Universal Soil Loss Equation Model Research Articles

Soil erosion prediction and spatiotemporal heterogeneity in driving effects of precipitation and vegetation on the northern slope of Tianshan Mountain

Soil erosion changes become more unclear in the future due to climate change and increased human activity. However, the spatiotemporal heterogeneity in the driving effects of key factors on soil erosion remains inadequately understood on the northern slope of the Tianshan Mountains (NSTM). In this paper, the spatiotemporal evolution of soil erosion on the NSTM was evaluated in the historical period (2000-2015) by the Revised Universal Soil Loss Equation (RUSLE) model. Subsequently, the FLUS-CMIP6-RUSLE model was employed to predict soil erosion in the future period (2030-2050). Then, the GTWR model was used to dissect the spatiotemporal heterogeneity in the driving effects of precipitation and vegetation. The results indicated that soil erosion on the NSTM fluctuated from 2000 to 2015, increasing by 1.77 t ha −1 yr −1. Soil erosion areas mainly occurred in Changji City, Fukang City, Hutubi County, Mulei County, Qitai County, and Turpan City. The soil erosion modulus in 2030, 2040, and 2050 under different climate scenarios will be 26.56,28.83, and 37.49 t ha −1 yr −1, respectively. The spatial pattern of soil erosion is similar to the historical period. Precipitation was the main factor intensifying soil erosion in the eastern and western counties from 2000 to 2020, while the soil erosion change in central counties was affected by vegetation. In the future, soil erosion change in the counties (i.e., Karamay, Yili, Tacheng, Shihezi, Manas, Qitai, and Mulei ) will be driven by precipitation. The vegetation will be the main driver of soil erosion change in central and eastern counties (i.e., Urumqi, Turpan, Fukang City, Jimsar County, and Changji City). This study firstly attempts to explore the spatiotemporal heterogeneity in the driving effects of precipitation and vegetation using Spatial-Temporal Geographically Weighted Regression (GTWR) in urban agglomerations of NSTM. It is meaningful to explore erosion-prone regions and to implement rational soil conservation measures in the study region and similar regions in the world.

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.

Climate change impairs the effects of vegetation improvement on soil erosion control in the Qinghai-Tibetan Plateau

Water erosion is a wide ecosystem degradation threat for the Qinghai-Tibetan Plateau (QTP) region and surrounding areas. Climate wetting and vegetation greening in the QTP could have complex impacts on water erosion. However, there is limited information about the erosion dynamics under climate change and the effects of vegetation improvement on erosion control in this alpine region. By calibrating the Revised Universal Soil Loss Equation (RUSLE) model, we estimated water erosion in the QTP and simulated future changes using different Shared Socioeconomic Pathway-Representative Concentration Pathway (SSP) scenarios. We showed intensified erosion and that the benefits of vegetation improvement to erosion control was impaired under climate change. Under the SSP585 scenario, annual water erosion in the QTP was predicted to increase by 0.35 t∙ha−1∙yr−1 and be dominated by an accelerative rise of rainfall erosivity (R factor of RUSLE). Compared with historical observations, the C factor of RUSLE under the SSP585 scenario was believed to reduce by 4.35 % on average during the 2081–2100 period, but predicted soil loss will increase by more than 90 %. Although more considerable vegetation improvement could occur under high climate forcing, there may also be more intensified rainfall erosivity and water erosion across the QTP than those under SSP126 and SSP245 scenarios. Our results suggest that mitigating climate change rather than vegetation improvement is essential to address climate-dominated ecosystem degradation threats such as water erosion in the QTP.

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.

