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https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Application of Remote Sensing and Geographic Information
Systems Technologies in Detecting
Groundwater Aquifers
C. Obunadike; S. Olisah
1,2Department of Hydrogeology, Freiberg University of Mining and Technology, Freiberg Germany.
callistusobunadike@gmail.com; somtobeolisah@gmail.com
Abstract
The advancement and rapid development in technologies such as Remote Sensing,
Geographic Information Systems (GIS) and Global positioning systems (GPS) holds
great promise in tackling current challenges facing hydrologists and hydrogeologists.
An integration of the tools offered by these technologies in collecting, processing,
analyzing, and modeling will help resolve a lot of challenges being faced in
groundwater exploration and management. RS and GIS are useful tools in the
recognition and delineation of aquiferous zones for potential groundwater. In the
study area, satellite images from LANDSAT-7 and ASTER as well as aeromagnetic
data and other ancillary data sets were analyzed to extract information on the
groundwater conduit and storage capacity of the lithology underlying the area.
Remotely sensed data and other additional data –including Land use and land cover,
slope map, drainage density, contact density, lineaments, and lithology- were
correlated and used to produce six thematic maps. Using an index overlay method on
these thematic maps, with the help of the GIS modeling software a groundwater
potential map of the study area was produced. The result of the study showed a
massive spatial variability of groundwater potential ranging from very good to poor.
Zones with good potential for groundwater were observed to cover about 36% of the
study area. The variability in groundwater potential correlated with variations in
structures, slope, geology, LU/LC, and drainage density in the area. While this work
is a qualitative one, more quantitative analyses using geophysical and hydrogeological
surveys are recommended to validate the existing data.
Keywords: Geographic Information System, Aquifer, Remote sensing, Global
Positioning System,
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Introduction
Groundwater exploration: limitations
Freshwater has always been a very critical resource through every stage of human
development and survival, dating back to the earliest records of history. Water
generally accounts for about 70% of the earth surface but only about 2.5% of it is
freshwater. Majority of this freshwater are locked in solid forms as ice, snow, and
permafrost with just about 30% of it available in liquid forms. Groundwater accounts
for approximately 98% of the fresh water available in liquid form.
Groundwater has become an extremely important and dependent source of water in
both rural and urban areas of developed and developing countries particularly because
of its relative flexibility and ease with which it can be harnessed (Todd and Mays,
2005). In most rural communities of developing countries, Groundwater is the most
common and most preferred source of water supply. This is because it provides more
potable water than any other source and eliminates the possibilities of occurrence or
spread of water-borne diseases. Groundwater plays an intricate part and is intertwined
in our environment/ecosystem; hence depletion of this resource will invariably bring
about serious environmental problems. Because it is both naturally hidden and occurs
in complex subsurface formations, there is little knowledge about groundwater and
this in turn affects the efficient management of this essential resource. With
increasing need for groundwater around the world consequent of high-rising
industrialization and urbanization, the rate of exploitation has also increased rapidly
and since this is uncontrolled, the quality and quantity of groundwater resources the
world over has also been affected negatively. Depletion and deterioration of
groundwater resources inadvertently leads to environmental, social, and economic
problems such as shortage/scarcity of water, land subsidence, drying of rivers and
springs, saltwater intrusion, poor farming output etc. To this effect, the concept of
Sustainable development has emerged in managing groundwater resources. According
to Dovers and Handmer (1995),
“Where there are threats of serious or irreversible environmental damage, lack of full
scientific certainty should not be used as a reason for postponing measures to prevent
environmental degradation, and development decisions should be guided by (a)
careful evaluation to avoid, whenever practicable, serious or irreversible damage to
the environment; and (b) an assessment of the risk-weighted consequences of various
options".
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
For effective management of groundwater resources, adequate and accurate
information must be known with respect to recharge and discharge (natural and
exploited) in a particular groundwater basin. With these data, the behavior of a
groundwater aquifer system can be estimated in the long term, enabling a
management process that ensures sustainable yield of groundwater resource.
Remote Sensing (RS)
The term ‘Remote Sensing’ was put together by Evelyn Pruitt, a scientist in the US
Office of Naval Research, when she realized that the term ‘aerial photography’ could
no longer describe accurately the many forms of imagery collected using radiations
outside the visible region of the electromagnetic spectrum (Campbell 1996). In the
following years, several satellites were launched for numerous remote sensing
purposes with varying dimensions and resolutions. This marked a revolution in the
use of remote sensing technologies.
