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1.
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   

2.
Sequential Gaussian Simulation(SGSIM)as a stochastic method has been developed to avoid the smoothing effect produced in deterministic methods by generating various stochastic realizations.One of the main issues of this technique is,however,an intensive computation related to the inverse operation in solving the Kriging system,which significantly limits its application when several realizations need to be produced for uncertainty quantification.In this paper,a physics-informed machine learning(PIML)model is proposed to improve the computational efficiency of the SGSIM.To this end,only a small amount of data produced by SGSIM are used as the training dataset based on which the model can discover the spatial correlations between available data and unsampled points.To achieve this,the governing equations of the SGSIM algorithm are incorporated into our proposed network.The quality of realizations produced by the PIML model is compared for both 2D and 3D cases,visually and quantitatively.Furthermore,computational performance is evaluated on different grid sizes.Our results demonstrate that the proposed PIML model can reduce the computational time of SGSIM by several orders of magnitude while similar results can be produced in a matter of seconds.  相似文献   

3.
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   

4.
基于网格数据的贵州土壤侵蚀敏感性评价及其空间分异   总被引：14，自引：1，他引：13  
在通用土壤侵蚀方程的基础上,建立了土壤侵蚀敏感性评价指标体系,利用GIS方法对影响土壤侵蚀敏感性的单因子进行评价,并将各因子进行网格化,运用网格数据的空间叠加分析方法对贵州省土壤侵蚀敏感性进行综合评价。在此基础上探讨了贵州省土壤侵蚀敏感性空间分异规律。通过与已有的土壤侵蚀现状图比较,发现土壤侵蚀高敏感区与水土流失严重区并不吻合,并进一步指出,脆弱的喀斯特环境是产生严重水土流失和导致石漠化的地质基础,强烈的人类活动是加速这一过程的主要驱动力量。   相似文献   

5.
In karst areas, accurately measuring and managing the spatial variability of soil water content (SWC) is very critical in settling numerous issues such as karst rocky desertification, ecosystem reconstruction, etc. In these areas, SWC exhibits strong spatial dependence, and it is a time and labor consuming procedure to measure its spatial variability. Therefore, estimation of this kind of soil property at an acceptable level of accuracy is of great significance. This study was conducted to evaluate and compare the spatial estimation of SWC by using ordinary kriging (OK) and cokriging (COK) methods with prime terrain variables, tending to predict SWC using limited available sample data for a 2,363.7 km2 study area in Mashan County, Guangxi Zhuang Autonomous Region, Southwest China. The measured SWC ranged from 3.36 to 26.69 %, with a mean of 17.34 %. The correlation analysis between SWC and prime terrain variables indicated that SWC showed significantly positive correlation with elevation (r is 0.46, P < 0.01), and significantly negative correlation with slope (r is ?0.30, P < 0.01); however, SWC was not significantly correlated with aspect in the study area. Therefore, elevation and slope were used as auxiliary data together for SWC prediction using COK method, and mean error (ME) and root mean square error were adopted to validate the prediction of SWC by these methods. Results indicated that COK with prime terrain variables data was superior to OK with relative improvement of 28.52 % in the case of limited available data, and also revealed that such elevation and slope data have the potential to improve the precision and reliability of SWC prediction as useful auxiliary variables.  相似文献   

6.
Soil erosion is one of most widespread process of degradation. The erodibility of a soil is a measure of its susceptibility to erosion and depends on many soil properties. Soil erodibility factor varies greatly over space and is commonly estimated using the revised universal soil loss equation. Neglecting information about estimation uncertainty may lead to improper decision-making. One geostatistical approach to spatial analysis is sequential Gaussian simulation, which draws alternative, equally probable, joint realizations of a regionalised variable. Differences between the realizations provide a measure of spatial uncertainty and allow us to carry out an error analysis. The objective of this paper was to assess the model output error of soil erodibility resulting from the uncertainties in the input attributes (texture and organic matter). The study area covers about 30 km2 (Calabria, southern Italy). Topsoil samples were collected at 175 locations within the study area in 2006 and the main chemical and physical soil properties were determined. As soil textural size fractions are compositional data, the additive-logratio (alr) transformation was used to remove the non-negativity and constant-sum constraints on compositional variables. A Monte Carlo analysis was performed, which consisted of drawing a large number (500) of identically distributed input attributes from the multivariable joint probability distribution function. We incorporated spatial cross-correlation information through joint sequential Gaussian simulation, because model inputs were spatially correlated. The erodibility model was then estimated for each set of the 500 joint realisations of the input variables and the ensemble of the model outputs was used to infer the erodibility probability distribution function. This approach has also allowed for delineating the areas characterised by greater uncertainty and then to suggest efficient supplementary sampling strategies for further improving the precision of K value predictions.  相似文献   

