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1.
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   

2.
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   

3.
Soil erodibility is one of the most important factors used in spatial soil erosion risk assessment. Soil information derived from soil map is used to generate soil erodibility factor map. Soil maps are not available at appropriate scale. In general, soil maps at small scale are used in deriving soil erodibility map that largely generalized spatial variability and it largely ignores the spatial variability since soil map units are discrete polygons. The present study was attempted to generate soil erodibilty map using terrain indices derived from DTM and surface soil sample data. Soil variability in the hilly landscape is largely controlled by topography represented by DTM. The CartoDEM (30 m) was used to derive terrain indices such as terrain wetness index (TWI), stream power index (SPI), sediment transport index (STI) and slope parameters. A total of 95 surface soil samples were collected to compute soil erodibility factor (K) values. The K values ranged from 0.23 to 0.81 t ha?1R?1 in the watershed. Correlation analysis among K-factor and terrain parameters showed highest correlation of soil erodibilty with TWI (r 2= 0.561) followed by slope (r 2= 0.33). A multiple linear regression model was developed to derive soil erodibilty using terrain parameters. A set of 20 soil sample points were used to assess the accuracy of the model. The coefficient of determination (r 2) and RMSE were computed to be 0.76 and 0.07 t ha?1R?1 respectively. The proposed methodology is quite useful in generating soil erodibilty factor map using digital elevation model (DEM) for any hilly terrain areas. The equation/model need to be established for the particular hilly terrain under the study. The developed model was used to generate spatial soil erodibility factor (K) map of the watershed in the lower Himalayan range.  相似文献   

4.
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   

5.
Scour and erosion potential of a soil are closely related to each other. Similarities or differences between them have not been defined fully and the terms are often used interchangeably or in association with one another. Erodibility is a property of soil that describes erosion potential. Therefore, a proper understanding of erodibility should help predict scour more accurately. In the past, researchers have looked into erosion of soils with the ultimate objective of understanding the erodibility with respect to the standard geotechnical properties. Most research has shown the difficulties associated with correlating erodibility to any one or more soil properties. The research described in this paper is mainly focused on the relationship between erodibility and dry unit weight of soil with varying fractions of fines. Soils tested using laboratory Jet Erosion Test (JET) indicated that the logarithm of erodibility makes a linear inverse relationship with the dry unit weight. In situ JETs confirmed the range of erodibilities established by the laboratory JETs. The best correlations between erodibility and dry unit weight appeared within a single category of soil as classified by the Unified Soil Classification System. In addition, it was also determined that the logarithm of erodibility is inversely related to the angle of internal friction of the fluvial soils tested during this investigation.  相似文献   

6.
Bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the Cuyahoga River watershed, northeastern Ohio, U.S.A. The relationship between landslides and various instability factors contributing to their occurrence was evaluated using a Geographic Information System (GIS) based investigation. A landslide inventory map was prepared using landslide locations identified from aerial photographs, field checks, and existing literature. Instability factors such as slope angle, soil type, soil erodibility, soil liquidity index, landcover pattern, precipitation, and proximity to stream, responsible for the occurrence of landslides, were imported as raster data layers in ArcGIS, and ranked using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each instability factor in controlling the spatial distribution of landslides, both bivariate and multivariate models were used to analyze the digital dataset. The logistic regression approach was used in the multivariate model analysis. Both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method, and by comparing the maps with the known landslide locations. The multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area. The logistic regression model produced a landslide susceptibility map at a scale of 1:24,000 that classified susceptibility into four categories: low, moderate, high, and very high. The results also indicated that slope angle, proximity to stream, soil erodibility, and soil type were statistically significant in controlling the slope movement.  相似文献   

