Spatial Distribution Of Soil Erosion Research Articles

Characteristics of Soil Physical Properties and Spatial Distribution of Soil Erosion on Ridge-Slope Farmland in the Black Soil Areas of Northeast China

Characteristics of Soil Physical Properties and Spatial Distribution of Soil Erosion on Ridge-Slope Farmland in the Black Soil Areas of Northeast China

Predicting soil erosion risk using the revised universal soil loss equation (RUSLE) model and geo‐spatial methods

AbstractAnthropogenic activities like overgrazing, deforestation and mismanaged land use accelerate soil erosion (SE), causing nutritional and organic matter loss. In this study, we predicted the annual rate of soil loss in the Salt Range, extending south from the Pothohar plateau, Pakistan, using the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model parameters and erosion probability zones were estimated using remote sensing and Geo‐Spatial methods. The annual average soil loss rates were calculated by considering five geo‐environmental factors, that is, slope length and steepness (LS), rainfall erosivity (R), cover management (C), soil erodibility (K), and conservation practice (P) range from 0–559 527, 1404–4431, 0–1, −0.14 to 1.64, and 0.2–122 respectively. This research determined that the yearly average rate of SE in the Salt Range varies from over 50 to above 350 . The distribution of land area across different SE probability zones reveals that a small portion (2.11%) is classified as High, a moderate portion (7.13%) falls under the category of Moderate, while the majority (90.7%) is classified as Low in terms of proneness towards erosion. The land devoid of vegetation and characterized by steep slopes is especially prone to SE. The Salt Range is highly vulnerable to SE risk due to climatic variations and improper land use practices. The result provides a spatial distribution of SE across the salt range, utilized for management planning processes and conservation at the policy level among decision‐makers and land‐use planners.

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

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

Spatio-Temporal Variations in Soil Erosion and Its Driving Forces in the Loess Plateau from 2000 to 2050 Based on the RUSLE Model

Assessing the spatio-temporal variability and driving forces of soil erosion on the Loess Plateau is critical for ecological and environmental management. In this paper, the Revised Universal Soil Loss Equation (RUSLE) model, the patch-generating land use simulation, and the Geographical Detector are used to investigate the spatio-temporal variations of the Loess Plateau’s soil erosion from 2000 to 2050. The results showed that: (1) The primary categories of soil erosion from 2000 to 2020 were moderate, mild, and slight, and the average level of soil erosion exhibited a decreasing and then an increasing tendency during the last 20 years. (2) Soil erosion was directly impacted by changes in land use, with cropland and forest being the primary land use and land cover changes in the study region. Cropland and construction land being turned into woodland between 2000 and 2020 resulted in a significant decrease in the severity of soil erosion. Projected soil erosion is expected to increase significantly between 2020 and 2050 due to arable land being converted into construction land. (3) The key variables impacting the spatial distribution of soil erosion were LUCC (Land-Use and Land-Cover Change), NDVI (Normalized Difference Vegetation Index), and slope, and the interplay of these variables may increase their ability to explain soil erosion. Grasslands with an NDVI ranging from 0.9 to 1, rain ranging from 0.805 to 0.854 m, a slope above 35°, and a terrain elevation ranging from 1595 to 2559 m were identified as having a high risk of soil erosion. Soil erosion prevention and management efforts should focus on the ecological restoration of upland areas in the future.

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 (<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.

Analysis of Factors Influencing Spatial Distribution of Soil Erosion under Diverse Subwatershed Based on Geospatial Perspective: A Case Study at Citarum Watershed, West Java, Indonesia.

The application of remote sensing data has been significant in modeling soil erosion. However, previous studies have fallen short in elucidating and lacked an understanding of the multifactor influencing erosion. This study addresses these limitations by employing the InVEST and the Geodetector models. Specifically, it aims (1) to delineate both spatial and temporal variations in soil erosion within the Citarum watershed from 2010 to 2020, (2) to identify the key drivers of soil erosion and unravel the underlying mechanisms, and (3) to identify the high-risk zones for soil erosion. Both models consider a range of natural predictors, including topography (slope factor), climate (precipitation factor), and vegetation cover (vegetation factor). In addition, they incorporate social parameters such as income per capita and population density, which interact with the watershed's position in the downstream, middle, and upper streams. The results reveal that, over a decade, the average soil erosion increased by 15.50 × 106 tons, marking a 16.65% surge. The impact of factors varies significantly across different subwatershed areas. For example, fraction vegetation cover interactions influence upper- and middle-stream regions, while the downstream area is notably affected by precipitation interactions. The high-risk erosion areas in the watershed are primarily influenced by slope, precipitation, and fractional vegetation cover. In these areas, factors causing high erosion risks include slope, precipitation, and other environmental variables categorized into strata. The study highlights the varying influential factors in different watershed areas.

