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Concept: Universal Soil Loss Equation


The multitemporal behavior of soil loss by surface water erosion in the hydrographic basin of the river Mourão in the center-western region of the Paraná state, Brazil, is analyzed. Forecast was based on the application of the Universal Soil Loss Equation (USLE) with the data integration and estimates within an Geography Information System (GIS) environment. Results had shown high mean annual rain erosivity (10,000, with great concentration in January and December. As a rule, soils have average erodibilities, exception of Dystroferric Red Latisol (low class) and Dystrophic Red Argisol (high class). Although the topographic factor was high (>20), rates lower than 1 were predominant. Main land uses comprise temporal crops and pasture throughout the years. The watershed showed a natural potential for low surface erosion. When related to usage types, yearly soil loss was also low (<50 ton.ha-1.year-1), with more critical scores that reach rates higher than 150 ton.ha-1.year-1. Soil loss over the years did not provide great distinctions in distribution standards, although it becames rather intensified in some sectors, especially in the center-eastern and southwestern sections of the watershed.

Concepts: Water, Geographic information system, Soil, Erosion, Surface runoff, Geomorphology, Weathering, Universal Soil Loss Equation


High levels of water-induced erosion in the transboundary Himalayan river basins are contributing to substantial changes in basin hydrology and inundation. Basin-wide information on erosion dynamics is needed for conservation planning, but field-based studies are limited. This study used remote sensing (RS) data and a geographic information system (GIS) to estimate the spatial distribution of soil erosion across the entire Koshi basin, to identify changes between 1990 and 2010, and to develop a conservation priority map. The revised universal soil loss equation (RUSLE) was used in an ArcGIS environment with rainfall erosivity, soil erodibility, slope length and steepness, cover-management, and support practice factors as primary parameters. The estimated annual erosion from the basin was around 40 million tonnes (40 million tonnes in 1990 and 42 million tonnes in 2010). The results were within the range of reported levels derived from isolated plot measurements and model estimates. Erosion risk was divided into eight classes from very low to extremely high and mapped to show the spatial pattern of soil erosion risk in the basin in 1990 and 2010. The erosion risk class remained unchanged between 1990 and 2010 in close to 87% of the study area, but increased over 9.0% of the area and decreased over 3.8%, indicating an overall worsening of the situation. Areas with a high and increasing risk of erosion were identified as priority areas for conservation. The study provides the first assessment of erosion dynamics at the basin level and provides a basis for identifying conservation priorities across the Koshi basin. The model has a good potential for application in similar river basins in the Himalayan region.

Concepts: Biodiversity, Geographic information system, Drainage basin, Erosion, Estimation, Remote sensing, Geomorphology, Universal Soil Loss Equation


A detailed description of the G2 erosion model is presented, in order to support potential users. G2 is a complete, quantitative algorithm for mapping soil loss and sediment yield rates on month-time intervals. G2 has been designed to run in a GIS environment, taking input from geodatabases available by European or other international institutions. G2 adopts fundamental equations from the Revised Universal Soil Loss Equation (RUSLE) and the Erosion Potential Method (EPM), especially for rainfall erosivity, soil erodibility, and sediment delivery ratio. However, it has developed its own equations and matrices for the vegetation cover and management factor and the effect of landscape alterations on erosion. Provision of month-time step assessments is expected to improve understanding of erosion processes, especially in relation to land uses and climate change. In parallel, G2 has full potential to decision-making support with standardised maps on a regular basis. Geospatial layers of rainfall erosivity, soil erodibility, and terrain influence, recently developed by the Joint Research Centre (JRC) on a European or global scale, will further facilitate applications of G2.

Concepts: Climate, Weather, Climate change, Erosion, Map, Vegetation, Environmental soil science, Universal Soil Loss Equation


The greatest obstacle to soil erosion modelling at larger spatial scales is the lack of data on soil characteristics. One key parameter for modelling soil erosion is the soil erodibility, expressed as the K-factor in the widely used soil erosion model, the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). The K-factor, which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500m) for the 25 EU Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032thahha(-1)MJ(-1)mm(-1) with a standard deviation of 0.009thahha(-1)MJ(-1)mm(-1). The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed.

