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[Objective] The aims of this study are to determine the spatial distribution of water erosion and its spatial-temporal variation in southwest alpine-canyon area, to explore driving mechanism of water erosion in study area, and to provide countermeasures for regional soil erosion and water loss control. [Methods] Based on GIS and RUSLE model, the spatial and temporal variation and driving mechanism of water erosion in southwest alpine-canyon area were analyzed by using the method of the Theil-Sen trend analysis. Mann- Kendall significance test and random forest model. [Results (1) The ratio of light erosion and mild erosion was over 78.05%. (2) Generally, during the period of 2000 to 2020, the range of water erosion amount varied between 4.606 3×108 t and 1.487 4×109 t and the annual value was 7.9664×108t. The range of rainfall erosion factor amount varied between 125.11 MJ mm/(hm².h.a) and 249,93 MJ mm/(hm² ha) and the annual value was 186.71 MJ mm/(hm²h.a). The range of vegetation coverage and management factor varied between 0.37 and 0.70 and the average value for years was 0.48. (3) Topographic factors was with the highest contribution to explain spatial variation of water erosion, accounting for 41%, followed by vegetation coverage and management factors, rainfall erosion factor, soil erodibility factor, soil and water conservation measure factor, accounting for 24%, 16%, 12%, and 8%, respectively. Moreover, spatial variation contribution of each factor varied with the soil and water conservation zone. [Conclusion] The severe erosion areas mainly distributed in the southern part of Eastern Xizang & West Sichuan Ecological Maintenance & Water Conservation Region. Eastern and northwest part of Southeast Xizang arctic-alpine Ecological Maintenance Region. Topographic factors and vegetation coverage and management factors was the main driving factors. In the Ecological Maintenance and Water Conservation Zone of Eastern Xizang & Western Sichuan, the Southeast Xizang Ecological Maintenance Arctic alpine Zone and the Northwest Yunnan Ecological Maintenance Zone, the measures that would improve the vegetation cover to increase the soil anti-erodibility was suggested. In the North Yunnan & Southwest Sichuan Water Storage and Soil Conservation Zone, vegetation cover should be improved while appropriate soil and water conservation measures could be placed. [ABSTRACT FROM AUTHOR]

Soil and water bioengineering (SWBE) measures in fire-prone areas are essential for erosion mitigation, revegetation, as well as protection of settlements against inundations and landslides. This study's aim was to detect erosive areas at the basin scale for SWBE implementation in pre- and post-fire conditions based on a wildfire event in 2019 in southern Ecuador. The Revised Universal Soil Loss Equation (RUSLE) was used in combination with earth observation data to detect the fire-induced change in erosion behavior by adapting the cover management factor (C-factor). To understand the spatial accuracy of the predicted erosion-prone areas, high-resolution data from an Unmanned Aerial Vehicle (UAV) served for comparison and visual interpretation at the sub-basin level. As a result, the mean erosion at the basin was estimated to be 4.08 t ha−1 yr−1 in pre-fire conditions and 4.06 t ha−1 yr−1 in post-fire conditions. The decrease of 0.44% is due to the high autonomous vegetation recovery capacity of grassland in the first post-fire year. Extreme values increased by a factor of 4 in post-fire conditions, indicating the importance of post-fire erosion measures such as SWBE in vulnerable areas. The correct spatial location of highly erosive areas detected by the RUSLE was successfully verified by the UAV data. This confirms the effectivity of combining the RUSLE with very-high-resolution data in identifying areas of high erosion, suggesting potential scalability to other fire-prone regions. [ABSTRACT FROM AUTHOR]

Accelerated erosion caused by human activities is the major triggering factor for the loss of soil and water resources in Ethiopia. Soil erosion is commonly regarded as a major environmental problem that affects the sustainability of agricultural production and downstream reservoir. To facilitate urgent policy intervention in sustainable land management, evaluating the amount of annual soil loss is inevitable. In this study, the Revised Universal Soil Loss Equation (RUSLE) and Geographic Information System (GIS) are integrated to analyse the amount of soil loss in the Neshe Dam Watershed of Ethiopia. The total amount of soil loss from the watershed (33,376 hectares) is about 1,252,935 tons per year. Furthermore, the potential average annual soil loss for the whole watershed is anticipated at 37.54 t ha−1 yr−1. In terms of sub-watersheds, very high soil loss (56.62 t ha−1 yr−1) was observed in sub-watershed B accounts (for 16.3%) and the least (16.06 t ha−1 yr−1) observed in sub-watershed E accounts (for 23.6%). The study concluded that if proper measures are not taken now, the accelerated erosion in the watershed will jeopardize the sustainability of agricultural production within the watershed and energy generated from the dam in the long term. [ABSTRACT FROM AUTHOR]

Numerous studies found that the main factor contributing to the export of sediment from the Ethiopian highlands is soil erosion, which is related to the expansion of cultivated land at the expense of steep slopes of natural vegetation covers. Plantation practices on steep slopes of bare surfaces and utilizing effective water and soil conservation measures on cultivated lands, may, however, have an impact on the rate of soil loss and the sediment yield ratio. Thus, the main objective of the research was to determine how changes in land use and land cover introduced by soil conservation affected the spatiotemporal variability in soil loss and sediment delivery ratio. RUSLE, InVEST, and integrated GIS technology were used to quantify the study's results. The expansion of farmed land at the expense of natural forests, woodlands, shrubs, and grasslands between 1990 and 2000 resulted in a significant increase in soil loss and sediment yield. This was mostly seen where natural vegetation cover areas on steep slopes were turned into cultivable land. Watershed development interventions significantly decreased both the mean annual soil loss and sediment yields between 2003 and 2021. For instance, from 2010 to 2021, the mean annual soil loss in the watershed dropped by 23.5 t ha−1 and the sediment export declined by 6.13 t ha−1yr−1. To prevent soil loss and sediment export at the extensive level, it is therefore concluded that sustainable soil management on cultivated land and plantation techniques on community land should be expanded up to the adjacent watersheds. [ABSTRACT FROM AUTHOR]

[Display omitted] • Erosion-based prioritization using a comprehensive approach is vital. • The Shazand sub-Watersheds, Iran, were prioritized using the game theory. • The Shazand sub-watersheds were prioritized differently in various scenarios. • The game theory-based Condorcet approach holistically and successfully ranked sub-watersheds. The soil erosion of the watershed is different in temporal and spatial scales due to complex climatic and environmental conditions and their dynamic state over time. Accordingly, it is necessary to prioritize sub-watersheds based on their dynamic conditions to reduce the destructive effects of soil erosion. However, this issue has to be sufficiently reported on the scale of the watershed. Towards that, a game theory-based prioritization approach was planned concerning changes in vegetation cover and rainfall in the Shazand Watershed of the Markazi Province, Iran. To achieve the purpose of the study, the amount of soil erosion was first estimated in different scenarios resulting from the changes in rainfall erosivity and vegetation cover using the RUSLE model. Next, pairwise comparisons between 24 sub-watersheds in all scenarios were made based on the sub-watersheds' ranks in soil erosion in each scenario. Considering all scenarios, the priority of the sub-watersheds was assessed using the Condorcet approach. The results showed that the mean soil erosion in the watershed ranged from 5.4 and 36 t ha−1 y−1 while applying various scenarios. It is worth mentioning that the prioritization results obtained from the Condorcet algorithm and the current soil erosion rate are highly similar. On the other hand, sub-watersheds 2 and 9 were selected as the first and last priority, providing an appropriate roadmap for managers and relevant departments to adopt appropriate soil erosion inhibition measures. [ABSTRACT FROM AUTHOR]

[Objective] The spatial and temporal characteristics of soil erosion in the Yarlung Zangbo River basin were explored, and effects of climate and vegetation cover changes on soil erosion were analyzed, in order to provide theoretical support for soil erosion prevention, ecosystem protection, and development and utilization of water and soil resources in alpine regions. [Methods] Based on the Revised Universal Soil Loss Equation（RUSLE）, the spatial and temporal change of soil erosion in the Yarlung Zangbo River basin from 1980 to 2017 was quantitatively evaluated. [Results] The soil erosion intensity in the the Yarlung Zangbo River basin showed an overall trend of first decreasing and then increasing from 1980 to 2017, the fluctuation of the multi-year average soil erosion modulus decreased from 1980 to 1999, while the annual average soil erosion modulus showed no significant increase trend from 2000 to 2017. The upper and middle areas of the basin had significant changes, while the erosion intensity in the lower areas increased first and then decreased. The annual average soil erosion modulus was significantly positively correlated with rainfall erosivity, with a Pearson correlation coefficient of 0.92, but not significantly correlated with NDVI. Among the different land use types, the most intense soil erosion occurred in unused land, followed by sparse grassland, which accounted for more than 54% of the total basin erosion due to its highest area ratio. [Conclusion] Rainfall is the main factor affecting the change of soil erosion intensity in the Yarlung Zangpo River basin, and the key areas for soil erosion prevention and control in the future should be the areas with relatively high rainfall in the lower reaches of eastern part of the basin, and focus on preventing soil erosion caused by extreme rainfall. [ABSTRACT FROM AUTHOR]

