Yang, Liu, Yang,Sun, & Beazley 2009 Multivariate and Geostatistical Analysis of Soil Salinity in...

8/2/2019 Yang, Liu, Yang,Sun, & Beazley 2009 Multivariate and Geostatistical Analysis of Soil Salinity in Nested Areas of the Y

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Multivariate and geostatistical analysis of wetland soil salinityin nested areas of the Yellow River Delta

M. YangA

, S. L. LiuA,C

, Z. F. YangA

, T. SunA

, and Robert BeazleyB

ASchool of Environment, State Key Laboratory of Water Environment Simulation,

Beijing Normal University, Beijing 100875, China.BDepartment of Natural Resources, College of Agriculture and Life Sciences, Fernow Hall 302,

Cornell University, Ithaca, NY 14853, USA.CCorresponding author. Email: [email protected]

Abstract. This study investigated scale dependency of certain soil salinity ions in topsoil horizons in the Yellow RiverDelta in north-east Shandong province, China. Factorial kriging analysis (FKA) was used to analyse spatial variability of

soil salinity ions (Na+, K+, Mg2+, Ca2+, Cl, SO42) sampled at 3 nested areas over a geologically contrasting region.

Correlation analysis and principal component analysis (PCA) were performed on the logarithmic variables, thenmultivariate geostatistics was used to investigate scale dependency of soil salinity spatial variability and auto- and

cross-variograms exhibited by 3 spatial structures: nugget effect, short-range, and long-range structures. Statistical analysis

showed that NaCl was the main salinity type over the 3 nested sample areas. In addition, the variables were random and

regional, which implied that a linear model of coregionalisation was feasible for the analysis of their spatial variability. The

coefficients of the coregionalisation matrix showed that the short-range structures of auto- and cross-correlation for soil

salinity were dominant at the large and middle-sized sample areas, while the long-range structure dominated at the small

area. The resulting structural correlation coefficients showed strong correlations between variables changing as a function

of spatial scale. These relationships between soil salinity variables at different spatial structures were not acquired by the

linear correlation coefficients.

PCA was then performed on the coregionalisation matrices at each sample area to summarise the relationships among

variables at different spatial structures. From the synthetic analysis of coregionalisation matrices, correlation matrices, and

principal components, we concluded that soil genesis and parent material may act on short-range variation of soil salinity,

while climate and topography may influence long-range structure at the large sample area. At the middle-sized sample area,

variations were mostly affected by mineral fertilisation at the short-range structure, while human activities such as irrigationand drainage in wetland restorations influenced the soil salinity spatial variability at the long-range scale. Vegetation and

groundwater table may also be important factors influencing the spatial variability of soil salinity at different spatial

structures at the small sample areas.

Additional keywords: spatial variability, salinity, factory kriging, PCA, coregionalisation.

Introduction

Spatial variability of soil properties results from complex

interactions between natural processes and management

practices at different spatial and temporal scales (Castrignanet al. 2000b; Lin et al. 2002). Soil-forming factors, such as

parent materials, biota, climate, and topography (Jenny 1941),

explain most of the general characteristic variability; however,

management practices may also affect soil variability

significantly (Marx et al. 1988; Dobermann 1994; Bocchi

et al. 2000; Mohawesh et al. 2008). Some of the several

possible factors that govern soil variability are likely to have

a short-range action, whereas others operate at longer distances.

As a consequence, soil variables are expected to be correlated in

a way that is scale-dependent (Castrignan et al. 2000a).

Despite the amount of literature published in the past 3

decades, knowledge about soil variability is still dispersed

and requires further synthesis (Heuvelink and Webster 2001;

Lin et al. 2005). In particular, there is a need to quantify soil

variability across multiple scales, which will enhance the use of

soil information in diverse applications. To determine scaledependency, factorial kriging analysis (FKA) developed by

Matheron (1982) was previously used in soil science

(Goovaerts 1992). FKA is a variant of kriging that aims to

estimate and map different sources of spatial variability

determined from the experimental variograms (Goovaerts

1992, 1998). This multivariate geostatistical technique allows

description of the spatial relationships, as well as separating

the sources of variation according to the spatial scales at which

they operate (Imrie et al. 2008).

