http://www.sciencedirect.com/science/article/pii/S1674987111001034

A comprehensive methodology that integrates Revised Universal Soil Loss Equation（RUSLE） model and Geographic Information System（GIS） techniques was adopted to determine the soil erosion vulnerability of a forested mountainous sub-watershed in Kerala,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 erodability（K）,slope length and steepness（LS）,cover management （C） and conservation practice（P） factors were computed to determine their effects on average annual soil loss in the area.The resultant map of annual soil erosion shows a maximum soil loss of 17.73 t h^-1 y^-1 with a close relation to grass land areas,degraded forests and deciduous forests on the steep side-slopes（with high LS ）.The spatial erosion maps generated with RUSLE method and GIS can serve as effective inputs in deriving strategies for land planning and management in the environmentally sensitive mountainous areas.     

Spatio-temporal changes in rate of soil loss and erosion vulnerability of selected region in the tropical forests of Borneo during last three decades

An attempt has been made to analyze the spatial-temporal characteristics of soil erosion vulnerability and soil loss from the forested region in the north-eastern Borneo, Sarawak, Malaysia during the last three decades (1991–2015) using the revised universal soil loss equation (RUSLE) and geographical information systems (GIS). The components of RUSLE such as rainfall erosivity (R), soil erodibility (K), slope-length and steepness (LS), cover management (C) and conservation practice (P) factors were grouped into two categories by keeping one set as temporally changing and others as static. Among them the R and C factors are calculated for the years 1991, 2001 and 2015 whereas the K and LS factors are considered for the single time frame. Because of the forested nature of the study area, the P factor is kept constant for the whole analysis. The R factor and C factor is shown changes in values and its distribution over the years, which reflected in the final soil loss and erosion vulnerability map as a change in the rate of erosion and spatial domain. The analysis of three time slices has shown that the maximum value of the soil loss per unit area i.e. at erosion hotspots, is relatively similar throughout at around 1636 to 1744 t/ha/y. This is the result of maximum values of R factor and C factor i.e. high rainfall erosivity combined with lack of vegetation cover in those hotspots, which are generally steeply sloping terrain. The reclassification of annual soil loss map into erosion vulnerability zones indicated a major increase in the spatial spread of erosion vulnerability from the year 1991 to 2015 with a significant increase in the high and critical erosion areas from 2.3% (1991) to 31.5% (2015). In 1991, over 84% of the study area was under low erosion vulnerability class but by the year 2015 only 12% was under low erosion vulnerability class. Moreover, a dynamic nature in the erosion pattern was found from the year 1991 to 2015 with more linear areas of land associated with higher rate of soil loss and enhanced erosion vulnerability. The linearity in the spatial pattern is correlated with the development of logging roads and logging activities which has been confirmed by the extraction of exposed areas from satellite images of different years of analysis. The findings of the present study has quantified the changes in vegetation cover from dense, thick tropical forest to open mixed type landscapes which provide less protection against erosion and soil loss during the severe rainfall events which are characteristic of this tropical region.     

Spatial prediction of soil erosion risk by remote sensing,GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley,Kerala, India

Siruvani watershed with a surface area of 205.54 km² (20,554 hectare), forming a part of the Western Ghats in Attapady valley, Kerala, was chosen for testing RUSLE methodology in conjunction with remote sensing and GIS for soil loss prediction and identifying areas with high erosion potential. The RUSLE factors (R, K, LS, C and P) were computed from local rainfall, topographic, soil classification and remote sensing data. This study proved that the integration of soil erosion models with GIS and remote sensing is a simple and effective tool for mapping and quantifying areas and rates of soil erosion for the development of better soil conservation plans. The resultant map of annual soil erosion shows a maximum soil loss of 14.917 t h⁻¹ year⁻¹ and the computations suggest that about only 5.76% (1,184 hectares) of the area comes under the severe soil erosion zone followed by the high-erosion zone (11.50% of the total area). The dominant high soil erosion areas are located in the central and southern portion of the watershed and it is attributed to the shifting cultivation, and forest degradation along with the combined effect of K, LS and C factor. The RUSLE model in combination with GIS and remote sensing techniques also enables the assessment of pixel based soil erosion rate.     

