ESTIMATING GROUNDWATER RECHARGE INTO A SHALLOW … … · Sajal Kumar Adhikary 1*, Tanmay Chaki 2, Md. Mahidur Rahman 1 and Ashim Das Gupta 3 1Department of Civil Engineering, Khulna

Journal of Engineering Science 04(1), 2013 11-22

ESTIMATING GROUNDWATER RECHARGE INTO A SHALLOW UNCONFINED

AQUIFER IN BANGLADESH

Sajal Kumar Adhikary1*, Tanmay Chaki2, Md. Mahidur Rahman1 and Ashim Das Gupta3

1Department of Civil Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh 2Water Resources Planning Division, Institute of Water Modelling (IWM), Dhaka, Bangladesh

3Water Engineering and Management, Asian Institute of Technology (AIT), Bangkok, Thailand

Received 01 September 2012; Accepted 18 October 2012

ABSTRACT

This paper presents a data conservative approach, in which quantitative groundwater recharge estimation in a

shallow unconfined aquifer is interpreted in details by the analysis of observed precipitation and water level

fluctuations records. Kushtia district in Bangladesh has been taken as a case study area based on the observed

data and information. In the adopted state-of-the-art methodology used for this study, well-known water table

fluctuation technique has been modified so that groundwater recharge in shallow aquifer can be estimated by

using the least available data and information. Observed time series of precipitation and groundwater levels

records at a few monitoring wells in the study area are the only data required to carry out the study. The

approach illustrated in this paper can be useful in any initial level of assessment in groundwater studies. In

addition, results can be applied as input data for developing numerical groundwater model for any groundwater

resource investigation in the study area or similar drainage basins in Bangladesh.

Keywords: Groundwater recharge, Kushtia, Precipitation, Unconfined aquifer, Water table fluctuation

1. INTRODUCTION

Groundwater (GW) recharge is defined as the fraction of total precipitation falling into a drainage basin, which

eventually reaches the water table in the saturation zone of an aquifer (Jukić and Jukić, 2004). It is a fundamental

component of GW systems (Sanford, 2002), because information on GW recharge rates is often necessary for

water resource management, inputs to regional GW models and predictions of climate change impacts (De Silva

and Ruston, 2007). Thus, GW recharge is a critical hydrological parameter, which may need to be estimated at a

variety of spatial and temporal scales depending on the application. Since GW recharge cannot be measured

directly, it is often estimated by using the results of hydrogeologic and geologic investigations, hydro-

meteorological data, observed discharges or GW level hydrographs (Jukić and Jukić, 2004). However, aquifer-

scale recharge estimation is often required for water resource assessment and management, whereas local-scale

recharge is critical to assessment of GW contamination from point sources. Estimation of GW recharge may be

required on temporal scales ranging from days to thousands of years. As aquifers are depleted, recharge

estimation have become more vital in determining appropriate levels of GW withdrawal. In addition, recharge

estimation is becoming more important for contaminant transport, as aquifer management expands from cleanup

of existing contamination to aquifer protection by delineation of areas of high recharge (Scanlon and Cook,

2002). Thus, understanding GW recharge and its accurate estimation is essential for the successful management

of water resources and modelling fluid flow and transport of contaminants within the subsurface (Healy, 2010;

Healy and Cook, 2002). Increasing demand for recharge estimation is forcing the researchers to develop

approaches for building a more thorough understanding of aquifer recharging process and quantifying recharge

rates that reduce uncertainties and increase confidence in recharge estimates (Scanlon and Cook, 2002).

The fraction of precipitation that reaches the phreatic zone in an aquifer depends upon several factors including

soil, topography, vegetation, and climate (Moon et al., 2004). Because of such influencing factors, determining

the GW recharge into aquifers is one of the great challenges in almost all the GW studies (Korkmaz, 1988).

