Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala,India,using Revised Universal Soil Loss Equation(RUSLE) and geo-information technology

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 vulner- ability 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 i 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.

Relationship between drought and soil erosion based on the normalized differential water index(NDWI)and revised universal soil loss equation(RUSLE)model

The Langat River Basin in Malaysia is vulnerable to soil erosion risks because of its exposure to intensive land use activities and its topography,which primarily consists of steep slopes and mountainous areas.Furthermore,climate change frequently exposes this basin to drought,which negatively affects soil and water conservation.However,recent studies have rarely shown how soil reacts to drought,such as soil erosion.Therefore,the purpose of this study is to evaluate the relationship between drought and soil erosion in the Langat River Basin.We analyzed drought indices using Landsat 8 satellite images in November 2021,and created the normalized differential water index(NDWI)via Landsat 8 data to produce a drought map.We used the revised universal soil loss equation(RUSLE)model to predict soil erosion.We verified an association between the NDWI and soil erosion data using a correlation analysis.The results revealed that the southern and northern regions of the study area experienced drought events.We predicted an average annual soil erosion of approximately 58.11 t/(hm^(2)·a).Analysis of the association between the NDWI and soil erosion revealed a strong positive correlation,with a Pearson correlation coefficient of 0.86.We assumed that the slope length and steepness factor was the primary contributor to soil erosion in the study area.As a result,these findings can help authorities plan effective measures to reduce the impacts of drought and soil erosion in the future.

Combining RUSLE model and the vegetation health index to unravel the relationship between soil erosion and droughts in southeastern Tunisia

Droughts and soil erosion are among the most prominent climatic driven hazards in drylands,leading to detrimental environmental impacts,such as degraded lands,deteriorated ecosystem services and biodiversity,and increased greenhouse gas emissions.In response to the current lack of studies combining drought conditions and soil erosion processes,in this study,we developed a comprehensive Geographic Information System(GIS)-based approach to assess soil erosion and droughts,thereby revealing the relationship between soil erosion and droughts under an arid climate.The vegetation condition index(VCI)and temperature condition index(TCI)derived respectively from the enhanced vegetation index(EVI)MOD13A2 and land surface temperature(LST)MOD11A2 products were combined to generate the vegetation health index(VHI).The VHI has been conceived as an efficient tool to monitor droughts in the Negueb watershed,southeastern Tunisia.The revised universal soil loss equation(RUSLE)model was applied to quantitatively estimate soil erosion.The relationship between soil erosion and droughts was investigated through Pearson correlation.Results exhibited that the Negueb watershed experienced recurrent mild to extreme drought during 2000–2016.The average soil erosion rate was determined to be 1.8 t/(hm2•a).The mountainous western part of the watershed was the most vulnerable not only to soil erosion but also to droughts.The slope length and steepness factor was shown to be the most significant controlling parameter driving soil erosion.The relationship between droughts and soil erosion had a positive correlation(r=0.3);however,the correlation was highly varied spatially across the watershed.Drought was linked to soil erosion in the Negueb watershed.The current study provides insight for natural disaster risk assessment,land managers,and stake-holders to apply appropriate management measures to promote sustainable development goals in fragile environments.

Modeling spatial and temporal change of soil erosion based on multi-temporal remotely sensed data

In order to monitor the pattern, distribution, and trend of land use/cover change （LUCC） and its impacts on soil erosion, it is highly appropriate to adopt Remote Sensing （RS） data and Geographic Information System （GIS） to analyze, assess, simulate, and predict the spatial and temporal evolution dynamics. In this paper, multi-temporal Landsat TM/ETM＋ re- motely sensed data are used to generate land cover maps by image classification, and the Cellular Automata Markov （CA_Markov） model is employed to simulate the evolution and trend of landscape pattern change. Furthermore, the Re- vised Universal Soil Loss Equation （RUSLE） is used to evaluate the situation of soil erosion in the case study mining area. The trend of soil erosion is analyzed according to total/average amount of soil erosion, and the rainfall （R）, cover man- agement （C）, and support practice （P） factors in RUSLE relevant to soil erosion are determined. The change trends of soil erosion and the relationship between land cover types and soil erosion amount are analyzed. The results demonstrate that the CA_Markov model is suitable to simulate and predict LUCC trends with good efficiency and accuracy, and RUSLE can calculate the total soil erosion effectively. In the study area, there was minimal erosion grade and this is expected to con- tinue to decline in the next few years, according to our prediction results.

