Average Annual Erosion Research Articles

Estimation of Soil Erosion by RUSLE Model Using Geoinformatics Techniques: A Case Study of Mulshi Reservoir Catchment, Pune District, Western Maharashtra.

In the western part of Maharashtra, where the Western Ghat highlands are located, soil erosion is a serious and persistent environmental issue. To make matters worse, the majority of the dams in this region run the risk of sedimenting their reservoirs, which is a serious concern. This research was being conducted on the Mulshi reservoir catchment area, Pune district of western Maharashtra having area of 250.25 km2. It is fact that the surface runoff of seasonal rainfall is more in this region due to the presence of western ghats. In this study, high resolution satellite images were used to estimate average annual soil erosion based on the five factors defined in the Revised Universal Soil Loss Equation (RUSLE) with the help of Geoinformatics techniques. Overlay of five parameters, viz., rainfall erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C) and support & conservations practices factor (P) has been done in ArcGIS (version 10.8) software. Annual average soil erosion of the catchment is 0 to 433.43 tons/hector/year and has been classified into Six categories, defined as very high, high, moderate, low, and very low and negligible according to the severity of erosion. The high values of soil erosion (>20 tons/hector/year) found on the highlands due to the high slope and bare lands. however, low values (<10 tons/hector/year) are found on the area occupied by reservoir and dense vegetation.

Modeling of soil erosion based on geospatial techniques and the RUSLE model for the Ugam Chatkal National Park, Uzbekistan

Soil erosion significantly impacts agriculture and the environment, posing threats to food security, economic stability, and social development . Today, modern technologies are effectively used by world scientists to assess soil erosion and take measures against it. This study leverages modern technological advancements for the evaluation and management of soil erosion, specifically within the Ugam Chatkal National Park in Tashkent region. This research employed Geographic Information Systems (GIS) and the Revised Universal Soil Loss Equation (RUSLE) model to assess erosion processes and quantify annual soil loss in the park. Thematic layers (R,P,C,K,LS) for this model were created using GIS. In this way, the impact of each factor on soil erosion can be seen more clearly. According to the obtained results, it was found that the R and LS factors have a greater effect on soil erosion than the other factors. Findings indicate a heightened susceptibility of the Ugam Chatkal National Park to water erosion. The estimated average annual soil erosion rate ranges between 0.00 and 250 tons per hectare per year (t ha-1 yr-1). The results underscore the need for developing and implementing strategic measures to prevent water erosion in the park and to mitigate its impacts. The study emphasizes the importance of current technologies in recognizing and addressing environmental issues, particularly soil erosion, which has far-reaching implications for ecological preservation and sustainable development. This study not only sheds light on the erosion dynamics of the Ugam Chatkal National Park, but it also stresses the critical importance of taking proactive actions to protect the park’s ecological integrity. Failure to treat erosion in time could result in lasting damage to the park’s rich biodiversity and scenic vistas. The integration of modern technologies such as GIS and the RUSLE model not only enhances our understanding of soil erosion processes but also empowers conservation efforts, offering a promising pathway towards ensuring the long-term sustainability of natural ecosystems.

Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method

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

Soil erosion risk assessment of the Lakhmess watershed (northwestern Tunisia) via the SEAGIS model: Inferred prioritization of risky sub-watersheds

