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UNIVERSITY OF GHANA 
 
 
 
COLLEGE OF BASIC AND APPLIED SCIENCE 
 
 
 
GEOPHYSICAL AND HYDROGEOLOGICAL CHARACTERISATION 
OF THE NASIA BASIN, NORTHERN GHANA 
 
 
ABDUL-SAMED ALIOU 
(10256943) 
 
A THESIS SUBMITTED TO THE UNIVERSITY OF GHANA IN 
PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD 
OF DOCTOR OF PHILOSOPHY IN EARTH SCIENCE 
 
DEPARTMENT OF EARTH SCIENCE 
 
 
 
NOVEMBER  2020 
 
 
 
 
 
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DECLARATION 
I hereby declare that this thesis is my own work produced from research under 
supervision and to the best of my knowledge, it contains no material previously 
published by another person nor material which has been accepted for the award of 
any other degree of any institution, except where due acknowledgement has been 
made. 
 
 
 22/11/2020 
Abdul-Samed Aliou                 
 
(Student)             Signature          Date 
 
 
 
Prof. Sandow Mark Yidana                        
(Principal Supervisor)                         Signature           Date 
 
 
 
 
Dr. Larry Pax Chegbeleh                          
(Co- Supervisor)                            Signature           Date 
 
 
 
 
Dr (Mrs) Yvonne Sena Akosua Loh                      
(Co-Supervisor)                               Signature           Date 
 
 
 
 23/11/2020 
Kurt Klitten            _______________ 
 
                    
(Co-Supervisor)                               Signature           Date 
 
 
 
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ABSTRACT 
The development of groundwater resources across the Voltaian Sedimentary Basin 
(VSB) is constrained by lack of knowledge on the location and suitability of 
aquifers for borehole construction. The main objective of this study is to evaluate 
the hydrogeological characteristics of the Nasia Basin to help in delineating suitable 
locations for groundwater exploration. An integrated geophysical investigation 
involving resistivity survey using 2D Electrical Resistivity Tomography (ERT), 
Electromagnetic Survey with EM34-3 and wireline logging of boreholes were 
employed to determine the resistivity of the different type of lithology occurring in 
the area and for providing information about the lateral and vertical extent of 
weathering and fracturing in the subsurface. A total of 58 ERT profiles were run at 
selected locations and at existing boreholes to obtain information about resistivity 
in different depth intervals. Majority of the profiles were run across the general 
strike of rocks of the VSB in the study area with length being either 400 m or 800 
m.  Five EM-34 ground conductivity profiles of 500-1000 m were conducted on 
each of two approximately parallel South-North traverses (EM campaign 1) to 
record the conductivity in the various geological formations and to assess its 
possible variation related to major geological structures. In addition to the two-
parallel profile (EM campaign 1), 12 other EM-34 profiles each 500 m long were 
carried out at six selected communities in the central part of the study area (EM 
campaign 2) with the objective of locating areas within the weathered zone capable 
of storing groundwater for small scale pilot irrigation from dug-wells. The 
conclusion of the integrated geophysical investigation is that combining 
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interpretations from various geophysical methods provides an improved 
characterisation of the hydrogeology. Secondly, hydrogeological investigations 
were conducted which involved analysis of pumping test results, evaluation of 
various methods of estimating aquifer transmissivity and assessment of the major 
factors controlling groundwater occurrence. Using remote sensing and 
geographical information system (GIS), thematic maps of slope, drainage density 
and lineament, geological formation were prepared. Other thematic maps such as 
static water level, regolith thickness, depth, recharge and transmissivity developed 
from kriging were incorporated into GIS. Finally, multi-criteria analysis and GIS 
techniques were used to integrate these thematic maps to delineate suitable zones 
to obtain a comprehensive groundwater potential map of the study area. The results 
also show that in certain locations such as portions of the Bimbilla formation, 
probability of obtaining aquifers is very low and therefore the target should be to 
locate thick regolith for groundwater storage. The results from the hydrogeological 
investigation indicate that cokriging gives better estimates of spatial aquifer 
transmissivity and therefore is a better approach considering the paucity of long 
duration pumping test data. Regression models and variographic analysis 
conducted confirmed the findings of previous researchers that groundwater within 
the study area is mainly structurally controlled and not by lithology. The 
groundwater potential map was classified into five zones that describe the 
potentiality of each cell in the study area for groundwater exploration. These classes 
are; very poor, poor, moderate, good and very good groundwater potential areas. It 
was found that 2% and 18% of the study area was classified as very good and good 
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potential areas respectively. These are areas found to be concentrated in the Kodjari 
formation southwest of the Panabako sandstone formation of the study area. About 
38% of the study area was classified as moderate potential which is sparsely 
distributed across the study area and 41% represent poor potential for groundwater 
exploration occurring mainly in the Bimbilla formation. Less than 1% of the study 
area was classified as very low potential areas and also concentrated in southeast 
of the study area. The reliability of the groundwater potential map was tested 
against successful and dry boreholes and the results showed that generally, the 
majority of high- and low-yielding boreholes fall in areas predicted by the map. 
Furthermore, a sensitivity analysis was performed to study the effect of each 
parameter on the overall groundwater map using the effective weight. It was found 
that the transmissivity was the most effective among the parameters that have the 
greatest influence on groundwater occurrence in the study area which is consistent 
with literature. The integrated geophysical method coupled with the comprehensive 
groundwater map has provided better information about subsurface geology of the 
Nasia Basin which is critical for understanding the lithological character in terms 
of hydrogeological conditions. The general concept of interpreting low resistivity 
zones as the presence of a sub-vertical fracture zone and therefore regarded as a 
favorable site has been refuted. A more scientific approach of interpreting 
geophysical results has been proposed for exploration hydrogeologist in the area 
and in similar geology. The groundwater potential map further shows that the 
potential for high yielding boreholes is limited to about 20% of the study area. 
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Therefore, other strategies to augment for increasing irrigation such as exploring 
the weathered zone for groundwater storage should be considered. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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TABLE OF CONTENTS 
 
DECLARATION .................................................................................................... ii 
ABSTRACT ........................................................................................................... iii 
TABLE OF CONTENTS ...................................................................................... vii 
LIST OF FIGURES ............................................................................................... ix 
LIST OF TABLES ............................................................................................... xiv 
DEDICATION ...................................................................................................... xv 
ACKNOWLEDGEMENT ................................................................................... xvi 
CHAPTER ONE ..................................................................................................... 1 
INTRODUCTION ............................................................................................... 1 
1.1 Background ........................................................................................... 1 
1.2  Problem Statement ................................................................................ 5 
1.3 Objectives of the Study......................................................................... 7 
1.4 Justification ........................................................................................... 7 
1.5  The Study Area ..................................................................................... 9 
1.6  Brief Background of Groundwater Development in the Voltaian ...... 16 
CHAPTER TWO .................................................................................................. 19 
LITERATURE REVIEW .................................................................................. 19 
2.1 Geophysical characterisation of Groundwater Systems ..................... 19 
2.2 Characterisation of Groundwater Systems Using Hydraulic Parameters
 23 
2.3 Remote Sensing (RS) and GIS for groundwater mapping ................. 30 
CHAPTER THREE .............................................................................................. 34 
RESEARCH METHODOLOGY ...................................................................... 34 
3.1 Sources of Data ................................................................................... 34 
3.2 Desk Study .......................................................................................... 34 
3.3 Reconnaissance Survey ...................................................................... 35 
3.4 Field Investigation .............................................................................. 35 
3.5 Data Processing and Analysis ............................................................. 45 
3.6 Remote Sensing and Geographical Information System .................... 53 
3.7 Groundwater Potential Map ................................................................ 55 
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CHAPTER FOUR ................................................................................................. 58 
RESULTS AND DISCUSSION ....................................................................... 58 
4.1 Geophysical Investigations ................................................................. 58 
4.2 Evaluation of Groundwater Characteristics ...................................... 105 
4.3 Analysis of aquifer response ............................................................. 116 
4.4 Estimation of hydraulic parameters ....................................................... 118 
4.5. Variographic analysis of hydrogeological parameters ..................... 131 
4.6 Groundwater Potential Map ................................................................... 141 
CHAPTER FIVE ................................................................................................ 168 
CONCLUSIONS AND RECOMMENDATIONS ......................................... 168 
REFERENCES ................................................................................................... 173 
APPENDICES .................................................................................................... 201 
APPENDIX 1-EM-34 PROFILES .................................................................. 201 
APPENDIX 2-ERT PROFILES ...................................................................... 220 
APPENDIX 3- GEOPHYSICAL WIRELINE LOGS .................................... 252 
APPENDIX 4- GEOLOGICAL LOGS .......................................................... 269 
APPENDIX 5A- ERT-PROFILES COMPARED TO WIRELINE LOGS FOR 
DWVP BOREHOLES..................................................................................... 279 
APPENDIX 5B- ERT-PROFILES COMPARED TO WIRELINE LOGS FOR 
DWVP BOREHOLES..................................................................................... 280 
APPENDIX 6- HISTOGRAMS OF 7 PARAMETERS ................................. 281 
APPENDIX 7- DRAWDOWN AND RECOVERY GRAPHS OF 26 
PUMPING TEST RESULTS > 6 HRS ........................................................... 283 
APPENDIX 8- RECHARGE ESTIMATES BASED ON CHLORIDE MASS 
BALANCE METHOD AT 60 BOREHOLES ................................................ 296 
APPENDIX 9- DESCRIPTIVE STATISTICS OF PARAMTERS ................ 298 
APPENDIX 10- MASTER TABLE WITH 233 BOREHOLES ..................... 299 
APPENDIX 12- RECLASSIFICATION OF PARAMETERS FOR MULTI-
CRITERIA ANALYSIS .................................................................................. 305 
APPENDIX 13- RECLASSIFICATION MAPS OF PARAMETERS ........... 307 
APPENDIX 14- SENSITIVITY ANALYSIS ................................................ 311 
  
 
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LIST OF FIGURES 
10Figure 1 - 1: Map of Nasia Basin of the White Volta Basin ..............................10 
Figure 1 - 2: Geological map of the study area ......................................................12 
Figure 3 - 1: Image of Geonics EM34-3 system (Xia et al. 2001).........................37 
Figure 3 - 2a: A map showing locations of EM traverse lines...............................39 
Figure 3 - 2a: A map showing the locations for the EM-points………………… 39 
Figure 3-2b: A map showing the locations of the ERT points………….……… 41                                                                              
Figure 3 - 3: Model builder for remote sensing .....................................................53 
Figure 3 - 4: Flow chart for developing groundwater potential map. ....................57 
Figure 4 - 1a: ERT-profile (RES2DINV processed) at DWVP09 Samene, drilled 
at ERT station 370...……………………………………………………………..59 
Figure 4 - 1b: ERT-profile (RES2DINV processed) at DWVP02 Tamboku, drilled 
at ERT station 410………………………………………………………………61  
Figure 4-1c: Geophysical wireline logs of borehole DWVP 02 Tamboku, drilled 
at ERT station 410...……………………………………………………………..62 
Figure 4.2: A geological map showing the locations of DWVP09-DWVP02-
Tenkpanga-DWVP01-HAP11…………………………………………………...65 
Figure 4 - 2 a: EM-34 profile of Tenkpanga in the Poubogou formation…….….66 
Figure 4 - 2 b: ERT profile of Tenkpanga in the Poubogou formation (AWB 
processed)………………………………………………………………...……...68  
Figure 4 - 2 c: 800 m long ERT profile (AWB processed) at DWVP01, Tamboku 
(drilled at station 420) in the Panabako sandstone formation…………………....70 
Figure 4 - 2 d: Geophysical wireline logs of borehole DWVP01(drilled at ERT st. 
420) in Panabako sandstone………………………………………………….…..71 
Figure 4 – 2 e: EM profile of HAP 11 (Nalerigu SHS at st. 265) in the Panabako 
sandstone formation….……………………………………………………..…..72 
Figure 4 – 2 f: ERT profile (RES2DINV processed) at borehole HAP 11, Nalerigu 
SHS (drilled at st. 265)….………………………………………………………73 
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Figure 4 - 2 g: Geophysical wireline logs of HAP 11 at Nalerigu SHS.………..75 
Figure 4.3a: A geological map showing DWVP 10 & Sandua – DWVP 05 - DWVP 
08 & Nakpaya – HAP 05 and Disiga – HAP 14 – Bugya Pala (WVB 11 & 
B2)…………………………………………………………………..…………...77                              
Figure 4 - 3b.: 400 m long ERT-profile (RES2DINV processed) at borehole DWVP 
10 Sakpa (at st. 120) located within “Bimbila old” underlain by the Kodjari 
formation………………………………………………….……….……..............78 
Figure 4 – 3c: Geophysical wireline logs of borehole DWVP10 Sakpa (drilled at st. 
120 on ERT-profile) showing Bimbila formation underlain from 73 m by Kodjari 
formation…………………………………………………………………………79 
Figure 4 – 3 d: EM Profile-A as one of two parallel SE-NW profiles at Sandua 
(“Bimbila old” formation)……………………………………………………..…80 
Figure 4-3e: 400 m long ERT-profile (RES2DINV processed) at borehole DWVP 
05 Kpodu (drilled at st.200) within “Bimbila middle” formation………………82 
Figure 4 – 3 f: Geophysical wireline logs at borehole DWVP05 at (Kpobu) (“middle 
Bimbila” formation)……………………………………………………………..83 
Figure 4 – 3g: Geophysical wireline logs of borehole DWVP08 at Nakpaya 
(“Bimbila middle” formation)..………………………………………………….84 
Figure 4 – 3h: 400 m ERT-profile (RES2DINV processed) at borehole DWVP08, 
Nakpaya (drilled at st. 160) within “Bimbila middle” formation….…………….85 
Figure 4 -– 3i: 500 m long EM profile-A in SSW-NNE direction at Nakpaya 
(“Bimbila middle” formation)…………………………………………………....86 
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Figure 4 -– 3j: ERT profile at borehole HAP 05 (Janga) at st.220 (younger part of 
Bimbila formation) after RES2DINV processing……………….……………..…87 
Figure 4 - 3k: Geophysical wireline logs in borehole HAP 5 Janga (younger part 
of Bimbila formation)…………………………………………………………....88 
Figure 4 - 3l: EM profile-A as one of two parallel NE-SW profiles at for selection 
of feasible site for dug-well construction (within Bimbila young formation)…...90 
Figure 4 - 4a: Figure 4-4a: Geophysical wireline logs of borehole HAP 14 Tuuni 
(at st. 120 on ERT-profile) in Kodjari formation underlain by Panabako 
sandstone……………………………………………..……………………….....92 
Figure 4 - 4b: The ERT-profile at borehole HAP 14 Tuuni (at st. 120) located 
within the Kodjari formation. ................................................................................93 
Figure 4 - 4c: Figure 4-4c: EM-34 response curves along traverses A and B, 
Tuuni (HAP 14)………………………………………………………………….94 
Figure 4 – 4d: Geophysical wireline logs from WVB 11(Bugya Pala)………….97 
Figure 4 - 4e: Geophysical wireline logs from CWSA borehole Bugya Pala-2....98 
Figure 4 – 4f: An illustration of how resistivity is compared using different 
methods……………………………………………………………….………...100  
Figure 4 - 5a: Box Plot for Depth (m) for the Nasia basin……………………..112 
Figure 4 - 5b: Box Plot for Thickness of Regolith (m) for the Nasia Basin.......112 
Figure 4 - 5c: Box Plot for Yield (m3/d) for the Nasia Basin…………………..113 
Figure 4 - 5d: Box Plot for SWL (m) for the Nasia Basin ...................................113 
Figure 4 - 6a: Histogram of Log-Transformed Depth (m) for the entire Nasia 
Basin………...………………………………………………….........................114 
Figure 4 - 6b: Histogram of Log-Transformed Thickness of Regolith (m)…….115 
Figure 4 - 6c: Histogram of Log-Transformed SWL (m)………………………115 
Figure 4 - 6d: Histogram of Log-Transformed Yield for the entire Nasia basin .116  
Figure 4 - 7a: A graph of drawdown (m) against Time (min) for Daboya No.2 
(illustrating boundary condition)….……………………………………………117 
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Figure 4 - 7b: Graph of Drawdown (m) against Time (min) for Walewale -WA-05 
(illustrating well loss and a high yielding aquifer condition) ..............................118 
Figure 4 - 8: Log-Transformed Specific Capacity for the Study area .................121 
Figure 4 - 9: Log-Transformed Transmissivity for the entire study area ............122 
Figure 4 - 10 (a): Untransformed Transmissivity and (b) Specific Capacity ......126 
Figure 4 - 11: (a)Transformed Transmissivity and (b) Specific Capacity ...........127 
Figure 4 - 12. Experimental variogram and fitted model for Transmissivity ......127 
Figure 4 - 13. Experimental variogram and fitted model for Specific Capacity ..129 
Figure 4 - 14: Cross variogram for transmissivity and specific capacity ............130 
Figure 4 – 15a. Transfored Histogram  of borehole Yield ...................................131 
Figure 4 – 15b. Untransformed Histogram of borehole Yield .............................131 
Figure 4 - 16: Variogram of Yield fitted with an exponential model ..................132 
Figure 4 - 17: Histograms showing Depth data ...................................................134 
Figure 4 - 18: Variogram of borehole depth fitted with an exponential model ...134 
Figure 4 - 19: Histograms showing SWL distribution .........................................135 
Figure 4 - 20: Variogram of SWL fitted with a variogram ..................................136 
Figure 4 - 21. Histogram showing distribution of raw and transformed Regolith
..............................................................................................................................137 
Figure 4 - 22: Variogram of Regolith fitted with an exponential model .............138 
Figure 4 - 23:  Histograms showing the distribution of Recharge .......................139 
Figure 4 - 24: Variogram for Recharge fitted with an exponential model ...........140 
Figure 4 - 27: 2-D Spatial distribution of aquifer transmissivity from cokriging for 
the study area .......................................................................................................142 
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Figure 4 - 28: Spatial distribution of Specific Capacity in the study area ...........143 
Figure 4 - 29: Spatial distribution of Depth in the study area. .............................144 
Figure 4 - 30: 2-D Spatial distribution of Regolith in the study area ..................145 
Figure 4 - 31: 2-D Spatial distribution of SWL in the study area ........................146 
Figure 4 - 32: Spatial distribution of Yield in the study area ...............................147 
Figure 4 - 33: Drainage Density in the study area ...............................................148 
Figure 4 - 34: Spatial distribution of Recharge in the study area ........................150 
Figure 4 - 35: Slope in the study area ..................................................................150 
Figure 4 - 36: Lineament map of the study area ..................................................151 
Figure 4 - 37: Reclassification of Depth of borehole ...........................................153 
Figure 4 - 38: Reclassification of SWL ...............................................................154 
Figure 4 - 39: Reclassification of aquifer Transmissivity ....................................155 
Figure 4 - 40: Groundwater Potential Map of the study area ..............................161 
Figure 4 - 41: Groundwater potential map with yield (m3/d) of productive 
boreholes ..............................................................................................................163 
Figure 4 - 42: Groundwater potential map with dry boreholes of study area ......164 
Figure 4 - 43: Sensitivity Analysis 1....................................................................166 
Figure 4 - 44: Sensitivity Analysis 2....................................................................167 
 
 
 
 
 
 
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LIST OF TABLES 
Table 3.1: Depth of investigation at different coil-separations in HD-mode and   
VD-mode……………………………………………………………………….38 
Table 4.1a: Resistivity of weathered zone and of bedrock from cases discussed 
………………………………………………………………………………..…102 
Table 4.1b: Resistivity of the geological formations of the Nasia Basin…….…104 
Table 4.2a: Summary of Groundwater Characteristics in Bimbilla Formation.. 107 
Table 4.2b: Summary of Groundwater Characteristics in Poubogou 
Formation……………………………………………………………………….108 
Table 4.2c: Summary of Groundwater Characteristics in Kodjari Formation….109 
Table 4.2d: Summary of Groundwater Characteristics in Panabako 
Sandstone……………………...………………………………………………..110 
Table 4.3 Scaled values and weights assigned to the different classes and 
parameters……… ……………………………………………………..……….159 
 
  
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DEDICATION 
I dedicate this work to the four women who have influenced my life in diverse 
ways: my lovely wife, Sally Adwoa Afriyie for the love, patience, sacrifice and 
support through this journey; my daughter, Wendkuuni Nhyira Aliou for being a 
gift and a blessing in my life; my dear mother, Mamata Mahama for the love, 
continuous prayer and support and finally my late aunt, Zainab Mahama who I 
affectionately call mama, for raising me to be the person I am.  
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ACKNOWLEDGEMENT 
My greatest appreciation goes to the Almighty God for His grace and mercy for 
seeing me through this journey.  
I am also grateful to the Danish International Development Agency (DANIDA) for 
providing the funding (project number 14-P02-GHA) to make this research 
possible.  
My profound gratitude goes to my principal supervisor and coordinator of the 
project, Prof. Sandow Mark Yidana for first given me the opportunity to be part of 
the project. I am truly grateful for his immense supervision, constructive criticism 
and continuous support. To my co-supervisors: Kurt Klitten, I say thank you for the 
direction, guidance and support; Dr (Mrs) Yvonne S. A. Loh, I appreciate all the 
encouragement and supervision and to Dr Larry-Pax Chegbeleh, I am most grateful 
for the guidance especially during the field work. 
To my boss Dr Julius Aptidon Awini, Managing Director-Hydronomics Limited, 
thank you for the patience and support you showed me during my study. God richly 
bless you.  
To my family, I appreciate your endurance and prayers during this period of my 
study.  
I also say a big thank you to Miss Elikplim Abla Dzikunoo for the encouragement, 
support and assistance. 
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Richard Mejida Adams and Evans Manu, I cannot forget the support you provided 
especially during the field campaign. Ben Emunah Aikins and Francis Andorful 
(RS & GIS Lab-UG), thank you for the time and assistance. 
Finally, to Abdul-Rahman Lutuf of Saha Consulting and Services Limited and 
Rexford A. Ani, I appreciate the words of encouragement.  God bless you 
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CHAPTER ONE 
INTRODUCTION 
1.1 Background 
Groundwater resources are increasingly recognised as a major strategic water 
resource in the context of a changing climate and human development. In many 
parts of the world, groundwater has been used as a reliable source of water for 
numerous purposes such as for irrigation, domestic and industrial uses. For 
instance, UNESCO (2012) suggests that, over 2.5 billion people worldwide rely on 
groundwater daily.  
 
In the wake of changing climate coupled with water scarcity, several researchers in 
the global scene have conducted various investigations in an effort to understand 
the hydrogeological properties of aquifers for proper management. Hunkeler (2016) 
for instance has advocated the use of geological method in the form of core 
sampling using rotary core drilling, augering or sonic vibratory drilling for 
characterising subsurface of terrains. This approach however has a lot of 
uncertainties because the information obtained is local and considering the high 
heterogeneous nature of some terrains, it would not be representative enough.  
 
Geophysical methods are also capable of characterising aquifers to produce good 
results especially where they are combined with other methods. Ashraf et al. (2018) 
developed subsurface soil profile based on the combined use of electrical resistivity 
with well log and pumping test and concluded that, the method was reliable in 
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identifying potential favorable zones within the area. Danielsen et al. (2007) on the 
other hand, used transient electromagnetic (TEM) and continuous vertical electrical 
sounding (CVES) to characterise the hydrogeology of Karoo stratigraphic sequence 
in Zimbabwe. A more accurate image of the entire subsurface profile was provided 
using both methods. 
 
Remotely sensed data has been used by Kexiang et al. (2017) for hydrogeological 
characterisation of the whole Brazil. Their study investigated the relationship 
between GRACE (Gravity Recovery and Climate Experiment)-
derived groundwater changes and geological conditions such as rock properties and 
aquifer types across Brazil in order to study the groundwater potential. The 
challenge however with this approach has to do with cost of the analysis especially 
when measuring or analyzing smaller areas and also it requires a special skill to 
analyze the images.  
 
Pumping test is widely accepted as the best method for aquifer characterisation 
according to Fetter (2001). Holland (2011) therefore used pumping test in 
characterising the hydrogeology of crystalline basement aquifers within the 
Limpopo Province of South Africa. The study focused on evaluating factors that 
influence borehole yields and aquifer transmissivities. A limitation with using 
pumping tests is the fact that it does not provide detailed information about the 
variability of hydraulic parameter as they are often related to narrow or limited site 
characterizations, thus, data obtained are only point estimates (Slater, 2002). 
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Tracer test according to Todd and Mays (2005) have also been used in the 
determination of aquifer hydraulic parameters though the procedure is simple in 
practice, results obtained are only approximations due to field limitations. For 
sparse areas, Yand et al. (2010) have proposed advanced modelling techniques as 
means of improving characterisation of the complex hydrogeological systems. 
They used groundwater flow modelling with nonlinear inverse calibration, 
advective transport, geochemical modelling and isotope study to characterize 
heterogeneous systems.  
 
In Ghana, besides the Hydrogeological Assessment Project (HAP) which was 
initiated to provide scientific background for groundwater-based supply projects as 
well as resource management studies of the Northern Regions of Ghana, studies on 
characterising the hydrogeology are limited especially for the Voltaian 
Sedimentary Basin (VSB). This is because most groundwater projects are for rural 
water supply. There are however a few investigations with various methods of 
characterising the hydrogeological terrains. Ewusi (2006) assessed the feasibility 
of various geophysical techniques for groundwater exploration and management in 
Northern Region. The major limitation of Ewusi (2006) is the reliance on only 
geophysics to map the groundwater resources even though new insights were 
presented. 
  
Yidana et al. (2011)  characterised the hydrogeological conditions of portions of 
the Voltaian with pumping test data using regression analysis. The challenge with 
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this work had to do with the limited data for some of the lithology. Darko (2001) 
on his part evaluated the groundwater resources in Ghana by classifying the 
hydrogeologic units on a regional scale. Besides the limited data used for the VSB, 
the method used in calculating transmissivity was based on different geological 
environment which can be misleading. 
 
Forkuor et al. (2013) also combined spatial parameters such as recharge rate, 
regolith thickness, transmissivity, borehole success rate and static water level to 
assess the groundwater development potential in Northern Ghana. The challenge 
with this work was the over simplification of the geology of the VSB for instance 
by generalizing all the rocks as sandstones instead of classifying them in their right 
geological units. 
 
In recent times, other researchers have used numerical groundwater flow 
simulations under steady state and transient conditions for hydrogeological studies 
in the White Volta basin (Attandoh et al. 2013; Darko, 2015; Ofosu-Addo et al. 
2008) among others. For a proper groundwater models to be developed, there is the 
need for a thorough understanding of the hydrogeological setting. 
 
Considering the number of limitations with the various methods stated above, a 
comprehensive method of characterizing the hydrogeology of the Voltaian 
supergroup is urgently required. A combined use of geophysics and hydraulic 
parameters would be adequately to characterise the hydrogeology. 
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1.2  Problem Statement 
The success rates of drilling wet boreholes yielding minimum 10 lpm within the 
VSB has been reported differently by various researchers to be below 60%. For 
instance, Dapaah-Siakwan and Gyau-Boakye (2000) reported a success rate of 56% 
in the VSB while Annon (2000) reported a 38% after evaluating boreholes drilled 
under the International Development Agency (IDA) funded project. Unihydro 
(2003) pegged the success rate based on then available data as 25%. In the 
2016/2017 financial year of World Vision International, a success rate of 50% was 
recorded for the Gushiegu and Karaga Districts.  
The dry boreholes or low yields recorded within the VSB is partly because of the 
lack of detailed understanding of the hydrogeology and its relevance for the 
geophysical data. There appears to be poor understanding of the physical basis of 
geophysical siting, and in consequence some geophysical techniques are being 
carried out wrongly and others are not being used to their full potential. Even where 
techniques are carried out and geophysical data are analysed correctly, they are 
often not interpreted appropriately. Community members as the ultimate 
beneficiaries of these boreholes, have their hopes are dashed wherever dry or low 
yielding boreholes are recorded. Donors also get disappointed about these results 
and have raised concerns about the huge sums of money that is spent on drilling 
projects. 
 
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Apart from the Hydrogeological Assessment Project (HAP) funded by Canadian 
International Development Agency between (2005-2011) and the White Volta 
Basin Monitoring Project (WVBP) funded by Danish International Development 
Agency (DANIDA) between (2004-2008), which carried out a comprehensive 
hydrogeological study of the Voltaian supergroup in Ghana, there has not been any 
hydrogeological studies in the area in recent time.   
 
Although there have been several drilling attempts at various parts of the Voltaian 
supergroup, most of them have been treated in isolation. This isolated use of site-
specific information represents a failure to develop a background understanding, a 
necessary condition for improving successful groundwater exploration. There has 
been no or little consensus on what groundwater targets are and no attempt has been 
made to document the geophysical character of the rocks, such as linking typical 
geophysical signatures to geological formation or lithology. Most hydrogeological 
investigations within the study area for groundwater exploration are usually based 
on just the electrical resistivity method. Further to that, there is little understanding 
of the probable depth of groundwater within the study area. Forkuor et al. (2013) 
assert that, little knowledge has been built on groundwater potential through the 
continuous collection, verification, registration and archival of hydrogeological 
data from drilled boreholes. 
 
The development of groundwater within this environment is notoriously 
complicated, with the high number of variable parameters. Detailed 
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hydrogeological study, which involves combining different geophysical methods 
and evaluation of hydraulic parameters to develop a comprehensive groundwater 
potential map is urgently needed. The map will aid groundwater development 
practitioners in selecting locations for groundwater development. Additionally, it 
could inform donor and other funding organisations in setting realistic goals and 
standards for future project success in what is a very difficult terrain to develop 
groundwater resources. 
1.3 Objectives of the Study 
The primary objective of this study is to determine the hydrogeological 
characteristics of the Nasia Basin of the White Volta Basin. 
The specific objectives of the study are to: 
• evaluate the current conventional methods of interpreting geophysical 
results in the area; 
• develop an appropriate approach for groundwater exploration in the various 
geological formations and document their electrical resistivities; 
• assess the aquifer parameters of the main lithologies and evaluate the factors 
that control groundwater potential within the area; 
• develop a comprehensive groundwater indicator/potential map to aid in 
groundwater exploration 
1.4 Justification 
The current approach in exploring groundwater has in general not produced the 
desired results, especially within the Voltaian terrain, considering the low success 
rates that has been recorded. Several reasons can be given to the poor results but 
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key among the reasons is a lack of thorough understanding of the geology and 
hydrogeology.  
This research aims to serve as blue print for the groundwater sector in the quest to 
enhance success rates of drilling wet boreholes yielding minimum 10 lpm in the 
area. It comprises several methods of characterising the hydrogeology ranging from 
remote sensing, geophysical studies, hydrogeology and geostatistics to produce a 
groundwater potential map. This would go a long way to help achieve goal 6 of the 
Sustainable Development Goals (SDG) thus achieving universal and equitable 
access to water.    
 
Secondly the outcome of this research goes to feed into a bigger project funded by 
DANIDA with the objective of harnessing groundwater for sustainable agriculture 
as a pilot activity within the project area. Therefore, the results would be used in 
determining prolific aquifers capable of yielding large volumes of water 
particularly in the dry season.  
 
This also feeds directly into the government’s quest to revolutionise agriculture 
through the ‘planting for food and jobs policy’ targeted at employing an estimated 
number of approximately 770,000 jobs per crop season and about 1.5 million jobs 
by the end of 2020 (MOFA, 2019). Having access to water can help farmers engage 
in irrigation especially in the dry season since most of the inhabitants in the study 
area are farmers and will help provide jobs for the youth and thereby help reduce 
poverty. 
 
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Finally, this research would provide relevant guidelines to all the organizations in 
the water sector for groundwater development in areas with similar geology.  
 
1.5  The Study Area 
1.5.1  Location  
The Nasia Basin (Figure 1-1) forms part of the nine sub-catchments of the White 
Volta Basin and it is located within Northern and North East Regions of Ghana 
specifically within East Mamprusi, West Mamprusi, Savelugu, Nanton, Karaga and 
Gushiegu Districts. The study area is bounded between longitude 0° and 1° W and 
latitude 9° 45° N and 10° 45° N with an area of approximately 5400 km2. Some of 
the major towns in the study area are Nalerigu, Gambaga, Walewale, Karaga, 
Gushiegu and Gbintiri.  
1.5.2  Climate and Vegetation 
The area lies within the tropical Continental or Interior Savannah Climatic Zone 
(Dickson and Benneh, 1995). The rainy season starts from May and ends in October 
with an annual rainfall which ranges between 1005 and 1150 mm, and the heaviest 
rains occurring in August. The mean monthly temperatures vary from about 36°C 
in March/April to about 27°C in August. Relative humidity is high during the rainy 
season (65-85%) but may fall to 20% during the dry season.   The area falls within 
the savannah vegetation zone, which is characterized by tall grasses that grow in 
tussocks, and widely scattered trees such as baobab and the dawa-dawa trees. 
 
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The climate is controlled by circulation patterns of two subtropical air masses; wet 
maritime monsoonal air from the Gulf of Guinea and Sahara Desert air from the 
interior of Northern Africa (Lutz et al. 2007). There is significant 
evapotranspiration mainly due to the warm and dry winds that blow across the 
Sahara Desert. Evapotranspiration ranges from 650 mm to 1300 mm and varies by 
season, location, local vegetation type and density (Kwei, 1997). 
 
 
Figure 1 - 1: Map of Nasia Basin of the White Volta Basin 
 
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1.5.3  Drainage 
The main drainage system in the study area is made up of the Nasia river, which is 
a tributary to the White Volta. The effect of the drainage system is seen mostly 
between Savelugu and West Mamprusi District covering the areas between Nabogu 
and Kukuobilla. These areas are prone to periodic flooding during the wet season, 
making them suitable for rice cultivation (Ghanadistricts, 2017). 
1.5.4  Geology 
The area is underlain by rocks belonging to both Oti-Pendjari Group and the 
Bombouaka Group of the Voltaian supergroup, which covers about 45% of the total 
landmass of Ghana. Based on a revised lithostratigraphy of the study area, there are 
four (4) main geological formations, Bimbilla, Kodjari, Panabako Sandstone and 
Poubogou formations (Carney et al. 2010). Figure 1-2 is a geological map of the 
study area with the various formations. 
 
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Figure 1 - 2: Geological map of the study area 
Carney et al. (2010) indicates that the Bimbila formation consists of green to khaki, 
micaceous laminated mudstones, siltstones and sandstones which represent a 
continuation of foreland basin deposition. The siltstones typically occur in thin, 
tabular beds with wind-rippled tops and low angle cross-bedding. Sandstones are 
thinly intercalated within the unit and also include the feature-forming, and thus 
readily mappable Bunya member. The Bunya sandstone member is about 50 m 
thick and outcrops in the southernmost portions of the study area. It consists of 
green - grey, medium to thickly-bedded, feldspathic and lithic-rich wacke 
sandstone. (Jordan et al. 2009).  
 
The formation, Kodjari sometimes referred to as the basal formation of the Oti-
Penjari Group. The tillites or tilloids of this group fill an erosional and slightly 
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angular unconformity of glacial origin. This group stratigraphically overlies the 
Bombouaka group (Sougy, 1971). Carney et al. (2010) indicate that the distinct 
triad of lithologies comprise basal tillites, cap-carbonates and laminated tuffs and 
ash-rich siltstone. Outcrops identified with south of the Gambaga massif, show 
them as crimson- weathering siliceous tuffaceous siltstones. Barfod et al. (2004) 
also suggest that it consists mainly of shales and siltstones, and lenses of various 
facies of sandstones, greywackes, limestones, silexites and tuffs. 
 
The Panaboko sandstone is estimated to be 150-200 m thick, and comprises hard, 
well-cemented, well sorted, medium-grained, quartzitic sandstones, which are 
particularly indurated and appear crystalline in the north of the area. Overlying the 
unit is a typically thin, gravelly laterite, sometimes capped by thin iron oxide 
cement (Carney et al. 2010; Jordan et al. 2009). 
 
The Poubogou formation of pro-delta mudstones and siltstones outcrops towards 
north along the Gambaga escarpment and is about 170 m thick and grades into the 
overlying nearshore-facies quartz arenites of the Panabako sandstone formation 
(Carney et al. 2010). According to Ayite et al. (2008), the Poubogou formation 
consists of green-grey micaceous mudstones and siltstones with thin intercalations 
of fine-grained sandstone.  
1.5.5  Hydrogeology 
The sedimentary rocks mentioned above are affected by the Pan-African tectonic 
events and have virtually lost their primary porosity due to cementation and 
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consolidation (Acheampong and Hess, 2000; Kesse, 1985). Dapaah-Siakwan and 
Gyau-Boakye (2000) suggest that the beds generally dip gently. Groundwater 
occurrence is dependent on secondary permeability resulting from fracturing and/or 
weathering of the rocks. This enhances hydraulic parameters such as storage, 
transmission and recharge potential (Banoeng-Yakubo et al. 2011). Banoeng-
Yakubo et al. (2011) indicate that the weathered zone of the Voltaian is highly 
variable due to the variability in the clay content. Dapaah-Siakwan and Gyau-
Boakye (2000) suggest that the average yield for productive boreholes (minimum 
yield of 10 lpm) in the area is between 6.2 m3/hr -8.5 m3/hr with success rates of 
less than 60%. Dapaah-Siakwan and Gyau-Boakye (2000) allude that parameters 
which control groundwater storage appear to be discrete entities of variable spatial 
extent.   
 
