Quantifying specific capacity and salinity variability in Amman Zarqa ...

Quantifying specific capacity and salinity variability in Amman Zarqa Basin, Central Jordan, using empirical statistical and geostatistical techniques F. Shaqour, R. Taany, O. Rimawi & G. Saffarini

Environmental Monitoring and Assessment An International Journal Devoted to Progress in the Use of Monitoring Data in Assessing Environmental Risks to Man and the Environment ISSN 0167-6369 Volume 188 Number 1 Environ Monit Assess (2016) 188:1-17 DOI 10.1007/s10661-015-5051-z

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Author's personal copy Environ Monit Assess (2016) 188:46 DOI 10.1007/s10661-015-5051-z

Quantifying specific capacity and salinity variability in Amman Zarqa Basin, Central Jordan, using empirical statistical and geostatistical techniques F. Shaqour & R. Taany & O. Rimawi & G. Saffarini

Received: 21 April 2015 / Accepted: 10 December 2015 # Springer International Publishing Switzerland 2015

Abstract Modeling groundwater properties is an important tool by means of which water resources management can judge whether these properties are within the safe limits or not. This is usually done regularly and in the aftermath of crises that are expected to reflect negatively on groundwater properties, as occurred in Jordan due to crises in neighboring countries. In this study, specific capacity and salinity of groundwater of B2/A7 aquifer in Amman Zarqa Basin were evaluated to figure out the effect of population increase in this basin as a result of refugee flux from neighboring countries to this heavily populated basin after Gulf crises 1990 and 2003. Both properties were found to exhibit a three-parameter lognormal distribution. The empirically calculated β parameter of this distribution mounted up to 0.39 m3/h/min for specific capacity and 238 ppm for salinity. This parameter F. Shaqour : O. Rimawi : G. Saffarini (*) Department of Applied and Environmental Geology, The University of Jordan, Amman, Jordan e-mail: [email protected] F. Shaqour e-mail: [email protected]

is suggested to account for the global changes that took place all over the basin during the entire period of observation and not for local changes at every well or at certain localities in the basin. It can be considered as an exploratory result of data analysis. Formal and implicit evaluation followed this step using structural analysis and construction of experimental semivariograms that represent the spatial variability of both properties. The adopted semivariograms were then used to construct maps to illustrate the spatial variability of the properties under consideration using kriging interpolation techniques. Semivariograms show that specific capacity and salinity values are spatially dependent within 14,529 and 16,309 m, respectively. Specific capacity semivariogram exhibit a nugget effect on a small scale (324 m). This can be attributed to heterogeneity or inadequacies in measurement. Specific capacity and salinity maps show that the major changes exhibit a northwest southeast trend, near As-Samra Wastewater Treatment Plant. The results of this study suggest proper management practices. Keywords Amman Zarqa Basin . Salinity . Three-parameter lognormal distribution . Geostatistics . Semivariance analysis . Kriging interpolation

O. Rimawi e-mail: [email protected]

Introduction R. Taany Department of Water Resources and Environmental Management, Faculty of Agricultural Technology, Al-Balqa Applied University, Al Salt, Jordan e-mail: [email protected]

It has long been recognized that modeling approaches are important tools in evaluating and quantifying environmental issues especially in nonpoint source pollution

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management (Al-Mahamid 2005; Al-Abed et al. 2005; Gupta et al. 2006; Al Tarazi et al. 2006; El Naqa et al. 2006; Al-Abed and Al-Sharif 2008, Goode et al. 2013). In a country such as Jordan, characterized by semiarid climate in its northern part and arid climate in its southern part, groundwater becomes the major source of supply for domestic and agricultural purposes. This implies effective resource management measures, especially in the basins that are most productive and heavily populated such as the Amman Zarqa Basin (AZB). As a result, several studies have been conducted on this basin to evaluate its hydraulic characteristics and address some environmental issues. Among which is the study of Bajjali 1997, where he used GIS to characterize the groundwater in a part of AZB, namely Dhuleil and Hallabat areas. The GIS output maps of salinity, nitrate, chloride, and stable isotope indicated clearly the deterioration of groundwater. This was attributed to the return flow from irrigation and extensive fertilizer application. Fitch (2001) carried out a socioeconomic study to evaluate various options for improving the management of groundwater in the AZB uplands. He found that the value of water used by most farms in the basin is quite low and that the basin is being seriously over-abstracted. Consequently, the water table is dropping, and the water quality is declining. Taany et al. (2009) assessed the spatial and temporal variability of groundwater level fluctuations in the Amman–Zarqa basin, during the period 2001–2005. Kriging mapped fluctuations have showed that drop and rise events are localized in the basin. Several suggestions were made to mitigate the rise and drop hazards in the detected sites. Al Kuisi et al. (2009) quantified the degree of contamination in the basin by evaluating the characteristic distribution and seasonal variation of nitrate and salinity concentrations during the period 1970–2005. They have found that there is a salinity buildup of about 8 × 10−2 μs/cm per day. In 2010, Al Kuisi and Abdulfattah evaluated the vulnerability of AZB groundwater to selenium and found that the upper aquifer of the basin (B2/A7) has a Se average of about 9 mg/l and attributed its variability to several sources. Goode et al. 2013 assessed groundwater levels and salinity in six groundwater basins in Jordan among which was AZB. They have found that long-term linear salinity trends are increasing in some areas in the basin (in 58 % of the studied wells). Al Kuisi et al. (2014) assessed the AZB groundwater’s vulnerability to contamination and found that 77 % of the basin exhibit very low to low vulnerability, 14 %