Integrating RUSLE Model with Cloud-Based Geospatial Analysis: A Google Earth Engine Approach for Soil Erosion Assessment in the Satluj Watershed

This study employed an advanced geospatial methodology using the Google Earth Engine (GEE) platform to assess soil erosion in the Satluj Watershed thoroughly. To achieve this, the Revised Universal Soil Loss Equation (RUSLE) model was integrated into the study, which was revealed through several analytical tiers, each with a unique function. The study commenced with estimating the R factor, which was carried out using annual precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS). The erodibility of the soil, which the K factor describes, was then calculated using the USDA soil texture classifications taken from the Open Land Map. The third layer emphasizes the LS factor, which analyzes slope data and how they affect soil erosion rates, using digital elevation models. To understand the impact of vegetation on soil conservation, the fourth layer presents the C factor, which evaluates changes in land cover, and the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 data. The P factor incorporates MODIS data to assess the types of land cover and slope conditions. Combining these layers with the RUSLE model produces a thorough soil loss map, revealing different levels of soil erosion throughout the Satluj Watershed. The preliminary findings indicate that 3.3% of the watershed had slight soil loss, 0.2% had moderate loss, and 1.2% had high soil erosion rates. And 92% had severe rates of soil erosion. After a thorough investigation, the detected regions were divided into risk classifications, providing vital information for the watershed’s land management and conservation plans. The mean soil loss throughout the watershed was determined to be 10,740 tons/ha/year. This novel method creates a strong foundation for evaluating soil erosion, while also highlighting the value of the cloud-based geospatial analysis and the RUSLE model in comprehending intricate environmental processes.

Long‐term effects of soil erosion on dryland crop yields in the Songnen Plain, Northeast China

AbstractThe elucidation of the influence of soil erosion on crop yields is of paramount importance to ensure food security. Remote sensing data has facilitated the acquisition of long‐term spatially‐distributed information on soil erosion modulus and crop yields. The Revised Universal Soil Loss Equation (RUSLE) model was employed to estimate the soil erosion modulus, while the Carnegie‐Ames‐Stanford Approach (CASA) model has been utilized to calculate the net primary productivity (NPP). This study integrated models to assess the impact of soil erosion on dryland crop yields in the Songnen Plain from 2000 to 2020. The findings reveal that the annual average soil erosion modulus for dry cropland in black soil region (BSR) of the Songnen Plain is 4.2 (t ha−1 yr−1), which is 1.5 times greater than that of the other region (OR). The greater cropland erosion within the BSR limited the trend for increasing crop yields, whereas the impact of erosion on crop yield dynamics in the OR was not significant. The areas where dryland crop yields diminished in the Songnen Plain because of soil erosion were predominantly concentrated within the BSR. Advancements in agricultural technology have partially mitigated the adverse consequences of erosion on yields.

Geoinformatics and RUSLE model-based soil erosion assessment in a tropical mountainous area of Chite watershed, Mizoram, India

Soil erosion remains a persistent menace to the sustainability of agriculture and the environment in tropical mountainous regions. Soil erosion assessment is therefore necessary to identify degraded land areas for implementing effective conservation and management strategies. Hence, this study focuses on estimating potential soil erosion and analyzing their spatial patterns in the Chite watershed, situated in the Eastern Himalayas, India, using the Revised Universal Soil Loss Equation (RUSLE) model in Geographic Information System (GIS) platform. Various datasets encompassing remote sensing, ground observations, and laboratory analysis were employed to prepare the model’s input factors. The estimated mean erosion rate of the study area is 6.10 t ha-1 year-1, which produces a total soil loss of about 357580.90 t year-1. Spatial analysis reveals that about 5.79% of the watershed is under a relatively severe erosion category, contributing 70.13% of the total soil loss. Soil erosion appraisal with respect to the land use/ land cover (LULC) indicates a considerable consequence of various anthropogenic activities in the watershed. Higher rates of soil erosion are mainly observed on the bare land, cropland, and settlement areas which are characterized by steep and continuous slopes. The present findings were also validated with previous work undertaken in some comparable regions. This research can serve as a reliable tool towards the development of successful soil conservation measures and for promoting sustainable land use planning in this ecologically sensitive tropical mountainous region.

