Groundwater Recharge potential: Integrated Water Management Strategies in Koye Feche, Oromia Region, Ethiopia

Groundwater Recharge potential: Integrated Water Management Strategies in Koye Feche, Oromia Region, Ethiopia | 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 Recharge potential: Integrated Water Management Strategies in Koye Feche, Oromia Region, Ethiopia Peniel Bafe Unto, Alemayehu Kassa Ewentie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6490153/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Water scarcity poses a significant challenge to agricultural productivity and food security, necessitating effective water management strategies. Effective water management strategies are essential to ensuring sustainable access to water for agricultural use. This study evaluates groundwater recharge potential to enhance agricultural water availability and promote sustainable water use. A cross-sectional research design was employed, utilizing GIS, remote sensing, and the Analytical Hierarchy Process (AHP) methods to identify potential recharge zones. Approximately 6% of the study area, primarily in the northwest, exhibits high groundwater recharge potential, while moderate and low recharge zones cover 63% and 31%, respectively. The results highlight the importance of integrating groundwater recharge strategies with agricultural water management to enhance irrigation sustainability. To address future water scarcity, integrated water management strategies must be developed, emphasizing groundwater recharge in high-potential areas. Sustainable water allocation policies and advanced irrigation techniques should be implemented to optimize agricultural water use. These measures will support long-term water security and improve agricultural resilience in water-scarce regions. Water scarcity Groundwater recharge Water management GIS Remote sensing Sustainability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction 1.1 Background Access to clean and reliable water sources is essential for human well-being, economic development, and environmental sustainability (UN-Water, 2021). Water is a physical, social, cultural, economic, and political resource critical to human health and well-being, as it is a human right vital for everyone's survival (Hunde and Itefa 2020). However, water shortages arise when the demand exceeds the supply, leading to undesirable pressure on society (Girsha et al. 2016). Disturbingly, the trend of freshwater withdrawal and consumption patterns is also alarming (UN-Water 2021). The water supply sector faces numerous challenges such as water shortage, urbanization, population growth, geographical setup of cities, non-functionality of water supply sources including some groundwater sources, high water usage, and a large population (Alemu and Dioha, 2020; Divakar et al. 2011; MacDonald et al. 2011; Roozbahani et al. 2015).Ethiopia, known as the water tower of Africa, boasts multiple water sources, including 12 river basins with an annual volume of 122 billion m3 and a groundwater potential of 2.6–6.5 billion m 3 . However, the availability of potable water is scarce (Varady et al. 2023). The current water authority of Sheger city, only 50% of the demand has been met since 2005(AAWSA, 2011). It is estimated that the unmet water demand between 2015 and 2030 may increase by 48% in the study area (Alemu and Dioha ,2020). Similarly, Gelan and Koye Feche areas are known for their water scarcity and insufficient water management. The limited supply of freshwater resources combined with progressive growth in water demand is likely to worsen in the future, highlighting the importance of quantifying the current and future water demand-supply gap. Groundwater accounts for 60% of the world's freshwater supply, covering only 0.6% of the world's water (EPA 2009). In Ethiopia, nearly 924,140 km 2 of the highlands and Rift Valley hold about 185 billion m 3 of groundwater stored in sedimentary, volcanic, and quaternary rocks (Alemayehu et al. 2006). The presence and intensity of groundwater recharge zones vary due to factors such as soil texture, infiltration capacity, precipitation rate, climate conditions, and plant cover (Mengistu et al. 2022). Accurate information on Groundwater recharge potential zones is vital for effective water management. In this study, GIS and remote sensing, combined with weighted overlay analysis based on AHP techniques, are used to identify potential recharge zones in the study area. The water sources in the study area, as well as neighboring areas, rely on groundwater. However, there is evidence of groundwater exploitation, emphasizing the need for stakeholder involvement to preserve this valuable resource (WEF, 2021). Gelan and Koye Feche relies solely on groundwater as its water source. However, from site visit and preliminary interviews with the experts some wells have ceased to provide water, it is a critical problem considering that groundwater is the only water source. This phenomenon indicates a severe depletion of groundwater resources, leading to an imbalance between water supply and demand (Berhanu, B., & Teshome, A. 2018). The findings are particularly relevant in light of Sheger City's rapid urbanization plans, which include large-scale infrastructure developments such as luxury apartments and high-water-demand facilities (Ermiyas, 2022). Urban expansion exerts immense pressure on already scarce water resources, exacerbating supply-demand imbalances and increasing the risk of groundwater depletion (Bouziotas et al., 2015). Additionally, inadequate understanding of groundwater recharge potential areas can lead to ineffective water management decisions, further threatening long-term water security (Abdul Bari & Vennila, 2013). By identifying high-potential recharge zones, this study aims to support the development of strategic groundwater management initiatives in Gelan and Koye Feche. The research findings will inform groundwater recharge projects by providing scientifically validated data for selecting suitable locations for artificial recharge interventions, such as rainwater harvesting, infiltration ponds, and recharge wells. These measures will contribute to the conservation and sustainable utilization of groundwater resources, mitigating depletion risks and ensuring long-term water availability for agricultural and domestic use. Ultimately, integrating groundwater recharge strategies with sustainable water management policies is essential to enhancing irrigation resilience, securing water supply, and fostering climate-adaptive agricultural development in water-scarce regions. 2. Materials & Methods 2.1. Description of the Study Area Koye Feche is sub-cities located in the Oromia region under Sheger city administrative division. This area is characterized by unique geographical and hydrogeological features that influence groundwater management which is located at approximately 8.9333° N latitude and 38.6667° E longitude (Fig. 1 ). The elevation ranges from approximately 1,800 to 2,500 meters above mean sea level. The primary water source in the area is groundwater, derived from underlying aquifers. The study considered the Akaki catchment as the primary groundwater recharge zone, located between 8°36′–9°12′ N and 38°40′–39°4′ E, with an area coverage of approximately 1,500 km². The average annual rainfall is 1,170 mm, with a bimodal distribution pattern, characterized by the Kiremt (June to September) and Belg (February to May) seasons. The topography consists of rolling plains, valleys, steep riverbanks, and ridged terrains in Entoto, Sebeta, and Yeka mountainous areas. 2.2. Ground water recharging zone In identifying the potential recharge zones, the study took the Akaki catchment as an area of recharging zone. The zone is located between 8°36ʹ–9°12ʹ N and 38°40ʹ 39°4ʹ E with an area coverage of around 1500 km 2 Range (Fig. 2 ). The average rainfall is 1170 mm from a data obtained from 1990 to 2024. The rainfall follows slightly a bimodal type of rainfall for two seasons, the Kiremt (heavy precipitation) from June to September and the Belg (light precipitation) from February to May. The zone has a slope of a flat to gentle slope and very strong to steep slopes. The area is 2000 m a.m.s.l in the downstream of the catchment and 3000 m a.m.s.l in the upstream with ridged terrains in Entoto, Sebeta, and Yeka mountainous areas. The zone has physiographic components rolling plains, valleys, steep river banks, hills, and mountains. The image in Fig. 3 show the boundary of the zone which is analyzed for the groundwater recharging zone. 2.2. Data Collection The study utilized various biophysical datasets to assess groundwater recharge potential. These datasets were collected from reliable web sources and standardized in the same file types for consistency in analysis. Several key parameters were considered in the assessment. Lineament density was included as an essential factor, as lineaments—linear geological features—often indicate fractures and faults that enhance groundwater infiltration. Land use and land cover (LULC) data were incorporated to identify areas with high permeability and low runoff, such as fallow agricultural lands and open forests, which support recharge. Slope data were analyzed to distinguish areas with gentle topography where water can accumulate and infiltrate instead of quickly running off steep surfaces. Additionally, soil type and geomorphological data were examined to map regions with deep, permeable soils that facilitate efficient vertical water percolation. Drainage density was also evaluated, as areas with lower drainage density generally have a higher potential for groundwater recharge. To generate these datasets, various sources and methods were used. The LULC data were obtained from the U.S. Geological Survey Global Visualization Viewer website, ensuring cloud cover did not exceed 30%. Soil data were collected from the FAO soil type website, with shapefiles extracted using ArcGIS 10.4. Slope information was derived from a digital elevation model (DEM) using the slope tool in the Arc Hydro toolbox of ArcGIS 10.4, where the percentage of slope was calculated. Geomorphological features were identified using a geomorphological map from the Ministry of Geology, while geological data were extracted from a global geological map at a 1:500,000 scale, with shapefiles clipped for the study area. Lineament density was determined by digitizing and identifying fault lines from a georeferenced geological map using ArcGIS 10.4. Finally, drainage density was mapped using a 30-meter resolution Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) dataset through a series of hydro-processing steps, including filling sinks, creating flow direction and accumulation maps, generating stream networks, establishing stream orders, and converting stream order data into drainage density values. This systematic approach ensured a comprehensive and reliable assessment of groundwater recharge potential by integrating multiple biophysical factors. 