贵州省乌江流域土壤侵蚀模拟——基于GIS、RUSLE和ANN技术的研究

本研究在GIS技术支撑下选择RUSLE模型作为基础模型,估算乌江流域20世纪80年代和90年代年均土壤侵蚀量,结合ANN技术,预测2001—2010年乌江流域的土壤侵蚀量,分析了该流域近30年来土壤侵蚀动态变化规律,以期为研究区土壤侵蚀防治工作提供理论依据。研究结果表明:应用RUSLE模型计算乌江流域年均土壤侵蚀模数,计算结果和以往土壤侵蚀调查估计的结果比较吻合,但由于RUSLE模型不计算重力侵蚀,因此计算结果仍与实测输沙模数有所出入。90年代潜在土壤侵蚀模数比80年代高,流域潜在土壤侵蚀呈增加趋势,其中三岔河流域和马蹄河/印江河流域年均潜在土壤侵蚀模数最高。3种主要土地覆被类型中,林地的土壤保持量最大,耕地次之,草地最少,这与非喀斯特地区在水土保持效果上通常林地草地旱地的结论有所不同。通过构建BP神经网络,预测得到乌江流域2001—2010年土壤侵蚀模数,结果显示,21世纪前10年,流域土壤侵蚀模数大幅降低,流域年均土壤侵蚀模数由90年代的23.13 t/(hm2·a)降低为1.01 t/(hm2·a)。三岔河流域的水土流失得到了控制,黔西、金沙、息烽、修文、贵阳、平坝、思南、石阡、沿河和松桃等县市应是"十二五"期间的水土流失重点治理对象。     

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。     

贵州茂兰喀斯特地区土壤全钙含量空间估算模型与迁移分析

文章在世界自然遗产地贵州荔波茂兰保护区采集土壤全钙数据,分析采用地理加权回归（GWR）方法进行空间分析的有效性,筛选识别影响土壤全钙空间分布的主要因子,建立喀斯特地区土壤全钙含量空间分布计算模型,获取研究土壤全钙空间分布基础数据。通过土壤流失方程（USLE）计算土壤侵蚀状况,对比分析土壤全钙与土壤侵蚀空间关联,揭示土壤全钙的空间迁移规律。结果表明:（1）在岩性一致条件下,相对高差和坡度是影响土壤全钙空间分布的主导因子;（2）GWR模型的预测精度优于全局回归的（OLS）,相关系数分别是0.41和0.39;（3）通过土壤全钙含量空间估算模型,计算得到研究区土壤全钙空间分布特征,土壤全钙为0 ~37.68 gkg-1。研究结论说明,在湿润气候的喀斯特地区,尽管植被覆盖度大,但土壤全钙空间分布仍然深受成土母质影响,喀斯特峰林土壤侵蚀强度大,土壤全钙含量高,物质迁移以流失为主,峰丛洼地土壤侵蚀强度小,土壤全钙含量低,物质迁移以淋溶流失为主。      

基于RUSLE的贵州省红枫湖流域土壤侵蚀时空变化特征

以贵州省红枫湖流域为研究对象,运用GIS和RUSLE模型分析了该流域1960～1986年、1987～1997年、1998～2004年三个时段内的年平均土壤侵蚀量和土壤侵蚀强度,并探讨了40多年来流域土壤侵蚀变化的时空变化特征。结果表明,过去40多年来,流域的土壤侵蚀经历了一个先增强再减弱的过程,土壤侵蚀强度空间分布呈西强东弱的格局,且流域西部呈明显先增强再减弱的特征,东部变化相对较小。     

RS技术在实时区域土壤侵蚀评价中的应用--以福建省花山溪流域为例

传统数据采集方法严重制约了土壤侵蚀评价实时、快速的要求。文章以福建花山溪流域为例,基于“通用土壤侵蚀方程式”(USLE)为土壤侵蚀定量评价模型,利用多时效、多光谱的遥感数据,在ERDAS8．5软件平台上,研究提取流域动态最易变化的植被、土地利用类型等信息的技术方法。研究结果证实,这种方法可以快速、准确地提取影响土壤侵蚀的基本参数．进而实现对区域流域土壤侵蚀的实时评价。     

通用土壤流失方程的研究进展及其改进方法

通用土壤流失方程是水力土壤侵蚀领域应用广泛的模型之一,由美国科学家Wischmeier和Smith在1965年首次提出。该方程较为全面地考虑了土壤侵蚀的影响因素,其形式为6个土壤侵蚀影响因子的乘积：     