Agricultural land abandonment and its impact on soil erosion in the Madi Watershed, Gandaki Province, Nepal

Agricultural land abandonment is the crucial issue of land use /land cover change in many parts of the world and it is directly related to soil erosion in the mountain areas. Nepal has also faced heavy agricultural land abandonment from the mountain regions in the last two decades. This study is concerned with agricultural land abandonment and its impact on soil erosion in the Madi Watershed, Gandaki Province of Nepal. This study used high-resolution aerial photography and Google Earth images to map cultivated land and its abandonment in 1995 and 2020. The Revised Universal Soil Loss Equation (RUSLE) model has been used to estimate soil erosion. Studies revealed that more than 40% of the agricultural land was abandoned from 1995 to 2020 and there is higher spatial variability of abandonment intensity. High altitude and steeper slope areas have a higher intensity of abandonment than in lower altitude and lower slope gradients. There is a significant impact of abandonment of agricultural land on reducing soil erosion from 1995 to 2020. The average rate of soil erosion was quite high in 1995 but decreased to 51.5 percent in 2020. There are higher rates of reduction in soil erosion at higher altitudes and marginal lands like steeper slopes and the areas having lower solar radiation because of the higher proportion of agricultural land abandonment. Thus, it can be concluded that agricultural land abandonment during the last 25 years significantly impacts soil erosion control in the study area.

Prioritization of Sub-Watershed Based on Soil Loss Estimation Using RUSLE Model

Soil erosion is one of the most crucial land degradation problems and is considered the most critical environmental hazard worldwide. The present study uses remote sensing data integrated with the geographical information system (GIS) technique and the revised universal soil loss equation (RUSLE) model for assessing the annual average soil loss of the Digaru watershed of India for 1999 and 2020. The estimated mean gross yearly soil loss from the entire watershed was 102716 t yr-1 in 1999 and 178931.6 t yr-1 in 2020. The overall average soil loss rate increased significantly between 1999 and 2020, rising from 4.73 t—ha-1yr-1 to 8.43 t—ha-1yr-1. The sub-watersheds are prioritized as high (≥ 40 t ha−1yr−1), moderate (20–40 t ha−1yr−1), and low (&amp;lt;20 t ha−1yr−1) based on the spatial distribution of soil erosion. Seven sub-watersheds have been grouped under low priority, followed by seven under moderate priority and one under high priority. This study demands instant attention for soil and water conservation efforts in highly eroded watershed areas.

Characterization of carbon fluxes, stock and nutrients in the sacred forest groves and invasive vegetation stands within the human dominated landscapes of a tropical semi-arid region

In the semi-arid plains of Southern India, outside the protected area network, sacred groves forests and the barren lands invaded by Prosopis juliflora are reckoned to be the major greenery, but have homogenous and heterogeneous vegetation respectively. This study attempted to compare 50 Sacred Groves Stands (SGS) and 50 monodominant Prosopis juliflora Stands (PJS) for the functional diversity, evenness, floral diversity, carbon stock and dynamics, carbon-fixing traits, dendrochronology of trees, soil nutrient profiles, and soil erosion. Quadrat sample survey was adopted to record stand density, species richness, abundance, basal area and leaf area index; composite soil samples were collected at depths 0–30 cm for nutrient profiling (N, P, K, and OC). Photosynthesis rate (µmole co2 m2/sec), air temperature (°c), leaf intracellular co2 concentration (ppm), ambient photosynthetic active radiation (µmole m2/sec), transpiration rate (m. mole H2O m2/sec) were determined for the 51 tree species existed in SGS and PJS using Plant Photosynthesis system. Structural Equation Model (SEM) was applied to derive the carbon sequestering potential and photosynthetic efficiency of eight dominant tree species using vital input parameters, including eco-physiological, morphological, and biochemical characterization. The Revised Universal Soil Loss Equation (RUSLE) model, in conjunction with ArcGIS Pro and ArcGIS 10.3, was adopted to map soil loss. Carbon source/sink determinations inferred through Net Ecosystem Productivity (NEP) assessments showed that mature SGS potentially acted as a carbon sink (0.06 ± 0.01 g C/m2/day), while matured PJS acted as a carbon source (−0.34 ± 0.12 g C/m2/day). Soil erosion rates were significantly greater (29.5 ± 13.4 ton/ha/year) in SGS compared to PJS (7.52 ± 2.55 ton/ha/year). Of the eight selected tree species, SEM revealed that trees belonging to the family Fabaceae [Wrightia tinctoria (estimated coefficient: 1.28, p = 0.02) > Prosopis juliflora (1.22, p = 0.01) > Acacia nilotica (1.21, p = 0.03) > Albizia lebbeck (0.97, p = 0.01)] showed comparatively high carbon sequestering ability.

