RUSLE Model Research Articles

Evaluation of soil erosion vulnerability in Hubei Province of China using RUSLE model and combination weighting method

Evaluation of soil erosion vulnerability in Hubei Province of China using RUSLE model and combination weighting method

Integrating calibrated PTFs and modified OpenKarHydro framework to map the responses of ecohydrological processes to climate change across the Loess Plateau

Climate change is exacerbating the risk of soil water stress, soil erosion and ecological degradation in the Loess Plateau (CLP). However, the spatiotemporal dynamics of ecohydrological processes, including soil water balance (SWB), soil erosion (SE) and vegetation net primary productivity (NPP), in response to climate change remain isolated and ambiguous. Additionally, existing studies often rely on limited measured data from specific watersheds with particular land use/cover types to establish the pedotransfer functions (PTFs) for single soil hydraulic parameter (SHP). There is a notable lack of comprehensive studies that explore PTFs encompassing all SHPs applicable to the entire CLP, predict the spatiotemporal response of ecohydrological processes to climate change, and identify future risk periods and regions for SWB, SE, and NPP. To address these gaps, we first screened the PTFs applicable for the whole CLP, and then projected the spatiotemporal dynamics of ecohydrological processes from 2020 to 2030 under CMIP6 scenarios by integrating the PTFs with the OpenKarHydro, RUSLE, and CASA model, and finally classified the soil water content, SE and NPP to identify their risk periods and regions. The results showed that the PTFs achieved satisfactory accuracy, with RMSEs for Ks, θs, θr, α, n, θfc and θw being 2.682, 0.109, 0.016, 0.111, 0.897, 0.060 and 0.058, BIASs of 1.365, 0.182, 0.012, 0.031, 0.214, 0.057 and 0.048, R2s of 0.445, 0.500, 0.430, 0.400, 0.694, 0.453 and 0.453, and NSEs of 0.645, 0.737, 0.874, 0.349, 0.567, 0.756 and 0.458, respectively. SE decreased significantly from 2020 to 2030, with an average annual rate of −6.18 %. Soil water storage decreased significantly between 2020 and 2030, and declining from southeast to northwest. The proportion of area in the wet zone decreased by 4 %, while the proportion of area in the dry zone increased by 11.9 % from 2020 to 2030. The average NPP in 2020–2030 is 320.07 gC·m−2·a−1, with the largest in summer and smallest in winter, and decreasing from southeast to northwest. The NPP increased significantly in 2020–2030, with average annual value and rate of 19.61 gC·m−2·a−1 and 70.84 %, respectively. The NPP increased significantly in spring, summer and autumn, but remained stable in winter. This investigation bridges the gap between existing soil properties, missing SHPs, the strong regional applicability of PTFs, and isolated ecohydrological processes. It is promises to provide valuable insights into the response to climate change in the CLP and another water-limited regions.

Analysis of Spatial Distribution of Ecosystem Services and Driving Factors in Northeast China

Northeast China is an important ecological barrier in China, and an in-depth understanding of the spatial distribution in ecosystem services （ESs）, and the driving factors is crucial for realizing the subsequent management and protection of ESs. In the study, we quantitatively assessed the characteristics of spatial distribution in ESs in Northeastern China using the InVEST, RWEQ, and RUSLE models and identified the driving factors of ESs spatial distribution in conjunction with the geodetector based on meteorological data, remote sensing data, and socio-economic data. The results showed that the spatial distribution of ESs in Northeast China had obvious spatial heterogeneity. The high values of habitat quality （HQ）, carbon sequestration （CS） services, and soil conservation （SC） services were mainly distributed in the northern part of the four eastern leagues of the Inner Mongolia Autonomous Region, the northern part of Heilongjiang Province, and the eastern part of Northeast China, which were high in fraction vegetation cover, and low values were mainly found in southwestern and eastern Heilongjiang Province, western Jilin Province, and western Liaoning Province. The high values of the water yield （WY） service and wind prevention and sand fixation （WPSF） service were distributed in the east of the Inner Mongolia Autonomous Region and the east of Liaoning Province. The high values of WY services and WPSF services were distributed in the eastern part of Northeast China and the four eastern provinces of the Inner Mongolia Autonomous Region. According to the geodetector results, slope had the strongest explanatory power for the spatial distribution of SC services with a q-value of 0.31, land use/cover change had the strongest explanatory power for the spatial distribution of HQ and CS services with q-values of 0.64 and 0.52, respectively, and fraction vegetation coverage and annual precipitation had the strongest explanatory power for the spatial distribution of WPSF and WY services with q-values of 0.24 and 0.64, respectively, and there were interactions among all the driving factors. The spatial distribution of ESs in Northeast China was mainly influenced by natural factors. The results will provide a scientific basis for subsequent management and enhancement of ESs in Northeast China.