[Objective]The aims of this study are to explore the characteristics of spatial and temporal dynamics of soil erosion in typical farmland conversion area under different topographic conditions before and after the'Grain for Green'project, and to provide scientific support for consolidating project results. [Methods]The RUSLE model was used to quantitatively analyze the spatial and temporal evolution characteristics of soil erosion intensity in Yan'an City from 1989 to 2019, and the distribution patterns of soil erosion on various elevations and slopes were explored in combination with topographic factors. The precipitation, vegetation and soil and water conservation factor affecting the change of soil erosion modulus were decomposed by the LMDI model and their contribution values were analyzed. The CA-Markov model was used to predict the soil erosion status of Yan'an City in 2029. [Results]The average soil erosion modulus in Yan'an City in 1989, 1999, 2009 and 2019 were 12 554. 80 t/(km²·a), 8 237. 17 t/(km²·a), 5 936. 57 t/(km²·a)and 4 473. 02 t/(km²·a), respectively. The erosion type was dominated by slight erosion, showing the spatial differentiation characteristics of low level in the south and high level in the north. The soil erosion intensity generally decreased with the increase of elevation, but the erosion increased at the fifth elevation. The soil erosion intensity was consistent with the slope gradient, and the erosion intensity increased with the increase of slope gradient. The inhibitory effect of vegetation factor and soil and water conservation factor on soil erosion had increased in recent years. The soil erosion situation in Yan'an City will be generally improved in 2029. [Conclusion]The soil erosion status in Yan'an City has improved significantly, but there is still the increased erosion in some areas. In the future, the high-altitude areas in the north and the bank areas of the Yellow River in the east of Yan'an City should be given more attention, meanwhile, the monitoring of high-slope areas should be strengthened. [ABSTRACT FROM AUTHOR]

Assessing the spatial distribution of the erosion process is considered a critical initial step to provide valuable insights to decision-makers for devising an effective erosion mitigation strategy to reduce erosion damages. This research was conducted based on a revised universal soil loss equation (RUSLE) model integrated with the geographic information environment (GIS) within the Wadi El Ghareg watershed located in the Menzel Bourguiba region in northeastern Tunisia to simulate the spatial distribution of erosion across the basin which has been experiencing adverse effects of climate change, characterized by periods of drought and heavy rainfall. The RUSLE incorporates several variables, including rainfall erosivity (R), soil erodibility (K), cover management (C), slope length (LS), and conservation practices (P), serving as key predisposition parameters in this research. For the validation process of the applied model, 200 points were selected to create an inventory map; the points were selected based on satellite images and field surveys. The obtained thematic maps were normalized by fuzzy logic and overlaid using the model equation in the GIS. The results identified the most severely eroded areas requiring immediate erosion control measures. Hence, the results reveal that about 1.71% of the area is covered under severe erosion risk, 0.13% area under high erosion risk, 0.26% area under moderate erosion risk, 0.27% area under low erosion risk, and 97.63% of the area under very low erosion risk. The accuracy of the model was evaluated based on the receiver operating characteristic curves (ROC) and the areas under the curves (AUC). The result showed that this model had an excellent predictive accuracy for soil erosion susceptibility, with AUC values of 0.967. The final produced map will be used as a basis for suggesting a framework that can help make practical policy recommendations to fight against erosion in the context of sustainable management of the watershed. [ABSTRACT FROM AUTHOR]

Organic carbon performs essential functions in soils, which act as sources or sinks of atmospheric organic carbon. Agricultural management affects the carbon cycle in the soil, with effects on climate change. One of the crops most vulnerable to climate change is coffee. Brazil is the world’s largest coffee producer, with a predominance of management under a conventional system, with sloping terrain and the absence of conservationist practices. The absence of conservationist practices increases in soil loss rates due to water management and carbon emissions, as well as a reduction in coffee production. This paper intended to estimate soil and organic carbon losses by RUSLE in coffee farms in southern Minas Gerais, south-eastern Brazil. Data were obtained from fieldwork, laboratory analysis, and cartographic products. The results indicated, exclusively for coffee crops, soil and carbon losses between 7 and 32 Mg ha−1 year−1 and 87 and 460kg ha−1 year−1, respectively. However, the highest soil losses occurred on sloping terrains with eucalyptus plantations located downhill, and the lowest losses occurred on flat land with native forests. Organic carbon losses were linked directly to soil losses, as a result from the land practices, slope and agricultural management adopted. These results can be used for the planning and priority definition of areas needing conservationist practices, such as green manuring, planting in contour and maintaining of vegetation between coffee rows, which are already used in some sites of the study area. [ABSTRACT FROM AUTHOR]

The second-largest wildfire in the history of South Korea occurred in 2022 due to strong winds and dry climates. Quantitative evaluation of soil erosion is necessary to prevent subsequent sediment disasters in the wildfire areas. The erosion rates in two watersheds affected by the wildfires were assessed using the revised universal soil loss equation (RUSLE), a globally popular model, and the soil erosion model for mountain areas (SEMMA) developed in South Korea. The GIS-based models required the integration of maps of the erosivity factor, erodibility factor, length and slope factors, and cover and practice factors. The rainfall erosivity factor considering the 50-year and 80-year probability of rainfall increased from coastal to mountainous areas. For the LS factors, the traditional version (TV) was initially used, and the flow accumulation version (FAV) was additionally considered. The cover factor of the RUSLE and the vegetation index of the SEMMA were calculated using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images acquired before and after the wildfire. After one year following the wildfire, the NDVI increased compared to during the year of the wildfire. Although the RUSLE considered a low value of the P factor (0.28) for post-fire watersheds, it overestimated the erosion rate by from 3 to 15 times compared to the SEMMA. The erosion risk with the SEMMA simulation decreased with the elapsed time via the vegetation recovery and stabilization of topsoil. While the FAV of RUSLE oversimulated by 1.65~2.31 times compared to the TV, the FAV of SEMMA only increased by 1.03~1.19 times compared to the TV. The heavy rainfall of the 50-year probability due to Typhoon Khanun in 2023 generated rill and gully erosions, landslides, and sediment damage in the post-fire watershed on forest roads for transmission tower construction or logging. Both the RUSLE and SEMMA for the TV and FAV predicted high erosion risks for disturbed hillslopes; however, their accuracy varied in terms of the intensity and extent. According to a comparative analysis of the simulation results of the two models and the actual erosion situations caused by heavy rain, the FAV of SEMMA was found to simulate spatial heterogeneity and a reasonable erosion rate. [ABSTRACT FROM AUTHOR]

Soil erosion poses a significant threat to agricultural production worldwide, with a still-debated impact on the current increase in atmospheric CO2. Whether erosion acts as a net carbon (C) source or sink also depends on how it influences greenhouse gas (GHG) emissions via its impact on crop yield and nutrient loss. These effects on the environmental impacts of crops remain to be considered. To fill this gap, we combined watershed-scale erosion modeling with life cycle assessment to evaluate the influence of soil erosion on environmental impacts of wheat production in the Ebro River basin in Spain. This study is the very first to address the full GHG balance of erosion including its impact on soil fertility and its feedback on crop yields. Two scenarios were simulated from 1860 to 2005: an eroded basin involving conventional agricultural practices, and a non-eroded basin involving conservation practices such as no-till. Life cycle assessment followed a cradle-to-farm-gate approach with a focus on recent decades (1985–2005). The mean simulated soil erosion of the eroded basin was 2.6 t ha−1 year−1 compared to the non-eroded basin. Simulated soils in both eroded and non-eroded basins lost organic C over time, with the former emitting an additional 55 kg CO2 ha−1 year−1. This net C source represented only 3% of the overall life cycle GHG emissions of wheat grain, while the emissions related to the increase of fertilizer inputs to compensate for N and P losses contributed a similar percentage. Wheat yield was the most influential parameter, being up to 61% higher when implementing conservation practices. Even at the basin scale, erosion did not emerge as a net C sink and increased GHG emissions of wheat by 7–70%. Nonetheless, controlling erosion through soil conservation practices is strongly recommended to preserve soils, increase crop yields, and mitigate GHG emissions. [ABSTRACT FROM AUTHOR]

The implementation of new technologies in agro-industrial activities has contributed to the minimization of problems due to soil depletion; however, the management of soil quality for agricultural use has been a neuralgic point due to the processes of infertility due to erosion. The objective of this research is to model soils degraded by agroindustrial interaction using Sentinel images in the municipality of Valledupar, Cesar (Colombia). Different national and international remote sensing images were used to obtain components for the application of the universal soil loss equation RUSLE. The results show a low to very high degree of erosivity - R factor, mainly critical towards the Sierra Nevada de Santa Marta (SNSM) and soils of very low erodibility - K factor. The LS factor shows varied length and slopes. Forests, crops and grasslands identified by factor C predominate towards the forest conservation zones of the mountainous landscape, while in the alluvial valley there are crops and shrublands together with sparse and semi-arid vegetation. It was found that the largest percentage of the study area has slight losses while the moderate to very high zones are strongly dominated by high slopes. [ABSTRACT FROM AUTHOR]

Soil erosion exacerbates land degradation, which has a detrimental impact on terrestrial ecosystems all over the world. The purpose of this study is to forecast future soil erosion in the Tumen River Basin (TRB) on the border between China and North Korea under changing land-use and climate conditions from 2030 to 2050 using the revised universal soil loss equation (RUSLE). Precipitation changes during the study period were examined under three shared socioeconomic pathway (SSP) scenarios. Future land use was simulated using a patch-generating land-use simulation (PLUS) model. As the scenario worsens, regions requiring sustained attention in the TRB are expected to rise steadily from 4% (1337 km2) in 2017 to 7.3% (2440 km2) in 2050. Under SSP5-8.5, soil erosion will reach 24.45 t ha-1yr-1 in 2050, which is 6 t ha-1yr-1 more than current soil loss. According to the land cover simulation, 2.7% (~925 km²) of forest area is anticipated to be lost by 2050, with the majority of that area converted to built-up, cropland, and grassland areas increasing by 0.5%, 1.3%, and 0.8%, respectively. This study is expected to enhance the spatial perception of soil erosion at different scales and provide scientific basis for soil and water management. [ABSTRACT FROM AUTHOR]

