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Applied Water Science

, Volume 6, Issue 1, pp 35–45 | Cite as

Deciphering transmissivity and hydraulic conductivity of the aquifer by vertical electrical sounding (VES) experiments in Northwest Bangladesh

  • Golam Shabbir Sattar
  • Mumnunul Keramat
  • Shamsuddin ShahidEmail author
Open Access
Original Article

Abstract

The vertical electrical soundings (VESs) are carried out in 24 selective locations of Chapai-Nawabganj area of northwest Bangladesh to determine the transmissivity and hydraulic conductivity of the aquifer. Initially, the transmissivity and hydraulic conductivity are determined from the pumping data of nearby available production wells. Afterwards, the T and K are correlated with geoelectrical resistance and the total resistivity of the aquifer. The present study deciphers the functional analogous relations of the geoelectrical resistance with the transmissivity and the total resistivity with the hydraulic conductivity of the aquifer in northwest Bangladesh. It has been shown that the given equations provide reasonable values of transmissivity and hydraulic conductivity where pumping test information is unavailable. It can be expected that the aquifer properties viz. transmissivity and hydraulic conductivity of geologically similar area can be determined with the help of the obtained equations by conducting VES experiments.

Keywords

VES Transmissivity Hydraulic conductivity Aquifer Chapai-Nawabganj 

Introduction

Basic elements of groundwater investigation involve determination of transmissivity and storage coefficient of the aquifers along with the geometry of the water-bearing zone. Pumping test is one of the suitable means for computing reliable and representative values of the hydraulic characteristics in aquifers (Ayers 1989; Kruseman and de Ridder 1994). Pumping test is an expensive process and therefore, long duration pump test is rarely carried out in practice. Surface geoelectric measurements provide an alternative approach for the estimation of some of the aquifer properties (Ahamed and deMarsily 1987; Khan et al. 2002). Though the geoelectrical methods alone, even under favorable conditions, do not replace test drilling to ascertain groundwater condition, yet in many cases can reduce the number of test drillings by giving a better selection of test borehole locations (Yadav and Abolfazli 1998). In past three decades, several investigators have studied the relations between aquifer parameters and geoelectric properties (Ponzini et al. 1984; Kelly and Frohlich 1985; Onuoha and Mbazi 1988; Mbonu et al. 1991; Kalinski et al. 1993; Frohlich et al. 1996; Dasargues 1997; Singhal et al. 1998; Niwas and De Lima 2006; Shevnin et al. 2006; Batte et al. 2010; Egbai 2011; Ezeh 2011; Majumdar and Das 2011; Sikandar and Christen 2012; Asfahani 2012; Niwas and Celik 2012; Nwosu et al. 2013; Ugada et al. 2013). Batte et al. (2010) correlated geoelectric data with aquifer parameters to delineate the groundwater potential of hard rock terrain in central Uganda. Egbai (2011) used information from vertical electrical sounding (VES) for the determination of the transmissivity of aquifers in Anwai, Delta State of Nigeria. Majumdar and Das (2011) characterized and estimated aquifer properties from electrical sounding data in Sagar Island region in south 24 Parganas of West Bengal, India. Sikandar and Christen (2012) estimated hydraulic conductivity using geoelectrical method in alluvial aquifers of Pakistan. Asfahani (2012) derived transmissivity of Quaternary aquifer from vertical electrical sounding measurements in the semiarid Khanasser valley region of Syria. Niwas and Celik (2012) estimated porosity and hydraulic conductivity of Ruhrtal aquifer in Germany using near-surface geophysical methods. Ugada et al. (2013) determined aquifer hydraulic characteristics of Umuahia area from Dar Zarrouk parameters. Nwosu et al. (2013) measured the hydraulic properties of the aquiferous zones using geoelectrical method for the evaluation of groundwater potentials in the complex geological area of Imo state, Nigeria. In all the above studies, mathematical equations were developed to estimate hydraulic aquifer property from surface electrical measurements. All the studies suggested that estimation of hydraulic conductivity and transmissivity from surface resistivity measurements is feasible. However, these relationships are area-specific and have limited applications in other area (Purvance and Andricevic 2000a, b; Niwas and De Lima 2006).

