Groundwater level monitoring and recharge estimation in the White Volta River basin of Ghana

Journal of African Earth Sciences 71–72 (2012) 80–86

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Groundwater level monitoring and recharge estimation in the White Volta River basin of Ghana Emmanuel Obuobie a,⇑, Bernd Diekkrueger b, William Agyekum a, Sampson Agodzo c a

Water Research Institute, Council for Scientific and Industrial Research, P.O. Box AH 38, Achimota, Accra, Ghana Department of Geography, University of Bonn, Meckenheimer Alle 166, D-53115 Bonn, Germany c Department of Agricultural Engineering, Kwame Nkrumah University of Science and Technology, University Post Office, Kumasi, Ghana b

a r t i c l e

i n f o

Article history: Received 12 September 2011 Received in revised form 12 June 2012 Accepted 19 June 2012 Available online 3 July 2012 Keywords: Ghana Groundwater level Groundwater recharge Water table fluctuation White Volta basin

a b s t r a c t Recharge quantification is an important pre-requisite for effectively managing groundwater resources as recharge estimates are needed to determine sustainable yields of groundwater aquifers for rational and sustainable exploitation of the resource. In this study, the water table fluctuation method has been applied in the White Volta River basin of Ghana (approx. 46,000 km2) to estimate seasonal fluctuations in groundwater levels in the basin and subsequently to estimate recharge to the groundwater for the 2006 and 2007 water years. Results show high seasonal and spatial variability in the water level, with a range of 1240–5000 mm in 2006, and 1600–6800 mm in 2007. Seasonal rainfall was found to be the main source of recharge to the aquifers in the basin as water level rise occurred only in the rainfall season. Recharge to groundwater in the White Volta basin was estimated to vary between 2.5% and 16.5% of the mean annual rainfall, with a mean recharge of 7–8%. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction The importance of freshwater is increasing very rapidly due to the fast growth in the world’s population, resulting in the increased demand for the resource world-wide. Notwithstanding the rising demand, the amount of freshwater available on earth is limited and unevenly distributed. According to UNEP (2002), about one third of the world’s population live in countries with moderate to high water stress with disproportionately high impacts on the poor. The study observed that, with the current projected human population growth, industrial development and expansion of irrigated agriculture in the next two decades, water demand will rise to levels that will make the task of providing water for human sustenance more difficult. According to a recent study by UNICEF/WHO, safe water supply coverage in sub-Saharan Africa (SSA) is estimated to be about 56% of the total population (WHO/UNICEF, 2005). The low coverage was explained by lack of investment in new infrastructure and population growth. Major sources of water supply for domestic, agricultural and industrial uses in arid and semi-arid areas in SSA include surface water (e.g., streams and rivers, lakes, ponds, dugouts, impoundment reservoirs, rainwater harvesting) and groundwater (e.g., springs, boreholes and hand-dug wells). Due to high spatial and temporal variability associated with rainfall in SSA, surface water sources are mostly unreliable, subject to high evapora⇑ Corresponding author. E-mail address: [email protected] (E. Obuobie). 1464-343X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jafrearsci.2012.06.005

tive losses, easily polluted, and insufficient to meet the rising demands. Groundwater sources are well suited to meet the dispersed demand of the growing rural population, which forms the larger proportion of the total population. In the White Volta basin of Ghana, groundwater use is of fundamental importance and a key resource for economic and social development. In 2005, about 44% of the basin inhabitants depended on groundwater sources for domestic water supply (Martin and van de Giesen, 2005). The figure may be higher in recent times. Generally, the microbiological and chemical quality of the basin’s groundwater is good for multipurpose use except for few areas where iron and excessive fluoride concentration are problematic. Over the past three decades, exploitation of groundwater in the basin has increased substantially for reasons including a policy by the Government of Ghana to set up water supply schemes for small towns and rural areas, based entirely on groundwater sources, and the quest of some inhabitants of the basin, mostly youth, to increase their income through dry season irrigated vegetable production. This has put stress on the resource in some localities, particularly, in the northeastern parts of the basin. While groundwater offers the opportunity to improve water supply coverage at relatively lower cost and with greater flexibility, it is of great importance to improve our knowledge of the resource for rational and sustainable development, use and management. Evaluation of groundwater involves several factors of which the recharge is paramount. Quantification of the recharge rate is prerequisite for efficient and sustainable management of groundwater (Scanlon et al., 2002; Chand et al., 2005). Quantification of the