Accelerated erosive processes are global threats that can be quantified and classified. The northern region of Paraná is known for its high grain yield and crop rotation (soybeans, corn, wheat and oats). The municipality of Bela Vista do Paraíso has the particularity of being located on two geological formations, a basic eruptive of the Serra Geral Formation (JKsg) and a sandstone of the Adamantina Formation (Ka). The use of GIS to quantify soil loss has been intensifying in earth sciences, in which the GISus-M software is relevant for its practicality. It was observed that the C factors, which in this case have their values determined by the methodology of Durigon et al. (2014), are linked to monthly soil loss due to erosion due to crop rotation and their fallow period, in addition to erodibility (K factor) also having its significance, mainly associated with LS factors. The use of monthly modeling for RUSLE expresses a better observation of the erosive processes that occur, in addition to contributing to soil conservation measures and the planning and ordering of local roads in the municipality. Keywords: erosion; RUSLE; geological formations; GISus-M. [ABSTRACT FROM AUTHOR]

Soil erosion is a formidable global challenge with far-reaching consequences. It results in the depletion of soil nutrients, land degradation, decreased agricultural output, heightened runoff, and the exacerbation of geological hazards such as landslides and debris fl ows. This study focuses on the assessment of soil erosion in the Beas Valley region of Kullu, Himachal Pradesh, situated in the Western Himalaya landscape of Northern India. The research employs various datasets and a welldefi ned methodology to analyze the complex interactions between climate, soil, topography, and land use in order to understand and mitigate soil erosion risks. The primary data sources utilized in this study include rainfall data from the Climate Research Unit at the University of East Anglia, soil data from the Food and Agriculture Organization, Digital Elevation Model (DEM) data from the Shuttle Radar Topography Mission, and satellite imagery from Landsat. The research methodology is based on the Revised Universal Soil Loss Equation (RUSLE), a widely accepted model for assessing soil erosion. The RUSLE equation (A= R·K·LS·C·P) incorporates several factors to quantify soil erosion rates. The R-factor, derived from monthly and annual rainfall data, is used to estimate erosivity. The K-factor, determined using soil type and composition, characterizes soil erodibility. The LS-factor considers slope and fl ow accumulation, while the C-factor is calculated based on the Normalized Difference Vegetation Index (NDVI) from satellite imagery. Lastly, the P-factor accounts for the effectiveness of conservation practices. This interdisciplinary approach provides valuable insights into the dynamics of soil erosion in the Beas Valley region. By leveraging cutting-edge data sources, fi led visit and a robust methodology, this study contributes to a better understanding of soil erosion processes in a fragile Himalaya ecosystem, facilitating informed land management decisions and environmental conservation efforts. [ABSTRACT FROM AUTHOR]

Soil erosion is a key factor in soil quality degradation and carbon balance in arid ecosystems. However, many models ignore the soil erosion process in arid regions, which may lead to limits in our understanding of ecosystem processes in arid regions. In this study, we added the soil erosion process according to field observed data of soil hydrothermal regimes and carbon flux. We validated this coupling version of IBIS (Integrated Biosphere Simulator) and RUSLE (RU–IBIS) by examining four different vegetation types and the carbon budget in the arid region on the Loess Plateau (LP). Our results indicated that the coupling model (RU–IBIS) produced more reliable simulations of the soil water content (with the r from 0.23–0.90 to 0.71–0.97) and evaporation (ET) (the average r was 0.76) and significantly improved the simulation of the leaf area index (LAI) (the average r was 0.95) and net primary production (NPP) (the average r was 0.95). We also conducted sensitivity experiments to determine how soil texture and aerodynamic roughness (Z0m) affect the soil water content. Moreover, it was revealed that specific leaf area (SLA) plays a key role in the simulation of NPP and NEE. Our study suggests that the coupled soil erosion process and parameterization can effectively improve the performance of IBIS in arid regions. These results need to be considered in future Earth system models. [ABSTRACT FROM AUTHOR]

Land degradation is an evident and severe environmental problem in Ethiopia. Since 2003 community-based watershed management has been implemented in the study area to reduce land degradation. This study aimed to investigate to what extent the community-based watershed management is effective in rehabilitating the land. Woody vegetative cover, land use/cover (LULC) change and soil erosion rate were selected variables to examine the effectiveness of the management measures. The LULC and woody vegetative cover spatial datasets were created from Landsat satellite images of 2003 and 2019 using ERDAS IMAGINE 2016® and ArcGIS10.8®. The soil loss rate was modelled using geoinformation techniques combined with the Revised Universal Soil Loss Equation. To assess the perception of farmers involved in the watershed rehabilitation activities, qualitative data were collected through Focus Group Discussions and Key Informant interviews. The results showed that the average annual soil loss rate in the study area was 49.5 tonnes (t)/hectare (ha−1)/year(y−1) in 2003 and 23.8 t ha−1y−1 in 2019, which is a 52% decrease over 16 years. Woody vegetative cover and grassland increased by 11% and 103%, respectively. These figures and respondents' responses, Focus Group Discussants and Key Informants showed that the community-based watershed management measures implemented in the study area were effective for improving vegetation cover and reducing soil loss (reducing land degradation). The sustainability of these achievements is contingent on the availability of a robust local organizational set-up complemented by the provision of natural resources development technologies, capacity building and security of land tenure. In sum, the findings in the study lead to conclude that the active involvement of farmers in land management and security of land tenure are effective approaches to reverse land degradation in the highlands of Ethiopia. [ABSTRACT FROM AUTHOR]

Northwest Hubei Province is a region prone to soil erosion. Investigating the spatiotemporal variations in soil erosion and identifying its underlying causes can offer valuable insights for guiding soil and water conservation efforts in the area. Based on the RUSLE model, we quantitatively analyzed the spatiotemporal variations in soil erosion in Northwest Hubei from 2005 to 2020. A geographical detector model was used to study the dominant factors of the spatial and temporal differentiation patterns of soil erosion, the degree of interactive coupling between multiple factors, and quantitative attribution of soil erosion-prone areas. The results indicate that, overall, the intensity of soil erosion in Northwest Hubei continued to decrease from 2005 to 2020. The average soil erosion modulus decreased by 16.3 t/(km²·a), and the soil erosion volume decreased by 200,000 t in 2020 compared to in 2005. Northwest Hubei showed a spatially divergent pattern of strong soil erosion intensity in the west and south and weak soil erosion in the central and north, with slight and mild erosion as a whole (accounting for 93% of the total erosion area). The land-use types in the study area were mainly forest land, and the proportions of forest land in 2005, 2010, 2015, and 2020 were over 60%. The soil erosion intensity of cultivated land was the highest among all land-use types. The area proportion of mild and above grade erosion intensity in this type was 85%, followed by forest land and grassland, with the percentage of the area of mild erosion intensity being 33.96% and 26.63%, respectively. From 2005 to 2020, each land-use type was transformed into another, and the transfer matrix results showed that cultivated land and grassland were mostly transformed into forest land, with a transfer area of 11.98%. Topographic factors markedly affected the soil erosion pattern: the soil erosion intensity in northwestern Hubei first increased and then decreased with the increase in elevation; the most significant erosion occured in the elevation zones of >200-500 m and >500-800 m, with proportions of the total erosion area being 47.7% and 31.8%, respectively. The soil erosion pattern was different under different slopes, and the area of >8° -25° was dominated by moderate, strong, and very strong erosion (the proportion of erosion is 55.4%); 65.6% of the area of >25° is strong and above high-intensity erosion. Given the impact of topographic factors on soil erosion, effective arrangements for soil erosion prevention and control measures should be made, keeping in mind the elevation distribution law and slope as the primary influencing factors. The results of geographical detection showed that slope and land use were the dominant factors affecting soil erosion, and the explanatory power of the two factors on soil erosion (q=0.479) was better than that of a single factor. Areas with slopes>35°, at elevations ranging between 500 and 800 m, showing an annual rainfall erosivity of 4,950.55-6,378.09 MJ·mm/(hm²·h·a), and cultivated land as the main land use type were identified as high-risk erosion areas. The results provide a scientific basis for soil erosion prevention, control, and land-use optimization in northwestern Hubei. [ABSTRACT FROM AUTHOR]

At a watershed scale, soil erosion occurs at a spatially variable rate, posing a significant danger to long-term resource management. The most serious issue has long been regarded as soil erosion. As a result, estimating soil loss and identifying the critical area for implementing optimum management techniques are essential to the programme's success. A numerical model called the sediment-rainfall-watershed area model (SRWA) is built using a spatially distributed RUSLE-based SDR hybridized model to estimate sediment yields in the upper Brahmaputra River watershed. The developed model has been calibrated and validated from 2001 to 2007 and 2008 to 2014, respectively. For the entire period, the statistical performance of the proposed SRWA model and the SDR-RUSLE-based model reveals a correlation coefficient of 0.93 and a Nash–Sutcliffe efficiency coefficient of 0.84. This demonstrates that the SRWA model may assess sediment yield at any upper Brahmaputra basin watershed/sub-watershed outlet. [ABSTRACT FROM AUTHOR]