Groundwater is a major component of people’s livelihood and agro-based economy in Northwest Bangladesh (Shahid and Hazarika 2010). About 75 % water for irrigation in the region comes from groundwater (Shahid 2010). However, overexploitation of groundwater in recent years has caused the groundwater level falls to the extent of not getting fully replenished in the recharge season. Actions are necessary to regulate the abstraction of groundwater in the area for sustaining rechargeable groundwater aquifers (Shahid and Hazarika 2010). Cost-effective estimation of aquifer properties is essential for this purpose. Therefore, the present study is carried out to establish the physical relationship between aquifer properties and geoelectrical properties of the area obtained by VES experiments.

The VES experiments have been conducted in Chapai-Nawabganj area located in the northwestern part of Bangladesh. It lies between the geographical coordinates having latitude 24°25′N and 25°43′N and longitude 88°06′E and 88°25′E (Fig. 1). The area covers about 475 km2 with population over 0.38 million, among which more than 60 percent depend directly or indirectly on agricultural work. The total cultivable land is about 340 km2. More than 55 % of cultivated land requires irrigation, which totally fulfilled from groundwater.
Fig. 1

Location map of study area

Geology of the area

Geomorphology of the area can be broadly divided into two zones (Rashid 1991) viz. western floodplain (70 % of total area) and the northeastern Pleistocene Terrace (30 % of total area), which is also known as the Barind Tract (Morgan and McIntire 1959). The uplifted terraces of Pleistocene sediments of Barind Tracts are more strongly weathered than the surrounding alluvium. In the areas with alluvial, the Barind Tract sediments can be found at depths of the order of 150–200 m or more. Four distinct physiographic sub-divisions are identified in the present study area (Sattar 2005). These are Padma floodplain, Padma–Mahananda floodplain, Mahananda floodplain, and the Barind Tract. The Padma and the Mahananda are that two prominent rivers and control the overall hydrogeomorphological activity. The upper aquifers in the region are unconfined or semi-confined in nature. The thickness of the exploitable aquifer ranges from 10 to 40 m. Jahan et al. (1994) computed that the specific yield of the aquifer in the area varies from 8 to 32 % with a general decreasing trend from north toward central portion. The maximum depth to groundwater table from land surface varies from 7 to 30 m (Asaduzzaman and Rushton 2006). The topography of the area is mainly flat with an average elevation of 25 m above the mean sea level. There is a mild surface gradient toward southeast (Shahid and Hazarika 2010).

Materials and methods

Theoretical background

From well-known Darcy’s law, the water discharge, Q (m3/s), may be expressed in the form (Nath et al. 2000):
$$Q = KI^{\prime } A$$
(1)
and the differential form of Ohm’s law can be written as (Nath et al. 2000):
$$J = \sigma E$$
(2)
where K = hydraulic conductivity (m/day), I  = hydraulic gradient, A = area of cross-section perpendicular to the direction of flow, J = current density (A/m2), \(\sigma\) = electrical conductivity (inverse of resistivity in a homogeneous, isotopic medium), and E = applied electrical field. These two fundamental laws of fluid flow and current flow may be utilized to find a probable relationship between electrical and hydraulic characters of the formation.
The geoelectrical resistivity, ρ, appears as the material specific constant of proportionality in the expression for the total resistivity (A) of the cylinder of length L and cross-sectional area D of uniform composition (Nath et al. 2000),
$$A \, = \, \rho \, L/D$$
(3)
The total resistivity can be obtained experimentally through Ohm’s law, R = V/I, where V is the potential difference between the ends of the cylinder and I is the total current flowing through the cylinder. The resistivity of the material is an intrinsic property of the material, that can be calculated as the product of the apparent resistance R app = V/I and a geometric factor K = A/L that carries information about geometry of the cylinder (Islami 2011).
Now if we consider a prism of unit cross-section, with thickness \(h\) and resistivity \(\rho\), the resistance (R) normal to the face of the prism, and the conductance (\(S\)) parallel to the face of the prism can be given as (Patra and Nath 1999),
$$R = h\rho$$
(4)
and
$$S = \frac{h}{\rho } = h\sigma$$
(5)
This is when considering of a prism of aquifer material having unit cross-sectional area and thickness h. R and S are Dar Zarrouk parameters with R as transverse resistance and S as longitudinal conductance (Zohdy 1974 and 1975). The transmissivity \(T\) (the product of hydraulic conductivity and aquifer thickness) can be derived in terms of R and S as (Patra and Nath 1999),
$$T = K\sigma R$$
(6)
and
$$T = \left( {\frac{K}{\sigma }} \right)S$$
(7)
It has been observed (Niwas and Singhal 1981) that either of the two propositions, \(K\sigma\) = constant or \(K/\sigma\) = constant could be true for an area under study, also valid for other areas with similar geological setting and water quality. Dasargues (1997) have used the overall resistivity of aquifer material to correlate it with hydraulic conductivity (K) using the relation (Patra and Nath 1999),
$$K\;\infty \;A$$
(8)
where
$$A\; = \;\sum {\rho_{i} }$$
(9)
where i represent different layers of aquifer. It is well-established fact that the variations in resistivity are due to the variations of geological formations with their characteristics’ compositions.

