Soil Erosion Vulnerability 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

Spatio-temporal soil loss modelling using RUSLE and sediment delivery into a reservoir in a semi-arid region of northern Nigeria

Soil loss is aggravated by uncontrolled deforestation, indiscriminate land clearing for agricultural activities, overgrazing, and urban development that leads to severe soil erosion over the land surface. The main objective of this research is to apply the Revised Universal Soil Loss Equation (RUSLE), in conjunction with remote sensing and GIS, to determine the temporal variation of soil loss from the Gubi watershed in the years 2000 and 2017 and to estimate the sediment delivery into the Gubi reservoir in Northern Nigeria. Datasets of rainfall, soil type, topography, cover management, and support practice were utilized to determine the five RUSLE factors. The research identified five levels of erosion ranging from severe to very low-risk areas. The spatial distribution of erosion hotspots in the catchment area was highlighted. Results revealed increasing levels of erosion from 14,244.31 tons/year in 2000 to 16,792.33 tons/year in 2017. Consequently, the catchment's sediment yield was 6903.65 tons/year and 9172.71 tons/year corresponding to sediment delivery ratios of 48.46 % and 54.62 % in 2000 and 2017 respectively. The sediment inflow into Gubi reservoir was 747.38 tons/year and 950.76 tons/year corresponding to 10.83 % and 10.37 % of the total sediment yield in 2000 and 2017 respectively. Soil erosion vulnerability index maps were produced and the erosion is expected to increase with time as agricultural activities and deforestation continue to occur. The sediment deposits have the potential to reduce the designed life of the reservoir. Best management practices, such as tree planting, mulching, and contour ridging are recommended for soil conservation. There is a need to assess the reservoir sediment deposits so that measures can be taken to maintain the reservoir operations properly.

Mapping of Soil Erosion Vulnerability in Wadi Bin Abdullah, Saudi Arabia through RUSLE and Remote Sensing

This study investigates soil loss in the Wadi Bin Abdullah watershed using the Revised Universal Soil Loss Equation (RUSLE) combined with advanced tools, such as remote sensing and the Geographic Information System (GIS). By leveraging the ALOS PALSAR Digital Elevation Model (DEM), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall data, and the Digital Soil Map of the World (DSMW), the research accurately evaluates soil loss loads. The methodology identifies significant variations in soil loss rates across the entire watershed, with values ranging from 1 to 1189 tons per hectare per year. The classification of soil loss into four stages—very low (0–15 t/ha/yr), low (15–45 t/ha/yr), moderate (45–75 t/ha/yr), and high (>75 t/ha/yr)—provides a nuanced perspective on soil loss dynamics. Notably, 20% of the basin exhibited a soil loss rate of 36 tons per hectare per year. These high rates of soil erosion are attributed to certain factors, such as steep slopes, sparse vegetation cover, and intense rainfall events. These results align with regional and global studies and highlight the impact of topography, land use, and soil properties on soil loss. Moreover, the research emphasizes the importance of integrating empirical soil loss models with modern technological approaches to identify soil loss-prone locations and precisely quantify soil loss rates. These findings provide valuable insights for developing environmental management strategies aimed at mitigating the impacts of soil loss, promoting sustainable land use practices, and supporting resource conservation efforts in arid and semi-arid regions.

Assessing soil erosion risk through geospatial analysis and magnetic susceptibility: A study in the Oued Ghiss dam watershed, Central Rif, Morocco

