Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

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.

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.

An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.

Soil pH Management across Spatially Variable Soils

Knowledge and management of soil pH, particularly soil acidity across spatially variable soils is important, although this is greatly ignored by farmers. The objective of the study was to evaluate in-field spatial variability of soil pH, and compare the efficiency of managing soil pH through site-specific method vs. uniform lime application. The study was conducted on three sites with study sites I and II （23°50＇ S; 29°40＇ E）, and study sites IIl （23°59＇ S; 28°52＇ E） adjacent to each other in the semi-arid regions of the Limpopo Province, South Africa. Soil samples were taken in four replicates from geo-referenced locations on a regular grid of 30 m. Soils were analyzed for pH, and SMP buffer pH. Soil maps were produced with Geographic Information System （GIS） software, and soil pH datasets were interpolated using a geostatistical tool of inverse distance weighing （IDW）. Soil pH in the fields varied from 3.93 to 7.00. An excess amount of lime as high as 30 t/ha under uniform lime application were recorded. These recommendations were in excess on field areas that needed little or no lime applications. Again, there was an under applications of lime as much as 35 t/ha for uniform liming applications. This under- and over-recommendations of lime based on average soil pH values suggests that uniform soil acidity correction and soil pH management strategy is not an appropriate strategy to be adopted in these fields with spatially variable soils. The field can be divided into lime application zones of （1） high rates of lime, （2） low rates of lime and （3） areas that requires no lime at all so that lime rates are applied per zone. A key to site-specific soil acidity correction with lime is to reach ideal soil pH for the crop in all parts of the field.

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.

On Spatial Variability and Distribution of Soil Nutrients in Different Nitraria tangutorum Bobr.Populations in Tsaidam Basin

In this paper,six important distribution areas of Nitraria tangutorum Bobr.in Tsaidam Basin were selected as research objects,to study spatial variability and distribution of soil nutrients in different N.tangutorum populations and analyze the relationship between soil nutrient contents and geographical location,by measuring soil pH and the contents of organic matter（OM）,total nitrogen（N）,total phosphorus（P）,total potassium（K）,hydrolysis N,available P and available K in soils.Results showed that：（1） soil nutrient contents among different populations showed significant spatial variability,and soil depth had a significant effect on soil nutrients contents,but the variation rules were not obvious.（2）Average pH and average contents of OM,total N,total P,total K,hydrolysis N,available P and available K in soils with different depths（0-15,15-30,30-45 cm） varied in the range of 8.37-9.21,3.34-20.68,0.18-1.21,0.35-0.75,16.12-22.04,5.13-553.28,1.10-52.54 and 103.83-562.28 mg/kg,respectively.（3） The analysis results of correlation between average values of pH and contents of nutrient indexes in soils with different depths（0-15,15-30,30-45 cm） showed that the correlation of these indexes were different.（4）OM and total N contents in soils with different depths（0-15,15-30,30-45 cm） all had a significant positive correlation with latitude and negative correlation with longitude and altitude,and the correlation of available P and available K contents in surface soils（0-15 cm） with latitude,longitude and altitude were significant positive,significant negative and significant negative,respectively;moreover,longitude and latitude also showed a significant impact on soil available K contents with the depth of 30-45 cm.In addition,comprehensive analysis result of nutrient contents showed that N.tangutorum populations in Huaitou Tala Town had the highest fertility,and the fertility levels of N.tangutorum populations in Chaka Town and Wulan Keke Town were relatively lower.

Probabilistic back-analysis of rainfall-induced landslides for slope reliability prediction with multi-source information

Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.

Spatial Variability of Nutrient Properties in Black Soil of Northeast China

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 1353 km, whereas the ranges of total K, AP, and AK were only from 6 to 138 km. The fit of the experimental scmi-variograms to the theoretical models indicated that except for AN, kriging could successfully interpolate other six variables. Thns, the geostatistical method used on a large scale could accurately evaluate the spatial variability of most black soil nutrient properties in Northeast China.

Spatial Patterns of Soil Heavy Metals in Urban-Rural Transition Zone of Beijing

To identify the main sources responsible for soil heavy metal contamination, 70 topsoils were sampled from the Daxing County in the urban-rural transition zone of Beijing. The concentrations of heavy metals Cu, Zn, Pb, Cr, Cd, Ni, As, Se, Hg, and Co; the soil texture; and the organic matter content were determined for each soil sample. Descriptive statistics and geostatistics were used to analyze the data, and Kriging analysis was used to estimate the unobserved points and to map the spatial patterns of soil heavy metals. The results showed that the concentrations of all the soil heavy metals exceeded their background levels with the exception of As and Se. However, only the Cd concentration in some areas exceeded the critical value of the national soil quality standard. The semivariance analysis showed that the spatial correlation distances for soil Cu, Zn, Cr, Cd, As, Ni, and Co ranged from 4.0 to 7.0 km, but soil Se, Pb, and Hg had a larger correlation distance. Soil Co, Se, Cd, Cu and Zn showed a strong spatial correlation, whereas the other soil heavy metals showed medium spatial correlation. Soil heavy metal concentrations were related to soil texture, organic matter content, and the accumulation of heavy metals in the soils, which was because of air deposition and use of water from the Liangshui, Xinfeng, and Fenghe rivers that are contaminated by wastewater and sewage for the purpose of irrigation of fields. Hence, a comprehensive treatment plan for these rivers should be formulated.

