In this study, a three-dimensional (3D) finite element modelling (FEM) analysis is carried out to investigate the effects of soil spatial variability on the response of retaining walls and an adjacent box culvert due ...In this study, a three-dimensional (3D) finite element modelling (FEM) analysis is carried out to investigate the effects of soil spatial variability on the response of retaining walls and an adjacent box culvert due to a braced excavation. The spatial variability of soil stiffness is modelled using a variogram and calibrated by high-quality experimental data. Multiple random field samples (RFSs) of soil stiffness are generated using geostatistical analysis and mapped onto a finite element mesh for stochastic analysis of excavation-induced structural responses by Monte Carlo simulation. It is found that the spatial variability of soil stiffness can be described by an exponential variogram, and the associated vertical correlation length is varied from 1.3 m to 1.6 m. It also reveals that the spatial variability of soil stiffness has a significant effect on the variations of retaining wall deflections and box culvert settlements. The ignorance of spatial variability in 3D FEM can result in an underestimation of lateral wall deflections and culvert settlements. Thus, the stochastic structural responses obtained from the 3D analysis could serve as an effective aid for probabilistic design and analysis of excavations.展开更多
Soil water content(SWC) is a key factor limiting ecosystem sustainability in arid and semi-arid areas of the Hexi Corridor of China, which is characterized by an ecological environment that is vulnerable to climate ch...Soil water content(SWC) is a key factor limiting ecosystem sustainability in arid and semi-arid areas of the Hexi Corridor of China, which is characterized by an ecological environment that is vulnerable to climate change. However, there is a knowledge gap regarding the large-scale spatial distribution of SWC in this region. The specific objectives of this study were to determine the spatial distribution patterns of SWC across the Hexi Corridor and identify the factors responsible for spatial variation of SWC at a regional scale. This study collected and analyzed SWC in the 0–100 cm soil profile from 109 field sampling sites(farmland, grassland and forestland) across the Hexi Corridor in 2017. We selected 17 factors, including land use, topography(latitude, longitude, elevation, slope gradient, and slope aspect), soil properties(soil clay content, soil silt content, soil bulk density, saturated hydraulic conductivity, field capacity, and soil organic carbon content), climate factors(mean annual precipitation, potential evaporation, and aridity index), plant characteristic(vegetation coverage) and planting pattern(irrigation or rain-fed), as possible environmental variables to analyze their effects on SWC. The results showed that SWC was 0.083(±0.067) g/g in the 0–100 cm soil profile and decreased in the order of farmland, grassland and forestland. The SWC in the upper soil layers(0–20, 20–40 and 40–60 cm) had obvious difference when the mean annual precipitation differed by 200 mm. The SWC decreased from southeast to northwest following the same pattern as precipitation, and had a moderate to strong spatial dependence in a large effective range(75–378 km). The SWC showed a similar distribution and had no significant difference between soil layers in the 0–100 cm soil profile. The principal component analysis showed that the mean annual precipitation, geographical position(longitude and latitude) and soil properties(soil bulk density and soil clay content) were the main factors dominating the variance of environmental variables. A stepwise linear regression equation showed that plant characteristic(vegetation coverage) and soil properties(soil organic carbon content, field capacity and soil clay content) were the optimal factors to predict the variation of SWC. Soil clay content could be better to explain the SWC variation in the deeper soil layers compared with the other factors.展开更多