E. Obuobie et al. / Journal of African Earth Sciences 71–72 (2012) 80–86

recharge is needed for example, to estimate the sustainable yield of groundwater aquifers. Knowledge of aquifer sustainable yield is important for rational and sustainable exploitation of the groundwater resource (Sanford, 2002; Sophocleous and Schloss, 2000; Gonfiantini et al., 1998; Scanlon et al., 2002). The objective of this study was to monitor the groundwater levels in the White Volta river basin of Ghana and to use this information to estimate recharge to the groundwater aquifers in the basin.

2. Recharge estimation methods Estimating groundwater recharge in arid- and semi-arid-regions can be difficult, since in such areas the recharge is generally low compared to the average annual rainfall or evapotranspiration, and thus difficult to determine precisely (Scanlon et al., 2002; Beekman and Xu, 2003). Recharge processes vary from one place to another, and there is no guarantee that a method developed and used for one locality will give reliable results when used in another. Therefore, it is necessary to identify the probable flow mechanisms and the important features influencing the recharge in a locality before deciding on the recharge method to use (Lerner et al., 1990). The recharge to a groundwater aquifer cannot be easily measured directly, and usually estimated by indirect means (Lerner et al., 1990). The accuracy of the indirect estimates is usually difficult to determine, and therefore it is recommended that recharge should be estimated using multiple methods to obtain more reliable values (USGS, 2008; Scanlon et al., 2002; Lerner et al., 1990). A wide variety of methods exists for estimating groundwater recharge, which have been designed to represent the actual physical processes of the recharge. Recharge estimation methods can be classified according to (i) hydro-geological provinces (Lerner et al., 1990), (ii) hydrologic zones (Scanlon et al., 2002; Beekman et al., 1996; Bredenkamp et al., 1995), (iii) physical, numeric modeling, and (iv) tracer techniques (Scanlon et al., 2002; Lerner et al., 1990; Kinzelbach et al., 2002). Scanlon et al. (2002) classified recharge methods on the basis of three hydrologic zones of studies namely surface water, unsaturated zone and saturated zone. Each of these zones provides a different set of data that can be used to estimate the groundwater recharge. Within each of the hydrologic zones, the recharge techniques were further classified into physical techniques, tracers and numerical modeling. Methods based on surface water studies include physical methods, e.g., channel-water budget, seepage meters and baseflow discharge; tracer methods, e.g., stable isotopes of oxygen and hydrogen; numerical modeling methods, e.g., deep percolation model and water budget equation. Methods based on the unsaturated zone studies include physical methods, e.g., lysimeters, Darcy’s law and zero-flux plane; tracer techniques, e.g., bromide, 3 H, and visible dyes, 36Cl, and Cl; numerical modeling methods, e.g., soil water storage routing, quasi-analytical approaches and numerical solutions to the Richards equation. Recharge estimation methods based on the saturated zone studies are physical methods, e.g., water table fluctuation and Darcy’s law; groundwater dating using traces such as CFC, 3H/3He, and 14C; and groundwater flow modeling. A detailed description of each of the above-mentioned techniques can be found in Scanlon et al. (2002), Scanlon et al. (2003), and Lerner et al. (1990). The water table fluctuation was used in this study because it gives more reliable estimates irrespective of the recharge mechanism prevailing in an area, weather piston or preferential flow. Its applicability in terms of temporal scale is wide, from a day to years. In addition, the method is easy to use, has low data needs and cost relatively low (USGS, 2008).

