Quantify Soil Erosion Research Articles

Exploring the unintended contribution of soil erosion research to microplastic contamination

The global scientific community, particularly soil erosion researchers, is increasingly recognizing the urgent need to address plastic pollution and the accumulation of microplastics in soil. Human activities such as urban expansion, food packaging, and the use of agricultural materials have driven a significant rise in microplastic contamination. The problem is compounded by the difficulty, expense, and time-intensive nature of analyzing soil erosion samples to accurately identify microplastic types, sources, and their effects. Soil erosion, while naturally shaping landscapes like hillslopes, deltas or river valleys, has evolved into a critical issue due to human-induced changes, threatening fertile agricultural zones and areas prone to landslides, with serious economic and human consequences. While validated methods exist to quantify soil erosion, runoff, and pollutant concentrations, these techniques inadvertently introduce microplastics into the environment by plastic materials during sampling, transport, and lab analysis. This not only skews historical and current data on microplastic levels in soils but also alters natural soil conditions around research plots. To address these challenges, this paper advocates for the refinement of measurement techniques to minimize contamination, a reassessment of current methodologies, and a concerted effort to reduce human impact on soil health, thereby safeguarding a critical natural resource.

Quantification of Soil Erosion Using Digital Soil Mapping and RUSLE Method for Coimbatore District, Tamil Nadu, India

Quantification of Soil Erosion Using Digital Soil Mapping and RUSLE Method for Coimbatore District, Tamil Nadu, India

Application of fallout radionuclide—137Cs for estimating soil erosion in steep hillslopes with diverse land use of North-western Indian Himalayas

Hilly and mountainous regions are significantly impacted by soil erosion, primarily due to rainfall-runoff processes occurring on the hillslope scale. Assessing soil erosion is crucial for quantifying the loss of soil carbon and nutrients, which diminishes the potential of soil ecosystem services and is critical for mitigating the impacts of climate change on food security. However, the Himalayan landscape poses serious challenges for assessing soil erosion due to its steep and rugged terrain, which hinders the use of conventional and modelling methods. The fallout radionuclide—137Cs has been extensively utilized as an environmental marker for investigating soil redistribution processes. Despite its potential, there is a notable lack of 137Cs-based soil erosion studies in the Himalayan region. In this context, we assessed the applicability of the fallout radionuclide—137Cs method in quantifying soil erosion rates and identifying erosion hotspots on two hillslopes of the Higher Himalayas. On the hillslope scale, we observed that soil erosion rates vary based on slope gradient and land use/land cover. Forested areas exhibited the lowest soil erosion rates compared to cultivated areas, while flat hillslope positions experienced lower erosion rates than steeper positions. The average net erosion rate for the Harsil hillslope varied among different hillslope positions, ranging from – 2.9 to – 15.6 t ha−1 yr−1. Similarly, in the Gangnani hillslope, the net erosion rates varied across different positions, ranging from – 5.6 to – 39 t ha−1 yr−1. Our findings confirm that the middle and lower hillslope positions are the most critical source areas with higher soil erosion rates, while hillslope positions with forest cover demonstrate the lowest erosion rates, identified as helpful in controlling soil erosion. The study has demonstrated the applicability of FRN as a soil erosion measurement method in the complex, rugged, and steep terrain of the Himalayas, highlighting the need for targeted conservation efforts to mitigate soil erosion and preserve ecosystem integrity.

Assessing decadal soil redistribution rates using 239+240Pu across diverse lithologies in Southeast Alaska

