Spatial Distribution Of Soil Erosion Research Articles

Estimation of annual rate and spatial distribution of soil erosion in the Jamuna basin using RUSLE model: A geospatial approach

Soil erosion poses a serious threat to the western Jamuna sub-basin area in Bangladesh, comprising of 8 districts and 202 unions. The Revised Universal Soil Loss Equation (RUSLE) methodology integrated with Remote Sensing and Geographic Information System (GIS) was applied to compute the annual soil erosion rate for the western Jamuna sub-basin area. Primarily open-source data was utilized in this study to propose a low-cost and rapid soil erosion monitoring framework. Results indicate that a total of 598,232 tons of soil is being lost annually from the study area. Furthermore, the study estimates a potential average soil erosion rate of approximately 29.5 tons/ha/year in the 202 unions. The spatial distribution of soil erosion suggests that the unions of Bogra, Sirajganj and Jamalpur districts undergo higher soil loss compared to the unions of Pabna and Natore districts. The three unions with dominant mean soil erosion rates are the Bhandarbari and the Sariakandi Paurashava of Bogra district, and the Kaoakola union of the Sirajganj district. Heavy rainfall after a prolonged dry season, the presence of steep slopes and the susceptibility of the unions located in proximity to the Bangali river and the Jamuna river leading to higher rates of surface runoff and floods resulted in higher rates of soil erosion. On the contrary, the Atgharia Paurashava and Debottar unions of Pabna district and the Moshinda union of Natore district are the unions with minimal soil erosion rates, which likely be attributed to the presence of flat terrain, low annual rainfall, higher vegetation cover and the predominance of clayey soils. Overall, the study identifies an increasing trend of soil erosion rates from the south-western part of the study area towards the central, north-eastern and south-eastern parts which are close to the Jamuna river. Such quantification of annual soil loss is necessary for the formulation and implementation of effective soil conservation strategies.

The Combined Use of 137Cs Measurements and Zr-Methods for Estimating Soil Erosion and Weathering in Karst Areas of Southwestern China

Soil physical erosion and chemical weathering quantification at the slope scale are important to reveal the material cycle of the ecosystem in the karst region, because of the high heterogeneity due to the complex habitats. The Zr-based mass balance method has been widely used to quantify long-term physical erosion and chemical weathering at the slope scale, but the method is still in the exploratory research stage for quantifying short-term physical erosion and chemical weathering. In this paper, sloping fields (nine sampling sites and two sloping fields) in Zunyi, within the Guizhou karst region, were studied. We attempted to quantify the short-term physical erosion and chemical weathering rates by 137Cs combined with the Zr-based mass balance method, and an ideal distribution curve of the Zr concentration in the soil surface layer of a karst region is proposed. The results showed the following: (1) The average soil erosion rate on the slope of the study area is 580 t/(km2·a), which is equivalent to 14% of the average value of the Wujiang River basin in which it is located. This shows that the spatial distribution of soil erosion in this area varies significantly. (2) The Zr concentration in the erosion profile (EUC (1)) corresponds to 48% of that in the deposition profile (DUC (3)). This indicates that physical erosion in the study area survives chemical weathering, which is also consistent with the relationship model hypothesis. In addition, the vertical distribution characteristics of Zr concentration in all profiles are basically consistent with the ideal hypothesis curve. (3) The chemical weathering rate of the topsoil has been preliminarily estimated to be around 30 t/(km2·a), and the ratio between the physical soil erosion and chemical weathering is 20:1. The results show that the physical erosion is dominant over the chemical weathering of topsoil, and the chemical weathering rate was proportional to the physical erosion. This study provides a new method for quantifying short-term soil erosion and weathering erosion at the slope scale in karst regions, which is important for regional ecological restoration and sustainable development.