A total of 1400 soil samples from the plow layer (0-20 cm) at an approximate interval of 5 km were collected in the autumn of 2002 over the entire black soil arable crops region to determine the spatial variability of...A total of 1400 soil samples from the plow layer (0-20 cm) at an approximate interval of 5 km were collected in the autumn of 2002 over the entire black soil arable crops region to determine the spatial variability of seven variables, such as total organic matter content (OMC), total N, total P, total K, alkali-dissolvable N (AN), available P (AP) and available K (AK), with classical statistics and geostatistical analysis across the entire black soil area in Northeast China. In nonsampled areas ordinary kriging was utilized for interpolation of estimated nutrient determinations. Classical statistics revealed highly significant (P≤0.01) correlations with all seven of the soil properties, except for OMC with AP and total K with AK. In addition, using coefficients of variation, all soil properties, except for total K, were moderately variable. A geostatistical analysis indicated that structural factors, such as parent material, terrain, and water table, were the main causes of the spatial correlations. Strong spatial correlations were noted with OMC, total N, total P, AN, and AP, while they were moderate for total K and AK. The effective spatial autocorrelation of OMC, total N, total P, and AN ranged from 1 037 to 1 353 km, whereas the ranges of total K, AP, and AK were only from 6 to 138 km. The fit of the experimental semi-variograms to the theoretical models indicated that except for AN, kriging could successfully interpolate other six variables. Thus, the geostatistical method used on a large scale could accurately evaluate the spatial variability of most black soil nutrient properties in Northeast China.展开更多
Soil carbon to nitrogen(C/N) ratio is one of the most important variables reflecting soil quality and ecological function,and an indicator for assessing carbon and nitrogen nutrition balance of soils.Its variation ref...Soil carbon to nitrogen(C/N) ratio is one of the most important variables reflecting soil quality and ecological function,and an indicator for assessing carbon and nitrogen nutrition balance of soils.Its variation reflects the carbon and nitrogen cycling of soils.In order to explore the spatial variability of soil C/N ratio and its controlling factors of the Ili River valley in Xinjiang Uygur Autonomous Region,Northwest China,the traditional statistical methods,including correlation analysis,geostatistic alanalys and multiple regression analysis were used.The statistical results showed that the soil C/N ratio varied from 7.00 to 23.11,with a mean value of 10.92,and the coefficient of variation was 31.3%.Correlation analysis showed that longitude,altitude,precipitation,soil water,organic carbon,and total nitrogen were positively correlated with the soil C/N ratio(P < 0.01),whereas negative correlations were found between the soil C/N ratio and latitude,temperature,soil bulk density and soil p H.Ordinary Cokriging interpolation showed that r and ME were 0.73 and 0.57,respectively,indicating that the prediction accuracy was high.The spatial autocorrelation of the soil C/N ratio was 6.4 km,and the nugget effect of the soil C/N ratio was 10% with a patchy distribution,in which the area with high value(12.00–20.41) accounted for 22.6% of the total area.Land uses changed the soil C/N ratio with the order of cultivated land > grass land > forest land > garden.Multiple regression analysis showed that geographical and climatic factors,and soil physical and chemical properties could independently explain 26.8%and 55.4% of the spatial features of soil C/N ratio,while human activities could independently explain 5.4% of the spatial features only.The spatial distribution of soil C/N ratio in the study has important reference value for managing soil carbon and nitrogen,and for improving ecological function to similar regions.展开更多
基金The authors would like to acknowledge the financial support provided by the National Natural Science Foundation of China(Grant No.41977240)the Fundamental Research Funds for the Central Universities(Grant No.B200202090).
文摘In this study, a three-dimensional (3D) finite element modelling (FEM) analysis is carried out to investigate the effects of soil spatial variability on the response of retaining walls and an adjacent box culvert due to a braced excavation. The spatial variability of soil stiffness is modelled using a variogram and calibrated by high-quality experimental data. Multiple random field samples (RFSs) of soil stiffness are generated using geostatistical analysis and mapped onto a finite element mesh for stochastic analysis of excavation-induced structural responses by Monte Carlo simulation. It is found that the spatial variability of soil stiffness can be described by an exponential variogram, and the associated vertical correlation length is varied from 1.3 m to 1.6 m. It also reveals that the spatial variability of soil stiffness has a significant effect on the variations of retaining wall deflections and box culvert settlements. The ignorance of spatial variability in 3D FEM can result in an underestimation of lateral wall deflections and culvert settlements. Thus, the stochastic structural responses obtained from the 3D analysis could serve as an effective aid for probabilistic design and analysis of excavations.