Influence of varied drought types on soil conservation service within the framework of climate change:insights from the Jinghe River Basin,China

Severe soil erosion and drought are the two main factors affecting the ecological security of the Loess Plateau,China.Investigating the influence of drought on soil conservation service is of great importance to regional environmental protection and sustainable development.However,there is little research on the coupling relationship between them.In this study,focusing on the Jinghe River Basin,China as a case study,we conducted a quantitative evaluation on meteorological,hydrological,and agricultural droughts(represented by the Standardized Precipitation Index(SPI),Standardized Runoff Index(SRI),and Standardized Soil Moisture Index(SSMI),respectively)using the Variable Infiltration Capacity(VIC)model,and quantified the soil conservation service using the Revised Universal Soil Loss Equation(RUSLE)in the historical period(2000-2019)and future period(2026-2060)under two Representative Concentration Pathways(RCPs)(RCP4.5 and RCP8.5).We further examined the influence of the three types of drought on soil conservation service at annual and seasonal scales.The NASA Earth Exchange Global Daily Downscaled Projections(NEX-GDDP)dataset was used to predict and model the hydrometeorological elements in the future period under the RCP4.5 and RCP8.5 scenarios.The results showed that in the historical period,annual-scale meteorological drought exhibited the highest intensity,while seasonal-scale drought was generally weakest in autumn and most severe in summer.Drought intensity of all three types of drought will increase over the next 40 years,with a greater increase under the RCP4.5 scenario than under the RCP8.5 scenario.Furthermore,the intra-annual variation in the drought intensity of the three types of drought becomes smaller under the two future scenarios relative to the historical period(2000-2019).Soil conservation service exhibits a distribution pattern characterized by high levels in the southwest and southeast and lower levels in the north,and this pattern has remained consistent both in the historical and future periods.Over the past 20 years,the intra-annual variation indicated peak soil conservation service in summer and lowest level in winter;the total soil conservation of the Jinghe River Basin displayed an upward trend,with the total soil conservation in 2019 being 1.14 times higher than that in 2000.The most substantial impact on soil conservation service arises from annual-scale meteorological drought,which remains consistent both in the historical and future periods.Additionally,at the seasonal scale,meteorological drought exerts the highest influence on soil conservation service in winter and autumn,particularly under the RCP4.5 and RCP8.5 scenarios.Compared to the historical period,the soil conservation service in the Jinghe River Basin will be significantly more affected by drought in the future period in terms of both the affected area and the magnitude of impact.This study conducted beneficial attempts to evaluate and predict the dynamic characteristics of watershed drought and soil conservation service,as well as the response of soil conservation service to different types of drought.Clarifying the interrelationship between the two is the foundation for achieving sustainable development in a relatively arid and severely eroded area such as the Jinghe River Basin.

Dynamic Monitoring of Soil Erosion for Upper Stream of Miyun Reservoir in the Last 30 Years