Previously, FKA has been successfully applied in various

fields including remote sensing (Oliver et al . 2000; van

Meirvenne and Goovaerts 2002; Rodgers and Oliver 2007;

CSIRO 2009 10.1071/SR08211 0004-9573/09/050486

CSIRO PUBLISHING

www.publish.csiro.au/journals/ajsr Australian Journal of Soil Research, 2009, 47, 486497


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Tarnavsky et al. 2008), hydrogeology (Wang et al. 2001; Linet al. 2004, 2006; Ryu et al. 2006), ecology (Castrignan et al.

2000b; Hernndez et al. 2007), and landscape (Bishop and Lark

2006). Within the field of soil science, it has been used to assess

variation in soil properties for crop management (Dobermannet al. 1995; Bocchi et al. 2000; Casa and Castrignan 2008),

geochemical exploration (Jimnez-Espinosa and Chica-Olmo

1999; Batista et al. 2001; Reis et al. 2004), pollution (Einax

and Soldt 1998; Lin et al. 2002; Rodrguez et al. 2008), and

analyses of underlying geochemical processes (Bourennane

et al. 2003; Xu and Tao 2004). Considerable work has been

done to investigate variability of soil basic properties (Brdossy

and Lehmann 1998; Qiu et al. 2001) and soil pollutants (Li et al.

2006; Tavares et al. 2008), particularly heavy metals (Reis et al.

2003; Xu and Tao 2004). However, quantification of soil salinity

variability at multiple scales is often desirable, especially in

wetlands where salinity accumulates in great amount and

threatens plants and animals.

Accumulation of soluble salts in soils has caused serious

problems in relation to agricultural development and naturalresources management. As soils become more saline, soil

moisture becomes less available to plants, until at higher

salinities water is drawn from the roots back into the soil

(Brady and Weil 2002). The classic conditions that promote

increasing soil salinity are drier climates, where irrigation is

irregular and evapotranspiration allows salts to become

concentrated in the upper part of the soil profile. However,

low-lying coasts, such as deltas where surface drainage is poor

andflooding during extremely high tides or storms is common,

can also foster conditions for high levels of soil salinity (Fang

et al. 2005). Salinities of 0.7 dS/m are less stressful to mostplants. Above this threshold, salt toxicity occurs, with different

plants becoming susceptible to perceptible salt stress at salinities

as low as 0.8 dS/m.The Yellow River Delta, the only large delta in China to

undergo extensive development, is characterised by extensive

coverage of saline soils (Liu and Drost 1997). A high proportion

of these occur in the most actively prograding areas inconjunction with recently formed estuarine wetlands. Despite

plans of the Chinese central government to enlarge the

agricultural production base in the Yellow River Delta, a lack

of information on the salinisation potential of regional soils

remains an impediment to developing a balanced and

ecologically sound plan to achieve this goal.

The main objective of this work was to study the spatial

variability of soil salinity over 3 nested areas in the Yellow River

Delta and seek possible explanations for their distributions inthe light of the statistical evidence. Here, the soil salinity was

characterised by calcium (Ca2+), potassium (K+), sodium (Na+),

magnesium (Mg2+), chloride (Cl), and sulfate (SO42) ions.

Each ion had its own distinctive distribution and it was difficult

to discern common patterns and to seek common causes for

them. Therefore, we deemed that an analysis of

coregionalisation would be more revealing than a univariate

geostatistical analysis. We examined the scale-dependent

correlation structure of some soil properties, proposing that it

can reflect the different sources of variability. Information about

soil variability is important in ecological modelling,

environmental prediction, precision agriculture, and natural

resources management (Lin et al. 2005). Consequently, studyon soil salinity variability in the Yellow River Delta can

provide important scientific data for natural resources

management and wetland restorations.

Materials and methodsThe study area

The study area is located at 11880701198100E and

378200388120 N in northern Shandong Province, China

(Fig. 1). It encompasses an area >6000 km2, from Ninghai,extending north-east to the Taoerhe River estuary, south-east

to the Xiaoqinghe River estuary, and eastward forming a fan

shape. This region lies within the newly created wetlands of the

Yellow River Delta, which extends from the mouth of YellowRiver to the Bohai Sea. The current course of Yellow River was

formed artificially in 1976 by changing the old course from the

Diaokou River to the Qingshui Gully. The soils in the delta

formed on marine sediments as a result of deposition of a large

amount of sand and mud transported by the Yellow River,together with lateral sea seepage. The Yellow River Delta is

characterised by extensive coverage of saline soils, a high

proportion of which occurs in the most actively prograding

areas in conjunction with recently formed estuarine wetlands.

