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The variations in soil erosion significantly impact regional ecological security. Under rapid urbanisation, extensive ecological restoration and climate change, soil erosion development in the red soil region of southern China is ambiguous. Therefore, this study investigated the current (1980s–2020) and future (2050) erosion characteristics in a typical soil erosion control catchment (Changting section catchment) in this region by using the Cellular Automata Markov model and CMIP6 data to predict future scenarios and the Revised Universal Soil Loss Equation to estimate soil erosion. The results showed significant changes in the vegetation coverage of major land uses from 1980s to 2020, which was mainly caused by continuous soil and water conservation (SWC). The land use subtypes that were obtained by reclassifying land use based on the threshold of vegetation cover on soil erosion control, reflect a continuous transformation from those with poor SWC effectiveness to those with great SWC effectiveness. Therefore, the estimated soil erosion intensity continued to decrease from 1980s to 2020, and the contribution of land use/land cover (LULC) impacts ranged from 74%–195%. However, predictions of land use subtypes indicated that LULC may be stable after 2020; thus, soil erosion changed little when the climate was almost unchanged in 2050. Under climate change scenarios, soil erosion may increase by 111%–121%, and the contribution of precipitation impacts was 63%–66%. The major driving factor of soil erosion changes may shift from LULC to precipitation after 2020. Therefore, in the future, the potential for reducing soil erosion by vegetation restoration may be limited, and more engineering measures should be applied to address the erosion risk caused by climate changes. This study provides prospects for land use/land cover and soil erosion in the red soil region of southern China. [ABSTRACT FROM AUTHOR]

Soil erosion by water is a serious problem in Ethiopia, contributing to diminishing crop yields and food shortages. Apart from understanding the magnitude, risk, and spatial distribution of the problem, identifying erosion hotspot areas is essential for effectively reversing the problem. This study aims to identify erosion hotspots in the Gotu watershed, in northeastern Ethiopia, using the revised universal soil loss equation (RUSLE) and incorporating local farmers' perspectives to prioritize conservation efforts. The RUSLE model reveals that 29,744.3 metric tons of soil is lost annually from the Gotu watershed, with an average loss of 65.3 to t ha⁻1 year⁻1. The main contributing factors to soil erosion in the watershed include undulating topography, loss of plant cover, and continuous cultivation. The highest soil loss rates (> 80 t ha⁻1 year⁻1) were found in the western, northern, and southern parts of the watershed, where cultivation occurs on moderate to steep slopes with sparse vegetation cover. These areas should be prioritized for conservation interventions. Farmers identified poor crop yields and damaged conservation structures as key indicators of soil erosion prevalence in the watershed. Increasing farmer's understanding of soil erosion and the importance of soil and water conservation is essential for effectively controlling soil erosion and improving food security in the area. [ABSTRACT FROM AUTHOR]

The current issue affecting land and water resources is soil erosion. Its numerous detrimental effects include deforestation, unfavorable farming methods, and decreased fertility, all of which impede socioeconomic growth. This research aimed to evaluate the effects of land use land cover (LULC) dynamics on soil erosion and its spatial distribution patterns over the Hare Watershed for the periods 2001–2021. Revised Universal Soil Loss Equation (RUSLE) model coupled with a geographic information system (GIS) was applied to estimate potential soil losses; this involved utilizing data on rainfall erosivity (R) using interpolating rainfall data, topography (LS) using digital elevation model (DEM), soil erodibility (K) using the William equation and digital soil map, conservation practices (P) using DEM and satellite images, and vegetation cover (C) using satellite images. To account for the change in LULC over the past 20 years, three separate maps of C‐factors were developed for the years 2001, 2011, and 2021. The remaining four factors were kept constant. Finally, after the successful running of the model, the estimated annual average soil loss rate (A) of the watershed is 211.38, 223.79, and 235.64 t·ha−1yr−1 for the years 2001, 2011, and 2021, respectively. This is a result of grazing areas and shrubland being lost for agricultural land. Moreover, in the past period, the watershed was found to show an increase in the yearly mean soil loss rate of 24.26 t·ha−1yr−1. Consequently, the northeastern, northwest, and southern regions of the watershed require the implementation of suitable land management. [ABSTRACT FROM AUTHOR]

Soil erosion poses significant ecological and socioeconomic challenges, driven by factors such as inappropriate land use, extreme rainfall events, deforestation, farming methods, and climate change. This study focuses on the Kozhikode district in Kerala, South India, which has seen increased vulnerability to soil erosion due to its unique geographical characteristics, increase in extreme events, and recent land use trends. The research employs RUSLE (Revised Universal Soil Loss Equation), considering multiple contributing factors such as rainfall erosivity (R), slope length and steepness (LS), cover management (C), conservation practices (P), and soil erodibility (K). The study is unique and novel, since it integrates extensive field data collected from agricultural plots across Kozhikode with the RUSLE model predictions, providing a more accurate and context-specific understanding of soil erosion processes and also suggesting management strategies based on risk priority. The study found that Kozhikode experiences an average annual soil loss of 28.7 tons per hectare. A spatial analysis revealed varying erosion risk levels across the district. 52.0% of the area experiences very slight erosion, 10.31% has slight erosion, 6.18% undergoes moderate erosion, 3.88% is moderately severe, 7.34% is at severe erosion risk, 5.6% has very severe erosion, and 14.65% faces extremely severe erosion. Field data collected from agricultural plots across Kozhikode were compared with RUSLE-predicted values, revealing a low root mean square error, indicating a strong correlation between observed and simulated data. Based on these findings, the district was categorized into low, medium, and high-priority regions, with tailored recommendations proposed for each. Implementing these measures could mitigate erosion, preserve soil fertility, and support the long-term sustainability of natural and agricultural ecosystems in Kozhikode. Given the practical challenges in estimating RUSLE factors in Southern India, where data scarcity is a common issue, this preliminary study underscores the need for expanded, long-term field observations to enhance understanding of soil erosion processes at the watershed level. [ABSTRACT FROM AUTHOR]