In northern Morocco, water erosion presents a critical risk for soil degradation and dam sedimentation, attributable to extreme climatic conditions, variegated topography, lithological vulnerabilities, and anthropogenic pressures. This study quantitatively and spatially evaluates soil erosion susceptibility within the catchment area of the Oued Ghiss dam in Central Rif, Morocco. Utilizing the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information Systems (GIS) and calibrated through magnetic susceptibility metrics, the study provides a detailed analysis of erosion factors. The model was applied using GIS to spatially analyze data on rainfall, soil type, topography, land cover, and management practices, with magnetic susceptibility measurements used to calibrate the model by identifying stable and erosion-prone areas. The resultant soil loss map reveals a mean erosion rate of 24 t/ha/year. Specifically, 37.28 % of the catchment area exhibits an erosion rate under 5 t/ha/year, 40.16 % between 5 and 10 t/ha/year, 22.77 % between 10 and 20 t/ha/year, and 0.86 % surpasses 20 t/ha/year. Magnetic susceptibility measurements across various soil profiles indicated that stable soils showed increased susceptibility towards the surface, while eroded soils displayed decreased susceptibility, with significant correlations found between magnetic susceptibility variations and the final risk map. This innovative calibration of the RUSLE model through magnetic susceptibility improves erosion vulnerability assessments and results in a more detailed soil loss map. The findings provide critical information for policymakers to develop targeted management plans for erosion-prone areas, particularly benefiting the Ghiss dam watershed, where these insights will aid in preventing dam siltation and enhancing water resource management. This confirms magnetic susceptibility's reliability in assessing soil erosion vulnerability and improving numerical models.

Soil erosion vulnerability in Brazilian semiarid

Soil erosion vulnerability in Brazilian semiarid

Soil Erosion Vulnerability Assessment in the Eco-Sensitive Himalayan Region Using Modeling Approach

Soil Erosion Vulnerability Assessment in the Eco-Sensitive Himalayan Region Using Modeling Approach

Assessing soil erosion vulnerability through geospatial morphometric analysis in the Oued Amter Basin (Northwest Morocco)

Assessing soil erosion vulnerability through geospatial morphometric analysis in the Oued Amter Basin (Northwest Morocco)

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.

RUSLE and GIS Based Soil Erosion Modelling in a Micro-Watershed (1E1C4a2) in the Budgam district of Jammu and Kashmir

Abstract: A comprehensive methodology that integrates Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) techniques was adopted to determine that soil erosion vulnerability of micro-watershed in J&K India. The spatial pattern of annual soil erosion rate was obtained by integrating geo-environmental variables in a raster based GIS method. GIS data layers including, rainfall erosivity (R). Soil erodibility (K), slope length and steepness (LS), cover management (C) and conservation Practice (P) factors were computed to determine their effect on average annual soil loss in the area. The resultant map of annual soil erosion shows maximum soil loss of 2.9246 t h Y with a close relation to grass land area, agricultural lands and orchards (with low LS) . The spatial erosion maps generated with RUSLE method and GIS can serve as effective inputs in deriving strategies for land planning management in the environmentally sensitive agricultural areas.

Evaluation of the Importance of Ecological Protection in Xiahe County

The importance of ecological protection is a comprehensive index reflecting the importance of ecosystem structure and function in a region, including ecosystem service function and ecological vulnerability. This paper takes Xiahe County as an example, based on the GIS and RS platform, combined with its natural environment characteristics, selects the importance of water conservation, soil and water conservation, and biodiversity maintenance in the evaluation of the importance of ecosystem service functions. Among which, we select grassland degradation vulnerability assessment and soil erosion vulnerability assessment as the basic ways to assess the Ecological vulnerability. Finally, the evaluation results are integrated, the importance of ecological protection is obtained, and the extremely important and important areas of ecological protection are identified.

The influence of land cover variation on soil erosion vulnerability around coal mining concession areas in South Borneo

The availability of information about soil erosion vulnerability is necessary as a primary consideration to determine the effort of soil conservation, particularly in the coal mining area. This study aimed to estimate the potential risk of soil erosion from land cover variation in a coal mining concession site in South Borneo. Data were taken from 18 stations of soil erosion monitoring which were evenly distributed in each land cover. Soil erosion vulnerability was quantified using the Universal Soil Loss Equation (USLE) method. The comparison mean of soil erosion among land cover types was evaluated using the Kruskal-Wallis test and followed by the Nemenyi test with a significant level of 5%. Results found that the potential risk of soil erosion was significantly different among land covers (p<0.05). The highest soil erosion vulnerability was noted in the reclamation area of 1,012.3 t ha<sup>-1</sup> year<sup>-1,</sup> while the lowest risk of soil erosion was observed in plantation forests of 47.9 t ha<sup>-1</sup> year<sup>-1</sup>. Surprisingly, the potential risk of soil erosion in natural forests was four times higher than in oil palm plantations. Besides being located in hilly areas with high slope levels, the vegetation density in natural forests was relatively low. However, our study recorded there were two critical factors that highly correlated to soil erosion vulnerability, i.e., soil erodibility (R = 0.89; p<0.05) and slope length and steepness (R = 0.85; p<0.05).