Quantitative identification of the covariation between sediment connectivity and soil erosion can contribute to provide the key information for watershed sediment management. However, this covariation and its spatiotemporal response mechanisms are still unclear, especially whether this covariation can be used as a basis for identifying critical source areas of sediment in large‐scale ecological restored watersheds. In this study, an integrated methodology framework by the revised universal soil loss equation (RUSLE), index of connectivity (IC) and sediment delivery ratio (SDR) was proposed to visually assess the spatiotemporal characteristics of erosion and sediment yield processes in the Yanhe Watershed with large‐scale ecological restoration from 1985 to 2020 and to identify the covariation between sediment connectivity and erosion in subbasins. The soil erosion estimated by RUSLE has decreased by over 80% since 1985 owing to increased vegetation cover and the effective implementation of soil conservation measures, but the upper reaches still have high erosion intensity due to differences in specific controlling factors such as topographic conditions and land cover, requiring focused soil conservation practice. The IC results showed that as the vegetation restoration and soil conservation measures in the Yanhe Watershed varied from year to year, their spatial and temporal patterns had a strong influence on the distribution of sediment connectivity, and some local areas in the middle reaches showed local minima of IC in 1995, 1998 and 2010 mainly due to the implementation of long‐term ecological restoration project. The developed IC‐Erosion maps indicated that areas with high connectivity but low erosion accounted for over 60% of the total watershed area from 1985 to 1999, demonstrating a reverse correlation between sediment connectivity and erosion. Meanwhile, over 40% of the erosion occurred in a few areas (approximately 20%) with high connectivity and high erosion from 2000 to 2004, which can be characterized as the critical areas of erosion. The methods and results of this study provide ideas for separately defining both erosion and connectivity and quantifying bi‐variable erosion–connectivity classification, which can be easily viewed on a scatterplot. [ABSTRACT FROM AUTHOR]

This study was conducted to determine the erosion risk caused by the change in land use/cover in Acısu Basin between 2000-2018. In the study where the RUSLE model was preferred, other parameters were kept constant, but the model was applied to the model twice separately with the land cover/use data of the relevant years and two independent erosion maps were created. With the study, how much soil is lost in Acısu Basin during the year, which regions are more exposed to erosion, and their distribution was classified and revealed. During the study, the data obtained from satellite images, topographic maps, and field observations were analyzed by GIS and IA techniques. Kappa statistical test was used for the accuracy of the findings. It was determined that the most change in the direction of growth in the basin in 18 years from 2000 to 2018 was 17.08 km² (3.35%) in grassland and pasture areas. On the other hand, it was determined that the highest change in the direction of the decrease occurred in plant change areas with a value of -23.05 km² (4.49%). It is seen that the change in the surface cover of the study area in this time interval caused the expansion of very light, light, and moderate erosion areas. Due to this change in the land surface of Acısu Basin covering 18 years, it was determined that there was an increase in the annual total of 197 t ha-1 y -1 and an increase in the average soil loss amount of 49 t ha-1 y -1 . [ABSTRACT FROM AUTHOR]

In order to reveal spatial and temporal variation characteristics of soil erosion in the upper reaches of Beiluo River watershed before and after the Grain for Green Project, the author used the revised universal soil loss equation（RUSLE) combined with GIS and RS technic to analyze the dynamic changes characteristics of soil erosion in the upper reaches of Beiluo River watershed before and after the Grain for Green Project from 1981 to 2010. The period from 1981 to 2010 was divided into three periods（1981-1990, 1991-2000 and 2001-2010）, which were numbered as I, Ⅱ and Ⅲ, respectively. The results show that a) the spatial distribution of soil erosion intensity in the upper reaches of Beiluo River watershed is higher in the northwest and lower in the southeast. During 1981 to 2010, the average soil erosion modulus of the watershed shows a continuous decreasing trend, especially after the Grain for Green Project, the soil erosion modulus is decreased significantly while the rainfall erosivity of the watershed shows a fluctuating increasing trend. The average annual soil erosion modulus of time period I, time period Ⅱ and time period Ⅲ is 8 612.62, 8 078.91 and 3 060.71 t/（km²·a), respectively. b) After the Grain for Green Project, the area of cultivated land in the watershed is decreased sharply, the area of woodland and grassland is increased significantly, and the vegetation coverage has increased greatly. The soil erosion modulus of main land use types follow the rule of cultivated land＞grassland＞woodland, that is, the soil erosion modulus is decreased with the increase of vegetation coverage. c) Soil erosion in the three periods is consistent with the distribution characteristics of the terrain. 1 350-1 550 m is the main elevation zone of soil erosion distribution in the watershed, which accounts for about 80% of the total erosion amount in the watershed, and 72% of the erosion amount is distributed in the slope＞15°. The average soil erosion modulus of different slope directions follow the pattern of sunny slope＞half-sunny slope＞half-shady slope＞shady slope. [ABSTRACT FROM AUTHOR]

Land use and management practices (LUMPs) play a critical role in regulating soil loss. The cover-management factor (C-factor) in Universal Soil Loss Equation (USLE)-type models is an important parameter for quantifying the effects of LUMPs on soil erosion. However, accurately determining the C-factor, particularly for large-scale assessments using USLE-type models, remains challenging. This study aims to address this gap by analyzing and comparing the methods used for C-factor quantification in 946 published articles, providing insights into their strengths and weaknesses. Through our analysis, we identified six main categories of methods for C-factor quantification in USLE-type modeling. Many studies have relied on empirical C-factor values for different land-use types or calculated C-factor values based on vegetation indices (VIs) in large study areas (>100 km2). However, we found that no single method could robustly estimate C-factor values for large-scale studies. For small-scale investigations, conducting experiments or consulting the existing literature proved to be more feasible. In the context of large-scale studies, employing methods based on VIs for C-factor quantification can enhance our understanding of the relationship between vegetation changes and soil erosion potential, particularly when considering spatial and spatiotemporal variations. For the global scale, we recommend the combined use of different equations. We suggest further efforts to develop C-factor datasets at large scales by synthesizing field-level experiment data and combining high-resolution satellite imagery. These efforts will facilitate the development of effective soil conservation practices, ensuring sustainable land use and environmental protection. [ABSTRACT FROM AUTHOR]

[Objective!] Disclosesing the spatiotemporal pattern and influencing factors of soil erosion in Shiyan City plays an important guiding role in soil and water conservation and water quality protection of Danjiangkou Reservoir. Met hods] Based on the dynamic monitoring data of soil and water loss and long-term observation data of monitoring stations in Shiyan City from 2005 to 2020, the temporal and spatial variation characteristics of soil and water losses in Shiyan City were explored, and the main influencing factors were quantitatively evaluated by RUSLE model. [Results] Soil and water loss in Shiyan City was in the containment stage from 2005 to 2011 and in the relatively stable stage from 201 2 to 2020. Tn 2020, the soil erosion in the central area of Shiyan City showed the characteristics of small area and high intensity. The three southern areas showed the characteristics of wide area and low intensity, while the northern area showed the characteristics of wide area and high intensity. For runoff plots with different land use types, the average soil erosion modulus of bare land plot was the highest〔2 320 t/(• a) J, followed by cultivated land, economic forest and grassland plot. The average soil erosion moduli of the three slope grades (0°〜1(几 1()。〜2()°, 2()。〜3()°) were 616.73 t/(km2 • a), 1 226.65 t/(km2 • a), 2 ()80.26 t/(km2 • a), respectively, indicating that the soil erosion was seriously aggravated after the slope gradient exceeded 10°. Compared with the natural vegetation coverage area, the soil erosion rates of Amorpha fruticosa hedgerow and ridge terrace area were significantly reduced, and the soil and water conservation effect of Amorpha fruticosa hedgerow was better. There was a significant binary linear or exponential function relationship between soil erosion modulus and slope and erosive precipitation in different land use types. [Conclusion] Yunxi County and Yunyang District are the key areas of soil and water losses in Shiyan City. The land use type, slope, vegetation cover type and erosive precipitation are the important influencing factors on soil and water losses in this area. These research results can provide scientific basis for the optimal allocation of soil and water conservation measures and the construction of erosion model in northwest Hubei Province. [ABSTRACT FROM AUTHOR]

A quarry lake is a quarry that is filled with water after abandonment. In the lake water, natural radionuclides contaminate with a certain level, as a result of atmospheric deposition, groundwater diffusion, soil erosion, and sediment–water–air interaction. In this study, 40K, 226Ra, and 232Th were analysed for surface soil and water in a specific quarry lake in Vietnam. The seasonable variation of radioactivity in water was investigated. It was found that the activity concentration correlated with the TDS of water and the higher activity concentration was found in the wet season. The RUSLE model was used to predict the average annual rate of soil erosion in the study site. Based on the model prediction, the inflow rate of 40K, 226Ra, and 232Th in water were 4.73, 36.75, and 768.5 GBq y−1 respectively. [ABSTRACT FROM AUTHOR]