7.
Spatial distribution of concentrations of radon gas in the soil is important for defining high risk areas because geogenic radon is the major potential source of indoor radon concentrations regardless of the construction features of buildings. An area of southern Italy (Catanzaro-Lamezia plain) was surveyed to study the relationship between radon gas concentrations in the soil, geology and structural patterns. Moreover, the uncertainty associated with the mapping of geogenic radon in soil gas was assessed. Multi-Gaussian kriging was used to map the geogenic soil gas radon concentration, while conditional sequential Gaussian simulation was used to yield a series of stochastic images representing equally probable spatial distributions of soil radon across the study area. The stochastic images generated by the sequential Gaussian simulation were used to assess the uncertainty associated with the mapping of geogenic radon in the soil and they were combined to calculate the probability of exceeding a specified critical threshold that might cause concern for human health. The study showed that emanation of radon gas radon was also dependent on geological structure and lithology. The results have provided insight into the influence of basement geochemistry on the spatial distribution of radon levels at the soil/atmosphere interface and suggested that knowledge of the geology of the area may be helpful in understanding the distribution pattern of radon near the earth’s surface.  相似文献   

8.
Digital Elevation Model (DEM) is one of the important parameters for soil erosion assessment. Notable uncertainties are observed in this study while using three high resolution open source DEMs. The Revised Universal Soil Loss Equation (RUSLE) model has been applied to analysis the assessment of soil erosion uncertainty using open source DEMs (SRTM, ASTER and CARTOSAT) and their increasing grid space (pixel size) from the actual. The study area is a part of the Narmada river basin in Madhya Pradesh state, which is located in the central part of India and the area covered 20,558 km2. The actual resolution of DEMs is 30 m and their increasing grid spaces are taken as 90, 150, 210, 270 and 330 m for this study. Vertical accuracy of DEMs has been assessed using actual heights of the sample points that have been taken considering planimetric survey based map (toposheet). Elevations of DEMs are converted to the same vertical datum from WGS 84 to MSL (Mean Sea Level), before the accuracy assessment and modelling. Results indicate that the accuracy of the SRTM DEM with the RMSE of 13.31, 14.51, and 18.19 m in 30, 150 and 330 m resolution respectively, is better than the ASTER and the CARTOSAT DEMs. When the grid space of the DEMs increases, the accuracy of the elevation and calculated soil erosion decreases. This study presents a potential uncertainty introduced by open source high resolution DEMs in the accuracy of the soil erosion assessment models. The research provides an analysis of errors in selecting DEMs using the original and increased grid space for soil erosion modelling.  相似文献   

9.
Assessment of soil erosion risk using SWAT model   总被引：3，自引：2，他引：1  
Soil erosion is one of the most serious land degradation problems and the primary environmental issue in Mediterranean regions. Estimation of soil erosion loss in these regions is often difficult due to the complex interplay of many factors such as climate, land uses, topography, and human activities. The purpose of this study is to apply the Soil and Water Assessment Tool (SWAT) model to predict surface runoff generation patterns and soil erosion hazard and to prioritize most degraded sub-catchment in order to adopt the appropriate management intervention. The study area is the Sarrath river catchment (1,491 km2), north of Tunisia. Based on the estimated soil loss rates, the catchment was divided into four priority categories for conservation intervention. Results showed that a larger part of the watershed (90 %) fell under low and moderate soil erosion risk and only 10 % of the watershed was vulnerable to soil erosion with an estimated sediment loss exceeding 10 t?ha?1?year?1. Results indicated that spatial differences in erosion rates within the Sarrath catchment are mainly caused by differences in land cover type and gradient slope. Application of the SWAT model demonstrated that the model provides a useful tool to predict surface runoff and soil erosion hazard and can successfully be used for prioritization of vulnerable areas over semi-arid catchments.  相似文献   

