Mapping and quantifying medium-term soil loss rates in mountain olive groves using unmanned aerial vehicle technology.

Olive groves are one of the main agroecosystems in the Mediterranean region, but water erosion, aggravated by inappropriate soil management, is compromising the environmental sustainability of these crops. National and international public organisations, including the European Union via its Common Agricultural Policy, have acknowledged the problem and recognise the need to quantify the effects of this process. However, the variability of currently available short-term soil erosion measurements, together with limited understanding of the underlying processes, mean there is considerable uncertainty about the long-term effects of soil erosion. This paper presents an innovative procedure called SERHOLIVE4.0 designed to measure and model long-term soil erosion rates in olive groves, by means of structure-from-motion (SfM) techniques by which image information is obtained from unmanned aerial vehicles (UAVs). For the present study, the procedure was evaluated in mountain olive groves, where the erosion rate was calculated from historical surface reconstructions. Overall, this approach was found to be practical and effective. The method includes the following steps: [1] measure the current relief using UAV technology; [2] reconstruct the historical relief from field measurements; [3] calculate soil truncation (h) and obtain a soil erosion rate map; [4] determine the erosive dynamics of the slope and establish the relation between tree truncation, slope and mounds. The method we describe presents the following advantages:•it quantifies soil losses by reference to existing tree mounds;•it is straightforward to apply;•its application enhances the calibration of erosion models.

Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia.

Soil erosion is a major environmental problem in Ethiopia, reducing topsoil and agricultural land productivity. Soil loss estimation is a critical component of sustainable land management practices because it provides important information about soil erosion hotspot areas and prioritizes areas that require immediate management interventions. This study integrates the Revised Universal Soil Loss Equation (RUSLE) with Google Earth Engine (GEE) to estimate soil erosion rates and map soil erosion in the Upper Tekeze Basin, Northern Ethiopia. SoilGrids250 m, CHIRPS-V2, SRTM-V3, MERIT Hydrograph, NDVI from sentinel collections and land use land cover (LULC) data were accessed and processed in the GEE Platform. LULC was classified using Random forest (RF) classification algorithm in the GEE platform. Landsat surface reflectance images from Landsat 8 Operational land imager (OLI) sensors (2021) was used for LULC classification. Besides, different auxiliary data were utilized to improve the classification accuracy. Using the RUSLE-GEE framework, we analyzed the soil loss rate in different agroecologies and LULC types in the upper Tekeze basin in Waghimra zone. The results showed that the average soil loss rate in the Upper Tekeze basin is 25.5 t ha-1 yr-1. About 63 % of the basin is experiencing soil erosion above the maximum tolerable rate, which should be targeted for land management interventions. Specifically, 55 % of the study area, which is covered by unprotected shrubland is experiencing mean annual soil loss of 34.75 t ha-1 yr-1 indicating the need for immediate soil conservation intervention. The study also revealed evidence that this high mean soil loss rate of the basin can be reduced to a tolerable rate by implementing integrative watershed management and exclosures. Furthermore, this study demonstrated that GEE could be a good source of datasets and a computing platform for RUSLE, in particular for data scarce semi-arid and arid environments. The results from this study are reliable for decision-making for rapid soil erosion assessment and intervention prioritization.

Non-trade-off Changes in Soil Conservation Service and Soil Loss on the Tibetan Plateau Underlying the Impacts of Climate Change and human activities.

Climate change and human activities have significantly influenced soil loss and the soil conservation service, posed threats to regional ecological sustainability. However, the relationships and underlying driving forces between potential soil loss, actual soil loss, and soil conservation service have not been well understood. Utilizing the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, we evaluated the soil conservation service on the Tibetan plateau from 1990 to 2020. We analyzed the spatial and temporal trends and examined the driving factors using linear regression, Pearson correlation, and random forest regression. The soil conservation service exhibited a complex pattern of increase followed by a decrease, with a turning point around 2010. Soil conservation service and soil loss demonstrated non-trade-off changes. The potential soil loss dominated the spatiotemporal patterns of soil conservation service on the Tibetan Plateau. Climatic factors significantly influenced the spatiotemporal patterns of soil conservation service, with annual precipitation emerging as the dominant driving factor, contributing approximately 20%. However, the impacts of human activities became more pronounced since 2010, and the contribution of vegetation to changes in soil conservation service was increased. The impact of the Normalized Difference Vegetation Index (NDVI) on soil conservation service for the grades I, II, and III increased by 13.19%, 3.08%, and 3.41%, respectively. Conversely, in northern Tibet before 2010 and eastern Three-River-Source after 2010, soil conservation service exhibited an increasing trend driven by both climate factors and human activities. Which indicates that the implementation of ecological restoration measures facilitated vegetation improvement and subsequently reduced actual soil loss. This study provides a scientific basis for resource management, land development strategies, and the formulation of ecological restoration measures on the Tibetan Plateau.

