Spatial Distribution of Salinity State in Depression

17 Egypt. J. Soil Sci. 52, No. 2, pp. 259 – 274 (2012)

ــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ

[email protected]

Spatial Distribution of Salinity State in Depression

Floor, Western Desert, Egypt

K. M. Darwish

* and Ahmed A. Afifi

**

*Soils and Water Technology Department, Arid Land Research

Institute, City of Scientific Research and Technology Applications,

Borg El-Arab, Alexandria and **

Soils & Water Use Department,

National Research Centre (NRC), Giza, Egypt.

SSESSING the vulnerability of soil salinity state is the first step

toward careful management of land resources to avoid or, at least,

to minimize impacts on agriculture. This requires careful monitoring of

the soil salinity status and variation to curb degradation trends, and

secure sustainable land use and management. Bahariya depression

faces a sever constraint in availability of good agricultural land. The

major limitations were associated to high soil salinity. The remote

sensing data and field observations show that new reclaimed areas

have been recently cultivated and developed widespread water logging,

seepage channels and soil salinisation. Geostatistical techniques

provide a means to study the heterogeneous nature of spatial

distributions of soil salinity. Ordinary kriging was applied to estimate

electrical conductivity (EC) measurements in some areas in Bahariya.

These EC spatial data were analyzed for autocorrelation and then used

to make optimal, statistically rigorous maps of the area sampled. In

kriging, more easily measured data sets of electrical conductivity (EC)

were incorporated to improve the estimation of salinity state. Skewness

and kurtosis were calculated and observed for all data to describe the

shape and symmetry of the geochemical data distribution along the

study area. The estimated spatial distributions of EC using the

geostatistical methods with various reduced data sets were compared

with the extensive salinity measurements in the large field.

Technically, there were different original factors have influenced the

final output of kriging logarithm, especially in the southern part.

Improving those factors could obtain more accurate results. The output

results suggest that sampling cost can be dramatically reduced and

estimation can be significantly improved using kriging technique. It is

now much easier for a decision maker to assess the salinity state for

agricultural and to locate the most suitable site for extension and

drilling wells. The present study demonstrates high efficiency for

kriging method to analyze complex spatial changes of salinity

mapping.

Keywords: Salt affected soils, spatial variability, Semi-variogram,

Ordinary kriging.

A

mailto:[email protected]

K. M. DARWISH AND A. A. AFIFI

Egypt. J. Soil Sci. 52, No. 2 (2012)

260

Salinity and alkalinity are the two of the most important problems on agricultural

production in arid and semi-arid regions and are threatening for sustainable

agricultural management (Ceuppens et al.,1997, Boivin et al., 2002 and Kilic

& Kilic, 2007). The amount of agricultural lands having salinity and alkalinity

problems increase continuously as related to climate, topography, groundwater

level and quality of irrigation water (Postel, 1989, Ayers and Westcot, 1989 and

Kilic & Kilic, 2007). There are mainly two kinds of soil salinity: naturally

occurring dry land salinity and human-induced salinity caused by the low quality

of water (Hinrich and Bohn, 1985). In both cases, the development of plants and

soil organisms are limited leading to low yields. No doubt that soil salinity limits

food production in many areas of the country and such soils have particular

physical and chemical properties that require specific management. As a first step

for the better management of salt-affected soils, soil salinity needs to be

monitored in space to determine where it is, where it is spreading to and the

temporal rate at which it is spreading to control its harmful effects.

Indeed, detection, delineation and mapping of saline and alkaline soils are the

first steps for the amelioration processes of salt-affected soils. The heterogeneous

distribution of the soil characteristics was called the soil spatial variability. The

spatial variability of soil makes it possible to get precise information from soil.

More detailed and accurate information of soil were asked due to the requirement

of management and sustainable utilization for agricultural soil. Therefore, more

attention was paid to studying the spatial variability of soil as an important

component part of precision agriculture (Paz-Gonzalez, 2000).

