Groundwater Potential Mapping Using GIS,remote Sensing and AHP: A Case Study of Kitgum and Pader Districts, Acholi Sub-Region, Uganda

Groundwater Potential Mapping Using GIS,remote Sensing and AHP: A Case Study of Kitgum and Pader Districts, Acholi Sub-Region, Uganda | 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 Research Article Groundwater Potential Mapping Using GIS,remote Sensing and AHP: A Case Study of Kitgum and Pader Districts, Acholi Sub-Region, Uganda Ahmed Abdirizak Dirie, Abdulkadir Ahmed Mohamed, Ambiga Kannapiran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9232280/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Groundwater is a critical freshwater resource in semi-arid regions, particularly in water-stressed areas northern Uganda where surface water is scarce and unreliable. This study employed a geospatial approach integrated with multi-criteria decision analysis to identify groundwater potential zones in Kitgum and Pader districts of the Acholi subregion, Uganda. Utilizing Geographic Information Systems (GIS), remote sensing, and the Analytic Hierarchy Process (AHP), six thematic layers controlling groundwater occurrence rainfall, drainage density, slope, soil type, geology, and land use/land cover were developed, standardized, and assigned normalized weights based on their hydrogeological significance. A weighted overlay analysis in ArcGIS was applied to synthesize these layers into a comprehensive groundwater potential zonation map. The result catagorised the study area into five zones: very low, low, medium, high, and very high potential. The analysis revealed that Kitgum District is predominantly characterized by very low to low groundwater potential zones 67.24% when it is combined, constrained by factors such as higher drainage density, and steeper slopes. Conversely, Pader District sgowed favorable conditions, with 85.5% of its area classified as high to very high groundwater potential, attributed to gentle slopes,and higher rainfall, that enhance infiltration capacity. Validation against yield data from 18 existing boreholes demonstrated a 61.1% alignment between the model and actual well performance, confirming the model's reliability. This study demonstrates the effectiveness of GIS-based and AHP as a cost-effective tool for preliminary groundwater exploration in data-scarce environments. The resulting groundwater potential map provides a vital scientific foundation for sustainable water resource management, enabling targeted borehole siting, informed aquifer development, and resilience-building strategies to enhance water security for communities and livelihoods in the Acholi sub-region. Analytic Hierarchy Process (AHP) Geographic Information Systems (GIS) Groundwater Potential Mapping Acholi Water Resource Management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Groundwater is a vital component, serving as the largest reservoir of liquid freshwater on the earth. It makes up roughly 30% of the Earth's total freshwater resources, while freshwater, itself only constitute 2.5% of all global water when the immense amount of ocean water are accounted for. [ 1 – 3 ]. All These factors position groundwater as an essential resource, particularly as the surface water sources are increasingly exploited, and faces contaminations from pollutants and climate change. This critical situation exacerbated the increasing demand of water, due to population growth and developmental pressures. [ 4 – 6 ] This freshwater resource is indispensable for maintaining ecosystem functions, supporting agriculture and industrial activities, and supplying domestic water[ 7 ]. It is particularly significant in arid and semi-arid regions where surface water is scarce. In these regions, groundwater serves as the primary source of water, making it vital for both economic and social development[ 4 ] Additionally, plays vital in reducing flood risks through infiltrating large amount of water into subsurface.[ 8 ] . Despite its importance, groundwater is vulnerable to various threats, including over-extraction, pollution, and the impacts of climate change, which can affect the groundwater quality and it’s availability. Sustainable management practices are essential for protecting groundwater, especially as climate change alters precipitation patterns and increases the frequency of extreme hydrological events [ 6 , 9 ]. Therefore, understanding the dynamics of groundwater systems and implementing effective management and adaptation strategies are crucial for ensuring the sustainable use of this critical resource[ 10 ]. The result of overexploitation to this valuable resource, water scarcity became a significant global issue impacting billions of people[ 11 ]. It resulted from a combination of various factors, including increased water use due to population growth, urbanization, and inadequate management of water resources. Then climate change further exacerbated the problem through a higher temperatures[ 12 , 13 ]. The an emerging concern is the uncertainty in water scarcity projections due to variations among climate models and socioeconomic pathways, which complicates effective policy-making and management responses [ 14 , 15 ] .Current models predicted that global warming of 2°C above present temperatures could substantially increase, highlighting the critical need for more accurate managements and strategic interventions [ 16 , 17 ]. During the past decades, global human water use has doubled and poses an additional threat, yet available freshwater resources are finite[ 18 ]. As a result, water scarcity has been prevalent in various regions of the world[ 19 ] .Changing hydro-climatic and socioeconomic conditions have aggravated water scarcity over the past decades [ 20 ]. A wide range of studies show that water scarcity will intensify in the future, as a result of both increased consumptive water use and, in some regions, due to climate chang[ 13 ]. A little attention has yet been paid to the impacts of climate variability on water scarcity conditions, despite its importance for adaptation planning[ 21 ]. Many studies indicated that low-income, non-industrialized nations with tropical or subtropical climates are more vulnerable to the negative impacts of the climate change [ 22 ]. Especially ,tropical regions are anticipated to be vulnerable due to institutional, financial, technological, and constraints, such as many East-African countries.[ 23 ]. For example, during the period of 2010–2020, Uganda experienced the most devastating droughts in history with massive impacts to over seven million people [ 24 ]. While, ranked as 15th on vulnerability and 147th on readiness, meaning the country is very vulnerable, sadly, unprepared to respond to climate change impacts[ 25 ]. In this type of situation, the key advantage of groundwater is its inherent resilience. Because it is stored in vast underground aquifers, providing a stable water supply that can buffer communities against the short-term shocks of climate variability or drought[ 26 ]. Recently, there has been a global increase in attempts to identify groundwater potential zones [ 27 ]. This attempts of delineating groundwater potential zones became an essential approach to tackle water-related issues globally[ 28 ]. According to Uganda Bureau of Statistics (UBOS) in 2024, more than 29% of the people in the Acholi subregion lack of access to an improved drinking water source (Kagurusi et al., 2025). Additionally, a study conducted in 2022 by Dalson Twecan showed that less than 20% of the farmers in Acholi subregion uses irrigation for agriculture, while other remaining percentage rely on precipitation which is not constant, particularly in the dry season. Poverty, a lack of awareness of sophisticated climate change adaptation strategies, the scarcity of irrigation technicians, and illiteracy could all be factors contributing to farmers poor irrigation usage[ 29 ]. The most severe drought recorded in Acholi sub-region in northern Uganda in the past few years severely damaged crops and cattle and had a detrimental effect on agriculture sectors[ 25 ]. These circumstances highlights the need of groundwater potential zones delineation and sustainable management techniques that are both spatially explicit and scientifically sound[ 30 ]. The integration of remote sensing and GIS techniques offered an effective means of identifying zones that are favorable for groundwater occurrence[ 30 ]. In the context of GWRZ delineation, the analytical hierarchy process (AHP) is utilized as a decision-making tool and offers a methodical framework for ranking criteria according to relative relevance. Researchers evaluate the importance of one criterion over another by using pairwise comparisons, which produces a more impartial and reliable weighting system[ 28 ]. The groundwater potential zones of Kitgum and Pedar, two districts in the Acholi area, were identified in this study. The findings of this study have immediate implications for groundwater resource management and development, in addition to socioeconomic development. In the long run, this research contributes to the fulfilment of national goals for economic growth, water security, and poverty alleviation, which benefits both local communities and more extensive regional development projects. Geospatial technology, which is noninvasive, was e used in this investigation. To achieve insightful the result of this study, we set three specific objectives, which eventually contribute to get a solution for our main objectives of Mapping Groundwater potential zones in Kitgum and Pedar Districts in Acholi subregion . To generate thematic maps using GIS (ArcGIS pro) To determine and assign normalized weights to all thematic layer To delineate ground-water potential zone 2. Materials and Methods 2.1. Description of the study area This study was carried out in the Northeastern Ugandan districts of Kitgum an Pedar, which is part of the Acholi subregion where violent wars experienced. notably, the 20-year (1986–2006) civil war between the Lord's Resistance Army (LRA) and the Uganda People's Defense Force (UPDF) had a significant impact on northern Uganda, particularly the Acholi sub-region. As a result of being forced into internally displaced persons (IDPs) concentration camps, the conflict had an impact on social, political, and economic service delivery and structures[ 31 ].the coverage area of Kitgum district is roughly about 4142.5km 2 and 3292.99km 2 for Pedar, which make the total area of this study approximately 7435.5. The rainy season in Acholi subregion lasts from March to October, typically May and August receive the highest precipitation. The dry season, on the other hand, can be harsh and lasts from late November through February. This region generally gets low temperatures of 18°C and maximum temperatures of 30°C[ 32 ]. The study area’s geography is a flat plateau with fertile arable soil suited for farming and wooded savannah flora. Due to their resistance to drought, the primary crops planted in the area include sorghum, finger millet, sesame, cotton, groundnuts, and cassava[ 25 ]. 2.2 Datasets and software Six thematic maps were selected for this study, in order to zonate groundwater potential zones, which are drainage density, geological map, soil map, Land use Land cover (LULC), rainfall map which was generated from rainfall data From UNMA (Uganda National Metrological Authority) and lastly slope map. Table 1 lists all the data and their sources GIS, particularly ArcGIS software, played a critical role in delineating groundwater potential zones[ 33 , 34 ]. In this study we utilized this powerful software and AHP to delineate groundwater potential zones in Kitgum and Pedar Districts in Acholi subregion. all data which are used in this study are presented in Table 1 Table 1 Data and their sources Factors Resolution Data Type Data Source Notes SRTM DEM 30 m Raster https://earthexplorer.usgs.gov Digital Elevation Model, source of elevation data Slope 30 m Raster Derived from SRTM DEM Calculated using DEM Drainage density 30 m Raster Derived from SRTM DEM Calculated using DEM Soil 30 m Raster Food and Agricultural Organization (FAO) https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/ Soil type classification Rainfall …… Excell UNMA Rainfall data Geological ……. PDF (Georeferenced) Uganda Directorate of Geological Survey and Mines. https://dgsm.go.ug/ Rock type classification Land use/land cover 30 m Raster Landsat 9 https://earthexplorer.usgs.gov/ Classification of land use and vegetation cover Groundwater yield – Excell Ministry of Water and Environment Location of groundwater wells, and yield capacity 2.3 The static and dynamic thematic maps Several thematic layers describing elements influencing groundwater occurrence were developed in order to identify groundwater potential zones. spatial alignment were also done, all layers were standardized by projecting them into the EPSG:4326 geographic coordinate system as supported by studies carried out by K. Joseph Pious [ 35 ]. Weights were assigned by using the AHP [ 36 , 37 ]. Pairwise comparisons were then done to determine the weights influencing groundwater potentiality, the factors or the thematic maps are categorized into static (e.g., lithology, slope) and dynamic (e.g., Land Use/Land Cover [LULC], rainfall) parameters to analyze.. [ 34 , 37 – 43 ] 2.3.1 Drainage Density A drainage density map, which shows the total length of all streams and rivers within a specific unit area mostly in kilometers as this study assigned, is an essential analytical tool in hydrogeological analyses[ 44 ]. It offers pivot information about the properties of surface runoff and the possibility of infiltration. A Digital Elevation Model (DEM) was downloaded from The USGS website and this DEM was imported Into Geographical Information System (GIS) software [ 37 , 45 ]. Digital Elevation Model (DEM) was processed using the Fill tool to fix data voids [ 1 ]. After determining each cell's steepest downhill path using the flow direction tool, cells with a high concentration of upstream drainage were identified using the flow accumulation tool[ 46 ]. Then the streams are generated and changed in to polyline [ 3 ]. These streams are then computed by the Line Density tool, which created a raster map that divides the terrain into zones with low to high drainage densities based on the total length of the stream within a certain search radius per unit area[ 41 ]. it is formally defined by a formula: \(\:\text{D}\text{d}=\frac{{\sum\:}_{i=1}^{i=n}\:Di}{A}\) km / km2 [ 40 ] Were Di = is the total recorded drainage length (km) Dd = is the drainage density and A = is an area in km2. 