Inverse reliability analysis and design for tunnel face stability considering soil spatial variability

The traditional deterministic analysis for tunnel face stability neglects the uncertainties of geotechnical parameters,while the simplified reliability analysis which models the potential uncertainties by means of random variables usually fails to account for soil spatial variability.To overcome these limitations,this study proposes an efficient framework for conducting reliability analysis and reliability-based design(RBD)of tunnel face stability in spatially variable soil strata.The three-dimensional(3D)rotational failure mechanism of the tunnel face is extended to account for the soil spatial variability,and a probabilistic framework is established by coupling the extended mechanism with the improved Hasofer-Lind-Rackwits-Fiessler recursive algorithm(iHLRF)as well as its inverse analysis formulation.The proposed framework allows for rapid and precise reliability analysis and RBD of tunnel face stability.To demonstrate the feasibility and efficacy of the proposed framework,an illustrative case of tunnelling in frictional soils is presented,where the soil's cohesion and friction angle are modelled as two anisotropic cross-correlated lognormal random fields.The results show that the proposed method can accurately estimate the failure probability(or reliability index)regarding the tunnel face stability and can efficiently determine the required supporting pressure for a target reliability index with soil spatial variability being taken into account.Furthermore,this study reveals the impact of various factors on the support pressure,including coefficient of variation,cross-correlation between cohesion and friction angle,as well as autocorrelation distance of spatially variable soil strata.The results also demonstrate the feasibility of using the forward and/or inverse first-order reliability method(FORM)in high-dimensional stochastic problems.It is hoped that this study may provide a practical and reliable framework for determining the stability of tunnels in complex soil strata.

Stochastic analysis of excavation-induced wall deflection and box culvert settlement considering spatial variability of soil stiffness

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.

Geospatial Coronavirus Vulnerability Regression Modelling for Malawi Based on Cumulative Spatial Data from April 2020 to May 2021

In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management tool to identify and model the vulnerabilities of Malawi’s districts in prioritizing health services as far as coronavirus prevalence and other infectious diseases are concerned. The aim of this study was to model coronavirus vulnerability in all districts in Malawi using Geographic Information System (GIS) to monitor the disease’s cumulative prevalence over the severely affected period between 2020 and 2021. To achieve this, four parameters associated with coronavirus prevalence, including population density, percentage of older people, temperature, and humidity, were prepared in a GIS environment and used in the modelling process. A multiscale geographically weighted regression (MGWR) model was used to model and determine the vulnerability of coronavirus in Malawi. In the MGWR modelling, the Fixed Spatial Kernel was used following a Gaussian distribution model type. The Results indicated that population density and older people (age greater than 60 years) have a more significant impact on coronavirus prevalence in Malawi. The modelling further shows that Malawi, between April 2020 and May 2021, Lilongwe, Blantyre and Thyolo were more vulnerable to coronavirus than other districts. This research has shown that spatial variability of Covid-19 cases using MGWR has the potential of providing useful insights to policymakers for targeted interventions that could otherwise not be possible to detect using non-geovisualization techniques.

Spatial and Temporal Variation of Particulate Matter (PM10 and PM2.5) and Its Health Effects during the Haze Event in Malaysia

This study aims to assess and compare levels of particulate matter(PM10 and PM2.5)in urban and industrial areas in Malaysia during haze episodes,which typically occur in the south west monsoon season.The high concentrations of atmospheric particles are mainly due to pollution from neighbouring countries.Daily PM concentrations were analysed for urban and industrial areas including Alor Setar,Tasek,Shah Alam,Klang,Bandaraya Melaka,Larkin,Balok Baru,and Kuala Terengganu in 2018 and 2019.The analysis employed spatiotemporal to examine how PM levels were distributed.The data summary revealed that PM levels in all study areas were right-skewed,indicating the occurrence of high particulate events.Significant peaks in PM concentrations during haze events were consistently observed between June and October,encompassing the south west monsoon and inter-monsoon periods.The study on acute respiratory illnesses primarily focused on Selangor.Analysis revealed that Klang had the highest mean number of inpatient cases for acute exacerbation of bronchial asthma(AEBA)and acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with values of 260.500 and 185.170,respectively.Similarly,for outpatient cases of AEBA and AECOPD,Klang had the highest average values of 41.67 and 14.00,respectively.Shah Alam and Sungai Buloh did not show a significant increase in cases during periods of biomass burning.The statistical analysis concluded that higher concentrations of PM were associated with increased hospital admissions,particularly from June to September,as shown in the bar diagram.Haze episodes were associated with more healthcare utilization due to haze-related respiratory illnesses,seen in higher inpatient and outpatient visits(p<0.05).However,seasonal variability had minimal impact on healthcare utilization.These findings offer a comprehensive assessment of PM levels during historic haze episodes,providing valuable insights for authorities to develop policies and guidelines for effective monitoring and mitigation of the negative impacts of haze events.