10.
文章以贵州花江喀斯特石漠化地区为研究区域,利用WEPP模型(坡面版)分别模拟2006年、2010年土壤侵蚀模数,并将实测数据与WEPP模型模拟值作比较,探讨WEPP软件在喀斯特石漠化地区的适用性。研究表明:WEPP模型对于模拟喀斯特石漠化地区土壤侵蚀有较大误差,对土壤侵蚀模数模拟的有效性系数均为负值,不适用于直接计算该区域土壤侵蚀模数。WEPP模型对微度侵蚀模拟精度不够,但能大体反映不同径流小区之间土壤侵蚀强弱的关系和生态修复过程土壤侵蚀的变化趋势。若要应用WEPP模型对喀斯特地区土壤侵蚀模数模拟计算,必须考虑水土的地下漏失、地表裸岩率、地形高度破碎等环境条件。裸岩率、土壤漏失、地形条件等都是WEPP模型修正所必须注意的内容。   相似文献   

11.
Assessing spatial variability of soil thickness is a critical issue for understanding and predicting slope processes. The present work was aimed at estimating the spatial scales at which the variation of pyroclastic cover thickness occurs in a sample area in the Sorrento Peninsula (Italy). Stochastic simulation was used to understand the spatial variability of pyroclastic cover thickness on Mount Pendolo and to assess its spatial uncertainty. In the study area, covering about 0.7 km2, thickness measurements were collected using electrical resistivity tomography profiles, continuous core drillings and steel rod penetrometric tests. Variographic analysis revealed the occurrence of an anisotropic behaviour along the N50 and N140 directions. In the latter anisotropic direction, a nested variogram was fitted including (1) a long-range component which could be related to large-scale factors, like the curvature of the slope and contributing area and (2) a shorter scale variation which is probably associated with the occurrence of denudation processes or to the articulate cover/bedrock interface. To assess the spatial variability and uncertainty of pyroclastic cover thickness, a stochastic simulation algorithm was used and 500 equally probable images of cover thickness were yielded. The results showed that a better thickness distribution map can be drawn by simulating the data collected on the slope and at the footslope separately. The approach also allowed delineating the areas characterized by greater uncertainty, suggesting supplementary measurements to further improve the cover thickness distribution model, thus reducing the uncertainty.  相似文献   

12.
Wind-erosion risk is a challenge that threatens land development in dry-land regions. Soil analysis, remote sensing, climatic, vegetal cover and topographic data were used in a geographic information system (GIS), using multi-criteria analysis (MCA) to map wind-erosion risk (Rwe) in Laghouat, Algeria. The approach was based on modelling the risk and incorporating topographic and climatic effects. The maps were coded according to their sensitivity to wind erosion and to their socio-economic potential, from low to very high. By overlapping the effects of these layers, qualitative maps were drawn to reflect the potential sensitivity to wind erosion per unit area. The results indicated that severe wind erosion affects mainly all the southern parts and some parts in the north of Laghouat, where wind-erosion hazard (Hwe) is very high in 43% of the total area, and which was affected mainly by natural parameters such as soil, topography and wind. The results also identified features vulnerable to Rwe. The product of the hazard and the stake maps indicated the potential risk areas that need preventive measures; this was more than half of the study area, making it essential to undertake environmental management and land-use planning.  相似文献   