The area has a monsoon climate of the warm-temperate zone.

The average annual temperature is 11.712.68C. The average

annual precipitation is 530630 mm, of which 70% is rainfall

during summer (MayJuly), and the ratio of evaporation to

precipitation is 3.22 on annual average. The groundwater

table in the delta is high, in general ranging from 1.6 to

2.4 m and the mineralisation degree is 32.4 g/L on average.

Soil samples and analytical methodsOur analysis is based on 118 samples collected in October and

November 2007. Soil samples were taken at 3 nested areas:

(1) the large area of the whole Yellow River Delta, (2) the middle

area of The Dawenliu Nature Reserve, and (3) the small area of

the core of The Dawenliui Nature Reserve (Fig. 1). Collected

soil samples were air-dried and crushed to pass through a 2-mm

mesh. Fifty-g subsamples were ground in a mortar to pass

through a 0.25-mm sieve. Samples were subsequently

transported to the laboratory for determination of soil salt

content, including Ca2+, K+, Na+, Mg2+, Cl, and SO42. Na+

and K+ which were determined by frame photometry, and Ca2+,

Mg2+, Cl, and SO42 by ion chromatography.

Multivariate geostatistical analysis

The theory underpinning factorial kriging analysis (FKA) has

been published in several texts (Goovaerts 1997; Wackernagel

1998; Pardo-Igzquiza and Dowd 2002). A brief outline is

provided below. The first step involves the definition of a

linear model of coregionalisation (LMC). Geostatistical

techniques rely on variograms, which are convenient

representations of the auto- and cross-correlation structures in

a spatially distributed dataset. Spatial scales of variation in our

context are related to different ranges observed in the

experimental semi-variogram. An experimental variogram is

calculated as follows:

Analysis of wetland soil salinity Australian Journal of Soil Research 487


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gh 12Nh

XNh

i 1

zxi zxi h2 1

where N is the number of data pairs approximately separated

by the vectorh, andz(xi) is the value of the regionalised variable

zof interest at location xi. Cross-variograms between 2 variables

za and zb may be calculated as follows:

gabh 1

2Nh

XNh

i 1

zaxi zaxi h zbxi zbxi h

2

Valid models which are commonly fitted to the experimentalvariograms include spherical, Gaussian, and exponential

functions. These are characterised by a sill, which represents

the covariance accounted for by the model, and a range, which

signifies the extent of spatial correlation. The value of the

variograms where the model approaches the abscissa is

referred to as the nugget effect. This encompasses the micro-

scale variation and any errors due to analytical, sampling, or

location measurements (Imrie et al. 2008).

The experimental semi-variograms and semi-cross-

variograms are modelled with nested structures, with each

structure representing a particular scale of variation, e.g. a

nugget effect, a short-range structure, and a long-range

structure (Pardo-Igzquiza and Dowd 2002). Such a

covariogram is a linear combination of Ns component

functions gu(h):

gabh XNsu 1

guabh XNsu 1

buab guh 3

where the buab are coefficients which represent the importance

of each spatial scale u on the relationships between the variables.

This LMC can be expressed in matrix terms:

Gh XNsu 1

Buguh 4

where G(h) is the p p variogram matrix and Bu

is a positivesemi-definite matrix of the coefficients babu . A measure of the

correlation between the variables za andzb at the spatial scale u

is given by:

ruab buabffiffiffiffiffiffiffiffiffiffiffiffiffiffibuaab

ubb

q 5

The structural correlation coefficients rabu differ from the

traditional product-moment correlation coefficients in that

they focus on specific spatial scales, filtering out the

processes operating over different distances (Castrignan

et al. 2000b).

Legend

Legend

Large scope

Middle scope

Small scope

Small scope

The delta

Core area

0 5 10 20 km

0 500 1000 2000 km

Yellow River

Yangtze River

The Yellow River Delta

0 15 30 60 m

N

N

N

Fig. 1. Location of the Yellow River Delta and samples at 3 nested areas.

488 Australian Journal of Soil Research M. Yang et al.


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Principal component analysis (PCA) can be performed on the

Bu matrices, in order to aid the identification of the major

processes operating at the spatial scales identified. The

interpretation of the resulting factors is based on their

correlation with the original variables.

