Assessment of soil erosion at large watershed scale using RUSLE and GIS: a case study in the Loess Plateau of China

Soil erosion is a serious problem in the Loess Plateau of China, and assessment of soil erosion at large watershed scale is urgently need. This study used RUSLE and GIS to assess soil loss in the Yanhe watershed. All factors used in the RUSLE were calculated for the watershed using local data. RUSLE‐factor maps were made. The mean values of the R‐factor, K‐factor, LS‐factor, C‐factor and P‐factor were 970 209 MJ km⁻² h⁻¹ a⁻¹, 0·0195 Mg h MJ⁻¹ mm⁻¹, 10·27, 0·33359 and 0·2135 respectively. The mean value of the annual average soil loss was found to be 14 458 Mg km⁻² per year, and the soil loss rate in most areas was between 5000 and 20 000 Mg km⁻² per year. There is more erosion in the centre and southeast than in the northwest of Yanhe watershed. Because of the limitations of the RUSLE and spatial heterogeneity, more work should be done on the RUSLE‐factor accuracy, scale effects, etc. Furthermore, it is necessary to apply some physical models in the future, to identify the transport and deposition processes of sediment at a large scale. Copyright © 2005 John Wiley & Sons, Ltd.     

Spatial Estimation of Soil Erosion Risk by Land‐cover Change in the Andes OF Southern Ecuador

Ecuador has the highest deforestation rate in South America, causing large‐scale soil erosion. Inter‐Andean watersheds are especially affected by a rapid increase of the population leading to the conversion of large areas of montane forest into pasture and cropland. In this study, we estimate soil erosion risk in a small mixed land‐use watershed in the southern Andes of Ecuador. Soil loss was estimated at a spatial resolution of 30 m, using the Revised Universal Soil Loss Equation (RUSLE) where the RUSLE factors were estimated on the basis of limited public available data. Land‐cover maps for 1976, 2008 and 2040 were created assuming increasing deforestation rates over the ensuing decades. Greater erosion rates are estimated for succession areas with agricultural cropland and pasture with maximum values of 936 Mg ha⁻¹ y⁻¹, where slopes and precipitation amounts are the greatest. Under natural forest vegetation, the estimated soil erosion rates are negligible (1·5 to 40 Mg ha⁻¹ y⁻¹) even at steep slopes and higher elevations where rainfall amounts and intensities are generally higher. When the entire watershed has undergone substantial deforestation in 2040, erosion values may reach 2,021 Mg ha⁻¹ y⁻¹. Vegetation cover is the most important factor for potential soil erosion. Secondary factors are related to rainfall (R‐factor) and topography (LS factors). Although the spatial predictions of potential soil erosion have only limited meaning for erosion risk, this method provides an important screening tool for land management and assessment of land‐cover change. Copyright © 2013 John Wiley & Sons, Ltd.     

Using the RUSLE to identify factors controlling erosion rates of mine soils

Observable differences in particle size, smoothness and compaction between cap site (slope 2·8 per cent) and batter site (slope 20·7 per cent) surfaces on the waste rock dump at Ranger Uranium Mine were quantified in terms of revised universal soil loss equation (RUSLE) parameter values. Cap site surface material had a K_m (erodibility corrected for sediment density) of 0·030 and batter site surface material had a K_m of 0·0056. Using these K_m values (derived from particle size distributions), slope length and steepness (LS) factors of 0·36 for the cap site and 3·66 for the batter site, and a cover (C) factor of 0·45 for the cap site and 0·16 for the batter site, the RUSLE predicts an erosion rate from the cap site that is 1·9 times greater than erosion from the much steeper batter site. The RUSLE indicates that the finer particle size and blocky soil structure of the cap site (D₅₀ = 0·91 mm) compared with the looser granular structure of the batter site (D₅₀ = 1·74 mm) strongly influence erosion. The predictions are similar to observed soil losses from erosion plots on these sites under rainfall simulation events, for which the measured erosion rate from the cap site was approximately twice that from the batter site. For the RUSLE to predict the observed erosion rates, the support practice (P) factor for the cap site would have to be approximately 30 per cent greater than the P factor for the batter site. The higher cap site P factor probably results from smoothing and compaction caused by vehicle movement across the surface. Compaction is considered to have greatly reduced infiltration capacity, thus increasing the erodibility of the cap site. Vehicles probably also crushed the surface material at the cap site, creating the observed finer particle size distribution and further increasing the erodibility. Compaction, through its effects on erodibility (K_m) and surface roughness (P), is concluded to be the major cause of higher erosion from the cap site, even though the slope steepness is 10 times less. Parameterisation of the RUSLE quantifies the differences between sites and explains the unexpected erosion rates observed. The results highlight the need for careful management of rehabilitated sites to avoid increases in erosion which may arise from compaction by machinery.     