Remote sensing in a broad sense refers to ‘the gathering and processing of
information about earths environment, particularly its natural and cultural resources,
through the use of photographs and related data acquired from an aircraft or satellite’
(Lillesand and Kiefer, 1987). In this regard, remote sensing does not only involve the
process of gathering data but also includes the arrangement and analysis of this data
to obtain meaningful spatial information.
Because groundwater lies in the subsurface and most current remote sensing medium
(air- and satellite-based) can only possibly penetrate a few centimeters below the
ground surface, groundwater hydrologists were late in using satellite data for
groundwater management. Despite these shortcomings, remote sensing has quite
remarkable potential in studies regarding regional groundwater flow and has proved
to be very useful in mapping large and inaccessible groundwater zones. The necessity
of remote sensing-based groundwater exploration is to demarcate and delineate all
possible features connected with the localization of groundwater (Elbeih, 2014).
Indicators of groundwater acquired through remote sensing methods may have the
advantage of providing essential data that are not readily available through
conventional methods. Examples of data that can be measure through remote sensing
methods are heat signatures, groundwater heads, changes in groundwater storage and
subsidence data while the indicators include runoff, water being discharged to the
surface carrying heat energy and vegetation surface water. Satellite technology is
assessed based on its ability to give adequate measure of fluxes, storage and potential
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of groundwater, and this data can be used if supplementary analysis is employed to
deduce groundwater behavior from surface expression. Satellite data are acquired
from space-based sensors that records and report data, which can be used in
groundwater investigations. The table below shows a list of such active sensors.
*Inoperative. End of mission 9.05.2012 **officially ended mission in Feb. 2011 ***Mission terminated on 23.04.2007
Sensor
Launch year
Ground
resolution (m)
Precipitation
Surface
temperature
Soil
moisture
Water
storage
Snow
water
Land
cover
Topography
AMSR-E
2002
5400–56,000
X
x
X
x
ASTER
1999
15, 30, 90
X
X
X
AVHRR
1991–2003
1100
X
X
X
X
GRACE
2002
300,000
X
*ENVISAT-RA2
2002
1000
x
X
Landsat-8
2013
30, 60
X
X
MODIS
1999
250, 500, 1000
X
X
**OrbView-2
1997
1100
x
X
***OrbView-3
2003
1, 4
X
X
X
RADARSAT-1
1995
10–100
X
SRTM
2000
30, 90
X
Depending on the problem to be solved or the investigations being made, matching
sensors are selected to ensure that the results contain useful and easily quantifiable
data. Each sensor provides data that is unique regarding a property of the earths
surface or shallow subsurface. These sensors are normally classified according to the
range of electromagnetic (EM) spectrum that they cover and the information they
provide is specific to each spectral band (like visible, microwave, infrared, near-
infrared etc.).
A major factor to application of remote sensing to groundwater is the recognition that
groundwater flow in the shallow subsurface is driven by surface forcing and
parameterized by geologic properties that can be inferred from surface data. A
particular successful application of remote sensing to groundwater has been the
identification of lineaments that are thought to be related to faults and fracturing in
hard rock (Elbeih, 2004).
Remotely sensed data are most useful in situations where they are used
simultaneously with GIS, numerical modeling, and ground-based data.
Table 1: A selected list of active space-based sensors that report data of potential use for investigations of groundwater.
(Modified after Becker et al., 2004)
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Figure1: Illustration of the Antarctic Mosaic (Source:
RADARSAT-1)
Figure 2: ASTER image of the White Nile (Source:
NASA/GSFC/U.S/Japan ASTER science team
Figure 3: MODIS, Southeastern DRC. Detected fires and
borders are highlighted (Source: NASA)
Figure 4: Natural-color image of Virginia and Washington D.C captured
by the OLI on Landsat-8 (Credit: NASA Earth Observatory)
Figure 5: GRACE gravity maps for Brazil over the years 2012-2014 (source: Physics Today)
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Geographic Information System (GIS)
“GIS is a system of hardware, software and procedures designed to support the
capture, management, manipulation, analysis, modeling and display of spatially
referenced data for solving complex planning and management problems” (Rhind,
1989). GIS essentially consists of a powerful set of tools that collects, sorts,
transforms and displays spatial data acquired in the real world for a given set of
purposes. The first time a geographic information system was developed was in the
early sixties in Canada and the system consisted of a few computer applications that
basically process map data. The cost of such technology then was very high, and the
processing speed was quite slow as the storage capacity of the computers was limited.