Spatial and Temporal Variability of N, P and K Balances for Agroecosystems in China

Nitrogen, phosphorus, and potassium balances for agroecosystems in China from 1993 to 2001 were calculated at national and provincial levels using statistical data and related parameters, and their spatial and temporal variabilities were analyzed with GIS to estimate the potential impacts of nutrient N, P and K surpluses or deficits to soil, water and air. At the national scale, the N and P balances from 1993 to 2001 showed a surplus, with the nitrogen surplus remaining relatively stable from 1997—2001. Although during this period the P surplus pattern was similar to N, it had smaller values and kept increasing as the use of phosphate fertilizer increased year by year. However, K was deficient from 1993 to 2001 even though from 1999 to 2001 the K deficit decreased. The spatial analysis revealed higher N surpluses in the more developed southeastern provinces and lowest in the western and northern provinces where there was less chemical fertilizer input. The serious K deficit mainly occurred in Shanghai and Beijing municipalities, Jiangsu, Zhejiang and Hubei provinces, and Xinjiang autonomous regions. For the years 1992, 1996 and 2001, N surpluses and K deficits had significant positive spatial correlations with per capita gross domestic product (GDP), per capita gross industrial output value, and per capita net income of rural households. This showed that the level of economic development played an important role on nutrient balances in the agroecosystems.

Spatial variability and pattern analysis of soil properties in Dehui city, Jilin province

Based on regionalized variable theory, semivariograms of geo-statistics wereused to research the spatial variation of soil properties quantitatively. The results showed thatthe semivariogram of soil organic matter is best described by spherical model, the best model forsemivariograms of soil total N and available K is exponential models and that of available P belongsto linear with sill model. Those soil properties have different spatial correlations respectively,the lag of organic matter is the highest and that of available P is the lowest, the spatialcorrelation of N and available K belongs to moderate degree. Spatial heterogeneities are differenttoo, the degree of organic matter and total N are higher, the degree of available K is in the nextplace and that of available P is the lowest. Influenced by the shape, topography and soil of thestudy area, all isotropies of available P are obvious in all directions while anisotropies of othersare manifested. According to the analytical results, supported by GIS, Kriging and IDW methods areapplied to describe and analyze the spatial distribution of soil properties. The results indicatethat soil organic matter, total N and available K are distributed regularly from northeast tosouthwest, while available P is distributed randomly.

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.

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.

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.

Delineation of Site-Specific Management Zones Based on Temporal and Spatial Variability of Soil Electrical Conductivity

A coastal saline field of 10.5 ha was selected as the study site and 122 bulk electrical conductivity (ECb) measurements were performed thrice in situ in the topsoil (0-20 cm) across the field using a hand held device to assess the spatial variability and temporal stability of the distribution of soil electrical conductivity (EC), to identify the management zones using cluster analysis based on the spatiotemporal variability of soil EC, and to evaluate the probable potential for site-specific management in coastal regions with conventional statistics and geostatistical techniques. The results indicated high coefficients of variation for topsoil salinity over all the three samplings. The spatial structure of the salinity variability remained relatively stable with time. Kriged contour maps, drawn on the basis of spatial variance structure of the data, showed the spatial trend of the salinity distribution and revealed areas of consistently high or consistently low salinity, while a temporal stability map indicated stable and unstable regions. On the basis of the spatiotemporal characteristics, cluster analysis divided the site into three potential management zones, each with different characteristics that could have an impact on the way the field was managed. On the basis of the clearly defined management zones it was concluded that coastal saline land could be managed in a site-specific way.

Small-Scale Spatial Variability of Soil Nutrients and Vegetation Properties in Semi-Arid Northern China

A field experiment was conducted at Kezuohouqi County, Inner Mongolia Autonomous Region of China, which was located on the southeastern edge of the Horqin Sandy Land, to study the spatial variability of soil nutrients for a smallscale, nutrient-poor, sandy site in a semi-arid region of northern China; to investigate whether or not there were 'islands of fertility' at the experimental site; and to determine the key nutrient elements that sustained ecosystem stability. Results obtained from geostatistical analysis indicated that the spatial distribution pattern of soil total nitrogen (STN) was far different from those of soil organic matter (SOM), total phosphorus (STP), and total potassium (STK). Compared to SOM, STP, and STK, STN had a lower structural heterogeneity ratio and a longer range, while other elements were all similar. In addition, STN had an isotropic spatial structure, whereas the others had an anisotropic spatial structure. The spatial structure patterns of herbage species, cover,and height also differed, indicating that spatial variability was subjected to different ecological factors. Differences in the spatial variability patterns among soil nutrients and vegetation properties showed that soil nutrients for a small-scale were not the primary limiting factors that influenced herbage spatial distribution patterns. Incorporating spatial distribution patterns of tree species, namely, Pinus sylvestris var. mongolica Litv. and shrub Lespedeza bicolor Turcz. in a research plot and using fractal dimension,SOM, STP, and STK were shown to contribute to the 'islands of fertility' phenomenon, however STN was not, possibly meaning that nitrogen was a key limiting element. Therefore, during restoration of similar ecosystems more attention should be given to soil nitrogen.