[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 erosion is a widespread concern that is indeed considered to be a significant environmental issue, and it has particularly severe consequences in less developed countries like Ethiopia. An effective watershed management procedure for establishing priority is supported by the identification of erosion-susceptible areas. Therefore, the main objective of the study was to assess soil erosion dynamics and its spatial pattern using a novel methodological framework combining the RUSLE and MCDA. The study used data on land use and cover, topography, soil, and climatic data. The analytical hierarchy process (AHP) were used to identify soil erosion-susceptible areas and the factors were weighted using a pairwise comparison matrix, and weights were combined using weighted overlay in GIS. Our results indicated that the mean annual soil loss rate was 27.10 t ha−1 yr−1, while the total soil loss from the entire study area was 3.11 Mt. The highest soil loss was observed in bare land (30.54 t ha−1 yr−1) and farmland (23.65 t ha−1 yr−1), which were considered as the most susceptible land types to erosion. Likewise, 10.3% of the study area is very highly susceptible; 20.2% is highly susceptible, 24.2% of the area is moderately susceptible, 27.1% is low, and 18.2% has very low susceptibility. The district's most significant erosion-susceptible areas are characterized by steep slopes that are composed of farmland and bare land. This suggests the majority of the area is susceptible to erosion, requiring interventions to reverse the alarming degradation level. The presented framework has a board application to estimate regional soil erosion and to identify spatial patterns of soil erosion. [ABSTRACT FROM AUTHOR]

Soil erosion and subsequent sedimentation pose significant challenges in the Sikkim Himalayas. In this study, we conducted an assessment of the impact of rainfall-induced soil erosion and sediment loss in South Sikkim, which falls within the Teesta Basin, employing Revised Universal Soil Loss Equation (RUSLE) and Sediment Yield Index (SYI) models. Leveraging mean annual precipitation data, a detailed soil map, geomorphological landforms, Digital Elevation Models (DEMs), and LANDSAT 8 OLI data were used to prepare the factorial maps of South Sikkim. The results of the RUSLE and SYI models revealed annual soil loss >200 t ha−1 yr−1, whereas mean values were estimated to be 93.42 t ha−1 yr−1 and 70.3 t ha−1 yr−1, respectively. Interestingly, both models displayed similar degrees of soil loss in corresponding regions under the various severity classes. Notably, low-severity erosion <50 t ha−1 yr−1 was predominantly observed in the valley sides in low-elevation zones, while areas with severe erosion rates >200 t ha−1 yr−1were concentrated in the upper reaches, characterized by steep slopes. These findings underscore the strong correlation between erosion rates and topography, which makes the region highly vulnerable to erosion. The prioritization of such regions and potential conservation methods need to be adopted to protect such precious natural resources in mountainous regions. [ABSTRACT FROM AUTHOR]

Soil erosion has been causing an imbalance in nature and the environment. It is mainly caused naturally but is also due to human interventions leading to desertification and possible contamination. Therefore, engineering, geography, and cartography have been allies in applying erosion models to predict, address, and remediate the impacts. Therefore, the Revised Universal Soil Loss Equation (RUSLE) and Soil and Water Assessment Tool (SWAT) linked to Geographic Information Systems (GISs) could boost decision making as tools to mitigate issues. This study applies the RUSLE and SWAT models from a geotechnical point of view to analyze a sub-watershed at Idanha-a-Nova (Portugal) over 4 years, showing a predominant erosion risk class with losses lower than 5 t.ha−1.year−1 (60 to 86%), characterized as very low risk. The modeling permitted the development of soils erosion susceptibility charts, in addition to material availability and the suitability for construction areas, exposing a replicable methodology that could contribute to minimizing environmental impacts while encouraging a more intelligent use of the land towards a greener exploration. [ABSTRACT FROM AUTHOR]

Soil erosion emerged as a significant land degradation concern, causing serious threat to soil ecosystem services in the Himalayan region. The complex topography of the region poses limitations to the measurement of soil redistribution (erosion, transport, and deposition) rate, necessitated for the effective soil conservation planning. The study investigated soil redistribution processes over a typical complex hillslope of the mid‐Himalayan region using the fallout radionuclide (FRN)—137Cs method and Revised Universal Soil Loss Equation (RUSLE) model. It involved a comparison of 137Cs measured soil redistribution and the RUSLE model estimates, aiming to assess its correspondence over the complex hillslope. Analysis of 137Cs measurements revealed the highest net erosion (−13.2 t ha−1 year−1) at the upper hillslope with a convex shape, while sediment deposition occurred at the lower (36.9 t ha−1 year−1) and valley (32.5 t ha−1 year−1) hillslope positions with a concave shape. The RUSLE model also estimated the highest erosion on the upper hillslope (−12.3 t ha−1 year−1) but the lowest erosion at the lower (−0.88 t ha−1 year−1) and valley (−0.32 t ha−1 year−1) hillslopes, that differed with the 137Cs method. The 137Cs method provided soil redistribution rate (either net erosion or deposition), whereas RUSLE model only showed the gross erosion rate. Thus, the estimate from RUSLE corresponds only to hillslope positions with a convex and straight shapes. The distribution of 137Cs measurements has clearly revealed the influence of slope shape and steepness in governing erosion and deposition processes at the hillslope positions with convex and concave slope shapes, respectively. In addition, terraces effectively trap sediments from upslope areas. Investigation of soil erosion using 137Cs measurement along with the RUSLE model helped to validate erosion and deposition processes over the hillslope positions. The study will help to suggest suitable conservation measures for the various hillslope positions in the mid‐Himalayan region. [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]

Soil erosion is considered to be a prominent problem, and its regular spatial assessment in the watershed is required. Thus, for the present study, the Kodar river basin in Chhattisgarh, India, was chosen for the investigation. Three different models, namely Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE), and Unit Power-Based Soil Erosion and Deposition (USPED), were chosen by incorporating remote sensing and GIS platforms and their performance were compared. On the basis of area's varied slope and land usage, the erosion rate was calculated. It was noted that a difference of 33,68632 t, −11570.08 t, and 1023.99 t in the sedimentation values of the USLE, RUSLE, and USPED models, respectively were observed. The observed sedimentation data was validated, and the USPED model was found to be the most authentic model among the selected three models. Hence, the present study will be helpful in better understanding of the complex interactions of the variables involved in predicting net erosion of soil. [ABSTRACT FROM AUTHOR]