13.
The watershed of the Ningxia–Inner Mongolia reach of the Yellow River suffers serious wind erosion hazards and the areas with high wind erosion probabilities need to be identified to help in the building of the correct wind-sand blown hazard protection systems. In this study, the Integrated Wind-Erosion Modelling System model and Normalized Difference Vegetation Index (NDVI) data set were used to identify the distributions of threshold wind speeds and wind erosion occurrence probabilities. Through field observations, the relationships among NDVI, vegetation cover, frontal area (lateral cover), roughness length, and threshold friction velocity were obtained. Then, using these relationships, the spatial distributions of threshold wind speeds for wind erosion at a height of 10 m for the different months were mapped. The results show that the threshold wind speed ranged from 7.91 to 35.7 m/s. Based on the threshold wind speed distributions, the wind erosion occurrence probabilities of different months were calculated according to the current wind speed. The results show that the distributions of wind erosion occurrence probabilities and threshold wind speeds were related to each other. The resulting maps of threshold wind speeds and wind erosion occurrence probabilities would help environmental and agricultural researchers in determining some strategies for mitigating or adapting from wind erosion hazards.  相似文献   

14.
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   

15.
Soil erodibility (K) affects sediment delivery to streams and needs to be appropriately quantified and interpolated as a fundamental geographic variable for implementing suitable catchment management and conservation practices. The spatial distribution of K for erosion modelling at non-sampling grid locations has traditionally been estimated using interpolation algorithms such as kriging which do not adequately represent the uncertainty of estimates. These methods cause smoothing effects through overestimating the low values and underestimating the large values. In this study observed values were used to implement a sequential Gaussian simulation (SGS) procedure to evaluate the certainty of modelled data. Soil erodibility values were computed using 41 soil samples taken from the top 10 cm soil layer regularly distributed across four catchments, 367–770 ha in area, within Kangaroo River State forest, New South Wales (NSW). One hundred realisations were applied in the simulation process to provide spatial uncertainty and error estimates of soil erodibility. The results indicated that values simulated by the SGS algorithm produced similar K values for the neighbouring cells. At the pixel level, the SGS approach generated a reliable estimation of soil erodibility in most areas. Spatial variation of the K factor in this study was strongly related to soil landscape differences across the catchments; within catchments slope gradient did not have a substantial impact on the numerical values of the K factor using pixel-by-pixel comparisons of raster grid maps.  相似文献   

16.
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   

17.
An attempt has been made to analyze the spatial-temporal characteristics of soil erosion vulnerability and soil loss from the forested region in the north-eastern Borneo, Sarawak, Malaysia during the last three decades (1991–2015) using the revised universal soil loss equation (RUSLE) and geographical information systems (GIS). The components of RUSLE such as rainfall erosivity (R), soil erodibility (K), slope-length and steepness (LS), cover management (C) and conservation practice (P) factors were grouped into two categories by keeping one set as temporally changing and others as static. Among them the R and C factors are calculated for the years 1991, 2001 and 2015 whereas the K and LS factors are considered for the single time frame. Because of the forested nature of the study area, the P factor is kept constant for the whole analysis. The R factor and C factor is shown changes in values and its distribution over the years, which reflected in the final soil loss and erosion vulnerability map as a change in the rate of erosion and spatial domain. The analysis of three time slices has shown that the maximum value of the soil loss per unit area i.e. at erosion hotspots, is relatively similar throughout at around 1636 to 1744 t/ha/y. This is the result of maximum values of R factor and C factor i.e. high rainfall erosivity combined with lack of vegetation cover in those hotspots, which are generally steeply sloping terrain. The reclassification of annual soil loss map into erosion vulnerability zones indicated a major increase in the spatial spread of erosion vulnerability from the year 1991 to 2015 with a significant increase in the high and critical erosion areas from 2.3% (1991) to 31.5% (2015). In 1991, over 84% of the study area was under low erosion vulnerability class but by the year 2015 only 12% was under low erosion vulnerability class. Moreover, a dynamic nature in the erosion pattern was found from the year 1991 to 2015 with more linear areas of land associated with higher rate of soil loss and enhanced erosion vulnerability. The linearity in the spatial pattern is correlated with the development of logging roads and logging activities which has been confirmed by the extraction of exposed areas from satellite images of different years of analysis. The findings of the present study has quantified the changes in vegetation cover from dense, thick tropical forest to open mixed type landscapes which provide less protection against erosion and soil loss during the severe rainfall events which are characteristic of this tropical region.  相似文献   

