Geostatistical analysis of variations in soil salinity in a typical irrigation area in Xinjiang, northwest China

Mamattursun Eziz， Mihrigul Anwar， XinGuo Li

1. College of Geographical Science and Tourism， Xinjiang Normal University， Urumqi， Xinjiang 830054， China

2. College of Resources and Environmental Science， Xinjiang University， Urumqi， Xinjiang 830046， China



Geostatistical analysis of variations in soil salinity in a typical irrigation area in Xinjiang, northwest China

Mamattursun Eziz1*， Mihrigul Anwar2， XinGuo Li1

1. College of Geographical Science and Tourism， Xinjiang Normal University， Urumqi， Xinjiang 830054， China

2. College of Resources and Environmental Science， Xinjiang University， Urumqi， Xinjiang 830046， China

ABSTRACT

Characterizing spatial and temporal variability of soil salinity is tremendously important for a variety of agronomic and environmental concerns in arid irrigation areas. This paper reviews the characteristics and spatial and temporal variations of soil salinization in the Ili River Irrigation Area by applying a geostatistical approach. Results showed that: （1） the soil salinity varied widely， with maximum value of 28.10 g/kg and minimum value of 0.10 g/kg， and was distributed mainly at the surface soil layer. Anions were mainly SO42-and Cl-， while cations were mainly Na+and Ca2+； （2） the abundance of salinity of the root zone soil layer for different land use types was in the following order: grassland ＞ cropland ＞ forestland. The abundance of salinity of root zone soil layers for different periods was in the following order: March ＞ June ＞ September； （3） the spherical model was the most suitable variogram model to describe the salinity of the 0-3 cm and 3-20 cm soil layers in March and June， and the 3-20 cm soil layer in September， while the exponential model was the most suitable variogram model to describe the salinity of the 0-3 cm soil layer in September. Relatively strong spatial and temporal structure existed for soil salinity due to lower nugget effects； and （4） the maps of kriged soil salinity showed that higher soil salinity was distributed in the central parts of the study area and lower soil salinity was distributed in the marginal parts. Soil salinity tended to increase from the marginal parts to the central parts across the study area. Applying the kriging method is very helpful in detecting the problematic areas and is a good tool for soil resources management. Managing efforts on the appropriate use of soil and water resources in such areas is very important for sustainable agriculture， and more attention should be paid to these areas to prevent future problems.

soil salinization； variation； geostatistics； Ili River Irrigation Area

1　Introduction

Soil salinization is one of the most serious eco-environmental problems in irrigation areas in arid lands （Hamid et al.， 2011）. Soil salinity is the major and most persistent threat to irrigated agriculture（Manoranjan et al.， 2001）. The global extent of primary salt-affected soils is about 955×106hm2， while secondary salinization affects some 77×106hm2， with 58% of these being in irrigated areas. Globally， nearly 20% of all irrigated land is salt-affected （Metternicht and Zinck， 2003）. The total area of saline soil in Xinjiang， China is about 8.48×106hm2， and 31.1% of the plantation area is threatened by salinization. Soil salinity， together with other soil physical and chemical properties， plays an important role in plant composition， productivity， and distribution because of the differences in tolerances of plant species to salinity. Excessive salt amounts adversely affect soil physical and chemical properties， as well as the microbiologicalprocesses （Abdelbasset et al.， 2009）. Effects of soil salinity are manifested in loss of stand， reduced plant growth， reduced yields， and in severe cases， crop failure. Remedial actions for soil salinization require reliable information to better set priorities and to choose the types of action that are most appropriate for combating soil salinization. Studies on the characteristics and spatio-temporal distribution of saline soil can provide such necessary basic information for the documentation of salinity changes and the anticipation of further degradation （Dennis et al.， 2007）. Therefore， soil salinization in irrigation areas is a significant issue that is theoretically and practically essential for sustainable development of agricultural land in arid regions.

