Integrating Remote Sensing and Multi-Criteria Decision Analysis for Groundwater Zoning in the Eastern Desert of Egypt

Integrating Remote Sensing and Multi-Criteria Decision Analysis for Groundwater Zoning in the Eastern Desert of Egypt | 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 Article Integrating Remote Sensing and Multi-Criteria Decision Analysis for Groundwater Zoning in the Eastern Desert of Egypt Hesham Ezz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6618206/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 The Eastern Desert of Egypt is a hyper-arid region where groundwater represents the most viable freshwater source for sustainable development. This study presents a comprehensive geospatial analysis integrating remote sensing data and the Analytical Hierarchy Process (AHP) within a GIS environment to delineate groundwater potential zones (GWPZ). Seven thematic layers—precipitation, lithology, slope, drainage density, soil type, land use/land cover (LULC), and lineament density—are selected based on their relevance to groundwater recharge and availability. These layers are standardized, classified, and weighted using AHP, yielding a consistent and validated spatial model. The final resulting GWPZ map categorizes the region into four classes: high, moderate, low, and very low potential, with the low potential zone dominating 71% of the area, followed by moderate (23%), very low (4%), and high potential zones (2%). This research offers a scalable and reliable framework for groundwater exploration and supports strategic water resource planning in arid regions with limited surface water availability. Earth and environmental sciences/Environmental sciences/Environmental impact Physical sciences/Engineering/Civil engineering Sustainable Groundwater Management Groundwater potential zones Remote sensing GIS AHP method Multi-Criteria Decision Analysis (MCDA) Egypt Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Egypt, situated within the hyper-arid belt of North Africa, faces one of the most pressing water scarcity challenges in the world. With over 95% of the country receiving less than 100 mm of rainfall annually, the nation relies heavily on the Nile River as its primary freshwater source in addition to groundwater as a second source of water (Abdel-Shafy & Kamel, 2016 ). However, the sustainability of the Nile is increasingly under threat due to rapid population growth, rising agricultural demands, industrial development, and transboundary pressures. Groundwater, therefore, represents a critical supplementary resource, particularly in regions distant from the Nile Valley, such as the Eastern Desert. According to the Food and Agriculture Organization (FAO), Egypt’s annual renewable water resources per capita have fallen well below the absolute scarcity threshold of 500 cubic meters, classifying it as a country experiencing absolute water scarcity (Fouad et al., 2023 ; Needs & Indicator, 2021 ). Compounding this crisis are the growing uncertainties posed by climate change, including elevated evapotranspiration, erratic rainfall, and declining upstream inflows, which collectively call for alternative and sustainable water sources. Groundwater constitutes a vital natural resource, particularly in arid and semi-arid regions where surface water availability is scarce and often insufficient to meet the growing demands of human populations and ecosystems (Heath, 1998 ). The Eastern Desert of Egypt, with its hyper-arid climate characterized by minimal rainfall and high evapotranspiration rates, faces significant challenges in securing adequate water supplies, making the identification and sustainable management of groundwater resources paramount. Accurate and up-to-date mapping of groundwater potential zones (GWPZ) is crucial for supporting informed decision-making in water resource management and for guiding future development activities in this region (Heath, 1983). Traditional methods of groundwater exploration, often relying on costly and time-consuming ground-based surveys, can be limited in their spatial coverage and efficiency (Abdalla et al., 2020 ; Heath, 1998 ). In recent decades, the integration of Geographic Information Systems (GIS) and remote sensing technologies has emerged as a powerful and cost-effective approach for assessing and mapping natural resources, including groundwater potential (Abdalla et al., 2020 ; Allafta et al., 2020 ; Das et al., 2019 ; Elewa & Qaddah, 2011 ; Morgan et al., 2022 ). Remote sensing provides valuable spatial data on various environmental factors that influence groundwater occurrence, such as precipitation intensities, lithology, surface slopes, lineaments, drainage patterns, vegetation cover, and land surface characteristics. To effectively integrate these diverse factors in assessing groundwater potential, multi-criteria decision analysis (MCDA) methods, such as the Analytical Hierarchy Process (AHP), have proven to be highly suitable (Alrawi et al., 2022 ; Arulbalaji et al., 2019 ; Ghanim et al., 2023 ; Kumar et al., 2016 ; Moshe & Beza, 2024 ; Moumane et al., 2024 ). The AHP method, developed by (Saaty, 1980 ), provides a structured framework for evaluating and weighting various criteria based on their relative importance in influencing a particular phenomenon, such as groundwater potential. Previous studies in Egypt have focused on mapping groundwater potential