Soil Erosion Assessment Using the RUSLE Model, Remote Sensing, and GIS in the Woybo Watershed, Ethiopia

Soil Erosion Assessment Using the RUSLE Model, Remote Sensing, and GIS in the Woybo Watershed, Ethiopia

Soil loss estimation using RUSLE model: Comparison of conventional and digital soil data at watershed scale in central Iran

Sustainable agriculture and eco-environment conservation face constant threats from soil erosion in mountainous regions. This study aimed to predict soil loss in the mountainous watershed by applying the Revise Universal Equation Soil Loss (RUSLE) and Sediment Delivery Ratio (SDR) models and to evaluate the efficacy of the RUSLE model through the observed data in a hydrometric station. Comparing a polygonal, and a digital soil map (prepared by the Kriging method) for deriving the K factor to estimate soil loss was another objective of the recent research. In this study, two scenarios were examined for the calculation of spatial variation of the K-factor; scenario I comprised soil data derived from a legacy soil map with eight soil units and the data from their representative soil profiles (i.e., traditional soil survey), and scenario II comprised K-factor derived from 100 studied sites using kriging technique (i.e., digital soil survey). The results of RUSLE modeling showed that there was no significant difference between scenario I (7.97 t/(ha. yr)) and scenario II (7.93 t/(ha. yr)) for predicting soil loss with the RUSLE model. This finding confirms that the RUSLE model with limited and legacy data can provide a reliable prediction of soil loss in the given watershed. Moreover, long and short-term periods were used to estimate soil loss, while in the long-term period, estimated soil loss had higher accordance with actual soil loss. Sediment delivery ratio was calculated using models of Vanoni, Boyce, USDA-SCS, and Slope-based models. The results indicated that the Slope-based model had the highest fitness (R2=0.946, ME=0.27, and RMSE=0.275 for scenario I and R2=0.9443, ME=0.26 and RMSE=0.40 for scenario II), with observed sedimentation rate at the hydrometric station at the outlet of the watershed. Overall, predicted soil loss severity map using the traditional soil map by RUSLE model could provide trustworthy information for decision makers and governors at the given and similar watersheds in semiarid regions.

Assessment of soil erosion risk in a hilly zone sub-watershed of Karnataka using geospatial technologies and the RUSLE model

ABSTRACT Human activities and climate change have sped up soil erosion, leading to annual losses of fertile soil and declining crop yields. The biggest concerns in land and water conservation are modeling and monitoring soil loss, as well as measuring the vulnerability and danger of active erosional processes. In the present study, a comprehensive approach used to evaluate the risk of soil erosion in the Ganjigatti sub-watershed of Karnataka, India, using the RUSLE model and GIS techniques. The annual average soil loss estimated using the RUSLE model ranged from 0 to 122.73 t ha−1 yr−1, with an average of 3.48 t ha−1 yr−1 in the Ganjigatti sub-watershed. The quantity of erosion was found to be greatly influenced by topography (LS factor) and land use and land cover (C factor). The results showed that a larger part of the study area, 98.34%, accounted for slight erosion (0–5 t ha−1 yr−1). The spatial pattern of erosion revealed high to very severe risk zones in the main stream of sub-watershed. The total soil loss was calculated to be 15,046.96 t yr−1 in the sub-watershed. The study’s results aid decision-makers, land administrators, environmentalists, and others in implementing effective land and water conservation measures in the Ganjigatti sub-watershed to control soil erosion.