The spatial distribution of salinity has important implications for soil and

water resources management (Douaik et al., 2007). In addition, identification of

the spatial distribution of soil characteristics has a vital role in most of the

bioenvironmental systems. Variability is one of the intrinsic characteristics of

soil quality and within the same ecosystem soil properties may show

significant spatial variations (Robinson and Metternicht, 2006). For this reason,

we opted to apply geostatistical methods in order to better understand spatial

variations of soil characteristics. Douaik (2005) mapped spatial variation of soil

salinity on the field scale using geostatistical approaches. However, where there

is a lack of spatial autocorrelation, a statistical calibration technique based on

multiple-linear regression for predicting multiple depths, field scale spatial

distribution of soil ECe is more appropriate (Douaik et al., 2007).

In Egypt, the Western desert covers approximately 700,000 km2, which are

more than two-thirds of the total surface area. The significant oases in the

Western desert are Siwa, Bahariya, Farafra, Dakhla and Kharga. Bahariya is a

natural topographic depression located nearly in the middle of the Western desert

of Egypt and comprising a total area of approximately 2000 km2. It is located

between latitudes 27º48′ and 28º30′ N and between longitudes 28º35′ and

29º10′ E and about 370 km southwest of Cairo (Fig.1) (Hamdan, 2012). The

depression is surrounded by plateau escarpment. The floor of the depression

consists of Sandstone, Clays and Marls. The area falls under the arid condition as

SPATIAL DISTRIBUTION OF SALINITY STATE IN DEPRESSION …


261

the total rainfall is (3-6) mm/year. Springs and wells are the main groundwater

resources for irrigation and civic purposes (Salem, 1987).

Fig. 1. Location map of Bahariya Oasis, Western Desert.

Based on the US Soil Taxonomy (2010) six taxonomic units (at great group

level) were identified in the study areas; Torripsamments, Pasmmaquents,

Torrifluvents, Torriorthents, Salorthids, Calciorthids (Darwish, 2004).

In Bahariya, it is found that the main unsuitability criteria restricting and

eliminate more extend of additional areas for cultivation is the excess of salts. A

salt-affected soil is a major soil degradation process affecting the agricultural

productivity in the Egyptian soils (Darwish, 1998). In Bahariya, although

irrigation has greatly increased the agricultural production potential, a rise in

water table results when irrigation-induced recharge is greater than the natural

discharge. In many irrigated areas rising water tables have subsequently led to

water-logging and associated salinity problems.

This study aims to delineate and map the salinity limiting factor pronounced

by EC (dS/cm) measurements that rating some areas as unsuitable for crop

production in Bahariya, using geostatistic techniques. It will be important for soil

management and enhancement of crop yield to study the spatial variability of soil

salinity measurements, to draw the precise map with soil information (Beckett

and Webster, 1971) and to make the regime of the spatial variability of soil

characteristics quite clearly, especially for carrying out precision agricultural in

the future.



262

Material and Methods

General situation of experimental area(s)

In this research, two study areas were selected. One is in the northern part of

Bahariya covering an area of about 118.3km2 and the second is covering partly

the southern part of it with an area of 77.5km2 (Fig.2).

Fig. 2. The study areas in North and South Baharyia.

Based on the pre-field and information obtained, 45 soil profiles and 71 soil

augers were examined in different locations. Some proposed observations were

added at the edge of the study areas to reduce the error of missing information.

Figures 3a and 3b show the location of the observation sites where soil

samples were taken. Four transects in area-1 and -2 in the southern one. Soil

salinity measurements (ECe) dS/cm were determined in the soil water extract out

of the saturated soil paste.

The determination of the sampling pattern

Total of six sample areas were selected and distributed over the study areas

with a fixed wide of 1km for each. The exact locations of the soil profiles and

auger points were precisely defined in the field by using the GPS “System

Cooperation MAGELLAN”-GPS NAV DLX-10 and plotted on the maps.

Briefly, the steps in a geostatistical study include: (a) exploratory data

analysis (b) structural analysis (calculation and modeling of variograms) (c)

making predictions (kriging or simulations).