The thickness of the regolith (excluding saprock) according to Carrier et al. (2008) 
averages around 6 m to 11 m and they have linked that to the relatively stable clay 
(shale) and quartz (sandstone) composition or by the fine texture or ductile nature 
of the sedimentary rocks found in the Voltaian. Bannerman (1990) revealed that the 
most productive fracture zones occur between the depths of 13 m and 80 m with an 
average of 27 m. Carrier et al. (2008) and Banoeng-Yakubo et al. (2011) indicate 
the average depth of boreholes is 55 m and it ranges between 45 m and 75 m. The 
productivity of the aquifers is generally low to moderate with transmissivity range 
between 0.3 m2/d to 267 m2/d and a mean of 11.9 m2/d. 
 
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The evaluation of boreholes by O’Dochartaigh et al. (2011) under the HAP and 
other projects indicated that depth range of boreholes for the Bunya Sandstone 
member in the Bimbilla formation is between 30 m to 70 m. They assert that in 
non-dry boreholes, recorded yields range from 5 l/min to 1000 l/min and concluded 
that the key groundwater target is likely to be thin fracture zones within the 
sandstones. For the Bimbilla formation, depth typically ranges between 20 m and 
80 m, and it appears to form a moderate to low productivity aquifer. Groundwater 
targets are the weathered- fracture zones and thin zones at lithological boundaries 
(O’Dochartaigh et al. 2011). For the Panabako sandstone formation, O’Dochartaigh 
et al. (2011) indicated that based on their assessment, the depth of boreholes ranged 
between 30 m and 70 m. The borehole yield ranged between 15 l/min and 35 l/min 
with groundwater target being the weathered-out fracture zones between 20 m and 
30 m. The Formation appears to form a moderate to high productivity aquifer. 
 
Groundwater recharge is reported to range between 1.8 % and 32 % of the annual 
average precipitation by Yidana and Koffie (2014) while Addai et al. (2016) arrived 
at a range of 73 mm/yr -110 mm/yr with an average of 94 mm/yr. Afrifa et al,. 
(2017) on their part, estimated the groundwater recharge in the Gushiegu district in 
the range of 13.9 mm/y - 218 mm/y, with an average of 89 mm/yr, representing 
about 1.4%-21.8% (average 8.9%) of the annual precipitation in the area. 
 
 
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1.6  Brief Background of Groundwater Development in the Voltaian 
 
The first assessment of water resources potential was by the Geological survey of 
Ghana in 1925. Researchers like Annan-Yorke and Cudjoe (1971), Gill (1969), 
Junner (1946) were among the few who started reporting on groundwater related 
works within the country and in the Voltaian specifically.  Junner (1946) proposed 
a division of the Voltaian into three layers based on lithology and reported about 
the presence of large springs along joints and bedding planes at certain locations. 
Annan Yorke and Cudjoe (1971) conducted their studies on the geology and 
hydrology of the Voltaian Sedimentary Basin and reported that it consisted of 
undeformed flat-lying sediments with lots of variation in lithology. Gill (1969) 
related borehole yields and the quality of water to the various geological formations 
in the country however with limited data. For instance, in the Northern Region, 36 
boreholes were used with an average depth of 105 m and average yield of 4 m³/h.  
 
Between 1984 and 1995, Water Resources Institute (WRI) of Council for Scientific 
and Industrial Research conducted an assessment for the entire country using 8000 
boreholes. In the Northern, North East and Savana Regions, 150 boreholes were 
used to arrive at a depth ranging between 35 m -150 m while yield was between 8 
l/min - 600 l/min.  Nii Consult (1998) also conducted a study of the White Volta 
Basin using river basins for both surface and groundwater resources and indicated 
that aquifers with yields varying from 0.6 m3/hr -18 m3/hr with a mean of 3.8 m3/hr 
were tapped by boreholes drilled to depths between 27 m -91.1 m with a mean of 
42.1 m.   
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Dapaah-Siakwan and Gyau-Boakye (2000) on their part divided the country into 
three hydrogeological provinces: the Precambrian igneous and metamorphic rocks 
was described as the Basement Complex comprising 54% landmass, Paleozoic 
sedimentary basin comprising 45% consisted of the Voltaian Sedimentary Basin 
and 1% for Cenozoic, Mesozoic, and Paleozoic sedimentary strata along the coast 
and the Quaternary alluvium along major stream courses.  The challenge with these 
descriptions has to do with lumping of the various geological formations, which 
could lead to wrong conclusion in terms of hydrogeological potential.  
 
The Canadian International Development Agency (CIDA) funded a 
Hydrogeological Assessment Project (HAP), which was implemented in 
conjunction with the Water Resources Commission (WRC) between 2006-2011. 
The HAP was aimed at improving the knowledge base of the hydrogeological 
setting in the Northern Ghana by establishing a borehole database with more than 
10000 borehole records. Furthermore, by drilling 27 deep monitoring boreholes 
additional to previously drilled 12 monitoring boreholes funded by DANIDA 
(White Volta Basin monitoring project), and by conducting geophysical wireline 
logging in nearly all of them.  
 
The most recent review of the hydrogeology of the Voltaian was conducted by 
Banoeng-Yakubo et al. (2011). They developed a new hydrogeological map of 
Ghana, which contained five hydrogeological provinces based on groundwater 
potential and lithology. The provinces are the Birimian Province, the Crystalline 
Basement Granitoid Complex Province, the Voltaian Province, the Pan African 
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Province, and Coastal Sedimentary Province. In their conclusion they indicated that 
most prolific aquifers were associated with deep weathering and fracturing and 
suggested that the hydrochemistry is controlled by the weathering of silicate 
minerals and cation exchange except for unconfined aquifers where anthropogenic 
activities influence the groundwater hydrochemistry.  
  
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CHAPTER TWO 
LITERATURE REVIEW 
 
Groundwater management has led to the need for accurate investigation and 
description of aquifers. A hydrogeological characterization of a specific area can 
be achieved by estimating the aquifer parameters.  
Several methods have been used by different researchers for hydrogeological 
characterisation, which include geophysical characterisation, aquifer test analysis, 
tracer test, numerical modelling, remote sensing and Geographical Information 
System (GIS) techniques.  
 
2.1 Geophysical characterisation of Groundwater Systems  
 
The application of geophysics for groundwater investigations has been extensively 
presented and reviewed by a lot of researchers (Kearey and Brooks, 1991; Milsom, 
2003; Reynolds, 2010; Telford et al. 1976). These applications include mapping the 
depth and thickness of aquifers (Albouy et al. 2001; Danielsen et al. 2007; Huntley, 
1986; Mazac et al. 1985; Robain et al. 1996), locating geological structures such as 
major fracture systems and fault zones (Batte et al. 2008; Christensen and Sørensen, 
1998) and mapping groundwater contamination (Adepelumi et al. 2008; Cimino et 
al. 2007; Goldman and Kafri, 2006; Kafri et al. 2007; Mills et al. 1988). The 
commonest among the geophysical methods are the electrical and the 
electromagnetic (EM) methods which have been reported as the most successful in 
terms of groundwater resources investigation (Danielsen et al. 2007; Gwaze et al. 
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2000; MacDonald et al. 2001; Sørensen and Søndergaard, 1999; Soupios et al. 
2010).  
An illustration of the use of the geophysical technique for a rural water supply in 
hard rock area in northern Nigeria was shown by Reynolds (1997). The area 
recorded a failure rate of more than 82% for boreholes prior to the use of geophysics 
but was dramatically reduced to less than 20% by using an integrated geophysical, 
geological and photogeological methods.  
 
Danielsen et al. (2007) carried out a geophysical and hydrogeological investigation 
in Zimbabwe using two geophysical methods; Continuous Vertical Electrical 
Sounding (CVES) and Transient electromagnetic and concluded that a combination 
of the geophysical methods is able to provide a substantially better image of the 
subsurface as compared to one method. By using combined geophysical methods; 
EM34; Vertical Electrical Sounding (VES) and Magnetic profiling, the main targets 
for groundwater within a low permeable sedimentary terrain in Nigeria were 
identified by MacDonald et al. (2001). Another example of using geoelectric 
method for groundwater exploration is shown by Dahlin and Owen (1998). They 
used 2D resistivity surveys combined with ground penetrating radar in shallow 
alluvial aquifers in Zimbabwe. The results were used to develop hydrogeological 
models of the aquifers for managing a drilling programme. Barker (1990) on his 
part, used micro-processor-controlled resistivity traversing for siting boreholes in 
hard rock terrain in Nigeria.  
 
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In Sub-Saharan Africa including Ghana the frequent use of the resistivity and EM 
method in the crystalline basement in particular has resulted in researchers 
developing specific techniques of interpreting results (Beeson and Jones, 1988; 
Olayinka and Baker, 1990). These techniques that have been helpful particularly in 
localizing sub-vertical fractured zones as low resistivity zones within the high 
resistivity basement rocks have been used by other researchers in other geological 
terrain and have led to wrong results (MacDonald et al. 2001). This was confirmed 
by Ó Dochartaigh et al. (2011) in evaluating geophysical practices in Northern 
Ghana. They concluded that there is a general lack of understanding of geophysical 
siting techniques among some groundwater practitioners. Like most of the 
researchers referred to below the different siting techniques was based on low 
resistivity/high conductivity anomalies as indicators for profitable drilling sites 
irrespective of the type of geological environment. Furthermore, the direction of 
the profiles compared to the strike of sedimentary formation is never taken into 
consideration as being important for the interpretation.  
 
Annon (2000) evaluated the results of eighty (80) boreholes drilled in the Voltaian 
under the  International Development Agency (IDA) funded project and arrived at 
a 35% to 38% success rate using the conventional four electrode resistivity method 
while Ewusi, et al. (2009) on his part, used the 2-D Multi-Electrode Resistivity 
Imaging (2-D MERI) technique and recorded a 60% success rate. Though there was 
an improvement in the success rate, the results would have been far better if other 
methods were incorporated. Owen (2005) attested to the capability of the MERI by 
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indicating that it could provide detailed continuous 2D map of the subsurface, 
identify different lithologies, delineate contact zones and faults, and measure the 
thickness of the weathered regolith. Mainoo et al. (2019) for instance after a 
thorough investigation using 2D electrical resistivity tomography to delineate 
groundwater potential zones in the VSB concluded that a careful application of the 
method with an in-depth understanding of the geology would significantly improve 
the success rate of exploring groundwater. MacDonald et al. (2001), however, is of 
the view that a combined use of different geophysical methods would yield a more 
favourable outcome than reliance on a single method.  
 
Chegbeleh et al. (2014) developed a method of data interpretation for the 
sedimentary formation of the Voltaian basin for groundwater exploration using the 
EM-34 method. Their scheme of interpretation was based on identifying EM highs 
along parallel traverses of EM profiles presented on one-dimensional (1-D) plots. 
Though their work is an improvement in the use of the EM-34, the conclusions 
arrived for the range of conductivity for the area is a challenge considering the 
heterogeneous nature of the place, the size of the area compared with the number 
of points used.  
Menyeh et al. (2005) used the EM-34 for siting in the Gushiegu-Karaga district 
which is within the Voltaian sedimentary basin and recorded a 60% success rate 
out of 100 boreholes that were drilled. They recommended the use of the EM-34 
for siting in the area.  But considering the complex nature of the VSB, the 60% 
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success rate is not conclusive enough. Such a terrain requires more than one method 
to be verified before a conclusion is made for which method is most beneficial.  
 
Klitten and Agyekum (2008) on their part characterised the lithology of the 
Voltaian sedimentary rocks using geophysical logs in drilled water wells in the 
Northern Region of Ghana.  Klitten and Agyekum (2008), indicate that the 
lithological sections derived from the geophysical logs provide more information 
and has led to some revisions of boundaries between lithological sub-units.  
 
2.2 Characterisation of Groundwater Systems Using Hydraulic 
Parameters 
 
Freeze and Chery (1979) suggest that groundwater exploration relies heavily on 
investigating the aquifer geometry and understanding hydrogeological parameters 
for an efficient groundwater management. Hydraulic aquifer parameters are 
fundamental for groundwater flow modeling. From literature, the main hydraulic 
parameters commonly used for characterisation include vertical and horizontal 
hydraulic conductivity, transmissivity and storage capacity. Field estimations of 
these hydraulic parameters are not always available at sufficient number of data 
points because of the cost and time in acquiring them and therefore many 
investigation techniques focus on the estimation of the spatial distribution based on 
often rather few data points.  
There are generally three methods of determining aquifer parameters according to 
Driscoll (1986): 1) using data collected during pumping tests, 2) analyzing 
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hydraulic properties of an aquifer material and 3) calculations based on laboratory 
tests. 
2.2.1  Aquifer response curves 
Apart from calculating aquifer parameters, the time drawdown diagrams 
determined from pumping test can provide information on hydrogeological 
conditions in the aquifer in terms of negative or positive hydraulic boundaries. 
Several studies (Darko, 2001; Samani et al. 2006; Holland, 2011; Xiao, 2014) have 
reported the use of time-drawdown graphs in a bid to explain the hydrogeological 
conditions around pumped boreholes in hard rocks.  
 
Samani et al.(2006) highlighted the strength of drawdown-derivative analysis in 
ground water evaluation of a heterogeneous aquifer. They integrated conventional 
time-drawdown analyses, derivative curves and geological information to identify 
the nature of heterogeneities and assess aquifer response to pumping. Though this 
approach helps in aquifer interpretation, it is under-utilized due to a lack of 
published case studies demonstrating its strengths and weaknesses. 
Holland (2011) focused on the challenge conducting pumping tests analysis by 
choosing an appropriate model that best fits the observed drawdown response. He 
therefore proposed a method for the analysis of pumping test data in weathered-
fractured rock aquifers and highlighted the importance of diagnostic plots for the 
detection of flow regimes and to help in selecting the appropriate theoretical model. 
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Xiao (2014) followed the approach of Holland (2011) by illustrating the possibility 
of using derivative pattern for diagnostic analysis of aquifer tests. 
In Ghana, Darko (2001) used drawdown curves to identify dominating flow 
regimes to help in calculation of transmissivity. He illustrated the various 
components of the aquifer of a borehole in Wenchi located in the VSB using only 
one borehole for the entire VSB. The limitation with Darko (2001) work is on the 
use of only one borehole for the assessment.  
2.2.2  Aquifer parameters 
 
Specific capacity of a borehole (discharge per hour per m drawdown) has been 
described by many researchers such as Mace (2001) and Yidana et al. (2011), 
Yidana et al. (2008) as dependent on factors such as aquifer setting, screen setting 
and pumping duration and usually calculated using a ratio of well discharge to 
drawdown after 180 minutes of pumping.  
A thorough evaluation of transmissivity estimation has been conducted by Mace 
(2001) where three main approaches of calculating transmissivity were identified 
including analytical, empirical and geostatistical method. Mace (2001) indicates 
that the analytical method involves using mathematical equations that are based on 
the theory of groundwater flow such as the Cooper- Jacob (1946) method. Jalludin 
and Razack (2004) used the Cooper-Jacob (1946) method to calculate 
transmissivity for volcanic and sedimentary aquifer systems in the Republic of 
Djibouti. This was after the boreholes were pumped for a period ranging between 
2 hr to 72 hr to meet the assumptions for this method. Yidana et al. (2011) used the 
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Copper-Jacob (1946) method to estimate transmissivity for boreholes in some 
portions of the VSB.  The challenges with this method is with the (1) unrealistic 
assumptions about the aquifer and well hydraulics and (2) limited information on 
the aquifer or the well.  
In many hydrogeological studies, it is not uncommon for specific capacity data to 
be in abundance compared to data on transmissivity. Various reasons explain this 
situation such as; high cost associated with carrying out pumping test, time involved 
in embarking on the exercise and also the availability of the right personnel to 
conduct the pumping test without compromising on the quality of data.  
Empirical methods are therefore developed to relate transmissivity with specific 
capacity by using paired values of both parameters for a specified pumping time 
(Mace, 2001). Empirical relationships were first used by Eagon and Iohe (1972) 
and improved upon and popularized by Razack and Huntley (1991). In Ghana, 
empirical relations relating transmissivity and specific capacity with limited data 
was first developed by Acheampong and Hess (2000). Yidana et al. (2008) 
popularized the method in Ghana using data from the Afram Plains portion of the 
Voltaian to derive a regression models. The results revealed that transmissivity 
exists in a non-linear relationship with specific capacity in the area. They concluded 
that the high regression coefficient showed a high dependence of transmissivity on 
specific capacity for the area. Aliou (2010) also followed the approach of Yidana 
et al. (2008) and developed a relationship for transmissivity and specific capacity 
for some portions of the northern part of the Voltaian of Ghana. The relationship 
obtained did confirm that of previous researchers in other areas though it was based 
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on a smaller part of the VSB. The duration for the pumping test though was shorter 
(6 hours) it still showed high coefficient of regression for the  estimated 
transmissivity and specific capacity. Darko and Krásný (2010) and Forkuor et al. 
(2013) also used an empirical relationship developed by Krásný (1997) to estimate 
regional transmissivity map for Ghana. Krásný (1997) relationship was too 
generalized without taking into consideration the difference in geology. HAP 
(2011) also established a relationship for transmissivity and specific capacity for 
Northern Ghana based on records of then existing monitoring boreholes. Their 
relationship however is an amalgamation of all the rocks from both the VSB and 
the basement complex in the Northern Ghana which makes the model not entirely 
reliable. Combining parameters derived from different geological setting can 
introduce too much uncertainty in the model (Mace, 2001). 
 
Using geostatistical techniques such as kriging, kriging with regression and 
cokriging for estimating aquifer transmissivity and other parameters has been 
suggested by a number of researchers to be very helpful and has yielded useful 
results over the years (Aboufirassi and Marino, 1984; Binsariti, 1980; Darling et al. 
1994; Delhomme, 1974; 1976; Mace, 2001; Prudic, 1991; Razack and Lasm, 2006), 
though not from Ghana. 
 
Razack and Lasm (2006) used ordinary kriging to estimate areawise distribution of 
transmissivity from specific capacity at few locations in a highly fractured aquifer 
in Western Ivory Coast. The purpose was to compare with other forms of 
geostatistical estimation of transmissivity. Yidana et al. (2011) developed a spatial 
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distribution map of transmissivity for portions of the Voltaian basin of Ghana.  
Carter et al. (2011) also employed ordinary kriging to estimate the spatial 
distribution of specific capacity of Newark Basin aquifer by using measured values. 
The challenge with the use of these approaches is with the practicality of having 
sufficient number of boreholes in a vast field.  
 
Other researchers have also combined kriging with estimated transmissivity from 
linear regression to produce spatial distribution maps. Examples include Forkuor et 
al. (2013) who used estimated transmissivity measurements based on Darko and 
Krásný (2010) relationship to produce interpolated map of transmissivity. Darko 
(2001) also used kriging with empirical relationship based on Jetel and Krasny 
(1968) to produce a regionalized transmissivity map of Ghana.  
Regression relationships combined with kriging to estimate values of aquifer 
transmissivity introduce two categories of errors; errors from the regression model 
and another from the interpolation procedures. These errors affect the quality of the 
final spatial distribution map produced and therefore renders these techniques 
inappropriate. 
 
Aboufirassi and Marino (1982) used cokriging to construct a transmissivity map 
for Yolo Basin, California by employing field measurements of transmissivity and 
specific capacity. The latter was used in a big scale because there was relatively 
few transmissivity data available. An important advantage of using cokriging under 
this circumstance is the ability to still obtain accurate estimations of transmissivity 
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on the basis of auto and cross-correlation and as well as obtaining estimation 
variances. Razack and Lasm (2006) used cokriging to estimate transmissivity of 
aquifers of the Western Ivory Coast. In Ghana, there have been few instances where 
researchers have estimated transmissivity (Forkuor et al. 2013; HAP, 2011; Yidana 
et al. 2011) based on kriging and kriging with regression but no examples exist on 
the application of cokriging.  
2.2.3  Variographic analysis 
A key component of using kriging and cokriging for estimating areawide 
distribution of transmissivity or other parameters is concerned with spatial 
variability. Isaak and Srivastava (1989) have elaborated on the assumptions 
underlying variograms for spatial variability. Researchers such as Razack and Lasm 
(2006) have also suggested that kriging estimation using variographic analysis is 
capable of  describing the spatial variability of parameters. They allude that 
variograms are fundamental to characterising complex aquifers. Carter et al. (2011) 
in predicting specific capacity for Newark basin aquifer system, developed 
semivariograms to determine whether significant variations had occur and were 
able to estimate parameters of the modeled variograms such as sill, nugget effect 
and range for describing spatial correlation.  Forkuor et al. (2013) indicates that 
kriging gives an idea of the precision of estimates by quantifying estimated 
variance. They experimented with different models and parameters and performed 
cross-validation to understand the variability of the parameters prior to modeling 
potential areas of groundwater of Northern Ghana. 
 
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2.3 Remote Sensing (RS) and GIS for groundwater mapping 
 
Conventional methods of exploration for characterising groundwater systems are 
not only tedious but also consume lots of time and money and require skilled 
manpower. Remote sensing and GIS have been proposed as viable supplementary 
tools of characterizing groundwater systems. The concept of integrating remote 
sensing and GIS has proved to be an efficient tool in groundwater studies (Akram 
and Wani, 2009; Mondal et al. 2009; Narendra et al. 2013; Singh et al. 2013). 
 
Jha et al. (2007) categorized the applications of remote sensing and GIS for 
groundwater studies into the following (1) exploration and assessment of 
groundwater resources (2) selection of artificial recharge sites (3) GIS-based 
subsurface flow and pollution modeling (4) groundwater pollution hazard 
assessment and protection planning (5) estimation of natural recharge distribution, 
and (6) hydrogeologic data analysis and process monitoring. Researchers such as 
Deepika et al. (2013) and Wahyuni et al. (2008) have demonstrated how remote 
sensing and GIS could be used to detect areas with high potential for groundwater 
exploration.  
Though the use of remote sensing and GIS in groundwater studies has been widely 
used in many parts of the world, there is limited work in that regard in Ghana. 
Sander et al. (1996) is among the first researchers who used RS data and GIS to 
develop a well-siting strategy in the VSB in Ghana. The data used include Landsat 
Thematic Mapper (TM), SPOT, and infrared aerial photography interpreted for 
linear vegetation, drainage and bedrock feature that indicate underlying 
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transmissive fracture zones. The integration of data in a GIS was valuable for 
effective analyses but also exposed the necessity of accounting for spatial reference 
and accuracy of data from different sources. The data used covered only the central 
part of the Voltaian and therefore the conclusions made for the entire area does not 
apply in all locations. Banoeng-Yakubo (2000) also used remote sensing and data 
from boreholes in the Upper West Region to identify different rock types and 
structural conditions favourable for siting high yielding boreholes.  
Several attempts have been made to map potential areas for groundwater globally. 
Forkuor et al. (2013) suggests that the potential maps vary in scale ranging from 
global assessment, a regional assessment, country-level analyses and basin-level 
assessment (MacDonald et al. 2001; MacDonald and Davies, 2000; Martin and van 
de Giesen, 2005; WHYMAP, 2008; Woodford et al. 2006). 
A groundwater potential map for Ghana was developed by Gumma and Pavelic 
(2013) using remote sensing and geographical information system (GIS) 
techniques. The factors they used are geomorphology, geology, slope, drainage 
density, annual rainfall, land use/land cover, and soil type. The parameters used 
however have limited influence on groundwater potential occurrence especially for 
the study area. The key to identifying groundwater potential is an evaluation of 
hydraulic parameters, which were absent under this circumstance. Forkuor et al. 
(2013) combined spatial layers for five critical factors—recharge rate, regolith 
thickness, transmissivity, borehole success rate and static water level—through a 
multi-criteria analysis approach to rank groundwater development potential from 
the viewpoint of groundwater availability and accessibility. The weakness with this 
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approach concerns the reclassification of the geology of Ghana into two. The 
Voltaian supergroup was generalized as “sandstones”, while the Precambrian rocks 
were generalised as “weathered rocks”. This assumption is flawed because there 
are several rocks within the VSB and not only sandstone. Therefore, the 
groundwater potential will either be overestimated or underestimated. The most 
comprehensive groundwater potential map in Ghana prepared recently by Ó 
Dochartaigh et al. (2011) is an assessment by the British Geological Survey of the 
groundwater potential in Northern region prior to the implementation of UNICEF 
Integrated Water and Sanitation (IWASH) project. The map comprised the various 
geological units in Northern Region, attribute table indicating groundwater 
development potential and recommendations for appropriate development 
techniques. The distribution was not uniform because it focused mainly on the 
selected communities, which benefitted from the IWASH project.  
 
In developing a groundwater potential map using multi-criteria analysis, there are 
several methods of combining thematic layers. The weighted overlay method used 
by Saraf and Chowdhury (1998) is recommended as a more robust approach. 
Forkuor et al. (2013) on their part used the fuzzy approach to standardize all the 
spatial layers into commensurate scales. While Gumma and Pavelic (2013) and 
Sander et al. (1996) used overlay method for combining the thematic layers to 
develop a siting methodology for the VSB. The efficacy of this method is based on 
the incorporation of human judgement in the analysis. It also takes into 
consideration the relative importance of each parameter. The most appropriate 
method for characterising groundwater system would be to combine the various 
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methods to get a comprehensive map. That approach would help in compensating 
for the limitations of the individual methods. The Voltaian sedimentary basin 
known generally to be highly heterogeneous hydrogeologically would require a 
holistic and comprehensive assessment to characterise the groundwater system as 
suggested by MacDonald et al. (2001). 
 
Several researchers have employed sensitivity analysis to provide valuable 
information on the validity of suitability maps that have been developed (Gogu and 
Dassargues, 2000; Napolitano and Fabbri, 1996; Nwer, 2005). In Ghana, few 
researches apply sensitivity analysis to determine the reliability of their maps. Most 
sensitivity analyses carried out are with respect to numeral models that have been 
developed (Attandoh et al. 2013; Lutz et al. 2007). 
 
 
 
 
 
 
 
 
 
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CHAPTER THREE 
RESEARCH METHODOLOGY 
The methodology consists of a desk study, reconnaissance survey and field 
investigations and analysis of data. 
3.1 Sources of Data 
The data used in this research were from both primary and secondary sources. The 
secondary data was acquired from Community Water and Sanitation Agency 
(CWSA-Tamale), Water Resources Commission, Water Research Institute (WRI)-
Accra, Catholic Relief Services, World Vision International, Terahydro Limited 
and Hydronomics Limited. The data include Geophysical survey reports, 
Geological logs of boreholes, geophysical wireline logs from a few boreholes, 
Pumping Test results and hydrochemistry data (for recharge estimate).  
 
3.2 Desk Study  
The desk study involved compiling and assessing the following data sets: 
topographic and geological maps, existing borehole information and previous 
hydrogeological work in the study area. The borehole data received from the 
organisations were sorted into usable (233 in Appendix 10 including the 10 project 
boreholes) and non-usable. For instance, borehole data without geographical 
coordinates were regarded non-usable and those which did not fall within the study 
area were discarded. For pumping test results, records of boreholes pumped for 6 
hours or more were of prime interest. 67 data set were therefore classified as usable 
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data (Appendix 7) out of which 16 are primary data set (produced under this study). 
The purpose of conducting this desk study was to have a detailed overview about 
the available data and the hydrogeology of the study area. Attention was also on 
lineament patterns and fractures, the presence of suitable aquifers and their 
thickness, mean aquifer depth and the expected lithological sequences, among 
others.  
3.3 Reconnaissance Survey 
The main purpose of the reconnaissance survey was to locate target areas for 
geophysical investigations and also to identify existing boreholes for pumping test. 
It comprised an assessment of topography, geology, stratigraphy, structural 
features, water points and soil surveys to detect sufficiently permeable strata that 
by virtue of their relative elevation or depression, geological history and hydrology 
could be water bearing. Furthermore, social, logistical and accessibility 
considerations were also taken into account. The reconnaissance survey did also 
include setting out geophysical traverse lines in the selected target areas. Finally, 
communities that were earmarked for drilling of the 10 project monitoring 
boreholes were visited and opinion leaders were made to understand the scope of 
work to be carried out. 
3.4 Field Investigation  
This comprised the following activities for providing primary data under this study: 
- Geophysical Survey 
- Drilling and Construction 
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- Geophysical logging 
- Pumping Test  
3.4.1 Geophysical Survey 
Geophysical techniques have long been used (since Wenner, 1915) to help in 
developing groundwater and various successes are recorded (Al-Shaibani, 2008; 
Chegbeleh et. al. 2014; Danielsen et al. 2007; Ewusi, 2006). However, for areas 
underlain by low permeability sediments such as the Nasia Basin, a comprehensive 
approach is required because of the low success rates in finding groundwater.  In 
this study primary as well as secondary data from the three geophysical methods, 
frequency domain conductivity using Geonics EM34-3XL, Electrical Resistivity 
Tomography (ERT) and geophysical logging of boreholes were used to assist in the 
characterising the hydrogeology of the Nasia Basin.  
3.4.1.1 Electromagnetic Survey (EM) 
The purpose of using this method is 1) to identify structural features; 2) for mapping 
of the geology; 3) as well as for comparing the results with electrical resistivity. 
The EM34-3XL frequency domain ground conductivity meter was used for the 
electromagnetic profiling survey and is shown in Figure 3-1.  
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Figure 3 - 1: Image of Geonics EM34-3 system (Xia et al. 2001) 
 
The Geonics EM34-3XL conductivity meter consists of a transmitter (Tx) and 
receiver (Rx) coils, a transmitter and receiver consoles (signal controller and 
measuring unit), reference cables (10 m, 20 m and 40 m) were used to determine 
terrain conductivity.  
Two field campaigns were undertaken for the conductivity measurements. For the 
first campaign, profiles were conducted at 10 locations laying on two parallel 
traverses starting from the North of the study area (Gambaga massif of the 
Panabako formation and the Poubogou formation) to the south (the Bunya 
sandstone member), selected using the database of the HAP (EM campaign I). 
Based on preliminary results obtained from interpretation of geophysical borehole 
logs, the Bimbilla formation was “loosely” divided into three sub-sections (the 
upper or elder zone-A, the middle zone-B and the lower or younger zone-C). At 
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each of those six locations within the Bimbilla formation two parallel profiles with 
mutual distance of 100 m were conducted and in direction South-North thus 
perpendicular to the expected strike of the formation. This second EM field 
campaign (EM campaign II) was focused on areas prone to flooding in order to 
ensure sufficient water for small scale pilot irrigation schemes. The 
Electromagnetic equipment provides a direct measurement of apparent 
conductivity in the region of the measuring coil using the principle of 
electromagnetic induction. The equipment is operated in the Horizontal dipole 
(HD=vertical position of the coils by staying on the ground) as well as in the 
Vertical dipole modes (VD=horizontal position of the coils by staying on the 
ground). The depth of investigation is approximately 0.75 times coil separation in 
HD-mode and 1.5 times coil separation in VD-mode.  
Accordingly, the depth of investigation (DOI) respectively for the HD-mode and 
VD-mode for the 3 applied coil separations is seen in Table 3.1. 
Table 3.1: Depth of investigation at different coil-separations in HD-mode and   
VD-mode. 
Coil separation: 10 m 20 m 40 m 
DOI for HD-mode 7.5 m 15 m 30 m 
DOI for VD-mode 15 m 30 m 60 m 
 
Both campaigns were conducted by using the 10 m and the 40 m coil separation, 
which provides the conductivity of a volume of rock down to four different depth, 
thus 7.5 – 15 – 30 and 60 m. Some of the collected EM-profiles (secondary data) 
from other investigations were conducted by using the 20 m and the 40 m coil 
separation. As seen from Table 3.1 that approach provides only three different 
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investigation depth because the 20 m VD-mode and the 40 m HD-mode has same 
investigation depth. 
Each of the 10 profiles on the first EM-campaign is 500-1000 m long traversing the 
various portions of the geological formations within the study area (Figure 3-2a) 
almost perpendicular to the general strike (E-W) of the formations. The 12 profiles 
at the 6 locations in the EM campaign II had lengths of 500 m. 
 
 
Figure 3 - 2a: A map showing the locations for the EM-points 
 
The eastern traverse runs from the Panabako sandstone in the Gambaga massif into 
the Kodjari formation and further crossing the Bimbilla and finally ends in the 
Bunya Sandstone member. The western traverse runs from the Poubogou 
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Formation into the Kodjari and further through Bimbila and finally ends in the 
Bunya sandstone member of the Bimbilla formation at Pigu.  
For each of the measurements, the transmitter operator stopped at the 
measurement station; the receiver operator moved the receiver coil backwards or 
forward until the meter indicated correct inter coil spacing and conductivity was 
then recorded from the second meter. The equipment was nulled at every site and 
after changing each cable and occasionally checking the battery level to ensure 
that the battery indicator was greater than 4.5. For accurate measurement with 
the EM instrument, alignment of coils is very important during measurement of 
ground conductivity. McNeil (1980) opines that measurement can be very 
sensitive to misalignment especially with the vertical mode. Measurements 
sensitive to human made structures which are conductive such as metallic 
objects, high electric cable, metallic roofing sheets among were avoided. The 
coils were also maintained in their respective coplanar modes of operation as 
much as possible at all times to reduce errors and for accurate measurements.  
The processed EM-data from campaign I and II are to be seen in Appendix 1. 
 
3.4.1.2 Electrical Resistivity Tomography (ERT)  
The ABEM Terrameter LS which employs the static Lund automatic Electrical 
Resistivity Imaging (ERI) system was used for the resistivity survey. A number of 
electrodes were secured into the ground and connected by electrode cables. A 
switching unit connected to a computer controls the transmission of current into the 
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ground through two of the electrodes, while potentials are measured over other 
electrode pairs (Dahlin and Zhou, 2004). The electrode spacing of 20 m was 
selected and the traverse was between 400 m to 800 m for each selected point.  
To drill the monitoring boreholes, 10 locations were selected for conducting the 
geophysical survey. These locations were deliberately selected on advice from the 
Danish counterpart GEUS on basis of the preliminary processing and interpretation 
of the AirTem data, and also with the view of having an even distribution of the 
boreholes within the study area. In addition to these 10 monitoring boreholes, 60 
successful boreholes fitted with hand pumps were identified in the study area, and 
were calibrated and electrical resistivity measurements recorded.  The locations of 
these ERT points is shown in Figure 3-4b. 
 
Figure 3-2b: A map showing the locations of the ERT points 
 
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The ERT was conducted between the EM-34-3 traverse lines covering a length of 
800 m. A multi-method configuration involving Schlumberger, Wenner, Gradient 
8 and Dipole-Dipole Array were used. The purpose of combining the different 
electrode configurations was to: 1) validate the results, 2) establish which 
configuration was suitable for the prevailing geological framework, 3) provide 
some level of confidence to support the decision making in selecting suitable 
drilling points. This is in line with the procedure carried out by Dahlin and Zhou 
(2004). The ERT-data set are to be seen in Appendix 2.  
3.4.2 Drilling and Construction 
Hydronomics Ltd, a private drilling company was engaged to drill the 10 boreholes 
to augment the data in the study area. In addition, 15 shallow wells selected across 
the study area with a view of having an even distribution were drilled through the 
weathered zone. The purpose was to determine the thickness as well as collect 
samples to estimate hydraulic conductivity of the weathered zone. The depth for 
these 15 shallow wells were up to a maximum of 15 m. The air-flush rotary method 
with rock-roller bits was used through the weathered zone and for the 10 deep 
monitoring boreholes the down-the-hole hammer bit was used for penetrating 
further into the underlying bedrock.  
The necessary hydrogeological information such as estimation of the airlift yield, 
potential fracture zones and water strike zones as well as the lithology were 
captured during the course of the drilling.  
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The Drillers report from the 10 deep boreholes as well as from the 15 shallow 
boreholes are to be seen in Appendix 4. Drilling record of existing boreholes 
(secondary data-Appendix 4) were used in estimating water strike zones. 
3.4.3 Pumping Test 
The pumping test was conducted only on six of the 10 monitoring boreholes drilled 
under this project because the remaining four did not have sufficient yield (dry or 
nearly dry). In addition to that, 10 existing boreholes fitted with hand pumps were 
selected and pump tested. These are boreholes that have data on geophysical survey 
and geological logs. Prior to the pumping test exercise, all pump heads were 
removed and care was taken not to drop the pump cylinder and riser pipes into the 
wells. The depth of the boreholes was measured using a calibrated rope tied to a 
heavy metal. The discharge rate was estimated during the pumping test. 
The duration of pumping for the boreholes varied from 6 hrs to 24 hrs for the 
constant discharge and between 3 to 6 hrs for recovery test.  The procedure for 
conducting both the constant discharge and recovery test were in line with the steps 
recommended by ICRC (2011) and MacDonald et al. (2005). 
 