The spatiotemporal variability of soil erosion in Crimea in the periods of 1958–1988 and 1989–2018 is estimated on the basis of RUSLE. The erosion rate varies from very slight to very high in the region. The most eroded areas are confined to the Southern Crimea mountains mainly corresponding to a combination of a high rainfall erosivity (R-factor) and relatively steep slopes (LS-factor). Soil loss in the first period slightly exceeded the similar value after 1989 approximately 13%, while the proportion of moderately to very highly eroded areas varied within the range of less than 1%. These changes are related to both the reduction of agricultural cultivated lands and the variability of soil erosion factors. The soil erosion formation is mainly due to the factors of rainfall erosivity, slope steepness, as well as the cover-management factor in Crimea. Based on the comparison of the actual suspended sediment yield in the gauges with the soil erosion values, most river areas are characterized by the accumulation of eroded material when no more than 50% of the washed-out products reach the gauges. [ABSTRACT FROM AUTHOR]

Soil erosion is expected to worsen in the future as a result of climate change, growing population demands, improper land use, and excessive exploitation of natural resources in India. Due to the growing population and changes in land use, it has become increasingly crucial to map and quantitatively assess soil for the purpose of sustainable agricultural usage and planning conservation efforts. The problem of soil erosion is mainly on steeper slopes with intense rainfall in parts of Western Ghats. The 20.17% of geographical area have been converted into wasteland due to soil erosion. The Revised Universal Soil Loss Equation (RUSLE) is a highly prevalent and effective technique utilized for estimating soil loss in order to facilitate the planning of erosion control measures. Despite the fact that RUSLE is accurately estimate sediment yields from gully erosion, it is an effective tool in estimating sheet and rill erosions losses from diverse land uses like agricultural to construction sites. The current study is mainly about combining the RUSLE model with GIS (Geographic Information System) to find out how much soil is being lost, particularly in Noyyal and Sanganur watersheds which is located in Coimbatore district of Tamil Nadu, India. This analysis is based on the soil order, with a significant proportion of alfisols and inceptisols being considered. The obtained outcome is contrasted with the established soil loss tolerance threshold, leading to the identification of the areas with the highest susceptibility to erosion. Within the narrower and more inclined section of the watershed, yearly soil loss scales from 0 to 5455 tonnes/ha/year, with an average annual loss of soil of 2.44 tonnes/ha. The severe soil erosion of 100 to 5455 tonnes/ha/year is found along the steep and greater slope length. The generated soil map was classified into six categories: very slight, slight, moderate, high, severe, and very severe. These classifications, respectively, occupied 6.23%, 14.88%, 10.56%, 15.70%, 7.73%, and 6.63% of the basin area. Based on the results of cross-validation, the estimated result of the present study was found to be very high compared to past studies conducted 0 to 368.12 tonnes/ha/year especially in very severe erosion zones. But very slight to severe erosion zones nearly matched with same level of soil loss. To protect the soil in the study area from erosion, more specific actions should be taken. These include micro-catchment, broad bed furrows, up-and-down farming, soil amendment with coconut coir pith composition, streambank stabilization with vegetation, and micro-water harvesting with abandoned well recharge. These actions should be carried out over time to make sure to work. [ABSTRACT FROM AUTHOR]

Soil erosion in coastal areas, driven by global change and human activity, poses a significant threat to ecological and economic stability. This research investigates water erosion in the southeast of the Ría de Arosa (Pontevedra, Spain), utilizing the Revised Universal Soil Loss Equation model and Geographic Information System technologies. Key factors analyzed include rainfall erosivity, soil erodibility, topography, land cover, and conservation practices. High-resolution maps (1 × 1 m pixels) identified areas at high risk of erosion. Vulnerable zones, such as coastal cliffs and vineyards, show severe erosion rates exceeding 50 t/ha/year (>5 mm/year), with the most extreme zones reaching up to 200 t/ha/year (>200 mm/year). These results emphasize that intervention could be required or recommended. Suggested measures include reforestation, effective agricultural land management, or the implementation of vegetative barriers to reduce erosion. These areas, characterized by steep slopes and sparse vegetation, are particularly susceptible to soil loss, necessitating specific conservation efforts. The results underscore the need for sustainable coastal management practices and preventive strategies to protect this vulnerable coastal zone. Implementing these measures is crucial to mitigating the impacts of soil erosion, preserving natural resources, and ensuring long-term ecological and economic resilience in the region. [ABSTRACT FROM AUTHOR]