zones in specific areas of the Eastern Desert, such as the Wadi Qena Basin (Abdalla et al., 2020 ; Megahed et al., 2023 ), the Wadi Hodein area (Zayed & Aly, 2023 ), and the central Eastern Desert (Ketkat et al., 2024 ; Khan et al., 2022 ; Morgan et al., 2022 ; Zhu & Abdelkareem, 2021 ). Despite these advancements, there remains a significant knowledge gap regarding the groundwater potential of the Eastern Desert. However, most of the studies have focused on relatively smaller watersheds or specific regions within the Eastern Desert, with few efforts aimed at large-scale, multi-factorial groundwater assessments in the basement-dominated desert regions. No study has comprehensively mapped the entire Eastern Desert of Egypt, despite its vast size and critical importance for future water resource management and sustainable development. Given the region’s tectonic complexity, heterogeneous lithology, and episodic hydrological inputs, a tailored GIS-AHP framework is essential for developing an effective and practical groundwater potential map. In this context, the selection of appropriate thematic layers is paramount. Based on hydrological theory, regional experience, and previous modeling efforts, this study considers seven critical factors: precipitation, lithiology, slope, land use/land cover (LULC), drainage density, soil type, and lineament density. Each of these parameters is known to significantly influence groundwater recharge, storage, or movement. For instance, rainfall provides the primary input for recharge, while geology and lineament density govern subsurface permeability and storage potential. Slope and drainage density regulate runoff and infiltration, whereas soil and land cover modulate infiltration rates and surface retention. The main objective of this study is to identify and map groundwater potential zones in the Eastern Desert of Egypt using an integrated GIS-AHP model. The approach involves reclassifying and standardizing seven thematic datasets, followed by applying AHP to derive factor weights, which are then combined using weighted overlay analysis. The resulting map aims to support sustainable groundwater exploration, water resource planning, and policy development, especially in rural and infrastructure-scarce regions of Egypt. In doing so, the study contributes to Egypt’s broader water security goals and provides a methodological framework applicable to other arid regions facing similar challenges. 2. Study Area The Eastern Desert of Egypt extends from the Nile Valley in the west to the Red Sea in the east, and from the borders of Sudan in the south to the outskirts of the Nile Delta in the north (Fig. 1 ). It encompasses a diverse landscape characterized by rugged mountains, arid plains, and numerous wadis (dry riverbeds). The climate of the Eastern Desert is predominantly hyper-arid, with extremely low and erratic rainfall, high temperatures, and significant potential for evaporation (Said, 2012 ). These harsh climatic conditions result in a scarcity of perennial surface water bodies, making groundwater the primary source of water for human consumption, agriculture, and other developmental activities. The topography of the Eastern Desert is dominated by the Red Sea Hills, a chain of rugged mountains running parallel to the Red Sea coast, reaching elevations of over 2,000 meters above sea level. Westward, the terrain gradually slopes down towards the Nile Valley, forming plateaus and undulating plains dissected by wadis that drain towards the Nile or the Red Sea. The geomorphology of the region plays a crucial role in influencing surface runoff and groundwater recharge. The socio-economic context of the Eastern Desert is characterized by scattered settlements and economic activities primarily centered around mining, tourism along the Red Sea coast, and limited agriculture in areas where groundwater is accessible. The increasing demand for water due to population growth and developmental projects necessitates a comprehensive understanding of the available groundwater resources and their potential. The Eastern Desert of Egypt is underlain by three primary groundwater aquifer systems: the Nubian Sandstone Aquifer, the Fissured Hard Rock Aquifers, and the Coastal Aquifers (Abdel-Shafy & Kamel, 2016 ). Due to the region's arid conditions, natural recharge is minimal and occurs primarily through episodic flash floods and wadi infiltration. Nevertheless, structurally favorable zones, particularly those with high lineament density, exhibit promising groundwater potential. Understanding and delineating these zones is essential for supporting long-term water security and guiding sustainable development in one of Egypt’s most water-scarce regions. 