2.3.2 Slope Slope, significant factor in assessing groundwater potential, as it directly governs the balance between surface runoff and subsurface infiltration [ 47 ]. Using the 30-m-resolution SRTM DEM, the slope was calculated in this study. And the approach is supported by the earlier work of Edukondal et al [ 48 ]. In areas with steep slopes, gravitational forces cause the precipitation drains to rapidly run off over the surface, drastically reducing the time for a rain to infiltrate into the subsurface and finally recharge aquifers below [ 49 ]. Conversely, flat or gently sloping terrain allows precipitation drains to remain for longer durations, maximizing its opportunity to percolate into the ground[ 50 ]. slope maps inversely correlate with groundwater potential zones of low slope are typically assigned high weights and classified as highly promising for groundwater potential, while steep slope areas are indicative of poor groundwater potential. This makes slope a critical, often primary, thematic layer in any groundwater potential zonation model [ 51 ] .Slope can be generated using slope tool in the GIS and derived from the DEM [ 40 ]. 2.3.3 Land Use land Cover (LULC) The Land Use and Land Cover (LULC) map is a crucial for evaluating how surface conditions influence groundwater[ 52 ]. The LULC map are generally categorized into classes such as forest, agricultural land, urban areas, and water bodies. Each category directly affects infiltration. For example, forests promote high infiltration, while paved urban areas generate rapid runoff, limiting water percolation[ 53 ]. In this study ,land set 9 LULC was downloaded and then supervised in ArcGIS pro, which integrated in other thematic maps to build a comprehensive model as supported by existing literatures [ 54 ]. The combination of these layers allows for the identification of zones where favorable surface conditions (like vegetation cover) coincide with permeable soils pinpointing areas of high groundwater potential. 2.3.4 Soil Map The type of soil whether it sand, silt, and clay are determined by its texture, which is a major criterion or process for classifying it[ 16 ]. Sandy soils are great for groundwater recharge because of their high particle size and extensive pore spaces, which function as a sieve and let water flow through quickly and easily [ 55 ]. Clayey soils, on the other hand, are made up of extremely small, closely packed particles that retain water and swell when wet [ 37 ]. This results in a nearly impenetrable barrier that significantly delays infiltration and increases surface runoff rather than recharge[ 56 ]. The soil data used in this study were obtained from FAO Food and Agricultural Organization 2.3.5 Geological map the geological map was downloaded from the Department of Geological Survey and Mines (DGSM) of Uganda, as PDF form. after georeferencing the PDF, thus, the geological structure of the study are was clearly assessed .Which is a foundational layer for analyzing groundwater potential. This map is critically important as it reveals the rock type laying on the study area that controls groundwater movement and storage[ 57 ]. While the soil map informs us about the initial infiltration in the upper few meters[ 58 ]. geological map is used to understand the underlying rock[ 57 ]. By integrating this geological map with the soil map and other datasets like land use and slope, this study built a robust model to identify groundwater potential zones; for instance, an area with sandy soil overlying a fractured limestone aquifer is classified as a very high-potential zone. 2.3.6 Rainfall thematic map Creation This research used a rainfall information obtained from the Uganda National Meteorological Authority (UNMA) to quantify groundwater potential. The precipitation dataset provided essential details regarding both the spatial distribution and the temporal patterns of rainfall events, information which is directly used in the model to calculate the total volume of water that is available for infiltration into the subsurface. The meteorological station records from UNMA consist of accurate and reliable long-term measurements, thereby forming a highly trustworthy and authentic foundation for the construction of the hydrological model. In the absence of this essential input, the model would lack the ability to accurately distinguish between geographic areas that are highly storage to groundwater potential and those regions that are not. The integration of this authoritative UNMA dataset fundamentally ensures that the final map delineating groundwater potential is grounded in robust, credible, and verifiable climatic information. 2.4 Weighting of the factors influences GWP Several researchers have utilized the MCDA-AHP method to evaluate the weight of various influencing layers on groundwater potential [ 25 , 59 , 34 ]. Their ranking is dependent on their importance to groundwater and recharge occurrences and usually uses Saaty's scale of relative importance value .[ 62 ] which is shown in Table 2 .the numbers 1–9 indicate the degree to which one factor has advantage over another. The ultimate goal is to derive a reliable priority vector (weight) for each thematic map and their classes, ensuring that the final assessment accurately reflects the real-world influence of each parameter on groundwater systems. Table 2 Saaty's 1–9 scale of pairwise comparison Intensity of Importance Definition Explanation 1 Equal importance Two elements contribute equally to the objective. 3 Moderate importance Experience and judgment slightly favor one element over another. 5 Strong importance Experience and judgment strongly favor one element over another. 7 Very strong importance One element is favored very strongly over another; its dominance is demonstrated in practice. 9 Extreme importance The evidence favoring one element over another is of the highest possible order of affirmation. 2, 4, 6, 8 Intermediate values Used to express intermediate judgments between the main definitions (e.g., 2 is between "Equal" and "Moderate" importance). To check the consistency of pairwise comparison it is calculated using Eq. 1 \(\:\text{C}\text{o}\text{n}\text{s}\text{i}\text{s}\text{t}\text{e}\text{n}\text{c}\text{y}\:\text{R}\text{a}\text{t}\text{i}\text{o}\:\left(\text{C}\text{R}\right)=\:\frac{\text{C}\text{o}\text{n}\text{s}\text{i}\text{s}\text{t}\text{e}\text{n}\text{c}\text{y}\:\text{I}\text{n}\text{d}\text{e}\text{x}\left(\text{C}\text{I}\right)\:}{\text{R}\text{a}\text{n}\text{d}\text{o}\text{m}\:\text{I}\text{n}\text{d}\text{e}\text{x}\:\left(\text{R}\text{I}\right)}\) [ 63 ] $$\:\text{C}\text{o}\text{n}\text{s}\text{i}\text{s}\text{t}\text{e}\text{n}\text{c}\text{y}\:\text{I}\text{n}\text{d}\text{e}\text{x}\left(\text{C}\text{I}\right)\:\frac{{\lambda\:}\text{m}\text{a}\text{x}-\text{n}}{n-1}$$ If CR ≤ 0.1, the relative weights assigned were reasonable and consistent, which is suitable for decision making, according to Saaty's [ 61 ]. Reevaluating the relative weight assignment or updating the pairwise comparison is necessary if CR > 0.1, which indicates that the weight assigned was inappropriate and inconsistent[ 59 , 60 ]. λmax is the principal Eigen vector and RI- is the random index value. Groundwater potential zone (GWPZ) map of this study was generated by overlying all thematic layers using the Weighted Overlay Analysis tool in ArcGIS [ 64 ]. as defined by: $$\:GWPI{\sum\:}_{i=1}^{n}\left(Wi\times\:Ri\right)$$ where: Wi ​ is the normalized weight, Ri ​ is the reclassified rating, n is the total number of thematic layers. This flowchart below in Fig. 3 is illustrating the scientifically grounded of this study. 3. Results and Discussions 3.1 Thematic maps 3.1.1 Drainage Density Figure 3 , shows the drainage density map of the study area, which clearly demonstrates a spatial variance in the density of the stream networks per square kilometer. the values of the classes range from very low of 0.001 up to 0.119 km/km², showed in dark blue color, to very high of 0.479 up to 0.597 km/km², showed in yellow color on the map. The distribution reveals a heterogeneity across the two districts, which the moderate to high drainage densities dominating large part of the central and southern sections. These areas are characterized by closely spaced stream channels, suggesting well-developed surface runoff systems. In contrast, lower drainage densities are more prevalent along the peripheral zones, indicating relatively sparse channel development. Areas with high drainage density are likely to have impermeable underlying materials, which promote rapid surface runoff, and results decrease of water into subsurface[ 30 ]. drainage density locations, where it is low, associated with permeable soils, and reduces infiltration through minimizing surface flow. Hydrologically expressing, high drainage density zones are more prone to erosion and flash floods[ 35 ] with a relatively rapid hydrologic response to rainfall events[ 65 ], whereas low-density areas may indicate favorable conditions for groundwater [ 66 ], which means a poorly drained basin with a slow hydrologic response.[ 65 ] Table 3 The percentage and the area covered each class of the drainage density Class Class Interval Category Area (m²) Percentage (%) 1 0.001–0.119 Very Low 1646117511.3 22.08 2 0.12–0.239 Low 2449875307.2 32.88 3 0.24–0.358 Moderate 2067147749.06 27.74 4 0.359–0.478 High 1132921450.19 15.21 5 0.479–0.597 Very High 153809198.47 2.06 Total 7,449,871,216.22 100 Each category or classes area was divided by the total area of 7,449,871,216.22 m 2 , and the resulting percentages were then multiplied by 100. This indicates that the district's low drainage density encompasses the most area (32.88%), while the very high drainage density represents the smallest area with 2.06%. The major land of Kitgum and Peder district area has typically well-balanced drainage characteristics, low and moderate, which together make up more than 60.62% of the district's area, as shown in Table 3 . This spatial distribution presented in Fig. 3 provides valuable insights for land use planning and water resource management. The extensive low-density areas offer favorable conditions for agriculture and groundwater recharge, while the more limited high-density zones require targeted erosion control and flood management strategies[ 67 ]. The map serves as an essential tool for identifying priority areas for conservation measures and guiding sustainable development initiatives that account for the district's varied hydrological characteristics. 3.1.2 Slope map Slope is a key factor in regulating groundwater potentiality from a hydrological perspective[ 68 ]. The broad brown and green zones are showing the areas with gentle slopes that tend to promote water infiltration into the subsurface, improving groundwater recharge and storage. Conversely, regions with steeper slopes encourage rapid surface runoff, limiting the time available for water to percolate into the ground. Thereby reducing groundwater potentiality [ 69 ]. Therefore, the spatial distribution shown in this map suggests that the central and southwestern parts of the study area are more favorable for groundwater infiltration chances, while the steeper northeastern zones are less suitable due to increased runoff and reduced infiltration capacity. The behavior of the slope of these two districts is illustrated in the Table 4 table. Over 63% of the land is made up of gentle slopes of less or equal to 1.64 degree. This is very suitable for groundwater replenishment, and agriculture activity which some amount of the irrigated water lastly will be part of subsurface water. Conversely, just 2.63% of the entire area is made up of steep slopes. Although these areas of steep slopes are crucial to mountainous ecosystems also known as montane ecosystem, but they are not the favorable for infiltration. Figure 4 shows the slope map of the study area Table 4 The slope range, area and the percentage of the total area of each class Class Slope Range (%) Area(m 2 ) Percentage of Total Area 1 0.001–1.642 4714834094.9 63.78% 2 1.642–6.75 2326856508.8 31.49% 3 6.58–15.25 154666255.97 2.09% 4 15.26–24.63 127238524.63 1.72% 5 24.64–59.82 67003357.004 0.91% TOTAL 7,449,871,216.22 100.00% 3.1.3 Land Use land Cover (LULC) Vegetation covered areas and trees makes up a significant percentage of the landscape, which is a prominent domination of the area according to the land use and land cover map. This suggests that the area is either cultivated or mostly used for pastoral purposes, which natural grasslands or shrublands serving as a primary land use. The sizable trees area indicates the presence of woodlands, which are likely to be found along water channels, in higher elevation zones, or in protected regions, such as, Jaka forest reserve. These places provide important ecosystem services like soil stabilization and habitat for biodiversity. By comparison, there are much less by area landscapes that have been utilized by humans. The small amounts of constructed space indicate a less urbanization. The paved areas, as ramifications reduces amount of water infiltrates. The area's aridity is further highlighted by the sparse water cover, suggesting that surface water supplies are limited and that the region relies heavily on rainwater harvesting or groundwater supplies. Water bodies form a very small part of the area, according to the land cover map. They can be seen as a rare feature rather than a distinguishing characteristic