基于随机森林算法的土壤侵蚀影响因子研究——以赣江上游流域为例

[目的]分析影响赣江上游流域土壤侵蚀的主要因素,为该区水土流失治理与科学管理提供科学依据。[方法]基于2015年Landsat 8遥感影像、MODIS NDVI数据、数字高程模型(DEM)、土壤类型和降雨数据,采用RUSLE模型和随机森林算法对赣江上游流域土壤侵蚀及其影响因子进行定量化分析。[结果] 2015年赣江上游流域土壤侵蚀强度由东南向西北逐渐加剧,总体上处于轻度侵蚀水平,土壤侵蚀总量为3.45×10~7 t/a,平均土壤侵蚀模数为1 046.38 t/(km~2·a),比南方红壤丘陵区土壤允许流失量[500 t/(km~2·a)]高出2倍之多;子流域9,11,15平均土壤侵蚀模数分别为1 672.66,1 715.83和1 565.36 t/(km~2·a),处于中度侵蚀级别,为研究区重点防治区域;其余子流域均为轻度侵蚀级别。[结论]各子流域的土壤侵蚀受植被覆盖与管理因子(C)和坡长坡度因子(LS)影响较大,两者重要程度分别在30%和20%以上,土壤可蚀性因子(K)和降雨侵蚀力因子(R)的重要程度偏低,均未超过10%。其中子流域9,11,21主要受LS因子影响,其余子流域均受C因子主控。     

Effect of vegetation cover on soil erosion in a mountainous watershed

We applied the Revised Soil Loss Equation (RUSLE) to assess levels of soil loss in a Geographic Information System (GIS). In this study, we used the k-NN technique to estimate vegetation cover by integrating Landsat ETM+ scenes and field data with optimal parameters. We evaluated the root mean square errors and significance of biases at the pixel level in order to determine the optimal parameters. The accuracy of vegetation cover estimation by the k-NN technique was compared to that predicted by a regression function using Landsat ETM+ bands and field measurements as well as to that predicted by the Normalized Difference Vegetation Index (NDVI). We used a regression equation to calculate the cover management (C) factor of the RUSLE from vegetation cover data. On the basis of the quantitative model of soil erosion, we explored the relationship between soil loss and its influencing factors, and identified areas at high erosion risk. The results showed that the k-NN method can predict vegetation cover more accurately for image pixels at the landscape level than can the other two methods examined in this study. Of those factors, the C-factor is one of the most important affecting soil erosion in the region. Scenarios with different vegetation cover on high-risk areas showed that greater vegetation cover can considerably reduce the loss of soil erosion. The k-NN technique provides a new method to estimate the C-factor for RUSLE erosion mapping. The quantitative model of different vegetation cover scenarios provides information on how vegetation restoration could reduce erosion.     

基于RUSLE模型的黑龙江省2000-2010年土壤保持量评价

黑龙江省是我国重要的粮食产区,同时也是东北地区重点生态保护区,黑龙江省土壤保持量的研究对维持生态安全与可持续发展有重要作用。基于黑龙江省2000年、2005年和2010年的降雨、土壤、高程等数据,结合GIS空间分析方法,运用修订的通用土壤流失方程(RUSLE),估算了2000—2010年黑龙江省土壤保持量,并对其空间分布及变化趋势进行模拟分析。结果表明:2000—2010年,黑龙江省土壤保持能力整体增强,土壤保持量增加了5.34%,且除牡丹江和哈尔滨地区外,各行政区的土壤保持量均有所增加;各土地利用类型的年均单位面积土壤保持量以森林最多,为3 384.36 t·km-2·a-1,裸地最少,为177.17 t·km-2·a-1,10年来除农田和灌丛外,各土地利用类型的单位面积土壤保持能力均增强;2000—2010年黑龙江省高等级土壤保持量比例及低等级转化成高等级土壤保持量的面积都在提高,黑龙江省土壤保持能力10年来趋于好转。     

