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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., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Soil loss is aggravated by uncontrolled deforestation, indiscriminate land clearing for agricultural activities, overgrazing, and urban development that leads to severe soil erosion over the land surface. The main objective of this research is to apply the Revised Universal Soil Loss Equation (RUSLE), in conjunction with remote sensing and GIS, to determine the temporal variation of soil loss from the Gubi watershed in the years 2000 and 2017 and to estimate the sediment delivery into the Gubi reservoir in Northern Nigeria. Datasets of rainfall, soil type, topography, cover management, and support practice were utilized to determine the five RUSLE factors. The research identified five levels of erosion ranging from severe to very low-risk areas. The spatial distribution of erosion hotspots in the catchment area was highlighted. Results revealed increasing levels of erosion from 14,244.31 tons/year in 2000 to 16,792.33 tons/year in 2017. Consequently, the catchment's sediment yield was 6903.65 tons/year and 9172.71 tons/year corresponding to sediment delivery ratios of 48.46 % and 54.62 % in 2000 and 2017 respectively. The sediment inflow into Gubi reservoir was 747.38 tons/year and 950.76 tons/year corresponding to 10.83 % and 10.37 % of the total sediment yield in 2000 and 2017 respectively. Soil erosion vulnerability index maps were produced and the erosion is expected to increase with time as agricultural activities and deforestation continue to occur. The sediment deposits have the potential to reduce the designed life of the reservoir. Best management practices, such as tree planting, mulching, and contour ridging are recommended for soil conservation. There is a need to assess the reservoir sediment deposits so that measures can be taken to maintain the reservoir operations properly., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

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., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Ethiopia is currently facing a major environmental problem caused by soil erosion. In order to tackle this problem, it is essential to implement a comprehensive watershed management approach and give priority to conservation efforts depending on the level of severity. Therefore, the objective of this research is to evaluate the mean annual soil erosion and rank the sub-watersheds for conservations in the Ayu watershed, utilizing the Revised Universal Soil Loss Equation (RUSLE) model and the Sub-Watershed Prioritization Tool (SWPT). RUSLE was utilized to predict the annual average soil erosion rate, while SWPT was applied to conduct Weighted Sum Analysis (WSA) for ranking sub-watersheds. Support Vector Machine (SVM) was employed for classifying land use and land cover. The Relative importance of morphometric and topo-hydrologic features in the SWPT was analyzed using a Random Forest model. The Bland-Altman plot and Wilcoxon Signed Rank Test were employed to assess the agreement in prioritizing watersheds between RUSLE results and the SWPT. Furthermore, field observations were conducted to validate the land use classification by collecting ground data. In addition, the study was enhanced with local viewpoints by conducting focus group discussions with agricultural experts and farmers to obtain qualitative insights and validation of resuts. The findings showed that soil loss varied from 0 to 110 t/ha/yr, with an average of 8.95 t/ha/yr, resulting in a total loss of 384365.3 tons annually. The comparison of RUSLE and SWPT showed a moderate positive relationship (r = 0.59). The results of the Bland-Altman plot indicate a consistent agreement between the two methods. However, there is inconsistency among the five sub watersheds. This study enhances the knowledge of soil erosion patterns and offers useful guidance for watershed conservation techniques. It can be also used as a beneficial framework for managing watersheds, with possible uses outside of the Ayu watershed., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

As an effect of forest degradation, soil erosion is among Ethiopia's most pressing environmental challenges and a major threat to food security where it could potentially compromise the ecosystem functions and services. As the effects of soil erosion intensify, the landscape's capacity to support ecosystem functions and services is compromised. Exploring the ecological implications of soil erosion is crucial. This study investigated the soil loss and land degradation in the Lake Abaya catchment to explore forest landscape restoration (FLR) implementation as a possible countermeasure to the effects. The study used a geographic information system (GIS)-based approach of the Revised Universal Soil Loss Equation (RUSLE) to determine the potential annual soil loss and develop an erosion risk map. Results show that 13% of the catchment, which accounts for approximately 110,000 ha, is under high erosion risk of exceeding the average annual tolerable soil loss of 10 t/ha/year. Allocation of land on steep slopes to crop production is the major reason for the calculated high erosion risk in the catchment. A scenario-based analysis was implemented following the slope-based land-use allocation proposal indicated in the Rural Land Use Proclamation 456/2005 of Ethiopia. The scenario analysis resulted in a reversal erosion effect whereby an estimated 3000 t/ha/year of soil loss in the catchment. Thus, FLR activities hold great potential for minimizing soil loss and contributing to supporting functioning and providing ecosystem services. Tree-based agroforestry systems are among the key FLR measures championed in highly degraded landscapes in Ethiopia. This study helps policymakers and FLR implementors identify erosion risk areas for future FLR activities. Thereby, it contributes to achieving the country's restoration commitment., (© 2024. The Author(s).)

The study investigates the influence of multispectral satellite data's spatial resolution on land degradation in the Urmodi River Watershed in which Kaas Plateau, a UNESCO World Heritage site, is located. Specifically, the research focuses on soil erosion and its risk zonation. The study employs Landsat 8 (30-m resolution) and Sentinel-2 (10-m resolution) data to assess soil erosion risk. The Revised Universal Soil Loss Equation (RUSLE) is used to quantify the average annual soil erosion output denoted by (A), by using its factors such as rainfall (R), soil erodibility (K), slope-length (LS), cover management (C), and support practices (P). R-factor was computed from MERRA-2 rainfall data, K-factor was derived from field soil sample-based analysis, LS factor was from Cartosat Digital Elevation Model-based data. The C factor was derived from NDVI of Landsat 8 and Sentinel-2, and the P factor was prepared from LULC derived from Landsat 8, and Sentinel-2 was incorporated in the final integration. The soil erosion hazard map ranged from slight to extremely severe. Remote sensing (RS)-based parameters like Land Use Land Cover (LULC) are derived from the Landsat 8 and Sentine-2 satellite data and used to compute the difference in the final outcome of the integration. The study found similarities in average annual soil loss (A) in plain areas, but differences in final soil erosion risk zone (A) were influenced by LULC map variations due to different cell sizes, P factor, and slope gradient. Notable differences were observed in soil erosion risk categories, particularly in high to very severe zones, with a cumulative difference of 73.85 km 2 . In addition to this, a scatterplot between the final outputs was computed and found the moderate (R 2 = 42.08%) correlation between Landsat 8 and Sentinel-2 imagery-based final average annual soil erosion (A) of RUSLE. The study area encompasses various landforms ranging from the plateau to pediplain, and in such situation, the water-led soil erosion categories vary depending on terrain condition along with its biophysical factors and, hence, need to analyze the need of such factors on the average annual soil erosion quantification. Different spatial resolution has an effect on the final output, and hence, there is a need to track this change at various spatial resolutions. This analysis highlights the significant impact of spatial resolution on land degradation assessment, providing precise identification of surface features and enhancing soil erosion risk zoning accuracy., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

With the accelerated development of urbanization, the exploration and usage of land resources is becoming more and more frequent, which leads to the decline of soil quality, resulting in a series of soil ecological issues, such as soil nutrient loss, soil quality degradation and destruction. At present, the contradiction between soil erosion and sustainable development of human society has become one of the hot issues studied by scholars. The Yellow River Basin is an important experimental area for high-quality development in China, constructing the Yellow River Ecological Economic Belt play an important role in China's regional coordinated development. Although most of the affected area of the Lower Yellow River (AALYR) is in the plain, they have a large population density and are in the historical farming area. In latest years, because of the development and transformation of modern society, their ecological environment has become more fragile and soil erosion problems has become increasingly serious. Studying and analyzing soil erosion is of vital meaning for ecological protection and can provide scientific support for soil conservation work. Depending on the data of precipitation, soil properties, land use, population, etc., this paper studies and analyzes the soil erosion in AALYR from 2000 to 2020 through the RUSLE. We found that during the 20 years the proportion of very slight and slight grade area increased, and the distribution of moderate and above erosion grade was less, mainly in Zibo, Jinan, Anyang, Zhengzhou, and Tai 'an. Nearly three quarters of the regional soil erosion grade didn't change, apart from the increase of slight grade area, the other erosion grades area showed a downward trend. We take the city, county and town zoning analysis find that as the scale decreases, the area of serious erosion grades increases, and the distribution is gradually detailed. Land use is the main influencing factor of erosion except DEM. Forestland and grassland are larger of the soil erosion in various types of land use. Through these conclusions in this paper, it is promising to provide theoretical references for the ecological environment governance and high-quality and sustainable development of great river basins of the world and similar regions., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Ltd.)