The Revised Universal Soil Loss Equation （RUSLE） was applied to assess the spatial distribution and dynamic properties of soil loss with geographic information system （GIS） and remote sensing （RS） technologies. To improve the accuracy of soil-erosion estimates, a new C-factor estimation model was developed based on land cover and time series normalized difference vegetation index （NDVI） datasets. The new C-factor was then applied in the RUSLE to integrate rainfall, soil, vegetation, and topography data of different periods, and thus monitor the distribution of soil erosion patterns and their dynamics during a 3o-year period of the upstream watershed of Miynn Reservoir （UWMR）, China. The results showed that the new C-factor estimation method, which considers land cover status and dynamics, and explicitly incorporates within-land cover variability, was more rational, quantitative, and reliable. An average annual soil loss in UWMR of 25.68, 21.04, and 16.8o t ha-1 a-1 was estimated for 1990, 2000 and 2010, respectively, corroborated by comparing spatial and temporal variation in sediment yield. Between 2000 and 2010, a 1.38% average annual increase was observed in the area of lands that lost less than 5 t ha-1 a^-1, while during 1990-2000 such lands only increased on average by o.46%. Areas that classified as severe, very severe and extremely severe accounted for 5.68% of the total UWMR in 2010, and primarily occurred in dry areas or grasslands of sloping fields. The reason for the change in rate of soil loss is explained by an increased appreciation of soil conservation by developers and planners. Moreover,we recommend that UWMR watershed adopt further conservation measures such as terraced plowing of dry land, afforestation, or grassland enclosures as part of a concerted effort to reduce on-going soil erosion.

Mapping surface water erosion potential in the Soummam watershed in Northeast Algeria with RUSLE model

The present study aims to estimate the annual soil loss in the Soummam watershed in the northeast of Algeria,using the Revised Universal Soil Loss Equation(RUSLE),geographic information system(GIS),and remote sensing(RS).RUSLE model has been used for modelling the main factors involved in erosive phenomena.The Soummam watershed covers a surface area of 9108.45 km^2 of irregular shape,northeast–southwest towards southeast.It is characterized by an altitude varying between 2 m in the northeast and 2308 m in the northwest.Results showed that the average erosivity factor(R)is 70.64(MJ·mm)/(ha·h·year)and the maximum value reaches 140(MJ·mm)/(ha·h·year),the average soil erodibility factor(K)is 0.016(t·h·ha)/(MJ·ha·mm)and maximum values reach 0.0204(t·h·ha)/(MJ·ha·mm)in the southeast regions of the watershed,the average slope length and steepness factor(LS)is 9.79 and the mean C factor is estimated to be 0.62.Thematic maps integration of different factors of RUSLE in GIS with their database,allowed with a rapid and efficient manner to highlight complexity and factors interdependence in the erosion risk analyses.The resulting map for soils losses,with an average erosion rate of 6.81 t/(ha·year)shows a low erosion(<7.41 t/(ha·year))which covers 73.46%of the total area of the basin,and a medium erosion(7.42 to 19.77 t/(ha·year)),which represents 17.66%of the area.Areas with extreme erosion risk exceeding 32.18 t/(ha·year)cover more than 3.54%of the basin area.The results can certainly aid in implementation of soil management and conservation practices to reduce the soil erosion in the Soummam watershed.

Addressing soil protection concerns in forest ecosystem management under climate change

Background: Climate change may strongly influence soil erosion risk, namely through variations in the precipitation pattern. Forests may contribute to mitigate the impacts of climate change on soil erosion and forest managers are thus challenged by the need to define strategies that may protect the soil while addressing the demand for other ecosystem services. Our emphasis is on the development of an approach to assess the impact of silvicultural practices and forest management models on soil erosion risks under climate change. Specifically, we consider the annual variation of the cover-management factor(C) in the Revised Universal Soil Loss Equation over a range of alternative forest management models to estimate the corresponding annual soil losses, under both current and changing climate conditions. We report and discuss results of an application of this approach to a forest area in Northwestern Portugal where erosion control is the most relevant water-related ecosystem service.Results: Local climate change scenarios will contribute to water erosion processes, mostly by rainfall erosivity increase.Different forest management models provide varying levels of soil protection by trees, resulting in distinct soil loss potential.Conclusions: Results confirm the suitability of the proposed approach to address soil erosion concerns in forest management planning. This approach may help foresters assess management models and the corresponding silvicultural practices according to the water-related services they provide.