3. Material and Methods This study employs an integrated geospatial methodology that combines remote sensing, multi-source geodata, and the Analytical Hierarchy Process (AHP) within a Geographic Information System (GIS) environment to delineate groundwater potential zones (GWPZ) in the Eastern Desert of Egypt. This approach enables the systematic evaluation of multiple hydrogeological and environmental parameters, each of which contributes to the spatial variability of groundwater occurrence and recharge. The methodology is structured into three major phases: (1) data acquisition and preparation, (2) thematic map generation and classification, and (3) multi-criteria integration using AHP-based weighted overlay analysis. A total of seven key thematic layers—precipitation, geology (lithology), slope, drainage density, soil type, land use/land cover (LULC), and lineament density are selected based on their relevance to groundwater dynamics in arid environments. Geospatial datasets are first standardized through georeferencing and projection into a common spatial framework (UTM). Derived products such as slope and lineament density were extracted from high-resolution Digital Elevation Models (DEMs), while other layers are prepared using satellite imagery, geological surveys, and global datasets. Each thematic map was then reclassified into five groundwater potential categories ranging from 1 (very high potential) to 5 (very low potential), based on their hydrogeological characteristics. In parallel, the AHP method is applied to derive the relative importance (weights) of each factor through pairwise comparison, normalization, and consistency verification. These weights are then applied in the weighted overlay analysis, which resulted in a composite GWPZ index map. The final map is further classified into five classes, from very high to very low groundwater potential zones. The overall methodology is summarized in Fig. 2 , which illustrates the flow from dataset processing to GWPZ map generation. 3.1 Parameters for Groundwater Potential Mapping The selection of appropriate thematic parameters is a fundamental step in developing accurate groundwater potential zone (GWPZ) maps, particularly in arid and structurally complex regions such as the Eastern Desert of Egypt. This study incorporates seven geospatial layers: precipitation, lithology, slope, drainage density, land use/land cover (LULC), soil type, and lineament density. These parameters are selected based on their hydrogeological significance, spatial availability, and proven application in previous studies in the Eastern Desert. 3.1.1 Precipitation Rainfall is a key driver of natural groundwater recharge; particularly where other sources are absent. In hyper-arid environments such as Egypt’s Eastern Desert, precipitation is both limited and erratic; however, occasional intense rainfall events, especially in mountainous areas, can lead to substantial infiltration through wadis, fractured bedrock, and ephemeral channels. Regions that receive relatively higher rainfall are generally more favorable for groundwater accumulation. In this study, historical rainfall data spanning the period from 2010 to 2020 is collected to assess spatial variability across the study area as illustrated in Fig. 3 a. The dataset is derived from the CRU-TS-4.06 gridded product by the Climatic Research Unit, University of East Anglia, and downscaled using WorldClim 2.1 to improve spatial resolution and apply bias correction (Harris et al., 2020 ). Annual averages are computed to represent mean rainfall depths across the region. 3.1.2 Lithology Geological formations play a fundamental role in controlling the subsurface storage and movement of groundwater. Lithologies such as fractured basement rocks and porous sedimentary units, particularly sandstone and limestone, tend to possess higher permeability and porosity, making them more conducive to groundwater accumulation. In contrast, compacted or crystalline formations, including shale and igneous rocks, typically act as barriers to groundwater flow due to their low hydraulic conductivity. In this study, geological formations, Fig. 3 b, are delineated and classified using geological maps provided by United States Geological Survey, ( 2002 ) based on their anticipated influence on groundwater occurrence and transmissivity. 3.1.3 Topographic Data (Slope) Slope controls the partitioning between infiltration and surface runoff. Gentle slopes favor water retention and percolation, thereby enhancing recharge, whereas steep slopes promote rapid runoff and erosion, reducing the potential for infiltration. Slope gradients are derived from a Digital Elevation Model (DEM) using standard terrain analysis techniques and classified according to recharge potential. The Digital Elevation Model (DEM) for Egypt's Western Desert has a resolution of 1 arc-second (about 30 meters) and is geo-referenced to UTM WGS84 Zone 36 North. Figure 4 a shows how this Digital Elevation Model (DEM) is made using ArcGIS and the Spatial Analyst Extension to get data from the ALOS World 3D − 30m (AW3D30) version 2.1 (Takaku & Tadono, 2017 ). 3.1.4 Drainage Density Drainage density is a widely used hydrological parameter that serves as an indirect indicator of surface permeability and infiltration capacity. It is defined as the total length of drainage channels per unit area and provides insight into the terrain’s ability to absorb or shed surface water. Generally, areas with low drainage density indicate higher infiltration potential due to more permeable surface materials, while high drainage density often corresponds to compacted or impermeable substrates that promote surface runoff and limit groundwater recharge. In this study, drainage density is derived through hydrological modeling in a GIS environment using a high-resolution Digital Elevation Model (DEM). The DEM is first preprocessed by applying flow direction and flow accumulation algorithms to delineate the drainage network. Stream segments are classified according to the Strahler stream order system to distinguish between primary and higher-order channels (Fig. 5 a). This hierarchical classification allowed for the identification of major flow paths and their distribution across the landscape. Once the drainage network is fully extracted and validated, the total stream length within each spatial unit is computed, and drainage density is calculated using a raster-based method. The resulting drainage density map offers a spatially continuous representation of infiltration potential across the study area, with distinct variations correlating to terrain features and geological controls. Figure 5 b displays the drainage network overlaid with stream orders and the derived drainage density classes, highlighting zones with differing hydrological responses and recharge capacities. 