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3. Water table fluctuation The water table fluctuation (WTF) method is one of the most widely used techniques for estimating groundwater recharge over a wide variety of climatic conditions (Scanlon et al., 2002; Hall and Risser, 1993; Healy and Cook, 2002). The use of the method requires knowledge of specific yield and changes in groundwater levels over time. Healy and Cook (2002) have attributed the wide use of this method to the abundance of available groundwater level data and the simplicity of estimating recharge rates from temporal fluctuations or spatial patterns of water levels. The WTF method is best suited for estimating recharge rates over short time periods in areas with shallow unconfined aquifers that display sharp rise and fall in water levels (Scanlon et al., 2002). The method has no assumptions regarding movement of water through the unsaturated zone and, therefore, the presence of preferential flow paths does not restrict its use. Recharge estimates with the WTF technique are actual and therefore more reliable, compared to potential recharge estimates given by other methods. The WTF method is based on the assertion that rises in water levels in unconfined aquifers are due to recharge water arriving at the water table, and that all other components of the groundwater budget, including lateral flow, are zero during the recharge period (Scanlon et al., 2002; Healy and Cook, 2002). The recharge rate can be estimated as the product of the water level rise and the specific yield of the groundwater aquifer material. Mathematically, the recharge can be expressed as:

R ¼ Sy dh=dt ¼ Sy Dh=Dt

ð1Þ

where R is groundwater recharge (mm/time); Sy is specific yield (dimensionless); Dh is peak rise in water level attributed to the recharge period (mm); and Dt is the time of the recharge period. Major assumptions inherent in this technique include: (i) rise and decline in levels of the water table in shallow unconfined aquifers are solely due to recharge and discharge of groundwater; (ii) the specific yield of aquifer is known and constant over the time period of the water table fluctuation; and (iii) the pre-recharge water level recession can be extrapolated to determine water level rise (Healy and Cook, 2002). These assumptions are not always the case and could be drawbacks of this method in some situations. For instance, changes in groundwater levels may not always be as a result of recharge or discharge. It could be caused by other factors such as evapotranspiration, changes in atmospheric pressure, presence of entrapped air and earth tides, or as a response to changes in stream stage for wells that are very close to streams (Delin et al., 2006). Previous studies have shown that obtaining a specific yield that is representative of a large area can be difficult. Besides, specific yield values vary with time as opposed to the assumption of a known and constant specific yield (Delin et al., 2006; Loheide et al., 2005; Sophocleous, 1985). There are no strict limits as to the range of recharge that can be estimated with the WTF method. Scanlon et al. (2002) gives annual recharge rates estimated using the WTF technique as ranging from 5 mm in the Tabalah Basin of Saudi Arabia to 247 mm in a small basin in eastern United States. In West Africa, the WTF method have been used by Martin (2005) and Sandwidi (2007) to estimate the annual groundwater recharge in the Atankwidi basin in Ghana (13–143 mm) and the Kompienga dam basin in Burkina Faso (44–244 mm). 4. Study area The study area, the White Volta River basin of Ghana, is located in northern Ghana (Fig. 1) and falls within the boundaries of latitudes 8°300 and 11°N, and longitudes 0.0° and 2°300 W. The study

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N

Burkina Faso Benin Togo

Ivory Coast

White Volta Basin Country Borders

Ghana

0

50 100Km

Fig. 1. Location map of White Volta basin of Ghana and monitoring (observed) wells.

basin is boarded to the north by the Republic of Burkina Faso, the north-eastern corner by the republic of Togo, with the remaining portions falling exclusively within Ghana. The total catchment area of the basin in Ghana is estimated to be about 45,804 km2, representing 43.7% of the estimated 104,752 km2 of the entire White Volta River basin system, which extends beyond the boundaries of Ghana to neighboring Burkina-Faso and Togo (MWH, 1998). According to the report of the population census done in Ghana in 2000, the basin has a population of about 1.6 million inhabitants, with annual growth rate of 1.5% (Codjoe, 2004; WRI, 2003; GSS, 2002). Settlement is largely rural and dispersed. The topography of the basin area is generally flat to gently rolling with few undulating hills. Based on the FAO-UNESCO (1994) soil classification legends, the dominant soils types in the basin are Luvisols and Lithosols, which together constitute more than 85% of the soil resources. The vegetation is dominantly Savannah, which consists of grasses, shrubs and trees. The geology is predominantly crystalline basement rock of Precambrian age that consists of granite-gneiss-greenstone rocks, strongly deformed metamorphic rocks and amorogenic intrusions (Kesse, 1985; Key, 1992). Other geological formations of importance in the basin are the Paleozoic consolidated sedimentary formation (locally known as the Voltaian formation), which consists of sandstone, shale, arkose, mudstone, sandy and pebbly beds; and limestone (MWH, 1998). Groundwater occurrence in the study area is predominantly semi-confined with interspersed confining clayey layers within the aquifer section. Aquifers in the study area are developed in hard rocks. They are localized and discontinuous. The mean annual rainfall ranges from 800 mm in the north to 1140 mm in the south (Shahin, 2002; Gyau-Boakye and Tumbulto, 2006). The mean monthly temperature is about 27 °C. The mean relative humidity varies from 80% at the peak of the rainy season in September to about 20% in January (peak of the harmattan period). Water table depth varies from ground level in the rainy season to about 15 m in the dry season. Previous monitoring of water levels in the study area revealed a sharp rise and decline in the water table during the wet and dry seasons, respectively (Martin, 2005).

