{"paper_id":"08eb699e-0d3a-46c9-8a69-58f625c4b021","body_text":"Geospatial assessment of groundwater potential zones in Abuja, Nigeria, using GIS-based weighted overlay analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Geospatial assessment of groundwater potential zones in Abuja, Nigeria, using GIS-based weighted overlay analysis David Mkpanam Nyong, Haulah Habeeb Muhammad, Adaobi Thelma Onyemaobi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7782208/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sustainable development of groundwater is vital in meeting the increasing water demand in rapidly growing and rapidly urbanizing areas like Abuja, Nigeria. This study aimed at mapping and classifying areas with varying groundwater potential within Abuja for the purpose of strategic groundwater development planning. Using Geospatial approaches, namely a weighted overlay technique, thematic maps for geology, elevation, land use/land cover, slope, drainage density, and precipitation were integrated based on their respective influences on groundwater availability. Each of the variables was given a weight depending on its importance in groundwater availability mapping. The final map delineated groundwater potential into three classes, namely, low, moderate, and high. The results of the study show that about 79% (5,736.2 km²) of Abuja has moderate groundwater potential, 7% (518.41 km²) has high potential, and 14% (1,024.61 km²) has low potential. The study concludes that although groundwater development is feasible for the vast area, there is a need for a focused approach for the high-potential area for the optimal use of this resource. These results provide critical guidance for policymakers and water resource managers for the purposes of facilitating sustainably managing groundwater in the scenario of the ongoing urbanization of Abuja. Groundwater Potential Geographic Information System (GIS) Weighted Overlay Analysis Abuja Nigeria Water Resource Management Multi-Criteria Decision Analysis (MCDA) Figures Figure 1 Figure 2 Figure 3 1. Introduction 1.1. Background and Review The availability of sustainable water sources is a major challenge for rapidly growing urban centers in the Global South. In Abuja, Nigeria's capital city, the uncontrolled growth in population and urbanization has put great pressure on available water sources, thus making the sustainable management of groundwater an important priority (Adelana et al., 2006). Groundwater also tends to represent a more reliable and sustainable water supply than surface water due primarily to its lesser sensitivity to seasonal changes and pollutions. Nevertheless, the effective development of groundwater relies on a thorough understanding of its spatial distribution in order to avoid over-extraction and to correctly target investments in water infrastructure. In the event of no such understanding, uncontrolled extraction might bring about the depletion of aquifers, land subsidence, and long-term environmental degradation. In this light, a scientific characterization of groundwater distribution is paramount for sustainably utilizing and managing practices. Traditionally, groundwater exploration has greatly depended on classical hydrogeological field surveys and drilling programs that have tended to be coupled with significant time commitments, costly outlays, and limitations in spatial coverages. Nevertheless, RS and GIS technologies have transformed groundwater research by providing the possibility for integrating and spatially interpreting multiple surface and subsurface variables that govern groundwater recharge and storage (Jha et al., 2010 ). By employing such technologies, multi-criteria decision analysis (MCDA) processes—such as the weighted overlay technique—enable the overall inclusion of vital parameters such as geology, topography, precipitation, and land use/land cover in comprehensive groundwater potential maps. This methodology has been systematically applied across a vast array of climatic and geological conditions and has emerged as effective and cost-effective for initial groundwater assessments (Murthy, 2000 ). The overlay weighting technique, which is considered as one of the most used approaches within Multi-Criteria Decision Analysis (MCDA), has been successfully utilized within different hydrogeological environments around the world. For example, Murthy ( 2000 ) explained its application within semi-arid regions of India, highlighting the contribution of geological structures and drainage intensity in the identification of groundwater availability. Similarly, Oikonomidis et al. ( 2015 ) applied this method within Greece, again proving its success within the context of the Mediterranean climate. The underlying principle of the technique involves the allocation of weightings for each thematic map according to its corresponding contribution towards groundwater recharge and storage, a process that is strongly founded on conventional hydrogeological principles (Todd & Mays, 2005 ). The application of GIS-based multi-criteria analysis is extremely valuable on Basement Complex terrains, which dominate vast swaths of Africa, as well as Nigeria. These Precambrian rocks have naturally weak primary porosity, and groundwater occurrence is almost entirely reliant on secondary porosity features that manifest due to fracturing and weathering activity. MacDonald et al. ( 2012 ) carried out a continent-scale examination of African groundwater stores, making reference to the normally weak-to-moderate yields that dominate these aquifers and once again making reference to the requirement for borehole site selection with careful consideration. Some studies carried out in Nigeria have utilized GIS and remote sensing approaches for groundwater mapping in similar environments. For example, studies that were conducted in the Sokoto Basin and the crystalline basement terrains have reported a significant linkage between lineament density, soil permittivity, and borehole yield (Offodile, 2002 ). While there are global-scale hydrogeological assessments for Nigeria, spatially confined high-resolution appraisals are scarce, especially for the Federal Capital Territory (FCT) of Abuja. Earlier studies have mostly covered larger regional settings or other Nigerian states. Thus, there is a critical demand for a precise, site-oriented evaluation for the purposes of Abuja's urban water resource planning. In response, this study designs an exhaustive groundwater potential map specific to the city's specific geological and climatic features. 