ABSTRACT The Revised Universal Soil Loss Equation (RUSLE) is the most widely used global soil erosion model. The poor performance of the RUSLE is solely dependent on the inherent structure of the RUSLE model to account for the soil erosion phenomena under varying topographic and climatic conditions. Considering the extensive global application of the RUSLE model, it is high time in research to identify the suitability of the RUSLE model under varying conditions. In this context, a global-scale review of the RUSLE model application was carried out to identify the best possible conditions for its reliable application and the conditions where it yielded poor performance. RUSLE is still the most frequently operational erosion simulation model despite its numerous deficiencies, weaknesses, and limitations. Nevertheless, the application over large and ungauged areas remains a real challenge due to the non-availability and quality of the required inputs.

Global-scale application of the RUSLE model: a comprehensive review

Soil loss is a continuous ecological problem. The loss of top fertile soil by erosion creates severe limitations to sustainable agricultural land use, which lead to reduced soil productivity and food insecurity. Soil loss is not affect only agriculture but also causes streams, lakes, dams, and reservoir sedimentation. Efforts should be focused to decrease risks of high erosion rates. This requires erosion prone area identification for urgent conservation plan. Techniques to know the spatial variability of erosion severity is essential for improving landuse management. The Revised Universal Soil Loss Equation (RUSLE) model with ArcGIS and Multi Criteria Evaluation (MCE) technique were used to estimate the annual soil loss rate and to identify high erosion risk areas in Gilegel beles watershed with a size of 770 km2, to prioritize area for conservation planning. land use land cover, soil map, digital elevation model (30m resolution), and twenty five years rainfall data at nine rain gage stations across the watershed were used to estimate soil loss in RUSLE model. The result showed that annual soil loss of the watershed ranges from 0 to 511.2ton/ha/yr with average of 28.68 ton /ha/ yr. Spatially 10.4% and 13.2% of the watershed was extremely sever and high soil erosion respectively. However, the remaining 46.4% and 30.03% of the area lies within low to moderate level. Our study revealed the most extensive soil loss rates were estimated in steep slopes, bare lands, crop land and biological degraded areas were aggravated soil loss problem. Finally, the combined models were effective for soil erosion estimation in small watersheds, so the outcome verified that integrated land management practices have needed to apply effective soil erosion control techniques like agroforestry, grass strips reduce runoff velocity and allow for infiltration, provide sediment trapping, erosion control blankets, dust control and percolation ditches for erosion prone areas and managing the inter catchment can be the best solution.

Potential soil loss estimation for conservation planning, upper Blue Nile Basin, Ethiopia

An understanding of the spatial variation and influence factors of soil nutrients in mining areas can provide a reference for land reclamation and ecological restoration. Daliuta was used as the study area. The spatial variability of soil nutrients was analyzed using traditional statistics and geostatistics. The effects of topography, mining history, and soil erosion were discussed. The results indicate that the soil pH of the Daliuta mining area is slightly acidic to slightly alkaline, and the soil organic matter, available nitrogen, available phosphorus, and available potassium belonged to the five levels (very low), six (extremely low), five (extremely low), and four (moderately low), respectively. The soil water and salt content indicated that the soil environment in the mining area is arid and has normal levels of salinity. The organic matter, available nitrogen, available phosphorus, available potassium, and soil salt varied moderately, and the pH did not change much, while the soil water varied strongly. The organic matter, pH, and soil salinity are moderately spatially autocorrelated, and the available nitrogen, available phosphorus, available potassium, and soil water are weakly spatially autocorrelated. Each nutrient index had a certain spatial trend effect. The slope, aspect, elevation, and topographic wetness index are the primary topographic factors that control the spatial distribution of soil nutrients. The organic matter, pH, and soil salinity are moderately spatially autocorrelated, and the available nitrogen, available phosphorus, available potassium, and soil water are weakly spatially autocorrelated. Each nutrient index had a certain spatial trend effect. The slope, aspect, elevation, and topographic wetness index are the primary topographic factors that control the spatial distribution of soil nutrients. Soil erosion and mining history are also important factors that lead to the spatial variation of soil nutrients.