Estimation of soil erosion using RUSLE in Caijiamiao watershed,China

Soil erosion is a serious environmental and production problem in China. In particular, natural conditions and human impact have made the Chinese Loess Plateau particularly prone to intense soil erosion area. To decrease the risk on environmental impacts, there is an increasing demand for sound, and readily applicable techniques for soil conservation planning in this area. This work aims at the assessment of soil erosion and its spatial distribution in hilly Loess Plateau watershed (northwestern China) with a surface area of approximately 416.31 km². This study was conducted at the Caijiamiao watershed to determine the erosion hazard in the area and target locations for appropriate initiation of conservation measures using the revised universal soil loss equation (RUSLE). The erosion factors of RUSLE were collected and processed through a geographic information system (GIS)-based approach. The soil erosion parameters were evaluated in different ways: The R-factor map was developed from the rainfall data, the K-factor map was obtained from the soil map, the C-factor map was generated based on Landsat-5 Thematic Mapper image and spectral mixture analysis, and a digital elevation model with a spatial resolution of 25 m was derived from topographic map at the scale of 1:50,000 to develop the LS-factor map. Support practice P factor was from terraces that exist on slopes where crops are grown. By integrating the six-factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the study area was obtained by the RUSLE model. The results showed that spatial average soil erosion at the watershed was 78.78 ton ha^?1 year^?1 in 2002 and 70.58 ton ha^?1 year^?1 in 2010, while the estimated sediment yield was found to be 327.96 × 10⁴ and 293.85 × 10⁴ ton, respectively. Soil erosion is serious, respectively, from 15 to 35 of slope degree, elevation area from 1,126 to 1,395 m, in the particular area of soil and water loss prevention. As far as land use is concerned, soil losses are highest in barren land and those in waste grassland areas are second. The results of the study provide useful information for decision maker and planners to take appropriate land management measures in the area. It thus indicates the RUSLE–GIS model is a useful tool for evaluating and mapping soil erosion quantitatively and spatially at a river watershed scale on a cell basis in Chinese Loess Plateau and for planning of conservation practices.     

Application of the modified universal soil loss equation (MUSLE) in prediction of sediment yield (Case study: Kengir Watershed, Iran)

Modified Universal Soil Loss Equation (MUSLE) application study is undertaken in order to estimate the sediment yield of the Kengir watershed in Iyvan City, Ilam Province, Iran. The runoff factor of MUSLE is computed using the measured values of runoff and peak rate of runoff at outlet of the watershed. Topographic factor (LS) and crop management factor(C) are determined using geographic information system (GIS) and field-based survey of land use/land cover. The conservation practice factor (P) is obtained from the literature. Sediment yield at the outlet of the study watershed is simulated for six storm events spread over the year 2000 and validated with the measured values. The high coefficient of determination value (0.99) indicates that MUSLE model sediment yield predictions are satisfactory for practical purposes.     

Regional soil erosion risk mapping using RUSLE,GIS, and remote sensing: a case study in Miyun Watershed,North China

This paper applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing technique, and geographic information system (GIS) to map the soil erosion risk in Miyun Watershed, North China. The soil erosion parameters were evaluated in different ways: the R factor map was developed from the rainfall data, the K factor map was obtained from the soil map, the C factor map was generated based on a back propagation (BP) neural network method of Landsat ETM+ data with a correlation coefficient (r) of 0.929 to the field collected data, and a digital elevation model (DEM) with a spatial resolution of 30 m was derived from topographical map at the scale of 1:50,000 to develop the LS factor map. P factor map was assumed as 1 for the watershed because only a very small area has conservation practices. By integrating the six factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the upper watershed of Miyun reservoir was obtained by the RUSLE model. The results showed that the annual average soil loss for the upper watershed of Miyun reservoir was 9.86 t ha⁻¹ ya⁻¹ in 2005, and the area of 47.5 km² (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.88% very low, 21.90% low, 6.19% moderate, 2.90% severe, and 1.84% very severe. Among all counties and cities in the study area, Huairou County is in the extremely severe level of soil erosion risk, about 39.6% of land suffer from soil erosion, while Guyuan County in the very low level of soil erosion risk suffered from 17.79% of soil erosion in 2005. Therefore, the areas which are in the extremely severe level of soil erosion risk need immediate attention from soil conservation point of view.     