Water erosion in Tunisian semi-arid regions causes harmful effects by silting reservoirs and reducing agricultural lands and soil fertility. Several factors are involved in the erosion process: rain erosivity, soil fragility, and degraded land cover on steep slopes associated with the intensification of inappropriate human practices. Thus, identifying erosion vulnerable sub-watersheds based on the assessed soil loss rate is very important to apply suitable conservation measures. The current research aimed to prioritize risky areas in the Lakhmess watershed, north-west Tunisia via the Soil Erosion Assessment using Geographical Information System (SEAGIS) model. To prioritize sub-watersheds vulnerable to soil erosion and sediment yield, the Lakhmess watershed, covering an area of 162 km2, was divided into 16 sub-watersheds (L1–L16), according to the hydrographic network. Then, the mean annual soil erosion rate and the mean annual sediment yield in the watershed were estimated by integrating the Revised Universal Soil Loss Equation (RUSLE) and the Sediment Delivery Ratio (SDR) in the SEAGIS model and their spatial distribution was determined. The obtained results indicate that the estimated average annual soil erosion rate is 4.2 t/ha/y and the annual sediment yield is 2.6 t/ha/y. Maner's SDR model was selected as the best model for estimating SY, with standard error, standard deviation, and coefficient of variation values of 0.75%, 0.01, and 0.45%, respectively. The prioritization of the Lakhmess sub-watersheds based on the estimated soil loss rate reveals that among the 16 sub-watersheds, three sub-watersheds (L10, L12, and L15) were identified as being in a very high priority soil erosion class. The high soil erosion rate and sediment yield in these sub-watersheds is explained by the steep slope and a high rainfall erosivity factor. Six sub-watersheds (L2, L4, L5, L6, L7, and L16) were found to belong to a very low priority soil erosion class, as they are characterized by a very gentle slope, which appears to be an extremely determining factor. These findings constitute a basis for decision makers to plan effective conservation measures to conserve agricultural lands, soil, and water resources in northwestern Tunisia.

A geospatial approach-based assessment of soil erosion impacts on the dams silting in the semi-arid region

Soil erosion significantly impacts dam functionality by leading to reservoir siltation, reducing capacity, and heightening flood risks. This study aims to map soil erosion within a Geographic Information Systems (GIS) framework to estimate the siltation of the K'sob dam and compare these estimates with bathymetric observations. Focused on one of the Hodna basin’s sub-basins, the K'sob watershed (1477 km2), the assessment utilizes the Revised Universal Soil Loss Equation (RUSLE) integrated with GIS and remote sensing data to predict the spatial distribution of soil erosion. Remote sensing data were pivotal in updating land cover parameters critical for RUSLE, enhancing the precision of our erosion predictions. Our results indicate an average annual soil erosion rate of 7.83 t/ha, with variations ranging from 0 to 224 t/ha/year. With a typical relative error of about 13% in predictions, these figures confirm the robustness of our methodology. These insights are crucial for crafting mitigation strategies in areas facing high to extreme soil loss and will assist governmental agencies in prioritizing actions and formulating effective soil erosion management policies. Future studies should explore the integration of real-time data and advanced modeling techniques to further refine these predictions and expand their applicability in similar environmental assessments.

Assessing geomorphological changes in coastal lagoon inlet using geospatial tools: A case study Moulay Bousselham lagoon, Morocco

Coastal monitoring is crucial for coastal management and both sustainable progress and environmental protection. This study aims to assess the morphodynamic changes undergone by an inlet, sandpits, and adjacent beaches of Moulay Bousselham lagoon (Morocco Atlantic coast), considered a RAMSAR site of significant biodiversity. Firstly, a Digital Shoreline Analysis System (DSAS) is applied to monitor shoreline evolution between 1949 and 2021, based on the End Point Rate (EPR) and Linear Regression Rate (LRR) methods, with the study area divided into south and north sides. Secondly, we mapped sandpit areas in the lagoon using recent satellite images and historical aerial photos covering a period of 75 years (1949–2024) within a GIS (Geographic Information System) environment. As a result, the average annual accretion rates on the north side, comprising 735 transects (1.45 km length), are +3.23 m/year (EPR) and +3.26 m/year (LRR). Additionally, the accretion phenomenon is observed in 117 transects, while erosion is recorded in 618 transects with −0.73 m/year and −0.81 m/year for EPR and LRR methods, respectively. The south side, comprising 1085 transects (2.46 km length), shows average annual erosion rates of −0.99 m/year (EPR) and −0.76 m/year (LRR) concerning the south part of the entrance inlet, with 278 transects showing accretion and 807 transects indicating erosion. Moreover, flood-dominated deltas (sandpit) mapping shows significant changes in the sandpit areas at the entrance inlet and inside the lagoon, with the area of accumulation increasing from around 155662,57 m2 in 1949 to 306020,22 m2 in March 2024. Inlet width measurements reveal fluctuations over time, from 53 m in 1949 to 177 m by 1988, reflecting dynamic coastal processes. However, by March 2024, the width decreased to 30 m, indicating sediment blockage and intense siltation. This analysis revealed that these sedimentation trends are mainly controlled by southward-directed littoral transport and the change in inlet position induced by several factors, including tidal currents, wave energy and river flow, particularly during winter seasons and extreme events (marine submersions and flooding). This article will help us to understand the relationship between the evolution of the coastline and its immediate influence on sediment supply to the lagoon.