3.1.5 Land Use / Land Cover (LULC) Land use and land cover (LULC) play a critical role in shaping surface hydrology and influencing infiltration rates. Vegetated areas, such as agricultural fields and shrublands, enhance infiltration by reducing surface runoff and promoting soil moisture retention, thereby supporting groundwater recharge. In contrast, impervious surfaces like urban developments and exposed barren lands restrict percolation, leading to increased runoff and reduced recharge potential. In this study, LULC data are sourced from the Environmental Systems Research Institute (ESRI) global land cover map, a widely recognized and reliable dataset in GIS-based environmental assessments (Fig. 4 b). ESRI is a global leader in geospatial software and provides high-quality spatial data that are essential for accurate hydrological and land surface analysis (Venter et al., 2022 ). 3.1.6 Soil Type Soil characteristics, particularly texture and structure, play a vital role in determining infiltration capacity and soil water retention (Fig. 6 a). Well-drained soils such as sandy and loamy types typically exhibit high permeability, allowing for greater vertical percolation and enhancing groundwater recharge. In contrast, clay-rich soils have lower permeability and tend to retain surface water, limiting infiltration into deeper layers. In this study, soil data are obtained from globally validated sources, including the works of Hengl et al. ( 2017 ) and Shangguan et al. ( 2014 ), which provide high-resolution, harmonized soil property datasets suitable for spatial hydrological modeling. 3.1.7 Lineament Density Lineaments—such as faults, fractures, and shear zones—play a critical role in facilitating groundwater movement, particularly in crystalline and hard rock environments where primary porosity is minimal (Yeh et al., 2016). In structurally complex regions like Egypt’s Eastern Desert, these features significantly enhance secondary porosity and serve as preferential pathways for infiltration and subsurface flow. Lineament patterns are extracted through remote sensing techniques and geological interpretation of satellite imagery, then processed in ArcGIS to generate a lineament density map. This map quantifies the concentration of structural features per unit area, offering a valuable proxy for fracture connectivity. Figure 6 b illustrates the spatial distribution of lineament density across the study area. 3.2 Analytical Hierarchy Process (AHP) for Groundwater Potential Mapping The Analytical Hierarchy Process (AHP) is a structured decision-making technique introduced by (Saaty, 1980 ) that enables the integration of multiple spatial and thematic parameters by assigning relative weights based on expert judgment and pairwise comparison. In the context of groundwater potential mapping, AHP has become a widely accepted method for synthesizing hydrogeological, topographical, climatic, and environmental data into a coherent spatial model. In this study, AHP is applied to evaluate and prioritize seven parameters influencing groundwater occurrence in the Eastern Desert of Egypt. These parameters include: precipitation, lithology, slope, drainage density, land use/land cover (LULC), soil type, and lineament density. The weights assigned to each parameter are derived from a normalized pairwise comparison matrix to compare each criterion against the others based on a 1–9 scale (1 = equal importance, 9 = extreme importance). These comparisons are used to calculate weights for each criterion, representing their relative influence on the goal. The process ensures consistency through the Consistency Ratio (CR), which should be less than 0.1 for reliable results. In this study, AHP is applied to assign weights to seven factors influencing groundwater potential in the Eastern Desert of Egypt, based on insights from existing literature and the specific hydrogeological characteristics of the study area. 4. Results and Discussion 4.1 Development of thematic maps To effectively implement the Analytical Hierarchy Process (AHP) using a weighted overlay in ArcGIS, the dataset is classified each into five categories (very high potential, high potential, moderate potential, low potential, and very low potential). This classification ensures a structured evaluation of multiple factors that influence spatial analysis, such as precipitation, lithology, slope, drainage density, land use/land cover (LULC), soil type, and lineament density. By standardizing these parameters, it is possible to systematically assign weights and rank their influence on the study area, facilitating a more data-driven decision-making process. Each dataset is classified based on its specific characteristics. For instance, precipitation values are categorized according to rainfall intensity, while slope classification followed terrain steepness. Similarly, geological formations, land cover types, soil classes, and lineament density are grouped based on their impact on hydrological and environmental processes. Below, we discuss the classification of each dataset in detail. 