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This study presents a novel approach that combines the standard procedure of frequency analysis with the rainfall erosivity factor (R factor) calculation method from the revised universal soil loss equation (RUSLE) handbook to estimate and map the R factor for various return periods using high spatiotemporal resolution precipitation product. The 0.1° 30-minute interval or half-hourly IMERG (Integrated Multi-satellitE Retrievals for Global precipitation measurement) precipitation data is utilized to generate the rainfall erosivity map of India for different return periods. Return level values of 2, 5, 10, 25, 50, and 100-year return periods are evaluated using the cumulative distribution function of best-fitted distribution, and a spatially distributed map of each return period is prepared. These maps will offer liberty to the stakeholders and policymakers to decide on the level of risk they are willing to take. Moreover, the high spatial resolution gridded data will minimize the probable error of interpolated maps in areas with a limited number of stations or no stations. In traditional practice, the average annual rainfall erosivity is considered for computing soil loss. However, this study has revealed that the average annual rainfall erosivity fell between return levels of 2 and 5-year return periods in around 94% of the studied area, highlighting the underestimation of rainfall erosivity. [ABSTRACT FROM AUTHOR]

The objective of this study is to provide a thorough assessment of soil erosion in the Hirshabelle state from 2020 to 2023, utilizing the Revised Universal Soil Loss Equation (RUSLE) and advanced geospatial technologies, particularly Google Earth Engine, to guide sustainable land management strategies. The study integrates multiple datasets, including CHIRPS for rainfall measurement, MODIS for land use analysis, and a digital elevation model for slope calculation, to offer a comprehensive understanding of the factors contributing to soil erosion. The rainfall erosivity (R) factor is calculated using CHIRPS data, while the soil erodibility (K-factor) is derived from the soil dataset. The topographic condition (LS-factor) is computed using the digital elevation model, and the cover-management (C) and support practice (P) factors are determined from the NDVI and land use data, respectively. The findings reveal considerable spatial variation in soil erosion across the Hirshabelle state. The results are categorized into five levels based on the severity of soil loss: very low (<5), low (5-10), moderate (10-20), high (20-40), and very high (≥40). While areas classified under “very low” soil loss are dominant, indicating relatively stable soils, regions under “very high” soil loss signal potential land degradation and the need for immediate intervention. Furthermore, the study revealed the intricate interplay of slope, vegetation, and land use in influencing soil erosion. Areas with steeper slopes and less vegetation were more susceptible to soil loss, emphasizing the need for targeted soil conservation measures in these regions. The land use factor played a crucial role, with certain land uses contributing more to soil erosion than others. [ABSTRACT FROM AUTHOR]

This study employs geospatial tools to examine erosion dynamics and its impact on sustainable development goals (SDGs). Landsat images of 30-m resolution from USGS, soil survey data from the field and FAO, and rainfall data obtained from Global Climatological Data were used in the study. Using rainfall-runoff, soil erodibility, steepness of slope, cover-management, and support practice, the study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil loss and map erosion-prone areas. The C-factor map revealed that open forest covers more significant parts (41.3%) of the study area, followed by grassland, shrub/tree, and built-up areas with 17.4%, 11.1%, and 10.7%, respectively. A total of 112 erosion sites were identified on the map and verified with GPS receivers. The estimated soil loss ranged between 0 and 38.73 t ha−1yr−1, with the very high (2.29–3.52 t ha-1yr-1) and most severe (3.52–38.73 t ha-1yr-1) classes significantly concentrated in areas with steep slopes and forest conversion. The low number of soil erosion sites observed in the south was associated with the closed forest, water bodies, gentle slope, and soil type (Cp), which is high in clay content (67.7%), making it less susceptible to erosion. The study highlights the interconnectedness of erosion with several SDGs, including Zero Hunger, Climate Action, Clean Water and Sanitation, Life on Land, Sustainable Cities and Communities. [ABSTRACT FROM AUTHOR]

Introduction: Soil erosion is a persistent environmental challenge in Ethiopia's Lake Ziway Watershed, posing severe risks to agricultural sustainability, food security, and ecosystem stability. Despite its significance, limited longitudinal studies have systematically analyzed soil erosion dynamics in this region or identified critical intervention areas. Methods: This study employs the Revised Universal Soil Loss Equation (RUSLE) model integrated with remote sensing and GIS techniques to perform a 30-year longitudinal analysis (1993–2023). Results: The analysis highlights spatial and temporal trends in both potential and modeled actual soil erosion rates, revealing substantial but fluctuating losses over the study period. In 1993, potential soil erosion ranged from 0 to 2,082 t/ha/yr, totaling approximately 3.76 million tonnes annually. By 2023, this had shifted to a range of 0–2,069 t/ha/yr, with an annual loss of 3.48 million tonnes. Modeled actual soil erosion followed a similar trajectory, decreasing from 2.58 million tonnes in 1993 to 2.30 million tonnes in 2023. Mean soil loss rates for potential erosion increased from 42.91 t/ha/yr in 1993 to 43.73 t/ha/yr in 2023, while modeled actual erosion rates rose from 38.79 t/ha/yr to 41.01 t/ha/yr. Discussion: These figures consistently exceed Ethiopia's acceptable soil loss threshold of 10–15 t/ha/y, highlighting the need for targeted interventions, particularly in erosion-prone areas. This study bridges critical knowledge gaps, providing actionable insights for policy development aimed at sustainable land management and agricultural resilience. [ABSTRACT FROM AUTHOR]