Soil erosion is an important ecological problem in Zhejiang Province, and understanding the spatial and temporal evolution of soil erosion risk and its driving forces is fundamental for soil conservation. This study quantitatively assessed the soil erosion risk in Zhejiang Province in 2000, 2010, and 2020 based on multi-source data and the RUSLE model at the provincial scale. Quantitative analysis was made of the driving forces of soil erosion risk in six natural zones by combining it with a geo-detector to fill the gaps in the current soil erosion research for Zhejiang Province. The results showed that during the period 2000-2020, Zhejiang Province achieved remarkable results in soil and water conservation. The soil erosion risk level was mainly slight erosion, and the proportion of slight erosion area increased year by year, while the risk areas of slight erosion and severe erosion were more stable and difficult to change. The change in soil erosion grade varied in each natural area of Zhejiang Province. There was a generally mild deterioration of soil erosion in the relatively flat terrain, which is closely related to the intensive human activities in the area, and the soil erosion in the northwest hilly area and the middle mountainous area in the south improved significantly. The slope factor had an important influence on soil erosion, with the highest explanatory power q value of 0.2668. The two-factor synergistic effect of slope and vegetation cover had a higher explanatory power with the highest q value of 0.3816, followed by slope and land use with a q value of 0.2869. This study provides a methodological reference for homogeneous-scale soil erosion research and erosion control in Zhejiang Province. [ABSTRACT FROM AUTHOR]

• The present study measured the C factor value using NDVI time series, extracted from Landsat 8 OLI imageries. • The forest areas of the basin are more prone to soil erosion, compared to any other LULC category. • Upraised slope significantly affects soil loss rate and sediment yield. • Around 174.31 Km2 (9.16 %) land area contributed 57.54 % of gross soil loss. • The upstream parts of the river basin are highly susceptible to soil erosion. As a major contributor of land degradation, water borne soil erosion poses a threat to environmental and socioeconomic sustainability worldwide. Recently, various anthropogenic factors have aggravated soil erosion related problems in most of the humid and semi arid parts of the developing countries like India. Therefore, accurate quantification of the spatially distributed soil erosion along with the identification of erosion hot spots is of paramount significance for designing extensive management strategies to combat erosion hazard. The present study aimed to estimate mean annual soil loss in a tropical river basin such as Kumari river basin (KRB, area: 195125 ha) in the Chotanagpur plateau region in India. In addition, the study also quantified mean sediment yield and identified the main sediment sources within the basin. For these purposes, the present study introduced a raster based spatially distributed approach that integrated RUSLE and sediment delivery ratio (SDR) models with remote sensing (RS) and GIS. The results depicted that annual soil loss within the basin varied from 0 to 208.34 t ha – 1 yr – 1 with an average of 1.92 t ha – 1 yr – 1. The computed annual soil loss was about 364.05 × 103 tonnes, of these, 209.46 × 103 (∼58 %) were lost from only 8.93 % of the total basin area at intolerable rate. Similarly, the estimated sediment yield within the basin ranged from 0 to 64.53 t ha – 1 yr – 1 with a mean of 1.15 t ha – 1 yr – 1 and followed a similar distribution pattern of soil erosion. Being characterized by steep topography, the forest patches in the upstream part of the basin showed the most critical erosion risk. The methodology adopted in the current research displayed acceptable correctness and enabled spotting of most vulnerable areas of water erosion and critical sediment sources, initiating a comprehensive anticipating aid for the implementation of proper land management and soil conservation plans. This perspective is surmised to be helpful for extensive study of water erosion in other tropical river basins. [ABSTRACT FROM AUTHOR]

This study aims to apply the Universal Erosion Model (RUSLE) on Wadi Rajeb Basin in order to estimate the susceptibility of soil to erosion. The research follows the analytical method for different data to represent the variables that have been dealt with in (GIS), such as: Digital Elevation Model (DEM), Data of soil properties, sentinel-2 image, etc. The study shows that the basin is subjected to erosion in varying levels. It also shows that 21% of the basin is highly exposed to soil erosion (572 ton/ha/year), and 34% of it is a moderate risk level for erosion (124-213 ton/ha/year), and the remaining areas (45% of the basin) appear to be at low risk (<0.24 ton/ha/year). The study recommends on the importance of providing techniques and methods to save the soil stabilization in the basin, especially in areas prone to erosion. [ABSTRACT FROM AUTHOR]

Estimating soil erosion by water is important in the management of watersheds. The growth of anthropogenic activities and climate change fluctuation in north-eastern Algeria are expected to influence land use/cover and soil sustainability. For this, the Revised Universal Soil Loss Equation (RUSLE), remote sensing (RS), geographic information system (GIS), and Machine Learning (ML) were combined to investigate the influence of Land Use Land Cover (LULC) changes on soil loss rates and its distribution in the Seybouse basin during a span of two decades between 1990 and 2010. The land use land cover classifications were categorized into six classes: water, dense forest, degraded forest, croplands, barren lands and urbanisation. The evaluation of the soil erosion distribution was based on the analyses of the LULC changes and RUSLE factors. The results reveal that the average annual soil loss based RUSLE in the basin for the years 1990, 2000, and 2010 were 12.73 t ha–1 y–1, 12.60 t ha–1 y–1, and 13.88 t ha–1 y–1 respectively. The study found that for the years 1990, 2000, and 2010 around 17, 15, and 20% areas are under the very severe and extremely severe erosion category, occurring mostly located in barren lands and degraded forests LULC classes. Overall, the results concluded that due to the impact of rainfall variability and decreased crop managements in the Seybouse basin, there was an increase in the surfaces of barren lands and degraded forests over crop lands and dense forests which resulted in the increase of erosion rates. [ABSTRACT FROM AUTHOR]

Model-based soil erosion studies have increased in number, given the availability of geodata and the recent technological advances. However, their accuracy remains rather questionable since the scarcity of field records hinders the validation of simulated values. In this context, this study aims to present a method for measuring sediment deposition at a typical Mediterranean catchment (870 ha) in Greece through high spatial resolution field measurements acquired by an Unmanned Aerial Vehicle (UAV) survey. Three-dimensional modeling is considered to be an emerging technique for surface change detection. The UAV-derived point cloud comparison, applying the Structure-from-Motion (SfM) technique at the Platana sediment retention dam test site, quantified annual topsoil change in cm-scale accuracy (0.02–0.03 m), delivering mean sediment yield of 1620 m3 ± 180 m3 or 6.05 t ha−1yr−1 and 3500 m3 ± 194 m3 or 13 t ha−1yr−1 for the 2020–2021 and 2021–2022 estimation. Moreover, the widely applied PESERA and RUSLE models estimated the 2020–2021 mean sediment yield at 1.12 t ha−1yr−1 and 3.51 t ha−1yr−1, respectively, while an increase was evident during the 2021–2022 simulation (2.49 t ha−1yr−1 and 3.56 t ha−1yr−1, respectively). Both applications appear to underestimate the net soil loss rate, with RUSLE being closer to the measured results. The difference is mostly attributed to the model's limitation to simulate gully erosion or to a C-factor misinterpretation. To the authors' better knowledge, this study is among the few UAV applications employed to acquire high-accuracy soil loss measurements. The results proved extremely useful in our attempt to measure sediment yield at the cm scale through UAV-SfM and decipher the regional soil erosion and sediment transport pattern, also offering a direct assessment of the retention dams' life expectancy. [ABSTRACT FROM AUTHOR]

Geohazard mapping using remote sensing and GIS is effective. Nam UN classic terrain with soil erosion and other geohazards. The Nam UN Basin's yearly soil loss and high erosion potential are estimated using RUSLE, remote sensing, and GIS. 14.26 t/ha/year of soil erosion is seen on the map. Soil erosion zones are also shown on the map. According to the study, 33.40 percent of the whole area (457.07 kilometers) is prone to severe soil erosion, while 7.72 percent (105.76 kilometers) is prone to high erosion. To decrease soil erosion, decision-makers use soil erosion prognosis analysis. The Analytical Hierarchy Process (AHP) was utilized to identify key soil erosion prone locations by incorporating geo-environmental variables such land use/land cover, geomorphology, Dem, drainage density, slope, elevation, LS factor, rainfall, soil texture, and soil depth. 33.40% of the region is highly prone to soil erosion. [ABSTRACT FROM AUTHOR]

Soil erosion is a natural occurrence in landforms but is a major threat that causes essential soil nutrients loss and it is hazardous to agriculture practices as depletion of soil makes the land less productive. The major factors influencing soil erosion are rainfall erosivity factor (R), soil erodibility factor (K), length and steepness factor (LS), cover management factor (C) and support practice factor (P). In most of the earlier research studies, the estimation of soil erosion has been done with Revised Universal Soil Loss Equation (RUSLE) but accuracy in analysis of C and P factors has not been considered. The main objective of the study is to explore the influence of different LULC classifier in determining the rate of erosion using RUSLE model. This study utilizes five classifiers such as Maximum Likelihood Classifier (MLC), Random Trees Classifier (RTC), Support Vector Machine Classifier (SVM) and Artificial Neural Network (ANN) and Mahalanobis Distance (MD) to generate the C and P factor. The Accuracy assessment, a statistical method is used to validate the classified Land Use / Land Cover (LULC). The obtained results show that the respective annual mean soil losses based on MLC, MD, ANN, SVM, RTC are 1.95, 2.08, 1.71, 2.19 and 1.85 tons−1 ha−1 yr−1. The estimated annual mean soil loss by using different classifiers is validated using a statistical method Receiver operating curve / Area under Curve. The accuracy level of RTC is 0.800 of Area Under the Curve (AUC) which is higher compared to conventional classifiers like MLC, MDC, ANN, SVM with the values 0.793, 0.707, 0.693, 0.793 of AUC, respectively. RTC, a Machine learning-based classifier, is observed to be more accurate than the conventional classifiers. The insights obtained from the present study will be very useful for land use planning and management and to undertake soil and water conservation measures. [ABSTRACT FROM AUTHOR]

