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UNIVERSITY OF GHANA 
 
 
HYDROGEOLOGICAL ASSESSMENT OF GROUNDWATER ON 
LEGON CAMPUS: APPLICATION OF GEOPHYSICAL AND 
NUMERICAL TECHNIQUES 
 
 
 
 
BY 
PATIENCE BOSOMPEMAA 
(10250248) 
 
 
 
THIS THESIS IS SUBMITTED TO THE EARTH SCIENCE 
DEPARTMENT, UNIVERSITY OF GHANA, LEGON IN PARTIAL 
FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF A 
MASTER OF PHILOSOPHY DEGREE IN GEOLOGY 
 
 
 
 
 
 
 
 
JULY, 2015
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DECLARATION  
 
I, Patience Bosompemaa, hereby declare that this thesis is a result of an original research 
undertaken under supervision by Prof. Sandow Mark Yidana and Dr. Larry- Pax Chegbeleh 
towards the award of Master of Philosophy in Geology in the Earth Science Department, 
University of Ghana. And that to the best of my knowledge, it has not been presented 
elsewhere for another degree except where due acknowledgement has been made in the 
text.  
  
  
……………………………………..                     Date………………………………   
PATIENCE BOSOMPEMAA 
(Student)   
  
  
………………………………………....               Date…………………………….   
PROF. SANDOW MARK YIDANA  
(Principal Supervisor)   
  
  
  
……………………………………                       Date…………………………….   
DR. LARRY-PAX CHEGBELEH   
(Co-Supervisor) 
 
 
 
 
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ABSTRACT 
 
The hydrogeological conditions of University of Ghana, Legon Campus was assessed using 
geophysical and numerical techniques. Data from borehole lithological logs and electrical 
resistivity survey was used to conceptualize the hydrogeological system of the terrain 
whiles using MODFLOW to generate groundwater flow simulation models under steady 
state conditions. The objective of the study was to conceptualize the hydrostratigraphy 
through geophysical techniques, develop a groundwater flow geometry for proper 
understanding of the mechanisms of groundwater flow in the area and to estimate 
groundwater budget and the responses of the aquifer system to stress. The calibrated steady 
state model together with a stochastic approach was used to simulate some scenarios of 
groundwater resource and its management in the area. The results of the study suggested 
three main lithostratigraphic units from geoelectric sections and the stratigraphic model 
and these units are phyllite, quartzite and laterite from the bottom to the top. Based on the 
borehole logs, the pseudo- sections and the geology of the area, the aquifer was encountered 
at a depth of about 38 m to 60 m which is attributed to probably the moderately weathered 
phyllite or quartzite giving an aquifer zone of thickness 22 m and this could be variable in 
space. The result suggests the dominance of local groundwater flow systems which results 
from the local changes in topography and hydraulic conductivity field. The estimated 
hydraulic conductivity values range between 4.0 m/day and 60.0 m/day and this is probably 
attributed to the local changes in the degree of weathering and/or fracturing in space. The 
estimated groundwater recharge ranges between 0.008 m/yr to 0.12 m/yr with an average 
of 0.064 m/yr representing 1% and 15%, with an average of 8.1% of the annual 
precipitation of 0.7 m in the area. The amount of water that entered the aquifer system 
through the general heads is 368368.08 m3/d and this is due to changes in the hydraulic 
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heads and the interconnectivity of the system with other aquifer systems. The contribution 
of recharge to the water budget seem low because the vertical percolation of rainwater is 
restricted due to the clayey nature of the terrain and the many physical developments which 
are seen on the Legon Campus.  From the scenario analysis that were performed, decrease 
in recharge did not have any significant change on the groundwater water system. However, 
when the initial abstraction rates were increased up to about 50%, dry cells were observed 
around some of the wells. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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DEDICATION  
 
This work is dedicated to my parents, Mr Frederick Owusu Afriyie and Madam Alice 
Serwah. To my entire family and friends, especially Mr Emmanuel Kweku Effah Asante 
who in one way or the other have contributed to my success. I am very grateful and God 
bless you. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ACKNOWLEDGEMENTS  
 
I am very grateful to God Almighty for his mercies and grace. My profound gratitude goes 
to my supervisors Prof. Mark Sandow Yidana and Dr Larry- Pax Chegbeleh for their 
immense supervision, hard work, constructive criticisms and support in making this thesis 
a success. God bless you abundantly. 
 I am also grateful to Prof. Daniel Asiedu, Dr. Thomas Armah, Prof Atta- Peters and Prof. 
Prosper Nude of the Earth Science Department, University of Ghana for their support. 
To Mr. Stephen Essel of SAL Consult Limited, Millicent Obeng Addai, Emmanuel Kofi 
Abitty, Elizabeth Darko, Patrick Banahene, Daniel Kwayisi, Patience Garrey, Richard 
Dogbe, Mr Kyeremeh of Frankatson Limited and Rev. and Mrs Aggrey I say thank you for 
your support.  
I wish to thank Ebiasah Sampson Odoom, Amoo Michael, Asem Enock Elikplim, Nipa 
Sebastian and Anyormi David Prince all of the Department of Earth Science, University of 
Ghana for their immense support. 
Finally, I would like to extend my gratitude to HEISA Engineering Company Ltd for their 
assistance. 
 
 
 
 
 
 
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TABLE OF CONTENTS 
DECLARATION .................................................................................................................. i 
ABSTRACT ........................................................................................................................ ii 
DEDICATION .................................................................................................................... iv 
ACKNOWLEDGEMENTS ................................................................................................. v 
LIST OF FIGURES ............................................................................................................ ix 
LIST OF TABLES ............................................................................................................. xii 
LIST OF APPENDICES ................................................................................................. xiii 
CHAPTER ONE .................................................................................................................. 1 
INTRODUCTION ............................................................................................................... 1 
1.1 BACKGROUND AND JUSTIFICATION ........................................................... 1 
1.2. RESEARCH OBJECTIVES ..................................................................................... 6 
1.3 STUDY AREA .......................................................................................................... 7 
1.3.1 LOCATION AND ACCESSIBILITY ................................................................ 7 
1.3.2. CLIMATE AND VEGETATION ...................................................................... 7 
1.3.3. TOPOGRAPHY AND DRAINAGE ................................................................. 7 
1.3.4. SOIL ................................................................................................................... 9 
1.3.5. GEOLOGY ......................................................................................................... 9 
1.3.6. HYDROGEOLOGY ........................................................................................ 13 
CHAPTER TWO ............................................................................................................... 14 
LITERATURE REVIEW .................................................................................................. 14 
2.1. GROUNDWATER RESOURCE DEVELOPMENT AND MANAGEMENT 
METHODS .................................................................................................................... 14 
2.2 GEOPHYSICAL APPLICATIONS IN HYDROGEOLOGICAL 
INVESTIGATIONS ................................................................................................... 15 
2.3 NUMERICAL TECHNIQUES IN HYDROGEOLOGICAL INVESTIGATIONS
 .................................................................................................................................... 21 
CHAPTER THREE ........................................................................................................... 31 
MATERIALS AND METHODS ...................................................................................... 31 
3.1 DESK STUDY ......................................................................................................... 31 
3.2 GEOPHYSICAL SURVEY ..................................................................................... 31 
3.3 GROUNDWATER MODELLING.......................................................................... 35 
3.3.1 CONCEPTUALIZATION OF THE HYDROGEOLOGICAL FRAMEWORK
 .................................................................................................................................... 37 
3.3.1.1 Generating stratigraphy .............................................................................. 37 
3.3.1.2 Generating solids ........................................................................................ 37 
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3.3.2 MODEL FORMULATION ............................................................................... 38 
3.3.3 NUMERICAL SIMULATION OF FLOW ....................................................... 42 
3.3.3.1 Model Calibration ....................................................................................... 43 
3.3.3.2 Sensitivity Analysis .................................................................................... 43 
3.3.3.3 Stochastic Approach ................................................................................... 44 
3.3.3.4 Analysis of Scenarios ................................................................................. 45 
CHAPTER FOUR ............................................................................................................. 46 
RESULTS AND DISCUSSIONS ..................................................................................... 46 
4.1 GEOPHYSICAL SURVEY ..................................................................................... 46 
4.1.2 CORRELATION BETWEEN APPARENT RESISTIVITY AND 
LITHOLOGY ............................................................................................................. 48 
4.1.3 DEDUCTIONS FROM RESISTIVITY PSEUDO-SECTIONS ....................... 50 
4.1.3.1 Section for Mensah Sarbah Hall (SB) ........................................................ 50 
4.1.3.2 Section for Hilla Limann Hall (HL) ........................................................... 51 
4.1.3.3 Section for Alexander Kwapong Hall (KWP) ............................................ 52 
4.1.3.4 Section for Valco Trust Hostel (VAL) ....................................................... 54 
4.1.3.5 Section for Legon Hall Annex C (LGC)..................................................... 55 
4.1.3.6 Section for Commonwealth Hall (CW) ...................................................... 57 
4.1.3.7 Section for University of Ghana Botanical Gardens (UGBBH1) ............... 58 
4.1.3.8 Section for University of Ghana Botanical Gardens (UGBBH2) ............... 59 
4.1.3.9 Section for Earth Science (ESC)................................................................. 61 
4.1.4 CONCEPTUALIZATION OF THE AQUIFER UNIT .................................... 62 
4.2 GROUNDWATER FLOW MODELLING ............................................................. 63 
4.2.1 STRATIGRAPHY............................................................................................. 63 
4.2.1.1 Cross-section............................................................................................... 64 
4.2.2 STEADY- STATE SIMULATIONS ................................................................ 65 
4.2.2.1 Hydraulic head ............................................................................................ 65 
4.2.2.2 The Hydraulic conductivity Field ............................................................... 70 
4.2.2.3 Groundwater recharge ................................................................................ 72 
4.2.2.4 Water budget ............................................................................................... 74 
4.2.3 STOCHASTIC MODEL ................................................................................... 76 
4.2.4 SCENARIO ANALYSIS .................................................................................. 79 
CHAPTER FIVE ............................................................................................................. 103 
CONCLUSIONS AND RECOMMENDATIONS .......................................................... 103 
5.1 CONCLUSIONS .................................................................................................... 103 
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5.2 RECOMMENDATIONS ....................................................................................... 105 
REFERENCES ................................................................................................................ 106 
APPENDIX ..................................................................................................................... 116 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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LIST OF FIGURES 
 
Figure 1.1: Location map of Legon and its surrounding areas showing the various 
communities ……………………………………….……………………………………. 8 
Figure 1.2: Map of Greater Accra Metropolitan Assembly (GAMA) of Ghana showing 
Legon Campus within the Togo Structural Unit……………….………………………   11 
Figure 1.3:  Geological map of the study area showing the various lithologies and borehole 
locations ………………………………………….……………………………………..  13 
Figure 3.1: An illustration for Schlumberger array and apparent resistivity (Morrison and 
Gasperikova, 2012)………………………………………………………………………………..33 
Figure 3.2: Map showing the various VES survey points …..……….…………………. 34 
Figure 3.3: Digitized map of the coverage area ……………………..……..……..……. 40 
Figure 3.4: Map in plain view showing grid over the active domain ………….………. 41 
Figure 3.5: Map in 3D showing grid over the active domain. ………………...……….. 42 
Figure 3.6: Map in 3D showing the spatial interpolation on the elevation data …..…… 42 
Figure 4.1: Curves types distribution in the study area ………………………….…….. 49 
Figure 4.2:  Resistivity pseudo section for SB ……………………………..………….. 52 
Figure 4.3: Resistivity pseudo section for HL ……………………………..…………... 53 
Figure 4.4: Resistivity pseudo section for AKWP ……………………..……………… 54 
Figure 4.5: Resistivity pseudo section for VAL ……………………..………………… 56 
Figure 4.6: Resistivity pseudo section for LGC ……………………..………………… 57 
Figure 4.7: Resistivity pseudo section for CW ……………………..…………………. 58 
Figure 4.8: Resistivity pseudo section for UGBBH1 ………………..………………… 60 
Figure 4.9: Resistivity pseudo section for UGBBH2 …………………..……………… 61 
Figure 4.10: Resistivity pseudo section for ESC ……………………...……………….. 62 
Figure 4.11: A 3D view of the borehole logs that was generated from the twelve borehole 
logs …………………………….…………………………….……...………………….. 64 
Figure 4.12: Oblique view of the solids created with some little amount of exaggeration 
depicting the lithostratigraphic framework of the basin ………...……………………... 65 
Figure 4.13: Selected cross sections cut from the solid model in different directions….. 66 
Figure 4.14: A match between the model computed heads and observed heads …...….. 67 
Figure 4.15: The most prominent groundwater flowpaths in the study area ……...…… 69 
Figure 4.16: Hydraulic head distribution of the calibrated model ……………………... 70 
Figure 4.17: Calibrated hydraulic conductivity field for the study area ………...……... 72 
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Figure 4.18: Recharge distribution for the study area ………………………..………… 74 
Figure 4.19: Eight solutions from the stochastic approach …………………………….. 78 
Figure 4.20: Standard deviation map for the hydraulic heads form the stochastic simulation 
……………………………………………………………………….............................. 79 
Figure 4.21: Hydraulic head distribution after 10% increment in abstraction rates ….... 82 
Figure 4.22: Hydraulic head distribution after 20% increment in abstraction rates …… 82 
Figure 4.23:  Hydraulic head distribution after 25% increment in abstraction rates ....... 83 
Figure 4.24: Hydraulic head distribution after 50% increment in abstraction rates …… 83 
Figure 4.25: Hydraulic head distribution after 100% increment in abstraction rates....... 84 
Figure 4.26: Hydraulic head distribution after 200% increment in abstraction rates....... 84 
Figure 4.27: Same abstraction rate with 10% decrease in recharge …………………… 86 
Figure 4.28: Same abstraction rate with 15% decrease in recharge …………………… 86 
Figure 4.29: Same abstraction rate with 20% decrease in recharge …………………… 87 
Figure 4.30:  Same abstraction rate with 25% decrease in recharge …………………... 87  
Figure 4.31:  Recharge reduced by 10% with abstraction rates increased by 10% …… 89 
Figure 4.32: Recharge reduced by 10% with abstraction rates increased by 20% …….. 89 
Figure 4.33: Recharge reduced by 10% with abstraction rates increased by 25% ….…. 90 
Figure 4.34: Recharge reduced by 10% with abstraction rates increased by 50% ….…. 90 
Figure 4.35: Recharge reduced by 10% with abstraction rates increased by 100% …… 91 
Figure 4.36: Recharge reduced by 10% with abstraction rates increased by 200%........  91 
Figure 4.37: Recharge reduced by 15% with abstraction rates increased by 10% …….. 94 
Figure 4.38: Recharge reduced by 15% with abstraction rates increased by 20%........... 94 
Figure 4.39: Recharge reduced by 15% with abstraction rates increased by 25% …..… 95 
Figure 4.40: Recharge reduced by 15% with abstraction rates increased by 50% …….. 95 
Figure 4.41: Recharge reduced by 15% with abstraction rates increased by 100% …...  96 
Figure 4.42: Recharge reduced by 15% with abstraction rates increased by 200% …...  96 
Figure 4.43: Recharge reduced by 20% with abstraction rates increased by 10% …...... 97 
Figure 4.44: Recharge reduced by 20% with abstraction rates increased by 20% …...... 97 
Figure 4.45: Recharge reduced by 20% with abstraction rates increased by 25% …...... 98 
Figure 4.46: Recharge reduced by 20% with abstraction rates increased by 50% …..... 98 
Figure 4.47: Recharge reduced by 20% with abstraction rates increased by 100% ....... 99 
Figure 4.48: Recharge reduced by 20% with abstraction rates increased by 200% ....... 99 
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Figure 4.49: Recharge reduced by 25% with abstraction rates increased by 10% ….... 100 
Figure 4.50: Recharge reduced by 25% with abstraction rates increased by 20% …… 100 
Figure 4.51: Recharge reduced by 25% with abstraction rates increased by 25% …… 101 
Figure 4.52: Recharge reduced by 25% with abstraction rates increased by 50% …… 101 
Figure 4.53: Recharge reduced by 25% with abstraction rates increased by 100% ….. 102 
Figure 4.54: Recharge reduced by 25% with abstraction rates increased by 200% ….. 102 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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LIST OF TABLES 
Table 4.1: Statistical summary of VES modelled results ………..……………………... 48 
Table 4.2: Summary of the 12 borehole logs from some boreholes drilled on Legon Campus 
…………………..………………………………………………………………………. 50 
Table 4.3: Water budget for the study area from the steady state model ………….……. 76 
Table 4.4: A table showing the initial flow rates for the scenario analysis ………....….. 80 
Table 4.5: A Table showing the change in hydraulic heads of the 14wells for the scenario 
of increasing abstraction up to 200% …………………………...……………………… 81 
Table 4.6: A Table showing the change in hydraulic heads of the 14wells for the scenario 
of maintaining abstraction with decreasing recharge by 10%, 15%, 20% and 25% …… 85 
Table 4.7: A Table showing the change in hydraulic heads of the 14wells for the scenario 
of decreasing recharge by 10% and increasing abstraction up to 200% ………..…..….. 88 
Table 4.8: A Table showing the change in hydraulic heads of the 14wells for the scenario 
of decreasing recharge by 15% and increasing abstraction up to 200% ……………..…. 92 
Table 4.9: A Table showing the change in hydraulic heads of the 14wells for the scenario 
of decreasing recharge by 20% and increasing abstraction up to 200% ………..……… 92 
Table 4.10: A Table showing the change in hydraulic heads of the 14 wells for the scenario 
of decreasing recharge by 25 % and increasing abstraction up to 200 %......................... 93 
 
 
 
 
 
 
 
 
 
 
 
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LIST OF APPENDICES 
 
Appendix 1: Vertical electrical sounding results for each station ……………..……… 117 
Appendix 2: Vertical electrical sounding model results for each station …………..…. 125 
 
 
  
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CHAPTER ONE 
INTRODUCTION 
 
1.1 BACKGROUND AND JUSTIFICATION 
 
Groundwater accounts for daily potable water supply for over 1.5 billion people worldwide 
with even a higher dependency during drought conditions (DFID, 2001). The use of 
groundwater for irrigation, industrialization and domestic activities has gained a lot of 
attention in recent years. The increasing popularity of groundwater resources as a solution 
to the water delivery system originates from the fact that the resource has certain features 
that make it attractive as a source of potable water supply (Quist et al., 1988). Groundwater 
resources management forms an important yardstick for measuring the progress of nations 
and it is the sole water resource in arid and semi-arid regions.  Also, groundwater resources 
have been proven to be the best buffer against the unremitting effects of climate 
change/variability on the global water resources. 
In Ghana, groundwater plays a vital role in the promotion of economic growth and 
reduction of poverty. There is rapidly increasing demand for water in industries particularly 
hydropower generation, agriculture, mining and environmental enhancement. Most urban 
communities in the country depend on tap water from the river basins which are developed 
and distributed through the pipe system in the area. Where rural communities are close 
enough to urban centres, they also tap into the extensions of the urban water delivery 
system. But most at times, these rural communities depend solely on groundwater resources 
for their day to day activities. Mygatt (2006) estimated that about 2 billion people in urban 
and rural communities worldwide depend on groundwater for daily consumption.  
 