because of how uncommon they are. An arid or semi-arid region where surface water supply is a crucial and perhaps limiting factor for both natural ecosystems and human activity is strongly affected by this acute shortage. There are significant ramifications to this near-absence of surface water. It implies that the majority of the region's demands are satisfied by groundwater resources or rainwater. The little patches of farmland and the enormous vegetation covered probably depend more on underground water supplies than on lakes or rivers. Figure 6 demonstrates the land use land cover of Kitgum and Pedar districts. According to the land use map, vegetation covers make up 66.46% of the entire 7,449,836,100 m² of the total area. Rangeland makes up 17.73%, while trees constitute 14.51%. Water bodies of 0.18% and built areas of 1.14%) cover insignificant proportions. The majority of the landscape is made up of natural land covers, such as trees, vegetations and rangeland, emphasizing the dominance of open green spaces over man-built or water bodies, the proportion of each class is shown in Table 5 . Table 5 the percentage of land use land cover of each class Class Land Cover Type Area (km²) Percentage of Total Area 1 Water 13753800 0.18% 2 Trees 1080747000 14.51% 4 Vegetation covers 4949316000 66.46% 5 Built Area 85023000 1.14% 6 Rangeland 1320996300 17.73% TOTAL 7,449,836,100 100% 3.1.4 Soil Map The soil categories in the study area reveals that Orthic Ferralsols (Fr) dominate the entire area of the two district, covering 4,641,247,890.3 m² or 62.3% of the study area. this type of soil has a potential yield (Yp) of 23 Mg/ha and a water-limited yield (Yw) of 11 Mg/ha, with its soil capability index (SCI) dropping to 30 under erosion and 20 under compaction due to factors such as shallow rooting[ 70 ]. Humic Gleysols (Gh) forms the second-largest category at 1,899,708,205.5 m² (24.5%), which is described as a poorly drained, fine-textured soil [ 71 ].While Lithosols account for 685,385,721.2 m² (9.2%( .The remaining 3% is Ferric Luvisols at just 223,495,083m², as shown in Fig. 7 .This distribution is critical for groundwater management, as the extensive coverage of Ferralsols suggests generally favorable conditions for infiltration, while the 25.50% coverage of Gleysols identifies important zones of water accumulation and potential surface-groundwater interaction. Table 6 shows the percentage of each type of soil and the area covers Table 6 the soil details of the study area Soil Code Soil Type Name Area (Km²) Percentage Fr Orthic Ferralsols 4,641,247,890.3 62.3% Gh Humic Gleysols 1,899,708,205.5 25.50% I Lithosols 685,385,721.2 9.2% Lf Ferric Luvisols 223,495,083 3% Total 7,449,836,900 100.00% 3.1.5 Geological Map The study area’s geology is a complex with a wide range of igneous and metamorphic rocks. This variety suggests an extended and active geological past that includes numerous instances of tectonic deformation, metamorphism, and magmatism. The six rock types shown in the map can be divided into two major groups: igneous and metamorphic rocks. The existence of an ancient basement complex that formed deep under the Earth's crust under extreme heat and pressure is suggested by the presence of high-grade metamorphic rocks. This metamorphic basement most likely forms the region's bedrock, affecting hydrology, soil formation, and also the topography. The geological framework of the region shaped by gniess and gnieses granitoids that constitute the predominant basement rock, covering 3153554842.19 m² and 3132729527.15m 2 , respectively .Representing 84.9% both of the total area. The metamorphic rock of charnockite forms a significant third unit, accounting for 463264656.6 m² or 6.22% of the area, as shown in Fig. 9 . The remaining geological composition is fragmented among minor units, where Amphibolit occupies 318,888,919.5 m² (4.28%), while Metagabbro and garanite formations are confined to just 25708065.7307m² which equivallant 0.34 $ and 353274934.266 m² (4.74%) respectively. Table 7 shows the percentage of each type of rocks and their area covers Table 7 Geological details of the study area Geological Material Coverage Area (m²) Percentage of Total Area Amphibiliots 318888919.49 4.28% Gneiss 3153554842.19 42.36% Gneissic granitiods 3132729527.15 42.06% Granite 353274934.266 4.74% Metagabbro 25708065.7307 0.35% Charnockite 463264656.612 6.21% Total 7,447,420,945.4387 100.00% 3.1.6 Rainfall Map The rainfall distribution of the study area is shown in Fig. 9 below. With rainfall measurements measured in millimeters. The values fall into five different classes, with the lowest 949 to 1042 mm and the highest 1285 to 1373 mm. There is a distinct gradient of increasing precipitation from the study area’s southwest to its northeast, rather than a constant fall of rain. This is more likely probable to increase the infiltration amount, as the southwest also have a gentle gradient than the northeast. As the slop map showed, it illustrated a higher elevations presence in the northeast, such hills or mountains, which condense the air and precipitates orographic rains. Since precipitation constitutes large amount of water that we have underground, it is essential to comprehend the rainfall distribution when delineating the ground water potential map[ 30 ]. Agriculture is directly impacted by the sharp contrast[ 25 ] between the wetter northeast and the dry southwest, which determines whether irrigation systems are required, which will be a good chance to infiltrate the water. as result, this map offers crucial data for district-level approaches to climate adaptation, natural resource management, and food security. 3.2 Weight Assignments of the thematic maps The study employed the Analytic Hierarchy Process (AHP), initiating the analysis by assigning ranks to of the selected thematic layers, as many previous studies utilized [ 72 ]. Subsequently, a pairwise comparison matrix was developed to model the groundwater influence regime in Table 5 . The consistency of this matrix was validated by calculating the consistency index and ratio. Following the normalized weights were derived and allocated to the thematic layers (Table 8 ). The final weights for the sub-features were computed by multiplying the average thematic layer weights (Table 9 ) by the normalized weights of their respective sub-features, following the standard AHP methodology. Table 8 Pairwise comparison matrix values of thematic layers 1) 2) 3) 4) 5) 6) Matrix Rainfall Soil Geology LULC D.Density Slope 1) Rainfall 1 4 1 2 2 2 2) Soil 1/4 1 1 2 4 1 3) Geology 1 1 1 1 2 1 4) LULC 1/2 1/2 1 1 2 1 5) D.Density 1/2 1/4 1/2 1/2 1 1 6) Slope 1/2 1 1 1 1 1 Table 9 Normalized Principal Eigenvector Factor Weight Rainfall 29.57% Soil 18.26% Geology 16.92% LULC 13.20% D.Density 8.78% Slope 13.26% 3.3 Groundwater Potential Zone The map of the groundwater potential zone (GWPZ) showed a distinct spatial variation between Kitgum district and Pader district. In Kitgum district is largely dominated by very low to low groundwater potential zones, indicating generally unfavorable conditions for groundwater occurrence. This pattern suggests the presence of limiting factors such as higher surface runoff, less permeable geological formations, and steeper slopes, all of which reduce infiltration and groundwater recharge. Conversely, Pader District is largely characterized by high to very high groundwater potential zones, showing more favorable hydrogeological conditions. These conditions include gentle slopes, hig precipitation amount, which enhanced infiltration capacity, which collectively support better groundwater storage and availability. The contrast between the two districts can be further explained by the influence of drainage density and other controlling factors used in the analysis of this study. Kitgum district, with its lower groundwater potential, is associated with higher drainage density, where closely spaced stream networks promote rapid surface runoff and limit the opportunity for water to infiltrate into the subsurface[ 39 ]. Conversely, Pader district exhibits lower drainage density, allowing more rainfall to percolate into the ground and recharge the aquifer system. Overall, the results highlight a clear hydrogeological distinction between the two districts, with Pader district offering more favorable conditions for groundwater development compared to Kitgum district, as shown in Fig. 10 and table 10 represent the percentage of each class. Table 10 Pedar District Area km 2 Percentage % Very Low 0 0% Low 44.265 1.3% Moderate 449.46 13.2% High 1600.35 47% Very High 1310.925 38.5% Total 3405 100% Kitgum Very Low 1445.68 35.74% Low 1274.175 31.5% Moderate 1011.25 25% High 184.45 4.56 Very High 129.44 3.2 Total 4045 100% 4 Conclusion This study demonstrated the efficacy of integrating Geographic Information Systems (GIS), remote sensing, and the Analytic Hierarchy Process (AHP) for delineating groundwater potential zones in Kitgum and Pader Districts of the Acholi sub-region, Uganda. The application of this multi-criteria decision analysis framework enabled the systematic integration of six thematic layers rainfall, drainage density, slope, soil type, geology, and land use/land cover, resulting in a spatially explicit groundwater potential zonation map that categorizes the study area into five distinct classes. The findings reveal a distinct hydrogeological contrast between the two districts. Kitgum District is predominantly characterized by very low to low groundwater potential zones, covering approximately 67.24% of its area, constrained by limiting factors such as higher drainage density, less permeable geological formations, and steeper slopes that collectively reduce infiltration and groundwater recharge. Conversely, Pader District exhibits favorable conditions, with over 85.5% of its area classified as high to very high groundwater potential, attributed to gentle slopes, higher rainfall amounts, and lower drainage density that enhance infiltration capacity and aquifer recharge. This unequal distribution emphasizes a landscape where groundwater is not evenly available, posing challenges for reliable water supply throughout various communities. The use of GIS-based techniques, remote sensing, and the AHP was successful in identifying groundwater potential in this semi-arid region with limited data. By methodically assigning weights and combining various thematic layers, the research created a spatially explicit and reproducible model that can aid in targeted water resource planning. Validation against yield data from 18 existing boreholes demonstrated a 61.1% agreement between predicted potential and actual well performance, confirming the model's overall reliability while identifying localized inconsistencies that warrant future refinement. In conclusion, this research underscores the critical need for targeted and sustainable groundwater management strategies in Kitgum and Pader Districts. Given the concentration of high-potential zones in Pader District, efforts should prioritize these locations for detailed hydrogeological investigation and strategic borehole siting. At the same time, the widespread low-potential areas in Kitgum District call for integrated water resource approaches, including rainwater harvesting, efficient irrigation practices, and conservation measures, to reduce pressure on scarce groundwater reserves. The resulting groundwater potential map provides a valuable decision-support tool for local authorities, development partners, and communities to enhance water security, build climate resilience, and support long-term socio-economic stability in the Acholi sub-region. 5 Validation of the results The groundwater potential model was validated using yield data from 18 boreholes across the study area. Based on the classification system where yields above 30 m³/h were considered very high, 20–30 m³/h high, 15–20 m³/h as moderate, 10–15 m³/h as low, and 5–10 m³/h as very low, the model achieved an overall alignment rate of 61.1%. The highest alignment rates were observed in the very high, high, moderate, and low yield classes, each achieving 66.7% alignment, while the very low yield class showed the lowest alignment at 33.3% as shown in Table 12 . The non-aligned boreholes of 38.9% provide valuable insights for model refinement. Boreholes ID 1 and ID 5 represent cases where actual yields deviated from predictions, suggesting the influence of localized geological features such as fractures, fault zones, or variations in weathering patterns that are not fully captured by the regional model parameters. These discrepancies highlight the need for incorporating higher-resolution geological data and localized structural mapping to improve predictive accuracy for groundwater exploration in similar semi-arid crystalline basement terrains. Table 11 Beerhall Yield classification Yield m 3 /h Class > 30 Very High Yield 20–30 High Yields 15–20 Moderate 10–15 Low 5–10 Very Low Table 12 Validation of the model ID of the bore hall Coordinates Yield in m 3 /h Alignment 1 32.8864713°E 3.1641167°N 12.3 Not aligned with the result as Low 2 32.8973558°E 3.1313555°N 22.78 Aligned with the Result 3 32.9589395°E 3.2016978°N 19.66 Not aligned with the result 4 32.9380934°E 3.2159653°N 16 Not aligned with the result 5 32.9543511°E 3.3595554°N 33 Not Aligned with the result 6 32.9192775°E 3.3841089°N 19 Aligned with the result 7 33.0639723°E 3.2885108°N 15.5 Aligned with the result 8 33.0998420°E 3.2833053°N 21 Not aligned with the result 9 33.1854458°E 3.2945847°N 14.8 aligned with the result 10 33.2248570°E 3.2699937°N 15.32 Aligned with the result 11 33.3477907°E 3.3660538°N 15.40 Aligned with the result 12 33.4743159°E 3.3581832°N 8.2 Not Aligned with the result 13 33.5308768°E 3.3983156°N 6.5 Aligned with the Result 14 33.4743159°E 3.3581832°N 6.8 Aligned with the Result 15 33.3904211°E 3.4053767°N 11 Aligned with the Result 16 32.6095974°E 3.0198017°N 26.7 Aligned with the Result 17 32.6367909°E 2.9976837°N 30.5 Aligned with the Result 18 32.6497590°E 2.9260987°N 31 Aligned with the Result Declarations We affirm that this research represents a unique scholarly contribution. The collection of the data process, including the gathering of data, its interpretation, and the conclusions drawn, was conducted exclusively for this work. The findings discussed have not appeared in any other publication. Acknowledgement The authors sincerely acknowledge and thank the members of the school of engineering and applied science (SEAS) at Kampala International University starting with the Dean Prof Mustapha Mohamud Lawan the head of the civil department Dr Sani Aliyu and for providing moral support, their technical advises were crucial to the successful completion of this research. Authors Contribution Ahmed Abdirizak Dirie: Research topic review and conceptualization, research gap analysis and writing, Prof.Ambiga: Supervision, A.A.M: Editing. Funding No funding was received for this research. Data availability The data are available on request from the authors. Conflict of interest The author declares that there were no competing interests. Ethical approval This study was conducted in accordance with ethical standards and guidelines of Kampala International University REC . Clinical trial number Clinical trial number is not applicable for this research. Consent to Participate Declarations Not applicable Consent to Publish declarations The authors declare that there are no conflicts of interest regarding the publication of this paper. References Alam M, Chauhan P, Narayan Thakural L, Malviya D, Ahmad R, Sajid M. Identification of groundwater recharge potential zone using geospatial approaches and multi criteria decision models in Udham Singh Nagar district, Uttarakhand, India. Adv Sp Res. 2025;75:1931–44. https://doi.org/10.1016/j.asr.2024.10.039 . Song Q, Liu Y, Wang Z, Xu Z. Assessing groundwater artificial recharge suitability in the Mi River basin using GIS, RS, and FAHP: a comprehensive analysis with seasonal variations. Appl Water Sci. 2025;15:1–21. https://doi.org/10.1007/s13201-025-02362-z . 