GIS-based Effect Assessment of Soil Erosion Before and After Gully Land Consolidation: A Case Study of Wangjiagou Project Region, Loess Plateau

The Loess Plateau is one typical area of serious soil erosion in the world. China has implemented ′Grain for Green′(GFG) project to restore the eco-environment of the Loess Plateau since 1999. With the GFG project subsidy approaching the end, it is concerned that farmers of fewer subsidies may reclaim land again. Thus, ′Gully Land Consolidation Project′(GLCP) was initiated in 2010. The core of the GLCP was to create more land suitable for farming in gullies so as to reduce land reclamation on the slopes which are ecological vulnerable areas. This paper aims to assess the effect of the GLCP on soil erosion problems by studying Wangjiagou project region located in the central part of Anzi valley in the middle of the Loess Plateau, mainly using the revised universal soil loss equation(RUSLE) based on GIS. The findings show that the GLCP can help to reduce soil shipment by 9.87% and it creates more terraces and river-nearby land suitable for farming which account for 27.41% of the whole study area. Thus, it is feasible to implement the GLCP in places below gradient 15°, though the GLCP also intensifies soil erosion in certain places such as field ridge, village land, floodplain, natural grassland, and shrub land. In short, the GLCP develops new generation dam land and balances the short-term and long-term interests to ease the conflicts between economic development and environmental protection. Furthermore, the GLCP and the GFG could also be combined preferably. On the one hand, the GFG improves the ecological environment, which could offer certain safety to the GLCP, on the other hand, the GLCP creates more farmland favorable for farming in gullies instead of land reclamation on the slopes, which could indirectly protect the GFG project.

Evaluating estimated sediment delivery by Revised Universal Soil Loss Equation(RUSLE)and Sediment Delivery Distributed(SEDD)in the Talar Watershed,Iran

The performance of the Revised Universal Soil Loss Equation(RUSLE)as the most widely used soil erosion model is a challenging issue.Accordingly,the objective of this study is investigating the estimated sediment delivery by the RUSLE method and Sediment Delivery Distributed(SEDD)model.To this end,the Talar watershed in Iran was selected as the study area.Further,700 paired sediment-discharge measurements at Valikbon and Shirgah-Talar hydrometric stations between the years 1991 and 2011 were collected and used in sediment rating curves.Nine procedures were investigated to produce the required RUSLE layers.The estimated soil erosion by RUSLE was evaluated using sediment rating curve data by two methods including least squares and quantile regression.The average annual suspended sediment load was calculated for each sub-watershed of the study area using the SEDD model.Afterwards,a sediment rating curve was estimated by least squares and quantile regression methods using paired discharge-sediment data.The average annual suspended sediment load values were calculated for two hydrometric stations and were further evaluated by the SEDD model.The results indicated that the first considered procedure,which utilized 15-min rainfall measurements for the rainfall factor(R),and the classification method of SENTINEL-2 MSI image for the cover management factor(C),offered the best results in producing RUSLE layers.Furthermore,the results revealed the advantages of utilizing satellite images in producing cover management layer,which is required in the RUSLE method.

Assessing and mapping soil erosion risk zone in Ratlam District, central India

Evaluation of physical and quantitative data of soil erosion is crucial to the sustainable development of the environment. The extreme form of land degradation through different forms of erosion is one of the major problems in the sub-tropical monsoon-dominated region. In India, tackling soil erosion is one of the major geo-environmental issues for its environment. Thus, identifying soil erosion risk zones and taking preventative actions are vital for crop production management. Soil erosion is induced by climate change, topographic conditions, soil texture, agricultural systems, and land management. In this research, the soil erosion risk zones of Ratlam District was determined by employing the Geographic Information System(GIS), Revised Universal Soil Loss Equation(RUSLE), Analytic Hierarchy Process(AHP), and machine learning algorithms(Random Forest and Reduced Error Pruning(REP) tree). RUSLE measured the rainfall eosivity(R), soil erodibility(K), length of slope and steepness(LS), land cover and management(C), and support practices(P) factors. Kappa statistic was used to configure model reliability and it was found that Random Forest and AHP have higher reliability than other models. About 14.73%(715.94 km^(2)) of the study area has very low risk to soil erosion, with an average soil erosion rate of 0.00-7.00×10^(3)kg/(hm^(2)·a), while about 7.46%(362.52 km^(2)) of the study area has very high risk to soil erosion, with an average soil erosion rate of 30.00×10^(3)-48.00×10^(3)kg/(hm^(2)·a). Slope, elevation, stream density, Stream Power Index(SPI), rainfall, and land use and land cover(LULC) all affect soil erosion. The current study could help the government and non-government agencies to employ developmental projects and policies accordingly. However, the outcomes of the present research also could be used to prevent, monitor, and control soil erosion in the study area by employing restoration measures.