The flash flood-induced erosion is the primary contributor to soil loss within the Indian Himalayan Region (IHR). This phenomenon is exacerbated by a confluence of factors, including extreme precipitation events, undulating topographical features, and suboptimal soil and water conservation practices. Over the past few decades, several flash flood events have led to the significant degradation of pedosphere strata, which in turn has caused landslides along with fluvial sedimentation in the IHR. Researchers have advocated morphometric, hydrologic, and semi-empirical methods for assessing flash flood-induced soil erosion in hilly watersheds. This study critically examines these methods and their applicability in the Alaknanda River basin of the Indian Himalayan Region. The entire basin is delineated into 12 sub-watersheds, and 13 morphometric parameters are analyzed for each sub-watershed. Thereafter, the ranking of sub-watersheds vulnerability is assigned using the Principal Component Analysis (PCA), compounding method (CM), Geomorphological Instantaneous Unit Hydrograph (GIUH), and Revised Universal Soil Loss Equations (RUSLE) approaches. While the CM method uses all 13 parameters, the PCA approach suggests that the first four principal components are the most important ones, accounting for approximately 89.7% of the total variance observed within the dataset. The GIUH approach highlights the hydrological response of the catchment, incorporating dynamic velocity and instantaneous peak magnifying the flash flood susceptibility, lag time, and the time to peak for each sub-watershed. The RUSLE approach incorporates mathematical equations for estimating annual soil loss utilizing rainfall-runoff erosivity, soil erodibility, topographic, cover management, and supporting practice factors. The variations in vulnerability rankings across various methods indicate that each method captures distinct aspects of the sub-watersheds. The decision-maker can use the weighted average to assign the overall vulnerability to each sub-watershed, aggregating the values from various methods. This study considers an equal weight to the morphometric, hydrological GIUH, and semi-empirical RUSLE techniques to assess the integrated ranking of various sub-watersheds. Vulnerability to flash flood-induced landslides in various sub-watersheds is categorized into three classes. Category I (high-priority) necessitates immediate erosion control measures and slope stabilization. Category II (moderate attention), where rainwater harvesting and sustainable agricultural practices are beneficial. Category III (regular monitoring) suggests periodic community-led soil assessments and afforestation. Sub-watersheds WS11, WS8, WS5, and WS12 are identified under category I, WS7, WS4, WS9, and WS6 under category II, and WS1, WS3, WS2, and WS10 under category III. The occurrence of landslides and flash-flood events and field observations validates the prioritization of sub-watersheds, indicating the need for targeted interventions and regular monitoring activities to mitigate environmental risks and safeguard surrounding ecosystems and communities. [ABSTRACT FROM AUTHOR]

Soil erosion is a complex and highly heterogeneous process with a wide range of environmental and economic impacts. Its estimation is particularly challenging and modelling is typically used for erosion estimation over large areas. The aim of this study was to compare the two leading empirical and physical erosion estimation models, i.e. the Revised Universal Soil Loss Equation (RUSLE) and the Water Erosion Prediction Project (WEPP). The models were calibrated and validated using data collected from field experiments conducted in agricultural lands of Mexico. The simulated rainfall experiments involved measuring erosion from field plots subjected to four tillage systems (No crop, Conventional tillage, Conventional tillage + residues, and Handspike) under two antecedent soil moisture conditions (dry and wet). Different calibration approaches based on the factors K and C for RUSLE, and interrill erodibility and hydraulic conductivity in WEPP were tested. The best-performing methods in RUSLE involved measuring the K factor and adopting the recommended C factor by the National Forestry Commission of Mexico. In WEPP, the best results were obtained when interrill erodibility was estimated from experimental measurements. Overall, RUSLE outperformed WEPP in most of the treatments except for CT under WAMC. [ABSTRACT FROM AUTHOR]

Kuruçay Stream basin, located in the Edremit district in northwest Turkey, is a small, narrow and long basin with an area of 17.3 km2, extending in the N–S direction, located on the southern slopes of Mount Ida. The study was carried out to determine the soil erosion susceptibility and distribution of Kuruçay Stream basin, as well as to estimate the annual average amount of soil loss through the Revised Universal Soil Loss Equation (RUSLE) model using Geographical Information Systems (GIS). To apply the equation to the basin, data layers for rainfall erosivity (R), soil erodibility (K), slope length (L) and slope steepness (S), landcover management (C), and support practice (P) factors were generated. The layers were then overlaid, calculations were made and erosion susceptibility classes were generated. Thus, the spatial distribution of erosion susceptibility classes and the annual estimated amount of soil loss were determined. Based on the results, the Kuruçay Stream basin was found to have five distinct erosion susceptibility levels: low, moderate, high, severe, and extremely severe. Accordingly, 76% of the basin has low, 11.3% moderate, 6% high, 2.9% severe and finally 3.7% very severe erosion susceptibility. The Kuruçay Stream basin shows similar characteristics with the neighbouring river basins in terms of erosion sensitivity.

This study assesses watershed vulnerability to erosion and sedimentation in Guyana using Digital Elevation Model (DEM) and Land Use/Land Cover (LULC) classification. It aims to assess erosion risk by examining rainfall erosivity, soil types, and spatial variability. The integration of DEM-derived slope data with LULC and rainfall erosivity shows a strong correlation between high rainfall (20,809.02–31,262.35 mm) and erosivity (21,433.29–32,200.22 MJ mm ha−1 h−1 yr−1), indicating increased erosion potential, particularly in southwestern regions. Soil analysis reveals Plinthic Acrisols, with high clay content, are more prone to erosion, whereas Orthic Ferralsols are less vulnerable. Slope analysis suggests flat terrain (89.16 %) has low erosion risk, but steeper slopes require interventions. Conservation strategies like reforestation, sustainable agriculture, contour plowing, and terracing are recommended to reduce erosion impacts. This integrated assessment provides insights for prioritizing soil conservation and adaptive management to address erosion risks in Guyana.

Using soil conservation practices such as contouring are able to reduce soil loss on arable land parcels. In the empirical model of the Universal Soil Loss Equation (USLE), these measures are taken into account by the P-factor for support practice management. In the context of application, there is usually a lack of sufficient data or suitable methodology to accurately determine the P-factor within a plot-specific analysis. In this study, we demonstrate the effort and benefit of deriving an individual P-factor for each land parcel within a typical application scale. For this purpose, we apply the Fast Line Detector algorithm to open remote sensing data of Google Earth from May 2016 in German low mountain range. The algorithm detects lines from tire tracks and seed rows, which allows to determine an individual main cultivation direction for each land parcel. The success rate was 94.9 % for 2495 land parcels with 26 different crops. The results show a major time advantage for the automated method when considering a large number of parcels. Subsequently, we used the detailed information obtained to calculate the P-factor under regional German conditions using the German standard DIN 19708 and, secondly, an approach based on revised USLE. It is apparent that the current German standard cannot be applied with the necessary level of detail for 78.1 % of all land parcels in this low mountain range study due to unsuitable equations and validity ranges for slope steepness and length and a non-consideration of ridges and off-grade contouring and therefore needs to be revised to avoid being restricted in its application.