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 May, 2026 Reviews received at journal 28 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor assigned by journal 28 Mar, 2026 Submission checks completed at journal 28 Mar, 2026 First submitted to journal 26 Mar, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9232280","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618299343,"identity":"9b672811-95e1-4b4a-b8cc-f34879728da3","order_by":0,"name":"Ahmed Abdirizak Dirie","email":"data:image/png;base64,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","orcid":"","institution":"Kamapala International University","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"Abdirizak","lastName":"Dirie","suffix":""},{"id":618299344,"identity":"777735ff-6d5e-40ff-9555-477d759c1e09","order_by":1,"name":"Abdulkadir Ahmed Mohamed","email":"","orcid":"","institution":"Kamapala International University","correspondingAuthor":false,"prefix":"","firstName":"Abdulkadir","middleName":"Ahmed","lastName":"Mohamed","suffix":""},{"id":618299345,"identity":"17cde652-1469-4f96-b1d4-4647ca434754","order_by":2,"name":"Ambiga Kannapiran","email":"","orcid":"","institution":"Kamapala International University","correspondingAuthor":false,"prefix":"","firstName":"Ambiga","middleName":"","lastName":"Kannapiran","suffix":""}],"badges":[],"createdAt":"2026-03-26 09:40:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9232280/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9232280/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106348168,"identity":"3c4e735a-1909-4d4c-a03f-d492108c9ba6","added_by":"auto","created_at":"2026-04-07 16:44:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":416887,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Study Area.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9232280/v1/815ec879c78f4673ce2fbfce.jpeg"},{"id":106348166,"identity":"2236d290-da32-468c-889a-f631d4cc40c5","added_by":"auto","created_at":"2026-04-07 16:44:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eData collection and analysis procedure\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9232280/v1/3a62f5a26f73026c25729e8a.png"},{"id":106404307,"identity":"a7eb9ab4-5e04-4844-b6f0-ab288e82e50d","added_by":"auto","created_at":"2026-04-08 09:15:48","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":450175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrainage Denisty map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9232280/v1/334542ffefe185898bb714cb.jpeg"},{"id":106404877,"identity":"7bca13ba-ea01-419d-9aa3-b226434678b1","added_by":"auto","created_at":"2026-04-08 09:17:19","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":537414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5: Slope map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9232280/v1/dc676c96cf2d127363684c50.jpeg"},{"id":106404846,"identity":"95211701-e842-4b80-af3c-a547a68d15cf","added_by":"auto","created_at":"2026-04-08 09:17:14","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":505946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6: Land Use or Land Cover (LULC) Map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9232280/v1/3c3e8088b0f1a1e206e82415.jpeg"},{"id":106403519,"identity":"ecba4c57-f426-42b2-9d2c-159216a113d5","added_by":"auto","created_at":"2026-04-08 09:14:26","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":370441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7 Soil types\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9232280/v1/e646c259792fcbc70c4a42bf.jpeg"},{"id":106348171,"identity":"3c58a87e-23cb-4ee9-85c8-fc8c4b46ea39","added_by":"auto","created_at":"2026-04-07 16:44:34","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":369319,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 8 Geological Map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9232280/v1/99cbea85235b0fd6e7816fac.jpeg"},{"id":106348172,"identity":"3b2203b1-42ad-47fc-8199-a530c169973e","added_by":"auto","created_at":"2026-04-07 16:44:34","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":391552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 9 the Percipitation(Rainfall) map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9232280/v1/9c3ffb3551f0b8a58a97ec92.jpeg"},{"id":106348174,"identity":"74c065b5-ac79-4985-9dfd-078f0a28af71","added_by":"auto","created_at":"2026-04-07 16:44:34","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":501143,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 10 Groundwater Potential Zone map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9232280/v1/94731912ab72e178dbe40db6.jpeg"},{"id":106724294,"identity":"2ca1becb-1015-46c3-b934-a1680b999bfd","added_by":"auto","created_at":"2026-04-12 18:27:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5167075,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9232280/v1/4218f155-2e19-4e2c-9983-36914cacefb9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGroundwater Potential Mapping Using GIS,remote Sensing and AHP: A Case Study of Kitgum and Pader Districts, Acholi Sub-Region, Uganda\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGroundwater is a vital component, serving as the largest reservoir of liquid freshwater on the earth. It makes up roughly 30% of the Earth's total freshwater resources, while freshwater, itself only constitute 2.5% of all global water when the immense amount of ocean water are accounted for. [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. All These factors position groundwater as an essential resource, particularly as the surface water sources are increasingly exploited, and faces contaminations from pollutants and climate change. This critical situation exacerbated the increasing demand of water, due to population growth and developmental pressures. [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThis freshwater resource is indispensable for maintaining ecosystem functions, supporting agriculture and industrial activities, and supplying domestic water[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It is particularly significant in arid and semi-arid regions where surface water is scarce. In these regions, groundwater serves as the primary source of water, making it vital for both economic and social development[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Additionally, plays vital in reducing flood risks through infiltrating large amount of water into subsurface.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eDespite its importance, groundwater is vulnerable to various threats, including over-extraction, pollution, and the impacts of climate change, which can affect the groundwater quality and it\u0026rsquo;s availability. Sustainable management practices are essential for protecting groundwater, especially as climate change alters precipitation patterns and increases the frequency of extreme hydrological events [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, understanding the dynamics of groundwater systems and implementing effective management and adaptation strategies are crucial for ensuring the sustainable use of this critical resource[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe result of overexploitation to this valuable resource, water scarcity became a significant global issue impacting billions of people[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It resulted from a combination of various factors, including increased water use due to population growth, urbanization, and inadequate management of water resources. Then climate change further exacerbated the problem through a higher temperatures[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe an emerging concern is the uncertainty in water scarcity projections due to variations among climate models and socioeconomic pathways, which complicates effective policy-making and management responses [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] .Current models predicted that global warming of 2\u0026deg;C above present temperatures could substantially increase, highlighting the critical need for more accurate managements and strategic interventions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring the past decades, global human water use has doubled and poses an additional threat, yet available freshwater resources are finite[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As a result, water scarcity has been prevalent in various regions of the world[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] .Changing hydro-climatic and socioeconomic conditions have aggravated water scarcity over the past decades [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A wide range of studies show that water scarcity will intensify in the future, as a result of both increased consumptive water use and, in some regions, due to climate chang[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A little attention has yet been paid to the impacts of climate variability on water scarcity conditions, despite its importance for adaptation planning[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany studies indicated that low-income, non-industrialized nations with tropical or subtropical climates are more vulnerable to the negative impacts of the climate change [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Especially ,tropical regions are anticipated to be vulnerable due to institutional, financial, technological, and constraints, such as many East-African countries.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For example, during the period of 2010\u0026ndash;2020, Uganda experienced the most devastating droughts in history with massive impacts to over seven million people [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. While, ranked as 15th on vulnerability and 147th on readiness, meaning the country is very vulnerable, sadly, unprepared to respond to climate change impacts[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this type of situation, the key advantage of groundwater is its inherent resilience. Because it is stored in vast underground aquifers, providing a stable water supply that can buffer communities against the short-term shocks of climate variability or drought[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Recently, there has been a global increase in attempts to identify groundwater potential zones [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This attempts of delineating groundwater potential zones became an essential approach to tackle water-related issues globally[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to Uganda Bureau of Statistics (UBOS) in 2024, more than 29% of the people in the Acholi subregion lack of access to an improved drinking water source (Kagurusi et al., 2025). Additionally, a study conducted in 2022 by Dalson Twecan showed that less than 20% of the farmers in Acholi subregion uses irrigation for agriculture, while other remaining percentage rely on precipitation which is not constant, particularly in the dry season. Poverty, a lack of awareness of sophisticated climate change adaptation strategies, the scarcity of irrigation technicians, and illiteracy could all be factors contributing to farmers poor irrigation usage[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The most severe drought recorded in Acholi sub-region in northern Uganda in the past few years severely damaged crops and cattle and had a detrimental effect on agriculture sectors[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These circumstances highlights the need of groundwater potential zones delineation and sustainable management techniques that are both spatially explicit and scientifically sound[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe integration of remote sensing and GIS techniques offered an effective means of identifying zones that are favorable for groundwater occurrence[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the context of GWRZ delineation, the analytical hierarchy process (AHP) is utilized as a decision-making tool and offers a methodical framework for ranking criteria according to relative relevance. Researchers evaluate the importance of one criterion over another by using pairwise comparisons, which produces a more impartial and reliable weighting system[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe groundwater potential zones of Kitgum and Pedar, two districts in the Acholi area, were identified in this study. The findings of this study have immediate implications for groundwater resource management and development, in addition to socioeconomic development. In the long run, this research contributes to the fulfilment of national goals for economic growth, water security, and poverty alleviation, which benefits both local communities and more extensive regional development projects. Geospatial technology, which is noninvasive, was e used in this investigation.