Depending on the available hydrological data and information, numerous techniques for estimating GW recharge

have been discussed intensively in the literatures (Venetics, 1971; Rushton and Ward, 1979; Caro and Eagleson,

1981; Johansson, 1987; Das Gupta and Paudyal, 1988; Korkmaz, 1988; Bekesi and McConchie, 1999; Arnold et

al., 2000; Edmunds et al., 2002; Flint et al., 2002; Lewis and Walker, 2002; Scanlon et al., 2002; Jukić and Jukić,

2004; Moon et al., 2004; Anuraga et al., 2006; Rahman and Roehrig, 2006; Ruston et al., 2006; Sharda et al.,

2006; Thomas and Tellam, 2006; Batelaan and de Smedt, 2007; Coes et al., 2007; De Silva and Ruston, 2007;

Park and Parker, 2008; Martínez et al., 2009; Nziku et al., 2009; Izuka et al., 2010; Jie et al, 2011; Singh, 2011;

Yin et al., 2011). However, most of these approaches often require much time and skill along with lots of

* Corresponding author : [email protected]

KUET @ JES, ISSN 2075-4914 / 04(1), 2013

JES an International Journal

12 Sajal Kumar Adhikary et. al. Estimating Groundwater Recharge into a Shallow Unconfined Aquifer……………….

hydrological data and information, since they apply numerical modeling techniques for estimating GW recharge

into aquifer. Very often, the necessary data and information are not readily available and the cost of using such

models may sometimes be prohibitively very high (Das Gupta and Paudyal, 1988) for a developing country like

Bangladesh. Therefore, simple and straightforward method should be developed for estimating GW recharge

from a few input variables that are relatively easy to collect for most regions. Measurements of GW level

fluctuations along with precipitation events establish a practical means of estimating temporally and spatially

variable GW recharge rates, which have been illustrated by using a number of methods in several literatures

(Veneetics, 1971; Rushton and Ward, 1979; Caro and Eagleson, 1981; Johansson, 1987; Das Gupta and Paudyal,

1988; Korkmaz, 1988; Sophocleous, 1991; Rai and Singh, 1995; Bierkens, 1998; Rai and Manglik, 1999;

Knotters and Bierkens, 2000; Coulibaly et al., 2001; Healy and Cook, 2002; Rai et al., 2006). However, GW

level fluctuation is not only worth of estimating recharge, but also it can provide a practical means of estimating

other hydrological parameters as well (Bauer et al., 2004). In this respect, the water-table fluctuation method can

be a good choice for estimating GW recharge in shallow aquifer, which requires only the knowledge of specific

yield and changes in water levels over time. Advantages of this approach include its simplicity and insensitivity

to the mechanism by which water moves through the unsaturated zone (Healy and Cook, 2002). This paper

presents a data conservative modified water-table fluctuation approach, in which quantitative estimation of GW

recharge in the shallow unconfined aquifer in Kushtia district of Bangladesh is interpreted by analyzing the

precipitation and GW level data. It is expected that the methodology described in this paper can be applied as

preliminary input parameters for developing and simulating a GW model in the numerical environment for any

GW resource investigation scheme in the study area or similar catchments in Bangladesh.

2. METHODOLOGY

2.2 System Conceptualization and Mathematical Formulation

Records of GW level fluctuations in monitoring wells are worth the cost and trouble of collecting only if they are

used as a basis for hydrologic interpretations and for subsequent applications in hydrological investigations.

Although water level records have been important to draw conclusions regarding the occurrence and

development of GW in specific areas under concern, many such records still await interpretations. Similarly, a

wealth of climatologic and other hydrologic data is in need of analysis before taking the final conclusions

(Korkmaz, 1988). In the wet period of a water year, when the precipitation occurs, the first rain wets vegetation

and accordingly other surfaces and then begins to infiltrate into the ground depending on the surface

characteristics.

Figure 1a GW level fluctuations in arithmetic scale caused by the recharge from precipitation


13

Figure 1b GW level fluctuations in log-scale caused by the recharge from precipitation

In general, it is possible to calculate the total level oscillation amplitude due to infiltration of the precipitated

water, if the overall recession regime (i.e. the behavior of the aquifer without external recharge) is known

(Korkmaz, 1988). It is generally found that the component forming a GW hydrograph having a recession can be

approximated by a simple exponential relationship (Subramanya, 1994) of the form (Figure 1) as expressed in

Equation (1).

tehh 0

α−= (1)

Where, h0 and h are the water level above discharge level at the beginning of the measurement period and at any

certain time (t), respectively and α is known as coefficient of recession or discharge coefficient.