5. Methods and data

this study while the other 13 (labeled – WRI) were installed and monitored by the CSIR-Water Research Institute, Ghana, as part of a Danish government-funded water resource information services project in the White Volta Basin. Three of the wells monitored by the WRI, Wa-Danko (WVB1-WRI), Wa-Northeast (WVB2-WRI) and Tumu (WVB3-WRI), are located within a distance of about 300 m from existing mechanized wells, and additional two (Navrongo, Gowrie–Tingre) are within 500 m downstream of irrigation dams (Agyekum et al., 2006). The groundwater hydrographs of these five wells may have been influenced by the mechanized wells and dams. Installation and monitoring of dataloggers started in December 2005. The dataloggers were programmed to record and store water level data at 6-h intervals. Data recorded were retrieved on quarterly basis. In addition to monitoring water levels, atmospheric pressure was monitored in seven wells using barometric dataloggers. The atmospheric pressure data were used for correcting the water level data. 5.2. Estimation of water level rise (Dh) Generally, water level rise in a monitoring well is computed as the difference between the peak of a water level rise and the value of the extrapolated antecedent recession curve at the time of the peak. According to Delin et al. (2006), the recession curve is the trace that the well hydrograph would have followed had there not been any recharge. There are two main methods for estimating the water level rise. These are the master recession curve (MRC) and graphical extrapolation. The MRC approach can be time consuming and though it has less subjectivity, there is the possibility of mistakenly including water level rises that did not happen as a result of recharge. The graphical extrapolation method, though has more subjectivity compared to the MRC, it is the simplest of the available methods and less time consuming. The graphical extrapolation method was used in this study to estimate the groundwater level rise in each of the monitoring wells. This was achieved by visually examining the entire water level data for each well and manually extrapolating the antecedent recession curves. The rise in water level during the recharge period was obtained as the difference between the peak of the rise and the low point of the extrapolated antecedent recession curve at the time of the peak.

5.1. Water level measurements 5.3. Specific yield Groundwater level measurements were obtained from 19 monitoring wells equipped with automatic water level recorders (data loggers) spread across the study area (Fig. 1; Table 1). Six of the 19 wells (labeled – GVP) were installed and monitored specifically for

The specific yield of a rock or soil is defined as the ratio of the volume of water, which after being saturated, will yield by gravity to its own volume (Meinzer, 1923 cited in Healy and Cook, 2002).

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E. Obuobie et al. / Journal of African Earth Sciences 71–72 (2012) 80–86 Table 1 Characteristics of wells monitored in the White Volta River basin of Ghana. Well location

Well ID

Elevation (m)

Geology

Well depth (m)

Pumping yield (m3/h)

Wa-Danko Wa-Northeast Tumu Navrongo Gowrie-Tinguri Bongo Datuku Bawku Ducie-Camp Yagbum Bugya-Pali Tinguri Galiwei Yorougu Tongo Kalijiisa Sumbrungu Sokabiisi Kpasenkpe

WVB1-WRI WVB2-WRI WVB3-WRI WVB4-WRI WVB5-WRI WVB6-WRI WVB7-WRI WVB8-WRI WVB9-WRI WVB10-WRI WVB11-WRI WVB12-WRI WVB13-WRI WVB14-GVP WVB15-GVP WVB16-GVP WVB17-GVP WVB18-GVP WVB19-GVP