A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing - a case study in Germany.

Soils provide habitat, regulation and utilization functions. Therefore, Germany aims to reduce soil sealing to 30 ha day - 1 by 2030 and to eliminate it by 2050. About 55 ha day - 1 of soil are damaged (average 2018-2021), but detailed information on its soil quality is lacking. This study proposes a new approach using geo-information and remote sensing data to assess agricultural soil loss in Lower Saxony and Brandenburg. Soil quality is assessed based on erosion resistance, runoff regulation, filter functions, yield potential and the Müncheberg Soil Quality Rating from 2006 to 2015. Data from the German Soil Map at a scale of 1:200,000 (BÜK 200), climate, topography, CORINE Land Cover (CLC) and Imperviousness Layer (IMCC), both provided by the Copernicus Land Monitoring Service (CLMS), are used to generate information on soil functions, potentials and agricultural soil loss due to sealing. For the first time, soil losses under arable land are assessed spatially, quantitatively and qualitatively. An estimate of the qualitative loss of agricultural soil in Germany between 2006 and 2015 is obtained by intersecting the soil evaluation results with the quantitative soil loss according to IMCC. Between 2006 and 2015, about 73,300 ha of land were sealed in Germany, affecting about 37,000 ha of agricultural soils. This corresponds to a sealing rate of 11 ha per day for Germany. In Lower Saxony and Brandenburg, agricultural soils were sealed at a rate of 1.9 ha day - 1 and 0.8 ha day - 1 respectively, removing these soils from primary land use. In Lower Saxony, 75% of soils with moderate or better biotic yield potential have been removed from primary land use, while in Brandenburg this figure is as high as 88%. Implementing this approach can help decision-makers reassess sealed land and support Germany's sustainable development strategy.

Quantification of soil erosion and sediment yield using the RUSLE model in Boyo watershed, central Rift Valley Basin of Ethiopia.

Changes in land use and land cover (LULC) are becoming recognized as critical to sustainability research, particularly in the context of changing landscapes. Soil erosion is one of the most important environmental challenges today, particularly in developing countries like Ethiopia. The objective of this study was evaluating the dynamics of soil loss, quantifying sediment yield, and detecting soil erosion hotspot fields in the Boyo watershed. To quantify the soil erosion risks, the Revised Universal Soil Loss Equation (RUSLE) model was used combined with remote sensing (RS) and geographic information system (GIS) technology, with land use/land cover, rainfall, soil, and management approaches as input variables. The sediment yield was estimated using the sediment delivery ratio (SDR) method. In contrast to a loss in forest land (1.7 %), water bodies (3.0 %), wetlands (1.5 %), and grassland (1.7 %), the analysis of LULC change (1991-2020) showed a yearly increase in the area of cultivated land (1.4 %), built-up land (0.8 %), and bare land (3.5 %). In 1991, 2000, and 2020, respectively, the watershed's mean annual soil loss increases by 15.5, 35.9, and 38.3 t/ha/y. Approximately 36 cm of the watershed's economically productive topsoil was lost throughout the study's twenty-nine-year period (1991-2020). According to the degree of erosion, 16 % of the watershed was deemed seriously damaged, while 70 % was deemed slightly degraded. Additionally, it is estimated for the year 2020 that 74,147.25 t/y of sediment (8.52 % of the total annual soil loss of 870,763.12 t) reach the Boyo watershed outlet. SW4 and SW5 were the two sub-watersheds with the highest erosion rates, requiring immediate conservation intervention to restore the ecology of the Boyo watershed.

Soil erosion is a major drive for nano & micro-plastics to enter riverine systems from cultivated land.