Spatial variability of soil organic carbon in the forestlands of northeast China

Soil organic carbon （SOC） is an effective indicator of soil fertility and productivity, and it varies spatially and temporally in relation to other soil properties. Spatial variability of SOC in the forestlands of northeast China was characterized using geostatistics. Soil samples at the depths of 0-20 era, 20-40 cm and 40-60 cm were collected from six- ty-three temporary plots to evaluate SOC concentration and density （SOCD） and other soil properties. We analyzed correlations between SOC and soil properties. Soil organic carbon concentrations were high. The total amount of C stored in soil （0-60 cm） was 16.23 kg·m-2 with the highest SOCD of 7.98 kg·m-2 in topsoil. Soil properties in most cases differed by horizon, suggesting different processes and effects in each horizon. Soil organic carbon had positive relationships with total N, P and K as well as readily available K, but did not show a significant posi- tive correlation with available P. Spatial factors including elevation, slope and aspect affected SOC distribution. Soil organic carbon at 0-60 cm had strong spatial autocorrelation with nugget/sill ratio of 5.7%, and moderate structured dependence was found at 0-20 cm, which indicated the existence of a highly developed spatial structure. Spatial distributionsof SOC concentration and SOCD were estimated using regres- sion-kriging, with higher prediction accuracy than ordinary kriging. The fractal dimension of SOC indicated the preferential pattern of SOC dis- tribution, with the greatest spatial heterogeneity and strongest spatial dependence in the northeast-southwest direction.

Spatial pattern of soil organic carbon in desert grasslands of the diluvial-alluvial plains of northern Qilian Mountains

The soil properties in arid ecosystems are important determinants of vegetation distribution patterns. Soil organic carbon （SOC） content, which is closely related to soil types and the holding capacities of soil water and nutrients, exhibits complex variability in arid desert grasslands; thus, it is essentially an impact factor for the distri- bution pattern of desert grasslands. In the present study, an investigation was conducted to estimate the spatial pattern of SOC content in desert grasslands and the association with environmental factors in the diluvial-alluvial plains of northern Qilian Mountains. The results showed that the mean values of SOC ranged from 2.76 to 5.80 g/kg in the soil profiles, and decreased with soil depths. The coefficients of variation （CV） of the SOC were high （ranging from 48.83% to 94.67%）, which indicated a strong spatial variability. SOC in the desert grasslands of the study re- gion presented a regular spatial distribution, which increased gradually from the northwest to the southeast. The SOC distribution had a pattern linked to elevation, which may be related to the gradient of climate conditions. Soil type and plant community significantly affected the SOC. The SOC had a significant positive relationship with soil moisture （P〈0.05）; whereas, it had a more significant negative relationship with the soil bulk density （BD） （P〈0.01）. However, a number of the variations in the SOC could be explained not by the environmental factors involved in this analysis, but rather other factors （such as grazing activity and landscape）. The results provide important references for soil carbon storage estimation in this study region. In addition, the SOC association with environmental variables also provides a basis for a sustainable use of the limited grassland resources in the diluvial-alluvial plains of north- ern Qilian Mountains.