3.4.4 Geophysical logging 
Wireline-logging was done after drilling of the 10 project monitoring wells in order 
to obtain a detailed lithological profile at each borehole, and to establish knowledge 
on the resistivity of the different formations. Furthermore, to validate the results 
and the interpretation of the respective ERT-profile. Wireline logging data from 
additional 7 boreholes previously drilled and used for monitoring by Water 
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Resources Commission under the Hydrogeological Assessment Project were made 
available and included in this study.  All the geophysical borehole logging was 
conducted by WRI using Robertson Geo wireline logging equipment and processed 
with Viewlog 4.0 from EarthFX (Klitten and Agyekum, 2019). 
  
In conducting the geophysical logging, Klitten and Agyekum (2019) indicated that 
the accessible depth section of each borehole was logged at 1 cm intervals using a 
set of five digital geologging tools (probes), Robertson’s (RG) Micrologger, a 
Winch, and a Computer. The logging tools used are: 1) Focused Guard (GLOG) 
tool to control the casing and screen setting and obtain information about the 
formation resistivity within the close vicinity of open-constructed borehole 
sections, 2). Dual Induction (DUIN) tool was undertaken to obtain information on 
formation conductivity, which is transformed into formation resistivity, even 
through plastic lined casings where the GLOG tool does not provide valid data, 3) 
Fluid Temperature and Conductivity (TCGS) logging tool conducted under static 
water condition to determine the log profile of groundwater salinity and 
temperature and 4) finally, High Resolution Impeller flowmeter (HRFM) logging 
tool to determine inflow rates in boreholes with yields higher than 20 lpm. The 
methodology used is in line with Klitten & Agyekum (2008), Agyekum & Klitten 
(2008), Agyekum (2009), and Agyekum et al. (2013) , all with reference to work 
on the Voltaian sedimentary rocks. 
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The processed wireline logging data from each of the 17 boreholes and the 
belonging interpretation are presented as comprehensive logging-sheets in 
Appendix 3. 
 
3.5 Data Processing and Analysis 
Prior to analysis, the data was manually inspected for quality assurance.  
3.5.1 Geophysical survey (EM34, ERT and borehole wireline logging) 
The interpretation of EM34 data is generally qualitative although their inversions 
can be done for layered models using some commercial programs (Inman, 1975). 
For this study, the results of the measured apparent conductivity in both horizontal 
dipole (HD) and vertical dipole (VD) modes were plotted on Microsoft Excel 
program against the station distance to give the measured conductivity. The 
response curves from both dipole modes were compared to determine anomalous 
regions. The criteria used for interpretation are in line with Chegbeleh et al. (2014) 
which is identifying possible gradual crossover and a gradual peak. Therefore, 
areas of interest for the interpretation were the crossovers which are locations where 
the VD response values exceeded the HD response values and also gradual peaks 
and sinks. These regions were used in mapping the geology based on the 
corresponding apparent conductivity.  
 
The resistivity data (ERT-profiles) at the 10 DWVP boreholes were processed with 
the RES2DINV version 3.5 (Loke, 2001), the raw data (the dat-file) is read into the 
software and edited by using the option ’change first electrode location’ to 
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reposition the first electrode at starting zero (0) mark of the profile line. The 
elevation information was also entered for each of the profile line in the inversion 
files and saved while the text files .txt were saved for documentation.  The 
approaches described by Loke and Dahlin (2002) and Loke et al. (2003) were used. 
The inversion method works by reducing the difference between the measured 
resistivities and the calculated response of the estimated model through a number 
of iterations, until satisfactory agreement between model response and field data is 
reached or no further improvement is possible (Dahlin 2001). This is usually 
represented in the value of the root mean square (RMS) error. The computer 
program automatically subdivides the subsurface into a number of blocks, and then 
uses a least-squares inversion scheme to determine the appropriate resistivity value 
for each block. The underlying model is a two-dimensional finite element model 
that accounts for topography. 
For all the 70 ERT-profiles, the processing was later repeated under study stay in 
Denmark by using the software “Aarhus Workbench” (AWB) version 5.8.0.8 
(Auken et al. 2009), which is a dedicated program package for management, 
processing, inversion, and visualization of geophysical data and models in a fully-
featured GIS environment. The dat-file was downloaded from the console of the 
ERT set-up into the AWB software. The dat-file is the main data file containing the 
measured DC and in order to ensure that the profile lines are correctly positioned 
on the GIS, an ewp file containing the coordinates was also created to help specify 
a coordinate system. A file was also created to supply the topography. It has a 
processing feature which shows the data points at each focus depth along with 
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pseudo section and electrode positions. The smooth method of inversion was 
carried out, and a priori information such as resistivity and layer thickness from 
geophysical logging of some of the boreholes was used to constrain the inversion. 
Compared with other 2D software which usually truncates the data around the start 
and end portion of the profile, this software is rather able to show the entire probing 
depth of the profile. The resulting resistivity 2D-profiles from the AWB-processing 
are shown in Appendix 2, however only 50 data-set were validated as being 
acceptable for AWB-processing.  
 
The hydrogeological interpretation of the geophysical data as presented in Chapter 
4 was based on the resultant 2D modeled images. The geophysical signatures were 
estimated from the resistivity values and the depths of penetration detected from 
the various profiles supported by knowledge about the lithological setting of the 
place from the borehole records. Priority was given to the AWB-processed images 
(profiles). 
 
For the geophysical logging, the software used for processing the data and 
presentation of the results is ViewLog 4.0 from EarthFX (Klitten & Agyekum, 
2019). It is a program designed for presentation of geophysical log data with 
geological logs and interpretations primarily for use in the non-petroleum sectors. 
The program was used to display conductivity log, resistivity log, gamma log, flow 
log and fluid conductivity & temperature logs. Detailed studies of the natural 
gamma enabled the correlation of the geophysical logs with the lithostratigraphic 
units within the study area. Analysis of the results from geophysical logging has in 
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this study focused on extracting resistivity of the encountered geological formations 
and comparing these with the ERT results and geologic logs. 
The general geophysical parameters of the various geological formation of the 
rocks was determined for the Nasia Basin based on comparison of the responses 
recorded from the three geophysical methods.  
3.5.2 Analysis of pumping test results  
In analysing pumping test data for aquifer parameters such as transmissivity, the 
Theis (1935) analytical model is the most common method that is used with specific 
assumptions (Kruseman and de Ridder, 1971). The Cooper-Jacob (1946) model has 
been suggested by Halford et al. (2006) to be the most convenient for single well 
tests. Acheampong and Hess (1998) for instance used this method in estimating 
aquifer parameters for the southern Voltaian Sedimentary Basin of Ghana. 
According to Todd (1959) and Acheampong and Hess (1998), the method was 
originally derived for isotropic porous media but aquifers with secondary 
permeability such as the study area exhibit homogeneous characteristics when 
sufficiently large volumes of water are considered and as such the Cooper-Jacob 
(1946) method could be used. Researchers such as Yidana et al. (2011) suggest that 
results from such tests produce reasonable hydraulic characteristics of aquifers for 
basin characterization.  
Halford et al. (2006) indicate that the Cooper-Jacob (1946) method requires that 
pumping should be done for a reasonable length of time at a sustained rate for 
transient conditions to be induced. Since the pumping test was conducted without 
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an observation borehole, only drawdown data from the pumped well is available. 
Drawdown is not only caused by the aquifer conditions, but also caused by “well 
loss” such as turbulent water flow which results in additional drawdown of the 
water level. To minimise the effect of such “well loss”, the early portions (1st log 
cycle) of the drawdown-time curve data is not used for the analysis (Acheampong 
and Hess, 1998; Darko, 2001).  
For this study, out of 67 pumping test data-set available, 41 boreholes were pumped 
at six (6) hours with three (3) hour recovery. These 41 data-set could not be used 
for the transmissivity calculation because of the short duration of pumping but were 
used for calculating specific capacity. The remaining test data from 26 boreholes 
were pumped between 12 hrs-24 hrs with a few pumped for 9 hrs and this pumping 
duration was considered to be significant enough to induce transient conditions as 
such have met the Cooper-Jacob (1946) method assumptions. 
The data from the pumping test was presented in Micosoft Excel and the drawdown 
and residual drawdown were estimated by subtracting the static water level from 
the dynamic water level. Semi-logarithmic graphs were constructed by plotting 
drawdown on the vertical axis against time in minutes on horizontal logarithmic 
axis representing the constant discharge curve for each data-set. The recovery data 
were similarly constructed by plotting the residual drawdown against logarithm of 
time in minutes after pump stop.  
Transmissivity (T) was therefore estimated on drawdown data as well as on 
recovery data for the 26 boreholes based on the Cooper-Jacob (1946) method as 
shown in equation 3.1 
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0.183Q
 T =          3.1 
△S
△S is the drawdown per log cycle in m, Q is discharge rate in m3/d and T in m2/d 
Since the pumping duration for all the 67 data-set was at least 6 hours, the Specific 
Capacity (SC) was calculated from drawdown after 360 minutes of pumping using 
equation 3.2. 
SC = Q/S         3.2 
where Q is the discharge rate in m3/d and S is the drawdown after 360 minutes of 
pumping.  
3.5.3 Estimation of Recharge 
 
Recharge is an essential parameter in determining groundwater potential as 
suggested by several researchers (Forkuor et al. 2013; Macdonald et al. 2016; 
MacDonald et al. 2001). Recharge estimates at 72 water points  were obtained from 
Addai et al. (2016) and shown in Appendix 8. They are based on Chloride Mass 
Balance method, thus the recharge Rg = (Clpr /Clgw )*Pr – where Clpr is the 
Chloride content in precipitation, Clgw is te Chloride content in groundwater, and 
Pr is the precipitation. This method was used due to its apparent conservative nature 
based on an assumption that, the only source of chloride in groundwater system 
comes from precipitation and that chloride does not react or decay in the process of 
reaching the groundwater system (Yidana and Koffie, 2013). Researchers such as 
Carrier et al. (2008) have used this method due to its simplicity in design.  
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3.5.4 Geostatistical Analysis  
For the present study, the available transmissivity estimates from 26 boreholes is 
not sufficient to characterise the hydrogeology of the area. Therefore, two 
geostatistical methods were used to estimate the transmissivity from specific 
capacity and the best approach was adopted. First and foremost, an empirical 
relationship in the form of regression analysis between transmissivity and specific 
capacity was established for two scenarios; a relationship between transmissivity 
for the entire Nasia Basin (based on the 26 boreholes) and another which is 
formation-specific (thus based on a rather low number of boreholes within each 
formation). Based on the results of the regression analysis, a comparison was then 
made between these two scenarios to determine the major factors that control 
groundwater within this highly heterogeneous environment. Even though the data 
for transmissivity is limited and would affect the purpose of the comparison, this 
approach could be built upon by future researchers with abundant data to 
characterise aquifers. The software used for the regression analysis is IBM SPSS 
Statistics version 20 (IBM Corp. 2011). 
 
Secondly because of the abundance of specific capacity data, transmissivity was 
estimated using cokriging as recommended by Aboufirassi and Mariño (1984). The 
software used is GS+ version 10 (Robertson, 2008). 222 specific capacity data were 
used as secondary variate and 26 transmissivity estimated using Cooper-Jacob 
(1946) method as the primary variate. Structural analysis using variograms was 
conducted prior to kriging and cokriging for all the parameters following procedure 
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adopted by (Aboufirassi and Mariño, 1984; Isaaks and Srivastava, 1989; Razack 
and Lasm, 2006).  
Interpolated maps using kriging were developed to estimate spatial distribution of 
depth, specific capacity, yield, static water level, regolith thickness, recharge and 
hydraulic conductivity at unsampled locations in the study area. The maps of the 
respective parameters were produced using GS+ version 10 (Robertson, 2008).  
Graphical representations of the dataset (histograms and box and whiskers) were 
constructed using SPSS for easy examination of parameters. The histogram was 
also used to check both the skewness and kurtosis of the dataset. Descriptive 
statistics such as central tendencies (mean, median and mode) and standard 
deviation were also estimated using the IBM SPSS Statistics software. Normal 
distribution and homoscedasticity are key requirements of optimal multivariate 
statistical analyses. All the parameters in the dataset did not meet the requirement 
for normal distribution which is very common for most earth science data, therefore 
they were transformed and also back transformed using the function within the GS+ 
version 10 (Robertson, 2008) to obtain a normal distribution since this is an 
essential requirement for optimal geostatistical modeling (Salifu et al. 2013; Yidana 
et al. 2011).  This was followed by construction of experimental variograms and 
cross-variogram for the selection of an appropriate theoretical model. The sill, 
nugget and range were determined to help in explaining the variability of the 
parameters. The final step was validating the model and creation of an interpolated 
map of the respective parameters. 
 
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3.6 Remote Sensing and Geographical Information System  
3.6.1 Satellite Imagery and Digital Elevation Model (DEM) 
Two satellite data, Advanced Spaceborne Thermal Emission and Reflection 
Radiometer (ASTER) Digital Elevation Model (DEM) were obtained from United 
States Geological Survey (USGS) Earth Explorer website (earthexplorer.usgs.gov). 
Figure 3-4 is the model builder using ArcGIS 10.6 (ESRI, 2011) for the estimation 
of slope and drainage density layers. 
 
 
Figure 3 - 3: Model builder for remote sensing 
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3.6.2 Slope and Drainage Density 
The slope was also derived from the DEM image using the slope tool in the 
spatial analyst tool in ArcGIS 10.6 (ESRI, 2011). 
Drainage density is the total length of all the streams and rivers in a drainage basin 
divided by the total area of the drainage basin (Pradhan and Youssef, 2010). The 
drainage analysis involves various steps to arrive at drainage density for the study 
area. The methodology used by Seidu (2017) was adopted for the analysis by 
filling in all sinks in the DEM of the Nasia Basin using the fill tool in ArcGIS 
10.6 (ESRI, 2011). The flow direction tool was used to create a raster data 
from the DEM to show all the direction that water flows on the surface. Other 
tools used are the stream to feature, stream order together with the conditional 
tool to create line density and subsequently the drainage density.  These steps 
are illustrated in Figure 3.4.  
 
3.6.3 Lineament density 
The lineaments are linear features on the Earth’s surface that reflect a general 
surface expression of underground fractures (Pradhan and Youssef, 2010). The 
lineament data for the study area was acquired from Water Resources Commission 
under the Hydrogeological Assessment Project (HAP) funded by the Canadian 
International Development Agency (CIDA) and also from a digitized map from the 
Ghana National Petroleum Commission. These two lineament files were converted 
into shapefiles and merged in ArcGIS 10.6 (ESRI, 2011). The merged lineament 
shapefile was then converted to a raster image to be used for multi criteria analysis. 
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3.7 Groundwater Potential Map  
Several researchers (Forkuor et al. 2013; Chandra et al. 2019; Gumma and Pavelic, 
2013) have integrated Remote Sensing (RS) and Geographical Information System 
(GIS) to produce groundwater potential map in an effort to manage groundwater 
resources.  
For the Nasia Basin, the files of the parameters used for the potential map were 
exported from GS+ to ArcGIS 10.6. The eight parameters are well depth, depth to 
static water level, regolith thickness, recharge, transmissivity, terrain slope, 
drainage density and lineament density. The next step was to mask these parameters 
with the shapefile of the Nasia Basin. After masking the parameters, they were 
converted to raster images for standardization. The raster images for each of the 
parameters were reclassified due to their disparate scales to make any combination 
meaningful (Forkuor et al. 2013). 
3.7.1 Weighted Overlay Analysis 
This is a combined analysis of multiclass maps and has the advantage of integrating 
human judgment based on experience about the local environment. No standard 
scale is required and it considers the relative importance of each parameter and the 
classes it belongs to (Saraf and Choudhury, 1998). 
Weights were assigned to the various parameters based on a probably subjective 
valuation of their contribution and influence on groundwater potential within the 
study. Studies by Forkuor et al. 2013; Gumma and Pavelic, 2013; and Nsiah et al. 
(2018) served as a guide in assigning the weights to the parameters. The thematic 
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maps were overlaid using the overlay tool in Arc GIS 10.6 to produce a 
comprehensive groundwater potential map. Figure 3.5 is a flowchart illustrating the 
steps followed in producing the groundwater potential map for the Nasia Basin. 
3.7.2 Classification of Groundwater Potential Map 
The groundwater potential was classified into five zones. This classification is 
based on the probability of each zone having a high yielding to low yielding 
potential based on the combination of the various thematic layers. The zones are as 
follows: (a) very poor (b) poor (c) moderate (d) good, and (e) very good.  
3.7.3 Validation of Groundwater Potential Map 
The reliability of the groundwater potential map was tested against measured 
borehole yields and also locations of dry boreholes. A number of researchers have 
used yield as an independent parameter in validating potential maps that have been 
developed (Forkuor et al. 2013; Gumma and Pavelic 2013). 
3.7.4 Sensitivity Analysis 
Sensitivity analysis is used to determine the influence of different criteria weights 
on the spatial pattern of a suitability map (Nwer, 2005). For this research, two 
scenarios to test the influence of the weights assigned to the individual parameters 
on the final groundwater potential map was performed. The approach used by 
Napolitano and Fabbri (1996) was employed to test the sensitivity of the map. 
 
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Figure 3 - 4: Flow chart for developing groundwater potential map 
 
 
 
 
 
 
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CHAPTER FOUR 
RESULTS AND DISCUSSION 
4.1 Geophysical Investigations  
The results of geophysical investigations are discussed based on two broad 
categories. The first approach evaluates the current method used by 
hydrogeologists in interpreting geophysical results within the Nasia Basin. The 
second approach seeks to determine the most appropriate method for groundwater 
exploration in view of the challenges faced with groundwater development in the 
study area. It must be noted that not all the geophysical results in the communities 
are presented in this chapter but selected results in each of the geological 
formations. Results of all primary as well as most of the secondary geophysical 
investigations using the electromagnetic method, electrical resistivity tomography 
(ERT) and geophysical wireline logging are included as Appendices 1, 2 and 3 
respectively. 
4.1.1 Evaluation of current hydrogeological interpretation method of 
geophysical investigations 
The electrical resistivity method is the most widely used method for groundwater 
prospecting in Ghana and has produced good results mainly for the crystalline 
basement. Within the VSB however, the results have been appalling for many 
reasons, example wrong interpretation of geophysical results. Figure 4.1a shows 
the electrical resistivity imaging (ERT-profile) at borehole DWVP09 and with 
traverse length of 800 m. Figure 4.1a illustrates an instance where wrong 
interpretation has resulted in a dry borehole. The ERT results from Figure 4.1a 
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indicates that station 370 with the pointed arrow was the point selected for drilling. 
The profile at the drilling point essentially shows a three-layer structure. A 20 m 
thick high resistivity top layer (>3500 Ωm) overlies a 40 m conductive layer (20 
Ωm), which in turn overlies a moderately resistive layer (80 Ωm). The root mean 
squared (RMS) error of electrical resistivity from the profile is 41.7%. Borehole 
DWVP09 is situated within the Panabako sandstone formation with the upper 
portion of the profile interpreted as laterite and dry weathered sandstone accounting 
for the high resistivity recorded. The second layer at point 370 m was interpreted 
as a possible wet fractured/saturated sandstone layer because the resistivity was 
very low, approximately 20 Ωm. 
 
Figure 4 - 1a: ERT-profile (RES2DINV processed) at DWVP09 Samene, drilled at 
station 370. 
 
This interpretation is consistent with the observations of Ewusi et al. (2009) who 
indicate that within the sandstone formation, resistivities below 70 Ωm should be 
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targeted.  Despite the low resistivity interpreted as fractures within the bedrock, the 
drilling was unsuccessful with borehole yield of 4 lpm which is below the minimum 
required yield of 10 lpm for even a hand pump. This indicates that the geological 
environment is complex as such the groundwater exploration must be based on a 
sound understanding of the geology and proper interpretation of the geophysical 
results (Mainoo et al. 2019).  
Another instance that explains the wrong interpretation of geophysical results is 
illustrated by the reliance on the root mean squared (RMS) error after iterations. 
The RMS error is the difference between the calculated and measured apparent 
resistivity values as a result of adjustment made to the resistivity of the model 
blocks subject to the smoothness constraints used. The aim is to obtain a model 
with the lowest possible RMS error. However, that model can sometimes show 
large and unrealistic variations in the resistivity values thereby not being the most 
probable model from a geological perspective. Figure 4.1b shows a 2 D electrical 
resistivity profile at borehole DWVP02 similarly located within the Panabako 
sandstone of the Voltaian supergroup. The borehole was drilled at station 410 m 
(with the pointed arrow) and the root mean squared error of electrical resistivity 
values is 42.8%. The results show three sections, a 10 m thin upper layer with very 
high resistivity (>2000 Ωm) overlying a low resistivity layer (<64 Ωm) but only to 
55 m depth followed by high resistivity (>5000 Ωm). The upper layer was 
interpreted as dry weathered sandstone followed by wet fractured layer with low 
resistivity and the high resistive layer beyond 55 m as the fresh sandstone bedrock.  
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Figure 4 - 1b: ERT-profile (RES2DINV processed) at DWVP02 Tamboku, drilled 
at station 410. 
 
Considering the high value of RMS error for this profile,  (42.8%), researchers such 
Ashraf et al. (2018) and Loke, et. al. (2003) have advocated for a rejection of the 
model if RMS error exceeds a threshold of 5% because according to them it 
indicates an unacceptable fit. This means the model badly represents the measured 
apparent resistivities.  However, after the drilling and pumping for 24 hrs, a yield 
of 132 lpm was recorded and with a final drawdown of 6 m only. It is noticeable 
on the wireline flow-log seen on Figure 4-1c, that both the two water bearing 
fractures are located in the boundary between a low resistivity and a high resistivity 
bed which is the boundary between two different lithological units. Irrespective of 
the high RMS of the processed ERT-profile, there is a relatively good fit to the 
actual resistivities and their variation towards depth on the wireline logs seen on 
Figure 4-1c.  
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Figure 4-1c: Geophysical wireline logs of borehole DWVP 02 Tamboku, drilled at 
ERT station 410. 
The shifts from high resistivity (>2000 Ωm) to low resistivity (<64 Ωm) at 10 m 
depth followed by again high resistivity (>5000 Ωm) on the ERT-profile are seen 
on the Calc. Resistivity-log at respectively 14 m and 44 m depth. Furthermore, the 
resistivity level on the latter fits very well to the level of the two upper layers on 
the ERT-profile, whereas the resistivity below 44 m depth on the wireline-log is 
100-500 Ωm, thus far much lower than the high resistivity (>5000 Ωm) at greater 
depth on the ERT-profile. 
 
This outcome of the ERT-profile at borehole DWVP02 contradicts that of Figure 
4.1a because both ERT-profiles have high values for RMS error, both boreholes 
were drilled in a low resistivity zone and in the same geological formation. This 
result clearly indicates the complex nature of the hydrogeology and implies there 
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are other controlling factors that should be considered when exploring for 
groundwater. 
4.1.2 Development of a more appropriate groundwater exploration 
method within the various geological formations 
Researchers such as Chegbeleh et al. (2014), Ewusi et al. (2009) and Menyeh et al. 
(2005) have provided techniques of improving the location of targets for water 
drilling in Ghana or provided methods of interpreting geophysical results for 
groundwater exploration. Though few successes have been recorded from some of 
these approaches, a lot needs to be done in a more holistic and scientific manner. 
For instance, Chegbeleh et al. (2014) talks about the ability of the EM equipment 
to detect fractured zones and for delineation of geological features, and also 
incorporating knowledge about local geology and other hydrogeological factors in 
interpretation. Comte et al. (2012), Khalil et al. (2012) and Van Dam (2010) have 
indicated that using more than one geophysical method enhances data interpretation 
and better results are obtained. 
The results of the geophysical investigations are discussed with respect to the 
geological formations within the two main geological groups (Bombouaka and 
Oti/Pendjari) in the study area. An integrated approach is applied to characterise 
the aquifers for the respective formations. The results of the processing of an ERT-
profile in terms of vertical distribution of resistivity like in the previous section 
(4.1.1) has been validated by comparison to the wireline resistivity log in the 
borehole drilled on basis of interpretation of the ERT-profile. In situations where 
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there are EM34-profiles at the same location or in close proximity, it has been 
included in the comparison. 
The ERT is expected to be useful for hydrogeological investigations as it is 
sensitive to the aquifer lithology (clay mineral content), the pore-water content and 
pore-water mineralization; it allows the spatial distribution of these important 
hydrogeological features to be characterized (Fetter 1988). However, 
differentiating among these features is difficult on the basis of the ERT results 
alone. Knowledge from wireline logs on resistivity of the actual geological 
formation aids in the processing and interpretation of ERT-data and EM34-data. 
Therefore, through coupling ERT with other methods of investigation or datasets 
this difficulty is resolved. 
 
4.1.2.1 Bombouaka Group 
There are two geological formations that fall within the Bombouaka group in the 
Nasia Basin, the Poubogou formation and the Panabako sandstone formation as 
seen in Figure 4.2 and in addition, the locations of boreholes in the Bombouaka 
Group with the respective geophysical data discussed in this chapter are shown.  
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Figure 4.2: A geological map showing the locations of DWVP09-DWVP02-
Tenkpanga-DWVP01-HAP11 
4.1.2.1.1 Poubogou Formation 
The Poubogou Formation forms a small portion of the basin and is made up of 
green-grey, micaceous mudstones and siltstones with intercalations of arenaceous 
and argillaceous material (Carney et al. 2010).  
The thin intercalations of siltstone and fine-grained sandstone according to Ayite 
et al. (2008), are typically planar bedded, with sharp top-surfaces and undulatory, 
slightly channelized bases which in places show spectacular ball-and-pillow load 
structures.   
Figure 4.2a shows results from the electromagnetic (EM) profiling measurements 
located at Tenkpanga (from campaign I) in both horizontal dipole (HD) and vertical 
dipole (VD) modes plotted against station distance. The conductivity measurements 
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in both modes are conducted by using the 20 m and 40 m coil separation, thus 
obtaining depth of investigation 15 m and 30 m as well as 30 m and 60 m 
respectively. Two sections of contrasting conductivity are visible; from SSW a high 
conductivity is recorded on both the 20 m and 40 m coils (0 to about st. 600 m) 
towards NNE is juxtaposed to a relatively low conductivity between 600 m and 
1000 m with a stream flowing from NW towards SE.  This stream is interpreted as 
a normal fracture network. Field observation confirms the presence of few 
sandstone outcrops towards north eastern part of the traverse. The high conductivity 
(unstable) between 0 and 600 m is probably associated with Poubogou mudstones 
and siltstones while the low conductivity towards NNE indicates presence of 
Panabako sandstone overlaying Poubogou formation. 
80 TENKPANGA - EM PROFILE
70
60
50
40
30
20
10
0
0 200 400 600 800 1000 1200
Distance(m)
SSW HD_20m VD_20m HD_40m VD_40m NN
 
Figure 4 - 2 a: EM-34 profile of Tenkpanga in the Poubogou formation 
 
Validation of the interpretation of the EM results was provided by an 800 m ERT 
profile undertaken along the same traverse. Figure 4-2b with the ERT profile after 
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AWB processing shows a general dip of layers towards NNE with increasing 
resistivity. The topmost layer (about 10 m) shows high resistivity which could 
represent the hard-lateritic cover and dry weathered materials. That fits well with 
the lower conductivity at 20 m coil separation compared to 40 m coil separation on 
the EM-34 profile, Figure 4-2a. Two contrasting resistivities are also observed 
below the 10 m depth confirming the conductivity variation along the EM profile.  
A relatively low resistivity is recorded from the SSW direction towards a higher 
resistivity in the NNE direction. Even though the two methods show similar 
resistivity/conductivity variation along the traverse and with depth, it is worth to 
notice that the ERT-resistivity in general seems far too high compared to the 
resistivity elaborated from the EM-34 irrespective of the coil separation and 
HD/VD mode. The ERT-resistivity is higher than 1000 Ωm in the uppermost 20 m 
compared to EM-conductivity of 15-40 mS/m equal to 60-25 Ωm for coil 20 m 
separation and in both modes. For both modes but with 40 m coil separation the 
EM-conductivity 30-60 mS/m equal to 30-15 Ωm, thus similarly much lower that 
the ERT-resistivities below 20 m depth. 
 
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Figure 4 - 2 b: ERT profile of Tenkpanga in the Poubogou formation (AWB 
processed). 
 
From the geophysical results for the Poubogou formation, it is evident that the 
traditional approach of interpreting geophysical results is inappropriate and may 
have contributed to the high number of unsuccessful boreholes within the VSB 
(Akudago et al. 2009).   
When prospecting for groundwater within the Poubogou formation, the main factor 
to be considered is most probably the contact between the Poubogou 
mudstone/siltstones and the occasionally overlaying Panabako sandstones which is 
reflected on ERT-profiles by the contrasting resistivity and by contrasting 
conductivities on EM-profiles in deep exploration modes (HV-40 and VD-40). 
Groundwater targets are not necessarily the low resistivity or high resistivity units 
in the profile. Secondly, the direction of the profiles compared to the expected strike 
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of the formation, as well as of the dip and the trend of the resistivity distribution 
should also be considered. 
4.1.2.1.2 Panabako Sandstone Formation 
This formation is mainly made up of fine-to medium-grained sparsely feldspathic 
quartz-arenites and it covers the north-north eastern portion of the Nasia basin. 
Results from ERT, geophysical borehole logging and Electromagnetic profiling at 
two more locations within the Panabako sandstone formation have been used to 
analyse the option for characterising the aquifers for groundwater exploration 
within this formation. Figure 4.2 c and Figure 4.2 d show the ERT-profile (also 
included in Appendix 2) and geophysical wireline logs of DWVP01 located at 
Tamboku. The AWB-processed 800 m long ERT-profile (Figure 4.2 c) shows three 
layers, with the upper 10-15 m thick layer having from SW towards NE two 
contrasting resistivities- a high resistivity from SW (>500 Ωm), station 0 to 340 m 
then a relatively low resistivity (100-200) Ωm from station 340-m to 800-m. The 
upper layer overlies a 20-30 m thick relatively low resistive layer (60-100) Ωm 
which is followed by a high resistive bedrock (>500 Ωm). A subvertical structure 
(located approximately between st.340-420m) indicated by two contrasting 
resistivity from the upper layer to the bedrock is observed.  The high resistivity at 
the upper layer is interpreted as dry weathered sandstone (or laterite) whereas the 
second layer with low resistivity is characteristic of fractured or intercalations of 
siltstone/clay.  
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Figure 4 - 2 c: 800 m long ERT profile (AWB processed) at DWVP01, Tamboku 
(drilled at station 420) in the Panabako sandstone formation. 
 
Since primary permeability within this geological environment is virtually non-
existent, interpretation of geophysical survey results should be targeted at sections 
that could indicate secondary permeability (Yidana et al. 2020). Figure 4.2 c for 
instance, shows a subvertical structure as mentioned above between stations 340 m 
to 420 m. Station 420 m was therefore selected for drilling which resulted in a yield 
of 22 lpm after 24 hr pumping test (specific capacity after 6 hours was 0.33 lpm/m). 
This geophysical result of the AWB-processing of the ERT-profile is much better 
corroborated with the result of the geophysical wireline-logging in borehole 
DWVP01 at station 420 m (Figure 4.2 d) than the comparison with the RES2DINV 
result as seen in Appendix 5A.  
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Figure 4 - 2 d: Geophysical wireline logs of borehole DWVP01(drilled at ERT st. 
420) in Panabako sandstone. 
 
Furthermore, several portions of the wireline logs (Gamma-, Conductivity- and 
Resistivity-log on Figure 4-2 d) show values interpreted as intercalation of 
mudstones/clay layers within the sandstone as suggested by Ayite et al. (2008), but 
neither seen on the drillers log nor on the log of the geologist. Notice also, that the 
resistivity log (Figure 4.2d) calculated from the conductivity-log shows 10 m upper 
layer with high resistivity followed by 30-40 m shifting but in average lower 
resistivity over shifting but in average high resistivity layers to 115 m depth, i.e. 
generally a three-layer situation with mutual resistivity proportion similar to the 
ERT-profile as it is described above. 
 
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One more case with analysis of different geophysical methods applied at same 
borehole in the Panabako formation is from borehole HAP 11 at Nalerigu SHS. 
(secondary data) with conductivity measurements from 20 m and 40 m coil 
separation over a traverse of 400 m. Comparison of the response curves from both 
the horizontal and vertical dipole modes with 40 m coil separation (deep 
exploration depth) shows three distinct peak anomalies at the stations C70, C175 
and C 265. These anomalies are thought associated with subvertical fracture zones 
in the bedrock. Therefore, station C265 on the EM-34 profile was selected for 
drilling and it yielded 24 lpm.  
 
Figure 4 – 2 e: EM profile at HAP 11 (Nalerigu SHS at st.265) in the Panabako 
sandstone formation 
 
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The ERT-profile (secondary data) along the same traverse as the EM-profile and in 
the same direction is shown on Figure 4-2f. It shows a two layered situation, a high 
resistivity, ≥1800 Ωm at the upper layer and gradually decreasing downwards to 
below 460 Ωm between 20 m to 30 m depth suggesting a possible change in 
lithology. The high resistivity is interpreted as lateritic cover and dry weathered 
sandstone, and the lower resistivity is interpreted as wet fractured sandstone or 
sandstone with intercalations of mudstone.  
 
Figure 4 – 2 f: ERT profile (RES2DINV processed) at borehole HAP 11, Nalerigu 
SHS (drilled at st. 265). 
When comparing the ERT-profile with the EM-profile there are significant 
differences and some similarities: The lower resistivity from st. 0 to st. 180 of the 
uppermost 20-40 m thick layer compared to the resistivity on the remaining section 
of the ERT-profile is not reflected by corresponding contrary conductivity variation 
on the EM-20 m coil-separation profile irrespective of the modes HD or VD. There 
is hardly any variation seen on the EM-20 m coil profile. The generally lower 
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resistivity below 20-40 m depth on the ERT-profile is confirmed by the generally 
higher conductivity on the EM-40 m coil separation profile compared to the EM-
20 m coil separation profile. Similarly, the conductivity variation along the EM-40 
m profile showing lower conductivity from st. 0 to st. 180 compared to the 
remaining section fits well to the higher resistivity below 30-40 m from st. 0 to st. 
180 on the ERT-profile compared to the remaining section on the ERT-profile. 
Summarizing the ERT-profile shows a two-layer situation, an extremely high 
resistivity (>1000Ωm) to 20 m depth followed by a somehow lower resistivity 
(<500 Ωm) to at least 60 m depth. Oppositely, the wireline logs (Gamma- and 
Calculated Resistivity-log on Figure 4-2g) unambiguously show the occurrence of 
6 different lithologies within the same depth of 60 m, and with following 
resistivities: 500-1000/ <40/ 250/ 75/ 1500/ 250 Ωm corresponding to sandstone/ 
claystone/ sandstone/ claystone/ sandstone quartsitic/ sandstone and with the 
following thicknesses: 10/ 4/ 17/ 2/ 11/ 20 m. Below the depth of 64 m, the 
resistivity gradually decreases to 100 Ωm at 83 m depth. Below this depth a 
completely different rock occurs with frequently varying resistivity below 100 Ωm 
as well as frequently varying but rather high gamma-radiation, a mudstone with 
frequent intercalations of siltstone and fine-grained sandstone, thus most probably 
the Poubogou Formation. 
Obviously, neither the ERT-profile nor the EM-profile with four investigation 
depth can reflect the details of a complex lithological profile as the one in borehole 
HAP 11 documented by the geophysical wireline logging, Figure 4-2g. Both 
methods do reflect, that the resistivity of a top layer is higher than the average 
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resistivity below. However, the magnitude of the resistivities on the ERT-profile 
seems generally too high compared to the resistivity-log (calculated from the 
Induction conductivity-log), whereas the EM-conductivities of the 20 m as well as 
of the 40 m coil separation in both modes correspond reasonable well to the 
conductivities on the Induction-log (Long coil distance = DUIN L.Cond). 
 