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]

Abstract Meghalaya is known for its complex environment due to its hilly terrain and heavy rains, which result in significant soil erosion challenges. The LS factor is the primary factor used to estimate soil loss. Acquiring accurate LS values has always been a significant challenge. The main aim of this research is to compute the LS factor in the Meghalaya region. Three methods are used to calculate the LS factor: (1) Wischmeier and Smith’s (1978) method, (2) Moore and Wilson’s (1992) method, and (3) Desmet and Gover’s (1996) method. All three approaches used the digital elevation model (DEM) in the spatial domain to determine the LS factor. The results of this investigation demonstrated significant variation in LS factor in all three methods. Wischmeier and Smith’s method yielded LS factor values ranging from 0 to 266.538, Moore and Wilson’s method resulted in values ranging from 0 to 112.631, and Desmet and Gover’s method resulted in values ranging from 0 to 683.679. This study concluded that Wischmeier and Smith’s method resulted in LS factor values distributed uniformly across the spatial domain. In contrast, Moore and Wilson’s method only yielded high values along the flow path and low values in other areas. Additionally, Desmet and Gover’s method identified high LS factors in regions with steep slopes near rivers and streamlines. This study can be helpful in anticipating soil erosion well before time and also in developing a slope management plan in the study region.

Human activities and climate change have sped up soil erosion, leading to annual losses of fertile soil and declining crop yields. The biggest concerns in land and water conservation are modeling and monitoring soil loss, as well as measuring the vulnerability and danger of active erosional processes. In the present study, a comprehensive approach used to evaluate the risk of soil erosion in the Ganjigatti sub-watershed of Karnataka, India, using the RUSLE model and GIS techniques. The annual average soil loss estimated using the RUSLE model ranged from 0 to 122.73 t ha−1 yr−1, with an average of 3.48 t ha−1 yr−1 in the Ganjigatti sub-watershed. The quantity of erosion was found to be greatly influenced by topography (LS factor) and land use and land cover (C factor). The results showed that a larger part of the study area, 98.34%, accounted for slight erosion (0–5 t ha−1 yr−1). The spatial pattern of erosion revealed high to very severe risk zones in the main stream of sub-watershed. The total soil loss was calculated to be 15,046.96 t yr−1 in the sub-watershed. The study’s results aid decision-makers, land administrators, environmentalists, and others in implementing effective land and water conservation measures in the Ganjigatti sub-watershed to control soil erosion.

Soil erodibility is challenging for agricultural soils, sustainable farming systems and water-food security in developing countries. Regional scale precise estimation of soil erosion is vital to save fertile soils. For this purpose, geospatial technology is the researchers’ top choice. The Revised Universal Soil Loss Equation (RUSLE) has the potential plausible estimates of soil erosion in the region. The study area comprises parts from two basins (Peshawar and Nizampur) having distinct land surface characteristics. This difference made the applicability of RUSLE model challenging. The influencing factors were combined using weighted sum in GIS. The study area was categorized into five soil erosion zones: very low (0–0.1 t ha−1 year−1), low (0.1–0.50 t ha−1 year−1), moderate (0.50–0.7 t ha−1 year−1) high (0.7–1 t ha−1 year−1), and very high (>1 t ha−1 year−1). The 1,748 km2 (174800 hectares) study region has an annual soil loss of 0.1 to > 1.0 t ha−1 year−1. The overall accuracy of the estimated soil erosion was found to be 90% with Kappa coefficient of 87.25% and receiver operating characteristics area under the curve (ROC-AUC) score of 0.819. This study’s will help to address the climate change-induced challenges of soil erosion in agricultural lands.

Due to ongoing cultivation, the Teaching and Research Farm of Kwara State University in Malete has seen quick and accelerated erosion. Run-off-related soil loss is a significant and persistent ecological problem in the study area. Information on soil loss is essential for promoting agricultural production and natural resource management. The average annual soil loss was calculated and mapped in this study using remote sensing and GIS. The soil loss was computed using the Revised Universal Soil Loss (RUSLE) Model. Using a topographic map at a scale of 1:50,000, an aster digital elevation model (DEM) with a spatial resolution of 20 m, a digital soil map at a scale of 1: 250,000, rainfall data spanning 39 years (1981-2020), and other data, RUSLE's soil loss variables were calculated. The RUSLE parameters were investigated and incorporated using a raster calculator in the geoprocessing tools in the arc-GIS 10.1 environment to estimate and map the annual soil loss of the research region. The results show that the annual soil loss in the study region ranged from 48.553 to 1,476.606 t ha-1 year-1 , cover around 100 ha of land. Most of the soil erosion affected areas are spatially situated in block 2B and 3A part of the farm. These are areas where low Ferric Luvisols and high Ferric Luvisols with higher soil erodibility character (21- 33) values are dominant. Therefore, it was found that the main causes of soil erosion were slope gradient and length, followed by soil erodibility parameters. The study therefore recommended using sustainable soil and water conservation methods to address the problem of soil erosion in the study area.

Meghalaya is known for its complex environment due to its hilly terrain and heavy rains, which result in significant soil erosion challenges. The LS factor is the primary factor used to estimate soil loss. Acquiring accurate LS values has always been a significant challenge. The main aim of this research is to compute the LS factor in the Meghalaya region. Three methods are used to calculate the LS factor: (1) Wischmeier and Smith's (1978) method, (2) Moore and Wilson's (1992) method, and (3) Desmet and Gover's (1996) method. All three approaches used the digital elevation model (DEM) in the spatial domain to determine the LS factor. The results of this investigation demonstrated significant variation in LS factor in all three methods. Wischmeier and Smith's method yielded LS factor values ranging from 0 to 266.538, Moore and Wilson's method resulted in values ranging from 0 to 112.631, and Desmet and Gover's method resulted in values ranging from 0 to 683.679. This study concluded that Wischmeier and Smith's method resulted in LS factor values distributed uniformly across the spatial domain. In contrast, Moore and Wilson's method only yielded high values along the flow path and low values in other areas. Additionally, Desmet and Gover's method identified high LS factors in regions with steep slopes near rivers and streamlines. This study can be helpful in anticipating soil erosion well before time and also in developing a slope management plan in the study region. [ABSTRACT FROM AUTHOR]