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Before the full potential of the groundwater resources can be harnessed, it must be explored 
and developed in a sustainable manner. Many scientific and traditional techniques have 
been utilized in the exploration for groundwater resources. In hydrogeological 
investigations, the conventional techniques that were used involved field surveys at two 
quite different spatial scales which are; low resolution tracer and pumping tests and high 
resolution borehole-based studies. For instance, core measurements provide information 
about a very small portion of typical field investigations at a very high resolution while 
surface geophysical techniques often provide lower resolution information but over larger 
spatial scales (Hubbard and Rubin, 2005). These techniques have been widely used in 
different areas, but are always impeded by insufficient information about the variations in 
the subsurface which does not usually apply to certain areas. 
In recent times, several works have been carried out by researchers in an attempt to bridge 
the wide gap between these two scales by using different innovative hydrogeological and 
geophysical tools with high to medium resolution capabilities. For instance, in several 
hydrogeological and geophysical studies, Ground Penetrating Radar (GPR) technique has 
been efficient in imaging near surface features under adequate surface conditions (Davis 
and Annan, 1989; Jol and Smith, 1991; Lawson et al., 1996; Al- fares et al., 2002; 
Chalikakis et al., 2011; Carrrère et al., 2013). However, this technique does not identify the 
features which influence water dynamics. Electrical Resistivity Tomography (ERT) 
technique has been used in certain areas (for example Karst) to identify features due to its 
robustness and sensitivity to water content (Zhou et al., 2000; Cardarelli et al., 2006; 
Descloitres et al., 2008; Clement et al., 2009; Valois et al., 2010; Okpoli, 2013). This 
technique however requires repeated efforts of data quality and error models especially in 
difficult measurement conditions to give accurate results. Seismic refraction/ Multichannel 
Analysis of Surface Waves (MASW) techniques have also been used to investigate layered 
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media and to perform both large and small scale survey which provides information about 
the stratigraphy, hydrogeology and bedrock topography (Steeples, 2000; Tian et al., 2002; 
Olona et al., 2010; Hayashi et al., 2014) but it is an active source technique that requires an 
impulsive signal such as those from sledgehammer or weight drop or swept vibratory signal 
such as vibroseis to generate surface waves. However, borehole and geophysical surface 
measurements are combined to develop ground water flow models for studies in 
groundwater systems which compile geologic and groundwater data to develop 
deterministic analysis of groundwater transport analysis, and complex conceptual site 
models. Although some decisions can be made using best engineering or best geologic 
judgement, in many instances human reasoning alone is inadequate to synthesize the 
conglomeration of factors involved in analysing complex groundwater problems 
(Anderson and Woessner, 2002). 
In conventional hydrogeological practice and groundwater resources assessments, 
numerical groundwater models are being constructed and these models are significant in 
performing complex environmental analyses that other conventional methods are incapable 
of assisting in analysing. These models are significant in performing complex analyses and 
in making informed predictions. Groundwater flow simulation models have been used to 
solve complex hydrogeological problems such as tracing of contaminant pathways, 
conceptualization and quantification of hydrogeological conditions of aquifer systems and 
using the information to plan future data requirements. They are also used to explore 
groundwater system dynamics such as surface water-groundwater interactions, recharge 
areas, seepage rates, transportation dynamics, prediction of changes in aquifer behaviour 
and as a management tool in the management of complex and extensive aquifer systems. 
Models can be used in an interpretive sense to gain insight into the controlling parameters 
in a site- specific setting or as a framework for assembling and organizing field data 
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formulating ideas about system dynamics (Anderson and Woessner, 2002). Ground water 
models have been used in other parts of the world and have proven over several decades to 
be useful tools for addressing a range of ground water problems and supporting the 
decision-making process. Different numerical methods are available to solve the 
groundwater flow equations upon the definition of boundary and initial conditions and the 
two common methods that are used for the solution of the groundwater flow equations are 
the finite differences and finite element methods. For instance, Mondal and Singh, (2009) 
developed a numerical model in Dindigul District (Tamilnadu), India, using the finite 
difference technique coupled with method of characteristics to predict total dissolved 
solids, TDS migration for the next 20 years. Surinaidu et al., (2014) used the finite 
difference method based numerical groundwater flow model to develop a steady state 
model, twenty conceptual layers of total thickness 320 m. Many researchers such Konikow 
(1977), Konikow and Bredehoeft (1978), Rushton and Redshaw (1979); Guiguer and Franz 
(1996); Molenat and Gascuel-Odoux (2002), McDonald and Harbaugh (2003), Bakker et 
al., (2004), Huysmans et al., (2006) have used different numerical techniques to solve 
complex hydrogeological problems. Although there are some errors and uncertainties in 
any modelling study with regard to hydrogeological understanding, the conceptual model 
design, and model calibration and prediction simulations, as well as recharge and 
evapotranspiration estimation and simulation, it is one of the best approach to simulate and 
predict aquifer conditions. The best tool available to help groundwater hydrologists meet 
challenges of prediction is usually a groundwater model (Anderson and Woessner, 2002). 
The limitations of groundwater models are that they are based on assumptions and 
simplification of the physical system and are not the exact replicas of the physical 
hydrogeology of the basin. Like all models, groundwater flow models cannot predict the 
future with absolute certainty. 
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 In Ghana, these models have not been so much popular in decision making due to the lack 
of data and expertise involved in the modelling. Nonetheless models should be used more 
because it will give a comprehensive and thorough knowledge about the current 
groundwater recharge and level in the country. The model allows more effective use of the 
available data; more complexities can be accounted for; and the implications of the 
assumptions used in the analysis and of the management decisions can be evaluated 
(Hamilton, 1982). The study undertakes an assessment of the hydrogeological conditions 
of rocks underlying University of Ghana, Legon Campus using a variety of geophysical 
and numerical modelling techniques. This study will help give a better appreciation of the 
hydrogeological conditions of the aquifers and assist in the management of the resource to 
ensure sustainability. 
In this particular area of study groundwater is being abstracted for supply to support 
municipal water supply especially to halls and academic departments because the public 
water supply system has serious challenges and are highly unreliable. Due to the increasing 
number of students and other supporting staff and its attended increase in the abstraction 
rate of the groundwater resources, it is imperative to assess the effects of these abstractions 
on the groundwater system and other ecosystem that depend on the groundwater systems. 
The best way to do this is not the use of yield data because the it only gives a point data but 
the numerical models combines the filed data and the hydrogeological conditions on the 
ground to assess the resource comprehensively and holistically. One added advantage of 
these models is their ability to estimate recharge with certain degree of accuracy depending 
on the data available and the expertise involved. The estimation of accurate groundwater 
recharge is a significant aspect of managing groundwater systems which will help identify 
recharge and discharge areas and good measures to be taken to protect such zones from 
contamination.  
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The numerical simulation is preceded by detailed conceptualization which involves 
geophysical surveys carried out to determine the hydrostratigraphy of the aquifer system 
in the area which will ultimately assist in determining the hydrogeological conditions of 
the aquifers. Since models are based on data on the hydrogeological conditions of an area, 
the conceptual framework arising from the geophysical surveys and borehole logs data will 
serve as initial inputs to a numerical groundwater flow model calibrated under steady state 
condition. Therefore, this will help constrain groundwater recharge and its spatial and 
temporal variation in the terrain. This study focuses on conceptualizing the 
hydrostratigraphy of the study area through geophysical techniques, developing a 
groundwater flow geometry and to estimate groundwater budget and the responses of the 
system to stress. 
 
1.2. RESEARCH OBJECTIVES 
 
The main objective of the research is to estimate groundwater recharge rates and its spatial 
and temporal variations in the study area through model calibration. The specific objectives 
include; 
 To conceptualize the hydrostratigraphy through geophysical techniques. 
 To develop a groundwater flow geometry for proper understanding of the 
mechanisms of groundwater flow in the study area. 
 To estimate groundwater budget and the responses of the aquifer system to 
stress.  
 
 
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1.3 STUDY AREA 
 
1.3.1 LOCATION AND ACCESSIBILITY 
 
Legon Campus of the University of Ghana is based at Legon, a suburb of the city Accra 
located in the Ga East Municipality of Ghana. It is situated about twelve kilometres 
northeast of the centre of Accra (Figure 1.1.) and falls between latitudes 5° 39' 0" North, 
and longitudes 0° 11' 0" West. The Campus is highly inhabited and has nearly 40,000 
students and other supporting staff living around (University of Ghana, 2015). 
 
1.3.2. CLIMATE AND VEGETATION  
 
The Municipality falls in the savannah agro-ecological zone. Rainfall pattern is bi-modal 
with the average annual temperature ranging between 25.1°C in August and 28.4°C in 
February and March. February and March are normally the hottest months (Ga East 
Municipal Assembly, 2006).  
The Municipality has two main vegetation namely shrub lands and grassland. The shrub 
lands occur mostly in the western outskirts and in the north towards the Aburi hills and 
consist of dense cluster of small trees and shrubs that grow to an average height of about 
five meters. The grassland which occurs to the southern parts of the district has now been 
encroached upon by human activities including settlements (Ga East Municipal Assembly, 
2006). 
 
1.3.3. TOPOGRAPHY AND DRAINAGE 
 
The land area consists of gentle slopes interspersed with plains in the west.  The Akwapim 
range rises steeply above the western end and lies generally at 375- 420 m north of Aburi 
and fall to 300 m southward.   
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Figure 1.1: Location map of Legon and its surrounding areas showing the various communities 
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There are a few rivers and seasonal streams most of which are threatened by human 
activities. These include the Sisami stream at Sesemi and the Dakubi at Ajako with other 
small ponds at Abloradjei, Sesemi, Danfa, Otinibi and Old Ashongman.  Most of these 
ponds are also threatened by human activities but the Assembly has made conscious efforts 
to preserve them for agricultural use. Feasibility studies are therefore being conducted for 
possibility of an Aqua-culture to generate income for the youth.  There are lots of ground 
water which have been tapped to provide potable water for the rural communities and small 
towns (Ga East Municipal Assembly, 2006). 
 
1.3.4. SOIL 
The soils in the Legon Campus being part of Accra can be divided into four main groups: 
drift materials resulting from deposits by wind-blown erosion; alluvial and marine motted 
clays of comparatively recent origin derived from underlying shales; residual clays and 
gravels derived from weathered quartzites, gneiss and schist rocks and lateritic sandy clay 
soils derived from weathered Accraian sandstone bedrock formations. In many low lying 
poorly-drained areas, pockets of alluvial ‘black cotton’ soils are found. These soils have a 
heavy organic content, expand, and contract readily causing major problems with 
foundations and footings. In some areas, lateritic soils are strongly acidic. Near the foothills 
of Accra are large areas of alluvial laterite gravels and sands. Many of these deposits are 
being exploited in an uncontrolled manner for constructional purposes (Accra Metropolitan 
Assembly, 2006). 
 
1.3.5. GEOLOGY 
Legon Campus is underlain by the Togo Structural Unit (Fig. 1.2.) which forms part of the 
Pan African Dahomeyide orogen exposed in the south-eastern part of Ghana. In general, 
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the rocks of the Togo Structural Unit form a range of mountains and hills trending in the 
north northeast to south southwest from the Volta River to the Ghana- Togo border and 
beyond (Kesse, 1985). The Togo Structural Unit is predominantly composed of quartz- 
sericite- schist, quartzites, phyllites and chlorite- schist (Grant, 1969; Junner and Hirst, 
1946; Robertson, 1925) as shown in Figure 1.2. In addition, phyllonites, cataclastics 
quartzites, micaceous quartzites and serpentinites form part of this Unit (Blay, 1971; Crook, 
1963). Hornstones, jaspers, hematite quartz- schist, pebbly grits and argillaceous schist 
with quartz pebbles also occur in this Unit (Kesse, 1985; Junner and Service, 1936).  
 
Figure 1.2: Map of Greater Accra Metropolitan Assembly (GAMA) of Ghana showing Legon 
Campus within the Togo Structural Unit 
 
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Junner and Service (1936) considered the hornstones, jaspers and hematite quartz- schist 
as post depositional rocks. The serpentinites occur along the western thrust fault which 
marks the western contact of the Togo Structural Unit to the Buem Structural Unit. The 
Togo Structural Unit overlies the Dahomeyan Structural Unit and forms its eastern 
boundary. It is bordered to the west by the Cape Coast granitoid complex rocks, the 
Voltaian and the Buem Structural Unit. Ahmed et al. (1977) suggested that the eastern 
boundary trust fault and the western boundary trust fault mark the eastern and western 
margin of the Togo Structural Unit respectively. The rocks in the Togo Structural Unit are 
highly deformed with intense folds, fractures, joints and faults which indicate a series of 
deformation of which the latest is related to the 600 Ma-500 Ma Pan African event 
(Kennedy, 1964; Grant, 1969; Crook, 1963; Robertson, 1925). The folds are predominantly 
isoclinal with axial planes inclined to south- east and minor recumbent folds with general 
dip of less than 300 (Kesse, 1985). Most of the rocks in the study area are dominantly 
quartzites and phyllites (Fig. 1.3.). The quartzites in the study area are mostly gray, medium 
to fine grained, thick foliated with joints and microfractures. The phyllites are mostly gray, 
fine –grained, thinly foliated with joint and microfractures. 
 
 
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Figure 1.3:  Geological map of the study area showing the various lithologies and borehole 
locations 
 
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1.3.6. HYDROGEOLOGY 
The Togo Structural Unit consists of rocks such as quartzites, phyllites and schists. The 
rocks are inherently impermeable and groundwater occurrence in the geologic formation is 
controlled mainly by the development of secondary porosity e.g. fractures, faults, joints 
and the associated weathered zone (Akiti, 1986). The Accra plain has no perennial river 
with the exception of the Volta River which forms the northern and eastern borders of the 
plain. A distinct hydrologic feature of the plain is the east-west watershed which divides 
the plain into north and south drainage systems. Within the plains, groundwater potential 
is relatively high at the foothills of the Akwapim Mountains. Recharge to the aquifer is by 
direct percolation of rainfall along the Akwapim Mountains (Akiti, 1986). Minor or indirect 
recharge also occurs mainly in the rainy season when fractures or mega joints intercept the 
ephemeral stream courses (Kortatsi, 2006). The channels act as conduits that allow water 
to recharge the aquifer. Recharge of the groundwater system is estimated at 15% of the 
annual precipitation. Boreholes drilled in the plains at average depth of 55 m have yielded 
value of 2.7 m3/h and transmissivity values are generally low due to the clayey nature of 
the regolith (Kortatsi, 2006). The transmissivity vary from 0.2 m2/h in the clayey regolith 
to 4.5 m2/d in the plain (Darko, 2001). Legon Campus forms part of the Pan African 
Province and it is controlled by secondary permeability. Due to deformation most of the 
rocks found in the study area have lost their inherent primary permeability. As a result of 
this, the hydrogeological characteristics of rocks in the area which is controlled by 
fractures, faults, folds and joints came as a result of tectonism that occurred in the area. 
 
 
  
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CHAPTER TWO 
LITERATURE REVIEW 
 
2.1. GROUNDWATER RESOURCE DEVELOPMENT AND MANAGEMENT 
METHODS 
Groundwater resource development is the use of proven approaches with innovative 
technologies to develop sustainable groundwater resource supplies. It involves three phases 
of development. The first phase is exploration for the resource which may involve 
geophysical techniques; the second phase is drilling to penetrate the aquifer through various 
drilling methods, under which the following can be conducted: logging, interpretation of 
borehole information, and water wells, well/borehole development, pump selection, 
pumping test and the third phase involves water quality analysis. 
In the last decades, local techniques such as dowsing and dug wells were used to explore 
and develop ground water. In the last half century, the usage of mechanical means to drill 
wells, the arrival of relatively cheap energy sources and the introduction of the submersible 
turbine pump has changed the way of development of groundwater all over the world.  
Regional hydrogeological investigations have traditionally been undertaken using pumping 
tests, tracers and other traditional methods. For instance, Meier et al. (1988) carried out an 
evaluation study to show that the Jacob’s method leads to a good approximation of the 
effective transmissivity of heterogeneous media when constrained to late time data and this 
method was based on the assumption of aquifer homogeneity. Marc et al. (2001) used 
chemical and isotopic tracers to conduct a study in a Small Mediterranean forested 
catchment during autumn recharge to investigate hydrological processes. The result 
showed that subsurface drainage through soil macroporosity is one of the dominant 
processes in the formation of streamflow generation but one of the weaknesses with this 
method is that in-situ analyses are probably not possible.  
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Groundwater resources management has to deal with balancing the exploitation of a 
complex resource (in terms of quantity, quality and surface water interactions) with the 
increasing demands of water and land users who can pose a threat to resource availability 
and quality (Tuinhof et al., 2002, 2006). Assessments of groundwater resources in terms of 
quality and recharge source are critical components of sustainable groundwater resources 
evaluation and management, especially in complex weathered/fractured crystalline 
bedrock terrain. The sustainable management of groundwater resources depends on the 
knowledge of the rates and spatial distribution of recharge to aquifers. The need for 
groundwater management does not usually arise until there is a significant decline in well 
yields and/or quality that affects one of the stakeholder groups (Yidana et al., 2008). Thus 
if there is uncontrolled pumping in a particular terrain it may damage the resource as a 
whole which may result in serious groundwater level decline, and in some cases aquifer 
saline intrusion or even land subsidence. Hence the management of groundwater resources 
suitable for optimal development has been of global significant for years now and this is 
crucial in recent times as the threat of climate change and its impacts on sustainable 
livelihoods will certainly have to be mitigated by the efficient management of groundwater 
resources for optimal development (Yidana et al., 2008). 
 