 
Figure 4 - 2 g: Geophysical wireline logs of HAP 11 at Nalerigu SHS  
 
In summary, within the Panabako sandstone formation water-bearing sub-
horizontal fractures if any are often related to distinct distinct lithological 
boundaries (like in DWVP 02 mentioned earlier) and occurrence of several 
significant intercalations of claystone in a sandstone are therefore good indicators 
for groundwater availability. Where there were intercalations of the different units, 
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the layers of each individual rock materials are easily distinguished by differences 
in gamma radiation and in resistivity on geophysical wireline logs in a borehole. 
Unfortunately, none of the two surface geophysical methods, ERT and EM, can 
provide such detailed information but only rough indication of the very general 
lithological profile and furthermore identification of eventual sub-vertical 
boundaries between significant different lithologies. Though, if the two methods 
are indicating that a certain depth section of the sandstone has lower resistivity than 
otherwise normal for that type of sandstone, it can be an indication of claystone 
intercalations thus being a positive indication on possibility for sub-horizontal 
water-bearing fractures along the boundary between the sandstone and the 
claystone intercalations. Therefore, the recommendation given by Ewusi et al. 
(2009), that within the sandstone formation, resistivities below 70 Ωm should be 
targeted, needs to be evaluated in terms of whether a low resistivity is a horizontal 
anomaly on a profile thus indication of a sub-vertical fracture zone – or an 
anomalous low resistivity at a certain depth thus indicating a section in the 
sandstone with claystone intercalations. 
4.1.2.2 Oti/Pendjari Group 
This group is made up of the Kodjari and the Bimbila formations within the study 
area as seen on the map below, Figure 4.3, where also the locations with boreholes 
and with geophysical data discussed in this section are shown. 
 
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Figure 4.3a: A geological map showing DWVP 10 & Sandua – DWVP 05 - DWVP 
08 & Nakpaya – HAP 05 and Disiga – HAP 14 – Bugya Pala (WVB 11 & B2). 
4.1.2.2.1 Bimbilla Formation 
This formation constitutes about two-thirds of the study area comprises green to 
khaki, micaceous laminated mudstones, siltstones and tabular, sharp-based 
sandstones. The Bunya Sandstone Member forms the top of this Formation, with 
few outcrops in the southernmost portions of the study area (Carney et al. 2010; 
Jordan et al. 2009). The four locations (DWVP 10, DWVP 05 & DWVP 08, and 
HAP 05) within this formation selected for discussion are expected to represent 
respectively elder-, middle- and younger part of the formation. 
The first case within the Bimbila formation selected for discussion is the 100 m 
deep borehole DWVP 10 at Sakpa representing the eldest part of the Bimbila 
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formation since the Kodjari formation is encountered at 73 m depth, Figure 4.3c. 
The 400 m ERT-profile at borehole DWVP 10 (Figure 4.3b) shows a 30-40 m top 
layer with low resistivity (25-50 Ωm) along most of the profile, though being above 
100 Ωm from st. 280. Below the top layer seems the resistivity gradually to increase 
towards greater depth and surprisingly to be more than 1000 Ωm below about 60 
m depth. The lateral variation of the resistivity indicates slightly dipping layers, but 
without indication of any significant resistivity anomaly. 
 
Figure 4 - 3b.: 400 m long ERT-profile (RES2DINV processed) at borehole DWVP 
10 Sakpa (at st. 120) located within “Bimbila old” underlain by the Kodjari 
formation. 
Comparison of the ERT profile and the wireline resistivity log in the 100 m deep 
borehole DWVP 10 (Figure 4.3c) shows a generally bad fit. Because the resistivity 
log shows in the uppermost 20 m only 15-20 Ωm, and below 20 m depth follows 
an almost uniform siltstone with resistivity of 20-30 Ωm to 85 m depth after which 
the resistivity is increasing to 50 Ωm only. Thus the significantly increasing 
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resistivity from being 50 Ωm at 30-40 m on the ERT-profile to much higher than 
1000 Ωm below 60 m depth does not tally at all with the wireline resistivity log. 
 
Figure 4 – 3c: Geophysical wireline logs of borehole DWVP10 Sakpa (drilled at 
st. 120 on ERT-profile) showing Bimbila formation underlain from 73 m by Kodjari 
formation. 
Opposite to what was not seen on any of the previous shown resistivity logs in 
boreholes in the Panabako formation, the resistivity of the uppermost 10-20 m of 
the Bimbila formation has always a lower resistivity than seen in the bedrock 
below, thus reflecting the weathering of the latter (Saprolite + Saprock). 
This is confirmed by the results of conductivity measurement from the 
electromagnetic profile in Figure 4.3d located at Sandua some 20 km WSW of 
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borehole DWVP 10, and also expected to represent the elder part of the Bimbilla 
formation. The EM profile-A is one of two parallel 500 m long and SSE-NNW 
directed profiles conducted in campaign II with the purpose of localizing a feasible 
site for dug-well construction in order to exploit groundwater from areas with as 
thick Saprolite & Saprock as possible. The dry borehole DWVP 08 is located about 
400 m NNE of the northern end of the EM profiles and was already drilled, when 
the EM survey was conducted. 
 
Figure 4 – 3 d: EM Profile-A as one of two parallel SE-NW profiles at Sandua 
(“Bimbila old” formation). 
The conductivity on the 10 m coil separation profile for both dipole modes is 
significantly higher than on the 40 m coil separation profiles. The VD-40 profile 
indicates a rather uniform bedrock with resistivity of 20-25 Ωm, whereas the three 
more shallow profiles, VD-10, HD-10 and HD-40 indicate thicker and more clayey 
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low resistivity (10 Ωm) overburden on the right section compared to the left section 
of the profile (the northern section compared to the southern). Accordingly, this 
northern section on the profile with thicker and more clayey overburden is 
considered to provide good groundwater storage thus a dug well was suggested to 
be constructed at st. 280 as shown on Figure 4.3d above. 
The second case within the Bimbilla Formation selected for discussion is the 100 
m deep borehole DWVP 05 at Kpodu (Pobbul), which is expected to represent the 
middle part of the Bimbila formation. Figure 4.3e and Figure 4.3f show respectively 
the ERT-profile and the geophysical wireline logs of borehole DWVP05 (Kpodu). 
The 400 m long ERT profile shows a very low resistivity (<15 Ωm) in the 30 m top 
layer along the first 180 m against a generally higher but varying resistivity in the 
30 m thick top layer along the last 220 m profile. The borehole DWVP 05 was 
suggested drilled at st. 200 being a relative resistivity-high anomaly (40 Ωm) in the 
top layer to 40m depth followed by an increase to more than 100 Ωm at 60 m, 
though even to much higher resistivity after st. 200. The lateral variation of the 
resistivity below 40 m depth does indicate a vertical sub-structure like a fault at st. 
200, and that was why the borehole was drilled there. However, the rock 
encountered was not particularly fractured, and the yield obtained was only 13 lpm 
and with 28.5 m after 6 hours pumping. 
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Figure 4-3e: 400 m long ERT-profile (RES2DINV processed) at borehole DWVP 
05 Kpodu (drilled at st.200) within “Bimbila middle” formation. 
 
Comparison of the ERT-profile and the resistivity log (calculated from the DUIN-
long log) in the 100 m deep borehole DWVP 05 (Figure 4-3f) gives a generally bad 
fit. Because the ERT-profile shows too high resistivities and does not reflect the 
very constant resistivity of 15-20 Ωm in the obviously homogeneous bedrock of 
siltstone seen on the resistivity log the whole way to the bottom of the borehole, 
though with even lower resistivity in the uppermost 20 m reflecting the Saprolite 
and Saprock. 
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Figure 4 – 3 f: Geophysical wireline logs at borehole DWVP05 at (Kpobu) 
(“Bimbila middle” formation). 
The third case within the Bimbila formation selected for discussion is the 100 m 
deep borehole DWVP08 at Nakpaya, which is also expected to represent the middle 
part of the Bimbila formation, though with a different lithology compared to DWVP 
05 as seen on Figure 4-3g below by having several intercalations of sandy beds in 
the siltstone. The sandy beds are indicated by peaks with low gamma-radiation and 
low conductivity. The thickness and resistivity of the Saprolite + Saprock is 
respectively 10 m and 5-15 Ωm. The resistivity of the bedrock from 10 m depth is 
varying between 10 and 70 Ωm, thus with the generally highest in the section from 
16 m to 32 m (having an average of 50 Ωm) and the generally lowest from 32 m to 
66 m (having an average of 15 Ωm). 
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Figure 4 – 3g: Geophysical wireline logs of borehole DWVP08 at Nakpaya 
(“Bimbila middle” formation). 
The ERT profile of DWVP 08 (Nakpaya), Figure 4.3h, generally shows a 15-20 m 
thick top layer with low but varying resistivity (10 Ωm -25 Ωm) along the 400 m 
profile, and interpreted as being Saprolite & Saprock. The bedrock below the top 
layer seems to have a resistivity of 75 Ωm, though with an 80 m wide sub-vertical 
structure in the central part of the profile with a resistivity of 40 Ωm only, which 
therefore could be a wide fractured and weathered zone. Accordingly, the borehole 
site was selected at st. 160, thus within this structure and also within a low 
resistivity anomaly (<20 Ωm) of the top layer. Irrespective of these indications on 
a prospective drilling site, the outcome was a dry borehole. 
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Figure 4 – 3h: 400 m ERT-profile (RES2DINV processed) at borehole DWVP08, 
Nakpaya (drilled at st. 160) within “Bimbila middle” formation. 
 
When comparing the ERT-profile and the resistivity log (calculated from the 
DUIN-long log) in the 100 m deep borehole DWVP 08 (Figure 4-3g) a fairly good 
fit is obtained for the uppermost low-resistivity layer and its thickness as well as on 
the average resistivity of 40 Ωm of the bedrock (siltstone with intercalations of 
sandstone). Though, the ERT-profile does not reflect the lithological variation of 
the siltstone as it is illustrated by the geophysical wireline logs, an example is by 
the varying resistivity from 20 m depth the whole way down to the bottom of the 
borehole. 
The EM profile at Nakpaya in Figure 4-3i is one of two parallel and 500 m long 
and SSW-NNE directed profiles conducted in campaign II with the purpose of 
localizing a feasible site for dug-well construction in order to exploit groundwater 
from areas with thick Saprolite & Saprock. The dry borehole DWVP 08 is located 
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about 400 m NNE of the northern end of the EM profiles and was already drilled, 
when the EM survey was conducted. 
 
Figure 4 -– 3i: 500 m long EM profile-A in SSW-NNE direction at Nakpaya 
(“Bimbila middle” formation).  
The VD-40 profile indicates a rather uniform bedrock along the profile with 
conductivity of 35-40 mS/m corresponding to resistivity of 25-30 Ωm of the 
bedrock, whereas the two more shallow profiles, VD-10 and HD-10 shows much 
higher and more varying conductivity reflecting much lower resistivity (10-15 Ωm) 
and varying thickness of the clayey overburden, thus maximum thickness expected 
from st. 100 to st. 340. Accordingly, this section on the profile with thicker and 
more clayey overburden is considered to provide good groundwater storage thus a 
dug well was suggested to be constructed at st. 100 as shown on Figure 4-3i. 
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As being representative for the younger part of the Bimbilla Formation HAP05 
(Janga) is selected as an example. The 2D ERT-profile shows at the borehole site 
st. 220 two layers (Figure 4.3j), thus an upper rather thin zone (<5m) with resistivity 
surprisingly higher than 100 Ωm and underlain by quite low resistivity 15-25 Ωm 
to 55 m depth. 
 
Figure 4 -– 3j: ERT profile at borehole HAP 05 (Janga) at st.220 (younger part of 
Bimbila formation) after RES2DINV processing. 
The borehole HAP 05 was drilled to a depth of 166 m, and the drillers log as well 
as the geologist log, Figure 4-3k, describe the whole penetrated section as being a 
homogeneous mudstone without any indications on fractures from a depth of 22 m, 
above which the rock was completely weathered (Saprolite). The resistivity 
wireline log (calculated from DUIN-long, in Figure 4-3k) shows that most of the 
22 m thick weathered zone has a much lower resistivity, 10 Ωm, than the top layer 
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seen on the ERT profile. The resistivity log as well as the Gamma-log further 
confirmed the occurrence of a very homogenous bedrock with a resistivity of 15-
22 Ωm and a Gamma-radiation of 60-70 cps (counts per second) the whole way 
down to 120 m. An obstacle at that depth made it impossible to continue wireline 
logging further downwards. Thus, the resistivity of the bedrock seen on the ERT 
profile was actually verified by the wireline logs. The comparison between the ERT 
profile at borehole HAP 05 (Janga) and the geophysical wireline resistivity log is 
also included in Appendix 5B. 
 
Figure 4 – 3k: Geophysical wireline logs in borehole HAP 5 Janga (younger part 
of Bimbila formation). 
The yield after drilling was 5 lpm which is below the minimum required yield for 
hand pump (10 lpm). Since the bedrock had no fractures, it implies that the recorded 
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yield of 5 lpm is a result of contribution from only the quite thick and clayey 
overburden (22 m) verified by the geophysical wireline logs.  Figure 4.3l shows 
results of apparent conductivity measurement using EM 34 at Disiga located some 
13 km ENE of borehole HAP 05, but still expected to represent the younger part of 
Bimbilla Formation. Even though the results are from a different area far away from 
HAP 05, the trend is consistent with the observations from results of HAP5. Thus, 
the high apparent conductivity (>100 mS/m and >60mS/m) for the two shallow 
profiles, HD-10 and VD-10 compared to the more uniform and much lower 
conductivity (40mS/m) on the deep profile VD-40 indicates a rather thick 
weathered zone (>10<20 m depth) above a homogeneous mudstone with a 
resistivity of 25 Ωm, which is quite similar to the observations in borehole HAP 
05. 
It was concluded from the EM-34 profile (Figure 4-3l) that the target for 
groundwater exploitation by dug-well construction should be the thicker weathered 
zone in the right section of the profile, thus towards the southwestern end of EM-
profile A. However, due to risk for frequent flooding of that area the dug-well was 
actually constructed at st. 90 shown on the profile. But even so, the dug-well is 
expected to benefit from the groundwater storage potential not being far away. 
 
 
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Figure 4 – 3l: EM profile-A as one of two parallel NE-SW profiles at for selection 
of feasible site for dug-well construction (within Bimbila young formation). 
Finally, the Bunya sandstone member, which forms the top of the Bimbilla 
formation, seems to have high potential for groundwater exploitation. Jordan et al. 
(2009) reports that the Bunya Sandstone occurs in the Karaga area as a series of 
disconnected outliers which are the evidence for the continuation of the synclinal 
structure (Dakar Syncline). This structural feature might have caused a higher 
secondary permeability and therefore could serve as good targets for groundwater 
exploration. Field observation and data from CWSA and World Vision show that 
several high yielding boreholes (see Appendix 10) used for small town and 
mechanized system in Karaga and Tong townships were completed in the Bunya 
sandstone member. Unfortunately, there do not exist any geophysical wireline 
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logging data from boreholes in the Bunya sandstone in the Nasia Basin. Therefore, 
validation of the evaluated resistivities from possible ERT-profiles and EM-profiles 
from locations within this sandstone could not be conducted. 
  
4.1.2.2.2 Kodjari Formation 
 
The first location to discuss in this section is the geophysical wireline logs of 
borehole HAP 14 at Tuuni (secondary data), which was thought to represent the 
elder part of the Bimbilla Formation, and is shown in Figure 4-4a below. However, 
the interpretation of the geophysical wireline logs has disclosed that the uppermost 
80 m of the 120 m deep borehole HAP 14 consist of the three sub-units of the 
Kodjari formation, thus the silexites, the limestone and the tillite, underlain by 
Panabako sandstone from 80 m depth. 
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Figure 4-4a: Geophysical wireline logs of borehole HAP 14 Tuuni (at st. 120 on 
ERT-profile) in Kodjari formation underlain by Panabako sandstone. 
The borehole HAP 14 was drilled at station 120 on the ERT profile shown in Figure 
4-4b below. The 400 m ERT-profile (processed by RES2DINV) shows a 10-15 m 
top layer with low resistivity (22 -38 Ωm) below which the resistivity is gradually 
increasing to more than 138 Ωm at about 60 m depth, thus without any indication 
of any significant lithology boundary. Furthermore, there is hardly any lateral 
variation in the resistivity along the profile, thus indicating the absence of any 
geological anomaly or structure. 
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Figure 4 - 4b: The ERT-profile at borehole HAP 14 Tuuni (at st. 120) located within 
the Kodjari formation. 
Comparison of the ERT profile and the wireline resistivity log of borehole HAP 14 
fits relatively well for the upper portion of the latter showing a 12 m top layer with 
resistivity of 15-25 Ωm followed by an almost uniform resistivity of 25-40 Ωm to 
80 m. Then followed by an erratic but in average higher resistivity (50-300 Ωm) 
after 80 m indicating sandstone with clay intercalations, the latter seen as low 
resistivity peaks and corresponding high gamma-radiation peaks, which is typical 
for certain sections of the Panabako formation (as an example see logs in DWVP 
02 on Figure 4-1d). This suggests that in areas underlain by the Kodjari formation, 
drilling deeper is encouraged if sufficient yield is not obtained, because it has the 
possibility of encountering water bearing Panabako sandstone. Though, the 
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groundwater in Panabako sandstone below Kodjari can be too saline like in this 
borehole HAP 14 where the fluid conductivity log shows values higher than 600 
mS/m, Figure 4-4a. 
In addition, Figure 4-4c illustrates two profiles A and B for HAP 14, Tuuni as 
reported by WRI in 2009.  Profile A was conducted from south towards north – and 
profile B from west towards east. The difference in the variation of the conductivity 
for both dipole modes between the two profiles is noticeable, because it most 
probably reflects whether the profile is perpendicular (profile A) or parallel (profile 
B) to dip of the sedimentary layer sequence.  
 
Figure 4-4c: EM-34 response curves along traverses A and B, Tuuni (HAP 14). 
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Another case of geophysical data from Kodjari area are the geophysical wireline 
logs of the two boreholes WVB11-Bugya Pala 1 and Bugya Pala 2, location seen 
on the geological map, Figure 4.3a. The geophysical logs are shown in Figure 4.4d 
and Figure 4.4e respectively. A high conductivity is recorded for the upper layer to 
a depth of about 20 m in both boreholes, and it was originally interpreted as the 
weathered zone. However, weathering is normally decreasing downwards thus 
being reflected by a gradually increasing resistivity towards depth. Obviously, this 
is not the case in any of the two boreholes, where the shift in conductivity thus 
resistivity at about 20 m depth is quite abrupt. It is worth noting that the extreme 
high conductivity in this upper zone particularly in borehole Bugya-Pala 2 most 
probably is caused by high content of conductive minerals other than clay since the 
gamma-radiation is not particular high. It might be magnetite rich Kodjari ashes or 
iron and manganese rich Kodjari limestone. Further studies of the near-surface 
layer at Bugya-Pala 2 borehole is necessary to confirm the observed unusual high 
conductivity and to determine the reason for it. The difference in gamma radiation 
for the two boreholes accounts for the difference in lithology, which is recorded by 
the driller only in the 56 m deep borehole WVB 11. Mudstone at bottom is here 
overlain by two types of bedded sandstones, where the upper one is having higher 
gamma-radiation and much lower resistivity than the lower one, and therefore re-
interpreted as Kodjari tillite. The lower one from 20 to 50 m depth in WVB 11 is 
re-interpreted as being Panabako sandstone based on the level and pattern of the 
gamma-log as well as of the resistivity-log. The mudstone at bottom might be the 
Poubogou formation. 
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The 40 m deep borehole Bugya-Pala 2 is located only 400 m north of WVB 11, and 
is without any information on lithology. Re-interpretation of the lithology based on 
the gamma-log and the resistivity-log (calculated from DUIN-Long conductivity 
log) concludes that a sandstone, possible Panabako, occurs at the lowermost 5 m at 
bottom overlain by 13 m mudstone, possible Kodjari tillite, and then the uppermost 
22 m highly conductive layer of Kodjari ashes or Kodjari limestone as mentioned 
above. The very different resulting lithology of boreholes with a mutual distance of 
only 400 m indicates a highly heterogeneous geological environment which might 
be explained by the Kodjari formation laying unconform on an uneven erosion 
surface of the Panabako formation. This requires a combination of various 
interpretation and observation when exploring for groundwater. It was observed for 
during the data collection period, in Bugya Pala for instance, the hand dug wells 
were uniquely located along a straight line and they had water throughout the year. 
This observation and interpretation of the geophysical logs means the thick 
weathered zone are good indicators for groundwater within this formation. 
Furthermore, the study has exposed a need for improvement of the geological 
description of the samples from drilling boreholes for groundwater exploitation. 
Jordan et al. (2009) indicates that ‘tillite-like conglomerate’ consisting of various 
rock fragments enclosed within an unsorted, quartzitic to feldspathic sandy matrix 
was identified in a borehole beyond 60 m depth in the Kodjari. This presupposes 
that drilling deeper boreholes within this formation has a possibility of encountering 
good fractures. However, as mentioned above under discussion of the borehole 
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HAP 14 there seems to be a risk for encountering too saline groundwater below the 
Kodjari formation. 
 
Figure 4 – 4d: Geophysical wireline logs from WVB 11(Bugya Pala)  
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Figure 4 - 4e: Geophysical wireline logs from CWSA borehole Bugya Pala-2.  
The extreme low and very uniform resistivity of Figure 4-4e of the uppermost 20 
m indicates a highly conductive rock type, because weathering (Saprolite) will 
normally decrease towards depth thus be reflected by a gradually increasing 
resistivity towards depth. 
 
4.1.3 Geophysical characteristics of rocks  
 
A general characteristic of the Voltaian sedimentary rocks obviously seems not 
sufficient for groundwater governance institutions and researches to make 
economical feasible development of the groundwater resources. Improvement of 
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the knowledge on the lithostratigraphy within the different formations of the VSB 
and on their respective geophysical and hydrogeological characteristics seems 
needed.  
In the UK for instance, Beamish (2013) used conductivity information from 
airborne electromagnetic survey to classify rock lithologies. His study has provided 
new information on the conductivity characteristics for many geological 
formations. New empirical map of the bedrock conductivity of the UK has also 
been developed. 
 
 
There are currently only some few researchers who have made documentation of 
resistivity of the different rock types in Ghana (Ewusi, 2006; Awini, 2008; 
Agyekum, 2009), however not related in details to the individual formations in the 
VSB. Having such information would be a good step for groundwater development 
especially for the Voltaian supergroup.  
Results from the electromagnetic profiles (Appendix 1) of some of the locations, 
electrical resistivity profiles (Appendix 2) at some boreholes and geologic logs in 
Appendix 4 were used to characterise the geophysical signature (resistivity) of the 
various geological formations. In some limited cases, geophysical logs from 
monitoring boreholes (Appendix 3) were used to validate the interpretation. The 
Appendix 5 consists of a summary of the interpretation from the combined 
geophysical methods. Similar combined use of different methods for characterising 
rocks has been used by other researchers (Comte et al. 2012; Danielsen et al. 2007; 
Sonkamble et al. 2014). 
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Figure 4-4f is an illustration of how the resistivity for HAP14 (Tuuni) was 
interpreted by combining various geophysical methods and geological log. The 
geological borehole description was matched with the electrical resistivity image 
(ERT-profile) at the drilled location. The resistivity was extracted and compared 
with the results from the geophysical logs (wireline) for confirmation. The case is 
presented in details in the previous section 4.1.2.2.2 Kodjari Formation.  
 
Figure 4 – 4f: An illustration of how resistivity is compared using different methods. 
 
The ERT-profile in Figure 4-4f illustrates an example where neither reflect the 
magnitude nor the variation with depth of the resistivity of the bedrock compared 
to the actual observation of the same on the wireline resistivity log. Furthermore, 
that the EM profiles HD-20 (blue) and VD-20 (red) provides information on the 
resistivity of the overburden, which fits quite well with the wireline resistivity log. 
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Table 4.1 summarizes the resistivity obtained of the weathered zone (Saprolite + 
Saprock) as well as of the bedrock by the three different methods, ERT-profiling, 
EM-34 profiling and wireline resistivity log, at each of the cases discussed in this 
Chapter 4.1. The wireline resistivity is read from the DUIN-long conductivity log 
after being transferred to Calculated DUIN-L resistivity. This because the Focussed 
Guard resistivity probe provides trustworthy resistivity only of open sections of 
boreholes (without casing and screen), and first from 10 m below the water table in 
the borehole. Therefore, the Guard resistivity probe does not provide information 
on resistivity of the weathered zone. 
The resistivity values from the ERT-profile and from the EM-34 profiles are read 
at the location on the profile of the borehole with wireline logs. The conductivity 
read in mS/m from the EM-34 profile is transferred to resistivity in Ωm by 
1000/conductivity. For EM-34 readings for the weathered zone is the HD-10 profile 
applied, though if not available then the HD-20 profile. EM-34 readings for the 
bedrock is the VD-40 profile. For ERT readings for the weathered zone is the 
average to 10-15 m depth applied, and for bedrock the dominating resistivity further 
down is applied. The readings of the resistivity of the respective weathered zone 
and the bedrock from the wireline log are determined by the pattern of the resistivity 
variation towards depth.   
 
 
 
 
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Table 4-1a: Resistivity of weathered zone and of bedrock from cases discussed  
Location Figure Formation 
Saprolite + Saprock Bedrock 
no. 
  EM ERT Calc.Res. EM ERT Calc.Res. 
HD-  log   log 
10 VD-
or 40 
20 
DWVP 09 4.1a Panabako 
20 & 300 & 
and - 3500 100-1000 - 
80 100 
4.1b 
DWVP 02 4.1c Panabako 
60 & 60 & 
and - >2000 100-1000 - 
>5000 100-500 
4.1d 
Tenkpanga 4.2a Poubogou 
25- 15- 50-
and >1000 - - 
60 30 500 
4.2b 
DWVP 01 4.2c Panabako 60-
200- 50-100 & 
and - 100-1000 - 100 & 
500 100-400 
4.2d >500 
HAP 11 4.2e, Panabako 
125- 60- 200-
4.2f above >1800 100-1000 100-250 
200 100 500 
and Poubogou - - 80-100 
- - - 
4.2g 
DWVP 10 4.3b Bimbila old - 20-30 15-20 - >500 20-30 
and above 
4.3c Kodjari 
Sandua 4.3d 10- - - 15- - - 
Bimbila old 
EM** 15 25 
DWVP 05 4.3e - 30-40 5-15 - >100 15-20 
Bimbila 
and 
middle 
4.3f 
DWVP 08  4.3g Bimbila 
- - 
Nakpaya and middle 10-25 5-15 40-75 10-70 
10- 25-
EM** 4.3h Bimbila -  - - 
15 30 
4.3i middle 
HAP 05 4.3j Bimbila 45 100- 10 30 15-25 15-22 
incl. and4.3k middle  300 
EM Not 
shown 
Disiga 4.3l Bimbila 20-
8-15 - - - - 
EM** young 30 
HAP 14 4.4a, Kodjari 
 4.4b, above 40-
22 20-40 10-20 25* 20-40 
4.4c Panabako 140 
- - - - 50-300 
(and - 
4.4f)  
WVB 11 = 4.4d Kodjari 
- - 100-300 - - 50 
Bugya tillite 
- - - - - 100-1000 
Pala 1 Panabako 
- - - - - <100 
Poubogou 
Bugya 4.4e Kodjari  
- - 5-10 - - 4 
Pala 2 Kodjari 
- - - - - 10 
tillite 
- - - - - >10 
Panabako 
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As described in the discussion of the different cases and seen in the table there are 
in general a bad fit between the resistivity from the ERT-profile of the weathered 
zone as well as of the bedrock compared to the factual resistivity on the wireline 
log. Most often is the resistivity from the ERT-profile far too high compared to the 
wireline resistivity log. Oppositely, the resistivity values evaluated from EM 
profiles corresponds quite well to the factual resistivity on the wireline log.  
 
Accordingly, values from the latter two methods are given higher priority in the 
following Table 4.1b, in which the range of resistivity for the various geological 
formations in the study area is summarized. The Panabako Sandstone Formation 
from the Table 4.1b has the highest resistivity for bedrock and weathered zone. This 
is expected since the rocks are according to Jordan et al. (2009) very hard and well 
cemented. Kesse (1985) also indicated that these rocks are generally well 
consolidated and are inherently impermeable. The resistivity of the weathered zone 
is quite often showing higher resistivity than the bedrock, which is opposite to what 
is seen for Bimbilla formation as well as for the Poubogou Formation.  
 
When it comes to the bedrock the resistivity of Bimbila seems relatively lower 
compared with that of the Poubogou Formation though both are composed of 
similar rocks (mudstone, siltstone with thin beds of sandstone). Telford et al. (1990) 
suggest that resistivity of some rock types varies with age and lithology. The 
Poubogou formation which belongs to the Bombouaka Group according to Jordan 
et. al. (2009) was deposited earlier than the Bimbilla Formation (Oti/ Pendjari 
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Group) and therefore could possibly be the explanation for why the fresh bedrock 
of Poubogou is having slightly higher resistivity than the fresh bedrock of Bimbila. 
The lower resistivity in the Bimbilla Formation is most probably caused by higher 
clay content.   
 The Kodjari has a similarly low resistivity as the Bimbila, which could be as a 
result of its composition, basal tillites, a cap-carbonate limestone and laminated 
tuffs and ash rich siltstones (Carney et al. 2010). In certain cases, it can even be 
extremely low caused by content of conductive minerals other than clay. 
 
There is a high variability of the resistivity generally in the area which supports the 
assertion by many researchers about the complex nature of the geology. Extreme 
caution such as including the direction of a geophysical EM or ERT profile and 
comparing them with the expected strike and dip of the sediments in the 
interpretation of such profiles, is needed when exploring for groundwater. Finally, 
it seems needed also to give more attention to the electrode contact to the terrain 
when conducting the ERT-profiling, because the insufficient electrode contact to 
terrain might be the reason for the generally too high resistivities obtained from this 
method.   
 
Table 4.1b: Resistivity of the geological formations of the Nasia Basin 
Saprolite+Saprock Bedrock 
Geologic Formation Resistivity Resistivity (Ohm-m) 
(Ohm-m) 
POUBOGOU  25-60  80-100 
PANABAKO SANDSTONE 100-1000  50-1000 
KODJARI 10-300 10-50 
KODJARI mineralized 5-10 4 
BIMBILLA 5-20  15-70 
 
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Based on the above discussion of the geophysical results, it is evident that there is 
a need to rethink with the concept of interpreting such results. The best approach 
would be to place much emphasis on the geology of the VSB, direction of their dip 
and strike, their trend of the resistivity. The purpose of geophysics is to gain better 
understanding of the geology which will then increase the knowledge on how to 
exploit the groundwater. 
 
4.2 Evaluation of Groundwater Characteristics 
The groundwater characteristics (depth, regolith-the entire weathered zone, static 
water level, depth of water strike and yield) in the study area has been grouped 
under the main geological formations. Tables 4.2a to 4.2d provide descriptive 
statistics of these parameters in the various geological formations.  
From the descriptive statistics table, despite the difference in the total number of 
boreholes sampled in each formation, the minimum depth of borehole is 31 m in all 
the formations except the Kodjari which has 21 m. The maximum depth of drilling 
however varied with the deepest in the Bimbilla formation (166 m), Poubogou 
formation having 92 m, Panabako sandstone (155 m) and 80 m for the Kodjari 
formation. The average depth of boreholes in all the formations ranged between 50 
– 60 m which is in consonance with that of Carrier et al. (2008). The low standard 
error for depth in each of the formations (2.05-3.01) m is an indication of the 
reliability of the mean. The high standard deviations for depth indicate the degree 
of variability and only few boreholes account for the high values. The high depth 
in Bimbilla formation (166 m) and Panabako sandstone formation (155 m) are 
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boreholes drilled under the HAP project for research as such do not represent the 
general depth of drilling in the area. The skewness and kurtosis indicate the high 
variability of depth for all the formations and show non normal distribution of the 
dataset. 
 
The thickness of regolith for all the formations in Table 4.2 ranges between 2 m-19 
m with an average between 7.2 m-8.55 m and the least is recorded in the Poubogou 
formation. The low value recorded for the Poubogou formation could be from the 
small number of boreholes in the dataset. The average thickness of regolith obtained 
for all the formation generally agrees with the findings of previous researchers 
(Banoeng-Yakubo et al. 2011; Carrier et al. 2008; Dapaah-Siakwan and Gyau-
Boakye, 2000). The reliability of the mean in each of the geological formations is 
indicated by the low values obtained for both the standard error and the standard 
deviation.  The skewness indicates that the data on thickness of regolith is normally 
distributed and a kurtosis of shows that there are fewer datasets at the tails of the 
distribution in each of the formations. 
 
The yield in all the formations in Table 4.2a-4.2d ranges from 5 m3/day to 720 
m3/day with mean ranging between 56 m3/d to 107 m3/d. The standard error and 
standard deviations are relatively high in all the formations indicating that the yield 
is spread out over large range. It is also observed that standard deviation is greater 
than the mean in all the formations which indicates a high variation between the 
values, and an abnormal distribution of the dataset. Yidana et al. (2020) suggests 
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that high variability in the borehole yield is indicative of a significant spatial 
variability in the aquifer hydraulic properties. The skewness indicates that the yield 
is not normally distributed. This is because most of the boreholes are designed for 
hand pump use (with minimum required yield of 10 lpm equal 14 m3/d) and 
therefore the target is not to drill further to obtain higher yields if sufficient yield is 
completed.  
 
 
 Table 4.2a: Summary of Groundwater Characteristics in Bimbilla Formation 
 N Rang Min Ma Mean Std. Varianc Skewness Kurtosis 
e x Devia e 
tion 
59.8 1.8 0.2 5.6 0.5
Depth (m) 92 135 31 166 2.26 21.69 470.56 
0 3 5 2 0 
Thickness 0.5 0.2 0.1 0.5
92 15 3 18 7.60 0.33 3.21 10.29 
of Regolith 7 5 8 0 
Yield 85.3 119.5 14296. 2.5 0.2 6.4 0.5
92 570 6 576 12.45 
(m3/d) 2 7 17 1 5 1 0 
2.7 0.2 8.8 0.5
SWL (m) 92 34 2 36 8.82 0.62 5.96 35.56 
0 5 6 0 
Depth of 
30.7 1.6 0.3 2.3 0.7
water strike 37 60 15 75 2.51 15.25 232.54 
3 1 9 9 6 
(m) 
  
 
 
 
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Statistic 
Statistic 
Statistic 
Statistic 
Std. Error 
Statistic 
Statistic 
Statistic 
Std. Error 
Statistic 
Std. Error 
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Table 4.2b: Summary of Groundwater Characteristics in Poubogou Formation 
 N Rang Mi Ma Mean Std. Varian Skewness Kurtosis 
e n x Devia ce 
tion 
50.3 0.5 3.1 0.9
Depth (m) 21 61 31 92 3.01 13.80 190.45 1.44 
8 0 6 7 
-
Thickness - 0.5 0.9
21 9 3 12 7.29 0.57 2.59 6.71 1.0
of Regolith 0.10 0 7 
7 
Yield 56.9 5798.4 0.5 5.4 0.9
21 260 14 274 16.62 76.15 2.50 
(m3/d) 2 8 0 9 7 
0.5 5.5 0.9
SWL (m) 21 14 2 16 5.95 0.66 3.01 9.05 1.84 
0 2 7 
Depth of            
11 25 36 31.0 3.22 35.57 31.00 - 1.2
water strike 3 
(m) 0 0.78 3 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Statistic 
Statistic 
Statistic 
Statistic 
Std. 
Error 
Statistic 
Statistic 
Statistic 
Std. 
Error 
Statistic 
Std. 
Error 
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Table 4.2c: Summary of Groundwater Characteristics in Kodjari Formation 
 N Rang Min Ma Mean Std. Varianc Skewness Kurtosis 
e x Devia e 
tion 
-
12.82 0.3 0.7
Depth (m) 39 59 21 80 52.59 2.11 164.46 0.18 0.1
4 8 4 
3 
Thickness 0.3 2.2 0.7
39 17 2 19 7.49 0.45 3.433 11.78 0.91 
of Regolith 8 8 4 
Yield 107.5 14.5 163.6 26790. 0.3 5.7 0.7
39 710 10 720 2.43 
(m3/d) 1 3 77 15 8 1 4 
0.3 2.0 0.7
SWL (m) 39 18 1 19 5.77 1.16 4.055 16.45 1.32 
8 3 4 
Depth of  
1.24 0.5 6.4 1.0
water strike 18 35 18 53 25.61 8.354 69.78 2.34 
4 5 4 
(m) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Statistic 
Statistic 
Statistic 
Statistic 
Std. 
Error 
Statistic 
Statistic 
Statistic 
Std. 
Error 
Statistic 
Std. 
Error 
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Table 4.2d: Summary of Groundwater Characteristics in Panabako Sandstone 
 N Rang Min Max Mean Std. Varianc Skewness Kurtosis 
e Devia e 
tion 
58.1 0.2 0.5
Depth (m) 82 124 31 155 2.77 25.07 628.54 2.04 4.67 
3 7 3 
Thickness 0.2 - 0.5
82 14 2 16 8.55 .374 3.39 11.49 0.10 
of Regolith 7 0.31 3 
Yield 426.2 5.7 432.0 70.5 6311.2 0.2 0.5
82 8.77 79.44 2.07 4.97 
(m3/d) 4 6 0 2 2 7 3 
0.32 0.2 - 0.5
SWL (m) 82 16 0.5 16 7.02 0.40 3.64 13.26 
7 7 0.72 3 
Depth of 
27.6 0.3 0.7
water strike 38 65 10 75 1.97 12.14 147.47 1.88 5.11 
6 8 5 
(m) 
 
From Tables 4.2a-4.2d, the range of SWL is 0.5 m-19 m and the average SWL for 
the geological formations is in the range of 5.77 m -8.82 m. The low standard error 
(<1 m) and standard deviations (<6 m) of SWL represent a high reliability of the 
mean. The Bimbilla formation which had a maximum drilling depth in the study 
area also had the highest SWL. The composition this formation, 
mudstone/siltstones with thin beds of sandstones (Jordan et al. 2009) could be the 
reason for this high SWL since these rocks generally are considered to have poor 
groundwater potential (Carrier et al. 2008).  
 