18.
The present study aims to investigate the relationships between several soil parameters (texture, organic matter and CaCO3 content) and the threshold wind velocity and erodibility of different soil types. Our aim was to determine the role of these soil parameters play in soil loss due to wind erosion and also to statistically evaluate these correlations. The erodibility studies were carried out in wind tunnel experiments, and the resulting data were analysed with multiple regression analysis and the Kruskal-Wallis test. We found that both the threshold wind speed and the erodibility of soils were mostly determined by silt fraction (0.05–0.02 mm), while sand fractions had a lesser effect on it. Our experiences with organic matter and CaCO3 similar, i.e. in spite of their correlation with the erosion, their contribution was not significant in the multivariate regression model. Consequently, based on mechanical composition of soils, one can predict threshold wind velocity and erodibility of soils.  相似文献   

19.
Estimation of soil erosion using RUSLE in Caijiamiao watershed,China   总被引：4，自引：1，他引：3  
Jinghu Pan  Yan Wen 《Natural Hazards》2014,71(3):2187-2205
Soil erosion is a serious environmental and production problem in China. In particular, natural conditions and human impact have made the Chinese Loess Plateau particularly prone to intense soil erosion area. To decrease the risk on environmental impacts, there is an increasing demand for sound, and readily applicable techniques for soil conservation planning in this area. This work aims at the assessment of soil erosion and its spatial distribution in hilly Loess Plateau watershed (northwestern China) with a surface area of approximately 416.31 km2. This study was conducted at the Caijiamiao watershed to determine the erosion hazard in the area and target locations for appropriate initiation of conservation measures using the revised universal soil loss equation (RUSLE). The erosion factors of RUSLE were collected and processed through a geographic information system (GIS)-based approach. 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 Landsat-5 Thematic Mapper image and spectral mixture analysis, and a digital elevation model with a spatial resolution of 25 m was derived from topographic map at the scale of 1:50,000 to develop the LS-factor map. Support practice P factor was from terraces that exist on slopes where crops are grown. By integrating the six-factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the study area was obtained by the RUSLE model. The results showed that spatial average soil erosion at the watershed was 78.78 ton ha?1 year?1 in 2002 and 70.58 ton ha?1 year?1 in 2010, while the estimated sediment yield was found to be 327.96 × 104 and 293.85 × 104 ton, respectively. Soil erosion is serious, respectively, from 15 to 35 of slope degree, elevation area from 1,126 to 1,395 m, in the particular area of soil and water loss prevention. As far as land use is concerned, soil losses are highest in barren land and those in waste grassland areas are second. The results of the study provide useful information for decision maker and planners to take appropriate land management measures in the area. It thus indicates the RUSLE–GIS model is a useful tool for evaluating and mapping soil erosion quantitatively and spatially at a river watershed scale on a cell basis in Chinese Loess Plateau and for planning of conservation practices.  相似文献   

20.
张晓平  王思敬  李黎  王彦兵 《岩土力学》2012,33(11):3465-3471
西北干旱地区土遗址受风化、风蚀等破坏严重,大量土质文物亟待加固抢修。加固后土遗址的各耐环境因素及加固机制研究是土遗址加固的理论基础。首次引入颗粒元程序PFC,通过改变模型中颗粒间平行连接强度,对硅酸钾（简称PS）加固前后的土样进行数值模拟。在考虑实际土样颗粒粒径和密度的前提下,拟合了生土PS加固前后的抗压和抗拉强度,并将拟合后的颗粒元模型应用于风蚀模拟。通过随机生成挟沙风颗粒,以一定的速度撞向土体,模拟挟沙风的吹蚀作用。挟沙风颗粒数与循环步数成正比例,因此,可以用挟沙风颗粒数来代表吹蚀时间的长短。挟沙风颗粒的速度则代表挟沙风风速。模拟结果表明,在20 m/s的挟沙风吹蚀作用下,风蚀程度随吹蚀时间的增加而增大,未加固土样的风蚀程度增幅度远大于加固土样;同样吹蚀时间条件下,加固土样的抗风蚀强度明显高于未加固土样。这些模拟结论与风洞试验结果的统计规律一致。本研究拟合的颗粒流模型可进一步应用于PS加固机制研究及耐风蚀、雨蚀、冻融等诸环境影响分析研究。  相似文献   

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