Concepts: Statistics, European Union, Soil, United Kingdom, Member State of the European Union, Treaty of Lisbon, Special Member State territories and the European Union, Universal Soil Loss Equation


The study aims to evaluate the significance of land cover delineation on soil erosion assessment. To that end, RUSLE (Revised Universal Soil Loss Equation) was implemented at the Upper Acheloos River catchment, Western Central Greece, annually and multi-annually for the period 1965-92. The model estimates soil erosion as the linear product of six factors (R, K, LS, C, and P) considering the catchment’s climatic, pedological, topographic, land cover, and anthropogenic characteristics, respectively. The C factor was estimated using six alternative land use delineations of different resolution, namely the CORINE Land Cover (CLC) project (2000, 2012 versions) (1:100,000), a land use map conducted by the Greek National Agricultural Research Foundation (NAGREF) (1:20,000), a land use map conducted by the Greek Payment and Control Agency for Guidance and Guarantee Community Aid (PCAGGCA) (1:5,000), and the Landsat 8 16-day Normalized Difference Vegetation Index (NDVI) dataset (30 m/pixel) (two approximations) based on remote sensing data (satellite image acquired on 07/09/2016) (1:40,000). Since all other factors remain unchanged per each RUSLE application, the differences among the yielded results are attributed to the C factor (thus the land cover pattern) variations. Validation was made considering the convergence between simulated (modeled) and observed sediment yield. The latter was estimated based on field measurements conducted by the Greek PPC (Public Power Corporation). The model performed best at both time scales using the Landsat 8 (Eq. 13) dataset, characterized by a detailed resolution and a satisfactory categorization, allowing the identification of the most susceptible to erosion areas.

Concepts: Statistics, Sediment, Soil, Erosion, Remote sensing, Land use, Geomorphology, Universal Soil Loss Equation


Inner Mongolia, an autonomous region of the People’s Republic of China, has experienced severe soil erosion following a period of rapid economic development and urbanization. To investigate how urbanization has influenced the extent of soil erosion in Inner Mongolia, we used urbanization and soil erosion data from 2000 through 2010 to determine the relationship between urbanization and soil erosion patterns. Two empirical equations-the Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ)-were used to estimate the intensity of soil erosion, and we performed backward linear regression to model how it changed with greater urbanization. There was an apparent increase in the rate of urbanization and a decrease in the area affected by soil erosion in 2010 compared to the corresponding values for 2000. The urban population stood at 11.32 million in 2010, which represented a 16.47% increase over that in 2000. The area affected by soil erosion in 2000 totaled 704,817 km², yet it had decreased to 674,135 km² by 2010. However, a path of modest urban development (rural-urban mitigation) and reasonable industrial structuring (the development of GDP-2) may partially reduce urbanization’s ecological pressure and thus indirectly reduce the threat of soil erosion to human security. Therefore, to better control soil erosion in Inner Mongolia during the process of urbanization, the current model of economic development should be modified to improve the eco-efficiency of urbanization, while also promoting new modes of urbanization that are environmentally sustainable, cost-effective, and conserve limited resources.

Concepts: Sediment, Urban area, People's Republic of China, Sustainability, Silt, Inner Mongolia, Geomorphology, Universal Soil Loss Equation


Soil losses must be quantified over watersheds in order to set up protection measures against erosion. The main objective of this paper is to quantify and to map soil losses in the Wadi Sahouat basin (2140 km2) in the north-west of Algeria, using the Revised Universal Soil Loss Equation (RUSLE) model assisted by a Geographic Information System (GIS) and remote sensing. The Model Builder of the GIS allowed the automation of the different operations for establishing thematic layers of the model parameters: the erosivity factor ®, the erodibility factor (K), the topographic factor (LS), the crop management factor ©, and the conservation support practice factor (P). The average annual soil loss rate in the Wadi Sahouat basin ranges from 0 to 255 t ha-1 year-1, maximum values being observed over steep slopes of more than 25% and between 600 and 1000 m elevations. 3.4% of the basin is classified as highly susceptible to erosion, 4.9% with a medium risk, and 91.6% at a low risk. Google Earth reveals a clear conformity with the degree of zones to erosion sensitivity. Based on the soil loss map, 32 sub-basins were classified into three categories by priority of intervention: high, moderate, and low. This priority is available to sustain a management plan against sediment filling of the Ouizert dam at the basin outlet. The method enhancing the RUSLE model and confrontation with Google Earth can be easily adapted to other watersheds.