These hindered the efficiency of converting maps into numerical forms.
“Despite all the technical constraints during the sixties, many of the basic techniques
of spatial data handling were invented and applied during that period” (Tomlinson,
1984). Between the sixties and seventies, geographic information handling analyzing
software were produced in hundreds, which satisfied certain specific data research
and management activities. The demand for GIS increased over the next decades with
an increased quest for improved natural resource management measures. GIS has
developed into an industry of its own today, and is an essential field of academic
study, not only being a useful tool for analyzing spatial data in academic research but
also aids in government services/projects, business decisions of cooperation and
generally helping the public to understand the world around them.
Integration of RS and GIS data
A basic requirement in the integration of GIS and RS data is that both sets of data
must be in the same geo-referencing system. This can be achieved through two
processes: ‘coordinate transformation’ and ‘Resampling’. Coordinate transformation
could either be through Rectification or Registration, depending on difference in
coordinates of the spatial datasets with respect to the geo-referencing system while
resampling is done for the interpolation of pixel values after rectification and
resampling for raster data. Today, advancement in computer hardware and software
technologies means that current capabilities of GIS/RS techniques in dealing with
data structure conversion have been largely expanded. Based on comprehensive
literature survey, the application of RS and GIS technologies in groundwater
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
hydrology to date has been categorized into six major groups (Jha and Peiffer, 2006).
The groups delineated include:
• Exploration and Assessment of Groundwater Resources
• Selection of sites for artificial recharge and water harvesting
• GIS-based subsurface flow and transport modeling
• Groundwater-pollution hazard assessment and protection planning
• Estimation of natural recharge distribution
• Hydrogeological data analysis and process monitoring
Figure 6: Recharge and discharge patterns generated by PRO-GIS and surface water maps from National
Hydrology Dataset (Source: esri.com)
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Methodology
Case Study: Application of RS and GIS in determining the
Groundwater potential in the crystalline basement of Bulawayo
Metropolitan Area, Zimbabwe
In most arid and semi-arid regions of Africa, the underlying lithology is crystalline
basement rocks and because of the high variability of the geologic and hydrogeologic
configuration of the associated basement aquifers, groundwater development in these
regions is complicated. The study area, Bulawayo is covered by crystalline complex
rock, and this partly causes their unreliable surface water supply. Consequently, the
region must face frequent water shortages annually. Because there is little, insufficient
information from previous research studies about groundwater occurrence and
distribution in the Bulawayo basement complex aquifer, the exploitation and
development of groundwater in the city to complement other water sources is
hindered. By using remote sensing and GIS techniques, the suitable zones for
recharging basement aquifers can be delineated.
Geology of the study area
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
The study area is mainly underlain by the crystalline basement rock that includes the
Bulawayo greenstone belt and its surrounding granite rock. According to the
geological report by Garson and Mtsvangwa (1995), the greenstone belt is flanked by
a belt of pyroblastic tonalitic to granodioritic gneisses, penetrated by later granitic
bodies. The greenstones comprise a succession of tightly folded metavolcanic and
metasedimentary rocks with generally steep dips away from the margins of the
granitic rocks. A second phase of deformation is associated with thrusting directed
from the eastern part of the area over a western hinterland consisting of granitic
gneiss and lower greenstone rocks. Superimposed in this phase is an episode in which
a strong east-west cleavage was developed. Faulting and shearing later occurred
mainly along north to north-north-west trends with some off-setting boundaries
(Chuma et al., 2013). The general geology underlying the study area is shown in the
diagram below.