Nano and micro-plastics (NMPs, particles diameter <5 mm), as emerging contaminants, have become a major concern in the aquatic environment because of their adverse consequences to aquatic life and potentially human health. Implementing mitigation strategies requires quantifying NMPs mass emissions and understanding their sources and transport pathways from land to riverine systems. Herein, to access NMPs mass input from agricultural soil to riverine system via water-driven soil erosion, we have collected soil samples from 120 cultivated land in nine drainage basins across China in 2021 and quantified the residues of six common types of plastic, including polyvinyl chloride (PVC), polymethyl methacrylate (PMMA), polypropylene (PP), polyethylene (PE), polycarbonate (PC), and polystyrene (PS). NMPs (Σ6plastics) were detected in all samples at concentrations between 3.6 and 816.6 µg/g dry weight (median, 63.3 µg/g) by thermal desorption/pyrolysis-gas chromatography-mass spectrometry. Then, based on the Revised Universal Soil Loss Equation model, we estimated that about 22,700 tonnes of NMPs may enter the Chinese riverine system in 2020 due to agricultural water-driven soil erosion, which occurs primarily from May to September. Our result suggested that over 90% of the riverine NMPs related to agricultural soil erosion in China are attributed to 36.5% of the country's total cultivated land, mainly distributed in the Yangtze River Basin, Southwest Basin, and Pearl River Basin. The migration of NMPs due to water-driven soil erosion cannot be ignored, and erosion management strategies may contribute to alleviating plastic pollution issues in aquatic systems.

Modeling soil loss under rainfall events using machine learning algorithms.

Soil loss is an environmental concern of global importance. Accurate simulation of soil loss in small watersheds is crucial for protecting the environment and implementing soil and water conservation measures. However, predicting soil loss while meeting the criteria of high precision, efficiency, and generalizability remains a challenge. Therefore, this study first used three machine learning (ML) algorithms, namely, random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to develop soil loss models and predict soil loss rates (SLRs). These soil loss models were constructed using field observation data with an average SLR of 1756.48 t/km2 from rainfall events and small watersheds in the hilly-gully region of the Loess Plateau, China. During training, testing and generalizability stages, the average coefficients of determination from the RF, SVM, and ANN models were 0.903, 0.860, and 0.836, respectively. Similarly, the average Nash-Sutcliffe coefficients of efficiency from the RF, SVM and ANN models were 0.893, 0.791 and 0.814, respectively. These results indicated that MLs have superior predictive performance and generalizability, and broad prospects for predicting SLRs. This study also demonstrated that the RF model outperformed better than the SVM and ANN models. Therefore, the RF model was used to simulate the SLR of each small watershed in the Chabagou watershed. Our results showed the four-year (2017-2020) average annual SLR of the small watersheds ranged from 0.73 to 1.63 × 104 t/(km2∙a) in the Chabagou watershed. Additionally, the results also indicated the SLR of small watersheds under the rainstorm event with a 100-year recurrence interval was 4.4-51.3 times that of other rainfall events.Furthermore, this study confirmed that bare land was the predominant source of soil loss in the Chabagou watershed, followed by cropland land and grassland. This study helps to provide the theoretical basis for deploying soil and water conservation measures to realize the sustainable utilization of soil resources in the future.

Responses of soil and water-related ecosystem services to landscape dynamics in the eastern Afromontane biodiversity Hotspot.