2.2 GEOPHYSICAL APPLICATIONS IN HYDROGEOLOGICAL 
INVESTIGATIONS  
Geophysics as a branch of physics deals with the application of physics to study the earth. 
It uses the principles, effects of differences in the physical properties of rocks such as 
elasticity, density, conductivity or resistivity, magnetism and radioactivity of the earth. 
Geophysical investigation is done to study the subsurface geological formation by 
measuring physical field(s) on or beneath the surface, in the borehole or in the air that are 
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influenced by the internal distribution of these physical properties. There are two main 
aspects of geophysics and these are pure and applied geophysics. Pure geophysics involves 
the study of the substantial parts of the planet whilst applied geophysics involves 
investigating the earth’s interior by taking measurements either at or near the earth surface 
(Telford et al., 1990). In other words, it provides a complimentary and non-invasive 
approach to remotely learn about the subsurface. On the basis of scale, geophysics is 
classified as global or general geophysics and exploration or applied geophysics. Global or 
general geophysics deals with the general aspects of the earth as a whole to determine its 
internal structure and dynamics. Examples include; variations in temperature with depths, 
plate tectonics, seismicity and causes of reversals in the earth’s magnetic field. Exploration 
or applied geophysics deals with the study of the upper layers of the earth’s crust in order 
to; explore and estimate mineral ore deposits, locate oil and gas deposits, identify 
groundwater points, study archaeological sites, investigate pollution and waste sites, locate 
cavities, underground tunnels and cable and image the subsurface geological setting for 
civil engineering problem (Kearey et al., 2002). Some physical fields are generated by an 
active experiment such as seismic, electrical and electromagnetic whereas other fields are 
passive and do not require any man made source.  Characteristics of these physical fields 
are controlled by the properties of the medium in which they propagate, as well as their 
source. It is the property of the medium which is determined by geophysical techniques 
and this is subsequently interpreted in terms of subsurface geological formation. Of all the 
physical properties of geological formation the electrical resistivity or conductivity is the 
highly varied (Palacky, 1987).   
Many hydrogeological problems have been resolved through the application of a suite of 
geophysical techniques. The application of a specific geophysical technique varies 
depending on the problems to be addressed. There are various approaches available for 
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regional hydro-geological investigations and for providing the necessary information 
required for optimal aquifer and basin yield management (Freeze and Cherry, 1979). 
Depending on the geological and hydrological environments, one or more of the different 
geophysical methods can be used in hydrogeological investigations such as ground-
penetrating radar, high-resolution seismic reflection and refraction, electrical 
resistivity/polarization, electromagnetic, microgravity and magnetic methods. The choice 
of geophysical technique employed for hydrogeological investigations depends on the 
existence of different physical properties between the subsurface materials being 
investigated. The thickness of unconsolidated material, depth to water table as well as the 
location and extent of fault and shear zones can be evaluated using geophysics (Carruthers 
and Smith, 1992). These methods could provide critical details on the geometries and 
characteristics of subsurface structures and formations that influence groundwater 
occurrence and flow. A lot of these methods provide a representative information on the 
presence of groundwater due to the fact that the geophysical properties of water can be 
quite different from those of the host soils and rocks. For example, Ground Penetrating 
Radar (GPR) technique has been efficient in imaging near surface features under adequate 
surface conditions (Davis and Annan, 1989; Jol and Smith, 1991; Lawson et al., 1996; Al- 
fares et al., 2002; Chalikakis et al., 2011; Carrrère et al., 2013) but this technique could not 
be able to identify the features which influence water dynamics. Electrical Resistivity 
Tomography (ERT) technique has been used in certain areas (for example Karst) to identify 
features due to its robustness and sensitivity to water content (Zhou et al., 2000; Cardarelli 
et al., 2006; Descloitres et al., 2008; Clement et al., 2009; Valois et al., 2010; Okpoli, 2013). 
This technique also requires a lot of repeated efforts in data quality and error models 
especially in difficult measurement conditions to give accurate results. Seismic refraction/ 
Multichannel Analysis of Surface Waves (MASW) techniques have also been used to 
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investigate layered media and to perform both large and small scale survey which provides 
information about the stratigraphy, hydrogeology and bedrock topography (Steeples, 2000; 
Tian et al., 2002; Olona et al., 2010; Hayashi et al., 2013). 
However, in this study, the electrical resistivity method was employed using the 
Schlumberger configuration for vertical electrical soundings. Accordingly, electrical 
resistivity/conductivity methods have extensively been used in solving various problems 
related to the hydrogeological investigations (Keller and Frischknecht, 1966).  Surface 
electrical resistivity method is one of the oldest and most commonly used geophysical 
exploration methods (Reynolds, 2011). Electrical resistivity (ER) surveying is mainly 
based on the principle that the distribution of electrical potential in the ground around a 
current-carrying electrode depends on the electrical resistivities and distribution of the 
surrounding soils and rocks. Applying an electrical direct current (DC) between two 
electrodes implanted in the ground and to measure the difference of potential between two 
additional electrodes that do not carry current is the usual practice in the field. ER is used 
to detect shallow structures and subtle changes in the soil apparent resistivity, which also 
makes it conductive for using it in geological and geophysical surveys (Telford et al., 
1990).  
The electrical resistivity method has some inherent limitations that affect the resolution and 
accuracy that may be expected (Ijeh, 2014).  Just as all other methods used for 
measurements of a potential field, the value of a measurement obtained at any location 
amounts to a weighted average of the effects produced over a large volume of material, 
with the nearby portions contributing most heavily.  This tends to produce smooth curves, 
which do not lend themselves to high resolution for interpretations (Ijeh, 2014). .  And also 
another feature that is common to all potential field geophysical methods is that a particular 
distribution of potential at the ground surface does not in most cases have a unique 
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interpretation.  Although these limitations should be recognized, the non-uniqueness or 
ambiguity of the resistivity method is scarcely less than with the other geophysical 
methods.  
The Vertical Electrical Sounding (VES) has been used in some parts of Africa and sub-
Sahara Africa extensively for different purposes. For example, Ibrahim (2013) conducted 
a geoelectric resistivity survey for site investigation in the East Matruh Area, North 
Western Desert, Egypt using resistivity data which was calibrated to the nearest boreholes 
in order to guide the interpretation. From the study the results were presented in litho-
resistivity section and it delineated four subsurface layers. Chuma et al. (2013) modelled 
the subsurface geology and the groundwater occurrence of the Matsheumlope low yielding 
aquifer in the Bulawayo Urban, Zimbabwe. The study used vertical electrical method to 
establish the depth, thickness and sequence of geological units in the low yielding aquifer. 
The results indicated high spatial variation of the subsurface formations and groundwater 
potential over short distance which indicated complex nature of the mapping basement 
aquifers. Bashir et al. (2014) carried out an investigation on the aquifer composition and 
its potential to yield groundwater in some selected towns in Bida Basin of North Central 
Nigeria using vertical electrical sounding method. The results showed five geoelectric 
layers in the study area and the groundwater bearing layer varies between 37– 70 m across 
the study area. 
Marere and Ojo (2014) carried out a geophysical investigation which involved the vertical 
electrical sounding (VES) electrical resistivity method carried out at Emeyel, Bayelsa state 
South- South Nigeria to determine the aquifer configuration which assisted in the sitting of 
high borehole yield using the Schlumberger configuration. The results suggested that two 
to three geoelectric layers were delineated and the second layer was the horizon with high 
groundwater potential occurrence. 
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Jatau et al. (2013) conducted a research on the geoelectrical drilling which was carried out 
in parts of the Abaji Area Council, Federal Capital Territory of North- Central Nigeria. The 
aim of the research was to establish groundwater potential of the area using seventy- two 
VES points. The study revealed 5- 7 lithologies sequence and the results indicated that the 
water bearing zone was within the moderate resistivity values obtained and the results 
correlated well with the existing geology. 
Alisiobi and Ako (2012) carried out an investigation on groundwater using combined 
seismic refraction and VES electrical resistivity methods around Ajebandele quarters, Ile- 
Ife, Osun State, Southwest Nigeria. The objective of the research was to determine the 
subsurface velocities, resistivities and thicknesses and also categorize the groundwater 
potential of the area. The study delineated a maximum of five layers with H, HK, AA, and 
HAA curve types and showed that the bedrock in the area occurred quite close to the 
surface. Although the number of layers delineated were different and the methods indicated 
viable aquifer at a fractured zone. 
Odoh et al. (2012) carried out a study in Ebonyi State, Southeast Nigeria on the prospecting 
groundwater fractured shale aquifer using an integrated suite of geophysical methods. The 
study used vertical electrical sounding method for Schlumberger configuration with the 
main objective of determining the formation resistivities and depth of the aquifer. The 
research suggested that the areas with low resistivity values were associated with the 
fractured zones which inferred high secondary porosity and high electrical conductivity. 
Based on the VES and horizontal profiling the integrated layer under the geoelectric was 
sand which signified a probable aquifer zone in Lagos University (Alabi et al., 2010). 
In Ghana there has not been much geophysical survey carried out for hydrogeological 
investigations and one of the few include: a geophysical study by Appiah et al. (2014) to 
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investigate   groundwater potential of Buma in the Gonja East District of Northern Ghana. 
The aim of the research was to delineate suitable site for groundwater development and 
also estimate the depth to the aquifer using electrical resistivity profiling and vertical 
electrical sound technique for Schlumberger configuration. The research identified a four 
layered subsurface structure and suggested that the third layer revealed a significant amount 
of weathering/fracturing which indicated the zone of potential groundwater location. 
Asare and Menyeh (2013) conducted a study on the geoelectrical investigation of 
groundwater resources and aquifer characteristics in some small communities in the 
Gushiegu and Karaga Districts of the Northern Region of Ghana using vertical electrical 
sounding for Schlumberger configuration. The research indicated a three-layer subsurface 
layer which was largely congruous to the weathering profile above the fresh bedrock and 
concluded that the interpreted VES sections led to the knowledge of the depths most likely 
to locate sustainable sources of groundwater. 
 
2.3 NUMERICAL TECHNIQUES IN HYDROGEOLOGICAL INVESTIGATIONS 
 
Models are tools that represent an approximation of a field situation. These models are 
applied to a variety of environmental issues and are particularly useful for interpreting and 
understanding the environmental issues having complex interaction of many variables in 
the system. Models also form an integral part of decision support systems by   means of 
managing water resources, contaminant transport, salinity and drainage and should not be 
regarded as just an end point in themselves. Groundwater modelling involves simulation 
of aquifer and its response to various input/output systems. It has emerged as an acceptable 
tool to support decision making process in groundwater management towards sustainability 
of groundwater resources and also it helps in analysis of many groundwater problems. 
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Groundwater Models have been applied to investigate a wide variety of hydrogeologic 
conditions. Since it is impossible to see into the sub-surface and observe the geological 
structures and the groundwater flow processes, boreholes are usually constructed and used 
for pumping and monitoring, and measuring the effects on water levels and other physical 
aspects of the system. Therefore, it is for this reason that groundwater flow models have 
been, and will continue to be used to investigate the important features of groundwater 
systems, and to predict their behavior under varied conditions.  
Groundwater flow models have been used widely to advise management decisions on the 
management of groundwater resources to meet growing needs and at the same time 
preserve ecological balance and water table stability (Yidana, 2008). Groundwater models 
are of the advantage of being used as a significant tool in solving complex hydrogeological 
problems such as tracing of contaminant pathways, conceptualization and quantification of 
hydrogeological conditions of aquifer systems and using the information to plan future data 
requirements.  
The use of numerical modelling techniques has the added advantage that it can be used for 
the purpose of forecasting recharge. Forecasting groundwater recharge has become 
important because of the impact of envisaged climate change and increased demand for 
groundwater resources in the future (Kirchner, 2003).  They are used to explore 
groundwater system dynamics and also used in an interpretive sense to gain insight into the 
controlling parameters in a site-specific setting or as a framework for assembling and 
organizing field data formulating ideas about system dynamics (Anderson and Woessner, 
2002). Groundwater models have proven over several decades to be useful in addressing a 
range of ground water problems which serves as a decision-making support system in 
addressing issues and have been widely used in other parts of the world. These models 
represent or approximate a real system and are tools that help in the organization and 
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understanding of hydrogeological data or the prediction of future hydrogeological events. 
Models are not a substitute for field investigations, but should be used as supplementary 
tools. Results are dependent on the quality and quantity of the field data available to define 
input parameters and boundary conditions (Wang and Anderson, 1982). 
Numerical modelling of groundwater is an attempt to simplify the physical hydrogeological 
system using physical equations and governing boundary conditions (Don et al., 2006). 
Numerical modelling techniques are currently being used and are among the most 
acceptable in carrying out basin wide hydrogeological assessments. There are different 
numerical methods that are used to solve groundwater flow equations upon the definition 
of boundary and initial conditions. The two common methods that are used for the solution 
of the groundwater flow equations are the finite differences and finite element methods. 
The finite difference methods are based on approximations of the partial differential 
equations by discrete linear changes over discrete space and/or time whilst the finite 
element methods serve as a spatial interpolation function between the calculated heads at 
the nodal points (Igboekwe and Achi, 2011). The success of numerical groundwater flow 
models in predicting subsurface flow phenomena depends on how adequately the 
groundwater flow equation is properly approximated to represent the real situation on the 
ground and how much each section of the domain is adequately represented in the 
conceptualization process (Yidana et al., 2012).  Many codes such as MODFLOW use the 
finite difference solution method and FEMWATER is a common code using the finite 
element solution method. The computer code MODFLOW, which is a full three-
dimensional groundwater flow model developed by the United State Geological Survey 
(McDonald and Harbaugh, 1988). 
Globally, groundwater numerical models have been used extensively in achieving various 
objectives using different techniques.  For instance, Surinaidu et al. (2014) used the finite 
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difference method based numerical groundwater flow model to develop a steady state 
model with twenty conceptual layers of total thickness 320 m. Konikow (1976); Konikow 
and  Bredehoeft (1978), Rushton and Redshaw  (1979),  Guiguer and Franz (1996), Molenat 
and Gascuel-Odoux (2002), McDonald and Harbaugh (2003), Bakker et al. (2004), 
Huysmans et al. (2006) have used different numerical techniques to solve complex 
hydrogeological problems and as a management tool to provide a framework for organising 
hydrologic data, quantifying the properties and behaviour  of  the  systems,  and  allowing  
quantitative  prediction  of  the  responses  of  those systems  to  externally  applied  stresses. 
He et al. (2007) used a three-dimensional finite element model to characterize the 
groundwater flow in coastal plain of the Seto Inland Sea, Japan. This study involved taking 
field data which described the aquifer system and translated the information into input 
variables which the model code used to solve governing equations of flow. The results of 
the study demonstrated a high correlation between ground surface elevation and the 
elevation and the groundwater level in the shallow coastal aquifer. Van der Gun and 
Lipponen (2010) also carried out a study on reconciliation of groundwater storage depletion 
due to pumping with sustainability using the numerical technique. The study reviewed 
constraints or considerations that examined how there were or may be incorporated in 
decision making process, and evaluate to what extent the resulting pumping rates and 
patterns created conditions that complied with the principles of sustainability. The study 
suggested that decisions to pump groundwater are motivated by people’s need for domestic 
water and by expected benefits of using water for a variety of activities. Mondal et al. 
(2011) conducted a study in southern India using numerical and geophysical techniques to 
produce a groundwater flow model for a tannery belt in the terrain, under both steady and 
transient conditions. The result of the study suggested that the total groundwater abstraction 
was about 80.43% of the groundwater recharge, but 10.25% was used up by 
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evapotranspiration. The study also showed that the modelled aquifer could sustain a 
pumping rate of 24,892 m3/day without further decline in water level activities.  
In Ghana, the numerical modelling techniques have not been so much used in the past due 
to the lack of data and expertise involved in the modelling but the inputs from the numerical 
model for this study will be used in making decisions for groundwater resource allocations 
and management. For example, Yidana et al. (2015) used a calibrated steady state 
numerical groundwater flow model to carry out a preliminary analysis of the 
hydrogeological conditions and groundwater flow in some parts of a crystalline aquifer 
system in Afigya Sekyere South District of Ghana. The results suggested that the hydraulic 
conductivity varies in the range of 1.04 m/d and 15.25 m/d which was consistent with 
ranges achieved from pump- tests carried out in areas of similar lithologies. The estimated 
groundwater recharge at calibration ranged between 4.3% and 1.3% of the total annual 
precipitation. The model suggested that when groundwater rates were decreased from the 
current levels by up to 30% and increased groundwater abstraction by up to 50% there was 
a drastic drawdown in the hydraulic head by up to 20% of the calibrated model.  
Yidana et al. (2015) developed a steady state model to carry out a numerical analysis of 
groundwater flow and potential in parts of a crystalline aquifer in the Northern Ghana. The 
objective was to estimate the regional distribution of a key aquifer hydraulic parameter and 
recharge and also to predict the possible effects of different abstraction for groundwater 
recharge scenarios on the sustainability of groundwater resources in the area. The results 
indicated that the estimated aquifer hydraulic conductivities range between 1.70 m/d and 
2.24 m/d and the estimated recharge about 3.6% to 16.4% of annual rainfall. The findings 
from this study suggested that at current recharge rates, the system could sustain 
groundwater abstraction when increased by up to 50% of the current abstraction rates with 
the minimal net drawdowns in the hydraulic head throughout the terrain and when 
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groundwater recharge was reduced by up to 10% and abstraction increased by 100%. 
However, an increase in groundwater abstraction by 100% with a reduction in recharge by 
10% through a 20 year period could result in changes in the general flow geometry 
especially in the northern parts of the study area. 
Yidana et al. (2014) carried out a study on the analysis of recharge and groundwater flow 
in parts of a weathered aquifer system in northern Ghana. The groundwater pattern in the 
area was conceptualized using a steady state groundwater flow model which suggested that 
there was no apparent preferred direction of groundwater flow in the area. The study results 
suggested that the hydraulic conductivities range between 0.35 m/d and 5.24 m/d which 
suggested that although the hydrostratigraphic unit under the study was largely a weathered 
zone, the hydraulic conductivities were reduced due to presence of materials in the clay 
range. 
Yidana et al. (2013) used a calibrated steady state numerical groundwater flow model to 
characterize the spatial distribution of a key hydraulic parameter in a crystalline aquifer in 
southwestern Ghana. The model was calibrated against water level of 22 boreholes in the 
area. Results indicated that the range of groundwater recharge was between 0.25% and 
9.13% of the total annual rainfall and the estimated hydraulic conductivity values in the 
range of 4.5 m/d to over 70 m/d to the simulated aquifer. However, the model suggested 
that with reduced recharge by up to 30% of the current rates, the system could only sustain 
increased groundwater abstraction by up to 150% of the current abstraction rates. 
Attandoh et al. (2012) carried out a study on the conceptualization of the hydrogeological 
system of some sedimentary aquifers in Savelugu–Nanton and surrounding areas, Northern 
Ghana. The objective of the study was to characterize the general groundwater flow pattern 
and provide local estimates of the distribution of hydraulic conductivity and recharge fields. 
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The result suggested a predominant NE– SW flow direction and the calibrated hydraulic 
conductivities range between 1.90 m/d and 10.81 m/d with an estimated vertical recharge 
that ranged between 0.3% and 4.1% of the annual rainfall. An increased abstraction by up 
to 50% suggested that it did not appear to register significant effects on groundwater 
budgets at the simulated recharge from the calibrated steady state model.  
Yidana et al. (2010) developed a groundwater flow model which was calibrated under 
steady state conditions for some aquifers of the southern Voltaian sedimentary system. The 
objective of the research was to determine the estimates of the hydraulic conductivities of 
the different hydrostratigrahic units of the southern Voltaian and the distribution of 
recharge from precipitation. The study suggested that the calibrated hydraulic 
conductivities ranged from 1.19 m/d to 6.3 m/d and the recharge ranged from 3.18e-05 m/d 
to 6.0e-04 m/d and using particle tracking six distinct flow paths were defined. 
Similarly, Yidana et al. (2010) carried out a study on the conceptual framework of 
groundwater flow in some crystalline aquifers in the southeastern Ghana. The objective of 
the study was to determine approximate levels of groundwater recharge, estimate aquifer 
hydraulic parameters, and then tested various scenarios of groundwater extraction under 
the current conditions of recharge using a steady state model of about 19 wells and 
boreholes. It was suggested that the groundwater flow patterns conformed to the general 
northeast- southwest structural grain of the country. The resulting recharge was estimated 
to range from 8.9x 10-5 m/d to 7.14x10-4 m/d which resulted in a basin wide average 
recharge of about 9.6% of total annual precipitation. The calibrated aquifer hydraulic 
conductivities ranged between 0.99 m/d and over 19.4 m/d. However, the study found that 
the groundwater extraction levels represented only 0.17% of the annual recharge.  
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Numerical flow simulation has been used to identify flow paths and its conditions in the 
subsurface. For instance, Wu (2005) carried out a study in Jina City of China and detected 
an impermeable stratum (diorite) that blocked the flow of groundwater in an attempt to use 
groundwater modelling to explore the groundwater flow conditions in aquifers that feed 
springs as a result of their intermittent discharges and dry up.  
There is always a significant amount of uncertainty associated with a groundwater model 
and this uncertainty can be associated with the conceptual model or with the data and 
parameters associated with the various components of the model.  Some model parameters 
such as hydraulic conductivity and recharge are particularly prone to uncertainty (Aquaveo, 
2011).  Calibrating a model to a rich set of observation data (monitoring wells, stream 
flows, etc.) may sometimes reduce this uncertainty.  However, calibration of data are often 
scarce and even well-calibrated models have a high level of uncertainty (Aquaveo, 2011). 
A stochastic approach in numerical modelling is a tool for estimating probability 
distributions of potential outcomes by allowing for random variation in one or more inputs 
over time. It is one method for dealing with uncertainty. With a stochastic approach, a set 
of models was constructed where each model in the set is thought to be equally probable 
(Aquaveo, 2011). Each model was then used to make the prediction or simulate a given 
scenario and the results were used to estimate a probability or risk that a certain outcome 
will occur.  
While this approach still relies on underlying model assumptions to generate initial 
parameter estimates, it more honestly reflects the uncertainty associated with modeling. 
Two basic methods for generating stochastic simulations are parameter randomization and 
indicator simulations (Aquaveo, 2011). The parameter randomization method has to do 
with the selection of model parameters that are randomized using either a Random 
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Sampling or Latin Hypercube approach and with this method each combination of input 
parameters defines a model instance.  
The indicator simulation approach deals with multiple equally probable realizations of the 
aquifer heterogeneity that are generated and each realization is used to define a model 
instance. Some of the studies that have been carried out using the stochastic approach are; 
Li et al., (2003) carried out a research on the computational practical method for stochastic 
groundwater modeling and suggested that the increase in computational effort is especially 
easy to accept since it offers the opportunity to assess groundwater model uncertainty in a 
scientifically credible way. 
Oreskes et al. (1994) carried out a study on the Verification, Validation, and Confirmation 
of Numerical Models in the Earth Sciences suggested that by using as numerous and 
diverse confirming observations as possible, it is reasonable to conclude that the 
conceptualization embodied in the model is not flawed. Therefore, a diversified set of 
statistical tests and evaluations for the model will provide a structured approach for 
evaluating the model predictions and building confidence in the decisions based on these 
predictions. Yeh (1992) carried out a study on the stochastic modelling of groundwater 
flow and solute transport in aquifers. The purpose of the paper was to address these 
problems and to provide an introductory overview of the stochastic approaches which have 
been developed recently to tackle these problems in field- scale aquifers. The research 
suggested that there are large uncertainties in predictions. These uncertainties arise mainly 
from our inability to depict detailed spatial distributions of hydrologic parameters in large-
scale aquifers. Although geological information is useful for defining large structures, 
some means of acquiring more detailed knowledge of the parameter distributions is 
necessary to improve our predictive capability. However, such techniques may not exist in 
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the foreseeable future unless there are some technological breakthroughs in field testing.  
Until then, we may have to rely on stochastic approaches to obtain probabilistic results.  
Although there are various errors and uncertainties in any modelling study in regard to 
hydrogeological understanding, the conceptual model design, and model calibration and 
prediction simulations, as well as recharge and evapotranspiration estimation and 
simulation, it is one of the best approach to simulate and predict aquifer conditions. The 
best tool available to help groundwater hydrologists meet challenges of prediction is 
usually a groundwater model (Anderson and Woessner, 2002).   
One of the limitations of groundwater models is that they are based on assumptions and 
simplification of the physical system and are not the exact replicas of the physical 
hydrogeology of the basin. Like all models, groundwater flow models cannot predict the 
future with absolute certainty but it allows more effective use of the available data; more 
complexities can be accounted for; and the implications of the assumptions used in the 
analysis and of the management decisions can be evaluated (Hamilton, 1982). This is the 
first research to be carried out on Legon Campus using geophysical and numerical 
techniques to assess the hydrogeological conditions that characterize the terrain and this 
makes the work unique. 
 