1.3. Novelty and Objectives This research addresses an important knowledge gap by conducting a detailed Geospatial-based groundwater potential assessment for Abuja, Nigeria, through the application of GIS and Remote sensing technology. This research is new because it combines detailed satellite images, especially from Sentinel-2, to classify land use and cover. It also uses a structured method to analyze these images, tailored specifically for the geology of the Basement Complex area in Abuja. The specific objectives of the study are to: Develop a groundwater potential map of Abuja by integrating thematic layers including geology, slope, drainage density, land use/land cover, soil type, and rainfall. Delineate the study area into zones of low, moderate, and high groundwater potential. Quantify the spatial extent of each potential zone and provide evidence-based recommendations for sustainable groundwater management and urban planning. Through these objectives, the study provides a valuable decision-support tool for urban planners, hydrogeologists, and water resource managers, contributing to the scientific foundation for the sustainable development and management of groundwater resources in Abuja’s rapidly urbanizing environment. 2. Materials and methods 2.1 Study Area Description Abuja the Federal Capital Territory (FCT), Nigeria is situated in the central part of Nigeria with coordinates 6°45'E to 7°45'E and 8°25'N to 9°25'N, The study area falls within the Guinea Savanna zone while most of its subsoil formations are underlain by Precambrian Basement Complex rocks which are characterized by having limited primary porosit. Aquifer systems are therefore mainly secondary, occurring within weathered regolith and fractured bedrock. Identification of groundwater potential in this geological context is relevant due to the fast-paced urbanization and increasing need for water in the region. Figure 1provides a detailed study area map (SAM) of Abuja, Nigeria. 2.2. Data Sources and Preparation The study employed a multi-thematic layer approach, integrating spatial datasets obtained from publicly available global sources. All datasets were standardized to the World Geodetic System 1984 (WGS 84), UTM Zone 32N, ensuring consistent spatial reference and compatibility during analysis. The six key thematic layers utilized are presented schematically in Fig. 2 . 2.2.1. Digital Elevation Model (DEM) and Derived Layers A 30-meter resolution Digital Elevation Model (DEM) was obtained from the United States Geological Survey (USGS) EarthExplorer platform. Two critical hydrological parameters were derived using ArcGIS Spatial Analyst tools: Slope: Generated from the DEM to quantify the gradient of the terrain, which influences surface runoff and groundwater infiltration. Drainage Density: Computed using the Line Density tool applied to a rasterized stream network extracted from the DEM. Drainage density reflects the degree of surface drainage dissection and inversely relates to infiltration potential. 2.2.2 Thematic Data Layers The additional thematic datasets employed in this study include: Geology: Sourced from the USGS World Geology Database, providing information on lithological formations and their associated hydrogeological properties. Land Use/Land Cover (LULC): Derived from Sentinel-2 imagery available through the Esri online platform. The LULC was classified into four categories: Built-up , Vegetation , Water bodies , and Barren land . Soil Type: Obtained from the Food and Agriculture Organization (FAO) global soil database, which includes data on soil texture, permeability, and infiltration capacity. Rainfall: Long-term mean annual precipitation data were extracted from the Climate Research Unit (CRU) database in point vector format, representing spatial rainfall variability across the FCT. 2.3. Methodology: GIS-Based Weighted Overlay Analysis The main analytical technique applied in the study was the Weighted Overlay Analysis, which integrates multiple environmental and geological factors to delineate groundwater potential zones. 2.3.1 Data Reclassification The entire thematic layers were reclassified to a unified scale of 1 to 4, in which 1 stands for least and 4 for the most suitable conditions for groundwater occurrence. Permeability potential was used for reclassification of the LULC map, assigning higher ratings to Water bodies and Vegetation owing to their infiltration nature. The IDW method was also used to interpolate the point rainfall data in preparation for the creation of a continuous raster surface, which was classified in terms of Low, Medium, High, and Very High rainfall regions. Similarly, slope, drainage density, soil, and geology layers have been reclassified in terms of their Corresponding hydrogeologic significance. 2.3.2. Assigned weights for groundwater potential factors. The relative importance of the contribution of each factor in controlling groundwater occurrence was determined from established hydrogeological principles. The weights assigned, which add up to 100%, are listed in Table 1 . Table 1 Assigned weights for groundwater potential factors. No. Thematic Layer Rationale Assigned Weight (%) 1 Geology Determines subsurface storage and flow characteristics. 25% 2 Drainage Density High density reduces infiltration; low density promotes it. 20% 3 Slope Steeper slopes increase runoff, reducing infiltration. 15% 4 Land Use/Land Cover Impacts surface sealing and natural recharge. 