Figure 7: Map showing the geological pattern in the study area. (Source: Chuma et al, 2013))
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Preparing the Thematic Maps
The study involved an extensive desk study and analysis of aeromagnetic data in
addition to the use of Remote Sensing and Geographic Information Systems. In
addition to intensive fieldwork, the data used for this study were gotten mostly from
topographic and geologic maps, LANDSAT-7 Enhanced Thematic Mapper plus
(ETM+) image of October 2005, ASTER DEM, Aeromagnetic data, geologic reports
and climatological records. Four processing pathways used in correlation with each
other were followed and they started with: the ASTER DEM data, LANDSAT ETM+
data, aeromagnetic data and the geologic data. The data from the remote sensors were
separately processed and from this the classes of land cover were deduced by a
method of image classification. The general pathways/methodology followed in this
study is shown in a simplified schematic flow chart below.
For the ArcGIS model, ancillary data were further processed to obtain the layers and
classes for each of the layers required for this model. The lineaments were visually
identified by interpretation of both ASTER DEM and LANDSAT ETM+ processed
images. For a clear identification of the lineaments from the LANDSAT ETM+ data,
a false color composite band combination of 754 (RGB) was used and this was
preceded by the principal component analysis/directional filtering using the
Figure 8: The methodology used to determine the groundwater potential zones in the study area
(Source: Chuma et al., 2013).
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Environment for Visualizing Image (ENVI 4.7) software. The radiation from the sun
shadows lineaments observed on the imagery and they were traced onto the base map.
Only a few lineaments were identified from the LANDSAT ETM+ because it was a
built-up area. On the other hand, the ASTER DEM DATA were converted into a
hillside terrain man with the help of ArcGIS. For a short distance, a drop in elevation
was observed from the hill shade surface lineaments.
Drainage pattern is one of the most important indicators of hydrogeological features,
because drainage pattern and density are controlled in a fundamental way by the
underlying lithology (Charon, 1974). Through the method of hand digitizing of the
drainage lines from processed LANDSAT ETM+ and geologic map, the drainage map
was produced.
The slope degree map was prepared by making use of the contours obtained from the
ASTER DEM 90m data and topographic map. The software, Surfer 8 was then used
to produce a 3-dimensional surface map.
Since the Land use and land cover (LULC) has an effect on the occurrence of
subsurface groundwater, a supervised classification was done on the LANDSAT
EMT+ image by means of maximum likelihood classification using ERDAS Imagine
version 9.2. With a false color combination of 432 (RGB), the different themes in the
image were identified.
Figure 9: The lineaments observed from the LANDSAT ETM+ (right) and ASTER DEM data (left) (Source:
Chuma et al., 2013)
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Groundwater Potential Modeling was necessary to delineate potential groundwater
sites within the study area. For this, every data set were integrated using the model
constructed in ArcGIS Model builder engine. Weighted Linear Combination (WLC)
produces the final map having each class individuals weight multiplied by the map
scores and then added to the result of the following Suitability equation:
S = ∑WiXi
Where S = Suitability, Wi = weight for each map score and Xi = individual map.
Weighting of the Thematic Maps
The six thematic layers were converted into a grid with related item weight generated
from the Analytic Hierarchy Process, AHP (Saaty, 1994). The six final thematic maps
were reclassified in the ArcMap (ArcGIS) according to the importance of each factor
in each raster image according to:
• Land use/land cover reclassification was carried out based on their ability to
infiltrate water into the ground and also to hold water within the ground.
• Drainage density raster was reclassified based on the recharge potential.
• Both pair-wise comparison and weight calculated for slope angle were carried
out since a flatter topography (i.e., with low slope angle) has better chances
for groundwater accumulation.
• Reclassified lineament map was produced based on the weight calculated after
a pair-wise comparison done. This is centered on the fact that zones closer to
lineaments are the highest zone of increased porosity and permeability, which
in turn have higher chances of accumulating groundwater.
• Reclassification of the lithologies weight was based on the susceptibility of the
rock to weathering.
• Contacts density map was based on the nature of the rocks in contact and the
density of the contacts. Contacts normally act as conduit of groundwater
movement in basement rocks.
The relative importance of each of the individual class in the same map/vector maps
were compared to one another using the AHP method. This resulted in important
matrices being produced using the calculated weights from the IDRSIS module. The
Mat Lab software was used to solve these matrices and hence calculate the
consistence values.