Soil erosion is a destructive consequence of land degradation caused by deforestation, improper farming practices, overgrazing, and urbanization. This irreversible effect negatively impacts the limited renewable soil resource, causing soil truncation, reduced fertility, and unstable slopes. To address the anticipation of erosion modulus resulting from long-term land use and land cover (LULC) changes, a study was conducted in the Swat District of Khyber Pakhtunkhwa (Kpk), Pakistan. The study aimed to predict and evaluate soil erosion concerning these changes using remote sensing (RS), geographic information systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE) model. We also evaluated the impact of the Billion Tree Tsunami Project (BTTP) on soil erosion in the region. Model inputs, such as rainfall erosivity factor, topography factor, land cover and management factor, and erodibility factor, were used to calculate soil erosion. The results revealed that significant soil loss occurred under 2001, 2011, and 2021 LULC conditions, accounting for 67.26%, 61.78%, and 65.32%, falling within the category of low erosion potential. The vulnerable topographical features of the area indicated higher erosion modulus. The maximum soil loss rates observed in 2001, 2011, and 2021 were 80 t/ha -1 /year -1 , 120 t/ha -1 /year -1 , and 96 t/ha -1 /year -1 , respectively. However, the observed reduction in soil loss in 2021 as compared to 2001 and 2011 suggests a positive influence of the BTTP on soil conservation efforts. This study underscores the potential of afforestation initiatives like the BTTP in mitigating soil erosion and highlights the significance of environmental conservation programs in regions with vulnerable topography., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Soil erosion is an important global phenomenon that can cause many impacts, like morphometry and hydrology alteration, land degradation and landslides. Moreover, soil loss has a significant effect on agricultural production by removing the most valuable and productive top soil's profile, leading to a reduction in yields, which requires a high production budget. The detrimental impact of soil erosion has reached alarming levels due to the exacerbation of global warming and drought, particularly in the arid climates prevalent in Tunisia and Algeria and other regions of North Africa. The influence of these environmental factors has been especially evident in the catchment of Mellegue, where profound vegetation loss and drastic changes in land use and cover, including the expansion of urban areas and altered agricultural practices, have played a significant role in accelerating water-induced soil loss between 2002 and 2018. The ramifications of these developments on the fragile ecosystems of the region cannot be overlooked. Accordingly, this study aimed to compare soil losses between 2002 and 2018 in the catchment of Mellegue, which is a large cross-border basin commonly shared by Tunisian-Algerian countries. The assessment and mapping of soil erosion risk were carried out by employing the Revised Universal Soil Loss Equation (RUSLE). This widely recognised equation provided valuable insights into the potential for erosion. Additionally, changes in land use and land cover during the same period were thoroughly analysed to identify any factors that may have contributed to the observed risk. By integrating these various elements, a comprehensive understanding of soil erosion dynamics was achieved, facilitating informed decision-making for effective land management and conservation efforts. It requires diverse factors that are integrated into the erosion process, such as topography, soil erodibility, rainfall erosivity, anti-erosion cultivation practice and vegetation cover. The computation of the various equation factors was applied in a GIS environment, using ArcGIS desktop 10.4. The results show that the catchment has undergone significant soil water erosion where it exhibits the appearance of approximately 14,000 new areas vulnerable to erosion by water in 2018 compared to 2002. Average erosion risk has also increased from 1.58 t/ha/year in 2002 to 1.78 in 2018, leading to an increase in total estimated soil loss of 54,000 t/ha in 2018 compared to around 25,500 t/ha in 2002. Maps of erosion risk show that highly eroded areas are more frequent downstream of the basin. These maps can be helpful for decision-makers to make better sustainable management plans and for land use preservation., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Soil Surface Roughness (SSR) is a physical feature of soil microtopography, which is strongly influenced by tillage practices and plays a key role in hydrological and soil erosion processes. Therefore, surface roughness indices are required when using models to estimate soil erosion rates, where tabular values or direct measurements can be used. Field measurements often imply out-of-date and time-consuming methods, such as the pin meter and the roller chain, providing inaccurate indices. A novel technique for SSR measurement has been adopted, employing an RGB-Depth camera to produce a small-scale Digital Elevation Model of the soil surface, in order to extrapolate roughness indices. Canopy cover coverage (CC) of the cover crop was also detected from the camera's images. The values obtained for SSR and CC indices were implemented in the MMF (Morgan-Morgan-Finney) model, to validate the reliability of the proposed methodology by comparing the models' results for sediment yields with long-term soil erosion measurements in sloping vineyards in NW Italy. The performance of the model in predicting soil losses was satisfactory to good for a vineyard plot with inter-rows managed with recurrent tillage, and it was improved using spatialized soil roughness input data with respect to a uniform value. Performance for plot with permanent ground cover was not so good, however it was also improved using spatialized data. The measured values were also useful to obtain C-factor for RUSLE application, to be used instead of tabular values., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

A highly visible form of soil erosion is gully, a significant geomorphological feature, resulting from water erosion and causing land degradation and deterioration. In arid and semi-arid environment, gully erosion is conceived as an important source of sediment supply washing out the top fertile soil and exposing lower soil layers. The present study is conducted on the lateritic terrain of Rupai watershed of eastern plateau fringe of India, where water erosion is a serious concern. In order to prepare a gully erosion vulnerability mapping, the analytical hierarchy process (AHP) model coupled with geospatial technology is adopted taking into account thirteen bio-physical factors. It is revealed that around 49% area of the watershed belongs to high to very high gully erosion vulnerability zone (GEVZ) followed by moderate risk zone of 31.64%. This model is validated performing an accuracy assessment, which is calculated to be 90.91%, and the value of Kappa co-efficient is 0.86. It is imperative to estimate the average annual soil loss alongside of delineating GEVZ; thus, the revised universal soil loss equation (RUSLE) model is used with geospatial technology. It unveils that the average estimated soil loss of the watershed varies from < 15 to 431 t ha -1 y -1 . Around 29% of the study area experiences high to very high (57 to > 147 t ha -1 y -1 ) soil erosion risk, where 68% area endures low level of soil erosion risk (< 15 t ha -1 y -1 ). The study of gully morphology depicts gully depth ranging from < 1 to 5 m (small to medium gully) with V and U shapes. Results obtained from this study may help in planning and management of land use and soil erosion conservation., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study evaluated soil erosion rates in the Shaqlawa district using the Geographical Information System (GIS)-based Revised Universal Soil Loss Equation (RUSLE) model. The primary objective was to identify areas within the district that are prone to significant erosion and develop appropriate soil conservation schemes accordingly. A combination of primary and secondary data from diverse sources was utilized to achieve this objective. The GIS-based RUSLE model used variables like soil erodibility (K), soil coverage (C), topographic effect (LS), rainfall runoff (R), and erosion control practices (P) to estimate the amount of soil that had been washed away in the study area. The study provided valuable information that can be used to plan and administer soil protection in the Shaqlawa district. The average yearly soil loss in the study region is estimated to be 65.66 t ha -1 year -1 . The district is experiencing significant soil erosion rates, which may have detrimental effects on agricultural productivity, water quality, and environmental health. The analysis revealed that Balisan, Hiran, Shaqlawa center, and part of the Salahaddin subdistrict are the most affected areas, with high values of LS and R factors contributing to significant soil erosion rates. These results underscore the importance of soil protection and management efforts in the Shaqlawa district. The combination of the RUSLE with GIS and remote sensing techniques has been recognized as an essential, cost-effective, and highly accurate approach for estimating soil erosion., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Soil erosion caused by water refers to the removal of topsoil by rainfall and runoff. Proper selection of an assessment method is crucial for quantifying the spatial variance of soil erosion. The Universal Soil Loss Equation (USLE) and its revised version (RUSLE) are widely used for modelling soil erosion. This study aimed to evaluate the effectiveness of the USLE-based soil erosion modelling in different agroecological regions of India, identify potential issues, and provide suggestions for future applications. The review revealed that little attention has been given to estimate soil erosion in high-priority land degradation regions of India. Additionally, many studies failed to thoroughly verify the authenticity of stated soil loss rates in their research regions either by overestimating or underestimating at least one of the five soil loss parameters. Furthermore, flaws in the application of methods to calculate these parameters leading to erroneous values were identified and suggestions for improvement were made. The USLE-based soil erosion modelling is an effective tool for quantifying soil erosion risk, but researchers should put emphasis on thoroughly verifying the methodologies adopted, unit conversions, and data availability for the estimation of soil loss parameters to improve the accuracy of their final results. This paper provides valuable insights to assist researchers in implementing USLE-based erosion models in diverse agroecological regions in India and elsewhere. However, for effective soil conservation and sustainable agriculture, further research is necessary to develop efficient techniques for using USLE-based soil erosion modelling to achieve a comprehensive understanding of erosion risk across different agroecological regions., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Soil erosion is a vector of disturbances to agricultural productivity and economic development in the western highlands of Ethiopia. Yet, tough vegetation cover loss swapped to other land uses could have amplified the soil loss rate at which land cover change preceded, but little is known about their effects on soil loss in the Limu-Seqa watershed. This study was designed to evaluate the historical trends of the effects of land use-land cover change on soil erosion dynamics as a threshold for potential monitoring of soil loss. Satellite image data of 1987, 2002, 2021, and DEM-20 m resolution were used. The RUSLE model was applied with primary parameters to generate soil loss. Findings show that average annual soil loss increased from 4.5 in 1987 to 13.5 t ha -1  yr -1 in 2002 and surpassed to 45.35 t ha -1  yr -1 in 2021 as a result of LULC changes, particularly the transition of forest and overgrazed land to cropland (43.83%) and dense-forest to poor-open-up forest (6.92%) between 1987 and 2021. Soil loss during the recent study period was substantially affected by a substantial LULC change, from forest to cropland. The severe and very severe erosion risk categories jointly cover more than half of the entire catchment, which contributes to two-thirds of the total mean annual soil loss in the watershed, which is found to be over and above soil loss tolerance (SLT) in Ethiopia and tropical regions. Therefore, given the robust economic and political status of priority conservation measures, red hues areas are significant., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Published by Elsevier Ltd.)

