catchments. Results of Multiple linear regression analysis showed a relationship between soil erosion rate (A) and USLE factors that soil erosion rate was most sensitive to the topographic factor (LS) followed by the support practice (P), soil erodibility (K), crop management (C) and rainfall erosivity factor (R). Barenland showed the most severe erosion, followed by croplands and plantation forests in the catchment.ConclusionsUse of the erosion severity classes coupled with various individual factors can help to understand the primary processes affecting erosion and spatial patterns in the catchment. This could be used for the site-specific implementation of effective soil conservation practices and land use plans targeted in erosion-prone locations to control soil erosion.