Wind Speed and Vegetation Coverage in Turn Dominated Wind Erosion Change With Increasing Aridity in Africa

AbstractWind erosion is one of the main causes of land degradation and desertification. Clarifying the spatiotemporal variations of wind erosion and the dominant factors of its spatial characteristics and the temporal trend will contribute to the establishment of appropriate wind erosion control and management practices, which is essential for combating global land degradation and strengthening ecological protection in drylands. Here, we assessed wind erosion in Africa during 2001–2020 based on the Revised Wind Erosion Equation (RWEQ). We also analyzed the influential factor of spatial characteristics and temporal variation based on machine learning and other methods under different aridity. Results revealed that the average annual wind erosion modulus was 16,672 t/km2/a in Africa during 2001–2020, with hyper‐arid areas and arid areas accounting for more than 90% of the total wind erosion modulus. The spatial characteristics of wind erosion were dominated by natural factors but not anthropogenic activities. Except in hyper‐arid areas, wind speed and vegetation coverage together dominated the spatial characteristics. Wind speed was the dominant factor in wind erosion change, while in arid and semi‐arid areas, the capability of vegetation coverage to affect wind erosion change was comparable to wind speed. It can be concluded that, although revegetation does contribute to the reduction of wind erosion in arid and semi‐arid areas, taking into account water resource constraints and land use conflicts, large plantations can be replaced with windbreaks to increase vegetation coverage while reducing near‐surface wind speed, which improves the sustainability of ecological projects aimed at combating land degradation and desertification.

Assessment of Soil Erosion within the Gayathripuzha River Basin Using RUSLE Model and GIS Tools

Soil erosion, a natural phenomenon accelerated by human activities, became a major problem for agricultural land use and for water quality concerns. Soil erosion by water is influenced by various factors like climate, nature of land surface, slope steepness, soil properties, land cover, and management practices. Estimation of soil erosion is essential to know the nature and quantity of soil loss and create specific strategies for soil conservation. This research paper thoroughly examines the susceptibility of soil erosion in the Gayathripuzha river basin in Kerala, India, which is encompassing a wide area of around 961 km². Employing the Revised Universal Soil Loss Equation (RUSLE), this investigation assesses the influential factors affecting soil erosion, which comprise rainfall (R Factor), soil erodibility (K Factor), topography (LS Factor), land usage (C Factor), and the impact of conservation measures (P Factor). The study was conducted using Geographic Information System (GIS) tools like ArcGIS 10.8 Desktop and integrating data on rainfall, soil properties, land topographic features, and land use. Rainfall erosivity is a major factor causing erosion in Gayathripuzha river basin and it ranges from 746.03 to 1222.35 MJ mm ha−1 h−1 y−1 in the river basin. Soil erodibility factor also varies widely within the region. Slope length and steepness factor showed that highland ecosystems are present only in a small area of the river basin. Variation in land use and land cover also influenced the soil erosion within the area. The estimated average annual soil erosion ranges from 0 to 133.18 t ha-1 yr-1 within the research site. The research found that soil erosion varied greatly across the study area. Regions with slopes over 50% had much higher erosion rates, while most of the study area had low erosion levels. Notably, the intensity of soil erosion demonstrated a positive correlation with slope percentage, with areas classified as bare ground demonstrating the highest erosion rates. These findings emphasize the necessity for targeted and comprehensive conservation strategies tailored to the diverse landscape of the region to effectively manage soil erosion and mitigate its adverse impacts.