Analysis of soil erosion characteristics in small watershed of the loess tableland Plateau of China

Soil is an essentially limited natural resource that natural and human-induced processes have both generated and damaged. Soil degradation has become one of the most crucial socio-economic and environmental problems since it produces deterioration in productivity and quality of soil resources. Soil erosion, a natural phenomenon that causes degradation of soil and, curves the soil surface away from natural physical forces. To reveal the main factors influencing the spatial distribution of soil erosion in the small watershed of the Loess Plateau, the present study has investigated the synergistic as well as the independent influence of land use, vegetation coverage, and slope on the spatial distribution characteristics of soil erosion in the Wangdonggou watershed in 2015. Soil samples have been collected and analyzed in the laboratory together with high-resolution satellite imagery and meteorological data and derived data from digital elevation model (DEM). The results have shown that soil erosion in Wangdonggou watershed in 2015 has been characterized by a slight erosion, highlighting a gradually increased intensity from North to South. Among different land-uses, woodland and grassland have caused more than 50% soil erosion in the study area, and the areas with vegetation coverage of ≥ 50% have been the main source of soil erosion, and they have been all affected by slope. Furthermore, the practice of expanding vegetation presence on the lower coverage of woodland and grassland, particularly where the slope is between 15°∼45°, and converting sloping woodland and grassland to the terrace have seemed to be effective strategies for controlling soil erosion in the Wangdonggou watershed. Finally, the current study has revealed that the RUSLE-GIS integrated model could be a useful tool to quantitatively and spatially map soil erosion at the watershed scale in the Loess Plateau, taking into account the provision of landscape services.

Paths of soil erosion controlled by typical soil and water conservation practices based on the SIMWE model: A case study of the Tongshuang watershed.

Due to the basic topographical characteristics of the gentle and long slope lengths in the Mollisol region of Northeast China, severe soil erosion is easily aggravated by the concentration of surface flow. The spatial distribution of water depth and hydrological connectivity index were introduced to evaluate the effects of typical soil and water conservation practices on the overland flow path and hydrological connectivity based on the GIS and SIMWE (SIMulated Water Erosion) model. We analyzed the effects of different soil and water conservation practices on the hydrological connectivity, water flow path, and spatial distribution of soil erosion and sediment yield by quantifying the variations of soil infiltration rate and surface manning roughness, as well as by constructing an artificial terrain digital elevation model (DEM). The results showed that: 1) terraces could effectively affect the hydrological connectivity of the slope and regulate flow path, with significant differences between the responses of hydrological connectivity and flow path under different forms of terraced fields and ridges. The characteristics of spatial distribution of soil erosion and sediment yield varied with changes in water flow path, which would eventually lead to the intensification of local erosion; 2) practices of vegetated buffer strips and contour tillage presented limited effectiveness on runoff path controlling, though they played a significant role in sediment retention; and 3) conservation tillage could reduce the hydrological connectivity and improve the retention capacity of runoff by increasing surface roughness. This study quantified the effects of different soil and water conservation practices on the hydrological connectivity, flow path, and spatial distribution of soil erosion and sediment yield, and could provide a theoretical reference for scientific layout of soil and water conservation practices in black soil region.

Spatial and agronomic assessment of water erosion on inland Pacific Northwest cereal grain cropland

The inland Pacific Northwest (PNW) is characterized by high erosion rates attributed to hilly topography, highly erodible silt loam soils, wet winter seasons with frequent freeze-thaw events, and widespread use of conventional tillage practices. Historically, annual water erosion from cropland in the region’s Palouse River basin averaged 20.6 t ha^-1, and more recent rates are 10, 13, and 11 t ha^-1 for the low (<380 mm y^-1), intermediate (380 to 460 mm y^-1), and high (>460 mm y^-1) precipitation zones. Identifying source areas for targeted, effective management requires understanding the factors affecting water erosion, especially how tillage practices and crop rotation interact in various topographic, soil, and climatic settings. The objectives of this study were to (1) understand how hillslope hydrological and water erosion processes are influenced by key environmental factors (soil, climate, and topography) and management practices (tillage and crop rotation), and (2) assess the spatial distribution of soil erosion at the county level and by precipitation zone over the last 30 years. We compiled various combinations of soil, climate, topography, tillage, and crop rotation, and simulated these combinations county by county with the Water Erosion Prediction Project (WEPP) model. Simulated average annual erosion rates were lowest in the low-precipitation zone and highest in the intermediate-precipitation zone, exceeding the USDA Natural Resources Conservation Service tolerable limit (11 t ha^-1) in many cases. Temporally, high precipitation events and fallow periods in the rotation greatly increase erosion. Average annual erosion rates in Whitman County are 13.6, 19.0, and 15.4 t ha^-1 in the three precipitation zones: low (<380 mm y^-1), intermediate (380 to 460 mm y^-1), and high (>460 mm y^-1), respectively. The intermediate-precipitation zone produced a total annual erosion of 4.2 × 10⁶ t, despite having the smallest area. Columbia County has the highest erosion rate with 23.7 t ha^-1 y^-1. Geospatial visualizations of the simulation results reveal critical source areas (“hotspots”) where estimated erosion rates are more than five times the average rates, providing crucial information for, and advancing our understanding of, targeting management and increasing efficiency of conservation practices.

