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Soil erosion is a hazard in every part of the world. The small-scale farmers often grapple with low agricultural production and food insecurity. This study was conducted to estimate soil loss in response to land use/cover change in 1990, 2000, 2010, and 2020 around Ghibe hydroelectric III dam in Southern Ethiopia. The Revised Universal Soil Loss Equation (RUSLE) model was applied using a geographic information system (GIS). Digital elevation model with 30 m to determine topographic factor and supportive practice (P) factor, rainfall from the National Meteorological Institute to calculate the erosivity (R) factor, a digital soil map of the world for erodibility (K) factor, and Landsat 5TM images of 1990, 2000, 2010, and the Landsat 8 OLI of 2020 to determine trends of land use/cover change and the cover management (C) factor were employed. The raster layers of topography, cover management, rainfall erosivity, soil erodibility, and conservation techniques were processed and multiplied using the GIS platform. The overall accuracy of supervised classification was 89.89. The results showed that the percentage of cropland and built-up areas increased by 11.93 and 32.44%, respectively throughout the three study periods. Conversely, the proportion of forest land, grassland, bare land, and bushland declined by 8.2, 9.3, 10.13, 8.6%, respectively. The average annual soil loss rates increased from 30.95 t ha−1 yr−1 in 1990 to 43.85 t ha−1 yr−1in 2020. This is significantly higher than the maximum threshold rate of erosion for Ethiopian highlands (11 t ha−1 yr−1). The local government officers, non-governmental organizations, and farmers who are trained should strengthen watershed management techniques. [ABSTRACT FROM AUTHOR]

Soil erosion is both the cause and effect of land degradation. Land use/land cover conversion that changes the inherent landscape structure of watersheds leads to soil loss increase. Pantabangan-Carranglan Watershed (PCW) as a major source of irrigation, electricity, biodiversity, livelihood, and other ecosystem services, thus, it is imperative to spatially and temporally estimate the soil erosion within its boundary to assist and guide decision-makers in planning conservation and management of the watershed. Using the Revised Universal Soil Loss Equation (RUSLE) model, remotely sensed data, soil analysis, and geographical information system, the soil erosion rate in PCW was estimated. Results showed that there is increasing soil erosion in PCW over time. In 2010 soil erosion rate was estimated to be 134 tons·ha-1·yr-1 which increased to 141 tons·ha-1·yr-1 and 154 tons·ha-1·yr-1 in 2015 and 2020, respectively. Considering the average soil erosion rate and land cover types in PCW, annual crop and open/barren land cover types have the highest average soil erosion rate through time with moderate and catastrophic erosion levels, respectively. [ABSTRACT FROM AUTHOR]

The Soil and Water Assessment Tool Plus (SWAT+) model depends on the Modified Universal Soil Loss Equation (MUSLE), which in turn depends on the peak runoff rate for the estimation of soil loss from a catchment. The SWAT+ model generates a random number for each day in the process of peak runoff rate estimation from the catchment. On the other hand, the model provides calibration parameters like the peak rate adjustment factor for sediment routing in the tributary channels and main channels. There is uncertainty that how likely the SWAT+ model estimates the actual peak runoff rates. An improved MUSLE does not depend on the peak runoff rate, and, therefore, it minimizes uncertainty related to the peak runoff rate when estimating soil loss from the catchment in the SWAT+ environment. On the other hand, the effect of the topographic factor of the MUSLE or improved MUSLE on soil erosion and sediment transport is not explicitly explained. Therefore, we modify the source code of the SWAT+ model for sediment yield estimation by replacing the improved MUSLE and the best-fitted equations of the topographic factor in the source code. The modified SWAT+ model showed better performance than the original SWAT+ model. [ABSTRACT FROM AUTHOR]

This study aimed at studying the feasibility of using a sediment model built in HEC – HMS incorporating Modified Universal Soil Loss Equation (MUSLE) in aiding the separation of sediment contribution as point and non-point, an important aspect in sediment pollution control. The model was developed and verified using a representative sub-catchment and a canal reach of a tropical climate. The field observations and model developed had a good agreement and indicated about 16% and 35% of total sediments in the canal may be from nonpoint sources for the dry and wet seasons, respectively. Results suggested that a major fraction of eroded sediment ended up in the main canal through the dense drainage network across the catchment. This meant sediment trapping should focus tributary drainage ditches or at point source inputs to canal rather than the main canal banks. The study recognized that HEC – HMS is also capable of simulating sediment generation with acceptable errors. Being a free software package, HEC – HMS would be an effective sediment modelling tool for jurisdictions where sediment analysis has been constrained by cost. [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 and water conservation is one of the most important parts of the national ecological civilization. The spatial control area of soil and water conservation can be divided to effectively manage the soil and water loss regions. However, it is still lacking in the spatial delineation in the regional division of soil and water conservation. Only a few studies have been focused on the spatial control of soil and water conservation, according to the small watershed. This study aims to explore the spatial control zoning for soil and water conservation, and then implement the differentiated protection and management measures. The universal soil loss equation (USLE) model was used to calculate the potential and actual soil erosion. The main influencing factors of soil erosion were determined by random forests. A self-organizing feature map (SOFM) was used to determine the spatial control zone of soil and water conservation in the Dabie Mountain area on a small catchment scale. The results showed that: 1) The average potential and actual soil erosion were 84415.7 t/(km²·a) and 210.25 t/(km²·a), respectively. The actual soil erosion was distributed mainly in 0-300 t/(km²·a) at the small watershed scale. There was the basically same distribution of spatial patterns under the potential and actual soil erosion. The high-value area was distributed mainly in the central and eastern mountain areas at the high elevation. 2) Vegetation coverage and slope were the main influencing factors of potential and actual soil erosion at the small watershed scale, indicating a significantly positive correlation with the potential soil erosion (P<0.01). The high vegetation cover area was concentrated in the hinterland of Dabie Mountain. The high slope area was extended from the west to the east along the ridgeline of Dabie Mountain. 3) The SOFM results showed that the spatial control zone of soil and water conservation was divided into three areas: key prevention, general prevention, and the rest area at the small watershed scale. Among them, the key prevention area involved 710 small watersheds with an area of 1 5287.4 km². There were 890 small watersheds in the general prevention area, covering an area of 18 874.4 km². Two prevention areas accounted for 61.2% of the study area. There was an outstanding difference between actual and potential soil erosion and slope among regions. The classification index can serve as the spatial control of soil and water conservation. The finding can provide theoretical support and decision-making on the spatial control regionalization for soil and water conservation. [ABSTRACT FROM AUTHOR]