1980—2020年延河甘谷驿流域土壤侵蚀评价与驱动因子分析

采用日降雨量、DEM、土壤类型、泥沙含量及多期NDVI等数据,基于修正通用土壤流失方程(RUSLE)和地理探测器,研究了国家生态退耕还林还草工程实施前后近41年延河甘谷驿流域土壤侵蚀动态与驱动因子。结果表明,1980—2020年研究区土壤侵蚀强度总体呈波动变化趋势,1980年、1990年、2000年、2010年和2020年平均侵蚀模数分别为6 746.30、5 740.28、6 389.56、5 450.46、5 480.56 t/(km~2·年)。1980—2000年研究区整体侵蚀强度逐渐增强,强烈及以上等级侵蚀面积占比逐渐增加,表现为“增蚀升级”的特点;2000年后研究区内土壤侵蚀强度开始降低,强烈及以上等级的侵蚀面积减少,总体表现为“减蚀降级”的特点。研究区土壤侵蚀强度随着坡度的升高而加剧,同时发现海拔1 000~1 200 m和1 200~1 400 m是研究区内侵蚀发生的主要高程带。2020年土地利用类型因子解释力最为突出,表明退耕还林还草工程实施效果显著,大面积的耕地向林草地转换是使得研究区2000年后土壤侵蚀强度降低的最主要原因。土壤侵蚀各影响因子的协同作用明显强于单一因子的影响。

基于RUSLE模型的秦岭—大巴山地土壤侵蚀时空特征分析

[目的]秦岭—大巴山地(秦巴山地)是我国重要的南北地理—生态过渡带主体,对秦巴山地的土壤侵蚀研究将有助于该区域的生态保护和水土资源管理。[方法]基于RUSLE模型计算秦巴山地的土壤侵蚀模数,并量化分析了该区域的土壤侵蚀的时空分布格局。[结果](1)2000—2020年秦巴山地的微度侵蚀面积呈上升趋势,轻度侵蚀及其以上等级的土壤侵蚀面积均呈下降趋势;从空间来看,秦巴山地东北和西南部的土壤侵蚀等级较高,中间较低;(2)秦巴山地的土壤侵蚀相对集中在500~1500 m、坡度15°~25°区域内;(3)秦巴山地发生土壤侵蚀最主要的土地利用类型为林地,耕地、林地的微度侵蚀以及草地的微度、剧烈侵蚀面积呈上升趋势;(4)秦巴山地土壤侵蚀主要分布在陕西、四川和甘肃,且甘肃和四川的剧烈侵蚀呈上升趋势。[结论]2000—2020年秦巴山地的侵蚀面积和强度呈“双下降”的态势,其整体侵蚀状况好转,但侵蚀分布存在明显空间差异。