Simulation and Prediction of Soil Erosion in Daning River Basin

Calculating soil erosion intensity and predicting its change trend is essential for the precise implementation of control measures. We had estimated and quantitatively analyzed the current situation of soil erosion in the Daning River basin in 2008, 2013 and 2018 using the Revised Universal Soil Loss Equation (RUSLE) model. We had also analyzed the correlation between distance from the river, elevation and slope, followed by the analysis and prediction of the soil erosion in the Daning River basin in 2023 by constructing a Cellular Automaton (CA)-Artificial Neural Network (ANN)-Markov model. The results demonstrated the total soil erosion to be 798.49, 746.38 and 628.78 10⁴ t in 2008, 2013 and 2018, respectively. The overall trend of soil erosion was found to be decreasing and the spatial distribution of soil erosion was strongly correlated with the distance from the river, elevation and slope. Based on the CA-ANN-Markov model prediction, the trend of soil erosion from 2018 to 2023 is consistent with that from 2008 to 2013. Light, moderate, and intense erosion demonstrated an increasing trend, whereas slight, very intense and intense erosion demonstrated a decreasing trend. By estimating and predicting soil erosion in the Daning River basin, this paper provides a reference for ecological restoration and the control of soil erosion in small watersheds under similar environments.

Soil erosion assessment in Ethiopia: A review

Soil erosion is a critical problem affecting rural livelihoods in Ethiopia. Large numbers of studies have been undertaken to identify critical areas of soil loss and prioritize conservation options. With the advancement of geospatial analysis techniques, the use of spatially distributed soil erosion assessment options has become increasingly common. The lack of database and documentation related to soil erosion assessment undermines coordination leading to duplication of efforts and in some instances generating contradictory results. The purpose of this study is to (1) review existing knowledge related to the extent and spatial distribution of soil erosion, (2) document the associated methods employed to assess soil erosion, and (3) assess the spatio-temporal dynamics of soil erosion and the determinant factors in Ethiopia. The review shows that there are about 170 peer-reviewed papers published in scientific journals related to soil erosion in Ethiopia. In those scientific articles, 15 different approaches were used to assess soil loss and sediment yield at different scales. Considering the data set, soil loss rate in Ethiopia varies between 0 and 220 t ha^–1 y^–1, and sediment yield ranges between 2 and 70 t ha^–1 y^–1. Based on the database, the national average gross soil erosion rate is estimated to be 38 t ha^–1 y^–1, while the net sediment yield is about 26 t ha^–1 y^–1. Generally, the observed gross soil loss is slightly lower than the mean value from cultivated lands reported in previous estimates (42 t ha^–1 y^–1) by Hurni (1993). The estimate made using the Revised Universal Soil Loss Equation gives the highest soil loss (51 t ha^–1 y^–1), while that based on field-survey approaches gives the lowest (20 t ha^–1 y^–1). The highest average net soil loss rate (40 t ha^–1 y^–1) is obtained using plot-level measurements, while the lowest (18 t ha^–1 y^–1) is obtained using the AGricultural Non-Point Source pollution model. The highest average soil erosion rate is observed in the moist agro-ecological zone (57 ± 7.8 t ha^–1 y^–1), while the lowest is obtained in the submoist (23.6 ± 2.7 t ha^–1 y^–1) following the arid zone (28.8 ± 6.5 t ha^–1 y^–1). The wide range of soil erosion estimates imply spatio-temporal dynamics of soil erosion in the country, which is mainly a reflection of heterogeneity of the various sites, mainly associated with different values of cover and management factors. Such knowledge can enable making informed conservation decisions by focusing on critical hotspots.