Soil erosion in North Africa modulates agricultural and urban developments as well as the impacts of flash floods. Existing investigations and associated datasets are mainly performed in localized urban areas, often representing a limited part of a watershed. The above compromises the implementation of mitigation measures for this vast area under accentuating extremes and continuous hydroclimatic fluctuations. To address this deficiency, we use the Revised Universal Soil Loss Equation to map surface erosion, providing the first insight into the decadal impacts of land degradation, which are largely unconstrained on North Africa's continental scale. We generate soil erosion maps for the major hydrological basins of North Africa using Google Earth Engine and multiple hydroclimatic and land use datasets, covering 5.8 million square kilometers. The generated geospatial dataset integrates land use, soil erodibility, slope, vegetation cover, and land practices. The resulting product is an expansive and publicly available Soil erosion susceptibility maps and rasters dataset (SESMAR). This dataset is a crucial step toward understanding the drivers of soil erosion in this vast, poorly characterized area as well as its potential to be used for future soil conservation campaigns for both agricultural and urban planning. We validate SESMAR using the Global Rainfall Erosivity Database (GloREDa) and the European Soil Data Centre (ESDAC) datasets as well as published peer-reviewed reports across 20 watersheds, demonstrating a robust agreement in assessing the average annual soil loss values and soil erosion classes in local areas covered by independent study teams. Our continental maps show commendable accuracy, supporting scientists, practitioners, and policymakers in their efforts for more resilient land management practices across North Africa to mitigate rising hydroclimatic extremes. [ABSTRACT FROM AUTHOR]

The estuary of River Badung is a containment for the Nusa Dua Estuary Reservoir, which requires an assessment of sedimentation and sediment transport to determine potential reductions in its storage capacity. Therefore, this study aims to quantify and optimize the transported sediment obtained through various control measures, accompanied by the analysis of sedimentation distribution patterns in the lower reaches of River Badung. In this study, three simulation methods were used, (1) Hydrologic Engineering Center's-River Analysis System (HEC-RAS) was applied to analyze sediment transport, (2) Aeronautical Reconnaissance Coverage Geographic Information System (ArcGIS) was implemented to process geometry data and then exported with Hydrologic Engineering Center's-Geospatial River Analysis System (HEC-GeoRAS), and (3) Universal Soil Loss Equation (USLE) method was used to optimize sedimentation control. The results showed that River Badung had significant erosion, with sediment volume determined by USLE and HEC-RAS methods being 8040 m3 and 8740 m3, respectively. This emphasized a difference of 697 m3 or 8.66%, with the condition of sediment control structures yielding the following outputs, (1) the structures of check dams 1 and 4 required a thorough repair, (2) the capacity of check dams 2 was almost full, and (3) check dams 3 was full. Based on these results, check dams 5 and groundsill were capable of accommodating sediment, with reservoir (check dams 5) needing adjustment for appropriate functioning. [ABSTRACT FROM AUTHOR]

Soil erosion has been a significant threat to agricultural productivity, dam sustainability, and ecosystem services in the highlands of Ethiopia. Despite some watershed-specific research on this problem, the spatiotemporal distribution of soil erosion risks is still rarely assessed in South Wollo. This study aims to monitor land cover changes, analyze landscape mosaics, and estimate soil loss in South Wollo from 1990 to 2020. The Revised Universal Soil Loss Equation (RUSLE) was utilized to estimate soil loss. Key informant interviews and field observations were conducted to gain further insights into land cover change and soil erosion. The results showed that agricultural land use was dominant, covering 50.75 % of the study area in 1990 and it continued to expand at an annual rate of 1.73 %, primarily on shrubland and grassland. The landscape structure was more heterogeneous in the north compared to the central and southern areas. In 1990, the mean soil loss was 21.13 t ha-1yr−1, totaling 41.53 million tons. By 2020, These figures had increased to 28.97 t ha-1yr−1 and 49.86 million tons, respectively. Areas experiencing very severe soil erosion (>50 t ha-1yr−1) expanded from 10.81 % (1990) to 13.57 % (2020). The magnitude of soil loss also differed among land cover types, with the highest rates observed in bare land (>85 t ha-1yr−1), followed by agricultural land (>30 t ha-1yr−1). Soil loss factors and landscape metrics exhibited a significant correlation with soil loss, though the strength and direction of these interactions varied. Land cover change and its associated soil erosion were primarily exacerbated by policy factors, including tenure insecurity and disregard for farmers' indigenous conservation knowledge. This study offers valuable insights into the trends of land cover transitions, landscape structure, and soil loss. It will help guide sustainable land management, aimed at increasing green legacy and reducing soil loss. [ABSTRACT FROM AUTHOR]

Despite nearly a century of research on water-related issues, water erosion remains one of the greatest threats to soil health and soil ecosystem services around the world. Yet, to date, data on water erosion needed to develop mitigation strategies are scarce, especially in the Sahelian regions. The current study therefore sets out to estimate annual soil losses caused by water erosion and to analyze trends over the period of 1981–2020 in the Mékrou watershed, located in the Middle Niger river sub-basin in West Africa. The Revised Universal Soil Loss Equation, remote sensing, and the Geographic Information System (GIS) were deployed in this study. Several types of data were used, including rainfall data, sourced from meteorological stations and reanalysis datasets, which capture the temporal variability of erosive forces. Soil properties, including texture and organic matter content, were derived from FAO global soil databases to assess soil erodibility. High-resolution digital elevation models (30 m) provided detailed topographic information, crucial for calculating slope length and steepness factors. Land use and land cover data were extracted from satellite imagery, enabling the analysis of vegetation cover and anthropogenic impacts over four decades. By integrating and treating these data, this study reveals that the estimated average annual amount of water erosion in the Mékrou watershed is 6.49 t/ha/yr over 1981–2020. The dynamics of the ten-year average are highly variable, with a minimum of 3.45 t/ha/yr between 1981 and 1990, and a maximum of 8.50 t/ha/yr between 1991 and 2000. Even though these average soil losses in the Mékrou basin are below the tolerable threshold of 10 t/ha/yr, mitigation actions are needed for prevention. In addition, the spatial dynamics of water erosion are noticeably heterogeneous. The study reveals that 72.7% of the surface area of the Mékrou watershed is subject to slight water erosion below the threshold, compared with 27.3%, particularly in the mountainous south-western part, which is subject to intense erosion above the threshold. This research is the first study of soil erosion quantification with the RUSLE method and GIS in the Mékrou watershed, and fills a critical knowledge gap of the water erosion in this watershed, providing insights into erosion dynamics and supporting future sustainable land management strategies in vulnerable Sahelian landscapes. [ABSTRACT FROM AUTHOR]