甘肃省黄河流域土壤侵蚀及其驱动机制

[目的]量化甘肃省黄河流域土壤侵蚀并探究其驱动机制,旨在为该区域水土流失治理和水土资源保护提供科学依据和方法参考。[方法]基于修正的土壤流失方程(RUSLE)模型对1980—2020年甘肃省黄河流域土壤侵蚀进行量化,使用Theil-Sen斜率方法分析了土壤侵蚀的年际变化,通过空间统计方法分析了不同下垫面的土壤侵蚀强度和分布规律,采用地理探测器探究了土壤侵蚀的驱动因子。[结果](1)1980—2020年,甘肃省黄河流域土壤侵蚀模数(A)均值为37.38 t/(hm^(2)·a),中度及以下侵蚀占全域面积70%,其中甘南以微度侵蚀为主,A值呈减小趋势;陇中和陇东以轻度和中度侵蚀为主,A值呈增加趋势。(2)全域农田A值最高且侵蚀总量最大,沼泽类湿地A值最低,密林地侵蚀总量最小;受分布面积影响,甘南草地土壤侵蚀量较显著,陇中和陇东农耕区侵蚀量最大;高侵蚀模数主要发生在海拔1000 m以下和10°~20°坡度区间。(3)甘南和陇东地区坡度对A值的作用最为显著,陇中地区土壤的作用更大;域内土壤侵蚀影响因子呈多元性,因子交互的解释力强于单因子;侵蚀高风险区域主要位于丰水、陡坡和植被稀疏地区。[结论]因区域气候和下垫面条件相异,土壤侵蚀发生的原因不尽相同;甘肃省黄河流域陇中、陇东地区存在较为严峻的土壤侵蚀问题,农田和低覆草地是该区水土流失治理的重点关注区域。

Soil loss estimation using rusle model to prioritize erosion control in KELANI river basin in Sri Lanka

Soil erosion contributes negatively to agricultural production,quality of source water for drinking,ecosystem health in land and aquatic environments,and aesthetic value of landscapes.Approaches to understand the spatial variability of erosion severity are important for improving landuse management.This study uses the Kelani river basin in Sri Lanka as the study area to assess erosion severity using the Revised Universal Soil Loss Equation (RUSLE) model supported by a GIS system.Erosion severity across the river basin was estimated using RUSLE,a Digital Elevation Model (15 × 15 m),twenty years rainfall data at 14 rain gauge stations across the basin,landuse and land cover,and soil maps and cropping factors.The estimated average annual soil loss in Kelani river basin varied from zero to 103.7 t ha-1 yr-1,with a mean annual soil loss estimated at 10.9 t ha-1 yr-1.About 70% of the river basin area was identified with low to moderate erosion severity (< 12 t ha-1 yr-1) indicating that erosion control measures are urgently needed to ensure a sustainable ecosystem in the Kelani river basin,which in turn,is connected with the quality of life of over 5 million people.Use of this severity information developed with RUSLE along with its individual parameters can help to design landuse management practices.This effort can be further refined by analyzing RUSLE results along with Kelani river sub-basins level real time erosion estimations as a monitoring measure for conservation practices.

基于GIS和RUSLE的黄土高原小流域土壤侵蚀评估

对基于上坡汇流面积的坡长因子算法进行改进,提出考虑上坡土地利用/覆盖对汇流影响的坡长因子算法,运用GIS和RUSLE评估黄土高原四面窑沟流域的土壤侵蚀强度及其与环境因素的关系。结果表明,流域多年平均侵蚀强度4399.79t/(km2·a),属中度侵蚀;侵蚀强度和侵蚀量均随坡度增加而显著增加,80.59%的侵蚀量来源于占流域总面积59.06%的25°以上坡度带;不同坡向的侵蚀强度表现为正阳坡>半阳坡>半阴坡>正阴坡,其中,占总面积45.07%的阳坡产生56.50%的侵蚀量;不同土地利用类型中,占总面积57.07%的草地产生96.37%的侵蚀量,成为目前流域内主要侵蚀产沙源。研究为应用修正通用土壤流失方程在黄土高原进行侵蚀评估提供技术范例,为该区侵蚀防治和水土资源利用提供有益参考。