Assessment of soil erosion risk in Chamchamal catchment using RUSLE integrated into GIS Techniques

In Iraqi Kurdistan Region, as in most Mediterranean countries, assessment of soil erosion risks is a challenging task mainly due to the non-availability or insufficiency of relevant data. In the current study, the soil erosion risks have been estimated by integrating the spatial data on potential erosion rates and soil loss tolerance limits for conservation planning at the sub-catchment level in Chamchamal district, Sulaimani, Iraqi Kurdistan Region. To achieve the above objectives, the study catchment was delineated with the support of GIS after obtaining the Dem of Chamchamal district. The remote sensing (RS) and a geographic information system (GIS) were used to estimate the spatial distribution of soil erosion across the Chamchamal district, to identify the risk of soil erosion, and to develop a conservation priority map. People can reach a common goal: to gain actionable intelligence from all types of data. Additionally, the study catchment was subdivided into 12 subcatchments using the same tool. The estimated annual soil loss for different subcatchments is characterized by a wide range of variations. It ranges from a minimum of 7.08 t ha-1 yr-1 at subcatchment 1 to as high as 252.31 t ha-1 yr-1 at sub-catchment 3. With a few exceptions, the soil tolerance limit at the investigated grid points was estimated at10 t ha-1 yr-1. The results also revealed that around 12% of the whole catchment has a negligible risk of soil erosion above the tolerance limits, and does not call for immediate soil conservation measures. The remaining area (454.96 km2) requires conservation planning with different prioritizations. Subcatchments 2, 3, 7, and 10 were identified as the worst affected districts in terms of soil erosion and therefore require immediate attention of natural resources. The weighted soil erosion risk index (WSERI) categorized sub-catchments 2, 3, 7, and 10 as being of topmost priority as it is the most severely affected areas. The recommended measures to conserve soil and water encompassed three broad categories, namely, vegetative, soil, and mechanical measures. Within the same sub-catchment, one or a combination of several measures can be implemented based on the nature and intensity of the problem.

The Link between Landscape Characteristics and Soil Losses Rates over a Range of Spatiotemporal Scales: Hubei Province, China.

Controlling soil erosion is beneficial to the conservation of soil resources and ecological restoration. Understanding the spatial distribution characteristics of soil erosion helps find the key areas for soil control projects and optimal scale for investing in a soil and water conservation project at the lowest cost. This study aims to answer the question of how the spatial distribution of soil erosion in Hubei Province changed between 2000 and 2020. Moreover, how do the effects of natural factors and human activities on soil erosion vary over the years? What are the differences in landscape pattern characteristics and the spatial cluster of soil erosion at multiple administrative scales? We simulated the spatial distribution of soil erosion in Hubei province from 2000 to 2020 by the Chinese Soil Loss Equation model at three administrative scales. We investigated the relationship between soil erosion and driving factors by Geodector. We explored the landscape pattern and hotspots of land at different levels of soil erosion by Fragstat and hotspot analysis. The results show that: (1) The average soil erosion rate decreased from 2000 to 2020. Soil erosion is severe in the mountainous areas of western Hubei province, while it is less severe in the central plains. (2) Land-cover type, precipitation, and normalized difference vegetation index are the most influencing factors of soil erosion in 2000–2010, 2015, and 2020, respectively. (3) The aggregation index values at the town scale are higher than those at the city and county scales, while the fractal dimension index values at the town scale are lower, which indicates that soil erosion projects are most efficient when the project unit is ‘town’. (4) At the town scale, if the hotspot area (6.84% of the total area) is treated as the protection target, it can reduce 50.42% of the total soil erosion of Hubei province. Hotspots of soil erosion overlap with high erosion zones, mainly in the northwestern, northeastern, and southwestern parts of Hubei province in 2000, while the hotspots in northwestern Hubei disappear in 2020. In conclusion, land managers in Hubei should optimize the land-use structure, soil and water conservation in slope land, and eco-engineering controls at the town scale.