Several investigations have been carried out in the past to relate aquifer parameters with geoelectric properties for different geological setup which have been discussed in detail in introduction section. In the present study, transverse resistance is correlated with aquifer transmissivity and the total resistivity is correlated with hydraulic conductivity to decipher the functional analogous relationships for Northwest Bangladesh.

Field survey and data processing

For the proposed study, 24 VES experiments were performed at pre-selected stations (Fig. 1) employing Schlumberger array. These stations were selected on the basis of reconnaissance survey, where emphasis was given on the proximity to the existing production wells. The field measurements were made with a minimum and maximum current electrode spacing (AB) of 400 and 1,000 m, respectively. The collected VES data were interpreted using both the multi-layer forward (Zohdy and Bisdorf 1989) and Inverse (Cooper 2001) methods. The intentions of the use of two models were to increase the acceptability of interpretation and hence furnish accurate information on groundwater-bearing formation underneath.

The pumping test data at 15 locations were collected from Barind Multi-purpose Development Authority (BMDA) and used for the estimation of aquifer hydraulic properties. Location of pumping test is also shown in Fig. 1. Same number is used in Fig. 1 to represent VES and pumping location. At each location, three-step pumping test for the period of 1,080 min, each step being of 360 min was conducted to study aquifer properties. The discharging rates of the steps were 4,893, 6,116 and 7,339 m3/day, respectively. However, drawdown data were collected at regular time intervals. Pumping test data available in the study area were ‘single-well test’. Considering the nature of data and their applicability to comply the different relevant equations, the Eden and Hazel (1973) method available in software StepMaster (version 2.0) was used to estimate transmissivity. The details of Eden and Hazel method can be found in Eden and Hazel (1973).

Result and discussions

Analysis of lithologs

On the basis of borehole information, the groundwater-bearing sedimentary sequences of the floodplain and Barind areas can be divided into several recognizable hydrostratigraphic units. The top clayey layer mainly consists of recent (floodplain) to older alluvium (Barind Tract) of Quaternary age. The textural characteristics of this unit are mainly clay, silt, and silty clay to very fine sand. Thickness of the zone also varies in accordance with its geomorphologic situation. In the floodplain area, the thickness ranges from 6 to 12 m. In and around the Barind region, thickness increases with the increase in elevation from 12 to 24 m.

The second layer is a composite aquifer. This is a common sandy unit of fine to medium-grained sand present just below the top layer in the floodplain area. Some times this zone is absent in the Barind region. Thickness of this zone varies from 5 to 15 m, and 3 to 10 m in the floodplain and Barind area, respectively.

The third layer is the main aquifer consisting of medium to coarse-grained sand, and coarse sand with gravels which serve as potential zones for groundwater storage, distribution and exploration. The main hydrogeological constrains of this porous zone is its uneven distribution below the composite aquifer layer. This zone is very thick (25–35 m) at few places around the floodplain area, and is relatively thin (10–15 m) in the Barind Tract. This indicates that the main water-bearing unit is gradually thinning from floodplain to the Barind area and is regarded as the ultimate constrain of water scarcity of the Barind Tract. Considering the development potential of groundwater in the area of study, this zone can be considered as most productive zone at both floodplain and the Barind Tract.