Prioritization of erosion susceptible watersheds using morphometric analysis and PCA approach: A case study of lower Sutlej River basin of Indian Punjab

Morphometry helps in understanding the behaviour of drainage characteristics with respect to various hydrological processes including infiltration, runoff, erosion and sediment transport. Morphometric analysis of river basins is an essential technique to the study the response of drainage basin in response to topological characteristics. The river basins' morphometric analysis is an important technique to prioritize the watersheds for implementation of soil and water management strategies. In this study, the morphometric characteristics of the lower Sutlej River have been determined using the geo-spatial techniques. The river basin, having area of 8577 km2, was delineated into the fourteen sub-watersheds (WS-1 to WS-14) in the GIS environment. The ALOS PALSAR DEM and ArcGIS were utilized to evaluate the morphometric parameters of the delineated watersheds. The calculated morphometric parameters were used to rank the watersheds in terms of soil erosion potential. The priority ranks to the watersheds were assigned as per compound parameter, which was calculated by averaging the ranks designated to each morphometric parameter. Watersheds with the lowest compound parameter values were given the highest priority rating, and vice versa. Based on the results WS-7 was assigned the first rank whereas WS-13 was assigned the 13th rank. The principal component analysis was performed to determine the highly correlated morphometric parameters. Out of the 18 parameters, 13 were found be highly correlated. The compound parameter obtained based on these highly correlated parameters also prioritized WS-7 as the most vulnerable watershed. Therefore, WS-7 should be selected for the implementation of soil and water conservation strategies. It can be concluded that morphometric analysis along with PCA in combination with GIS can be helpful in prioritizing the watersheds in terms of soil erosion vulnerability and water management.

Ecological Vulnerability in the Red Soil Erosion Area of Changting under Continuous Ecological Restoration: Spatiotemporal Dynamic Evolution and Prediction

Sustainable restoration of degraded ecosystems is a major environmental concern in several regions of China. Changting is one of the severely affected water- and soil-loss areas in southern China that have been under continuous management for the last 30 years. Taking the typical red soil erosion area in Changting, Fujian, as the research object, an evaluation index system with 30 m resolution was developed based on the Sensitivity–Resilience–Pressure (SRP) model. Spatial principal component analysis, Global Moran’s I, the LISA cluster map, and the CA-Markov model were employed to dynamically evaluate and predict the ecological vulnerability of the red soil erosion area in Changting. The findings revealed that the ecological vulnerability of the red soil erosion area in Changting has obvious spatial differences and topography, meteorological, and economic and social variables are the primary driving factors of ecological vulnerability. The analysis of spatial distribution of ecological vulnerability showed significant sets of contiguous locations of severe and mild ecological vulnerability. The total index of ecological vulnerability in the study area reduced by 9.49% from 2000 to 2020, yet it was still just mildly vulnerable. The proportion of severe and extremely vulnerable areas declined by 4.87% and 5.61%, respectively. The prediction results for the coming ten years showed that the ecological vulnerability of red soil erosion in Changting will tend to improve. In summary, it is found that after years of continuous ecological management in the red soil erosion area of Changting, the ecological restoration effect of the soil erosion area is obvious.

An Assessment of Ecological Protection Importance Based on Ecosystems Services and Vulnerabilities in Tibet, China

Tibet has a unique ecosystem and unique species resources and is an important ecological protection area in China and surrounding countries. Ecological protection in Tibet needs to be strengthened and targeted regionally. Therefore, it is necessary to evaluate the importance of ecological protection and its spatial difference for policy formulation. In this study, the importance of biodiversity conservation, water retention, soil retention, and sand prevention, as well as the vulnerability of soil erosion and land desertification, was assessed, and the importance of ecological protection was classified. The extremely important ecological protection areas covered an area of 615,000 km2, mainly located in the Northern Tibet Plateau, the southern foot of the Himalayas, the Hengduan Mountains, and the Gangdise-Nyainqentanglha Mountains. The relationship between land-use type, elevation, and ecological protection importance was studied. The results showed that the most important ecological protection area consisted of forest and grassland in low-altitude areas or uninhabited areas 5000 m above sea level. The spatial characteristics of dominant ecological functions in different regions were identified by comparing the importance of different factors. The study found that there are significant differences in the dominant factors of important ecological protection areas in Southeast Tibet, Northern Tibetan Plateau, and Central Tibet. The results provide an effective reference for ecological function zoning and ecological protection policy formulation in Tibet, as a scientific basis for promoting ecological protection and restoration in Tibet.