2.3. Data analysis The identification of groundwater recharge potential areas was carried out using Geographic Information System (GIS) techniques. The data analysis process began with organizing and processing relevant datasets, including geological data, Shuttle Radar Topography Mission (SRTM) data, and Landsat imagery, to develop a groundwater potential zone map. Several key tasks were performed during this process, such as creating a drainage network, calculating drainage density, extracting elevation data, generating lineament data, computing lineament density, digitizing geological data, and classifying land use and land cover. A Digital Elevation Model (DEM) with a spatial resolution of 30 meters was extracted to represent the study area and its surroundings. Additional thematic maps, including slope, drainage density, and geomorphology maps, were derived from this DEM. To ensure consistency in the analysis, all retrieved data were standardized by assigning a common scale. Higher values were assigned to attributes more favorable for groundwater recharge, while lower values were given to less suitable attributes. The following parameters were standardized and overlaid: slope, soil type, land use/land cover (LULC), geology, drainage density, lineament density, and geomorphology. Weight values were assigned to each dataset based on their significance in groundwater recharge. By allocating percentage influences (%), the relative importance of each criterion was determined. Finally, all thematic layers were combined using a weighted overlay module in ArcGIS to generate the groundwater potential map. 2.3.1. Selection of Criteria The first step in the Analytic Hierarchy Process (AHP) approach involves selecting the key factors that influence groundwater recharge. A structured framework was developed to categorize and assess these factors based on their significance (Fig. 3 ). This framework was established by carefully selecting variables that affect groundwater recharge, as outlined in previous studies (Boroushaki & Malczewski, 2008). The identified factors were classified to determine potential recharge zones. The relative importance of each factor was evaluated using a nine-point scale to assess its contribution to groundwater recharge. 2.3.2. AHP Method for Groundwater Recharge Zone The assessment of groundwater recharge potential zones was conducted using multi-criteria analysis based on the AHP method (Moisa et al., 2023). Criteria weights for spatial data were calculated using a scientific ratio scale ranging from 1 to 7. This facilitated the evaluation of factors such as soil texture, soil drainage, slope, lineament density, drainage density, and land use/land cover (LULC) in modeling potential groundwater recharge zones. A pairwise comparison matrix was employed to reclassify weight parameters according to their influence and significance. This approach enabled the mapping of groundwater recharge potential zones within the study area (Moisa et al., 2022; Gao et al., 2023). 2.3.3. Estimating Relative Weights To minimize overfitting and reduce statistical noise, the AHP method excludes highly interdependent factors. Expert opinions, eigenvector principles, and eigenvalue techniques were utilized to establish the rankings of various parameters (Kabeto et al., 2014). The primary eigenvalue method played a crucial role in determining factor rankings, while expert judgment and eigenvector analysis helped assign appropriate weightings (Malczewski, 2006). The consistency ratio, which ensures the reliability of the weight assignments, was calculated using the formula: \(\:\varvec{C}\varvec{o}\varvec{n}\varvec{s}\varvec{i}\varvec{s}\varvec{t}\varvec{e}\varvec{n}\varvec{c}\varvec{y}\:\varvec{R}\varvec{a}\varvec{t}\varvec{i}\varvec{o}\:=\frac{\varvec{C}\varvec{I}}{\varvec{R}\varvec{I}}\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\left(1\right)\) where CI represents the Consistency Index, and RI is the Random Consistency Index, dependent on the matrix dimension. 2.3.4. Integration of Thematic Layers The spatial distribution of groundwater recharge within the study area was visualized through the creation of a groundwater recharge potential map. This was achieved using the weighted overlay tool in ArcGIS 10.4. The analysis involved integrating reclassified layers of lithology, slope, lineament density, soil type, land use/land cover (LULC), and drainage density, considering their respective percentage contributions to groundwater recharge. The Weighted Overlay analysis tool assigned values to each input raster layer, categorizing the results into five recharge potential levels: very high, high, medium, low, and very low. Each factor’s cell values were multiplied by their corresponding weights, and the results were summed to produce the final map (Esri, 2014; Raviraj et al., 2017). The methodology is illustrated in Fig. 3 , where two key processes are highlighted: reclassification of individual layers and integration of the reclassified layers using the weighted overlay analysis technique. This technique is guided by an AHP-based pairwise comparison matrix. The equation used for mapping the groundwater recharge potential zone is: $$\:\varvec{G}\varvec{R}\varvec{P}\varvec{Z}\:=\:\varvec{¼}\:\varvec{G}\varvec{w}\varvec{G}\varvec{r}\:+\:\varvec{S}\varvec{w}\varvec{S}\varvec{r}\:+\:\varvec{L}\varvec{u}\varvec{L}\varvec{v}\:\varvec{w}\varvec{l}\varvec{u}\varvec{l}\varvec{c}\:+\:\varvec{D}\varvec{d}\varvec{w}\varvec{D}\varvec{d}\:+\:\varvec{L}\varvec{d}\varvec{w}\varvec{L}\varvec{d}\varvec{r}\:+\:\varvec{S}\varvec{g}\varvec{w}\varvec{S}\varvec{g}\:+\:\varvec{G}\varvec{m}\varvec{W}\varvec{G}\varvec{m}\dots\:\dots\:..\:\:\left(2\right)$$ where GRPZ represents the groundwater recharge potential zone, G is geology, S is soil type, Lu is land use/land cover, Dd is drainage density, Ld is lineament density, Sg is slope gradient, and Gm is geomorphology. The subscripts indicate the weight and ranking assigned to each criterion, ensuring a structured integration of all thematic layers. To further refine the analysis, a suitability index (SI) was calculated using the following formula: $$\:\varvec{S}\varvec{I}\:=\:\varvec{\varSigma\:}\left(\varvec{W}\varvec{i}\:*\:\varvec{X}\varvec{i}\right)\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:..\dots\:.\left(3\right)$$ Where SI represents the suitability index, Wi denotes the weight assigned to factor i, and Xi represents the normalized criterion score for the respective factor. By using this formula, a comprehensive assessment of groundwater recharge potential was achieved, balancing the contributions of each factor. 2.3.5. Weighted Overlay Analysis The weighted overlay analysis technique, as presented (Esri, 2014), was used to integrate multiple spatial layers into a cohesive groundwater recharge potential model. This technique utilizes the pairwise comparison matrix from the AHP approach, ensuring a consistent value scale across all layers. Each input layer was reclassified into categories representing very high, high, moderate, low, and very low recharge potential. The relative weights of the groundwater recharge categories were determined using the AHP pairwise comparison matrix, ensuring the reliability of the weighting process. The calculated weights were then used to generate a spatial distribution map illustrating recharge potential across the study area, providing a detailed and practical perspective on groundwater recharge potential within the zone. This refined approach ensures that the analysis remains transparent, scientifically robust, and accessible to a broader audience, facilitating further research and policy applications. 3. Result and discussion 3.1. Groundwater Recharge Potential Zones Groundwater recharge potential zones (GRPZ) were identified using the Weighted Overlay analysis technique in ArcGIS 10.4. Thematic layers, including geology, soil type, slope, lineament density, land use/land cover (LULC), drainage density, and geomorphology, were integrated to classify the study area into different recharge potential zones. The classification resulted in three recharge potential zones: high, moderate, and low. The analysis revealed that a significant portion of the study area falls within the high and moderate recharge zones, indicating areas most suitable for groundwater recharge and storage 3.2. Analytical Hierarchy Process (AHP) for Groundwater Recharge Potential The AHP method was applied to assign weights to spatial data based on multi-criteria analysis. A numerical scale of 1–9, following moissa scale (Moisa et al. 2023), was used to rank the influencing factors. A pairwise comparison matrix was employed to determine the relative importance of geomorphology, slope, lineament density, drainage density, soil, geology, and LULC (Table 1 ) Table 1: Weightage Values of Thematic Maps Matrix LULC Geology Slope Drainage density Soil Lineament density Geomorphology Weight value (%) 1 2 3 4 5 6 7 LULC 1 1 3 3 3 3 5 5 24.7 Geology 2 1/3 1 3 3 3 3 3 20 Slope 3 1/3 1/3 1 1 3 3 5 12.9 Drainage density 4 1/3 1/3 1 1 1 2 3 8.7 Soil 5 1/3 1/3 1/3 1 1 1 3 14.3 Lineament density 6 1/5 1/3 1/3 ½ 1 1 1 8 Geomorphology 7 1/5 1/3 1/5 1/3 1/3 1 1 9.4 3.3. Consistency Ratio To validate the reliability of the weight assignment, the consistency ratio was calculated. The obtained consistency ratio was 0.07, which is below the acceptable threshold of 0.1, indicating that the weight assignments are consistent and suitable for Weighted Overlay analysis (Moisa et al., 2022). 