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1.
Soil erosion modeling of a Himalayan watershed using RS and GIS   总被引：5，自引：1，他引：4  
Employing the remote sensing (RS) and geographical information system (GIS), an assessment of sediment yield from Dikrong river basin of Arunachal Pradesh (India) has been presented in this paper. For prediction of soil erosion, the Morgan-Morgan and Finney (MMF) model and the universal soil loss equation (USLE) have been utilized at a spatial grid scale of 100 m × 100 m, an operational unit. The average annual soil loss from the Dikrong river basin is estimated as 75.66 and 57.06 t ha−1 year−1 using MMF and USLE models, respectively. The watershed area falling under the identified very high, severe, and very severe zones of soil erosion need immediate attention for soil conservation.  相似文献   

2.
Soil erosion is a serious environmental problem in Indravati catchment. It carries the highest amount of sediments compared with other catchments in India. This catchment spreading an area of 41,285 km2 is drained by river Indravati, which is one of the northern tributaries of the river Godavari in its lower reach. In the present study, USLE is used to estimate potential soil erosion from river Indravati catchment. Both magnitude and spatial distribution of potential soil erosion in the catchment is determined. The derived soil loss map from USLE model is classified into six categories ranging from slight to very severe risk depending on the calculated soil erosion amount. The soil erosion map is linked to elevation and slope maps to identify the area for conservation practice in order to reduce the soil loss. From the model output predictions, it is found that average erosion rate predicted is 18.00 tons/ha/year and sediment yield at the out let of the catchment is 22.30 Million tons per annum. The predicted sediment yield verified with the observed data.  相似文献   

3.
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。   相似文献   

4.
The Universal Soil Loss Equation (USLE) is an erosion estimation model to assess the soil losses that would generally result from splash, sheet, and rill erosion. At the present study, spatial distribution of different erosion prone areas were identified by USLE model to determine the average annual soil losses at Mashhad plain, northeast of Iran. Soil losses were estimated on a 100?×?100 m cell basis resolution by overlaying the five digital parameter layers (R, K, LS, C, P). To determine the critical soil loss regions at the plain, cell-based USLE parameters were multiplied by Arc-GIS ver.9.3. The estimated annual soil losses values were subsequently grouped into five classes ranging from 0 to 0.25 t/h/year around the trough line of the plain at Kashaf-rud River to 2–10 t/ha/year at the hills and pediment plains. Our results indicated a good correlation between land units of hills and pediment plains with the values of soil losses at the study area (R 2 ?=?0.72), also the statistical analysis exhibited a high correlation between land use/cover of dry farming and soil losses (R 2 ?=?0.78).  相似文献   

5.
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.  相似文献   

6.
The universal soil loss equation (USLE) is an erosion model to estimate average soil loss that would generally result from splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting soil losses and planning control practices by the effective integration of the GIS-based procedures to estimate the factor values on a grid cell basis. This study was performed for five different lands uses of Indağı Mountain Pass, Cankırı to predict the soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Of the USLE factors, rainfall-runoff erosivity factor (USLE-R) and topographic factor (USLE-LS) were greatly involved in GIS. These were surfaced by correcting USLE-R site-specifically using DEM and climatic data and by evaluating USLE-LS by the flow accumulation tool using DEM and watershed delineation tool to consider the topographical and hydrological effects on the soil loss. The study assessed the soil erodibility factor (USLE-K) by randomly sampled field properties by geostatistical analysis. Crop management factor for different land-use/land cover type and land use (USLE-C) was assigned to the numerical values from crop and flora type, canopy and density of five different land uses, which are plantation, recreational land, cropland, forest and grassland, by means of reclassifying digital land use map available for the site. Support practice factor (USLE-P) was taken as a unit assuming no erosion control practices. USLE/GIS technology together with the geostatistics combined these major erosion factors to predict average soil loss per unit area per unit time. Resulting soil loss map revealed that spatial average soil loss in terms of the land uses were 1.99, 1.29, 1.21, 1.20, 0.89 t ha−1 year−1 for the cropland, grassland, recreation, plantation and forest, respectively. Since the rate of soil formation was expected to be so slow in Central Anatolia of Turkey and any soil loss of more than 1 ton ha−1 year−1 over 50–100 years was considered as irreversible for this region, soil erosion in the Indağı Mountain Pass, to the great extent, attained the irreversible state, and these findings should be very useful to take mitigation measures in the site.  相似文献   