RETRACTED ARTICLE: Assessment of soil erosion using RUSLE and GIS: a case study of the Maotiao River watershed,Guizhou Province,China

Due to the existence of fragile karst geo-ecological environments, such as environments with extremely poor soil cover, low soil-forming velocity, and fragmentized terrain and physiognomy, as well as inappropriate and intensive land use, soil erosion is a serious problem in Guizhou Province, which is located in the centre of the karst areas of southwestern China; evaluation of soil loss and spatial distribution for conservation planning is urgently needed. This study integrated the revised universal soil loss equation (RUSLE) with a GIS to assess soil loss and identify risk erosion areas in the Maotiao River watershed of Guizhou. Current land use/cover and management practices were evaluated to determine their effects on average annual soil loss and future soil conservation practices were discussed. Data used to generate the RUSLE factors included a Landsat Thematic Mapper image (land cover), digitized topographic and soil maps, and precipitation data. The results of the study compare well with the other studies and local data, and provide useful information for decision makers and planners to take appropriate land management measures in the area. It thus indicates the RUSLE–GIS model is a useful tool for evaluating and mapping soil erosion quantitatively and spatially at a larger watershed scale in Guizhou.     

Estimating of water erosion in semiarid regions using RUSLE equation under GIS environment

Water erosion is one of the main forms of land degradation in Algeria, with a serious repercussion on agricultural productivity. The purpose of this study is to estimate the soil loss of Wadi El-Ham watershed in the center of Algeria, this study aims also to evaluate the effectiveness and reliability of the use of the Revised Universal Soil Loss Equation (RUSLE) under a Geographic Information System in this field. The RUSLE model involves the main factors of erosion phenomena, namely, rain aggressiveness, soil erodibility, topographic factor, land cover index and the anti-erosive practices factor. Using this approach, the specific erosion in Wadi El-Ham watershed is estimated as 5.7 (t/ha/yr) in the entire watershed area. This result is compared to the measured suspended sediment at the Rocade-Sud gauging station situated outlet the watershed. These data consist of 1293 instantaneous measures of the water discharge and the suspended sediment concentration recorded during 21 years. Through this comparison, the used approach of RUSLE under GIS estimates the soil loss in Wadi El-Ham in Hodna region of Algeria with an error of 7.5%. Consequently, the results obtained in cartographic format make it possible to target the areas requiring priority action for a larger scale analysis to find appropriate solutions to combat erosion and to protect the natural environment.     

Soil erosion risk assessment of the Keiskamma catchment,South Africa using GIS and remote sensing

This paper examines the soil loss spatial patterns in the Keiskamma catchment using the GIS-based Sediment Assessment Tool for Effective Erosion Control (SATEEC) to assess the soil erosion risk of the catchment. SATEEC estimates soil loss and sediment yield within river catchments using the Revised Universal Soil Loss Equation (RUSLE) and a spatially distributed sediment delivery ratio. Vegetation cover in protected areas has a significant effect in curtailing soil loss. The effect of rainfall was noted as two pronged, higher rainfall amounts received in the escarpment promote vegetation growth and vigour in the Amatole mountain range which in turn positively provides a protective cover to shield the soil from soil loss. The negative aspect of high rainfall is that it increases the rainfall erosivity. The Keiskamma catchment is predisposed to excessive rates of soil loss due to high soil erodibility, steep slopes, poor conservation practices and low vegetation cover. This soil erosion risk assessment shows that 35% of the catchment is prone to high to extremely high soil losses higher than 25 ton ha⁻¹ year⁻¹ whilst 65% still experience very low to moderate levels of soil loss of less than 25 ton ha⁻¹ year⁻¹. Object based classification highlighted the occurrence of enriched valley infill which flourishes in sediment laden ephemeral stream channels. This occurrence increases gully erosion due to overgrazing within ephemeral stream channels. Measures to curb further degradation in the catchment should thrive to strengthen the role of local institutions in controlling conservation practice.     