7.
基于全国第二次土壤普查得到的6 241个典型土壤剖面数据,采用主成分分析方法和径向基函数神经网络模型建立不同植被类型—土纲单元内土壤有机质与气候、地形和植被等环境因子间的非线性关系,模拟全国表层土壤有机质的空间分析格局。结果表明,该模型具有较准确的预测能力,性能指数达到1.94。与普通克里格法、反比距离法和多元回归模型相比,神经网络模型对621个验证点模拟结果与实测值的相关系数为0.799,分别提高了0.265、0.181和0.120,平均绝对误差分别降低了4.25、4.43和2.34 g/kg,平均相对误差分别降低了30.16%、32.66%和5.93%,均方根误差则分别降低了8.61、8.24和6.24 g/kg;从模拟结果图来看,神经网络模型能够提供更多的细节信息。该方法为大尺度土壤性质空间分布模拟提供了有益的参考。  相似文献   

8.
青藏高原土壤有机碳储量（soil organic carbon stocks, SOCS）对于区域生态环境演替具有重要作用, 但是其空间分布数据还比较缺乏, 特别是季节冻土区的数据较少。基于378个土壤剖面数据, 结合与土壤有机碳（soil organic carbon, SOC）相关的地形、 气候以及植被等环境因子, 使用地理加权回归（geographically weighted regression, GWR）模型模拟了青藏高原季节冻土区0 ~ 30 cm、 0 ~ 50 cm、 0 ~ 100 cm和0 ~ 200 cm深度的SOC总量和空间分布。结果表明： 青藏高原季节冻土区SOCS自东南向西北递减, 表层0 ~ 200 cm的SOC总量约15.37 Pg; 季节冻土区不同植被类型SOC从大到小依次为森林、 灌丛、 高寒草甸、 高寒草原和高寒荒漠; 各土壤类型中棕壤、 黑钙土和泥炭土的SOC最大, 而棕钙土、 棕漠土、 灰棕漠土、 风沙土、 石质土、 盐土、 冷钙土、 寒漠土以及冷漠土的SOC最小。研究结果给出了青藏高原季节冻土区SOC的总量、 空间分布及规律, 可为相关地球模式的发展提供基础数据。  相似文献   

9.
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10.
To obtain data on heavy metal contaminated soil requires laborious and time-consuming data sampling and analysis. Not only has the contamination to be measured, but also additional data characterizing the soil and the boundary conditions of the site, such as pH, land use, and soil fertility. For an integrative approach, combining the analysis of spatial distribution, and of factors influencing the contamination, and its treatment, the Mollifier interpolation was used, which is a non-parametric kernel density regression. The Mollifier was capable of including additional independent variables (beyond the spatial dimensions x and y) in the spatial interpolation and hence explored the combined influence of spatial and other variables, such as land use, on the heavy metal distribution. The Mollifier could also represent the interdependence between different heavy metal concentrations and additional site characteristics. Although the uncertainty measure supplied by the Mollifier at first seems somewhat unusual, it is a valuable feature and supplements the geostatistical uncertainty assessment.  相似文献   

11.
荆江段河岸粘性土体抗冲特性试验   总被引：2，自引：0，他引：2       下载免费PDF全文
粘性土体具有较强的抗冲能力,其抗冲性强弱决定了河岸的侵蚀速率。为了估算河岸的侵蚀速率,确定粘性土体的起动条件、侵蚀系数及两者之间的数量关系非常重要,为此开展了荆江段河岸粘性土体起动条件与冲刷特性的水槽试验研究。根据试验结果获得了粘性土体的起动流速与土体液限/自然含水率之间关系以及起动切应力与干密度、起动切应力与液性指数之间的定量表达式,综合反映了粘性土起动条件与其物理性质指标之间的关系;结合冲刷特性试验结果,还获得了土体侵蚀系数随起动切应力的变化规律,并与已有其他试验结果进行了比较。结果表明:荆江河岸粘性土侵蚀系数均比相同条件下其他公式计算值偏大,这与该试验土体中粘粒含量相对较低、土体结构受到扰动等因素有关;根据试验结果,拟合得到了荆江河岸粘性土侵蚀系数与起动切应力之间定量关系式,其相关系数R2=0.90,故该关系式能为荆江段崩岸过程的计算提供参考依据。  相似文献   

