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....     

岩石裸露与水土流失关系研究现状及石漠化因子研究存在的问题

基岩裸露是喀斯特地区的常见现象,形成了类似荒漠景观的"石漠化",而石漠化的发生发展影响了区域土壤侵蚀机理和结果,故需加大石漠化因子(D)与水土流失定量关系研究,以便修正土壤流失方程。因此,在中外文献查阅的基础上,总结了岩石裸露对坡面水土流失的影响相关研究现状。结果表明,岩石裸露对坡面水土流失的影响研究结论较为离散,主要观点可分为2大类:一类观点认为,随着坡面岩石裸露率的增加,水土流失呈线性、二次函数及指数等趋势衰减,其中以指数衰减为主要方式;另一类观点认为,岩石裸露对坡面水土流失的影响具有复杂性,与坡度、土壤类型、降雨阶段及岩石裸露率区间等有关,在一定条件下,随着岩石裸露率的增加,水土流失加剧。对于我国喀斯特区D因子研究现状,主要存在2个方面的问题:一是相关定量观测试验较少,致使RUSLE等常用土壤流失方程在喀斯特地区的应用受到较大限制;二是野外定位观测作为研究土壤侵蚀各因子的重要手段,但在喀斯特地区建造天然岩溶裸露小区的难度极大。研究结果可为我国喀斯特区石漠化因子(D)的定量研究及相关土壤流失方程的修正提供参考。     

Mapping soil erosion risk in Rondônia,Brazilian Amazonia: using RUSLE,remote sensing and GIS

This article discusses research in which the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia. Soil map and soil survey data were used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover‐management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Enhanced Thematic Mapper Plus image. Assuming the same climatic conditions and no support practice in the study area, the rainfall–runoff erosivity (R) and the support practice (P) factors were not used. The majority of the study area has K values of less than 0·2, LS values of less than 2·5, and C values of less than 0·25. A soil erosion risk map with five classes (very low, low, medium, medium‐high, and high) was produced based on the simplified RUSLE within the GIS environment, and was linked to land use and land cover (LULC) image to explore relationships between soil erosion risk and LULC distribution. The results indicate that most successional and mature forests are in very low and low erosion risk areas, while agroforestry and pasture are usually associated with medium to high risk areas. This research implies that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Amazonia. Copyright © 2004 John Wiley & Sons, Ltd.     

Stratified vegetation cover index: A new way to assess vegetation impact on soil erosion

Vegetation cover is an important parameter used in assessing the relationship between vegetation and soil erosion. However, the intensity of soil erosion actually changes not only with vegetation cover but also with differences in vegetation type and structure. How to integrate the cumulative effect of the different growth forms making up a vegetation community into one index for inclusion in soil loss predictive equations is an open research question. This paper proposes a method to separately measure the cover of different vegetation strata, estimate their contribution to reducing soil loss, and then to integrate this into a single vegetation index called the stratified vegetation cover index (C_s). The results show that C_s is more effective than projected vegetation cover for the assessment of soil erosion and also can overcome the disadvantages of vegetation indices such as NDVI. This means that C_s is a good substitute for vegetation cover or cover-related vegetation indices in studies on the relationship between vegetation and soil erosion. The concept of Cs may help the local governors or forest department understand the importance of vegetation structure and make right management decisions.     

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.     

Soil erodibility and its prediction in semi-arid regions

ABSTRACT

Pedotransfer functions (PTFs) have been used to save time and cost in predicting certain soil properties, such as soil erodibility (K-factor). The main objectives of this study were to develop appropriate PTFs to predict the K-factor, and then compare new PTFs with Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) K-factor models. The K-factor was measured using 40 erosion plots under natural rainfall in Simakan Watershed, an area of 350 km² in central of Iran. The Regression Tree (RT) and Multiple Linear Regression (MLR) were used to develop PTFs for predicting the K-factor. The result showed that the mean of measured K was 0.01 t h MJ^?1 mm^?1. The mean K value predicted by USLE and RUSLE was 2.08 and 2.84 times more than the measured K, respectively. Although calcium carbonate was not considered in the original USLE and RUSLE K-factors, it appeared in the advanced PTFs due to its strong positive significant impact on aggregate stability and soil infiltration rate, resulting in decreased K-factor. The results also showed that the RT with R² = 0.84 had higher performance than developed MLR, USLE and RUSLE for the K estimation.     