An evaluation of the depth of water strike to determine the probable depth of 
encountering water within each of the formation is illustrated in Table 4.2 (a-d). 
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Statistic 
Statistic 
Statistic 
Std. Error 
Statistic 
Statistic 
Statistic 
Std. Error 
Statistic 
Std. Error 
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The average depth of striking water ranges between 25 m -31 m for all the 
formations. This is similar to the observations made by Bannerman (1990) who 
reported that the average depth for most productive fractures is 27 m. The results 
in Table 4.2a to 4.2d also point to the fact that, apart from the Poubogou formation, 
the average depth of water strikes over the study area exceeds the average thickness 
of regolith (weathering depth) suggesting that productive yields are mainly derived 
from water-bearing fractures struck at depth. This agrees with conclusions of 
researchers like Carrier et al. (2008) and Ewusi et al. (2009). Nsiah et al. (2018) in 
determining the groundwater potential zone of the Nabogo in the Northern Region 
of Ghana revealed that groundwater strike in the basin is at depths ranging from 3.5 
m - 68.98 m with a mean depth of 24.4 m. The map they generated showed that, for 
a high potential zone water could be intercepted at a depth of about 30 m to 43 m 
during drilling. This conclusion even though is not within the study area, however 
falls within the broader VSB therefore can generally be said to compare with the 
findings of this work 
To explore the distribution of the groundwater parameters for the entire study area, 
statistical methods such as Box plots and Histograms were used as shown in Figure 
4.5a-4.5d and Figure 4.6a-4.6d respectively.    
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Figure 4 - 5a: Box Plot for Depth (m) for the Nasia basin 
 
Figure 4 - 5b: Box Plot for Thickness of Regolith (m) for the Nasia basin 
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Figure 4 - 5c: Box Plot for Yield (m3/d) for the Nasia basin 
 
 
Figure 4 - 5d: Box Plot for SWL (m) for the Nasia basin 
 
Figures 4.5a to 4.5d clearly show extreme variabilities which suggest that a lot of 
processes influence these parameters in the study area. For instance, the minimum 
and maximum values for parameters such as depth and yield for the entire study 
area in Tables 4.2a to 4.2d are 21m -166 m and 5 m3/d - 720 m3/d respectively. 
These extreme variation is an indication of the complexity of the hydrogeology of 
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the study area and suggest that the parameters are controlled possibly by discrete 
entities created due to weathering or fracturing (Yidana et al. 2011).  
Besides that, depth is mainly influenced by the ability to encounter water as such 
drilling is stopped once sufficient yield is encountered. In certain situation also, 
financial considerations influence the depth to which drilling is completed. In order 
to make savings from these projects, most of the boreholes are completed before at 
depths not exceeding between 50 m -100 m even when the yields are low. 
 
The data showed in Tables 4.2 (a-d) and Appendix 6, high degree of skewness is 
displayed which is very common for geoscience data.  However multivariate 
statistical analyses require both normal distribution and homoscedasticity, 
therefore, all the data were log-transformed as illustrated in Figures 4.6 (a-d) for 
the entire study area. Even though the transformed parameters do not depict perfect 
normal distribution, they are however an improvement over the raw data.  
 
 
Figure 4 - 6a: Histogram of Log-Transformed Depth (m) for the entire 
Nasia basin 
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Figure 4 - 6b: Histogram of Log-Transformed Thickness of Regolith (m) 
 
 
 
Figure 4 - 6c: Histogram of Log-Transformed SWL (m) 
 
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Figure 4 - 6d: Histogram of Log-Transformed Yield for the entire Nasia basin 
 
4.3 Analysis of aquifer response  
A total of 67 boreholes pump tested at different duration (6-48 hours) of constant 
discharge and 3 -12 hours recovery is presented in Appendix 7.  
Various geologic and hydrogeologic conditions such as recharge, fracture, 
groundwater storage in the vicinity of the well can result in a number of different 
types of drawdown curves. 
Figures 4.7a and 4.7b are graphs of drawdown against time which illustrate 
different aquifer response within the study area.   
Figure 4.7a is a graph of constant discharge for a borehole in Daboya No.2 in the 
West Mamprusi District with drawdown against time. It is observed from the graph 
that, the curve steeps after 200 minutes which indicates an aquifer limited by 
boundary of some kind. Calculation of aquifer parameters according to Driscoll 
(1980) should not be made in any part of the slope of the curve reflecting a 
boundary. The borehole is situated within the Bimbilla formation and the geologic 
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log (Appendix 4) shows intercalations of mudstone and siltstone between 18 m – 
31 m. Mudstone/Siltstone generally do not produce good aquifers therefore are 
interpreted as low permeable aquitard under this circumstance and could possibly 
be the cause of the steepening in Figure 4.7a. 
Constant Discharge-Daboya No.2
Bimbilla Formation
Discharge rate=12 lpm, SWL=6.12
0
5
10
15
20
25
30
1 10 100 1000 10000
Time (min)
 
Figure 4 - 7a: A graph of drawdown (m) against Time (min) for Daboya 
No.2(illustrating boundary condition) 
 
The graph in Figure 4.7b also illustrates a case of well loss for the first 5 minutes 
with a significant drop in drawdown followed by a good aquifer indicated by the 
quick response in recovery. This compares well with the observations made by 
Acheampong and Hess (1998). Within the study area, the Kodjari is known to 
produce high yielding boreholes and could have accounted for the rapid recovery 
in yield as seen in the curve. The composition of the Kodjari formation, thus having 
a distinctive triad of lithologies; basal tillites and diamictons overlain by cap-
carbonate and laminated tuffs and ash-rich siltstones. These comprise among 
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others, matrix-supported conglomeratic lenticles with small pebble size clasts of 
quartz and various metamorphic lithologies (Carney et al. 2010). These materials 
have the ability of enhancing secondary permeability and thereby increasing the 
groundwater potential.  
CONSTANT DISCHARGE FOR WALEWALE-WA-05
KODJARIFORMATION
DISCHARGE RATE=140 lpm, SWL=3.64m
TIME (min)
1 10 100 1000 10000
0
5
10
15
20
25
 
Figure 4 - 7b: Graph of Drawdown (m) against Time (min) for Walewale -WA-
05(illustrating well loss and a high yielding aquifer conditions) 
 
4.4 Estimation of hydraulic parameters 
Evaluation of the yielding potential of aquifers require fairly an accurate 
determination of hydraulic characteristics. Several methods are used in estimating 
these parameters and they include laboratory and pumping test analysis. 
 
 
 
 
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4.4.1 Specific Capacity (SC) 
Results of specific capacity for the study area are shown in Appendix 9 based on 
220 boreholes. The SC was estimated from drawdown after 360 minutes of 
pumping. The purpose of selecting drawdown after 360 minutes as indicated earlier 
was to make use of the large secondary data with final drawdown after 6 hours. The 
specific capacity for the study area ranges between 0.12 m3/d/m to 960 m3/d/m with 
an average of 17.00 m3/d/m. The specific capacity values are skewed more towards 
boreholes in the Bunya sandstone member of the Bimbilla formation and the Kodjari 
formation. The skewness of the specific capacity values is associated with the high 
yields that have been obtained within these formations. The high mean specific 
capacity of the boreholes implies they have intercepted either numerous small open 
fractures or a single fracture (Acheampong and Hess, 1998; Rushton, 1987). The 
specific capacity values  generally are comparable with  those of  Yidana et al. 
(2011)  except for two boreholes that are located in the Kodjari (Walewale-WA-01 
and Walewale 1-Norst). Several other factors such as well setting, pumping duration  
and aquifer setting (aquifer type) can influence specific capacity (Mace, 2001). Well 
setting is linked to factors such as the radius (which is inversely proportional to 
specific capacity) and degree of penetration of the aquifer.  
4.4.2 Transmissivity  
Various methods were used in estimating transmissivity and they include empirical 
and geostatistics.  
 
 
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4.4.2.1 Analytical Method of estimating Transmissivity 
Transmissivity estimated using the Cooper Jacob (1946) model for single-well test 
for 26 boreholes with pumping duration varying between 9 hrs and 48 hrs is shown 
in Appendix 9. The transmissivity in the area varies significantly with a minimum 
value 0.25 m2/day and a maximum of 263 m2/day and an average value of 32 
m2/day. From Appendix 9, it can be deduced that only two borehole had high 
transmissivity (Walewale-WA-01 and Walewale 1-Norst). Aside these two 
boreholes, the values of transmissivity compares generally well with the findings 
of  Yidana et al. (2011) and Darko and Krásný (2010) with their transmissivity 
estimates been below 50 m2/day. The two boreholes with the high transmissivity 
values were drilled in the Kodjari formation whilst the boreholes used by (Yidana 
et al. 2011) were within the Bimbilla formation. The Bimbilla formation beside the 
Bunya sandstone member are dominated by mudstone/siltstone and generally have 
low yields. The Kodjari formation on the other hand, is comprised of matrix-
supported conglomeratic lenticles with small pebble size clasts of quartz and 
various metamorphic lithologies (Carney et al. 2010) which are capable of 
enhancing its permeability. The major limitation with approach of estimating 
transmissivity has to do with the limited number of boreholes for the entire study 
area. 
4.4.2.2 Empirical Method of estimating Transmissivity 
Empirical methods using regression models have been used by (Acheampong and 
Hess, 1998; Yidana et al. 2011; Yidana et al. 2008) to help in predicting 
transmissivity in areas with only specific capacity data. The specific capacity and 
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transmissivity data were log transformed to meet the requirement of normality 
(Figure 4.8 and Figure 4.9 respectively) prior to being used for the regression 
model. Though the figures do not represent a perfect normal distribution but are a 
significant improvement from the raw data. Two categories of regression 
relationships were conducted, the first involved transmissivity estimates for the 
entire 26 study area with the corresponding specific capacity. The second 
regression relationship developed was based on data specific to the geologic 
formation. LogT was used as the dependent variable and Log SC, the independent 
variable. 
 
Figure 4 - 8: Log-Transformed Specific Capacity for the Study area 
 
 
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Figure 4 - 9: Log-Transformed Transmissivity for the entire study area 
 
Equation 4.1 and 4.2 illustrate the relationship between 26 transmissivity and 
corresponding specific capacity data sets for the entire study area. 
𝐿𝑜𝑔𝑇 = 0.970 𝐿𝑜𝑔𝑆𝐶 + 0.020       
 (4.1) 
𝑇 = 1.047 𝑆𝐶0.970         
 (4.2) 
𝑅2 = 0.954  
The coefficient of determination, R2, implies that 95 % of the transmissivity in the 
area is due to the specific capacity data. This value generally implies that the sample 
size model is good enough to render the model applicable within the Nasia basin. 
Acheampong and Hess (1998) however indicate that considering the small sample 
size, the applicability of such a model to the rest of the basin is unclear. 
The value of the coefficient of determination suggests high dependence of 
transmissivity on specific capacity. Researchers like Acheampong and Hess (1988), 
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Yidana et al (2008) have developed similar  relationship but for the Southern part 
of the Voltaian but this is an improvement similar to Yidana et al. (2011) 
relationship for a portion of the Northern Voltaian. The coefficient of regression 
compares well with that of Yidana et al. (2011). 
The second regression model (formation-specific) based on data from the Bimbilla, 
Kodjari and Panabako Sandstone formations. It must be acknowledged that the 
regression relationship for the formation specific is based on limited data. Most of 
the pumping test results did not meet a critical assumption in the Cooper-Jacob 
(1946) method, the requirement for long duration pumping (Kruseman & de Ridder, 
1971). They were pumped for less than 6 hours and therefore could not be used for 
the analysis. The Bimbilla formation had 10 boreholes, the Panabako Sandstone 
formation 9 boreholes and the Kodjari formation had only 7 boreholes.  
The regression relationship for Bimbilla formation is illustrated in equation 4.3 and 
4.4, that for the Kodjari is equation 4.5 and 4.6. The Panabako sandstone is 
represented by equation 4.7 and 4.8. 
 
𝐿𝑜𝑔𝑇 = 0.919 𝐿𝑜𝑔𝑆𝐶 + 0.086       
 (4.3) 
𝑇 = 1.219 𝑆𝐶0.919         
 (4.4) 
𝑅2 = 0.895  
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The coefficient of determination, R2, for the rocks within Bimbilla formation is 
89.5% which means that the about 90% of the transmissivity in the study area can 
be predicted by specific capacity.  
𝐿𝑜𝑔𝑇 = 0.992 𝐿𝑜𝑔𝑆𝐶 + 0.004       
 (4.5) 
𝑇 = 1.009 𝑆𝐶0.992         
 (4.6) 
𝑅2 = 0.949  
From equations 4.5 and 4.6, the coefficient of determination, R2, suggests that there 
is a large dependence (over 94%) of aquifer transmissivity on specific capacity 
within rocks of the Kodjari formation.  
 𝐿𝑜𝑔𝑇 = 0.942 𝐿𝑜𝑔𝑆𝐶 + 0.23       
 (4.7) 
𝑇 = 1.698 𝑆𝐶0.942         
 (4.8) 
𝑅2 = 0.976  
Within the Panabako sandstone formation, a coefficient of determination, R2, of 
97.6% is obtained implying that about 98% of the aquifer transmissivity is 
influenced by the specific capacity. This value agrees with the model for sandstones 
estimated by Yidana et al. 2011 for the Northern part of the Voltaian. 
The coefficients of determination in all the three formations shown above indicate 
a strong dependence of transmissivity on specific capacity. Yidana et al. (2011) 
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indicates that the significance of these models gives credence to the complexity of 
the Voltaian aquifers significant variations in aquifer properties in space. Thus 
formation-specific model would yield better optimal results than one relationship 
that applies to the larger Voltaian.  
Though all the coefficient of regression from the equations above suggest a strong 
relationship, the representativeness and accuracy of the original data used is unclear 
based on the limited sample size (Acheampong and Hess, 1998; Yidana et al. 2011).   
The results of transmissivity calculated from these regression models (equation 4.1 
to 4.8) is presented in Appendix 10. A comparison between the transmissivity 
estimates for the entire study area and that of formation-specific shows a relatively 
small difference, less than 1.5 m2/day with most of the values below 0.6 m2/day. 
Since the difference between the two set of transmissivity values is not significant, 
it means that groundwater within the Nasia sub-basin is structurally controlled and 
not by lithology. In appendix 11 for instance, the thickness of the regolith in all the 
boreholes is thin and this means, the storage ability of the aquifer would be low and 
therefore would not be able to contribute to groundwater potential as suggested by 
Carrier et al. (2008) and Ewusi et al. (2009). The structural control of groundwater 
in the area is confirmed by researchers such as Yidana et al. (2008) by suggesting 
that horizontal fractures and joints resulting from secondary fracturing are the main 
determinants of both transmissivity and specific capacity. Acheampong and Hess 
(1998) have also indicated that the nature, aperture and degree of interconnection 
between joints determine the hydrogeological fortunes of the rocks in the area.  
 
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4.4.2.3 Geostatistical Method of estimating Transmissivity 
To improve on the accuracy of predicting transmissivity with less error based on 
limited data, cokriging was used by combining 26 transmissivity records and 222 
specific capacity data. This approach has been used by researchers such as 
Aboufirassi and Mariño (1984), Lance et al. (1996) and fairly recently by Razack 
and Lasm (2006). 
To depict the structural character of transmissivity and specific capacity for the 
terrain, variographic analysis was conducted on these parameters. Experimental 
variograms of transmissivity (lnT) and specific capacity (ln SC) were determined 
as shown in Figure 4.12 and 4.13 respectively. Prior to that, the data for 
transmissivity and specific capacity were transformed from their skewed raw form 
as shown in Figures 4.10 (a and b) into Figures 4.11 (a and b). 
 
 
Figure 4 - 10 (a): Untransformed Transmissivity and (b) Specific Capacity 
 
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Figure 4 - 11: (a)Transformed Transmissivity and (b) Specific Capacity 
 
The experimental variogram is presented in Figures 4.12 and Figure 4.13 for 
transmissivity and specific capacity respectively. Both variograms are fitted with a 
spherical model and their cross variogram is presented in Figures 4.14. Appendix 
11 contains cross validated results of the transmissivity and specific capacity which 
were used in selecting the appropriate model. 
 
  
 
 
 
 
 
 
 
Figure 4 - 12. Experimental variogram and fitted model for Transmissivity 
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The variogram of Transmissivity displayed in Figure 4.12, has a nugget effect of 
1.06 m4/d2. The nugget effect indicates that aquifer transmissivity within the terrain 
is highly variable and significantly controlled by distance. Researchers such as 
Isaaks and Srivastava (1989) and Razack and Lasm (2006) have associated the 
occurrence of the nugget effect to either sample errors or subtle variability in the 
data of the parameter. Several factors account for this variability in aquifer 
transmissivity in the study area. Yidana et al. (2011) for instance suggest that the 
variation in thickness of the weathered zone can influence the variability in 
transmissivity. They associated the variation to the low-grade metamorphism 
during the Pan-African tectonic event coupled with high temperature and pressure 
which has reduced the primary permeability of the rocks. Their conclusion was that, 
hydrogeological properties in the terrain are based on the degree of secondary 
permeability which varies significantly in the study area. This assertion is supported 
by Jalludin and Razack (2004) who reported that transmissivity is decreased by the 
effects such as weathering, hydrothermal and volcanic activities whereas tectonics 
activities rather enhance transmissivity.  
 
Aside the nugget effect, the variogram has a sill of 2.025 m4/d2 which indicates the 
total variance and it also has a range of 30348 m which indicates the distance 
between the sampling points at which the sill is reached. The presence of the sill 
and range indicate that distance has control over transmissivity in the study area. 
Beyond this range, the variance measured between the data points is independent 
from the respective data points and therefore correlation does not exist. Other 
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factors that are likely to contribute to the high degree of variability of transmissivity 
in the area could be to proximity to stream network, proximity to lineament among 
others. Rainfall in the study area is erratic as such its contribution to stream network 
would be seasonal which would lead to a variable transmissivity. Pradhan (2009) 
suggest that the stream network indirectly influences the groundwater potential of 
an area due to its relation to surface runoff and permeability.  
 
Figure 4 - 13. Experimental variogram and fitted model for Specific Capacity 
 
Specific capacity and transmissivity are closely related and such similar factors 
would contribute to their distribution in the basin. Figure 4.13 shows that the 
experimental variogram is fitted with spherical model with a nugget effect of 1.339 
m6/d2/m2. This means that the parameter under consideration is highly variable and 
it is controlled by distance. The sill and range of specific capacity from Figure 4.13 
are 1.560 m6/d2/m2 and 4661m respectively. Specific capacity is related to aquifer 
setting, well setting and pumping duration (Mace, 2001; and Yidana et al. 2011). 
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Transmissivity is one of the attributes of aquifer setting and since aquifer 
transmissivity is highly variable within the terrain, it presupposes that factors that 
contribute to the variability in transmissivity also affect specific capacity.  
 
A cross variogram of transmissivity and specific capacity is illustrated in Figure 
4.14 which has a nugget effect of 1.084 indicating the level of variability of the 
parameter, a sill of 2.185 that shows the total variance and a range of 3055 which 
shows the distance at which the variance measured between data points to be 
independent from the respective data points. Thus, correlation ceases to exist 
between the points beyond the range.  
 
Figure 4 - 14: Cross variogram for transmissivity and specific capacity 
 
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4.5. Variographic analysis of hydrogeological parameters 
4.5.1 Yield 
In order to develop a good kriged map of borehole yield in the study area, 220 
boreholes were used to develop a good variogram. The yield data was first 
transformed from the skewed nature (Figure 4.15 a) to normalized form as shown 
in Figure 4.15 (b) before being used for the variograms. 
 
Figure 4 - 15. Histograms of borehole Yield  
 
The transformed yield data shown in Figure 4.15 does not show a perfect normal 
distribution but rather an improvement over the raw data. The experimental 
variogram for the borehole yield is presented in Figure 4.16.  
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Figure 4 - 16: Variogram of Yield fitted with an exponential model 
 
The variogram of yield shown in Figure 4.16 is fitted with an exponential 
theoretical model with a nugget effect of 0.843 m6/d2, a sill of 1.195 m6/d2 and a 
range of 3253 m. The nugget effect describes the level of variability that is 
characterized by the study area. This means that borehole yield is significantly 
controlled by distance. Yield has been reported by many researchers to be 
influenced by various factors. Nsiah et al. (2018) for instance reports that yield 
within the Nabogo basin is related to fracturing of the rocks and poor fracture 
network would contribute to the variability of borehole yield. Acheampong and 
Hess (1998) as reported by Yidana et al. (2011)  that the large variations are 
controlled by discrete entities created in the wake of weathering and/or fracturing 
of the primary rock. Darko and Krásný (2010) attribute the variability of yields to 
the impervious nature of the rocks though they contain structures that enhance 
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percolation of water to form limited groundwater reservoirs that are structurally 
dependent and discontinuous in occurrence. Other factors such as slope, proximity 
of boreholes to stream, the extent of well development, the construction materials 
used and the appropriateness of the drilling method all have a play in the variability 
of yield. Inadequate well development and the choice of inappropriate screen for 
the material of the aquifer zone may also affect the distribution of well yield. The 
sill and range give an indication of the influence of distance on the yield. The 
presence of these two attributes of the variogram clearly suggest that distance has 
control over yield within the study area. The results of cross validation of the yield 
prior to selection of the appropriate theoretical model is shown in Appendix 13. 
The purpose of this cross-validation technique is to compare estimated and true 
values using only yield information available in the sample data set. 
 
4.5.2 Depth  
The depth data used for the variographic analysis was first transformed as a 
requirement for geostatistical measure as shown in Figure 4.17. A total of 220 
boreholes with record on depth were used for this analysis. The variogram for the 
depth of boreholes is displayed in Figure 4.18 which was fitted with an exponential 
theoretical model. 
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Figure 4 - 17: Histograms showing Depth data 
 
 
 
 
 
 
 
 
Figure 4 - 18: Variogram of borehole depth fitted with an exponential model 
 
The variogram of depth displayed in figure 4.18 above has a nugget effect which 
shows the level of variability of depth within the study area. The nugget effect from 
the above variogram is 0.0607 m2. The sill and the range for the depth from the 
variogram are 0.1224 m2 and 11431m. Appendix 13 contains the cross validated 
figure of variogram for depth which was used in selecting the appropriate 
theoretical model. The variability can be associated to the fact that boreholes 
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generally within the study area are need base. As such the focus has always been to 
obtain water at a minimum yield of 10 l/min rather than any other reason. Therefore, 
drilling stops immediately sufficient yield is attained and this does not take in 
consideration the general depth of drilling in the study. A lot of researchers have 
attempted to relate borehole yields with depth but no concrete scientific conclusion 
has been reached so far. Within the Voltaian for instance, Asare and Klitten (2008) 
evaluated the results of five borehole drilled beyond 100 m. All the boreholes were 
highly productive however the results contradict that from the HAP monitoring 
boreholes. Depths beyond 150 m could not even yield up to 10 l/min which refutes 
any conclusion that would have been made by Asare and Klitten (2008).  
4.5.3 Static Water Level (SWL)  
Results of the distribution of SWL are shown in Figure 4.19 with raw data (non-
transformed) of SWL as well as normalized SWL (transformed). 
 
Figure 4 - 19: Histograms showing SWL distribution 
 
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The variogram for SWL is shown in figure 4.20 fitted on an exponential theoretical 
model. The variogram contains a nugget effect with a value of 0.1986 m2, a sill and 
a range of 0.2818 m2 and 423.5 m respectively. Cross validation of the variogram 
is presented in Appendix 11. 
Ayamsegna and Amoateng-Mensah (2002) suggest that, the variation in SWL of 
boreholes in the terrain is mainly due to excessive usage. Aside that, they also 
indicated that high temperatures and extended dry seasons are to blame for the early 
drying up of surface water sources, which could recharge the groundwater system. 
Forkuor et al. (2013) have confirmed the conclusions of Ayamsegna and 
Amoateng-Mensah (2002) by suggesting that SWL fluctuates within a range from 
season to season due to variations in recharge rate and pumpage.  
 
 
Figure 4 - 20: Variogram of SWL fitted with a variogram 
 
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4.5.4 Regolith   
Regolith has been defined by Eggleton (2001) as the entire unconsolidated or 
secondarily re-cemented cover that overlies more coherent bedrock that has been 
formed by weathering, erosion, transport and/or deposition of the older material  
 
The regolith for this study area was determined from geological logs of successful 
boreholes totaling 220. Figure 4.21 is an illustration of a raw data without 
transformation (left) and also the transformed data (right). This was necessary since 
the raw data is skewed and would require being normalized for geostatistical 
analysis. 
 
 
Figure 4 - 21. Histogram showing distribution of raw and transformed Regolith 
 
 
 
The variogram for the regolith is shown in Figure 4.22, fitted with an exponential 
theoretical model. The nugget effect of is 0.1304 m2/y2, the sill and the range are 
0.2207 m2/y2 and 3327 m respectively. The results for the cross validation of the 
regolith is presented in Appendix 11. Even though the regolith is relatively thin, the 
level of variability is significant as seen in the nugget effect. MacDonald et al. 
(2001) and Carrier et al. (2008) suggest that the relatively thin regolith may be as a 
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result of the presence of stable clay or the presence of quartz in sandstones, which 
are resistant to weathering.  
The regolith can also significantly be affected by topography. Generally, locations 
with high topography such as the Gambaga areas under the Panabako sandstone 
formation are exposed more to weathering than low topographic areas due to 
gradient. Larger surface areas of the high topographic area are available for the 
breakdown of the geologic materials.  
 
Figure 4 - 22: Variogram of Regolith fitted with an exponential model 
 
4.5.5 Recharge   
A total of 73 data points was used in performing this analysis. The recharge for this 
study are based on values quoted by Addai et al. (2016) using the Chloride Mass 
Balance method (Appendix 8). 
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The data was transformed from its raw skewed nature for normal distribution as 
shown in Figure 4.23.  
 
Figure 4 - 23:  Histograms showing the distribution of Recharge  
 
The variogram for the recharge is displayed in Figure 4.24 and it is fitted with an 
exponential theoretical model. The model has a nugget effect of 0.00667 m2/yr2, a 
sill and a range of 0.01251 m2/yr2 and 8364 m respectively.  Appendix 11 contains 
the results of the cross validation of recharge. 
Recharge in the study area is prominent during the wet season when meteoric water 
goes beyond the permeable portions of the lateritic and sandy soils through the 
weathered zone into the fractured aquifer, particularly in places where there is 
hydraulic continuity between the weathered zone and the underlying fissures. 
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Figure 4 - 24: Variogram for Recharge fitted with an exponential model 
 
The nugget effect implies that recharge in the area is highly variable and as Carrier 
et al. (2008) indicates that, the spatial variability is related to the nature of the 
material of the unsaturated zone and the variability in rainfall patterns. Apambire 
(2000) suggest that recharge is variable and based his estimates on a function of 
lithology and total annual rainfall. According to Forkuor et al. (2013), factors such 
as geology, rainfall (intensity and distribution), land use, and soil type have been 
quoted by Sandwidi (2007) and Obuobie (2008) as the cause of variability to 
recharge.  
 
  
 
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4.6 Groundwater Potential Map 
The groundwater potential map was developed from Remote Sensing and 
Geographic Information System using multi criteria analysis (weighted overlay 
technique). The procedures used by Gumma and Pavelic (2013) were implemented 
in developing the groundwater potential map. 
4.6.1 Thematic/Interpolation Maps 
Interpolation maps were developed for all the hydrogeological parameters that 
influence groundwater potential within the study area. Kriging and cokriging 
geostatistical techniques of interpolation were used based on the results of the 
variograms for each of the hydrogeological parameter. 
 
4.6.1.1 Aquifer Transmissivity  
Figure 4.27 shows the results of cokriging to estimate aquifer transmissivity for the 
study area. The aquifer transmissivity appears to be low/moderate from the NE 
direction and increases towards the middle part of the study area. Then finally 
reduces towards the SW around Janga. The high transmissivity zone observed in 
the middle portion is associated with the Kodjari formation which according to Ó 
Dochartaigh (2011) is the most productive in the terrain in terms of groundwater 
potential. High transmissivity also noticed around the south west portion of the 
study area. These are associated with the Bunya sandstone members within the 
Bimbilla formation. It is observed that the locations showing high transmissivity 
are also having high values for yield.  
 
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Figure 4 - 27: 2-D Spatial distribution of aquifer transmissivity from cokriging 
for the study area 
 
4.6.1.2 Specific Capacity 
Spatial distribution map of specific capacity based on variograms developed in the 
study area is presented in Figure 4.28. The specific capacity map follows the same 
pattern as the transmissivity map. From the two maps shown in Figure 4.28 below, 
it is obvious that about two thirds of the study fall within the low specific capacity 
zone with values below 5 m3/d/m. Comparing this specific capacity map with the 
geological map of the study area clearly shows that the low values are associated 
with rocks of the Panabako sandstone formation possibly places without fractures. 
The Poubogou and the Bimbilla formations also show indication of low values 
which maybe because of the dominant impermeable mudstone-siltstone rock type 
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with very low potential for groundwater. The middle part of the study area 
corresponds to the Kodjari formation and the lower part of the study area is within 
the Bunya Sandstone member of the Bimbilla formation. These two locations show 
indications of medium to high specific capacity. There are few spots within the 
Panabako Sandstone that show high values of specific capacity. The results from 
the map follows the trend observed by Ó Dochartaigh et al. (2011) 
 
Figure 4 - 28: Spatial distribution of Specific Capacity in the study area 
 
4.6.1.3 Depth 
The spatial distribution of depth in the study area is shown in the kriged map 
presented in Figure 2.29.  The map shows that most of the boreholes in the study 
area are drilled to depth less than 60m with few boreholes beyond 60m. The 
variability of depth is mainly because most boreholes are based on community need 
as such drilling ends as soon as an appreciable yield is obtained.  
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Figure 4 - 29: Spatial distribution of Depth of productive boreholes in the study 
area. 
 
4.6.1.4 Thickness of Regolith 
The spatial distribution of the thickness of the weathered zone in the study area is 
depicted in the kriged image of Figure 4.30. Generally, there is a thin thickness of 
the regolith in the study area. This compares with the conclusions made by Carrier 
et al. (2008) for the entire Voltaian basin. It can be observed that most of the 
boreholes covering about two-thirds of the study area have a thickness of 
approximately 7m. There are however pockets of relatively thicker weathered 
zones, more than 10m scattered about the study area. The north eastern and south 
western part of the study area have slightly higher weathered zone thickness. 
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Figure 4 - 30: 2-D Spatial distribution of Regolith in the study area 
 
4.6.1.5 Static Water Level (SWL) 
Figure 4.31 shows a kriged 2-D spatial distribution of static water level in the study 
area. It is observed that, the middle portion towards the eastern part of the study 
have low SWL below 6m. These areas are underlain by rocks of the Kodjari and 
Panabako sandstone formations and similar trend is also observed in the spatial 
distribution of specific capacity and transmissivity of the study area. High SWL is 
recorded for boreholes located within the Bimbilla formation and a small part of 
the Panabako Sandstone formation (North of the study area). SWL may fluctuate 
within a range partly due to variation in recharge and demand from pumpage 
(Forkuor et al. 2013). The high values of SWL indicate that access to groundwater 
is a challenge and the vice versa for low SWL. The Bimbilla formation is generally 
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considered to have low groundwater due to the impermeable mudstone/siltstone 
rocks that dominate the formation.  
 
 
 
Figure 4 - 31: 2-D Spatial distribution of SWL in the study area 
 
4.6.1.6 Yield 
The kriged yield map of the study area is shown in Figure 4.32. The map shows a 
general feature of low yield across the study area with some level of variability 
which can be attributed to the fact that the entities considered as water bearing 
structures are discrete (Acheampong and Hess, 1988). The variability of yield in 
the study area supports the assertion that groundwater occurrence is mainly due to 
secondary permeability. The high yielding boreholes are located in the southern 
part of the study area which is within the Bunya sandstone member and few spots 
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within the central part of the study area (Kodjari formation). The potential of the 
Bunya sandstone member has been stated by Ó Dochartaigh et al. (2011) as very 
favourable for groundwater occurrence. 
-m3/d 
 
Figure 4 - 32: Spatial distribution of Yield in the study area 
 
4.6.1.7 Drainage density 
The drainage density for the study area is shown in Figure 4.34. The drainage 
mimics the major river (River Nasia). The flow is from the east and terminates at 
the south west. Overall, higher drainage densities are noted in the south east and 
south west parts (Bimbilla formation) than in the northern side (Kodjari and 
Panabako sandstone formations) of the study area. 
High drainage density implies that a large proportion of the precipitation goes as 
surface runoff which explains why the Bimbilla formation has low groundwater 
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potential. Low drainage density suggests that, rainfall infiltrates the ground and few 
channels are required to carry the runoff.  
 
 
Figure 4 - 33: Drainage Density in the study area 
 
 
4.6.1.8 Recharge 
Figure 4.34 shows the spatial distribution of recharge in the study area. 
Groundwater recharge potential can be considered to be relatively high in the 
central part and low in the rest of the study area. It is observed that the recharge 
follows the pattern of the drainage density. This could mean that groundwater is 
recharged mainly from drainage system which is as a result of infiltration from 
rainfall.    The low recharge is attributed to the significant clay content in the 
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overburden which tends to impede vertical infiltration and percolation (Attandoh et 
al. 2013). 
 
4.6.1.9 Slope 
The slope of the study area is presented in Figure 4.35. Areas with steep slopes such 
as the Northern part of the area which has the Gambaga escarpment will have more 
run off occurring leading to reduced infiltration.  Magesh et al. (2011) suggests that 
this will reduce recharge which eventually will affect groundwater potential. As 
infiltration is inversely related to slope, a gentle slope promotes an appreciable 
groundwater infiltration.  
From the slope map it can be seen that the central part of the study area has nearly 
level and gentle slopes which means these areas are more likely to have higher 
infiltration and therefore higher recharge. It is observed that the slope and drainage 
density share similar patterns, thus higher values towards lower portion of north 
west (Janga community).  Nearly level slope and gentle slope are favourable for 
groundwater accumulation since the resident time for rainwater is longer compared 
to steeply sloping surfaces (Arnous, 2016; Waikar and Nilawar, 2014). 
 
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Figure 4 - 34: Spatial distribution of Recharge in the study area 
 
 
Figure 4 - 35: Slope in the study area 
4.6.1.10 Lineament  
 The general pattern of lineament in Figure 4.37 is E-W direction with a few in the 
NW-SE direction. The presence of the lineaments can be attributed to both geologic 
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and geomorphic processes like fractures or linear valleys which contribute in 
accumulation of groundwater in the subsurface. More lineament features are 
observed in the middle and upper portion of the study which could explain the 
relative high groundwater in those areas. 
 