 
 
 
 
 
 
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CHAPTER THREE 
MATERIALS AND METHODS 
 
3.1 DESK STUDY 
 
Desk study was carried out to acquire information about the area of study. Literature and 
data on geology, hydrogeology as well as topographic maps of the study area were collected 
and reviewed. Information on some boreholes drilled in the study area which includes 
coordinates, depth, yield, static water level (SWL) and borehole logs were obtained from 
HEISA Engineering Company Limited. The locations, together with their corresponding 
ground elevations were verified and determined with the help of the Garmin Vista GPS. 
The location of all the wells as well as the ground elevations were determined using the 
Garmin Vista GPS. The hydraulic heads from all the wells were determined by subtracting 
the water levels from the ground elevations. Pumping test was carried out on some few 
boreholes in the study area to help give an idea about the hydrogeological parameters 
(hydraulic conductivity, transmissivity) of the terrain. It also aided in assigning appropriate 
values to the parameters during the calibration of the numerical model although the 
pumping test data was not used in the analysis. 
 
3.2 GEOPHYSICAL SURVEY 
 
Geophysical survey was conducted to determine and delineate the subsurface layer 
parameters such as thicknesses and resistivities of the study area. In this study the Electrical 
Resistivity method was employed to determine the resistivity variation with depth of the 
subsurface formation. Electrical techniques, especially the resistivity surveys, are the most 
popular of geophysical methods for groundwater surveys because they often give a strong 
response to the subsurface conditions and are relatively cost- effective (Ernstson and Kirsch 
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2006a). The Vertical Electrical Sounding was employed because it is capable of detecting 
the slightest change in pore water conductivity (Banoeng -Yakubo et al., 2005). Different 
factors affect the resistivity in the subsurface. Pore spaces in rock particles may be in the 
form of intergranular voids, joint, or fracture openings and closed pores such as bubbles or 
vugs in or without lavas (Telford et al., 1990). Interconnected pores only contribute 
effectively to conductivity and the geometry of the interconnections. Single VES should 
only be applied in areas, where the ground is assumed to be horizontally layered with very 
little lateral variation, since the sounding curves only can be interpreted using a horizontally 
layered earth (1D) model. The ABEM SAS-4000 Terrameter was used. The Global 
Positioning System (GPS) was used to determine the coordinates of the boreholes where 
the VES was conducted.  
During the geophysical data acquisition, a calibrated rope and a data sheet were used for 
taking field data. The Schlumberger array was adopted for the study involving four 
collinear electrodes with the two outer electrodes being the current electrodes and the two 
inner electrodes the potential electrodes. The ABEM SAS-4000 Terrameter was placed at 
the centre peg where the borehole was located. The potential electrodes were kept fixed at 
0.5 m away from the centre peg while the current electrode positions were varied at 
predetermined intervals. A series of measurements of resistance values were made by 
increasing the current electrode spacing in successive steps about a fixed point from 1.5 to 
200 m along a survey line of 20 m. The potential electrodes were fixed in the same position 
(0.5 m) until the observed voltage and resistance were too small to be measured where it 
was increased to 5 m. The resistivity of the ground was measured by sending current into 
the ground at the current electrodes and the corresponding potential difference was 
measured at the potential electrodes. The ground resistance measurements at every VES 
point were automatically displayed on the digital readout screen for four cycle and then 
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written down on the field data sheet. The resistance measurements were repeated twice to 
ensure consistency of the field data. The apparent resistivity (ohm-meter) of the subsurface 
formation was calculated using the formula (Equation 3.1) by Morrison and Gasperikova 
(2012).  
…………. 3.1 
 
Figure 3.1: An illustration for Schlumberger array and apparent resistivity (Morrison and 
Gasperikova, 2012) 
 
 Where Ρa is the apparent resistivity, V/I is the resistance where ‘V’ is the voltage and ‘I’ 
is the current, ‘b’ is the distance between the current electrode and the potential electrode 
and ‘a’ is the distance between the potential electrodes. The apparent resistivity values 
obtained with increasing values of electrode separations were used to estimate the 
thickness, depth and apparent resistivities of the subsurface formations using IPI2WIN 
software. In the software, a bi–log plot was made with apparent resistivity on the ordinate 
axis and the current electrode spacing (AB/2) along the abscissa, the Schlumberger 
configuration was selected and an automatic iteration button pressed to carry out multiple 
iterations. This then gave a model of how the subsurface resistivities vary with depth by 
marching the observed curve with a standard curve. A correlation was then made of 
apparent resistivity with the geology, borehole logs and literature which gave interpretation 
of the VES results. 
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A total of forty-three Vertical Electrical Soundings (VES) were carried out in nine selected 
areas within the study area (Figure 3.2).  
 
 
Figure 3.2: Map showing the various VES survey points 
 
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Five VES were carried out at eight locations each and three VES at one of the locations at 
5 m intervals.  Since Legon is still under development the existence of settlements, roads 
and pavements in the area made it difficult to establish a continuous traverse line at some 
borehole locations limiting the survey to nine locations where there was enough space to 
carry out.  
 
3.3 GROUNDWATER MODELLING 
 
Groundwater Modelling System (GMS) 7.1 software was used to estimate recharge rates, 
characterize aquifer parameters and to simulate the groundwater flow systems in the terrain.  
This software system was selected because it has numerous applications in preparation and 
quick exchange of data in standard form and also the existence of different numerical codes 
for solving a lot of different hydrogeological problems. The software package incorporates 
the United States Geological Survey’s Modular three dimensional Finite Differences 
groundwater flow code, MODFLOW-2000 (Harbaugh et al., 2000), the Finite Element 
code, FEMWATER (Lin et al., 1997), and several solute transport codes. Therefore, 
depending on the data available and the objectives of the research to be achieved, a variety 
of codes were selected for the simulation. MODFLOW is based on the Darcy law and the 
law of conservation of mass as illustrated in Equation 3.2 and this governs groundwater 
flow under transient conditions. 
h 2h 2h 2h
Ss  Kx  K y  K2 2 z Wt x y z2 …………  3.2 
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Where Ki, W, and Ss, respectively, refer to the hydraulic conductivity in the ith direction, 
sources/sinks, and the aquifer specific storage. This equation has been used in diverse forms 
to model groundwater flow depending on the prevailing conditions.  
In this present study, steady state conditions were assumed. In this respect, the time variable 
nature of the hydraulic head on the right hand side of equation 3.2 was regarded negligible 
because the current sinks are not considered significant enough to cause such changes. Thus 
under equilibrium conditions a steady state is assumed when inflow exactly balances 
outflow and there is no change in storage or where groundwater extraction is insignificant 
compared to the storage. As such, equation 3.3 was used: 
  2h  2h  2h
K x  K y  K z   0
 x
2 y 2 z 2  ………  3.3 
 
Both numerical groundwater flow modelling codes, (MODFLOW and FEMWATER) are 
flexible to use in the GMS system. On this basis, the United States Geological Survey’s 
Modular Finite Diference Groundwater Flow Modelling code, MODFLOW-2000 
(Harbaugh, 2000), was chosen to simulate the groundwater flow system in the study area. 
MODFLOW was chosen for this study since it is arguably the most tested finite difference 
numerical code which has proven to predict hydrogeological systems correctly within the 
limits of the available data and field conceptualization. Even though MODFLOW cannot 
simulate multiphase flow and flow in the unsaturated zone, it can simulate confined, leaky 
and unconfined aquifers (Anderson and Woessner, 2002). 
 
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3.3.1 CONCEPTUALIZATION OF THE HYDROGEOLOGICAL FRAMEWORK 
3.3.1.1 Generating stratigraphy 
A set of borehole logs data with codes assigned to the various lithologies together with 
their corresponding bottom and top elevation data was imported into GMS in excel form. 
A 3D view of the borehole logs was generated with each colour representing a different 
type of lithology. Horizon IDs were assigned at borehole contacts which is the interface 
between different materials on a borehole log and the horizons were created and numbered 
consecutively in the order that the strata are “deposited” that is from the bottom up 
(Aquaveo, 2008). The slightly weathered to fresh quartzite or phyllite was numbered as 
“0”, the weathered phyllite and phyllite as “1” and “2” respectively and the lateritic top soil 
as “3”. 
   
3.3.1.2 Generating solids 
 
A polygon was generated in a form of a polygonal boundary around the boreholes to serve 
as the boundary for the creation of Triangulated Irregular Network (TIN) (Aquaveo, 2008). 
The polygons that was defined was used to create a TIN and the solids were created from 
the borehole horizons using the interpolation scheme. It was interpolated to cover the entire 
domain using Krigging methods. TINs were formed by connecting a set of XYZ points 
(scattered or gridded) with edges to form a network of triangles. The surface was assumed 
to vary in a linear fashion across each triangle. From this a simple solid model was created 
consisting of three layers of different materials. The solids were given a better view by 
cutting three cross sections through the solid model.  
  
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3.3.2 MODEL FORMULATION 
 
There was no river and stream networks in the study area to be digitized out and 
incorporated into the model. The domain was divided into zones where coverages were 
created for hydraulic conductivities and recharge as presented in Fig. 3.3. The zonations 
were based on the knowledge of the geophysical data, geology and topography of the area 
to give indications to where there may be possible high conductivities as well as recharge 
and discharge areas. In the coverage for hydraulic conductivity, polygons were created to 
simulate spatial variations in hydraulic conductivity in the area. Initial values were assigned 
appropriately based on knowledge of the characteristics of rock types in the study area, 
geophysical data, borehole logs, and geology of the area. Similarly, the recharge coverage 
was designed to simulate variabilities in groundwater recharge at different locations in the 
area. The terrain was conceptualized as a single layer model based on the available data. 
From the geophysical survey and borehole logs, the lower limits of the aquifer system were 
conceptualized as a confining layer to coincide with the impervious material beneath. The 
upper limit was modelled as a convertible layer to coincide with semi-confining conditions 
prevailing in the area. In the absence of physical boundaries to limit flow on all sides, the 
vertical walls were all conceptualized as General Head Boundaries, GHB (Anderson and 
Woessner, 2002). This would enable the simulation of net groundwater flow across the 
boundaries of the terrain. The GHB is a head dependent condition which simulates flow 
across a boundary based on the head difference across the boundary and the conductance 
of the material across the boundary (Equation 3.4). Consider;  
Q = Cd dℎ …………   3.4 
Where Q, Cd, and dℎ are, respectively, the flow across the boundary, the conductance of 
the material, and the hydraulic head difference across the boundary. 
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Figure 3.3: Digitized map of the coverage area 
 
 
An observation coverage was created to accommodate hydraulic data from 22 boreholes 
for the purpose of calibration. Coverages were similarly created by the GHB condition and 
the northern, southern, eastern and the western sections of the model were assigned general 
head boundaries because there were no obvious physical or geographical boundaries in the 
model domain. A grid system was then automatically developed to overlay the entire 
domain. A uniform rectangular grid system was used for the entire domain as the intended 
purpose was to generalise flow without focus on any particular location. A uniform square 
grid system of dimensions 70 m × 70 m was automatically developed over the 
conceptualized model of the entire domain to begin the simulation under steady-state 
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conditions (Figs 3.4 and 3.5). This was done to ensure that a general flow was generated 
without focussing on any particular location. The study area has an approximate area of 
8,535,043.509 m2  (8.53 km2).  
Data of the top and bottom elevation of the aquifer were imported as text files and modelled 
using the 2D scatter point sets. The spatial interpolation map (Fig. 3.6.) shows the highest 
elevation above 55 m and the lowest around 3.6 m and this gives an idea about the recharge 
and discharge zones.  
 
 
 
Figure 3.4: Map in plain view showing grid over the active domain 
 
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Figure 3.5: Map in 3D showing grid over the active domain. 
 