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。     

Modelling soil erodibility in mountain rangelands of southern Kyrgyzstan

Soil erosion in mountain rangelands in Kyrgyzstan is an emerging problem due to vegetation loss caused by overgrazing. It is further exacerbated by mountain terrain and high precipitation values in Fergana range in the south of Kyrgyzstan. The main objective of this study was to map soil erodibility in the mountainous rangelands of Kyrgyzstan. The results of this effort are expected to contribute to the development of soil erodibility modelling approaches for mountainous areas. In this study, we mapped soil erodibility at two sites, both representing grazing rangelands in the mountains of Kyrgyzstan and having potentially different levels of grazing pressure. We collected a total of 232 soil samples evenly distributed in geographical space and feature space. Then we analyzed the samples in laboratory for grain size distribution and calculated soil erodibility values from these data using the Revised Universal Soil Loss Equation (RUSLE) K-factor formula. After that, we derived different terrain indices and ratios of frequency bands from ASTER GDEM and LANDSAT images to use as auxiliary data because they are among the main soil forming factors and widely used for prediction of various soil properties. Soil erodibility was significantly correlated with channel network base level (geographically extrapolated altitude of water channels), remotely sensed indices of short-wave infrared spectral bands, exposition, and slope degree. We applied multiple regression analysis to predict soil erodibility from spatially explicit terrain and remotely sensed indices. The final soil erodibility model was developed using the spatially explicit predictors and the regression equation and then improved by adding the residuals. The spatial resolution of the model was 30 m, and the estimated mean adjusted coefficient of determination was 0.47. The two sites indicated different estimated and predicted means of soil erodibility values (0.035 and 0.039) with a 0.05 significance level, which is attributed mainly to the considerable difference in elevation.     

东北薄层黑土区作物轮作防治坡面侵蚀的效果与C值研究

作物轮作通过影响通用土壤流失方程(USLE)中作物覆盖和管理因子C值的变化和改良土壤性质而减少坡面土壤侵蚀。基于东北薄层黑土区连续6年大豆—红小豆轮作和裸露休闲坡面小区的径流泥沙和降雨资料,分析了2011—2016年研究区侵蚀性降雨特征,探讨了作物轮作防治坡面土壤侵蚀的效果,研究了作物轮作C值的年内和年际动态变化。结果表明:研究区所有侵蚀性降雨皆发生在5—10月,其降雨量占全年降水量的32.5%～68.1%,且年内和年际分布不均。对于5°坡度的裸露小区,土壤侵蚀主要发生在6—8月,坡面径流量和土壤流失量分别为48.4mm和1 388.2t/(km~2·a);对于5°坡度的作物轮作小区,土壤侵蚀主要发生在5—7月,坡面径流量和土壤流失量分别为19.5mm和166.7t/(km~2·a)。与裸露休闲小区相比,作物轮作小区可使黑土坡面年径流量和土壤流失量减少59.7%和88.0%。大豆—红小豆轮作措施的多年平均C值为0.12,其中大豆作物的C值为0.04,变化范围0.007～0.080;红小豆作物的C值为0.38,变化范围0.28～0.46。大豆和红小豆作物的C值月变化分别为0.01～0.24和0.01～0.80,呈先减少后增加的变化趋势。大豆—红小豆轮作对东北薄层黑土区坡面土壤侵蚀防治有明显效果,研究结果可为薄层黑土区土壤侵蚀定量评价和预报模型的建立提供基础数据。     