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
FACTORS
Lineaments
Lithology
Contacts
Slope
Drainage
LULC
Weight
Weight [%]
Lineaments
1
2
2
3
8
9
0.3455
35
Lithology
½
1
2
4
7
8
0.2905
29
Contacts
½
½
1
3
5
7
0.1995
20
Slope
1/7
¼
1/3
1
3
6
0.0946
9
Drainage
1/8
1/7
1/5
1/3
1
5
0.0483
5
Land Use
1/9
1/8
1/7
1/6
1/5
1
0.0216
2
The integrated layer is made up of grids that were grouped into different groundwater
potential zones using suitable logical reasoning and conditioning, also laying
emphasis on the hydrogeological condition of each cell grid. Then the layers were
overlaid using a weighted sum in the ArcMap module. The six factors affecting
groundwater are imposed on each other in the ArcSene environment (ArcGIS) and is
illustrated in the figure below. Each polygon of the thematic layers was separately
labeled before being registered. The final thematic layer has each polygon designated
based on their interpretation of groundwater occurrence and the weights based on
interpretation of the probability level of occurrence assigned to each polygon.
Table 2: Weight applied in ArcMap weight sum scheme (Source Chuma et al., 2013)
Figure 10: Integration of the Thematic Maps that gives the potential zones (Source: Chuma et al, 2013)
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Results and Discussion
A particularly useful aspect in delineating the extent of the Bulawayo Crystalline
basement rock is the Geospatial analysis of the aeromagnetic data. High magnetic
susceptibility values were observed, indicating the presence of igneous rocks
composed mainly of basalt and granite. The combination of these 2 rock formations
give rise to the Bulawayo crystalline basement and as a result is the reason for the
study area being mostly underlain by basement aquifers.
Basement aquifers are essentially phreatic in character but may respond to localized
abstraction in a semi-confined fashion, if the rest water level occurs in a low
permeability horizon (Wright, 1992). The greenstone belt that makes up the study
area geology forms the crystalline rock formation, which favors the occurrence of the
basement aquifer in the area.
Figure 11: The geospatial analysis of aeromagnetic data indicating Bulawayo crystalline basement
rocks (Source: Chuma et al, 2013)
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Regional Structural Lineaments analysis
Lineaments are visual, surface manifestation of structurally controlled features such
as faults, fractures and rock contacts, their high density may represent highly
connected fractures that offer favorable conditions for the accumulation of
groundwater. The lineament analyses in our study area are extracted from both
remotely sensed data and geological images/maps and they gave essential clues on the
subsurface features controlling the movement and/or storage of groundwater. Also,
other lineaments like joints and fractures could provide information on surface
features and are responsible for infiltration of surface runoff water from the surface
down to the subsurface. Most of the topographic lineaments within the study area
correlate with fracture zones, faults, and lithological contrasts along fold belts and in
crystalline basement rocks.
Figure 12: Geologic structures, extracted lineaments and contacts (Source: Chuma et al, 2013)
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Using the regional structural lineament map, a rose diagram was developed on the
Integrated Land and Water Information System 3.3 (ILWIS) software, and this
indicated the clear direction and magnitude of lineaments.
The major direction of the lineaments is northwesterly, though more west-north-west
trends occur in western part of the study area. The ENE-WSW to NE-SW trending
lineaments is largely as a result normal faults with the possibility of a minimal
Figure 13: Rose diagram showing directions and lengths of lineaments in Bulawayo (Source:
Chuma et al., 2013)
Figure 14: Drainage pattern overlaid on the DEM. (Source: Chuma et al., 2013)
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transcurrent movement. This is the reason most of the rivers in the study area flow in
the western direction. Bulawayo has four major rivers and many other smaller ones.
They all drain from the central part of the town to the western and northern direction.
The drainage map of the area, which is overlain using the DEM software, shows the
network of surface water bodies and the flow directions.