The document discusses the environmental effects of natural processes and human activities on the water environment in watersheds. It highlights the impact of pollutants from agricultural, urban, and industrial sources on water quality, as well as the accumulation of nutrients and heavy metals. The papers in the special issue focus on topics such as nutrient accumulation, heavy metal contamination, and the origin and transportation of major ions and solutes in river water. Additionally, the document addresses the impact of hydraulic characteristics on water environments in large reservoirs, emphasizing the need for continued research to understand and mitigate the effects of human activities on water quality. [Extracted from the article]

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]

Construction sites rely on erosion control practices to protect bare slopes and prevent soil loss. The effectiveness of certain erosion controls is often under-evaluated if they are not a part of a product evaluation program. Furthermore, erosion controls in general are not fully understood regarding how their performance can be affected by site specific variables, such as soil variations. This study used large-scale rainfall simulators to evaluate how a commonly used erosion control on construction sites, broadcasted straw mulch, performs on three common soil types in Alabama. The study at the Auburn University, Stormwater Research Facility (AU-SRF) used the industry standard testing method and three different soil types: sand, loam, and clay in accordance with ASTM D6459-19, the standard test method for testing rolled erosion control products' (RECPs) performance in protecting hillslopes from rainfall-induced erosion. As required by ASTM D6459-19, the rainfall simulators simulated a storm of varying 20 min increments of 2 in./h (5.08 cm/h), 4 in./h (10.16 cm/h), and 6 in./h (15.24 cm/h). A total of nine bare soil tests on the 4:1 test plots was performed with an average total soil loss of 1977 lb (897 kg), 236.2 lb (107 kg), and 114.2 lb (51.8 kg) for sand, loam, and clay, respectively. The average erodibility K-factor for each soil type is calculated to be 0.37 (sand), 0.043 (loam), and 0.013 (clay). Nine straw tests were performed on the 4:1 plots, with an average total soil loss of 44.31 lb (20.1 kg), 6.74 lb (3.1 kg), and 17.13 lb (7.8 kg) for sand, loam, and clay, respectively. Straw testing indicated substantial soil loss reduction with average cover management C-factor values under the revised universal soil loss equation (RUSLE) method of 0.021, 0.047, and 0.193 for sand, loam, and clay applications, respectively. This variation in C-factor across the three soil types indicates that the single C-factor, often reported by product manufacturers, is not adequate to imply performance. [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]

Agriculture continues to be one of the most important sources of nonpoint source pollution to surface water bodies. Consequently, it is critical to identify and prioritize high‐contributing agricultural fields and sub‐field areas for reducing soil erosion and sediment delivery by implementing best management practices (BMPs). Current erosion risk assessment tools are either complex modelling approaches or rely on a simplified reality and generalized assumption. The Daily Erosion Project (DEP) is a daily estimator of precipitation, hillslope runoff, detachment and soil loss covering ~630 000 km2 across the Midwest United States. These estimations are reported daily and publicly at the hydrologic unit code 12 watershed resolution (approximately 100 km2). The main objective of this study was to develop a new tool (named Overland Flow Element tool [OFEtool]) that downscales the watershed scale of DEP to estimate average runoff and soil displacement within a field, helping to locate erosive hotspots at multiple scales. We also demonstrated the applicability of OFEtool in Bennet Creek‐Sugar Creek in East Central Iowa (the United States) and compared its results with other erosion vulnerability tools such as the Soil Vulnerability Index for Cultivated Cropland (SVI‐cc) and a GIS‐based Revised Universal Soil Loss Equation (RUSLE). The same erosion risk classes and ranges (low, moderate, moderately high and high) were implemented for all indexes. The advantages of the OFEtool compared to the SVI‐cc and RUSLE models are related to the use of an event‐based modelling approach, such as DEP, with updated soil loss estimates based on temporal changes in climate inputs and land use and management. The OFEtool uses a 6‐year time frame and a more up‐to‐date field inputs, while RUSLE provides a long‐term average and SVI‐cc only considers soil and topographical factors for risk assessment. Results indicated that the spatial distribution of vulnerable fields (and parts of the fields) followed a similar trend as other tested indices. However, the risk level associated with each tool differed (SVI‐cc > RUSLE > OFEtool). These differences could arise from intrinsic disparities within the tools (inputs, timing, processes considered, assumptions). While currently limited to the DEP domain and relying on the DEP random sampling scheme, further research is warranted to validate the tool at other Midwest locations and ensure it captures the watershed's landscape variability (combination of terrain, soil, land use and management) required to identifying critical erosion hotspots. [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]