基于RS/GIS和RUSLE的流域土壤侵蚀定量研究——以福建省吉溪流域为例

借用地理信息系统(GIS)和遥感(ERDAS)技术,以修正的通用水土流失方程(RUSLE)为核心,根据吉溪流域2001年和2003年的遥感影像解译数据和统计资料得到地貌、降雨量、植被等资料,量化流域土壤侵蚀评价因子,对吉溪流域土壤侵蚀进行了定量化分析.结果表明,吉溪流域年均土壤侵蚀模数为2 339.21t/km2,侵蚀强度属轻度,2003年较2001年土壤侵蚀有所改善,中度侵蚀及其以上等级的土壤侵蚀量分别占总侵蚀量的48.71%和77.51%.最后确定流域土壤侵蚀重点治理的区域和流域最佳管理措施(BMPS),为吉溪流域水土保持和水环境保护研究工作提供科学依据.     

密云沙厂小流域土壤侵蚀GIS和RS评价方法研究

从GIS和RS研究的角度对沙厂小流域实测的土壤侵蚀数据进行分析,建立基于3S的土壤侵蚀综合评价指标模型,快速地计算出土壤侵蚀量,并对其侵蚀的动态过程进行监测,为沙厂小流域综合治理的快速决策提供科学的平台和依据.     

新农小流域植被恢复的土壤侵蚀效应评估

水土流失问题每年都对自然环境造成巨大的破坏,在目前的治理实践中,植被建设是治理水土流失最有效的方式。在土壤侵蚀综合治理的过程中,最小的治理单位就是小流域。新农小流域处于柳河西岸,地势低洼,水力侵蚀和风力侵蚀共同作用,导致水土流失严重。新农市政府采用工程建设与植被恢复相结合的方式来治理土壤侵蚀。经过不懈的努力,取得了一定的成效。本文就对新农小流域植被恢复的土壤侵蚀效应进行评估,利用RUSLE修正土壤流失方程,在ARCGIS10.2平台中,输入新农流域的基本数据,对2007-2015九个年份的土壤侵蚀进行模拟,进而对新农小流域植被恢复之后的土壤侵蚀效应进行评估。     

桂江流域土壤侵蚀估算及其时空特征分析

桂江流域的水土流失现状研究对珠江三角洲的水生态安全有重要的现实意义。采用修正的通用土壤流失方程（RUSLE）估算了桂江流域的土壤侵蚀模数与年侵蚀总量,分析流域内土壤侵蚀的时空分布特征,探讨了影响该区域土壤侵蚀强度的自然与人文因素。结果表明,桂江流域51.8%的地表都在发生不同程度的土壤侵蚀。从全流域平均土壤侵蚀强度来看,属于中度侵蚀。从土壤侵蚀面积来看,约85%的地表处于微度、轻度与中度侵蚀。4-6月的全流域平均土壤侵蚀强度最大,侵蚀总量也是最大的。流域的土壤侵蚀主要发生在高程在30~600m的低山丘陵-高地地貌区内的林地与耕地中。流域内岩溶区的土壤侵蚀强度随着石漠化程度从无到中度逐渐增加,轻、中度石漠化区的土壤侵蚀强度达到强度侵蚀等级。      

Application of the revised universal soil loss equation model on landslide prevention. An example from N. Euboea (Evia) Island, Greece

This study aims to evaluate the application of the Revised Universal Soil Loss Equation (RUSLE) on predicting of sites susceptible to slope failures caused by the soil erosion. It was carried out at the northern part of Euboea Island presenting one of the highest frequencies of landslide occurrence in Greece. All required datasets for the application of the RUSLE model were engaged to compile a potential soil erosion map of the study area. In addition, historical slope failure data were used to evaluate the produced map. Moreover, the frequency ratio statistical analysis was applied for the verification of the soil erosion map. The results showed a satisfactory agreement between the soil erosion intensity zones and distribution of landslides events. Therefore, the RUSLE model can be applied for the localization of sites susceptible to landslides that were prepared or triggered by the soil erosion. Finally, taking under consideration the contribution of erosion mechanisms over the landslides manifestation, a combination of measures were proposed for preventing and supporting these catastrophic phenomena.     

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.     

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.     