A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction

Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting accurate rainfall helps early detection of soil erosion vulnerability and can minimise the damages by taking appropriate measures caused by severe storms, droughts and floods. This study aims to predict soil erosion probability using the deep learning approach: long short-term memory neural network model (LSTM) and revised universal soil loss equation (RUSLE) model. Daily rainfall data were gathered from five agro-meteorological stations in the Central Highlands of Sri Lanka from 1990 to 2021 and fed into the LSTM model simulation. The LSTM model was forecasted with the time-series monthly rainfall data for a long lead time period, rainfall values for next 36 months in each station. Geo-informatics tools were used to create the rainfall erosivity map layer for the year 2024. The RUSLE model prediction indicates the average annual soil erosion over the Highlands will be 11.92 t/ha/yr. Soil erosion susceptibility map suggests around 30 % of the land area will be categorised as moderate to very-high soil erosion susceptible classes. The resulted map layer was validated using past soil erosion map layers developed for 2000, 2010 and 2019. The soil erosion susceptibility map indicates an accuracy of 0.93 with the area under the receiver operator characteristic curve (AUC-ROC), showing a satisfactory prediction performance. These findings will be helpful in policy-level decision making and researchers can further tested different deep learning models with the RUSLE model to enhance the prediction capability of soil erosion probability.

Soil Erosion Vulnerability Mapping in Selected Rural Communities of uThukela Catchment, South Africa, Using the Analytic Hierarchy Process

Soil erosion remains one of the main causes of land degradation, affecting many countries across the globe including South Africa. In rural communities with much reliance on agriculture, soil erosion is an important threat to food security. Therefore, mapping erosion-prone areas is an essential step towards adopting appropriate erosion mitigation and soil conservation measures. The objectives of this study were to (i) assess and model soil erosion vulnerability based on the Analytic Hierarchy Process (AHP) approach in Hoffenthal and KwaMaye communities within the uThukela Catchment, South Africa; and (ii) identify the relevant sustainable interventions and remedial strategies to combat soil erosion in the study area. The AHP was employed to map soil erosion vulnerability and derive the percentage weights of geo-environmental parameters contributing to soil erosion: rainfall, slope, drainage density, soil type, vegetation cover, and land use/land cover. The AHP model showed that slope, vegetation cover, and rainfall had the most considerable influence on soil erosion with factor weights of 29, 23, and 18%, respectively, in the study area. Further, this study revealed that high-risk soil erosion areas occupy 21% of the total study area, while very high-risk areas are about 14%, and the east and central areas are most vulnerable to soil erosion. Validation of the AHP model (overall accuracy = 85%; kappa coefficient = 0.70) results suggests that the predictive capacity of the model was satisfactory. Therefore, the developed soil erosion vulnerability model can serve as an important planning tool to prioritize areas for soil conservation and erosion management approaches like sustainable agriculture and bioengineering interventions.

Modelling climate change impact on soil loss and erosion vulnerability in a watershed of Shiwalik Himalayas