Like any other exponential formula, on a semi-logarithmic paper when water level above discharge level is

plotted to the log scale and time to the arithmetic scale, the recession curves plot as nearly as straight lines

(Figure 1b). In the logarithmic system with base 10, the formula is converted into the linear form as described in

Equation (2)

t43429.0hloghlog 0 α−= (2)

The shape of the recession curve depends on the water yielding properties of the aquifer material, the

transmissivity and the aquifer geometry (Johansson, 1987).

The recovery of the water level, ∆H under natural hydrological conditions is a mirror image of the recession

curve obtained from GW level fluctuations. The recovery of the water level varies from year to year, depending

on the amount of total precipitation (Rt) in wet period of a water year (Figure 1a). However, GW levels are

influenced by the seasonal cycles (Almedeij and Al-Ruwaih, 2006; Adhikary et al., 2012), which are relevant to

different factors such as recharge from precipitation, evapotranspiration, overland flow and runoff, and discharge

from wells and shows a seasonal pattern of fluctuations (Healy, 2010). The degree of correlation between

fluctuations of GW level and variations in total precipitation (Rt) in wet period of a water year provides an

evidence as to the freeness of the connection between the aquifer recharge and total precipitation (Rt) in wet

period of a water year (Korkmaz, 1988). In this study, the direct estimation of GW recharge in shallow aquifer is

considered using recovery of the GW level (∆H) and total precipitation (Rt) during wet period in a water year


(Figure 2). The linear regression model developed by the relationship between ∆H and Rt can be expressed by

Equation (3).

tbRaH +=∆ (3)

Where, ∆H is recovery of GW level, and Rt is total precipitation during the wet period in the water year under

consideration, a and b are the regression coefficients of the linear regression model expressed by Equation (3).

This process is diagrammatically presented in Figure 2.

Figure 2 Relationship of total precipitation in wet period and recovery of the aquifer water level

Figure 2 indicates that the precipitation intercept, Re is the intersection of the total precipitation recovery straight

line with zero-total precipitation axis. It represents the amount of surface runoff and evapotranspiration for the

same period. Recovery or recharge from rainfall is a function of the amount of total precipitation (Rt). If the

intercept is Re, the recharge (Rs) can be estimated by the Equation (4), which is expressed as

eRtcRsR −= (4)

Where, Rtc is the computed total precipitation by means of Equation (3) during the wet period in a water year.

However, the major features of this proposed methodology is that it indirectly estimates the water losses such as

evapotranspiration and surface runoff. It can be found by subtracting the recharge amount from the total

precipitation quantity.

2.2 Estimation of Groundwater Recharge

In this paper, a modified data conservative GW fluctuation technique is presented for estimating GW recharge in

a shallow unconfined aquifer of Bangladesh based on the statistical relationship between the precipitation and

water levels. The analysis is based on the original conditions, which are unaffected by heavy pumping. Observed

monthly precipitation records and weekly GW levels are the only data required to carry out the study. Data have

been collected from the central database of Bangladesh Water Development Board (BWDB). For this study,

daily rainfall data at a precipitation data collection station (BWDB rainfall station ID: CL41) and GW level

fluctuation data at a monitoring well (BWDB GW level monitoring station ID: GT5015004) are collected for the

16 years period from 1992 to 2007. However, these two representative monitoring stations located in the study

area have been selected depending on the data availability for a longer period. Shallow aquifer located in the

upper part of the Gorai River catchment (mainly Kushtia district area) in Bangladesh has been selected as a case

study area (Figure 3). The upper part of aquifer in this region is unconfined in nature (Halcrow, 1993; Adhikary,


15

2009) and GW is extensively extracted from its shallow depth due to the increased irrigation activities (Halcrow,

1993; Adhikary et al., 2011; Adhikary et al., 2012).

