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.

Factor value determination and applicability evaluation of universal soil loss equation in granite gneiss region

Six types of runoff plots were set up and an experimental study was carried out to examine natural rate of soil and water loss in the granite gneiss region of northern Jiangsu Province in China. Through correlation analysis of runoff and soil loss during 364 rainfall events, a simplified and convenient mathematical formula suitable for calculating the rainfall erosivity factor （R） for the local region was established. Other factors of the universal soil loss equation （USLE model） were also determined. Relative error analysis of the soil loss of various plots calculated by the USLE model on the basis of the observed values showed that the relative error ranged from -3.5% to 9.9% and the confidence level was more than 90%. In addition, the relative error was 5.64% for the terraced field and 12.36% for the sloping field in the practical application. Thus, the confidence level was above 87.64%. These results provide a scientific basis for forecasting and monitoring soil and water loss, for comprehensive management of small watersheds, and for soil and water conservation planning in the region.

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.

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.

An Integrated GIS/RS Approach for Soil Erosion Assessment and Modeling in Syrian Coastal Soils

An integrated remote sensing (RS) and geographic information system (GIS) technique was employed to characterize the spatial distribution of the risk of soil erosion by water on Latakia district, Syria. The universal soil loss equation (USLE) was used to calculate the annual soil loss rates for Latakia soils. Mainly, remote sensing data, soil survey, land use inventory, elevation data and climatic atlases are used as resource data sets to generate USLE factor values. The results revealed that integration of GIS/RS with USLE was a practical and effective approach for monitoring soil erosion over large areas.

Soil Erosion under Different Land Use Types and Zones of Jinsha River Basin in Yunnan Province,China

Severe soil erosion in the middle and upper reaches of Yangtze River has been regarded as a major environmental problem. The on-site impact of soil erosion on agricultural production and the off-site impact on floods and sedimentation in Yangtze Rive are well known. A quantitative assessment of soil erosion intensity is still scanty for developing appropriate soil erosion control measures for different land use types and zones in this region. This article constructs a localized USLE and estimates the average soil loss in the Jinsha River Region in Yunnan Province, one of the priority areas for soil erosion control in the middle and upper reaches of Yangtze River. The estimation is done under different land uses and zones in this basin. The estimation shows that while soil erosion in the cultivated land is the most severe, 36～40% of the garden and forest land suffers from soil erosion of various degrees due to lack of ground cover and other factors. Soil erosion in the pasture is modest when the ground cover is well maintained. It also confirmed that terracing can reduce soil erosion intensity significantly on the cultivated land. Research findings suggest that sufficient attention must be paid to regeneration of the ground cover in reforestation programs. In addition to mass reforestation efforts, restoration of grassland and terracing of the cultivated land should also play an important role in erosion control.

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.

Quantitative Remote Sensing Study on Regional Soil Erosion——Bin County of Heilongjiang as an Example

Based on Chinese soil loss equation （CSLE） model, this paper utilized technical advantages of RS and geographic information system （GIS） on data access and erosion factors database building to study prediction methods of regional soil erosion. The spatial analysis module of ARCGIS platform was applied to study the spatial distribution of erosion and the inter-relations of the factors influencing regional soil erosion in the research area. As a result, the mean soil erosion modulus of Bin County is 3 555.42 t/（km^2.a）, which suggests moderate degree erosion. The mean soil erosion modulus of clayey meadow soil is higher than those of dark brown soil and black soil. Vegetation factor values are between 0.1-0.2. The mean slope gradient and slope length values are respectively 1.335 and 6.061 which shows slope length is a dominant factor. And soil type, vegetation coverage and topographic factors have remarkable relevance to each other. Therefore, RS, GIS and CSLE are applicable in regional scale to disclose spatial distribution characteristics of soil erosion and to analyze the characteristics of dominant soil erosion factor quantitatively.

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.

Forest soil conservation based on eco-service provision unit method and its value in Anji County,Huzhou,Zhejiang,China

We propose an eco-service provision unit method for estimating the benefit and spatial differences of forests in controlling soil erosion.A total of 197 eco-service provision units were grouped on 1424.43 km2 of forest according to differences in vegetation,slope,soil,and rainfall.The amount of soil conservation and its economic value were estimated.The forests in Anji County prevent4.08 9 105 tons of soil from eroding annually,thereby avoiding 1.36 9 104 tons of nutrient loss（on-site cost） and preventing 149 tons of nutritive elements from entering water systems（off-site cost）.From an economic perspective,the soil nutrient conservation in the forests of Anji County generated an annual benefit of 43.37 million RMB（Chinese Currency,6.20 RMB = US$1）.On average,each hectare of ecological forest contributed up to 436 RMB annually because of soil conservation.Ecological complexes with higher rainfall intensity,such as broadleaf forest and red soil on slope gradients [25°,contributed the highest soil conservation benefits.This study identified and quantified the dominant contributors and magnitudes of soil conservation provided by forests.This information can benefit decision making regarding differentiated ecological compensation policies.

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.

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.