This study assesses the effectiveness of a distributed physically based hydrological model (MARINE) to investigate erosion estimation during flash floods compared with other widely used empirical models derived from the Universal Soil Loss Equation (USLE) like Revised Universal Soil Loss Equation (RUSLE) and Modified Universal Soil Loss Equation (MUSLE). It is carried out on a small catchment in south‐eastern France, the Claduègne catchment. To compare the erosion volumes simulated by the three models, MARINE, MUSLE and RUSLE, a sensitivity analysis on the model parameters is carried out. According to physics‐based simulations, flood events fall into two categories: those dominated by raindrop erosion and those dominated by shear stress erosion. The results show that the erosion simulated by the three methods are comparable, except for events dominated by raindrop erosion suggesting that further research is needed to improve raindrop erosion within MARINE. Simulations from the MARINE model provide access to the spatio‐temporal variability of erosion dynamics during the event and can also be used to produce erosion/deposition maps, which are useful for environmental decision‐makers and planners in identifying areas at risk from erosion and deposition hazards. [ABSTRACT FROM AUTHOR]

Soil erosion risk assessment enables the identification of areas requiring priority treatment and avoids wasting human and material resources. The factor scoring method used in existing studies has high subjectivity, and the method of expressing erosion risk according to the soil erosion intensity ignores the random nature of the occurrence of erosion; therefore, neither method accurately reflects the risk of soil erosion. In order to address this issue, this study proposes a soil erosion risk assessment method that integrates the outcome and the probability of occurrence of soil erosion by means of a probabilistic statistical model. Subsequently, experimental research is conducted in the Dali River Basin. On the basis of long time-series data, using mathematical statistics as a tool and drawing on the empirical frequency formula, the probabilistic statistical risk assessment model is combined with the Modified Universal Soil Loss Equation (RUSLE) model to account for the probability of regional soil erosion at different intensity levels in the long time-series, which is combined with the intensity of erosion to carry out soil erosion risk assessment. The results of our study show the following: (1) The central and southwestern regions of the Dali River Basin (DRB) present medium and high levels of soil erosion risk, with the proportion of low-risk areas increasing annually, accounting for 78.97% of the DRB in 2020, while extremely high-risk areas account for only 0.40% of the DRB. (2) The major components impacting soil erosion risk in the DRB, as revealed by the geodetector, are the normalized difference vegetation index (NDVI) and slope, where the interaction between the two dominated the spatial variation in soil erosion risk. (3) Comparing the soil erosion risk and its status in the coming years, the proposed assessment method based on the occurrence probability can reveal the future soil erosion risk better than the traditional assessment method. [ABSTRACT FROM AUTHOR]

Lahars are hazardous mixtures of rock and water that flow rapidly in the form of debris flows or mudflows. The continuous eruptions at Sakurajima supply volcanic ash to form on the surface, reducing the permeability of the ground. As the surface permeability decreases, surface runoff occurs and eventually lahar starts. Therefore, Sakurajima Island suffers from lahars triggered by heavy rains every year. Sheet erosion by surface runoff decreases the thickness of volcanic ash on the ground and subsequently increases hydraulic conductivity. This cycle continues due to the eruptions of the Sakurajima volcano and the lahars formed by heavy rains. Depending on the lahar type and amount of surface runoff, the flow density and block sizes carried in the lahar vary. In this study, the relationship between the volcanic ash thickness and the hydraulic conductivity of the ground was investigated in the Arimura River basin of Sakurajima volcano between 2015 and 2020. Monthly volcanic ash thickness was measured 2 km from the craters. The dynamic change of volcanic ash thickness due to accumulation and erosion was investigated by the Universal Soil Loss Equation and the newly developed Discharge Dependent Erosion methods. Block sizes of the lahars were estimated by vibrographs and CCTV camera. The hydraulic conductivity of the ground was calculated with a Kinematic Wave Model developed specifically for this study. Based on the relationship between volcanic ash thickness and hydraulic conductivity, a risk assessment chart was created considering the block sizes carried by the lahars. [ABSTRACT FROM AUTHOR]