BangladeshKushtia District (Study Area)

N

EW

S

Figure 3 Location of studied aquifer (Kushtia district) in Bangladesh

By analyzing the monthly precipitation data and water level records belonging to the 16 years period from 1992

to 2007, the amount of rainfall causing a rise in water level is determined. These rainfall amounts are called

“total precipitation (Rt)” causing the rise in water level. The water level rise caused by this total precipitation is

termed as “water level rise due to GW recharge (∆H)” and can be found from the plotted graph directly. This

procedure is repeated year after year for the period of total records of 16 years from 1992 to 2007 and the

relationship between these two variables Rt and ∆H is established by using simple statistical technique. However,

∆H is a function of the precipitation amount above a threshold precipitation value, which represents the

precipitation causing surface runoff, evapotranspiration, and subsurface flow or only one of them depending on

the local hydrogeological conditions in the study area (Korkmaz, 1988). These values of water losses can be

obtained by setting ∆H = 0 in the regression equation, which has been schematically presented in Figure 2. The

recharge amount for each water year is then calculated by subtracting the threshold precipitation from the total

precipitation causing GW level rise in that particular year.

3. RESULTS AND DISCUSSION

Shallow aquifer located in the upper part of the Gorai River catchment (mainly Kushtia district area) in

Bangladesh has been selected as a case study area (Figure 3) for this study based on the data availability from

secondary source (i.e. Bangladesh Water Development Board). It covers an area of 1621 square kilometres and is

bounded by Rajshahi, Natore and Pabna districts to the North, Chuadanga and Jhenaidah districts to the South,

Rajbari district to the east and West Bengal and Meherpur district to the west. The upazillas (sub-district) are

Kushtia Sadar, Kumarkhali, Daulatpur, Mirpur, Bheramara and Khoksa. The Ganges, the Gorai, Mathabhanga,

Kaligonga and Kumar are the main rivers flowing through the district. The average maximum and minimum


temperatures are 37.8 0C and 11.2 0C, respectively with an average annual rainfall of 1,898 mm. The GW levels

naturally fluctuate in response to a sequence of climatic events and to constraints imposed by the hydrogeologic

and topographic characteristics, which is presented in Figure 4.

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 20072

4

6

8

10

12

14

AJOJAJOJAJOJAJOJAJOJAJOJAJOJAJOJAJOJAJOJAJOJAJOJAJOJAJOJAJOJAJO0

200

400

600

800

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Water Year

Gro

un

dw

ate

r L

ev

el

(m.P

WD

)P

rec

ipit

ati

on

(m

m)

Water Year

Figure 4 Weekly GWL hydrograph and monthly precipitation hyetograph in the study area for 16 years

Figure 4 reveals that the aquifer is recharged in the wet period of a water year (April to next March in a year) in

Bangladesh (Mirza, 1997) due to the heavy rainfall in that time and discharged out of it in the dry period as there

is not much rainfall available in that time. In addition, evapotranspiration and water withdrawal takes place in

various ways by numerous human interventions, which cause the depletion of the GW level. However, GW

recharge is the largest in the wet period of a water year, especially in the monsoon season, when plants are

dormant and evapotranspiration rates are comparatively less. Besides, in the dry period of a water year, when

evapotranspiration rates along with the GW extraction from underground shallow aquifer exceed the available

moisture from precipitation, GW recharge to the water table is negligible in the shallow unconfined aquifer and

consequently, GW levels decline.


17

Figure 5 Fluctuations in water levels caused by recharge from precipitation in the study area

The recovery of the water level (∆H) and the total precipitation (Rt) during the wet period in each water year for

16 years period from 1992 to 2007 are determined, which are presented in Figure 5. A linear regression model

during the whole 16 years period is established and is schematically presented in Figure 6. The results of linear

regression analysis are presented in Table 1. The regression equation is found as represented in Equation (5).

tR∆H ×+= 0.005211.06585- (5)

The average precipitation intercept (Re) of the case study aquifer is found as 204.58 mm (Figure 6), which

represents the amount of surface runoff and evapotranspiration for the same period. This result demonstrates that