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of the basin exhibit moderate vulnerability, and around 5 % of the basin can be considered as a high vulnerability zone. The results of the aforementioned studies have shown clearly that many factors are responsible for variability of groundwater parameters in the basin. Some are extrinsic, such as population growth, vegetation, and land use. Others are intrinsic such as aquifer rock type, porosity, and permeability. In the last few decades, Jordan has witnessed rapid population growth enhanced by fluxes of people from neighboring countries due to political issues, mainly from the Gulf countries during early 90s and recently as a result of the 2003 Gulf War. This increase of population created high stresses on the limited water resources, especially in the heavily populated Amman Zarqa Basin. Accordingly, any management measure that has to be taken in this regard needs to know the spatial and temporal behaviors over a certain period of time. This prompted most of the aforementioned studies of Amman Zarqa Basin and this study as well. This study aims, also, to quantify empirically the Amman Zarqa Basin B2/A7 aquifer’s specific capacity and salinity changes, taking the year 1990 as a datum, using a well-known statistical approach in gold mining, and verifying the results by investigating their spatial variability using kriging interpolation techniques.

Geological and hydrological aspects The Amman Zarqa Basin is located in the northern highlands of Jordan (Fig. 1) and extends further to Syria in the east. It occupies an area of about 4049 km2 of which 310 km2 is located in Syria. It is dominated topographically and structurally by the Amman Zarqa structure, which is a synclinal depression trending NE–SW (Al-Abed and Al-Sharif 2008). Three aquifer systems occur in this basin: the upper aquifer system (B2/A7), the middle aquifer (A4), and the lower aquifer system (K) (Humphreys 1983; Al Kuisi et al. 2012). The first two aquifers are the prevailing ones. The upper aquifer system, which is the primary focus of this study, is unconfined and consists of massive dolomitic limestones and dolomites with intercalated beds of sandy limestone, marl, chalk, chert, and phosphate. The B2/A7 aquifer system is the most important aquifer system in Jordan in terms of productivity and areal extent. Its thickness varies between 40 m in the

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Fig. 1 Location map of Amman-Zarqa Basin (modified after Ta’any et al. 2009)

west and southeast and 300 m in the east and averages 220 m (BGR, MWI 2001). The groundwater flow in this aquifer is generally influenced by the recharge discharge areas’ topography and structure. According to Salameh and Bannayan (1993), the total recharge to the AZB is round 90 MCM/year. In late 70s and early 80s, declines in the water level were noticed in both governmental and private wells and ranged between 0.67 and 2.0 m per year (MWI 2000). Prior to the year 1990 and the successive crises in the neighboring countries, the TDS values ranged between 260–680 ppm, and the water type was Ca and Mg carbonate. Recently, a salinity buildup was noticed in the northern, northeastern, and eastern parts of the basin. For example, it has been reported that salinity exceeded 2700 ppm in some private wells in the northeastern part of the basin (Bajjali 1997; MWI 2000; Al Kuisi et al. 2009; Goodie et al. 2013). This salinity buildup as well as specific capacity values will be assessed before and after the year 1990 for the upper aquifer only (B2/A7) using both empirical and geostatistical techniques.

Methodology To evaluate specific capacity and salinity in AZB groundwater before and after the 1990 crisis, our methodology included the following: –

Literature review relating to salinity and specific capacity in the B2/A7 aquifer as it represents the

– –

upper aquifer in the basin and the one to be affected first by extrinsic and intrinsic factors. Data compilation and analysis Assessing specific capacity and salinity spatial variability using empirical and geostatistical techniques.