\u003c/p\u003e \u003cp\u003eTo achieve insightful the result of this study, we set three specific objectives, which eventually contribute to get a solution for our main objectives of Mapping Groundwater potential zones in Kitgum and Pedar Districts in Acholi subregion .\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo generate thematic maps using GIS (ArcGIS pro)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo determine and assign normalized weights to all thematic layer\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo delineate ground-water potential zone\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Description of the study area\u003c/h2\u003e \u003cp\u003eThis study was carried out in the Northeastern Ugandan districts of Kitgum an Pedar, which is part of the Acholi subregion where violent wars experienced. notably, the 20-year (1986\u0026ndash;2006) civil war between the Lord's Resistance Army (LRA) and the Uganda People's Defense Force (UPDF) had a significant impact on northern Uganda, particularly the Acholi sub-region. As a result of being forced into internally displaced persons (IDPs) concentration camps, the conflict had an impact on social, political, and economic service delivery and structures[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].the coverage area of Kitgum district is roughly about 4142.5km\u003csup\u003e2\u003c/sup\u003e and 3292.99km\u003csup\u003e2\u003c/sup\u003e for Pedar, which make the total area of this study approximately 7435.5. The rainy season in Acholi subregion lasts from March to October, typically May and August receive the highest precipitation. The dry season, on the other hand, can be harsh and lasts from late November through February. This region generally gets low temperatures of 18\u0026deg;C and maximum temperatures of 30\u0026deg;C[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study area\u0026rsquo;s geography is a flat plateau with fertile arable soil suited for farming and wooded savannah flora. Due to their resistance to drought, the primary crops planted in the area include sorghum, finger millet, sesame, cotton, groundnuts, and cassava[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Datasets and software\u003c/h2\u003e \u003cp\u003eSix thematic maps were selected for this study, in order to zonate groundwater potential zones, which are drainage density, geological map, soil map, Land use Land cover (LULC), rainfall map which was generated from rainfall data From UNMA (Uganda National Metrological Authority) and lastly slope map. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists all the data and their sources\u003c/p\u003e \u003cp\u003eGIS, particularly ArcGIS software, played a critical role in delineating groundwater potential zones[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this study we utilized this powerful software and AHP to delineate groundwater potential zones in Kitgum and Pedar Districts in Acholi subregion. all data which are used in this study are presented in Table \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\u003eData and their sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRTM DEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\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/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital Elevation Model, source of elevation data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDerived from SRTM DEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCalculated using DEM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrainage density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDerived from SRTM DEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCalculated using DEM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFood and Agricultural Organization (FAO) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/\u003c/span\u003e\u003cspan address=\"https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSoil type classification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026hellip;\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUNMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRainfall data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026hellip;\u0026hellip;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDF (Georeferenced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUganda Directorate of Geological Survey and Mines. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dgsm.go.ug/\u003c/span\u003e\u003cspan address=\"https://dgsm.go.ug/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRock type classification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use/land cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLandsat 9\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/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClassification of land use and vegetation cover\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroundwater yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinistry of Water and Environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation of groundwater wells, and yield capacity\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=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The static and dynamic thematic maps\u003c/h2\u003e \u003cp\u003eSeveral thematic layers describing elements influencing groundwater occurrence were developed in order to identify groundwater potential zones. spatial alignment were also done, all layers were standardized by projecting them into the EPSG:4326 geographic coordinate system as supported by studies carried out by K. Joseph Pious [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Weights were assigned by using the AHP [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Pairwise comparisons were then done to determine the weights influencing groundwater potentiality, the factors or the thematic maps are categorized into static (e.g., lithology, slope) and dynamic (e.g., Land Use/Land Cover [LULC], rainfall) parameters to analyze.. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38 CR39 CR40 CR41 CR42\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Drainage Density\u003c/h2\u003e \u003cp\u003eA drainage density map, which shows the total length of all streams and rivers within a specific unit area mostly in kilometers as this study assigned, is an essential analytical tool in hydrogeological analyses[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. It offers pivot information about the properties of surface runoff and the possibility of infiltration. A Digital Elevation Model (DEM) was downloaded from The USGS website and this DEM was imported Into Geographical Information System (GIS) software [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDigital Elevation Model (DEM) was processed using the Fill tool to fix data voids [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. After determining each cell's steepest downhill path using the flow direction tool, cells with a high concentration of upstream drainage were identified using the flow accumulation tool[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Then the streams are generated and changed in to polyline [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These streams are then computed by the Line Density tool, which created a raster map that divides the terrain into zones with low to high drainage densities based on the total length of the stream within a certain search radius per unit area[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. it is formally defined by a formula:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{D}\\text{d}=\\frac{{\\sum\\:}_{i=1}^{i=n}\\:Di}{A}\\)\u003c/span\u003e \u003c/span\u003e km / km2 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWere\u003c/p\u003e \u003cp\u003eDi\u0026thinsp;=\u0026thinsp;is the total recorded drainage length (km)\u003c/p\u003e \u003cp\u003eDd\u0026thinsp;=\u0026thinsp;is the drainage density and\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;is an area in km2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Slope\u003c/h2\u003e \u003cp\u003eSlope, significant factor in assessing groundwater potential, as it directly governs the balance between surface runoff and subsurface infiltration [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Using the 30-m-resolution SRTM DEM, the slope was calculated in this study. And the approach is supported by the earlier work of Edukondal et al [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In areas with steep slopes, gravitational forces cause the precipitation drains to rapidly run off over the surface, drastically reducing the time for a rain to infiltrate into the subsurface and finally recharge aquifers below [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Conversely, flat or gently sloping terrain allows precipitation drains to remain for longer durations, maximizing its opportunity to percolate into the ground[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eslope maps inversely correlate with groundwater potential zones of low slope are typically assigned high weights and classified as highly promising for groundwater potential, while steep slope areas are indicative of poor groundwater potential. This makes slope a critical, often primary, thematic layer in any groundwater potential zonation model [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] .Slope can be generated using slope tool in the GIS and derived from the DEM [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Land Use land Cover (LULC)\u003c/h2\u003e \u003cp\u003eThe Land Use and Land Cover (LULC) map is a crucial for evaluating how surface conditions influence groundwater[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The LULC map are generally categorized into classes such as forest, agricultural land, urban areas, and water bodies. Each category directly affects infiltration. For example, forests promote high infiltration, while paved urban areas generate rapid runoff, limiting water percolation[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In this study ,land set 9 LULC was downloaded and then supervised in ArcGIS pro, which integrated in other thematic maps to build a comprehensive model as supported by existing literatures [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The combination of these layers allows for the identification of zones where favorable surface conditions (like vegetation cover) coincide with permeable soils pinpointing areas of high groundwater potential.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Soil Map\u003c/h2\u003e \u003cp\u003eThe type of soil whether it sand, silt, and clay are determined by its texture, which is a major criterion or process for classifying it[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Sandy soils are great for groundwater recharge because of their high particle size and extensive pore spaces, which function as a sieve and let water flow through quickly and easily [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Clayey soils, on the other hand, are made up of extremely small, closely packed particles that retain water and swell when wet [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This results in a nearly impenetrable barrier that significantly delays infiltration and increases surface runoff rather than recharge[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The soil data used in this study were obtained from FAO Food and Agricultural Organization\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Geological map\u003c/h2\u003e \u003cp\u003ethe geological map was downloaded from the Department of Geological Survey and Mines (DGSM) of Uganda, as PDF form. after georeferencing the PDF, thus, the geological structure of the study are was clearly assessed .Which is a foundational layer for analyzing groundwater potential. This map is critically important as it reveals the rock type laying on the study area that controls groundwater movement and storage[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. While the soil map informs us about the initial infiltration in the upper few meters[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. geological map is used to understand the underlying rock[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. By integrating this geological map with the soil map and other datasets like land use and slope, this study built a robust model to identify groundwater potential zones; for instance, an area with sandy soil overlying a fractured limestone aquifer is classified as a very high-potential zone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6 Rainfall thematic map Creation\u003c/h2\u003e \u003cp\u003eThis research used a rainfall information obtained from the Uganda National Meteorological Authority (UNMA) to quantify groundwater potential. The precipitation dataset provided essential details regarding both the spatial distribution and the temporal patterns of rainfall events, information which is directly used in the model to calculate the total volume of water that is available for infiltration into the subsurface. The meteorological station records from UNMA consist of accurate and reliable long-term measurements, thereby forming a highly trustworthy and authentic foundation for the construction of the hydrological model. In the absence of this essential input, the model would lack the ability to accurately distinguish between geographic areas that are highly storage to groundwater potential and those regions that are not. The integration of this authoritative UNMA dataset fundamentally ensures that the final map delineating groundwater potential is grounded in robust, credible, and verifiable climatic information.