The spatial and temporal dynamics of soil salinization are important issues in soil salinization studies（Zhao et al.， 2005； Mehmet and Turgut， 2006）. With the increasing concern of soil salinization in irrigated areas， the monitoring of the spatio-temporal dynamics of soil salinity in an aquifer can act as an early warning device in land degradation. Irrigation of agricultural land in arid regions requires that water be applied in excess of evapotranspiration to prevent salt accumulation in the root zone （Prendergast et al.，2004）. However， applications of excessive water，chemical fertilizers， and inefficient irrigation methods threaten the sustainability of groundwater （Stigter et al.， 2006）. The expansion of irrigated agriculture and intensive agricultural activities induce the risk of soil quality degradation.

There are many management tools that can help managers to make appropriate decisions given the available soil resources. Geostatistical methods have been proven to be an applicable and reliable tool for better management and conservation of soil， water resources， and sustainable development of any area（Kumar et al.， 2005； Reghunath et al.， 2005； Theodossiou and Latinopoulos， 2007）. For example， Triantafilis et al. （2004） used the non-linear kriging method for mapping the salinity risk of farmlands using saline groundwater as the source of irrigation. Results of their study explained the potential of geostatistics to simulate the critical conditions of salinization. In this study， we applied geostatistics for analyzing the spatio-temporal variations of soil salinity in the Ili River Irrigation Area （IRIA）. Such quantitative analyses are urgently needed， will be useful in land use management of the IRIA， and can provide a scientific basic for soil salinity control and sustainable agriculture in the study area.

2　Materials and methods

2.1Study area

The IRIA study area is situated in the northern slope of the Tianshan Mountains and the southern part of the Ili River， Xinjiang Uyghur Autonomous Region，China. It ranges from 80°54′E to 81°12′E and from 43°44′N to 43°56′N， with an altitude ranging from 560-750 m a.s.l.， with a total area of 424.6 km2（Figure 1）. The study area has an arid continental climate with an average annual temperature of 8.9 °C， an average annual rainfall of 277 mm， and an average annual evaporation capacity of 1，594 mm. The accumulated active temperature ≥10 °C is about 3，100 °C. The annual daily sun duration is 2，100 h. The coldest month （January） generally has a mean air temperature of -9.3 °C， while the warmest month （July） usually has an average air temperature of 22.9 °C（Mamattursun et al.， 2012）.

Figure 1　Location of the sampling sites

Seriphidium transillense， Phragmites australia，Amaranthus retroflexus， Populus alba var. pyramidalis，Elaeagnus oxycarpa， and Hippophae rhamnoides are the main plant species in the study area. Cotton， wheat，and rice are the dominant crops in the region. The peak irrigation period for all crops is June-August，whereas irrigation of winter wheat starts in November. Agricultural developments depend on irrigation supplemented by groundwater resources to certain extent. The irrigation of these water-intensive crops and seepage from the canal network are the most likely causes of changes in groundwater salinity in the study area. A hydrological imbalance， underpinned by improper agricultural practices， has caused the water table to rise in the topsoil. This rise of the water table has led to increased soil salinization and degradation of the relatively limited soil resources in the study area （Wang et al.， 2008）.

2.2Sampling and testing

Figure 1 shows the geographical location of the soil sampling points. The coordinates system used in Figure 1 is the Universal Transverse Mercator（UTM）. The datum of this system is the World Geodetic System of 1984 （WGS 1984） upon which Global Positioning System （GPS） measurements were made. A total of 544 soil samples from 56 sampling sites were taken in March （136 samples）， June（136 samples）， and September （136 samples） of 2009. The soil samples were taken randomly， and according to the spatial variation parameters of soil salinity，the total numbers of soil samples were suitable for a spatial analysis.