AbstractQuantifying soil redistribution rates, including both erosion and deposition, is critical for understanding erosion processes, landscape evolution, land management strategies, and the carbon cycle. In the Northeast Pacific coastal temperate rainforest, the interaction of perhumid climate and dense coniferous forest tends to form Spodosols which are soils characterized by a subsurface accumulation of organic matter and iron and aluminum oxides, across a range of contrasting lithologies. Deep Spodosols are frequently found on steep backslopes (up to 60%) of colluvial deposits, where shallower soils would typically be expected. We hypothesized that deep Spodosols in Southeast Alaska indicate slope stability, exhibiting negligible soil redistribution rates and stable surfaces regardless of the lithology. Our objective was to quantify soil redistribution rates for Spodosols formed on steep slopes across a range of lithologies in hilly and mountainous areas of Juneau, AK. We used 239+240Pu isotopes to quantify soil erosion and deposition rates in Spodosols formed on colluvial deposits from tonalite, slate, metavolcanic rock, and phyllite. 239+240Pu measurements revealed negligible soil redistribution rates for all studied pedons, ranging from erosion rates of 0.51 t/ha/year to deposition rates up to 0.43 t/ha/year. No difference was detected between the hill and mountain landforms, further supporting the idea that Spodosols could indicate slope stability over decadal timescales across the region. Understanding the resilience of Spodosols to erosion processes in varied lithologies and landforms on steep slopes is paramount for making informed decisions regarding sustainable land use, landslide risk mitigation, and effective carbon sequestration strategies.

Quantifying soil erosion and soil organic carbon conservation services in indian forests: A RUSLE-SDR and GIS-based assessment

Indian forests, having sixteen types of vegetation, provide various ecosystem services. Forests reduce water erosion of soil, reduce floods and conserve low river flow during dry seasons. Present study estimated annual sedimentation, texture, soil organic carbon control service of forests of India using the Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Ratio (SDR) approach through GIS technique along with identifying important drivers responsible for erosion in forests. The soil erosion control service was based on five factors viz., rainfall erosivity, soil erodibility, slope, crop management, conservation practice factor and SDR across the various forest dominated river basins. Geo-detector modelling was performed to evaluate environmental drivers responsible for soil erosion. Valuation of soil control service of the India’s forests was estimated based on damage and replacement cost method. Annual soil loss rate was maximum in Tropical Thorn Forest and in Ganga River basin. Maximum sediment control and SOC conservation against the soil erosion control service by India’ forests were due to Tropical Dry Deciduous Forest with a total annual sedimentation control and SOC conservation of 33,056,560 tonnes and 3,31,115.06 tonnes, respectively. The conservative estimate of the economic value of soil conservation service by the forests of India was US$ 535.6 M with an average of 4.40 US$ ha−1 and a maximum share of value for Tropical Dry Deciduous Forest i.e. 294.18 million US$ and average maximum value for Littoral and Swamp Forest (28.91 US$ ha−1). The soil erosion in Tropical and Sub-tropical Forest was influenced by the interactive effects between NDVI and Bulk Density. In contrast, soil erosion in the temperate and alpine area was influenced by anthropogenic activity in combination with climatic factors. Present study accounts the role of India’s forests for the soil conservation service and may provide inputs to implement forest and reservoir management strategies besides providing information for Integrated Watershed management and Catchment Treatment Plans.

Source, preservation and re-suspension of 239,240Pu in a well dated peat core collected from northwest China

Owning to the high radiotoxicity in high concentrations, as well as the irreplaceability in quantifying soil erosion rates, demarcating the Anthropocene, and dating of sediment, anthropogenic 239,240Pu have drawn high attention. However, the source in specific areas, preservation characteristics in different environment media, and re-distribution process after the cessation of atmospheric nuclear weapons tests, have not been fully understood, which obscures the exact start year, temporal variation, and deposition flux of 239,240Pu in sedimentary records, and hinders the wide application of 239,240Pu in environment study. A sediment core from the Yiwu peat bog with dominance of atmospheric deposition in the source material, simple sedimentary environment, and high dust deposition flux, was collected to examine the 239,240Pu, and explore the source, preservation, and re-distribution process. The double peaks of 239,240Pu in 1952 CE and 1963 CE, as well as 240Pu/239Pu ratios of 0.163–0.190 with an average of 0.177 ± 0.010 confirmed that the 239,240Pu source originated predominantly from global fallout. The minimal vertical migration of plutonium in the Yiwu peat core was attributed to the near-neutral pH condition. The high inventory of 128 ± 7 Bq m−2, as well as the atypical negative correlation between 239,240Pu concentrations and organic matter content (r = − 0.79, P < 0.01), was attributed to the contribution of 239,240Pu re-suspended with dust from the neighboring Gobi Desert, particularly in the cold and dry years. The total re-suspended 239,240Pu was estimated to be 77 Bq m−2, exceeding the direct fallout level of 51 Bq m−2 during 1945–2016 CE. In this study, the specified deposition pattern of 239,240Pu after the cessation of atmospheric nuclear weapons was established, providing an important standard for multiple environmental studies, and the re-suspended amount of 239,240Pu in a typical arid area was quantified for the first time.