Introduction Soil erosion by water is one of the most common environmental problems worldwide and is considered a serious risk for sustainability in developing countries. Water erosion on a global scale is one of the most critical types of soil and environmental degradation due to its geographical extent and ecological effects. In this regard, effectively controlling sediment load is an important component in watershed management. In the formulation of a watershed management strategy, the estimation of sediment delivery ratio (SDR) plays a significant role. SDR is defined as the sediment yield from an area divided by the gross erosion of that same area. SDR is expressed as a percentage and represents the efficiency of the watershed in moving soil particles from areas of erosion to the point where sediment yield is measured. One of the problems in estimating the SDR in watersheds is the lack of proper information on the amount of soil erosion and sediment yield. In this context, the Sanganeh soil conservation research station, having measured soil erosion and sediment yield of small watersheds, is a suitable place to evaluate the accuracy of the RUSLE model and estimate the ratio of sediment delivery on the scale of small watersheds. The current research aims to achieve two goals: a) determining the accuracy of the RUSLE model in estimating soil erosion based on the measurements in the erosion plots, and b) estimating the SDR using the estimated soil erosion values as well as the sediment yield measured at the outlet small watershed are planned. Materials and Methods Considering the importance of soil erosion and the study of sediment processes in semi-arid rangeland ecosystems, the Khorasan Razavi Agricultural and Natural Resources Research Center (KANRRC) assessed some micro-watersheds for the collection of storm-wise runoff and associated sediment. The Sanganeh research micro-watershed, located 100 km from Mashhad City (northeast Iran), is one of the watersheds selected for this study. The watershed area, the longest waterways, and the mean slope of the watershed are 1.2 ha, 145.0 m, and 31.2%, respectively. The study watershed consists of semi-arid rangeland dominated by Bromus tectorum and Artemisia diffusa, with a coverage of 50%. The soil is Entisol and Aridosol, young, with a maximum depth of 30 cm. The mean electrical soil conductivity (EC), soil organic matter (OM), clay, sand, silt, and surface rock fragments of soils are 1.81, 1.57, 10.6, 54.7, 34.7, and 5%, respectively. In this research, three experimental small watersheds with areas between 4300-12000 m2 were selected along with the erosion plots in them. Then, 24 rainfall events related to two periods of 2006-2009 and 2016-2018 were recorded along with the corresponding data of runoff and sediment in watersheds and plots. In this study, water flow and sediment yield were monitored at the main outlet of the micro-watersheds and plots. The runoff volume was calculated after each storm event by multiplying the depth of collected water, measured using an iron ruler at five points in the tank (corners and central), by the surface area of the collector. The collected runoff and sediment were then mixed thoroughly and one sample was taken to determine sediment concentration and sediment yield. Then, by collecting the required information (includeing rainfall erosivity, topography, conservation practice, soil erodibility, and cover-crop management factors), the RUSLE model was run and compared with the observation data of the plots. The storm-wise soil erosion predictions were compared with observed data based on the criteria of the coefficient of determination (R2 ) and relative estimation error (RE). In the following, by modifying the RUSLE model and observing the sedimentation data of the studied watersheds, the value of the SDR was estimated. Results and Discussion After collecting the required information, the RUSLE model was implemented at the plot scale. The accuracy of the model was evaluated using erosion plot data, which was not confirmed due to huge overestimations of RUSLE. Next, to achieve more accurate results, regression types (linear, exponential, power, etc.) were used between the observed and estimated values of soil erosion (RUSLE). After applying the correction coefficient, this model was able to estimate the average erosion rate of the whole period are 12, 17, and 2% for E1, E4, and E6 watersheds, respectively, which is within the acceptable range of soil erosion modeling. Therefore, it can be said that the accuracy of the modified RUSLE model (by regression model) in estimating the average soil erosion during the period is higher than the event-based scale. Also, the prediction of maximum event estimation error for E1, E4, and E6 watersheds was 25.7, 35.8, and 21.6%, respectively. After evaluating the accuracy of the RUSLE model at the plot scale and in order to know the amount of soil erosion at the watershed scale, the values of L, S, K, and C factors for the watersheds were calculated based on a weighted average and entered into the modified model. Therefore, the results of the RUSLE model were generalized to the watershed scale. In the final stage, by dividing the amount of erosion by the corresponding amounts of sediment yield measured at the outlet of watersheds, the ratio of sediment delivery was calculated. The average SDR of the entire period in the E1, E4, and E6 watersheds are 42.2, 41.5, and 39.7%, respectively, and in the maximum events, it is one or two percent higher. Conclusion Overall, the results of this research showed that using the modified RUSLE model, it is possible to estimate the average soil erosion in the Sangane soil conservation station and also estimate the SDR. Therefore, this approach can be used in executive programs in similar areas. According to the obtained results, the classification of rainfall data based on the rain erosive factor and then the evaluation of the RUSLE model can provide more accurate results. In addition, in this research, due to the small area of the watersheds, waterway processes did not play a role in the deposition and transfer of eroded soils. It is also suggested that similar research could be done in larger watersheds. Finally, considering the determination of the SDR in this area, it is recommended to evaluate the accuracy of the experimental methods for determining the SDR. [ABSTRACT FROM AUTHOR]

Soil loss (SL) and its related sedimentation in mountainous areas affect the lifetime and functionality of dams. Darbandikhan Lake is one example of a dam lake in the Zagros region that was filled in late 1961. Since then, the lake has received a considerable amount of sediments from the upstream area of the basin. Interestingly, a series of dams have been constructed (13 dams), leading to a change in the sedimentation rate arriving at the main reservoir. This motivated us to evaluate a different combination of equations to estimate the Revised Universal Soil Loss Equation (RUSLE), Sediment Delivery Ratio (SDR), and Reservoir Sedimentation (RSed). Sets of Digital Elevation Model (DEM) gathered by the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), AQUA eMODIS NDVI V6 data, in situ surveys by echo-sounding bathymetry, and other ancillary data were employed for this purpose. In this research, to estimate the RSed, five models of the SDR and the two most sensitive factors affecting soil-loss estimation were tested (i.e., rainfall erosivity (R) and cover management factor (C)) to propose a proper RUSLE-SDR model suitable for RSed modeling in mountainous areas. Thereafter, the proper RSed using field measurement of the bathymetric survey in Darbandikhan Lake Basin (DLB) was validated. The results show that six of the ninety scenarios tested have errors <20%. The best scenario out of the ninety is Scenario #18, which has an error of <1%, and its RSed is 0.46458 km3·yr−1. Moreover, this study advises using the Modified Fournier index (MIF) equations to estimate the R factor. Avoiding the combination of the Index of Connectivity (IC) model for calculating SDR and land cover for calculating the C factor to obtain better estimates is highly recommended. [ABSTRACT FROM AUTHOR]

The western edge of the Jord Tannourine-Aaqoura plateau (Lebanon) is a Mediterranean mountain region that unites the suitable conditions for water erosion: abundant rain, steep slopes, ravaged natural vegetation cover, and soil susceptible to degradation. However, the agricultural terraces developed for decades remain a robust practice to limit runoff and expand the useful agricultural area. To determine water erosion and the role of anti-erosion practices, the adopted methodology is based on the universal USLE equation revised (RUSLE). The combination of model parameters in a GIS has made it possible to define that water erosion can exceed 51 t/ha/year, mainly in sectors marked by the absence of agricultural terraces, and not associated with dense plant cover. In contrast, terraced slopes induce losses of less than 16 t/ha/year. The implantation of a network of metal rods does the validation. Faced with this risk of degradation, it seems essential to recommend protective agricultural techniques. [ABSTRACT FROM AUTHOR]

Soil conservation services can weaken and improve soil erosion, and by analyzing its spatial distribution characteristics and influencing factors, it can improve soil conservation capacity and reduce regional soil erosion. Firstly, the amount of soil erosion in the study area was estimated based on the reviewed universal soil loss equation (RUSLE) model and the integrated evaluation of ecosystem services and trade offs (INVEST) model. Secondly, a more accurate soil erosion estimation model was used to evaluate soil conservation services in the study area. Finally, the main factors affecting the spatial distribution of soil conservation services in the study area were discussed by means of multiple linear regression and geographical detectors. The results show that the difference between the estimated results of the RUSLE model and the actual soil erosion modulus in the study area is 201.02t/(km²·a), which is of low precision. The difference between the estimated results of the InVEST model and the actual soil erosion modulus in the study area is 33.11 t/ (km²·a), with high precision. The total soil conservation in the study area is 13.01×108 t. The areas with strong soil conservation capacity are in the northeast and southwest of the study area, while the west, middle and southeast of the study area have weak soil conservation capacity. The climatic factors affecting soil conservation services are mainly precipitation and temperature, and the landscape indices affecting soil conservation services are mainly sprawl index, landscape segmentation degree, separation index and Shannon uniformity index. The research results can provide a scientific basis for the sustainable development of ecosystem services. [ABSTRACT FROM AUTHOR]