Spatial variability of soil water erosion: Comparing empirical and intelligent techniques

Soil water erosion (SWE) is an important global hazard that affects food availability through soil degradation, a reduction in crop yield, and agricultural land abandonment. A map of soil erosion susceptibility is a first and vital step in land management and soil conservation. Several machine learning (ML) algorithms optimized using the Grey Wolf Optimizer (GWO) metaheuristic algorithm can be used to accurately map SWE susceptibility. These optimized algorithms include Convolutional Neural Networks (CNN and CNN-GWO), Support Vector Machine (SVM and SVM-GWO), and Group Method of Data Handling (GMDH and GMDH-GWO). Results obtained using these algorithms can be compared with the well-known Revised Universal Soil Loss Equation (RUSLE) empirical model and Extreme Gradient Boosting (XGBoost) ML tree-based models. We apply these methods together with the frequency ratio (FR) model and the Information Gain Ratio (IGR) to determine the relationship between historical SWE data and controlling geo-environmental factors at 116 sites in the Noor-Rood watershed in northern Iran. Fourteen SWE geo-environmental factors are classified in topographical, hydro-climatic, land cover, and geological groups. We next divided the SWE sites into two datasets, one for model training (70% of the samples = 81 locations) and the other for model validation (30% of the samples = 35 locations). Finally the model-generated maps were evaluated using the Area under the Receiver Operating Characteristic (AU-ROC) curve. Our results show that elevation and rainfall erosivity have the greatest influence on SWE, while soil texture and hydrology are less important. The CNN-GWO model (AU-ROC = 0.85) outperformed other models, specifically, and in order, SVR-GWO = GMDH-GWO (AUC = 0.82), CNN = GMDH (AUC = 0.81), SVR = XGBoost (AUC = 0.80), and RULSE. Based on the RUSLE model, soil loss in the Noor-Rood watershed ranges from 0 to 2644 t ha^–1yr^?1.     

Evaluation of sediment yield and soil loss by the MPSIAC model using GIS at Golestan watershed, northeast of Iran

Watershed degradation due to soil erosion and sedimentation is considered to be one of the major environmental problems in Iran. In order to address the critical conditions of watershed degradation in arid and semiarid regions, a study based on the Modified Pacific Southwest Inter-Agency Committee (MPSIAC) model was carried out at Golestan watershed, northeast of Iran. The model information layers comprising nine effective factors in erosion and sedimentation at the watershed site were obtained by digitalization and spatial interpolation of the basic information data in a GIS program. These factors are geology, soil, climate, runoff, topography, land cover, land use, channel, and upland erosion. The source data for the model were obtained from available records on rainfall and river discharge and sediment, topography, land use, geology, and soil maps as well as field surveys and laboratory analysis. The results of the MPSIAC model indicated that 60.75 % (194.4 km²) and 54.97 % (175.9 km²) of the total watershed area were classified in the heavy sedimentation and erosion classes, and the total basin sediment yield and erosion were calculated as 4,171.1 and 17,813.4 m³ km^?2 year^?1, respectively. In the sensitivity analysis, it was found that the most sensitive parameters of the model in order of importance were topography (slope), land cover and use, runoff, and channel erosion (R ²?=?0.92–0.94), while geology, climate (rainfall), soil, and upland erosion factors were found to have moderate effect to the model output (R ²?=?0.74–0.59).     