基于GIS和RS的退耕还林工程土壤保育价值评估——以陕西吴起县四面窑沟流域为例

在全国退耕还林工程典型地区陕西省吴起县境内,选取四面窑沟流域为研究对象,采用RS和GIS监测退耕还林工程的具体实施方式及其面积,并提出考虑土地覆盖汇流影响的改进坡长因子,利用ArcGIS和RUSLE评估流域在退耕前后的土壤侵蚀强度变化,在此基础上,采用机会成本法和替代价格法评估流域退耕还林工程的土壤保育价值。结果表明：1997-2004年,流域内实际开展退耕还林工程1895．8hm^2,其中,荒山造林369．99hm^2、退耕还林357．48hm^2、退耕还草901．72hm^2、人工封育266．61hm^2;退耕前（1997年）后（2004年）流域土壤侵蚀模数减少了4644．04t／（km^2·a）,侵蚀强度由极强度降低为中度;退耕还林工程每年产生土壤保育价值1324．3万元,其中,减少土壤侵蚀效益8．11万元、减少肥力流失效益1180．22万元、减少泥沙淤积效益124．45万元、培育土壤效益11．52万元。     

Erodibility of soils of the upper rainforest zone,southeastern Nigeria

The susceptibility of some soils in the high rainfall zone of Nigeria to soil erosion must be measured regularly for better soil management. A number of techniques have been adopted for the determination of this soil loss parameter. The aim of this study is to determine the soil characteristics that relate significantly to erodibility. Soil samples collected from 0–20 cm depth from 10 different locations in the upper rainforest area were analysed for particle size distribution, water‐stable aggregates, exchangeable cations, organic carbon, soil dispersion and aggregating indices. The soils are mainly Acrisols, Nitosols, Gleysols and Ferralsol in the FAO classification while their textures are sands to sandy‐clay‐loam. They are very unstable in water as reflected in the higher values of WSA >0·50 mm and the mean‐weight diameter that ranged from 0·50 to 2·03 mm. The dispersion ratio for the soils are between 0·26 and 0·69 while clay dispersion ratio also ranged from 0·24 to 0·80. Revised universal soil loss equation (RUSLE) erodibility model values (K) were from 0·03 to 0·06 Mg h MJ⁻¹ mm⁻¹. These parameters can be effectively used in predicting soil erodibility, though their predictability varied in ranking of soil erodibility. In spite of this variability these indices can be used for potential erosion hazard determination by agricultural extension staff to avoid crop failures and other negative influence of soil erosion. The soil parameters are easy to determine and will be a valuable instrument when faster approaches to erosion control measures are required. Copyright © 2003 John Wiley & Sons, Ltd.     

贵州省猫跳河流域土壤侵蚀量计算及其背景空间分析

以贵州省猫跳河流域为研究区，在GIS 技术支撑下，应用修正的通用土壤流失方程计算研究区的土壤侵蚀量，分析土壤侵蚀的空间分布格局，对土壤侵蚀与其环境背景因子包括海拔高程、坡度、坡向和土地利用类型等进行叠加和空间统计分析，揭示土壤侵蚀与其环境背景等因子的空间关系，为土壤侵蚀的有效防治和治理提供科学依据。结果表明：研究区平均土壤侵蚀模数为28.6 t/(hm²·a)，1200～1400 m的海拔高程带、6°～25°坡度带和南坡是发生土壤侵蚀的主要区域，也是水土流失防治及治理的重点区域。在各种土地利用类型中，旱地发生土壤侵蚀面积和侵蚀量最大，其次是灌草地，水田最小。在县域中，清镇市土壤侵蚀面积和土壤侵蚀量最大，其次是平坝县和修文县，息烽县土壤侵蚀面积和土壤侵蚀量最小。     