This research used the Revised Universal Soil Loss Equation (RUSLE) with Sediment Delivery Ratio (SDR) model. The analytic hierarchy process (AHP) method, while also incorporating the use of a geographic information system (GIS) and remote sensing (RS) to predict the annual soil loss rate and spatialise the processes of water erosion at the scale of the Loukkos Watershed, Morocco. The RUSLE model and AHP parameters were estimated using RS data, and the erosion vulnerability zones were determined using GIS. We used five parameters, including precipitation erosivity, soil erodibility, slope length and steepness, vegetation cover, and soil erosion control practices in the RUSLE. For the AHP technique, we used seven geo‐environmental factors, including annual average precipitation, drainage density, lineament density, slope, soil texture, land use/land cover and landform maps. The results of RUSLE indicated that the average annual soil loss varied from 0 to 2388.27 t·ha−1·year−1. The total estimated annual potential soil loss was approximately 40 790 220.11 t·ha−1·year−1, and a sediment yield estimated by RUSLE‐SDR was 8 647 526.66 t·ha−1·year−1, equivalent to 6.65 Mm3. This value is very close to the measured value of 6.81 Mm3, for a difference of 0.16 Mm3. Furthermore, the results of the AHP indicate that the soil erosion potential index varies from 0 to 0.205315 t·ha−1·year−1. Overall, nearly 13.7% of the area suffered from severe soil erosion exceeding 50 t·ha−1·year−1. Approximately 80% of the Loukkos Watershed area experienced only slight erosion, while the remaining 6% incurred moderate erosion. Integrating GIS and RS into the RUSLE model and AHP helped us robustly estimate the extent and degree of erosion risk. Territorial decision‐makers should adopt our results to develop soil conservation strategies, water management plans and other necessary soil and water conservation measures for this region. [ABSTRACT FROM AUTHOR]

The Tiga Dam, a primary hydraulic structure in northern Nigeria, is subjected to intense hydrological stress during the rainy season, posing potential risks to its structural integrity. This study investigates the geotechnical properties and stability of the Tiga Dam in Kano State, Nigeria. Twelve soil samples from the downstream area were analyzed for specific gravity, grain size distribution, Atterberg limits, compaction parameters, permeability, and shear strength. The dam's stability was assessed using Plaxis 2D under various reservoir conditions. Soil erodibility was evaluated using the Revised Universal Soil Loss Equation (RUSLE), and a linear regression model with noise was developed to predict soil expansion rates. The results showed heterogeneous soil properties, with specific gravity ranging from 2.11 to 2.63 and permeability from 3.40 × 10−9 to 1.49 × 10−7 m/s. Stability analysis revealed factors of safety of 1.322, 1.006, 1.002, and 1.147 for high reservoir, rapid drawdown, slow drawdown, and low reservoir conditions, respectively. The RUSLE K factor ranged from 0.055 to 0.145, indicating low to moderate soil erodibility. The expansion rate model demonstrated high accuracy (R2 = 0.989) in predicting seasonal and long-term soil expansion trends, with peak rates increasing from 16.94 mm/month in 2010–2013 to 19.45 mm/month in 2017–2020. This comprehensive analysis provides crucial insights into the Tiga Dam's geotechnical behavior, highlighting potential vulnerabilities and the need for targeted management strategies to ensure long-term stability and safety. [ABSTRACT FROM AUTHOR]

Soil loss is a crucial problem due to its importance in agricultural and livestock production, where intensive human activities have exacerbated erosive processes. In this framework, the RUSLE model emerges as a crucial tool for soil loss estimation, with the LS factor being a central component of the model. However, several research studies have shown that an inadequate selection of LS can overestimate soil erosion in complex topographies. This study assesses the overestimation of the RUSLE (Revised Universal Soil Loss Equation) model by slope length and steepness factor, focusing on a complex terrain characterized by abrupt changes in elevation and slope. Seven divergent equations were used to evaluate the LS factor and compared with the results of the RUSLE model. The findings revealed that the models proposed by Desmet and Govers and Moore and Brusch proved best suited to terrain with such characteristics, avoiding over-estimation. The meticulous selection of the LS model was highlighted as a crucial aspect, underlining the importance of an accurate methodology in soil loss measuring in topographically complex environments. This study contributes to the understanding and improvement of erosion models, providing valuable guidelines to address soil loss overestimation by LS-Factor. [ABSTRACT FROM AUTHOR]

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Anthropogenic activities like overgrazing, deforestation and mismanaged land use accelerate soil erosion (SE), causing nutritional and organic matter loss. In this study, we predicted the annual rate of soil loss in the Salt Range, extending south from the Pothohar plateau, Pakistan, using the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model parameters and erosion probability zones were estimated using remote sensing and Geo‐Spatial methods. The annual average soil loss rates were calculated by considering five geo‐environmental factors, that is, slope length and steepness (LS), rainfall erosivity (R), cover management (C), soil erodibility (K), and conservation practice (P) range from 0–559 527, 1404–4431, 0–1, −0.14 to 1.64, and 0.2–122 t.ha−1.year−1$$ \left(t.{ha}^{-1}.{year}^{-1}\right) $$ respectively. This research determined that the yearly average rate of SE in the Salt Range varies from over 50 to above 350 t.ha−1.year−1$$ \mathrm{t}.{\mathrm{ha}}^{-1}.{\mathrm{year}}^{-1} $$. The distribution of land area across different SE probability zones reveals that a small portion (2.11%) is classified as High, a moderate portion (7.13%) falls under the category of Moderate, while the majority (90.7%) is classified as Low in terms of proneness towards erosion. The land devoid of vegetation and characterized by steep slopes is especially prone to SE. The Salt Range is highly vulnerable to SE risk due to climatic variations and improper land use practices. The result provides a spatial distribution of SE across the salt range, utilized for management planning processes and conservation at the policy level among decision‐makers and land‐use planners. [ABSTRACT FROM AUTHOR]