This study investigates soil loss in the Wadi Bin Abdullah watershed using the Revised Universal Soil Loss Equation (RUSLE) combined with advanced tools, such as remote sensing and the Geographic Information System (GIS). By leveraging the ALOS PALSAR Digital Elevation Model (DEM), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall data, and the Digital Soil Map of the World (DSMW), the research accurately evaluates soil loss loads. The methodology identifies significant variations in soil loss rates across the entire watershed, with values ranging from 1 to 1189 tons per hectare per year. The classification of soil loss into four stages—very low (0–15 t/ha/yr), low (15–45 t/ha/yr), moderate (45–75 t/ha/yr), and high (>75 t/ha/yr)—provides a nuanced perspective on soil loss dynamics. Notably, 20% of the basin exhibited a soil loss rate of 36 tons per hectare per year. These high rates of soil erosion are attributed to certain factors, such as steep slopes, sparse vegetation cover, and intense rainfall events. These results align with regional and global studies and highlight the impact of topography, land use, and soil properties on soil loss. Moreover, the research emphasizes the importance of integrating empirical soil loss models with modern technological approaches to identify soil loss-prone locations and precisely quantify soil loss rates. These findings provide valuable insights for developing environmental management strategies aimed at mitigating the impacts of soil loss, promoting sustainable land use practices, and supporting resource conservation efforts in arid and semi-arid regions. [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]

The source region of the Yellow River (SRYR), known as the "Chinese Water Tower", is currently grappling with severe soil erosion, which jeopardizes the sustainability of its alpine grasslands. Large-scale soil erosion monitoring poses a significant challenge, complicating global efforts to study soil erosion and land cover changes. Moreover, conventional methods for assessing soil erosion do not adequately address the variety of erosion types present in the SRYR. Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR's needs. By leveraging soil erosion data measured by 137Cs from 521 locations and employing the random forest (RF) algorithm, a new soil erosion model was formulated. Key findings include that: (1) The RF soil erosion model significantly outperformed the revised universal soil loss equation (RUSLE) model and revised wind erosion equation (RWEQ) model, achieving an R2 of 0.52 and an RMSE of 5.88. (2) The RF model indicated that from 2001 to 2020, the SRYR experienced an average annual soil erosion modulus (SEM) of 19.32 t·ha−1·y−1 with an annual total erosion in the SRYR of 225.18 × 106 t·y−1. Spatial analysis revealed that 78.64% of the region suffered low erosion, with erosion intensity declining from northwest to southeast. (3) The annual SEM in the SRYR demonstrated a downward trend from 2001 to 2020, with 83.43% of the study area showing improvement. Based on these findings, measures for soil erosion prevention and control in the SRYR were proposed. Future studies should refine the temporal analysis to better understand the influence of extreme climate events on soil erosion, while leveraging high-resolution data to enhance model accuracy. Insights into the drivers of soil erosion in the SRYR will support more effective policy development. [ABSTRACT FROM AUTHOR]

Mitigating soil erosion's effects have been prioritized since the early 20th century. Rainfall simulators and analytical prediction models are used to determine soil erosion susceptibility. This study used different methodologies to measure soil erodibility in two hydrographic sub-basins, the Renato and Caiabi, in the Middle and Upper Teles Pires River in Mato Grosso state, Brazil. The rainfall simulator showed a higher range of K-factor values for the Renato sub-basin of 0.0009 to 0.0086 Mg × h × (MJ × mm)−1 and a lower range of K-factor values for the Caiabi sub-basin of 0.0014 to 0.0031 Mg × h × (MJ × mm)−1. Soil loss equations similarly estimated a higher range of K-factor values for the Renato of 0.0008 to 0.0990 Mg × h × (MJ × mm)−1 and a lower range of K-factor values for the Caiabi of 0.0014 to 0.0846 Mg × h × (MJ × mm)−1. There was no significant difference at the 5% level for the K factor determined by the rainfall simulator for both sub-basins. Equations specified in Bouyoucos (1935) and Lombardi Neto and Bertoni (1975) showed significant correlation (5%) for farming systems in the Caiabi sub-basin. Indirect methodologies that performed well for correlation were equations 2 and 3 from Roloff and Denardin (1994), which use iron and aluminum as parameters. Soil erosion was most influenced by physical texture parameters of the region's soil. [ABSTRACT FROM AUTHOR]

The changes in land use in Goiás state due to the advancement of agriculture and livestock have caused a major influence on soil losses. This study identified the potential soil loss in the state and alone in 183 watershed basins for water catchment in the state of Goias using the universal soil loss equation (USLE), and their R, K, LS, and CP factors were obtained by geoprocessing tool. The results generated a scenario where most of the area of the state, 77.6%, shows weak erosive sensitivity, 8.5% moderate, and 6.9% erosive susceptibility with high erosive susceptibility. Concerning soil loss of funding basins it was found that twelve basins showed very high class and two in severe erosive susceptibility, 64% of the basins were framed with low and moderate erosive susceptibility. Thus, the use of USLE in conjunction with the Geoprocessing tool was effective in the spatial representation of soil loss, identifying areas most vulnerable to the erosion process, and assisting in proposing priority measures for soil conservation. [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 poses a severe threat to water quality and soil health in semi-arid regions. Assessing soil erosion and sedimentation at the watershed-scale is crucial for managing water resources and soil. In Iran, empirical models, e.g., the erosion potential method (EPM), modified Pacific Southwest Inter-Agency Committee (MPSIAC), and universal soil loss equation (USLE), have drawn more attention than other models, due to the availability of input data and lack of reliable hydrometric station, especially for a long time span. In the present article, we evaluated the soil erosion intensity and sediment yield in the Chehelgazi mountainous watershed (western Iran) utilizing the EPM, MPSIAC, and USLE, combined with the sediment delivery ratio (SDR) module, and then compared them with the observations. The required input data for EPM, MPSIAC, and USLE models all were created in ArcMap 10.8 and ENVI 5.5. The sediment yield amount and soil erosion zonation map were then obtained by the models mentioned and validated by the hydrometric data. According to the results, most portions of the watershed were subject to moderate erosion risk. Moreover, the annual average sediment yield of 196.86, 99.30, and 84.84 kt year−1 assessed by EPM, MPSIAC, and USLE combined with SDR, respectively, suggested the superiority of USLE-SDR and MPSIAC. As compared with the observed amounts of 86.46 kt year −1, the efficiency of USLE-SDR was astonishing. Overall, we conclude that the USLE model coupled with the Boyce (1975) SDR formula is the best for estimating sediment yield at Chehelgazi mountainous watersheds, while MPSIAC is better suited for mapping soil erosion state. [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]