Modeling soil erosion using RUSLE and GIS in a watershed occupied by rural settlement in the Brazilian Cerrado

The installation of a rural settlement complex in the watershed stream Indaiá has promoted changes in land-use and vegetation cover dynamics; however, the effects of intensive agriculture and cattle farming in rural settlements on soil loss rates are not well known. Predictive models implemented in geographic information systems have proven to be effective tools for estimating erosive processes. The erosion predictive model Revised Universal Soil Loss Equation (RUSLE) is a useful tool for analyzing, establishing and managing soil erosion. RUSLE has been widely used to estimate annual averages of soil loss, by both interrill and rill erosion, worldwide. Therefore, the aim of this work was to estimate the soil loss in the watershed stream Indaiá, using the RUSLE model and geoprocessing techniques. To estimate soil loss, the following factors were spatialized: erosivity (R), erodibility (K), topography (LS), land-use and management (C) and conservation practices (P); the annual soil loss values were calculated using the RUSLE model equation. The estimated value of soil loss in the hydrographic basin ranged from 0 to 4082.16 Mg ha^?1 year^?1 and had an average value of 47.81 Mg ha^?1 year^?1. These results have demonstrated that 68.16 % of the study area showed little or no soil loss based on the Food and Agriculture Organization’s (FAO 1980) classification. When comparing the average value of soil loss obtained using the RUSLE model with the Natural Potential for Erosion, a 16-fold reduction in soil was found, which highlighted the fact that vegetation cover (C factor) has a greater influence than other factors (R, K and LS) on soil loss prediction attenuation. These results lead to the conclusion that soil loss occurs by different methods in each settlement in the basin and that erosive processes modeled by geoprocessing have the potential to contribute to an orderly land management process.     

Simulating climate change impact on soil erosion using RUSLE model <Emphasis Type="BoldItalic">−</Emphasis> A case study in a watershed of mid-Himalayan landscape

Climate change, particularly due to the changed precipitation trend, can have a severe impact on soil erosion. The effect is more pronounced on the higher slopes of the Himalayan region. The goal of this study was to estimate the impact of climate change on soil erosion in a watershed of the Himalayan region using RUSLE model. The GCM (general circulation model) derived emission scenarios (HadCM3 A2a and B2a SRES) were used for climate projection. The statistical downscaling model (SDSM) was used to downscale the precipitation for three future periods, 2011–2040, 2041–2070, and 2071–2099, at large scale. Rainfall erosivity (R) was calculated for future periods using the SDSM downscaled precipitation data. ASTER digital elevation model (DEM) and Indian Remote Sensing data – IRS LISS IV satellite data were used to generate the spatial input parameters required by RUSLE model. A digital soil-landscape map was prepared to generate spatially distributed soil erodibility (K) factor map of the watershed. Topographic factors, slope length (L) and steepness (S) were derived from DEM. Normalised difference vegetation index (NDVI) derived from the satellite data was used to represent spatial variation vegetation density and condition under various land use/land cover. This variation was used to represent spatial vegetation cover factor. Analysis revealed that the average annual soil loss may increase by 28.38, 25.64 and 20.33% in the 2020s, 2050s and 2080s, respectively under A2 scenario, while under B2 scenario, it may increase by 27.06, 25.31 and 23.38% in the 2020s, 2050s and 2080s, respectively, from the base period (1985–2013). The study provides a comprehensive understanding of the possible future scenario of soil erosion in the mid-Himalaya for scientists and policy makers.     

Soil erosion modeling of a Himalayan watershed using RS and GIS

Employing the remote sensing (RS) and geographical information system (GIS), an assessment of sediment yield from Dikrong river basin of Arunachal Pradesh (India) has been presented in this paper. For prediction of soil erosion, the Morgan-Morgan and Finney (MMF) model and the universal soil loss equation (USLE) have been utilized at a spatial grid scale of 100 m × 100 m, an operational unit. The average annual soil loss from the Dikrong river basin is estimated as 75.66 and 57.06 t ha⁻¹ year⁻¹ using MMF and USLE models, respectively. The watershed area falling under the identified very high, severe, and very severe zones of soil erosion need immediate attention for soil conservation.     