Spatial Variability of Soil Chemical Properties in the Reclaiming Marine Foreland to Yellow Sea of China

Precise information about the spatial variability of soil properties is essential in developing site-specific soil management, such as variable rate application of fertilizers. In this study the sampling grid of 100 m × 100 m was established to collect 1 703 soil samples at the depth of 0-20 cm, and examine spatial patterns including 13 soil chemical properties （pH, OM, NH4^＋, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn） in a 1 760 ha rice field in Haifeng farm, China, from 6th to 22nd of April, 2006, before fertilizer application and planting. Soil analysis was performed by ASI （Agro Services International） and data were analyzed both statistically and geostatistically. Results showed that the contents of soil OM, NH4^＋, and Zn in Haifeng farm were very low for rice production and those of others were enough to meet the need for rice cultivation. The spatial distribution model and spatial dependence level for 13 soil chemical properties varied in the field. Soil Mg and B showed strong spatial variability on both descriptive statistics and geostatistics, and other properties showed moderate spatial variability. The maximum ranges for K, Ca, Mg, S, Cu and Mn were all - 3 990.6 m and the minimum ranges for soil pH, OM, NH4^＋, P, Fe, and Zn ranged from 516.7 to 1 166.2 m. Clear patchy distribution of N, P, K, Mg, S, B, Mn, and Zn were found from their spatial distribution maps. This proved that sampling strategy for estimating variability should be adapted to the different soil chemical properties and field management. Therefore, the spatial variability of soil chemical properties with strong spatial dependence could be readily managed and a site-specific fertilization scheme for precision farming could be easily developed.

Reliability analysis of slopes considering spatial variability of soil properties based on efficiently identified representative slip surfaces

Slope reliability analysis considering inherent spatial variability(ISV)of soil properties is timeconsuming when response surface method(RSM)is used,because of the"curse of dimensionality".This paper proposes an effective method for identification of representative slip surfaces(RSSs)of slopes with spatially varied soils within the framework of limit equilibrium method(LEM),which utilizes an adaptive K-means clustering approach.Then,an improved slope reliability analysis based on the RSSs and RSM considering soil spatial variability,in perspective of computation efficiency,is established.The detailed implementation procedure of the proposed method is well documented,and the ability of the method in identifying RSSs and estimating reliability is investigated via three slope examples.Results show that the proposed method can automatically identify the RSSs of slope with only one evaluation of the conventional deterministic slope stability model.The RSSs are invariant with the statistics of soil properties,which allows parametric studies that are often required in slope reliability analysis to be efficiently achieved with ease.It is also found that the proposed method provides comparable values of factor of safety(FS)and probability of failure(Pf)of slopes with those obtained from direct analysis and lite rature.

Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines

This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.

Spatial variability of organochlorine pesticides (DDTs and HCHs) in surface soils from the alluvial region of Beijing,China

The spatial variability in the concentrations of 1,2,3,4,5,6-hexachlorocyclohexane （HCH） and 1,1,1-trichloro-2,2-bis-（p-chloro- phenyl） ethane （DDT） in surface soils was studied on the basis of the analysis of 131 soil samples collected from the surface layer （0-20 cm depth） of the alluvial region of Beijing, China. The concentrations of total HCHs （including α-,β-, γ-, and δ-isomers） and total DDTs （including p,p＇-DDT, p,p＇-DDD, p,p＇-DDE, and o,p＇-DDT） in the surface soils tested were in the range from nondetectable to 31.72 μg/kg dry soil, with a mean value of 0.91, and from nondetectable to 5910.83 μg/kg dry soil, with a mean value of 32.13, respectively. It was observed that concentrations of HCHs in all soil samples and concentrations of DDTs in 112 soil samples were much lower than the first grade （50 μg/kg） permitted in ＂Environment quality standard for soils in China （GB 15618-1995）＂. This suggests that the pollution due to organochlorine pesticides was generally not significant in the farmland soils in the Beijing alluvial region. In this study, the spatial distribution and trend of HCHs and DDTs were analyzed using Geostatistical Analyst and GS＋（513）. Spatial distribution indicated how these pesticides had been applied in the past. Trend analysis showed that the concentrations of HCHs, DDTs, and their related metabolites followed an obvious distribution trend in the surface soils from the alluvial region of Beijing.