12.
Recoverable mineralisation at a given mining selectivity is traditionally modelled from sparse data grids by non-linear geostatistical techniques such as Uniform Conditioning. This method estimates the tonnage and grade of mineralisation which can be extracted as small selective minable blocks from large blocks (panels), whose grade is modelled by Ordinary Kriging. Uniform Conditioning technique estimates the proportions of recoverable mineralisation in each panel without specifying the actual locations of the economically extractable blocks. This inability to predict a spatial location of the recoverable mineralisation is a major disadvantage of the conventional Uniform Conditioning method. A new approach, called Localised Uniform Conditioning, has been developed to overcome this limitation. This method applies the grade–tonnage relationships modelled by the Uniform Conditioning technique to the spatial grade distribution patterns approximated by direct kriging of the small blocks from the sparse data grid. This approach estimates localised selective mining units grades conforming to the proper grade–tonnage curves obtained by the Uniform Conditioning method as well as maintaining the relative spatial grade distribution pattern indicated by the directly kriged small block grades. The advantage of this approach is essentially dependent upon the data available for ranking the small blocks within a panel in increasing order of their grade. Ordinary Kriging of the small blocks can be used for their ranking providing the kriged estimates produce a meaningful indication of the relative grade pattern. Where the data is sparse and not close to a panel, or their distribution is characterised by a strong short-range variability, the advantages of using the Localised Uniform Conditioning approach are more limited.  相似文献   

13.
Land degradation is still a very common problem in the mountains of Asia because of inappropriate land use practice in steep topography. Many studies have been carried out to map shifting cultivation and areas susceptible to soil erosion. Mostly, estimated soil loss is taken as the basis to classify the level of soil loss susceptibility of area. Factors that influence soil erosion are: rainfall erosivity, soil erodibility, slope length and steepness, crop management and conservation practices. Thus the reliability of estimated soil loss is based on how accurately the different factors were estimated or prepared. As each and every small pixel of our earth surface is different from one area to another, the manner in which the study area was discretized into smaller homogenous sizes and how the most accurate and efficient technique were adopted to estimate the soil loss are very important. The purpose of this study is to produce erosion susceptibility maps for an area that has suffered because of shifting cultivation located in the mountainous regions of Northern Thailand. For this purpose, an integrated approach using RS and GIS-based methods is proposed. Data from the Upper Nam Wa Watershed, a mountainous area of the Northern Thailand were used. An Earth Resources Data Analysis System (ERDAS) imagine image processor has been used for the digital analysis of satellite data and topographical analysis of the contour data for deriving the land use/land cover and the topographical data of the watershed, respectively. ARCInfo and ARCView have been used for carrying out geographical data analysis. The watershed was discretized into hydrologically, topographically, and geographically homogeneous grid cells to capture the watershed heterogeneity. The soil erosion in each cell was calculated using the universal soil loss equation (USLE) by carefully determining its various parameters and classifying the watershed into different levels of soil erosion severity. Results show that during the time of this study most of the areas under shifting cultivation fell in the highest severity class of susceptibility.  相似文献   

14.
Assessment of uncertainty due to inadequate data and imperfect geological knowledge is an essential aspect of the subsurface model building process. In this work, a novel methodology for characterizing complex geological structures is presented that integrates dynamic data. The procedure results in the assessment of uncertainty associated with the predictions of flow and transport. The methodology is an extension of a previously developed pattern search-based inverse method that models the spatial variation in flow parameters by searching for patterns in an ensemble of reservoir models. More specifically, the pattern-searching algorithm is extended in two directions: (1) state values (such as piezometric head) and parameters (such as conductivities) are simultaneously and sequentially estimated, which implies that real-time assimilation of dynamic data is possible as in ensemble filtering approaches; and (2) both the estimated parameter and state variables are considered when pattern searching is implemented. The new scheme results in two main advantages—better characterization of parameters, especially for delineating small scale features, and an ensemble of head states that can be used to update the parameter field using the dynamic data at the next instant, without running expensive flow simulations. An efficient algorithm for pattern search is developed, which works with a flexible search radius and can be optimized for the estimation of either large- or small-scale structures. Synthetic examples are employed to demonstrate the effectiveness and robustness of the proposed approach.  相似文献   