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.

Unraveling soil filling and transport in fissures on karst slopes using multiple tracers

The fragile ecological environment and unique surface–subsurface geological duality of karst regions have led to complex processes and rates of underground soil loss, which is subject to much controversy. However, to ensure the ecological sustainability of karst areas, it is imperative to address this issue systematically using innovative study methods. With focuses on ten fissures and fivecross-sections in a typical karst area in southwestern China, this study applied unmanned aerial vehicles for field surveys and indoor modeling techniques to investigate the distribution density of fissures in typical karst areas, which is crucial for calculating underground soil loss. By measuring magnetic susceptibility, organic carbon content, and particle size of fissure soils, the primary sources of soil in the fissures were identified. Additionally, the rate of soil loss passing through karst fissure was calculated using 137Cs tracing and 14C dating techniques. Results indicate that (1) the proportion of fissure area per square kilometer of land is 1.59 %; (2) the soil in the fissures is mainly filled with the topsoil outside the fissures, and (3) the average rate of soil filling into the fissures is 16.13 t km−2 yr−1, account for only 5.8 % of the total soil erosion modulus. The study shows that soil filling into fissures is the main process of underground soil loss on karst slopes, and the rate of soil loss through fissures is very small. This study offers valuable insights into soil movement processes and quantification of soil erosion in karst regions, thus aiding in the preservation and sustainable management of these unique ecosystems.

Quantifying Soil Erosion in Drought-Impacted Central Odisha, India, through Geospatial Mapping with RUSLE

Soil degradation in Odisha poses a significant conservation concern for the local environment. The present research focused on a region in central Odisha State, India, affected by drought conditions. Several models have emerged to assess soil loss, with the Revised Universal Soil Loss Equation (RUSLE) standing out as the most suitable option. The erosion computation process entails utilizing the digital elevation model (DEM), Landsat-9 imagery, and soil data from several sources accessible in different forms and scales. The present analysis took into account various elements, namely, crop management factor (C), practice management factor (P), slope length factor (LS), steepness factor of the slope (S), and rainfall factor (R). Multiplying these factors yielded the average rate of soil erosion. Areas with a high slope length factor, such as those in Kandhamal, Kalahandi, and Nuapada, have a high erosion rate. The study reveals that 57% of the land in the study area experiences very low to moderate soil erosion at a rate of 2–10 tons per hectare per year, while 43% faces moderately to very severe erosion at a rate of 10–25 tons per hectare per year. Erosion hotspots, covering 32,205 square kilometers, are mainly identified in agricultural and forested hilly areas where slopes exceed 10°, such as those in Kandhamal, Kalahandi, and Nuapada, which have a high erosion rate. These districts are especially vulnerable to soil loss and resulting climate action (Sustainable Development Goals-13) because of frequent and severe rainfall, shifting agricultural practices, a thin surface soil covering, natural erosion, and barren hills. The research emphasizes the urgent need for implementing conservation and management measures to protect high-risk areas from further degradation. In conclusion, the study underscores the effectiveness of the RUSLE-GIS model in conducting quantitative and spatial assessments of soil erosion on a river watershed scale. The model is deemed crucial in formulating conservation strategies to address the identified erosion issues in the tropical highlands of the area.