Land degradation in the form of soil erosion is a worldwide challenge and make environmental problem that affects crop yields, makes livelihoods difficult, and creates crises. The main objective of this study was to measure soil loss using the Revised Universal Soil Loss Equation (RUSLE) Model in Horo district, Western Ethiopia. RUSLE with a Geographical Information System (GIS) was used to quantify soil loss using rainfall, soil, a digital elevation model (DEM), and satellite image datasets as factor value inputs. Those factors are erosivity (R), erodibility (K), topography (LS), cover management (C), and conservation support practice (P) layer values that can be interactively used using weighted overlay in ArcGIS 10.8. The result shows that the maximum and minimum potential annual soil loss of the study area ranged from nil (0.01 t/ha/yr) on plain surfaces to 216.01 t/ha/yr. The average annual soil loss rate in the study area was 13.27 t ha/yr. The highest mean annual soil loss of 216.01 t/ha/yr were observed from farmland and it was the largest portion of the study area, which covered about 64243.02 ha and represented about 73.75% of the total. As a result, forest land (16383.23 ha) was the second-largest, accounting for 18.81% of the total area. Consequently, the study revealed that the farmland was more vulnerable to erosion than other land uses and land cover types. Hence, information on average annual soil loss is important for selecting appropriate conservation measures to reduce on-site soil loss and its off-site effects. Therefore, farmers and other expected bodies should have focused on soil conservation and management practices at the highest soil loss severity classes, which must get priority for conservation by stakeholders, agents, and the government., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors.)

Annually, millions of hectares of land are affected by wildfires worldwide, disrupting ecosystems functioning by affecting on-site vegetation, soil, and above- and belowground biodiversity, but also triggering erosive off-site impacts such as water-bodies contamination or mudflows. Here, we present a soil erosion assessment following the 2017's wildfires at the European scale, including an analysis of vegetation recovery and soil erosion mitigation potential. Results indicate a sharp increase in soil losses with 19.4 million Mg additional erosion in the first post-fire year when compared to unburned conditions. Over five years, 44 million Mg additional soil losses were estimated, and 46% of the burned area presented no signs of full recovery. Post-fire mitigation could attenuate these impacts by 63-77%, reducing soil erosion to background levels by the 4th post-fire year. Our insights may help identifying target policies to reduce land degradation, as identified in the European Union Soil, Forest, and Biodiversity strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