308 313 312 179 181 224 194 224 276 242 143 185 211 179 280 198 194 124 130

Granite Granite Granite Granite Granite Granite Birimian Granite Basal sandstone Mainly sandstone Mudstone & shale Mudstone & shale Mudstone & shale Granite Granite Birimian Granite Granite Shale & sandstone

104.0 37.5 105.0 72.0 90.0 35.0 90.0 100.0 78.0 100.0 56.0 51.0 100.0 60.0 60.0 54.0 60.0 43.0 56.0

1.2 2.4 30.0 10.2 12.0 2.7 1.2 18.0 0.7 27.0 0.5 7.2 0.3 4.7 1.5 1.7 1.2 1.0 5.4

Table 2 Statistics on specific yield (Sy) from 17 studies (Johnson, 1967). Texture

Clay Silt Sandy clay Fine sand Medium sand Coarse sand Gravelly sand Fine gravel Medium gravel Coarse gravel

Sy

0.02 0.08 0.07 0.21 0.26 0.27 0.25 0.25 0.23 0.22

Coefficient of variation (%)

Minimum specific yield

Maximum specific yield

Number of determinations

59 60 44 32 18 18 21 18 14 20

0.00 0.03 0.03 0.10 0.15 0.20 0.20 0.21 0.13 0.12

0.05 0.19 0.12 0.28 0.32 0.35 0.35 0.35 0.26 0.26

15 16 12 17 17 17 15 17 14 13

NB: The values were determined with different methods.

In simple terms, the specific yield is a fraction of the porosity of an aquifer that can be drained by gravity. The value depends on the grain size, shape and distribution of pores and compaction of the strata (Gupta and Gupta, 1999). The specific yield value can be calculated from porosity and specific retention using the relation below (Healy and Cook, 2002):

Sy ¼ /  Sr ;

ð2Þ

where / is porosity and Sr is specific retention (the volume of water retained by the rock per unit volume of rock). The value of specific yield can be estimated with various methods including laboratory- and field- methods such as aquifer pumping test, volume-balance methods, water-budget methods, geophysical methods, and field capacity tests (Healy and Cook, 2002; Lerner et al., 1990). The complexity of determining the specific yield value has resulted in a wide range of values for the same textural class as reported in various literature (e.g., Tables 2 and 3). Such variations have been attributed to natural heterogeneity in geologic material, differences in determination methods and largely to the amount of time spent in determining the specific yield value (Prill et al., 1965, cited in Healy and Cook, 2002). According to Lerner et al. (1990) specific yield values determined from laboratory measurement of drainable porosity over a reasonable sample size is preferred to values determined from pumping tests which are often very different because they are derived for short times. And in situations where laboratory measurements of spe-

Table 3 Specific yield values used in recharge estimation in India (Sinha and Sharma, 1988 cited in Lerner et al., 1990). Material

Range of specific yield

Sandy alluvium Valley fills Silt/clay rich alluvium Sandstone Limestone Highly karstified limestone Granite Basalt Laterite Weathered phyllite, shale, schist, and associated rocks

0.12–0.18 0.10–0.14 0.05–0.12 0.01–0.08 0.03 0.07 0.02–0.04 0.01–0.03 0.02–0.04 0.01–0.03

cific yield values are not available, the use of standard values in literature is recommended. As there are no laboratory determined specific yield values for the study basin, specific yield values were selected from literature (Table 3), based on the geologic material of the aquifers in the study area and guilded by the range of specific yield value (0.01– 0.05) – in Shahin (2002), for the weathered zone material in neighboring Burkina Faso. Ghana and Burkina Faso, to a large extent, have the same geology.