The Peruvian Amazon faces an increasing threat of soil erosion, driven by unsustainable agricultural practices and accelerated deforestation. In Neshuya (Ucayali region), agricultural activity has intensified since 2014, but the effect on soil erosion is unknown. The present study aimed to evaluate the increase in erosion levels, at a sub-basin of the central–eastern Amazon of Peru, in a Geographic Information System (GIS) environment. The revised universal soil loss equation (RUSLE) model was used for assessing the effect of vegetation cover change from 2016 to 2022. In the Neshuya sub-basin (973.4 km2), the average erosion increased from 3.87 to 4.55 t ha−1 year−1, on average. In addition, there is great spatial variability in the values. In addition, 7.65% of the study area (74.52 km2) exceeds the soil loss tolerance limit (15 t ha−1 year−1). The deforestation rate was 17.99 km2 year−1 and by 2022 the forested area reached 237.65 km2. In conclusion, the transition from forest to farmland was related to the most critical erosion values. Unsustainable soil management practices can be the underlying explanation of changes in soil chemical and physical properties. Also, social dynamic changes and differences in landscape patterns play a role. [ABSTRACT FROM AUTHOR]

The study focused on the erosional dynamics of the land adjoining the Western Ghats before and after the extreme rainfall and mega flood event in 2018. The study area is situated on the windward side of the Western Ghats in the southwestern region of the Indian subcontinent, which is renowned for its orographic rainfall patterns. To assess the temporal soil loss / temporal soil erosion (TSE), the Revised Universal Soil Loss Equation (RUSLE) was used. The variables of soil erodibility (K) and slope length and steepness (LS) were held constant, while the factors of rainfall erosivity (R) and cover management (C) were analyzed using datasets from the years 2017, 2018, and 2022 (referred to as TSE 1, TSE 2, and TSE 3, respectively). The analysis revealed the impact of the extreme rainfall event on the soil erosional characteristics of the study area. There was a noticeable variation in the rate of annual soil loss and erosion severity across the region. Notably, during the time frame of TSE 2, both the maximum value of annual soil loss (527 t. ha− 1. y− 1) and the mean soil loss (6.20 t. ha− 1. y− 1) experienced a remarkable increase compared to TSE 1 and TSE 3. Additionally, areas with high soil erosion severity witnessed a 47-fold increase, while areas with low erosion severity exhibited a significant reduction in the year 2018. However, in TSE 3, despite receiving higher rainfall, the areas with higher erosion severity displayed a reduction, indicating a re-stabilization of the terrain against erosion. The analysis also highlighted the crucial role of extreme rainfall in influencing the severity of erosion in the study area. In comparison to TSE 1, TSE 2 experienced a significant threefold increase in rainfall erosivity, reaching a peak value of 33,711 MJ mm ha− 1 h− 1 year− 1, whereas it was only 13,806 MJ mm ha− 1 h− 1 year− 1 in TSE 1. Similarly, in TSE 3, the highest erosivity of rainfall was documented at 34,901 MJ mm ha− 1 h− 1 year− 1, accompanied by a maximum annual rainfall of 4853 mm. The rise in erosivity value was primarily attributed to the intense and severe fluctuations in monthly rainfall. However, when comparing TSE 3 with TSE 2, the overall erosion was found to be decreased. These findings indicate that the severity of erosion observed in 2018 (TSE 2) was a consequence of the extreme rainfall events that occurred within a short period of time. This trend continued in subsequent years with varying severity, as the state witnessed various extreme rainfall events. While the research successfully provided insights into the spatial and temporal dynamics of erosion severity in the region, there is a growing necessity to investigate the long-term and decadal variability in rainfall erosivity in the study area. Such an investigation is essential for the formulation of effective measures to control and mitigate soil erosion in the state. Consequently, these results also emphasize the importance of implementing an effective land use policy to address the adverse impacts of the impending climate change related extreme weather events. [ABSTRACT FROM AUTHOR]

Empirically based approaches, like the Universal Soil Loss Equation (USLE), are appropriate for estimating mass movement attributed to rill erosion. USLE and its associates become widespread even in spatially extended studies in spite of its original plot-level concept, as well as with certain constraints on the supply of suitable input spatial data. At the same time, there is a continuously expanding opportunity and offer for the application of remote sensing (RS) imagery together with machine learning (ML) techniques to model and monitor various environmental processes utilizing their versatile benefits. The present study focused on the applicability of data-driven geospatial models for predicting soil erosion in three vineyards in the Upper Pannon Wine Region, Central Europe, considering the seasonal variation in influencing factors. Soil loss was formerly modeled by USLE, thus providing non-observation-based reference datasets for the calibration of parcel-specific prediction models using various ML methods (Random Forest, eXtreme Gradient Boosting, Regularized Support Vector Machine with Linear Kernel), which is a well-established approach in digital soil mapping (DSM). Predictions used spatially exhaustive, auxiliary, and environmental covariables. RS data were represented by multi-temporal Sentinel-2 satellite imagery data, which were supplemented by (i) topographic covariates derived from a UAV-based digital surface model and (ii) digital primary soil property maps. In addition to spatially quantifying soil erosion, the feasibility of transferring the inferred models between nearby vineyards was tested with ambiguous outcomes. Our results indicate that ML models can feasibly replace the empirical USLE model for erosion prediction. However, further research is needed to assess model transferability even to nearby parcels. [ABSTRACT FROM AUTHOR]