Results and discussionSummary statistics

Table 1 summarises the statistics of all measured data. The

distribution of the soil salinity ions at different sample areas was

acquired by geostatistical module in ArcGIS software. The

distributions of the original variables at the 3 areas were

asymmetric, only Na+ at the middle area and K+ and Ca2+

ions at the small area had skewness coefficient


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Extracting the latent roots and vectors from the correlationmatrices may reveal the relations among the variables more

clearly. The first 2 eigenvalues are listed in Table 2. At the large

and middle areas, the first accounted for ~50% of the variance,

and the second for 19.32% and 24.82%, respectively. However,the first accounted for 45.23% and the second for 28.22% at

the small sample area. All the eigenvalues obtained at the 3 areas

confirmed the results of the above correlation analyses that

the correlations between the variables are complicated and

dispersive. The correlation coefficients between the variables

and principal components are reported in Table 3.

The variable Na+ and Cl, which determined the

salinealkaline types of the soils, appeared closely related,

positively loading on the first principal component at the

large area, whereas K+ was strongly correlated with the

second principal component. This seemed to suggest that soil

salinisation was formed on marine sediments due to lateral sea

seepage in the prograding process of the Yellow River Delta

(Weng and Gong 2006). Geological process and monsoon

climate may also be important factors.

The same principal components were present at the middleand small areas. Na+ and Clwere positively associated with the

first principal component and also showed a strong link with

SO42, Mg2+, and Ca2+, which were the second principal

components compared to the large area. This may beattributed to some other processes superimposing on soil

salinity characteristics.

In this context, Na+ and Cl were strongly correlated,

positively loading on the first principal component, which

Table 4. Coefficients of double-spherical model fitted to the principal

components

Area Component Nugget Sill1 Sill2 Range1 (m) Range2 (m)

Large Factor1 0.216 0.954 0.774 2120 4080

Factor2 0 0.686 0.259 2120 4080

Middle Factor1 1.38 1.5 0.39 1080 2050

Factor2 0.39 1.005 0.525 1080 2050

Small Factor1 0.784 1.512 0.532 45 105

Factor2 0.238 1.377 0.544 45 105

0 20 40 60 80 100 120

0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

lhl (m)

0 20 40 60 80 100 120

0

0.4

0.8

1.2

1.6

2.0

2.4

2.8

3.2

0 300 600 900 1200 1500 1800 2100 2400 2700

0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

0 300 600 900 1200 1500 1800 2100 2400

0

0.4

0.8

1.2

1.6

2.0

2.4

2.8

3.2

(|h|)

0 500 1000 1500 2000 2500 3000 3500 4000 4500

0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7

0 500 1000 1500 2000 2500 3000 3500 4000 4500

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Large sample scope

Middle sample scope

Small sample scope

Fig. 3. Spherical auto-variograms of the principal components plotted as points. Solid lines are those of the fitted models of linear

coregionalisation.



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suggested that NaCl may be the main salinity type in the YellowRiver Delta, but there were different variables related with the

second principal component, K+ at the large area and Mg2+ and

Ca2+ at the middle and small area. This showed different salinity

characteristics formed from many different processes, such asmineral fertilisation, geological genesis, climate, vegetation,

lateral seepage, channel change, groundwater tables, and

human activities.

Spatial analysis and coregionalisation

Classical statistical analysis cannot separate the different sources

of spatial variability affecting soil salinity at the site surveyed.

This required a particular statistical approach that combinesclassical factor analysis for describing the correlation

structure of a multivariate dataset with geostatistics, to take

into account the regionalised nature of the variables.

Consequently coregionalisation analysis was performed(Castrignan et al. 2000b).

Coregionalisation analysis

Before attempting to fit a linear model of coregionalisation,we calculated the experimental auto- and cross-variograms of

the 6 salinity ions based on the correlation matrices and the PCA

aforementioned. We calculated the scores of the principal

components at each sample area, and then calculated thevariograms of the first 2 principal components of the

variables. The 6 graphs of the first 2 principal components at

the 3 nested areas displayed a steady increase in semi-variance

with increasing lag distance to ~2120 m at the large sample area,

1080 m at the middle sample area, and 45 m at the small sample

area. Then it reached a maximum more slowly at>4080, 2050,and 105 m, respectively. To represent the whole variograms

with both short- and long-range structures of each factor we

fitted a double-spherical model with a nugget (except for the

factor 2 at the large sample area). The values of the coefficients

are given in Table 4. The solid lines in Fig. 3 represents these

fitted models.