Our compilation of data relied heavily on the data bank of the Ministry of Water and Irrigation (MWI 2007). Capped and abandoned wells were excluded. Wells that are still in use whether privately owned or governmental were used. Well data such as coordinates, elevations, depth, depth to water, yield, drawdown, testing date, and measured specific capacity and salinity values were entered into a Microsoft Excel spreadsheet. The obtained well (n = 123) data were then separated into two subsets: one for the wells dug during and before 1990 and the other one for wells dug after 1990. The compiled wells, with already determined specific capacities and salinities, dug during and before 1990 mounted up to 70 wells and for the period after 1990 mounted up to 53 wells. Applying the Kruskal–Wallis test shows that there is a significant difference between the two subsets [H = 15.2 (1, N = 123), p > 0.05]. To evaluate and quantify the recent specific capacity and salinity trends over the entire period of observation, data from the compiled 123 wells were used (Appendix). What should be mentioned in this regard is that the wells representing the two time intervals are not the same.

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Data analysis General method Both specific capacity and salinity were subjected to the same analytical treatment. This included the following: – – – –

Determining the type of distribution and calculating the statistical moments. Determining the spatial variability of every parameter by constructing experimental semivariograms and fitting hypothetical models into it. Mapping the spatial distribution using kriging interpolation techniques. Making inferences based on the constructed histograms, probability plots, semivariograms, and kriging contoured estimates.

The statistical treatment was carried out using GS+ (Version 5) and Rockware 15 software packages. Statistical analysis To determine the type of distribution, graphical histograms were constructed for both salinity and specific capacity (Fig. 2). First, the original data were used, and the constructed histograms were found to exhibit a lognormal distribution (as inferred from the histograms). To validate this conclusion, the raw data were ln transformed and used to construct new histograms to see whether the new histograms would reflect a bellshaped distribution. As shown in Fig. 2, it is evident that the studied variables approximate the lognormal distribution. To validate this conclusion, new frequency tables were constructed for the original data so that the upper category limits would have exponential relationships (Tables 1 and 2). This has been done for the 123 wells data set representing the entire period of observation. The upper category limits were then plotted versus cumulative relative frequencies on lognormal probability paper (Figs. 3 and 4). If the plotted points fit on a straight line, we conclude that the distribution is a two-parameter lognormal distribution. If not and the plotted values fit on a straight line that bends upward or downward at its lower limits, then we have a three-parameter lognormal distribution (for further details, see David 1977; Krige 1978; and Rendu 1978).

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The three-parameter lognormal distribution is a skewed distribution in which the logarithm of any linear function of a given variable is normally distributed. The distribution is applied to the frequency analysis of floods, annual flows, and monthly flows, and a comparison with other commonly used methods suggests that it can be successfully used for the purpose of this study (Sangal and Biswas 1970; Charbeneau 1978; Stedinger 1980; Koutsoyiannis 2008; and Aristizabal 2012). A three-parameter lognormal distribution indicates that the variable under consideration might have witnessed enrichment or impoverishment processes. In other words, an increase or decrease in the original values. Accordingly, the inference that can be made from Figs. 3 and 4 is that both salinity and specific capacity values exhibit a three-parameter lognormal distribution as the lines bend upward at their lower limits. According to Rendu (1978) and David (1977), a certain value could be subtracted or added to straighten the lines, i.e., linearize the plotted line. The calculation of this value (β) can be carried out according to Rendu (1978) as follows:
 β ¼ m2 – f 1 f 2 = ðð f 1 þ f 2 Þ – 2mÞ where m is the sample value corresponding to 50 % cumulative frequency, and f1 and f2 are the sample values corresponding to 15 and 85 % cumulative frequencies. The calculated values for β were found to be 230 ppm for salinity and 0.39 m3/h/min for specific capacity. These amounts were then subtracted from the raw salinity and specific capacity values, and new frequency tables were constructed (Tables 1 and 2). The upper category limits of the newly constructed frequency tables were then plotted versus their cumulative relative frequencies. The results are shown as dotted straight lines on Figs. 3 and 4. In other words, the lines became straightened with no upward or downward bends at their lower limits. This justifies going ahead with the lognormality assumption. The justifications and benefits of doing so are discussed in detail in Rendu 1978. Structural analysis The geostatistical method which has been applied in this regard involved the following steps: (1) modeling the spatial variability of the studied variables by