Non-point source pollution poses a significant threat to global water security, and risk assessment and key source area (CSA) identification are critical for its management. While source–sink landscape models are widely used for non-point source pollution evaluation, their application in karst regions is challenged by ecological fragility, shallow soil layers, and severe soil erosion, limiting their effectiveness in accurately identifying pollution risks and CSAs. This study focuses on the Caohai Lake basin in southwestern China; it integrates the landscape-weighted load index (LWLI) and the universal soil loss equation (USLE) to assess non-point source pollution risks in the basin with the aim of precisely delineating critical source areas (CSAs). Total phosphorus (TP) and total nitrogen (TN) served as key predictors of water quality, and their responses to the LWLI and USLE were analyzed in the karst environment. The results revealed the following: (1) source landscapes cover 65% of the basin area, with cropland (40%) being the primary contributor to nitrogen pollution; (2) the LWLI and USLE explain 50–67% of the TP and TN variations during the wet season, with a sharp increase in water quality risk when the LWLI exceeds 0.75; and (3) high-risk and very high-risk areas account for 36.3% and 15.3% of the basin, respectively, and are concentrated in the northwest and south, where intensive agriculture and severe soil erosion dominate. These findings provide a scientific basis for non-point source pollution control in the Caohai Lake basin. [ABSTRACT FROM AUTHOR]

Open-pit mining disturbs the earth's surface, impacts geological characteristics, and releases many pollutants including heavy metals, radionuclides, and poisonous gases into the environment. This study investigated the difference between the activity levels of 226Ra radionuclide in the abandoned quarry region (region A) and the active quarry region (region B). In the surface soil, the mean values of activity concentrations were 40 Bq kg−1 and 55 Bq kg−1 in region A and region B, respectively. The statistical analysis shows that the obtained values of 226Ra concentrations in Region B are higher and more dispersed than those in Region A. For four study quarry lakes, the ranges of activity concentrations in water and sediment were (0–19.4 mBq L−1) and (25–71 Bq kg−1), respectively. The levels of 226Ra in lake water are within the recommended value of 0.74 Bq L−1 of the United States Environmental Protection Agency (USEPA) for radiation safety. This water can be considered for drinking, agricultural irrigation, and fish farming. The Revised Universal Soil Loss Equation (RUSLE) estimated the soil loss due to soil erosion in the abandoned quarries. It was predicted that the soil erosion degrees in the study area were from very low class to moderately high class according to soil loss classification. The Quantitative Water, Air, Sediment Interaction (QWASI) was validated to predict 226Ra activity concentrations in the water and sediment of quarry lakes. Based on Student's t-test, the predicted values of 226Ra activity concentrations for study quarry lakes agreed with the measured values (confidence interval 95%). [ABSTRACT FROM AUTHOR]

Soil erosion is a widespread issue causing land degradation globally.This study aims to determine the spatial distribution of annual soil erosion through the utilization of the Revised Universal Soil Loss Equation (RUSLE) model in a sub-basin, situated in the southwestern region of Pakistan. To accomplish this, numerous data mining techniques were employed, along with machine learning algorithms, to produce thematic layers (K, R, LS, C, and P) that served as input parameters for the RUSLE model. According to the resultant model, soil erosion in the study area ranged from 0.00 to 866 tons per hectare per year. The estimated values for rainfall-runoff erosivity (R), soil erodibility(K), topography (LS), and cover management (C), factors ranged from 147 to 191 (MJ.mm.ha-1.h-1year-1), 0.0229 to 0.0259 (t.ha.MJ-1mm-1), 0.002 to 360.77, and 0.001 to 1, respectively. The statistics revealed that 58% of the land in the study area experiences a very low degree of soil erosion, at an erosion rate less than 13.58 t/ha/year. About 24% of the study area faces low erosion, with an erosion rate spanning from 13.58-44.16 t/ha/year. 13% of the area is demarcated as moderate soil erosion severity, at an erosion rate ranging from 44.16-81.53.14 t/ha/year. On the other hand, 5% of the study area experienced high to very high soil erosion, with an erosion rate of 81.53-866.34 t/ha/year. The northern part (Takatu range), north-eastern part (zarghoon range) eastern-central (Murder range), and western-southern (Chiltan range), which are characterized by steep slopes and barren land, experience high to very high severty of soil erosion.Conclusivily Remote Sensing and GIS, in combination with the RUSLE model, are significant for identifying input factors for modeling soil erosion and resource management. This study will assist policymakers in pinpointing the erosionprone areas in the hill tracts that urgently need soil conservation measures.Additional lack in upto-date field data himpered in model validation, hence is recommended to conduct field based studies to validate the model-derived results and create a reliable soil erosion database for the study area. [ABSTRACT FROM AUTHOR]

Waduk Mrica merupakan infrastruktur vital yang berperan sebagai Pembangkit Listrik Tenaga Air (PLTA), sumber irigasi, pengendali banjir di bagian hilir, dan perikanan. Kemampuan waduk untuk menjalankan fungsinya tergantung pada kapasitas penyimpanannya, yang dipengaruhi oleh tingkat sedimentasi akibat erosi pada Daerah Aliran Sungai (DAS). Untuk mengatasi masalah pendangkalan dan dampak erosi tanah, diperlukan pemahaman mendalam mengenai tingkat bahaya erosi dan nilai sedimentasi dalam periode waktu tertentu. Penelitian ini bertujuan memberikan gambaran menyeluruh mengenai laju erosi di DAS Serayu dan dampaknya terhadap pendangkalan Waduk Mrica dalam rentang waktu 2018-2022 dengan memanfaatkan citra satelit Sentinel-2. Penelitian meliputi prediksi laju erosi DAS Serayu menggunakan metode Universal Soil Loss Equation, kemudian dilakukan estimasi kedalaman Waduk Mrica dengan algortima Random Forest yang dikalibrasi dengan data perekaman batimetri in situ. Hasil penelitian ini menunjukkan estimasi kedalaman menggunakan citra Sentinel-2 mampu memberikan informasi kedalaman 0-25 m di Waduk Mrica pada periode 2018-2022, dengan nilai R2 pada rentang 0,83-0,92, MAE pada rentang 0,367-0,593, RMSE pada rentang 0,819-1,401 m. Hasil penelitian menunjukkan tren penurunan total volume Waduk Mrica dari tahun 2018 hingga 2022, menandakan berkurangnya kapasitas penyimpanan waduk. Terjadi variasi laju erosi, total erosi, dan sediment yield di DAS Serayu selama periode tersebut. Sub DAS Serayu Hulu secara konsisten menjadi kontributor terbesar terhadap total erosi dibanding Sub DAS lainnya. Terdapat hubungan antara sedimen hasil erosi DAS Serayu dan pendangkalan Waduk Mrica pada tahun 2019 dan 2020, tetapi pada tahun 2021 dan 2022 terjadi ketidaksesuaian di mana sedimen DAS meningkat sedangkan sedimen waduk menurun yang disebabkan adanya tindakan pengendalian sedimen waduk.