Relationship between annual soil erosion and surface runoff in Wadi Hanifa sub-basins

Abstract Soil erosion is the major environmental risk that causes topsoil loss, which decreases fertility in agricultural land. Spatial estimation of soil erosion is essential for conserving land resources and for developing control plans. This study evaluated soil erosion in 17 sub-basins of the Wadi Hanifa catchment using the Gavrilović model. The soil erosion rate is categorized into three classes based on the annual volume of rainfall and runoff. About 21% of the drainage area of Wadi Hanifa is affected by erosion severity, and 33% of the area is affected by severe erosion. The correlation obtained between the annual soil erosion and annual volumes of rainfall and runoff is significant. It is concluded from the results that the drainage area of the north-western part suffers from a high soil erosion risk. The NRCS-CN and erosion predict that the Gavrilović model can be applied for estimating and analyzing the spatial distribution of soil erosion over more drainage watersheds in Saudi Arabia. Now, the GIS techniques are one of the best tools for developing the conservation and management planning processes of soil resources.

Feasibility Assessment of a Magnetic Layer Detection Method for Field Applications

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

Integrated Use of Remote Sensing and GIS in estimating Soil Erosion in the Tukvar Tea Plantation Area, Darjeeling, India by RUSLE Modelling

Soil erosion is one of the major threats to food security and agricultural sustainability worldwide. Numerous factors including relief, slope, land use, land cover, rainfall pattern, soil texture, conservation techniques and anthropogenic factors, combine to cause soil erosion. The enormity and spatial distribution of soil erosion should be known for effectively assessing and mapping erosion-prone areas. In the hill region, soil loss is a significant component in decreasing stability and persistent loss causes landslides. So in order to study this, various soil erosion models have come up amongst which RUSLE has been adopted by many researchers. The goal of the current study is to forecast the projected soil loss in Tukvar tea plantations of the Darjeeling district. This study will provide an estimate of the amount and rate of erosion in the Darjeeling district's Tukvar tea plantations. This study revealed that the leading factors to soil erosion are slope factors and rainfall erosivity. The geo-coded reference of the geographic extent of soil erosion-prone areas will be useful for micro-level planning and will serve as a useful tool for managing and conserving soil.

Soil erosion assessment by RUSLE model using remote sensing and GIS in an arid zone

ABSTRACT In this research, we used the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) to predict the annual rate of soil loss in the District Chakwal of Pakistan. The parameters of the RUSLE model were estimated using remote sensing data, and the erosion probability zones were determined using GIS. The estimated length slope (), crop management (), rainfall erosivity (), soil erodibility (), and support practice () range from 0–68,227, 0–66.61%, 0–0.58, 495.99–648.68 0.15–0.25 , and 1 respectively. The results indicate that the estimated total annual potential soil loss of approximately 4,67,064.25 is comparable with the measured sediment loss of 11,631 during the water year 2020. The predicted soil erosion rate due to an increase in agricultural area is approximately 164,249.31 . In this study, we also used Landsat imagery to rapidly achieve actual land use classification. Meanwhile, 38.13% of the region was threatened by very high soil erosion, where the quantity of soil erosion ranged from 365487.35 . Integrating GIS and remote sensing with the RUSLE model helped researchers achieve their final objectives. Land-use planners and decision-makers use the result's spatial distribution of soil erosion in District Chakwal for conservation and management planning.

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

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

Landscape pattern analysis using GIS and remote sensing to diagnose soil erosion and nutrient availability in two agroecological zones of Southern Mali