Spatial Variability of Soil Cation Exchange Capacity in Hilly Tea Plantation Soils Under Different Sampling Scales

Studies on the spatial variability of the soil cation exchange capacity （CEC） were made to provide a theoretical basis for an ecological tea plantation and management of soil fertilizer in the tea plantation. Geostatistics were used to analyze the spatial variability of soil CEC in the tea plantation site on Mengding Mountain in Sichuan Province of China on two sampling scales. It was found that, （1） on the small scale, the soil CEC was intensively spatially correlative, the rate of nugget to sill was 18.84% and the spatially dependent range was 1 818 m, and structural factors were the main factors that affected the spatial variability of the soil CEC; （2） on the microscale, the soil CEC was also consumingly spatially dependent, and the rate of nugget to sill was 16.52%, the spatially dependent range was 311 m, and the main factors affecting the spatial variability were just the same as mentioned earlier. On the small scale, soil CEC had a stronger anisotropic structure on the slope aspect, and a weaker one on the lateral side. According to the ordinary Kriging method, the equivalence of soil CEC distributed along the lateral aspect of the slope from northeast to southwest, and the soil CEC reduced as the elevation went down. On the microscale, the anisotropic structure was different from that measured on the small scale. It had a stronger anisotropic structure on the aspect that was near the aspect of the slope, and a weaker one near the lateral aspect of the slope. The soil CEC distributed along the lateral aspect of the slope and some distributed in the form of plots. From the top to the bottom of the slope, the soil CEC increased initially, and then reduced, and finally increased.

Spatial variability effect of internal friction angle on the post-failure behavior of landslides using a random and non-Newtonian fluid based SPH method

This study proposed a random Smoothed Particle Hydrodynamics method for analyzing the post-failure behavior of landslides,which is based on the Karhunen-Loeve(K-L) expansion,the non-Newtonian fluid model,and the OpenMP parallel framework.Then,the applicability of this method was validated by comparing the generated random field with theoretical result and by simulating the post-failure process of an actual landslide.Thereafter,an illustrative landslide example was created and simulated to obtain the spatial variability effect of internal friction angle on the post-failure behavior of landslides under different coefficients of variation(COVs) and correlation lengths(CLs).As a conclusion,the reinforcement with materials of a larger friction angle can reduce the runout distance and impact the force of a landslide.As the increase of COV,the distribution range of influence zones also increases,which indicates that the deviation of influence zones becomes large.In addition,the correlation length in Monte Carlo simulations should not be too small,otherwise the variation range of influence zones will be underestimated.

Spatial and temporal variability of daily precipitation in Haihe River basin, 1958-2007

The seasonal variability and spatial distribution of precipitation are the main cause of flood and drought events. The study of spatial distribution and temporal trend of precipitation in river basins has been paid more and more attention. However, in China, the precipitation data are measured by weather stations （WS） of China Meteorological Administration and hydrological rain gauges （RG） of national and local hydrology bureau. The WS data usually have long record with fewer stations, while the RG data usually have short record with more stations. The consistency and correlation of these two data sets have not been well understood. In this paper, the precipitation data from 30 weather stations for 1958-2007 and 248 rain gauges for 1995-2004 in the Haihe River basin are examined and compared using linear regression, 5-year moving average, Mann-Kendall trend analysis, Kolmogorov-Smirnov test, Z test and F test methods. The results show that the annual precipitation from both WS and RG records are normally distributed with minor difference in the mean value and variance. It is statistically feasible to extend the precipitation of RG by WS data sets. Using the extended precipitation data, the detailed spatial distribution of the annual and seasonal precipitation amounts as well as their temporal trends are calculated and mapped. The various distribution maps produced in the study show that for the whole basin the precipitation of 1958-2007 has been decreasing except for spring season. The decline trend is significant in summer, and this trend is stronger after the 1980s. The annual and seasonal precipitation amounts and changing trends are different in different regions and seasons. The precipitation is decreasing from south to north, from coastal zone to inland area.