0 500 1000 1500 2000 2500 3000 3500 4000 45000

0.05

0.45

0.40

SO42

Na+

0 700 1400 2100 2800 3500 4200 4900 5600 6300

0

0.002

0.004

0.006

0.008

0.012

0.010

0.014

0.016

0 700 1400 2100 2800 3500 4200 4900 5600 6300

0

0.0024

0.0020

0.0028

0.0032

0.0036 K+

0 700 1400 2100 2800 3500 4200 4900 5600 6300 0

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

(|h|)

Mg2+

0 700 1400 2100 2800 3500 4200 4900 5600 6300

0

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.004

Ca2+

0 700 1400 2100 2800 3500 4200 4900 5600 6300

0

0.02

0.04

0.06

0.08

0.10

0.12

0.140.16

|h| (m)

Cl

0.35

0.30

0.25

0.10

0.15

0.20

0.0016

0.0012

0.0008

0.0004

Fig. 4. Spherical auto-variograms of the soil standardised variables plotted as points at the large scale. Solid lines are those of the

fitted models of linear coregionalisation.



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Although each soil attribute has its own more-or-lessdistinctive distribution, there are some similarities. Therefore,

an analysis of coregionalisation would be more revealing with

common spatial patterns and aid in seeking common causes

for them at different spatial structures (Castrignan et al.2000b).

Figure 4 shows the linear models of coregionalisation as it

fitted the 6 direct auto-variograms superimposed on the

coefficients at the large sample area (Table 5; auto-variogram

at the middle and small area and cross-variograms are not

reported). They show that the short-range structure of auto-

correlation was dominant in almost all the variables at the

3 areas. The short-range structure dominated the cross-

variograms between Na+ and K+ (negative), Na+ and Cl

(positive), K+ and Mg2+ (positive), Mg2+ and SO42

(negative), and Ca2+ and SO42 (positive) at the large area;

Na+ and SO42(negative), K+ and Mg2+ (negative), K+ and Ca2+

(positive), and Cl and SO42 (negative) at the middle area; and

Na+ and Cl (positive), K+ and Cl (positive), and Ca2+ and

SO42

(negative) at the small area. However, the long-rangestructure dominated mainly that of Na+ and K+ (negative) at the

middle sample area, and Na+ and Mg2+ (negative), Na+ and Ca2+

(negative), Na+ and SO42 (negative), K+ and Mg2+ (positive),

Mg2+ and Ca2+ (negative), and Mg2+ and SO42(negative) at the

small area.

By synthetic analyses, we found that there were differentspatial structures for the cross-varigrams between different soil

variables. The short-range structure dominated at the large

sample area, long-range structure at the small sample area,

and at the middle sample area, the dominant spatial structureswere between the short-range and long-range structures by

different auto- and cross-variograms. Previous studies mostly

examining only one study area showed that the main spatial

structure was different; some exhibited long-range structure

(Bourennane et al . 2003), and some showed short-range

structure (Imrie et al. 2008). The importance of each structure

reflected the influence of the corresponding spatial variable or

pair of variables on soil variation. However, the whole set of

multivariate spatial relations can be better represented by their

nugget and structural correlation coefficients, which allows

focusing on a specific spatial structure, filtering the effects of

the other structures of variation. This difference showed that

sampling interval and area may influence spatial variability of

soil salinity to a certain extent, and the right amount of samples

was important for exact analysis of spatial variability and itssources at different spatial structures (Pye et al. 2006).