7.
The construction of roads has direct and indirect impacts on soil erosion, with spatio-temporal variations existing among different levels of road zones. Aiming to quantitatively analyze the soil loss, this paper explored the relationship between the erosion of soil and its distance from the road in Fengqing county, Southwest China in 1987 and 2004, respectively. The average soil erosion was calculated and expressed with grid map using universal soil loss equation (USLE) model based on GIS and RS. Along the different levels of roads classified as trunk, county, town, village and unpaved road, the buffer zones were subdivided into five stripes, each of which was 200 meters wide. The average soil erosion modulus of each buffer zone was also counted. Results show that the soil loss decreases with increasing distance to the road except rare trunk roads in the region. In addition, the declined intensity varies with the different levels of roads. Soil erosion was more serious along the lower level road than the higher ones. And soil erosion was more severe for all levels of roads in 1987 than those in 2004 because much more rainfalls affected the situation of soil erosion in 1987.  相似文献   

8.
A grid-based erosion model is developed by integrating the distributed hydrological model, BTOPMC, with the modified USLE to estimate soil erosion and sediment outflow during single storms. The possible sheet, rill, channel erosion types, and sediment transport processes are considered within each grid under the model structure. Instead of representing the sheet erosion and rill erosion separately, the classic USLE method is modified to simulate the lumped sheet–rill erosion during storms. In the modification, the runoff ratio and a relevant correction coefficient are brought into the R-factor which improves the model’s applicability in predicting erosion during single storms. Instead of representing a grid with a unique erosion type, a channel component is assumed to exist in each grid, and its width varies with the upstream contributing area of the grid. This assumption avoids the problems that are caused by the difference between the channel widths in the upstream area and the downstream area if the grid is simply recognized as a channel grid. It also enables the model to be applicable in simulating soil erosion and sediment outflow from a large catchment. Through a case study in the Lushi catchment, China, the results show an overall satisfactory accuracy for the selected events. Moreover, by analyzing the spatial distribution of soil erosion or deposition, the erosion-prone areas are identified for the prioritization purpose.  相似文献   

9.
Undulating landscapes of Chhotanagpur plateau of the Indian state of Jharkhand suffer from soil erosion vulnerability of varying degrees. An investigation was undertaken in some sections of the Upper Subarnarekha River Basin falling within this state. An empirical equation known as Universal Soil Loss Equation (USLE) was utilized for estimating the soil loss. Analysis of remote sensing satellite data, digital elevation model (DEM) and geographical information system (GIS)–based geospatial approach together with USLE led to the soil erosion assessment. Erosion vulnerability assessment was performed by analyzing raster grids of topography acquired from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM data. LANDSAT TM and ETM+ satellite data of March 2001 and March 2011 were used for inferring the land use–land cover characteristics of the watershed for these years, respectively. USLE equation was computed within the GIS framework to derive annual soil erosion rates and also the areas with varying degrees of erosion vulnerability. Erosion vulnerability units thus identified covered five severity classes of erosion ranging from very low (0–5 ton ha?1 yr?1) to very severe (> 40 ton ha?1 yr?1). Results indicated an overall increase of erosion in the year 2011 as compared to the erosion computed for the year 2001. Maximum soil erosion rate during the year 2001 was found up to 40 ton ha?1 yr?1, whereas this went up to 49.80 ton ha?1 yr?1 for the year 2011. Factors for the increase in overall erosion could be variation in rainfall, decrease in vegetation or protective land covers and most important but not limited to the increase in built-up or impervious areas as well.  相似文献   