Spatial variability of soil properties in red soil and its implications for site-specific fertilizer management

Assessing spatial variability and mapping of soil properties constitute important prerequisites for soil and crop management in agricultural areas. To explore the relationship between soil spatial variability and land management, 256 samples were randomly collected at two depths (surface layer 0–20 cm and subsurface layer 20–40 cm) under different land use types and soil parent materials in Yujiang County, Jiangxi Province, a red soil region of China. The pH, soil organic matter (SOM), total nitrogen (TN), cation exchange capacity (CEC), and base saturation (BS) of the soil samples were examined and mapped. The results indicated that soils in Yujiang were acidified, with an average pH of 4.87 (4.03–6.46) in the surface layer and 4.99 (4.03–6.24) in the subsurface layer. SOM and TN were significantly higher in the surface layer (27.6 and 1.50 g kg–1, respectively) than in the subsurface layer (12.1 and 0.70 g kg–1, respectively), while both CEC and BS were low (9.0 and 8.0 cmol kg–1, 29 and 38% for surface and subsurface layers, respectively). Paddy soil had higher pH (mean 4.99) than upland and forest soils, while soil derived from river alluvial deposits (RAD) had higher pH (mean 5.05) than the other three parent materials in both layers. Geostatistical analysis revealed that the best fit models were exponential for pH and TN, and spherical for BS in both layers, while spherical and Gaussian were the best fitted for SOM and CEC in the surface and subsurface layers. Spatial dependency varied from weak to strong for the different soil properties in both soil layers. The maps produced by selecting the best predictive variables showed that SOM, TN, and CEC had moderate levels in most parts of the study area. This study highlights the importance of site-specific agricultural management and suggests guidelines for appropriate land management decisions.

Spatial variability of soil organic carbon and total nitrogen in the hilly red soil region of Southern China

To obtain accurate spatial distribution maps of soil organic carbon(SOC)and total nitrogen(TN)in the Hetian Town in Fujian Province,China,soil samples from three depths(0–20,20–40,and 40–60 cm)at 59 sampling sites were sampled by using traditional analysis and geostatistical approach.The SOC and TN ranged from 2.26 to 47.54 g kg-1,and from 0.28 to 2.71 g kg-1,respectively.The coefficient of variation for SOC and TN was moderate at 49.02–55.87%for all depths.According to the nuggetto-sill ratio values,a moderate spatial dependence of SOC content and a strong spatial dependence of TN content were found in different soil depths,demonstrating that SOC content was affected by both extrinsic and intrinsic factors while TN content was mainly influenced by intrinsic factors.Indices of cross-validation,such as mean error,mean standardized error,were close to zero,indicating that ordinary kriging interpolation is a reliable method to predict the spatial distribution of SOC and TN in different soil depths.Interpolation using ordinary kriging indicated the spatial pattern of SOC and TN were characterized by higher in the periphery and lower in the middle.To improve the accuracy of spatial interpolation for soil properties,it is necessary and important to incorporate a probabilistic and machine learning methods in the future study.

Probabilistic stability analysis of embankment slopes considering the spatial variability of soil properties and seismic randomness

The safety of embankments under seismic conditions is a primary concern for geotechnical engineering societies.The reliability analysis approach offers an effective tool to quantify the safety margin of geotechnical structures from a probabilistic perspective and has gained increasing popularity in geotechnical engineering.This study presents an approach for probabilistic stability analysis of embankment slopes under transient seepage considering both the spatial variability of soil parameters and seismic randomness.The spatial varying soil parameters are firstly characterized by the random field theory,where a large number of random field samples of the soil parameters can be readily generated.Then,the factor of safety(FS)of the embankment slope under seismic conditions corresponding to each random field sample is evaluated through performing seismic stability analysis based on the pseudo-static method.A hypothetical embankment example is adopted in this study for illustration,and the influences of shear strength parameters,seismic coefficient,and the external water level on the embankment slope failure probability are systematically investigated.Results show that the coefficient of variation of the friction angle and the horizontal scale of fluctuation have more significant effects on the embankment slope failure probability.Besides,the seismic coefficient also affects the embankment slope failure probability considerably.For a given external water level,the failure probability corresponding to the downstream slope of the embankment is larger than that in the upstream slope.