In this research, we used the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) to predict the annual rate of soil loss in the District Chakwal of Pakistan. The parameters of the RUSLE model were estimated using remote sensing data, and the erosion probability zones were determined using GIS. The estimated length slope ( L S), crop management ( C), rainfall erosivity ( R), soil erodibility ( K), and support practice ( P) range from 0–68,227, 0–66.61%, 0–0.58, 495.99–648.68 M J / m m. t. h a − 1. y e a r − 1 , 0.15–0.25 M J / m m. t. h a − 1. y e a r − 1 , and 1 respectively. The results indicate that the estimated total annual potential soil loss of approximately 4,67,064.25 t. h a − 1. y e a r − 1 is comparable with the measured sediment loss of 11,631 t. h a − 1. y e a r − 1 during the water year 2020. The predicted soil erosion rate due to an increase in agricultural area is approximately 164,249.31 t. h a − 1. y e a r − 1 . In this study, we also used Landsat imagery to rapidly achieve actual land use classification. Meanwhile, 38.13% of the region was threatened by very high soil erosion, where the quantity of soil erosion ranged from 365487.35 t. h a − 1. y e a r − 1 . Integrating GIS and remote sensing with the RUSLE model helped researchers achieve their final objectives. Land-use planners and decision-makers use the result's spatial distribution of soil erosion in District Chakwal for conservation and management planning. [ABSTRACT FROM AUTHOR]

Northeast China is a prominent base for commercial grain production nationwide. Soil erosion, a primary cause of land degradation and grain yield decrease, has become an imminent issue and is still not well documented in Northeast China. Thus, a comprehensive assessment of soil erosion in Northeast China is essential for deepening our understanding of various geological and agricultural issues, such as control of regional water and soil losses, anti-degeneration attempts on black soil, preservation of land fertility, and safeguarding of national food security. Based on the Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ) models, this paper comprehensively assessed the water and wind erosion in Northeast China from 2000 to 2020 and analyzed the current situation, as well as the spatial and temporal evolution of soil erosion. The results suggest the following: (1) The degree of soil erosion in Northeast China was mainly slight, and water erosion was more severe than wind erosion. Water and wind erosion in bare land and grassland were more intensive than in cropland and forests. The Liao River Plain (LRP) has undergone relatively intensive water erosion, while the wind erosion in the Greater Kinggan Mountains Region (GKMR) was more intensive than in other sub-regions. (2) A slight intensifying trend of water erosion could be observed in Northeast China from 2000 to 2020, where the area of slight water erosion decreased and that of light and intensive water erosion increased. The water erosion in the Changbai Mountain Region (CBMR), the Sanjiang Plain (SJP), and the Songnen Plain (SNP) intensified, while the LRP has undergone slower water erosion than before. The water erosion in bare land and cropland intensified, while the water erosion in grassland and forests slowed down. Compared to the first decade (2000–2010), the second decade (2010–2020) in the timespan of study had a reversed trend of water erosion from intensifying to moderating, which means water erosion was alleviated. (3) A moderating trend in wind erosion could be found in Northeast China from 2000 to 2020, where the area of slight wind erosion increased and that of light, moderate, and intensive wind erosion decreased. The wind erosion in the LRP showed a pronounced decrease, and the wind erosion in bare land and cropland also considerably decreased. Compared to the first decade, the amount of wind erosion in the second decade decreased by 18.2%, but the rate in the second decade decreased slowly or even increased. These two facts indicate that wind erosion in Northeast China has alleviated, but this trend is gradually slowing down. Soil erosion is caused by multiple factors, such as climate, topography, soil, and human activities. This study provides important implications for our understanding of soil erosion control and management in Northeast China. In sub-regions with severe erosion, such as the LRP and the GKMR, we can adopt methods such as zero tillage, cross ridge tillage, and straw mulching according to the local characteristics of soil erosion to slow down the process. [ABSTRACT FROM AUTHOR]

Intensive cropland expansion for an increasing population has driven soil degradation worldwide. Modeling how agroecosystems respond to variations in soil attributes, relief and crop management dynamics can guide soil conservation. This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image (spectroscopy technique), which uses time series remotely sensed environmental, agricultural and anthropic variables, in the southeast region of São Paulo State, Brazil. The availability of the open-access satellite images of Tropical Rainfall Measuring Mission (TRMM) and Landsat satellite images provided ten years of rainfall data and 35 years of exposed soil surface. The bare soil surface and agricultural land use were extracted, and the multi-temporal rainfall erosivity was assessed. We predict soil maps' attributes (texture and organic matter) through innovative soil spectroscopy techniques to assess the soil erodibility and soil loss tolerance. The erosivity, erodibility, and topography obtained by the Earth observations were adopted to estimate soil erosion in four scenarios of sugarcane (Saccharum spp.) residue coverage (0%, 50%, 75%, and 100%) in five years of the sugarcane cycle: the first year of sugarcane harvest and four subsequent harvesting years from 2013 to 2017. Soil loss tolerance means 4.3 Mg ha−1 exceeds the minimum rate in 40% of the region, resulting in a total soil loss of ~6 million Mg yr−1 under total coverage management (7 Mg ha−1). Our findings suggest that sugarcane straw production has not been sufficient to protect the soil loss against water erosion. Thus, straw removal is unfeasible unless alternative conservation practices are adopted, such as minimum soil tillage, contour lines, terracing and other techniques that favor increases in organic matter content and soil flocculating cations. This research also identifies a spatiotemporal erosion-prone area that requests an immediately sustainable land development guide to restore and rehabilitate the vulnerable ecosystem service. The high-resolution spatially distribution method provided can identify soil degradation-prone areas and the cropland expansion frequency. This information may guide farms and the policymakers for a better request of conservation practices according to site-specific management variation. [ABSTRACT FROM AUTHOR]

The Biome of Brazilian savannah, Cerrado, an important ecological-economic region with a large production of commodities, has in its northern portion the recent agricultural frontier outside the Amazon. Problems derived from environmental degradation, such as erosive processes and the loss of soil productivity have shown their relevance. In this context, this paper analyses the space-time variabilities in soil losses rates calculated for watersheds draining the Emas National Park (PNE), and National Park of Brasilia (PNB), and their surroundings by applying the Revised Universal Soil Loss Equation (RUSLE) for the years 1970,1985 and 2018. Results indicate that the higher average rates were calculated for the surroundings of PNB, 10 ton.h-1, hangingfrom 3.8 to 20.8. Oppositely, for the PNE soil loss rates were 10 times lower from those of PNB, where no significant differences were observed, with average rate of 0.3 ton.h-1.a-1 (hangingfrom 0.1 to 0.5). Oppositely, the controlling factors of soil loss differed among the two sites, as for the PNB land-use changes controlled the time-space variabilities, and for the PNE only the morphologic differences played this role. The rates calculated for the PNE and surroundings differ from the general trend observed for other Savannahs in the world. [ABSTRACT FROM AUTHOR]

Roads have provided convenience to mankind, but much natural destruction occurs when roads are constructed adjacent to mountains. Route selection was based on a qualitative methodology of expert judgment, and the road was planned with the operator’s objectives and minimum costs in the first place. In this study, we propose a new road route planning method that uses a quantitative and specific method using a genetic algorithm. By focusing on ’habitat destruction’ and ’soil erosion due to cut soil’, which are major problems caused by the construction of roads, the conflicting objectives of ’minimization of costs for users and builders’ are combined and weighted for each purpose. Compared to the existing road, the proposed road could have an 85.52% increase in biodiversity conservation and a 4% improvement in soil loss stability. This model allows decision makers to efficiently generate scenarios by adjusting the weights of goals or substituting data. [ABSTRACT FROM AUTHOR]

Soil erosion is a severe environmental problem worldwide, especially in tropical regions. The Revised Universal Soil Loss Equation (RUSLE), one of the universally accepted empirical soil erosion models, is quite commonly used in tropical climatic conditions to estimate the magnitude and severity of soil erosion. This study, apart from identifying the role of individual parameters in influencing the results of the RUSLE, also aims at refining the RUSLE results by incorporating the state-of-the-art technique Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) in a GIS environment by utilizing its ability to measure minute surface changes in millimetre levels. Apart from this novel approach of prioritising soil erosion classes using PSInSAR, the eroding surface conditions were also studied using low coherence value (<0.75 in this study). The spatially and temporally averaged annual soil loss and net soil erosion (2015-2019), derived through RUSLE and transport limited sediment delivery (TLSD) approach, respectively, was improved by spatially integrating the PSInSAR velocity map. The integrated methodological framework is demonstrated for a tropical river basin in South India (Muvattupuzha River Basin [MRB]), which shows a mean rate of net soil loss of 6.8 ton/ha/yr, and nearly 8% of the area experiences deposition. Our approach to improve the accuracy of RUSLE-based soil erosion classes using PSInSAR techniques clearly demarcated the areas that call for utmost priority in implementing management practices. The corollary results show that the very severe soil erosion class is characterized by PSI velocity with higher negative values, followed by the successively lower classes. Results strongly suggest that RUSLE output can be improved as well as validated using a velocity map derived from radar data. [ABSTRACT FROM AUTHOR]