Analysis of Factors Affecting the Occurrence of Groundwater
Groundwater occurrence is generally affected by several surface and subsurface
factors. These factors were obtained for our study area, processed, and analyzed. The
Figure 15: Reclassified thematic maps of the study indicating the land use and densities of slope,
drainage, lineaments and contacts (Source: Chuma et al., 2013)
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
thematic maps for all six parameters were reclassified and incorporated in the AHP
system. Comparing the drainage system of the area to its structural composition. It
was observed that the drainage system of the area is structurally controlled to follow
the lineaments directions. The drainage patterns observed (dendritic and parallel
pattern) are indicative of the structures present, which act as conduits or storage for
subsurface water. The general drainage pattern observed in Bulawayo points that the
high drainage density occurs in the western and northern parts while the low and
moderate drainage density concentrates on the central and southern part of the study
area. This distribution can be due to weak zones created during faulting, shearing and
geological contacts. From the evaluated LANDSAT ETM+ image and topographic
map (year 2005), it is observed that settlements in Zimbabwe have expanded a great
deal since its independence in 1980. This negatively affected the groundwater
recharge of the area since people had to clear the natural forests and vegetation for
construction purposes. Settlements are generally found to be quite unsuitable for
infiltration.
Figure 16: The three-dimensional analysis of the study area (Source: Chuma et al., 2013)
The above figure shows the three-dimensional (3D) surface map of the study area and
was produced using ASTER DEM data, which was then processed and analyzed in
the Surfer 8 software to get the final map. The abrupt changes in terrain noticed on the
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
map explain the variability of the slopes in all directions. It also depicts the probable
direction of the geologic structures in the area that affects the movement and storage
of groundwater. The topography of the land surface determines the general direction
of groundwater flow and invariably influences its discharge and recharge.
Occurrence of Groundwater in Bulawayo
The six thematic maps were integrated to produce a map, which was reclassified and
from these five categories of groundwater potential zones were deciphered (shown in
figure 17 below). The amount of groundwater is mostly a function of topographic
effect and the structural geology environment. The spatial distributions of the
different groundwater potential zones deciphered from the model generally depict
regional patterns of drainage, lineaments, lithology, and landform.
Figure 17: Groundwater potential zones identified from the weighted factors (Source: Chuma et
al, 2013)
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
The low to poor zones are observed to be spatially distributed mostly along the ridges
where the slope degree is very high, and lithology is compact and far from
lineaments. The crystalline rock formation, which is formed by the greenstone belt in
the area, favors the occurrence of basement aquifer there. Groundwater is generally
controlled by secondary porosity resulting from mechanical and chemical weathering.
This weathering processes lead to the formation of fractures and faults, which act as
storage/conduit for groundwater occurrence.
The groundwater potential zones classification according to the model is represented
statistically according to the categories the pie chart diagram shown above. From this
representation, about 36% of the entire area is classified as high groundwater potential
zones and are mainly clustered in the southern and south-eastern part of the study
area. Some high potential zones also occur in the northern part, but the source area
has the possibility of being vulnerable to sewage system contamination. A great
correlation can be observed between the designated ‘high potential’ areas and the
location of the Matsheumhlope aquifer (Rusinga and Taigbenu, 2004). On the other
hand, areas having moderate groundwater potential are adjudged to have a
combination of contributions from slope, lithology, land use/cover and landform.
The groundwater flow direction is seen to vary which is attributed to the variation and
localization of the fractures within the basement rock. This characteristic of the
basement rock makes it hard to exploit the available groundwater especially if there is
not enough information on the groundwater occurrence in the area. Judging by the
information from the drainage pattern of the study area, the flow direction of
groundwater is generally in the north and north-west direction.
Figure 18: Pie chart of the classified categories obtained from groundwater
potential model (Source: Chuma et al, 2013)
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
Conclusions
The study shows that there is a wide spatial variability of groundwater potential in the
study area. The high potential zones in the area are directly related to the volcanic
rock, which is characterized by secondary structures, interconnected pore spaces,
gentle slopes and lower drainage density. The low to poor groundwater potential
zones lie in the massive basement units are far from lineaments. Some other moderate
potential zones occur near the sewer drainage systems and hence should be avoided to
prevent water contamination.
Parts of the study area characterized by surface expression of lineaments are
hydrogeological insignificant because of the high slope, which makes it part of the
poor groundwater potential zones in the area. Also, low drainage density areas induce
more infiltration thereby resulting in good groundwater potential zones, in sharp
contrast to high drainage density areas.
While the result of this study is qualitative, it is however recommended that more
quantitative investigations are done to fully understand the groundwater potentials of
the area and hence, create a hydrogeological model for an effective management and
exploitation of groundwater in Bulawayo.
https://doie.org/10.0202/2023550809 C. Obunadike, S. Olisah
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