Conditional simulation of USLE/RUSLE soil erodibility factor by geostatistics in a Mediterranean Catchment,Turkey

Soil erosion is a major environmental problem that threatens the sustainability and productivity of agricultural areas. Assessment and mapping of soil erosion are extremely important in the management and conservation of natural resources. The universal soil loss equation (USLE/RUSLE) is an erosion model that predicts soil loss as a function of soil erodibility (K-factor), as well as topographic, rainfall, cover, and management factors. The traditional approach assumes that one soil erodibility value represents the entire area of each soil series. Therefore, that approach does not account for spatial variability of soil series. This study was carried out to evaluate the use of the sequential Gaussian simulation (SGS) for mapping soil erodibility factor of the USLE/RUSLE methodology. Five hundred and forty-four surface soil samples (0–20 cm) were collected from the study area to determine the soil erodibility. A simulation procedure was carried out on 300 realizations, and histogram and semivariogram of the simulation were compared to the observed values. The results showed that the summary statistics, histogram, and semivariogram of the simulation results were close to the observed values. In contrary to the traditional approach and kriging, 95% confidence interval of the simulated realizations was formed in order to determine uncertainty standard deviation map, and the uncertainty was explained numerically. The SGS produced a more reliable soil erodibility map and it can be more successfully used for monitoring and improving effective strategies to prevent erosion hazards especially to improve site specific management plans.     

火后泥石流流域坡面侵蚀物源动储量估算方法探讨

火后泥石流具有易发、突发等特征,尤其在林火发生当年异常活跃,林火干扰会加剧土壤侵蚀,若将常规泥石流坡面侵蚀物源动储量估算方法应用于火后泥石流将使计算结果偏小,会导致拦砂坝等治理工程库容设计有效性不足。本文采用修正通用土壤流失方程(RUSLE),以四川省雅江县八角楼乡火烧迹地D4流域为例,通过野外调查、室内外试验、遥感解译等手段,计算了研究区的坡面侵蚀物源动储量。结果表明:在火后5年内,D4流域坡面侵蚀物源动储量约为3.28×104 m3,中度和重度火烧区贡献量较大,合计占比高达93.90%。随着后期植被恢复和土壤物理及水文地质性质改善,坡面土壤侵蚀模数将会降低,坡面侵蚀物源动储量也随之减少。该研究成果对火后泥石流应急治理工程(如拦砂坝等)的库容、结构设计具有指导借鉴意义。     

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.     

用定量遥感方法监测UNDP试区小流域水土流失研究

简要介绍了水土流失定量遥感方法的组成和基本原理,重点叙述了它在小流域治理试区的应用作业和应用结果,并讨论了它的适应性、准确性、实用性和应用前景等。该法的监测模型可与美国近年刊出的RUSLE相媲美,且其因子算式算法系由我国实测资料所建,故更适合我国水土流失实际的应用效果,尤其适用于遥感和GIS数据的微机处理。其应用结果,不仅有比常规调查法和定性遥感法更准确实用的流失总量、各级面积的统计数据和流失现状图,而且还有流失治理规划的防治强度预报图。在UNDP试区应用表明,该法所建立的系统,也可对小流域(大于7km2)每年实施监测,以获水土流失变化动态的准确信息。     

Soil erosion and sediment yield modeling using RS and GIS techniques: a case study, Iran

Water erosion is a serious and continuous environmental problem in many parts of the world. The need to quantify the amount of erosion, sediment delivery, and sediment yield in a spatially distributed form has become essential at the watershed scale and in the implementation of conservation efforts. In this study, an effort to predict potential annual soil loss and sediment yield is conducted by using the Revised Universal Soil Loss Equation (RUSLE) model with adaptation in a geographic information system (GIS). The rainfall erosivity, soil erosivity, slope length, steepness, plant cover, and management practice and conservation support practice factors are among the basic factors that are obtained from monthly and annual rainfall data, soil map of the region, 50-m digital elevation model, remote sensing (RS) techniques (with use of Normalized Difference Vegetation Index), and GIS, respectively. The Ilam dam watershed which is located southeast part of Ilam province in western Iran is considered as study area. The study indicates that the slope length and steepness of the RUSLE model are the most effective factors controlling soil erosion in the region. The mean annual soil loss and sediment yield are also predicted. Moreover, the results indicated that 45.25%, 12.18%, 12.44%, 10.79%, and 19.34% of the study area are under minimal, low, moderate, high, and extreme actual erosion risks, respectively. Since 30.13% of the region is under high and extreme erosion risk, adoption of suitable conservation measures seems to be inevitable. So, the RUSLE model integrated with RS and GIS techniques has a great potential for producing accurate and inexpensive erosion and sediment yield risk maps in Iran.     

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.     

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.