263

Figures 4a and 4b show the frequency distribution of EC (dS/cm) values in

study area-1 and -2 respectively. The first one exhibit abnormal distribution,

while the second is normally distributed, which does not deemed statistical

transformation (e.g., Ln) prior to kriging. Although the Ln transformation EC

semivariogram can give a better fitting, when using it for kriging purposes the

problem of back transformation of the kriged estimated is limiting its usability.

Fig. 3a. Location of sample points in study area-1 (North).

Fig. 3b. Location of sample points in study area-2 (South).



264

Fig. 4a. Frequency distribution of ECe values in area-1.

Fig. 4b. Frequency distribution of ECe values in area-2.

Method of geostatistical analysis

The spatial variability of EC data was analyzed using semi-variance function. The

correlation lag range of soil salinity EC values was determined according to the sill of

semivariogam and the spherical models were set up. And then, the spatial distribution

maps of EC in the two areas were drawn by using Kringing interpolation.

Semi-variance function method

Semivariance function is used to measure the degree of variation in soil

characteristics ECe between sampling points and to determine the reasonable

space of sampling. Semivariance is an autocorrelation statistic defined as:

γ(h) = [½ N(h)] Σ [ zi – z

i+h ]

2 (1)

where

γ(h) = Semivariance for interval distance class h;

zi = measured sample value at point i;



265

zi+h

= measured sample value at point i+h; and

N(h) = total number of sample couples for the lag interval h.

Semivariance is evaluated by calculating γ(h) for all possible pairs of points in

the ECe data set and assigning each pair to an interval class h. The Lag Class

Distance Interval defines how pairs of points will be grouped into lag classes.

It is found that the distribution of the ECe measurements in spatial for area-1

and -2 is isotropic or direction-nondependent geometric model. The isotropic

model can be described based on three parameters:

• Nugget Variance or C0

• Sill or C0+C – the model asymptote

• Range or A – the separation distance over which spatial dependence is ap-

parent. Sometimes this is called the effective range in order to distinguish

range (A) from a model’s range parameter (A0).

Spherical isotropic model

The spherical isotropic model is a modified quadratic function for which at

some distance A0

pairs of points will no longer be autocorrelated and the

semivariogram reaches an asymptote. The formula used for this model is:

γ(h) = C0 + C [1.5(h / A

0) - 0.5(h / A

0)

3

] (2) for h ≤ A0

γ(h) = C0 + C for h >A

0 (3)

where γ(h) = semivariance for interval distance class h,

h = the lag distance interval,

C0 = nugget variance ≥ 0,

C = structural variance ≥ C0 and

A0 = range parameter. In the case of the spherical model, the effective range A = A

0.

Sill C0 + C and correlation lag range h of soil characteristics can be got relying

on the semivariograms based on the formula (2). The semivariogams showing the

spatial variability of soil salinity data characteristics may be drawn be semi-

variance function according to the information obtained from the ECe

measurements.

Kriging

Kriging is the technique of making optimal, unbiased estimates of

regionalized variable at unsampled locations using the structural properties of the

semivariogram and the initial set of data values (Davis, 2003). In this research the

ordinary block kriging method was used. Block kriging provides an estimate for a

discrete area around an interpolation point. In environmental work block kriging



266

is usually more appropriate. The block is defined as the rectangular area around a

point that is not included in an adjacent block.

Results and Discussion

Spatial variability of ECe values in study areas

The statistical results of the ECe measurements for area-1 and area-2 are listed

in Table 1. The statistical analysis can just explain the sample difference of ECe

characteristics in volume and in homogeneity, but it cannot describe the spatial

variability difference of the salinity data. So, it is necessary to analyze the spatial

variability of the data above by semivariance function.

TABLE 1. Statistical summary of ECe (dS/cm) data .

Items Mean Standard

Deviation Sample variance Min. Max.