ABSTRACT Rainfall erosivity is one of the most dynamic factors in the soil erosion process. The increase in soil erosion caused by high rainfall erosivity, and the subsequent loss of soil nutrients, can lead to reduced food production and ecosystem services. This research program under the New South Wales (NSW) Climate Change Adaptation Strategy, assesses rainfall pattern change, rainfall erosivity and erosion risk across NSW under future climate conditions. Daily rainfall erosivity and erosion risk were modelled by Revised Soil Loss Universal Equation (RUSLE) approach and compared with that driven by observed rainfall data. Future rainfall erosivity and soil erosion risk change were investigated from daily precipitation projection of the updated NSW and Australian Regional Climate Modelling (NARCliM1.5) for two future scenarios, RCP4.5 and RCP8.5, from the historical (1986–2005) to far future (2060–2079) periods. The annual average rainfall erosivity is projected to increase about 8% under RCP 4.5 and further decrease 5% under RCP 8.5 in NSW due to the predicted temperature rises. More frequent heavy rainfall events are projected to occur during summer (December–January–February), and the rainfall from these extreme rainfall events is expected to account for 51% of the total annual rainfall in the far future. NARCliM‐derived results underestimate annual rainfall erosivity compared with observation‐derived erosivity. There are greater instability (root mean squared error [RMSE]: 803.2) and erosivity uncertainty (Bias: 16%~48%) in high rainfall zones. At a monthly scale, dry months (June–July–August) are becoming drier, while wet months (December–January–February) are becoming wetter and more erosive. 67% of NSW is predicted to experience increased rainfall erosivity under RCP4.5, whereas most of NSW will shift to drought and its consequent effects under the high‐end emission scenario (RCP 8.5). To address the dual challenges of excessive wetness in coastal and north‐east NSW and increasing aridity in Western NSW, it is necessary to develop climate change adaptation management strategies based on high‐risk areas and monthly or seasonal conditions. With the emerging launch of NARCliM2.0, we anticipate further improvements of these predictions will be achieved by more accurate models and data at higher spatial and temporal resolutions. [ABSTRACT FROM AUTHOR]

Estimating sediment yield in a river is a challenging task in the water resources field. Different methods are available for estimating sediment erosion and yield, but generally they are not spatially distributed in nature. This paper presents the application of the Revised Universal Soil Loss Equation (RUSLE) for estimating soil erosion and integrates it with spatially distributed Sediment Delivery Ratio (SDR) to calculate sediment yield in a Himalayan river. The study area is Kabeli sub-catchment, located upstream of the Koshi River Basin in the eastern part of Nepal. The Kabeli River is where numerous hydropower projects are envisaged, and sediment-related issues are of major concern. With the use of the RUSLE, the mean annual soil erosion is estimated at 35.96 tons/ha/yr. The estimated specific sediment yield (SSY) from the distributed SDR method is 6.74 tons/ha/yr, which is close to the observed SSY of 7.26 tons/ha/yr using the data records of ~8 years. Based on correlation analysis, the topographic factor ( L S ) is the most sensitive RUSLE parameter with respect to sediment erosion. The sloping areas near the river hillslope are particularly vulnerable to soil erosion. The results indicate that the approach employed in this study may be potentially applied in other catchments with similar physiographic characteristics for the estimation of sediment yield. [ABSTRACT FROM AUTHOR]

Soil erosion is a major issue in the Indian Himalayan region, affecting both mountainous areas and the Terai. In the Terai, significant surface soil loss is driven by factors such as sandy soils, shallow soil depth, high rainfall, and the erosive force of young rivers. Human activities, including the conversion of forests and grasslands to croplands and settlements, along with poor agricultural practices, exacerbate the problem. This pilot-scale study aimed to quantify surface soil erosion and the loss of soil organic matter and nutrients in a watershed of the eastern Himalayan Terai region of India, utilizing the Revised Universal Soil Loss Equation (RUSLE) model on a Geographic Information System (GIS) platform. The results revealed substantial soil loss ( x ¯ = 32.0 Mg ha−1 yr−1), along with the removal of organic matter ( x ¯ = 0.95 Mg ha−1 yr−1), available nitrogen ( x ¯ = 1.49 kg ha−1 yr−1), available phosphorus (P2O5) ( x ¯ = 0.50 kg ha−1 yr−1), and available potassium (K2O) ( x ¯ = 5.02 kg ha−1 yr−1). Ground surveys indicated that a significant portion of the local population was directly or indirectly affected by the annual loss of fertile topsoil, with farmers, agricultural workers, and tea garden owners being the most impacted. The erosion problem of Terai region remains unheard of as it does not cause direct damage like landslides. However, loss of topsoil every year declines the land productivity and curbs the agricultural financial benefit margin. The study recommends expanding soil erosion monitoring and modelling across the entire eastern Himalayan Terai region. Being a cost and time friendly reliable method, use of RUSLE on the GIS platform can be the best option for that. With updated erosion data, comprehensive management measures can be developed involving policymakers, administrators, researchers, and local communities. [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]