BackgroundSoil is a basic natural resource for the existence of life on earth, and its health is a major concern for rural livelihoods. Poor soil health is directly associated with reduced agricultural land productivity in many sub-Saharan countries, such as Mali. Agricultural land is subjected to immense degradation and the loss of important soil nutrients due to soil erosion. The objective of the study was to diagnose the spatial distribution of soil erosion and soil nutrient variations under different land use in two agroecological zones of Southern Mali using the Geographical Information System (GIS) software, the empirically derived relationship of the Revised Universal Soil Loss Equation, in-situ soil data measurement and satellite products. The soil erosion effect on agricultural land productivity was discussed to highlight the usefulness of soil and water conservation practices in Southern Mali.ResultsThe results of the land use and land cover change analysis from 2015 to 2019 revealed significant area reductions in water bodies, bare land, and savanna woodland for the benefit of increased natural vegetation and agricultural land. There was significant variation in the annual soil loss under the different land use conditions. Despite recordings of the lowest soil erosion rates in the majority of the landscape (71%) as a result of field-based soil and water conservation practices, the highest rates of erosion were seen in agricultural fields, resulting in a reduction in agricultural land area and a loss of nutrients that are useful for plant growth. Spatial nutrient modelling and mapping revealed a high deficiency and significant variations (p < 0.05) in nitrogen (N), phosphorus (P), potassium (K), and carbon (C) in all land use and land cover types for the two agroecologies.ConclusionsThe study highlighted the inadequacies of existing field-based soil and water conservation practices to reduce soil erosion and improve landscape management practices. The findings of the study can inform land management planners and other development actors to strategize and prioritize landscape-based intervention practices and protect catchment areas from severe erosion for the enhanced productivity of agricultural fields.

Study on Soil Erosion Driving Forces by Using (R)USLE Framework and Machine Learning: A Case Study in Southwest China

Soil erosion often leads to land degradation, agricultural production reduction, and environmental deterioration, which seriously restricts the sustainable development of regions. Clarifying the driving factors of soil erosion is the premise of preventing soil erosion. Given the lack of current research on the driving factors/force changes of soil erosion in different regions or under different erosion intensity grades, this paper pioneered to use machine learning methods to address this problem. Firstly, the widely used (Revised) Universal Soil Loss Equation ((R)USLE) framework was applied to simulate the spatial distribution of soil erosion. Then, the K-fold algorithm was used to evaluate the accuracy and stability of five machine learning algorithms for fitting soil erosion. The random forest (RF) method performed best, with average accuracy reaching 86.35%. Then, the Permutation Importance (PI) and the Partial Dependence Plot (PDP) methods based on RF were introduced to quantitatively analyze the main driving factors under different geological conditions and the driving force changes of each factor under different erosion intensity grades, respectively. Results showed that the main drivers of soil erosion in Chongqing and Guizhou were cover management factors (PI: 0.4672, 0.4788), while that in Sichuan was slope length and slope factor (PI: 0.6165). Under different erosion intensity grades, the driving force of each factor shows nonlinear and complex inhibitory or promoting effects with factor value changing. These findings can provide scientific guidance for the refined management of soil erosion, which is significant for halting or reversing land degradation and achieving sustainable use of land resources.

Application of the Unit Stream Power Erosion and Deposition (USPED) Model at Fort Campbell, Kentucky/Tennessee

During the last century, numerous models have been developed to predict soil erosion and concomitant sediment deposition. Most have been shown to be inadequate and/or inaccurate in one respect or another. Historically, erosion model results have been compared with sediment outflow from watersheds, providing no information regarding sources and sinks of erosion and deposition within them. This paper documents the application of the new-generation Unit Stream Power Erosion and Deposition (USPED) model at the U.S. Army's Fort Campbell, Kentucky / Tennessee. The USPED model is relatively simple to apply and its novel approach compares model results with observed soil erosion and sediment deposition spatially across the landscape. The model results matched visually-estimated erosion/sediment deposition values 85% of the time, providing an encouraging test of the model. The approach we used provided information regarding the intensity and spatial distribution of soil erosion and sediment deposition that can be used to optimize the placement and size of soil erosion and sediment control efforts.

Increased precipitation weakenes the positive effect of vegetation greening on erosion

Soil erosion is a major global soil degradation problem that threatens land, freshwater and oceans. Rainfall erosivity has led to an increasing in the global soil erosion rate, while vegetation restoration is a safeguard measure to reduce soil erosion. Therefore, probing the influence of precipitation and vegetation on the spatial distribution of soil erosion is important for understanding the mechanism of erosion. In order to assess the degree of global soil erosion, based on the RUSLE model, a global soil erosion data set from 2000 to 2015 (0.25°× 0.25°) was created, showing that soil erosion was increasing in 70.80% of the study area, where precipitation was the dominant factor. Different grades of erosion showed that the soil erosion area of mild and above mild erosion increased by 44.88 106 ha, an increase of 5.39%. Spatial erosion is mainly distributed in Asia and North America. The difference from North America is that the erosion in Asia showed a decreasing trend during the study period. Different climatic zones show that erosion mainly occurs in the temperate zone, accounting for 39.97% of the area. Precipitation and vegetation increasing significantly in 24.43% and 16.74% of the regions. However, the proportion of regions where precipitation and vegetation had a negative contribution to erosion was 29.12% and 53.81%. Above results will deepen our understanding of the mechanism of erosion.