Adaptation of the revised universal soil loss equation to map spatial distribution of soil erosion in tropical watersheds: a GIS/RS-based study of the Upper Mahaweli River Catchment of Sri Lanka

Adaptation of the revised universal soil loss equation to map spatial distribution of soil erosion in tropical watersheds: a GIS/RS-based study of the Upper Mahaweli River Catchment of Sri Lanka

Spatial distribution of soil erosion and sediment yield in the Koshi River Basin, Nepal: a case study of Triyuga watershed

Spatial distribution of soil erosion and sediment yield in the Koshi River Basin, Nepal: a case study of Triyuga watershed

Quantitative Analysis of Factors Influencing Spatial Distribution of Soil Erosion Based on Geo-Detector Model under Diverse Geomorphological Types

The Loess Plateau of China suffers from severe erosion, which results in a great variety of economic and ecological problems. For scientific control of soil erosion, the key questions urgently to be addressed in this paper are: (1) Which are the driving factors under diverse geomorphological types? (2) Do these driving factors operate independently or by interacting? (3) Which zones under diverse geomorphological types suffer from severe erosion and need more attention? In this paper, the RUSLE model was applied here to demonstrate the spatio-temporal variations in soil erosion from 2010 to 2017 in Yan’an City, and the Geo-detector model proved to be a useful tool to solve the questions mentioned above. The results showed that the average erosion modulus in Yan’an City decreased by 1927.36 t/km2·a from 2010 to 2017. The intensity of soil erosion in the northern Baota District, central Ganquan County, Luochuan County, Ansai County, and Zhidan County decreased to varying degrees. The effect size of driving factors affecting soil erosion varied significantly in diverse geomorphological types. The effect size of interaction between land-use types and vegetation coverage, land-use types and slope, slope and precipitation was higher than that of a single factor. High-risk zones with severe erosion were closer to cultivated land and forest land with steep slopes (>25°) in the mid-elevation hills of Yan’an City. Additionally, based on the specificity of the study area, the Geo-detector model performed better in a relatively flat region, and factors with macroscopic spatial distributions weaken its explanatory power on soil erosion on a regional scale. Based on data selection, data of different accuracy sparked the issue of “data coupling”, which led to the enormous deviation of model results in mid-elevation plains. Results from our analysis provide insights for a more ecologically sound development of Yan’an City and provide references for the scientific use of the Geo-detector model.

Machine learning applications for water-induced soil erosion modeling and mapping

Assessment of water-induced soil erosion as a crucial part of soil conservation plans is costly and time-consuming when applied to an extensive area. In this study, we propose a methodology based on recording the annual soil erosion in a portion of the study area using erosion pins and assessing the spatial distribution of soil erosion for the entire area using machine learning techniques. First, soil erosion pins were installed, and the amount of soil loss in each pin was recorded. The controlling factors of soil erosion (percentage of vegetation canopy, curvature, slope degree, slope length, percentage of sand, percentage of silt, and percentage of clay) were determined, and the dataset was divided into training (75% of the data) and testing (25% of the data) subsets. Three machine learning algorithms, namely boosted regression trees (BRT), deep learning (DL), and multiple linear regression (MLR), were employed to identify the relationship between soil erosion and its controlling factors. Then, the methods were evaluated by comparison between the predicted and observed values on the testing subset using statistical coefficients including coefficient of determination (R-squared), normalized root mean squared error (NRMSE), and Nash-Sutcliffe efficiency (NSE). Results show that the BRT outperformed the other algorithms in the assessment of the annual soil erosion (R-squared: 0.92, NSE: 0.9, and NRMSE: 0.32). Finally, the optimal algorithm (BRT) was selected to estimate the spatial distribution of soil erosion across the entire study area, and the final erosion map was verified using additional verification pins.