15% 5 Soil Type Controls infiltration rate into the subsurface. 10% 6 Rainfall The primary source of groundwater recharge. 10% 7 Digital Elevation Model (DEM) Base for deriving slope and drainage. 5% Total - - 100% 2.3.2 Overlay Analysis and Zoning The reclassified raster layers were all merged using the Weighted Sum Tool in ArcGIS 10.8. Each of the thematic layers was initially multiplied by its own weight, which is an expression of its relative significance, prior to summation, therefore yielding a continuous Groundwater Potential Index (GPI). Thereafter, the resulting raster was grouped into three groundwater potential zones, that is, Low, Medium, and High, utilizing the Natural Breaks (Jenks) classification scheme. Area of each class was calculated in its entirety for the examination of its distribution within the study FCT. The final groundwater potential map is depicted in Fig. 3 (Results section). 3. Results The weighted overlay analysis produced a detailed groundwater potential map for the Federal Capital Territory of Abuja (Fig. 3 ). The delineated study area was clearly segmented into Low, Medium, and High groundwater potential zones. The quantitative assessment indicated that medium potential zones prevail, encompassing roughly 79% (5,736.2 km²) of the entire region. High potential zones comprise approximately 7% (518.41 km²), mainly situated in areas characterized by gentle slope gradients and diminished drainage density, whereas low potential zones represent the remaining 14% (1,024.61 km²). The quantitative analysis of the zonal areas is summarized in Table 2 . The results indicate that most of Abuja land surface, amounting to approximately 5,736.20 km² (79%), falls under medium groundwater potential. High potential areas are predominantly limited, occupying 518.41 km² (7%) of the total area. Low potential areas occupy 1,024.61 km² (14%). The spatial analysis revealed that the high potential zones were primarily linked to regions with mild slopes, reduced drainage densities, and particular geological formations that support groundwater storage. The majority class, represented by the medium potential zone, suggests that groundwater development is generally somewhat feasible throughout the region. Table 2 Areal extent and percentage of groundwater potential zones in Abuja. No. Groundwater Potential Zone Area (km²) Area (%) 1 Low 1,024.61 14% 2 Medium 5,736.20 79% 3 High 518.41 7% Total - 7,279.22 100% 4. Discussion High groundwater potential (79%) in Abuja is typical for the hydrogeological conditions of Basement Complex terrains, wherein aquifer productivity is largely dictated by secondary porosity from fractures and weathering (MacDonald et al., 2012 ). Relatively small areas of high productivity (7%) indicate the spatial resolution typical for these second-order features. Lastly, high groundwater potential areas have close agreement with low relief and low drainage density areas, thus supporting the beneficial role of subdued slopes and low runoff in groundwater recharge (Jha et al., 2010 ). However, the approach maintains certain deficiencies in its methodology. Trivariate weighted overlay method concludes subsurface conditions primarily from surface proxies, thus never experiencing direct verification of aquifer depth and continuity, for example. While geometric and slope serve as reliable proxy variables, verification in the field using borehole information or geophysical traverses is essential for more accurate characterization of subsurface components. Lastly, the process of assigning weights involves an element of expert subjectivity, which may in the future be minimized in subsequent studies using methods like the Analytic Hierarchy Process (AHP) in favor of more objectivity. In spite of these liabilities, the analysis provides a low-cost, spatially bounded first-level groundwater potential evaluation, valuable in data-scarce environments. Synthesis of remote sensing and the GIS allows for a replicable model for groundwater regional analysis in other parts of West Africa, where similar hydrogeologic and data deficiencies predominate. 5. Conclusion The research utilized a GIS-assisted weighted overlay method in the mapping of suitable groundwater locations in Abuja, Nigeria. By integrating seven key thematic layers, namely, drainage density, geology, slope, land use/land cover, soil, rainfall, and elevation, an efficient spatially inclusive groundwater zonation map was produced. The result showed that roughly 79% of the study area is characterized by medium groundwater potential, 7% is of high potential, and 14% is low potential regions. From these findings, it is evident that, despite the fact that groundwater is developable in the bulk of Abuja, locations of high potential should be satisfactorily targeted for efficient resource exploitation. Generally, the paper gives scientific justification for sustainable water management and urban water planning in the Federal Capital Territory. The groundwater potential map thus produced is used as decision support for water resource managers, policymakers, and planners who deal with the issues of fast urbanization and rising water demand. Ground-truth validation should be the focus in future studies using borehole logs, time-series monitoring, and the results of pumping test in order to characterize groundwater dynamics in response to changing climatic and land use conditions. Declarations Funding “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.” Competing Interests “The authors have no relevant financial or non-financial interests to disclose.” Author Contributions “All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by David Mkpanam Nyong , Haulah Habeeb Muhammad, and Adaobi Thelma OnyemaobiThe first draft of the manuscript was written by David Mkpanam Nyong and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.” Acknowledgments The authors gratefully acknowledge the United States Geological Survey (USGS), the Food and Agriculture Organization (FAO), and