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Analysis of Spatial Variability and Influencing Factors of Soil Nutrients in Western China: A Case Study of the Daliuta Mining Area

Soil erosion is the most serious problem that affects economic development, food security, and ecosystem services, which is the main concern in Ethiopia. This study focused on quantifying soil erosion rate and severity mapping of the Megech watershed for effective planning and decision-making processes to implement protection measures. The RUSLE model integrated with ArcGIS software was used to accomplish the objectives. The six RUSLE model parameters: erosivity, erodibility, slope length and steepness, cover management, and erosion control practices were used as input parameters to compute the average annual soil loss and identify erosion hotspots in the watershed. The RUSLE estimated a total soil loss of 1,399,210 t yr−1 from the watershed with a mean annual soil loss of 32.84 t ha−1yr−1. The soil erosion rate varied from 0.08 to greater than 500 t ha−1yr−1. A severity map with seven severity classes was created for 27 sub-watersheds: low (below 10), moderate (10–20), high (20–30), very high (30–35), severe (35–40), very severe (40–45) and extremely severe (above 45) in which the values are in ton ha−1yr−1. The area coverage was 6.5%, 11.1%, 8.7%, 22%, 30.9%, 13.4%, and 7.4% for low, moderate, high, very high, severe, very severe, and extremely severe erosion classes, respectively. About 82% of the watershed was found in more than the high-risk category which reflects the need for immediate land management action. This paper could be important for decision-makers to prioritize critical erosion hotspots for comprehensive and sustainable management of the watershed. 

Soil loss estimation and severity mapping using the RUSLE model and GIS in the Megech watershed, Ethiopia

Assessment of soil erosion extent using RUSLE model integrated with GIS and RS: the case of Megech-Dirma watershed, Northwest Ethiopia

Soil erosion is a difficult forceful practice by which useful surface soil is removed, conveyed, and stored at a detached place causing the exposure of subsurface soil and siltation in reservoirs and natural streams. It also degraded different agricultural areas at different rates and different locations as well as created gulleys, but these degraded areas, degradation rates, and gulley locations were not well identified and quantified. Therefore; the core objective of this study is to evaluate soil erosion rate and to identify soil erosion hotspot areas as well as to identify gulley locations using Revised Universal Soil Loss Equation (RUSLE), Multi-criteria Evaluation Analysis Techniques, and Geographic Information System (GIS). Land Use/Land Cover (LU/LC) map, slope map, rainfall map, and Digital Elevation Model (DEM) were the necessary inputs used for this study. The results of this study indicated that the potential annual soil loss of the watershed ranged from 0.0 to 706.7 tons per hectare per year with an average annual soil loss rate of 27.7 tons per hectare per year. From the total area of the watershed (859.2 km2), the potential area for gully expansion is 63km2. The overall analysis indicated that 4.8% of the total watershed is highly sensitive; 54.24% is moderately sensitive; 17.69% is marginally sensitive while, 23.28% is currently not sensitive and the remaining 0.06% is a /constraint to erosion. Hence, the Areas which were categorized under highly and moderately sensitive classes needed direct mediation for better conservation planning. It is concluded that Gulley, LU/LC, Topographical wetness index, and soil types are factors that enable soil erosion from high to low, respectively. It is recommended that the concerned bodies should have to take care of the area from further soil erosion through creating awareness among the society about the sustainable use of natural resources and conservation methods.

Assessment of soil erosion rate and hotspot areas using RUSLE and multi-criteria evaluation technique at Jedeb watershed, Upper Blue Nile, Amhara Region, Ethiopia

Tseganeh Getnet

Papers

Assessment of soil erosion rate and hotspot areas using RUSLE and multi-criteria evaluation technique at Jedeb watershed, Upper Blue Nile, Amhara Region, Ethiopia