Calibrating a rainfall erosivity assessment model at regional scale in Mediterranean area

A simplified regression model is here calibrated on the basis of rainfall data records of Sicily (southern Italy), in order to show the model reliability in assessing the R-factor of the Universal Soil Loss Equation and its revised version (RUSLE) and to provide an estimate of long-term rainfall erosivity at medium-regional scale. The proposed model is a rearrangement of a former simplified model, formulated for the Italian environment, grouping three easily available rainfall variables on various time scales, which has been shown to be more successful than others in reproducing the rainfall erosive power over different locations of Italy. A geostatistical interpolation procedure is then applied for generating the regional long-term erosivity map with associated standard error. Areas with severe erosive rainfalls (from 2,000 up to more than 6,000 MJ mm ha⁻¹ h⁻¹) are pointed out which will correspond to areas suffering from severe soil erosion. Solving the problem of calculating the R-factor value in the RUSLE equation by means of such a simplified model here formulated will allow to predict the related soil loss. Moreover, given the availability of long time-series of concerned rainfall data, it will be possible to analyse the variability of rainfall erosivity within the last 50 years, and to investigate the application of RUSLE or similar soil erosion models with forecasting purposes of soil erosion risk.     

基于RS与GIS的麻地沟土壤侵蚀及其生态环境效应研究

以GIS、RS和修正的土壤流失方程(RUSLE)为基础,结合遥感影像解译数据、地形图及降雨资料等,对麻地沟的土壤侵蚀进行了定量分析,并根据土壤侵蚀定量计算结果,结合景观生态学的方法,分析土壤侵蚀引起生态环境效应的因子,包括植被指数和景观指数等,对麻地沟地区的生态环境效应进行了分析。结果表明:研究区土壤侵蚀状况正在逐步好转,坡耕地和坡荒地是发生土壤侵蚀面积相对较高的两种土地利用类型,在保证当地居民所需坡耕地的情况下,修造梯田和植树造林是控制土壤侵蚀最有效的方式。     

Modeling impacts of sediment delivery ratio and land management on adsorbed non-point source nitrogen and phosphorus load in a mountainous basin of the Three Gorges reservoir area, China

Agricultural nonpoint source (NPS) pollution at the Three Gorges reservoir area in China has been increasingly recognized as a threat to aquatic environment in recent years due to the serious eutrophication problem. Adsorbed NPS pollution is one of the major forms of NPS pollution in mountainous regions, the essential of the adsorbed NPS pollution is soil loss. Thus, simple, highly sensitive and continuous methods are required to simulate and quantify sediments yield at watershed scales. It is imperative to construct an integrated model to estimate the sediment yield and adsorbed NPS pollution load. According to the characteristics of climate, hydrology, topography, geology, geomorphology and land use types in Three Gorges reservoir area, a GIS-based dynamic-integrated-distributed model of annual adsorbed NPS load was presented in view of impacts of the rainfall intensity, sediment delivery ratio (SDR) and land management, where the temporally dynamic-continuous model of annual sediment yield was established by modifying the revised Universal Soil Loss Equation (RUSLE), and the spatially integrated-distributed model of annual adsorbed NPS load was then developed via the correlation between sediment yield and adsorbed NPS load. Furthermore, a case study of the Jialing River basin in China was applied to validate the integrated model, the dynamic-distributed coupling among GIS technology, sediment yield model, and adsorbed NPS load model was achieved successfully. The simulation results demonstrate the following: (1) runoff and sediment are influenced greatly by rainfall intensity, SDR and vegetation cover; rainfall and land management show high sensitivities to the integrated model; the average annual adsorbed TN and TP pollution loads from 2006 to 2010 decreased by 76 and 74 % compared with the previous treatment (1990), respectively. (2) Spatio-temporal variations of adsorbed NPS nitrogen and phosphorus load are mainly related to different land use types and the background level of nutriments in topsoil; different land use types have different contribution rates; the largest contribution rates of adsorbed total nitrogen (TN, 58.9 %) and total phosphorus (TP, 53 %) loads are both from the dryland cropland. (3) The identification of critical source areas can help to implement the prevention and control measures aiming at the reduction of water environmental pollution. These results will provide useful and valuable information for decision makers and planners to take sustainable land use management and soil conservation measures for the control of sediment pollution in the Three Gorges reservoir area. The application of this model in the catchment shows that the integrated model may be used as a major tool to assess sediment yield risks and adsorbed NPS pollution load at mountainous watersheds.     