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Fig. 2 Constructed histograms for the three data sets

constructing experimental semivariograms and fitting hypothetical models to it and (2) using the fitted hypothetical models to construct maps using kriging interpolation techniques to figure out whether the changes in the variable values are localized or extend over the whole area under consideration and whether they remained constant or changed over the years. The justifications behind semivariogram analysis and kriging are described in Clark (1979), David (1977), and Isaaks and Srivastava (1990). However, the following is a brief summary of the method used in this study

in constructing maps using kriging interpolation techniques based on the fitted semivariograms. Generally speaking, groundwater specific capacity or salinity values at locations close to each other are expected to be approximately the same. Meanwhile, at locations that are distant apart might be similar or different. This change from strong dependence to independence can be measured and quantified (Gajem et al. 1981; Gundogdu and Guney 2007; and Ta’any et al. 2009). This change can be quantified with a semivariogram, which is a plot of semivariance

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Table 1 Salinity frequency table, with category limits having exponential relationships Salinity

Salinity—β (230)

Bin

Frequency

Cum. f

Cum. f%

Bin

Frequency

Cum. f

Cum. f%

200

1

1

0.81

200

65

65

52.85

300

21

22

17.89

300

15

80

65.04

450

45

67

54.47

450

18

98

79.67

675

30

97

78.86

675

12

110

89.43

1012.5

13

110

89.43

1012.5

4

114

92.68

1518.75

7

117

95.12

1518.75

5

119

96.75

2278.13

4

121

98.37

2278.13

2

121

98.37

3417.19

1

122

99.19

3417.19

1

122

99.19

5125.78

1

123

100.00

5125.78

1

123

100.00

versus separation distance of the well values (points). The semivariogram of a measured variable is defined as N ðhÞ

γ ð hÞ ¼

1 X ½Zxi − Z ðxi þ hÞ2 2N ðhÞ i¼1

where N is the number of data pairs at wells h distance apart, and xi and x i + h are the measured salinities or specific capacities at xi and x i + h wells that are h distance apart. The nugget effect, sill, and range of influence characterize semivariograms (Figs. 5 and 6). The range of influence is the range over which the variable values are spatially correlated. The value at which the semivariogram levels off is called the sill. If γ(h) is unlikely to pass through the origin but appears to be a positive intercept is called the nugget effect. The resulting semivariogram is referred to as experimental semivariogram that can be fit to one of several hypothetical ones (e.g., spherical, Gaussian, exponential, or

linear). Specific capacity and salinity that fitted hypothetical models are shown in Figs. 5 ans 6, which turned out to be exponential. The adopted hypothetical models were then validated using jackknife analysis where measured variables were removed and estimated via kriging. Estimated versus actual values can then be correlated. A perfect fit would have a regression coefficient of 1.0 (Robertson, 2000). Ordinary kriging techniques were then applied to interpolate groundwater specific capacity and salinity values at unsampled locations. This type of kriging can be used with the presence or absence of a trend. The kriging interpolated estimates for specific capacity and salinity before (n = 70) and after (n = 53) the year 1990 are illustrated in Figs. 7, 8, 9, and 10. Combined data for the entire period of observation were also used to account for the recent status of both specific capacity and salinity in the B2/A7 aquifer of the Amman Zarqa Basin (Figs. 11 and 12).

Table 2 Specific capacity frequency table, with category limits having exponential relationships Specific Capacity

Specific Capacity—β (0.39)

Bin

Frequency

Cum. f

cum. f%

Bin

Frequency

Cum f

Cum f%

0.1

1

1

0.81

0.1

19

15

12.20

0.5

14

15

12.20

0.5

16

28

22.76

2.5

34

49

39.84

2.5

21

54

43.90

12.5

26

75

60.98

12.5

19

75

60.98

62.5

25

100

81.30

62.5

25

100

81.30

312.5

13

113

91.87

312.5

13

113

91.87

1562.5

10

123

100

1562.5

10

123

100.00

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Fig. 3 Lognormal probability plots for salinity values

Results and discussions Groundwater properties vary spatially and temporally because of the synergistic effect of intrinsic and extrinsic factors that operate at different scales and intensities (Douaik et al. 2011). Characterizing the spatial and temporal variability of these properties is an important tool in their effective management. To do so, numerous samples have to be taken from monitoring wells repeatedly as conditions change. Accordingly, groundwater properties Fig. 4 Lognormal probability plots for specific capacity values

are measured only at certain locations and times. Where there is no well groundwater, properties should be predicted. Such predictions and their related certainties can be calculated using adequate statistical models. In this study, the assessment of the type of distribution of two groundwater properties, namely salinity and specific capacity of the B2/A7 aquifer of the Amman Zarqa Basin, showed that their data are positively skewed and can be fit to a three-parameter lognormal distribution (TPLN). As already mentioned, this type of

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Fig. 5 Fitted experimental semivariograms for specific capacity and salinity of B2/A7 groundwater aquifer of Amman-Zarqa Basin

distribution is commonly encountered in gold mines (Rendu 1978) and is applied in different fields of science (Limpert et al. 2001; Koutsoyiannis 2008). In gold mines, researchers have accounted for this type of

distribution as due to enrichment or impoverishment processes that have affected the variable under consideration and were able to calculate empirically the amount of impoverishment or enrichment (β) as shown

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Fig. 6 Fitted experimental semivariograms for both global specific capacity and global salinity

in Figs. 3 and 4. In our case, both salinity and specific capacity have witnessed increase in their values with the passage of time. The calculated increase was found to be 230 ppm and 0.39 m3/h/min for salinity and specific capacity, respectively.