Soil samples collected in the field were analyzed for chemical constituents， such as electrical conductivity （EC）， anions （HCO3-， Cl-， CO32-， and SO42-），and cations （Na+， K+， Ca2+， and Mg2+）. Soil salinity and EC were measured using digital meters immediately after sampling. The concentrations of Mg2+，CO32-， and HCO3-were determined by volumetric titrations， AgNO3was used to estimate Cl-， a flame photometer was used to measure Na+and K+ions， and EDTA titrations were used to measure SO42-and Ca2+. The accuracy of the chemical analysis was verified by calculating ion-balance errors， where the errors were generally around 10%.

2.3Geostatistical analysis methods

Geostatistical methods can be used to ideally describe the spatial variability of the environment and reveal the spatial heterogeneity and spatial patterns of natural phenomena. Analyses using the semivariogram model and the kriging interpolation are the most common geostatistical analysis methods（Goovaerts， 1997）.

2.3.1The semivariogram model

The main tool in geostatistics is the semivariogram， which expresses the spatial dependence between neighboring observations （Yang et al.， 2005）. The semivariogram， γ（h）， can be defined as one-half the variance of the difference between the attribute values at all points separated by h， as follows:

where γ（h） is the semi-variance， h is the sampling distance， Z（x） indicates the magnitude of the variable，and N（h） is the total number of pairs of attributes that are separated by a distance h.

Prior to the geostatistical estimation， we require a model that enables us to compute a variogram value for any possible sampling interval. A variogram model can be used to indicate both the structural and random aspects of a variable， such as soil salinity. The most commonly used variogram models are the spherical model， the exponential model， and the Gaussian model （Goovaerts， 1997； Wang， 1999；Hamid et al.， 2011）. The most appropriate semivariogram model was selected by comparing these models by the following parameters: mean error， root mean square， average standard error， root mean square standardized error， determination coefficient，and sum of the residual squares （Hu and Lu， 2009）. ArcGIS 9.2 was used for choosing the most appropriate semivariogram model.

2.3.2Kriging interpolation

Kriging interpolation is an exact interpolation estimator used to find the best linear unbiased estimate. It is based on certain mathematical models and statistical models， and it is used to derive the weight coefficients from the measured values of the nearby measurement points， and then to predict them （Pucci and Murashige， 1987； Hamid et al.，2011）. Kriging weight coefficients are calculated by using the semivariogram figure reflecting the spatial structure of the data. They are determined not only by the semivariogram figures and the distances to the prediction points， but also by the spatial relations of the measured values of the nearby measurement points （Yang et al.， 2008）. The spatial variability of the related points in the study area can be estimated by using the monitoring data of the sampling sites and the location relations between the sampling sites and the semivariogram model.

3　Results

3.1Conventional statistics of soil salinity

Descriptive statistics， including minimum values，maximum values， mean values， standard deviation（St.D）， and coefficient of variation （CV） for soil salinity from 136 sampling points， are summarized in Table 1.

Table 1 shows that the soil salinity varied widely，with a maximum value of 28.10 g/kg and a minimum value of 0.10 g/kg for the 0-3 cm soil layer， a maximum value of 8.70 g/kg and a minimum value of 1.37 g/kg for the 3-20 cm soil layer， a maximum value of 6.20 g/kg and a minimum value of 1.09 g/kg for the 20-40 cm soil layer， and a maximum value of 4.60 g/kg and a minimum value of 0.10 g/kg for the 40-60 cm soil layer. The mean values of soil salinity were 5.05， 1.37， 1.09， and 0.88 g/kg for the 0-3，3-20， 20-40， and 40-60 cm soil layers， respectively. The overall mean top-soil layer salinity value of 5.05 g/kg falls into the category of moderately saline soil（Rhoades et al.， 1992）. The abundance of the major ions was in the following order: Cl-＞ SO42-＞Ca2+＞ Na+＞ K+＞ Mg2+＞ CO32-＞ HCO3-for the 0-3 cm soil layer， SO42-＞ Cl-＞ Na+＞ Ca2+＞Mg2+＞ K+＞ CO32-= HCO3-for the 3-20 cm soil layer， SO42-＞ Cl-＞ Ca2+＞ Na+＞ Mg2+＞ K+＞CO32-= HCO3-for the 20-40 cm soil layer， and SO42-＞ Na+＞ Cl-＞ Ca2+＞ Mg2+＞ K+= CO32-= HCO3-for the 40-60 cm soil layer. The CVs of soil salinity were fairly high. This could have been due to uneven crop growth and non-uniform management practices， resulting in marked changes in soil salinity over small distances. The CVs of anions and cations in the soil also exhibited remarkable variability，ranging from a maximum value of 650% to a minimum value of 50%. The salinity and main ions of the 0-3 cm soil layer were higher than the salinity and main ions of the 3-20 cm， 20-40 cm， and 40-60 cm soil layers. The salinity of the surface soil layer （0-3 cm） accounted for 60.19% of the salinity of 0-60 cm soil layer， indicating that the soil salinity was distributed mainly in the top soil layer.