Figure 4 - 36: Lineament map of the study area   
 
4.6.2 Multi Criteria Analysis (Weighted Overlay Technique) 
 
4.6.2.1 Reclassification of parameters 
All the thematic layers for the various parameters were reclassified as a requirement 
for the multicriteria analysis. The values for the reclassification ranged between 1 
and 5 depending on the variability of the parameter. The borehole depth was 
reclassified into three categories based on the available data, a thorough evaluation 
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of borehole records and knowledge from experience in drilling boreholes in the 
study area. The acceptable depth of borehole by Community Water and Sanitation 
Agency (CWSA) is 40 m, but most boreholes drilled averaged around 60m and 
maximum of 100m. Therefore, the manual function in Arc GIS 10.6 classification 
criteria was used to classify depth into three (3) categories. Figure 4.38 is an 
illustration of how the rescaling of depth for the area was performed. The SWL 
layer was zoned into three using natural breaks of values (Figure 4.39). Low values 
were interpreted to have high potential for groundwater because some of those 
boreholes are very productive even in the dry season compared with boreholes 
where the SWLs are high. Further to that, the shallower depths indicates the 
groundwater is easily accessible (Martin & van de Giesen, 2005). Transmissivity 
was divided into 5 classes using the natural break function of classification in Arc 
GIS 10.6. The purpose of using the natural break function is to cater for the 
heterogeneous nature of the terrain. Figure 4.40 is an illustration of how the 
reclassification was performed. 
 
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Figure 4 - 37: Reclassification of Depth of borehole 
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Figure 4 - 38: Reclassification of SWL 
 
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Figure 4 - 39: Reclassification of aquifer Transmissivity 
 
The illustration of the remaining parameters (lineament, density, Recharge, 
Regolith and Drainage density) is presented in Appendix 12. The classification for 
these parameters was based on the natural breaks function in Arc GIS 10.6. This 
was to cater for the variability of each parameters. New maps were developed from 
the reclassified parameters and are presented in Appendix 13.  
 
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4.6.2.2 Assignment of score factors and weight 
The strength of the weighted overlay technique according to Gumma and Pavelic 
(2013) is based on the fact that human judgment/expert opinion including previous 
literature can be incorporated into the analysis. Table 4.4 shows the scaled values 
and weights assigned to the different classes and parameters. The attributes of the 
reclassified features were assigned scores based on their influence on groundwater 
potential. A scale factor ranging 1 to 5 was adopted where 1,2,3,4 and 5 represent 
very low, low, moderate, high and very high. Secondly, weights (% of influence) 
totaling 100% were assigned to the various parameters taking account of their likely 
importance for groundwater occurrence and replenishment. The higher the 
percentage weight, the more influence a particular parameter will have in the 
groundwater potential. 
 
Based on literature, expert knowledge on drilling and data on depth in the study 
area the same value of scale factor was assigned to the reclassified depth range. 
There is no direct relationship about the influence of depth to groundwater potential 
in the study area. Therefore, a value of 2 representing low influence was assigned 
for all the classes of depth with a 5% influence weight. Slope gradient influences 
the infiltration of rainfall in that steeper slopes generate less recharge because water 
runs rapidly off the surface during rainfall, allowing less time for infiltration and 
recharge. Therefore, high scores were assigned to the plain region with lower slope 
because low runoff is usually a good recharge zone. A weight of 10% was assigned 
to slope representing its contribution to groundwater in the study area.   
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Lineaments connote presence of secondary permeability. The occurrence of 
groundwater in the study area according to Banoeng-Yakubo et al. (2011) is 
characterised by the presence of secondary porosity. Therefore, distance close to 
lineaments would offer good opportunities for groundwater development and for 
that matter a weight of 15% has been assigned to lineament layer (Murthy and 
Mamo, 2009). Regolith is a good indicator of an aquifer’s storativity implying that 
high regolith thickness is an indication of higher storage. The study area generally 
has thin regolith as suggested by Carrier et al. (2008) and also from the current data. 
Therefore, a weight of 5% was assigned to regolith thickness. 
 
Groundwater recharge is associated to the characteristics of the drainage system. 
Generally, the denser the drainage system, the lower is the recharge rate. For that 
matter, a higher scale factor was assigned to less dense drainage areas and vice 
versa.  Singh et al. (2013) indicates that low drainage density implies greater 
groundwater percolation and accumulation. Therefore, recharge rate will also be 
high. A weight of 10% was assigned to both drainage density and groundwater 
recharge since they are interconnected in terms of their influence on groundwater 
potential.  
Likewise, information on SWLs implies availability of water in terms of 
accessibility. Thus, based on the classes of SWL layer, high potential is associated 
with shallower depths whereas high values of SWL connotes low potential (Forkuor 
et al. 2013; Nsiah et al. 2018). A weight of 10 % was assigned to the SWL layer as 
its overall contribution to groundwater in the area. Other factors such 
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evapotranspiration was considered in assigning the weight to SWL. The geology of 
the study area was reclassified into the formations and was rated according to the 
influence of each formation on the groundwater potential. Different scale factors 
were assigned to the formations based on the lithological descriptions, geophysical 
interpretation and the inherent hydraulic characteristics of the rocks in the various 
formations.  Table 4.4 shows the scale factor and the weight (10% influence) that 
was assigned to geology as its contribution to the groundwater potential of the study 
area. Transmissivity according to Krásný (1997) has been accepted as the best 
hydraulic parameter to express groundwater abstraction possibilities and to be 
represented in hydrogeological maps. Holland (2012) indicates that the magnitude 
of transmissivity helps in understanding the water-bearing characteristics of 
hydrogeological bodies and is a decisive factor for groundwater-abstraction 
possibilities. In view of the above assertions, a weight of 25% representing the 
highest weight was assigned to transmissivity. High scale factors also represent 
high transmissivity and vice versa.  
These approach of assigning scores and weight follows the procedure adopted by 
Forkuor et al. (2013), Gumma and Pavelic (2013) and Nsiah et al. (2018) who 
applied similar method to produce groundwater potential map for Northern region, 
Ghana and Gushiegu district respectively.  
 
 
 
 
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Table 4.3 Scaled values and weights assigned to the different classes and 
parameters. 
Scale 
 Potential Weight (% 
Parameter Reclassified  Value/Factor 
of Score Influence) 
score   
1 2 Low 
Depth 2 2 Low 5 
3 2 Low 
          
1 3 Moderate 
Regolith 2 4 High 5 
3 5 Very High 
          
1 1 Very Low 
2 2 Low 
Recharge 3 3 Moderate 10 
4 4 High 
5 5 Very High 
          
1 5 Very High 
SWL 2 4 High 10 
3 3 Moderate 
          
1 1 Very Low 
2 2 Low 
Aquifer Transmissivity 3 3 Moderate 25 
4 4 High 
5 5 Very High 
          
Bimbilla 2 Low 
Poubogou 2 Low 
Geological formation Panabako 10 
3 Moderate 
Sandstone 
Kodjari 4 High 
          
1 5 Very High 
Lineament 2 4 High 15 
3 3 Moderate 
          
1 5 Very High 
Drainage Density 2 4 High 10 
3 3 Moderate 
          
1 5 Very High 
Slope 2 4 High 10 
3 3 Moderate 
TOTAL SCORE       100 
 
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4.6.2.3 Groundwater Potential Map 
The overlay tool in Arc GIS 10.6 was used to produce the comprehensive 
groundwater potential map in Figure 4.41. The groundwater potential map has been 
divided into five classes: (a) very poor (b) poor (c) moderate (d) good, and (e) very 
good across.  
 
It indicates that places with moderate groundwater potential covers an area of 2010 
km2 and it is widely distributed across most parts of the study area. This area 
corresponds with most part of the Bimbilla, Poubogou and some portion of the 
Panabako Sandstone formation. This outcome is in sync with results from other 
researchers who have also indicated that the groundwater potential of the Bimbilla 
is low (Ewusi et. al. 2009). The portion of Bimbilla that has a moderate groundwater 
potential is associated with the Bunya sandstone member at the lower part of the 
Bimbilla formation and also the central part of Bimbilla formation. Carney et al. 
(2010) suggests that the Bunya sandstone member is very young and with a 
reasonable high porosity compared to the other units within the Voltaian. Field 
observation indicates that the central part of the Bimbilla formation in most places 
is covered by ephemeral streams which could have contributed to the moderate 
groundwater potential. About 2205 km2 of the map represent areas with poor 
groundwater potential which is dominated by the Bimbilla formation with few 
places in the Poubogou formation. These areas are comprised of mudstones and 
siltstone which are considered highly impermeable. The total area that falls under 
the category of good groundwater potential covers 985 km2 and 104 km2 also 
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represents the category of very good groundwater potential. Most of the very good 
to good groundwater potential falls within the Kodjari formation and southwest of 
the Panabako sandstone formation.   
 
 
Figure 4 - 40: Groundwater Potential Map of the study area 
 
The Kodjari Formation is considered the most prolific unit within the study area 
based on an evaluation of drilling records in the Voltaian basin (Ó Dochartaigh et 
al. 2011). Where there are fractures or intercalation of other units within the 
Panabako sandstone formation, high yielding aquifers are recorded. 
 
The very poor groundwater potential covers an area of 22 km2 which is within the 
mudstone siltstone areas of the Bimbilla formation. The Bimbilla formation is 
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known to be generally of poor groundwater potential. The very poor groundwater 
potential of the Bimbilla formation is evident in the outcome of the low yields that 
have been recorded under the Hydrogeological Assessment Project-HAP (1 dry 
well and HAP05 at Janga and HAP 14) as wells as poor yields under the Danida 
White Volta Basin Project -DWVP 04, DWVP06 and DWVP08, DWVP10 
(Appendix 7).  
 
4.6.3 Validation of Groundwater Potential Map 
The reliability of the potential map in predicting probability of obtaining 
groundwater was tested using both yield and dry boreholes. The yield map was 
superimposed on the groundwater potential map to identify the trend of distribution 
and it is as shown in Figure 4.42. 
 
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Figure 4 - 41: Groundwater potential map with reported yield (m3/d) of 
productive boreholes  
 
Figure 4.42 shows that the distribution of boreholes with high yields generally 
follow the same pattern as the very good to good groundwater potential zones. 
Similarly, the poor groundwater potential also follows the pattern as the low 
yielding boreholes. There are however few instances where high yielding boreholes 
are located in the low groundwater potential zones or vice versa. This is expected 
considering the level of heterogeneity of the study area with high variability in 
almost all the hydrogeological parameters.   
 
Furthermore, 117 dry boreholes extracted from the HAP database were used in 
validating the groundwater potential map. These dry boreholes were superimposed 
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on the groundwater potential map as shown in Figure 4.43. The distribution of the 
dry boreholes follows the pattern of groundwater potential zones of the study area. 
Few dry boreholes happen to fall within the zones with good groundwater potential 
which is expected when dealing with a highly heterogeneous environment  
 
Based on the results of validation obtained from these maps (Figure 4.42 and 4.43), 
the groundwater potential map developed can be regarded as reliable in predicting 
the groundwater potential of the study area. 
 
 
 
 
Figure 4 - 42: Groundwater potential map with dry boreholes of study area 
4.6.4 Sensitivity Analysis 
 
The purpose of sensitivity is to determine the influence of different criteria weights 
on the spatial pattern of a suitability map (Nwer, 2005). For this study, two 
scenarios were used to determine the sensitivity of the groundwater potential map 
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to slight changes in the weights assigned to the parameters following the approach 
by (Atandoh et al. 2013; Gogu and Dassargues 2000; Napolitano and Fabbri, 
1996;). Several scenarios have been tried to determine the sensitivity of the 
groundwater potential map, the most suitable illustrations are in Appendix 14.  
Figure 4.44 is the first scenario for the sensitivity analysis and it involved tweaking 
the weight of transmissivity and geological formation while maintaining the rest of 
the parameters. The result (Figure 4.44) though slightly similar to the trend of the 
groundwater potential map, a significant variation is observed. High yielding 
boreholes are located in the poor and moderate zones of the Bimbilla formation. 
This map implies that transmissivity has a greater influence in groundwater 
potential of the study area. Therefore the groundwater in the area is structurally 
controlled and not by lithology (Acheampong and Hess, 1998; Banoeng-Yakubo et 
al. 2011; Yidana et al. 2011).  
 
 
 
 
 
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Figure 4 - 43: Sensitivity Analysis 1 
 
 
The second scenario involved slight increment in the weight of lineament with a 
corresponding decrease in transmissivity. This was informed by the significant 
number of features on the lineament map in Figure 4.37. The results of the 
sensitivity analysis 2 suggest that a greater proportion of the Bimbilla formation 
has good groundwater potential but interestingly, the high yielding boreholes do 
not follow that trend. This is also at variance with what pertains on the ground and 
from literature. It therefore means that though lineament has an influence on the 
potential of groundwater, the influence should not be over-estimated, transmissivity 
remains the single most important parameter that has a higher influence on 
groundwater potential. As Krásný (1997) stipulates, “transmissivity has been 
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accepted as the best hydraulic parameter to express groundwater abstraction 
possibilities and to be represented in hydrogeological maps”. 
 
Figure 4 - 44: Sensitivity Analysis 2 
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CHAPTER FIVE 
CONCLUSIONS AND RECOMMENDATIONS 
 
5.1 Conclusions 
The study is the first attempt in characterising the hydrogeology of the Nasia basin 
with comprehensive data ranging from geophysical to hydrogeological and to 
produce a groundwater potential map. The research has demonstrated that the 
current conventional methods of exploring for groundwater by interpreting low 
resistive layers or zones from geophysical survey as suitable locations are 
inappropriate and has contributed to the significant number of low yielding and dry 
boreholes that is recorded in the Nasia Basin and the Voltaian Supergroup at large.  
Based on an integration of various geophysical methods, the geophysical 
characteristics of the rocks within the geological formations in the study area have 
been established to guide future groundwater endeavors. The results indicate that 
the regolith resistivity for the Panabako sandstone formation is between is 100 Ωm 
to 1000 Ωm and the bedrock is from 50 Ωm to 1000 Ωm. The Bimbilla formation 
has a regolith resistivity from 5 Ωm to 20 Ωm and a bedrock of 15 Ωm to 70 Ωm. 
The Kodjari and Poubogou formations have regolith resistivity ranging from 10 
Ωm to 300 Ωm and 25 Ωm to 60 Ωm respectively. The bedrock resistivity for the 
Kodjari formation ranges between 10 Ωm to 50 Ωm while that for the Poubogou 
formation is from 80 Ωm to 100 Ωm. When interpreting geophysical results, the 
focus should be on understanding the geology from the direction of the profiles 
compared to the expected strike of the formation as well as of the dip and the 
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trend/pattern of distribution of resistivity. The contact from these features are 
targets worth exploring. The results also show that in places such as the Bimbilla 
formation, not all locations are worth exploring but rather the weathered zone could 
hold significant water that can be tapped. 
Results of the hydraulic properties indicate that the average depth of striking water 
in the Nasia Basin exceeds the thickness of the regolith suggesting that productive 
yields are mainly derived from water bearing structures. Further to that, structural 
analysis conducted using variograms and regression models indicate that the 
groundwater in the study area is mainly structurally controlled. This corroborates 
the findings of research conducted in the Southern part of the Voltaian Supergroup 
in Ghana. Based on the limited data from the pumping test, geostatistical methods 
were employed to determine aquifer transmissivity of under sampled, using 
regression models and cokriging technique. Cokriging using specific capacity as 
the secondary parameter has proven very reliable and useful in providing better 
spatial estimation of transmissivity of the Nasia Basin. Cokriging is reliable 
because it considers the spatial auto-correlation of transmissivity and the spatial 
cross-correlation between transmissivity and specific capacity due to its abundance.  
A comprehensive groundwater potential map is developed based on wide range of 
parameters which influence the occurrence of groundwater in the study area. The 
study demonstrates that geospatial and multi-criteria analysis techniques in 
combination with reliable field data could be used for assessment of groundwater 
potential zones. Based on the map, an area of 104 km2 is classified as having very 
good groundwater potential and about 985 km2 is considered having good potential. 
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These are areas that fall within the Kodjari formation and southwest of the 
Panabako sandstone formation.  About 2205 km2 represent areas with poor 
groundwater potential dominated by the Bimbilla formation with few places in the 
Poubogou formation and are comprised of mudstones and siltstone which are 
considered highly impermeable. The map indicates that places with moderate 
groundwater potential cover an area of 2010 km2 and they are widely distributed 
across most parts of the study area. South west of the Nasia basin with an area of 
22 km2 is considered to have poor groundwater potential. This is made up of 
siltstones and mudstone belonging to the Bimbilla formation.  
An assessment of the results of the groundwater potential map indicates that only 
about 20% of the study area is capable of yielding high boreholes thus fall within 
the good to very good potential zones. Therefore, other strategies to augment for 
increasing irrigation such as exploring the weathered zone for groundwater storage 
should be considered. The reliability of the groundwater potential map was tested 
against successful and dry boreholes and the results from sensitivity analysis shows 
high degree of confidence in the prediction. 
Exploration hydrogeologist in the area could use the groundwater potential map as 
well as the methodology for interpreting geophysical results as a guide when 
selecting areas for hydrogeological studies especially when high yielding boreholes 
are required for mechanized systems. 
 
 
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5.2 Recommendations 
• The geophysical studies conducted under this research was based on only 
successful boreholes, it is recommended that further studies be conducted 
on dry boreholes and the results interpreted along with the respective 
geologic logs. This would give a proper understanding of the geology of the 
area. 
• Few boreholes were used within the Poubogou formation and therefore it is 
recommended that further studies targeted at obtaining detailed 
hydrogeological data within the Poubogou formation be undertaken. 
• The Kodjari formation should be explored further especially within Bugya 
Pala where two monitoring boreholes produced contrasting results. In 
addition, during the recognisance survey, it was observed that hand dug 
wells in the community were situated along a straight line and they never 
run out of water even in the dry seasons.  
• It is further recommended that, apparent conductivity measurement using 
EM-34 method be undertaken on a wider area and incorporated into a GIS 
environment. This will help to present and analyze the data in contour form 
instead of as individual lines so that an overall picture of the variation of 
electrical conductivity (resistivity) of the area can be understood. 
Superimposing the depth, yield, static water level data on the contoured map 
can result in establishing the interesting relationship between the 
parameters.  
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• The Bimbilla formation which most part of it falls under the very low, low 
to moderate groundwater potential should be investigated further. The 
groundwater dynamics of the regolith should be studied in detail to help 
ascertain its viability for storing groundwater in a sustainable manner as 
proposed from this study. 
• Further geophysical studies using geophysical logging should be conducted 
on more existing boreholes to help in providing more information that 
would help in the interpretation of the subsurface lithology.  
• In addition, extensive pumping test for a minimum duration of 24 hrs should 
be conducted on existing boreholes in the individual geological formations 
to help in determining the aquifer parameters and compare with the findings 
from this study.   
• The groundwater potential map should be developed for the individual 
geological formations using the same approach in this study then compared 
with the results from this research. This will enhance the understanding of 
both aquifer behaviour and the occurrence of groundwater which will lead 
to better understanding of the hydraulic connection between the regolith and 
the underlying fractured bedrock. 
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Yidana, S. M. Ophori, D. & Banoeng-Yakubo, B. (2008). Hydrogeological and 
hydrochemical characterization of the Voltaian Basin: The Afram Plains area, 
Ghana. Environmental Geology, 53(6), 1213–1223. 
https://doi.org/10.1007/s00254-007-0710-1 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
200 
 
University of Ghana http://ugspace.ug.edu.gh
APPENDICES 
APPENDIX 1-EM-34 PROFILES 
BANAWA-KODJARI FORMATION-SAMPLE OF INTERPRETATION  
 
 
 
 
 
 
 
 
 
 
 
 
 
201 
 
University of Ghana http://ugspace.ug.edu.gh
 
Location: Gambaga – Nalerigu 
 
10m Coil Separation 40m Coil Separation 
Bearing Profile Length: 800 m GAMBAGA
Profile No.     A Date:  10/5/2017 Bearing Profile Length: 800 m  
     STATION 10-m Separation Remarks Profile No.     A Date:  10/5/2017
HD VD      STATION 40-m Separation Remarks
0 29 18 HD VD
0 22 24  
20 24 16
20 24 26
40 7 6
40 18 21
60 24 28 60 21 23
80 29 21 80 18 22  
100 28 26 100 27 32
120 37 26 120 16 21
140 33 18 140 22 21
160 28 20 160 22 23  
180 38 29 180 19 21
200 40 33 200 20 27
220 31 26 220 19 23
240 21 24  
240 30 23
260 22 16
260 38 30
280 22 17
280 37 24
300 17 15
300 32 11 320 15 14  
320 17 12 340 15 17
340 16 10 360 15 10 under pylons
360 35 35 380 15 14
380 39 23 400 32 19  
400 14 34 420 22 16
420 37 17 440 26 15
440 35 26 460 21 13
480 20 14  
460 30 21
500 12 11
480 38 33
520 18 16
500 25 21
540 23 18
520 24 13 560 16 11  
540 23 20 580 12 11
560 19 9 600 15 14
580 34 26 620 18 14
600 31 24 640 21 20  
620 34 21 660 30 17
640 33 20 680 17 14
660 33 17 700 22 18
720 21 14
680 37 24
740 24 20
700 31 26
760 19 17
720 34 28
780 17 11
740 38 34 800 19 17
760 18 13
780 16 21
800 26 20
820 42 38
840 17 15
860 41 26
 
 
 
 
 
 
 
202 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
Location: Kpalvaka 
 
10m Coil Separation 40m Coil Separation 
 
KPALVAKA KPALVAKA
Bearing Profile Length: 800 m 10m Coil Separation Bearing Profile Length: 800 m
Profile No.     A Date:  10/6/2017
Profile No.     A Date:  10/6/2017
     STATION 40-m Separation Remarks
     STATION 10-m Separation Remarks
HD VD
HD VD 0 41 33
0 52 47 20 37 30
20 33 34 40 38 37
40 56 44 60 38 37
60 49 44 80 41 38
100 40 31
80 41 45
120 38 38
100 45 33 140 42 40
120 43 40 160 44 40
140 45 37 180 33 40
160 45 31 200 39 38
180 34 17 220 42 33
240 50 38
200 45 44
260 37 44
220 46 42 280 36 36
240 48 47 300 41 31
260 51 41 320 34 38
280 44 38 340 35 38
300 46 36 360 40 32
320 39 41 380 34 38 40m Coil Separation 
400 47 36
340 31 26
420 39 36
360 46 28 440 28 36
380 42 30 460 38 33
400 33 30 480 34 37
420 43 44 500 40 33
440 50 42 520 40 36
540 33 31
460 34 44
560 32 35
480 54 37 580 40 34
500 48 50 600 38 44
520 34 43 620 39 21
540 19 42 640 40 37
560 53 42 660 44 42
580 47 21 680 38 38
700 40 34
600 40 48
720 42 36
620 39 48 740 40 35
760 44 32
780 38 38
800 32 32
820 33 28  
 
 
 
 
 
 
 
203 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
 
 
 
204 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
 
 
205 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
 
 
 
 
 
206 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
 
 
207 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
 
208 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
 
 
 
 
209 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
210 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
211 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
 
212 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
213 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
214 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
215 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
 
 
216 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
217 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
 
 
 
 
 
 
218 
 
University of Ghana http://ugspace.ug.edu.gh
 
 
219 
 
University of Ghana http://ugspace.ug.edu.gh
APPENDIX 2 -ERT PROFILES 
 
1. DWVP01 
 
2. DWVPO2 
 
220 
 
University of Ghana http://ugspace.ug.edu.gh
3. DWVP06
 
4. DWVP07
 
221 
 
University of Ghana http://ugspace.ug.edu.gh
5. BANAWA 1 
 
6. DIDOGLL 
222 
 
University of Ghana http://ugspace.ug.edu.gh
7. DINDANE DRY 
 
8. JEMAKA 
 
223 
 
University of Ghana http://ugspace.ug.edu.gh
9. BANAWA 2 
 
10. BOAMASA BH1 
 
224 
 
University of Ghana http://ugspace.ug.edu.gh
11. BOAMASA BH 2 
 
12. DAGBIRIBORE BH1 
 
225 
 
University of Ghana http://ugspace.ug.edu.gh
13. DAGBIRIBORE BH2 
 
14. DISIGA 
 
226 
 
University of Ghana http://ugspace.ug.edu.gh
15. GBALIGA 
 
16. JEMAKA 2 
 
227 
 
University of Ghana http://ugspace.ug.edu.gh
17. KATANI 
 
18. KOLINVA 
 
228 
 
University of Ghana http://ugspace.ug.edu.gh
19. NABULUGU BH 2 
 
20. KPALVOKO 
 
229 
 
University of Ghana http://ugspace.ug.edu.gh
21. LOAGRI 
 
22. MANCHUGU 
 
230 
 
University of Ghana http://ugspace.ug.edu.gh
23. MOATANKURA 
 
24. NAMBRUGU 
 
231 
 
University of Ghana http://ugspace.ug.edu.gh
25. NALERIGU 
 
26. NASIA 
 
232 
 
University of Ghana http://ugspace.ug.edu.gh
27. KUKUA-ARTESIAN 
 
28. LATARE
 
233 
 
University of Ghana http://ugspace.ug.edu.gh
29. NAGBOO 
 
30. MANIE 
 
234 
 
University of Ghana http://ugspace.ug.edu.gh
31. TICHIRIGATABA 
 
32. NANORI 
 
235 
 
University of Ghana http://ugspace.ug.edu.gh
33. NYANSON 
 
34. NYINGALE 
 
236 
 
University of Ghana http://ugspace.ug.edu.gh
35. POMO 
 
36. SHEVOYA-CHIEF HOUSE 
 
237 
 
University of Ghana http://ugspace.ug.edu.gh
37. SURUGU  
 
38. TAMBOKO 
 
238 
 
University of Ghana http://ugspace.ug.edu.gh
39. TINGURI 1 
 
40. TINGURI 2 
 
239 
 
University of Ghana http://ugspace.ug.edu.gh
41. TINGURI 3 
 
42. TINKAYA 
 
240 
 
University of Ghana http://ugspace.ug.edu.gh
43. TUGBANG 
 
44. YIBEE 
 
 
 
241 
 
University of Ghana http://ugspace.ug.edu.gh
45. NASIA ERT/EM 
 
46. PIGU 
 
242 
 
University of Ghana http://ugspace.ug.edu.gh
47. BANAWA ER/EM 
 
48. WUNDUA 
 
243 
 
University of Ghana http://ugspace.ug.edu.gh
 
49. DWVP04 
 
50. DWVP9 
 
244 
 
University of Ghana http://ugspace.ug.edu.gh
51. DWVP03 
 
52. DWVP05 
 
53. DVWP08 
 
245 
 
University of Ghana http://ugspace.ug.edu.gh
54. DWVP10 
 
55. Gbimsi 
 
56. Guabulga 
 
 
246 
 
University of Ghana http://ugspace.ug.edu.gh
57.   Mishio 
 
58.  Daboya No. 2 Chps Compound 
 
59.Sooba Community 
 
247 
 
University of Ghana http://ugspace.ug.edu.gh
60. Sooba Primary School 
 
61.Tinguri Pope John RC  
 
62. Shelinyoya  
 
248 
 
University of Ghana http://ugspace.ug.edu.gh
63. Zanguya 
 
64.Nasia Chps Compound 
 
65. Loagri Primary 
 
249 
 
University of Ghana http://ugspace.ug.edu.gh
66.Loagri Community 
 
67. Kparigu 
 
68.Kukua 
 
250 
 
University of Ghana http://ugspace.ug.edu.gh
69.Kolinva 
 
70. Takorayili 
 
251 
 
University of Ghana http://ugspace.ug.edu.gh
 
APPENDIX 3- GEOPHYSICAL WIRELINE LOGS 
 
1. DWVP 01 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
252 
 
University of Ghana http://ugspace.ug.edu.gh
 
2. DWVP02 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
253 
 
University of Ghana http://ugspace.ug.edu.gh
 
3. DWVP03 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
254 
 
University of Ghana http://ugspace.ug.edu.gh
 
4. DWVP04 
 
 
 
 
 
 
 
 
 
255 
 
University of Ghana http://ugspace.ug.edu.gh
 
5. DWVP05 
 
 
 
 
 
 
 
 
 
 
256 
 
University of Ghana http://ugspace.ug.edu.gh
 
6. DWVP06  
 
 
 
 
 
 
 
 
 
 
 
 
 
257 
 
University of Ghana http://ugspace.ug.edu.gh
 
7. DWVP07  
 
 
 
 
 
 
 
 
 
 
258 
 
University of Ghana http://ugspace.ug.edu.gh
 
8. DWVP08 
 
 
 
 
 
 
259 
 
University of Ghana http://ugspace.ug.edu.gh
 
9. DWVP09 
 
 
 
 
 
 
260 
 
University of Ghana http://ugspace.ug.edu.gh
 
10. DWVP10 
 
 
 
 
 
 
261 
 
University of Ghana http://ugspace.ug.edu.gh
 
11. HAP05 
 
 
 
 
 
 
 
262 
 
University of Ghana http://ugspace.ug.edu.gh
 
12. BUGYA PALA-2 
 
 
 
 
 
 
 
 
 
263 
 
University of Ghana http://ugspace.ug.edu.gh
 
13. WVB-11 
 
 
 
 
 
 
 
 
 
264 
 
University of Ghana http://ugspace.ug.edu.gh
 
14. WVB12 
 
 
 
 
 
265 
 
University of Ghana http://ugspace.ug.edu.gh
 
15. HAP 11 
 
 
 
 
 
 
 
266 
 
University of Ghana http://ugspace.ug.edu.gh
 
16. HAP 14 
 
 
 
 
 
 
 
 
 
267 
 
University of Ghana http://ugspace.ug.edu.gh
 
17. HAP 12 
 
 
 
268 
 
University of Ghana http://ugspace.ug.edu.gh
 
APPENDIX 4- GEOLOGICAL LOGS 
 
1. DWVP01 
 DANIDA WHITE VOLTA PROJECT BH status: Successful ✓
Coordinates   Dry
Northings: 778042
Eastings: 1147263
BOREHOLE RECORD
Community Tamboku
Drilling contractor HYDRONOMICS LTD. Drill rig Method ROTARY AIR
Drilling start date 04-03-17 Compl. date 05-03-17 Operator Theo
TEST PUMPING  Date: 18-03-17 Conductivity Top of screen * m
Dynamic WL *          N   I  L L         m Pump type Total Iron Static WL *                m
Static WL *                          m Pumping rate (Q)                  m³/h Manganese Potential drawdown               m
Drawdown (s)           #  V   A  L  U   E m! Duration      h Nitrate  Potential yield 8l/min
 * Levels to ground level datum Specific capacity (Q/s)          m³/h/m Fluoride  Depth of borehole * 150 m
SIZE PROFILE TIME/ WATER ZONES WELL DIAGRAM 
DEPTH CUMULATIVE
M/MIN Q (l/min)
12" Lateritic material
Clay cutter Light  brown clay 2m Sanitary Seal
 
10 10m Backfill 10
Highly weathered sandstone
20 20
 
moderately weathered sandstone
30 30
6.5" 
hammer bit  43m PVC Plain
40 40
24m gravel pack
50 micaceous sandstone 50
60 60
70 70
dry fractures
80 80
90 90
100 100
110 110
fine grained sandstone
120 120
dry fractures
130 130
140 140
150 6 150
Gravel for gravel pack 23m LM Remarks and stoppages:
Screen Length 9 LM
Casing length 43 LM well was left over night to be constructed
Installation of grout seal 5 M
Cleaning & development 30 MINS Prepared by: HYDRONOMICS LTD
Centralisers fitted     No   
Safety cap fitted Yes  No  Approved:  
Backfill aband. BH Yes  No
Cement for grout 50 KG  
Platform construction date  
Distance from last BH KM
 
 
 
 
 
 269 
 Temporary
 SCALE
 
 
University of Ghana http://ugspace.ug.edu.gh
 
2. DWVP02 
 DANIDA WHITE VOLTA PROJECT BH status: Successful ✓
Coordinates   Dry
Northings: 775912
Eastings: 1144848
BOREHOLE RECORD
Community TAMBOKU TOWNSHIP
Drilling contractor HYDRONOMICS LTD. Drill rig KLR Method ROTARY AIR
Drilling start date 05-03-17 Compl. date 06-03-17 Operator Theo
PUMPING TEST  Date: 13-03-17 Conductivity Top of screen * nil m
Dynamic WL *             1  6  . 9   4    m Pump type PEDROLLO Total Iron Static WL *                m
Static WL *               9  . 5   5     m Pumping rate (Q) 132L/MIN Manganese Potential drawdown               m
Drawdown (s)               7  . 3   9   m Duration 24      h Nitrate  Potential yield
 * Levels to ground level datum Specific capacity (Q/s) Fluoride  Depth of borehole * 100 m
SIZE PROFILE TIME/ WATER ZONES WELL DIAGRAM 
DEPTH CUMULATIVE
M/MIN Q (l/min)
11"
Reddish lateritic material  to slightly weathered Sanitary Seal
Clay cutter sandstone  
5 m Backfill 5
 PVC Plain
weathered sandstone with moist cuttings
10  10
fresh  sandstone
15 15
6.5" 
hammer bit c 6
20 6 20
25 25
30 light grey fresh siltstone 30
cement grout
35 Gravel pack 35
60
 PVC Screen
40 40
45 45
100
50 50
55 55
60 60
65 65
70 70
  fresh siltstone
75 75
80 80
85 85
fresh siltstone
90 90
95 95
100 100
Gravel for gravel pack 16 LM Remarks and stoppages:
Screen Length 9 LM
Casing length 36 LM
Installation of grout seal 2 M
Cleaning & development 60 MINS Prepared by: SAMED
Centralisers fitted     No   
Safety cap fitted Yes  No  Approved:  
Backfill aband. BH Yes  No
Cement for grout 50 KG  
Platform construction date  
Distance from last BH KM
 
 
 
 
 
 
 
 270 
 Temporary
 SCALE
 
 
University of Ghana http://ugspace.ug.edu.gh
 
3. DWVP03 
 DANIDA WHITE VOLTA PROJECT BH status: Successful ✓
Coordinates   Dry
Northings: 763386
Eastings: 1130550
BOREHOLE RECORD
Community SHIENVOYA
Drilling contractor HYDRONOMICS LTD. Drill rig KLR Method ROTARY AIR
Drilling start date 08-03-17 Compl. date 08-03-17 Operator Theo
PUMPING TEST  Date: Conductivity Top of screen * nil m
Dynamic WL *          N   I  L          m Pump type Total Iron Static WL *                m
Static WL *                          m Pumping rate (Q) Manganese Potential drawdown               m
Drawdown (s)                        m Duration      h Nitrate  Potential yield
 * Levels to ground level datum Specific capacity (Q/s) Fluoride  Depth of borehole * 100 m
SIZE PROFILE TIME/ WATER ZONES WELL DIAGRAM 
DEPTH CUMULATIVE
M/MIN Q (l/min)
11"
Reddish lateritic material  to slightly weathered 2m Sanitary Seal
Clay cutter siltstone  
5 5
12m PVC Plain
8m Backfill
10  10
fresh  siltstone  
15 1m Gravel pack 15
6.5" 
hammer bit
20 20
25 25
30 light grey fresh siltstone 30
35 35
40 40
45 45
50 50
55 55
60 60
C 5
65 65
70 70
  fresh siltstone
75 75
80 80
85 85
fresh siltstone
90 90
95 95
100 100
Gravel for gravel pack 2 LM Remarks and stoppages:
Screen Length nil LM
Casing length 12 LM
Installation of grout seal 2 M
Cleaning & development MINS Prepared by: HYDRONOMICS LTD
Centralisers fitted     No   
Safety cap fitted Yes  No  Approved:  
Backfill aband. BH Yes  No
Cement for grout 50 KG  
Platform construction date  
Distance from last BH KM
 
 
 
 
 
 
 271 
 Temporary
 SCALE
 
 
University of Ghana http://ugspace.ug.edu.gh
 
4. DWVP04 
 DANIDA WHITE VOLTA PROJECT BH status: Successful ✓
Coordinates   Dry
Northings: 740848
Eastings: 1119935
BOREHOLE RECORD
Community KUKOBILA
Drilling contractor HYDRONOMICS LTD. Drill rig KLR Method ROTARY AIR
Drilling start date 12-03-17 Compl. date 12-03-17 Operator Theo
PUMPING TEST  Date: Conductivity Top of screen * nil m
Dynamic WL *             8  7  . 5   8    m Pump type Total Iron Static WL *                m
Static WL *               6  . 2   5     m Pumping rate (Q) Manganese Potential drawdown               m
Drawdown (s)             8  1  . 3   3   m Duration      h Nitrate  Potential yield
 * Levels to ground level datum Specific capacity (Q/s) Fluoride  Depth of borehole * 100 m
SIZE PROFILE TIME/ WATER ZONES WELL DIAGRAM 
DEPTH CUMULATIVE
M/MIN Q (l/min)
11"
Reddish lateritic material  to slightly weathered 2m Sanitary Seal
Clay cutter siltstone  
5 3m Backfill 5
14m PVC Plain
clayey material
10  10
9m PVC Screen
fresh  siltstone  
15 8 15
6.5" 
hammer bit
20 8 20
16m Gravel pack
25 25
30 light grey fresh siltstone 30
35 35
40 40
45 45
50 50
55 55
60 60
65 65
70 70
  fresh siltstone
75 75
80 80
85 85
fresh siltstone
90 90
95 95
100 100
Gravel for gravel pack 16 LM Remarks and stoppages:
Screen Length 9 LM
Casing length 14 LM
Installation of grout seal 2 M
Cleaning & development 60 MINS Prepared by: HYDRONOMICS LTD
Centralisers fitted     No   
Safety cap fitted Yes  No  Approved:  
Backfill aband. BH Yes  No
Cement for grout 50 KG  
Platform construction date  
Distance from last BH KM
 