Fig. 7 Kriging interpolated specific capacity values before (n = 70)

The factors that have caused this increase acted on the studied aquifer for a long period of time and as a whole; therefore, the increase can be accounted for as an indicator of global variability (i.e., covers the basin for the entire period of observation) as opposed to local

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Fig. 8 Kriging interpolated specific capacity values after 90 (n = 53)

variability (at every well site at different time instants). The main drawback to this technique is that it requires a great deal of measurements (Rendu 1978; Sangal and Biswas 1970; Stedinger 1980; Koutsoyiannis 2008; Aristizabal 2012). Accordingly, the estimated β values can be used as an exploratory data analysis of global variability at a certain point of time. The empirically calculated β values for specific capacity and salinity were 0.39 m3/h/min and 238 ppm, respectively.

Fig. 9 Kriging interpolated salinity values before 90 (n = 70)

On the other hand, the assessment of both variables using data before and after the year 1990 and the related observed increase or decrease can be accounted for as pertaining to events during a certain time interval. To account for the variability of a certain groundwater property, its type of distribution should be studied first, and if it turned out to be a three-parameter lognormal distribution, the beta constant (β) would be a measure of the increase or the decrease in this property with the

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Fig. 10 Kriging interpolated salinity values after 90 (n = 53)

passage of time. What has been done is a simple empirical calculation and is not sufficient. Though it says empirically how much the increase or the decrease in the studied variability is, it does not tell us where such a change took place. To locate where these changes took place, the changes exhibited by the property under consideration can be examined locally at every well site. However, in areas having no monitoring wells, one cannot tell, and the following can be done:

1. Model the spatial variability of the property under consideration by constructing and fitting a semivariogram that would account for its spatial distribution in the investigated area. In our case, it turned out to be exponential with and without nugget effect for both specific capacity and salinity Figs. 5 and 6. 2. Cross validating the model to check whether it would reproduce the spatial variability or not.

Fig. 11 Kriging interpolated specific capacity values for the entire period of observation (n = 123)

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Fig. 12 Kriging interpolated salinity values for the entire period of observation (n = 123)

3. Determining the exact location where the change took place using kriging interpolation techniques by building kriged estimates as shown in Figs. 7, 8, 9, 10, 11, and 12. The results of structural analysis (semivariogram analysis) showed that specific capacity values used to exhibit an exponential model with nugget effect before 1990 and remained to exhibit the same model but with different parameters. What have changed are the nugget effect and the sill, i.e., the parameters of the model. Both nugget and sill values decreased from around 485 to 84 and 3583 to 1055 (m3/h/min)2, respectively. This means that the inherited randomness (nugget effect) in the specific capacity values is decreasing and that the sill values (variance) are also decreasing. This implies that the aquifer is suffering from an ongoing change. For example, comparing locally the specific capacity values near As-Samra Wastewater Treatment Plant using Figs. 7 and 8, one can say that the central part of the basin have witnessed a major decrease in this property after the year 1990 indicating thus the impact of population increase and flourishing agricultural activities on this property as reflected by over pumping. Such impacts have been emphasized by Ta’any et.al (2009); Al Kuisi et al. (2009), (2010), (2012), and (2014); and Goode et al. (2013).

However, globally, the basin as a whole witnessed an increase, namely in its northern and southeastern parts. This could be attributed to different intrinsic and extrinsic factors. Regarding salinity values, before and after 1990, they also exhibit an exponential model without nugget effect but with different sills. As shown in Fig. 5, a striking increase took place from 5025 to 70,494 (ppm)2. This increase in salinity variability can be accounted for as due to extensive salinisation at certain localities as compared to others since 1990. This is clearly demonstrated in Figs. 9 and 10. A careful examination of both figures shows that the salinity values before 1990 were around 450 as demonstrated by salinity contour interval in the vicinity of AS-Samra Wastewater Plant, whereas after 1990, the salinity values increased to around 750. This highlights as well the impact of population increase on this property. Globally (Fig. 6), the sills of both specific capacity and salinity mounted up to around 2303 and 31,464 (ppm)2, respectively, and the empirically measured increase for both properties exhibit a NW–SE trend as shown in Figs. 11 and 12. Accordingly, the results of the aforementioned steps can be used to locate both point and nonpoint sources of pollution and can be used later as an evidence for adopting certain management measures.