At the current times, soil erosion is the major problem that affects land and water resources, especially in Ethiopia's highlands. Due to the dynamics of land use land cover change, land degradation, and soil erosion increase significantly and result in a loss of fertile soil every year and lead reduction in agricultural production. This study was therefore designed to explore the land use land cover (LULC) dynamics from 1986 to 2020, to estimate mean annual soil erosion rates and identify erosion hotspot areas from 1986 to 2020, and finally, to evaluate the impacts of land use land cover change on soil loss of 1986 to 2020. For this, Landsat imageries of 4 years from 1986 to 2020 were used. Maximum likelihood supervised classification methods were used to classify LULCs. The dynamics of LULC change were used as an input for measuring soil loss by employing the combination of geospatial technologies with the revised universal soil loss equation (RUSLE). The LULC maps of 1986, 1997, 2009, and 2020 were used for identifying crop management (C) factor and conservation practice (P) factor. Rainfall erosivity factor (R), soil erodibility factor (K), and slope length and steepness factor (LS) were also used as sources of data. Based on the five factors, soil erosion intensity maps were prepared for each year. Results showed that the annual soil loss in the watershed ranged from 0 to 3938.66 t/ha/year in 1986, 0 to 4550.94 t/ha/year in 1997, 0 to 5011.21 t/ha/year in 2009, and 0 to 6953.23 t/ha/year in 2020. The annual soil loss for the entire watershed was estimated at 36.889, 42.477, 47.805, and 48.048 t/ha/year in 1986, 1997, 2009, and 2020, respectively. The mean soil loss of 1986, 1997, 2009, and 2020 was higher in cultivated land followed by shrub land, grazing land, and forest land. Mean soil loss increased from 1986 to 1997, from 1997 to 2009, and from 2009 to 2020. This is because of the expansion of agricultural land at the expense of grazing land and shrub land. Therefore, urgent soil and water conservation practices should be made in hotspot areas., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Soil erosion and sedimentation contribute to deteriorating water quality, adverse alterations in basin hydrology and overall ecosystem biogeochemistry. Thus, understanding soil erosion patterns in catchments is critical for conservation planning. This study was conducted in a peri-urban Inner Murchison Bay (IMB) catchment on the northern shores of Lake Victoria since most soil erosion studies in Sub-Saharan Africa have been focused on rural landscapes. The study sought to identify sediment sources by mapping erosion hotspots using the revised universal soil loss equation (RUSLE) model in appendage with field walks. RUSLE model was built in ArcGIS 10.5 software with factors including: rainfall erosivity, soil erodibility, slope length and steepness, land cover and support practices. The model was run, producing an erosion risk map and field assessments conducted to ground-truth findings and identify other hotspots. The percentage areas for RUSLE modelled erosion rates were: 66.8% for 0-2 t ha -1  year -1 ; 10.8% for 2-5 t ha -1  year -1 ; 10.1% for 5-10 t ha -1  year -1 ; 9% for 10-50 t ha -1  year -1 and 3.3% for 50-100 t ha -1  year -1 . Average erosion risk was 7 t ha -1  year -1 and the total watershed erosion risk was 197,400 t year -1 , with croplands and steep areas (slope factor > 20) as the major hotspots (> 5 t ha -1  year -1 ). Field walks revealed exposed soils, marrum (gravel) roads and unlined drainage channels as other sediment sources. This study provided the first assessment of erosion risk in this peri-urban catchment, to serve as a basis for identifying mitigation priorities. It is recommended that tailored soil and water conservation measures be integrated into physical planning, focusing on identified non-conventional hotspots to ameliorate sediment pollution in Lake Victoria., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Erosion of soil by water coupled with human activities is considered as one of the most serious agents of land degradation, posing severe threat to agricultural productivity, soil health, water quality, and ecological setup. The assessment of soil erosion and recognition of problematic watersheds are pre-requisite for management of erosion hazards. In the present study, Revised Universal Soil Loss Equation (RUSLE) integrated with remote sensing (RS) and geographic information system (GIS) has been used to assess the soil erosion in lower Sutlej River basin of Punjab, India, and prioritize the watersheds for implementation of land and water conservation measures. The total basin area was about 8577 km 2 which was divided into 14 sub-watersheds with the area ranging from 357.8 to 1354 km 2 . The data on rainfall (IMD gridded data), soil characteristics (FAO soil map), topography (ALOS PALSAR DEM) and land use (ESRI land use and land cover map) were prepared in the form of raster layers and overlaid together to determine the average annual soil loss. The results revealed that the average annual soil loss varied from 1.26 to 25 t ha -1 , whereas total soil loss was estimated to be 2,441,639 tonnes. The spatial distribution map of soil erosion showed that about 94.4% and 4.7% of the total area suffered from very slight erosion (0-5 t ha -1  year -1 ) and slight erosion (5-10 t ha -1  year -1 ), respectively, whereas 0.11% (9.38 km 2 ) experienced very severe soil loss (> 25 t ha -1  year -1 ). Based on estimated average annual soil loss of sub-watersheds, WS8 was assigned the highest priority for implementation of soil and water conservation measures (323.5 t ha -1  year -1 ), followed by WS9 (303.8 t ha -1  year -1 ), whereas WS2 was given last priority owing to its lowest value of soil loss (122.02 t ha -1  year -1 ). The present study urges that conservation strategies should be carried out in accordance with the priority ranking of diverse watersheds. These findings can certainly be used to implement soil conservation plans and management practices in order to diminish soil loss in the river basin., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting accurate rainfall helps early detection of soil erosion vulnerability and can minimise the damages by taking appropriate measures caused by severe storms, droughts and floods. This study aims to predict soil erosion probability using the deep learning approach: long short-term memory neural network model (LSTM) and revised universal soil loss equation (RUSLE) model. Daily rainfall data were gathered from five agro-meteorological stations in the Central Highlands of Sri Lanka from 1990 to 2021 and fed into the LSTM model simulation. The LSTM model was forecasted with the time-series monthly rainfall data for a long lead time period, rainfall values for next 36 months in each station. Geo-informatics tools were used to create the rainfall erosivity map layer for the year 2024. The RUSLE model prediction indicates the average annual soil erosion over the Highlands will be 11.92 t/ha/yr. Soil erosion susceptibility map suggests around 30 % of the land area will be categorised as moderate to very-high soil erosion susceptible classes. The resulted map layer was validated using past soil erosion map layers developed for 2000, 2010 and 2019. The soil erosion susceptibility map indicates an accuracy of 0.93 with the area under the receiver operator characteristic curve (AUC-ROC), showing a satisfactory prediction performance. These findings will be helpful in policy-level decision making and researchers can further tested different deep learning models with the RUSLE model to enhance the prediction capability of soil erosion probability., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Sedimentation has a prominent impact on the functionality and lifetime of reservoirs and is a growing concern for stakeholders. Various parameters influence sedimentation caused by soil erosion. Here we have examined fifty Italian reservoirs to determine sedimentation rates and storage capacity loss. The reservoirs studied have an average age of 78 years as of 2021, with the highest loss of capacity observed, equal to 100%, for Ceppo Morelli. For the fifty Italian catchments covering north, south, central and islands of Italy, we found the mean annual sediment yield varying between 17-4000 m 3 /km 2 . year. Six of fifty reservoirs studied (Quarto, Colombara, Ceppo Morelli, Fusino, Vodo and Valle di Cadore) are already in a very critical situation in terms of storage capacity loss. Out of the fifty reservoirs, half of them will reach their half-life year by 2050. For example, for the Fusino reservoir located in northern Italy, we observed a loss of 90% of the storage volume as of 2020 with respect to its operation year 1974, compared to 6% in 2015 as available in literature. Modelling the sediment delivery ratio (SDR) is an open question, due to the lack of adequate data and uncertainties about the variability in hydrological, geomorphological, climate and landcover parameters. Here, we addressed the issue with a simplified multiple regression approach based on sediment delivery ratio values retrieved by the RUSLE model. We found different multi regressions for reservoirs belonging to the Alpine and Apennine regions. This analysis offers a starting point for the management and prioritization of adaptation and remediation policies necessary to address reservoir sedimentation., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In modeling studies, the use of spatial data derived from geographic information systems and remote sensing applications to simulate the impact of phenological and seasonal changes on soil loss has a promising effect on the accuracy of predictions. The objective of this work was to estimate the C-factor (cover management) as a dynamic-factor RUSLE (revised universal soil loss equation) model based on an NDVI (Normalized Difference Vegetation Index) approach derived from high-resolution Landsat 8 and Landsat ETM7 satellite images for 140 different rain-fed wheat parcels in terms of seasonal and phenological-based by the integrated use of remote sensing and GIS. Overall, it was found that the highest C values, an average of 0.70, were estimated for the emergence period of the wheat, while the lowest value of 0.06 was found in the booting period. Seasonally, the estimated average C values in these parcels were 0.69, 0.63, 0.13, and 0.44 for the autumn, winter, spring, and summer, respectively. Corresponding soil losses for those seasons were 1.70, 1.55, 0.28, and 1.13 t ha -1  year -1 respectively. Comparatively, without considering the phenological growing periods of wheat, the annual predicted soil loss rate was 11.5% higher than the conditions considered. The present study concluded that an assessment of seasonal and phenological changes in the C-factor for fragile ecosystems with weak crop-cover development could significantly improve the accuracy of the RUSLE model predictions and effectively manage limited soil and water resources., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In recent years, the Cerrado biome in Brazil (Brazilian savannah) has faced severe environmental problems due to abrupt changes in land use/cover (LUC), causing increased soil loss, sediment yield and water turbidity. Thus, this study aimed to evaluate the impacts of soil loss and sediment delivery ratio (SDR) over the last 30 years to simulate future scenarios of soil losses from 2050 to 2100 and to investigate an episode of sediment delivery that occurred in the Rio da Prata Basin (RPB) in 2018. In this study, the following were used: an estimation of soil losses for 1986, 1999, 2007 and 2016 using the Revised Universal Soil Loss Equation (RUSLE), an estimation of SDR, sediment export and sediment deposition using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, an association of RUSLE factor C to LUC data for 2050 and 2100 based on the CA-Markov hybrid model, and an estimation of future soil erosion scenarios for 2050 and 2100. The results show that over the last 30 years (1986-2016), there has been a reduction in the areas of highly intense and severe degrees. Future soil erosion scenarios (2050-2100) showed a 13.84% increase in areas of soil loss >10 Mg ha -1  year -1 . The results highlighted the importance of assessing the impacts of LUC changes on soil erosion and the export of sediments to agricultural watersheds in the RPB, one of the best ecotourism destinations in Brazil. In addition, the increase in soil loss in the region intensified sediment yield events and increased water turbidity. Furthermore, riparian vegetation, although preserved, was not able to protect the watercourse, showing that it is essential to adopt the best management practices in the agricultural production areas of the basin, especially where ramps are extensive or the slope is greater than 2%, to reduce the runoff velocity and control the movement of sediments on the surface towards the drainage canals. The results of this study are useful for drawing up a soil and water conservation plan for the sustainable production of agriculture and maintenance of ecosystem services in the region., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Soil erosion accelerated by poor agricultural practices, land degradation, deprived infrastructure development and other anthropogenic activities has important implications for nutrient cycling, land and lake productivity, loss of livelihoods and ecosystem services, as well as socioeconomic disruption. Enhanced knowledge of dynamic factors influencing soil erosion is critical for policymakers engaged in land use decision-making. This study presents the first spatio-temporal assessment of soil erosion risk modelling in the Winam Gulf, Kenya using the Revised Universal Soil Loss Equation (RUSLE) within a geospatial framework at a monthly resolution between January 2017 and June 2020. Dynamic rainfall erosivity and land cover management factors were derived from existing datasets to determine their effect on average monthly soil loss by water erosion. By assessing soil erosion rates with enhanced temporal resolution, it is possible to provide greater knowledge regarding months that are particularly susceptible to soil erosion and can better inform future strategies for targeted mitigation measures. Whilst the pseudo monthly average soil loss was calculated (0.80 t ha -1  month -1 ), the application of this value would lead to misrepresentation of monthly soil loss throughout the year. Our results indicate that the highest erosion rates occur between February and April (average 0.95 t ha -1  month -1 ). In contrast, between May and August, there is a significantly reduced risk (average 0.72 t ha -1  month -1 ) due to the low rainfall erosivity and increased vegetation cover as a result of the long rainy season. The mean annual gross soil loss by water erosion in the Winam Gulf catchment amounts to 10.71 Mt year -1 , with a mean soil loss rate of 9.63 t ha -1  year -1 . These findings highlight the need to consider dynamic factors within the RUSLE model and can prove vital for identifying areas of high erosion risk for future targeted investigation and conservation action., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2021. Published by Elsevier B.V. All rights reserved.)