3.4. Ranking of Thematic Layers The ranking of each thematic layer was determined based on previous studies (Shahid et al., 2000; Mitiku et al., 2023; Amjed & Majed, 2022; Kumar et al., 2019). The rankings are presented in Table 2 . Table 2 Rank of thematic maps Factors Class Rank Weightage (%) LULC bare ground 1 25 built area 1 Crop 4 Range land 1 Tree 4 Vegetation 5 Water 5 Soil Clay 5 15 Sandy-loam 4 Clay-loam 2 Geology Middle-biocene basalt 2 20 Miocene, ignimbrite, agglomerate 4 Quaternarnary,basalt, and spatter hayalcola 3 Oligocene, tuff, tranchyte 2 pliocene basalt and tranchyte 4 pliocene ignimbrite rhayolite 5 Slope 0–7 5 13 7–15 4 15–25 3 25–36 2 > 36 1 Drainage Density <1 5 8 1.0–2.0 4 2.0–3.0 3 3.0–4.0 2 4.0–5.0 1 Lineament Density 0–7.075760965 5 8 7.07576- 20.3043755 4 20.34756 -32.917658 3 32.91759–48.607441 2 48.60742–78.465417 1 Geomorphology Structural Landforms 4 10 Residual Landforms 2 7-Residual Landforms 2 8-Volcanic Landforms 3 9-Volcanic Landforms 3 Alluvial Landform 5 Residual Landforms 2 Volcanic Landforms 3 3.5. Thematic Layers 3.5.1. Land Use/Land Cover (LULC) : The study area comprises various LULC types, including bare land, water bodies, agricultural land, built-up areas, and forests. These were classified based on their recharge potential, with water bodies and forests having the highest recharge capacity and built-up areas having the least (Fig. 4 ). 3.5.2. Slope map : Slopes were categorized into five classes: 0–7°, 7–15°, 15–25°, 25–36°, and > 36°. Lower slopes (0–7°) were ranked highest for recharge potential, while steeper slopes (> 36°) were ranked lowest (Fig. 5 ). Among this divisions of slopes (0°–2°) classified as best for recharging which is 5, 7–15° ranked as good which is 4 and moderate slopes 15–25° ranked as 3 and the slops with gradient 25–36°, and greater than 36°ranked as number 2 and 1 which shows list for recharging. 3.5.3. Geology : The study area features diverse geological formations, including basalt, ignimbrite, and tuff. These were ranked according to their groundwater recharge potential, with permeable formations receiving higher scores. The geological setting of the study area comprises a diverse geological setting which can broadly classified as middle-biocene basal, Miocene, ignimbrite, agglomerate, Quaternarnary, basalt, basalt, basalt, and spatter hayalcola Oligocene, tuff, trachyte, Pliocene basalt and trachyte, Pliocene ignimbrite rhyolite. This geological formation ranked based on their suitability for groundwater recharging and ranked as 5 for the excellent and 1 for the least suitable as shown in Fig. 6 . 3.5.4. Drainage Density : Drainage patterns were analyzed to assess groundwater recharge potential. Areas with lower drainage density (0–1 km/km²) exhibited the highest recharge potential, while higher density areas (4–5 km/km²) had lower potential. In order to analyze the drainage patterns, the stream order values were reclassified and grouped, resulting in the creation of a drainage density map. This map was further divided into four distinct categories: 0 − 1 km/km² (classified as very high), 1–2.1 km/km² (classified as high which is 5), 2,1–3 km/km² (classified 4), and 3–4 km/km² (classified as 3), 3–4 km/km² (classified as very high), 4–5 km/km² (classified as least suitable). Thus, areas falling within the range of 0–1 km/km² exhibit a very high groundwater recharging potential, while areas within the range of 4–5 km/km² indicate a low groundwater potential (Fig. 7 ). 3.5.5. Lineament Density : Lineament density was derived from a DEM analysis, with higher densities in the central region indicating greater groundwater recharge potential, consistent with previous studies (Ardakani et al., 2022). The lineament data was calculated from a 30×30 resolution DEM within the study area, revealing a complex network of intersecting geological features (Fig. 8 ). The central part generally exhibits higher lineament density, which is favorable for groundwater recharge. The convergence of these high-density lineaments was ranked accordingly, with the last two classifications in Fig. 6 assigned ranks 4 and 5, while the first two received the lowest rankings. Assigning weights to the lineament densities was done by prioritizing areas with heightened density, as they indicate greater groundwater susceptibility. Previous studies (Ardakani et al., 2022) have also confirmed that high lineament density enhances groundwater recharge potential. 3.5.6. Soil map : Three soil types were identified: clay, sandy loam, and clay loam. Sandy loam soils were given higher weights due to their superior infiltration capacity. The role of soil in regulating the infiltration and percolation of surface water into the aquifer is of paramount importance, making the soil map a foundational element in the process of delineating groundwater potential zones. Within the study area, three primary soil types have been identified according to FAO soil classification, as outlined below. clay loam and sandy loam soils were accorded higher weights due to their favorable characteristics, followed by clay soils, which were assigned relatively lower weights. This systematic approach ensures a comprehensive consideration of soil types and their varying capacities to contribute to groundwater recharge processes (Fig. 9 ). 3.5.7. Geomorphology : The study area consists of residual landforms, volcanic landforms, and alluvial deposits, with residual landforms covering 38% of the area and assigned the highest weight due to their significant recharge capacity. Structural landforms are also identified as important groundwater recharge sources and given higher weightage. This classification framework helps distinguish and evaluate the varying groundwater prospects associated with each geomorphic unit, as detailed in Fig. 10 . 3.6. Groundwater Recharge Zones Groundwater potential zones in the study area were delineated using a Weighted Overlay analysis in ArcGIS, incorporating thematic layers such as land use, geology, geomorphology, soil type, slope, drainage density, and lineament density. These thematic maps were overlaid using their respective weightages, as shown in Table 3. The final classification divided the study area into three recharge potential zones: high, moderate, and low. The high recharge zones, covering 6% of the area, are primarily located in the northwest. Moderate recharge zones, covering 63% of the area, are found predominantly in the southern and northeastern regions. The low recharge zones, covering 31% of the area, are mainly concentrated in the central region. The final groundwater recharge potential map (Fig. 11 ) offers valuable insights for sustainable groundwater management and conservation strategies, highlighting areas with varying recharge potentials. 4. Conclusion 4.1. Conclusion This study effectively delineated groundwater recharge potential zones by integrating multiple thematic maps using the Weighted Overlay Analysis in ArcGIS. The classification of recharge zones into low, moderate, and high potential provided critical insights into the spatial distribution of groundwater recharge capacity within the study area. Findings indicate that the northwestern region exhibits the highest groundwater recharge potential, making it a prime area for groundwater conservation and sustainable utilization. Moderate recharge potential covers the majority of the study area (63%), highlighting the need for conservation practices to maintain recharge rates. However, the central region, which accounts for 31% of the study area, has the lowest recharge potential due to unfavorable geomorphological, soil, and slope characteristics. The presence of steep slopes, low lineament density, and impermeable geological formations in these areas significantly restricts groundwater infiltration. The study's consistency ratio of 0.07 confirms the reliability of the weightage assigned to different thematic factors, ensuring robust decision-making for water resource management. The ranking of thematic layers further validated that land use/land cover, geology, and slope are the most influential factors in determining groundwater recharge potential, followed by lineament density, drainage density, soil type, and geomorphology. To enhance groundwater recharge in low-potential areas, artificial recharge structures such as percolation tanks, check dams, and recharge wells should be constructed, while afforestation and soil conservation measures can help improve infiltration. In high-recharge zones, sustainable groundwater extraction and protection measures should be prioritized, including controlled pumping and contamination prevention strategies. Local authorities must integrate recharge maps into water resource planning, enforce land-use regulations to protect recharge areas, and promote public awareness on sustainable groundwater use. Future studies should utilize advanced remote sensing, machine learning, and hydrogeological modeling to refine recharge assessments and guide long-term groundwater management Declarations Acknowledgments The author would like to thank Ministry of Geology, Addis Ababa water and sewerage authority, ministry of water and energy, and the meteorology agency for their generous support and resources, which have facilitated this research endeavor. Consent to participate Informed consent was obtained from all participants involved in the study. Data availability statement All relevant data are included in the paper or its supplementary information. Conflict of interest The authors declare there is no conflict. Funding Declaration This research received no external funding. Clinical Trial Number Clinical trial number: not applicable. Clinical Trial Registration Details This study is not a clinical trial. Clinical trial registration: not applicable. Ethics and Consent to Publish Declarations Ethics approval and consent to participate: This study did not involve any experiments on humans or animals. Ethical approval and consent to participate were therefore not required. Consent for publication: Not applicable. This manuscript does not contain any individual person's data in any form (including individual details, images, or videos), and consent for publication is not required. Author Contribution declaration Peniel Bafe Unto and Alemayehu Kassa: Conceptualized the study, designed the research methodology, and conducted data analysis using Arch GIS software, remote sensing, and the Analytical Hierarchy Process (AHP) methods. Additionally, contributed to the interpretation of results and drafted the manuscript References Alemayehu, T., Legesse, D., Ayenew, T., Mohammed, N., & Waltenigus, S. (2006). Degree of groundwater vulnerability to pollution in Addis Ababa, Ethiopia. In Y. Xu & B. 