Modelling Soil Erosion Using RUSLE and GIS for Kadalundi River Basin in Kerala, India

The main objective of the study was to estimate the potential average annual soil loss from Kadalundi river basin using RUSLE model and to prepare the spatial distribution map of soil erosion hazard using GIS to suggest suitable soil conservation and management measures for the basin. The individual factor maps for each factor in the RUSLE equation were prepared and multiplied in raster calculator to obtain the spatial distribution map of soil erosion with the help of ArcGIS. The daily rainfall data, basic soil data, Digital Elevation Model (DEM) and satellite imageries were used as the input data for the calculation of rainfall erosivity factor, soil erodibility factor, topographic factor, cover management factor and conservation practice factor. The results indicated an average annual erosion of 5.48 t·ha-1·yr-1 in the basin during the period of 2001 to 2021. The total quantity of soil washed away from the basin was 694836.60 t·yr-1. The estimated values of R-factor ranged from 938.97 MJ·mm·ha-1·h-1·yr-1 to 1102.65 MJ·mm·ha-1·h-1·yr-1, and the computed erodibility factor values varied between 0.0075 to 0.025 t·h·MJ⁻1·mm⁻1. The topographic factor value of the basin was found between 0 to 9.07. The basin had C-factor values of 0.08 to 1.10 and the P-factor values varied from 0.1 to 1. The spatial distribution map of erosion suggest that major part of the basin is under slight erosion class and only a minor part is under very severe erosion class. Areas in the very severe erosion class is characterised by moderate to steep slopes, or in other words areas with less slope is having slight erosion. As most of the area of the basin is having slight erosion, simple agronomical measures can be adopted for the mitigation purposes.

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.

Soil erosion and environmental degradation in Nepal: A Review of Environmental Policies

Soil erosion is a natural process that occurs more frequently in mountainous regions in the Himalayas due to the complex landforms. Nepal also experiences severe forms of soil erosion due to its geographical complexity. This study examines the issues of soil erosion in relation to the environment and evaluates the effectiveness of existing environmental policies in Nepal, using secondary sources of data. The study reveals that Nepal has an average annual soil erosion rate of 25 tons per hectare per year, resulting in the total annual loss of 369 metric tons. This rate is higher compared to other areas around the world. The middle mountain region of Nepal has a higher mean annual soil erosion rate per hectare per year (which is of 38.4 tons) than other physiographic regions. Soil erosion is caused by various factors such as rainfall, water flow, land cover, geographical setting, and conservation practices – which leads to a decrease in agricultural productivity. Both on-site and off-site effects pose a significant risk to agriculture, development projects and the environment. It is suggested that the steps have to be taken to the environmental safeguard for reducing soil erosion and mitigate the problem through government agencies by emphasizing on effective implementation measures of the existing environmental policies, regulations, and development plans.

Revealing soil erosion and sediment sources using 137Cs and fingerprinting in an agroforestry catchment

Soil erosion and accompanying sediment issues have a significant impact on environmental sustainability. However, there is not currently a clear understanding of the relationship between soil erosion and sediment sources at the catchment scale, which has led some researchers to regard areas with severe soil erosion as the main sediment source. This view does not apply in all circumstances.•This paper uses 137Cs to study the average annual erosion rate of different land use types. The results show that the erosion rates of woodland, sparse woodland and farmland areas were 400, 1756, and 2771 t·km−2·a−1, respectively, multiplied by the corresponding area. Surface soil erosion levels in the catchments of the three types accounted for 11%, 26% and 25%, respectively, and the total accounted for 62% when the gully bank accounted for 38%.•Fingerprinting technique results show that the relative source contributions of woodland, farmland, sparse woodland and gully bank areas to sediment at the small catchment outlet were 4%, 10%, 48% and 38%, respectively. The results reveal that the detachment process of soil and the transport process of sediment with runoff were internally related and independent of each other.•By comparing on-site erosion rates with downstream source contributions, we have a clearer understanding of soil erosion and subsequent sediment transport process in the agroforestry catchment in mountainous areas.We emphasize the importance of governance and management in areas with severe soil erosion, mainly targeting types such as hillslope framland. In addition, sparse woodland and gully banks should be given more attention, as they account for a large proportion of sediment in the downstream catchment.