Table 1　Descriptive statistics of soil salinity in the study area （g/kg）

3.2The soil salinity of different land use types

Previous studies showed that the soil salinity of different land use/cover types varies due to the different effects of groundwater withdrawal， irrigation，and transpiration （Mamattursun et al.， 2010）. Therefore， we analyzed the change of soil salinization in different land use/cover types （Table 2）.

Table 2 shows that the soil salinity of grassland varied from 0.10 g/kg to 8.70 g/kg， while the salinity of forestland varied from 0.20 g/kg to 1.10 g/kg and the salinity of cropland varied from 0.10 g/kg to 4.00 g/kg. The mean values of soil salinity were 2.50，0.60， and 1.10 g/kg for grassland， forestland， andcropland， respectively. This indicates that the grassland was slightly salinized. The abundance of the major ions of grassland was in the following order: Cl-＞ SO42-＞ Na+＞ Ca2+＞ Mg2+＞ K+＞ CO32-= HCO3-. The abundance of the major ions of forestland was in the following order: SO42-＞ Na+＞Cl-＞ Mg2+＞ Ca2+= K+= CO32-= HCO3-. The abundance of the major ions of cropland was in the following order: SO42-＞ Na+＞ Ca2+＞ Cl-＞ Mg2+＞K+＞ CO32-= HCO3-. The CVs of soil salinity for different land use types were relatively high， which could have been due to non-uniform management practices， resulting in marked changes in soil salinity over small distances.

Table 2　Change of soil salinity and ion components for different land use/cover （LUCC） types （g/kg）

3.3The soil salinity in different periods

The changes in soil salinity in March， June， and September are shown in Table 3. The soil salinity in March varied from 0.10 g/kg to 10.30 g/kg， while it varied from 0.10 g/kg to 8.70 g/kg in June and from 0.06 g/kg to 6.17 g/kg in September. The mean values of soil salinity were 1.61， 1.37， and 1.17 g/kg for March， June， and September， respectively. The CVs of soil salinity in different periods were also relatively high， possibly due to irrational agricultural activities， resulting in marked changes in soil salinity over small distances.

3.4Spatial structure analysis of soil salinity

The results of spatial structure analysis of soil salinity are summarized in Table 4. By fitting a variogram model to the data， it was found that the spherical model was the most suitable variogram model to describe the salinity of the 0-3 cm and 3-20 cm soil layers in March and June， and the 3-20 cm soil layer in September； the exponential model was the most suitable variogram model to describe the salinity of the 0-3 cm soil layer in September.

The spatial heterogeneity of a variable in a variogram model can be represented by the values of nugget， sill， and range. Nugget and sill characterize the random aspect of the variable， whereas range characterizes the structural aspect. The ratio of nugget variance to sill variance could be regarded as a criterion to classify the spatial dependence of groundwater levels and salinity. If the ratio is less than 25%， the variable has strong spatial dependence； between 25% and 75%，the variable has moderate spatial dependence； and greater than 75%， the variable shows only weak spatial dependence （Li， 1998）.