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Kumar, P., Herath, S., Avtar, R., & Takeuchi, K. (2016). Mapping of groundwater potential zones in Killinochi area, Sri Lanka, using GIS and remote sensing techniques. Sustainable Water Resource Management, 2, 419–430. MacDonald, R. I., Green, P., Balk, D., Fekete, B. M., Revenga, C., Todd, M., & Montgomery, M. (2011). Urban growth, climate change, and freshwater availability. PNAS, 108(15), 6312–6317. Majed, I., & Amjed, S. (2022). Delineating potential sites for artificial groundwater recharge using a mathematical approach to remote sensing and GIS techniques. Water Supply, 22(4), 4230-4246. Malczewski, J. (2006). GIS-based Multi criteria decision analysis: A survey of the literature. International Journal of Geographical Information Science, 20(7), 703-726. Mengistu, T. D., Chang, S. W., Kim, I. H., Kim, M. G., & Chung, I. M. (2022). Determination of potential aquifer recharge zones using geospatial techniques for proxy data of Gilgel Gibe catchment, Ethiopia. Water, 14(9), 1362. Mitiku, M. B., Mengistu, M. G., Geda, F. N., Dereje, G. O., Misgana, L. D., Kiros, T. D., ... G, O. G. (2023). Evaluation of the groundwater recharge potential zone by using GIS and remote sensing in Ziway Abijata sub-basin, Central Rift Valley of Ethiopia. Water Supply, 23(8), 3416-3431. Moisa, M. B., Feyissa, M. E., Dejene, I. N., Tiye, F. S., Deribew, K. T., Roba, Z. R., Gemeda, D. O. (2023). Evaluation of land suitability for Moringa oleifera tree cultivation by using Geospatial technology and remote sensing: the case of Dhidhessa Catchment, Abay Basin, Ethiopia. Oil Crop Science, 8(1), 45-55 Roozbahani, R., Schreider, S., & Abbasi, B. (2015). Optimal water allocation through a multi-objective compromise between environmental, social, and economic preferences. Environmental Modelling & Software, 64, 18–30. Shahid, S., Nath, S., & Roy, J. (2000). Groundwater potential modelling in a soft rock area using a GIS. International Journal of Remote Sensing, 21, 1919-1924. United Nations. (2021). Valuing water: Facts and figures. Retrieved from https://www.globalwaters.org/sites/default/files/un_water_development Varady, R. G., Albrecht, T. R., Modak, S., Wilder, M. O., & Gerlak, A. K. (2023). Transboundary water governance scholarship: A critical review. Environments, 10(2), 27. World Economic Forum. (2021). The Global Risks Report. Geneva, Switzerland. Girsha, W. D., Adlo, A. M., Garoma, D. A., & Beggi, S. K. (2016). Assessment of water, sanitation and hygiene status of households in Welenchiti Town, Boset Woreda, East Shoa Zone. Ethiopia Sci J Publ Health, 4(6), 435. AAWSA. (2011). Annual Report 2010. Berhanu, B., & Teshome, A. (2018). The impact of rapid urban population growth on water demand: the case of Addis Ababa city, Ethiopia. International Journal of Water Resources Development, 34(2), 192-205. Abdul Bari, J., & Vennila, G. (n.d.). Studies on various thematic maps for identifying groundwater potential zones of Bhavan Taluk, Erode District, India. Pollution Research, 32(1), 55–60. Boroushaki, S., & Malczewski, J. (2008). Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Computers & Geosciences, 34(4), 399-410. ESRI. (2014). Model Builder, Spatial Analyst 2.0, ArcView GIS. Environmental Systems Research Institute. Raviraj, A., Kuruppath, N., & Kannan, B. (2017). Identification of potential groundwater recharge zones using remote sensing and geographical information system in Amaravathy Basin. Journal of Remote Sensing GIS, 6(4), 1-10. Kabeto, J., Adeba, D., Regasa, M. S., & Leta, M. K. (2022). Groundwater Potential Assessment Using GIS and Remote Sensing Techniques: Case Study of West Arsi Zone, Ethiopia. Water, 14(12), 1838. Additional Declarations No competing interests reported. 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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-6490153","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458700229,"identity":"7dcbb428-9569-443e-af5f-36b5be3f547b","order_by":0,"name":"Peniel Bafe Unto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACAwbGByCah4Gd+QCQlpAhQguzAUQLM1sCSAsP0VoYGJh5oHoJAXP2w4yPCyruyPAz83x+daPGAujCw0c34NNi2ZPMbDzjzDMeyWbebdY5x4AO40lLu4HXYQfyj0nzth3mMTjMu804hw2oRYLHDL+W84/Zf/P+O8xjf5jnmXHOP2K03EhmY+ZtANrCzMP8OLeNCC2WMx4zS884dphH4jCbGXNunwQPGyG/mPMnM34uqDlsz9/e/Phzzrc6OX72w8fwagEBZijNJgEmCSlH1sL8gRjVo2AUjIJRMPIAACKsQOHn+SFNAAAAAElFTkSuQmCC","orcid":"","institution":"Addis College","correspondingAuthor":true,"prefix":"","firstName":"Peniel","middleName":"Bafe","lastName":"Unto","suffix":""},{"id":458700230,"identity":"5b61364f-84e0-4783-9228-348d19224b62","order_by":1,"name":"Alemayehu Kassa Ewentie","email":"","orcid":"","institution":"Addis College","correspondingAuthor":false,"prefix":"","firstName":"Alemayehu","middleName":"Kassa","lastName":"Ewentie","suffix":""}],"badges":[],"createdAt":"2025-04-20 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area\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6490153/v1/ce7ac21d2ee64fd56f622b5f.png"},{"id":83228910,"identity":"43e4d9b8-1447-470b-ad48-b8db188f84b9","added_by":"auto","created_at":"2025-05-21 12:31:44","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":144729,"visible":true,"origin":"","legend":"\u003cp\u003eBoundary of recharge zone\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6490153/v1/4b36eb8058a69d3f4e619c50.jpeg"},{"id":83228907,"identity":"b824483f-549d-49f7-801c-93b12fc8fc7e","added_by":"auto","created_at":"2025-05-21 12:31:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95206,"visible":true,"origin":"","legend":"\u003cp\u003eStudy frame work to delineate recharge 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map\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6490153/v1/52d1b19dbee817b7a8364741.png"},{"id":83229504,"identity":"ca1fba1c-6089-4d15-b624-6e05b9ca6bd9","added_by":"auto","created_at":"2025-05-21 12:39:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":120857,"visible":true,"origin":"","legend":"\u003cp\u003eGeology map\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6490153/v1/5395d1da0218d1c3f4d50aee.png"},{"id":83228916,"identity":"50fd5bc3-9408-402a-b6ce-1f36bf1caa14","added_by":"auto","created_at":"2025-05-21 12:31:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":251073,"visible":true,"origin":"","legend":"\u003cp\u003eDrainage density map\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6490153/v1/0aeedf5c78524d782e410ac1.png"},{"id":83228922,"identity":"a6548601-368a-48c0-82b1-d6c41e084fa0","added_by":"auto","created_at":"2025-05-21 12:31:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":265241,"visible":true,"origin":"","legend":"\u003cp\u003eLineament density map\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6490153/v1/19d7e79178668ba889942b92.png"},{"id":83230590,"identity":"f2ec31ce-a1e5-4e87-9b3f-a7a358a701f1","added_by":"auto","created_at":"2025-05-21 13:03:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":134603,"visible":true,"origin":"","legend":"\u003cp\u003eSoil map\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6490153/v1/2eb04bc2bfe46edb379fe68a.png"},{"id":83229510,"identity":"7b7165cd-2e6c-4967-bcd6-bcfc65bc474d","added_by":"auto","created_at":"2025-05-21 12:39:45","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":217745,"visible":true,"origin":"","legend":"\u003cp\u003eGeomorphology map\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6490153/v1/e659c0b2de439faa4a4b5f1d.png"},{"id":83228921,"identity":"e3b59302-2985-438a-ba2e-5fa172f44bec","added_by":"auto","created_at":"2025-05-21 12:31:44","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":175382,"visible":true,"origin":"","legend":"\u003cp\u003ePotential ground water recharging area\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-6490153/v1/3d86942436fb2759d9fde50a.png"},{"id":90967137,"identity":"436aa7ac-391d-415e-b5e0-818e4abff83a","added_by":"auto","created_at":"2025-09-10 06:47:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3018929,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6490153/v1/2651bb5d-4eb7-4d0f-b89d-2f89d001b794.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Groundwater Recharge potential: Integrated Water Management Strategies in Koye Feche, Oromia Region, Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Background\u003c/h2\u003e \u003cp\u003eAccess to clean and reliable water sources is essential for human well-being, economic development, and environmental sustainability (UN-Water, 2021). Water is a physical, social, cultural, economic, and political resource critical to human health and well-being, as it is a human right vital for everyone's survival (Hunde and Itefa 2020). However, water shortages arise when the demand exceeds the supply, leading to undesirable pressure on society (Girsha et al. 2016). Disturbingly, the trend of freshwater withdrawal and consumption patterns is also alarming (UN-Water 2021).\u003c/p\u003e \u003cp\u003eThe water supply sector faces numerous challenges such as water shortage, urbanization, population growth, geographical setup of cities, non-functionality of water supply sources including some groundwater sources, high water usage, and a large population (Alemu and Dioha, 2020; Divakar et al. 2011; MacDonald et al. 2011; Roozbahani et al. 2015).Ethiopia, known as the water tower of Africa, boasts multiple water sources, including 12 river basins with an annual volume of 122\u0026nbsp;billion m3 and a groundwater potential of 2.6\u0026ndash;6.5\u0026nbsp;billion m\u003csup\u003e3\u003c/sup\u003e. However, the availability of potable water is scarce (Varady et al. 2023). The current water authority of Sheger city, only 50% of the demand has been met since 2005(AAWSA, 2011). It is estimated that the unmet water demand between 2015 and 2030 may increase by 48% in the study area (Alemu and Dioha ,2020). Similarly, Gelan and Koye Feche areas are known for their water scarcity and insufficient water management. The limited supply of freshwater resources combined with progressive growth in water demand is likely to worsen in the future, highlighting the importance of quantifying the current and future water demand-supply gap.