Enhancing basin sustainability: Integrated RUSLE and SLCC in land use decision-making

Sustainable agriculture and eco-environment conservation face constant threats from soil erosion in mountainous regions. This study utilizes remote sensing and GIS support to predict soil erosion and sediment yield by applying the Revise Universal Equation Soil Loss (RUSLE) and Sediment Delivery Ratio (SDR) models. The findings highlight that the basin adversely experiences an average annual soil erosion rate of 108.47 t ha−1 yr−1, leading to a significant sediment influx of 206.03 × 106 t/yr at downstream. The RUSLE-SDR model performs satisfactorily, with percent bias (PBIAS) values below 25%. Based on the RUSLE output, the study integrates a threshold of over 100 t ha−1 yr−1 into the adapted slopeland capability classification (SLCC), along with slope gradient derived from Digital Elevation Model (DEM) and soil depth, to categorize the entire basin into five capability classes. Each class is associated with specific soil erosion control treatments for agricultural activities and forestry. The study suggests the four land use/land cover (LULC) scenarios with different prioritizations, aimed to optimize land resource utilization and conserve the eco-environment. Scenarios #3 and #4, in particular, demonstrate promising benefits for the eco-environment by substantially increasing forest coverage to 81.27% and 85.07% of the total area, respectively. In contrast, scenarios #1 and #2 prioritize agricultural development. Due to the challenge posed by rugged terrains, the application of LULC scenarios must be adhered strictly, considering a specific class of the SLCC with its treatments. The study offers a timely and feasible approach for soil erosion investigation and land use adjustment to support effective basin management. However, ensuring the health and sustainability of the basin ecosystem may necessitate additional measures, such as proper planning for riparian zones and engineering solutions.

Assessing the Effects of Conservation Measures on Soil Erosion in Arasbaran Forests Using RUSLE

Vegetation cover is known as one of the most dominant parameters in soil erosion that can considerably affect soil erosion drivers. This study aimed to assess the effects of vegetation cover on soil conservation in Arasbaran Forests, Iran. A part of Arsbaran forests has been protected for 45 years. The other part has not been under protection during these years. This study was carried out in order to investigate the effects of forest protection management on the changes in the amount of soil erosion and compare it with the non-protection sector. To this end, 66 samples were grouped in the two selected elevation classes. Out of every three sample plots, one plot was randomly selected for collecting soil samples. Landsat 8 images and a Digital Elevation Model were utilized for sample collection via ENVI (Environment for Visualizing Images) and GIS (Geographic Information System), respectively. Then, the Revised Universal Soil Loss Equation (RUSLE) was employed to estimate the annual soil loss in the studied sites. The results showed the annual soil erosion of 9.84 and 10.06 tons per hectare/year for protected and non-protected areas, respectively. Moreover, the average annual soil erosion of 9.95 tons per hectare/year was calculated for the whole Arasbaran Forests. The results of the statistical test revealed no significant difference between protected and non-protected sites in terms of erosion rates (p > 0.05). Based on the findings, despite the non-significant and slightly lower soil loss per unit area in the protected site, there is a notable soil loss throughout the entire non-protected area. It appears necessary to conduct a thorough review of existing conservation laws and to closely monitor their effective implementation. This step is crucial for enhancing the effectiveness of forest conservation management in mitigating soil erosion. The results show that absolute forest protection alone cannot make a big difference in preventing soil erosion. In this regard, there is a need to carry silviculture measures to manage protected forest stands to increase the sustainability of the forest. Obviously, in the case of proper management along with protection, it is possible to have a greater effect in preventing soil erosion.