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Summary and conclusions This study aimed at evaluating the variability of both specific capacity and salinity of the B2/A7 aquifer in Amman Zarqa Basin at different time intervals, namely after the time datum 1990, using statistical and geostatistical techniques. The first step involved determining the type of distribution exhibited by both properties. Both specific capacity and salinity exhibit a threeparameter lognormal distribution. The empirically calculated parameter (β = 0.39 m3/h/min and 238 ppm for both specific capacity and salinity, respectively) can be used to account for the increase or decrease of the property under consideration and can be considered as an indicator of global variability (all over the basin) as compared to local variability (at certain locations) (Sangal and Biswas 1970; Stedinger 1980; Koutsoyiannis 2008; and Aristizabal 2012). This method ignores the spatial coordinates and consequently can be used as an exploratory data analysis according to Douaik et al. (2011). For a formal and implicit evaluation of the spatial variability of the B2/A7 specific capacity

and salinity groundwater properties before and after the year 1990, structural analysis have been used to model the variability using experimental semivariograms. The constructed models were validated and then adopted to map the basin using kriging interpolation techniques (Figs. 7, 8, 9, and 10). The constructed maps (Figs. 8 and 10) pinpointed clearly where in the basin the eminent changes took place. In our case, the central part of the basin, where the As-Samra Wastewater Treatment Plant is located, suffered major increase in salinity values and decrease in specific capacity values after the time datum of 1990. What has been accounted for is due to population increase and flourishing agricultural activities. Figures 11 and 12 display the current situation of both properties and their trends. Acknowledgments The authors would like to express their sincere appreciation and gratitude to the Ministry of Water and Irrigation and namely to Eng. Mohammad Momani for the kind permission to use the data bank files. Thanks are also due to colleague Dr. Susan White, La Trobe University, Australia and the journal’s reviewers for improving the original manuscript.