4.1.1 Precipitation classification Precipitation data, illustrated in Fig. 3 a, is categorized into five classes based on daily rainfall intensity (mm/day). The lowest category, 0–5 mm/day, represents arid or semi-arid regions with minimal rainfall. Areas receiving between 6–10 mm/day fall into the second category, indicating slightly wetter conditions. Moderate precipitation zones, ranging from 11–20 mm/day, form the third class. Higher rainfall intensities of 21–30 mm/day characterize the fourth category, while the highest precipitation class, more than 31 mm/day, corresponds to regions experiencing significant daily rainfall. This classification helps assess the potential influence of precipitation on surface runoff and erosion. 4.1.2 Lithology classification The geological formations in the study area, as shown in Fig. 3 b, are reclassified based on their hydrogeological properties—primarily porosity, permeability, and their capacity to facilitate groundwater recharge and storage. Lithological units such as Quaternary (undivided) and Holocene deposits are categorized as having very high groundwater potential, due to their unconsolidated nature and high infiltration capacity. Formations like the Tertiary, Tertiary-Cretaceous, and Carboniferous units are assigned high potential status, as they generally possess moderate porosity and may contain secondary permeability through fracturing or weathering. Sedimentary units including the Cretaceous, Lower Cretaceous, and Jurassic are considered to have moderate potential, reflecting their ability to store groundwater primarily in fractures or bedding planes. More compact formations such as the Cretaceous-Jurassic and Cretaceous-Carboniferous sequences are classified as having low potential, due to their typically reduced permeability. Finally, very low groundwater potential is attributed to Precambrian basement rocks, Mesozoic Igneous, and Tertiary Igneous formations, which are generally impermeable and lack sufficient secondary porosity except where structurally disrupted. This classification scheme reflects both regional hydrogeological knowledge and established principles regarding lithologic influence on groundwater occurrence. It supports the broader AHP-based groundwater modeling by providing a geologically informed thematic layer for integration with other spatial parameters. 4.1.3 Slope classification Slope is a key topographic factor influencing surface runoff and infiltration, both of which are critical in determining groundwater recharge potential. As shown in Fig. 4 a, the slope of the study area is derived from a high-resolution Digital Elevation Model (DEM) and classified into five categories based on gradient. These categories are then reinterpreted in the context of groundwater favorability. Areas with gentle slopes (0–5%) are classified as having very high groundwater potential, as they favor water accumulation and infiltration with minimal runoff. Zones with moderate slopes (5.1–10%) are assigned high potential, reflecting their suitability for moderate infiltration while still maintaining limited runoff. Slopes ranging from 10.1–20% are considered to have moderate potential, as water retention begins to diminish and runoff increases. Steeper slopes between 20.1% and 30% are classified as low potential, due to rapid runoff and limited infiltration time. Finally, areas with very steep slopes (more than 30.1%) are assigned very low potential for groundwater recharge, as these terrains facilitate fast surface water discharge and are typically erosion-prone. This classification ensures that slope-induced hydrological processes are accurately reflected in the overall groundwater potential model. 4.1.4 Drainage density classification Drainage density is a crucial indicator of the infiltration capacity and surface permeability of a landscape, which directly influences groundwater recharge potential. The drainage density are classified into five categories, as shown in Fig. 5 b, and interpreted in terms of groundwater potential. Areas with low drainage density (0–0.29) are classified as having very high groundwater potential, indicating well-drained, permeable surfaces that promote infiltration and reduce surface runoff. Zones with moderately low drainage density (0.30–0.42) are assigned high potential, still reflecting favorable infiltration conditions. Drainage densities ranging from 0.43 to 0.52 are categorized as moderate potential, suggesting a balance between runoff and recharge. Regions with higher drainage density (0.53–0.65) are considered to have low potential, typically associated with less permeable surfaces and increased surface flow. Finally, areas with very high drainage density (0.66–0.93) are assigned very low groundwater potential, as these are usually compact or rocky terrains where rapid runoff limits infiltration. This classification ensures the drainage network’s influence is accurately incorporated into the groundwater potential assessment. 