the Climate Research Unit (CRU) for providing the open-access data essential for this research. We also thank the anonymous reviewers for their insightful comments and suggestions, which greatly improved the quality of this manuscript. Ethics approval : “ Not applicable”. Consent to participate : “ Not applicable”. Consent for publication : “ Not applicable”. Availability of data and materials The datasets used in this study are publicly available from the United States Geological Survey (USGS), the Food and Agriculture Organization (FAO), and the Climate Research Unit (CRU). Processed data supporting the findings of this study are available from the corresponding author on reasonable request. References Adelana SMA, Olasehinde PI, Vrbka P (2008) A quantitative estimation of groundwater recharge in part of the Sokoto Basin, Nigeria. J Environ Hydrology 16(4):1–16 Food and Agriculture Organization (FAO) (2015) World Reference Base for Soil Resources 2014, update 2015. International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106. FAO, Rome Jha MK, Chowdary VM, Chowdhury A (2010) Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques. Hydrogeol J 18(7):1713–1728. https://doi.org/10.1007/s10040-010-0631-z MacDonald AM, Bonsor HC, Dochartaigh Ó, B. É., Taylor RG (2012) Quantitative maps of groundwater resources in Africa. Environ Res Lett 7(2):024009. https://doi.org/10.1088/1748-9326/7/2/024009 Murthy KSR (2000) Groundwater potential in a semi-arid region of Andhra Pradesh – a geographical information system approach. Int J Remote Sens 21(9):1867–1884. https://doi.org/10.1080/014311600209788 Offodile ME (2002) Groundwater study and development in Nigeria. Mecon Geology and Engineering Services Oikonomidis D, Dimogianni S, Kazakis N, Voudouris K (2015) A GIS/Remote Sensing-based methodology for groundwater potentiality assessment in Tirnavos area, Greece. J Hydrol 525:197–208. https://doi.org/10.1016/j.jhydrol.2015.03.056 Todd DK, Mays LW (2005) Groundwater Hydrology, 3rd edn. Wiley USGS EarthExplorer (2023) EarthExplorer . U.S. Geological Survey. https://earthexplorer.usgs.gov/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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05:12:57\",\"extension\":\"jpeg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":269415,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGroundwater Potential Map of Abuja showing the spatial distribution of Low, Medium, and High potential zones.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7782208/v1/ebdbfbec7799674a89543c38.jpeg\"},{\"id\":95655791,\"identity\":\"79c343fb-a2c9-4c30-890f-280ab748a3d2\",\"added_by\":\"auto\",\"created_at\":\"2025-11-11 16:16:57\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2060284,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7782208/v1/43054ace-97d7-4d4d-bead-0685c21bc3e8.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Geospatial assessment of groundwater potential zones in Abuja, Nigeria, using GIS-based weighted overlay analysis\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cdiv id=\\\"Sec2\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1.1. Background and Review\\u003c/h2\\u003e\\u003cp\\u003eThe availability of sustainable water sources is a major challenge for rapidly growing urban centers in the Global South. In Abuja, Nigeria's capital city, the uncontrolled growth in population and urbanization has put great pressure on available water sources, thus making the sustainable management of groundwater an important priority (Adelana et al., 2006). Groundwater also tends to represent a more reliable and sustainable water supply than surface water due primarily to its lesser sensitivity to seasonal changes and pollutions. Nevertheless, the effective development of groundwater relies on a thorough understanding of its spatial distribution in order to avoid over-extraction and to correctly target investments in water infrastructure. In the event of no such understanding, uncontrolled extraction might bring about the depletion of aquifers, land subsidence, and long-term environmental degradation. In this light, a scientific characterization of groundwater distribution is paramount for sustainably utilizing and managing practices.\\u003c/p\\u003e\\u003cp\\u003eTraditionally, groundwater exploration has greatly depended on classical hydrogeological field surveys and drilling programs that have tended to be coupled with significant time commitments, costly outlays, and limitations in spatial coverages. Nevertheless, RS and GIS technologies have transformed groundwater research by providing the possibility for integrating and spatially interpreting multiple surface and subsurface variables that govern groundwater recharge and storage (Jha et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). By employing such technologies, multi-criteria decision analysis (MCDA) processes\\u0026mdash;such as the weighted overlay technique\\u0026mdash;enable the overall inclusion of vital parameters such as geology, topography, precipitation, and land use/land cover in comprehensive groundwater potential maps. This methodology has been systematically applied across a vast array of climatic and geological conditions and has emerged as effective and cost-effective for initial groundwater assessments (Murthy, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe overlay weighting technique, which is considered as one of the most used approaches within Multi-Criteria Decision Analysis (MCDA), has been successfully utilized within different hydrogeological environments around the world. For example, Murthy (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e) explained its application within semi-arid regions of India, highlighting the contribution of geological structures and drainage intensity in the identification of groundwater availability. Similarly, Oikonomidis et al. (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) applied this method within Greece, again proving its success within