Monitoring and Quantifying Soil Erosion and Sedimentation Rates in Centimeter Accuracy Using UAV-Photogrammetry, GNSS, and t-LiDAR in a Post-Fire Setting

Remote sensing techniques, namely Unmanned Aerial Vehicle (UAV) photogrammetry and t-LiDAR (terrestrial Light Detection and Ranging), two well-established techniques, were applied for seven years in a mountainous Mediterranean catchment in Greece (Ilioupoli test site, Athens), following a wildfire event in 2015. The goal was to monitor and quantify soil erosion and sedimentation rates with cm accuracy. As the frequency of wildfires in the Mediterranean has increased, this study aims to present a methodological approach for monitoring and quantifying soil erosion and sedimentation rates in post-fire conditions, through high spatial resolution field measurements acquired using a UAV survey and a t-LiDAR (or TLS—Terrestrial Laser Scanning), in combination with georadar profiles (Ground Penetration Radar—GPR) and GNSS. This test site revealed that 40 m3 of sediment was deposited following the first intense autumn rainfall events, a value that was decreased by 50% over the next six months (20 m3). The UAV–SfM technique revealed only 2 m3 of sediment deposition during the 2018–2019 analysis, highlighting the decrease in soil erosion rates three years after the wildfire event. In the following years (2017–2021), erosion and sedimentation decreased further, confirming the theoretical pattern, whereas sedimentation over the first year after the fire was very high and then sharply lessened as vegetation regenerated. The methodology proposed in this research can serve as a valuable guide for achieving high-precision sediment yield deposition measurements based on a detailed analysis of 3D modeling and a point cloud comparison, specifically leveraging the dense data collection facilitated by UAV–SfM and TLS technology. The resulting point clouds effectively replicate the fine details of the topsoil microtopography within the upland dam basin, as highlighted by the profile analysis. Overall, this research clearly demonstrates that after monitoring the upland area in post-fire conditions, the UAV–SfM method and LiDAR cm-scale data offer a realistic assessment of the retention dam’s life expectancy and management planning. These observations are especially crucial for assessing the impacts in the wildfire-affected areas, the implementation of mitigation strategies, and the construction and maintenance of retention dams.

Does spatial resolution matter in the estimation of average annual soil loss by using RUSLE?-a study of the Urmodi River Watershed (Maharashtra), India.

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 rainfalldata, 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 km2. In addition to this, a scatterplot between the final outputs was computed and found the moderate (R2 = 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.

Effect of grass cover and abandonment on soil surface changes and soil properties in traditional vineyards in Vráble viticultural region in southwestern Slovakia

Land use transition, related to the extensification of management and agricultural abandonment, is a widespread phenomenon across European landscapes. In this study, we examined the effects of protective grassing and abandonment on soil protection and regeneration in traditional vineyards of the Vráble viticultural region, located in the temperate climate zone of central Europe. We analysed changes in the soil surface level, soil profile, and soil properties at various slope positions: shoulder, backslope, and footslope. Variability in the soil surface level was assessed using the poles height method, which was developed to quantify soil erosion and deposition. The results showed that both measures positively affected soil development. In the abandoned vineyard, the soil surface level increased by 0.72 mm yr−1 on the slope shoulder, 0.26 mm yr−1 on the backslope, and 3.58 mm yr−1 on the footslope. In contrast, the cultivated vineyard exhibited minimal changes in the slope shoulder (0.02 mm yr−1), erosion on the backslope (4.52 mm yr−1), and deposition on the footslope (19.30 mm yr−1). The vineyard that was hoed for half of the analysed period and then grassed showed erosion rates of 0.25 mm yr−1 on the shoulder, 1.48 mm yr−1 on the backslope, and deposition of 3.42 mm yr−1 on the footslope. All slope profiles showed evidence of erosion-accumulation processes resulting from past management practices without protective vegetation cover; however, the development of a new humic layer was observed in grassed and abandoned vineyard. Grass cover and abandonment positively influenced soil organic carbon (SOC) content, bulk density, and porosity.

Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020)

In soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (RE), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (ED), which relates RE to precipitation. The RE requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for RE estimation. This study evaluates the performance of a new gridded dataset of RE and ED in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000–2020. By using this method, a correlation of 0.94 was found between PISCO_reed and RE obtained by the observed data. An average annual RE for Peru of 7840 MJ · mm · ha−1· h−1 was estimated with a general increase towards the lowland Amazon regions, and high values were found on the North Pacific Coast area of Peru. The spatial identification of the most at risk areas of erosion was evaluated through a relationship between the ED and rainfall. Both erosivity datasets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision.