The study predicted the soil erosion map of the Coimbatore district, Western part of Tamil Nadu, India, to assess the potential of Digital Soil Mapping (DSM) against the empirical model, Revised Universal Soil Loss Equation (RUSLE). A total of 386 surface soil samples were collected and their properties used to predict soil erosion spatially. The Geographical Information System (GIS) enabled RUSLE factors to be derived spatially and integrated for annual soil loss estimation. The soil loss values calculated based on the RUSLE factors ranged from 0 to 88.58 t ha–1 yr–1. The spatial soil erosion was further predicted based on the SCORPAN model using the decision tree methodology to compare the suitability of the approaches in erosion mapping. The modelling tool, Cubist, was adopted to model the continuous soil erosion properties with 33 environmental covariates as independent variables. The LS factor, profile curvature, geology, mean annual rainfall, maximum air temperature, land use, minimum air temperature, elevation, slope and geomorphology covariates were the most influencing variables in predicting soil loss. The Cubist model's rules are applied to the NLCD-Cubist classifier to generate a soil erosion map, and the annual soil loss ranged from 0 to 52.54 t ha–1 yr–1. The spatial pattern of soil erosion classes indicated that the Coimbatore district's western part has a high erosion risk. Based on the validation sample points (n = 190), the DSM approach showed a higher concordance correlation (r = 0.857), whereas the RUSLE model showed less agreement (r = 0.632). From the concordance correlation values, it could be inferred that the decision tree algorithm performed better than the RUSLE erosion model. However, from the error map generated, it is advisable to have more soil observations with suitable environmental covariates to improve digital soil erosion mapping accuracy. The DSM approach is exploited well with point-based soil erosion loss information. The RUSLE model would perform well if the R, K, LS, C, and P factor maps were available at uniform scale and spatial resolution. [ABSTRACT FROM AUTHOR]

[Objective] This study aims to explore the application of traditional mixed pixel decomposition method in drone remote sensing technology, and to propose a fast estimation method for vegetation cover and management factor (C factor) at a small scale. [Methods] Drone aerial photography was used to capture remote sensing imagery of land use in the Guli area of Jiangning, Nanjing. An object-oriented classification method was utilized to extract the coverage of various land types. The C value of the research area was computed based on the mixed pixel decomposition C factor model, and the method′s accuracy was evaluated by comparing it with previous research results. [Results] The object classification results (vegetation, bare land, and non-photosynthetic land) have an overall accuracy of over 95%. Based on the mixed pixel decomposition C factor model, the estimated C values for forestland, cultivated land, and grassland in the Guli area are 0.057, 0.176, and 0.043, respectively, which are close to existing research results. [Conclusion] Estimating C factor based on UAV remote sensing is feasible and more efficient compared to the traditional method of runoff plot observation. [ABSTRACT FROM AUTHOR]

Objective］ The study of spatial-temporal variations and influencing factors of water-related ecosystem services under complex terrain in Southwest China can serve as a foundation for the ecological management and sustain able development in this region. Methods］ The annual water yield and soil erosion in Southwest China from 1 992 to 2020 were evaluated to explore the multi-dimensional (including time, space and elevation) variation characteristics by the Tn VEST model and universal soil loss equation. Tn ad dition, the relative weight analysis method was used to quantify the relative contribution rate of climate and land use change to water-related ecosystem services. Results］ (1) From 1992 to 2020, water yield in Southwest China showed a downward trend, with a notable mutation occurring in 2002; soil erosion showed an increasing trend, with a mutation observed in 1 997. (2) The water yield decreased and soil erosion increased with rising altitude. (3) The relative contribution rate of precipitation to both water yield and soil erosion in karst areas was found to be greater than that in non-karst areas. Conclusion］ Climate change has a more significant impact on water yield and soil erosion, while land use change has a less obvious impact. This study will contribute to the ecological protection in complex terrain (with different elevations and different land forms) and provide a scientific basis for land management and ecological restoration measures in karst areas. [ABSTRACT FROM AUTHOR]

Global sensitivity analysis (GSA) of the revised universal soil loss equation (RUSLE) factors is in its infancy but is crucial to rank the importance of each factor in terms of its non-linear impact on the soil erosion rate. Hence, the goal of this study was to perform a GSA of each factor of RUSLE for a soil erosion assessment in southern Bahia, Brazil. To meet this goal, three non-linear topographic factor (LS factor) equations alternately implemented in RUSLE, coupled with geographic information system (GIS) software and a variogram analysis of the response surfaces (VARSs), were used. The results showed that the average soil erosion rate in the Pardo River basin was 25.02 t/ha/yr. In addition, the GSA analysis showed that the slope angle which is associated with the LS factor was the most sensitive parameter, followed by the cover management factor (C factor) and the support practices factor (P factor) (CP factors), the specific catchment area (SCA), the sheet erosion (m), the erodibility factor (K factor), the rill (n), and the erosivity factor (R factor). The novelty of this work is that the values of parameters m and n of the LS factor can substantially affect this factor and, thus, the soil loss estimation. [ABSTRACT FROM AUTHOR]

The article aimed to identify areas vulnerable to soil loss and erosion in the Serra Dourada State Park and its two-kilometer surroundings, with the additional aim of verifying whether the delimitation of the Conservation Unit had a preservation effect. The years 1985, 2003 and 2022 were adopted as a time frame, therefore considering a time frame before and after the 2003 park creation decree. Three soil loss diagnoses were generated from the application of the Universal Loss Equation of Soil (USLE). It was observed that the main changes in soil loss classes were due to the conversion of areas with natural cover and appropriation for agricultural activities. Through visual inspection of satellite images, 294 erosion points were identified, of which 121 are within the park and 26 are critical erosions. The results show that the creation of the Conservation Unit was not enough to guarantee environmental preservation, however, it reduced the advancement of degradation over space. [ABSTRACT FROM AUTHOR]