6. Results and discussions 6.1. Groundwater level rise The highest monthly rainfall for the study area in 2006 and 2007 were measured in August and groundwater levels were highest in September/October (Figs. 2a–2d). Although the rainfall season in the study area starts in April/May, groundwater level in all wells started to rise only in June/July when about 40% of the annual rainfall had occurred. The 2- to 4-month lag between the start of the rainfall season and beginning of groundwater level rise can be described as a period of refilling of the soil due to moisture deficit inherited from the past dry season. The lag suggests that there are threshold effects and a non-linear relationship between rainfall and recharge in the study area. Additionally, the lag suggests that most wells in the study area recharge slowly. Similar observations

150

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100 14 50 18 0

Rainfall/Cummulative recharge (mm)

Rainfall Groundwater level Cummulative recharge Sumbrungu

Fig. 2c. Groundwater hydrograph and bar graphs of daily rainfall at Bongo in the White Volta basin of Ghana.

Rainfall Groundwater level Cummulative recharge

250

200

8 150 100 12 50

16

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have been made by Sandwidi (2007) and Martin (2005) in the Kompienga dam and Atankwidi basins, respectively. A critical examination of the groundwater hydrographs and groundwater level data for the two study years (2006 and 2007) suggests groundwater recharge in the White Volta basin of Ghana is almost entirely from the seasonal rainfall, since water level rise occurred mostly in the rainfall period. Though there was some accumulation of recharge in the dry season possibly due to regional flow of groundwater, this is negligible. Therefore, it can be reasonably concluded that, contribution to groundwater recharge in the White Volta basin of Ghana by aquifers outside the study is insignificant. The annual and spatial variations in groundwater levels were quite high. The recorded annual groundwater level rise in 2006 ranged from 1240 to 5000 mm, with a mean value and coefficient of variation of 2652 mm and 0.42, respectively. For 2007, annual groundwater level ranged from 1600 to 6800 mm. The mean value was 3577 mm, with coefficient of variation of 0.39. Annual rainfall measurements for 2006 and 2007 were 870 and 1294 mm, respectively. The highest and lowest water level rises in 2006 were recorded at Tumu and Bongo respectively (Figs. 2a–2d). In 2007, the highest water level rise was recorded in Bongo and the lowest at Bugya-Pali. The groundwater level rise measured at Kpasenkpe is rather high and may have been influenced by lateral flow due to its close proximity (within 100 m) to the main channel of the White Volta River.

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Fig. 2b. Groundwater hydrograph and bar graphs of daily rainfall at Sumbrungu in the White Volta basin of Ghana.

Navrongo

4

Rainfall/Cummulative recharge (mm)

7

7

7

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ov

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Fig. 2a. Groundwater hydrograph and bar graphs of daily rainfall at Tumu in the White Volta basin of Ghana.

Water level depth below ground (m)

0

M

0

16

Rainfall Groundwater level Bongo Cummulative recharge

Rainfall/Cummulative recharge (mm)

200

Water level depth below ground (m)

Tumu

8

Water level depth below ground (m)

Rainfall Groundwater level Cummulative recharge

Rainfall/Cummulative recharge (mm)

E. Obuobie et al. / Journal of African Earth Sciences 71–72 (2012) 80–86

Ja

Water level depth below ground (m)

84

Fig. 2d. Groundwater hydrograph and bar graphs of daily rainfall at Navrongo in the White Volta basin of Ghana.

A comparison of the mean annual groundwater level rise shows an increase of 35% in 2007 over that of 2006. The mean annual rainfall in 2007 shows an increase of 50% over that of 2006. In most of the observed wells, there were no short-term water level fluctuations in response to daily rainfall events. This is most likely due to attenuation of water table fluctuation as a result of the large storage capacity of the monitored wells. 6.2. Recharge estimates Recharge for each of the monitoring wells was estimated by multiplying the groundwater level rise with the specific yield values of the aquifer material in which the wells are situated. The mean annual recharge estimated for 2006 ranged from 28 to 150 mm, representing 3.5–16.5% of the annual rainfall (Table 4). For 2007, the mean annual recharge varied from 32 to 204 mm (2.5–16% of the annual rainfall). For 2006, the largest and smallest recharge values were estimated for Bongo and Galiwei, respectively, while for 2007, the largest and the smallest recharge were estimated for Bongo and Bugya-Pali, respectively. Recharge within the basin differed spatially because of differences in soils, landuse/-cover, climate, and physiography. An attempt was made at spatially interpolating the recharge estimates obtained for the individual boreholes. For the two years studied, the annual recharge in the White Volta basin of Ghana largely ranged from 68