In our study， the nugget effect of less than 1.1 for soil salinity indicated the existence of a strong spatial auto-correlation for these elements （Yang et al.， 2008）. The low nugget effect reflects the fact that the variation of soil salinity was highly spatially structured，and also that there was little or no variability of soil salinity in shorter distances of the range values. This led us to the conclusion that the fitted semivariogram model well represented the spatial structure of variation of soil salinity. In our study the nugget-to-sill ratios of March， June， and September were less than 75%， indicating that the soil salinity in those months had a strong spatial dependence. The nugget-to-sill ratios of soil salinity also revealed that the spatial heterogeneity of soil salinity was caused mainly by the spatial structure， and the soil salinity was jointly affected by the spatial structure and the stochastic factors. The spatial correlation of the soil salinity might be caused by the spatial structure， such as natural fac-tors including the terrain， landforms， climate， and soil types. The ranges of soil salinity of March， June， and September simulated in the semivariogram model varied from 0.93 km to 2.98 km， indicating that our sampling density was suitable for the study area. However， the ranges were similar among the measurement periods despite the differences in irrigation intensity between the measurement periods.

Table 3　Change of soil salinity and its ion components in different periods （g/kg）

Table 4　The spatial variation parameters of soil salinity in the study area

3.5The kriged map of soil salinity

Ordinary kriging was applied for estimation of soil salinity in March， June， and September across the study area （Figure 2）. This figure shows that higher soil salinity was distributed in the central parts and lower soil salinity was distributed in the marginal parts of the study area. Soil salinity tended to increase from the marginal parts to the central parts across the study area. The soil salinity in the central parts of the study area exceeded 28 g/kg and the maximum value reached 28.10 g/kg. The soil salinity in the marginal parts was low； its minimum value was about 0.10 g/kg.

4　Discussion

This research showed that geostatistical analysis methods are very useful for estimating changes in the spatio-temporal dynamics of soil salinization at the local level. In many cases， geostatistical analysis methods may be the most economically feasible way to gather regular soil salinization information over large areas. Results of our study indicated that in these spatial distribution maps of soil salinity the similarity of the spatial patterns of the soil salinity in each measurement period was quite striking: soil salinity was higher in the central and in the marginal parts of the region even in March， when no or rare irrigations occurred. Interpolation and comparison of the soil salinity maps in each measurement period showed that the spatial distribution of soil salinity was very similar to those of the averaged March， June， and September measurements.

Agricultural wells are denser in central parts of the study area than in the northern parts， and they directly exert much pressure on groundwater levels and salinity. Furthermore， there are more agricultural canals in the central parts of the area than in the southern parts，which also directly exert much pressure on the groundwater level. Therefore， the groundwater level in the central parts of the region is not as deep as elsewhere. Because of drought， soil water evaporates，leaving salt in the soil and groundwater. Also， higher-EC groundwater used for irrigation contributes salt to the soil， causing soil salinization. The groundwater table in the central parts of the study area has now almost reached the ground surface in some areas，causing an advanced stage of soil salinization. This indicates that more attention should be paid to these areas to prevent future problems.

Figure 2　Spatial distribution of soil salinity obtained by the kriging method

Reasonable irrigation rates - well-adjusted to the water demands of crops - and suitable changes in land use should not result in an unbalance of the regional water quantity. However， the water resources in the study area have been used for agriculture for a long time， mostly for irrigation by traditional flooding（although some croplands have adopted new irrigation techniques）. As a result， water has been lost in the transportation process and excess water has been infiltrated， leading to a rise of the groundwater level to near the surface， becoming the origin of soil salinization. Bio-drainage can be enhanced by cultivating salt-tolerant trees and shrubs that have a high rate of evapotranspiration. Vegetation such as Populus euphratica， Halocnemum strobilaceum， Halogeton arachnoideus， Halostachys caspica， Haloxylon ammodendron， T. taklamakanensis， and Tamarix arceuthoides are suitable to be grown and have great benefits to control soil salinization in the area（Mamattursun et al.， 2010）. Biological drainage has proven to be very effective in lowering shallow groundwater tables and facilitating some leaching of salts from the surface layers of salinized soils. Enhancing the integrated use of surface water and groundwater will optimize the use of water resources.