 
 
 
 
 272 
 Temporary
 SCALE
 
 
University of Ghana http://ugspace.ug.edu.gh
 
5. DWVP05 
 DANIDA WHITE VOLTA PROJECT BH status: Successful ✓
Coordinates   Dry
Northings: 783088
Eastings: 1122217  
BOREHOLE RECORD
Community KPOBU
Drilling contractor HYDRONOMICS LTD. Drill rig KLR Method ROTARY AIR
Drilling start date 13-03-17 Compl. date 14-03-17 Operator Theo
PUMPING TEST  Date: 19-03-17 Conductivity Top of screen * nil m
Dynamic WL *             3  7  . 1   5    m Pump type PEDROLLO Total Iron Static WL *                m
Static WL *               8  . 7   0     m Pumping rate (Q)           0.762 m³/h Manganese Potential drawdown               m
Drawdown (s)             2  8  . 4   5   m Duration 6      h Nitrate  Potential yield 10 l/min
 * Levels to ground level datum Specific capacity (Q/s)          m³/h/m Fluoride  Depth of borehole * 100 m
SIZE PROFILE TIME/ WATER ZONES WELL DIAGRAM 
DEPTH CUMULATIVE
M/MIN Q (l/min)
11"
2m Sanitary Seal
Clay cutter Reddish lateritic ma teor hiaigl hly weathered  
sandstone
5 5
10 9m Backfill 10
2m Grout
moderately  weathered  siltstone
15 15
6.5" 
hammer bit
8m Gravel pack
20 21m PVC Plain 20
25 25
8
C
30 light grey fresh siltstone 30
35 35
40 40
45 45
50 50
C
C 10
55 55
60 60
C 10
65 65
70 70
light grey fresh with slight fractures siltstone
75 75
80 80
85 85
90 90
95 95
100 100
Gravel for gravel pack 8 LM Remarks and stoppages:
Screen Length nil LM
Casing length 21 LM After drilling, there was little flow so the team waited for 
Installation of grout seal 2 M 45mins for water to accumulate for airlifting 
Cleaning & development 30 MINS Prepared by: HYDRONOMICS LTD
Centralisers fitted     No   
Safety cap fitted Yes  No  Approved:  
Backfill aband. BH Yes  No
Cement for grout 50 KG  
Platform construction date  
Distance from last BH KM
 
 
 
 
 
 
 
 
 273 
 Temporary
 SCALE
 
 
University of Ghana http://ugspace.ug.edu.gh
 
6. DWVP06 
 DANIDA WHITE VOLTA PROJECT BH status: Successful ✓
COORDINATES   Dry
Northings: 785282
Eastings: 1109841
BOREHOLE RECORD
Community TANYELI
Drilling contractor HYDRONOMICS LTD. Drill rig KLR Method ROTARY AIR
Drilling start date 14-03-17 Compl. date 15-03-17 Operator Theo
PUMPING TEST  Date: 19-03-17 Conductivity Top of screen * nil m
Dynamic WL *          N   I  L          m Pump type PEDROLLO Total Iron Static WL *                m
Static WL *               3  . 5   0     m Pumping rate (Q)           m³/h Manganese Potential drawdown               m
Drawdown (s)                        m Duration      h Nitrate  Potential yield
 * Levels to ground level datum Specific capacity (Q/s)          m³/h/m Fluoride  Depth of borehole * 100 m
SIZE PROFILE TIME/ WATER ZONES WELL DIAGRAM 
DEPTH CUMULATIVE
M/MIN Q (l/min)
11"
Reddish lateritic material  to slightly weathered 2m Sanitary Seal
Clay cutter siltstone  
5 5
10 9m Backfill 10
2m Grout
fresh  siltstone 16m PVC Plain
15 2m Gravel pack 15
6.5" 
hammer bit
20 20
25 25
30 light grey fresh siltstone 30
35 35
40 40
45 45
50 50
55 55
60 60
C 5
65 65
70 70
  fresh siltstone
75 75
80 80
85 85
90 90
95 95
100 100
Gravel for gravel pack 8 LM Remarks and stoppages:
Screen Length nil LM
Casing length 16 LM there was little water when the hole was left overnight
Installation of grout seal 2 M
Cleaning & development 30 MINS Prepared by: HYDRONOMICS LTD
Centralisers fitted     No   
Safety cap fitted Yes  No  Approved:  
Backfill aband. BH Yes  No
Cement for grout 50 KG  
Platform construction date  
Distance from last BH KM
 
 
 
 
 
 
 
 
 274 
 Temporary
 SCALE
 
 
University of Ghana http://ugspace.ug.edu.gh
 
7. DWVP07 
 DANIDA WHITE VOLTA PROJECT BH status: Successful ✓
Coordinates   Dry
Northings: 813968
Eastings: 1123607
BOREHOLE RECORD
Community SALIWIA
Drilling contractor HYDRONOMICS LTD. Drill rig Method ROTARY AIR
Drilling start date 16-03-17 Compl. date 16-03-17 Operator Theo
TEST PUMPING  Date: 18-03-17 Conductivity Top of screen * m
Dynamic WL *             5   1  . 2      m Pump type Total Iron Static WL *                m
Static WL *             5   . 2  5       m Pumping rate (Q)                  m³/h Manganese Potential drawdown               m
Drawdown (s)           4  5   . 9  1     m Duration      h Nitrate  Potential yield 45l/min
 * Levels to ground level datum Specific capacity (Q/s)          m³/h/m Fluoride  Depth of borehole * 75 m
SIZE PROFILE TIME/ WATER ZONES WELL DIAGRAM 
DEPTH CUMULATIVE
M/MIN Q (l/min)
12" Lateritic material
Clay cutter Light  brown clay 2m Sanitary Seal
 
5 5
Highly weathered light brown clay mixed 
10 19m Backfill 10
fresh siltstone
15 15
6.5" 
hammer bit  23m PVC Plain
20 20
2m gravel pack
25 fresh siltstone 25
30 30
35 35
40 40
45 45
50 50
55 55
Light grey siltstone
60 60
134
65 65
70 45 70
75 45 75
Gravel for gravel pack 19m LM Remarks and stoppages:
Screen Length nil LM
Casing length 23 LM well was drilled closed to a valley. Two wells in the community are uphill and have low 
Installation of grout seal 5 M yield.There have been several unsuccessful attempts in the community
Cleaning & development 30 MINS Prepared by: HYDRONOMICS LTD
Centralisers fitted     No   
Safety cap fitted Yes  No  Approved:  
Backfill aband. BH Yes  No
Cement for grout 50 KG  
Platform construction date  
Distance from last BH 13 KM
 
 
 
 
 
 
 
 
 275 
 Temporary
 SCALE
 
 
University of Ghana http://ugspace.ug.edu.gh
 
8. DWVP08 
 DANIDA WHITE VOLTA PROJECT BH status: Successful
Coordinates   Dry ✓
Northings: 734974
Eastings: 1122909
BOREHOLE RECORD
Community NAKPAYA
Drilling contractor HYDRONOMICS LTD. Drill rig KLR Method ROTARY AIR
Drilling start date 11-03-17 Compl. date 11-03-17 Operator Theo
PUMPING TEST  Date: Conductivity Top of screen * nil m
Dynamic WL *          N   I  L          m Pump type Total Iron Static WL *                m
Static WL *                          m Pumping rate (Q) Manganese Potential drawdown               m
Drawdown (s)                        m Duration      h Nitrate  Potential yield
 * Levels to ground level datum Specific capacity (Q/s) Fluoride  Depth of borehole * 100 m
SIZE PROFILE TIME/ WATER ZONES WELL DIAGRAM 
DEPTH CUMULATIVE
M/MIN Q (l/min)
11"
Reddish lateritic material  to slightly weathered 2m Sanitary Seal
Clay cutter siltstone  
5 5
12m PVC Plain
8m Backfill
10 10
2m Gravel pack
fresh  siltstone  
15 15
6.5" 
hammer bit
20 20
25 25
30 light grey fresh siltstone 30
35 35
40 40
45 45
50 50
55 55
60 60
C 5
65 65
70 70
  fresh siltstone
75 75
80 80
85 85
fresh siltstone
90 90
95 95
100 100
Gravel for gravel pack 2 LM Remarks and stoppages:
Screen Length nil LM
Casing length 12 LM
Installation of grout seal 2 M
Cleaning & development MINS Prepared by: HYDRONOMICS LTD
Centralisers fitted     No   
Safety cap fitted Yes  No  Approved:  
Backfill aband. BH Yes  No
Cement for grout 50 KG  
Platform construction date  
Distance from last BH KM
 
 
 
 
 
 
 
 
 
 
 276 
 Temporary
 SCALE
 
 
University of Ghana http://ugspace.ug.edu.gh
 
9. DWVP09 
 DANIDA WHITE VOLTA PROJECT BH status: Successful ✓
Coordinates   Dry
Northings: 761494
Eastings: 1157299
BOREHOLE RECORD
Community SAMENE
Drilling contractor HYDRONOMICS LTD. Drill rig KLR Method ROTARY AIR
Drilling start date 07-03-17 Compl. date 08-03-17 Operator Theo
PUMPING TEST  Date: Conductivity Top of screen * nil m
Dynamic WL *             5  8  . 4   8    m Pump type PEDROLLO Total Iron Static WL *                m
Static WL *               7  . 0   0     m Pumping rate (Q) 4L/MIN Manganese Potential drawdown               m
Drawdown (s)             5  1  . 4   8   m Duration 6      h Nitrate  Potential yield
 * Levels to ground level datum Specific capacity (Q/s) Fluoride  Depth of borehole * 100 m
SIZE PROFILE TIME/ WATER ZONES WELL DIAGRAM 
DEPTH CUMULATIVE
M/MIN Q (l/min)
11"
Reddish lateritic material  to slightly weathered 2m Sanitary Seal
Clay cutter siltstone  
5 8m Backfill 5
18m PVC Plain
clayey material
10  10
fresh  siltstone
15 15
6m PVC Screen
6.5" 
hammer bit c 6
20 6 20
16m Gravel pack
25 25
30 light grey fresh siltstone 30
35 35
40 40
45 45
50 50
55 55
60 60
65 65
70 70
  fresh siltstone
75 75
80 80
85 85
fresh siltstone
90 90
95 95
100 100
Gravel for gravel pack 16 LM Remarks and stoppages:
Screen Length 9 LM
Casing length 14 LM
Installation of grout seal 2 M
Cleaning & development 60 MINS Prepared by: HYDRONOMICS LTD
Centralisers fitted     No   
Safety cap fitted Yes  No  Approved:  
Backfill aband. BH Yes  No
Cement for grout 50 KG  
Platform construction date  
Distance from last BH KM
 
 
 
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10. DWVP10 
 DANIDA WHITE VOLTA PROJECT BH status: Successful ✓
Coordinates   Dry
Northings: 814303
Eastings: 1138258
BOREHOLE RECORD
Community SAAKPA
Drilling contractor HYDRONOMICS LTD. Drill rig KLR Method ROTARY AIR
Drilling start date 16-03-17 Compl. date 16-03-17 Operator Theo
PUMPING TEST  Date: 19-03-17 Conductivity Top of screen * nil m
Dynamic WL *          N   I  L          m Pump type PEDROLLO Total Iron Static WL *                m
Static WL *               3  . 5   0     m Pumping rate (Q)           m³/h Manganese Potential drawdown               m
Drawdown (s)                        m Duration      h Nitrate  Potential yield
 * Levels to ground level datum Specific capacity (Q/s)          m³/h/m Fluoride  Depth of borehole * 100 m
SIZE PROFILE TIME/ WATER ZONES WELL DIAGRAM 
DEPTH CUMULATIVE
M/MIN Q (l/min)
11"
Reddish lateritic material  to slightly weathered 2m Sanitary Seal
Clay cutter siltstone  
5 5
10 9m Backfill 10
2m Grout
fresh  siltstone 16m PVC Plain
15 2m Gravel pack 15
6.5" 
hammer bit
20 20
25 25
30 light grey fresh siltstone 30
35 35
40 40
45 45
50 50
55 55
60 60
C 5
65 65
70 70
  fresh siltstone
75 75
80 80
85 85
C 8
Slight fractures at 87m with reduced dust C
90 90
95 95
100 100
Gravel for gravel pack 8 LM Remarks and stoppages:
Screen Length nil LM
Casing length 16 LM
Installation of grout seal 2 M
Cleaning & development MINS Prepared by: HYDRONOMICS LTD
Centralisers fitted     No   
Safety cap fitted Yes  No  Approved:  
Backfill aband. BH Yes  No
Cement for grout 50 KG  
Platform construction date  
Distance from last BH KM  
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APPENDIX 5A- ERT-PROFILES COMPARED TO WIRELINE LOGS FOR DWVP BOREHOLES 
 
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APPENDIX 5B- ERT-PROFILES COMPARED TO WIRELINE LOGS FOR DWVP BOREHOLES 
 
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APPENDIX 6- HISTOGRAMS OF 7 PARAMETERS 
 
 
 
 
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Static Water Level (m) 
 
Specific Capacity (m3/d/m) 
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APPENDIX 7- DRAWDOWN AND RECOVERY GRAPHS OF 26 PUMPING TEST RESULTS > 6 HRS 
 
Location: Daboya No. 2 
 
 
 
 
 
 
 
Location: Karaga 1 
 
 
 
 
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Location: Karaga 2 
 
 
 
 
 
 
 
Location: Karaga 3 
 
 
 
 
 
 
 
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Location: Karaga 4 
 
 
 
 
 
 
 
Location: Gambaga SHS 1 
 
 
 
 
 
 
 
 
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Location: Gambaga SHS 2 
 
 
 
 
 
 
 
Location: Gambaga SHS 3 
  
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Location: Gambaga SHS 4 
  
 
 
 
 
 
Location: Gambaga GB-01 
 
 
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Location: Nalerigu NA-01 
 
 
 
 
 
 
Location: Karaga Yepala 
 
 
 
 
 
 
 
 
 
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Location: Karaga 
 
 
 
 
 
 
 
Location: Walewale-WA-05 
 
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Location: Walewale-WA-01 
 
 
 
 
 
 
 
Location: Walewale-WA-02 
 
 
 
 
 
 
 
 
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Location: Walewale-WA-03 
 
 
 
 
 
 
 
 Location: Tong 
 
 
 
 
 
  
 
 
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Location: DWVP 02 
 
 
 
 
 
 
 
 
Location: Walewale – 1 – NORST 
 
 
 
 
 
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Location: Walewale – 2 – NORST 
 
 
 
 
 
 
 
Location: Walewale – 3 – NORST 
  
 
 
 
 
 
 
 
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Location: Wundua 1 
 
 
 
 
 
 
 
 
Location: Wundua 2 
 
  
 
 
 
 
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Location: Gushegu S-7 
 
 
 
 
 
 
 
Location: Gushegu H-25-02 
 
 
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APPENDIX 8- RECHARGE ESTIMATES BASED ON CHLORIDE MASS 
BALANCE METHOD AT 60 BOREHOLES 
 
Cl_Concentra
Cl_Concentr Recharg
EASTI NORTH tion in Precipitatio
ID ation in e-Rg %Rg 
NGS INGS precipitation n (m/yr) 
Groundwate (m/yr) 
(mg/L) 
r (mg/L) 
GW001 788676 1138932 5.06 1.31 1.1 0.285 28.50 
GW003 788491 1135548 657.86 1.31 1.1 0.002 0.22 
GW004 788455 1147997 8.37 1.31 1.1 0.172 17.25 
GW005 787922 1148355 144.31 1.31 1.1 0.010 1.00 
GW006 788320 1150174 164.14 1.31 1.1 0.009 0.88 
GW007 795566 1152185 94.56 1.31 1.1 0.015 1.53 
GW008 795318 1150418 2.07 1.31 1.1 0.696 69.59 
GW009 789863 1158668 82.96 1.31 1.1 0.017 1.74 
GW010 788681 1164391 248.28 1.31 1.1 0.006 0.58 
GW011 797847 1170387 47.55 1.31 1.1 0.030 3.04 
GW015 778224 1154314 37.68 1.31 1.1 0.038 3.83 
GW016 780823 1159615 64.12 1.31 1.1 0.023 2.25 
GW017 780054 1164851 61.28 1.31 1.1 0.024 2.35 
GW018 780140 1165276 39.06 1.31 1.1 0.037 3.69 
GW019 778162 1163815 33.45 1.31 1.1 0.043 4.31 
GW020 773080 1158983 48.25 1.31 1.1 0.030 2.99 
GW021 770662 1154159 7.95 1.31 1.1 0.182 18.16 
GW022 760580 1157946 44.08 1.31 1.1 0.033 3.27 
GW023 761556 1153446 7.68 1.31 1.1 0.188 18.79 
GW024 766280 1151079 10.77 1.31 1.1 0.134 13.40 
GW025 740625 1146125 5.12 1.31 1.1 0.282 28.20 
GW026 742355 1149687 46.29 1.31 1.1 0.031 3.12 
GW027 744714 1145441 54.87 1.31 1.1 0.026 2.63 
GW028 745750 1146207 18.93 1.31 1.1 0.076 7.62 
GW029 748714 1145545 31.97 1.31 1.1 0.045 4.51 
GW030 755458 1142101 21.57 1.31 1.1 0.067 6.69 
GW031 759007 1139515 49.40 1.31 1.1 0.029 2.92 
GW032 764343 1129821 5.72 1.31 1.1 0.252 25.24 
GW033 766851 1137447 6.11 1.31 1.1 0.236 23.60 
GW034 756705 1137331 30.40 1.31 1.1 0.047 4.75 
GW035 750921 1132636 34.64 1.31 1.1 0.042 4.17 
GW036 747004 1137902 61.68 1.31 1.1 0.023 2.34 
GW037 722076 1109007 33.93 1.31 1.1 0.043 4.25 
GW038 735303 1122376 39.32 1.31 1.1 0.037 3.67 
GW039 740305 1119629 253.91 1.31 1.1 0.006 0.57 
 
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Cl_Concentra
Cl_Concentr Recharg
EASTI NORTH tion in Precipitatio
ID ation in e-Rg %Rg 
NGS INGS precipitation n (m/yr) 
Groundwate (m/yr) 
(mg/L) 
r (mg/L) 
GW040 781433 1094718 101.31 1.31 1.1 0.014 1.42 
GW041 783023 1122017 206.50 1.31 1.1 0.007 0.70 
GW042 787438 1121223 10.77 1.31 1.1 0.134 13.40 
GW045 790748 1118809 8.21 1.31 1.1 0.176 17.58 
GW046 783072 1115950 229.55 1.31 1.1 0.006 0.63 
GW047 781558 1113424 232.68 1.31 1.1 0.006 0.62 
GW048 782309 1111850 28.95 1.31 1.1 0.050 4.99 
GW049 781902 1108714 41.92 1.31 1.1 0.034 3.44 
GW050 784456 1111204 39.47 1.31 1.1 0.037 3.66 
GW051 780799 1103998 142.02 1.31 1.1 0.010 1.02 
GW052 781025 1100930 202.43 1.31 1.1 0.007 0.71 
GW053 787415 1097843 125.27 1.31 1.1 0.012 1.15 
GW054 791144 1097675 186.39 1.31 1.1 0.008 0.77 
GW055 796316 1097722 121.09 1.31 1.1 0.012 1.19 
GW056 809345 1101161 204.97 1.31 1.1 0.007 0.70 
GW057 813502 1102172 189.34 1.31 1.1 0.008 0.76 
GW058 816360 1106532 198.04 1.31 1.1 0.007 0.73 
GW059 819613 1113513 35.95 1.31 1.1 0.040 4.01 
GW060 819225 1118333 52.56 1.31 1.1 0.027 2.75 
GW061 817330 1124857 16.65 1.31 1.1 0.087 8.67 
GW062 819609 1125200 215.97 1.31 1.1 0.007 0.67 
GW063 818450 1139704 14.14 1.31 1.1 0.102 10.21 
GW064 807585 1142444 205.04 1.31 1.1 0.007 0.70 
GW065 798912 1144860 201.67 1.31 1.1 0.007 0.72 
GW066 779330 1164579 25.08 1.31 1.1 0.058 5.75 
 742559 1149527 60.00 1.31 1.1 0.024 2.41 
 737585 1149844 34.50 1.31 1.1 0.042 4.18 
 740379 1124007 7.65 1.31 1.1 0.189 18.87 
 780342 1166037 13.81 1.31 1.1 0.105 10.45 
 804970 1098155 2.42 1.31 1.1 0.597 59.69 
 755894 1141695 7.67 1.31 1.1 0.188 18.81 
 759069 1138944 3.55 1.31 1.1 0.407 40.65 
 766584 1137462 5.70 1.31 1.1 0.253 25.32 
 761186 1149104 6.00 1.31 1.1 0.241 24.05 
 760551 1157994 26.00 1.31 1.1 0.056 5.55 
 740654 1144976 54.00 1.31 1.1 0.027 2.67 
 741607 1144659 38.00 1.31 1.1 0.038 3.80 
 
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APPENDIX 9- DESCRIPTIVE STATISTICS OF PARAMTERS 
 
 
 
 Total Range Minimu Maximu Mean Std. Variance Skewness Kurtosis 
No. m m Deviation 
     Std.    Std.  Std. 
Error Error Error 
Well Depth (m) 222 145 21.0 166 57.43 1.46 21.75 473.06 2.01 0.16 5.81 0.33 
Thickness of Regolith 
222 17 2.0 19 7.98 0.22 3.23 10.43 0.44 0.16 0.25 0.33 
(m) 
Yield (m3/d) 222 714 6.0 720 84.24 7.76 115.67 13378.79 2.70 0.16 8.35 0.33 
SWL (m) 222 36 0.5 36 7.38 0.33 4.87 23.72 2.53 0.16 10.82 0.33 
Transmissivity (m2/d) 26 264 0.25 264 32.46 11.72 59.75 3570.57 3.12 0.46 9.96 0.89 
Specific Capacity 
222 960 0.12 960 16.45 4.95 73.80 5446.61 10.60 0.16 126.51 0.33 
(m3/d/m) 
           
  
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APPENDIX 10- MASTER TABLE WITH 233 BOREHOLES 
Specific 
Drawdow Specific Transmissivity-
Thickness LogRegoli Discharge Discharge LogYield Capacity LogSC(m3/ Transmissivity 
Community BH ID  Formation Northing Easting LogD(m) SWL (m) LogSWLF (irmst) water strike n(m (m) ) at Capacity Entire Nsia 
Depth of th (m) (l/min) (m3/d) (m3/d) (l/min/m d/m) (m2/d)-sormation-
360mins (m3/d/m) (m2/d)
(m) Regolith ) specific
Karaga 1 DWVP220 BIMBILLA 1098267 781857 50 1.7 8 0.9 40 57.6 1.76 35.62 1.55 5.49 7.29 10.49 1.06 10.54 10.54
Karaga 2 DWVP221 BIMBILLA 1098648 781359 52 1.72 3 0.48 250 360 2.56 29.19 1.47 13.41 18.64 26.85 1.44 14.35 14.35
Karaga 3 DWVP222 BIMBILLA 1098172 782058 40 1.6 7 0.85 150 216 2.33 31.53 1.5 4.86 30.86 44.44 1.66 23.12 23.12
Karaga 4 DWVP223 BIMBILLA 1098712 781772 40 1.6 6 0.78 300 432 2.64 12.09 1.08 7.48 40.11 57.75 1.77 37.47 37.47
Karaga Yepala DWVP230 BIMBILLA 1098871 781296 50 1.7 5 0.7 120 172.8 2.24 13.25 1.12 10.96 10.95 15.77 1.22 10.72 10.72
Karaga DWVP231 BIMBILLA 1098680 780915 55 1.74 3 0.48 130 187.2 2.27 14.45 1.16 8.38 15.51 22.34 1.37 11.05 11.05
Tong              DWVP236 BIMBILLA 1099506 776216 65 1.81 5 0.7 400 576 2.76 9.75 0.99 11.84 33.78 48.65 1.70 25.1 25.10
GUSHIEGU-S-7 DWVP242 BIMBILLA 1098426 805150 93 1.97 5 0.7 70 100.8 2 6.4 0.81 3.13 22.36 32.20 1.52 34.16 34.16
GUSHIEGU-H-25-02 DWVP243 BIMBILLA 1098553 804261 93 1.97 6 0.78 30 43.2 1.64 11.13 1.05 6.15 4.88 7.02 0.90 8.69 8.69
DABOYA C150 DWVP50 BIMBILLA 1113018 730148 52 1.72 10 1 12 17.28 1.24 6.12 0.79 20 15.54 0.77 1.11 0.32 0.45 0.45
TUUNI HAP 14 BIMBILLA 1139654 788493 120 2.08 12 1.08 10 14.4 1.16 3.42 0.53 35 31.42 0.32 0.46 0.16 0.60 0.49
JANGA HAP 05 BIMBILLA 1108645 722136 166 2.22 15 1.18 5 7.2 0.86 7.65 0.88 16 26.43 0.19 0.27 0.10 0.37 0.29
TINGURI WVB12 BIMBILLA 1100980 809369 51 1.71 12 1.08 150 216 2.33 8.94 0.95 27 4.91 30.55 43.99 1.65 39.47 41.11
KUKOBILA DWVP4 BIMBILLA 1119935 740848 100 2 12 1.08 12 17.28 1.24 6.25 0.8 15 81.33 0.15 0.21 0.08 0.29 0.23
KPOBU DWVP5 BIMBILLA 1122217 783088 100 2 18 1.26 13 18.72 1.27 8.7 0.94 27 28.45 0.46 0.66 0.22 0.83 0.70
SALINWIA DWVP7 BIMBILLA 1123607 813968 75 1.88 10 1 80 115.2 2.06 5.25 0.72 63 45.91 0.13 0.19 0.08 0.26 0.21
NYANSON DWVP11 BIMBILLA 1097722 796354 34 1.53 4 0.6 41 59.04 1.77 6.4 0.81 20 5.21 7.87 11.33 1.09 11.35 11.03
NYINGALE DWVP12 BIMBILLA 1099834 794165 58 1.76 9 0.95 10 14.4 1.16 4.2 0.62 32 8.12 1.23 1.77 0.44 2.06 1.82
DIDOGLL DWVP13 BIMBILLA 1108845 757832 31 1.49 7 0.85 43 61.92 1.79 5 0.7 20 5.5 7.82 11.26 1.09 11.28 10.96
GBALIGA DWVP14 BIMBILLA 1109553 786728 53 1.72 5 0.7 70 100.8 2 3.21 0.51 22 4.26 16.43 23.66 1.39 22.32 22.53
JAMAGA DWVP15 BIMBILLA 1108713 781901 55 1.74 4 0.6 30 43.2 1.64 10.64 1.03 32 29.09 1.03 1.49 0.40 1.76 1.54
LOARI DWVP16 BIMBILLA 1136756 738217 46 1.66 5 0.7 25 36 1.56 8.06 0.91 26 13.08 1.91 2.75 0.57 3.09 2.79
DISIGA DWVP17 BIMBILLA 1106822 738717 55 1.74 5 0.7 15 21.6 1.33 10.45 1.02 32 6.5 2.31 3.32 0.64 3.67 3.35
NAMBURUGU DWVP18 BIMBILLA 1138624 787296 50 1.7 5 0.7 30 43.2 1.64 4.43 0.65 22 8.1 3.70 5.33 0.80 5.67 5.31
NASIA CHIPS DWVP41 BIMBILLA 1123690 740519 75 1.88 6 0.78 10 14.4 1.16 31.29 1.5 46 38 0.26 0.38 0.14 0.50 0.41
LOAGRI PRIM. DWVP42 BIMBILLA 1137100 737852 75 1.88 5 0.7 10 14.4 1.16 10.56 1.02 22 58.7 0.17 0.25 0.10 0.34 0.27
LOAGRI COMM. DWVP43 BIMBILLA 1136616 738087 75 1.88 6 0.78 10 14.4 1.16 8.52 0.93 41 60.64 0.16 0.24 0.09 0.33 0.26
SOOBA COMM. DWVP44 BIMBILLA 1112634 731113 75 1.88 5 0.7 10 14.4 1.16 9.1 0.96 17 64.51 0.16 0.22 0.09 0.30 0.24
SOOBA PRIM. DWVP45 BIMBILLA 1112654 730866 75 1.88 3 0.48 10 14.4 1.16 9.4 0.97 27 63.09 0.16 0.23 0.09 0.32 0.25
DABOYA CHIPS DWVP46 BIMBILLA 1113758 730454 75 1.88 3 0.48 10 14.4 1.16 8.42 0.93 18 62.11 0.16 0.23 0.09 0.32 0.25
SAKULO DWVP47 BIMBILLA 1111415 792672 50 1.7 4 0.6 10 14.4 1.16 6.99 0.84 36 58.89 0.17 0.24 0.09 0.33 0.26
NAMBURUGU DWVP48 BIMBILLA 1121067 787370 60 1.78 3 0.48 10 14.4 1.16 6.24 0.8 42 47.54 0.21 0.30 0.11 0.40 0.33
KOFIYIRI A170 DWVP49 BIMBILLA 1105606 723505 52 1.72 3 0.48 10 14.4 1.16 9.1 0.96 15 35.08 0.29 0.41 0.15 0.54 0.44
SOOBA A-0 DWVP51 BIMBILLA 1112147 731209 70 1.85 5 0.7 5 7.2 0.86 7.2 0.86 52.6 0.10 0.14 0.06 0.20 0.16
SOOBA A50 DWVP52 BIMBILLA 1112189 731202 94 1.97 3 0.48 4 5.76 0.76 7.8 0.89 48.14 0.08 0.12 0.05 0.17 0.13
JANGA DWVP59 BIMBILLA 1105056 722110 100 2 6 0.78 20 28.8 1.46 8.3 0.92 75 19.11 1.05 1.51 0.40 1.78 1.56
SAFAM DWVP60 BIMBILLA 1103887 724651 50 1.7 8 0.9 40 57.6 1.76 4.28 0.63 19 22.57 1.77 2.55 0.55 2.88 2.60
KUKOBILA -BH30 DWVP61 BIMBILLA 1117418 740053 60 1.78 8 0.9 50 72 1.86 3.31 0.52 25 20.86 2.40 3.45 0.65 3.80 3.48
KUKOBILA-BH31 DWVP62 BIMBILLA 1118433 738768 100 2 9 0.95 20 28.8 1.46 5.78 0.76 75 25.44 0.79 1.13 0.33 1.36 1.18
KUKOBILA-BH32 DWVP63 BIMBILLA 1117410 739444 80 1.9 5 0.7 11 15.84 1.2 6.12 0.79 42 13.69 0.80 1.16 0.33 1.40 1.21
KUKOBILA-BH33 DWVP64 BIMBILLA 1114466 738328 100 2 8 0.9 20 28.8 1.46 11.68 1.07 27 18.44 1.08 1.56 0.41 1.83 1.61
KUKOBILA-BH 34 DWVP65 BIMBILLA 1116160 738508 50 1.7 9 0.95 65 93.6 1.97 5.05 0.7 45 5.24 12.40 17.86 1.28 17.24 17.15
YIBEE DWVP70 BIMBILLA 1121288 801782 40 1.6 12 1.08 14 20.16 1.3 12.6 1.1 19 8.3 1.69 2.43 0.54 2.76 2.48
Nakpaya Sch. DWVP71 BIMBILLA 1122375 735306 34 1.53 10 1 47 67.68 1.83 5.62 0.75 21 8.09 5.81 8.37 0.97 8.59 8.22
MANCHUGU DWVP72 BIMBILLA 1118672 815267 34 1.53 10 1 24 34.56 1.54 5.31 0.73 24 7.4 3.24 4.67 0.75 5.02 4.67
Pomo DWVP73 BIMBILLA 1106940 813190 72 1.86 6 0.78 12 17.28 1.24 7.5 0.88 47 42.11 0.28 0.41 0.15 0.54 0.44
Surugu DWVP75 BIMBILLA 1109553 812054 44 1.64 6 0.78 15 21.6 1.33 3.3 0.52 22 1.13 1.63 0.42 1.91 1.68
Tugban DWVP78 BIMBILLA 1108880 815915 43 1.63 10 1 12 17.28 1.24 2.39 0.38 23 1.04 1.50 0.40 1.77 1.55
Komoayili-05 DWVP119 BIMBILLA 1103400 785127 50 1.7 10 1 300 432 2.64 7.31 0.86 4.24 70.75 101.89 2.01 85.40 92.86
kamoayili-04 DWVP120 BIMBILLA 1103413 785123 55 1.74 6 0.78 120 172.8 2.24 9.5 0.98 8.36 14.35 20.67 1.34 19.72 19.76
Tong              DWVP121 BIMBILLA 1115314 802064 65 1.81 6 0.78 400 576 2.76 9.75 0.99 12.75 31.37 45.18 1.66 40.45 42.19
Achinayili DWVP122 BIMBILLA 1115508 802102 70 1.85 6 0.78 10 14.4 1.16 5.4 0.73 34.87 0.29 0.41 0.15 0.54 0.44
Duna DWVP123 BIMBILLA 1112294 781788 45 1.65 10 1 180 259.2 2.41 8.48 0.93 7.44 24.19 34.84 1.55 31.85 32.79
Talole DWVP124 BIMBILLA 1112421 797937 52 1.72 11 1.04 13 18.72 1.27 6.32 0.8 13.86 0.94 1.35 0.37 1.61 1.40
Gbankoni DWVP126 BIMBILLA 1141642 804855 68 1.83 8 0.9 30 43.2 1.64 12 1.08 26.2 1.15 1.65 0.42 1.93 1.70
Dimia DWVP127 BIMBILLA 1145168 771642 70 1.85 6 0.78 85 122.4 2.09 11.7 1.07 26.73 38.49 1.60 34.91 36.12
Loagri #1 DWVP142 BIMBILLA 1136623 738765 63 1.8 5 0.7 20 28.8 1.46 8.81 0.94 9.6 2.08 3.00 0.60 3.35 3.04  
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Specific 
Drawdow Specific Transmissivity-
LogRegoli Discharge Discharge LogYield Capacity LogSC(m3/
Community BH ID  Formation Northing Easting LogD(m) SWL (m) LogSWLF (irmst) water strike n(m (m) ) at Capacity Entire Nsia 
Thickness th (m) (l/min) (m3/d) (m3/d) (l/min/m d/m)360mins (m3/d/m) Transmissivity (m2/d)
Depth of ) (m2/d)-sormation-
(m) Regolith specific
Nakpaya DWVP145 BIMBILLA 1120620 734510 44 1.64 3 0.48 16 23.04 1.36 6.11 0.79 10.36 1.54 2.22 0.51 2.54 2.27
Wungu 1 DWVP154 BIMBILLA 1141576 735749 44 1.64 10 1 10 14.4 1.16 5.55 0.74 31.24 0.32 0.46 0.16 0.60 0.49
Wungu 2 DWVP155 BIMBILLA 1141004 734987 50 1.7 10 1 30 43.2 1.64 1.5 0.18 16.95 1.77 2.55 0.55 2.88 2.60
Wungu 3 DWVP156 BIMBILLA 1141808 736288 61 1.79 6 0.78 25 36 1.56 10.13 1.01 22.02 1.14 1.63 0.42 1.91 1.68
Wungu 4 DWVP157 BIMBILLA 1141322 735368 51 1.71 8 0.9 10 14.4 1.16 2.64 0.42 22.46 0.45 0.64 0.21 0.81 0.68
Wungu Nab DWVP158 BIMBILLA 1142020 736320 50 1.7 8 0.9 40 57.6 1.76 8.83 0.95 8.15 4.91 7.07 0.91 7.36 6.98
Nasia DWVP160 BIMBILLA 1126568 739463 50 1.7 5 0.7 280 403.2 2.61 8.39 0.92 15.04 18.62 26.81 1.44 25.04 25.43
Loagri DWVP161 BIMBILLA 1136305 738511 50 1.7 8 0.9 100 144 2.16 6.71 0.83 13.2 7.58 10.91 1.08 10.96 10.63
Najong DWVP164 BIMBILLA 1140734 817320 90 1.95 9 0.95 55 79.2 1.9 13.14 1.12 17.72 3.10 4.47 0.74 4.83 4.47
Nanyiri DWVP165 BIMBILLA 1127796 822717 75 1.88 15 1.18 90 129.6 2.11 6.86 0.84 5.43 16.57 23.87 1.40 22.50 22.72
Kukobila bh1 DWVP166 BIMBILLA 1120514 739645 45 1.65 10 1 50 72 1.86 4.35 0.64 7.09 7.05 10.16 1.05 10.26 9.92
Kukobila bh3 DWVP168 BIMBILLA 1120529 739636 42 1.62 12 1.08 75 108 2.03 4.75 0.68 3.27 22.94 33.03 1.53 30.33 31.14
Kukobila bh4 DWVP169 BIMBILLA 1120681 739349 54 1.73 10 1 200 288 2.46 3.14 0.5 5.98 33.44 48.16 1.69 42.89 44.89
Karaga BH1 DWVP173 BIMBILLA 1098178 781783 33 1.52 6 0.78 21 30.24 1.48 1.7 0.23 26.48 0.79 1.14 0.33 1.37 1.19
Karaga BH2 DWVP174 BIMBILLA 1097924 782037 36 1.56 10 1 66 95.04 1.98 4.47 0.65 5.04 13.10 18.86 1.30 18.12 18.08
Karaga BH3 DWVP175 BIMBILLA 1099067 781148 33 1.52 11 1.04 110 158.4 2.2 5.06 0.7 19.52 5.64 8.11 0.96 8.34 7.97
Tiya I DWVP179 BIMBILLA 1113418 730729 34 1.53 5 0.7 16 23.04 1.36 7.7 0.89 6.34 2.52 3.63 0.67 3.99 3.66
Tiya II DWVP180 BIMBILLA 1113799 731237 36 1.56 10 1 64 92.16 1.96 6.35 0.8 17.89 3.58 5.15 0.79 5.50 5.13
Tiya III DWVP181 BIMBILLA 1112910 730348 54 1.73 10 1 10 14.4 1.16 7 0.85 8.88 1.13 1.62 0.42 1.90 1.67
Wungu DWVP182 BIMBILLA 1140596 735174 46 1.66 5 0.7 10 14.4 1.16 9.76 0.99 13.85 0.72 1.04 0.31 1.26 1.09
Gbitugu DWVP186 BIMBILLA 1100905 772284 55.58 1.74 3 0.48 120 172.8 2.24 16.2 1.21 27.78 40.00 1.61 36.17 37.49
Kanshegu DWVP188 BIMBILLA 1095968 802991 56 1.75 3 0.48 20 28.8 1.46 8.11 0.91 0.96 1.38 0.38 1.64 1.43
Kpubo DWVP190 BIMBILLA 1122017 783027 46 1.66 10 1 10 14.4 1.16 8.31 0.92 0.38 0.54 0.19 0.69 0.58
Kpugi DWVP191 BIMBILLA 1102549 801440 65 1.81 15 1.18 28 40.32 1.61 11.48 1.06 7.33 10.55 1.06 10.63 10.29
Kunaayili DWVP192 BIMBILLA 1096890 804478 61 1.79 10 1 32 46.08 1.66 6.82 0.83 3.10 4.47 0.74 4.83 4.47
Kunkundanyili DWVP193 BIMBILLA 1119883 740221 48 1.68 10 1 25 36 1.56 21.24 1.33 8.20 11.80 1.11 11.78 11.47
Nalogu DWVP198 BIMBILLA 1113101 805679 42 1.62 5 0.7 10.11 14.56 1.16 6.93 0.84 0.77 1.11 0.32 1.34 1.16
Namborigu DWVP199 BIMBILLA 1138777 806700 55 1.74 8 0.9 10 14.4 1.16 9.2 0.96 0.54 0.78 0.25 0.97 0.82
Manie DWVP201 BIMBILLA 1123083 803547 62.1 1.79 10 1 12 17.28 1.24 10.41 1.02 0.53 0.76 0.25 0.95 0.80
Yilang DWVP210 BIMBILLA 1127927 786384 65 1.81 6 0.78 13 18.72 1.27 10.98 1.04 0.38 0.55 0.19 0.70 0.59
Yizegu DWVP211 BIMBILLA 1120296 783287 61.9 1.79 10 1 13 18.72 1.27 10.6 1.03 0.42 0.60 0.20 0.76 0.64
Zagsilari DWVP212 BIMBILLA 1131456 749858 44 1.64 11 1.04 38 54.72 1.74 7.29 0.86 3.21 4.62 0.75 4.98 4.62
Bonglna DWVP216 BIMBILLA 1127245 783467 59.9 1.78 5 0.7 36 51.84 1.71 10.54 1.02 3.09 4.45 0.74 4.81 4.46
Sedugo DWVP217 BIMBILLA 1114339 766456 34.9 1.54 10 1 15 21.6 1.33 7.97 0.9 1.97 2.83 0.58 3.17 2.87
Baliga DWVP218 BIMBILLA 1109374 786487 61.6 1.79 10 1 151 217.44 2.34 7.08 0.85 7.74 11.14 1.08 11.17 10.85
TINKAYA DWVP36 Poubugou 1145542 748719 65 1.81 10 1 28 40.32 1.61 4.2 0.62 32 19.22 1.46 2.10 0.49 2.41 2.15
GUABULINGA DWVP40 Poubugou 1152283 752445 92 1.96 6 0.78 10 14.4 1.16 9.21 0.96 36 51.01 0.20 0.28 0.11 0.38 0.30
Zasilari DWVP82 Poubugou 1150291 745461 44 1.64 5 0.7 38 54.72 1.74 7.29 0.86 25 11.85 3.21 4.62 0.75 4.98 4.62
Tenkpanga DWVP131 Poubugou 1153416 746636 46 1.66 5 0.7 15 21.6 1.33 6.7 0.83 1.92 7.81 11.25 1.09 11.27 10.95
Gbimsi 1 DWVP136 Poubugou 1150642 742552 40 1.6 10 1 30 43.2 1.64 2.17 0.34 12.64 2.37 3.42 0.65 3.77 3.45
Gbimsi 2 DWVP137 Poubugou 1150590 743240 55 1.74 9 0.95 20 28.8 1.46 3.79 0.58 16.73 1.20 1.72 0.43 2.01 1.77
Guabulga 1 DWVP138 Poubugou 1151827 751945 45 1.65 9 0.95 80 115.2 2.06 3.77 0.58 4.7 17.02 24.51 1.41 23.06 23.31
Guabulga 2 DWVP139 Poubugou 1151783 752440 45 1.65 9 0.95 45 64.8 1.81 3.78 0.58 5.10 7.34 0.92 7.61 7.24
Guabulga 3 DWVP140 Poubugou 1151750 751934 44 1.64 6 0.78 15 21.6 1.33 2.17 0.34 33.86 0.44 0.64 0.21 0.81 0.68
Nayorko 1 DWVP146 Poubugou 1146656 744797 54 1.73 4 0.6 15 21.6 1.33 6.67 0.82 8.26 1.82 2.62 0.56 2.95 2.67
Nayorko 2 DWVP147 Poubugou 1147291 745686 72 1.86 3 0.48 20 28.8 1.46 5.91 0.77 10.96 1.82 2.63 0.56 2.96 2.67
Guabulga 4 DWVP159 Poubugou 1151651 751528 49 1.69 9 0.95 190 273.6 2.44 5.68 0.75 14.58 13.03 18.77 1.30 18.04 18.00
Gbimsi Woda-Fong DWVP176 Poubugou 1149613 743302 34 1.53 10 1 10 14.4 1.16 7.29 0.86 12.21 0.82 1.18 0.34 1.42 1.23
Gbimsi Woda-Fong DWVP177 Poubugou 1149613 743937 34 1.53 8 0.9 12 17.28 1.24 8.8 0.94 11.95 1.00 1.45 0.39 1.72 1.50
Gbimsi-Mugu DWVP178 Poubugou 1149994 743048 31 1.49 6 0.78 15 21.6 1.33 4.15 0.62 8.02 1.87 2.69 0.57 3.03 2.73
Gaagbini DWVP185 Poubogou 1146282 746151 43.8 1.64 3 0.48 45 64.8 1.81 4.69 0.67 4.48 6.45 0.87 6.76 6.39
Guabulga DWVP187 Poubogou 1151836 752135 50 1.7 5 0.7 190 273.6 2.44 5.68 0.75 13.03 18.77 1.30 18.04 18.00
Manga DWVP194 Poubogou 1155070 751786 61 1.79 6 0.78 10 14.4 1.16 7.3 0.86 0.47 0.67 0.22 0.84 0.71
Sandanfongu DWVP204 Poubogou 1148826 742520 55.3 1.74 10 1 16 23.04 1.36 4.55 0.66 2.38 3.43 0.65 3.78 3.46
Sayoo DWVP205 Poubpoou 1149270 742821 48 1.68 12 1.08 15 21.6 1.33 16.5 1.22 9.32 13.42 1.16 13.26 13.00
Guakudo Sch DWVP141 Panabako sandstone 1132637 750921.8 50 1.70 8 0.90 10 14.4 1.158362 3.89 0.59 0.455581 0.66 0.21906989 1.14 0.70
Walewale-WA-01 DWVP233 Kodjari 1145528 739351 72 1.86 9 0.95 500 720 2.86 2.63 0.42 2.18 229.36 330.28 2.52 263.52 263.52
Walewale-WA-03 DWVP234 Kodjari 1145740 738955 55 1.74 9 0.95 250 360 2.56 6.35 0.8 5.75 43.48 62.61 1.80 70.84 70.84
Walewale-WA-02 DWVP235 Kodjari 1145713 738822 60 1.78 10 1 240 345.6 2.54 4.6 0.66 11.54 20.80 29.95 1.49 14.95 14.95  
 300 
University of Ghana http://ugspace.ug.edu.gh
 