This summary is about a special issue of the journal Environmental Science and Pollution Research that focuses on innovative methodologies in natural hazard management, specifically related to remote sensing and risk assessment. The issue covers various topics such as fire regime dynamics, coastal vulnerability assessment, landslide susceptibility, seismic response of structures, flood impact assessment, microbial communities in anaerobic digestion, tropical cyclone risk assessment, and cloudburst event forecasting. The articles aim to provide insights for policy-making, emergency response, and sustainable development, with the goal of enhancing the safety and resilience of communities worldwide. [Extracted from the article]

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]

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]

Soil erosion inventory of a river basin is vital for effective soil conservation and sustainable development considering the ongoing Global change. In the current study, gross soil erosion rates (A) are estimated for a mountainous river in a tropical climate, the Karamana River Basin (KRB), southern India by utilizing the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model computes 'A', as a product function of five variables; rainfall erosivity (R), soil erodibility (K), topographic factor (LS), crop management factor (C), and erosion control practice factor (P). To compute these five parameters, the digital elevation model, slope, normalized vegetation index (NDVI), rainfall and land use/land cover (LULC) are used along with the texture data of soil samples collected from KRB. The estimated A values range between 0 to 738.44 t ha−1 year−1, with an average of 10.22 t ha−1 year−1 whereas the sediment yield (SY) estimated by utilizing the sediment delivery ratio (SDR) model ranges between 0 to 246.68 t ha−1 year−1 with an average of 1.58 t ha−1 year−1 for KRB. For further validation, a soil erosion potential index (SEPI) map is developed to identify the soil erosion-prone zones of KRB using the geo-environmental thematic maps following the analytic hierarchy process (AHP). Finally, rainfall impacts on soil erosion are evaluated and quantified for KRB, considering the recent marked variations in rainfall that are induced by the extreme climatic events (cyclones/high rainfall/floods) along the West coast of India. The outcome of this study suggests that increasing rainfall by one standard deviation of the long-term average causes a significant increase in low and medium soil erosion regions. [ABSTRACT FROM AUTHOR]

According to reports, Ethiopia is one of the countries in sub-Saharan Africa with the worst affected by soil erosion. It has both on-site and off-site consequences on biophysical and socioeconomic settings in an area. The study area is heavily affected by soil erosion forming diverse erosion structures, particularly in the upper course of the watershed. Hence, this work seeks to estimate the geographically distributed annual soil loss rate and mapping of soil erosion hazard hotspot areas in the watershed using the Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions. The RUSLE parameters, such as rainfall erosion factor (R-factor), soil erodibility factor (K-factor), slope steepness and slope length factor (LS-factor), land cover factor (C-factor), and conservation practice factor (P-factor) were considered as data input for the analysis to quantify the soil loss rate in the study area. A digital elevation model (DEM) with a 12.5 × 12.5-meter resolution was employed for catchment delineation and determination of the LS factor.The mean yearly rainfall data from the surrounding rain gauge stations was used to analyze the R-factor. The results of the current conditions showed that the average typical soil loss rate from the entire watershed is 23.8 t ha-1 yr-1, and the quantity of soil loss from the study area ranged from 0 to 776.71tan /ha-1 yr-1. Nonetheless, Tiro Afeta experiences mean soil erosion at a rate of roughly 50.2 t ha-1 yr-1, exceeding the acceptable threshold of 11 t ha-1 yr-1. Determining the sustainability of soil production requires assessment, particularly in cases where significant yearly soil erosion occurs. Due to intensive agricultural activities in the Xiro Afeta watershed, significant soil erosion is predominantly occurring in thissteep upper region. Consequently, this area urgently requires appropriate soil protection measures. [ABSTRACT FROM AUTHOR]