4.1.5 Land use/land cover (LULC) classification Land use and land cover (LULC) conditions have a significant influence on groundwater recharge, primarily through their impact on infiltration rates and surface runoff. In this study, the LULC map is reclassified into groundwater potential categories based on the permeability and hydrological behavior of each land cover type. As shown in Fig. 4 b, areas covered by water bodies and flooded vegetation are assigned a very high groundwater potential classification, as these surfaces support direct recharge and high moisture retention. Similarly, croplands and bare ground are categorized as high potential zones due to their relatively permeable surfaces, which allow moderate infiltration under certain conditions. Rangelands and tree-covered areas are assigned a moderate potential, reflecting their mixed capacity to promote infiltration depending on vegetation density and soil structure. In contrast, urban and built-up areas are classified as having very low groundwater potential due to their impervious surfaces that inhibit infiltration and significantly increase runoff. Snow and ice-covered areas, although limited in the study region, are also considered very low potential, given their limited contribution to groundwater recharge in arid climates. This classification ensures that the LULC layer accurately reflects the spatial variability in surface permeability and its influence on groundwater occurrence across the Eastern Desert. 4.1.6 Soil type classification Soil characteristics, particularly texture and permeability, are essential in determining the rate and volume of groundwater infiltration. In this study, the soil map is reclassified into categories of groundwater potential based on the physical and hydrological properties of each soil type. As shown in Fig. 6 a, Fluvisols and Arenosols are classified as having very high groundwater potential due to their high porosity and excellent infiltration capacity. These soils are typically found in alluvial environments or sandy terrains that facilitate rapid percolation. Regosols and Andosols are assigned a high potential rating. These soils are well-drained and generally formed from unconsolidated material, allowing for moderate to high infiltration. Soils such as Umbrisols, Lixisols, Kastanozems, and Calcisols are considered to have moderate groundwater potential, reflecting intermediate permeability and variable water retention characteristics. In contrast, Podzols and Ferralsols are classified as having low groundwater potential, as they tend to be compacted, rich in iron and aluminum oxides, and often exhibit poor drainage properties that hinder infiltration. This classification approach ensures that the soil layer used in the AHP model accurately represents the variability in infiltration capacity and its influence on groundwater recharge across the study area. 4.1.7 Lineament density classification Lineaments are critical in controlling groundwater occurrence in hard rock terrains, where primary porosity is minimal and groundwater movement relies heavily on secondary structural pathways. This lineament density map is reclassified into five classes to assess their relative contribution to groundwater potential, as shown in Fig. 6 b. Areas with very high lineament density (0.1–0.11) are assigned very high groundwater potential, as they represent structurally complex zones that facilitate recharge through enhanced secondary porosity and permeability. Zones with high density values (0.08–0.09) are categorized as having high potential, indicating well-connected fracture systems. Moderate potential is assigned to areas with lineament densities between 0.05 and 0.07, where structural influence on recharge is present but less intense. Low potential areas are identified in regions with densities ranging from 0.03 to 0.04, where fewer fractures limit water movement. Finally, zones with very low lineament density (0–0.02) are considered least favorable for groundwater recharge, as they lack significant structural conduits. This classification ensures that fracture-controlled groundwater movement is adequately represented in the overall potential mapping, particularly important in the crystalline terrains of the Eastern Desert. 4.2 AHP calculations and weights The AHP analysis yielded the normalized weights for each factor, reflecting their relative importance based on expert knowledge. The derived weights from the AHP process are shown in Table 1 . The consistency ratio (CR) calculated for the pairwise comparison matrix equals to 0.011 which is well below the acceptable threshold of 0.1, indicating a consistent and reliable weighting scheme. The final weights derived for the seven parameters are as shown in Table 1 . Table 1 AHP weights for the different parameters Parameter Weight % Precipitation 32.31% Lithology 17.89% Slope 12.63% Drainage Density 10.6% LULC 5.37% Soil Type 10.6% Lineament Density 10.6% These weights reflect a balanced consideration of both hydroclimatic drivers and structural-geological controls that shape groundwater dynamics in the Eastern Desert of Egypt. Rainfall, assigned the highest weight (32.31%), reflects its critical role as the primary recharge mechanism, despite the region’s hyper-arid nature. Although rainfall is sporadic, episodic precipitation events—particularly in highland zones—can