Structural correlation coefficients

In this case study, the simple productmoment correlation

coefficients did not reveal the real relationships among the

Table 5. Coefficients of double spherical coregionlisation models for variograms

Area Structure Na+ K+ Mg2+ Ca2+ Cl SO42

Large Nugget 0.0006 0.00016 0.00153 0.00144 0 0

Sill1 0.08 0.00174 0.0085 0.00258 0.0756 0.284

Sill2 0.0034 0.00082 0.00423 0.00198 0.0585 0.064Middle Nugget 0.018 0.0252 0.0008 0 0.06 0.02

Sill1 0.12 0.133 0.0736 0.09 0.26 0.144

Sill2 0.075 0.0273 0.0192 0.034 0.092 0.022

Small Nugget 0.0234 0.0198 0.0096 0.0098 0.05 0.0035

Sill1 0.0459 0.0282 0.0189 0.0096 0.06 0.029

Sill2 0.0261 0.0048 0.0054 0.0018 0.044 0.012

Na+K+ Na+Mg2+ Na+Ca2+ Na+Cl Na+SO42 K+Mg2+ K+Ca2+

Large Nugget 0 0 0 0 0 0 0.0005

Sill1 0.003 0.0039 0.004 0.011 0.0282 0.0028 0.0019

Sill2 0.0011 0.0034 0.0029 0.0053 0.0102 0.0009 0.0015

Middle Nugget 0.016 0 0 0.09 0.028 0.0054 0

Sill1 0.048 0.02 0.0639 0.068 0.084 0.0166 0.0135

Sill2 0.054 0.0134 0.036 0.044 0.012 0.0068 0.0081

Small Nugget

0.0024 0 0 0.026 0 0

0.004Sill1 0.0426 0.0114 0.0072 0.066 0.01 0.0033 0.0544

Sill2 0.0168 0.0118 0.0086 0.016 0.02 0.004 0.02

K+Cl K+SO42 Mg2+Ca2+ Mg2+Cl Mg2+SO4

2 Ca2+Cl Ca2+SO42 ClSO4

2

Large Nugget 0 0 0.00215 0.00052 0.018 0 0.012 0

Sill1 0.0065 0.0067 0.0035 0.007 0.164 0.0095 0.123 0.009

Sill2 0.0025 0.0042 0.0013 0.004 0.0735 0.0074 0.006 0.006

Middle Nugget 0.0486 0.012 0.009 0 0 0 0.0084 0.028

Sill1 0.0414 0.0358 0.0378 0.0255 0.0148 0.064 0.0438 0.146

Sill2 0.0162 0.014 0.0114 0.0138 0.0058 0.02 0.012 0.062

Small Nugget 0 0 0.0016 0.0036 0.0016 0 0 0.0084

Sill1 0.0183 0.0108 0.005 0.0102 0.005 0.0069 0.005 0.0312

Sill2 0.0066 0.0054 0.0056 0.0026 0.0056 0.0035 0.0015 0.0126



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variables, since it averaged out distinct changes in the correlationstructures occurring at different spatial scales. Pooling all the

spatial structures, the only significant correlations between the

variables were observed for the pairs of Na+ and K+, Cl, SO42

at each sample area (Fig. 2). In contrast, filtering the differentcomponents disclosed interesting correlations between the

variables, changing as a function of spatial scales

(Castrignan et al. 2000b).

At the large sample area, in the shortest spatial structure

(nugget effect), the most relevant correlation appeared between

Ca2+ and Mg2+ (negative), and to a less extent between Cl and

SO42 (positive). As the nugget effect always comprised an

unknown variance caused by procedural errors, we chose to

direct our attention to correlation structures at short- and long-

range scales. At plot-size level, Ca2+ and Mg2+ revealed strong

correlations, whereas at longer spatial structure, high

correlations were between Na+ and K+, as well as Na+ and

Mg2+. Cl appeared correlated, but to a lesser extent, with Ca2+

(Fig. 5a).

At the middle sample area, the correlation structures lookedsomewhat different, and the short-range structure showed

SO42 was strongly correlated with Cl, and slightly with Na+

and Ca2+. However, strong correlations were shown between

Na+ and K+ atP< 0.01, and Cland Ca2+ aswell asCa2+ and Na+

at P< 0.05 (Fig. 5b).At the small sample area, the strong correlations between

Na+ and Cl and Cl and SO42 showed at the 3 spatial scales,

while there were also strong correlations between K+ and Na+,

Cl, SO42, and between Mg2+ and Ca2+, at the short- and long-

range spatial structures (Fig. 5c).

From the analyses at 3 sample areas, there were strong

correlations between Na+ and Cl, K+ at the large and small

areas, Cl and Ca2+ at the large and middle area, as well as

between other variables. In fact, there were some correlations

between the soil base-exchange ions which could be found in

vegetation ecology (Guo and Tang 1999; Li et al. 2002).