Effect of climate change on soil erosion rate in a tropical Indian catchment

Abstract This study analyzes the variations in soil erosion rate in a tropical catchment in India using the empirical Revised Universal Soil Loss Equation (RUSLE) model integrated with climate change scenarios from an ensemble of global climate models (GCMs) included in the NEX Global Daily Downscaled Projections Coupled Model Intercomparison Project Phase 6 dataset for Shared Socioeconomic Pathways (SSP) 126 and SSP 585. A set of seven GCMs are initially selected. Based on their ability to simulate the rainfall for the current scenario, the PROMETHEE-II method is used to rank the GCMs and the top four best-performing models are used for further analysis. Soil erosion rates projected for the future climate scenarios are compared with the current scenario. In the near future and in the middle of the century, soil erosion rates under the SSP 126 scenario are projected to be higher than that under the SSP 585 scenario. The ensemble average soil erosion rate is projected to increase by 15.41%–25.94% toward the end of the century for different emission scenarios, and the areas susceptible to high and very high soil erosion rates are projected to increase to 40.3%.

A review of soil erosion modeling in Nigeria using the Revised Universal Soil Loss Equation model

AbstractSoil erosion is a severe soil degradation problem that endangers the actualization of Sustainable Development Goals. It affects agricultural production by reducing soil fertility via topsoil translocation, leading to soil quality deterioration. A review of soil erosion modeling using the Revised Universal Soil Loss Equation (RUSLE) model shows that Nigeria lags behind other developing countries like Ethiopia despite the massive improvements provided by remote sensing and geographic information system (GIS) technologies. Fewer than 50 articles were found on the Google Scholar search engine that used the RUSLE model for empirical studies on soil erosion. Only four research articles were found on the ScienceDirect website. Additionally, there were differences in the findings from studies across Nigeria. Such disparities were due to differences in the approaches used to estimate key factors in the RUSLE model. Therefore, we recommend that the Ministries of Agriculture and Environment take soil erosion research seriously by adopting proactive soil conservation and management measures. Ensemble models can be used including machine learning approaches rather than focusing mainly on structural intervention. In addition, there should be a harmonized look‐up table for the cover management factor and conservation practices factor that fairly represent the various ecoclimatic regions. The limitations to the model's use are highlighted, and a way forward is suggested.

Soil Loss Estimation by Water Erosion in Agricultural Areas Introducing Artificial Intelligence Geospatial Layers into the RUSLE Model