\u003c/p\u003e \u003cp\u003eGroundwater accounts for 60% of the world's freshwater supply, covering only 0.6% of the world's water (EPA 2009). In Ethiopia, nearly 924,140 km\u003csup\u003e2\u003c/sup\u003e of the highlands and Rift Valley hold about 185\u0026nbsp;billion m\u003csup\u003e3\u003c/sup\u003e of groundwater stored in sedimentary, volcanic, and quaternary rocks (Alemayehu et al. 2006). The presence and intensity of groundwater recharge zones vary due to factors such as soil texture, infiltration capacity, precipitation rate, climate conditions, and plant cover (Mengistu et al. 2022).\u003c/p\u003e \u003cp\u003eAccurate information on Groundwater recharge potential zones is vital for effective water management. In this study, GIS and remote sensing, combined with weighted overlay analysis based on AHP techniques, are used to identify potential recharge zones in the study area.\u003c/p\u003e \u003cp\u003eThe water sources in the study area, as well as neighboring areas, rely on groundwater. However, there is evidence of groundwater exploitation, emphasizing the need for stakeholder involvement to preserve this valuable resource (WEF, 2021).\u003c/p\u003e \u003cp\u003eGelan and Koye Feche relies solely on groundwater as its water source. However, from site visit and preliminary interviews with the experts some wells have ceased to provide water, it is a critical problem considering that groundwater is the only water source. This phenomenon indicates a severe depletion of groundwater resources, leading to an imbalance between water supply and demand (Berhanu, B., \u0026amp; Teshome, A. 2018).\u003c/p\u003e \u003cp\u003eThe findings are particularly relevant in light of Sheger City's rapid urbanization plans, which include large-scale infrastructure developments such as luxury apartments and high-water-demand facilities (Ermiyas, 2022). Urban expansion exerts immense pressure on already scarce water resources, exacerbating supply-demand imbalances and increasing the risk of groundwater depletion (Bouziotas et al., 2015). Additionally, inadequate understanding of groundwater recharge potential areas can lead to ineffective water management decisions, further threatening long-term water security (Abdul Bari \u0026amp; Vennila, 2013).\u003c/p\u003e \u003cp\u003eBy identifying high-potential recharge zones, this study aims to support the development of strategic groundwater management initiatives in Gelan and Koye Feche. The research findings will inform groundwater recharge projects by providing scientifically validated data for selecting suitable locations for artificial recharge interventions, such as rainwater harvesting, infiltration ponds, and recharge wells. These measures will contribute to the conservation and sustainable utilization of groundwater resources, mitigating depletion risks and ensuring long-term water availability for agricultural and domestic use. Ultimately, integrating groundwater recharge strategies with sustainable water management policies is essential to enhancing irrigation resilience, securing water supply, and fostering climate-adaptive agricultural development in water-scarce regions.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Materials \u0026 Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Description of the Study Area\u003c/h2\u003e \u003cp\u003eKoye Feche is sub-cities located in the Oromia region under Sheger city administrative division. This area is characterized by unique geographical and hydrogeological features that influence groundwater management which is located at approximately 8.9333\u0026deg; N latitude and 38.6667\u0026deg; E longitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The elevation ranges from approximately 1,800 to 2,500 meters above mean sea level. The primary water source in the area is groundwater, derived from underlying aquifers.\u003c/p\u003e \u003cp\u003eThe study considered the Akaki catchment as the primary groundwater recharge zone, located between 8\u0026deg;36\u0026prime;\u0026ndash;9\u0026deg;12\u0026prime; N and 38\u0026deg;40\u0026prime;\u0026ndash;39\u0026deg;4\u0026prime; E, with an area coverage of approximately 1,500 km\u0026sup2;. The average annual rainfall is 1,170 mm, with a bimodal distribution pattern, characterized by the Kiremt (June to September) and Belg (February to May) seasons. The topography consists of rolling plains, valleys, steep riverbanks, and ridged terrains in Entoto, Sebeta, and Yeka mountainous areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Ground water recharging zone\u003c/h2\u003e \u003cp\u003eIn identifying the potential recharge zones, the study took the Akaki catchment as an area of recharging zone. The zone is located between 8\u0026deg;36ʹ\u0026ndash;9\u0026deg;12ʹ N and 38\u0026deg;40ʹ 39\u0026deg;4ʹ E with an area coverage of around 1500 km\u003csup\u003e2\u003c/sup\u003e Range (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The average rainfall is 1170 mm from a data obtained from 1990 to 2024. The rainfall follows slightly a bimodal type of rainfall for two seasons, the Kiremt (heavy precipitation) from June to September and the Belg (light precipitation) from February to May. The zone has a slope of a flat to gentle slope and very strong to steep slopes. The area is 2000 m a.m.s.l in the downstream of the catchment and 3000 m a.m.s.l in the upstream with ridged terrains in Entoto, Sebeta, and Yeka mountainous areas. The zone has physiographic components rolling plains, valleys, steep river banks, hills, and mountains. The image in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show the boundary of the zone which is analyzed for the groundwater recharging zone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Collection\u003c/h2\u003e \u003cp\u003eThe study utilized various biophysical datasets to assess groundwater recharge potential. These datasets were collected from reliable web sources and standardized in the same file types for consistency in analysis. Several key parameters were considered in the assessment. Lineament density was included as an essential factor, as lineaments\u0026mdash;linear geological features\u0026mdash;often indicate fractures and faults that enhance groundwater infiltration. Land use and land cover (LULC) data were incorporated to identify areas with high permeability and low runoff, such as fallow agricultural lands and open forests, which support recharge. Slope data were analyzed to distinguish areas with gentle topography where water can accumulate and infiltrate instead of quickly running off steep surfaces. Additionally, soil type and geomorphological data were examined to map regions with deep, permeable soils that facilitate efficient vertical water percolation. Drainage density was also evaluated, as areas with lower drainage density generally have a higher potential for groundwater recharge.\u003c/p\u003e \u003cp\u003eTo generate these datasets, various sources and methods were used. The LULC data were obtained from the U.S. Geological Survey Global Visualization Viewer website, ensuring cloud cover did not exceed 30%. Soil data were collected from the FAO soil type website, with shapefiles extracted using ArcGIS 10.4. Slope information was derived from a digital elevation model (DEM) using the slope tool in the Arc Hydro toolbox of ArcGIS 10.4, where the percentage of slope was calculated. Geomorphological features were identified using a geomorphological map from the Ministry of Geology, while geological data were extracted from a global geological map at a 1:500,000 scale, with shapefiles clipped for the study area. Lineament density was determined by digitizing and identifying fault lines from a georeferenced geological map using ArcGIS 10.4. Finally, drainage density was mapped using a 30-meter resolution Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) dataset through a series of hydro-processing steps, including filling sinks, creating flow direction and accumulation maps, generating stream networks, establishing stream orders, and converting stream order data into drainage density values. This systematic approach ensured a comprehensive and reliable assessment of groundwater recharge potential by integrating multiple biophysical factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data analysis\u003c/h2\u003e \u003cp\u003eThe identification of groundwater recharge potential areas was carried out using Geographic Information System (GIS) techniques. The data analysis process began with organizing and processing relevant datasets, including geological data, Shuttle Radar Topography Mission (SRTM) data, and Landsat imagery, to develop a groundwater potential zone map. Several key tasks were performed during this process, such as creating a drainage network, calculating drainage density, extracting elevation data, generating lineament data, computing lineament density, digitizing geological data, and classifying land use and land cover.\u003c/p\u003e \u003cp\u003eA Digital Elevation Model (DEM) with a spatial resolution of 30 meters was extracted to represent the study area and its surroundings. Additional thematic maps, including slope, drainage density, and geomorphology maps, were derived from this DEM. To ensure consistency in the analysis, all retrieved data were standardized by assigning a common scale. Higher values were assigned to attributes more favorable for groundwater recharge, while lower values were given to less suitable attributes. The following parameters were standardized and overlaid: slope, soil type, land use/land cover (LULC), geology, drainage density, lineament density, and geomorphology. Weight values were assigned to each dataset based on their significance in groundwater recharge. By allocating percentage influences (%), the relative importance of each criterion was determined. Finally, all thematic layers were combined using a weighted overlay module in ArcGIS to generate the groundwater potential map.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Selection of Criteria\u003c/h2\u003e \u003cp\u003eThe first step in the Analytic Hierarchy Process (AHP) approach involves selecting the key factors that influence groundwater recharge. A structured framework was developed to categorize and assess these factors based on their significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This framework was established by carefully selecting variables that affect groundwater recharge, as outlined in previous studies (Boroushaki \u0026amp; Malczewski, 2008). The identified factors were classified to determine potential recharge zones. The relative importance of each factor was evaluated using a nine-point scale to assess its contribution to groundwater recharge.