Quantifying soil erosion and influential factors in Guwahati's urban watershed using statistical analysis, machine and deep learning

Soil erosion is a complex environmental issue influenced by rapid climate change, resource exploitation, and soil degradation etc. These factors have triggered global acceleration of soil erosion, primarily due to rapid transformation of topographical features and landscape composition. Guwahati, a thriving financial hub in northeast India, witnessing significant landscape change on both the banks of the Brahmaputra river therefore becomes disaster-prone zones. Hence, the objective of the present study is to identify soil erosion factors and assess its impact using statistical, machine learning, and deep learning techniques. It employs Revised Universal Soil Loss Equation (RUSLE) model for soil erosion estimation, furthermore analyzing physical attributes such as morphometrics, topography, drainage networks, and land use fragmentation indicators. Partial Least Squares Regression (PLSR), Random Forest (RF) sensitivity analysis, and Deep Neural Network (DNN) techniques are used in the study. The RUSLE model showed a significant range of soil erosion rates in the study area, spanning from 168.16 to 188.60 tonnes/hectare/year. Particularly, Silsako, Bharalu, North Guwahati, and Foreshore experiences the most severe soil loss. Amongst all influential factors contributing to soil erosion, the most important key parameters are rainfall, drainage density, landscape fragmentation components (such as cohesion index, edge density, and Shannon diversity index), along with stream frequency and basin relief, as indicated by the RF and DNN models. Furthermore, the PLSR analysis assigned linear weights to variables, highlighting the effectiveness of 14 out of 15 independent predictors derived from basin characteristics in accurately estimating soil erosion. This study provides important quantitative insights through rigorous scientific analysis, enabling well-informed decisions in urban watershed management within the Brahmaputra region. Furthermore, it enhances understanding of the area's urgent needs, societal implications, and environmental conditions related to soil erosion.

Geomorphological changes in young soils with sparse vegetation: Mathematical modeling and numerical simulation

This article presents an improved mathematical model and numerical simulation for weathering of large areas with complex topography. It uses the equations of momentum, temperature, and humidity in turbulent air and for heat and water infiltration into soils. A mathematical model is also presented to calculate the soil porosity fraction produced by physical rock weathering in areas where soil is produced from intrusive rocks (batholiths). An algorithm based on air velocity, humidity (rainfall), temperature variation, and soil topography was developed to quantify soil erosion and change of relief at each point and time step in air, at the ground surface, and within the soil. This results in a complete air-soil model based on conservation laws that have not previously been applied to large areas of the earth's surface. The mathematical model is solved using large-scale numerical simulations applied to an area of 6.6 km2 in the Sierra Nevada batholith of California, USA. The results show that the wind velocity and resulting erosion is greater in areas with steeper slopes and that moisture accumulates mainly in low and flat areas; therefore, erosion is not uniform throughout the study area. In addition, computer simulations localized calculations to discrete grid cells within the porous (saprolite) fraction of the soil produced by freezing and thawing of water in rock. Results indicate that this physical mechanism is the primary contributor to weathering of rock at the study area.

Quantifying Soil Erosion and Identifying Critical Source Areas Under Current Management in the Holeta Watershed, Awash Basin, Ethiopia

Quantifying Soil Erosion and Identifying Critical Source Areas Under Current Management in the Holeta Watershed, Awash Basin, Ethiopia

Feasibility Assessment of a Magnetic Layer Detection Method for Field Applications