Human activities are the key driver of water erosion changes in northeastern China

AbstractNortheast China is experiencing severe soil erosion, resulting in land degradation and nutrient loss. Among them, water erosion poses the greatest threat to food security. Combining various multi‐source data, we applied the modified soil erosion equation (RUSLE) to evaluate the water erosion modulus in Northeast China (1985–2021). We used the threshold segmentation and residual analysis method to quantify the relative roles of climate change and human activities on water erosion. The outcomes were as follows: (1) In recent decades, the average annual water erosion modulus in northeastern China was 804.78 t·km−2·a−1, decreasing with a slope of −11.114 t·km−2·a−1. In 24% of the study area, the erosion modulus increased significantly, mainly in grassland and farmland, due to grassland degradation and expansion of sloping cropland. In 20% of the study area, erosion modulus decreased significantly, mainly spread in forests, which was attributed to the implementation of important ecological restoration projects by the Chinese government, such as afforestation programs and returning farmland to forest. (2) During the research process, there were spatial variations in water erosion drivers in Northeast China. For areas with significant increases in erosion, 20% of the total study area were caused by anthropogenic factors, and 3% were attributed to natural factors. For areas where erosion had significantly improved, 18% of the total study area was attributed to ecological restoration, and 2% to natural factors. (3) Based on the results of the study, ecological restoration of forests and grasses, management of sloping cropland, and conservation tillage were recommended to reduce soil erosion in Northeast China.

Spatial Estimation of Soil Erosion Using RUSLE Model: A Case Study of Sangareddy Telangna State, India

Soil erosion is a major environmental challenge that affects agricultural productivity, water quality, and land management. To address this issue, accurate estimation and spatial mapping of soil erosion rates are essential for effective soil conservation planning and management. This study focuses on utilizing the Revised Universal Soil Loss Equation (RUSLE) model to estimate and map soil erosion rates in the Sangareddy district of Telangana State, India. The RUSLE model integrates several factors contributing to soil erosion, including rainfall erosivity, soil erodibility, slope length and steepness, cover management factor, and conservation practices. These factors were derived from geospatial datasets, including rainfall records, soil characteristics, topographic data, land use/land cover maps, and conservation practices information. By applying the RUSLE model, the study estimates the average annual soil erosion rates across the Sangareddy district. The spatial distribution of soil erosion is then mapped using geographic information system (GIS) techniques, providing valuable insights into the area’s most susceptible to erosion. These findings can inform decision-makers and land managers in prioritizing and implementing appropriate soil conservation strategies to mitigate erosion and protect valuable soil resources. The spatial estimation of soil erosion using the RUSLE model provides a scientific basis for land-use planning, conservation program implementation, and policy formulation to promote sustainable land management practices. It highlights the need for effective erosion control measures, such as contour farming, terracing, afforestation, and appropriate land use practices, in order to minimize soil erosion and preserve the soil quality in Sangareddy and similar regions.

ANALYSIS OF RAINFALL EROSIVITY INDEX USING THE BOLS AND LENVAIN METHODS

Indonesia has a tropical climate, which causes variations in the intensity of rainfall. With a high level of rainfall intensity, Indonesia is vulnerable to. The ability of to cause erosion is called erosivity. The aim of the study was to determine the rain erosivity index as a potential erosion control plan. The employed methods were the Bols and Lenvain methods involving the rainfall consistency test, analysis of average rainfall, and analysis of the erosivity index of rain. The results indicate that only Penengahan Station was eligible for the rainfall analysis with determination coefficient values (R2) of 0.9904 and 0.9889 with the Bols and Lenvain methods respectively. The annual average erosivity index value with the Bols method was 1762.23, while with the Lenvain method, it was 1280.19. With the results in mind, it is safe to infer that the use of the Bols equation is safer to apply to a plan concerning erosion potential mitigation caused by rainfall in the sub-sub watershed area of Khilau as the Bols method yielded a larger value.