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2.3.2. AHP Method for Groundwater Recharge Zone\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe assessment of groundwater recharge potential zones was conducted using multi-criteria analysis based on the AHP method (Moisa et al., 2023). Criteria weights for spatial data were calculated using a scientific ratio scale ranging from 1 to 7. This facilitated the evaluation of factors such as soil texture, soil drainage, slope, lineament density, drainage density, and land use/land cover (LULC) in modeling potential groundwater recharge zones. A pairwise comparison matrix was employed to reclassify weight parameters according to their influence and significance. This approach enabled the mapping of groundwater recharge potential zones within the study area (Moisa et al., 2022; Gao et al., 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Estimating Relative Weights\u003c/h2\u003e \u003cp\u003eTo minimize overfitting and reduce statistical noise, the AHP method excludes highly interdependent factors. Expert opinions, eigenvector principles, and eigenvalue techniques were utilized to establish the rankings of various parameters (Kabeto et al., 2014). The primary eigenvalue method played a crucial role in determining factor rankings, while expert judgment and eigenvector analysis helped assign appropriate weightings (Malczewski, 2006). The consistency ratio, which ensures the reliability of the weight assignments, was calculated using the formula:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{C}\\varvec{o}\\varvec{n}\\varvec{s}\\varvec{i}\\varvec{s}\\varvec{t}\\varvec{e}\\varvec{n}\\varvec{c}\\varvec{y}\\:\\varvec{R}\\varvec{a}\\varvec{t}\\varvec{i}\\varvec{o}\\:=\\frac{\\varvec{C}\\varvec{I}}{\\varvec{R}\\varvec{I}}\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\left(1\\right)\\)\u003c/span\u003e \u003c/span\u003ewhere CI represents the Consistency Index, and RI is the Random Consistency Index, dependent on the matrix dimension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2.3.4. Integration of Thematic Layers\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe spatial distribution of groundwater recharge within the study area was visualized through the creation of a groundwater recharge potential map. This was achieved using the weighted overlay tool in ArcGIS 10.4. The analysis involved integrating reclassified layers of lithology, slope, lineament density, soil type, land use/land cover (LULC), and drainage density, considering their respective percentage contributions to groundwater recharge. The Weighted Overlay analysis tool assigned values to each input raster layer, categorizing the results into five recharge potential levels: very high, high, medium, low, and very low. Each factor\u0026rsquo;s cell values were multiplied by their corresponding weights, and the results were summed to produce the final map (Esri, 2014; Raviraj et al., 2017).\u003c/p\u003e \u003cp\u003eThe methodology is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, where two key processes are highlighted: reclassification of individual layers and integration of the reclassified layers using the weighted overlay analysis technique. This technique is guided by an AHP-based pairwise comparison matrix. The equation used for mapping the groundwater recharge potential zone is:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{G}\\varvec{R}\\varvec{P}\\varvec{Z}\\:=\\:\\varvec{\u0026frac14;}\\:\\varvec{G}\\varvec{w}\\varvec{G}\\varvec{r}\\:+\\:\\varvec{S}\\varvec{w}\\varvec{S}\\varvec{r}\\:+\\:\\varvec{L}\\varvec{u}\\varvec{L}\\varvec{v}\\:\\varvec{w}\\varvec{l}\\varvec{u}\\varvec{l}\\varvec{c}\\:+\\:\\varvec{D}\\varvec{d}\\varvec{w}\\varvec{D}\\varvec{d}\\:+\\:\\varvec{L}\\varvec{d}\\varvec{w}\\varvec{L}\\varvec{d}\\varvec{r}\\:+\\:\\varvec{S}\\varvec{g}\\varvec{w}\\varvec{S}\\varvec{g}\\:+\\:\\varvec{G}\\varvec{m}\\varvec{W}\\varvec{G}\\varvec{m}\\dots\\:\\dots\\:..\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere GRPZ represents the groundwater recharge potential zone, G is geology, S is soil type, Lu is land use/land cover, Dd is drainage density, Ld is lineament density, Sg is slope gradient, and Gm is geomorphology. The subscripts indicate the weight and ranking assigned to each criterion, ensuring a structured integration of all thematic layers.\u003c/p\u003e \u003cp\u003eTo further refine the analysis, a suitability index (SI) was calculated using the following formula:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{S}\\varvec{I}\\:=\\:\\varvec{\\varSigma\\:}\\left(\\varvec{W}\\varvec{i}\\:*\\:\\varvec{X}\\varvec{i}\\right)\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:..\\dots\\:.\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere SI represents the suitability index, Wi denotes the weight assigned to factor i, and Xi represents the normalized criterion score for the respective factor. By using this formula, a comprehensive assessment of groundwater recharge potential was achieved, balancing the contributions of each factor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2.3.5. Weighted Overlay Analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe weighted overlay analysis technique, as presented (Esri, 2014), was used to integrate multiple spatial layers into a cohesive groundwater recharge potential model. This technique utilizes the pairwise comparison matrix from the AHP approach, ensuring a consistent value scale across all layers. Each input layer was reclassified into categories representing very high, high, moderate, low, and very low recharge potential. The relative weights of the groundwater recharge categories were determined using the AHP pairwise comparison matrix, ensuring the reliability of the weighting process. The calculated weights were then used to generate a spatial distribution map illustrating recharge potential across the study area, providing a detailed and practical perspective on groundwater recharge potential within the zone.\u003c/p\u003e \u003cp\u003eThis refined approach ensures that the analysis remains transparent, scientifically robust, and accessible to a broader audience, facilitating further research and policy applications.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Result and discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Groundwater Recharge Potential Zones\u003c/h2\u003e\n \u003cp\u003eGroundwater recharge potential zones (GRPZ) were identified using the Weighted Overlay analysis technique in ArcGIS 10.4. Thematic layers, including geology, soil type, slope, lineament density, land use/land cover (LULC), drainage density, and geomorphology, were integrated to classify the study area into different recharge potential zones.\u003c/p\u003e\n \u003cp\u003eThe classification resulted in three recharge potential zones: high, moderate, and low. The analysis revealed that a significant portion of the study area falls within the high and moderate recharge zones, indicating areas most suitable for groundwater recharge and storage\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Analytical Hierarchy Process (AHP) for Groundwater Recharge Potential\u003c/h2\u003e\n \u003cp\u003eThe AHP method was applied to assign weights to spatial data based on multi-criteria analysis. A numerical scale of 1\u0026ndash;9, following moissa scale (Moisa et al. 2023), was used to rank the influencing factors. A pairwise comparison matrix was employed to determine the relative importance of geomorphology, slope, lineament density, drainage density, soil, geology, and LULC (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;font-size:12px;font-family:\"Calibri\",sans-serif;color:#44546A;font-style:italic;text-align:;;'\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eTable 1: Weightage Values of Thematic Maps\u003c/span\u003e\u003c/p\u003e\n \u003ctable style=\"border: none;width:6.5in;margin-left:4.25pt;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.3pt;border:solid black 1.0pt;border-right: none;background:white;padding:0in 5.4pt 0in 5.4pt;height:60.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eMatrix\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.55pt;border-top: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-right: none;background: white;padding: 0in 5.4pt;height: 60pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.45pt;border-top: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-right: none;background: white;padding: 0in 5.4pt;height: 60pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eLULC\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.55pt;border-top: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-right: none;background: white;padding: 0in 5.4pt;height: 60pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eGeology\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.45pt;border-top: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-right: none;background: white;padding: 0in 5.4pt;height: 60pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eSlope\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.5pt;border-top: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-right: none;background: white;padding: 0in 5.4pt;height: 60pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eDrainage density\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5pt;border-top: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-right: none;background: white;padding: 0in 5.4pt;height: 60pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eSoil\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.5in;border-top: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-right: none;background: white;padding: 0in 5.4pt;height: 60pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eLineament density\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.05pt;border-top: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-right: none;background: white;padding: 0in 5.4pt;height: 60pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eGeomorphology\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89.65pt;border: 1pt solid black;padding: 0in 5.4pt;height: 60pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;line-height:105%;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:105%;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eWeight value (%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131.3pt;border-top: none;border-left: 1pt solid black;border-bottom: 1pt solid black;border-right: none;background: white;padding: 0in 5.4pt;height: 17.