Application of Remote Sensing and GIS for Soil Erosion Analysis on Baitarani River Basin by RUSLE Model

Abstract: 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 Keonjhar and Mayurbhanj 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 14,218 square kilometers, are mainly identified in agricultural and forested hilly areas where slopes exceed 100, such as those in Keonjhar and Mayurbhanj, 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.

Spatiotemporal dynamic of soil erosion in the Roraya River Basin based on RUSLE model and Google Earth Engine

Spatiotemporal dynamic of soil erosion in the Roraya River Basin based on RUSLE model and Google Earth Engine

Integrating portable X-ray fluorescence site survey and ArcGIS models for rapid risk assessment and mitigation strategies at an abandoned arsenic mine site: a case study

ABSTRACT Australia’s metalliferous abandoned mine sites (MAMSs), pose tangible threats to the environment and human health. To address these concerns, our study utilised state-of-the-art handheld XRF technology to conduct a real-time assessment of the Mole River arsenic mine site. The data revealed notably elevated levels of arsenic and manganese, with the southeast corner of the site identified as a contaminant hotspot. We used a tiered risk assessment approach to compare the detected contaminant concentrations to the Australian health investigation levels (tier 1). This led us to a broader examination of erosion vulnerabilities and the potential migration of contaminants (tier 2). Further, a hydrological assessment (tier 3) identified significant erosion in southern regions, indicating the potential for contaminants to be transported off-site through surface water runoff to Sam’s Creek and Mole River. The proximity of a reservoir to these runoff pathways brought forth additional challenges, especially during heavy rainfall events. Subsequent laboratory analysis of water samples reinforced our findings, as they confirmed heightened arsenic concentrations in Mole River downstream, accentuating the potential risks to ecosystems and human health. By integrating the XRF contour map and erosion assessment with the RUSLE model, valuable insights are gained into critical hotspots with high contamination and erosion potential. By directing rehabilitation efforts towards critical hotspots, resources can be allocated more efficiently and cost-effectively.

Analysing the influence of surface greening on soil conservation in China using satellite remote sensing

Global surface greening is very important to slow down regional warming, promote ecological balance and sustainable development, and China has made a significant contribution to this in the past 30 years, and effectively alleviated environmental problems such as land degradation, water shortage and air pollution by rapidly increasing vegetation coverage. Previous studies have shown that vegetation plays a positive role in alleviating soil erosion, but there is no quantitative description of runoff erosion and sediment transport under vegetation evolution with long time series and high resolution in China area. In this study, we use the RUSLE model and multi-source satellite remote sensing data to simulate overland soil erosion in China from 1990 to 2020 and analyze both the temporal and spatial variation of vegetation avoided erosion (VAE) and its correlation with multiple driving factors, such as Gross Primary Production (GPP). The results show that: 1)The distribution has increased by 12.89 % from 1990 to 2020, which is significant, and indicates that greening of the surface has significantly prevented soil and water loss;2)The spatial correlation strength of VAE-GPP in China is roughly in line with the division of the Hu Huanyong line (Hu-line), which is the result of the combined effect of vegetation distribution and human activities;3)The efficiency of VAE was significantly improved, especially the forest, accounting for about 76.50 % of the total, which was twice the sum of the other types. The research conclusion is of great significance to soil erosion prevention and control, soil ecological improvement, vegetation dynamic planning and regional sustainable development.