Area 1 (N) 37.27 46.51 2163.06 0.96 178.0

Area 2 (S) 51.26 39.41 1552.79 0.86 132.0

Figures 5a and 5b illustrate the semivariance value of area-1 and -2. The sill

of ECe in area-1 & 2 are 2622.0m and 1845.0m and their correlation lag range

3960.0m and 1150.0m respectively.

The sampling interval can be determined based on the semivariograms. This

proves the existence of correlation between spatial variability of the soil salinity

data, which belongs to nugget effect.

Fig. 5a. Isotropic variogram (spherical model) of ECe (dS/cm) in area-1.



267

Fig. 5b. Isotropic variogram (spherical model) of ECe (dS/cm) in area-2.

The variogram shows a relative nugget effect of 11.7% for study area-1 and

10.0% for area-2, which cloud be calculated through this ratio [(C0/(C

0 + C))

x100] between nugget variance and sill. This variability caused by random

factors. The nugget effect looks more significant in study area-2 than area-1. The

correlation lag range of area-2 was obviously smaller than that of area-1 in the

north of Bahariya.

According to the forgoing discussion, the salinity status in the study areas is

closely related to the position in the landscape. Thus, the variogram analysis can

be used to investigate the spatial structure of this primary variable. This analysis,

based on samples distributed over the investigated areas, is essential before the

actual spatial estimation by kriging can be performed. As mentioned before the

experimental variograms calculated on the EC data were well fitted to the

spherical variogram model. Both variograms show some impotent facts:

- They are rising gradually for study area-1 and rapidly for area-2 and then

levels off to become constant for larger distances.

- The sill is reached asymptotically as a character of the spherical model.

As a conclusion, the results obtained from the variogram analysis of the EC data

indicate that the salinity data show an identical spatial structure. The reason for

less fitting of the EC variograms is due to the quite limitation of number of the

EC data available, the uneven spatial distribution of the sample localization’s and

the effect of some extreme EC values.

Spatial interpolation of EC data using kriging

After calculating the semi-variogram parameters and choosing the best

variogram model with its building parameters that fit with spherical isotropic

model, the ordinary block kriging algorithms was applied to interpolate the EC

data using GeoStat program.

The resulting block-kriged maps of EC values for the study areas-1 and area-2 are

illustrated in Fig. 6a and 6b.



268

Normally, the smoothness of spatial distribution map illustrated the degree of

spatial variability in soil salinity data characteristics. If the degree of spatial

variability is small, that means the spatial distribution of soil salinity data is even

and less sampling points are needed to draw an accurate soil salinity map;

otherwise, more sampling points are needed.

35,679E+06 35,685E+06 35,690E+06 35,695E+06

"m east"

3134583

3138434

3142286

"m

no

rth

"

"EC dS/cm"

143

134

124

115

105

96

86

77

67

58

48

39

29

19

10

0

Fig. 6a. Interpolate-Kriging map of the study area-1 north of Bahariya.

35,658E+06 35,662E+06 35,666E+06 35,670E+06

"m east"

3097399

3100634

3103869

"m n

ort

h"

EC dS/cm

118

111

104

97

89

82

75

67

60

53

46

38

31

24

16

9

Fig. 6b. Interpolate-Kriging map of the study area-2 south of Bahariya.

The complicated and less smooth spatial distribution of EC data in study area-1

(Fig. 6a) suggested higher spatial variability of salinity data EC (dS/cm), while,

the simple and smooth spatial distribution indicated lower spatial variability of

EC data in study area-2 (Fig. 6b).



269

To assess the accuracy of the kriging estimated maps, there is a cross

validation analysis for evaluating effective parameters for kriging. In cross-

validation analysis each measured point in a spatial domain is individually

removed from the domain and its value estimated via kriging as though it were

never there. In this way a graph can be constructed of the estimated vs. actual

values for each sample location in the domain. The cross validation analysis of

study areas-1 and area-2 are presented in Fig. 7a and b.