The lack of current data on the spatial distribution of soil erosion hinders the ability to predict soil erosion on slopes. To address this problem, we present a simple and sensitive soil erosion measurement method called the magnetic layer detection (MLD) method. This method can measure the changes in soil layer thickness (SLT) at a site featuring a pre-buried magnetic layer (ML) using the relationship between magnetic susceptibility and soil thickness, and then use this information to determine the erosion or accumulation at that point. To verify the adaptability and accuracy of this method, we designed three field tests. First, we conducted a simulated sediment siltation experiment using the erosion pin (EP) method as the control. This experiment demonstrated the accuracy of the MLD method for measuring SLT (Rad2 = 0.97, NSE = 88.14%, and RMSE = 1.17 mm). Next, a simulated rainfall experiment with the runoff plot (RP) method as a control was used to demonstrate the effectiveness of the MLD method under water erosion. The results demonstrated that this method can reliably estimate soil erosion on both bare plots (Rad2 = 0.83, NSE = 71.78%, and RMSE = 0.56 mm) and sparse grass plots (Rad2 = 0.90, NSE = 81.77%, and RMSE = 0.25 mm), with performance that is better than that of the traditional EP method. Finally, a soil scouring experiment, again with the RP method as a control, was designed to verify whether the MLD method could accurately measure the erosion of a slope after the soil was scoured by surface runoff. The MLD method could accurately measure the slope erosion (Rad2 = 0.91, NSE = 89.55%, and RMSE = 0.42 mm), with results superior to those of the traditional EP method. The MLD method’s results were similar to those from the laser scanner (LS) method, but more accurate and with less associated cost and data processing time. Therefore, the MLD method has potential as a reliable measurement method that can provide useful guidance for elucidating the spatial distribution of soil erosion and predicting slope soil erosion. This approach could be used to obtain raw data to quantify soil erosion on the Loess Plateau of China and beyond.

Soil erosion rates evaluation via 137Cs and RUSLE modeling in the Sigilil watershed (northeastern Tunisia)

Soil erosion is a natural phenomenon which causes many problems such as the reduction of soil fertility and silting of dams. In this study, the 137Cs activities and the RUSLE model were used to quantify soil erosion rates in the Sgilil river watershed of northeastern Tunisia, and where 20% of the agricultural land is devoted to the cultivation of olive trees. In addition, the erosion dynamics and its spatial variation were inferred. The net erosion rates in the cultivated field based on the measured 137Cs activities and using the Fallout Radionuclide Conversion Models range from 2.24 to 34.23 t ha−1 yr−1 when the Proportional Model is applied and from 1.49 to 30 t ha−1 yr−1 when the Mass Balance Model 2 is applied. The average erosion rates of the Sgilil river watershed estimated by the RUSLE model after multiplying five erosion factors for an average year (1990–2019) is 6 t ha−1 yr−1. Based on the RUSLE model results, around 52% of the watershed area falls within the slight erosion risk class (0–2 t ha−1 yr−1) and 11% within the very severe erosion risk class (> 30 t ha−1 yr−1). The center of the watershed, where the olive cultivated field is located belongs to a high to severe erosion risk class. Results attained through the isotopic approach and those found through the RUSLE modeling are in agreement.

Mapping and Quantification of Soil Erosion and Sediment Delivery in Poorly Developed Urban Areas: A Case Study

Poorly developed regions in the Gaza Strip, Palestine, face significant risks to public safety, the environment, and stormwater infrastructure due to soil erosion and deposition. This study is the first of its kind to investigate soil erosion in this area. A revised universal soil loss equation (RUSLE) model was utilized and validated using field measurements of accumulated sediments at three major stormwater detention basins from 2014 to 2020. High-resolution maps were created to capture the urbanization effect and to further improve the future prediction of urbanization. The findings revealed that the highest potential for sediment generation in the Gaza governorate occurred over the slopes of the eastern ridge, which drain toward the city center. Sediment generation ranged from 1784 to 4281 ton/ha for the years of 2018 and 2020, respectively. The average sediment delivery ratio (SDR) was calculated to be 0.00134. The estimations for sediment export ranged from 0 to 135.3 ton/ha for the year 2020, with an average of 0.0737 ton/ha. The urban areas exhibited the least sediment export rate; however, the model revealed abnormal behavior for a dataset of the field measurements which was ascribed to the impact of destruction/reconstruction activities in the corresponded watersheds that followed the war in 2014. This conducted research stands as a pioneering effort in quantifying and cartographically representing sediment erosion potential within the Gaza Strip. Thus, it serves as an indispensable point of reference for future researchers in terms of the employed parameterization and calibration methodology. Furthermore, it holds distinct significance as an unparalleled resource for experts and stakeholders who are invested in comprehending the ramifications of erosion on urban landscapes and drainage systems.

Soil erosion in diverse agroecological regions of India: a comprehensive review of USLE-based modelling.

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.