三峡库区土壤侵蚀遥感定量监测——基于GIS和修正通用土壤流失方程的研究

在GIS技术的支持下,通过遥感技术和野外调查进行信息采集,选用修正的通用土壤流失方程(RUSLE)估算了整个三峡库区的年均土壤侵蚀量,分析了土壤侵蚀的空间分布特征,计算了不同土地利用类型的土壤保持能力,并对库区进行了防治强度预报。结果表明,三峡库区年均土壤侵蚀量为18476.27×104t/a,平均土壤侵蚀模数为3316.53 t/(km2.a),属于中度侵蚀。库区年均土壤保持量为48427633×104t/a,其中有林地、其他林地和高中覆盖度草地的年均土壤保持量最大,水田保持土壤能力最强,旱地保持土壤能力最小。库区预防强度预报结果为2005年三峡库区需要治理和急需治理的面积占总面积的25.67%,所占比例较大,预防和治理土壤侵蚀的任务比较艰巨。

基于GIS的岩溶槽谷区小流域土壤侵蚀量估算

在修正的通用土壤流失方程(RUSLE)中引入石漠化因子,以重庆市南川区木渡河小流域为研究对象,在GIS技术的支持下,对岩溶槽谷区小流域土壤侵蚀量的估算方法进行了探讨.研究结果表明,研究区现实土壤侵蚀量40011.51 t/a,土壤保持量835963.94 t/a.计算研究区及不同土地利用类型的现实土壤侵蚀模数时,石漠化程度越高,总面积现实土壤侵蚀模数和可流失面积现实土壤侵蚀模数的差异越大;在岩溶区,使用可流失面积现实土壤侵蚀模数更能真实的反映岩溶区土壤侵蚀的状况.研究区水田和林地土壤保持能力最大,旱地土壤保持能力最小,控制土壤侵蚀的有效措施是在裸岩山地和陡坡地植树造林,大于25°的陡坡地退耕还林还草,扩大林地面积,减小陡坡耕地面积.

基于GIS的黄土高原小流域土壤侵蚀定量评价

以小流域为单元进行土壤侵蚀量的定量评价研究,是探索土壤流失规律和评价流域治理效益的一个重要途径和内容。根据修正通用土壤流失方程(RUSLE),在ArcGIS软件平台下,以QuickBird遥感影像作为主要数据源,计算了黄土高原南小河沟小流域土壤侵蚀量并定量分析了土壤侵蚀量与坡度和土地利用的关系。结果表明:南小河沟小流域年土壤侵蚀量88 504.2 t/a,平均土壤侵蚀模数为2 438.98 t/(km2.a),属于轻度接近于中度侵蚀强度,土壤侵蚀得到有效控制。随着坡度的增大土壤侵蚀量明显增加,>25°区域产生全流域80%以上的土壤侵蚀量。不同植被类型的土壤侵蚀模数依次为:天然草地>未成林地>疏林地>灌木林地>人工草地>乔木林地。改善疏林地、未成林地和天然草地的结构,加强难利用地和荒坡地的治理,提高植被覆盖度,是降低土壤侵蚀量的主要途径。

基于RUSLE的土壤侵蚀量研究——以南水北调中线陕西水源区为例

目的对南水北调中线陕西水源区的土壤侵蚀进行了初步研究,为水源区的生态保护、水土保持和生态补偿提供重要的依据。方法在GIS技术的支持下,利用遥感影像解译资料、数字高程模型(DEM)及土壤、降雨等数据,对土壤流失方程RUSLE中各因子进行了量化分析,从而实现对南水北调中线陕西水源区土壤侵蚀量的估算,并对结果进行土壤侵蚀强度分级。结果通过计算可得,在南水北调中线陕西水源区,微度侵蚀面积占12.87%,轻度侵蚀面积占30.4%,中度侵蚀面积占30.66%,强度侵蚀面积占26.07%。土壤侵蚀空间分布具有明显的地带性和垂直地带性规律。结论南水北调中线水源区陕西段绝大部分地区属于强度侵蚀地区,仅有少数地区达到微度侵蚀,水土保持任务很艰巨。