2000 2001 2002 20032

4

6

8

10

12

1416

A J A O D F A J A O D F A J A O D F A J A O D F0

100

200

300

400

500

600

700

2000 2001 2002 2003

Water Year

Gro

un

dw

ate

r L

eve

l (m

.PW

D)

∆H = 8.64m ∆H = 6.8m∆H = 6.53m

∆H = 5.94m

Pre

cip

itati

on

(m

m)

Water Year

1633.6 1477.41486.01580.4

2004 2005 2006 20072

4

6

8

10

12

1416

A J A O D F A J A O D F A J A O D F A J A O D0

100

200

300

400

500

600

700

800

2004 2005 2006 2007

Water Year

Gro

un

dw

ate

r L

ev

el (m

.PW

D)

∆H = 6.51m∆H = 8.40m ∆H = 6.27m

∆H = 6.81m

Pre

cip

ita

tio

n (

mm

)

Water Year

1567.1 1668.61788.5 1504.0

1996 1997 1998 19992

4

6

8

10

121416


100

200

300

400

500

600

700

1996 1997 1998 1999

Water Year

Gro

un

dw

ate

r L

ev

el

(m.P

WD

)

∆H = 8.45m ∆H = 8.98m∆H = 9.35m

∆H = 6.93m

Pre

cip

itati

on

(m

m)

Water Year

1928.31389.6 1909.9 2015.4

1992 1993 1994 19952

4

6

8

10

12

1416


100

200

300

400

500

1992 1993 1994 1995

Water Year

Gro

un

dw

ate

r L

ev

el

(m.P

WD

)

∆H = 6.01m ∆H = 8.9m∆H = 6.45m

∆H = 6.82m

Pre

cip

ita

tio

n (

mm

)

Water Year

1329.81386.2 1709.5 1644.5


the proposed methodology described in this paper indirectly estimates the water losses such as

evapotranspiration and surface runoff. It can be found by subtracting the recharge amount from the total

precipitation amount. The obtained result is often useful in any initial level of assessment, where model input is

primarily necessary while conducting detailed study using the numerical GW modeling technique.

0 400 800 1200 1600 2000 24000

2

4

6

8

10

12R

eco

ve

ry,

∆H

(m

)

Total precipitation in wet period, Rt (mm)

∆H = -1.06585 + 0.00521 x Rt

Re = 204.58 mm

Figure 6 Relationship of total precipitation in wet period and groundwater recovery

Table 1 Results of linear regression analysis

Type of relation No. of

observations

Correlation

coefficient (r)

Standard error

of estimate Regression Equation

Total precipitation-

GW level recovery 16 0.84 1.49 tR∆H ×+= 0.005211.06585-

However, the computed values (Rtc) of total precipitation by means of the developed regression model for water

years from 1992 to 2007 are summarized in Table 2. The results of the recharge (Rs) computation based on the

available precipitation for the total 16 years period from 1992 to 2007 by using the Equation (4) are presented in

Figure 7. The annual GW recharge in the studied aquifer for the period ranges from 1794 mm in 1999 to 1140

mm in 2003 (Figure 7). The annual average recharge during the 16 years period is estimated as 1413 mm.

However, this is about 74 percent of the average annual precipitation (1898 mm) occurs in the study area.

Estimated GW recharge by using the methodology proposed in this study is finally compared with the previous

studies conducted in the present study area under the studied aquifer (Halcrow, 1993; Adhikary, 2009), which is

presented in Figure 8. However, Healy (2010) describes that since actual recharge rates are unknown, so there

are no standards by which the accuracy of estimated recharge can be evaluated. Figure 8 shows that the estimated

GW recharge values for the whole 16 years period in this study are reasonably closer to those estimated by the

two previous studies (e.g. Halcrow, 1993; Adhikary, 2009). Hence, the obtained result is regarded as quite

reasonable for further applications. The estimated GW recharge based on three different studies presented in

Figure 7 indicates that the modified GW fluctuation approach proposed in this study overestimates in some years

as well as underestimates in other time periods with respect to the study conduced by Halcrow (1993) and

Adhikary (2009). This proves the acceptability of the methodology proposed in this study in estimating GW

recharge in an unconfined aquifer having shallow depth. However, it is noteworthy that these two previous

studies applied the long-term water balance approach and numerical GW modeling technique, respectively for

estimating the GW recharge in the studied aquifer. It is now widely recognized that both methods usually

provide almost accurate results as they take into account lots of hydrological parameters (Moon et al., 2004),

which actually influence the recharging process of the aquifer.