10.
Riparian zones act as important buffer zones for non-point source pollution, thus improving the health of aquatic ecosystems. Previous research has shown that riparian zones play an important role, and that land use has an important effect, on phosphorus (P) retention. A spatial basin-scale approach for analyzing P retention and land use effects could be important in preventing pollution in riparian zones. In this study, a riparian phosphorus cycle model based on EcoHAT was generated with algorithms from soil moisture and heat models, simplified soil and plant phosphorus models, plant growth models, and universal soil loss equations. Based on remote sensing data, model performance was enhanced for spatial and temporal prediction of P retention in the riparian zone. A modified soil and plant P model was used to simulate the soil P cycle of a riparian zone in a temperate continental monsoon climate in northern China. A laboratory experiment and a field experiment were conducted to validate the P cycle model. High coefficients of determination (R 2) between simulated and observed values indicate that the model provides reliable results. P uptake variations were the same as the net primary productivity (NPP) trends, which were affected by soil temperature and moisture in the temperate continental monsoon climate. Beginning in June, the monthly content increased, with the maximum appearing in August, when the most precipitation and the highest temperatures occur. The spatial distribution of P uptake rates from March to September showed that areas near water frequently had relatively high values from May to August, which is contrary to results obtained in March, April, and September. The P uptake amounts for different land uses changed according to expectation. The average monthly P uptake rates for farmlands and grasslands were more than those for orchards and lowlands, which had moderate P uptake rates, followed by shrubs and forests. The spatial distribution of soil erosion demonstrated that the soil erosion came primarily from high-intensity agricultural land in the western and central areas, while the northern and eastern study regions, which were less affected by human activity, experienced relatively slight soil erosion. From the point of view of P pollution prevention, the spatial structure of riparian zones and the spatial distribution of land use around the Guanting reservoir are thus not favorable.  相似文献   

11.
The devastating effect of soil erosion is one of the major sources of land degradation that affects human lives in many ways which occur mainly due to deforestation, poor agricultural practices, overgrazing,wildfire and urbanization. Soil erosion often leads to soil truncation, loss of fertility, slope instability, etc.which causes irreversible effects on the poorly renewable soil resource. In view of this, a study was conducted in Kelantan River basin to predict soil loss as influenced by long-term land use/land-cover(LULC) changes in the area. The study was conducted with the aim of predicting and assessing soil erosion as it is influenced by long-term LULC changes. The 13,100 km~2 watershed was delineated into four sub-catchments Galas, Pergau, Lebir and Nenggiri for precise result estimation and ease of execution. GIS-based Universal Soil Loss Equation(USLE) model was used to predict soil loss in this study. The model inputs used for the temporal and spatial calculation of soil erosion include rainfall erosivity factor,topographic factor, land cover and management factor as well as erodibility factor. The results showed that 67.54% of soil loss is located under low erosion potential(reversible soil loss) or 0-1 t ha~(-1) yr~(-1) soil loss in Galas, 59.17% in Pergau, 53.32% in Lebir and 56.76% in Nenggiri all under the 2013 LULC condition.Results from the correlation of soil erosion rates with LULC changes indicated that cleared land in all the four catchments and under all LULC conditions(1984-2013) appears to be the dominant with the highest erosion losses. Similarly, grassland and forest were also observed to regulate erosion rates in the area. This is because the vegetation cover provided by these LULC types protects the soil from direct impact of rain drops which invariably reduce soil loss to the barest minimum. Overall, it was concluded that the results have shown the significance of LULC in the control of erosion. Maps generated from the study may be useful to planners and land use managers to take appropriate decisions for soil conservation.  相似文献   