Determining the Soil Erodibility for an Experimental Basin in the Semi-Arid Region Using Geoprocessing

Erosion is the natural process which has the greatest environmental impact, and is the principal trigger for desertification around the globe. The main model used to estimate soil loss by erosion is the Universal Soil Loss Equation (USLE), which unites the major factors that influence erosion into one equation. The soil erodibility factor (K) is the component of this equation that represents soil physics, and is defined as the inherent capacity of the soil to withstand disintegration of its particles and their subsequent transport. The use of geostatistics is seen as an alternative in spatializing this variable from sampled to non-sampled points. The aim of this study therefore, was to determine the soil erodibility factor for an experimental basin in the semi-arid region of Brazil, in addition to generating the soil erodibility map using geostatistics. Disturbed and undisturbed soil samples were collected from 35 points, and laboratory samples were processed to determine the granulometry, permeability and organic matter of the soil, data which are used to determine the K-factor. Kriging was performed to spatialize the study variable, when spherical, exponential and Gaussian semivariograms were tested for generation of the soil erodibility map, these being evaluated by their respective deviations resulting from cross-validation. The mean value of K for the Haplic Luvisol was 0.0328 ton·ha·h/ha·MJ·mm;for the eutrophic Red-Yellow Argisol it was 0.0258 ton·ha·h/ha·MJ·mm;and for the Fluvic Neosol, it was 0.0424 ton·ha·h/ha·MJ·mm. The experimental basin is classified as highly erodible. The semivariogram that presented the best fit for generating the soil erodibility map of the study area was Gaussian.

Estimation of Non-point Source Pollution Loads Under Uncertain Information

Many kinds of uncertainties are involved, such as random, fuzzy, grey, unascertained property and so on, in soil erosion process. To exactly predict the non-point source pollution loads, some uncertainties should be taken into consideration. Aiming at the deficiency of present blind number theory being helpless for fuzziness, a novel blind number, i.e. extended-blind number, was introduced by substituting a set of triangular fuzzy numbers （TFNs）, expressed as a-cuts, for interval values in present blind number, and the expected value of extended-blind number was also brought forward by referring to the current blind number theory. On the basis of denoting the parameters of Uni- versal Soil Loss Equation （USLE） as extended-blind parameters, a novel USLE model was established for quantitatively evaluating soil erosion loss and non-point source pollution loads. As a case, the uncertain USLE was employed for predicting the soil erosion loss and non-point source pollution loads of absorbed nitrogen and phosphorus in a dis- trict in the Hangbu-Fengle River basin, in the upstream of Chaohu Lake watershed. The results show that it is feasible in theory to extend blind number into fuzzy environment and reliable on conclusion to apply extended-blind number theory for predicting non-point source pollution loads.

基于RUSLE模型的河南省黄河流域土壤侵蚀研究

为了更好地研究黄河河南段的土壤侵蚀情况,以河南省黄河流域为研究区域,采用地理信息系统和遥感技术,融合遥感影像、DEM数据、降雨数据和土壤数据等,将RUSLE模型应用到该区域的土壤侵蚀研究中。结果表明:该区域的年均土壤侵蚀模数为861.78 t/(km^2·a),年均土壤侵蚀总量为305.44×10^5 t/a,轻度以上的土壤侵蚀多发生在河南西部的一些省辖市内。其中,土壤侵蚀等级为轻度以下的区域,其面积占研究区总面积的88.39%,该区域主要分布在0°~10°坡度范围内,主要的土地利用类型为耕地和草地;中度侵蚀和强烈侵蚀的区域,二者的总面积占比为10.71%,主要分布在15°~25°坡度范围内,土地利用类型主要为林地;极强烈侵蚀和剧烈侵蚀的区域面积所占比例较小,占比共计0.9%,该区域零星分布在坡度范围大于25°的草地和林地区域。该研究成果揭示了河南省黄河流域的土壤侵蚀情况,可为区域水土流失治理提供科学依据。

Mapping the Risk of Water Erosion in the Watershed of the Ningxia-Inner Mongolia Reach of the Yellow River, China

Mapping and assessing soil-erosion risk can address the likelihood of occurrence of erosion as well as its consequences. This in turn provides precautionary and relevant suggestions to assist in disaster reduction. Because soil erosion by water in the watershed of the Ningxia-Inner Mongolia reach of the Yellow River is closely related to silting of the upper reaches of the Yellow River, it is necessary to assess erosion risk in this watershed. This study aims to identify the soil-erosion risk caused by water in the watershed of the Ningxia-Inner Mongolia reach of the Yellow River from 2ool to aOlO. Empirical models called Chinese Soil Loss Equation （CSLE） and Modified Universal Soil Loss Equation （MUSLE） were used to predict the erosion modulus in slope surfaces and gullies. Then the soil erosion risk in this watershed was assessed according to the classification criteria of soil erosion intensities （SL19o-2oo7）. The study results showed that the range of values of the erosion modulus in this watershed was o-44,733 t/km2/a. More than 20% of the total watershed area was found to present an erosion risk, with the regions at risk mainly located in channels and their upper reaches, and in mountainous areas. To determine the regression coefficients of the erosion factors with respect to erosion modulus, a GWR （geographically weighted regression） was carried out using the ArcGIS software. It was found that the topographic factor has the highest contribution rate to the soil erosion modulus, while the highest contribution rate of the erosion factors to the erosion modulus and the largest values of the factors were not located in the same places. Based on this result, the authors propose that slope management is the most important task in preventing soil loss in this watershed, and the soil- conservation projects should be built according to the eontribution rate of the erosion factors.