19

Table 2 Summary of GW recharge computation for the studied aquifer

Water Year

Annual

Precipitation, Ry

(mm)

Recovery of GW

level, ∆H

(m)

Computed Total

Precipitation, Rtc

(mm)

Recharge, Rs

(mm)

1992 1633.60 6.01 1356.21 1151.63

1993 2301.20 8.90 1912.83 1708.25

1994 1450.60 6.45 1442.58 1238.00

1995 1782.50 6.82 1513.60 1309.02

1996 1719.70 6.93 1534.71 1330.13

1997 2190.50 8.45 1826.46 1621.88

1998 2250.60 8.98 1928.19 1723.61

1999 2267.60 9.35 1999.20 1794.62

2000 1919.10 8.64 1862.93 1658.35

2001 1720.80 6.80 1509.76 1305.18

2002 1868.70 6.53 1457.94 1253.36

2003 1897.60 5.94 1344.69 1140.11

2004 1861.80 6.51 1454.10 1249.52

2005 1996.50 8.40 1816.86 1612.28

2006 1664.00 6.27 1408.03 1203.45

2007 1841.80 6.81 1511.68 1307.10

Annual Rainfall

Evapotranspiration & Surface Runoff

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

0

400

800

1200

1600

2000

2400

2800

Am

ou

nt o

f W

ate

r (m

m)

Year

Recharge

Figure 7 Estimated GW recharge for 16 years (1992-2007) in the study area


19921993

19941995

19961997

19981999

20002001

20022003

20042005

20062007

0

400

800

1200

1600

2000

2400

GW

Re

ch

arg

e (

mm

)

Year

Present Study

Adhikary (2009)

Halcrow (1993)

Figure 8 Comparison of estimated GW recharge by different studies for studied aquifer

4. CONCLUSIONS

This study presents a modified water table fluctuation technique for estimating GW recharge by using only the

precipitation and water level information for a shallow unconfined aquifer in Bangladesh. The overall goal of

this study has been a synthesis of the results obtained by analyzing the GW level fluctuations and precipitation

records. By following this principle, the study establishes a data conservative approach of estimating GW

recharge in any shallow unconfined aquifer under concern. However, the accuracy of the results totally depends

on the base of water level monitoring data. This study reveals that the shallow GW in the study area is recharged

mainly from the precipitation sources. Besides, the annual average GW recharge in the studied aquifer is found

as 1413 mm for total 16 years period from 1992 to 2007, which is about 74 percent of the average annual

precipitation. However, the study concludes that the proposed data conservative methodology presented in this

study provides appropriate mechanisms for indirect estimation of the water losses such as evapotranspiration and

surface runoff, which will make this method useful in future hydrological computation for GW resource

assessment and management in the field of water resources engineering. Finally, the present study conclusively

proves that the proposed methodology described in this study will open a window of opportunity for any sorts of

initial level assessment of GW resources in a shallow unconfined aquifer in Bangladesh, where shortest possible

data and information are available.

ACKNOWLEDGEMENTS

The authors acknowledge the valuable data supports from the central database of the Bangladesh Water

Development Board (BWDB), Dhaka, Bangladesh. The authors express the special gratitude to the Editors for

Special Issue of Journal of Engineering Science (JES), and anonymous reviewers for their constructive

comments and valuable suggestions to improve the quality of this work.

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ESTIMATING GROUNDWATER RECHARGE INTO A SHALLOW … … · Sajal Kumar Adhikary 1*, Tanmay Chaki 2, Md. Mahidur Rahman 1 and Ashim Das Gupta 3 1Department of Civil Engineering, Khulna