Human activities have significant influence on soil erosion in karst areas. The spatial and temporal evolution of soil erosion in Guizhou Province was evaluated using the Revised Universal Soil Loss Equation (RUSLE), which revealed an increasing trend in the initial data analysis for the soil erosion modulus. To disclose the impact of human activities on regional soil erosion, the soil erosion in 2000, 2010, and 2020 was analyzed. The results show the following: (1) The average values of the soil erosion modulus in the study area for 2000, 2010, and 2020 were 4.479, 4.945, and 5.806 t·hm−2·a−1, respectively; when considering human activities without the influence of rainfall erosivity, these values were 4.679, 4.963, and 4.799 t·hm−2·a−1. The influence of human activities on soil erosion is gradually becoming a positive force. (2) According to the Spearman regression analysis, the top four factors related to soil erosion in 2000 and 2010 were soil loss risk (E, 0.721 and 0.737), anti-erosion factors (Pr, − 0.236 and − 0.221), rock exposure rate (0.222 and 0.279), and altitude (0.210 and 0.195). In 2020, the top four factors were Pr (0.725), land surface temperature (LST, 0.268), NDVI (− 0.232), and E (0.186). In the first two stages, soil erosion is closely related to natural factors, while in 2020, soil erosion is more closely related to human activities. (3) The geographically weighted regression (GWR) showed the highest range of regression coefficients for Pr (150), followed by E and NDVI (25), rock exposure rate (10), and land surface temperature (LST) (1.5). The rainfall erosivity is increasing annually as a consequence of global climate change. This rise in rainfall erosivity has resulted in a corresponding increase in soil erosion in the study area, which obscures the positive impact of human activities in the reduction of soil erosion. [ABSTRACT FROM AUTHOR]

Water erosion is a significant global threat due to the high soil loss rate and all its consequent implications. Technologies to predict erosion are strongly related to measurements and vice versa. Measurements can simply provide empirical evidence of the erosion process and are hard to extrapolate in time and space. Measurements were used to develop some erosion models, such as the Universal Soil Loss Equation (USLE), and also for their calibration and validation. Several measurement techniques are used to collect soil erosion data at different spatial and temporal scales, but they cannot be considered fully accurate in any experimental condition. Each technique exhibits advantages and disadvantages, so extensive knowledge of their feasibility, accuracy, and limitations is required to correctly plan experiments and use the performed measurements. In this paper, recent scientific developments on the measurement of rainfall erosivity, soil loss at the plot scale, and rill and gully erosion using close-range photogrammetry are presented. Further considerations are made on the quality of soil erosion measurements and the usefulness and importance of measuring plot soil loss. Our critical analysis highlighted that the techniques reported in the literature are a solid basis, which, however, should be developed to improve their range of applicability and data quality. [ABSTRACT FROM AUTHOR]

The Brazilian Savanna (Cerrado) is the second‐largest South American biome that corresponds to almost two‐third of the national agricultural production. Extensive agricultural‐driven land‐use changes have significantly altered the landscape, causing increased soil erosion. Furthermore, projections of climate change effects on the Cerrado raise concerns about the potential exacerbation of soil loss and its consequences on ecosystem sustainability. This study investigated soil loss for the Cerrado ecosystem by assessing current rates and projecting the potential effects of future climate change. Current soil loss was based on experimental plots (100 m2) during 7 years maintained under typical main land cover in Brazil (sugarcane, pasture, Cerrado, and bare soil). Erosivity, by using the Universal Soil Loss Equation (USLE), was estimated from observations, parameters of erodibility, and land cover. To assess the future soil loss (2100), we used the calibrated USLE equation with yearly erosivity derived from 12 downscaled and bias‐corrected SSP2‐4.5 and SSP5‐8.5 scenarios of CMIP6 climate model projections. Current agricultural practices induce considerable erosion, where sugarcane has 3.4 times higher soil loss as compared with the natural soil cover. Regarding future SSP2‐4.5 and SSP5‐8.5 scenarios (2100), we estimated an increase of 4.9% and 7.6% in soil loss, respectively, for all land covers. The observed soil loss rates underscore the critical importance of implementing sustainable land management practices to mitigate further soil degradation. Climate change may impose additional stress on the Cerrado ecosystem, amplifying the urgency for adaptive measures to safeguard this important biome. [ABSTRACT FROM AUTHOR]

It is of common experience that the sizeable portion of sediment yield generated over a period in a catchment occurs largely due to only a few extreme storm events rather than the numerous small rain events, suggesting the vital role of rain duration besides its magnitude. This paper presents a novel empirical approach based on the Soil Conservation Service Curve Number equation and Universal Soil Loss Equation coupled with a sediment yield model to predict sediment yield resulting from a storm of a certain duration (t) and return period (T). To this end, an empirical relation relating potential erosion (A) with 'T' and 't' is proposed and calibrated/validated using historical rainfall-runoff-sediment series data from ten Indian and 12 United States (US) watersheds, respectively. It is calibrated with a low nRMSE, high NSE, and very less PBIAS values in all the sub-watersheds (mean nRMSE ≤ 0.285, NSE ≥ 0.84 and PBIAS < ± 10%). In validation, the proposed approach shows an excellent performance in 9 of the 10 sub-watersheds of India (0.77 ≤ NSE ≤ 0.98, 0.85 ≤ R2 ≤ 0.99 and PBIAS ≤ ± 20%) a good performance in 8 out of 12 sub-watersheds of the USA (0.50 ≤ NSE ≤ 0.97, R2 > 0.91 and PBIAS ≤ ± 30%). Finally, a correlation between the calibrated empirical parameters of the proposed relation and the catchment characteristics was ascertained, which showed that the size and stream length influence these parameters more in large sub-watersheds, while slope and stream length influence more in small sub-watersheds. [ABSTRACT FROM AUTHOR]

In the highland areas of Ethiopia, farming communities are struggling with severe soil erosion, which is putting significant strain on ecosystems and food production capabilities. Many smallholder farmers in the basin cultivate fragile soils on steep slopes, often without proper management practices, leading to substantial soil loss, ecosystem degradation, and economic setbacks. The study aimed to assess the impact of soil–water conservation (SWC) practices on controlling soil erosion and enhancing soil nutrients in the Abay Basin. Utilizing Landsat 5 TM, Landsat 7 ETM+, and Sentinel 2 sensors available through Google Earth Engine (GEE), the study analyzed historical and recent land use and land cover changes (LULCC). The revised universal soil loss equations (RUSLE) model within the Integrated Valuation of Ecosystem Services and Trade‐Offs (InVEST) application was used for the analysis. The findings indicated that the mean soil loss values were 33.2 tons/ha in 2007, 48.6 tons/ha in 2012, 32.1 tons/ha in 2017, and 31.8 tons/ha in 2022. The higher mean soil loss in 2012 was attributed to significant human intervention in the sample watersheds after 2012, which impacted natural ecosystems. The widespread adoption of sustainable land management (SLM) systems was hindered by factors such as the labor‐intensive nature of the technologies, difficulty in identifying the best SWC technologies, and a lack of continuous follow‐up and maintenance for physical SWC technologies. The study emphasized that these challenges were largely due to a lack of commitment and weak community participation during the planning and implementation stages. Therefore, it is crucial to enhance the involvement of residents and nongovernmental organizations in conservation efforts. The study concluded that expanding the scope and scale of local community participation is essential for future SWC interventions. [ABSTRACT FROM AUTHOR]