5　Conclusions

With an extensive field survey of soil salinity in 2009， and with many interviews of local experts， we have shown that the use of geostatistics can be an effective means of acquiring information on soil salinization changes. In an arid zone oasis such as the IRIA，extensive field-based survey methods can be difficult and expensive to implement due to restricted accessibility. However， in such areas a limited amount of field sampling combined with geostatistical analysis methods can produce reasonably accurate large-scale information at relatively little cost.

In this research， we examined the characteristics and spatio-temporal dynamics of soil salinity in the IRIA， where the entire irrigated area is suffering from various degrees of soil salinization. The observed spatial distribution of soil salinity in the IRIA revealed that central parts of the area were at risk of grave land degradation due to higher groundwater salinity and deeper groundwater levels. The higher soil salinity in the study area was also associated with improper human activities， specifically mismanaged agricultural practices.

It is concluded that application of geostatistics can produce better insight into soil salinization and can lead to valuable solutions for those critical conditions which endanger soil resources. Applying the kriging method is also helpful in detecting the problematic areas and is a good tool for soil resources management. Hence，management efforts in the appropriate use of soil and water resources in salinized areas are very important for sustainable agriculture. The future sustainable agriculture of salinized areas is highly dependent on the present management of the soil and water resources.

Acknowledgments:

The authors are grateful to the anonymous reviewers for their critical reviews and comments on drafts of this manuscript. This research was funded by the National Natural Science Foundation of China （Nos. 41201032， 41561073， and U1138302）.

Abdelbasset L， Mokded R， Tahar G， et al.， 2009. Effectiveness of compost use in salt-affected soil. Journal of Hazardous Materials， 171: 29-37. DOI: 10.1016/j.jhazmat.2009.05.132.

Dennis LC， James DR， Jirka Š， 2007. Leaching requirement for soil salinity control: Steady-state versus transient models. AgriculturalWaterManagement，90:165-180.DOI: 10.1016/j.agwat.2007.02.007.

Goovaerts P， 1997. Geostatistics for Natural Resources Evaluation. New York: Oxford University Press.

Hamid Y， Mamattursun E， Mihrigul M， et al.， 2011. Variations in groundwater levels and salinity in the Ili River Irrigation Area，Xinjiang， northwest China: A geostatistical approach. International Journal of Sustainable Development & World Ecology，18: 55-64. DOI: 10.1080/13504509.2011.544871.

Hu XL， Lu L， 2009. Spatio-temporal variability of groundwater level in the middle Heihe River Basin. Journal of Desert Research， 29: 777-784.

Kumar S， Sondhi SK， Phogat V， 2005. Network design for groundwater level monitoring in upper Bari Doab canal tract，Punjab，India.IrrigationDrainage，54:431-442. DOI: 10.1002/ird.194.

Li HB， 1998. Theory and methodology of spatial heterogeneity quantification. Applied Ecology， 9: 651-657.

Mamattursun E， Hamid Y， Anwar M， et al.， 2010. Oasis land-use change and its effects on the oasis eco-environment in Keriya Oasis， China. International Journal of Sustainable Development &WorldEcology，17:244-252.DOI: 10.1080/13504500903211871.

Mamattursun E， Hamid Y， Zulpiya M， et al.， 2012. The response of soil salinization to characteristics of groundwater in Ili River Valley. Journal of China Hydrology， 32: 14-20.