Spatial distribution of soil erosion and its impacts on soil productivity in Songnen typical black soil region

Soil erosion is an important reason for land degradation. The black soil region of Northeast China is the main grain production base in China, however, the black soil productivity in this region is at risk and unsustainable now because there was few soil conservation measures in nearly 100 years of cultivation. To assess the impact of erosion on land productivity, both 210 sampling points for 137Cs measurement and 112 sampling points for soil profile features were laid out on cultivated land of Songnen typical black soil region in combination with the 2010 China land use map. The current soil erosion/deposition rate and land productivity index PI were calculated over the study area, and then the PI values were predicted erosion by 20, 50, 100, and 200 years without soil and water conservation measures based on the current erosion rate and soil profile characteristics. The results showed that the average soil erosion rate of cultivated land in the study area was 705.8 t/(km2⋅a), and 77.0% of the cultivated land had soil erosion rate larger than the soil loss tolerance of 200 t/(km2⋅a). Spatially, the soil erosion rate in the northern region was greater than that in the southern region significantly, which was mainly affected by slope steepness. The relationship between soil erosion rate (SL) and slope steepness (θ) in degree was established according to the results of 137Cs measured at 210 sample points: SL = 556.4∗θ or SL = 0.464∗θ in mm/a (given soil bulk density is 1200 kg/m3). The current mean value of PI for cultivated land was 0.59, decreasing significantly from north to south, which was mainly influenced by organic matter content. The predicted PI values decreased significantly with the increase of erosion years in the northern area. This is related to the topography of rolling and hills in the northern area with a greater soil loss rate, leading to a rapid decrease of organic matter content and the increase of sand content in the topsoil in the future. The spatial distributions of the current soil erosion rates and predicted PI values after years of erosion for cultivated land would be helpful for soil conservation policy-making.

Soil Erosion Characteristics and Scenario Analysis in the Yellow River Basin Based on PLUS and RUSLE Models.

Soil erosion is an important global environmental issue that severely affects regional ecological environment and socio-economic development. The Yellow River (YR) is China's second largest river and the fifth largest one worldwide. Its watershed is key to China's economic growth and environmental security. In this study, six impact factors, including rainfall erosivity (R), soil erosivity (K), slope length (L), slope steepness (S), cover management (C), and protective measures (P), were used. Based on the revised universal soil loss equation (RUSLE) model, and combined with a geographic information system (GIS), the temporal and spatial distribution of soil erosion (SE) in the YR from 2000 to 2020 was estimated. The patch-generating land use simulation (PLUS) model was used to simulate the land-use and land-cover change (LUCC) under two scenarios (natural development and ecological protection) in 2040; the RUSLE factor P was found to be associated with LUCC in 2040, and soil erosion in the Yellow River Basin (YRB) in 2040 under the two scenarios were predicted and evaluated. This method has great advantages in land-use simulation, but soil erosion is greatly affected by rainfall and slope, and it only focuses on the link between land-usage alteration and SE. Therefore, this method has certain limitations in assessing soil erosion by simulating and predicting land-use change. We found that there is generally slight soil erosivity in the YRB, with the most serious soil erosion occurring in 2000. Areas with serious SE are predominantly situated in the upper reaches (URs), followed by the middle reaches (MRs), and soil erosion is less severe in the lower reaches. Soil erosion in the YRB decreased 11.92% from 2000 to 2020; thus, soil erosion has gradually reduced in this area over time. Based on the GIS statistics, land-use change strongly influences SE, while an increase in woodland area has an important positive effect in reducing soil erosion. By predicting land-use changes in 2040, compared to the natural development scenario, woodland and grassland under the ecological protection scenario can be increased by 1978 km2 and 2407 km2, respectively. Soil erosion can be decreased by 6.24%, indicating the implementation of woodland and grassland protection will help reduce soil erosion. Policies such as forest protection and grassland restoration should be further developed and implemented on the MRs and URs of the YR. Our research results possess important trend-setting significance for soil erosion control protocols and ecological environmental protection in other large river basins worldwide.