12.
This study is aimed at the evaluation of the hazard of soil erosion and its verification at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Precipitation, topographic, soil, and land use data were collected, processed, and constructed into a spatial database using GIS and remote sensing data. Areas that had suffered soil erosion were analysed and mapped using the Universal Soil Loss Equation (USLE). The factors that influence soil erosion are rainfall erosivitiy (R) from the precipitation database, soil erodibility (K) from the soil database, slope length and steepness (LS) from the topographic database, and crop and management (C) and conservation supporting practices (P) from the land use database. Land use was classified from Landsat Thematic Mapper satellite images. The soil erosion map verified use of the landslide location data. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys.  相似文献   

13.
Sand production by soil erosion in small watershed is a complex physical process. There are few physical models suitable to describe the characteristics of the intense erosion in domestic loess plateau. Introducing support vector machine (SVM) oriented to small sample data and possessing good extension property can be an effective approach to predict soil erosion because SVM has been applied in hydrological prediction to some extent. But there are no effective methods to select the rational parameters for SVM, which seriously limited the practical application of SVM. This paper explored the application of intelligence-based particle swarm optimization (PSO) algorithm in automatic selection of parameters for SVM, and proposed a prediction model by linking PSO and SVM for small sample data analysis. This method utilized the high efficiency optimization property and swarm paralleling property of PSO algorithm and the relatively strong learning and extending capacity of SVM. For an example of Huangfuchuan small watershed, its intensive fragmentation and intense erosion earn itself the name of “worst erosion in the world”. Using four characteristics selection algorithms of correlation feature selection, the primary affecting factors for soil erosion in this small watershed were determined to be the channel density, ravine area, sand rock proportion, and the total vegetation coverage. Based on the proposed PSO–SVM algorithm, the soil erosion modulus in the small watershed was predicted. The accuracy of the simulation and prediction was good, and the average error was 3.85%. The SVM predicting model was based on the monitoring data of sand production. The construction of the SVM erosion modulus prediction model for the small watershed comprehensively reflected the complex mechanism of soil erosion and sand production. It had certain advantage and relatively high practical value in small sample prediction in the discipline of soil erosion.  相似文献   

14.
The rapid erosion of soil by wind and water has been a problem since man began cultivating the land. Moreover soil erosion, as a hazard, has always been associated mainly with agriculture in the tropical and semi-arid areas. Soil loss through rill, gully and sheet erosion is a major environmental problem in India. Among all the predictive equations developed to estimate soil loss, the most accepted, used, convenient and suitable technique, for smaller areas like hillslopes and fields, is the Universal Soil Loss Equation (USLE). This method has been applied to the cultivated fields on either side of the gullied banks of the Adula and Mahalungi rivers, to estimate soil loss from fields under different crops. Rainfall data from the IMD has been used for the purpose. Field slope measurements, textural analysis of soil and determination of soil organic matter have also been carried out. Finally the soil loss has been computed from the generated data. The results have been used to ascertain whether the soil loss in the area is within or beyond the tolerance limit. It has been found that the soil loss in these areas have exceeded the tolerance limit and hence require due attention.  相似文献   

15.
祁连山石羊河上游山区土壤侵蚀的环境因子特征分析   总被引：3，自引：1，他引：2  
在GIS技术支持下, 运用通用水土流失方程USLE, 对祁连山北坡东段的哈溪林区的土壤侵蚀量及空间分布进行了模拟运算, 并定量分析了各种环境因子与土壤侵蚀之间的关系. 结果显示: 研究区平均土壤侵蚀模数为25.1 t·hm-2·a-1, 微度和轻度侵蚀面积占总面积的80%, 而强度到剧烈侵蚀产生的侵蚀量占78.3%; 各土地类型土壤侵蚀模数由高到低依次是裸地>草地>农田>灌丛>乔木林, 裸地侵蚀量占到总侵蚀量的54.9%; 乔木林和灌木林95%以上侵蚀面积属微度侵蚀区, 农田中度到剧烈侵蚀的面积比例达到35.9%, 高于草地和其他植被类型, 而草地剧烈侵蚀面积比例高于农田. 海拔高度范围与土壤流失量之间的关系与植被的海拔分布范围明显相关; 土壤平均侵蚀模数随坡度的增加而增大, 土壤侵蚀量主要分布在15°～45°的坡度范围, 不同植被覆盖下土壤流失随坡度变化的趋势可在一定程度上反映该类植被对土壤流失的防止作用.  相似文献   