The investigation of soil and water-related ecosystem services (ES) and recognizing the spatiotemporal effects of land-use and land cover changes (LULC) are essential for the formulation of conservation strategies. The research employed the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and the Revised Universal Soil Loss Equation (RUSLE) models for the assessment of ES. The study was carried out in the Kaffa Forest Biosphere Reserve in Ethiopia, which is part of the eastern Afromontane biodiversity hotspots. The aim of this investigation was to examine and map the temporal and spatial fluctuations in sediment retention, soil erosion, and water yield resulting from LULC modifications between 1986 and 2019, and to provide forecasts for the next three decades. According to the RUSLE analysis, the landscapes exhibited estimated soil losses ranging from zero to 1.5 tons ha-1 yr-1 in 1986, 2009, and 2019, respectively. The mean annual sediment exports for the years 1986, 1999, and 2019 were estimated to be 12.6, 9.9-, and 28.7-tons ha-1, respectively. The water yield of the site experienced a notable increase from 9.8 × 109 m3 in 1986 to 19.6 × 10 9 m3 in 1999, and subsequently rose to 39.3 × 109 m3 in 2019, which is considered to be a disadvantageous to the site. The study found a significant positive correlation between water yield and the expansion of settlement area (r = 0.99, P = 0.015) as well as agricultural land (r = 0.99, P = 0.05). It was also found that significant positive correlation found between vegetation dense area such as forest (r < 0.999, P < 0.001) and shrub & bamboo (r = 0.998, P = 0.036) with sediment retention service. The investigation discovered that there existed tradeoffs between the ES of sediment retention and water yield as the slope increased. The results may be attributed to the presence of dense vegetation cover on the elevated slope regions, rendering them unsuitable for agricultural activities, and the concurrent expansion of arable lands in the lower slope areas, which are flat terrains more conducive to cultivation. The transition from land with more vegetation density to land with lower or no vegetation coverage resulted in an increase in soil loss and water yield, while simultaneously decreasing the sediment retention service. Therefore, the findings can be used as a document to guide decision-makers to design soil-water conservation technologies to enhance the ecological integrity of the biosphere reserve.

Soil erosion estimation and erosion risk area prioritization using GIS-based RUSLE model and identification of conservation strategies in Jejebe watershed, Southwestern Ethiopia.

Erosion of soil refers to the process of detaching and transporting topsoil from the land surface by natural forces such as water, wind, and other factors. As a result of this process, soil fertility is lost, water bodies' depth is reduced, water turbidity rises, and flood hazard problems, etc. Using a numerical model of erosion rates and erosion risks in the Jejebe watershed of the Baro Akobo basin in western Ethiopia, this study mapped erosion risks to prioritize conservation measures. In this study, the Revised Universal Soil Loss Equation (RUSLE) model was used, which was adapted to Ethiopian conditions. To estimate soil loss with RUSLE, the rainfall erosivity (R) factor was generated by interpolating rainfall data, the soil erodibility (K) factor was derived from the soil map, the topography (LS) factor was determined from the digital elevation model (DEM), cover and management (C) factor derived from the land use/cover data, and conservation practices (P) factor generated from digital elevation model (DEM) and land use/cover data were integrated with remote sensing data and the GIS 10.5 environment. The findings indicated that the watershed annual soil loss varies from nearly 0 on a gentle slope of forest lands to 265.8 t ha-1 year-1 in the very steep slope upper part of the watershed, with a mean annual soil loss of 36.2 t ha-1 year-1. The total annual soil loss in the watershed is estimated to be around 919,886.5 tons per year. To minimize the amount of soil erosion in the watershed that had been most severely affected, we identified eight conservation strategies that could be implemented. These strategies were based on the participatory watershed development (PWD) principles established by the Ethiopian government and the severity of the erosion in the watershed. The study's findings showed that a GIS-based RUSLE soil erosion assessment model can provide a realistic prediction of the amount of soil loss that will occur in the watershed. This tool can also help identify the priority areas for implementing effective erosion control measures.

Soil erosion estimation by RUSLE model using GIS and remote sensing techniques: A case study of the tertiary hilly regions in Bangladesh from 2017 to 2021.

Soil erosion is one of the major environmental threats in Bangladesh, especially in the tertiary hilly regions located in the northeastern and southeastern parts of the country. The revised universal soil loss equation (RUSLE), combined with Geographic Information System, is a reliable methodology to estimate the potential soil loss in an area. This research aimed to use the RUSLE model to estimate the soil erosion in the tertiary hill tracts of Bangladesh from 2017 to 2021. The erosivity factor was determined from the annual average precipitation, and erodibility factor was estimated from FAO soil database. The elevation model was used to analyze slope length steepness factors, while land use land cover was used to compute cover management factor. Lastly, land use and elevation were integrated to estimate the support practice factor. Results revealed that the potential mean annual soil loss in 2017, 2019, and 2021 was 68.77, 69.84, and 83.7 ton ha-1 year-1 from northeastern and 101.72, 107.83, and 114.04 ton ha-1 year-1 from southeastern region, respectively. Although total annual rainfall was high in 2017, soil loss was found higher in 2021 which indicates the impact of land use change on erosion. This investigation will help the policymakers to identify the erosion-vulnerable areas in the hill tracts that require immediate soil conservation practices. Additionally, there is no latest field-based data available for the country for the validation, and hence, it is recommended to conduct field-based studies for validating the model-derived results and creating a reliable soil erosion database for the country.