lead to significant localized recharge through infiltration in wadis and fractured terrains. Lithology, ranked second (17.89%), strongly influences groundwater storage and movement through lithological properties such as porosity and permeability. In this region, fractured Precambrian basement rocks and porous sedimentary formations serve as key aquifer hosts. Factors such as slope (12.63%), drainage density (10.60%), soil type (10.60%), and lineament density (10.60%) are assigned nearly equal weights due to their interlinked roles in governing surface runoff, infiltration efficiency, and subsurface flow dynamics. Notably, lineament density is emphasized due to the dominance of fracture-controlled aquifers in the crystalline terrains of the Eastern Desert. In contrast, land use/land cover (LULC) received the lowest weight (5.37%), consistent with its relatively localized influence on groundwater processes in the study area. Given the sparse vegetation and limited anthropogenic land cover, its impact is minimal compared to the more dominant physical and structural factors. The weighting scheme developed in this study aligns with and builds upon a range of published AHP-based groundwater assessments across Egypt. Rainfall weights in the literature vary widely—from 6.8% (Khan et al., 2022 ) to 30% (Elewa & Qaddah, 2011 ) depending on the climatic setting and emphasis on recharge. Our assignment of 32.31% is consistent with studies in hyper-arid regions where recharge is largely episodic but remains critical for groundwater sustainability. The lithology weight of 17.89% falls within the commonly cited range of 3–20%, (El-Sayed & Elgendy, 2024 ; Elewa & Qaddah, 2011 ), reinforcing its central role in subsurface water behavior . Similarly, our equal weighting of drainage density, soil type, and lineament density (10.6% each) reflects a balanced approach where all three contribute meaningfully to recharge potential. The final weights assigned in this study fall well within the ranges reported in similar groundwater potential mapping studies across Egypt. According to the literature, drainage density weights have ranged from 0.25–23% (Abdalla et al., 2020 ; El-Sayed & Elgendy, 2024 ), with higher values generally reported in studies where runoff control is a dominant factor, such as in Wadi Abu Marzouk and the Central Eastern Desert. Soil type weights vary between 5% and 18.2% (El-Sayed & Elgendy, 2024 ; Morgan et al., 2022 ), reflecting the differing significance of soil permeability and retention properties across study areas. Similarly, lineament density weights span a broad range from 2–23% (Abdalla, 2012 ; El-Sayed & Elgendy, 2024 ), with the highest values observed in tectonically complex regions where fracture-controlled aquifers prevail. The nearly equal weights assigned to these three factors in our model reflect their interrelated contributions to groundwater recharge in the structurally diverse and geomorphologically varied terrain of the Eastern Desert, while remaining well-aligned with published methodologies. LULC weights reported in literature typically range from 4.5–7% (El-Sayed & Elgendy, 2024 ; Morgan et al., 2022 ). Our allocation of 5.37% fits this trend, acknowledging its secondary role compared to hydrogeological parameters. Overall, the weight distribution adopted in this study is both scientifically grounded and context-specific. It offers a balanced framework for evaluating groundwater potential in the Eastern Desert, where rainfall and geologic controls dominate recharge, but structural features and terrain characteristics significantly influence storage and movement. 4.3 Integration of AHP and Remote Sensing Data The integration of Analytical Hierarchy Process (AHP) with remote sensing-derived thematic layers is performed within a Geographic Information System (GIS) environment to produce the final Groundwater Potential Zones (GWPZ) map. This integrated approach allowed for the objective combination of multiple geospatial datasets, each representing factors that control groundwater occurrence in the Eastern Desert of Egypt. Seven thematic maps—precipitation, lithology, slope, drainage density, soil type, land use/land cover (LULC), and lineament density—are generated and reclassified into five groundwater potential classes (1–5), where 1 indicates very high potential and 5 indicates very low potential. The reclassification is based on hydrological behavior, infiltration capacity, and geological properties, supported by field knowledge and published literature as mentioned before. Each thematic map is standardized to this scale to ensure compatibility in the subsequent overlay operation. The classified layers are then multiplied by their respective AHP weights and summed to produce a groundwater potential map (GWPZ) map, which indicates areas with varying potential for groundwater occurrence. The equation (Eq. 1 ) for the weighted overlay method, employed using GIS environment, in this study is: $$\:\varvec{G}\varvec{W}\varvec{P}\varvec{Z}=\:\sum\:_{\varvec{i}=1}^{\varvec{n}}\left({\varvec{W}}_{\varvec{i}}\times\:{\varvec{F}}_{\varvec{i}}\right)$$ 1 Where: W: AHP-derived weights for each factor F: Classified thematic map for each factor n: number of studied factors (7) The result is a composite raster map representing a continuous groundwater potential map across the region. This map is then classified into zones corresponding to their potential to groundwater. Lower values represent higher groundwater potential, consistent with the adopted classification scheme. This methodology leverages both expert-based decision weighting and high-resolution spatial data, enabling a more accurate and scalable assessment of groundwater resources in arid environments where field data are sparse or difficult to obtain. 