The regression coefficient, which is describing the linear regression equation

is for area-1 = 0.1 and for area-2 = -2.2. The standard error of the regression

coefficient (SE = 0.39, 1.07 for area-1 & area-2 respectively). The r2 value is the

proportion of variation explained by the best-fit line (in case of area 1= 0.1% and

27.0% for area-2) and the y-intercept of the best-fit line is also provided. The SE

Prediction term is defined as SD x (1 - r2) 0.5, where SD = standard deviation of

the actual data (46.5 and 33.7 for areas-1 and area-2, respectively).

Fig. 7a. Cross validation (Kriging) of study area-1.

Fig. 7b. Cross validation (Kriging) of study area-2.



270

It is clear that the spatial variability of area-2 is comparatively less than that in

area-1. The main reason for this weakness of spatial variability in area-2 could be

referred to the lack of EC sampling points available and to the high influence of

the EC (dS/cm) extreme values on neighboring locations. This results in larger

tendency of the kriged estimates to higher salinity categories.

However, the visual interpretation of both kriged maps in relation to the DEM

(Digital Elevation Model) grid image of each area give a good impression that the

estimation of salinity values is logical, taken into consideration the location from

the salt effected soils (playa), the relatively lower elevation units (depressions)

and the position in landscape in the Oasis. To demonstrate this quite simply is by

overlaying the interpolated kriged maps with the DEM grid image of each area

(Fig. 8a1-2 & 8b1-2).

Figures 8a-2 and 8b-2 illustrate obviously how the 2D interpolated maps

geographically and geomorphologic-ally overlapped. The relatively high EC

values that are pronounced and presented as connected counter lines are

overlapping the areas where low elevation and much salinity features are

available. This quite validating the estimated kriged maps obtained.

In addition, the spatial distribution pattern and isotropic variogram model

(spherical) of the estimations of both interpolated maps were quite similar.

Fig. 8a-1. The kriged map overlaying the DEM of area-1 (North of Bahariya).



271

Fig. 8a-2. The kriged map overlaying the DEM grid image of area-1 (North of Bahariya).

Fig. 8b-1. The kriged map overlaying the DEM of area-2 (South of Bahariya).

Fig. 8b-2. The kriged map overlaying the DEM grid image of area-2 (South of Bahariya).

96 98 100 102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134 136 138 140

35658000 35660000 35662000 35664000 35666000 35668000

3098000

3100000

3102000

Elevation m

0 2000 4000 m

EC (dS/cm)



272

Conclusion

The spatial distribution maps drawn based on kriging interpolation method

explain clearly the spatial variability of soil salinity measurements in north and

south study areas of Bahariya Oasis.

Indeed, there are different original factors have influenced the final output of

the kriging logarithm technique. Those factors can be related to the issues of

sampling, the spatial distribution of the soil salinity measurements in the space,

the total number of the observation points and the variability of the EC data set

obtained. Improving those factors especially in the south study area-2 would play

an important role for receiving more accurate results out of this interpolation

method. The spatial distribution characteristics not only related to the processing

of formation, but also related to the result of the specific agricultural production

system and mode in Bahariya.

It is worth to mention, that the results suggest that sampling cost can be

dramatically reduced and estimation can be significantly improved using kriging

technique. Of course, the cost vs. benefit balance of such a detailed approach still

needs to be explored. In addition, consequent studies are recommended relying on

the current results including cokriging geostatistical technique.

At the end of the whole procedure, it is still manage successfully to use the

obtained interpolated EC salinity maps and to suggest better resource

management practices for the selected study areas of Bahariya Oasis.