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Weighting of the factors influences GWP\u003c/h2\u003e \u003cp\u003eSeveral researchers have utilized the MCDA-AHP method to evaluate the weight of various influencing layers on groundwater potential [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Their ranking is dependent on their importance to groundwater and recharge occurrences and usually uses Saaty's scale of relative importance value .[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] which is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e .the numbers 1\u0026ndash;9 indicate the degree to which one factor has advantage over another. The ultimate goal is to derive a reliable priority vector (weight) for each thematic map and their classes, ensuring that the final assessment accurately reflects the real-world influence of each parameter on groundwater systems.\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\u003eSaaty's 1\u0026ndash;9 scale of pairwise comparison\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensity of Importance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExplanation\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\u003eEqual importance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTwo elements contribute equally to the objective.\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\u003eModerate importance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExperience and judgment slightly favor one element over another.\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\u003eStrong importance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExperience and judgment strongly favor one element over another.\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\u003eVery strong importance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOne element is favored very strongly over another; its dominance is demonstrated in practice.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtreme importance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe evidence favoring one element over another is of the highest possible order of affirmation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2, 4, 6, 8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntermediate values\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eUsed to express intermediate judgments between the main definitions (e.g., 2 is between \"Equal\" and \"Moderate\" importance).\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo check the consistency of pairwise comparison it is calculated using Eq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{C}\\text{o}\\text{n}\\text{s}\\text{i}\\text{s}\\text{t}\\text{e}\\text{n}\\text{c}\\text{y}\\:\\text{R}\\text{a}\\text{t}\\text{i}\\text{o}\\:\\left(\\text{C}\\text{R}\\right)=\\:\\frac{\\text{C}\\text{o}\\text{n}\\text{s}\\text{i}\\text{s}\\text{t}\\text{e}\\text{n}\\text{c}\\text{y}\\:\\text{I}\\text{n}\\text{d}\\text{e}\\text{x}\\left(\\text{C}\\text{I}\\right)\\:}{\\text{R}\\text{a}\\text{n}\\text{d}\\text{o}\\text{m}\\:\\text{I}\\text{n}\\text{d}\\text{e}\\text{x}\\:\\left(\\text{R}\\text{I}\\right)}\\)\u003c/span\u003e \u003c/span\u003e [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{C}\\text{o}\\text{n}\\text{s}\\text{i}\\text{s}\\text{t}\\text{e}\\text{n}\\text{c}\\text{y}\\:\\text{I}\\text{n}\\text{d}\\text{e}\\text{x}\\left(\\text{C}\\text{I}\\right)\\:\\frac{{\\lambda\\:}\\text{m}\\text{a}\\text{x}-\\text{n}}{n-1}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIf CR\u0026thinsp;\u0026le;\u0026thinsp;0.1, the relative weights assigned were reasonable and consistent, which is suitable for decision making, according to Saaty's [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Reevaluating the relative weight assignment or updating the pairwise comparison is necessary if CR\u0026thinsp;\u0026gt;\u0026thinsp;0.1, which indicates that the weight assigned was inappropriate and inconsistent[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. λmax is the principal Eigen vector and RI- is the random index value. Groundwater potential zone (GWPZ) map of this study was generated by overlying all thematic layers using the Weighted Overlay Analysis tool in ArcGIS [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. as defined by:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:GWPI{\\sum\\:}_{i=1}^{n}\\left(Wi\\times\\:Ri\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eWi\u003c/em\u003e​ is the normalized weight,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eRi\u003c/em\u003e​ is the reclassified rating,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003en\u003c/em\u003e is the total number of thematic layers.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis flowchart below in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is illustrating the scientifically grounded of this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussions","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Thematic maps\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Drainage Density\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, shows the drainage density map of the study area, which clearly demonstrates a spatial variance in the density of the stream networks per square kilometer. the values of the classes range from very low of 0.001 up to 0.119 km/km\u0026sup2;, showed in dark blue color, to very high of 0.479 up to 0.597 km/km\u0026sup2;, showed in yellow color on the map. The distribution reveals a heterogeneity across the two districts, which the moderate to high drainage densities dominating large part of the central and southern sections. These areas are characterized by closely spaced stream channels, suggesting well-developed surface runoff systems. In contrast, lower drainage densities are more prevalent along the peripheral zones, indicating relatively sparse channel development.\u003c/p\u003e \u003cp\u003eAreas with high drainage density are likely to have impermeable underlying materials, which promote rapid surface runoff, and results decrease of water into subsurface[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. drainage density locations, where it is low, associated with permeable soils, and reduces infiltration through minimizing surface flow. Hydrologically expressing, high drainage density zones are more prone to erosion and flash floods[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] with a relatively rapid hydrologic response to rainfall events[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], whereas low-density areas may indicate favorable conditions for groundwater [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], which means a poorly drained basin with a slow hydrologic response.[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe percentage and the area covered each class of the drainage density\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage (%)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u0026ndash;0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1646117511.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.08\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u0026ndash;0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2449875307.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.88\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u0026ndash;0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2067147749.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.74\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.359\u0026ndash;0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1132921450.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.21\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.479\u0026ndash;0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153809198.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e7,449,871,216.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEach category or classes area was divided by the total area of 7,449,871,216.22 m\u003csup\u003e2\u003c/sup\u003e, and the resulting percentages were then multiplied by 100. This indicates that the district's low drainage density encompasses the most area (32.88%), while the very high drainage density represents the smallest area with 2.06%. The major land of Kitgum and Peder district area has typically well-balanced drainage characteristics, low and moderate, which together make up more than 60.62% of the district's area, as shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThis spatial distribution presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides valuable insights for land use planning and water resource management. The extensive low-density areas offer favorable conditions for agriculture and groundwater recharge, while the more limited high-density zones require targeted erosion control and flood management strategies[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The map serves as an essential tool for identifying priority areas for conservation measures and guiding sustainable development initiatives that account for the district's varied hydrological characteristics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Slope map\u003c/h2\u003e \u003cp\u003eSlope is a key factor in regulating groundwater potentiality from a hydrological perspective[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The broad brown and green zones are showing the areas with gentle slopes that tend to promote water infiltration into the subsurface, improving groundwater recharge and storage. Conversely, regions with steeper slopes encourage rapid surface runoff, limiting the time available for water to percolate into the ground. Thereby reducing groundwater potentiality [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Therefore, the spatial distribution shown in this map suggests that the central and southwestern parts of the study area are more favorable for groundwater infiltration chances, while the steeper northeastern zones are less suitable due to increased runoff and reduced infiltration capacity.\u003c/p\u003e \u003cp\u003eThe behavior of the slope of these two districts is illustrated in the Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e table. Over 63% of the land is made up of gentle slopes of less or equal to 1.64 degree. This is very suitable for groundwater replenishment, and agriculture activity which some amount of the irrigated water lastly will be part of subsurface water. Conversely, just 2.63% of the entire area is made up of steep slopes. Although these areas of steep slopes are crucial to mountainous ecosystems also known as montane ecosystem, but they are not the favorable for infiltration. Figure\u0026nbsp;4 shows the slope map of the study area\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe slope range, area and the percentage of the total area of each class\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope Range (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea(m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of Total Area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u0026ndash;1.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4714834094.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.642\u0026ndash;6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2326856508.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.49%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.58\u0026ndash;15.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154666255.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.09%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.26\u0026ndash;24.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127238524.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.64\u0026ndash;59.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67003357.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7,449,871,216.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.00%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Land Use land Cover (LULC)\u003c/h2\u003e \u003cp\u003eVegetation covered areas and trees makes up a significant percentage of the landscape, which is a prominent domination of the area according to the land use and land cover map. This suggests that the area is either cultivated or mostly used for pastoral purposes, which natural grasslands or shrublands serving as a primary land use. The sizable trees area indicates the presence of woodlands, which are likely to be found along water channels, in higher elevation zones, or in protected regions, such as, Jaka forest reserve. These places provide important ecosystem services like soil stabilization and habitat for biodiversity.\u003c/p\u003e \u003cp\u003eBy comparison, there are much less by area landscapes that have been utilized by humans. The small amounts of constructed space indicate a less urbanization. The paved areas, as ramifications reduces amount of water infiltrates. The area's aridity is further highlighted by the sparse water cover, suggesting that surface water supplies are limited and that the region relies heavily on rainwater harvesting or groundwater supplies.\u003c/p\u003e \u003cp\u003eWater bodies form a very small part of the area, according to the land cover map. They can be seen as a rare feature rather than a distinguishing characteristic because of how uncommon they are. An arid or semi-arid region where surface water supply is a crucial and perhaps limiting factor for both natural ecosystems and human activity is strongly affected by this acute shortage. There are significant ramifications to this near-absence of surface water. It implies that the majority of the region's demands are satisfied by groundwater resources or rainwater. The little patches of farmland and the enormous vegetation covered probably depend more on underground water supplies than on lakes or rivers. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrates the land use land cover of Kitgum and Pedar districts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the land use map, vegetation covers make up 66.46% of the entire 7,449,836,100 m\u0026sup2; of the total area. Rangeland makes up 17.73%, while trees constitute 14.51%. Water bodies of 0.18% and built areas of 1.14%) cover insignificant proportions. The majority of the landscape is made up of natural land covers, such as trees, vegetations and rangeland, emphasizing the dominance of open green spaces over man-built or water bodies, the proportion of each class is shown in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e .\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ethe percentage of land use land cover of each class\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\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand Cover Type\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\u003ePercentage of Total Area\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\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13753800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18%\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\u003eTrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1080747000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.51%\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\u003eVegetation covers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4949316000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.46%\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\u003eBuilt Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85023000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14%\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\u003eRangeland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1320996300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7,449,836,100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\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=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Soil Map\u003c/h2\u003e \u003cp\u003eThe soil categories in the study area reveals that Orthic Ferralsols (Fr) dominate the entire area of the two district, covering 4,641,247,890.3 m\u0026sup2; or 62.3% of the study area. this type of soil has a potential yield (Yp) of 23 Mg/ha and a water-limited yield (Yw) of 11 Mg/ha, with its soil capability index (SCI) dropping to 30 under erosion and 20 under compaction due to factors such as shallow rooting[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Humic Gleysols (Gh) forms the second-largest category at 1,899,708,205.5 m\u0026sup2; (24.5%), which is described as a poorly drained, fine-textured soil [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].While Lithosols account for 685,385,721.2 m\u0026sup2; (9.2%( .The remaining 3% is Ferric Luvisols at just 223,495,083m\u0026sup2;, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e .This distribution is critical for groundwater management, as the extensive coverage of Ferralsols suggests generally favorable conditions for infiltration, while the 25.50% coverage of Gleysols identifies important zones of water accumulation and potential surface-groundwater interaction. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the percentage of each type of soil and the area covers\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ethe soil details of the study area\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\u003eSoil Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil Type Name\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\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrthic Ferralsols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,641,247,890.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGh\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumic Gleysols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,899,708,205.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLithosols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e685,385,721.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLf\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFerric Luvisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223,495,083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7,449,836,900\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.00%\u003c/b\u003e\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=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5 Geological Map\u003c/h2\u003e \u003cp\u003eThe study area\u0026rsquo;s geology is a complex with a wide range of igneous and metamorphic rocks. This variety suggests an extended and active geological past that includes numerous instances of tectonic deformation, metamorphism, and magmatism. The six rock types shown in the map can be divided into two major groups: igneous and metamorphic rocks. The existence of an ancient basement complex that formed deep under the Earth's crust under extreme heat and pressure is suggested by the presence of high-grade metamorphic rocks. This metamorphic basement most likely forms the region's bedrock, affecting hydrology, soil formation, and also the topography.\u003c/p\u003e \u003cp\u003eThe geological framework of the region shaped by gniess and gnieses granitoids that constitute the predominant basement rock, covering 3153554842.19 m\u0026sup2; and 3132729527.15m\u003csup\u003e2\u003c/sup\u003e, respectively .Representing 84.9% both of the total area. The metamorphic rock of charnockite forms a significant third unit, accounting for 463264656.6 m\u0026sup2; or 6.22% of the area, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The remaining geological composition is fragmented among minor units, where Amphibolit occupies 318,888,919.5 m\u0026sup2; (4.28%), while Metagabbro and garanite formations are confined to just 25708065.7307m\u0026sup2; which equivallant 0.34\u003cspan\u003e$\u003c/span\u003e and 353274934.266 m\u0026sup2; (4.74%) respectively. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the percentage of each type of rocks and their area covers\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeological details of the study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeological Material\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoverage Area (m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of Total Area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmphibiliots\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e318888919.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGneiss\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3153554842.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGneissic granitiods\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3132729527.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGranite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e353274934.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.74%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetagabbro\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25708065.7307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharnockite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e463264656.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7,447,420,945.4387\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.00%\u003c/b\u003e\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=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.1.6 Rainfall Map\u003c/h2\u003e \u003cp\u003eThe rainfall distribution of the study area is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e below. With rainfall measurements measured in millimeters. The values fall into five different classes, with the lowest 949 to 1042 mm and the highest 1285 to 1373 mm. There is a distinct gradient of increasing precipitation from the study area\u0026rsquo;s southwest to its northeast, rather than a constant fall of rain. This is more likely probable to increase the infiltration amount, as the southwest also have a gentle gradient than the northeast. As the slop map showed, it illustrated a higher elevations presence in the northeast, such hills or mountains, which condense the air and precipitates orographic rains.\u003c/p\u003e \u003cp\u003eSince precipitation constitutes large amount of water that we have underground, it is essential to comprehend the rainfall distribution when delineating the ground water potential map[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Agriculture is directly impacted by the sharp contrast[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] between the wetter northeast and the dry southwest, which determines whether irrigation systems are required, which will be a good chance to infiltrate the water. as result, this map offers crucial data for district-level approaches to climate adaptation, natural resource management, and food security.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Weight Assignments of the thematic maps\u003c/h2\u003e \u003cp\u003eThe study employed the Analytic Hierarchy Process (AHP), initiating the analysis by assigning ranks to of the selected thematic layers, as many previous studies utilized [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Subsequently, a pairwise comparison matrix was developed to model the groundwater influence regime in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The consistency of this matrix was validated by calculating the consistency index and ratio. Following the normalized weights were derived and allocated to the thematic layers (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The final weights for the sub-features were computed by multiplying the average thematic layer weights (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) by the normalized weights of their respective sub-features, following the standard AHP methodology.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePairwise comparison matrix values of thematic layers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMatrix\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGeology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eD.Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSlope\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\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\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\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\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\u003eGeology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\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\u003eLULC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\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\u003eD.Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\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\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNormalized Principal Eigenvector\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.26%\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=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Groundwater Potential Zone\u003c/h2\u003e \u003cp\u003eThe map of the groundwater potential zone (GWPZ) showed a distinct spatial variation between Kitgum district and Pader district. In Kitgum district is largely dominated by very low to low groundwater potential zones, indicating generally unfavorable conditions for groundwater occurrence. This pattern suggests the presence of limiting factors such as higher surface runoff, less permeable geological formations, and steeper slopes, all of which reduce infiltration and groundwater recharge. Conversely, Pader District is largely characterized by high to very high groundwater potential zones, showing more favorable hydrogeological conditions. These conditions include gentle slopes, hig precipitation amount, which enhanced infiltration capacity, which collectively support better groundwater storage and availability.\u003c/p\u003e \u003cp\u003eThe contrast between the two districts can be further explained by the influence of drainage density and other controlling factors used in the analysis of this study. Kitgum district, with its lower groundwater potential, is associated with higher drainage density, where closely spaced stream networks promote rapid surface runoff and limit the opportunity for water to infiltrate into the subsurface[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Conversely, Pader district exhibits lower drainage density, allowing more rainfall to percolate into the ground and recharge the aquifer system. Overall, the results highlight a clear hydrogeological distinction between the two districts, with Pader district offering more favorable conditions for groundwater development compared to Kitgum district, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e and table 10 represent the percentage of each class.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cp\u003eTable 10\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePedar District\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e449.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1600.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1310.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.5%\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\u003e3405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKitgum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1445.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.74%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1274.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1011.