 
  
Figure 3.6: Map in 3D showing the spatial interpolation on the elevation data 
  
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3.3.3 NUMERICAL SIMULATION OF FLOW 
 
After all the initial values were assigned to the various coverages in the map model, 
MODFLOW was initialized and the conceptual was mapped to MODFLOW/MODPATH 
in order to get the model ready for numerical simulation. The top and bottom elevation data 
which were in default states were converted to the actual field values. The solver and flow 
packages in MODFLOW-2005 (Harbaugh, 2005) are available under the global options in 
the GMS. Three flow packages are available: these are the Layer Property Flow (LPF), the 
Block Centred Flow (BCF), and the Hydrogeologic Unit Flow packages (HUF). There are 
five solver packages:  these are strongly implicit procedure, the Pre-Conditioned Conjugate 
Gradient (PCG), the Slice Successive Over-relaxation (SOR), and the Geometric Multi-
grid (GMG) approach (Yidana et al., 2013).  
In this study, the LPF and PCG were, respectively, chosen as the flow and solver packages 
to simulate the conditions in the domain.  The default Layer Property Flow (LPF) package 
was used to model the aquifer in the study area under convertible conditions.  In the LPF 
package, the user defines the layer elevations, horizontal hydraulic conductivities and 
vertical anisotropies and MODFLOW computes the cell-by-cell conductance using the 
hydraulic conductivity values and the layer geometry. In addition, the LPF package has 
only two options for layer types: convertible or confined. In this study, the convertible 
option was selected because from the geophysical survey the aquifer units in the study area 
are mostly semi-confined. This allows MODFLOW the flexibility to assign confined or 
unconfined conditions to locations based on the computed water level. Using ordinary 
kriging the model was interpolated to cover the entire domain. It was then possible to obtain 
spatial variations in the thickness of the aquifer using the top and bottom elevations. The 
starting heads were automatically generated from the data imported during the 
conceptualization. 
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3.3.3.1 Model Calibration 
 
Model calibration is the process of adjusting the model to reflect field conditions. It is only 
the calibrated model that can be used to simulate scenarios and characterize the hydraulics 
of the terrain being modelled. The hydraulic head data was the only observation data used 
to calibrate the model as there are no known springs and other drains in the area. The 
hydraulic head was calculated by subtracting the static water level (SWL) from the 
elevation of the ground. The calibration objective was therefore to ensure that the model 
computed hydraulic head data closely matched the observed data within a margin of error 
established during the conceptualization process. The model was manually calibrated 
initially by varying the hydraulic conductivities, head stage and recharge and run each time. 
The calibration target was set at ±2.5 m, which means that for each of the observation wells, 
calibration was said to have been achieved whenever the observed and model computed 
hydraulic heads were within 2.5 m of each other. The process was later switched to the 
automatic approach through the Parameter Estimation (PEST) because of its ability to limit 
parameter value ranges and parallel process utilities and the pilot point method was used 
to simulate the hydraulic conductivity. The pilot point approach makes it possible to 
simulate a continuous hydraulic conductivity field for proper characterization of the 
aquifer. 
3.3.3.2 Sensitivity Analysis 
Sensitivity analysis is recommended after calibration of every model. The purpose of 
sensitivity analysis is to measure the stability of the model in the face of subtle changes in 
some of the model parameters. It was performed by making slight variations in the key 
parameters and noting the effects on the calibrated model. When a calibrated model shows 
significant departures from calibration after slight variations in the key parameters, it is 
said to be unstable and can therefore not be relied upon to predict scenarios. Under such 
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circumstances, the model will have to be recalibrated. In this project, sensitivity analysis 
was performed automatically through the PEST. In this way, histograms would be 
generated at the end of the calibration to indicate parameter sensitivities. 
3.3.3.3 Stochastic Approach 
 
A stochastic model is a tool for estimating probability distributions of potential outcomes 
by allowing for random variation in one or more inputs over time and/or space. A stochastic 
modelling approach is one method for dealing with uncertainty. With a stochastic approach, 
a set of models was constructed where each model in the set is thought to be equally 
probable. Each model was then used to make the prediction or simulate a given scenario 
and the results were used to estimate a probability or risk that a certain outcome will occur. 
While this approach still relies on underlying model assumptions to generate initial 
parameter estimates, it more honestly reflects the uncertainty associated with modelling.  
Two basic methods for generating stochastic simulations are parameter randomization and 
indicator simulations and with this study the parameter randomisation method was used 
because each combination of input parameters defines a model instance. With the indicator 
simulation approach, multiple equally probable realizations of the aquifer heterogeneity are 
generated and each realization is used to define a model instance (Aquaveo, 2011).  
In this study the Latin Hypercube method was used and this is due to the fact that it allows 
for a greater degree of confidence with fewer model runs. The stochastic option in GMS 
was selected before the various parameter (recharge, hydraulic conductivity) data was 
edited. The number of runs was set and the parameter values for each run was entered. This 
was a convenient way to set up the sensitivity run of the model and the stochastic 
simulation. The standard deviation, mean and the number of segment for each of the 
parameters were entered. MODFLOW was then run in a stochastic mode showing a 
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spreadsheet of a set of parameter values associated with each model with sixteen model 
solutions that has converged successfully and had been checked by default. The total 
number of model simulation was sixteen. The model was run to estimate a probability or 
risk that a certain outcome will occur for sixteen solutions Aquaveo, 2011). 
3.3.3.4 Analysis of Scenarios 
  
Since the model was conceptualized and calibrated under steady state conditions, it is not 
appropriate for modelling fluctuations in groundwater storage. However, the steady state 
model with the eight stochastic solutions were used in a limited fashion to evaluate recharge 
and abstraction scenarios. Out of the 22 observation, 14 wells with available yields were 
used in this study. The yields estimated during pumping tests were applied as the initial 
abstraction rates. In the first scenario analyses, abstraction rates were increased by 10%, 
20%, 25%, 50%, 100%, and 200% whilst maintaining recharge at the calibrated rates. In 
the second scenario, the abstraction rates remained same whilst the recharge was decreased 
by 10%, 15%, 20% and 25%. In the third scenario, the recharge was deliberately reduced 
by 10%, 15%, 20%, and 25% whilst abstraction rates were increased at 10%, 20%, 25%, 
50%, 100%, and 200%. These scenarios were determined somewhat arbitrarily to provide 
a preliminary test of the capacity of the system to contain probable stresses. Although not 
a transient model, the simulation of these scenarios will provide the same information 
regarding the tolerable groundwater abstraction rates in the area. 
 
 
 
 
 
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CHAPTER FOUR 
RESULTS AND DISCUSSIONS 
4.1 GEOPHYSICAL SURVEY   
A total of Forty-three (43) vertical electrical soundings (VES) were carried out at nine 
borehole locations with five (5) VES each conducted at eight borehole locations and except 
for one location where three (3) VES were conducted due to lack of space to carry out the 
survey. The geophysical survey was conducted to assist in the conceptualization of the 
terrain concerning the identification of the extent of aquifers in the vertical lithological 
succession. The results and interpretation of each VES station are presented in Appendix 
1.  
Since this study is within the Togo formation, a maximum investigation depth of about 80 
m was chosen for the purpose of the study in order to determine the aquifer zone and 
thickness within the terrain. The apparent resistivity plots will help give a better 
understanding in the variations of the lateral resistivity. Vertical electrical sounding curves 
were interpreted quantitatively to determine the depth to each lithological layer with 
corresponding apparent resistivity to the basement bedrock (Appendix 2) and correlated 
with available borehole logs to determine their corresponding geological formations.  
Various apparent resistivity configurations are used to describe the various curve types. 
Assuming ρ1, ρ2, and ρ3 are the apparent resistivities of three subsurface layers beginning 
with ρ1 at the top, followed by ρ2 and ρ3, then four possible curve types can be deduced 
according to Lowrie (2007).  With the curve types description, whenever ρ1 > ρ2 < ρ3, an 
H-type curve is defined, ρ1 < ρ2 < ρ3 gives an A-type curve, ρ1 < ρ2 > ρ3 gives a K-type 
curve and Q-type curve has ρ1 > ρ2 > ρ3 but in situations where more than three subsurface 
layers exist the resulting type curve is obtained by combining these curve types 
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consecutively at any given time. The results of the study suggest that three to five 
subsurface layers exist in the area resulting in ten different resistivity sounding curve types: 
The H (ρ1 > ρ2 < ρ3), A (ρ1 < ρ2 < ρ3), K (ρ1 < ρ2 > ρ3), KH (ρ1 < ρ2 > ρ3 < ρ4), QH 
(ρ1 > ρ2 > ρ3 < ρ4), HK (ρ1 > ρ2 < ρ3 > ρ4), KQH (ρ1 < ρ2 > ρ3 > ρ4 < ρ5), QQH (ρ1 > 
ρ2 > ρ3 > ρ4 < ρ5), KHK (ρ1 < ρ2 > ρ3 < ρ4 > ρ5), and HAK (ρ1 > ρ2 < ρ3 < ρ4 > ρ5) 
type curves. A statistical summary of the VES modelling results using the IPI2WIN 
software is presented in Table 4.1. 
Table 4.1.  Statistical summary of VES modelled results 
APPARENT RESITIVITY OF LAYERS (Ohm-m)  THICKNESS OF LAYER (m) 
 ρ1 ρ2 ρ3 ρ4 ρ5 T1 T2 T3 T4 
Min 7.41 4.36 25.3 1.5 6.9 0.75 1.05 5.4 30.6 
Max 785 929 24512 7542 11290 30.3 77.9 170 139 
Mean 268.66 318.31 1267.43 1616.8 4275.76 2.38 11.54 52.69 71.69 
 
The distribution of the curve types (Figure 4.1.) are KH (32%), HK (19%), QH (9%), K 
(9%), KQH (7%), A (7%), QQH (5%), HAK (5%), H (5%) and KHK (%) signifying KH 
curve types as predominant while KHK curve types being the least occurrence in the study 
area. 
 
 
 
 
 
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CURVES TYPE  DISTRIBUTION
2% 5% 5%
7%
9%
19%
32%
5%
9% 7%
H A KH KQH QH QQH HK K KHK HAK
 
Figure 4.1: Curves type distribution in the study area 
 
4.1.2 CORRELATION BETWEEN APPARENT RESISTIVITY AND LITHOLOGY 
 
The geology of the study area consists of rocks of the Togo Structural Unit, such as 
quartzite, phyllite and schist (Fig. 1.3.). Since the earth is not homogeneous these different 
rock materials in the subsurface respond differently to the flow of applied external electrical 
current and hence a variation in apparent resistivity. Various researchers have tried to 
combine drill logs, apparent resistivity and other geophysical studies to correlate the 
lithology of a given area. Vertical electrical sounding method has been applied and the 
results were able to delineate different geoelectric sections which were correlated with 
available borehole logs; to determine their corresponding geological formations (Bashir et 
al., 2014). A summary of the twelve borehole logs drilled in the study area is shown in 
Table 4.2. During the drilling of these boreholes laterite was encountered at an average 
depth of about 9 m from the ground surface. The permeability of the laterite gives an 
indication of the intensity of runoff and interflow that occurs in the area and also regulates 
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groundwater recharge to underlying bedrock. Moderate to highly weathered 
quartzite/phyllite was encountered at an average depth from 9 m to 38 m which indicates 
that it could be an aquifer zone since the rocks are weathered enough to enhance the 
permeability of the rocks. However, groundwater could not be extracted in large quantities 
for supply. Moderately weathered quartzite/phyllite was mostly found at an average depth 
of about 38 m to 60 m and this could be the aquifer zone where more yield was encountered 
and as well as secondary permeability indicating the presence of water. Low weathered to 
fresh quartzite/phyllite was also found at an average depth of about 60 m to 80 m which 
acts as the confining layer due to the massive nature and the degree of fracturing and other 
secondary permeability observed in the borehole lithological logs.  
 
Table 4.2. Summary of the 12 borehole logs from some boreholes drilled on Legon Campus 
 
DEPTH RANGE (m) GEOLOGICAL FORMATION 
0 to 9 laterite 
  
9 to 38 moderate to highly weathered quartzite/phyllite 
  
38 to 60 moderately weathered quartzite/phyllite 
  
60 to 85 slightly weathered to fresh quartzite/ phyllite 
 
 
However, in order to determine the depth and thickness of the aquifer, this is used together 
with literature on the geology of the area to interpret the pseudo sections from the VES 
results. The results and interpretation of these soundings are discussed in Appendix 1 with 
a summary in Table 4.1. 
 
 
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4.1.3 DEDUCTIONS FROM RESISTIVITY PSEUDO-SECTIONS 
 
Nine pseudo-sections of the resistivity data were prepared from the computer derived one 
dimensional layered model using the VES results to illustrate resistivity variations within 
some segments of the study area by combining all the VES results for each location. The 
selections of sounding stations for cross sections were based on closeness to each other 
along the survey line and the availability of enough space to carry out the survey. 
4.1.3.1 Section for Mensah Sarbah Hall (SB) 
 
The resistivity pseudo-section SB shown in Figure 4.2 is a combination of the five VES 
results along a survey line of about 20 m with the depth of investigation of about 80 m. A 
statistical summary of the result and interpretation of these VES are shown in Appendix 1. 
From the pseudo-section very low apparent resistivity ranging from 0 Ωm– 42.2 Ωm is 
recorded beneath VES3 and 4 covering a depth of about 6- 15 m and 7-  38 m respectively. 
Based on the borehole logs and the geology of the area it is inferred that it could probably 
be the highly weathered phyllite or quartzite with high level of saturation. The low apparent 
resistivity ranging from 42.2- 64.9 Ωm that extends from VES1 to the midway of VES 4 
and 5 could be highly weathered phyllite or quartzite with low of saturation. The midrange 
apparent resistivity values of about 64.9- 115 Ωm extend throughout the area from VES1 
to 5 and were encountered at 34– 65 m from VES1 to 3 where the borehole is sited then to 
a deeper depth. This could be attributed to moderately weathered formation of phyllites 
and quartzites which could be the aquifer zone. High apparent resistivity values ranging 
from 115 Ωm and above are observed at the topmost and beneath at a depth of about 65m 
to 80 m which extends from VES1 to 3. The topmost material was observed to be lateritic 
topsoil and beneath could probably be slightly weathered to fresh phyllites or quartzites. 
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Figure 4.2:  Resistivity pseudo section for SB 
 
4.1.3.2 Section for Hilla Limann Hall (HL) 
 
The plot of the resistivity pseudo-section is shown in Figure 4.3 with five vertical electrical 
soundings conducted along a survey line of about 20 m covering a depth of investigation 
of about 80 m. A statistical summary of the results and interpretation of these VES are 
discussed in Appendix 1.  Low apparent resistivity values ranging from 0– 183 Ωm were 
observed in the midcentral part of the section from a depth of about 9- 80 m from VES1 to 
2 and VES4. At VES3 where the borehole is sited and VES5, depth range is about 8– 30 
m. The very low apparent resistivity ranging from 0- 127 Ωm at VES2 and 4 could be 
attributed to highly weathered phyllite or quartzite with high level of saturation. The low 
resistivity materials could be probably high weathered phyllite or quartzite with low or no 
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saturation. The midrange apparent resistivity of about 183- 298 Ωm observed at VES3 and 
5 at a depth of about 30– 80m could be attributed to moderately weathered formation of 
phyllites and quartzites which could probably be the aquifer zone. High apparent resistivity 
ranging from 298 Ωm and above, extending from VES1 to 5 are observed at the topmost 
part of the section at a depth of about 9 m from the ground surface. Based on the geology, 
borehole logs and field observations this could be attributed to lateritic topsoil.  
4.1.3.3 Section for Alexander Kwapong Hall (KWP) 
 
The plot of resistivity pseudo-section (Figure 4.4.) involves five vertical electrical 
soundings conducted along a survey line of about 20 m covering a depth of investigation 
of about 80 m. A statistical summary of the results and interpretation of these VES are 
presented in Appendix 1. Low apparent resistivity values ranging from 0– 268 Ωm were 
observed at the extreme portions of the section from VES1 to midway of VES2 and 3 at 
one end from a depth of about 7– 80 m and to the other end from midway of VES3 and 4 
to VES5 from a depth of about 9– 80 m. 
 
 
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Figure 4.3: Resistivity pseudo section for HL 
 
 Figure 4.4: Resistivity pseudo section for AKWP 
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The very low apparent resistivity ranging from 0- 164 Ωm at VES1 and 2 could be 
attributed to highly weathered phyllite or quartzite with high level of saturation. The low 
resistivity materials could be probably high weathered phyllite or quartzite with low or no 
saturation. The midrange apparent resistivity of about 268- 5188 Ωm observed at VES3 
from a depth of about 9 – 80 m could be attributed to moderate to highly weathered phyllites 
or quartzites which could be a groundwater potential zone based on the geology and 
borehole logs. High apparent resistivity ranging from 518 Ωm and above, extending from 
VES1 to 5 are observed at the topmost part of the section at a depth up to 9 m from the 
ground surface and this could be attributed to lateritic topsoil. 
4.1.3.4 Section for Valco Trust Hostel (VAL) 
 
The plot of resistivity pseudo section (Figure 4.5.) reveals three VES points conducted 
along a survey line of about 10 m and the depth of investigation of about 45 m. A statistical 
summary of the results and interpretation of the VES are discussed in Appendix 1. From 
the pseudo-section, low apparent resisitivity in the range of 123 Ωm – 231 Ωm is recorded 
at midways of VES1 and 2 and VES2 and 3 covering a depth of up to 45 m from the ground 
surface. This could probably be moderate to highly weathered phyllite or quartzite with 
low level or no saturation. The midrange apparent resistivity of about 231- 534 Ωm 
observed at midway of VES2 and 3 could be attributed to moderately weathered phyllite, 
which could be the aquifer zone and those observed around VES1 up to a depth of about 
10 m from the surface could also be attributed to lateritic topsoil. High apparent resistivity 
values of 534 Ωm and above are observed at the extreme end of the section at VES3 at a 
depth of about 10– 45 m. This could be probably low weathered to fresh phyllite or 
quartzite.    
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 Figure 4.5: Resistivity pseudo section for VAL 
 
4.1.3.5 Section for Legon Hall Annex C (LGC) 
 
The resistivity pseudo-section (Fig. 4.6.) involves five VES points along a survey line of 
about 20 m with the depth of investigation of about 80 m. A statistical summary of the 
results and interpretation of these VES are discussed in Appendix 1. The pseudo-section 
shows generally low apparent resistivity at the central portion extending from VES1 to 5. 
The very low resistivity ranging from 0 Ωm – 271 Ωm observed from midway of VES1 
and 2 to VES4 at depth of about 10– 30 m could probably be the highly weathered phyllite 
or quartzite with high level of saturation. The low apparent resistivity ranging from 271- 
341 Ωm at a depth of about 8– 49 m could be moderate to highly weathered phyllite or 
quartzite with low level of saturation.  
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Figure 4.6: Resistivity pseudo section for LGC 
 
The midrange apparent resistivity of about 341- 464 Ωm extends from a depth of about 32- 
80 m throughout the section from VES1 to 5 and this could be attributed to moderately 
weathered formation of phyllites and quartzites which could probably be a groundwater 
potential zone. High apparent resistivity values ranging from 464 Ωm and above are 
observed at the topmost part of the section at a depth up to 8 m from the surface. This 
topmost material observed could suggest lateritic topsoil. 
  
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4.1.3.6 Section for Commonwealth Hall (CW) 
The plot of the resistivity pseudo section (Figure 4.7.) shows five vertical electrical 
conducted along a survey line of about 20 m and the depth of investigation is about 80 m. 
A statistical summary of the result and interpretation of these VES are discussed in 
Appendix 1.  From the pseudo-section low apparent resisitivity ranging from 251 Ωm – 
501 Ωm is observed at the topmost portion extending from VES1 to 5 up to a depth 
saturation. But at VES3 where the borehole is sited and VES5, very low apparent resistivity 
of 0- 251 Ωm are observed up to a depth of about 4 m could be attributed to highly 
weathered phyllite or quartzite with high level of saturation. These observations are due to 
the fact that Commomwealth Hall (CW) is located on a highland and therefore most of the 
lateritic top soil might have been eroded. The midrange apparent resistivity of about 501- 
1259 Ωm are observed at depth 0– 14 m at VES1 and extends to about 80 m at VES2 to 5. 
 