Assessment of soil erosion processes and accurately identifying erosion drivers is crucial for effective control and conservation strategies. We introduced a new approach involving the integration of the SDR of the InVEST and Geodetector models to explore the spatio-temporal variability of erosion and investigate the key drivers contributing to soil erosion. The results revealed an average soil loss increase of 19 to 37 t ha-1 yr-1 in the watershed from 1990 to 2020, respectively. Cropland areas had the highest growth in soil loss from 26.53 in 1990 to 118.76 t ha-1 yr-1 in 2020. The Geodetector revealed that the key variables influencing erosion are vegetation, slope, precipitation, and aspect factors. The interaction of slope and aspect, slope and precipitation, had the strongest influence on erosion than other variables, with q values of 0.80 and 0.79, respectively. The approach of this study can be used in other regions characterized by severe soil erosion. [ABSTRACT FROM AUTHOR]

Healthy soil is the key foundation of the world's agriculture and an essential resource to ensure the world's food security. Soil erosion is one of the serious forms of soil degradation and a major threat to sustainable terrestrial ecosystems. In this study, we utilized a continuous Landsat satellite image dataset to map soil erosion changes (1990–2020) based on the RUSLE model across the Pearl River Basin. The study results indicated that: (1) The multi-year area-specific soil erosion average in the Pearl River Basin is approximately 538.95 t/(km2·a) with an annual soil loss of approximately 353 million tons; (2) The overall soil erosion displayed a decreasing trend over the past 30 years with an annual decreasing rate of −13.44(±1.53) t/(km2·a); (3) Soil erosion, dominated by low- and moderate-level erosion, primarily occurred in the tributary basin of Xijiang River, especially in the areas with slopes > 15°, low vegetation coverage, or poorly managed forests; (4) the NDVI and land cover were the dominant factors regulating soil erosion dynamics versus the insignificant role of precipitation played in the erosion procedure. The study results are valuable for soil erosion management and water conservation in the Pearl River Basin. [ABSTRACT FROM AUTHOR]

This research dealt with the topic of modeling the soil lost in a semi-arid desert area in the Ma'an watershed and its surroundings in Ma'an Governorate using the global equation model, GIS, and remote sensing technology. Erosion is one of the most important geomorphological processes, and its intensity varies according to its causative factors. Soil erosion risks were assessed by applying a mathematical model within the GIS environment based on six factors affecting soil erosion. To apply the RUSAL model to the study area, a number of variables and factors must be derived based on the data of rainfall rates, land uses, soil characteristics, slope values, and vegetation index (NDVI). Several layers were established for the previous factors and then combined to produce a map for evaluating the risk of erosion, which was divided into five degrees. The results showed that only 0.004% of the study area falls within the severe and very severe classes, and 99.29% of the study area falls within the very light class, which reflects a very low susceptibility to erosion. The results also showed that rainfall and the topographic factor, which includes the degree of slope, contribute effectively to the occurrence of water erosion in the studied area. The study also proved that geographic information systems and remote sensing technology are effective tools in mapping soil and its related problems, especially that of water erosion. [ABSTRACT FROM AUTHOR]

This study examines the watersheds in the Bastora river basin and builds a geographic model within ARC GIS software. The work was divided into three phases. The first phase was to obtain the results of 14 morphometric parameters to determine priorities, by using AHP model to extract the relative weights of the parameters. Then, these weights are adopted in building (SDI, TOPSIS and SAW) models to determine levels of priorities. The outputs were classified into five categories according to the degree of their susceptibility to erosion. The second phase includes creating another model to provide necessary data and tools for building the RUSLE model. The final phase validates of pairs of models (TOPSIS-RUSLE), (SDI-RUSLE), and (SAW-RUSLE) using least squares tool for comparison between the results. The results indicate a highly statistically significant relationship among all the previous pairs of models with clear preference recorded for (TOPSIS-RUSLE) models relationship. [ABSTRACT FROM AUTHOR]

The present paper attempts to quantify the amount of soil loss occurring in the Mayurakshi River basin of eastern India with the help of the empirical Revised Universal Soil Equation (RUSLE) Model. The areas of high erosion were identified and their spatial and temporal patterns and trends were monitored for the years 2008 and 2018. It was observed that the average soil loss increased by about 6% from 2008 to 2018. Among a variety of causal factors, the Shannon’s Entropy Index used in this study has identified the slope length and steepness factor (LS factor) as the most significant causal factor of erosion in the studied river basin. One of the novel approaches used in this study is the application of landscape ecological metrics in the domain of temporal erosion monitoring. An important conclusion drawn from the analysis is that the patches of high and moderate erosion are increasing significantly in terms of areal extent, whereas the extent of the patches of low erosion is decreasing with time. Therefore, appropriate management strategies prioritised on the high and moderate erosion patches are the need of the hour. [ABSTRACT FROM AUTHOR]

In this study, soil loss by water in the Chambal river basin (CHB) has been estimated using Google Earth Engine and Geographic Information System platforms employing the Revised Universal Soil Loss Equation (RUSLE). Google Earth Engine, a cloud-based platform, has been used for generating land use/land cover map from the voluminous satellite remote sensing data. The observed mean rate of soil erosion in the CHB is 1.17 t/ha/yr. The study reveals that very severe type of soil erosion in the Chambal river basin is found in the gullies/ravines and barren lands with the highest mean rate of soil erosion in the gully areas at 13.44 t/ha/yr. Catchment-wise soil loss estimates suggest that the four catchments namely Kali Sindh, Lower Chambal, Upper Chambal and Parbati are experiencing much higher soil loss in comparison to others; hence these catchments are prioritised for soil conservation efforts. Sensitivity analysis of individual factors and their interaction effect indicates that LS factor is the most influential factor in the study area followed by C factor. Research highlights: Estimates soil loss by water for the first time at basin scale in the Chambal river basin (mean rate 1.17 t/ha/yr) by integrating Google Earth Engine and GIS employing RUSLE. Provides insight into the spatial pattern and status of soil erosion in the study area. Reveals very severe type of soil erosion in the areas of gullies/ravines with the highest mean rate 13.44 t/ha/yr and 5.92 t/ha/yr, respectively. LS factor is the most influential factor in the study area, followed by C factor. Prioritises Kali Sindh, Lower Chambal, Upper Chambal and Parbati catchments for soil conservation efforts based on much higher soil loss. [ABSTRACT FROM AUTHOR]

Background: Pelosols are the Soil of the Year 2022 in Germany, Austria and Switzerland. They represent soils with a high clay content (≥45%) in the diagnostic P horizon. Pelosols are nutrient‐rich, have a strong capacity for swelling and shrinking, have a challenging water balance with a high portion of nonplant available water and are affected by high traction. Such special characteristics make them challenging soils under agricultural management. Aims: The occurrence, land use management and soil erosion risk of Pelosols in Germany were investigated and compared to their clay‐rich soil counterparts on a global scale. Methods: We intersected soil maps of the German and international digital soil mapping with soil erosion data. Results: A high percentage (63%) of Pelosols in Germany are under agricultural use, from which two‐thirds are arable farming. Simultaneously, Pelosols have a high risk for soil erosion by water and are the fourth most endangered soil type compared to all soil types in Germany. The average soil erosion loss of Pelosols used for agricultural practices assessed by the Revised Universal Soil Loss Equation (RUSLE) is 2.24 t ha−1 year−1 compared to an average erosion loss of all agriculturally used soils in Germany of 1.65 t ha−1 year−1. From an international perspective, Pelosols in Germany are mostly mapped as haplic Cambisols or haplic Luvisols, as they do not necessarily meet the diagnostic properties of the clay‐rich Vertisol soil type. Most Vertisols are classified as Pelosols, but Pelosols do not necessarily fulfil the diagnostic criteria of Vertisols. Vertisols on a global scale have an even higher soil erosion risk than Pelosols in Germany (3.5 t ha−1 year−1). Conclusions: Pelosols and Vertisols, despite their high percentages for agricultural use, have a high soil erosion risk compared to other soil types and thus need special care under agricultural use and adapted protective land use management. [ABSTRACT FROM AUTHOR]

• RUSLE/IC/SDR was explored for simulation of agricultural erosion management. • Erosion management measures (EMM) were simulated at two catchments. • Effectiveness of EMM's varied by catchment and field parcel. • RUSLE/IC/SDR is a promising tool for high-resolution and large scale simulations. Models play a crucial role in guiding agricultural erosion management, though their incorporation of sediment connectivity and management strategies varies. This study evaluated the RUSLE/IC/SDR model's potential for simulating agricultural erosion management at both the field scale and across two catchments. We tested the model's ability to simulate erosion management measures at a high spatial resolution (2 m × 2 m) across diverse topographies, assessed whether incorporating sediment connectivity improves RUSLE-based erosion management planning within catchments, and explored its capacity to tailor measures based on local connectivity characteristics. Our findings showed significant variability in sediment sources and connectivity. The simulation of no-till and buffer strip measures effectively demonstrated their varying effectiveness across fields and catchments. At the catchment scale, erosion management planning that incorporates sediment connectivity through the RUSLE/IC/SDR approach did not contribute to significant additional sediment delivery reduction compared to using RUSLE alone. However, at the field scale, RUSLE/IC/SDR offered improved opportunities for tailoring erosion management measures to local sediment connectivity characteristics. These simulations highlight both the potential and limitations of RUSLE/IC/SDR, advancing our understanding of its application for erosion management. In conclusion, while RUSLE/IC/SDR represents a valuable extension of RUSLE, further research is needed to fully realize its practical applications. Nonetheless, it shows promise for high-resolution simulation of sediment connectivity and erosion management at the field scale, across large catchments and regions. [ABSTRACT FROM AUTHOR]