Concepts: Geographic information system, Erosion, Map, Topography, Remote sensing, Cartography, Google Earth, Universal Soil Loss Equation


Traditionally, the Universal Soil Loss Equation (USLE) and the revised version of it (RUSLE) have been applied to predicting the long term average soil loss produced by rainfall erosion in many parts of the world. Overtime, it has been recognized that there is a need to predict soil losses over shorter time scales and this has led to the development of WEPP and RUSLE2 which can be used to predict soil losses generated by individual rainfall events. Data currently exists that enables the RUSLE2, WEPP and the USLE-M to estimate historic soil losses from bare fallow runoff and soil loss plots recorded in the USLE database. Comparisons of the abilities of the USLE-M and RUSLE2 to estimate event soil losses from bare fallow were undertaken under circumstances where both models produced the same total soil loss as observed for sets of erosion events on 4 different plots at 4 different locations. Likewise, comparisons of the abilities of the USLE-M and WEPP to model event soil loss from bare fallow were undertaken for sets of erosion events on 4 plots at 4 different locations. Despite being calibrated specifically for each plot, WEPP produced the worst estimates of event soil loss for all the 4 plots. Generally, the USLE-M using measured runoff to calculate the product of the runoff ratio, storm kinetic energy and the maximum 30-minute rainfall intensity produced the best estimates. As to be expected, ability of the USLE-M to estimate event soil loss was reduced when runoff predicted by either RUSLE2 or WEPP was used. Despite this, the USLE-M using runoff predicted by WEPP estimated event soil loss better than WEPP. RUSLE2 also outperformed WEPP.

Concepts: Scientific method, Mathematics, Prediction, Futurology, Soil, Erosion, Erosion control, Universal Soil Loss Equation


Agricultural land use change, especially corn expansion since 2000s, has been accelerating to meet the growing bioenergy demand of the United States. This study identifies the environmentally sensitive lands (ESLs) in the U.S. Midwest using the distance-weighted Revised Universal Soil Loss Equation (RUSLE) associated with bioenergy land uses extracted from USDA Cropland Data Layers. The impacts of soil erosion to downstream wetlands and waterbodies in the river basin are counted in the RUSLE with an inverse distance weighting approach. In a GIS-ranking model, the ESLs in 2008 and 2011 (two representative years of corn expansion) are ranked based on their soil erosion severity in crop fields. Under scenarios of bioenergy land use change (corn to grass and grass to corn) on two land types (ESLs and non-ESLs) at three magnitudes (5%, 10% and 15% change), this study assesses the potential environmental impacts of bioenergy land use at a basin level. The ESL distributions and projected trends vary geographically responding to different agricultural conversions. Results support the idea of re-planting native prairie grasses in the identified High and Severe rank ESLs for sustainable bioenergy management in this important agricultural region.

Concepts: Agriculture, United States, Soil, Surface runoff, Minnesota, Geomorphology, Deforestation, Universal Soil Loss Equation


Understanding the occurrence of erosion processes at large scales is very difficult without studying them at small scales. In this study, soil erosion parameters were investigated at micro-scale and macro-scale in forests in northern Iran. Surface erosion and some vegetation attributes were measured at the watershed scale in 30 parcels of land which were separated into 15 fire-affected (burned) forests and 15 original (unburned) forests adjacent to the burned sites. The soil erodibility factor and splash erosion were also determined at the micro-plot scale within each burned and unburned site. Furthermore, soil sampling and infiltration studies were carried out at 80 other sites, as well as the 30 burned and unburned sites, (a total of 110 points) to create a map of the soil erodibility factor at the regional scale. Maps of topography, rainfall, and cover-management were also determined for the study area. The maps of erosion risk and erosion risk potential were finally prepared for the study area using the Revised Universal Soil Loss Equation (RUSLE) procedure. Results indicated that destruction of the protective cover of forested areas by fire had significant effects on splash erosion and the soil erodibility factor at the micro-plot scale and also on surface erosion, erosion risk, and erosion risk potential at the watershed scale. Moreover, the results showed that correlation coefficients between different variables at the micro-plot and watershed scales were positive and significant. Finally, assessment and monitoring of the erosion maps at the regional scale showed that the central and western parts of the study area were more susceptible to erosion compared with the western regions due to more intense crop-management, greater soil erodibility, and more rainfall. The relationships between erosion parameters and the most important vegetation attributes were also used to provide models with equations that were specific to the study region. The results of this paper can be useful for better understanding erosion processes at the micro-scale and macro-scale in any region having similar vegetation attributes to the forests of northern Iran.

Concepts: Soil, Ecosystem, Erosion, Map, Surface runoff, Region, Geomorphology, Universal Soil Loss Equation