The existing digital soil maps are mainly characterized by coarse spatial resolution and are not up to date; thus, they are unable to support the physical process-based models for improved predictions. The overarching objective of this work is oriented toward a data-driven approach and datacube-based tools (Soil Data Cube), leveraging Sentinel-2 imagery data, open access databases, ground truth soil data and Artificial Intelligence (AI) architectures to provide enhanced geospatial layers into the Revised Universal Soil Loss Equation (RUSLE) model, improving both the reliability and the spatial resolution of the final map. The proposed methodology was implemented in the agricultural area of the Imathia Regional Unit (northern Greece), which consists of both mountainous areas and lowlands. Enhanced soil maps of Soil Organic Carbon (SOC) and soil texture were generated at 10 m resolution through a time-series analysis of satellite data and an XGBoost (eXtrene Gradinent Boosting) model. The model was trained by 84 ground truth soil samples (collected from agricultural fields) taking into account also additional environmental covariates (including the digital elevation model and climatic data) and following a Digital Soil Mapping (DSM) approach. The enhanced layers were introduced into the RUSLE’s soil erodibility factor (K-factor), producing a soil erosion layer with high spatial resolution. Notable prediction accuracy was achieved by the AI model with R2 0.61 for SOC and 0.73, 0.67 and 0.63 for clay, sand, and silt, respectively. The average annual soil loss of the unit was found to be 1.76 ton/ha/yr with 6% of the total agricultural area suffering from severe erosion (&gt;11 ton/ha/yr), which was mainly found in the mountainous border regions, showing the strong influence of the mountains in the agricultural fields. The overall methodology could strongly support regional decision making and planning and environmental policies such as the European Common Agricultural Policy (CAP) and the Sustainable Development Goals (SDGs).

Estimating Soil Erosion and the Effect of Changing Land Use and Land Cover in the Bringi Catchment’s Devalgam Watershed Using RS and GIS

Abstract: One of the main factors contributing to soil degradation worldwide is soil erosion. The geographical variability of erosion at the Devalgam watershed was evaluated using the Revised Universal Soil Loss Equation (RUSLE) model in a GIS environment. DEM ASTER (30 m × 30 m). The model was fed with annual rainfall data from 1990, 2000, 2010, and 2020 as well as soil and LULC maps. In the Devalgam watershed, the mean annual soil loss ranged from 0 to 127.32, 140.34, 146.49, and 187.23 t/ha/yr for the years 1990, 2000, 2010, and 2020. The mean annual sol loss was calculated to be 16.6, 18.3,19.1, and 22.08 t/ha/yr. According to a zonal statistical analysis of soil erosion for various types of land cover, open forests and barren areas were more likely to experience erosion with the least susceptible to erosion were vegetation, built-up areas, orchards, and agriculture, with estimates of 85.12 and 52.35 t/ha/yr, respectively. According to the current study, managing natural resources is increasingly dependent on the LULC change in the Devalgam Watershed. GIS and remote sensing technologies have shown to be useful tools for analyzing LU/LC changes on a watershed-by-watershed basis. Six LU/LC classes were used to categorize the study area: built-up, barren terrain, vegetation, farmland, woodland, and orchards. and it was discovered that, out of the total area, or 2058.618 hectares, the least area was covered by orchards (0.5%), while the highest area was covered by forest (30.5%), followed by barren ground (25.7%). The examination of the overlay of analysis of the changes from 1990 to 2020 was conducted using LANDSAT-5 1990 over LANDSAT-8 OLI 2020. The findings also indicate that whereas the area under agricultural, builtup areas, barren land, and orchards expanded by 123%, 36.5%, 0.5%, and 36.4%, respectively, the area under forests and vegetation dropped by 17.3% and 48%. With a kappa coefficient of 0.84, 0.71, 0.75, and 0.87, respectively, the overall accuracy for the categorized imageries LANDSAT-5 (1990), LANDSAT-7 (2000), LANDSAT-7 TM (2010), and LANDSAT-8 OLI (2020) was determined to be 86.6%, 76.6%, 80%, and 90%.respectively

Assessment of Soil Loss from Land Cover Changes in the Nan River Basin, Thailand

This study investigates soil loss erosion dynamics in the Nan River Basin, Thailand, focusing on the impact of land cover changes. Utilizing the Universal Soil Loss Equation (USLE) model, key factors, including rainfall erosivity, soil erodibility, topography, and land cover, are analyzed for the years 2001 to 2019. The findings reveal a substantial increase in human-induced soil erosion, emphasizing the pressing need for effective mitigation measures. Severity classification demonstrates shifting patterns, prompting targeted conservation strategies. The examination of land cover changes indicates significant alterations in the satellite image (MODIS), particularly an increase in Deciduous forest (~13.21%), Agriculture (~0.18%), and Paddy (~0.43%), and decrease in Evergreen Forest (~13.73%) and Water (~0.12%) cover types. Deciduous forest and Agriculture, associated with the highest soil loss rates, underscore the environmental consequences of specific land use practices. Notably, the increase in Deciduous forest and Agriculture significantly contributes to changes in soil loss rates, revealing the interconnectedness of land cover changes and soil erosion in ~18.05% and ~8.67%, respectively. This study contributes valuable insights for informed land management decisions and lays a foundation for future research in soil erosion dynamics. Additionally, the percentage increase in Agriculture corresponds to a notable rise in soil loss rates, underscoring the urgency for sustainable land use practices.