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E. Obuobie et al. / Journal of African Earth Sciences 71–72 (2012) 80–86 Table 4 Recharge values estimated in the White Volta basin of Ghana for water years 2006/2007. Well

Aquifer material (topsoil texture)

Mean specific yield

Year

Dh (mm)

Mean recharge (mm)

% Of rainfall

WVB7-WRI WVB13-WRI WVB3-WRI

Birimian (sandy clay loam) Mudstone & shale (Clay sandy loam) Granite (Sandy loam)

0.035 0.020 0.030

WVB4-WRI

Granite (Sandy clay loam)

0.030

WVB5-WRI

Granite (Sandy loam)

0.030

WVB6-WRI

Granite (Sandy loam)

0.030

WVB9-WRI WVB11-WRI

Basal sandstone (Clay sand loam) Mudstone & shale (Sandy clay loam)

0.030 0.020

WVB14-GVP

Granite (Sandy loam)

0.030

WVB10-WRI

Sandstone (Sandy clay loam)

0.030

WVB1-WRI WVB8-WRI WVB2-WRI WVB12-WRI WVB15-GVP WVB16-GVP WVB17-GVP WVB18-GVP WVB19-GVP

Granite (Sandy loam) Granite (Sandy clay loam) Granite (Sandy clay loam) Mudstone & shale (Clay sandy loam) Granite (Sandy loam) Birimian (Sandy clay loam) Granite (Sandy clay loam) Granite (Sandy clay loam) Shale & sandstone (Sandy clay loam)

0.030 0.030 0.030 0.020 0.030 0.035 0.030 0.030 0.030

2006 2006 2006 2007 2006 2007 2006 2007 2006 2007 2006 2006 2007 2006 2007 2006 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007

4200 1380 1240 3199 2606 3533 2320 2310 5000 6800 2800 2109 1600 2691 3522 2181 3500 2920 2875 2080 3778 4148 2268 3435 4605 6659

147 28 38 96 78 106 70 69 150 204 84 42 32 81 106 66 105 88 87 63 76 125 68 103 138 200

17.0 3.5 4.5 7.5 9.0 8.0 8.0 5.5 16.5 16.0 9.5 4.5 2.5 9.0 8.0 8.0 8.5 6.5 6.5 4.5 6.0 8.5 5.0 8.0 10.5 16.0

to 108 mm, representing 8–10% of the long-term mean annual rainfall (1100 mm) of the area. Places with large annual recharge values exceeding 100 mm tend to be concentrated in the northeastern part of the basin and include places like Datuku, Tongo, Bongo and Yorogou. The overall recharge to groundwater in the study basin was estimated using the area weights of the monitoring wells determined with the Thiessen polygon method and was estimated to be 70 mm (8.0% of the annual rainfall) in 2006 and 92 mm (7.0% of the mean annual rainfall) in 2007. The difference in the recharge values for the two study years could be attributed to differences in the annual rainfall distribution and intensity. The recharge values obtained in this study are reasonable and within the range of estimates obtained with the WTF and other methods in previous groundwater studies done in other parts of the Volta Basin and in many arid/semi-arid areas in Africa (e.g., van der Sommen and Geirnaert, 1988; Houston, 1982; Friesen et al., 2005; Darko and Krasny, 2003; Martin, 2005; Nyagwambo, 2006; Ayenew et al., 2006; Sandwidi, 2007; Obuobie, 2008), although the values obtained in this study were a bit higher than results obtained in some of the previous studies mentioned. Since the specific yield values used in this study were obtained from literature and not measured for the specific aquifers in the basin, some level of error was expected. The reliability of the study results can be improved by using specific yield values determined for the aquifes in the study basin. 7. Conclusion The water table fluctuation method for estimating recharge to groundwater aquifers has been used widely to estimate recharge in different climatic conditions. This method is easy to apply, has low data needs and cost relatively less. It requires data of specific yield and changes in groundwater level over time. This method has been applied in this study to analyze groundwater level fluctuations in the White Volta Basin of Ghana for 2006 and 2007 water years as well as to quantify recharge to groundwater in the basin. Findings from groundwater level monitoring show high seasonal and spatial variability in the water level, with a range of 1240–

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