Erosion is seen as a major productivity problem in the world. Unplanned agricultural practices caused by human activities initiate the soil erosion process. Especially in sloping areas, agricultural activities without soil conservation measures accelerate this process. This study prepared land use planning (LUP) scenarios to reduce soil losses in the Mikail Stream Micro-Basin, which has an erosion problem and a rough topography. ILSEN land evaluation method, which is formed by interpreting FAO land evaluation principles according to Turkish conditions, was used in the creation of the scenarios. Soil conservation (terracing and contour agriculture) land-use types (LUT) that can help in erosion reduction were included in the ILSEN method and 8 different LUP scenarios were created. Soil protected (terracing and contour farming) LUTs that can help reduce erosion were included in the ILSEN method and 8 different LAP scenarios were created. The RUSLE Method integrated with the Geographic Information System (GIS) was used to calculate the estimated amount of soil loss caused by the scenarios created. For land evaluation and erosion studies, serial-based soil map of the area and Google Earth images were used. Scenario 7 created has reduced soil loss by 79% compared to the present land use (Scenario 8) of the basin. While the soil loss caused by the present land-use of the basin was 335.95 tons ha -1  year -1 on average, the amount of soil loss caused by the 7th scenario was calculated as 69.05 tons ha -1  year -1 on average. The results showed that the ILSEN land evaluation method can be a model to be used in the creation of erosion-reducing LUP scenarios in areas with erosion problems., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Soil erosion valuation at a spatial scale is crucial for assessing natural resource quality in a farming country like Ethiopia. The study's goal was to determine the rate of soil erosion in the Megech-Dirma catchment in Northwest Ethiopia using the Revised Universal Soil Loss Equation model aggregation with Geographic Information System and Remote Sensing. Sediment yield and transport were also estimated using sediment delivery ratio. Revised Universal Soil Loss Equation model data inputs included precipitation data for the R value, soil data for the K value, land cover data from satellite images for the C and P value, and topographical data from a Digital Elevation Model for the LS component. It was completed using the ArcGIS 10.4 software. The mean annual soil loss is 110.60 t ha -1  yr -1 . Each year, a total of 8499.74 t ha -1  yr -1 of soil eroded and on average resulting in 1605.30 t/km 2 /yr, sediment material has been transported to the stream channels and deposited with a sediment delivery ratio of 1.87. The strength of soil erosion in the area is divided into six categories. The erosion rate classes were 46.38 percent (0-12 t ha -1  yr -1 ) low, 13.63 percent (12-20 ha -1  yr -1 ) moderate, 9.22 percent (20-35 ha -1  yr -1 ) high, 12.30 percent (35-50 ha -1  yr -1 ) very high, 7.20 percent (50 up to 100 ha -1  yr -1 ) severe, and 11.27 percent (>100 ha -1  yr -1 ) very severe erosion. According to erosion severity, 46.38 percent of the watershed is at risk of low erosion, while 11.27 percent is at risk of extremely severe erosion. The north and northeastern sections of the watershed have a moderate to extremely severe erosion risk due to steep slopes, high rainfall, and weak conservation measures. The severely eroded parts of the plateau and steep portions are proposed to be covered by plantation, stone bund, and check dam constructions., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This study investigated the changes in soil erosion associated with land use change from 2000 to 2020 in a mountainous rural area. Land use change was detected using Landsat images and soil erosion was estimated using the revised universal soil loss equation (RUSLE). The results show that deforestation and fallow cultivation caused substantial soil loss, whereas conversion from uncultivated land to cropland reduced soil erosion. A conversion from 711 ha cropland and 234 ha forestland to uncultivated land increased the average soil loss from 17 ton·ha -1 ·year -1 to 42 ton·ha -1 ·year -1 and the area of eroded soil at the very high level from 276 to 1058 ha between 2000 and 2010. In contrast, a wide expansion of cropland from 637 ha uncultivated land decreased the average soil loss from 42 ton·ha -1 ·year -1 to 32 ton·ha -1 ·year -1 and the area of eroded soil at the very high level from 1058 to 690 ha between 2010 and 2020. We suggest management measures such as forest protection, afforestation, reforestation, fruit tree development, and soil erosion control practices in coffee and maize cultivation to reduce soil erosion., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Human population growth and subsequent land use intensification are closely linked to contemporary increases in sediment and associated contaminants fluxes to fluvial systems, lakes, reservoirs, and coastal zones worldwide. In most urban areas, reservoirs that are the main source of fresh water supply, if not effectively managed, suffer from water quality decline and loss of capacity associated with accelerated siltation. This study analyzes watershed soil losses and sediment accumulation rates in two reservoirs in the Occoquan river basin, a sub-watershed of the Chesapeake Bay in the suburbs of the greater Washington, DC area. Lake Manassas is located in the upper reaches of the basin, characterized by mixed land use and cover of mostly forest, residential areas, and agriculture, whereas Occoquan Reservoir is located in the more urbanized lower reach of the basin in the heavily populated suburban zone south of Washington, DC. Five sediment cores from each lake were used in 210 Pb-based sediment accumulation rates analysis, and GIS-based Revised Soil Loss Equation (RUSLE) model and a sediment delivery ratio (SDR) were used to evaluate basin soil losses and sediment fluxes to the fluvial systems. 210 Pb sediment accumulation rate estimates in Occoquan Reservoir range from 0.26 g cm -2 year -1 in the upper reaches to 0.37 g cm -2 year -1 in the lower reaches. Lake Manassas also had comparable accumulation values ranging from 0.22 to 0.40 g cm -2 year -1 . RUSLE/SDR estimated watershed sediment fluxes were 0.26 Mg ha -1 year -1 (Mg-mega gram) in the upper watershed, which is significantly higher than 0.07 Mg ha -1 year -1 estimates for the lower reaches of the watershed. The variability in the reservoirs' sediment accumulation rates and basin soil losses reflects the variability of land use and cover, basin slopes, and erosion mitigation efforts within the watershed. The lower reaches, though more urbanized, have well-developed storm drain systems limiting run-off related soil losses. The well-managed riparian zones surrounding both reservoirs also limit sediment fluxes, hence the relatively low sediment accumulation rates. Although surficial sediment sources seem to be well managed, some of these efforts might be associated with the uptick in intrinsic sediment sources, leading to localized high sediment accumulation in the mouth of tributaries draining the high-intensity urban areas of the basin., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The Himalayan basins are characterised by severe soil erosion rates and several basins are among the largest sediment dispersal systems in the world. Unsustainable agricultural activities increase the soil erosion rates and influence the overall hydro-geomorphic regime of river basins. Consequently, the water holding capacity of soil reduces, which enhances the flood risk in the lowland regions. In addition, excessive sediment flux severely affects the reservoir capacity in the mountainous regions, thus amplifying the flood hazard in the upland regions. Here, we have analysed two large and hydro-geomorphically diverse Himalayan River basins, namely, the Ganga Basin (GBA) from source to Allahabad in northern India and the Kosi Basin (KB) draining through Nepal and north Bihar plains in eastern India. Based on RULSE and region-specific SDR modelling framework, which includes model calibration, validation and uncertainty assessment, we demonstrate that spatial variation in rainfall, hydrogeomorphic conditions, the presence of hydraulic structures, and large-scale agricultural activities influence the overall pattern of sediment production and transport in these two large river basins. Total soil erosion in GBA and KB are estimated to be ~404 × 10 6 t/y and ~724 × 10 6 t/y respectively, a large part of which comes from the mountainous regions in both basins. Sediment yield at the mountain exits of the GBA and KB are computed as 14.1 × 10 6 t/y and 86.4 × 10 6 t/y respectively, which work out to be ~5% and ~15% of total soil erosion from the respective contributing areas of the KB and GBA respectively. Similarly, sediment yields at outlets in the alluvial plains are estimated to be 32.2 × 10 6 t/y and 37.3 × 10 6 t/y in the GBA and the KB, respectively suggesting that a large part of sediments are accommodated in the alluvial plains of KB. These results have significant implications for sediment management in the Himalayan River basins., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The Saranda forest region, which is well known for its biodiversity in India, is now confronted by rapid socio-economic development, particularly the hilltop mining activities. Hilltop mining areas of this region have always been responsible for producing excessive soil erosion in the associated river basin. This erosion phenomenon becomes hazardous during the rainy season, thereby contributing to various environmental problems, and consequently, necessitating soil erosion control planning in the Saranda forest. Hence, this study aimed to estimate average annual soil erosion in the Saranda region in terms of the spatial distribution using the Geographic Information System (GIS) integrated Revised Universal Soil Loss Equation (RUSLE) model. The erosion was quantified at a spatial resolution of 10 m (pixel by pixel) using the GIS-based RUSLE inputs. This study also applies GIS integrated Analytic Hierarchy Process (AHP) model to identify the favorable zones for sediment deposition in the study area. On the basis of erosion severity, the entire study area is classified into six categories (very low to extreme). The study reveals that the Saranda forest's average annual soil erosion is 76 tons per hectare per year (t/ha/yr). Approximately 63% of the total area is categorized under very low to low erosion category, and the relevant area is mainly covered by forest land, whereas the mining region comprises less than 1% of the total study area with extremely high soil erosion (156 t/ha/yr) potential. As envisaged from the present study, the erosion-prone mining areas are located within a 1-5 km range of the adjacent Karo and Koina rivers, thereby, necessitating the erosion control strategy to avoid the possible threats. From this perspective, the study also investigates the favorable zones for sediment deposition using the GIS integrated AHP model to suggest the appropriate erosion control measures. Finally, the RUSLE and AHP models are combined on the GIS platform to identify the distressed catchment area. Moreover, 42% (41,060 ha) of the total area is disturbed due to the present mining activities, which involves 11 sub-watersheds, and their associated 50 micro-watersheds. From the context of watershed conservation, erosion control measures are also recommended. The methodology adopted in this study can be easily extended to any global mining-dominated catchment for sustainable conservation planning., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The information about different morphometric parameters of any watershed is necessary for better watershed management and planning. This study aimed to investigate morphometric characteristics, to assess the soil erosion risk, and to prioritize different sub-watersheds of the Koyna River basin, India, with two different approaches using geospatial technology. Different linear, shape, and relief parameters of the basin were estimated and analyzed. The linear and shape parameters indicated that the basin has less flood hazard. The relief parameters indicated that the basin has moderate roughness and unevenness. The parallel drainage pattern is dominant inside the basin due to the highly elongated nature of the basin. The bifurcation ratio (R b ) indicated lithological and geological variations inside the basin. Two different approaches namely morphometric analysis and empirical Revised Universal Soil Loss Equation (RUSLE) method were applied for prioritization of different sub-watersheds. Rainfall, soil, digital elevation model (DEM), and normalized difference vegetation index (NDVI) data were used for identifying erosion-prone zones with RUSLE analysis. Based on RUSLE analysis, the entire study area was divided into five soil erosion risk classes namely very slight (80.43 %), slight (14.94 %), moderate (3.21 %), severe (0.79 %), and very severe (0.63%), respectively. Most of the study area was found to be under a very slight soil erosion vulnerability class based on the RUSLE approach. The conservation practices should be carried out as per the priority ranking of different sub-watershed based on soil erosion rates. The results found in this study can surely assist in the implementation of soil conservation planning and management practices to reduce soil loss in the Koyna River basin of India., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Soil erosion is simultaneously driven by multiple factors. Identifying the dominant controlling factors and quantifying the contribution of each factor would be helpful to sustain water and soil resources. China's Loess Plateau was taken as an example area to investigate the above issues since it is the most eroded region in the world, and its soil loss is being controlled by a large-scale revegetation program. We extended the Revised Universal Soil Loss Equation (RUSLE) to large-scale erosion estimation with the aid of GIS for the period of 1986-2015, analyzed the relationship between erosion and controlling factors by correlation and wavelet coherence analysis, and quantified the contribution of each factor to erosion change by the elasticity coefficient method. Results showed that the soil erosion decreased from 1013 t·km -2 ·a -1 in 1991-1995 to 595 t·km -2 ·a -1 in 2011-2015, with a downward trend in the whole period. Spatially, most areas had soil erosion of slight intensity, and the areas with high-intensity erosion concentrated in a northeast-southwest strip with hilly-gully landscapes or densely distributed rivers. The changes in surface conditions including vegetation cover and soil conservation measures had dominant effects on the spatial heterogeneity of erosion, their contribution to erosion reduction was 119%. But rainfall erosivity increased soil erosion, and it had a contribution to erosion reduction of -28%. These results are helpful in understanding the mechanism behind the changes in soil erosion and providing information for sustainable soil and water management and vegetation restoration., Competing Interests: Declaration of competing interest The authors have no conflict of interests to declare., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Agricultural expansion and overgrazing are globally recognized as key contributors to accelerated soil degradation and surface erosion, with direct consequences for land productivity, and environmental health. Measured impacts of livestock grazing on soil physical properties and ground cover are absent in soil loss models (e.g., Revised Universal Soil Loss Equation, RUSLE) despite significant impacts to surface erosion. We developed a novel model that captures changes to ground cover and soil properties (permeability and structure) as a function of grazing intensity (density, duration, history, and stock type), as well as soil clay and water contents. The model outputs were integrated within RUSLE to calculate a treaded soil erodibility (K tr ) and grazed cover factors (C gr ) at seasonal timescales (3-month windows) to account for variability in soil moisture content, grazing practices, vegetation growth and senescence, and rainfall. Grazed pastures and winter-forage paddocks exhibit distinct changes in soil erodibility and soil losses, which are most pronounced for wet soils when plant cover is reduced/minimal. On average, typical pasture grazing pressures increase soil erodibility by 6% (range = 1-90%), compared to 60% (18-310%) for intensive winter forage paddocks. Further, negligible ground cover following forage crop grazing increases surface erosion by a factor of 10 (± 1 3 ) relative to grazed pastures, which exhibit soil losses (μ = 83 t km -2 yr -1 ; range = 11.6 to 246) comparable to natural uncropped catchments (100-200 t km -2 yr -1 ). Exacerbated soil losses from winter forage-crop paddocks (μ = 1,100 t km -2 yr -1 ) arose from significant degradation of soil physical properties and exposing soils directly to rainfall and runoff. We conclude that proactive decisions to reduce treading damage and avoid high-density grazing will far exceed reactive practices seeking to trap sediments lost from grazed lands., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The Blue Nile basin is one of the hot-spots of soil erosion areas in Ethiopia. However, the impact of land use changes on soil erosion is poorly understood in the Tagaw watershed. Hence, the objective of the study is to assess the impact of land use changes on soil erosion in Tagaw watershed over the last 31 years. Rainfall, soil, satellite images and topographic data are acquired from field survey and secondary sources. A Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The mean annual and total potential soil losses of the watershed are 19.3, 22.9, 26 and 0.06-503.56, 0.11-516.67, and 0.00-543.5 tons ha -1 yr -1 for 1995, 2006 and 2016 respectively. The highest soil loss is found for bare land. The RUSLE model further showed that the highest soil erosion occurred in 2016 whereas the lowest soil erosion occurred in 1995., Competing Interests: The authors declare no conflict of interest., (© 2021 The Authors. Global Challenges published by Wiley‐VCH GmbH.)