小流域植被覆盖与工程措施因子遥感监测研究

[目的]通过对长江委丹江治理工程重点项目区西河小流域植被覆盖与工程措施因子(CP)进行遥感监测研究,为丹江流域以及长江上游地区水土流失定量检测、土壤侵蚀综合防治和评价提供科学的决策依据。[方法]利用2010年丹江流域商南县西河小流域环境小卫星遥感数据,采用Erdas软件对遥感数据进行处理,得到影像中各类地物植被覆盖度。利用卜兆宏等水土流失定量遥感监测模型(QRSM模型)植被因子与植被覆盖度关系式算法,即用土壤流失量遥感监测植被因子算式开展计算和分析。[结果]西河小流域北部治理区和南部山区植被覆盖和工程措施因子值相当低;在河道两岸有局部区域出现了植被覆盖与工程措施因子高值区,主要是由于该区域居民房屋建筑和道路建设破坏了植被;中部人口集中的区域,治理程度较低,仅有较少区域出现植被覆盖集中连片高值区。[结论]影响水土流失众多客观自然因素中植被覆盖因子的影响最大,另一方面,人类过度开发利用土地资源引起陆地生态系统发生变化,因此研究水土流失应综合考虑地理、自然和经济发展,因地制宜,合理规划,确保经济持续稳定发展和生态安全。     

Monitoring the effects of land use and cover type changes on soil moisture using remote-sensing data: A case study in China's Yongding River basin

We used a radiation-transfer equation estimate of July surface temperatures (T_s) in China's Yongding River basin based on thermal infrared Landsat TM images from 1987 and 2005 and Landsat ETM+ images from 2000. Based upon the T_s–NDVI relationship space, we analyzed the scatterplot of T_s versus NDVI to calculate a temperature–vegetation dryness index (TVDI). We used a linear regression model between soil moisture and TVDI to estimate soil moisture to depths of 10 and 20 cm. We produced a land use and cover type map by classification of the Landsat images, and used the map to study the influence of land use and cover type changes on soil moisture. Some areas of farmland in 1987 had been converted into grassland by 2000, and soil moisture mainly increased, with increases ranging from 20 to 60%. From 2000 to 2005, most of the grassland in the northern part of the study area and some grassland in the central area were converted into farmland, and soil moisture decreased by up to 60%. Soil moisture decreased most obviously in areas where forest was converted into grassland, with decreases ranging from 60 to 100% in most areas.     

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.     

Erosion risk assessment: A contribution for conservation priority area identification in the sub-basin of Lake Tana,north-western Ethiopia

Soil erosion is a serious environmental problem arising from agricultural intensification and landscape changes. Improper land management coupled with intense rainfall has intricated the problem in most parts of the Ethiopian highlands. Soil loss costs a profound amount of the national GDP. Thus, quantifying soil loss and prioritizing areas for conservation is imperative for proper planning and resource conservation. Therefore, this study has modeled the mean soil loss and annual sediment yield of the Gumara watershed. Landsat 5 TM, Landsat 7ETM+, and Landsat 8 OLI were used for land use land cover (LULC) change analysis. Besides these, other datasets related to rainfall, digital soil map, Digital Elevation Model, reference land use, and cover (LULC) ground truth points were used to generate parameters for modeling soil loss. The watershed was classified into five major land-use classes (water body, cultivated land, grazing land, built-up and forest and plantation) using a maximum likelihood algorithm covering a period of the last 30 years (1988–2019). The mean annual soil loss and sediment yield were quantified using RUSLE, Sediment delivery ratio (SDR), and Sediment Yields models (SY). The analysis result unveils that within the past 30 years, the watershed has undergone significant LULC changes from forest & plantation (46.33%) and grazing land to cultivated land (31.59%) with the rate of ?1.42km²yr^-1 and -2.80km²yr^-1 respectively. In the same vein, the built-up area has expanded to cultivated and grazing land. Subsequently, nearly 15% (207 km²) of the watershed suffered from moderate to very severe soil loss. On average, the watershed losses 24.2 t ha?¹ yr?¹ of soil and yields 2807.02 t ha?¹ yr?¹ sediment. Annually, the watershed losses 385,157 t ha?¹ yr?¹ soil from the whole study area. Among the admirative districts, Farta (Askuma, Giribi, Mahidere Mariam and Arigo kebeles), Fogera (Gazen Aridafofota and Gura Amba kebeles), East Este (Witimera kebele), and Dera (Gedame Eyesus and Deriana Wechit kebeles) districts which cover 50% of the watershed were found severely affected by soil erosion. Thus, to curve back this scenario, soil and water conservation practices should prioritize in the aforementioned districts of the watersheds.     