15.
Dimensionality reduction methods such as principal components analysis (PCA) provide a means of identifying trends in soil characteristics which may be represented by a wide range of variables. However, these characteristics may be highly spatially variable and so the results from PCA represent, in some sense, an “average” of locally distinct characteristics. One approach to account for these local differences is to introduce a geographical weighting scheme into the PCA process. In this paper, such an approach is assessed in the exploration of soil characteristics in the state of Pennsylvania, USA. Data from 878 georeferenced soil profiles which include different soil parameters (n = 12) were extracted from the National Soil Survey Center database. Where data are parts of compositions (e.g., percentages of sand, silt, and clay), analysis using raw data is not appropriate and such data were transformed using log ratios (specifically, balances). Single variables (i.e., those which are not parts of compositions) were logged. The first two principal components explain over 50% of the variance. The mapped values suggest marked spatial variation in soil characteristics, but it is not possible to assess which of these variables explain most variation in particular regions from the simple maps of raw variables. Geographically weighted PCA (GWPCA) provides additional information which is obscured by PCA, and it also provides a set of component scores and loadings at all data locations. The soil variable with the largest loading at most locations of Pennsylvania is the logged base saturation (BSln), and this supports the findings of the conventional PCA analysis. While BSln loads most highly in most of the eastern third, the middle and the south west of the state, the northwest is less spatially consistent in terms of the variables which explain most variation. For GWPC 1, the variable with the second largest loading at most locations (i.e., primarily the south and west) is CEC.B1 (the log ratio of Ca, Mg, and Na to K and EXACID), while CEC.B2 (the log ratio of Ca and Mg to Na), pHln (logged pH) and BSln dominate in other areas. The GWPCA results suggest that there is marked spatial variation in multivariate soil characteristics across Pennsylvania state and that results from standard PCA obscure this considerable variation.  相似文献   

16.
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   

17.
The characteristics and the erodibility potentials of soils from Benin, Ogwashi-Asaba, and Nanka formations in Anambra state, Nigeria were studied. The study involved an integration of field observations, geotechnical analyses, and topographical modelling. About ten gullies were identified in the field. However, six major gullies labeled BN1, BN2, OA1, OA2, NK1, and NK2 were chosen and studied extensively to represent the three geologic formations. The results showed that soil properties, anthropogenic activities, topography, fierce surface runoff, and sparse vegetation are the factors that commonly and systematically influence the characteristics and erodibility potentials of all these soils. However, anthropogenic activities and soil properties play the most, dominant roles in the erosional processes. The specific gravity of the soils are similar, with values ranging from 2.50 to 2.69. Compaction test revealed all the soils were loose and collapsible. Grain size analysis further revealed that they have low content of fines (<25%, except for OA1 with 58.87%). The plasticity index of the fines indicated that they are nonplastic to low plastic soils (values range from 0–12%). The soils are highly to moderately permeable, with values ranging from 4.22×10?6m/sec to 4.34×10?4m/sec. All have low cohesion ranging from 1–7kPa, except for the OA1 with 27kPa. Based on the study, the three geologic formations have similar characteristics and high erodibility potentials; though OA1 has varied characteristics and thus, lower erodibility potential.  相似文献   

18.
How to integrate environmental geographic information and biodiversity data combined with management measures to effectively assess soil health is still an unresolved problem. This paper suggests an approach for systematically estimating soil quality and guiding ecological management. First, canonical correspondence analysis is used to predict the distributions of plant species or microorganism communities, principle pollutants and environmental variables from which spatial and environmental data are extracted by the geographic information system (GIS). Secondly, geostatistical methodologies are then used to estimate and quantify the spatial distribution characteristic of the species and pollutants and to create maps of spatial uncertainty and hazard assessment through ArcGis technology. Finally, redundancy analysis provides a suggestion about better management strategy and environmental factor for improving soil health and biodiversity. The combination of these methods with “3S” techniques as an assessment approach effectively meets the challenges for estimation and management in different soil environments.  相似文献   

19.
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   

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