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(Receive 14/10/2012;

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274

بالصحراء ةالتوزيع المكانى لحالة ملوحة التربة فى أراضى منخفض

الغربية, مصر

خالذ دمحم درويش

* عفيفىأحمذ و أحمذ عبذ الفتاح

**

* ــ لس ذىظيا األراض ايا

ــ عذ تؽز األراض اماؼح

ذيح األتؽاز

ــ تزض اعزبــ اعي ارطثيماخ ارىظي

ذريحاألسى**

لس األراض

ــ زازوش ام ثؽ ــ أسرغالي ايا

ــ اعيشج

. صز

يعرثز ذميي اؽساسيح ذرظح ؼح ارزتح اخطاخ األ ؽ إدارج ظيذج

ارد األراضي, ؼيس ذعة أ ع األل ذساعذ ف اؽذ ارأشيزاخ اغيز

طة رصذ دليك ؽاح ذػ األراض ذثاي زغت ع اشراعح. ذا ير

االذعااخ ؽذ ارذر ف األراض اشراعي، ذأي اسرخذا ادارج

سرذا ظيذ ألراضي. ذا اطك عذ ا خفض اثؽزي تاصؽزاء

اصزي اغزتي ياظ ذؽذيا وثيز ف ذافز األراض اصاؽح شراع. ذا

ح ذػ ارزت. ذص تيااخ االسرشعار ع تعذ وذه اشياراخ اؽمي تسثة شى

ااطك اؽذيصح األسرصالغ حدرا وثيز ف ذؽذيذ ااطك ارأشزج تاألالغ خاص

افرمذ ألدارج اعيذ ا ذعث در ف سيادج األراض اغذل, ارسزب

رمياخ األؼصاء اعغزافي ذعرثز ف رزت. , سيادج ذػ ا اائ لاخ از

سي ؼذيص ظيذ ذراسح ارثاياخ ف طثيعح ارسيع اىاي ألراض ارأشز

تاألالغ.

رلع ذمذيز لي لياساخ Ordinary krigingف ذ اذراس ذ ذطثيك ذميح

ااطك في ( وذالح ع ؼاح ذػ ارزت ف تعضECارصي اىزتائي )

ااؼاخ اثؽزيح. لذ اسرخذد اثيااخ اىايح مياساخ ارصي اىزتائي

(EC ع )autocorrelation ش اسرخذد ألراض خزائظ إؼصائيح دليمح ،

ذ ادراض ععح لي تيااخ krigingاطك ازاد دراسرا خالي ذميح

تشى رىا رؽسي ارمذيز ارلع ؽاح (ECلياساخ ارصي اىزتائي )

, ذ ؼح ارزتح ف ااطك ذؽد اذراس. خالي ارؽي األؼصائ ثيااخ

( الؼظر عيع Skewness and kurtosisؼساب ذ ارعاف ارفزطػ )

اثيااخ وذه صف شى ذاش ارسيع األؼصائ اطثيع ثيااخ

يائيح خالي اطك اذراسح ف شاي ظب ااؼ. ذد مارح اعيوي

( تاسرخذا ذمياخ اعيسراذسريه ع ECذسيعاخ اثيااخ امذرج اىايح )

ااؼيح ععاخ أخز لياساخ ؼح ارزتح في اطك اسع أش

اائي ارؽص عي افيح، ان عا خرفح عثد در ف ارأشيز ع ااذط

، خاصح في اعشء اعتي ااؼ. kriging logarithmتأسرخذ ذمي

تأخذ ذ اعا ف األعرثار, يى اؽصي ع رائط أوصز دلح.

ارائط ارؽص عيا ذشيز إ أ ذىفح ذعيع اعياخ اطك اذراسح

ه امي ارلع يى ذؽسا تشى وثيز تاسرخذا يى خفضا تشى وثيز، وذ

. خالي اخزائظ ارائط ارؽص عيا ف ذ اذراسح يس krigingذميح

ع صاع رخذ امزار ذميي ؼاح ؼح ارزتح ف ااطك ذؽد اذراس

الع ؼفز تشى ألع أدق وذه ذؽذيذ اطك ارسع اشراع األسرصالغ

ذ اذراسح ساعذخ ف ذضيػ اىفاءج اىرسث خالي آتار ايا اعفي.

رؽي ارغيزاخ اىايح اعمذج ordinary kriging logarithmذطثيك ذميح

رس خزائظ ارسيعاخ اىاي ؼح ارزت ف ااطك ذؽد اذراسح.

Spatial Distribution of Salinity State in Depression

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