基金sponsored by the National Natural Science Foundation of China (41530854, 41571130081)
文摘Soil water content(SWC) is a key factor limiting ecosystem sustainability in arid and semi-arid areas of the Hexi Corridor of China, which is characterized by an ecological environment that is vulnerable to climate change. However, there is a knowledge gap regarding the large-scale spatial distribution of SWC in this region. The specific objectives of this study were to determine the spatial distribution patterns of SWC across the Hexi Corridor and identify the factors responsible for spatial variation of SWC at a regional scale. This study collected and analyzed SWC in the 0–100 cm soil profile from 109 field sampling sites(farmland, grassland and forestland) across the Hexi Corridor in 2017. We selected 17 factors, including land use, topography(latitude, longitude, elevation, slope gradient, and slope aspect), soil properties(soil clay content, soil silt content, soil bulk density, saturated hydraulic conductivity, field capacity, and soil organic carbon content), climate factors(mean annual precipitation, potential evaporation, and aridity index), plant characteristic(vegetation coverage) and planting pattern(irrigation or rain-fed), as possible environmental variables to analyze their effects on SWC. The results showed that SWC was 0.083(±0.067) g/g in the 0–100 cm soil profile and decreased in the order of farmland, grassland and forestland. The SWC in the upper soil layers(0–20, 20–40 and 40–60 cm) had obvious difference when the mean annual precipitation differed by 200 mm. The SWC decreased from southeast to northwest following the same pattern as precipitation, and had a moderate to strong spatial dependence in a large effective range(75–378 km). The SWC showed a similar distribution and had no significant difference between soil layers in the 0–100 cm soil profile. The principal component analysis showed that the mean annual precipitation, geographical position(longitude and latitude) and soil properties(soil bulk density and soil clay content) were the main factors dominating the variance of environmental variables. A stepwise linear regression equation showed that plant characteristic(vegetation coverage) and soil properties(soil organic carbon content, field capacity and soil clay content) were the optimal factors to predict the variation of SWC. Soil clay content could be better to explain the SWC variation in the deeper soil layers compared with the other factors.
基金Project supported by the National Basic Research Program (973 Program) of China (No. 2005CB121108) the Heilongjiang Provincial Natural Science Foundation of China (No. C2004-25).
文摘A total of 1400 soil samples from the plow layer (0-20 cm) at an approximate interval of 5 km were collected in the autumn of 2002 over the entire black soil arable crops region to determine the spatial variability of seven variables, such as total organic matter content (OMC), total N, total P, total K, alkali-dissolvable N (AN), available P (AP) and available K (AK), with classical statistics and geostatistical analysis across the entire black soil area in Northeast China. In nonsampled areas ordinary kriging was utilized for interpolation of estimated nutrient determinations. Classical statistics revealed highly significant (P≤0.01) correlations with all seven of the soil properties, except for OMC with AP and total K with AK. In addition, using coefficients of variation, all soil properties, except for total K, were moderately variable. A geostatistical analysis indicated that structural factors, such as parent material, terrain, and water table, were the main causes of the spatial correlations. Strong spatial correlations were noted with OMC, total N, total P, AN, and AP, while they were moderate for total K and AK. The effective spatial autocorrelation of OMC, total N, total P, and AN ranged from 1 037 to 1 353 km, whereas the ranges of total K, AP, and AK were only from 6 to 138 km. The fit of the experimental semi-variograms to the theoretical models indicated that except for AN, kriging could successfully interpolate other six variables. Thus, the geostatistical method used on a large scale could accurately evaluate the spatial variability of most black soil nutrient properties in Northeast China.
基金Under the auspices of National Science and Technology Support Program of China(No.2014BAC15B03)the West Light Funds of Chinese Academy of Sciences(No.YB201302)
文摘Soil carbon to nitrogen(C/N) ratio is one of the most important variables reflecting soil quality and ecological function,and an indicator for assessing carbon and nitrogen nutrition balance of soils.Its variation reflects the carbon and nitrogen cycling of soils.In order to explore the spatial variability of soil C/N ratio and its controlling factors of the Ili River valley in Xinjiang Uygur Autonomous Region,Northwest China,the traditional statistical methods,including correlation analysis,geostatistic alanalys and multiple regression analysis were used.The statistical results showed that the soil C/N ratio varied from 7.00 to 23.11,with a mean value of 10.92,and the coefficient of variation was 31.3%.Correlation analysis showed that longitude,altitude,precipitation,soil water,organic carbon,and total nitrogen were positively correlated with the soil C/N ratio(P < 0.01),whereas negative correlations were found between the soil C/N ratio and latitude,temperature,soil bulk density and soil p H.Ordinary Cokriging interpolation showed that r and ME were 0.73 and 0.57,respectively,indicating that the prediction accuracy was high.The spatial autocorrelation of the soil C/N ratio was 6.4 km,and the nugget effect of the soil C/N ratio was 10% with a patchy distribution,in which the area with high value(12.00–20.41) accounted for 22.6% of the total area.Land uses changed the soil C/N ratio with the order of cultivated land > grass land > forest land > garden.Multiple regression analysis showed that geographical and climatic factors,and soil physical and chemical properties could independently explain 26.8%and 55.4% of the spatial features of soil C/N ratio,while human activities could independently explain 5.4% of the spatial features only.The spatial distribution of soil C/N ratio in the study has important reference value for managing soil carbon and nitrogen,and for improving ecological function to similar regions.