the context of the Mediterranean climate. The underlying principle of the technique involves the allocation of weightings for each thematic map according to its corresponding contribution towards groundwater recharge and storage, a process that is strongly founded on conventional hydrogeological principles (Todd \\u0026amp; Mays, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe application of GIS-based multi-criteria analysis is extremely valuable on Basement Complex terrains, which dominate vast swaths of Africa, as well as Nigeria. These Precambrian rocks have naturally weak primary porosity, and groundwater occurrence is almost entirely reliant on secondary porosity features that manifest due to fracturing and weathering activity. MacDonald et al. (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e) carried out a continent-scale examination of African groundwater stores, making reference to the normally weak-to-moderate yields that dominate these aquifers and once again making reference to the requirement for borehole site selection with careful consideration. Some studies carried out in Nigeria have utilized GIS and remote sensing approaches for groundwater mapping in similar environments. For example, studies that were conducted in the Sokoto Basin and the crystalline basement terrains have reported a significant linkage between lineament density, soil permittivity, and borehole yield (Offodile, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e). While there are global-scale hydrogeological assessments for Nigeria, spatially confined high-resolution appraisals are scarce, especially for the Federal Capital Territory (FCT) of Abuja. Earlier studies have mostly covered larger regional settings or other Nigerian states. Thus, there is a critical demand for a precise, site-oriented evaluation for the purposes of Abuja's urban water resource planning. In response, this study designs an exhaustive groundwater potential map specific to the city's specific geological and climatic features.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1.3. Novelty and Objectives\\u003c/h2\\u003e\\u003cp\\u003eThis research addresses an important knowledge gap by conducting a detailed Geospatial-based groundwater potential assessment for Abuja, Nigeria, through the application of GIS and Remote sensing technology. This research is new because it combines detailed satellite images, especially from Sentinel-2, to classify land use and cover. It also uses a structured method to analyze these images, tailored specifically for the geology of the Basement Complex area in Abuja.\\u003c/p\\u003e\\u003cp\\u003eThe specific objectives of the study are to:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eDevelop a groundwater potential map of Abuja by integrating thematic layers including geology, slope, drainage density, land use/land cover, soil type, and rainfall.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eDelineate the study area into zones of low, moderate, and high groundwater potential.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eQuantify the spatial extent of each potential zone and provide evidence-based recommendations for sustainable groundwater management and urban planning.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003eThrough these objectives, the study provides a valuable decision-support tool for urban planners, hydrogeologists, and water resource managers, contributing to the scientific foundation for the sustainable development and management of groundwater resources in Abuja\\u0026rsquo;s rapidly urbanizing environment.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"2. Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1 Study Area Description\\u003c/h2\\u003e\\u003cp\\u003eAbuja the Federal Capital Territory (FCT), Nigeria is situated in the central part of Nigeria with coordinates 6\\u0026deg;45'E to 7\\u0026deg;45'E and 8\\u0026deg;25'N to 9\\u0026deg;25'N, The study area falls within the Guinea Savanna zone while most of its subsoil formations are underlain by Precambrian Basement Complex rocks which are characterized by having limited primary porosit. Aquifer systems are therefore mainly secondary, occurring within weathered regolith and fractured bedrock. Identification of groundwater potential in this geological context is relevant due to the fast-paced urbanization and increasing need for water in the region. Figure\\u0026nbsp;1provides a detailed study area map (SAM) of Abuja, Nigeria.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2. Data Sources and Preparation\\u003c/h2\\u003e\\u003cp\\u003eThe study employed a multi-thematic layer approach, integrating spatial datasets obtained from publicly available global sources. All datasets were standardized to the World Geodetic System 1984 (WGS 84), UTM Zone 32N, ensuring consistent spatial reference and compatibility during analysis. The six key thematic layers utilized are presented schematically in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.2.1. Digital Elevation Model (DEM) and Derived Layers\\u003c/h2\\u003e\\u003cp\\u003eA 30-meter resolution Digital Elevation Model (DEM) was obtained from the United States Geological Survey (USGS) EarthExplorer platform. Two critical hydrological parameters were derived using ArcGIS Spatial Analyst tools:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eSlope: Generated from the DEM to quantify the gradient of the terrain, which influences surface runoff and groundwater infiltration.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eDrainage Density: Computed using the \\u003cem\\u003eLine Density\\u003c/em\\u003e tool applied to a rasterized stream network extracted from the DEM. Drainage density reflects the degree of surface drainage dissection and inversely relates to infiltration potential.