基于USLE的甘南川西北土壤侵蚀研究

甘南川西北位于黄河和长江上游源区,量化研究该区土壤侵蚀对河源区生态安全保障和地方经济可持续发展具重要意义。论文采用多种数据方法,基于USLE就甘南川西北2000—2015年间土壤侵蚀的时空分布特征及变化规律进行量化评估。结果表明:(1)降雨侵蚀力因子R值介于65~411(MJ·mm)/(hm²·h·a),高值区主要分布在东南部,空间分布与该区降雨格局基本一致;(2)土壤可蚀性因子K值介于0.19~0.41(t·hm²·h)/(hm²·MJ·mm),高值呈斑块状零星分布,与地带性土壤物化性状有关;(3)坡长坡度因子LS值介于0~8.24,高值主要分布在中北部高山地带,低值分布在东北部和西南部地形较平缓区域;(4)植被覆盖管理因子C值介于0~1,高值集中分布在研究区的西北部与西南部,与该区植被覆盖稀疏有关;(5)基于USLE的甘南川西北年侵蚀量为3.3×108 t/a,总体表现为轻度侵蚀;(6)2000—2015年间,研究区土壤侵蚀呈减弱态势,与增温背景下植被活动增强有关。     

青藏高原坡度坡长因子(LS)空间格局及影响因素分析

地形是影响土壤侵蚀的主要因素,但目前关于青藏高原地形因子的分布格局和影响因素有待研究。基于1弧秒分辨率的SRTM(shuttle radar topography mission)高程数据,计算坡度、坡长、LS因子(slope length and steepness factors,LS),结合高程积分和Hack剖面等,对青藏高原LS因子的分布格局、统计分布特征和影响因素进行研究,结果表明：(1)坡度、坡长和LS因子这3个地形指标,均表现出高原内部小、四周高山大的格局,内流区与外流区的坡度均值分别为6.55°和14.3°,坡长均值分别为122.9,172.2 m,LS均值分别为4.8和12.7;(2)青藏高原LS因子整体受坡度影响,但高原边缘陡峭地区LS因子主要受坡长影响;(3)青藏高原6条主要河流的Hack剖面都呈上凸形态,该地区地貌演化整体上处于幼年期;(4)青藏高原LS因子的分布特征与土壤侵蚀类型及其组合有对应关系：周边地区的高值对应冰川侵蚀—水蚀,西北部的低值对应水力—冻融侵蚀和风蚀,东南部边缘向高原内部过渡地区的较高值对应水力—重力侵蚀。通过分析LS因子的分布格局和统计特征...     

修正的通用土壤流失方程中各因子单位的确定

[目的]明确和规范修正的通用土壤流失方程(RUSLE)中各因子的单位,使得RUSLE在中国具体应用过程中更加科学和便捷。[方法]通过对国内外RUSLE应用实践的总结和对比研究,并分析其科学合理性,找出最为普遍应用的、准确的RUSLE各因子的单位,明确不同单位类型之间的转化系数。[结果]国内RUSLE的应用,大部分是通过各地区建立的各因子统计模型转换成国际制单位系统,最后相乘得到的是以国际制单位表示的土壤侵蚀量,另一部分则是通过相应的各因子统计模型计算得到各个因子以美制单位系统表示的计算结果,最后再乘以224.2将土壤侵蚀量转换为国际制单位。国内主流侵蚀估计中使用的单位焦耳系统,单位面积有两种即km^2和hm^2。土壤流失量A常用的国际制单位为t/hm^2或t/km^2;降雨侵蚀力因子R常用的国际制单位为(MJ·mm)/(hm^2·h·a)或(MJ·mm)/(km^2·h·a);土壤可蚀性因子K的常用国际制单位为(t·hm^2·h)/(hm^2·MJ·mm)或(t·km^2·h)/(km^2·MJ·mm)。不同地区建立的计算方法通过相应的转换系数转换成国际制单位。最后,R和K因子的单位系统的一致性是RUSLE应用的关键步骤。[结论]R和K因子通过相应的单位转换系数转换为国际制单位以及两者的单位一致性是土壤侵蚀评估的重要基础。     

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.     