Revisiting classic water erosion models in drylands: The strong impact of biological soil crusts

Soil erosion and subsequent degradation has been a contributor to societal collapse in the past and is one of the major expressions of desertification in arid regions. The revised universal soil loss equation (RUSLE) models soil lost to water erosion as a function of climate erosivity (the degree to which rainfall can result in erosion), topography, soil erodibility, and land use/management. The soil erodibility factor (K) is primarily based upon inherent soil properties (those which change slowly or not at all) such as soil texture and organic matter content, while the cover/management factor (C) is based on several parameters including biological soil crust (BSC) cover. We examined the effect of two more precise indicators of BSC development, chlorophyll a and exopolysaccharides (EPS), upon soil stability, which is closely inversely related to soil loss in an erosion event. To examine the relative influence of these elements of the C factor to the K factor, we conducted our investigation across eight strongly differing soils in the 0.8 million ha Grand Staircase-Escalante National Monument. We found that within every soil group, chlorophyll a was a moderate to excellent predictor of soil stability (R² = 0.21–0.75), and consistently better than EPS. Using a simple structural equation model, we explained over half of the variance in soil stability and determined that the direct effect of chlorophyll a was 3× more important than soil group in determining soil stability. Our results suggest that, holding the intensity of erosive forces constant, the acceleration or reduction of soil erosion in arid landscapes will primarily be an outcome of management practices. This is because the factor which is most influential to soil erosion, BSC development, is also among the most manageable, implying that water erosion in drylands has a solution.     

Evaluation of the predictive capabilities of Landsat 7 ETM+ on 50‐cm mean annual soil temperature in the Transylvanian Plain,Romania

Mean annual soil temperature has important implications for crops as well as for soil classification and formation. Landsat 7 Enhanced Thematic Mapper Plus (ETM+) band‐6 was analysed to determine its relationship with mean annual soil temperature (MAST) at 50 cm in the Transylvanian Plain, Romania. Band‐6 is available in both high and low gain formats from the United States Geological Survey; for our study only high gain was evaluated because of the increased resolution that it provides. Both of the gain levels of band‐6 are measured at 10.4–12.5 µm (thermal infrared), at 60‐m spatial resolution. Four different months of Landsat 7 ETM+ data were used to predict MAST and compared with 50‐cm soil temperature data measured on‐site with in situ sensors and data logging stations. Despite no correction for land cover differences across the plain, strong relationships were found between the Landsat‐predicted and field measured MAST with a coefficient of determination (R²) for July, August, December and February of 0.63. Multiple regression analysis (MAST_Regression) provided a weaker relationship, when compared with MAST_{in situ}, with a coefficient of determination (R²) of 0.42. Significant differences existed between urban and agricultural land covers, as identified by Coordination of Information on the Environment (CORINE) data. The use of Landsat 7 ETM+ could reduce the time and expense of large field studies for determining MAST. These data could then be used for temperature models of entire regions, for a range of land management options.     

黄土高原玉米坡耕地C因子的估算研究

C因子作为土壤侵蚀预报模型中人为可控制的一个重要的因子,对减少土壤侵蚀和控制水土流失有很大的影响。因此以黄土高原坡耕地典型作物玉米为研究对象,通过进行人工降雨模拟试验,研究了玉米5个不同生育期近地表状况的变化特征,根据玉米不同生育期产沙量计算C值。结果表明,植被覆盖度、株高和结皮厚度均随着玉米生育期的延长而逐渐增加,地表粗糙度随着生育期延长呈现先减小后增加的趋势。产沙量随着玉米的生长逐渐减小,减沙效益随着玉米生育期的延长不断增加。在前人以植被覆盖度计算C值模型的基础上,以植被覆盖度作为关键因子,将株高、土壤结皮、地表粗糙度作为调节因子建立当地C值模型,得到较好的玉米坡耕地的C值模型(模型R²=0.94,RMSE=0.017,MAE=0.014,NSE=0.992)。研究结果根据近地表状况变化特征建立C值计算公式,提高了C值估算的准确性和其在黄土高原的适用性,为提高黄土高原土壤侵蚀预报模型精度提供科学依据。     

Prediction of Soil Cation Exchange Capacity Using Different Soil Parameters by Intelligent Models