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\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\u003e4045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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"},{"header":"4 Conclusion","content":"\u003cp\u003eThis study demonstrated the efficacy of integrating Geographic Information Systems (GIS), remote sensing, and the Analytic Hierarchy Process (AHP) for delineating groundwater potential zones in Kitgum and Pader Districts of the Acholi sub-region, Uganda. The application of this multi-criteria decision analysis framework enabled the systematic integration of six thematic layers rainfall, drainage density, slope, soil type, geology, and land use/land cover, resulting in a spatially explicit groundwater potential zonation map that categorizes the study area into five distinct classes.\u003c/p\u003e \u003cp\u003eThe findings reveal a distinct hydrogeological contrast between the two districts. Kitgum District is predominantly characterized by very low to low groundwater potential zones, covering approximately 67.24% of its area, constrained by limiting factors such as higher drainage density, less permeable geological formations, and steeper slopes that collectively reduce infiltration and groundwater recharge. Conversely, Pader District exhibits favorable conditions, with over 85.5% of its area classified as high to very high groundwater potential, attributed to gentle slopes, higher rainfall amounts, and lower drainage density that enhance infiltration capacity and aquifer recharge. This unequal distribution emphasizes a landscape where groundwater is not evenly available, posing challenges for reliable water supply throughout various communities.\u003c/p\u003e \u003cp\u003eThe use of GIS-based techniques, remote sensing, and the AHP was successful in identifying groundwater potential in this semi-arid region with limited data. By methodically assigning weights and combining various thematic layers, the research created a spatially explicit and reproducible model that can aid in targeted water resource planning. Validation against yield data from 18 existing boreholes demonstrated a 61.1% agreement between predicted potential and actual well performance, confirming the model's overall reliability while identifying localized inconsistencies that warrant future refinement.\u003c/p\u003e \u003cp\u003eIn conclusion, this research underscores the critical need for targeted and sustainable groundwater management strategies in Kitgum and Pader Districts. Given the concentration of high-potential zones in Pader District, efforts should prioritize these locations for detailed hydrogeological investigation and strategic borehole siting. At the same time, the widespread low-potential areas in Kitgum District call for integrated water resource approaches, including rainwater harvesting, efficient irrigation practices, and conservation measures, to reduce pressure on scarce groundwater reserves. The resulting groundwater potential map provides a valuable decision-support tool for local authorities, development partners, and communities to enhance water security, build climate resilience, and support long-term socio-economic stability in the Acholi sub-region.\u003c/p\u003e"},{"header":"5 Validation of the results","content":"\u003cp\u003eThe groundwater potential model was validated using yield data from 18 boreholes across the study area. Based on the classification system where yields above 30 m\u0026sup3;/h were considered very high, 20\u0026ndash;30 m\u0026sup3;/h high, 15\u0026ndash;20 m\u0026sup3;/h as moderate, 10\u0026ndash;15 m\u0026sup3;/h as low, and 5\u0026ndash;10 m\u0026sup3;/h as very low, the model achieved an overall alignment rate of 61.1%. The highest alignment rates were observed in the very high, high, moderate, and low yield classes, each achieving 66.7% alignment, while the very low yield class showed the lowest alignment at 33.3% as shown in Table \u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe non-aligned boreholes of 38.9% provide valuable insights for model refinement. Boreholes ID 1 and ID 5 represent cases where actual yields deviated from predictions, suggesting the influence of localized geological features such as fractures, fault zones, or variations in weathering patterns that are not fully captured by the regional model parameters. These discrepancies highlight the need for incorporating higher-resolution geological data and localized structural mapping to improve predictive accuracy for groundwater exploration in similar semi-arid crystalline basement terrains.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBeerhall Yield classification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYield m\u003csup\u003e3\u003c/sup\u003e/h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery High Yield\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh Yields\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation of the model\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\u003eID of the bore hall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYield in m\u003csup\u003e3\u003c/sup\u003e/h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlignment\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\u003e32.8864713\u0026deg;E 3.1641167\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot aligned with the result as Low\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\u003e32.8973558\u0026deg;E 3.1313555\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAligned with the Result\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\u003e32.9589395\u0026deg;E 3.2016978\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot aligned with the result\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\u003e32.9380934\u0026deg;E 3.2159653\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot aligned with the result\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\u003e32.9543511\u0026deg;E 3.3595554\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot Aligned with the result\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\u003e32.9192775\u0026deg;E 3.3841089\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAligned with the result\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\u003e33.0639723\u0026deg;E 3.2885108\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAligned with the result\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.0998420\u0026deg;E 3.2833053\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot aligned with the result\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.1854458\u0026deg;E 3.2945847\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ealigned with the result\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.2248570\u0026deg;E 3.2699937\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAligned with the result\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.3477907\u0026deg;E 3.3660538\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAligned with the result\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.4743159\u0026deg;E 3.3581832\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot Aligned with the result\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.5308768\u0026deg;E 3.3983156\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAligned with the Result\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.4743159\u0026deg;E 3.3581832\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAligned with the Result\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.3904211\u0026deg;E 3.4053767\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAligned with the Result\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.6095974\u0026deg;E 3.0198017\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAligned with the Result\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.6367909\u0026deg;E 2.9976837\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAligned with the Result\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.6497590\u0026deg;E 2.9260987\u0026deg;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAligned with the Result\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":"Declarations","content":"\u003cp\u003eWe affirm that this research represents a unique scholarly contribution. The collection of the data process, including the gathering of data, its interpretation, and the conclusions drawn, was conducted exclusively for this work. The findings discussed have not appeared in any other publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely acknowledge and thank the members of the school of engineering and applied science (SEAS) at Kampala International University starting with the Dean Prof Mustapha Mohamud Lawan the head of the civil department Dr Sani Aliyu and \u0026nbsp;for providing moral support, their technical advises were crucial to the successful completion of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAhmed Abdirizak Dirie: Research topic review and conceptualization, research gap analysis and writing, Prof.Ambiga: Supervision, A.A.M: Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available on request from the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that there were no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with ethical standards and guidelines of Kampala International University REC .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number is not applicable for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest regarding the publication of this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlam M, Chauhan P, Narayan Thakural L, Malviya D, Ahmad R, Sajid M. Identification of groundwater recharge potential zone using geospatial approaches and multi criteria decision models in Udham Singh Nagar district, Uttarakhand, India. Adv Sp Res. 2025;75:1931\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.asr.2024.10.039\u003c/span\u003e\u003cspan address=\"10.1016/j.asr.2024.10.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong Q, Liu Y, Wang Z, Xu Z. Assessing groundwater artificial recharge suitability in the Mi River basin using GIS, RS, and FAHP: a comprehensive analysis with seasonal variations. 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Trends, preferences, and status of indigenous and introduced. (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Analytic Hierarchy Process (AHP), Geographic Information Systems (GIS), Groundwater Potential Mapping, Acholi, Water Resource Management","lastPublishedDoi":"10.21203/rs.3.rs-9232280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9232280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater is a critical freshwater resource in semi-arid regions, particularly in water-stressed areas northern Uganda where surface water is scarce and unreliable. This study employed a geospatial approach integrated with multi-criteria decision analysis to identify groundwater potential zones in Kitgum and Pader districts of the Acholi subregion, Uganda. Utilizing Geographic Information Systems (GIS), remote sensing, and the Analytic Hierarchy Process (AHP), six thematic layers controlling groundwater occurrence rainfall, drainage density, slope, soil type, geology, and land use/land cover were developed, standardized, and assigned normalized weights based on their hydrogeological significance. A weighted overlay analysis in ArcGIS was applied to synthesize these layers into a comprehensive groundwater potential zonation map. The result catagorised the study area into five zones: very low, low, medium, high, and very high potential. The analysis revealed that Kitgum District is predominantly characterized by very low to low groundwater potential zones 67.24% when it is combined, constrained by factors such as higher drainage density, and steeper slopes. Conversely, Pader District sgowed favorable conditions, with 85.5% of its area classified as high to very high groundwater potential, attributed to gentle slopes,and higher rainfall, that enhance infiltration capacity. Validation against yield data from 18 existing boreholes demonstrated a 61.1% alignment between the model and actual well performance, confirming the model's reliability. This study demonstrates the effectiveness of GIS-based and AHP as a cost-effective tool for preliminary groundwater exploration in data-scarce environments. The resulting groundwater potential map provides a vital scientific foundation for sustainable water resource management, enabling targeted borehole siting, informed aquifer development, and resilience-building strategies to enhance water security for communities and livelihoods in the Acholi sub-region.\u003c/p\u003e","manuscriptTitle":"Groundwater Potential Mapping Using GIS,remote Sensing and AHP: A Case Study of Kitgum and Pader Districts, Acholi Sub-Region, Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 16:44:29","doi":"10.21203/rs.3.rs-9232280/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-05T06:09:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T20:14:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T20:42:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102727065109420392076359243261010982543","date":"2026-04-07T06:43:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262491994717244390275987979705878419363","date":"2026-04-06T14:08:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58062499803904430943474368652846507903","date":"2026-04-04T18:04:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T02:28:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-28T14:48:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-28T14:48:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-03-26T09:31:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23a39cc5-4800-4a3b-ae08-a5de8c326ddd","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-05T06:09:30+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T06:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 16:44:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9232280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9232280","identity":"rs-9232280","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}