The lack of soil erosion data and other information about watersheds continues to limit soil erosion modeling. To overcome these limitations, many researchers have turned to machine learning models to analyze and model the complex water erosion processes and integrate them with empirical models. The Beni Haroun dam watershed faces soil erosion due to specific geo-environmental settings and land practices. It poses serious threats to agricultural and natural resource development. For these reasons, this study attempts to identify soil erosion susceptible zones using the Revised Universal Soil Loss Equation (RUSLE) using five key factors (rainfall erosivity, soil erodibility, topography, cover management and conservation practice factor) in GIS environment. Furthermore, we integrated the five RUSLE parameters and the model outputs into two machine learning (ML) algorithms, namely Random Forest (RF) and Random Tree (RT). The proposed models underwent training on 70% of the dataset and were subsequently validated on the remaining 30%. Our results indicated that the most vulnerable to severe soil erosion was concentrated in northwest regions, in contrast to the southeastern regions, which most occupy low erosion and moderate erosion. RUSLE and RT-based RUSLE models yielded nearly identical results in classifying erosion severity, estimating the annual average soil erosion at 17.5 and 17.69 (t ha–1y–1), respectively. In contrast, the Random Forest RF-based RUSLE model presented slightly divergent findings 23.89 (t ha–1y–1). Overall, these findings contribute to the identification of the area's most vulnerable to soil erosion, providing valuable insights to inform land management and conservation strategies. [ABSTRACT FROM AUTHOR]

Quantification of basin characteristics from morphometric analysis helps understand the physical process of erosion. This study performed morphometric analysis and uses the revised universal soil loss equation (RUSLE) on a pixel basis to estimate the space–time variability of soil loss in order to prioritize erosion-prone areas. The morphometric factors considered in the study included linear, areal, and relief aspects of the basin. Average annual soil loss was estimated using the RUSLE model for 21 years (1995–2005) and then used to correlate with morphometric parameters to find out the prioritization criteria for the Sutlej catchment. The Sutlej basin, covering an area of 21,595 km2 , is a fifth-order dendritic drainage basin. Its mean bifurcation ratio is 4.06. Based on the values of morphological parameters, prioritization was being done for eight subwatersheds of the Sutlej basin. It was found that the average annual soil loss was strongly correlated with the time of concentration, drainage density, and texture ratio, but not with other morphological characteristics. The study suggested that indices based on morphometric parameters should not be preferred alone for prioritizing catchments prone to erosion. This research works shows the nexus between morphological parameters and their direct relationship with the erosion to prioritize the Sutlej catchment and its subwatersheds. Unlike previous applications, this integrated methodology not only enhances the accuracy of erosion risk evaluation but also addresses the limitations of relying solely on morphometric factors. The results reveal significant correlations between certain morphometric factors and soil loss, emphasizing the need for a multifactorial approach. Additionally, the present study contributes new insights into subwatershed prioritization for targeted erosion control efforts. These innovative findings advance the field by providing a more comprehensive and effective strategy for predicting and managing soil erosion in diverse geographical contexts. Correlation of morphological parameters with erosion rate based on RUSLE can play a significant role to prioritize the watersheds, which is not enough single handedly to demarcate the erosion-prone areas in the catchment for further treatment to conserve the soil loss in the respective region. [ABSTRACT FROM AUTHOR]

The Qinling region in central China, known as the 'Dragon Vein of China', is a vital ecological barrier facing significant soil erosion challenges. This study aims to enhance soil erosion management and analyse the spatiotemporal changes of soil erosion in the northern foothills of the Qinling Mountains. We collected data on precipitation, terrain, land use types, and soil in the designated region. Using GIS technology and the Revised Universal Soil Loss Equation (RUSLE) model, we created a detailed soil erosion map and analysed its evolution from 2018 to 2022. Results show a significant reduction in soil erosion in 2020–2021 despite a general upward trend in other years. Innovation includes integrating remote sensing with RUSLE for high-precision mapping and introducing a hierarchical approach for erosion risk assessment. The study found erosion peaks in summer and autumn, with higher levels in the southern parts compared to the northern parts. Influential factors include climate variables, human activities, soil, and vegetation types. The average soil erosion modulus in 2023 is 233.515 t/(km2·a), with total soil erosion of 85,233.046 t/a, mainly concentrated in the valley and mountain basin areas. This research provides a theoretical basis for improving the natural environment and implementing comprehensive soil and water conservation measures in the Qinling region, offering a model for similar ecological regions globally. [ABSTRACT FROM AUTHOR]