The bottom layer in the region is commonly the Barind clay particularly at the Barind Tract. This clay is very hard and compact when it is dried. Its normal color is black but at places it varies from strong brown to very pale brown. Usually in the floodplain this lies just below main aquifer layer but at few places, especially in the Barind, it appears below the top aquitard layer or even very close to the surface and also to a considerable depth (40–45 m).

Water table in the area lies below the top aquiclude and hence developed a favorable confined condition for the main aquifer. The water in the aquifer is usually fresh.

Correlation between lithologs and VES logs

It is well recognized that the VES signature reflects the underground lithology up to which it reaches. The VES models have been compared with the available lithologs of the nearby location to observe the relation between them. The VES logs along with the nearest lithologs of different physiographic sub-divisions are presented in Fig. 2. It can be observed that in most cases the Forward models show better relation with the lithologs than those of the Inversion model. Vertical distributions of the VES logs are in good agreement with the lithologs having negligible discrepancy among them.
Fig. 2

Comparison of VES logs and lithologs in a Padma–Mahananda floodplain area; b Padma Floodplain; c Mahananda Floodplain; and d The Barind Tract

Aquifer thickness

Aquifer thicknesses from borehole data are interpolated using krigging method to prepare the map of aquifer thickness of the study area as shown in Fig. 3. Aquifer thickness is more than 10 m in most parts of the study area. The thickness is found to be less in Barind tract where it varies between 9.1 and 22.8 m. Aquifer thickness in other geomorphic region is more compared to Barind tract. In some parts of floodplain regions, aquifer thickness is found to be more than 35 m.
Fig. 3

Spatial distribution of aquifer thickness prepared from litholog data

Transverse resistance and total resistivity

Transverse resistance and total resistivity computed from VES data are interpolated to prepare the corresponding maps of the study area which are shown in Fig. 4a, b, respectively. The transverse resistance in the study area is found to vary between 480 and 5,375 ohm−1. Overall, it is found to be less in Barind tract compared to other geomorphic regions. Total resistivity, on the other hand, is found to vary between 66 and 300 ohm in the study area.
Fig. 4

Maps showing the spatial distribution of a transverse resistance; b total resistivity prepared from VES data

Transmissivity from transverse resistance

In hydrogeological investigations, transverse resistance (R) has been found to be functionally analogous to transmissivity (\(T\)) (Cassiani and Medina 1997; Niwas and Singhal 1985). The value of transverse resistance computed from VES data and transmissivity of the aquifer computed from pumping test data at different sites near to the VES locations are summarized in Table 1. The transverse resistance R and the corresponding available transmissivity, T, from the pumping test data are plotted in Fig. 5. The scatter plot reveals a linear relationship between T and R in the form of:
$$T = 0.3079\; \times \;R + 299.81.$$
(10)
The shape of the relation between aquifer properties and geophysical parameters can be linear or non-linear. Non-linearity arises due to heterogeneity or variations of lithological composition with directions. Alluvial aquifers are not free of clay. However, in only few cases, clay lenses are found within aquifer in the study area. Therefore, it is considered that the aquifer in the study area can be distinguished by low effect of clay content. Scatter plot of data shows linear relation between aquifer and geophysical parameters. When tried to fit with non-linear and linear equations, regression coefficient (r) is found to be higher for linear equation. Many other researchers also deduced linear relation between aquifer and geophysical parameters, considering geology and groundwater quality, remaining fairly constant within the area of interest (Niwas and Singhal 1981, 1985; Harb et al. 2010; Chachadi and Gawas 2012). Therefore, linear equations are derived to relate aquifer and geophysical parameters in the present study.
Table 1

Transmissivity of the aquifer determined from geoelectrical parameters

Physiographic sub-division

VES nos.