Assessing soil erosion after fire and rehabilitation treatments in NW Spain: Performance of rusle and revised Morgan–Morgan–Finney models

Although the Revised Universal Soil Loss Equation (RUSLE) and the revised Morgan–Morgan–Finney (MMF) are well‐known models, not much information is available as regards their suitability in predicting post‐fire soil erosion in forest soils. The lack of information is even more pronounced as regards post‐fire rehabilitation treatments. This study compared the soil erosion predicted by the RUSLE and the revised MMF model with the observed values of soil losses, for the first year following fire, in two burned areas in NW of Spain with different levels of fire severity. The applicability of both models to estimate soil losses after three rehabilitation treatments applied in a severely burned area was also tested. The MMF model presented reasonable accuracy in the predictions while the RUSLE clearly overestimated the observed erosion rates. When the R and C factors obtained by the RUSLE formulation were multiplied by 0·7 and 0.865, respectively, the efficiency of the equation improved. Both models showed their capability to be used as operational tools to help managers to determine action priorities in areas of high risk of degradation by erosion after fire. Copyright © 2010 John Wiley & Sons, Ltd.     

陕西省耕地土壤可蚀性因子

[目的]土壤可蚀性因子是计算土壤侵蚀的一个重要因子,对陕西省耕地土壤可蚀性因子展开研究,可为陕西地区的耕地土壤侵蚀计算及评价提供科学依据。[方法]以陕西省9个地区的耕地土壤实测数据为基础,利用通用土壤流失方程USLE(universal soil loss equation)、修订土壤流失方程RUSLE2(revised universal soil loss equation version 2)、侵蚀生产力影响模型EPIC(erosion productivity impact calculator)中可蚀性因子K值的计算公式以及几何平均粒径公式和几何平均粒径—有机质Dg-OM公式,计算不同耕地土壤质地条件下的土壤可蚀性因子。[结果]RUSLE2的极细砂粒转换公式在陕西黄土丘陵沟壑区平均低约14.53%,在陕南地区平均高约32.91%,使用修正公式后平均误差分别为7.81%和13.14%;对比分析K值的估算值与实测值,子洲县实测K值为0.002 69〔(t·hm2·h)/(hm2·MJ·mm)〕,Dg-OM模拟计算均值为0.0297〔(t·hm2·h)/(hm2·MJ·mm)〕;水蚀预报模型WEPP(water erosion prediction project)中的细沟间可蚀性(Ki)和细沟可蚀性(Kr),与USLE的K值相关系数分别为0.738 6和0.607 4。[结论]极细砂粒转换修正公式的计算误差小于RUSLE2模型;Dg-OM模型适合陕西黄土丘陵沟壑区及长武县、杨凌区和安康市典型耕地土壤;WEPP中Ki和Kr,当土壤砂粒含量小于30%,USLE的K值与WEPP的Ki和Kr值有强相关性。     

Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed

This research integrates the Revised Universal Soil Loss Equation (RUSLE) with a Geographic Information System (GIS) to model erosion potential for soil conservation planning within the Sierra de Manantlán Biosphere Reserve (SMBR), Mexico. Mountainous topography and a tropical uni-modal precipitation regime characterize this region. These unique climatic and topographic characteristics required a modification of the standard RUSLE factors and their derivation. The resulting RUSLE–GIS model provides a robust soil conservation planning tool readily transferable and accessible to other land managers in similar environments. Future pressure to expand agriculture and grazing operations within the SMBR will unquestionably accentuate the already high rate of soil erosion and resultant sediment loading of watercourses occurring in this region. Until recently there did not exist a reliable or financially viable means to model and map soil erosion within large remote areas. An increase in the reliability and resolution of remote sensing techniques, modifications and advancements in watershed scale soil erosion modelling techniques, and advances in GIS, represent significantly improved tools that can be applied to both monitoring and modelling the effects of land use on soil erosion potential. Data used in this study to generate the RUSLE variables include a Landsat Thematic Mapper image (land cover), digitized topographic and soil maps, and tabular precipitation data. Soil erosion potential was modelled within Zenzontla, a sub-catchment of the Rı́o Ayuquı́la, located in the SMBR, and the results are presented as geo-referenced maps for each of the wet and dry precipitation seasons. These maps confirm that high and extreme areas of soil loss occur within the Zenzontla sub-catchment, and that erosion potential differs significantly between wet and dry seasons.     