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;line-height:normal;'\u003e\u003cspan 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style=\"width:89.65pt;border:solid black 1.0pt;border-top:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:19.75pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003e14.3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.3pt;border-top:none;border-left:solid black 1.0pt;border-bottom:solid black 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:22.45pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eLineament density\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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New Roman\",serif;color:black;'\u003e\u0026nbsp;1/5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:31.55pt;border-top:none;border-left:solid black 1.0pt;border-bottom:solid black 1.0pt;border-right:none;background:yellow;padding:0in 5.4pt 0in 5.4pt;height:17.95pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;1/3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:31.45pt;border-top:none;border-left:solid black 1.0pt;border-bottom:solid black 1.0pt;border-right:none;background:yellow;padding:0in 5.4pt 0in 5.4pt;height:17.95pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003e1/5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:31.5pt;border-top:none;border-left:solid black 1.0pt;border-bottom:solid black 1.0pt;border-right:none;background:yellow;padding:0in 5.4pt 0in 5.4pt;height:17.95pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;1/3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:26.5pt;border-top:none;border-left:solid black 1.0pt;border-bottom:solid black 1.0pt;border-right:none;background:yellow;padding:0in 5.4pt 0in 5.4pt;height:17.95pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003e1/3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:.5in;border-top:none;border-left:solid black 1.0pt;border-bottom:solid black 1.0pt;border-right:none;background:yellow;padding:0in 5.4pt 0in 5.4pt;height:17.95pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003e1 \u0026nbsp; \u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.05pt;border-top:none;border-left:solid black 1.0pt;border-bottom:solid black 1.0pt;border-right:none;background:#00B050;padding:0in 5.4pt 0in 5.4pt;height:17.95pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003e1 \u0026nbsp; \u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:89.65pt;border:solid black 1.0pt;border-top:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:17.95pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003e9.4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Consistency Ratio\u003c/h2\u003e\n \u003cp\u003eTo validate the reliability of the weight assignment, the consistency ratio was calculated. The obtained consistency ratio was 0.07, which is below the acceptable threshold of 0.1, indicating that the weight assignments are consistent and suitable for Weighted Overlay analysis (Moisa et al., 2022).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Ranking of Thematic Layers\u003c/h2\u003e\n \u003cp\u003eThe ranking of each thematic layer was determined based on previous studies (Shahid et al., 2000; Mitiku et al., 2023; Amjed \u0026amp; Majed, 2022; Kumar et al., 2019). The rankings are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRank of thematic maps\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeightage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebare ground\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"7\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebuilt area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCrop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSandy-loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay-loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eGeology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle-biocene basalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"6\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiocene, ignimbrite, agglomerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuaternarnary,basalt, and spatter hayalcola\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOligocene, tuff, tranchyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epliocene basalt and tranchyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epliocene ignimbrite rhayolite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026ndash;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eDrainage Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u0026ndash;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u0026ndash;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0\u0026ndash;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0\u0026ndash;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eLineament Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;7.075760965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.07576- 20.3043755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.34756 -32.917658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.91759\u0026ndash;48.607441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.60742\u0026ndash;78.465417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eGeomorphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStructural Landforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual Landforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7-Residual Landforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8-Volcanic Landforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9-Volcanic Landforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlluvial Landform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual Landforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolcanic Landforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Thematic Layers\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003e3.5.1. Land Use/Land Cover (LULC)\u003c/strong\u003e: The study area comprises various LULC types, including bare land, water bodies, agricultural land, built-up areas, and forests. These were classified based on their recharge potential, with water bodies and forests having the highest recharge capacity and built-up areas having the least (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.5.2. Slope map\u003c/strong\u003e: Slopes were categorized into five classes: 0\u0026ndash;7\u0026deg;, 7\u0026ndash;15\u0026deg;, 15\u0026ndash;25\u0026deg;, 25\u0026ndash;36\u0026deg;, and \u0026gt;\u0026thinsp;36\u0026deg;. Lower slopes (0\u0026ndash;7\u0026deg;) were ranked highest for recharge potential, while steeper slopes (\u0026gt;\u0026thinsp;36\u0026deg;) were ranked lowest (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Among this divisions of slopes (0\u0026deg;\u0026ndash;2\u0026deg;) classified as best for recharging which is 5, 7\u0026ndash;15\u0026deg; ranked as good which is 4 and moderate slopes 15\u0026ndash;25\u0026deg; ranked as 3 and the slops with gradient 25\u0026ndash;36\u0026deg;, and greater than 36\u0026deg;ranked as number 2 and 1 which shows list for recharging.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.5.3. Geology\u003c/strong\u003e: The study area features diverse geological formations, including basalt, ignimbrite, and tuff. These were ranked according to their groundwater recharge potential, with permeable formations receiving higher scores. The geological setting of the study area comprises a diverse geological setting which can broadly classified as middle-biocene basal, Miocene, ignimbrite, agglomerate, Quaternarnary, basalt, basalt, basalt, and spatter hayalcola Oligocene, tuff, trachyte, Pliocene basalt and trachyte, Pliocene ignimbrite rhyolite. This geological formation ranked based on their suitability for groundwater recharging and ranked as 5 for the excellent and 1 for the least suitable as shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.5.4. Drainage Density\u003c/strong\u003e: Drainage patterns were analyzed to assess groundwater recharge potential. Areas with lower drainage density (0\u0026ndash;1 km/km\u0026sup2;) exhibited the highest recharge potential, while higher density areas (4\u0026ndash;5 km/km\u0026sup2;) had lower potential.\u003c/p\u003e\n \u003cp\u003eIn order to analyze the drainage patterns, the stream order values were reclassified and grouped, resulting in the creation of a drainage density map. This map was further divided into four distinct categories: 0 \u0026minus;\u0026thinsp;1 km/km\u0026sup2; (classified as very high), 1\u0026ndash;2.1 km/km\u0026sup2; (classified as high which is 5), 2,1\u0026ndash;3 km/km\u0026sup2; (classified 4), and 3\u0026ndash;4 km/km\u0026sup2; (classified as 3), 3\u0026ndash;4 km/km\u0026sup2; (classified as very high), 4\u0026ndash;5 km/km\u0026sup2; (classified as least suitable). Thus, areas falling within the range of 0\u0026ndash;1 km/km\u0026sup2; exhibit a very high groundwater recharging potential, while areas within the range of 4\u0026ndash;5 km/km\u0026sup2; indicate a low groundwater potential (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.5.5. Lineament Density\u003c/strong\u003e: Lineament density was derived from a DEM analysis, with higher densities in the central region indicating greater groundwater recharge potential, consistent with previous studies (Ardakani et al., 2022). The lineament data was calculated from a 30\u0026times;30 resolution DEM within the study area, revealing a complex network of intersecting geological features (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The central part generally exhibits higher lineament density, which is favorable for groundwater recharge. The convergence of these high-density lineaments was ranked accordingly, with the last two classifications in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e assigned ranks 4 and 5, while the first two received the lowest rankings. Assigning weights to the lineament densities was done by prioritizing areas with heightened density, as they indicate greater groundwater susceptibility. Previous studies (Ardakani et al., 2022) have also confirmed that high lineament density enhances groundwater recharge potential.