Erosion and connectivity are spatially varied processes key to determining sediment transport and delivery to downstream waterbodies. However, we find few studies that explicitly model the linkages of where erosion and connectivity coincide and where they contradict, particularly in urbanizing settings. In this study, we couple in-stream aquatic sensing, the Revised Universal Soil Loss Equation (RUSLE), the Index of Connectivity (IC), and the Sediment Delivery Ratio (SDR), together with Monte Carlo uncertainty analysis, to generate a new Erosion-Connectivity Mapping (ECM) framework. We evaluate ECM accuracy with field assessment of thirty-five sites spread across five lowland watersheds (mean slope <5°) in Johnson County, Kansas, USA, which differ primarily in their land use, ranging from 21% to 89% urban. RUSLE modeling results indicate erosion is controlled by topography with high risk areas near streambanks roadway systems. SDR and IC were positively related at the five sites (R2 = 0.78, p < 0.05) with the highest values in the most urbanized watershed, indicating that anthropogenic change augments connectivity. The ECM results indicate that while only 5±1% of the study area is both highly erodible and highly connected, these areas represent 37±4% of total watershed-scale erosion. Our modeling results indicate that erosion is more likely to be the limiting factor in sediment transport, as opposed to connectivity, as there are generally more locations that are well-connected to hydrologic transport but resistant to erosion. Our field assessment provided broad support for the ECMs; however, field assessment indicated that geospatial modeling underpredicts how closely related erosion and connectivity are in the field and we suggest that future models consider this coupling more explicitly. This study provides a method for combining RUSLE and IC in a new tool (ECM) to identify spatial patterns in sediment erosion-connectivity to aid in the understanding and management of watershed sedimentation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