Specific 
Drawdow Specific Transmissivity-
LogRegoli Discharge Discharge LogYield Capacity LogSC(m3/
Community BH ID  Formation Northing Easting LogD(m) SWL (m) LogSWLF (irmst) water strike n(m (m) ) at Capacity Entire Nsia 
Thickness th (m) (l/min) (m3/d) (m3/d) (l/min/m d/m)360mins (m3/d/m) Transmissivity (m2/d)
Depth of ) (m2/d)-sormation-
(m) Regolith specific
Walewale 1-Norst DWVP237 Kodjari 1145025 741256 80 1.9 5 0.7 400 576 2.76 0.64 -0.19 1.4 285.71 411.43 2.62 178.66 178.66
Walewale 2-Norst DWVP238 Kodjari 1144920 741468 75 1.88 8 0.9 130 187.2 2.27 5.64 0.75 24.57 5.29 7.62 0.94 3.06 3.06
Walewale 3-Norst DWVP239 Kodjari 1144602 741362 67 1.83 8 0.9 20 28.8 1.46 15.06 1.18 39.73 0.50 0.72 0.24 0.29 0.29
BUGYA PALA WVB11 Kodjari 1137749 746795 56 1.75 19 1.28 9 12.96 1.11 7.26 0.86 30 0.3 30.00 43.20 1.65 42.30 40.40
BOAMASA_BH1 DWVP20 Kodjari 1137330 756709 42 1.62 10 1 50 72 1.86 2.56 0.41 20 7.32 6.83 9.84 1.04 9.75 9.62
BOAMASA_BH2 DWVP21 Kodjari 1137635 756753 42 1.62 10 1 30 43.2 1.64 3.01 0.48 20 9.31 3.22 4.64 0.75 4.62 4.64
BANAWA_BH1 DWVP30 Kodjari 1145281 744554 38 1.58 11 1.04 17 24.48 1.39 2.55 0.41 28 26.37 0.64 0.93 0.29 0.94 0.98
BANAWA_BH2 DWVP31 Kodjari 1145442 745076 45 1.65 9 0.95 34 48.96 1.69 2.98 0.47 36 11.26 3.02 4.35 0.73 4.34 4.36
SHELUVUYA DWVP39 Kodjari 1137658 766388 51 1.71 10 1 10 14.4 1.16 6.89 0.84 18 32.41 0.31 0.44 0.16 0.45 0.47
Shevoya-chief palaceDWVP74 Kodjari 1137617 766798 49 1.69 6 0.78 45 64.8 1.81 2.7 0.43 20 17.63 2.55 3.68 0.67 3.67 3.71
Shelinvoya DWVP81 Kodjari 1162601 764082 63 1.8 6 0.78 60 86.4 1.94 8.42 0.93 24 7.14 8.40 12.10 1.12 11.97 11.76
ZANGUM DWVP86 Kodjari 1149885 737659 71 1.85 10 1 20 28.8 1.46 5.51 0.74 53 50.61 0.40 0.57 0.20 0.58 0.61
ZANGUM DWVP87 Kodjari 1149817 737567 38 1.58 5 0.7 15 21.6 1.33 5.47 0.74 21 17.18 0.87 1.26 0.35 1.27 1.31
TAMPULUNGU DWVP99 Kodjari 1146049 739640 72 1.86 3 0.48 10 14.4 1.16 7.7 0.89 37.21 0.27 0.39 0.14 0.40 0.42
TAMPULUNGU DWVP100 Kodjari 1147119 739557 63 1.8 7 0.85 7 10.08 1 8.45 0.93 42.11 0.17 0.24 0.09 0.24 0.26
KPARIGU DWVP104 Kodjari 1139273 758840 44 1.64 10 1 80 115.2 2.06 0.58 -0.24 21 4.23 18.91 27.23 1.45 26.76 25.82
BOAMASA DWVP105 Kodjari 1137638 756758 42 1.62 9 0.95 65 93.6 1.97 1.49 0.17 23 11.8 5.51 7.93 0.95 7.87 7.80
BOAMASA DWVP106 Kodjari 1137330 756710 42 1.62 9 0.95 45 64.8 1.81 3.23 0.51 23 11.6 3.88 5.59 0.82 5.56 5.56
GUAKUDO DWVP107 Kodjari 1132622 750724 47 1.67 9 0.95 22 31.68 1.5 4.08 0.61 21 16.63 1.32 1.90 0.46 1.91 1.95
BUGYAKURA DWVP109 Kodjari 1136335 747346 21 1.32 6 0.78 21 30.24 1.48 5.24 0.72 23 20.72 1.01 1.46 0.39 1.47 1.51
KPERIGA DWVP110 Kodjari 1143666 741126 58 1.76 8 0.9 12 17.28 1.24 6.75 0.83 17 0.71 1.02 0.31 1.03 1.07
KPERIGA DWVP111 Kodjari 1143646 741156 47 1.67 5 0.7 20 28.8 1.46 5.74 0.76 27 21.45 0.93 1.34 0.37 1.35 1.39
TAMPULUNGU DWVP114 Kodjari 1145988 739659 53 1.72 6 0.78 15 21.6 1.33 9.34 0.97 32 15.08 0.99 1.43 0.39 1.44 1.48
TAKORAYIRI DWVP115 Kodjari 1138125 750711 36 1.56 3 0.48 15 21.6 1.33 1.71 0.23 21 12.2 1.23 1.77 0.44 1.78 1.82
Tangbini DWVP128 Kodjari 1147315 797489 52 1.72 5 0.7 12 17.28 1.24 1.98 0.3 13.42 0.89 1.29 0.36 1.30 1.34
Bugya PALA Bh1 DWVP133 Kodjari 1138104 746289 39 1.59 3 0.48 300 432 2.64 2.95 0.47 0.45 666.67 960.00 2.98 916.86 817.99
Bugya PALA Bh2 DWVP134 Kodjari 1138331 746288 45 1.65 7 0.85 50 72 1.86 1.46 0.16 9.72 5.14 7.41 0.92 7.36 7.31
Bugya PALA Bh3 DWVP135 Kodjari 1141971 738002 48 1.68 5 0.7 140 201.6 2.3 2.15 0.33 10.17 13.77 19.82 1.32 19.53 18.97
Guakudo Sch DWVP141 Kodjari 1132637 750922 50 1.7 8 0.9 10 14.4 1.16 3.89 0.59 0.46 0.66 0.22 0.67 0.70
Shelinvoya 2 DWVP150 Kodjari 1138528 767149 49 1.69 3 0.48 20 28.8 1.46 8.2 0.91 7.5 2.67 3.84 0.68 3.83 3.86
shelinvoya 3 DWVP151 Kodjari 1137385 765371 39 1.59 2 0.3 38 54.72 1.74 7.55 0.88 8.02 4.74 6.82 0.89 6.78 6.74
Tampulungu 1 DWVP152 Kodjari 1146783 738955 72 1.86 10 1 15 21.6 1.33 10.82 1.03 30.05 0.50 0.72 0.24 0.73 0.76
Tampulungu 2 DWVP153 Kodjari 1147291 738320 63 1.8 15 1.18 15 21.6 1.33 11.76 1.07 18.98 0.79 1.14 0.33 1.15 1.19
Katigri DWVP189 Kodjari 1148070 805834 50 1.7 2 0.3 17 24.48 1.39 12.8 1.11 2.26 3.26 0.63 3.26 3.29
Galabisi DWVP219 Kodjari 1139059 782419 55.1 1.74 5 0.7 12 17.28 1.24 3.51 0.55 0.93 1.34 0.37 1.35 1.39
Gambaga SHS1 DWVP224 Panabako sandstone 1165331 780206.3 55 1.740363 6 0.78 10 14.4 10.18 24.14 0.287674 0.41 10.35 0.15 0.25
Gambaga SHS2 DWVP225 Panabako sandstone 1165736 780373.2 54 1.73 6 0.78 20 28.80 1.46 11.74 1.070 17.81 1.12 1.62 0.42 0.96 0.95
Gambaga SHS3 DWVP226 Panabako sandstone 1165546 780778.3 94 1.97 5 0.70 11 15.84 1.20 13.45 1.129 33.09 0.33 0.48 0.17 1.01 1.01
Gambaga SHS4 DWVP227 Panabako sandstone 1165927 780516.2 58 1.76 8 0.90 9 12.96 1.11 12.63 1.101 25 0.36 0.52 0.18 0.30 0.30
Gambaga BG-01 DWVP228 Panabako sandstone 1164902 780802.1 65 1.81 10 1.00 300 432.00 2.64 6.4 0.806 19.66 15.26 21.97 1.36 13.26 13.26
Nalerigu-NA-01 DWVP229 Panabako sandstone 1165069 787260.6 60 1.78 9 0.95 180 259.20 2.41 5.72 0.757 10.06 17.89 25.77 1.43 16.59 16.59
Wundua 1 DWVP240 Panabako sandstone 1143467 769873.8 55 1.74 10 1.00 200 288.00 2.46 2.92 0.465 3.22 62.11 89.44 1.96 65.88 65.88
Wundua 2 DWVP241 Panabako sandstone 1143298 770805.2 65 1.81 8 0.90 150 216.00 2.33 2.96 0.471 13.6 11.03 15.88 1.23 10.68 10.68
TAMBOKU DWVP2 Panabako Sandstone 1144848 775912 100 2.00 12 1.08 132 190.08 2.28 9.55 0.980 37 5.76 22.92 33.00 1.53 13.48 13.48
NALERIGU SHS HAP 11 Panabako Sandstone 1163361 785424.2 141 2.15 12 1.08 24 34.56 1.54 8.21 0.914 32 16.08 1.49 2.15 0.50 3.49 2.20
DAGBIBORE DWVP1 Panabako Sandstone 1147263 778042 150 2.18 16 1.20 22 31.68 1.50 6.02 0.780 25 65.78 0.33 0.48 0.17 0.85 0.52
SAMENE DWVP9 Panabako Sandstone 1157299 761494 100 2.00 16 1.20 4 5.76 0.76 7 0.845 18 47.48 0.08 0.12 0.05 0.23 0.14
MOATANKURA DWVP19 Panabako Sandstone 1138624 755848 37 1.57 6 0.78 13 18.72 1.27 6.04 0.781 28 19.33 0.67 0.97 0.29 1.65 1.01
NABULUGU_BH1 DWVP22 Panabako Sandstone 1141502 755701.9 37 1.57 9 0.95 85 122.40 2.09 2.52 0.401 28 4.21 20.19 29.07 1.48 40.60 27.51
NABULUGU_BH2 DWVP23 Panabako Sandstone 1141893 755809 34 1.53 7 0.85 11 15.84 1.20 7.3 0.863 19 6.07 1.81 2.61 0.56 4.19 2.65
TAMBOKU_BH1 DWVP24 Panabako Sandstone 1144472 776445 31 1.49 10 1.00 60 86.40 1.94 1.87 0.272 28 9.29 6.46 9.30 1.01 13.88 9.11
TAMBOKU_BH2 DWVP25 Panabako Sandstone 1144025 776184.9 50 1.70 11 1.04 18 25.92 1.41 3.22 0.508 20 16.28 1.11 1.59 0.41 2.63 1.64
DAGBRIBOARI_BH1 DWVP26 Panabako Sandstone 1137635 756753 40 1.60 12 1.08 30 43.20 1.64 4.11 0.614 26 21.08 1.42 2.05 0.48 3.34 2.10
DAGBRIBOARI_BH2 DWVP27 Panabako Sandstone 1149487 777801 60 1.78 12 1.08 18 25.92 1.41 2.64 0.422 36 16.54 1.09 1.57 0.41 2.59 1.62
BURUGU DWVP28 Panabako Sandstone 1148333 774050 50 1.70 13 1.11 60 86.40 1.94 5.2 0.716 36 15.27 3.93 5.66 0.82 8.69 5.62
KPARIGU DWVP29 Panabako Sandstone 1139487 759089 34 1.53 10 1.00 35 50.40 1.70 14.03 1.147 19 18.37 1.91 2.74 0.57 4.39 2.79
LATARE DWVP32 Panabako Sandstone 1154501 783293.8 40 1.60 10 1.00 55 79.20 1.90 4.77 0.679 20 12.11 4.54 6.54 0.88 9.96 6.47
TSICHIRIGA DWVP33 Panabako Sandstone 1161725 796740.1 34 1.53 15 1.18 80 115.20 2.06 2.6 0.415 19 18.8 4.26 6.13 0.85 9.37 6.08
NANORI DWVP34 Panabako Sandstone 1159614 780838 45 1.65 12 1.08 20 28.80 1.46 5.25 0.720 20 23.11 0.87 1.25 0.35 2.09 1.30  
 301 
University of Ghana http://ugspace.ug.edu.gh
 
Specific 
Drawdow Specific Transmissivity-
LogRegoli Discharge Discharge LogYield Capacity LogSC(m3/
Community BH ID  Formation Northing Easting LogD(m) SWL (m) LogSWLF (irmst) water strike n(m (m) ) at Capacity Entire Nsia 
Thickness th (m) (l/min) (m3/d) (m3/d) (l/min/m d/m)360mins (m3/d/m) Transmissivity (m2/d)
Depth of ) (m2/d)-sormation-
(m) Regolith specific
NAGBOO DWVP35 Panabako Sandstone 1106940 813190 78 1.89 10 1.00 60 86.40 1.94 13.12 1.118 20 20.84 2.88 4.15 0.71 6.48 4.16
TINGURI 1 DWVP37 Panabako Sandstone 1145240 750929.3 101 2.00 8 0.90 10 14.40 1.16 6.83 0.834 22 82.79 0.12 0.17 0.07 0.33 0.19
ZANGUGA DWVP38 Panabako Sandstone 1150234 755785.8 92 1.96 9 0.95 10 14.40 1.16 8.46 0.927 16 52.91 0.19 0.27 0.10 0.50 0.30
GBANDABILA DWVP54 Panabako Sandstone 1140414 801312.3 50 1.70 2 0.30 6 8.64 0.94 5.17 0.713 42.37 0.14 0.20 0.08 0.38 0.22
SILOMBOMA DWVP55 Panabako Sandstone 1150871 797919.1 45 1.65 10 1.00 108 155.52 2.19 5.99 0.777 16 24.35 4.44 6.39 0.87 9.74 6.33
SAGADUGU DWVP56 Panabako Sandstone 1146329 756743.6 80 1.90 15 1.18 24 34.56 1.54 9.66 0.985 24 17.19 1.40 2.01 0.48 3.28 2.06
SAKAGO DWVP57 Panabako Sandstone 1171588 793854.2 60 1.78 10 1.00 35 50.40 1.70 6.43 0.808 35 12.68 2.76 3.97 0.70 6.23 3.99
JAWANI DWVP58 Panabako Sandstone 1148166 787460.3 100 2.00 10 1.00 20 28.80 1.46 3.34 0.524 75 23.61 0.85 1.22 0.35 2.05 1.27
BUZULUNGU DWVP66 Panabako Sandstone 1153095 762035 100 2.00 10 1.00 7 10.08 1.00 7.45 0.872 50 43.42 0.16 0.23 0.09 0.43 0.25
TAKORAYILI-A150 DWVP67 Panabako Sandstone 1138808 750499.1 50 1.70 9 0.95 10 14.40 1.16 4.13 0.616 18 37.74 0.26 0.38 0.14 0.69 0.41
KOLINVAR DWVP68 Panabako Sandstone 1150873 795584.2 65 1.81 15 1.18 15 21.60 1.33 12.78 1.107 47 33.65 0.45 0.64 0.22 1.12 0.68
NAMANGU DWVP69 Panabako Sandstone 1151614 771877.5 90 1.95 10 1.00 6 8.64 0.94 10.41 1.017 43 46.44 0.13 0.19 0.07 0.35 0.20
Tinguri 2 DWVP76 Panabako Sandstone 1145195 751951 42 1.62 10 1.00 70 100.80 2.00 1.6 0.204 10 11.76 5.95 8.57 0.98 12.85 8.41
Tinguri 3 DWVP77 Panabako Sandstone 1144754 752171 49 1.69 11 1.04 85 122.40 2.09 7.83 0.894 21 5.83 14.58 20.99 1.34 29.88 20.06
Gbinduri DWVP80 Panabako Sandstone 1153796 754199.1 50 1.70 8 0.90 60 86.40 1.94 5.66 0.753 21 13.28 4.52 6.51 0.88 9.91 6.44
KPARIGU DWVP84 Panabako Sandstone 1139387 758756.2 50 1.70 10 1.00 162 233.28 2.37 2.13 0.328 42 5.22 31.03 44.69 1.66 60.87 41.75
NABULUGA DWVP94 Panabako Sandstone 1141509 755698.6 49 1.69 5 0.70 30 43.20 1.64 4.31 0.634 35 16.92 1.77 2.55 0.55 4.11 2.60
GBANI DWVP101 Panabako Sandstone 1144522 752327.1 36 1.56 5 0.70 100 144.00 2.16 1.82 0.260 23 5.79 17.27 24.87 1.41 35.05 23.65
KPARIGU DWVP104 Panabako Sandstone 1139273 758840.3 44 1.64 10 1.00 80 115.20 2.06 0.58 -0.237 21 4.23 18.91 27.23 1.45 38.18 25.82
GUAKUDO DWVP107 Panabako Sandstone 1132622 750723.6 47 1.67 9 0.95 22 31.68 1.50 4.08 0.611 21 16.63 1.32 1.90 0.46 3.12 1.96
KPERIGA DWVP111 Panabako Sandstone 1143646 741155.5 47 1.67 5 0.70 20 28.80 1.46 5.74 0.759 27 21.45 0.93 1.34 0.37 2.24 1.39
TAMPULUNGU DWVP114 Panabako Sandstone 1145988 739659.3 53 1.72 6 0.78 15 21.60 1.33 9.34 0.970 32 15.08 0.99 1.43 0.39 2.38 1.48
TAKORAYIRI DWVP115 Panabako Sandstone 1138125 750710.8 36 1.56 3 0.48 15 21.60 1.33 1.71 0.233 21 12.2 1.23 1.77 0.44 2.91 1.82
Dindane DWVP125 Panabako Sandstone 1169646 800304 60 1.78 10 1.00 12 17.28 1.24 6.17 0.790 9.49 1.26 1.82 0.45 2.99 1.87
Bongbini DWVP129 Panabako sandstone 1158152 773613 70 1.85 10 1.00 10 14.40 1.16 12.3 1.090 15.59 0.64 0.92 0.28 1.58 0.97
Kpikparigbini DWVP130 Panabako sandstone 1158236 793848 52 1.72 10 1.00 25 36.00 1.56 12.56 1.099 14.56 1.72 2.47 0.54 3.98 2.52
Tenkpanga DWVP131 Panabako sandstone 1153416 746636 46 1.66 5 0.70 15 21.60 1.33 6.7 0.826 1.92 7.81 11.25 1.09 16.60 10.95
Boiyini DWVP132 Panabako sandstone 1145373 762934 44 1.64 5 0.70 42 60.48 1.78 2.1 0.322 8.71 4.82 6.94 0.90 10.54 6.86
Mimima 1 DWVP143 Panabako sandstone 1149069 761497.9 43 1.63 6 0.78 20 28.80 1.46 8 0.903 7.62 2.62 3.78 0.68 5.94 3.80
mimima 2 DWVP144 Panabako sandstone 1150593 761116.9 45 1.65 3 0.48 130 187.20 2.27 0.5 -0.301 5.89 22.07 31.78 1.52 44.16 30.00
Nayorko 1 DWVP146 Panabako sandstone 1146656 744797.3 54 1.73 4 0.60 15 21.60 1.33 6.67 0.824 8.26 1.82 2.62 0.56 4.20 2.66
Nayorko 2 DWVP147 Panabako sandstone 1147291 745686.3 72 1.86 3 0.48 20 28.80 1.46 5.91 0.772 10.96 1.82 2.63 0.56 4.22 2.67
Sagadugu #1 DWVP148 Panabako sandstone 1147418 757751.3 40 1.60 3 0.48 55 79.20 1.90 4.41 0.644 11.51 4.78 6.88 0.90 10.45 6.80
Sagadugu #2 DWVP149 Panabako sandstone 1147164 756227.3 44 1.64 5 0.70 47 67.68 1.83 11 1.041 16.16 2.91 4.19 0.72 6.54 4.20
Shelinvoya 2 DWVP150 Panabako sandstone 1138528 767149.4 49 1.69 3 0.48 20 28.80 1.46 8.2 0.914 7.5 2.67 3.84 0.68 6.03 3.86
shelinvoya 3 DWVP151 Panabako sandstone 1137385 765371.4 39 1.59 2 0.30 38 54.72 1.74 7.55 0.878 8.02 4.74 6.82 0.89 10.36 6.74
Tampulungu 1 DWVP152 Panabako sandstone 1146783 738955.3 72 1.86 10 1.00 15 21.60 1.33 10.82 1.034 30.05 0.50 0.72 0.24 1.24 0.76
Tampulungu 2 DWVP153 Panabako sandstone 1147291 738320.3 63 1.80 15 1.18 15 21.60 1.33 11.76 1.070 18.98 0.79 1.14 0.33 1.92 1.19
Nanori 2 DWVP162 Panabako sandstone 1160118 781775.6 43 1.63 9 0.95 14 20.16 1.30 9.63 0.984 17.93 0.78 1.12 0.33 1.90 1.17
Saamini BH DWVP163 Panabako sandstone 1158149 760725.3 43 1.63 10 1.00 24 34.56 1.54 4.64 0.667 10.05 2.39 3.44 0.65 5.44 3.47
Langbinsi BHC DWVP170 Panabako sandstone 1156598 765653.6 55 1.74 10 1.00 100 144.00 2.16 9.26 0.967 22.43 4.46 6.42 0.87 9.79 6.36
Sakogu BHC DWVP171 Panabako sandstone 1171330 797784.7 46 1.66 6 0.78 70 100.80 2.00 12.7 1.104 16.91 4.14 5.96 0.84 9.13 5.92
SAKAGO BHD DWVP172 Panabako sandstone 1171584 797530.7 44 1.64 6 0.78 160 230.40 2.36 8.33 0.921 14.39 11.12 16.01 1.23 23.15 15.43
Bantambari DWVP183 Panabako sandstone 1159596 768179.7 50 1.70 3 0.48 10 14.40 1.16 11.3 1.053 0.51 0.74 0.24 1.28 0.78
Binduri DWVP184 Panabako sandstone 1148914 754934.8 50 1.70 4 0.60 50 72.00 1.86 5.66 0.753 4.11 5.91 0.84 9.05 5.87
Moatani DWVP195 Panabako Sandstone 1138341 756460.3 49.9 1.70 8 0.90 15 21.60 1.33 4.5 0.653 2.31 3.33 0.64 5.28 3.37
Moatani DWVP196 Panabako Sandstone 1139217 756437.5 49.9 1.70 8 0.90 13.17 18.96 1.28 5.63 0.751 1.12 1.61 0.42 2.65 1.66
Nalerigu DWVP197 Panabako Sandstone 1163497 785407.2 155 2.19 9 0.95 24 34.56 1.54 8.21 0.914 1.49 2.15 0.50 3.49 2.20
Namenyala DWVP200 Panabako Sandstone 1156387 771747.8 43.4 1.64 9 0.95 13 18.72 1.27 9.76 0.989 0.49 0.70 0.23 1.21 0.74
Sagadugu No.2 DWVP202 Panabako Sandstone 1145609 758145.6 40.1 1.60 9 0.95 25 36.00 1.56 7 0.845 1.72 2.48 0.54 4.00 2.53
Sagadugu No.2 DWVP203 Panabako Sandstone 1146249 757413.4 44 1.64 15 1.18 47 67.68 1.83 11 1.041 2.91 4.19 0.72 6.54 4.20
Singbini DWVP206 Panabako Sandstone 1143727 756762.4 34.9 1.54 10 1.00 150 216.00 2.33 3.27 0.515 2.21 3.18 0.62 5.04 3.21
Takoratinga DWVP207 Panabako Sandstone 1163740 787114.8 37.8 1.58 10 1.00 18 25.92 1.41 15.92 1.202 3.33 4.80 0.76 7.44 4.79
Tintariga DWVP208 Panabako Sandstone 1158623 802874.9 68.1 1.83 6 0.78 11.1 15.98 1.20 11.93 1.077 0.62 0.89 0.28 1.52 0.93
Tintariga DWVP209 Panabako Sandstone 1158613 801638.1 68.16 1.83 6 0.78 11.13 16.03 1.20 12.29 1.090 0.63 0.91 0.28 1.55 0.95
Zambulugu DWVP213 Panabako Sandstone 1161575 797371.1 31.2 1.49 10 1.00 100 144.00 2.16 4.54 0.657 4.21 6.07 0.85 9.28 6.02
Zigum DWVP214 Panabako Sandstone 1155376 784647 49.5 1.69 8 0.90 154 221.76 2.35 2.61 0.417 8.38 12.07 1.12 17.73 11.72
Zojiligu DWVP215 Panabako Sandstone 1165225 799157.5 68.2 1.83 6 0.78 10.5 15.12 1.18 6.58 0.818 1.85 2.66 0.56 4.27 2.71  
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APPENDIX 11- CROSS VALIDATION FOR GRAPHS FOR COKRIGING 
 
 
Cross validation of Transmissivity 
 
 
 
 
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Cross validation of Specific Capacity model 
 
 
 
 
 
 
 
 
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APPENDIX 12- RECLASSIFICATION OF PARAMETERS FOR MULTI-
CRITERIA ANALYSIS 
 
Reclass of Lineament 
 
Reclass of recharge 
 
 
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Reclass of Regolith 
 
Drainage density 
 
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APPENDIX 13- RECLASSIFICATION MAPS OF PARAMETERS 
 
DEPTH 
 
REGOLITH 
 
 
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RECHARGE 
 
STATIC WATER LEVEL 
 
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TRANSMISSIVITY 
 
LINEAMENT  
 
 
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DRAINAGE NETWORK 
 
SLOPE 
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APPENDIX 14- SENSITIVITY ANALYSIS 
                
GROUNDWATER POTENTIAL  SENSITIVITY 1 SENSITIVITY 2 
Scale 
 Potential of Weight (% Weight (% 
Parameter Reclassified  Value/Factor Weight (% Influence) 
Score Influence) Influence) 
score   
1 2 Low 
Depth 2 2 Low 5 5 5 
3 2 Low 
              
1 3 Moderate 
Regolith 2 4 High 5 5 5 
3 5 Very High 
              
1 1 Very Low 
2 2 Low 
Recharge 3 3 Moderate 10 10 10 
4 4 High 
5 5 Very High 
              
1 5 Very High 
SWL 2 4 High 10 10 10 
3 3 Moderate 
Aquifer 1 1 Very Low 
25  10 15 
Transmissivity 2 2 Low 
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3 3 Moderate 
4 4 High 
5 5 Very High 
              
Bimbilla 2 Low 
Poubogou 2 Low 
Geological formation Panabako 10 25 10 
3 Moderate 
Sandstone 
Kodjari 4 High 
              
1 5 Very High 
Lineament 2 4 High 15 15 25 
3 3 Moderate 
              
1 5 Very High 
Drainage Density 2 4 High 10 10 10 
3 3 Moderate 
              
1 5 Very High 
Slope 2 4 High 10 10 5 
3 3 Moderate 
TOTAL SCORE       100   100 100 
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