Conservational tillage (NT) is widely recognized globally for its efficacy in mitigating soil loss due to wind and water erosion. However, a systematic large-scale estimate of NT's impact on soil loss reduction in Northeast, China's primary granary, remains absent. This study aimed to investigate the spatial and temporal variability of soil erosion under NT compared to conventional tillage (CT) in the black soil region and to analyze the underlying mechanisms driving these erosions. The Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ) models were employed, incorporating previously published plot/watershed data to estimate the potential reduction of water and wind erosion by NT in this region. Results indicated that under CT practices, water- and wind-induced soil losses were widely distributed in the arable land of Northeast China, with intensities of 2603 t km−2 a−1 and 34 t km−2 a−1, respectively. Furthermore, the erosive processes of water and wind erosion were significantly reduced by 56.4% and 91.8%, respectively, under NT practices compared to CT. The highest efficiency in soil conservation using NT was observed in the mountainous regions such as the Changbai Mountains and Greater Khingan Mountains, where water erosion was primarily driven by cropland slopes and wind erosion was driven by the wind speed. Conversely, the largest areas of severe erosion were observed in the Songnen Plain, primarily due to the significant proportion of arable land in this region. In the plain regions, water-induced soil loss was primarily influenced by precipitation, with light and higher levels of erosion occurring more frequently on long gentle slopes (0–3°) than on higher slope areas (3–5°). In the temporal dimension, soil loss induced by water and wind erosion ceased during the winter under both tillage systems due to snow cover and water freezing in the soil combined with the extremely cold climate. Substantial reductions were observed under NT from spring to autumn compared to CT. Ultimately, the temporal and spatial variations of soil loss under CT and NT practices were established from 2010 to 2018 and then projected onto a cropland map of Northeast China. Based on this analysis, NT is recommended as most suitable practice in the southern regions of Northeast China for maintaining soil health and crop yield production, while its suitability decreases in the northern and eastern regions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Purpose: Identification of hotspots of accelerated erosion of soil and organic carbon (OC) is critical to the targeting of soil conservation and sediment management measures. The erosion risk map (ERM) developed by Lilly and Baggaley (Soil erosion risk map of Scotland, 2018) for Scotland estimates erosion risk for the specific soil conditions in the region. However, the ERM provides no soil erosion rates. Erosion rates can be estimated by empirical models such as the Revised Universal Soil Loss Equation (RUSLE). Yet, RUSLE was not developed specifically for the soil conditions in Scotland. Therefore, we evaluated the performance of these two erosion models to determine whether RUSLE erosion rate estimates could be used to quantify the amount of soil eroded from high-risk areas identified in the ERM. Methods: The study was conducted in the catchment of Loch Davan, Aberdeenshire, Scotland. Organic carbon loss models were constructed to compare land use specific OC yields based on RUSLE and ERM using OC fingerprinting as a benchmark. The estimated soil erosion rates in this study were also compared with recently published estimates in Scotland (Rickson et al. in Developing a method to estimate the costs of soil erosion in high-risk Scottish catchments, 2019). Results: The region-specific ERM most closely approximated the relative land use OC yields in streambed sediment however, the results of RUSLE were very similar, suggesting that, in this catchment, RUSLE erosion rate estimates could be used to quantify the amount of soil eroded from the high-risk areas identified by ERM. The RUSLE estimates of soil erosion for this catchment were comparable to the soil erosion rates per land use estimated by Rickson et al. (Developing a method to estimate the costs of soil erosion in high-risk Scottish catchments, 2019) in Scottish soils except in the case of pasture/grassland likely due to the pastures in this catchment being grass ley where periods of surface vegetation cover/root network absence are likely to have generated higher rates of erosion. Conclusion: Selection of suitable erosion risk models can be improved by the combined use of two sediment origin techniques—erosion risk modelling and OC sediment fingerprinting. These methods could, ultimately, support the development of targeted sediment management strategies to maintain healthy soils within the EU and beyond. [ABSTRACT FROM AUTHOR]

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]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil degradation exacerbates the depletion of soil resources and poses a threat to agricultural productivity and environmental sustainability. To address this issue, initiatives focusing on soil and water conservation (SWC) were carried out in the Tana Basin watershed located in Ethiopia. However, their spatial distribution, status, and effects of SWC activities on soil erosion were not evaluated in the entire study watershed. Thus, a revised universal soil loss equation (RUSLE) was adopted to identify soil erosion prone areas and evaluate the effect of SWC activities on soil erosion in Ethiopia. The finding shows the average soil loss of the catchment is 18.65 t/ha/year (moderate), and the detached and transported soil is 7934 tons/year. By implementing bunds in croplands and restricting grazing in degraded bushlands, it is feasible to reduce current soil erosion by 47% of the overall soil loss. The catchment area shows varying degrees of soil erosion, with a minimal rate in some areas, while others experience slight, moderate, severe, and very severe rates of soil loss, which make up 15.5%, 13.3%, 12.8%, and 10.9%, respectively. The prediction efficiency of the RUSLE using ground‐measured soil erosion data results (R2 = 0.90) revealed reliability. Consequently, the initial situation (soil bund and enclosure) results in a 47% reduction in erosion, the second situation involves increased expenses in materials and labor by 53% compared to the first scenario, and the third situation converts all deteriorated bushlands into forest land by 10%. Therefore, recommended SWC practices should be adopted in the Kecha and similar watersheds to minimize soil erosion and sustain soil productivity. [ABSTRACT FROM AUTHOR]

Soil erosion is one of the major threats to soil sustainability and a global environmental issue causing serious losses of the fertile upper layer of soil, affecting land productivity. Among natural processes and human activity factors, the highest sensitivity of soil loss rate is related to climate changes, as well as land cover/land use transformations. The aim of our paper is to assess the efficacy of various land cover and land use management practices under current climate conditions, as a decision‐making indicator in searching for sustainable soil‐use solutions. The approach is focused on two complementary case studies from the non‐arable hilly area of Romanian Subcarpathians and it is based on aggregating and processing Earth Observation (EO) techniques together with the Revised Universal Soil Loss Equation (RUSLE) equation. The workflow follows three stages: (1) the assessment of the present‐day status of soil erosion, as baseline scenario; (2) the analysis of historical soil erosion dynamics within the last 35 years; and (3) the prediction of soil loss rates in different scenarios of changed conditions related to land cover management and support practices against erosion. The results demonstrate the effectiveness of human interventions in soil erosion prevention, mitigation, or conservation. Soil‐improving management through vegetative measures and soil practices, like grazing management and mulching/manure application, together with forest recovery on eroded slopes may reduce soil loss rates by 50%–70%. However, abandoning the land and allowing the environment to change uncontrollably is a regional‐specific strategy that could accelerate soil erosion on the slopes that are already affected, while decelerating on the others by forest and shrubs regrowth. The significance of our research is related to the identification of the optimal soil use strategies that balance the local communities' economic interests with the effectiveness of sustainable soil management practices, thereby assisting in the achievement of the UN Sustainable Development Goals (SDGs) as indicators for a sustainable future. [ABSTRACT FROM AUTHOR]