Figure 4.7: Resistivity pseudo section for CW 
 
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This could probably be attributed to moderate to highly weathered phyllite or quartzite 
which could be the aquifer zone. High apparent resistivity of 1259 Ωm and above are 
observed at the extreme end of the section at VES1 extending from a depth of about 14– 
80 m. This could be probably low weathered to fresh phyllite or quartzite.  
4.1.3.7 Section for University of Ghana Botanical Gardens (UGBBH1) 
The plot of resistivity pseudo-section (Fig. 4.8.) involves five vertical electrical soundings 
conducted along a survey line of about 20 m covering a depth of investigation of about 80 
m. A statistical summary of the results and interpretation of these VES are discussed in 
Appendix 1.  Low apparent resistivity values ranging from 0– 75 Ωm were observed at 
VES2, VES3 and VES4 from a depth of about 0– 80 m, 0– 12 m and 0– 40 m respectively. 
From VES1 to 2 and VES4, the low resistivity materials could be probably high weathered 
phyllite or quartzite with low or no saturation. The midrange apparent resistivity of about 
75- 237 Ωm observed at VES1 and VES3 to 5 could be attributed to moderate to highly 
weathered formation of phyllites and quartzites and could probably be the aquifer zone. 
High apparent resistivity ranging from 237 Ωm and above at VES1 up to a depth of about 
10 m from the ground surface could probably be lateritic top soil. 
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 Figure 4.8: Resistivity pseudo section for UGBBH1 
 
4.1.3.8 Section for University of Ghana Botanical Gardens (UGBBH2) 
 
The plot of the resistivity pseudo-section is shown in Figure 4.9 involving five vertical 
electrical soundings conducted along a survey line of about 20 m covering a depth of 
investigation of about 45 m. A statistical summary of the results and interpretation of these 
VES are discussed in Appendix 1. Low apparent resistivity values ranging from 0– 20 Ωm 
were observed at the central portion where there is an existing borehole at VES3. 
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 Figure 4.9: Resistivity pseudo section for UGBBH2 
 
It trends from midway of VES1 and 2 to the midway of VES4 and 5 at depth 0– 5 m and 
extends quite deep to a depth of about 9 m from the ground surface at VES3. This low 
apparent resistivity values were confirmed on the ground by the presence of stagnant water 
around the neighbourhood of the borehole. The low resistivity materials could be probably 
highly weathered phyllite or quartzite with high level of saturation. The midrange apparent 
resistivity of about 20- 100 Ωm observed beneath the stretch of the survey line and this 
could be attributed to moderate to highly weathered formation of phyllites and quartzites. 
High apparent resistivity ranging from 100 Ωm and above at VES5 from 40– 45 m could 
probably be low weathered to fresh phyllite or quartzite.  
  
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4.1.3.9 Section for Earth Science Department (ESC) 
 
The plot of the resistivity pseudo-section is shown in Figure 4.10 involving five vertical 
electrical soundings conducted along a survey line of about 20 m covering a depth of 
investigation of about 80 m.  
 
 Figure 4.10: Resistivity pseudo section for ESC 
 
  
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A statistical summary of the result and interpretation of these VES are discussed in 
Appendix 1. Low apparent resistivity values ranging from 0– 285 Ωm were observed at 
VES1 and VES3, VES4 and VES5 at depth of about 10– 40 m, 8– 70 m and 22– 45 m, 8– 
45 m respectively. The low resistivity could be probably highly weathered phyllite or 
quartzite with low or no saturation. The midrange apparent resistivity of about 285- 433 
Ωm observed VES1 to 5 could be attributed to moderate to highly weathered formation of 
phyllites and quartzites which could be the aquifer zone. High apparent resistivity ranging 
from 433 Ωm and above are observed at the topmost part of the section from VES1 to 5 up 
to a depth of about 10 m from the ground surface could probably be lateritic top soil.   
 
4.1.4 CONCEPTUALIZATION OF THE AQUIFER UNIT  
 
Conceptualization of an aquifer unit is a means of achieving a graphic idealisation of the 
actual geologic conditions. Pseudo-sections from the geophysical survey were developed 
to help determine the aquifer zone which is probably areas of low resistivity and also give 
an idea about the thickness and depth of the aquifer. Based on the borehole logs, the pseudo- 
sections and the geology of the area, the aquifer was encountered at a depth of about 38 m 
to 60 m which could be attributed to the moderately weathered formation giving an aquifer 
zone of thickness of about 22 m and this could be variable in space. The aquifer system 
was considered to be semiconfined because the upper boundary of the aquifer is not 
restricted by any confining layer. This is because from the pseudo sections there could be 
impermeable and permeable layer established at the topmost part of the aquifer. Beneath 
the aquifer there could be rocks that are slightly weathered to fresh phyllite or quartzite and 
this could be impermeable hence the top of the aquifer was conceptualized as unconfined 
to coincide with atmospheric conditions. This unit was conceptualized as a single layer. 
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4.2 GROUNDWATER FLOW MODELLING 
 
4.2.1 STRATIGRAPHY 
 
The three dimensional view of the borehole logs that was generated from the twelve 
borehole logs with each colour representing a different type of lithology is shown below 
(Fig. 4.11) The reddish brown is a lateritic top soil, the light yellow is a weathered quartzite 
and the metallic gold is a weathered phyllite. 
 
Figure 4.11: A 3D view of the borehole logs that was generated from the twelve borehole logs 
 
The subsurface data obtained from the lithological well logs was used to develop a 
stratigraphic succession of Legon Campus using the horizon to solids approach as shown 
in Fig. 4.12. The solid model reveals the presence of three main lithostratigraphic units. 
From bottom to top, these units are; phyllite, quartzite and laterite. The laterites form the 
top most extensive formation and covers the entire areas followed by the quartzites and 
then the phyllites of the Togo Structural Unit. Thicknesses of the laterite ranges from 75 m 
- 84 m, quartzite ranging from 64 m - 95 m and phyllite ranges from 15 m - 78 m. 
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Figure 4.12: Oblique view of the solids created with some little amount of exaggeration depicting 
the lithostratigraphic framework of the basin 
 
The stratigraphic succession depicted from the solid model (Fig. 4.12.) of the Legon 
Campus corresponds to the results from the geophysical survey and the geology of the 
Togo formation. Thus, the resulting model is geologically reasonable and may be close to 
the actual stratigraphic distribution. 
4.2.1.1 Cross-section 
This section (Fig. 4.13.) represents various variations both lithostratigraphically from 
bottom to top and relative thickness as observed in different portions within the study area. 
It corresponds generally to each borehole stratigraphic information. 
 
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Figure 4.13: Selected cross sections cut from the solid model in different directions 
 
4.2.2 STEADY- STATE SIMULATIONS  
4.2.2.1 Hydraulic head 
Hydraulic head is the mechanical energy that causes groundwater to flow. It can be 
calculated in two ways: the sum of pressure head (hp) and elevation head (z), or h= (hp+z) 
and the difference between the land surface and depth of water or h= land elevation – depth 
to water. Where the pressure head (hp) is the height that water rises in a piezometer (a well 
that is open only at the top and bottom of its casing), the elevation head (z) is the elevation 
of the bottom of the piezometer or measuring point in feet above sea level. 
Knowledge on the distribution of the hydraulic head in a field provides useful information 
on the groundwater flow direction in that particular system. The relative levels of the 
hydraulic head between two stratigraphic units in a formation will determine the relative 
horizontal flow between the two. Consequently, groundwater will move vertically from the 
unit with higher head to that with relatively low head (Fetter, 2001).  The distribution of 
hydraulic heads from the steady state simulation was obtained through calibration of the 
model. From the relationship between the observed and model computed hydraulic heads 
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for the 22 wells used for the calibration (Figure 4.14.), it can be seen that there is a 
reasonable match between the observed and model computed values suggesting a close fit. 
This indicates that the model is reasonably calibrated within the limits of the data used and 
is therefore a representative of the hydrogeological conditions prevailing in the study area 
and that accurate predictions/ scenario analysis can be made. 
 
 
Figure 4.14: A match between the model computed heads and observed heads 
 
  
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The calibrated model under steady state condition (Fig. 4.15.) represent the distribution of 
hydraulic heads in the terrain whiles Figure 4.16 represent the distribution of groundwater 
flow patterns. From the hydraulic head map (Fig. 4.15.), the hydraulic heads generally 
range from 50 m to 124 m with an average head of 87 m. The distribution suggests that the 
highest hydraulic heads are in the north western and north eastern part of the terrain and 
the lowest hydraulic heads are in the south eastern, south western and the northern part of 
the terrain. Previous studies in similar geologic materials in Ghana are not available for 
immediate comparison. But comparing with work done in parts of Eastern Region by 
Yidana et al. (2011) in the same rock terrain the hydraulic heads range from 60 m to 120 
m which is similar to the range of hydraulic heads in the area of study. In their case, the 
major controls on groundwater flow were fractures and fracture induced weathered zones 
with preferred orientation (Yidana et al., 2011). 
The pattern and distribution of groundwater contours generally follow the pattern of the 
topography which gives an idea about the flow systems in the study area. The highest heads 
were around Commonwealth Hall and Faculty of Law where the elevation is high and the 
lowest heads were around parts of Botanical Gardens, Hilla Limann and its surrounding 
areas where elevations were observed to be low. 
According to Toth (1963), local groundwater flow systems occur where the surface 
topography has a well-defined local relief. With an increase in depth-to-width ratio, 
intermediate flow systems can occur, in which at least one local flow system exists between 
their recharge and discharge areas. Regional flow systems normally have their recharge 
area in the basin divide and the discharge area at the valley bottom. Eight prominent flow 
paths have been defined in the study area (Fig. 4.15.) through detailed particle tracking 
from MODPATH (Pollock, 1994). 
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Figure 4.15: The most prominent groundwater flowpaths in the study area 
 
However, there is no clearly defined direction of flow, indicating that the local structural 
entities that control the flow system are not oriented in any preferred direction. Local flow 
systems apparently dominate the flow in the area. This may be attributed to the fact that 
groundwater flow and the general hydrogeological conditions of the aquifers in the study 
area are purely based on the weathered and fractured zones. Where the aquifer is highly 
heterogeneous with respect to the key aquifer hydraulic parameters, flow is largely 
haphazard and local flow systems are predominant. The same is true when the terrain is of 
considerably variable topography (Fetter, 2001). 
 
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Figure 4.16: Hydraulic head distribution of the calibrated model 
 
Although, complex topographical variations have a bearing in dictating the flow systems, 
it has been noted that even where the topography is largely flat just like the study area, 
significant spatial variations in the horizontal hydraulic conductivities can induce local 
flow systems. The variety of local flow systems, have led to the development of several 
hydraulic barriers to flow in the northern and middle sections of the study area. The flow 
pattern observed in this area is unique and shows the unique nature of the materials in the 
terrain. However, the few northeast-southwest flows into the Keta lagoon are similar to the 
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observations among the crystalline basement aquifer system in south eastern Ghana and 
the Afram plains (Yidana et al., 2011).  
 
4.2.2.2 The Hydraulic Conductivity Field 
 
The hydraulic conductivity field is an essential output of the calibrated groundwater flow 
model as it helps in conceptualizing the general pattern of the transmissive properties of 
the aquifer and certainly helps in the management of the groundwater resources from it. A 
smooth map of the horizontal hydraulic conductivity field in the domain was established 
through the pilot point method (Hill, 1998).  The calibrated hydraulic conductivity values 
(Fig. 4.17.) range from 4.0 m/day to 60.0 m/day and adequately explains the observed 
groundwater flow pattern (Fig. 4.15.). The hydrogeological properties of the aquifers in the 
study area are controlled by secondary permeabilities created in the wake of fracturing 
and/or weathering, which create ingresses for increased groundwater recharge and storage.  
The hydrogeological properties of the rocks are based on the pervasiveness of weathering 
and fracturing of the rocks. Where the degree of secondary permeability is high, the 
hydraulic conductivities are high, and the lithologies serve as good aquifers. The highest 
hydraulic conductivity values are in southwestern (Valco Trust Hostel and Hilla Limann 
Hall areas) and southern (Alexander Kwapong Hall and its surroundings) parts of the study 
area and the lowest in the northern (University of Ghana Botanical Gardens), northwestern 
(Commonwealth Hall) and eastern (Mensah Sarbah Hall Annex D areas) sections of the 
study area. Thus the high hydraulic conductivity values are as a result of the induced 
secondary permeability such as faults, joints, folds and fractures that accompany the 
deformational processes. The high hydraulic conductivity areas must be highly fractured 
and weathered to have registered such high values. From the geophysical results and 
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borehole logs, most of the wells were completed in the weathered zone, which is clearly 
variable in terms of thickness and clay content. 
 
 
Figure 4.17: Calibrated hydraulic conductivity field for the study area 
 
The high difference in the hydraulic conductivity data is consistent with the resistivity 
pseudo sections in Fig. 4.2 to 4.10. Generally, these results reveal lateritic materials on the 
ground surface which could probably be clay. However usually in areas where clay content 
is reduced, hydraulic conductivity is extremely high, and vice versa. Hence the high 
hydraulic conductivity which is observed in the southern portion of the study area may 
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possibly mean that the underlying material basically made up of quartzites and phyllites as 
shown in the resistivity pseudo-sections and the summary of the 12 lithological logs. 
A similar approach by Yidana et al. (2012) in the Densu Basin in the Southern Ghana where 
the Togo formation underlie the southeastern part of the basin which is similar to the 
lithology in this study area resulted in a range of 2 m/d to 40 m/d which falls within the 
range of values for hydraulic conductivity of this study. And this suggests significantly 
heterogeneous aquifer system. 
 
4.2.2.3 Groundwater recharge 
 
Accurate estimation of recharge rates in a particular area is difficult due to the many 
parameters that influence recharge rates. However, knowledge on recharge rate is essential 
in any sustainable groundwater management decision making process. Model calibration 
with adequate field data has been proven to be appropriate methodology to be used for 
recharge estimates. The spatial distribution of groundwater recharge in the study area 
through the model calibration can be observed in Figure 4.18. The estimated groundwater 
recharge at the point of calibration ranges between 0.008 m/yr to 0.12 m/yr with an average 
of 0.064 m/yr representing 1% and 15% with an average of 8.1% of the annual precipitation 
of 0.7 m of annual rainfall in the area. From the recharge map (Fig. 4.18.) it can be seen 
that the highest recharge occurs towards the south and the rest of the terrain have low 
recharge rates. These estimates are in tandem with the work of Yidana et al., (2012) who 
used numerical simulations in calculating the recharge rates in the Densu River Basin. They 
estimated average recharge rates to be 13% of the annual precipitation in the area.  Several 
other workers (Addai et al., 2015; Oteng-Mensah et al 2014; Yidana et al., 2012; Yidana 
et al., 2010; Adomako et al., 2010; Lutz et al., 2007) have estimated recharge rates in Ghana 
using different methodologies. All these works found recharge rates to range from 5% to 
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10% of annual rainfall in each of the study areas. Even though the study area receives the 
same amount of rainfall and other climatic conditions are the same, the recharge rates 
distributions are not the same throughout. This can be attributed to the hetereogenities in 
the geology of the area. The study area is underlain by the Togo Structural Units which 
have been affected by the Pan African Orogeny. These rocks are metamorphosed and 
highly fractured. The fracture density of these rocks is not the same throughout the study 
area. Where the overburden is thick, vertical infiltration is expected to be restricted there 
by reducing the amount of water that would have recharged the aquifer systems. 
 
Figure 4.18: Recharge distribution for the study area 
 
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This is because this part of the country including the study area throughout the year has 
high evapotranspiration accompanied with high temperatures and low relative humidities 
which significantly reduce the fraction of precipitation that begins transit into the aquifer 
system. The high conductivity values (Fig. 4.18.) of some of the lowlands enhanced their 
infiltration capacity to recorded high recharge rates in the area due to the highly 
fractured/weathered rocks present. The reduced groundwater recharge in the study area 
could be attributed to high levels of evaporation of infiltrating precipitation whose 
downward trajectory is limited due to significant clay content or the lateritic top soil. Also, 
the Legon Campus has seen a lot of modern infrastructural modifications. Most part of 
campus is paved with rubber lining underneath. This prevents infiltrating water to get into 
the groundwater systems. So even though the geology of the study area is basically Togo 
Structural units with a lot of pervasive structures such as faults, joints and folds the amount 
of infiltrating water is reduced due to these linings and concrete.  
 
4.2.2.4 Water budget 
 
A water budget reflects the relationship between input and output of water through a 
particular region. The water budget that was acquired from the whole aquifer system 
basically from the groundwater flow model is presented in Table 4.3. The amount of water 
that enter the aquifer system through the general heads is 368368.08 m3/d. This amount is 
accounted for by the changes in the hydraulic heads and the interconnectivity of the system 
with other aquifer systems and surface water bodies. This means that subsurface inflows 
are very critical to groundwater resources sustenance in the terrain. Some stagnant surface 
water bodies were observed in some part of the study area especially around the University 
of Ghana Botanical Gardens (UGBBH2). Due to the inflows of surface water bodies to 
groundwater resource, proper sanitation measures must be taken in order to safe guard the 
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integrity of the groundwater resources in the study area. Any contamination activities that 
happen on the surface will eventually end up in the groundwater system.  
The contribution of recharge to the water budget seems low. This buttresses the earlier 
point that vertical percolation of rainwater is restricted due to the clayey nature of the 
terrain and the many physical developments which are seen on the Legon Campus. The 
total amount of water the system receives is 368731.62 m3/d. Assuming the student 
population and other supporting staff of about 43,000 and the per capita water consumption 
is 50 litres which translates into 0.05 m3, this will amount to 1548.00 m3/d of water which 
is far below the estimated amount from the water budget analysis. This means that enough 
water is available for other activities such as laboratory work since the University of Ghana 
is a vibrant research institution. Also the many eateries around can tap into the system 
without any major issues. However, care must be taken so that the groundwater resources 
are not depleted since day in and day out, more inundation attending urban developments 
are seen around.  
Looking at the water budget (Table 4.3), the amount of water that leaves the system is 
368728.09 m3/d which is slightly higher than the input amount. The wetlands that were 
observed around the botanical garden even during the dry season give an indication of 
baseflow occurring in the area. Baseflow is enhanced where the aquifers are unconfined 
and in unrestricted hydraulic connection with surface water bodies (Yidana, 2010). 
However, baseflow estimate is beyond the scope of this work. 
 
 
 
 
 
 
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Table 4.3 Water budget for the study area from the steady state model 
 
BUDGET TERM FLOW (m3/d) 
Flow Budget for Zone1 
IN:  
Constant heads 0 
General heads 368368.08 
Recharge 363.54 
Total IN 368731.62 
OUT:  
Constant heads 0 
General heads 368728.09 
Recharge 0 
Total OUT 368728.09 
SUMMARY:  
IN - OUT 3.53 
Percent Discrepancy 0.000958 
 
 
4.2.3 STOCHASTIC MODEL 
With a stochastic approach, a set of models is constructed where each model in the set is 
thought to be equally probable. Each model is then used to make the prediction or simulate 
a given scenario and the results are used to estimate a probability or risk that a certain 
outcome will occur. The stochastic approach was carried out to show that the solutions for 
the steady state model is unique and this was done by using sixteen (16) different set of 
solutions. Eight random solutions were chosen from the sixteen solutions for analysis for 
the stochastic approach. These solutions (Fig. 4.19.) are almost the same. This shows that 
the steady state model is very unique to the study area thus there is no significant difference 
among the distribution of hydraulic heads in all the solutions.  
  