ABSTRACT

The traditional methods for the measurement of soil cation exchange capacity (CEC) are time-consuming and laborious. It is also difficult to maintain stability for long-term experiments and projects. Therefore, it is necessary to develop an indirect approach such as pedotransfer functions (PTFs) to estimate this property from more easily available soil data. The aim of this study was to compare multiple linear and nonlinear regression, classification and regression trees (C&RT), artificial neural network (ANN) model included multiple layer perceptron (MLP) and k-nearest neighbors (k-NN) to develop PTFs for predicting soil CEC. Soil samples, 929, were used into two subsets for training and testing of the models. Sensitivity and statistical analyzes were conducted to determine the most and the least influential variables affecting soil CEC. The prediction capability of models was assessed by statistical indicators included the normalized root-mean-square error (NRMSE) and the coefficient of determination (R²). Results of the present investigation showed that the k-NN and ANN models had the ability to estimate soil CEC by computing easily measurable variables with a guarantee of authenticity, reliability, and reproducibility. Therefore, the results of this study provide a superior basis for predicting soil CEC and could be applied to other parts of the world with similar challenges.     

Assessment of sediment connectivity from vegetation cover and topography using remotely sensed data in a dryland catchment in the Spanish Pyrenees

Purpose

Many Mediterranean drylands are characterized by strong erosion in headwater catchments, where connectivity processes play an important role in the redistribution of water and sediments. Sediment connectivity describes the ease with which sediment can move through a catchment. The spatial and temporal characterization of connectivity patterns in a catchment enables the estimation of sediment contribution and transfer paths. Apart from topography, vegetation cover is one of the main factors driving sediment connectivity. This is particularly true for the patchy vegetation cover typical of many dryland environments. Several connectivity measures have been developed in the last few years. At the same time, advances in remote sensing have enabled an improved catchment-wide estimation of ground cover at the subpixel level using hyperspectral imagery.

Materials and methods

The objective of this study was to assess the sediment connectivity for two adjacent subcatchments (~70 km²) of the Isábena River in the Spanish Pyrenees in contrasting seasons using a quantitative connectivity index based on fractional vegetation cover and topography data. The fractional cover of green vegetation, non-photosynthetic vegetation, bare soil and rock were derived by applying a multiple endmember spectral mixture analysis approach to the hyperspectral image data. Sediment connectivity was mapped using the index of connectivity, in which the effect of land cover on runoff and sediment fluxes is expressed by a spatially distributed weighting factor. In this study, the cover and management factor (C factor) of the Revised Universal Soil Loss Equation (RUSLE) was used as a weighting factor. Bi-temporal C factor maps were derived by linking the spatially explicit fractional ground cover and vegetation height obtained from the airborne data to the variables of the RUSLE subfactors.

Results and discussion

The resulting connectivity maps show that areas behave very differently with regard to connectivity, depending on the land cover and on the spatial distribution of vegetation abundances and topographic barriers. Most parts of the catchment show higher connectivity values in August as compared to April. The two subcatchments show a slightly different connectivity behaviour that reflects the different land cover proportions and their spatial configuration.

Conclusions

The connectivity estimation can support a better understanding of processes controlling the redistribution of water and sediments from the hillslopes to the channel network at a scale appropriate for land management. It allows hot spot areas of erosion to be identified and the effects of erosion control measures, as well as different land management scenarios, to be studied.     

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.     

Modelling soil erosion and sediment yield at a catchment scale: the case of Masinga catchment,Kenya

Development of improved soil erosion and sediment yield prediction technology is required to provide catchment stakeholders with the tools they need to evaluate the impact of various management strategies on soil loss and sediment yield in order to plan for the optimal use of the land. In this paper, a newly developed approach is presented to predict the sources of sediment reaching the stream network within Masinga, a large‐scale rural catchment in Kenya. The study applies the revised universal soil loss equation (RUSLE) and a developed hillslope sediment delivery distributed (HSDD) model embedded in a geographical information system (GIS). The HSDD model estimates the sediment delivery ratio (SDR) on a cell‐by‐cell basis using the concept of runoff travel time as a function of catchment characteristics. The model performance was verified by comparing predicted and measured plot runoff and sediment yield. The results show a fairly good relationship between predicted and measured sediment yield (R²=0·82). The predicted results show that the developed modelling approach can be used as a major tool to estimate spatial soil erosion and sediment yield at a catchment scale. Copyright © 2006 John Wiley & Sons, Ltd.