Examining Soil Erosion Responses to Grassland Conversation Policy in Three-River Headwaters, China

Soil erosion in the Three-River Headwaters (TRH) region has continued to intensify in recent decades due to human activities and climate change. To reverse this situation, the Chinese central government has launched the Subsidy and Incentive System for Grassland Conservation (SISGC). As a sign of the effectiveness of SISGC implementation, the dynamic changes of soil erosion can provide timely feedback for decision makers and managers. The Revised Universal Soil Loss Equation (RUSLE) model was used to simulate the spatial distribution of soil erosion before and after SISGC implementation, and Mann–Kendall (MK) test to reveal the effect of policy implementation. The results showed that: (1) the soil erosion in the TRH was mainly mild (83.83% of the total eroded area), and the average soil erosion rate and the total erosion were 13.63 t ha−1 y−1 and 323.58 × 106 t y−1 respectively before SISGC implementation; (2) SISGC implementation has curbed soil erosion. After SISGC implementation, the total soil erosion decreased by 3.80%, which showed obvious differences between grassland types; (3) The influences of SISGC were mainly because it has increased vegetation cover, further decreasing soil erosion. However, soil erosion in Alpine grassland has deteriorated, indicating direct targeted policymaking should be on the agenda. Furthermore, SISGC should be continued and grassland-type-oriented to restore the grassland ecosystem.

Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing

An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources.

Soil Erosion Estimation Using RUSLE Modeling and Geospatial Tool: Case Study of Kathmandu District, Nepal

Revised Universal Soil Loss Equation (RUSLE) model is applied in this study to evaluate the risk of erosion in Kathmandu district. The calculation of erosion requires certain data from various sources available in different formats and scales. Geographic Information System (GIS) was used which allowed considerable time savings in the processing of spatial data, screening the effects of each factor affecting soil erosion. Among various erosion factors, topography, rainfall, soil properties, and soil conservation practices were used for the study. Average soil loss was calculated by multiplying these factors. Final results of soil erosion rates were separated into six classes based on erosion severity, in which 2.18% of land (> 80Mg ha-1yr-1), followed by 2.85% of land (40-80 Mg ha-1yr-1), 5.56% of land (20-40 Mg ha-1yr-1), 8.73% of land (10-20 Mg ha-1yr-1), 10.53% of land (5-10 Mg ha-1yr-1) and 70.14% of land (0-5 Mg ha-1yr-1), falls under very severe, severe, very high, moderate and low severity zone respectively. Area having high slope length (LS) factor has high erosion rate. In Dakshinkali, Nagarjun and Budanilkantha area, there is high erosion rate. From the result, spatial distribution of soil erosion across Kathmandu district, can be applied for management and controlling the erosion.

Soil erosion risk and sediment yield assessment with universal soil loss equation and GIS: in Dijo watershed, Rift valley Basin of Ethiopia

Soil erosion is the main drivers in the world and Ethiopia in particular. This study has been conducted at Dijo watersheds in the Rift valley Basins of Ethiopia to estimate soil erosion rate and identify erosion hotspot areas for proper planning using Geographic Information System and Universal Soil Loss Equation adapted to Ethiopian condition. 64 years mean annual rainfall data for estimating erosivity factor, digital soil map for estimating soil erodibility factor, Digital Elevation Model for estimating topographic (LS) factor, Land use land cover for cover factor detection from Ethiopian ministry of water resources. The result reveals that the soil loss ranges from 0 ton/ha/year in flat slope to 38.09 ton/ha/year from steep slopes. The average soil loss rate is 2.2 tons per hectare per year and has been classified into three erosion severity classes as very low, low and moderate. The result also reveals that most of the watershed erosion severity evaluated under very low and low soil erosion severity classes covering 97.3% of the watershed areas which is due to the effect of mixed plantation of various tree and terraces. However, moderate soil erosion in the upper parts of the watershed could be due to the inherent characteristics of vertisols, lack of vegetation cover and terraces which should be given first priority for conservation interventions. From the gross soil erosion, 43,762 ton/year sediment yields have been estimated at watershed outlet. Policy aim at keeping land productivity will need to focus to reduce low and moderate soil erosion through terracing, inter-cropping, contour farming, strip cropping, conservation tillage, mulching and biological stabilizers based on their slope range, soil type and land use type. The current finding on erosion was evaluated based on the past 10 years land use land cover scenario; therefore, soil erosion might be reduced if the current land use land cover scenario considered. Finally, the integration of USLE and GIS is an effective tool in mapping the spatial distribution of soil erosion from the entire watershed. The moderate and low soil erosion severity areas should be managed through terracing, inter-cropping, contour farming, strip cropping, conservation tillage, mulching and biological stabilizers based on their slope range, soil type and land use type. Free grazing and cultivation of steep slope(Northern parts) contributed for moderate soil erosion in the watershed should be managed by cut–carry system, limiting the number of cattle units to be grazed in the specific plot of land and leaving the marginal steep slope areas with no ground covers for natural regeneration. Finally, the current finding on erosion was evaluated based on the past 10-year land use land cover scenario. Therefore, the soil erosion could be reduced if the current land use land cover scenario is considered.