Assessment of soil erosion hazard and its relation to land use land cover changes: Case study from alage watershed, central Rift Valley of Ethiopia.

Soil erosion by water and wind is among the most crucial land degradation processes in Ethiopia. This is also the case for Alage watershed located in the cental Rift Valley system. This study aimed at assessment of soil erosion hazard and its relation to land use land cover change in the watershed during the period from 1984 to 2016 for a better land management. The study is based on application of Remote Sensing (RS) and Geographical Information System (GIS) to extract inputs factor values for the Revised Universal Soil Loss Equation (RUSLE). Time-series satellite imageries of Landsat TM 1984, ETM+ 2000 and OLI 2016 were used for land use land cover change detection and determination of cover management (C) factor of the RUSLE. Biophysical data such as rainfall, soil properties, land management practices including soil and water conservation measures within the watershed were collected using field survey and secondary data sources. Slope steepness and slope length factors were derived using Digital Elevaition Model (DEM). Long-term average annual soil loss rates were estimated by the RUSLE integrated with GIS for 1984, 2000 and 2016. Using satellite imageries, the land use land cover and changes within the watershed during the three periods were obtained through a supervised classification with maximum likelihood algorithim. The results of land use land cover change indicated that the proportion of rain-fed cropland, bare land and built up areas increased by 17.4%, 5.9% and 2.9% respectively over the three study period. In contrast the proportion of bush/shrub land, irrigated cropland, grass land, forested areas and waterbodies decresaed by 15.5%, 4.7%, 3.4%, 2.3% and 0.3% respectively during the same period. Estimated average annual soil loss rates showed an increasing trend from 24.3 ton ha-1 yr-1 in 1984 to 38 ton ha-1 yr-1 in 2016. Increasing trends of average annual soil loss rate is attributed to increased proportion of cropland, bare land and built up areas during those periods leading to decreased protective vegetation cover. Hotspot areas within the watershed require implementation of land management practices to prevent further degradation and expansion of gullies. This study is relevant to demonstrate environmental implication of land use land cover change for future land management practices and land use policy in the Rift Valley of central Ethiopia.

Assessment, regionalization, and modeling rainfall erosivity over Brazil: Findings from a large national database.

In this study, we used a large national database to assess the rainfall erosivity (RE) patterns in time and space over the Brazilian territory. Thereby, RE and erosivity density (ED) values were obtained for 5166 rainfall gauges. Also, the concentration of the RE throughout the year and the RE's gravity center locations were analyzed. Finally, homogeneous regions regarding RE values were delimited and estimative regression models were established. The results show that Brazil's mean annual RE value is 5620 MJ mm ha-1 h-1 year-1, with considerable spatial variation over the country. The highest RE magnitudes were found for the north region, while the northeast region shows the lowest values. Regarding the RE's distribution throughout the year, in the southern region of Brazil, it is more equitable, while in some spots of the northeastern region, it is irregularly concentrated in specific months. Further analyses revealed that for most of the months, the RE's gravity centers for Brazil are in the Goiás State and that they present a north-south migration pattern throughout the year. Complementarily, the ED magnitudes allowed the identification of high-intensity rainfall spots. Additionally, the Brazilian territory was divided into eleven homogeneous regions regarding the RE patterns and for each defined region, a regression model was established and validated. These models' statistical metrics were considered satisfactory and, thus, can be used to estimate RE values for the whole country using monthly rainfall depths. Finally, all database produced are available for download. Therefore, the values and maps shown in this study are relevant for improving the accuracy of soil loss estimates in Brazil and for the establishment of soil and water conservation planning on a national scale.

Simulating with a combination of RUSLE GIS and sediment delivery ratio for soil restoration.