Transverse resistance (R) (ohm-m2)

Transmissivity from pumping Test (T) (m2/day)

Calculated transmissivity T = 0.3079 × R + 299.81 (m2/day)

Error (%)

Padma Floodplain

VES 01

2,250

912

993

9

VES 19

2,025

1,124

923

18

VES 22

5,375

1,820

1,955

7

VES 24

4,846

x

1,792

a

Padma–Mahananda Floodplain

VES 03

2,660

938

1,119

19

VES 17

1,548

662

776

17

VES 23

1,903

x

886

a

Mahananda Floodplain

VES 02

1,800

781

854

9

VES 06

960

764

595

22

VES 10

2,560

932

1,088

17

VES 12

1,947

x

899

a

VES 13

4,014

1,846

1,536

17

VES 14

2,020

810

922

14

VES 15

1,565

627

782

24

VES 16

3,600

x

1,408

a

VES 18

2360

x

1,026

a

VES 20

2,156

x

964

a

VES 21

2,029

1,018

925

9

Barind Tract

VES 04

2,575

x

1,093

a

VES 05

1,085

x

634

a

VES 07

960

x

595

a

VES 08

2,131

1,160

956

18

VES 09

480

375

448

19

VES 11

1,562

882

781

11

aIndicated the calculated values where there are no pumping test data

Fig. 5

Relation between the transverse resistance and transmissivity

The maps of aquifer transmissivity estimated from transverse resistance using Eq. (10) and that obtained from pumping test are shown in Fig. 6a, b, respectively. It can be seen from the maps that spatial distribution of transmissivity values calculated from transverse resistance matched well with that obtained through pumping test. It has also been found that the calculated T value in the VES locations, where pumping test data are not available (viz, VES locations 04, 05, 07, 12, 18, 20, 21 and 23) also well matched with the T of surrounding physiographic sub-divisions (Table 1). Therefore, it can be remarked that T (transmissivity) of the study area can be calculated from the VES data using Eq. 10.
Fig. 6

Spatial distribution of aquifer transmissivity obtained from a transverse resistance; and b pumping test

Hydraulic conductivity from aquifer total resistivity

Aquifer total resistivity (A) estimated from VESs is correlated with the hydraulic conductivity values computed from the analysis of pumping test at 15 borehole locations near to the VES points. The plot of aquifer resistivity along abscissa and hydraulic conductivity along ordinate is presented in Fig. 7. This scatter plot also shows a linear relationship between K and A which can be written in the form:
$$K = 0.3712 \times A - 7.3727$$
(11)
Hydraulic conductivity (K) estimated by pumping test and Eq. (11) are presented in Table 2. It is apparent from the Table 2 that values calculated by aforementioned equation give reasonable estimation of K for the respective regions which belong to different physiographic sub-divisions. The maps of aquifer hydraulic conductivity estimated from total resistivity using Eq. (11) and that obtained from pumping test are shown in Fig. 8a, b, respectively. It can be seen from the maps that spatial distribution of aquifer hydraulic conductivity values calculated from total resistivity match well with that obtained through pumping test.
Fig. 7

Relation between the aquifer resistivity and hydraulic conductivity

Table 2

Hydraulic conductivity of the aquifer determined from geoelectrical parameters

Physiographic sub-division

VES nos.

Total resistivity of the aquifer (A) (ohm-m)

Hydraulic conductivity from pumping Test (K) (m/day)

Predicted hydraulic conductivity from the equation K = 0.3712 × A − 7.372 (m/day)

Error (%)

Padma Floodplain

VES 01

66

34.4

31

10

VES 19

255

69.6

75

8

VES 22

215

80.2

66

18

VES 24

300

x

85

a

Padma–Mahananda Floodplain

VES 03

143

60

49

18

VES 17

114

33.5

42

26

VES 23

163

x

54

a

Mahananda Floodplain

VES 02

132

45

46

3

VES 06

225

67

68

1

VES 10

145

43.4

49

14

VES 12

195

x

61

a

VES 13

212

85

65

24

VES 14

200

52.9

62

18

VES 15

229

57.8

69

19

VES 16

185

x

59

a

VES 18

205

x

63

a

VES 20

85

x

36

a

VES 21

154.5

43.6

52

18

Barind Tract

VES 04

115

x

42

a

VES 05

272

x

79

a

VES 07

172

x

56

a

VES 08

155

50

52

4

VES 09

120

42

44

4

VES 11

110

46.9

41

12

aIndicated the calculated values where there are no pumping test data

Fig. 8

Spatial distribution of hydraulic conductivity values obtained from a pumping test; and b total resistivity