基于GIS和RUSLE的高山峡谷区土壤侵蚀研究——以云南省泸水县为例

[目的]研究区域土壤侵蚀,揭示水土流失的空间分异规律,为区域水土保持和生态农业建设提供理论指导依据。[方法]应用GIS和RUSLE模型对云南省泸水县的土壤侵蚀进行研究。RUSLE模型中的因子包括降雨侵蚀力、土壤可蚀性、坡度坡长因子、植被覆盖和水土保持措施因子,运用GIS空间分析模块,获取泸水县土壤侵蚀模数空间分布图,根据SL 190-2007的分级标准进行土壤侵蚀强度分级,并分析该区土壤侵蚀强度空间分布格局。[结果](1)从各强度侵蚀面积上看,泸水县2014年土壤侵蚀以微度侵蚀为主,占总面积的86.86%,但从平均土壤侵蚀模数看,土壤侵蚀量为4.24×10~6 t,平均侵蚀模数为1 373.1t/(km~2·a),土壤侵蚀强度属于轻度侵蚀;(2)土壤侵蚀较严重区与未利用地、耕地空间分布基本一致,在坡度25°~50°的范围内,侵蚀面积占总侵蚀面积的75%,并且在该坡度段上的耕地面积占总耕地的63%,剧烈侵蚀集中分布在未利用地上,中度以上剧烈以下强度侵蚀集中分布在该坡度段上的耕地上,说明该坡耕地、未利用地对土壤侵蚀的贡献最大,要加强对未利用地的生态治理。[结论]坡度大,陡坡垦殖和未利用地的不合理利用是该区土壤侵蚀加重的主要原因,坡度在25°以上的地区不适宜耕种,应优化农业产业结构如实施退耕还林还草等措施,才能有效的保持水土。     

植被覆盖和降雨因子变化及对东北黑土区土壤侵蚀的影响

[目的]研究东北黑土区植被覆盖和降雨侵蚀力因子对土壤侵蚀时空变化的影响程度,为该区水土流失治理和可持续发展提供科学依据.[方法]运用修正后的通用土壤流失方程(RUSLE)得到了2000-2018年东北黑土区土壤侵蚀分布特征,并探究土壤侵蚀模数与因子时空分布变化规律,得出侵蚀模数对于植被覆盖和降雨侵蚀力因子变化的敏感性....     

基于GIS与RUSLE的榆林市土壤侵蚀空间分布研究

利用3S技术,采用美国通用的水土流失方程(RUSLE),对榆林市2001和2010年的土壤侵蚀状况及其空间分布特征进行了计算分析。结果表明,榆林市2001年平均土壤侵蚀模数为4411 t/(km²·a),年均侵蚀总量为1.93×10⁸t;2010年的平均土壤侵蚀模数为6237 t/(km²·a),年均侵蚀总量为2.72×10⁸t。2001-2010年榆林市各区县的土壤侵蚀变化状况有着明显的空间差异,府谷、神木、榆阳、横山、靖边、佳县和子洲7个区县的土壤侵蚀类型发生了由中强度向高强度侵蚀的转化,土壤状况不断恶化。而定边、米脂、吴堡、绥德和清涧5县的土壤侵蚀类型由高强度向低强度侵蚀转变,水土流失状况得到有效遏制。     

Assessment of soil erosion hazard in Kilie catchment,East Shoa,Ethiopia

This paper reports on a field study conducted in Kilie catchment, East Shoa Zone, Ethiopia to assess the rate of soil erosion by employing a soil loss prediction model (Universal Soil Loss Equation) integrated with in remote sensing and geographical information systems (RS/GIS), environment and gully measurement techniques. The final soil erosion risk map was produced after multiplication of the six factors involved in the USLE and RS/GIS. Gully measurement showed that the erosion rate is higher for the upland areas than the lowlands due to inappropriate soil and water conservation measures, free grazing by animals and conversion of hillside areas into farmlands. About 97·04 per cent of the study catchment falls within a range of 0–10 t ha⁻¹ yr⁻¹ sheet/rill erosion rate. We found that 2·17 per cent of the study area in the uplands has a soil erosion rate falling between 10 and 20 t ha⁻¹ yr⁻¹. About 0·8 per cent of the study area in the uplands is hit by severe sheet/rill erosion rate within the range of 20–60 t ha⁻¹ yr⁻¹. Gully erosion extent in the study area was evaluated through gully measurement and quantification methods. Gully density of 67 m ha⁻¹ was recorded in the catchment. The gully to plot area ratio was found to be 0·14 on average. Hence, in the upland areas, sustainable land management practices are required in order to reduce the rate of soil erosion. Copyright © 2011 John Wiley & Sons, Ltd.