Erosion by water is the main cause of land degradation. Landscapes degraded by erosion need to be restored in many respects, and particularly in terms of ecosystem services. From an economic and management perspective, care is needed to select priority areas and determine the means to be applied to restore them. Globally, the model most commonly used to produce scenarios for the prevention of soil losses is the Revised Universal Soil Loss Equation (RUSLE). This study of the subbasin of the Sulakyurt Dam Basin in Turkey aims (1) to identify the distribution of soil losses over time and by location, and (2) to grade the priority areas for the prevention of soil losses by means of a simulation. The average potential soil losses in the area under study are estimated at 42.35 t ha-1 year-1, and the average actual losses at 39.49 t ha-1 year-1. According to the simulation, 27.61% of the study area (2782 ha) is of the highest priority for soil restoration. In our study, forests have the highest soil losses, which is contrary to the natural protection that forests provide against erosion. The high rates are due to the slope, the forest area is very steep. So it is the slope factor that outweighs the vegetation cover factor. Of the forest areas, 41.74% (1766 ha) falls within the areas of highest priority. The study serves as a guide for landscape planning and the determination of erosion risk in restoration efforts, and for identifying the methods to be adopted during the restoration work to reduce the loss of soil.

Impacts of Eucalyptus plantation on soil and water losses in a typical small watershed in mountainous area of southern China.

Unreasonable exploitation of artificial forest causes severe soil erosion in the mountainous areas of sou-thern China. The spatial-temporal variations of soil erosion in typical small watershed with artificial forest has signifi-cant implications for artificial forest exploitation and sustainable development of mountainous ecological environment. In this study, we used revised universal soil loss equation (RUSLE) and geographic information system (GIS) to evaluate the spatial and temporal variations of soil erosion and its key drivers of Dadingshan watershed in mountainous area of western Guangdong. The results showed that the erosion modulus was 1948.1 t·km-2·a-1 (belonging to light erosion) in the Dadingshan watershed. However, the spatial variation of soil erosion was substantial, with variation coefficient of 5.12. The maximal soil erosion modulus was 191127 t·km-2·a-1. Slight erosion (<500 t·km-2·a-1) accounted for 80.6% of the total watershed area. The moderate erosion and above (>2500 t·km-2·a-1) were mainly distributed in young Eucalyptus forest area with less than 30% of the vegetation coverage, which contributed nearly 75.7% of total soil erosion. During 2014-2019, the interannual variations of mean erosion of Dadingshan catchment was modest, but the spatial variation of soil erosion was large. Vegetation cover, slope, and rainfall were key drivers of such variation. The destruction of natural vegetation resulted by plantation exploitation was the primary cause of soil erosion in afforestation areas. Soil erosion significantly increased with the increases of slope gradient in the young forest area, which was aggravated by extreme rainfall. However, soil erosion gradually decreased with the increases of the age of Eucalypt plantation. Therefore, the hot spot of soil erosion was young forest areas of Eucalypt plantation with slope >25°, and the key period for soil erosion control was the first 2-3 years after Eucalyptus planting. We suggested that reasonable afforestation measures should be used in area with >25° slopes, and that the destruction of natural vegetation should be avoided on hillslope with >35° slope gradient. The road construction standards and forest management should be further improved to address the challenge of extreme rainfalls.

Evaluating soil loss under land use management and extreme rainfall.