Conclusion

In the complex floodplain–Barind geologic environment of the Chapai-Nawabganj area, the need for costly random drilling, resulting in dry holes or marginal production from wells can largely be eliminated by the judicious application of low-cost geoelectrical studies. Two inherent electrical properties of the earth materials viz. geoelectrical resistance (R) and the total resistivity (A) of the aquifer are easy to measure by conducting VES experiments. These two properties of aquifer materials have functionally analogous relation with the T and K, respectively. These are \(T = 0.3079\; \times \;R + 299.81\) and \(K = 0.3712 \times A - 7.3727\). These equations were also authenticated by estimating aquifer parameters at some locations where pumping test information is not available. It is notwithstanding that the linear lines indicate a minor discrepancy over the transverse resistance and aquifer total resistivity. Therefore, it can be applied in geologically similar area where any information relating to pumping well or borehole available for the identification of the potential groundwater bearing horizon.

Analysis of lithological data shows that the second and the third lithological layers consists of medium to coarse-grained sand, and coarse sand with gravels serve as potential zones for groundwater storage, distribution and abstraction in the study area. The main water-bearing unit is gradually thinning from floodplain to the Barind area and is regarded as the ultimate constrain of water scarcity of the Barind Tract. The hydraulic conductivity is found to vary between 31 and 85 m/day, and the transmissivity to vary between 448 and 1,955 m2/day in the study area. The transmissivity is found higher in the floodplain and less in Barind tract. The hydraulic properties of the aquifer reveal that floodplain regions are highly potential for groundwater abstraction.