Soil erosion has been and continue to be a major threat to environmental degradation especially in the developing countries. Accurate estimation of soil loss will provide reliable information in the management and mitigation solutions to soil erosion. In this study, the soil loss in an erosion prone Anambra State of South East region of Nigeria was estimated. Due to the complex nature of the catchment characteristics of Anambra State, soil loss cannot be estimated precisely by mere application of conventional soil erosion model. Hence a site-specific methodology was developed and applied. Revised Universal Soil Loss Equation (RUSLE) was integrated with the Geographic Information System (GIS) of the environment using remote sensing to build the model. 40-years rainfall data was collated from the Nigeria Meteorological Agency and analyzed. The various parameter of RUSLE which includes: Rainfall Erosivity (R), Soil Erodibility (K), Topography (LS), Land Use and Land Cover (C), and Erosion Control practices (P) were developed and imposed into ArcGIS 10.6 to estimate the amount of annual soil loss in the area. The result indicated that about 27.58km² (0.59%) of the study area have very low erosion rate of 0 – 5 t ha¹year-1, while the rates of erosion in 1311.52km² (28.01%), 538.59km² (11.50%), 1649.08km² (35.22%), 959.09km² (20.48%), and 196.76km² (4.20%) of the study area are 5–10, 10–15, 15–25, 25–50 and >50 t ha-1year-1respectively. This knowledge will help decision makers in managing the land degradation problems in Anambra State of Nigeria. Doi: 10.28991/CEJ-2024-010-07-014 Full Text: PDF [ABSTRACT FROM AUTHOR]

In Mediterranean regions, soil erosion is one of the most serious challenges with land degradation. The current work aims to study the water erosion in the watershed of Wadi Rmel located in the North-East of Tunisia, using the SWAT model, RUSLE method and, PAP/RAC approach for the year 2020. These methods are concentrated on the integration of factors influencing soil erosion and then validated by chosen spectral indices (NDVI, IB and, MSAVI), that are the most used to characterize the state of soil degradation. SWAT model demonstrates that the most eroded zones are located in South-West zones, in sub-basins number 10, 17 and 26. These areas presented a degraded vegetation cover with an NDVI value near zero. Moreover, the resulting map of the RUSLE method shows that the most erosive zone is located in the regions of the southwest. This part presents a high percentage of BI, more than 20%. On the other hand, the analysis of the erosive map conditions, presented by the PAP/RAC guidelines, shows also that the very high erosive state is located in southwestern areas with various uncovered zones. The same spatial distribution of soil erosion is found by the three used methods and confirmed by the spectral indices. Finally, this study results are beneficial for decision-makers to identify priority areas for intervention and to evaluate the existing management plans. Moreover, the application of spectral indices provides a useful tool to predict soil erosion hazard and to validate modeling results in case of the absence of field missions and observations. [ABSTRACT FROM AUTHOR]

The identification of areas prone to soil erosion in ungauged river basins is crucial for timely preventive measures, as erosion causes significant damage by lowering soil productivity and filling reservoirs with sedimentation. This study proposes a novel approach to prioritize sub-watersheds (SWs) in Ponnaniyar river basin. It utilizes different combinations of five objective-based weighting methods and seven Multi-criteria Decision Making (MCDM) techniques under outranking and synthesis methods with soil loss, morphometry, land use/land cover (LULC), and topography parameters. The results obtained from different hybrid models are validated using metrics like percentage and intensity of change. The findings reveal that MW-PROMETHEE (53.85%) and CRITIC-WASPAS (8.31) perform best in prioritizing areas based on morphometry, while CRITIC-TOPSIS (48.35% and 7.58) is more effective in prioritizing areas based on land use/land cover (LULC) and topography. The grade average method is used to integrate the rankings from 71 models: 35 based on morphometry, 35 based on LULC, and 1 based on the RUSLE model. The analysis identifies SW2 with a grade value of 4.34 as severely affected by soil erosion, followed by SW11 (5.45), SW5 (5.56), and SW9 (5.68), all falling within the very high priority level. This study recommends implementing appropriate water harvesting structures, which might be helpful in mitigating soil degradation, promoting soil conservation, and ensuring sustainable agricultural productivity. [ABSTRACT FROM AUTHOR]

For effective water and land resource management in data-scarce areas, it is imperative to investigate the spatial variability of sediment yield using a rapid, reliable, and affordable approach. The current study demonstrated the use of tools and models viz. Geographic Information System (GIS), Revised Universal Soil Loss Equation (RUSLE), and Sediment Delivery Ratio (SDR) based approach for the assessment of soil loss and sediment yield rate in Lake Hawassa Sub-basin. Input data used were soil, rainfall, digital elevation model (DEM), and land use and land cover change (LULC) maps. The result of the study showed that there was significant and widespread soil loss and sediment yield on cultivated land dominated by moderate, steep and very steep slopes with little vegetation and barren areas. An estimated mean of 16.36 t/ha/year, or 1.97 million tonnes of soil is lost annually by sheet and rill erosion across the Sub-basin. The sediment delivery ratio (SDR) at the outlet of the lake sub-basin was 0.249. Accordingly, the quantified sediment yield at the sub-basin outlet, taking into account the average-based soil loss and sediment delivery ratio, was found to be 4.07 t/ha/year. The annual siltation rate of the lake from these two forms of erosion was found to be 1.01 cm/year. Therefore, depending on the severity of the soil loss, appropriate Best Management Practices (BMP) should be applied to reduce the rate of soil loss and sediment yield to protect the lake and its ecosystem. [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]

Soil erosion refers to the soil removing process, from the terrain surfaces. It is the results of the combined effects of energy from rainfall and runoff, topography, soil properties and land uses. The process is greatly enhanced by human activities such as extensive agriculture, urbanization construction, and inappropriate farm and crop management practices. Soil loss from continent entering marine waters could affect marine fisheries. Erosion or soil loss on Pulau Payar was estimated using Revised Universal Soil Loss Equation (RUSLE) based on rainfall and site investigation of the soil and slope characteristics. Soil loss from a fully covered forest dominated by natural erosion process which was estimated about 3.52 ton/ha/year, which is considered as non-significant erosion to low erosion. While, for the worst case scenario of a fully disturbed area, land clearing and bare soils would give an estimates of 9,813 ton/ha/year. Medium disturbance, for example, will create a medium erosion of 801 ton/ha/year. Based on these scenario of land uses' activities, Pulau Payar should be kept at a minimal disturbance in order to minimize the impact of siltation on the surrounding marine environment. [ABSTRACT FROM AUTHOR]