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Figure 4.19: Eight solutions from the stochastic approach 
  
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Further analysis was performed using the statistical analysis tool in MODFLOW under 
stochastic simulations. From the standard deviation map that was developed for the various 
hydraulic heads from the stochastic simulation, it was observed that the highest variance in 
the hydraulic heads were in the central part of the terrain and also towards the south eastern 
part. Generally, the overall standard deviations of the hydraulic heads are very low. Most 
parts of the terrain are characterized by a low standard deviation of 0.01 which is depicted 
by the blue colour. Thus the hydraulic heads that are in these areas are very unique as 
compared to the high standard deviation zones (Fig. 4.20.) of 0.16 which are depicted by 
the colour red. 
 
 
Figure 4.20: standard deviation map for the hydraulic heads from the stochastic simulation 
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4.2.4 SCENARIO ANALYSIS 
Scenario analysis is done to gauge the possible impacts of reduced rainfall and the 
corresponding effects on groundwater recharge in the area, following climate 
change/variability and increased population with its attending increased demand for water. 
It is expected that with the increasing interest in groundwater abstraction for most uses 
within the campus, the resource will soon be commercially explored to serve the whole 
university community. The calibrated steady state model was used to predict the effects of 
various scenarios of reduced recharge rates and increased abstraction rates on the hydraulic 
head while in the stochastic mode. Fourteen abstraction wells were created and flow rates 
reflective of the water consumption in the area were assigned to them. These flow rates 
assigned were advised by the yield of the various boreholes used for this present study. 
From Table 4.4 it is seen that all the flow rates of the wells are almost within the same 
range except for Valco Trust Hostel which has an initial flow rate of 216 m3/d. 
 
Table 4.4 A table showing the initial flow rates for the scenario analysis 
initial flow rate 
Tube Wells (m3/d) 
ISH1_BH21 -101 
HILLA_LIMANNBH22 -144 
HILLA_LIMANNBH23 -108 
ALEXANDER_KWAPONGBH24 -144 
ELIZABETH_FRANCES_SEYBH25 -144 
JEAN_NELSON_AKABH26 -173 
JEAN_NELSON_AKABH27 -108 
JEAN_NELSON_AKABH28 -101 
ELIZABETH_FRANCES_SEYBH29 -115 
ELIZABETH_FRANCES_SEYBH30 -101 
LAW_FACULTYBH16 -130 
LEGON_ANNEXC_BH2 -101 
VALCO_GRAD_HOSTELBH4 -216 
EARTH_SCIENCE_DEPT -130 
 
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This is because Valco Trust Hostel depend solely on the groundwater resources unlike the 
other places where they are being supplemented with pipe water from Ghana Water 
Company Limited. 
For the first scenario, the estimated recharge rates remain same with increased abstraction 
rates by 10%, 20%, 25%, 50%, 100% and 200% to check the effects on the hydraulic head 
(Table 4.5). This scenario was performed to address the issue of increase in the student 
population and demand for more water as the lifestyle of people get complex. Thus the 
implication of this observation is that groundwater will be progressively be mined from the 
aquifers if recharge is maintained constant, and abstraction rates are increased as a result 
of increased population and demand in the ensuing years.  
 
Table 4.5 A Table showing the change in hydraulic heads of the 14wells for the scenario of 
increasing abstraction up to 200%  
 
well 1 2 3 4 5 6 7 
Initial 84.4853 80.4116 66.2998 60.7832 51.5316 48.8589 51.5523 
10% 84.1075 80.1048 65.8105 59.7651 50.4172 48.1122 50.7803 
20% 83.7296 79.7979 65.321 58.6611 49.1886 47.3103 49.9472 
25% 83.4852 79.5423 64.9167 57.9211 48.6331 46.9446 49.5954 
50% 82.7222 79.1412 65.3094 0 45.1389 45.1389 47.6333 
100% 80.9091 77.7664 63.6554 0 0 0 0 
200% 77.29 75.0331 60.2549 0 0 0 0 
        
well 8 9 10 11 12 13 14 
Initial 57.9728 61.4165 58.1392 62.1643 58.4668 59.9465 62.5935 
10% 57.1912 60.7238 56.9583 61.5773 57.9013 59.4637 62.1252 
20% 56.3883 60.0282 55.3886 60.9851 57.3343 58.9787 61.6551 
25% 56.0886 59.791 54.449 61.0048 57.1117 58.8158 61.5651 
50% 54.2677 58.3736 0 60.0729 56.4822 58.2557 60.6979 
100% 0 0 0 59.1176 54.6624 56.7473 59.0652 
200% 0 0 0 64.5242 0 57.4396 59.1574 
        
 
 
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There is no obvious shift in the nature of the potential field when the current abstraction 
rates from the existing pumping wells are increased by up to 25% as shown in Figure 4.21, 
Figure 4.22, and Figure 4.23. This means that the groundwater resources within the domain 
would be sustainable up to 25% increase in abstraction. Excessive pumping can lead to 
groundwater depletion, wherein groundwater is extracted from an aquifer at a rate faster 
than it can be replenished.  
As the initial abstraction rates assigned to the various wells were increased from 50% up 
to 200%, there is a drastic decline in hydraulic heads indicated by the dry cells (Figure 4.24 
to 4.26) in the southern portion of the domain but the general groundwater flow geometry 
remained the same as achieved. The consequence of this will be a drastic reduction in 
groundwater supply thereby affecting the livelihood of the people. The many organisms 
apart from human beings that rely heavily on the ecosystem will also be greatly affected.  
 
 
Figure 4.21: Hydraulic head distribution after 10% increment in abstraction rates 
 
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Figure 4.22: Hydraulic head distribution after 20% increment in abstraction rates 
 
 
Figure 4.23:  Hydraulic head distribution after 25% increment in abstraction rates 
 
 
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Figure 4.24: Hydraulic head distribution after 50% increment in abstraction rates 
 
Figure 4.25: Hydraulic head distribution after 100% increment in abstraction rates 
 
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 Figure 4.26: Hydraulic head distribution after 200% increment in abstraction rates 
 
Significant drawdown in the average hydraulic head will affect the functioning of the 
overall ecosystems which depend on higher groundwater levels for sustenance. Depletion 
can have significant effects on surface and unsaturated subsurface (vadose) waters, and on 
the terrestrial, riparian, and other ecosystems which depend on these waters. The roots 
trees, grasses, shrubs also depend on groundwater for their survival by tapping water from 
the aquifer system so the dry cells representing dry wells means that the groundwater table 
will be so low that their roots cannot reach for water and this could affect the vegetation 
causing them to die out after a period of time. 
For the second scenario, the abstraction rates remained same whilst the recharge was 
decreased by 10%, 15%, 20% and 25% to check the effects on the hydraulic head (Table 
4.6). The recharge rates were reduced to check the response of the groundwater water 
system to the advent of climate change/variability which have been observed over the years 
to cause rainfall patterns to be erratic and reduced in amount.  
 
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Table 4.6 A Table showing the change in hydraulic heads of the 14wells for the scenario of 
maintaining abstraction with decreasing recharge by 10%, 15%, 20% and 25% 
 
well 1 2 3 4 5 6 7 
Initial 84.4853 80.4116 66.2998 60.7832 51.5316 48.8589 51.5523 
10% 84.4783 80.3986 66.2663 60.7504 51.516 48.8491 51.541 
15% 84.4748 80.3921 66.2495 60.734 51.5082 48.8442 51.5354 
20% 84.4713 80.3857 66.2328 60.7176 51.5004 48.8392 51.5298 
25% 84.4679 80.3792 66.2161 60.7011 51.4926 48.8343 51.5241 
        
well 8 9 10 11 12 13 14 
Initial 57.9728 61.4165 58.1392 62.1643 58.4668 59.9465 62.5935 
10% 57.9591 61.4033 58.1311 62.1497 58.4629 59.9421 62.5835 
15% 57.9522 61.3967 58.127 62.1424 58.461 59.94 62.5835 
20% 57.9454 61.3901 58.123 62.1351 58.4591 59.9378 62.5802 
25% 57.9385 61.3835 58.119 62.1278 58.4571 59.9356 62.5769 
        
 
As it is shown in Figures 4.27, 4.28, 4.29 and 4.30 respectively. There is no obvious shift 
in the nature of the potential field when the recharge was decreased by up to 25%. 
 
  
Figure 4.27: Same abstraction rate with 10% decrease in recharge 
 
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Figure 4.28: Same abstraction rate with 15% decrease in recharge 
 
 
Figure 4.29: Same abstraction rate with 20% decrease in recharge 
 
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Figure 4.30:  Same abstraction rate with 25% decrease in recharge 
 
From the scenario analysis, it is observed that there was very little reduction in the basin 
wide drawdown of the hydraulic heads in the study area. This means that if the current 
trend in recharge rates decrease with time due to low humidity and low rainfall and the 
wells are pumped at their initial rates the groundwater resource in Legon Campus will still 
be sustainable. In this case the ecosystem will not be endangered and there will be no sea 
water intrusion. This observation ties in well with early submission that recharge to the 
groundwater system is through sub-surface flows rather than direct recharge from 
precipitation. In the light of this, research must be done to ascertain the sources of the 
recharge so that they can be protected from contamination. 
In the third instance, worse case scenarios were analyzed. Firstly, 10% reduction in 
recharge rates and an increment in the abstraction rates up to 200% of the fourteen wells 
was performed and changes in the hydraulic heads were again noted (Table 4.7). Again 
15%, 20%, and 25% reduction in recharge rates and an increment in the abstraction rates 
up to 200% of the fourteen wells was performed and changes in hydraulic heads were also 
recorded. 
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Table 4.7 A Table showing the change in hydraulic heads of the 14wells for the scenario of 
decreasing recharge by 10% and increasing abstraction up to 200% 
 
well 1 2 3 4 5 6 7 
Initial 84.4853 80.4116 66.2998 60.7832 51.5316 48.8589 51.5523 
10% 84.0831 80.0589 65.6736 59.6245 50.4561 48.132 50.8175 
20% 83.7052 79.7521 65.184 58.5083 49.2327 47.3331 49.9896 
25% 83.5163 79.5987 64.9391 57.9028 48.5638 46.9042 49.5417 
50% 82.6978 79.0955 65.1731 0 45.2095 45.1084 47.6841 
100% 80.8847 77.7205 63.5186 0 0 0 0 
200% 77.2654 74.9867 60.1166 0 0 0 0 
        
well 8 9 10 11 12 13 14 
Initial 57.9728 61.4165 58.1392 62.1643 58.4668 59.9465 62.5935 
10% 57.2482 60.7857 57.0425 61.7506 57.9348 59.5077 62.2046 
20% 56.45 60.0946 55.5075 61.1591 57.368 59.0228 61.7347 
25% 56.0253 59.7284 54.2794 60.8568 57.0826 58.7778 61.4975 
50% 54.3354 57.7189 0 60.2463 56.5152 58.2993 60.7771 
100% 0 0 0 59.3126 54.7063 56.7941 59.1477 
200% 0 0 0 64.5912 0 57.4961 59.2437 
        
 
 
For the first part of this scenario where recharge was deliberately reduced by 10% with 
increased abstraction rates by 10%, 20%, 25%, 50%, 100%, and 200%. It is observed that 
there was slight reduction of drawdown of the hydraulic heads in the study area as 
abstraction is increased by up to 25% abstraction (Fig. 4.31 to 4.33) but at 50% and  
 
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Figure 4.31.:  Recharge reduced by 10% with abstraction rates increased by 10% 
 
Figure 4.32. Recharge reduced by 10% with abstraction rates increased by 20% 
 
 
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Figure 4.33. Recharge reduced by 10% with abstraction rates increased by 25% 
 
 
Figure 4.34. Recharge reduced by 10% with abstraction rates increased by 50% 
 
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Figure 4.35. Recharge reduced by 10% with abstraction rates increased by 100% 
 
 
Figure 4.36. Recharge reduced by 10% with abstraction rates increased by 200% 
 
beyond up to 200% shown in Figure 4.34 to 4.36, there is a drastic decline in hydraulic 
heads indicated by the dry cells in the southern portion of the domain. This means that the 
groundwater resource is sustainable when recharge is reduced up to 25% and abstraction is 
increased up to 25%. Again same scenario was made where there was 15%, 20% and 25% 
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reduction in recharge rates and an increment in the abstraction rates up to 200% of the 
fourteen wells and changes in the hydraulic heads were noted (Tables 4.8, 4.9 and 4.10). 
Table 4.8 A Table showing the change in hydraulic heads of the 14wells for the scenario of 
decreasing recharge by 15% and increasing abstraction up to 200% 
 
well 1 2 3 4 5 6 7 
Initial 84.4853 80.4116 66.2998 60.7832 51.5316 48.8589 51.5523 
10% 84.0806 80.0543 65.6626 59.6128 50.4446 48.125 50.8087 
20% 83.7027 79.7474 65.173 58.4956 49.2197 47.3253 49.9796 
25% 83.5138 79.594 64.9281 57.8892 48.5501 46.8961 49.5315 
50% 82.6953 79.0909 65.1622 0 45.1887 45.0987 47.6722 
100% 80.8822 77.7159 63.5077 0 0 0 0 
200% 77.2628 74.982 60.1054 0 0 0 0 
        
well 8 9 10 11 12 13 14 
Initial 57.9728 61.4165 58.1392 62.1643 58.4668 59.9465 62.5935 
10% 57.2374 61.7328 57.03145 61.7328 57.9308 59.5028 62.1965 
20% 56.4382 61.1412 55.4921 61.1412 57.3639 59.018 61.7265 
25% 56.0134 60.8388 54.2511 60.8388 57.0786 58.7729 61.4893 
50% 54.3226 60.2286 0 60.2286 56.5112 58.2946 60.7691 
100% 0 59.2928 0 59.2928 54.7011 56.789 59.1393 
200% 0 64.7131 0 64.7131 0 57.4788 59.229 
 
  
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Table 4.9 A Table showing the change in hydraulic heads of the 14wells for the scenario of 
decreasing recharge by 20% and increasing abstraction up to 200% 
 
well 1 2 3 4 5 6 7 
Initial 84.4853 80.4116 66.2998 60.7832 51.5316 48.8589 51.5523 
10% 84.0781 80.0496 65.6516 59.601 50.4332 48.1181 50.7999 
20% 83.7002 797428 65.162 58.4828 49.2066 47.3175 49.9697 
25% 83.5113 79.5894 64.9171 57.8755 48.5363 46.888 49.5213 
50% 82.6928 79.0862 65.1513 0 45.1677 45.089 47.6602 
100% 80.8797 77.7112 63.4966 0 0 0 0 
200% 77.2603 74.9772 60.0941 0 0 0 0 
        
well 8 9 10 11 12 13 14 
Initial 57.9728 61.4165 58.1392 62.1643 58.4668 59.9465 62.5935 
10% 57.2265 60.7635 57.0204 61.7151 57.9268 59.498 62.1884 
20% 56.4265 60.0717 55.4766 61.1234 57.3599 59.0131 61.7184 
25% 56.0015 59.7053 54.2224 60.8209 57.0745 58.7679 61.4811 
50% 54.3097 58.421 0 60.2109 56.5073 58.2897 60.761 
100% 0 0 0 59.273 54.6959 567839 59.1309 
200% 0 0 0 64.6924 0 57.4741 59.2211 
        
 
Table 4.10 A Table showing the change in hydraulic heads of the 14wells for the scenario of 
decreasing recharge by 25% and increasing abstraction up to 200% 
 
well 1 2 3 4 5 6 7 
Initial 84.4853 80.4116 66.2998 60.7832 51.5316 48.8589 51.5523 
10% 84.0755 80.045 65.6406 59.5892 50.4217 48.1111 50.7912 
20% 83.6977 79.7381 65.151 58.47 49.1935 47.3097 49.9597 
25% 83.5088 79.5847 64.9061 57.8619 48.5223 46.8799 49.511 
50% 82.6903 79.0816 65.1403 0 45.1465 45.0792 47.6482 
100% 80.8804 77.7134 63.508 0 0 0 0 
200% 77.2577 74.9725 60.0829 0 0 0 0 
        
well 8 9 10 11 12 13 14 
Initial 57.9728 61.4165 58.1392 62.1643 58.4668 59.9465 62.5935 
10% 57.2156 60.7537 57.0093 61.6973 57.9228 59.4931 62.1803 
20% 56.4148 60.0602 55.4611 61.1056 57.3559 59.0082 61.7103 
25% 55.9896 59.6937 54.1928 60.8029 57.0704 58.763 61.4729 
50% 54.2969 58.4086 0 60.1932 56.5034 58.285 60.7529 
100% 0 0 0 59.2555 54.6938 56.7806 59.1236 
200% 0 0 0 64.6718 0 57.4694 59.2131 
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Same observations occurred for reduced recharge at 15%, 20% and 25% as shown in Figure 
4.37 to 4.54. Reductions in groundwater recharge resulting from climate change/variability 
effects could further lead to drastic drawdowns in the hydraulic head as groundwater 
abstraction rates is increased by 50%, 100% and 200%. As was observed from the tables, 
there was drawdown of almost 70 m which can cause aquifer dewatering. 
 