It is of great significance to understand the spatial-temporal change of soil erosion in the Southern Yunnan Mountainous Area, especially for soil and water conservation planning and ecolo-gical protection in the world cultural heritage area. Based on the RUSLE model and GIS/RS space information technology, we examined the spatial-temporal variation of soil erosion and its relationship with environmental factors in Yuanyang County from 2005 to 2015. The results showed that, during the study period, the spatial differentiation of soil erosion in Yuanyang County was substantial. The soil erosion area was concentrated on the southeast and southwest region. Erosion widely distributed among both sides of Tengtiao River. Extreme and severe erosion presented a point distribution pattern. The average soil erosion modulus was 11.06 t·hm -2 ·a -1 from 2005 to 2015. The erosion level was basically slight and mild, accounting 80% of the total. Mild and moderate erosion contributed nearly 50% of total annual soil erosion, which was the key level of soil erosion control. During the study period, soil erosion had been slowed down with the improvement of forest cover. The terrain was complicated and changeable in Yuanyang County, and soil erosion distribution had a prominent correlation with altitude and slope. The formation of this distribution pattern was driven by numerous natural and human factors. The key area of soil and water protective governance were below 500 m, above 1500 m and slope located at 25°-45°.

A novel model was developed to estimate heavy metal flux at regional scale by using the Revised Universal Soil Loss Equation (RUSLE) to estimate soil erosion. This model was then used to estimate the fluxes of heavy metals including Zn, Cu, Cr, Ni, Cd, and As in three mono-lithologic regions in upper Pearl River Basin including carbonate rock (CR) basin, black shale (BS) basin, and basalt (BT) basin. Results show that the total annual erosions of the watershed were 8.56 × 10 5 t a -1 , 3.26 × 10 6 t a -1 , and 5.09 × 10 5 t a -1 in CR, BT, and BS basins, respectively. The heavy metal flux was lowest for Cd (0.87 kg km -2 a -1 , 0.46 kg km -2 a -1 , and 1.07 kg km -2 a -1 in CR, BS, and BT basins, respectively). The heavy metal flux was highest for Zn in CR basin (16.29 kg km -2 a -1 ), Cr in BS basin (27.25 kg km -2 a -1 ) and Cu in BT basin (259.59 kg km -2 a -1 ). These findings have important implication to understand transport and distribution of heavy metals in the Pearl River Basin, and make regulations for controlling of non-point source heavy metal pollution.

Soils provide vital ecosystem services, from sequestering carbon to providing food and moderating floods. Soil erosion threatens the provisioning of these services and degrades downstream water quality. Vegetation plays an important role in soil retention: by holding it in place, soil can continue to provide ecosystem goods and services and protect water resources. The aims of this study were to: (1) develop a 30-meter resolution map of erosion in the conterminous United States, and (2) quantify the soil retention service of natural vegetation. Using the Revised Universal Soil Loss Equation and physiographic and remote sensing datasets, we estimated sheet and rill erosion. We also developed a map of sediment delivery ratio to connect erosion to downstream delivery using hydrologic connectivity. The estimated sheet and rill erosion in the conterminous United States was 1.55 Pg yr -1 , of which 0.52 Pg yr -1 reached waterbodies. Natural land cover prevents 12.3 Pg yr -1 of sheet and rill erosion and 5.1 Pg yr -1 in delivery to waterbodies. The value of natural land cover in retaining sediment is a function of the land cover, physiographic characteristics, and spatial context. This study has implications for spatial prioritization of natural land cover preservation and agricultural land management to minimize sediment erosion and delivery., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

Human-induced agricultural and developmental activities cause substantial alteration to the natural geography of a landscape; thereby accelerates the geologic soil erosion process. This necessitates quantification of catchment-scale soil erosion under both retrospective and future scenarios for efficient conservation of soil resources. Here, we present a revised universal soil loss equation (RUSLE) based soil erosion estimation framework at an unprecedentedly high spatial resolution (30 × 30 m) to quantify the average annual soil loss and sediment yield from an agriculture-dominated river basin. The input parameters were derived by using the observed rainfall data, soil characteristics (soil texture, hydraulic conductivity, organic matter content), and topographic characteristics (slope length and percent slope) derived from digital elevation model (DEM) and satellite imageries. The developed approach was evaluated in the Brahmani River basin (BRB) of eastern India, wherein the different RUSLE inputs, viz., rainfall erosivity (R factor), soil erodibility (K factor), topographic (LS factor), crop cover (C factor), and management practice (P factor) factors have the magnitude of 1937 to 4867 MJ mm ha -1  h -1  year -1 , 0.023 to 0.039 t h ha MJ -1  ha -1  mm -1 , 0.03 to 74, 0.16 to 1, and 0 to 1, respectively. The estimated average annual soil loss over the BRB ranged from 0 to 319.55 t ha -1  year -1 , and subsequent erosion categorization revealed that 54.2% of basin area comes under extreme soil erosion zones in the baseline period. Similarly, the sediment yield estimates varied in the range of 0.96 to 133.31 t ha -1  year -1 , and 35.81% area were identified as high soil erosion potential zones. The extent of erosion under climate change scenario was assessed using the outputs of HadGEM2-ES climate model for the future time scales of 2030, 2050, 2070, and 2080 under the four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The severity of soil erosion under climate change is expected to have a mixed impact in the range of -25 to 25% than the baseline scenario. The outcomes of this study will serve as a valuable tool for decision-makers while implementing management policies over the BRB, and can be well extended to any global catchment-scale applications.