Topsoil loss is a widespread environmental concern causing adverse impacts on natural and human systems. Severe weather accompanied with human activities can exacerbate this issue degrading soil health and consequently accelerating global and regional food insecurity. Erosion impairs soil physical and chemical properties such as infiltration rate, water holding capacity, loss of nutrients including soil carbon and nitrogen. Although, temporal properties of a rainfall event have meaningful implications, spatial heterogeneity of a rainfall contributes substantially and cannot be overlooked. Therefore, in this study, we investigated soil loss using weather radar NEXRAD data. We developed extreme rainfall (ER) scenarios and land use practices (nomgt, S0, S1, S2, and S3) and evaluated the watershed response. We found that grazing can manifold soil loss, and if accompanied with extreme rainfalls, soil loss accelerates impacting different subbasins each time. Our results suggest that spatial heterogeneity of ERs can be more significant in individual extreme rainfalls, however, over a year, soil moisture and type of the management practices (grazing and farming) could contribute more to topsoil loss. We classified watershed subbasins into different classes of soil loss severity to determine the soil loss hotspots. Soil loss can go as high as 350 (ton/ha/yr) under the ERs. Land use practices can increase erosion by 3600%. Slight increase in rainfall concentration (S1) can put vulnerable subbasins in extremely severe class (>150 ton/ha/yr). Under moderate increase in the rainfall concentration (S2) more subbasins fall into extremely severe category yielding approximately 200 ton/ha/yr. Under high increase in rainfall concentration (S3) almost all the subbasins fall into the extremely severe class yielding >200 ton/ha/yr. We found that in vulnerable subbasins, up to 10% increase in (Concentration Ratio Index) CI can increase annual soil loss up to 75%. Single ER can generate up to 35% of annual soil loss. Under one ER event soil loss hotspot subbasins can lose up to 160 ton/ha/day. 32% and 80% increase in rainfall amount for an ER event can increase soil loss by 94% and 285% respectively. The results, also, reveal that grazing and farming can be responsible for up 50% of soil loss. Our findings indicate the importance of site-specific managements to mitigate soil loss and all the consequences. Our study can help in better soil loss management implementation. Insights of our study may also help in water quality control and flood mitigation planning efforts.

Spatial modeling of soil loss as a response to land use-land cover change in Didessa sub-basin, the agricultural watershed of Ethiopia.

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.

GIS based soil loss assessment using RUSLE model: A case of Horo district, western Ethiopia.

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.

Predicting soil erosion potential under CMIP6 climate change scenarios in the Chini Lake Basin, Malaysia.

Climate change and soil erosion are very associated with environmental defiance which affects the life sustainability of humans. However, the potency effects of both events in tropical regions are arduous to be estimated due to atmospheric conditions and unsustainable land use management. Therefore, several models can be used to predict the impacts of distinct climate scenarios on human and environmental relationships. In this study, we aimed to predict current and future soil erosion potential in the Chini Lake Basin, Malaysia under different Climate Model Intercomparison Project-6 (CMIP6) scenarios (e.g., SSP2.6, SSP4.5, and SSP8.5). Our results found the predicted mean soil erosion values for the baseline scenario (2019-2021) was around 50.42 t/ha year. The mining areas recorded the highest soil erosion values located in the southeastern part. The high future soil erosion values (36.15 t/ha year) were obtained for SSP4.5 during 2060-2080. Whilst, the lowest values (33.30 t/ha year) were obtained for SSP2.6 during 2040-2060. According to CMIP6, the future soil erosion potential in the study area would reduce by approximately 33.9% compared to the baseline year (2019-2021). The rainfall erosivity factor majorly affected soil erosion potential in the study area. The output of the study will contribute to achieving the United Nations' 2030 Agenda for Sustainable Development.

Soil loss tolerance in the context of the European Green Deal.

Soil erosion by water, and the consequent loss of a non-renewable resource, is a relevant environmental issue which has economic, ecologic, and social repercussions. In the context of the European Green Deal, the increasing awareness of soil Ecosystem Services is leading to give the due relevance to this problem. Notwithstanding the recent soil conservation strategies adopted by the Common Agricultural Policy had positive effects, the concern regarding this topic is drastically increasing for the normalization of extraordinary rainfall events due to climate change. Recent events occurred in Europe demonstrated that landscape protection is often inadequate and interventions to prevent damages due to hydrogeological instability are scarce. The determination of a "tolerable" soil loss TSL is useful to establish a quantitative standard to measure the effectiveness of strategies and techniques to control soil erosion. However, soil conservation strategies/works designed by the mean annual value of the climatic variable, as the rainfall erosivity factor R, are not appropriate for some erosive events which produce intolerable sediment yield values. Therefore, the adoption of an adequate TSL, which could help to ensure the protection of soil functions and a sustainable soil use, should be a primary goal to reach for policy makers. In this paper, a new method to define the tolerable soil loss is proposed. This approach is based on the statistical analysis of the measured annual values of R and leads to the determination of the cover and management factor for which the maximum tolerable soil loss is equal to the annual soil loss of given return period. The analysis demonstrated that to limit soil erosion to the tolerable soil loss, interventions to change land use, reduce field length or apply support practices can be carried out.

Gully erosion vulnerability modelling, estimation of soil loss and assessment of gully morphology: a study from cratonic part of eastern India.

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.