The Implementation of Land Use Scenario to Mitigate Erosion Rate in Pidekso Dam Catchment Area

Land use in Pidekso Dam Catchment Area is the largest in moorland and rice fields, so the potential for erosion rates that produce sedimentation is relatively high and dramatically affects the operating life of the dam. This study aims to compare several land use implementation scenarios to obtain a longer operating life at Pidekso Dam. The amount of erosion rate is calculated based on the Universal Soil Loss Equation (USLE) model with the integration of Geographic Information Systems for land use implementation scenarios. The calculation results before the land use implementation scenario showed the magnitude of the erosion rate at Pidekso Dam Catchment Area of 971846.151 tons/year with a sediment yield of 116026.553 tons/year, including a very severe erosion hazard level. The operating life of Pidekso Dam is estimated to last only 33.54 years from the planned life of 50 years. Based on the analysis of several land use scenarios using vegetative and mechanical methods for the longer operating life of Pidekso Dam with the construction of sediment barriers, which reached 53.99 years.

Assessment of potential soil erosion in Mongolia based on the RUSLE model and RCP 8.5 scenario

AbstractDue to its relatively high altitude and a continental climate, environmental issues related to soil erosion and land degradation more seriously affect the ecosystem and crop production in Mongolia. As detailed soil erosion and land degradation assessments have only been performed in some regions of Mongolia, the purpose of this study is to evaluate the soil vulnerability on a national scale and predict future soil erosion under climate change prior to establishing an appropriate management plan. The revised universal soil loss equation model was chosen for this study, which reflects geographic and climatic characteristics in a vast area as the Mongolian territory. We evaluated soil erosion using historical data from 1993 and 2013 and generated predictions in the near (2041–2060) and far future (2061–2080) periods under the representative concentration pathway 8.5 scenario. The soil erosion rates were divided into six classes: very low, low, medium, high, very high and extreme. The total of 46,039 km2 of the Mongolian territory was classified as very high and extreme risk, which is predicted to increase up to 48,961 and 51,769 km2, in the near and far future periods, respectively. At the national scale, most of the soil erosion appeared in bare area, as it covers more than 60% of the total area. Meanwhile, the highest soil erosion risk was expected in high mountain ranges and ecologically vulnerable regions of the Gobi Desert and steppe area. This study identifies three land cover types with high priority for management and areas that should be considered first in the sustainable national land use and soil management plans considering regional characteristics.

Soil erosion vulnerability and soil loss estimation for Siran River watershed, Pakistan: an integrated GIS and remote sensing approach.