Modeling Soil Erosion Vulnerability Using RUSLE: A Case Study Upstream of Nangal Reservoir, India

Simulation of soil erosion is an important phenomenon in watershed management studies because of the dynamic nature of soil. The present study demonstrates the capabilities of remote sensing coupled with GIS to extract soil erosion vulnerabilities using RUSLE model. Therefore this study understands spatial distribution of soil erosion at a macro-watershed scale. The input parameters for RUSLE model include the rainfall and erosivity (R) derived using the TRMM satellite precipitation product for a long term implementing regression model. The soil erodibility (K) factor is estimated using available soil map, and the topographic factor (LS) from 12m ALOS PALSAR DEM. The management practice information was extracted from the land use / cover map based on 30m multi spectral LANDSAT-8 of three season datasets. The RUSLE model predicted very low, low, moderate, high and very high erosion in 84.8%, 7.9%, 4.4%, 2.3% and 0.7% of reviewed area, respectively. The results were validated with ground infromation and NDVI, being the main identifiers of soil erosion. As presence of high vegetation indicates higher strength to hold the soil particles together and prevent soil erosion, correlation coefficient was generated to find accuracy of the results with NDVI of the area. The strong positive correlation (0.5) provided scientific support that prediction of soil erosion by RUSLE can be effectively used for watershed management and combating action plan for soil erosion.

Spatial estimation of soil erosion using RUSLE modeling: a case study of Dolakha district, Nepal

BackgroundSoil erosion causes topsoil loss, which decreases fertility in agricultural land. Spatial estimation of soil erosion essential for an agriculture-dependent country like Nepal for developing its control plans. This study evaluated impacts on Dolakha using the Revised Universal Soil Loss Equation (RUSLE) model; analyses the effect of land use and land cover (LULC) on soil erosion.ResultsThe soil erosion rate categorized into six classes based on the erosion severity, and 5.01% of the areas found under extreme severe erosion risk (> 80 Mg ha−1 year−1) addressed by decision-makers for reducing its rate and consequences. Followed by 10% classified between high and severe range from 10 to 80 Mg ha−1 year−1. While 15% and 70% of areas remained in a moderate and low-risk zone, respectively. Result suggests the area of the north-eastern part suffers from a high soil erosion risk due to steep slope.ConclusionsThe result produces a spatial distribution of soil erosion over Dolakha, which applied for conservation and management planning processes, at the policy level, by land-use planners and decision-makers.

Spatial distribution of water and wind erosion and their influence on the soil quality at the agropastoral ecotone of North China

Abstract: In semiarid regions, wind and water erosion are serious environmental and ecological problems around world. They have different impacts on soil quality over a range of spatial scales. Analyzing the spatial distribution of soil erosion and understanding the impacts of wind and water erosion on soil quality at the regional scale is vital for mitigating soil erosion risk. In this study we explore the spatial distributions of water and wind erosion around Zhangjiakou city which suffers both water and wind erosion contemporaneously, and detect the influence of soil erosion on soil quality. We find that annual wind erosion intensities range 4.99–10.05 t ha−1 yr−1, and annual water erosion intensities range 2.92–4.14 t ha−1 yr−1. Areas with higher potential wind and total erosion risk occur mainly in northwest and southeast Zhangjiakou city, whereas potential water erosion risk is highest in the southwest and central regions. The highest erosion rates are concentrated in gentle parts of the landscape, where agriculture has led to low vegetation cover. Steeper portions of the landscape, which remain forested, have lower erosion rates. These spatial patterns are dominated by higher wind erosion, which correlates with surface soil coarsening and higher water infiltration, whereas the soil water holding capacity decreases with increasing wind erosion rates. In regions with high water infiltration rates, we find the intensity of water erosion weakened. To mitigate risks of soil degradation, suitable erosion measures should be implemented according to the dominant erosion type, which varies in space.