16.
This paper applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing technique, and geographic information system (GIS) to map the soil erosion risk in Miyun Watershed, North China. The soil erosion parameters were evaluated in different ways: the R factor map was developed from the rainfall data, the K factor map was obtained from the soil map, the C factor map was generated based on a back propagation (BP) neural network method of Landsat ETM+ data with a correlation coefficient (r) of 0.929 to the field collected data, and a digital elevation model (DEM) with a spatial resolution of 30 m was derived from topographical map at the scale of 1:50,000 to develop the LS factor map. P factor map was assumed as 1 for the watershed because only a very small area has conservation practices. By integrating the six factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the upper watershed of Miyun reservoir was obtained by the RUSLE model. The results showed that the annual average soil loss for the upper watershed of Miyun reservoir was 9.86 t ha−1 ya−1 in 2005, and the area of 47.5 km2 (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.88% very low, 21.90% low, 6.19% moderate, 2.90% severe, and 1.84% very severe. Among all counties and cities in the study area, Huairou County is in the extremely severe level of soil erosion risk, about 39.6% of land suffer from soil erosion, while Guyuan County in the very low level of soil erosion risk suffered from 17.79% of soil erosion in 2005. Therefore, the areas which are in the extremely severe level of soil erosion risk need immediate attention from soil conservation point of view.  相似文献   

17.
西南喀斯特区土层浅薄、成土速率低等特点决定了其允许土壤流失量小,土壤一旦流失,极难恢复,土壤侵蚀及其造成的石漠化现象已成为制约该区可持续发展最严重的生态环境问题。文章首先明晰西南喀斯特区土壤侵蚀特征,从坡面、小流域和区域三个尺度上系统概括西南喀斯特区土壤侵蚀的相关研究进展。针对当前喀斯特区土壤侵蚀研究野外径流小区、小流域及区域空间尺度数据缺少和相关研究模型限制性强等不足,建议从不同尺度深入研究喀斯特区土壤侵蚀发生发展规律及时空演化格局,并结合高新遥感、地球物理探测技术及模型,同步监测坡面—小流域—区域土壤流失,对土壤侵蚀进行定量评估,结合不同空间尺度土壤侵蚀特征构建系统性水土保持生态恢复治理模式和监测系统评价体系。   相似文献   

18.
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19.
Drought is a natural phenomenon posing severe implications for soil, groundwater and agricultural yield. It has been recognized as one of the most pervasive global change drivers to affect the soil. Soil being a weakly renewable resource takes a long time to form, but it takes no time to degrade. However, the response of soil to drought conditions as soil loss is not manifested in the existing literature. Thus, this study makes a concerted effort to analyze the relationship between drought conditions and soil erosion in the middle sub-basin of the Godavari River in India. MODIS remote sensing data was utilized for driving drought indices during 2000–2019. Firstly, we constricted Temperature condition index (TCI) and Vegetation Condition Index (VCI) from Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) derived from MODIS data. TCI and VCI were then integrated to determine the Vegetation Health Index (VHI). Revised Universal Soil Loss Equation (RUSLE) was utilized for estimating soil loss. The relationship between drought condition and vegetation was ascertained using the Pearson correlation. Most of the northern and southern watersheds experienced severe drought condition in the sub-basin during 2000–2019. The mean frequency of the drought occurrence was 7.95 months. The average soil erosion in the sub-basin was estimated to be 9.88 t ha?1 year?1. A positive relationship was observed between drought indices and soil erosion values (r value being 0.35). However, wide variations were observed in the distribution of spatial correlation. Among various factors, the slope length and steepness were found to be the main drivers of soil erosion in the sub-basin. Thus, the study calls for policy measures to lessen the impact of drought and soil erosion.  相似文献   

20.
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.  相似文献   

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