Therefore, from the estimation of the structural correlation

coefficients, we were able to summarise the causes of the

(a) Large sample area

(b) Middle sample area

(c) Small sample area

Nugget effect

Na+

K+

Mg2+

Ca2+

Cl

SO42

Na+

K+

Ca2+

Mg

Cl

SO42

Na+

K+

Mg2+

Ca2+

Cl

SO42

Na+

K+

Mg2+

Ca2+

Cl

SO42

Na+

K+

Mg2+

Ca2+

Cl

SO42

Na+

K+

Ca2+

Mg

Cl

SO42

Na+

K+

Mg2+

Ca2+

Cl

SO42

Na+

K+

Mg2+

Ca2+

Cl

SO42

Na+ K+ Mg2+ Ca2+ Cl SO42

Na+

K+

Ca2+

Mg

Cl

SO42

Na+ K+ Mg2+ Ca2+ Cl SO42 Na+ K+ Mg2+ Ca2+ Cl SO42

Short-range Long-range

Fig. 5. Correlation matrices for each spatial structure at the 3 areas.



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variability of soil salinity at each spatial structure at the 3 nestedsample areas.

At the large sample area, plot-size spatial variation was

mostly affected by environmental factors such as climate and

topography, which controlled soil genesis on the whole(Bruelheide and Udelhoven 2005). At the long-range scale,

the monsoon climate, characterised by high evaporation

discharge and relatively low precipitation, and rolling

topography imposing on the vertical and lateral flow, may

have played some role in the spatial structure of soil salinity.

At short-range structure, spatial variation was likely

dominated by parent material (river alluvium and marine

sediments in the bottom) being influenced by tides and the

Yellow River.

At the middle sample area, spatial variability at short-rangestructure was mostly influenced by mineral fertilisation, which

carried a lot of soluble salts accumulating in the soil and

consisting principally in various proportions of sodium and

calcium cations, as well as chloride and sulfate anions (Wang

et al. 2006). Long-range spatial variability was potentially

affected by irrigation and draining in wetland restoration

processes, which influenced the salinisation and leaching of

cations in soils (Shan 2007).

Based on the analyses above, at the small sample area, plot-

size spatial variation was mostly caused by the distribution of

vegetation, especially by halo-tolerant plants, which influenced

soil salinity by secreting or absorbing salts in soils, and

alleviated salinity stress (Song et al. 2003). Additionally

SO42

K+

Cl

Mg2+

Ca2+

Na+

35.2%

37.8% Mg2+

Ca2+

SO42

K+

Cl

Na+

27.7%

54.2%

Na+Mg2+

K+

SO42

Cl

Ca2+

35.3%

58%

SO42

Na+

Cl

Mg2+

K+

Ca2+

67%

24.3%

SO42

K+Na+Cl Mg

2+

Ca2+

63%

17.2 %

Na+

K+

Ca2+Mg2+

Cl

SO42

74.3%

13.4%

Na+

K+

Ca2+

SO42

Mg2+

Cl

72.3%

25.5%

Ca+

Na+

SO42

Mg2+

Cl

K+

51.1%

27.3%

Na+

K+

Mg2+

Cl

Ca2+

SO42

75.3%

18.9%

Large sample area

Middle sample area

Small sample area

Nugget effect Short-range Long-range

Fig. 6. Correlation circles for the structural variation at the 3 scales.



10/12

groundwater tables may be the cause of spatial variability at thelong-range scale. Alluvial soils in the delta were subject to

seepage or flooding during high tides and coastal storms, and

their poor drainage from high groundwater tables allowed salts

to accumulate due to inadequate leaching (Fang et al. 2005).Because the 3-sample area was nested, the spatial structure at the

3 areas had some overlaps.

Regional factors analyses

In order to summarise the relationships among the variables

at different spatial structures, a PCA was performed on the

regionalisation matrices for each sample area. The spatial

interrelations among the variables, as described above by

coregionalisation matrices for each sample area, could be

clearly displayed in the circles of correlations corresponding

to different spatial scales at each sample area (Saporta 1990)

(Fig. 6), where the pair of coordinates of each variable was

determined by the pair of correlation coefficients between the

spatial component of the variables and the first 2 regionalisedfactors.