Inference of Reference Conditions for Nutrient Concentrations of Chaohu Lake Based on Model Extrapolation

In the mid-eastern China,there are few or no lakes which are in the absence of anthropogenic disturbances,or their sediments remain undisturbed.As a result,the reference lakes distribution and paleolimnological reconstruction approaches usually are inappropriate to estimate lake reference conditions for nutrients.This yields the necessity of using the extrapolation methods to estimate the lake reference conditions for nutrients within those regions.The lake reference conditions for nutrients could be inferred inversely from the law of mass conservation,current lake nutrient concentration,and the loadings from watershed.Considering the scarcity of hydrological and water quality data associated with lakes and watersheds in China,as well as the low requirement of the watershed nutrient loadings models for these data,the soil conservation service(SCS) distributed hydrological model and the universal soil loss equation(USLE) were applied.The SCS model simulates the runoff process of the watershed,thereby calculating dissolved nutrients annually.The USLE estimates the soil erosion and particulate nutrients annually in a watershed.Then,with the loadings from atmospheric deposition and point source,the previous annual average nutrient concentrations could be acquired given the current nutrient concentrations in a lake.Therefore,the nutrient reference conditions minimally impacted by human activities could be estimated.Based on the proposed model,the reference conditions for total nitrogen and total phosphorus of Chaohu Lake,Anhui Province,China are 0.031 mg/L and 0.640 mg/L,respectively.The proposed reference conditions estimation model is of clear physical concept,and less data required.Thus,the proposed approach can be used in other lakes with similar circumstances.

Assessing the USLE Crop and Management Factor C for Soil Erosion Modeling in a Large Mountainous Watershed in Central China

Due to the impoundment of the Yangtze River, the Three Gorges Dam in China fosters high land-use dynamics. Soil erosion is expected to increase dramatically. One of the key factors in soil erosion control is the vegetation cover and crop type. However, determining these factors adequately for the use in soil erosion modeling is very time-consuming especially for large mountainous areas, such as the Xiangxi （香溪） catchment in the Three Gorges area. In our study, the crop and management factor C was calculated using the fractional vegetation cover （CFvc） based on Landsat-TM images from 2005, 2006, and 2007 and on literature studies （CLIT）. In 2007, the values of CFvc range between 0.001 and 0.98 in the Xiangxi catchment. The mean CFVC value is 0.05. CLIT values are distinctly higher, ranging from 0.08 to 0.46 with a mean value of 0.32 in the Xiangxi catchment. The mean potential soil loss amounts to 120.62 t/ha/a in the Xiangxi catchment when using CLIT for modeling. Based on CFVC, the predicted mean soil loss in the Xiangxi catchment is 11.50 t/ha/a. Therefore, CLIT appears to bemore reliable than the C factor based on the fractional vegetation cover.

Land use change and soil erosion in the Maotiao River watershed of Guizhou Province

Due to the extremely poor soil cover, a low soil-forming rate, and inappropriate intensive land use, soil erosion is a serious problem in Guizhou Province, which is located in the centre of the karst areas of Southwest China. In order to bring soil erosion under control and restore environment, the Chinese Government has initiated a serious of ecological re- habilitation projects such as the Grain-for-Green Programme and Natural Forest Protection Program and brought about tremendous influences on land-use change and soil erosion in Guizhou Province. This paper explored the relationship between land use and soil erosion in the Maotiao River watershed, a typical agricultural area with severe soil erosion in central Guizhou Province. In this study, we analyzed the spatio-temporal dynamic change of land-use type in Maotiao River watershed from 1973 to 2007 using Landsat MSS image in 1973, Landsat TM data in 1990 and 2007. Soil erosion change characteristics from 1973 to 2007, and soil loss among different land-use types were examined by integrating the Revised Universal Soil Loss Equation （RUSLE） with a GIS environment. The results indicate that changes in land use within the watershed have significantly affected soil erosion. From 1973 to 1990, dry farmland and rocky desertified land significantly increased. In contrast, shrubby land, other forestland and grassland significantly decreased, which caused accelerated soil erosion in the study area. This trend was reversed from 1990 to 2007 with an increased area of land-use types for ecological use owing to the implementation of environmental protection programs. Soil erosion also significantly varied among land-use types. Erosion was most serious in dry farmland and the lightest in paddy field. Dry farmland with a gradient of 6°-25° was the major contributor to soil erosion, and conservation practices should be taken in these areas. The results of this study provide useful information for decision makers and planners to take sustainable land use management and soil conservation measures in the area.