Soil erosion is one of the main issues that endangers global ecosystems. This study explored the spatiotemporal distribution of soil erosion and its drivers in the karst plateau mountainous region. A detailed examination of topography, soil, vegetation, land use, and precipitation data from 2000 to 2020 was conducted in Bijie City using the revised universal soil loss equation model. We also explored the driving forces using a geographical detector. The findings show that between 2000 and 2020, soil erosion first decreased, followed by an increase. The southwest, south, and northern regions contained the highest intensity of soil erosion. Land use, slope, and precipitation are the primary factors influencing soil erosion, with slopes having the greatest impact. By improving our understanding of the dynamics of soil erosion and the primary variables that influence it in karst plateau mountainous environments, our findings can assist in the development of strategies and technical support for sustainable soil and water conservation. [ABSTRACT FROM AUTHOR]

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]

The Yellow River Basin (YRB) is an important grain production base, and exploring the spatiotemporal heterogeneity and driving factors of soil erosion in the YRB is of great significance to the ecological environment and sustainable agricultural development. In this study, we employed the Revised Universal Soil Loss Equation (RUSLE) in conjunction with Transport-Limited Sediment Delivery (TLSD) to explore a modified RUSLE-TLSD for use assessing net water erosion. This modification was performed using sediment data, and the explanatory power of driving factors was assessed utilizing an optimal parameters-based geographical detector (OPGD). The results demonstrated that the modified RUSLE-TLSD can accurately simulate the spatiotemporal distribution of net water erosion (NSE = 0.5766; R2 = 0.6708). From 2000 to 2020, the net water erosion modulus in the YRB ranged between 1.62 and 5.33 t/(ha·a). Specifically, the net water erosion modulus decreased in the YRB and the middle reaches of the YRB (MYRB), but it increased in the upper reaches of the YRB (UYRB). The erosion occurred mainly in the Loess Plateau region, while the deposition occurred mainly in the Hetao Plain and Guanzhong Plain. The Normalized Difference Vegetation Index (NDVI) and slope emerged as significant driving factors, and their interaction explained 31.36% of YRB net water erosion. In addition, the redistribution of precipitation by vegetation and the slope weakened the impact of precipitation on the spatial pattern of net water erosion. This study provides a reference, offering insights to aid in the development of soil erosion control strategies within the YRB. [ABSTRACT FROM AUTHOR]

The Massarosa wildfire, which occurred in July 2022 in Northwestern Tuscany (Italy), burned over 800 hectares, leading to significant environmental and geomorphological issues, including an increase in soil erosion rates. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates with a multi-temporal approach, investigating three main scenarios: before, immediately after, and one-year post-fire. All the analyses were carried out using the Google Earth Engine (GEE) platform with free-access geospatial data and satellite images in order to exploit the cloud computing potentialities. The results indicate a differentiated impact of the fire across the study area, whereby the central parts suffered the highest damages, both in terms of fire-related RUSLE factors and soil loss rates. A sharp increase in erosion rates immediately after the fire was detected, with an increase in maximum soil loss rate from 0.11 ton × ha−1 × yr−1 to 1.29 ton × ha−1 × yr−1, exceeding the precautionary threshold for sustainable soil erosion. In contrast, in the mid-term analysis, the maximum soil loss rate decreased to 0.74 ton × ha−1 × yr−1, although the behavior of the fire-related factors caused an increase in soil erosion variability. The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables. [ABSTRACT FROM AUTHOR]

The Karst plateau region has a unique natural erosion environment and sharp human–land conflicts. This study selected Bijie City, Northwest Guizhou, as the study area. To quantitatively analyze the human and natural impacts on soil erosion in this area, this paper evaluated the anthropogenic and natural soil erosion based on the Revised Universal Soil Loss Equation (RUSLE) coupled with the Quantitative Analytical Model (QAM). The results showed the following: (1) the total soil erosion modulus in the study area showed an increasing trend: 37.86 t/(ha·a) in 2010, 42.12 t/(ha·a) in 2015, and 48.67 t/(ha·a) in 2020; (2) human activities reduced soil erosion, with an anthropogenic soil erosion modulus of −13.79 t/(ha·a) in 2015 and −17.36 t/(ha·a) in 2020, indicating that human activities, such as projects of returning farmland to forests and rocky desertification control, played a key role in decreasing soil erosion in the study area.; and (3) the percentage of the area of soil erosion deterioration dominated by natural factors (AGN) is gradually decreasing, 89.47% in 2015 and 81.85% in 2020; the percentage of the area of soil erosion deterioration dominated by human activities (AGH) is increasing from 6.17% in 2015 to 13.80% in 2020; and the percentage of the area of soil erosion mitigation caused by human activities (ALH) and the area of soil erosion not affected by natural and human activities (NNH) showed no significant change. This result suggests more attention should be paid to the area of AGH to control soil erosion. This study analyzed the roles of natural factors as well as human activities in the Karst plateau, enriched the application scope of the QAM, and provided new ideas for theoretical research in this field. [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]

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 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]

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]

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 document discusses the importance of soil carbon sequestration in forest ecosystems to achieve carbon neutrality and mitigate climate change. It highlights the impact of nitrogen deposition on soil organic carbon and the role of thinning intensities in influencing soil carbon cycling. Additionally, the document addresses soil erosion risks and the need for sustainable management practices to protect vulnerable areas. Overall, the research presented in the document aims to enhance understanding of forest ecosystem dynamics and promote sustainable development practices. [Extracted from the article]

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]

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]

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 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]