Soil erosion is a problematic issue with detrimental effects on agriculture and water resources, particularly in countries like Pakistan that heavily rely on farming. The condition of major reservoirs, such as Tarbela, Mangla, and Warsak, is crucial for ensuring an adequate water supply for agriculture in Pakistan. The Kunhar and Siran rivers flow practically parallel, and the environment surrounding both rivers' basins is nearly identical. The Kunhar River is one of KP's dirtiest rivers that carries 0.1 million tons of suspended sediment to the Mangla reservoir. In contrast, the Siran River basin is largely unexplored. Therefore, this study focuses on the Siran River basin in the district of Manshera, Pakistan, aiming to assess annual soil loss and identify erosion-prone regions.Siran River average annual total soil loss million tons/year is 0.154. To achieve this, the researchers integrate Geographical Information System (GIS) and remote sensing (RS) data with the Revised Universal Soil Loss Equation (RUSLE) model. Five key variables, rainfall, land use land cover (LULC), slope, soil types, and crop management, were examined to estimate the soil loss. The findings indicate diverse soil loss causes, and the basin's northern parts experience significant soil erosion. The study estimated that annual soil loss from the Siran River basin is 0.154 million tons with an average rate of 0.871 tons per hectare per year. RUSLE model combined with GIS/RS is an efficient technique for calculating soil loss and identifying erosion-prone areas. Stakeholders such as policymakers, farmers, and conservationists can utilize this information to target efforts and reduce soil loss in specific areas. Overall, the study's results have the potential to advance initiatives aimed at safeguarding the Siran River watershed and its vital resources. Protecting soil resources and ensuring adequate water supplies are crucial for sustainable agriculture and economic development in Pakistan.

Modeling of soil erosion risk in a typical tropical savannah landscape

Tropical savannah landscapes are faced with high soil degradation due to climate change and variability coupled with anthropogenic factors. However, the spatiotemporal dynamics of this is not sufficiently understood particularly, in the tropical savannah contexts. Using the Wa municipality of Ghana as a case, we applied the Revised Universal Soil Loss Equation (RUSLE) model to predict the potential and actual soil erosion risk for 1990 and 2020. Rainfall, soil, topography and land cover data were used as the input parameters. The rate of predicted potential erosion was in the range of 0–111 t ha−1yr−1 and 0–83 t ha−1yr−1 for the years 1990 and 2020, respectively. The prediction for the rate of potential soil erosion risk was generally higher than the actual estimated soil erosion risk which ranges from 0 to 59 t ha−1yr−1 in 1990 and 0 to 58 t ha−1yr−1 in 2020. The open savannah areas accounted for 75.8 % and 73.2 % of the total soil loss in 1990 and 2020, respectively. The validity of the result was tested using in situ data from a 2 km2 each of closed savannah, open savannah and settlement area. By statistical correlation, the predicted soil erosion risk by the model corresponds to the spatial extent of erosion damages measured in the selected area for the validation. Primarily, areas with steep slopes, particularly within settlement, were identified to have the highest erosion risk. These findings underscore the importance of vegetation cover and effective management practices in preventing soil erosion. The results are useful for inferences towards the development and implementation of sustainable soil conservation practice in landscapes with similar attributes.

Application of Revised Universal Soil Loss Equation (RUSLE) model for the estimation of soil erosion and prioritization of erosion-prone areas in Majuli Island, Assam, India

Soil erosion is a serious issue, causing loss of agricultural productivity, increase in sediment deposit in the riverbeds, and damage to the ecological balance of the affected areas. Proper assessment of the soil erosion rate is essential for managing natural resources. The present study employs a GIS-based RUSLE (Revised Universal Soil Loss Equation) model to estimate annual soil loss in Majuli River Island of Assam, India. Annual average rainfall, soil properties, topographic characteristics, and LULC were taken as inputs to identify the soil erosion susceptible areas. The result revealed that annual soil loss of the study area ranges between 0 to 711 t ha−1 yr−1, with a mean annual soil loss of 23.02 t ha−1 yr−1. The entire region was classified into six soil loss severity classes, around 90 % of the area was found to be very slightly affected (&lt; 5 t ha−1 yr−1) by soil erosion, around 5 % slightly affected (5 – 10 t ha−1 yr−1), roughly 3% moderately affected (10 – 20 t ha−1 yr−1), around 1% moderate high (20 – 40 t ha−1 yr−1), nearly 0.3 % area affected severely (40 -80 t ha−1 yr−1) and very severely affected areas (&gt; 80 t ha−1 yr−1) contributes 0.1 %. A total of six priority levels of conservation were demarcated village-wise; priority level I requires immediate attention, and so on. The research outcome can help effectively implement conservation and management practices to check soil erosion in the study area.