At the small area, the first 2 factors explained ~78% of nugget

variance: the first one was highly correlated with Na+ and Ca2+

(negatively) and also with SO42 (positively), whereas the

second was positively correlated with Mg2+ and Cl

. Thissuggested that there were various salinity types at a very

short range and basic salt is predominant, which is at

variance with the results of Weng and Gong (2006), who

thought that Na+ and Cl were significantly correlated and themain salinity type was sodium chloride. However, at this spatial

structure components also included procedural errors and hence

one should interpret them with caution (Castrignan et al.

2000b).The first 2 components of the short-range structure explained

>97% of the overall variance at this spatial scale, where the Na+,Ca2+, and K+ are strongly correlated with the first component,

whereas Clwas weighing alone slightly over the second factor.

At long-range structure, the components weighing over the

first 2 components were similar to that at the short-range

structure, except for the exchange of Cl and Ca2+ loading on

the first 2 components. This seemed to suggest that salinity

variation at the short-range structure can be characterised by

calcareous ingredients (Xu 2003), and at the long-range structure

by soluble ions.

Proceeding to the larger sample area, no differences of

importance are observed, except for the different performance

of Ca2+

and Cl

at the nugget and plot-size level and Mg2+

at thelong-range structure. At the nugget and plot-size, Ca2+ and Cl

had a reversed impact on the first 2 components, which suggests

that different processes may influence spatial variability of Ca2+

and Cl in different sample scales, as for Mg2+. Some of these

processes may include the infiltration of sea water owing to the

proximity of the study site to the shore, watering and draining in

wetland restoration, the geological nature of the rock, the rise of

the groundwater table to within few meters of soil surface, and

the previous accumulation of salt (Shao et al. 2008).

In extending our sample area great differences were

observed. In the nugget variance, the first is highly correlated

with K+ and SO42, whereas the second is negatively correlated

with Mg2+ and positively with Ca2+. At short-range structure,Cl influenced spatial variation independently from other

variables on the first component, and also, slightly, together

with Na+. This may be attributed to several factors such as low

groundwater tables caused by down-draught of sea water, andhigh evaporation. SO4

2 was highly correlated with the Mg2+

and Ca2+, which suggested that the parent material was likely

alkaline (Wang et al. 2004). Cl was correlated with Ca2+, and

weighed commonly over the first components, whereas other

variables controlled the second component.

Conclusions

The FKA described above enabled the factors underlying the

variation in soil salinity to be examined at 3 different spatial

scales at 3 nested sample areas.

At the large area, sampled in the whole Yellow River Delta,

spatial variability of soil salinity was affected by historic or

intrinsic environment conditions, such as climate, geology,

topography, soil genesis, and parent material, which acted ondifferent spatial structures. Because there was an unknown

variance caused by procedural errors at the scales shorter

than the sampling density, we focused our attention on the

short- and long-range structures. At the short-range structure,

variations in parent material geology of the major structural

divisions of the continent to sea were detected. At the long-range

scale the influence of monsoon climate and topography became

apparent.

At the middle sample area, variations were mostly affected by

mineral fertilisation at the short-range structure, while human

activities such as irrigation and drainage in wetland restorations

influenced the variations of soil salinity at the long-range scale.

At the small sample area, an area of 200 m by 200 m, vegetation

and groundwater table may influence the spatial variability ofsoil salinity at the short and long structures.

PCA showed that salinity types were slightly different atdifferent spatial structures and significantly at the 3 sample areas.

Compared to our analysis using univariate geostatistics, which

showed that NaCl was the main salinity type, analysis using

multivariate geostatistics showed similar results, but exhibitedmore detailed information at different spatial structures. With

this type of analysis it is possible to have better management of

spatial and temporal variability associated with all aspects of

wetland restoration for the purpose of improving soil propertiesand environmental quality.

Multivariate geostatistical techniques such as factorial

kriging analysis have facilitated the separation of the different

sources of spatial variation at different scales. This method has

proved very useful for several main reasons (Bocchi et al. 2000;

Castrignan et al. 2000a) and is likely to be applicable to the

future datasets that will emerge as part of the Global

Geochemical Reference Network (Imrie et al. 2008).

Acknowledgements

This study was supported by the National Key and Important Program for

Basic Research of China (No. 2006CB403303) and the National Natural

Sciences Foundation of China (No. 40871237; No. 40501067). The authors

would like to thank instructor Liu R M for guiding in operation of

multivariate geostatistical techniques.



11/12

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