Understanding the impact of climate change on soil erosion is required to plan effective soil conservation and management practices in hilly and mountainous landscape. Though, few studies have been executed in Indian Himalayas, much less in Shiwalik Himalayas. Thus, keeping this in view, the aim of the study is to estimate the possible impact of projected climate change scenarios on soil loss and erosion vulnerability using Statistical Downscaling Model (SDSM), MarkSim Weather Generator, Agricultural Policy Environmental eXtender (APEX) model and AHP method. The present study downscaled four climate scenarios on the near-future, noted the 2020 s (2011–2040), mid-future, noted 2050 s (2011–2070) and far-future, noted 2080 s (2071–2100/2095) under two Special Report Emission Scenarios (SRES), A2 and B2 with two Representative Concentration Pathway (RCP), 4.5 and 8.5 scenarios. Both SRES and RCP scenarios predicted an increase in temperature and annual rainfall depth during 21st century. Calibrated APEX model was used to simulate future soil loss. The study exposed that the average annual soil loss might increase up to 46.94%, 38.80%, 35.34% and 25.93% in the A2, B2, RCP 4.5 and RCP 8.5 scenarios respectively from the base period (1985–2014). The vulnerability assessment indicates, most of the areas might be under risk from slightly to highly vulnerable and was found that forest land cover could resist this erosion vulnerability to a certain extent. The findings of the study showed a possibility for climate change to increase the rate of soil loss unless conservation strategies or proper land use plans are remunerated. This study offers scientists and policymakers to a detailed understanding of the future impact of rainfall on soil loss and erosion vulnerability in the Shiwalik Himalayas.

Predicting soil erosion susceptibility associated with climate change scenarios in the Central Highlands of Sri Lanka

Soil erosion hazard is one of the prominent climate hazards that negatively impact countries’ economies and livelihood. According to the global climate index, Sri Lanka is ranked among the first ten countries most threatened by climate change during the last three years (2018–2020). However, limited studies were conducted to simulate the impact of the soil erosion vulnerability based on climate scenarios. This study aims to assess and predict soil erosion susceptibility using climate change projected scenarios: Representative Concentration Pathways (RCP) in the Central Highlands of Sri Lanka. The potential of soil erosion susceptibility was predicted to 2040, depending on climate change scenarios, RCP 2.6 and RCP 8.5. Five models: revised universal soil loss (RUSLE), frequency ratio (FR), artificial neural networks (ANN), support vector machine (SVM) and adaptive network-based fuzzy inference system (ANFIS) were selected as widely applied for hazards assessments. Eight geo-environmental factors were selected as inputs to model the soil erosion susceptibility. Results of the five models demonstrate that soil erosion vulnerability (soil erosion rates) will increase 4%–22% compared to the current soil erosion rate (2020). The predictions indicate average soil erosion will increase to 10.50 t/ha/yr and 12.4 t/ha/yr under the RCP 2.6 and RCP 8.5 climate scenario in 2040, respectively. The ANFIS and SVM model predictions showed the highest accuracy (89%) on soil erosion susceptibility for this study area. The soil erosion susceptibility maps provide a good understanding of future soil erosion vulnerability (spatial distribution) and can be utilized to develop climate resilience.

Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

Evaluation of the Risk Induced by Soil Erosion on Land Use. Case Study: Guruslău Depression

Changes in land use, increasing of agricultural areas to the detriment of wooded ones, and poor management of agricultural land, along with the impact of current changes in the climate (reflected in the increase of the climate aggression index) makes soil erosion one of the main risks associated with improper land use, with a direct impact on its productivity and an indirect impact on human beings. The aim of this study is to assess the risk induced by surface soil erosion on land use, using as our main method of investigation the development of two models of integrated spatial analysis of the territory: a derived model of the universal soil loss equation (USLE) and a qualitative model that integrates the result of soil erosion assessment with the database representing the land use. This was carried out in order to highlight the impact on the territory. The spatial analysis models were developed on a structure of vector spatial databases, through which the soil type, soil texture, climate aggression coefficient, and land use were mapped, and alphanumeric databases, representing the market cost of land, in EUROs, that highlight the quality of cultivated land (in terms of productive economic potential). The induced risk estimation is based on a qualitative rating of soil erosion vulnerability on a scale from 1 to 5 (1-low vulnerability; 5-high vulnerability) and of the reduction of the economic value of the land (according to the vulnerability rating). The implemented methodology highlights the quantitative risk, with a maximum value of about 46.000 EUROs, spatially identified on large surfaces on the outskirts of the Jibou municipality. It is mainly caused by the impact of soil erosion on large areas of orchards, which provide necessary products for human consumption. The present methodology can be implemented on similar areas and can be used as a model of good practices in risk assessment based on financial losses by local public authorities.