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.5.6. Soil map\u003c/strong\u003e: Three soil types were identified: clay, sandy loam, and clay loam. Sandy loam soils were given higher weights due to their superior infiltration capacity. The role of soil in regulating the infiltration and percolation of surface water into the aquifer is of paramount importance, making the soil map a foundational element in the process of delineating groundwater potential zones. Within the study area, three primary soil types have been identified according to FAO soil classification, as outlined below. clay loam and sandy loam soils were accorded higher weights due to their favorable characteristics, followed by clay soils, which were assigned relatively lower weights. This systematic approach ensures a comprehensive consideration of soil types and their varying capacities to contribute to groundwater recharge processes (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.5.7. Geomorphology\u003c/strong\u003e: The study area consists of residual landforms, volcanic landforms, and alluvial deposits, with residual landforms covering 38% of the area and assigned the highest weight due to their significant recharge capacity. Structural landforms are also identified as important groundwater recharge sources and given higher weightage. This classification framework helps distinguish and evaluate the varying groundwater prospects associated with each geomorphic unit, as detailed in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Groundwater Recharge Zones\u003c/h2\u003e\n \u003cp\u003eGroundwater potential zones in the study area were delineated using a Weighted Overlay analysis in ArcGIS, incorporating thematic layers such as land use, geology, geomorphology, soil type, slope, drainage density, and lineament density. These thematic maps were overlaid using their respective weightages, as shown in Table 3. The final classification divided the study area into three recharge potential zones: high, moderate, and low. The high recharge zones, covering 6% of the area, are primarily located in the northwest. Moderate recharge zones, covering 63% of the area, are found predominantly in the southern and northeastern regions. The low recharge zones, covering 31% of the area, are mainly concentrated in the central region. The final groundwater recharge potential map (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e) offers valuable insights for sustainable groundwater management and conservation strategies, highlighting areas with varying recharge potentials.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Conclusion\u003c/h2\u003e \u003cp\u003eThis study effectively delineated groundwater recharge potential zones by integrating multiple thematic maps using the Weighted Overlay Analysis in ArcGIS. The classification of recharge zones into low, moderate, and high potential provided critical insights into the spatial distribution of groundwater recharge capacity within the study area.\u003c/p\u003e \u003cp\u003eFindings indicate that the northwestern region exhibits the highest groundwater recharge potential, making it a prime area for groundwater conservation and sustainable utilization. Moderate recharge potential covers the majority of the study area (63%), highlighting the need for conservation practices to maintain recharge rates. However, the central region, which accounts for 31% of the study area, has the lowest recharge potential due to unfavorable geomorphological, soil, and slope characteristics. The presence of steep slopes, low lineament density, and impermeable geological formations in these areas significantly restricts groundwater infiltration.\u003c/p\u003e \u003cp\u003eThe study's consistency ratio of 0.07 confirms the reliability of the weightage assigned to different thematic factors, ensuring robust decision-making for water resource management. The ranking of thematic layers further validated that land use/land cover, geology, and slope are the most influential factors in determining groundwater recharge potential, followed by lineament density, drainage density, soil type, and geomorphology.\u003c/p\u003e \u003cp\u003eTo enhance groundwater recharge in low-potential areas, artificial recharge structures such as percolation tanks, check dams, and recharge wells should be constructed, while afforestation and soil conservation measures can help improve infiltration. In high-recharge zones, sustainable groundwater extraction and protection measures should be prioritized, including controlled pumping and contamination prevention strategies. Local authorities must integrate recharge maps into water resource planning, enforce land-use regulations to protect recharge areas, and promote public awareness on sustainable groundwater use. Future studies should utilize advanced remote sensing, machine learning, and hydrogeological modeling to refine recharge assessments and guide long-term groundwater management\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author would like to thank Ministry of Geology, Addis Ababa water and sewerage authority, ministry of water and energy, and the meteorology agency for their generous support and resources, which have facilitated this research endeavor.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants involved in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are included in the paper or its supplementary information.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare there is no conflict.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration Details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is not a clinical trial. Clinical trial registration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Publish Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve any experiments on humans or animals. Ethical approval and consent to participate were therefore not required.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person\u0026apos;s data in any form (including individual details, images, or videos), and consent for publication is not required.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeniel Bafe Unto and Alemayehu Kassa: \u0026nbsp; Conceptualized the study, designed the research methodology, and conducted data analysis using Arch GIS software, remote sensing, and the Analytical Hierarchy Process (AHP) methods. Additionally, contributed to the interpretation of results and drafted the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlemayehu, T., Legesse, D., Ayenew, T., Mohammed, N., \u0026amp; Waltenigus, S. (2006). Degree of groundwater vulnerability to pollution in Addis Ababa, Ethiopia. In Y. Xu \u0026amp; B. 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Studies on various thematic maps for identifying groundwater potential zones of Bhavan Taluk, Erode District, India. Pollution Research, 32(1), 55\u0026ndash;60.\u003c/li\u003e\n \u003cli\u003eBoroushaki, S., \u0026amp; Malczewski, J. (2008). Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Computers \u0026amp; Geosciences, 34(4), 399-410.\u003c/li\u003e\n \u003cli\u003eESRI. (2014). Model Builder, Spatial Analyst 2.0, ArcView GIS. Environmental Systems Research Institute.\u003c/li\u003e\n \u003cli\u003eRaviraj, A., Kuruppath, N., \u0026amp; Kannan, B. (2017). Identification of potential groundwater recharge zones using remote sensing and geographical information system in Amaravathy Basin. Journal of Remote Sensing GIS, 6(4), 1-10.\u003c/li\u003e\n \u003cli\u003eKabeto, J., Adeba, D., Regasa, M. S., \u0026amp; Leta, M. K. (2022). Groundwater Potential Assessment Using GIS and Remote Sensing Techniques: Case Study of West Arsi Zone, Ethiopia. Water, 14(12), 1838.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Water scarcity, Groundwater recharge, Water management, GIS, Remote sensing, Sustainability","lastPublishedDoi":"10.21203/rs.3.rs-6490153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6490153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater scarcity poses a significant challenge to agricultural productivity and food security, necessitating effective water management strategies. Effective water management strategies are essential to ensuring sustainable access to water for agricultural use. This study evaluates groundwater recharge potential to enhance agricultural water availability and promote sustainable water use. A cross-sectional research design was employed, utilizing GIS, remote sensing, and the Analytical Hierarchy Process (AHP) methods to identify potential recharge zones. Approximately 6% of the study area, primarily in the northwest, exhibits high groundwater recharge potential, while moderate and low recharge zones cover 63% and 31%, respectively. The results highlight the importance of integrating groundwater recharge strategies with agricultural water management to enhance irrigation sustainability. To address future water scarcity, integrated water management strategies must be developed, emphasizing groundwater recharge in high-potential areas. Sustainable water allocation policies and advanced irrigation techniques should be implemented to optimize agricultural water use. These measures will support long-term water security and improve agricultural resilience in water-scarce regions.\u003c/p\u003e","manuscriptTitle":"Groundwater Recharge potential: Integrated Water Management Strategies in Koye Feche, Oromia Region, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-21 12:31:40","doi":"10.21203/rs.3.rs-6490153/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6cb42669-26e1-49fd-9424-8f50248d1921","owner":[],"postedDate":"May 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T06:39:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-21 12:31:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6490153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6490153","identity":"rs-6490153","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}