Appendix

Table 3 Used data from Amman Zarqa Basin aquifer B2/A7 Well ID

Easting

Northing

Elevation

Depth

Test Date

Yield

Drawdown

Specific Capacity

Salinity

AL1031

274269

1173884

585

150

September 25 1966

172

0.14

1228.57

346

AL1041

272486

1171481

576

155

December 17 1964

39

50

0.78

269

AL1043

266987

1171506

557

234

November 27 1960

43

36.3

1.18

333

AL1072

266429

1171549

557

161

May 31 1968

95

0.6

158.33

307

AL1073

266498

1171015

556

165

November 23 1969

45

75

0.60

544

AL1075

268286

1170782

568

85

December 31 1982

100

0.1

1000.00

345

AL1763

283730

1169855

590

105

May 1 1979

250

0.3

833.33

284

AL1082

269143

1168674

596

130

January 1 1965

19

38

0.50

365

AL1086

273694

1171447

569

130

October 3 1973

77

0.5

154.00

281 230

AL1093

282979

1166340

585

400

July 20 1971

250

1.54

162.34

AL1114

289048

1161722

598

91

July 12 1975

30

1.8

16.67

215

AL1119

254930

1177165

613

153

February 16 1980

41

1.83

22.40

395

AL1158

251455

1173485

472

29

July 1 1970

60

12

5.00

499

AL1166

257200

1168750

560

124

August 1 1960

70

39

1.79

569

AL1230

256690

1170130

540

102

January 14 1986

90

0.4

225.00

690

AL1303

252141

1160190

581

100

July 1 1969

152

3.8

40.00

448

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Table 3 (continued) Well ID

Easting

Northing

Elevation

Depth

Test Date

Yield

Drawdown

Specific Capacity

Salinity

AL1306

251375

1159300

592

50

September 1 1966

25

1.3

19.23

704

AL1312

250590

1158835

603

60

May 1 1975

25

31.2

0.80

589

AL1324

253400

1159495

614

140

September 10 1963

77

18

4.28

336

AL1332

253160

1158950

630

91

April 18 1966

110

0.25

440.00

326

AL1622

253710

1173850

550

87

June 7 1978

77

2.35

32.77

378

AL1337

247640

1156200

692

135

November 26 1973

65

25

2.60

384

AL1338

247775

1156050

695

320

March 1 1970

122

52

2.35

400

AL1352

248848

1158792

622

80

May 20 1973

126

4

31.50

608

AL2720

249750

1157250

780

79

April 8 1992

66

1.63

40.49

402

AL1547

255710

1170510

530

175

March 20 1984

260

70

3.71

692

AL2449

290982

1185422

767

300

September 12 1989

65

0.07

928.57

250

AL1652

256658

1160056

638

170

September 26 1961

12

14

0.86

480

AL1672

254500

1171800

537

38

November 6 1963

147

4.2

35.00

1030

AL1684

245118

1159577

775

101

May 1 1976

58

4.7

12.34

1565

AL1685

270090

1167590

604

175

July 1 1966

43

13.9

3.09

506

AL1705

279325

1165865

585

140

June 1 1959

21

48

0.44

360

AL1752

269550

1169450

586

130

February 28 1965

79

38.4

2.06

339

AL1776

273200

1171400

568

130

October 3 1973

77

1.5

51.33

281

AL1803

250450

1147610

825

205

March 17 1983

30

29.47

1.02

492

AL1806

254035

1151940

720

190

May 1 1982

51

39.42

1.29

835

AL1813

241006

1152556

726

60

October 27 1960

93

11.3

8.23

666

AL1816

242022

1153980

705

38

March 25 1959

162

0.36

450.00

413

AL1820

242223

1154370

702

170

September 28 1963

275

5.2

52.88

275

AL1824

239900

1151500

750

134

May 13 1966

250

52.9

4.73

422

AL1828

242000

1152200

714

164

May 16 1971

250

9

27.78

600

AL1167

257465

1169795

529

106

November 1 1973

70

25

2.80

294

AL1835

240800

1152300

750

193

March 18 1971

250

3.6

69.44

646

AL1847

245450

1156050

725

225

April 7 1976

46

15.58

2.95

384

AL1855

248250

1155730

700

180

July 20 1987

35

32.65

1.07

740

AL1899

254870

1166920

586

113

January 17 1966

167

4.8

34.79

666

AL1923

250400

1158750

620

60

September 29 1975

25

31.2

0.80

589

AL2426

242540

1155160

680

116

December 9 1985

84

10

8.40

722

AL2564

281160

1166880

587

140

July 10 1987

90

0.08

1125.00

218

AL2565

275915

1179020

630

275

September 8 1987

15

70

0.21

428

AL2567

282720

1165200

589

165

July 10 1987

100

4.32

23.15

390

AL2568

286000

1165500

590

154

July 11 1987

110

0.21

523.81

198

AL2569

284900

1163350

594

144

August 17 1987

110

2.2

50.00

474

AL2570

283720

1163990

590

111

May 23 1987

100

1.13

88.50

422

AL2573

286000

1163000

590

162

August 19 1987

100

1.22

81.97

224

AL2574

282140

1166670

586

152

June 7 1987

97

3.91

24.81

250

AL2576

262730

1176400

554

103

September 20 1986

90

3.62

24.86

294

AL2587

282030

1167830

600

150

June 22 1985

89

0.4

222.50

262

AL2600

271800

1169380

580

100

December 31 1966

80

13.1

6.11

377

AL3315

262625

1145310

850

350

May 7 1987

40

26

1.54

672

Author's personal copy Environ Monit Assess (2016) 188:46

Page 15 of 17 46

Table 3 (continued) Well ID

Easting

Northing

Elevation

Depth

Test Date

Yield

Drawdown

Specific Capacity

Salinity

AL3408