References

  1. Ahamed S, de Marsily G (1987) Comparison of the geophysical methods for estimating transmissivity and specific capacity. Water Resour Res 23:1717–1723CrossRefGoogle Scholar
  2. Asaduzzaman M, Rushton KR (2006) Improved yield from aquifers of limited saturated thickness using inverted wells. J Hydrol 326:311–324CrossRefGoogle Scholar
  3. Asfahani J (2012) Quaternary aquifer transmissivity derived from vertical electrical sounding measurements in the Semi-Arid Khanasser Valley region. Syria. Acta Geophysica 60(4):1143–1158Google Scholar
  4. Ayers JF (1989) Conjunctive use of geophysical and geological data in the study of an alluvial aquifer. Ground Water 27:625–632CrossRefGoogle Scholar
  5. Batte AG, Barifaijo E, Kiberu JM, Kawule W, Muwanga A, Owor M, Kisekulo J (2010) Correlation of geoelectric data with aquifer parameters to delineate the groundwater potential of hard rock terrain in Central Uganda. Pure appl Geophys 167(12):1549–1559CrossRefGoogle Scholar
  6. Cassiani G, Medina MA Jr (1997) Incorporating auxiliary geophysical data into groundwater flow parameter estimation. Groundwater 35:79–91CrossRefGoogle Scholar
  7. Chachadi AG, Gawas PD (2012) Correlation study between geoelectrical and aquifer parameters in West Coast Laterites. Int J Earth Sci Eng 5(2):282–287Google Scholar
  8. Cooper GRJ (2001) V E S 1.30, Forward modeling and inversion of Schlumberger resistivity soundings for Microsoft Windows. University of the Witwatersrand, Johannesburg, SAGoogle Scholar
  9. Dasargues A (1997) Modeling base flow from an alluvial aquifer using hydraulic-conductivity data obtained from a derived relation with apparent electrical resistivity. J Hydrogeol 5:97–108CrossRefGoogle Scholar
  10. Eden RN, Hazel CP (1973) Computer and graphical analysis of variable discharge pumping test of wells. Inst Eng Australia Civil Eng Trans 15:5–10Google Scholar
  11. Egbai JC (2011) Vertical electrical sounding for the determination of aquifer transmissivity. Aust J Basic Appl Sci 5(6):1209–1214Google Scholar
  12. Ezeh CC (2011) Geoelectrical studies for estimating aquifer hydraulic properties in Enugu State, Nigeria. Int J Phys Sci 6(14):3319–3329Google Scholar
  13. Frohlich RK, Fisher JJ, Summerly E (1996) Electric-hydraulic conductivity correlation in fractured crystalline bedrock, Central Landfill, Rhode Island, USA. J Appl Geophys 35:249–259CrossRefGoogle Scholar
  14. Harb N, Haddad K, Farkh S (2010) Calculation of transverse resistance to correct aquifer resistivity of groundwater saturated zones : implications for estimating its hydrogeological properties. Leban Sci J 11(1):105–115Google Scholar
  15. Islami N (2011) Geoelectrical resistivity method for salt/brackish water mapping. J Coast Dev 14(2):104–114Google Scholar
  16. Jahan CS, Mazumder QH, Ghose SK, Asaduzzaman M (1994) Specific yield evaluation: Barind Area, Bangladesh. J Geol Soc India 44:283–290Google Scholar
  17. Kalinski RJ, Kelly WE, Bogardi I, Pesti G (1993) Electrical resistivity measurements to estimate travel times through unsaturated ground water protective layers. J Appl Geophys 30:161–173CrossRefGoogle Scholar
  18. Kelly WE, Frohlich RK (1985) Relations between aquifer electrical and hydraulic properties. Ground Water 23:182–189CrossRefGoogle Scholar
  19. Khan AA, Akhter SH, Ahmed KM, Hasan MA (2002) VES signature in soft rock groundwater exploration vis-à-vis geoenvironmental implications. In: Sherif et al. (eds) Groundwater hydrology, vol. 2, pp 179–193. Balkema Publishers, LeidenGoogle Scholar
  20. Kruseman GP, de Ridder NA (1994) Analysis and evaluation of pumping test data, 3rd edn., vol. 11. International Institute for Land Reclamation and Development, WageningenGoogle Scholar
  21. Majumdar RK, Das D (2011) Hydrological characterization and estimation of aquifer properties from electrical sounding data in Sagar Island region, South 24 Parganas, West Bengal, India. Asian J Earth Sci 4(2):60–74CrossRefGoogle Scholar
  22. Mbonu PDC, Ebeniro JO, Ofoegbu CO, Ekine AS (1991) Geoelectric sounding for the determination of aquifer characteristics in parts of the Umuahia area of Nigeria. Geophysics 56:284–291CrossRefGoogle Scholar
  23. Morgan P, McIntire WG (1959) Quaternary geology of Bengal basin. Geol Soc Am Bull 70:319–342CrossRefGoogle Scholar
  24. Nath SK, Patra HP, Shahid S (2000) Geophysical prospecting for ground water. Oxford and IBH Publishing, New DelhiGoogle Scholar
  25. Niwas S, Celik M (2012) Equation estimation of porosity and hydraulic conductivity of Ruhrtal aquifer in Germany using near surface geophysics. J Appl Geophys 84:77–85CrossRefGoogle Scholar