Figure 4.37. Recharge reduced by 15% with abstraction rates increased by 10% 
 
 
Figure 4.38. Recharge reduced by 15% with abstraction rates increased by 20% 
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Figure 4.39. Recharge reduced by 15% with abstraction rates increased by 25% 
 
 
Figure 4.40. Recharge reduced by 15% with abstraction rates increased by 50% 
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Figure 4.41. Recharge reduced by 15% with abstraction rates increased by 100% 
 
 
Figure 4.42. Recharge reduced by 15% with abstraction rates increased by 200% 
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Figure 4.43: Recharge reduced by 20% with abstraction rates increased by 10% 
 
 
Figure 4.44. Recharge reduced by 20% with abstraction rates increased by 20% 
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Figure 4.45: Recharge reduced by 20% with abstraction rates increased by 25% 
 
 
Figure 4.46: Recharge reduced by 20% with abstraction rates increased by 50% 
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Figure 4.47. Recharge reduced by 20% with abstraction rates increased by 100% 
 
 
Figure 4.48: Recharge reduced by 20% with abstraction rates increased by 200% 
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Figure 4.49.  Recharge reduced by 25% with abstraction rates increased by 10% 
 
 
Figure 4.50. Recharge reduced by 25% with abstraction rates increased by 20% 
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Figure 4.51. Recharge reduced by 25% with abstraction rates increased by 25% 
 
 
Figure 4.52. Recharge reduced by 25% with abstraction rates increased by 50% 
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Figure 4.53: Recharge reduced by 25% with abstraction rates increased by 100% 
 
 
Figure 4.54. Recharge reduced by 25% with abstraction rates increased by 200% 
 
This huge decline suggests that an increase in abstraction beyond 50% will dry up those 
wells lowering the groundwater level below sea level which could lead to sea water 
intrusion or intrusion of contaminated water and also affect the functioning of the 
ecosystems. 
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CHAPTER FIVE 
CONCLUSIONS AND RECOMMENDATIONS 
5.1 CONCLUSIONS 
Detailed geophysical survey using the vertical electrical sounding method was conducted 
to delineate the hydrostratigraphic units of the area that was used in the conceptualization 
of the numerical groundwater flow model. The geophysical survey was investigated up to 
a depth of about 80m which revealed three to five subsurface layers from the modelled 
VES curves giving rise to ten different resistivity sounding curve types. The distribution of 
the curve types showed that KH curve is predominant while KHK curves is the least 
occurrence curve in the study area. The results were able to delineate different geoelectric 
sections that were correlated with available borehole logs and geology to determine the 
corresponding geological formations, thicknesses and depth of the aquifer systems in the 
terrain.  
The geoelectric sections and the stratigraphic model revealed three main lithostratigraphic 
units and these units are phyllite, quartzite and laterite from the bottom to the top. Based 
on the borehole logs, the pseudo- sections and the geology of the area the aquifer was 
encountered at a depth of about 38 m to 60 m which was attributed to be probably the 
moderately weathered phyllite or quartzite giving an aquifer zone of thickness 22 m and 
this could be variable in space. The aquifer system was considered to be semiconfined and 
this unit was conceptualized as a single layer. 
A steady state numerical groundwater flow model was calibrated and the result suggests 
that aquifer hydraulic conductivity values in the area ranges between 4.0 m/day and 60.0 
m/day. The highest hydraulic conductivity values are in southwestern and southern parts 
of the study area and the lowest in the northern, northwestern and eastern sections of the 
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study area. The variation in the hydraulic conductivity in the terrain is attributed to the local 
variations in the degree of weathering and/or fracturing in space. Local flow systems 
dominate the flow in the area since there is no clearly defined direction of flow. This 
indicates the local structural entities that control the flow system are not oriented in any 
preferred direction. This may be attributed to the fact that groundwater flow and the general 
hydrogeological conditions of the aquifers in the study area are purely based on the 
weathered and fractured zones. Where the aquifer is highly heterogeneous with respect to 
the key aquifer hydraulic parameters, flow is largely haphazard and local flow systems are 
predominant. The same is true when the terrain is of considerably variable topography. The 
estimated groundwater recharge from the calibrated steady-state model ranges between 
0.008 m/yr to 0.12 m/yr with an average of 0.064 m/yr representing 1% and 15% with an 
average of 8.1% of the annual precipitation of 0.7 m of annual rainfall in the area. The 
highest recharge occurred towards the south and the rest of the terrain have low recharge 
rates. 
From the water table budget, the source of recharge to the groundwater resource is sub-
surface inflows. The amount of water that enter the aquifer system through the general 
heads is 368368.08 m3/d. This amount is accounted for by the changes in the hydraulic 
heads and the interconnectivity of the system with other aquifer systems. The contribution 
of recharge to the water budget seem low due to the fact that the vertical percolation of 
rainwater is restricted due to the clayey nature of the terrain and the many physical 
developments which are seen on the Legon Campus. From the water budget the amount of 
water that leaves the system is 368728.09 m3/d which is slightly higher than the input 
amount. Even though no research has been in the area to ascertain baseflow in the area, the 
wetlands that were observed around the botanical garden even during the dry season give 
an indication of baseflow occurring in the area. 
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From the scenario analysis that were performed, decrease in recharge did not have any 
significant change on the groundwater water system. However when the initial abstraction 
rates were increased up to about 50%, dry cells were observed around some of the wells. 
 
5.2 RECOMMENDATIONS  
 A holistic hydrogeological investigation involving qualitative groundwater 
resources must be done in the study to know the quality of the groundwater resource 
being consumed. 
 The sources of sub-surface inflows into the groundwater system in the area must be 
studied using tracers so that they can be delineated and protected from 
contamination. 
 The high recharge areas should be delineated and protected so they are not 
encroached upon to diminish the already low recharge in the study area. 
 
 
 
 
 
  
 
  
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APPENDIX 
 
Appendix 1: Vertical electrical sounding results for each station 
Appendix 2: Vertical electrical sounding model results for each station 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Appendix 1. 
Summary of vertical electrical sounding results 
  ρa Thickness Depth Curve  
Station Layer (ohm-m) (m) (m) type Interpretations 
SBVES1 1 98.3 0.75 0.75 KH laterite 
 2 142 3.94 4.69  laterite 
 3 40.4 56.7 61.4  moderate to highly weathered phyllite/quartzite with low or no water saturation 
 4 7542    hard or consolidated material 
SBVES2 1 110 0.762 0.762 KQH laterite 
 2 174 1.87 2.64  laterite 
 3 50.6 27.7 30.3  highly weathered phyllite/quartzite with low or no water saturation 
 4 32 30.6 60.9  moderately weathered phyllite/quartzite 
 5 11290    hard or consolidated material 
SBVES3_BH 1 134 0.75 0.75 QH laterite 
  2 73.6 1.37 2.12   laterite 
 3 38.2 46 48.1  moderate to highly weathered phyllite/quartzite saturated with water 
 4 272    hard or consolidated material 
SBVES4 1 184 1.26 1.26 QH laterite 
 2 72.4 5.42 6.68  laterite 
 3 25.3 61.5 68.2  moderate to highly weathered phyllite/quartzite saturated with water 
 4 6616    hard or consolidated material 
SBVES5 1 72.5 1.66 1.06 KH laterite 
 2 139 12.3 13.4  laterite 
 3 62.6 106 119  moderate weathered phyllite/quartzite 
SBVES1 1 98.3 0.75 0.75 KH laterite 
 2 142 3.94 4.69  laterite 
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Appendix 1 (continued) 
 
  ρa Thickness Depth Curve  
Station Layer (ohm-m) (m) (m) type Interpretations 
 3 62.6 106 119  moderate weathered phyllite/quartzite 
 4 4903    hard or consolidated material 
HLVES1 1 156 0.75 0.75 KQH laterite 
 2 929 1.05 1.05  laterite 
 3 214 5.93 7.49  laterite 
 4 129 139 141  highly weathered phyllite/quartzite with low or no water saturation 
 5 4372    hard or consolidated material 
HLVES2 1 244 0.75 0.75 KH laterite 
 2 437 4.34 5.18  laterite 
 3 109 170 178  moderate to highly weathered phyllite/quartzite saturated with water 
 4 2074    hard or consolidated material 
HLVES3_BH 1 176 0.75 0.75 KH laterite 
 2 341 3.31 4.06  laterite 
 3 126 27.2 31.3  moderate to highly weathered phyllite/quartzite with low or no  water saturation 
 4 311    hard or consolidated material 
HLVES4 1 179 0.75 0.75 KH laterite 
 2 285 3.98 4.49  laterite 
 3 118 144 148  moderate to highly weathered phyllite/quartzite saturated with water 
 4 2317    hard or consolidated material 
HLVES5 1 231 0.75 0.75 KH laterite 
 2 454 4.66 5.41  laterite 
 3 145 73.7 79.1  moderately weathered phyllite/quartzite 
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Appendix 1 (continued) 
 
  ρa Thickness Depth Curve  
Station Layer (ohm-m) (m) (m) type Interpretations 
 4 637    hard or consolidated material 
AKWPVES1 1 601 1.38 1.38 QQH laterite 
 2 355 4.92 6.3  laterite 
 3 210 45.2 51.5  highly weathered phyllite/quartzite saturated with water 
 4 41.7 47 98.5  highly weathered phyllite/quartzite saturated with water 
 5 8875    hard or consolidated material 
AKWPVES2 1 615 1.55 1.55 QQH laterite 
 2 395 4.22 5.77  laterite 
 3 217 24.4 30.2  highly weathered phyllite/quartzite saturated with water 
 4 101 134 164  highly weathered phyllite/quartzite saturated with water 
 5 9073    hard or consolidated material 
AKWPVES3_BH 1 684 0.75 0.75 QH laterite 
 2 406 7.4 8.15  laterite 
 3 284 87.7 95.9  moderately weathered phyllite/quartzite 
 4 614    hard or consolidated material 
AKWPVES4 1 610 1.07 1.07 HK laterite 
 2 296 2.18 3.24  laterite 
 3 464 13.9 17.1  moderately weathered phyllite/quartzite 
 4 223    hard or consolidated material 
AKWPVES5 1 785 3.87 3.87 QH laterite 
 2 412 7.94 11.8  laterite 
 3 141 95.1 107  highly weathered phyllite/quartzite with low or no water saturation 
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Appendix 1 (continued) 
 
  ρa Thickness Depth Curve  
Station Layer (ohm-m) (m) (m) type Interpretations 
 4 1678    hard or consolidated material 
VALVES1 1 476 0.75 0.75 HK laterite 
 2 228 1.67 2.42  laterite 
 3 404 5.4 7.82  laterite 
 4 185    hard or consolidated material 
VALVES2_BH 1 144 5.9 5.9 K highly weathered phyllite/quartzite with low or no water saturation 
 2 363 2.83 8.73  highly weathered phyllite/quartzite with low or no water saturation 
 3 121    hard or consolidated material 
VALVES3 1 398 1.03 1.03 H laterite 
 2 270 7.63 8.66  laterite 
 3 1260    hard or consolidated material 
LGCVES1 1 442 0.845 0.845 KH laterite 
 2 579 5.26 6.1  laterite 
 3 242 47 53.1  moderate to highly weathered phyllite/quartzite with low or no  water saturation 
 4 1113    hard or consolidated material 
LGCVES2 1 232 0.75 0.75 KH laterite 
 2 426 8.02 8.77  laterite 
 3 195 40.6 49.3  moderate to highly weathered phyllite/quartzite saturated with water 
 4 577    hard or consolidated material 
LGCVES3_BH 1 302 0.856 0.856 KHK laterite 
 2 425 4.63 5.49  laterite 
 3 202 43.5 49  moderate to highly weathered phyllite/quartzite saturated with water 
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Appendix 1 (continued) 
  ρa Thickness Depth Curve  
Station Layer (ohm-m) (m) (m) type Interpretations 
 4 2068 35.7 84.7  moderately weathered phyllite/quartzite 
 5 22.2    hard or consolidated material 
LGCVES4 1 304 0.776 0.776 KH laterite 
 2 421 4.68 5.45  laterite 
 3 243 37.3 42.8  moderate to highly weathered phyllite/quartzite with low or no  water saturation 
 4 424    hard or consolidated material 
LGCVES5 1 379 0.75 0.75 KH laterite 
 2 705 3.97 4.72  laterite 
 3 258 39.7 44.5  moderate to highly weathered phyllite/quartzite with low or no  water saturation 
 4 604    hard or consolidated material 
CWVES1 1 451 0.75 0.75 HK highly weathered phyllite/quartzite with low or no water saturation 
 2 278 5.26 6.01  moderately weathered phyllite/quartzite 
 3 9267 11 17  low weathered to fresh quartzite 
 4 1242    hard or consolidated material 
CWVES2 1 373 0.75 0.75 HK highly weathered phyllite/quartzite with low or no water saturation 
 2 269 9.86 10.6  moderately weathered phyllite/quartzite 
 3 5749 14.9 25.5  low weathered to fresh quartizite 
 4 694    hard or consolidated material 
CWVES3_BH 1 206 0.75 0.75 HAK highly weathered phyllite/quartzite saturated with water 
 2 146 5.31 6.06  highly weathered phyllite/quartzite with low or no water saturation 
 3 616 11 17.1  moderately weathered phyllite/quartzite 
 4 3928 37.1 48.2  moderately weathered phyllite/quartzite 
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Appendix 1 (continued) 
  ρa Thickness Depth Curve  
Station Layer (ohm-m) (m) (m) type Interpretations 
 5 6.9    hard or consolidated material 
CWVES4 1 516 0.75 0.75 HK highly weathered phyllite/quartzite with low or no water saturation 
 2 265 8.87 9.62  highly weathered phyllite/quartzite with low or no water saturation 
 3 3869 14.8 24.4  moderately weathered phyllite/quartzite 
 4 1157    hard or consolidated material 
CWVES5 1 340 0.75 0.75 HAK highly weathered phyllite/quartzite saturated with water 
 2 137 1.37 2.12  highly weathered phyllite/quartzite with low or no water saturation 
 3 414 14.9 17  moderately weathered phyllite/quartzite 
 4 1847 31.1 48.1  moderately weathered phyllite/quartzite 
 5 338    hard or consolidated material 
UGBBH1VES1 1 414 0.75 0.75 KQH laterite 
 2 783 5.29 6.04  laterite 
 3 221 11 17  laterite 
 4 106 119 136  moderately weathered phyllite/quartzite 
 5 229    hard or consolidated material 
UGBBH1VES2 1 42.5 0.818 0.818 KH laterite 
 2 86.9 3.25 4.07  laterite 
 3 30.8 117 121  highly weathered phyllite/quartzite saturated with water 
 4 3690    hard or consolidated material 
UGBBH1VES3_BH 1 41.8 12.8 12.8 K highly weathered phyllite/quartzite with low or no water saturation 
 2 624 26.3 39.1  moderately weathered phyllite/quartzite 
 3 140    hard or consolidated material 
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Appendix 1 (continued) 
 
  ρa Thickness Depth Curve  
Station Layer (ohm-m) (m) (m) type Interpretations 
UGBBH1VES4 1 15.5 0.825 0.825 K highly weathered phyllite/quartzite saturated with water 
 2 93.2 14.6 15.4  moderately weathered phyllite/quartzite with low water saturation 
 3 36.2 17.2 32.6  moderately weathered phyllite/quartzite 
 4 104    hard or consolidated material 
UGBBH1VES5 1 108 0.75 0.75 K laterite 
 2 253 8.16 8.91  laterite 
 3 120    hard or consolidated material 
UGBBH2VES1 1 21.2 3.43 3.43 A laterite 
 2 59.9 59.5 62.9  moderately weathered phyllite/quartzite 
 3 254    hard or consolidated material 
UGBBH2VES2 1 16.9 2.42 2.42 HK highly weathered phyllite/quartzite with low or no water saturation 
 2 12 5.4 7.82  moderate to highly weathered phyllite/quartzite with low or no  water saturation 
 3 248 17.4 25.2  low to moderately weathered phyllite quartizite 
 4 25.2    hard or consolidated material 
UGBBH2VES3_BH 1 7.41 0.75 0.75 HK highly weathered phyllite/quartzite saturated with water 
 2 4.36 4.85 5.6  highly weathered phyllite/quartzite with low or no water saturation 
 3 537 10.2 15.8  moderately weathered phyllite/quartzite 
 4 1.5    hard or consolidated material 
UGBBH2VES4 1 22.6 0.75 0.75 HK highly weathered phyllite/quartzite with low or no water saturation 
 2 14.9 7.09 7.84  moderate to highly weathered phyllite/quartzite with low or no  water saturation 
 3 94.8 74.4 82.2  low to moderately weathered phyllite quartzite 
 4 9.45    hard or consolidated material 
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Appendix 1 (continued) 
  ρa Thickness Depth Curve  
Station Layer (ohm-m) (m) (m) type Interpretations 
UGBBH2VES5 1 24.5 1.74 1.74 A laterite 
 2 48.8 46.5 48.2  moderately weathered phyllite/quartzite 
 3 2190    hard or consolidated material 
ESCVES1 1 396 7.89 7.89 H laterite 
 2 239 68.4 76.3  moderately weathered phyllite/quartzite 
 3 543    hard or consolidated material 
ESCVES2 1 321 36.5 36.5 A moderately weathered phyllite/quartzite 
 2 53.1 3.65 40.2  low weathered to fresh quartizite 
 3 108030    hard or consolidated material 
ESCVES3_BH 1 215 1.25 1.25 KH laterite 
 2 335 9.64 10.9  laterite 
 3 139 133 144  highly weathered phyllite/quartzite saturated with water 
 4 4342    hard or consolidated material 
ESCVES4 1 285 4.13 4.13 KH laterite 
 2 577 11.8 15.9  laterite 
 3 155 36.8 52.8  moderately weathered phyllite/quartzite 
 4 691    hard or consolidated material 
ESCVES5 1 176 0.75 0.75 KH laterite 
 2 361 9.21 9.96  laterite 
 3 194 136 146  highly weathered phyllite/quartzite with low or no water saturation 
  4 6289       hard or consolidated material 
VES, vertical electrical sounding; BH, borehole; SB, Mensah Sarbah hall; HL, Hilla Limann, AKWP, Alexander Kwapong; VAL, Valco trust 
hostel; LGC, Legon hall annex c; CW, Commonwealth hall; UGB, University of Ghana botanical gardens; ESC, Earth Science Department 
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Appendix 2 
 
 
 
 SBVES1 model result SBVES2 model result 
  
 
 
SBVES3 model result SBVES4 model result 
 
  
 
SBVES5 model result HLVES1 model result 
  SBVES5 model result   
 
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Appendix 2 (continued) 
 
HLVES2 model result HLVES3 model result
 SBVES3 model result   SBVES3 model result  
  
 
 HLVES5 model result HLVES5 model result
  SBVES3 model result SBVES3 model result   
  
 
AKWPVES1 model result AKWPVES2 model result
HLVES4 model result
 
 SBVES3 model result 
 
 SBVES3 model result SBVES3 model result   
 
  
 
AKWPVES3 model result AKWPVES4 model result
 126  SBVES3 model result SBVES3 model result 
    
  
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Appendix 2 (continued) 
 
AKWPVES5 model result VALVES1 model result
  SBVES3 model result SBVES3 model result   
  
 
VALVES2 model result VALVES3 model result
  SBVES3 model result SBVES3 model result   
  
 
LGCVES1 model result LGCVES2 model result
 SBVES3 model result   SBVES3 model result  
  
 
LGCVES3 model result LGCVES4 model result
  SBVES3 model result   SBVES3 model result  
  
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Appendix 2 (continued) 
 
LGCVES5 model result CWVES1 model result
 SBVES3 model result   SBVES3 model result  
  
 
CWVES2 model result CWVES3 model result
 SBVES3 model result   SBVES3 model result  
  
 
CWVES4 model result CWVES5 model result
 SBVES3 model result   SBVES3 model result  
  
 
UGBBH1VES1 model result UGBBH1VES2 model result
 SBVES3 model result   SBVES3 model result  
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Appendix 2 (continued) 
 
UGBBH1VES3 model result UGBBH1VES4 model result
 SBVES3 model result   SBVES3 model result  
  
 
UGBBH1VES5 model result UGBBH2VES1 model result
  SBVES3 model result SBVES3 model result   
  
 
UGBBH2VES2 model result UGBBH2VES3 model result
  SBVES3 model result SBVES3 model result   
  
 
UGBBH2VES4 model result UGBBH2VES5 model result
   SBVES3 model result SBVES3 model result   
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Appendix 2 (continued) 
 
ESCVES1 model result ESCVES2 model result
 SBVES3 model result   SBVES3 model result  
  
 
ESCVES3 model result ESCVES4 model result
 SBVES3 model result   SBVES3 model result  
  
 
ESCVES5 model result
 SBVES3 model result  
 
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