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.2.2 Thematic Data Layers\\u003c/h2\\u003e\\u003cp\\u003eThe additional thematic datasets employed in this study include:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eGeology: Sourced from the USGS World Geology Database, providing information on lithological formations and their associated hydrogeological properties.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eLand Use/Land Cover (LULC): Derived from Sentinel-2 imagery available through the Esri online platform. The LULC was classified into four categories: \\u003cem\\u003eBuilt-up\\u003c/em\\u003e, \\u003cem\\u003eVegetation\\u003c/em\\u003e, \\u003cem\\u003eWater bodies\\u003c/em\\u003e, and \\u003cem\\u003eBarren land\\u003c/em\\u003e.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eSoil Type: Obtained from the Food and Agriculture Organization (FAO) global soil database, which includes data on soil texture, permeability, and infiltration capacity.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eRainfall: Long-term mean annual precipitation data were extracted from the Climate Research Unit (CRU) database in point vector format, representing spatial rainfall variability across the FCT.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3. Methodology: GIS-Based Weighted Overlay Analysis\\u003c/h2\\u003e\\u003cp\\u003eThe main analytical technique applied in the study was the Weighted Overlay Analysis, which integrates multiple environmental and geological factors to delineate groundwater potential zones.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.3.1 Data Reclassification\\u003c/h2\\u003e\\u003cp\\u003eThe entire thematic layers were reclassified to a unified scale of 1 to 4, in which 1 stands for least and 4 for the most suitable conditions for groundwater occurrence. Permeability potential was used for reclassification of the LULC map, assigning higher ratings to Water bodies and Vegetation owing to their infiltration nature. The IDW method was also used to interpolate the point rainfall data in preparation for the creation of a continuous raster surface, which was classified in terms of Low, Medium, High, and Very High rainfall regions. Similarly, slope, drainage density, soil, and geology layers have been reclassified in terms of their Corresponding hydrogeologic significance.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.3.2. Assigned weights for groundwater potential factors.\\u003c/h2\\u003e\\u003cp\\u003eThe relative importance of the contribution of each factor in controlling groundwater occurrence was determined from established hydrogeological principles. The weights assigned, which add up to 100%, are listed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eAssigned weights for groundwater potential factors.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo.\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eThematic Layer\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eRationale\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eAssigned Weight (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGeology\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eDetermines subsurface storage and flow characteristics.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e25%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDrainage Density\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHigh density reduces infiltration; low density promotes it.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e20%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSlope\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eSteeper slopes increase runoff, reducing infiltration.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e15%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLand Use/Land Cover\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eImpacts surface sealing and natural recharge.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e15%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSoil Type\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eControls infiltration rate into the subsurface.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e10%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eRainfall\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eThe primary source of groundwater recharge.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e10%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDigital Elevation Model (DEM)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eBase for deriving slope and drainage.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e100%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.3.2 Overlay Analysis and Zoning\\u003c/h2\\u003e\\u003cp\\u003eThe reclassified raster layers were all merged using the Weighted Sum Tool in ArcGIS 10.8. Each of the thematic layers was initially multiplied by its own weight, which is an expression of its relative significance, prior to summation, therefore yielding a continuous Groundwater Potential Index (GPI). Thereafter, the resulting raster was grouped into three groundwater potential zones, that is, Low, Medium, and High, utilizing the Natural Breaks (Jenks) classification scheme. Area of each class was calculated in its entirety for the examination of its distribution within the study FCT. The final groundwater potential map is depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e (Results section).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003eThe weighted overlay analysis produced a detailed groundwater potential map for the Federal Capital Territory of Abuja (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). The delineated study area was clearly segmented into Low, Medium, and High groundwater potential zones. The quantitative assessment indicated that medium potential zones prevail, encompassing roughly 79% (5,736.2 km\\u0026sup2;) of the entire region. High potential zones comprise approximately 7% (518.41 km\\u0026sup2;), mainly situated in areas characterized by gentle slope gradients and diminished drainage density, whereas low potential zones represent the remaining 14% (1,024.61 km\\u0026sup2;).