Reservoir sedimentation in mountainous watersheds, such as the Zagros mountains, is high. Reservoir sedimentation reduces water storage and threatens water resource management. This study aims to simulate reservoir sedimentation in Dokan Lake, Iraq for 55 years, beginning in 1959. Multiple combinations of the revised universal soil loss equation—sediment delivery ratio (RUSLE-SDR) method have been tested using geographic information system (GIS) and remote sensing techniques. Two types of satellite precipitation data, six Digital Elevation Model (DEM), Harmonized World Soil Database (HWSD), and AQUA eMODIS NDVI V6 data have been used to prepare forty-eight RUSLE-SDR models. These models have been evaluated using three sets of in situ measurements (one topographic map and two bathymetric data sets) to select the suitable models. The rate of error test shows that the best model by far is model #38, which has an error of less than 8.31% for the three in situ measurements. The annual reservoir sedimentation rate (between 1959 and 2014) at Dokan Lake is 0.0034 km3·y−1 (6,347,800 ton·y−1). This rate has been decreased to 0.0027 km3·y−1 (2018 to present) due to short-term fluctuations in precipitation and temperature and construction of the Sardasht Dam, which contributed to the decrease in the amount of the water mass reached to Dokan lake. The sediment delivery ratio which is based on the basin model shows better results than the sediment delivery ratio which depends on the IC model although the SDR IC model is working well, too. As a result of neglecting this aspect, one-third of Dokan Lake volume has been lost already due to reservoir sedimentation. The results show that the GPM data is better than the TRMM data in estimating the R factor. Moreover, the best RUSLE-SDR model shows that the MODIS land cover (MCD12Q1) performs better than the AQUA eMODISNDVI V6 to estimate the cover-management (C) factor, which comprises the preventing effects of the crop or vegetation cover. We propose constructing check dams in the Dokan Lake basin to reduce reservoir sedimentation is among the most important measures to increase the dam's lifespan. [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]

Rural communities in ecologically sensitive areas are confronted with environmental challenges and land waste. The hollowing out of villages because of the steady loss of young people has brought these problems to the fore. Research on the remediation of rural settlements in ecologically sensitive regions is scarce. This paper focuses on Tong Yu County, a region located in the agricultural and pastoral intertwined zones of western Jilin, which is known for its ecological sensitivity. On the basis of the results of the geographical assessment of the area via hierarchical analysis (AHP method) and soil and water conservation capacity evaluation via the revised universal soil loss equation model (RUSLE) and the net primary production quantitative index model (NPP model), we propose a rural development program that divides the study area into three different zones with different development orientations, namely, potential enhancement, agglomeration development, and expansion restrictions. Moreover, we conceived a rural settlement remediation scheme using the kernel density estimation method and provided remediation strategies for rural settlements of various density levels. The rural settlement development program and the rural settlement remediation scheme led to the optimization of rural settlements. Notably, with increasing kernel density, the area of rural settlements tends to increase and then decrease. Our research helps save 5.059 km2 of land resources in the study area and offers guidance for improving the rural settlement layout in Tong Yu County. [ABSTRACT FROM AUTHOR]

The article discusses the potential for leveraging the Global Biodiversity Framework (GBF) to improve the management of transboundary watersheds. It highlights the need for coordinated efforts and collaboration between countries to address complex environmental issues that cross borders. The article suggests that the GBF should include explicit targets and indicators for transboundary management to ensure equitable accountability and incentivize action. It also emphasizes the importance of recognizing shared responsibilities and incorporating transboundary conservation incentives within the framework. The article provides examples of successful transboundary cooperation and calls for scaling up such efforts to achieve global conservation goals.The text discusses the importance of transboundary watershed management within the Global Biodiversity Framework (GBF) and its potential to improve conservation efforts. It highlights the need for improved data sharing and coordination among stakeholders to effectively manage shared resources. The text also suggests that the recommendations for transboundary watershed management can be applied to other forms of management across large spatial scales. The implementation of transboundary planning is complex but can promote stakeholder engagement and contribute to achieving the GBF targets.This document provides a list of countries and territories, along with their respective regions, population, and land area. The information is presented in a concise and factual manner, without any judgments or opinions. It is a useful resource for library patrons conducting research on specific countries or regions.The given text provides data on sediment run-off entering different countries' Exclusive Economic Zones (EEZs) and the estimated amount of sediment for each country. The data is presented [Extracted from the article]