Assessing soil erosion risk in Meghalaya, India: integrating geospatial data with RUSLE model

Assessing soil erosion risk in Meghalaya, India: integrating geospatial data with RUSLE model

Evaluating soil loss and sediment yield for sustainable management of the Hassan II dam within Morocco’s Upper Moulouya watershed using RUSLE model and GIS

Evaluating soil loss and sediment yield for sustainable management of the Hassan II dam within Morocco’s Upper Moulouya watershed using RUSLE model and GIS

Spatiotemporal Differentiation and Its Attribution of the Ecosystem Service Trade-Off/Synergy in the Yellow River Basin

Clarifying the spatio-temporal patterns of ecosystem services trade-off/synergy relationships (ESTSs) and their attribution in the Yellow River Basin is crucial to constructing ecological civilization in China. This study first analyzed the spatio-temporal change of ecosystem services (ESs) including the water yield, soil conservation, carbon sequestration, and habitat quality in the Yellow River Basin during 2000–2020 based on the InVEST and RUSLE models. Then, the spatial autocorrelation methods were used to quantify the spatio-temporal differentiation of ESTSs, and the Geo-detector method was employed to identify the contributions of driving factors associated with the natural, social-economic, and regional policy aspects of the ESTSs. Finally, the random forest and analysis of variance methods were used to validate the reasonability of major driving factors obtained by the Geo-detector. The main findings include: (1) In 2000–2020, water yield, soil conservation, and habitat quality increased, and carbon sequestration decreased. The ESs had a spatial pattern of high in the east and low in the west. (2) Overall, there were synergistic relationships between the four Ess. In the spatial distribution of ESTSs, the expansion of the synergy zone and trade-off zone occupied the majority. The synergy zones tended to be concentrated in the northwest and southeast of the study area. In contrast, the trade-off zones were more scattered than the synergy zone, mainly focused on the east-central and southwestern parts of the Yellow River Basin. (3) Geo-detector and random forest both showed that natural factors had a strong explanatory power on ESTSs, in which NDVI is a key driver. Both the results of Geo-detector and the analysis of variance showed that the interactions between natural factors exerted the most significant influence on ESTSs, followed by the interaction between natural factors and socio-economic factors.

Soil erosion-based sub-watershed prioritization through coupling various crop management and erosivity scenarios using game theory

The soil erosion of the watershed is different in temporal and spatial scales due to complex climatic and environmental conditions and their dynamic state over time. Accordingly, it is necessary to prioritize sub-watersheds based on their dynamic conditions to reduce the destructive effects of soil erosion. However, this issue has to be sufficiently reported on the scale of the watershed. Towards that, a game theory-based prioritization approach was planned concerning changes in vegetation cover and rainfall in the Shazand Watershed of the Markazi Province, Iran. To achieve the purpose of the study, the amount of soil erosion was first estimated in different scenarios resulting from the changes in rainfall erosivity and vegetation cover using the RUSLE model. Next, pairwise comparisons between 24 sub-watersheds in all scenarios were made based on the sub-watersheds' ranks in soil erosion in each scenario. Considering all scenarios, the priority of the sub-watersheds was assessed using the Condorcet approach. The results showed that the mean soil erosion in the watershed ranged from 5.4 and 36 t ha−1 y−1 while applying various scenarios. It is worth mentioning that the prioritization results obtained from the Condorcet algorithm and the current soil erosion rate are highly similar. On the other hand, sub-watersheds 2 and 9 were selected as the first and last priority, providing an appropriate roadmap for managers and relevant departments to adopt appropriate soil erosion inhibition measures.

Correction to: The effect of land use and land cover changes on soil erosion in semi-arid areas using cloud‑based google earth engine platform and GIS‑based RUSLE model

Correction to: The effect of land use and land cover changes on soil erosion in semi-arid areas using cloud‑based google earth engine platform and GIS‑based RUSLE model

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.

Identifying the effects of landscape pattern on soil conservation services on the Qinghai-Tibet Plateau