Rainfall Induced Soil Erosion and Sediment Yield Assessment in Upper Brahmaputra River Basin

Abstract Riverbank erosion, aggradation, and river bed degradation are significant threats to most Indian watersheds. A quantitative valuation of the spatial distribution of soil erosion at a watershed level is vital for sustainable watershed planning and management practice. Very few studies can be found that focus on soil loss with sediment yield patterns in the upper Brahmaputra river basin due to massive erosion-deposition processes. Therefore, it is intended to study the soil loss and sediment yield pattern in the upper Brahmaputra river basin at the selected outlet of Majuli Island, located in South Asia. The principal objective is to evaluate soil erosion and sediment yield transported downstream of Majuli Island using the revised universal soil loss equation (RUSLE) and sediment delivery ratio (SDR) models, respectively, for 1979-2014. The study is conducted based on data availability and efficient models like RUSLE and SDR to measure soil erosion and sediment yield. Also, sediment yield maps for 36 years are prepared for the whole catchment considering rainfall as a variable. The maximum area contributing to soil erosion is observed around the hilly region having steep slopes. However, the highest sediment deposition is found near Majuli Island. This study would help take preventive measures for the erosion process and watershed management to minimize soil erosion in the watershed.

Assessment of soil erosion risk using an integrated approach of GIS and Analytic Hierarchy Process (AHP) in Erzurum, Turkiye

Assessing soil erosion hazards and mapping the spatial distribution of soil erosion have an essential role in sustainable forest management. In this study, the potential soil erosion risk was evaluated through the Analytical Hierarchy Process (AHP) and Geographic Information Systems (GIS) in the Oltu forest planning unit, Erzurum. Seven erosion-related criteria, including slope, bedrock type, relative relief, drainage density and frequency, rainfall, and land use/land cover (LULC) were used for the present assessment. According to the AHP analysis, the slope was the most influential factor (21%) followed by bedrock type (19%), land cover (17%), and relative relief (14%) in the soil erosion process. The soil erosion risk in the study area was strongly influenced by the LULC where 59.46% is bare land with high erosion risk and 12.07%, with the lowest risk, is in an area with any forest cover. The estimated soil erosion risk was classified into five different classes namely very low, low, moderate, high, and very high. The results showed that this study area is highly prone to soil erosion. The larger proportion of the area (39.16%) is exposed to high to very high erosion, mainly determined by forest cover and geomorphology. To analyze the accuracy of the soil erosion risk map, 40 points were selected randomly in this study area. In these points, predicted values were compared to the real values obtained by Google Earth-colored images. The area under the ROC curve (AUC) method was applied to validate the efficiency of the AHP which showed a satisfactory accuracy of 81.00%. Findings presented that including the more influencing factors with a slope instead of including only the slope contributes to a more accurate erosion risk map. This study highlighted that GIS-based multi-criteria decision-making is a valuable and practical tool for decision-makers and land managers in creating soil erosion susceptibility maps and determining high-priority areas that require conservation measures for sustainable land use management by reducing the economic and ecological impacts of soil loss. Also, this approach can be practically applied in other planning units.

Temporal and Spatial Variation and Driving Forces of Soil Erosion on the Loess Plateau before and after the Implementation of the Grain-for-Green Project: A Case Study in the Yanhe River Basin, China.

To curb soil erosion, the Grain-for-Green Project has been implemented in the Loess Plateau region, and there have been few quantitative evaluations of the impact of ecological engineering on the spatial distribution of soil erosion on the Loess Plateau. In this paper, we used ArcGIS software, the Revised Universal Soil Loss Equation (RUSLE) model and the Geographic Detector (GeoDetector) model to investigate the changes in the spatial distribution of soil erosion and driving forces before and after the implementation of the Grain-for-Green Project in Yanhe River Basin, a typical area on the Loess Plateau. After the implementation of the Grain-for-Green Project, the soil erosion showed a decreasing trend over time and from local improvement to global optimization in space. The implementation of the Grain-for-Green Project led to changes in the dominant driving force of the spatial distribution of soil erosion, with the dominant driving force changing from the slope factor to the vegetation coverage factor. The main driving force of the two-factor interaction on soil erosion spatial differentiation changed from the slope factor and other factors to the vegetation coverage and other factors. The Grain-for-Green Project mainly influenced soil erosion by increasing the vegetation cover. The effect of the Grain-for-Green Project on the spatial distribution of soil erosion had hysteresis and spatial differences, and the direct and indirect driving forces generated by ecological engineering reached more than 50% on average.