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe quantitative analysis of the zonal areas is summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. The results indicate that most of Abuja land surface, amounting to approximately 5,736.20 km\\u0026sup2; (79%), falls under medium groundwater potential. High potential areas are predominantly limited, occupying 518.41 km\\u0026sup2; (7%) of the total area. Low potential areas occupy 1,024.61 km\\u0026sup2; (14%).\\u003c/p\\u003e\\u003cp\\u003eThe spatial analysis revealed that the high potential zones were primarily linked to regions with mild slopes, reduced drainage densities, and particular geological formations that support groundwater storage. The majority class, represented by the medium potential zone, suggests that groundwater development is generally somewhat feasible throughout the region.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eAreal extent and percentage of groundwater potential zones in Abuja.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo.\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGroundwater Potential Zone\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eArea (km\\u0026sup2;)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eArea (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLow\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1,024.61\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMedium\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5,736.20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e79%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHigh\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e518.41\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7,279.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e100%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eHigh groundwater potential (79%) in Abuja is typical for the hydrogeological conditions of Basement Complex terrains, wherein aquifer productivity is largely dictated by secondary porosity from fractures and weathering (MacDonald et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). Relatively small areas of high productivity (7%) indicate the spatial resolution typical for these second-order features. Lastly, high groundwater potential areas have close agreement with low relief and low drainage density areas, thus supporting the beneficial role of subdued slopes and low runoff in groundwater recharge (Jha et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eHowever, the approach maintains certain deficiencies in its methodology. Trivariate weighted overlay method concludes subsurface conditions primarily from surface proxies, thus never experiencing direct verification of aquifer depth and continuity, for example. While geometric and slope serve as reliable proxy variables, verification in the field using borehole information or geophysical traverses is essential for more accurate characterization of subsurface components. Lastly, the process of assigning weights involves an element of expert subjectivity, which may in the future be minimized in subsequent studies using methods like the Analytic Hierarchy Process (AHP) in favor of more objectivity.\\u003c/p\\u003e\\u003cp\\u003eIn spite of these liabilities, the analysis provides a low-cost, spatially bounded first-level groundwater potential evaluation, valuable in data-scarce environments. Synthesis of remote sensing and the GIS allows for a replicable model for groundwater regional analysis in other parts of West Africa, where similar hydrogeologic and data deficiencies predominate.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThe research utilized a GIS-assisted weighted overlay method in the mapping of suitable groundwater locations in Abuja, Nigeria. By integrating seven key thematic layers, namely, drainage density, geology, slope, land use/land cover, soil, rainfall, and elevation, an efficient spatially inclusive groundwater zonation map was produced.\\u003c/p\\u003e\\u003cp\\u003eThe result showed that roughly 79% of the study area is characterized by medium groundwater potential, 7% is of high potential, and 14% is low potential regions. From these findings, it is evident that, despite the fact that groundwater is developable in the bulk of Abuja, locations of high potential should be satisfactorily targeted for efficient resource exploitation. Generally, the paper gives scientific justification for sustainable water management and urban water planning in the Federal Capital Territory. The groundwater potential map thus produced is used as decision support for water resource managers, policymakers, and planners who deal with the issues of fast urbanization and rising water demand. Ground-truth validation should be the focus in future studies using borehole logs, time-series monitoring, and the results of pumping test in order to characterize groundwater dynamics in response to changing climatic and land use conditions.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e“The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.”\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e“The authors have no relevant financial or non-financial interests to disclose.”\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e“All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by\\u0026nbsp;\\u003c/em\\u003e\\u003cstrong\\u003e\\u003cem\\u003eDavid Mkpanam Nyong\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u003cem\\u003e,\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cem\\u003e\\u0026nbsp;Haulah Habeeb Muhammad, and Adaobi Thelma OnyemaobiThe first draft of the manuscript was written by \\u003cstrong\\u003eDavid Mkpanam Nyong\\u003c/strong\\u003e and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.”\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors gratefully acknowledge the United States Geological Survey (USGS), the Food and Agriculture Organization (FAO), and the Climate Research Unit (CRU) for providing the open-access data essential for this research. We also thank the anonymous reviewers for their insightful comments and suggestions, which greatly improved the quality of this manuscript.