  26. Niwas S, De Lima OAL (2006) Correlating electrical and hydraulic conductivity of a general aquifer model: concept and application. J Geol Soc India 67(6):730–736Google Scholar
  27. Niwas S, Singhal DC (1981) Estimation of aquifer transmissivity from Dar-Zarrouk Parameters in Porous Media. J Hydrol 50:393–399CrossRefGoogle Scholar
  28. Niwas S, Singhal DC (1985) Aquifer transmissivity of porous media from resistivity data. J Hydrol 82:143–153CrossRefGoogle Scholar
  29. Nwosu LI, Nwankwo CN, Ekine AS (2013) Geoelectric investigation of the hydraulic properties of the aquiferous zones for evaluation of groundwater potentials in the complex geological area of imo state, Nigeria. Asian J Earth Sci 6(1):1–15CrossRefGoogle Scholar
  30. Onuoha KM, Mbazi FCC (1988) Aquifer transmissivity from electrical sounding data: The case of Ajali Sandstone aquifers, South West of Enugu, Nigeria: Groundwater and mineral resources of Nigeria. Vieweg-Verlag, Wiesbaden, pp 17–30Google Scholar
  31. Patra HP, Nath SK (1999) Schlumberger geoelectric sounding in ground water. Principles, interpretation and applications. Balkema Publishers, Rotterdam, p 153Google Scholar
  32. Ponzini G, Ostroman A, Mollinai M (1984) Empirical relation between electrical transverse resistance and hydraulic transmissivity. Geoexploration 22:1–15CrossRefGoogle Scholar
  33. Purvance DT, Andricevic R (2000a) On the electrical-hydraulic conductivity correlation in aquifers. Water Resour Res 36:205–213Google Scholar
  34. Purvance DT, Andricevic R (2000b) Geoelectrical characterization of the hydraulic conductivity field and its spatial structure at variable scales. Water Resour Res 36:215–224Google Scholar
  35. Rashid H (1991) Geography of Bangladesh. Oxford University Press Limited, DhakaGoogle Scholar
  36. Sattar GS (2005) Combined analysis of geoelectrical and hydrogeological data for the evaluation of groundwater potentiality in Chapai-Nawabganj area of Bangladesh, Unpublished Ph.D. thesis. University of RajshahiGoogle Scholar
  37. Shahid S (2010) Spatial assessment of groundwater demand in Northwestern Bangladesh. Int J Water 5(3):267–283CrossRefGoogle Scholar
  38. Shahid S, Hazarika MK (2010) Groundwater droughts in the northwestern districts of Bangladesh. Water Resour Manag 24(10):1989–2006CrossRefGoogle Scholar
  39. Shevnin V, Delgado-Rodríguez O, Mousatov A, Ryjov A (2006) Estimation of soil hydraulic conductivity on clay content, determined from resistivity data. Paper presented at the 19th symposium on the application of geophysics to engineering and environmental problems, SAGEEP 2006: geophysical applications for environmental and engineering hazzards—advances and constraints 2:1464–1473Google Scholar
  40. Sikandar P, Christen EW (2012) Geoelectrical sounding for the estimation of hydraulic conductivity of alluvial aquifers. Water Resour Manag 26(5):1201–1215CrossRefGoogle Scholar
  41. Singhal DC, Niwas S, Shakeel M, Adam EM (1998) Estimation of hydraulic characteristics of alluvial aquifers from electrical resistivity data. J Geol Soc India 51:461–470Google Scholar
  42. Ugada U, Ibe KK, Akaolisa CZ, Opara AI (2013) Hydrogeophysical evaluation of aquifer hydraulic characteristics using surface geophysical data: a case study of Umuahia and environs, southeastern Nigeria. Arab J Geosci 1–12 (in press)Google Scholar
  43. Yadav GS, Abolfazli A (1998) Geoelectrical soundings and their relationship to hydraulic parameters in semi-arid regions of Jalore, north-western India. J Appl Geophys 39:35–51CrossRefGoogle Scholar
  44. Zohdy AAR (1974) Use of Dark Zarrouk curves in the interpretation of vertical electrical sounding data. U.S. Geol Surv Bull 41:1313-DGoogle Scholar
  45. Zohdy AAR (1975) Automatic interpretation of Schlumberger sounding curves using modified Dark Zarrouk function. U.S. Geol Surv Bull 39:1313-EGoogle Scholar
  46. Zohdy AAR, Bisdorf RJ (1989) Programs for the automatic processing and interpretation of Schlumberger sounding curves in Quick BASIC 4.0. U.S. Geological Survey Open-File Report, 89–137Google Scholar

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  • Golam Shabbir Sattar
    • 1
  • Mumnunul Keramat
    • 1
  • Shamsuddin Shahid
    • 2
    Email author
  1. 1.Geophysics Laboratory, Department of Applied Physics and ElectronicsUniversity of RajshahiRajshahiBangladesh
  2. 2.Department of Hydraulics and Hydrology, Faculty of Civil EngineeringUniversiti Teknologi Malaysia (UTM)Johor BahruMalaysia

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