Soil loss due to erosion has a huge impact on worldwide economy and environment. The Himalayan region is extremely vulnerable to erosion due to rugged terrain, erratic precipitation and excessive anthropogenic pressures. This study attempts to assess the spatial distribution of soil loss for managing soil disintegration rates in the western Himalayas using a GIS modeling approach. Factors affecting soil erosion were assessed and mapped using primary data from the field and secondary data. Map layers were developed for each identified factors and modeled using weighted overlay analysis. The rainfall-runoff erosivity, soil erodibility, topographic, cover management and support parameters varied around 361.75 MJ mm/ha/h/yr, (0.024–0.051) t ha h/ha/MJ/mm, 0–585.372, 0–1 and 0–1 respectively. The yearly soil disintegration rate varied between 0 and 6098.44 t ha/yr. The maximum area (137,165.30 ha) of the district's total area (146,295.142 ha) was under the less vulnerable class and the minimum (259.92 ha) was under the severely vulnerable category. The findings reported 70.24% of the area was under the less vulnerable class, followed by extremely vulnerable (10.48%) > highly vulnerable (7.40%) > severely vulnerable (7.19%) > moderately vulnerable (4.69%). The maximum (810 t/ha) and minimum (15 t/ha) mean soil loss was found under severely vulnerable and less vulnerable categories. The findings will provide site specific data regarding soil loss and vulnerability for effective management of soils in the eco-sensitive region. [ABSTRACT FROM AUTHOR]

IntroductionThe Northeast black soil area is an important marketable grain base in China. However, due to soil erosion, the black soil layer has been gradually thinning and its quality deteriorating. Therefore, accurately assessing the extent of soil erosion in this region is essential for the protection and sustainable utilization of black soil resources.MethodsIn this study, linear and nonlinear models were compared combined with remote sensing images to invert soil organic carbon (SOC). In the scenario of SOC change, temporally variable soil erodibility factor were obtained. Then based on the RUSLE model and GIS technology, land use, rainfall, soil texture and digital elevation model (DEM) were used to evaluate the temporal and spatial variation characteristics of soil erosion in black soil region from 1995 to 2020 in Hulan River Basin. The main influencing factors were explored by random forest model and analyzes in combination with eco-geological.ResultsThe findings are as follows: The random forest (RF) model was optimal for SOC inversion (2020: R2 = 0.64, RMSE = 0.70, 2010: R2 = 0.66, RMSE = 0.35). The erosion intensity was mainly slight or mild while mean annual soil loss firstly decreased then increased from 1995 to 2020 reaching a rate of 1020.16 t km−2 y−1 by 2020. Rainfall and topography were the main driving factors of soil conservation changes, and soil erosion was more likely to occur in the eco-geological environment of the neutral rock hilly woodland area.DiscussionThe results provide insights into spatial distribution characteristics of black soils erosion which are crucial for preventing further degradation and ensuring national food security.

Soil loss is aggravated by uncontrolled deforestation, indiscriminate land clearing for agricultural activities, overgrazing, and urban development that leads to severe soil erosion over the land surface. The main objective of this research is to apply the Revised Universal Soil Loss Equation (RUSLE), in conjunction with remote sensing and GIS, to determine the temporal variation of soil loss from the Gubi watershed in the years 2000 and 2017 and to estimate the sediment delivery into the Gubi reservoir in Northern Nigeria. Datasets of rainfall, soil type, topography, cover management, and support practice were utilized to determine the five RUSLE factors. The research identified five levels of erosion ranging from severe to very low-risk areas. The spatial distribution of erosion hotspots in the catchment area was highlighted. Results revealed increasing levels of erosion from 14,244.31 tons/year in 2000 to 16,792.33 tons/year in 2017. Consequently, the catchment's sediment yield was 6903.65 tons/year and 9172.71 tons/year corresponding to sediment delivery ratios of 48.46 % and 54.62 % in 2000 and 2017 respectively. The sediment inflow into Gubi reservoir was 747.38 tons/year and 950.76 tons/year corresponding to 10.83 % and 10.37 % of the total sediment yield in 2000 and 2017 respectively. Soil erosion vulnerability index maps were produced and the erosion is expected to increase with time as agricultural activities and deforestation continue to occur. The sediment deposits have the potential to reduce the designed life of the reservoir. Best management practices, such as tree planting, mulching, and contour ridging are recommended for soil conservation. There is a need to assess the reservoir sediment deposits so that measures can be taken to maintain the reservoir operations properly.

China has been facing severe organic pollution and the nonpoint source export from surface soil is usually overlooked in coastal areas. In this paper, from the perspective of catchment and attenuation, we have constructed a risk assessment method for coastal nonpoint source pollution (NSP) by applying the revised universal soil loss equation (RUSLE) and geostatistical analysis. Combined with the soil and water monitoring data, we have simulated regional NSP risks originating from surface soil organic matter (SOM) in the Yellow River Delta (YRD). Field surveys have verified the significant positive correlations between watershed SOM exporting risks and estuarine chemical oxygen demand (COD) fluxes during the rainy season. There present obvious logarithmic relations between the COD fluxes and the rainfall quantities, and the surface organic NSP risks. Larger NSP contributing sources are mainly located in the areas with higher soil exposure and stronger land-sea interactions. It should focus on the agricultural areas and improve relevant fertilizing and tillage methods to reduce source-exporting risks. Additionally, the summer rainfall concentrating periods need to be controlled emphatically, and vegetation-improving strategies need to be supplemented with the spatiotemporal-specific managements. This study provides a new insight for getting early terrestrial warning information for offshore organic water pollution, and presents research references for similar regional NSP issues.