\\u003c/p\\u003e\\n\\n\\u003cp\\u003eEthics approval\\u003cstrong\\u003e: \\u003cem\\u003e“\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cem\\u003eNot applicable”.\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConsent to participate\\u003cstrong\\u003e: \\u003cem\\u003e“\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cem\\u003eNot applicable”.\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for publication\\u003cstrong\\u003e: \\u003cem\\u003e“\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cem\\u003eNot applicable”.\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used in this study are publicly available from the United States Geological Survey (USGS), the Food and Agriculture Organization (FAO), and the Climate Research Unit (CRU). Processed data supporting the findings of this study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAdelana SMA, Olasehinde PI, Vrbka P (2008) A quantitative estimation of groundwater recharge in part of the Sokoto Basin, Nigeria. J Environ Hydrology 16(4):1\\u0026ndash;16\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFood and Agriculture Organization (FAO) (2015) World Reference Base for Soil Resources 2014, update 2015. International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106. FAO, Rome\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eJha MK, Chowdary VM, Chowdhury A (2010) Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques. Hydrogeol J 18(7):1713\\u0026ndash;1728. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s10040-010-0631-z\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s10040-010-0631-z\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMacDonald AM, Bonsor HC, Dochartaigh \\u0026Oacute;, B. \\u0026Eacute;., Taylor RG (2012) Quantitative maps of groundwater resources in Africa. Environ Res Lett 7(2):024009. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1088/1748-9326/7/2/024009\\u003c/span\\u003e\\u003cspan address=\\\"10.1088/1748-9326/7/2/024009\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMurthy KSR (2000) Groundwater potential in a semi-arid region of Andhra Pradesh \\u0026ndash; a geographical information system approach. Int J Remote Sens 21(9):1867\\u0026ndash;1884. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/014311600209788\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/014311600209788\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eOffodile ME (2002) Groundwater study and development in Nigeria. Mecon Geology and Engineering Services\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eOikonomidis D, Dimogianni S, Kazakis N, Voudouris K (2015) A GIS/Remote Sensing-based methodology for groundwater potentiality assessment in Tirnavos area, Greece. J Hydrol 525:197\\u0026ndash;208. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.jhydrol.2015.03.056\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.jhydrol.2015.03.056\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTodd DK, Mays LW (2005) Groundwater Hydrology, 3rd edn. Wiley\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eUSGS EarthExplorer (2023) \\u003cem\\u003eEarthExplorer\\u003c/em\\u003e. U.S. Geological Survey. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://earthexplorer.usgs.gov/\\u003c/span\\u003e\\u003cspan address=\\\"https://earthexplorer.usgs.gov/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Groundwater Potential, Geographic Information System (GIS), Weighted Overlay Analysis, Abuja, Nigeria, Water Resource Management, Multi-Criteria Decision Analysis (MCDA)\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7782208/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7782208/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eSustainable development of groundwater is vital in meeting the increasing water demand in rapidly growing and rapidly urbanizing areas like Abuja, Nigeria. This study aimed at mapping and classifying areas with varying groundwater potential within Abuja for the purpose of strategic groundwater development planning. Using Geospatial approaches, namely a weighted overlay technique, thematic maps for geology, elevation, land use/land cover, slope, drainage density, and precipitation were integrated based on their respective influences on groundwater availability. Each of the variables was given a weight depending on its importance in groundwater availability mapping. The final map delineated groundwater potential into three classes, namely, low, moderate, and high. The results of the study show that about 79% (5,736.2 km\\u0026sup2;) of Abuja has moderate groundwater potential, 7% (518.41 km\\u0026sup2;) has high potential, and 14% (1,024.61 km\\u0026sup2;) has low potential. The study concludes that although groundwater development is feasible for the vast area, there is a need for a focused approach for the high-potential area for the optimal use of this resource. These results provide critical guidance for policymakers and water resource managers for the purposes of facilitating sustainably managing groundwater in the scenario of the ongoing urbanization of Abuja.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Geospatial assessment of groundwater potential zones in Abuja, Nigeria, using GIS-based weighted overlay analysis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-07 05:12:52\",\"doi\":\"10.21203/rs.3.rs-7782208/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"54cb9c5c-9dc9-4c9c-9068-760dcd4b6a49\",\"owner\":[],\"postedDate\":\"October 7th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-11T02:23:42+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-07 05:12:52\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7782208\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7782208\",\"identity\":\"rs-7782208\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}