262200

1168100

585

100

June 30 1979

70

7.75

9.03

292

AL3424

268260

1170750

580

259

October 9 1990

50

9

5.56

710

AL3509

256250

1168000

575

236

January 29 1985

81

15.68

5.17

288

AL3554

284500

1161000

620

210

January 1 1983

67

14.9

4.50

412

CD1366

239280

1144150

920

235

July 25 1983

18

65

0.28

400

CD1054

235600

1139510

820

180

May 19 1981

63

30.75

2.05

320

CD1317

238760

1136940

785

213

August 15 1987

65

4

16.25

371

CD1360

234100

1138505

840

213

April 9 1980

57

20.3

2.81

337

CD1403

237360

1137210

795

202

April 29 1980

53

4.28

12.38

343

CD3020

234570

1139700

820

201

September 28 1981

20

27.57

0.73

304

AL1097

279791

1167222

590

121

May 7 1998

25

18

1.39

224

AL1186

255455

1169275

541

130

May 7 1998

12

41

0.29

390

AL1536

276800

1178100

628

204

May 7 1998

5

6

0.83

710

AL1654

254833

1165970

582

137

May 7 1998

82

20

4.10

1376

AL1694

252535

1156850

690

112

May 7 1998

30

18.6

1.61

425

AL1849

245165

1154825

729

205

May 7 1998

25

37.35

0.67

334

AL2529

265820

1158720

710

371

May 7 1998

45

43.5

1.03

1814

AL2530

264000

1165500

638

320

May 7 1998

52

138

0.38

838

AL2532

249715

1164035

610

400

May 7 1998

55

18

3.06

1220

AL2566

276550

1165500

610

129

May 7 1998

110

0.13

846.15

275

AL2571

279250

1166500

590

171

May 7 1998

120

7.3

16.44

307

AL2572

278500

1165000

600

129

May 7 1998

100

2.5

40.00

402

AL2575

283250

1165250

588

143

May 7 1998

120

1.23

97.56

365

AL2579

273500

1171750

570

210

May 7 1998

65

14.7

4.42

569

AL3133

281281

1164420

599

169

June 11 1993

65

65

1.00

592

AL3140

260150

1177490

556

218

June 29 1992

40

102

0.39

290

AL3145

259580

1179360

580

202

December 14 1993

60

0.1

600.00

490

AL3146

255770

1177870

640

292

May 11 1992

60

53.5

1.12

400

AL3268

255380

1170550

540

154

March 25 1991

144

55.55

2.59

2112

AL3284

281179

1165247

595

135

October 29 1991

91

1.5

60.67

682

AL3287

247829

1158879

624

452

May 17 1992

87

101.2

0.86

384

AL3290

246000

1158000

710

226

May 16 1991

37

64.9

0.57

1541

AL3305

278977

1166458

592

132

May 12 1993

62

29.9

2.07

400

AL3317

253360

1142250

900

390

July 4 1993

60

21

2.86

646

AL3358

259250

1163130

620

268

July 26 1995

36

14.2

2.54

700

AL3359

263500

1168500

600

390

December 9 1993

47

60.85

0.77

647

AL3402

257720

1169150

118

195

July 30 1994

60

0.4

150.00

1440

AL3404

245270

1161530

780

185

October 12 1996

60

42

1.43

512

AL3419

289200

1178300

690

287

March 18 1995

78

2.12

36.79

228

AL3442

257030

1162580

620

341

September 14 1997

70

2.36

29.66

570

AL3445

280734

1165405

595

137

March 11 1997

106

1.22

86.89

530

AL3489

254150

1160350

635

300

January 25 1997

30

70.78

0.42

414

AL3490

258470

1163030

607

348

December 9 1997

74

1.35

54.81

512

AL3534

252175

1158240

640

280

January 11 2000

50

10

5.00

540

Author's personal copy 46

Environ Monit Assess (2016) 188:46

Page 16 of 17

Table 3 (continued) Well ID

Easting

Northing

Elevation

Depth

Test Date

Yield

Drawdown

Specific Capacity

Salinity

AL3537

267670

1161600

700

512

August 15 1998

50

28.5

1.75

1363

AL3542

243725

1158710

680

210

November 17 1999

45

43.5

1.03

447

AL3584

240950

1168340

710

140

September 23 1997

40

31.5

1.27

610

AL3627

246140

1174630

480

150

November 17 2002

17

19

0.89

1050

AL3649

245110

1147025

890

425

September 10 2002

10

235

0.04

337

AL3656

245770

1158700

640

188

June 23 2003

140

5.3

26.42

855

AL3661

277050

1166670

620

110

October 3 2001

50

0.5

100.00

480

AL3714

258036

1172521

590

283

March 29 2004

55

10.15

5.42

4710

AL3715

271775

1169425

600

210

June 1 2004

60

2

30.00

1230

AL3720

254100

1158773

650

400

April 20 2004

34

123

0.28

416

AL3721

271570

1171310

600

300

August 4 2004

60

120

0.50

3117

AL3754

253150

1155150

830

255

August 1 2006

10

79

0.13

650

AL3829

271800

1173300

591

250

November 25 2007

17

92

0.18

260

AL3969

266318

1171444

557

200

October 29 1994

60

0.5

120.00

610

CD1283

232580

1141680

920

349

September 12 1998

15

33

0.45

377

CD1298

233070

1144230

900

190

September 12 1998

32

46.1

0.69

768

CD1407

242200

1141950

840

365

September 12 1998

18

64.2

0.28

416

CD3380

228800

1140600

920

360

August 24 1999

18

42.15

0.43

509

CD3381

231950

1141530

900

325

September 6 1999

20

42

0.48

435

Before 1990: All data above the grey row including the year 90 (n = 70). After 1990: All data below the grey highlighted row (n = 53)

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