Manoranjan KM， Sadiqul IB， Danielito TF， 2001. Soil salinity reduction and prediction of salt dynamics in the coastal ricelands of Bangladesh. Agricultural Water Management， 47: 9-23. DOI: 10.1016/S0378-3774（00）00098-6.

Mehmet C， Turgut Y， 2006. Hydro-chemical evaluation of groundwater quality in the Cavuscayi Basin， Sungurlu-Corum，Turkey.EnvironmentalGeology，50:323-330. DOI: 10.1007/s00254-006-0211-7.

Metternicht GI， Zinck JA， 2003. Remote sensing of soil salinity: Potentials and constraints. Remote Sensing of Environment， 85: 1-9. DOI: 10.1016/S0034-4257（02）00188-8.

Prendergast JB， Calvin WR， William LH， 2004. A model for conjunctive use of groundwater and surface for control of irrigation salinity. Irrigation Science， 14: 167-175. DOI: 10.1007/BF00190188.

Pucci AA， Murashige JAE， 1987. Application of universal krigingto an aquifer study in New Jersey. Ground Water， 25: 672-678. DOI: 10.1111/j.1745-6584.1987.tb02207.x.

Reghunath R， Sreedhara Murthy TR， Raghavan BR， 2005. Time series analysis to monitor and assess water resources: A moving average approach. Environmental Monitoring and Assessment，109: 65-72. DOI: 10.1007/s10661-005-5838-4.

Rhoades JD， Kandiah A， Mashali AM， 1992. The use of saline waters for production. FAO Irrigation and Drainage Paper No. 48， Rome， pp. 130-136.

Stigter TY， Ribeiro L， Carvalho DA， 2006. Application of a groundwater quality index as an assessment and communication tool in agro-environmental policies - Two Portuguese case studies.JournalofHydrology，327:578-591. DOI: 10.1016/j.jhydrol.2005.12.001.

Theodossiou N， Latinopoulos P， 2007. Evaluation and optimisation of groundwater observation networks using the kriging methodology. Environmental Modelling Software， 22: 414-415.

Triantafilis J， Odeh IOA， Warr B， et al.， 2004. Mapping of salinity risk in the lower Namoi Valley using non-linear kriging methods. Agricultural Water Management， 69: 203-231. DOI: 10.1016/j.agwat.2004.02.010.

Wang HW， Zhang XL， Qiao M， 2008. Assessment and dynamic analysis of the eco-environmental quality in the Ili River Basin based on GIS. Arid Land Geography， 31（2）: 215-221.

Wang Z， 1999. Geostatistics and Its Application in Ecology. Beijing: Science Press， pp. 53-87.

Yang FG， Cao SY， Liu XN， et al.， 2008. Design of groundwater level monitoring network with ordinary kriging. Journal of Hydrodynamics， 20: 339-346. DOI: 10.1016/S1001-6058（08）60066-9.

Yang YJ， Yang JS， Liu GM， et al.， 2005. Space-time variability and prognosis of soil salinization in Yucheng City， China. Pedosphere， 15: 797-804.

Zhao CY， Wang YC， Chen X， et al.， 2005. Effects of groundwater level fluctuation on its chemical composition in karst soils of Lithuania.EcologicalModelling，187:341-351. DOI: 10.1007/s00254-007-1164-1.

Eziz M， Anwar M， Li XG， 2016. Geostatistical analysis of variations in soil salinity in a typical irrigation area in Xinjiang， northwest China. Sciences in Cold and Arid Regions， 8（2）： 0147-0155.

10.3724/SP.J.1226.2016.00147.

*Correspondence to: Mamattursun Eziz， Vice Professor of College of Geographical Science and Tourism， Xinjiang Normal University. No. 102， Xinyi Road， Urumqi， Xinjiang 830054， China. E-mail: oasiseco@126.com

August 29， 2015Accepted: December 6， 2015