Land use patterns affect soil erosion processes and soil conservation services. Identifying key landscape pattern affecting soil conservation services is critical for improving soil conservation services and optimizing land use patterns. In this study, the Qinghai-Tibet Plateau (QTP), where ecosystem services are important, was selected as research area. Based on land use, meteorological, soil and vegetation data, the RUSLE model was used to estimate soil conservation quantitatively from 2000 to 2020. The temporal changes in landscape metrics were analyzed using Fragstats, and the response of soil conservation services to landscape metrics was analyzed by correlation analysis. The landscape pattern showed an obvious temporal change trend. In general, the overall landscape of the QTP became more diversified and fragmented, and the landscape shape tended to become more complicated. Patch density (PD), edge density (ED), landscape shape index (LSI), splitting index (SPLIT), Shannon's diversity index (SHDI), and Shannon's evenness index (SHEI) showed increasing trends, while largest patch index (LPI), mean patch area (AREA_MN), aggregation index (AI), and contagion index (CONTAG) showed decreasing trends. At the class level, the variations of landscape pattern metrics of different land use types were big difference. In the 20-year period, soil conservation services were generally on the rise. The spatial heterogeneity of soil conservation services decreased from the south-east to the north-west. PD and ED were positively correlated with soil conservation function, while AREA_MN and AI had a negative correlation with soil conservation function. The effect of landscape pattern metric variation on soil conservation capacity significantly differed among different land use types. Results of the research offer reasonable evidence for landscape management, water and soil retention function improvement and regional ecological protection on the QTP.

Interactive Effects of Ecological Land Agglomeration and Habitat Quality on Soil Erosion in the Jinsha River Basin, China

Soil erosion is a significant global environmental issue and a crucial aspect of global change. Exploring the interactive effect of ecological land agglomeration and habitat quality on soil erosion can effectively guide the positive intervention of ecological restoration activities. The study calculated the comprehensive ecological land agglomeration with Fragstats 4.2 and the habitat quality with InVEST 3.7.0 for the years 2000, 2010, and 2020 within the Jinsha River Basin in Yunnan, China. In addition, the RUSLE model was utilized to calculate soil erosion in the study area. The Geographic and Temporally Weighted Regression (GTWR) model was employed to obtain the regression coefficients and their spatial and temporal variations. The findings of this study revealed the following: (1) During the study period, there was an overall 29.06% reduction in the soil erosion modulus with an annual rate of 1.70% reduction on average, accompanied by an increase in both the comprehensive ecological land agglomeration and habitat quality. Soil erosion was more severe in the eastern regions than in the western ones and the other two indicators were higher in the northeast and southwest. (2) The GTWR results demonstrate that comprehensive ecological land agglomeration and habitat quality were negatively correlated with soil erosion, with results of −0.1383 and 0.0021, respectively. However, in northwest regions, there was a significant positive correlation between habitat quality and soil erosion. (3) The interaction term between comprehensive ecological land agglomeration and habitat quality was significantly negatively correlated with soil erosion with a result of −0.0299, and the interaction coefficients have regional variations. This study offers valuable guidance for land-use development and soil and water conservation in the Jinsha River Basin.

Proposal of erosion control scenarios for the reduction of soil losses in the Wadi Isser basin using the RUSLE model, northwest Algeria

Proposal of erosion control scenarios for the reduction of soil losses in the Wadi Isser basin using the RUSLE model, northwest Algeria

Study on the trade-off/synergy between ecosystem services and human well-being based on land use in Weinan City, China.

Change of land use have important impacts on ecosystem services (ESs) and human well-being (HWB), yet the trade-off/synergy among land use, ESs, and HWB has still not received sufficient attention at city regional scales level. Weinan City in the southern of the Loess Plateau of China was taken as the study area. Based on ArcGIS, InVEST model, and RUSLE model, the characteristics of spatial and temporal variations of land use and ESs from 2000 to 2020 were analyzed, and the trade-off/synergy relationship between land use, ESs, and HWB was quantified using correlation analysis. The results indicated that the area of cultivated land decreased significantly and the area of built-up land increased significantly from 2000 to 2020 in Weinan City. The grain production, soil conservation, and water yield functions showed an increasing trend, which was a synergistic relationship with HWB. Carbon storage and habitat quality functions showed a decreasing trend, which was a trade-off relationship with HWB. The index value of HWB has increased significantly, mainly in the added value of agricultural and rural per capita income. Land use intensity has a trade-off relationship with GP, WY functions, and HWB. There are many factors that affect this trade-off/synergy relationship, such as land use patterns, economic development, and population growth. The study can provide a theoretical basis for the sustainable development of regional economy and nature.