The runoff-sediment relationship in the Yellow River Basin of China is still grim. People pay more and more attention to non-point source (NPS) pollution caused by surface pollutants migrating into the receiving water body with rainfall runoff. The particulate load of pollutants adsorbed in the soil and sediment by erosion and denudation and migration into water is also quite serious. It is necessary to deeply analyze the quantitative relationship between particulate nitrogen and phosphorus (PN/PP) load and soil loss. The soil erosion estimation of different administrative units in the study basin is obtained by the revised universal soil loss equation (RUSLE). The spatial distribution and the variation characteristics at different slopes and different land use of PN/PP load are discussed. An empirical equation of particulate organic load is used to calculate the PN/PP load. The results show that the multi-annual average erosion modulus of the basin is 358.33 t/(km 2 ∙a); the multi-annual average soil erosion reaches 9.62 million tons. The PN/PP load caused by soil loss reaches 11,107.1 t and 7909.3 t, and the export coefficients are 4.13 kg/hm 2 and 2.94 kg/hm 2 , respectively. Spatial distribution of the PN/PP load is in step with the soil erosion distribution. Soil erosion is prone to occur in the region under the slope of 8 ~ 25°, the NPS load of PN/PP are relatively large, and the average export coefficients of PN/PP are 7.17 kg/hm 2 and 5.06 kg/hm 2 . With the increase of the slope, the PN/PP load export coefficient increases first and then decreases. Agricultural land (AGRL), forest land (FRST), and pasture (PAST) are the land use types that contribute the most to the PN/PP load and soil erosion, and the average export coefficients of PN/PP are 4.54 kg/hm 2 and 3.23 kg/hm 2 , respectively. The variability of natural elements, the unevenness and heterogeneity of spatial distribution, and the heavy involvement of human activities will have a conspicuous impact on the soil erosion and NPS pollution processes in the basin. The research on the influence of single factor and combined factors on NPS pollution process can be strengthen and provides scientific theoretical basis for formulating reasonable and efficient water and soil conservation measures and NPS pollution control scheme, so as to achieve effective control and scientific management of environment pollution.

Assessing the potential impacts of different land management practices helps to identify and implement sustainable watershed management measures. This study aims to assess a change in soil erosion rate under different land management practices in the Gilgel Abay watershed of the upper Blue Nile basin, Ethiopia. The Revised Universal Soil Loss Equation (RUSLE) model that was adapted to the Ethiopian highlands context was employed to estimate the rate of soil erosion. The impact of land management practices on soil erosion was estimated for three scenarios, which were baseline, intensive cultivation, and extensive cultivation scenarios. At the baseline scenario, the mean annual soil erosion was estimated at ~32.8 t ha -1 yr -1 , which is equivalent to a loss of ~13.66 Mt yr -1 from the entire watershed. While the rate of soil erosion reduced to ~11.3 t ha -1 yr -1 during the implementation of intensive cultivation management practice, which reduced the total soil loss in the watershed by 65%. On the other hand, under the extensive cultivation scenario, the mean annual soil erosion rate increased to ~34.4 t ha -1 yr -1 . The findings suggest that implementing agricultural intensification management practices can significantly reduce soil erosion in the watershed., (© 2020 Published by Elsevier Ltd.)

Land managers often need to predict watershed-scale erosion rates after disturbance or other land cover changes. This study compared commonly used hillslope erosion models to simulate post-fire sediment yields (SY) at both hillslope and watershed scales within the High Park Fire, Colorado, U.S.A. At hillslope scale, simulated SY from four models- RUSLE, AGWA/KINEROS2, WEPP, and a site-specific regression model-were compared to observed SY at 29 hillslopes. At the watershed scale, RUSLE, AGWA/KINEROS2, and WEPP were applied to simulate spatial patterns of SY for two 14-16 km 2 watersheds using different scales (0.5-25 ha) of hillslope discretization. Simulated spatial patterns were compared between models and to densities of channel heads across the watersheds. Three additional erosion algorithms were implemented within a land surface model to evaluate effects of parameter uncertainty. At the hillslope scale, SY was only significantly correlated to observed SY for the empirical model, but at the watershed scale, sediment loads were significantly correlated to observed channel head densities for all models. Watershed sediment load increased with the size of the hillslope sub-units due to the nonlinear effects of hillslope length on simulated erosion. SY's were closest in magnitude to expected watershed-scale SY when models were divided into the smallest hillslopes. These findings demonstrate that current erosion models are fairly consistent at identifying areas with low and high erosion potential, but the wide range of predicted SY and poor fit to observed SY highlight the need for better field observations and model calibration to obtain more accurate simulations., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

In recent years, forest fires have increased in terms of frequency, extent and intensity, especially in Mediterranean countries. Climate characteristics and anthropogenic disturbances lead forest environments to display high vulnerability to wildfires, with their sustainability being threatened by the loss of vegetation, changes on soil properties, and increased soil loss rates. Moreover, wildfires are a great threat to property and human life, especially in Wildland-Urban Interface (WUI) areas. In light of the impacts and trends mentioned above, this study aims to assess the impact of the Mati, Attika wildfire on soil erosion. The event caused 102 fatalities, inducing severe consequences to the local infrastructure network; economy; and natural resources. As such, the Revised Universal Soil Loss Equation (RUSLE) was implemented (pre-; post-fire) at the Rafina, Attika watershed encompassing the Mati WUI. Fire severity was evaluated based on the Normalized Burn Ratio (NBR). This index was developed utilizing innovative remotely sensed Earth Observation data (Sentinel-2). The high post-fire values indicate the fire's devastating effects on vegetation loss and soil erosion. A critical "update" was also made to the CORINE Land Cover (CLC) v. 2018, by introducing a new land use class namely "Urban Forest", in order to distinguish the WUI configuration. Post-fire erosion rates are notably higher throughout the study area (4.53-5.98 t ha -1  y -1 ), and especially within the WUI zone (3.75-18.58 t ha -1  y -1 ), while newly developed and highly vulnerable cites now occupy the greater Mati area. Furthermore, archive satellite data (Landsat-5) revealed how the repeated (historical) wildfires have ultimately impacted vegetation recovery and erosional processes. To our knowledge this is the first time that RUSLE is used to simulate soil erosion at a WUI after a fire event, at least at a Mediterranean basin. The realistic results attest that the model can perform well at such diverse conditions, providing a solid basis for soil loss estimation and identification of high-risk erosion areas., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2019 The Authors.)

Through an extensive bibliographic review, this contribution underlines the urgency and challenges to quantify soil erosion rates (ERs) in developing countries. It subsequently elaborates on the combined application of GIS-based RUSLE, generalized likelihood uncertainty estimation (GLUE) principles and sediment delivery ratio functions (SDR) to quantify ERs at country scale for these countries, as they commonly have limited measurements to that purpose. The methodology, termed RUSLE-GGS (RUSLE-GIS-GLUE-SDR) herein, comprises the following sequence: (1) construction of ER samples using RUSLE-GIS based on freely available local/global geoenvironmental observations and field relations, (2) construction of area-specific sediment yield samples utilizing SDR transfer functions, and (3) assessment of the most behavioral samples by means of bias analysis and cross validation. Its application to Peru allows obtaining 5-km resolution ER and potential erosion maps for the years 1990, 2000, and 2010. RUSLE-GGS is highly replicable and could potentially be used as an initial standard and systematic method to estimate ERs in developing countries through the active participation of local scientists. Thus, it potentially can contribute to improve the capacity building in such countries and set an initial frame to compare the evolution of soil erosion in their territories towards attaining Goal 15 of the UN 2030 Agenda for Sustainable Development., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Gypsiferous soils in karstic landscapes are important areas in terms of biodiversity and geology. One of the geological formations occurring in gypsum regions is the doline lakes. Subsidence dolines must be preserved because of their formation and fossil heritage. Erosion is one of the most serious types of degradation among these types of lakes. The Bakkal Lake is one of the most important doline lake in central Turkey and is at risks with sediment flow. In this study, we aimed to determine how erosion poses a threat to the geological heritage in areas such as doline lakes and to simulate what measures can be taken to protect them. RUSLE/GIS/remote-sensing technologies were used to estimate the distribution and amount of sediment flowing into the lake. According to the results of the study, the amount of sediment transported to the lake was estimated to be 2.73tha -1 y -1 , and the total amount of sediment transported was 2876 m 3 y -1 . The time until Bakkal Lake is filled with sediment flux was calculated to be 698 years. The simulation was developed by offering protection measures to reduce sediment flow to the lake. As a result of the simulation, it was calculated that the amount of sediment flowing into the lake decreased to 2.29 tha -1 y -1 and the filling time increased to 833 years. The study showed that a doline lake, which under natural processes would not be filled for thousands of years, will soon be filled unless radical measures are not taken. This study asserts that more planners should use simulations to model sediment flow to better select appropriate conservation measures.

This data article describes the multiple ecosystem services in Beijing and surrounding areas, including grain providing, water yield, carbon sequestration, soil retention, purified water service, cultural services, and habitat quality. These data are mainly from public data sets such as the Harmonized World Soil Database. These data can be used to improve the optimization of human well-being in the social-ecological system and further achieve regional sustainable development., (© 2020 The Author(s).)