西南喀斯特区土壤侵蚀研究进展

西南喀斯特区土层浅薄、成土速率低等特点决定了其允许土壤流失量小,土壤一旦流失,极难恢复,土壤侵蚀及其造成的石漠化现象已成为制约该区可持续发展最严重的生态环境问题。文章首先明晰西南喀斯特区土壤侵蚀特征,从坡面、小流域和区域三个尺度上系统概括西南喀斯特区土壤侵蚀的相关研究进展。针对当前喀斯特区土壤侵蚀研究野外径流小区、小流域及区域空间尺度数据缺少和相关研究模型限制性强等不足,建议从不同尺度深入研究喀斯特区土壤侵蚀发生发展规律及时空演化格局,并结合高新遥感、地球物理探测技术及模型,同步监测坡面—小流域—区域土壤流失,对土壤侵蚀进行定量评估,结合不同空间尺度土壤侵蚀特征构建系统性水土保持生态恢复治理模式和监测系统评价体系。      

Estimation of soil erosion using RUSLE in a GIS framework: a case study in the Buyukcekmece Lake watershed,northwest Turkey

This study was aimed at predicting soil erosion risk in the Buyukcekmece Lake watershed located in the western part of Istanbul, Turkey, by using Revised Universal Soil Loss Equation (RUSLE) model in a GIS framework. The factors used in RUSLE were computed by using different data obtained or produced from meteorological station, soil surveys, topographic maps, and satellite images. The RUSLE factors were represented by raster layers in a GIS environment and then multiplied together to estimate the soil erosion rate in the study area using spatial analyst tool of ArcGIS 9.3. In the study, soil loss rate below 1 t/ha/year was defined as low erosion, while those >10 t/ha/year were defined as severe erosion. The values between low and severe erosion were further classified as slight, moderate, and high erosion areas. The study provided a reliable prediction of soil erosion rates and delineation of erosion-prone areas within the watershed. As the study revealed, soil erosion risk is low in more than half of the study area (54%) with soil loss <1 t/ha/year. Around one-fifth of the study area (19%) has slight erosion risk with values between 1 and 3 t/ha/year. Only 11% of the study area was found to be under high erosion risk with soil loss between 5 and 10 t/ha/year. The severe erosion risk is seen only in 5% of the study area with soil loss more than 10 t/ha/year. As the study revealed, nearly half of the Buyukcekmece Lake watershed requires implementation of effective soil conservation measures to reduce soil erosion risk.     

Scale effects of eroded sediment transport in Wujiang River Basin,Guizhou Province,China

In recent years, research on spatial scale and scale transformation of eroded sediment transport has become a forefront field in current soil erosion research, but there are very few studies on the scale effect problem in Karst regions of China. Here we quantitatively extracted five main factors influencing soil erosion, namely rainfall erosivity, soil erodibility, vegetative cover and management, soil and water conservation, and slope length and steepness. Regression relations were built between these factors and also the sediment transport modulus and drainage area, so as to initially analyze and discuss scale effects on sediment transport in the Wujiang River Basin (WRB). The size and extent of soil erosion influencing factors in the WRB were gauged from: Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), precipitation data, land use, soil type and Normalized Difference Vegetation Index (NDVI) data from Global Inventory Modeling and Mapping Studies (GIMMS) or Advanced Very High Resolution Radiometer (AVHRR), and observed data from hydrometric stations. We find that scaling effects exist between the sediment transport modulus and the drainage area. Scaling effects are expressed after logarithmic transformation by a quadratic function regression relationship where the sediment transport modulus increases before decreasing, alongside changes in the drainage area. Among the five factors influencing soil erosion, slope length and steepness increases first and then decreases, alongside changes in the drainage area, and are the main factors determining the relationship between sediment transport modulus and drainage area. To eliminate the influence of scale effects on our results, we mapped the sediment yield modulus of the entire WRB, adopting a 1 000 km2 standard area with a smaller fitting error for all sub-basins, and using the common Kriging interpolation method.