Distribution Characteristics of Rainfall Erosivity in Jiangsu Coastal Areas

The issue of regional soil and water loss caused by human activity is particularly severe in coastal regions. Since coastal reclamation areas are a valuable land reserve resource, it is of practical significance to understand the distribution characteristics of rainfall erosion and its impact on soil erosion for the prediction, evaluation, and management of regional soil and water resources. Rainfall erosivity should be updated and estimated from simplified indices. This paper analyzed the observed rainfall data of field runoff plots in Dongtai City, Jiangsu Province, between 2011 and 2017. According to the standard of erosive rainfall in coastal areas, reporting 10.8 mm of rainfall or 7.6 mm·h−1 of I30 (maximum 30 min rainfall intensity), the annual average erosive rainfall frequency in Dongtai City was 37.7 and the annual erosive rainfall was 1082.0 mm on average, which accounted for 51.6% and 90.6% of the total rainfall frequency and the total rainfall, respectively. Moreover, the annual average rainfall erosivity in the region from 2011 to 2017 was 7717.4 MJ·mm·hm−2·h−1. The annual distribution of rainfall erosivity was irregular, with an average monthly erosivity value of 4501.8 MJ·mm·hm−2·h−1. Since the accumulated rainfall erosivity of Dongtai City in the flood season (May to September) accounted for 88.1% of the total rainfall erosivity, it is essential to focus on preventing soil and water loss in the flood season. This paper established a rainfall-based model and a composite model and intensity appropriate for a single event and monthly rainfall erosivity in the region. Both models can be used to calculate the annual rainfall erosivity, but only the composite model based on rainfall amount and intensity is recommended for calculating single and monthly rainfall erosivity levels in Jiangsu coastal areas. The empirical formulas in Jiangsu coastal areas can be updated using more recent rainfall data and assess soil erosion risk accurately.

Assessing current and future soil erosion under changing land use based on InVEST and FLUS models in the Yihe River Basin, North China

The Yihe River Basin is a key area for water conservation and soil erosion control in northern China. The excessive development of land resources is a major factor causing soil erosion and ecological degradation. However, the impacts of land use change on soil erosion in the basin are not yet clearly. Understanding the complex relationship between land use and soil erosion is an important way to promote the development of land resources utilization and ecological construction from cognition to decision-making. This study simulated the temporal-spatial changes of soil erosion in the basin from 1956 to 2020 using Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and evaluated the changes of soil erosion under different land use scenarios from 2020 to 2050 using Future Land Use Simulation (FLUS) model. From 1956 to 2020, the overall soil erosion intensity showed a slight decreasing trend, and the average annual soil erosion modulus was 38.21 t/ha/year. Soil erosion intensity was higher in the central and northern mountainous areas, while it was lower in the flat alluvial plains in the south. Arable land (4.07 t/ha/year) was the largest contributor to the amount of soil erosion, and land use changes caused the soil erosion intensity to fluctuate and decrease after 1995. From 2020 to 2050, soil erosion varied widely under different land use scenarios, and the land use pattern targeting ecological priority development would effectively mitigate soil erosion. Therefore, optimizing land use patterns and structures are critical initiatives to prevent soil erosion.

Analysis of spatiotemporal heterogeneity and influencing factors of soil erosion in a typical erosion zone of the southern red soil region, China

The southern red soil region (SRSR) of China, a region that is environmentally sensitive, suffers severe soil erosion (SE) as a result of climate change and human activity. The factors driving SE changes in the SRSR of China under changing settings have, however, only been the subject of a small number of research. Changting County is a typical SE area in the SRSR of China. The spatiotemporal variation characteristics of SE in Changting County from 2000 to 2020 were investigated using the revised universal soil loss equation (RUSLE), while the dominant factors influencing the spatiotemporal dynamic change of SE were analyzed using the optimal parametric geographical detector (OPGD) and logarithmic mean Divisia Index (LMDI). The results showed that the average annual soil erosion modulus in Changting County shows a decreasing trend, with the SE modulus decreased from 10.00 t·hm−2·a−1 in 2000 to 3.38 t·hm−2·a−1 in 2020. Slight SE intensity was the dominant SE class, accounting for more than 75% of the total area, and the proportion is trending upwards. Natural and anthropogenic factors showed significant effects on the spatial variation of SE, of which land use and vegetation cover were the main factors influencing the spatial distribution of SE. Furthermore, the interaction between these two dominant factors explained greatly the spatial variation of SE. The mitigation of SE was mainly attributed to the increase in vegetation cover following the implementation of various ecological projects, contributing to the reduction of SE by 68.7%. In addition, the changes in land use types and rainfall amounts also played positive roles in reducing SE, showing contributions of 20.2% and 11.1%, respectively.