Optimised Spatial Sampling Scheme for Soil Electriclal Conductivity Based on Variance Quad-Tree (VQT) Method

The acquisition of precise soil data representative of the entire survey area, is a critical issue for many treatments such as irrigation or fertilization in precision agriculture. The aim of this study was to investigate the spatial variability of soil bulk electrical conductivity （ECb） in a coastal saline field and design an optimized spatial sampling scheme of ECb based on a sampling design algorithm, the variance quad-tree （VQT） method. Soil ECb data were collected from the field at 20 m interval in a regular grid scheme. The smooth contour map of the whole field was obtained by ordinary kriging interpolation, VQT algorithm was then used to split the smooth contour map into strata of different number desired, the sampling locations can be selected within each stratum in subsequent sampling. The result indicated that the probability of choosing representative sampling sites was increased significantly by using VQT method with the sampling number being greatly reduced compared to grid sampling design while retaining the same prediction accuracy. The advantage of the VQT method is that this scheme samples sparsely in fields where the spatial variability is relatively uniform and more intensive where the variability is large. Thus the sampling efficiency can be improved, hence facilitate an assessment methodology that can be applied in a rapid, practical and cost-effective manner.

Geostatistical Assessment of the Spatial Distribution of Some Chemical Properties in Calcareous Soils

Spatial patterns of soil fertility parameters and other extrinsic factors need to be identified to develop farming practices that match agricultural inputs with local crop needs. Little is known about the spatial structure of nutrition in Iran. The present study was conducted in a 132-ha field located in central Iran. Soil samples were collected at 0-30 cm depth and were then analyzed for total nitrogen （N）, available phosphorus （P）, available potassium （K）, available copper （Cu）, available zinc （Zn）, available iron （Fe） and available manganese （Mn）. The results showed that the contents of soil organic matter, Cu and Zn in Marvdasht＇s farms were low. The spatial distribution model and spatial dependence level for soil chemical properties varied in the field. N, K, carbonate calcium equivalent （CaCO3） and electrical conductivity （EC） data indicated the existence of moderate spatial dependence. The variograms for other variables revealed stronger spatial structure. The results showed a longer range value for available P （480 m）, followed by total N （429 m）. The value of other chemical properties values showed a shorter range （128 to 174 m）. Clear patchy distribution of N, P, K, Fe, Mn, Cu and Zn were found from their spatial distribution maps. This proved that sampling strategy for estimating variability should be adapted to the different soil chemical properties and field management. Therefore, the spatial variability of soil chemical properties with strong spatial dependence could be readily managed and a site-specific fertilization scheme for precision farming could be easily developed.

Spatial variability and its main controlling factors of the permafrost soil-moisture on the northern-slope of Bayan Har Mountains in Qinghai-Tibet Plateau

The soil moisture movement is an important carrier of material cycle and energy flow among the various geo-spheres in the cold regions.Thus, this research takes the north slope of Bayan Har Mountains in Qinghai-Tibet Plateau as a case study.The present study firstly investigates the change of permafrost moisture in different slope positions and depths. Based on this investigation, this article attempts to investigate the spatial variability of permafrost moisture and identifies the key influence factors in different terrain conditions. The method of classification and regression tree(CART) is adopted to identify the main controlling factors influencing the soil moisture movement. The relationships between soil moisture and environmental factors are revealed by the use of the method of canonical correspondence analysis(CCA). The results show that: 1) Due to the terrain slope and the freezing-thawing process, the horizontal flow weakens in the freezing period. The vertical migration of the soil moisture movement strengthens. It will lead to that the soil-moisture content in the up-slope is higher than that in the down-slope. The conclusion is contrary during the melting period. 2) Elevation, soil texture, soil temperature and vegetation coverage are the main environmental factors which affect the slopepermafrost soil-moisture. 3) Slope, elevation and vegetation coverage are the main factors that affect the slope-permafrost soil-moisture at the shallow depth of 0-20 cm. It is complex at the middle and lower depth.

A Study of Spatial Variability on Aquifer Hydraulic Conductivity K

A structural analysis of K of an aquifer system in the study area is presented, and the main direction and degree of the variability of K are found by using the unstationary regionalized variable theory of geostatistics. Optimal estimation of K has been made by universal kriging method (U K M ). Both spatial variability distribution map and division map of K are given.