4.4 Groundwater Potential Zone Mapping 4.4.1 Spatial distribution and interpretation of GWPZ The integration of these weighted thematic layers in the GIS environment resulted in the generation of a GWPZ map for the Eastern Desert of Egypt is developed through the integration of seven spatially significant factors using a weighted overlay model driven by the Analytical Hierarchy Process (AHP). The resulting map categorizes the region into four groundwater potential classes: high, moderate, low, and very low. It is noteworthy that the “very high” potential class did not appear in the final output, likely due to the combined limitations of rainfall scarcity, impermeable geology, and rugged terrain. This classification enables an in-depth understanding of the hydrogeological setting of the region and its influence on groundwater recharge and storage. As illustrated in Fig. 7 and Table 2 , the low potential zone dominates, covering approximately 71% of the study area. This is followed by moderate potential zones at 23%, very low potential zones at 4%, and high potential zones accounting for only 2%. These proportions highlight the hydrogeological constraints across most of the Eastern Desert, while also identifying localized areas where favorable conditions align for targeted groundwater development. Table 2 Areas of different groundwater potential zones Zones Area (Km 2 ) % of Total Area High Potential 3648 2% Moderate Potential 48061 23% Low Potential 150453 71% Very Low Potential 8670 4% The low groundwater potential class dominates the landscape, covering approximately 71% of the study area. These zones are largely concentrated in the central belt and southwestern parts of the region, where Precambrian basement rocks and Mesozoic igneous formations prevail. These formations are known for their low primary porosity and poor permeability. Coupled with steep slopes and high drainage density, these geological settings facilitate rapid surface runoff and restrict infiltration. The limited rainfall in these areas, typically in the range of 0 to 10 mm/year, further reduces recharge potential, making groundwater development in these zones challenging. The moderate potential zones, accounting for 23%, are distributed across the northeastern and southeastern regions, particularly in areas where Cretaceous and Tertiary sedimentary units are present. These regions exhibit moderate lineament density, gentler slopes, and balanced drainage characteristics, all of which support partial recharge, especially when intersected by fractured zones or overlying moderately permeable soils such as Regosols and Lixisols. Rainfall in these areas ranges between 11 and 20 mm/year, contributing to seasonal recharge potential, especially in structurally controlled basins. Although covering only 2% of the total area, high groundwater potential zones are of great significance due to their favorable combination of hydrogeological factors. These zones are primarily located in the northernmost tip and southeastern edge of the Eastern Desert. In these regions, precipitation intensities reach up to 75 mm/year, particularly near the Red Sea foothills and Gebel Elba area, driven by orographic rainfall. Additionally, these areas are underlain by unconsolidated Quaternary and Holocene deposits, which offer high permeability and shallow aquifer conditions. They also exhibit low drainage density, low slope gradients, and high lineament density, all of which enhance infiltration and subsurface water retention. These zones are considered the most promising for sustainable groundwater development and should be prioritized for further hydrogeological surveys and well installation. In contrast, very low potential zones, which constitute about 4% of the region, are mainly located in steep mountainous terrain along the eastern escarpments and isolated patches in the southwest. These areas are geologically dominated by crystalline basement rocks, have very steep slopes (often > 30%), and are characterized by high surface runoff. Moreover, rainfall in these zones is minimal (often < 5 mm/year), and the structural conditions offer limited pathways for infiltration. These zones are thus poorly suited for groundwater development without substantial intervention such as artificial recharge or engineered water harvesting systems. The distribution of groundwater potential classes emphasizes the dominance of less favorable zones, a pattern that aligns with the arid climate, hard-rock geology, and topographic constraints typical of the Eastern Desert. However, the spatial association between higher rainfall regions, permeable lithologies, and structural features such as faults and fractures highlights the importance of integrating multiple thematic layers for accurate groundwater targeting. Overall, the resulting GWPZ map provides critical spatial insight for guiding groundwater exploration, land-use planning, and water management strategies. It is especially relevant in the context of climate change and growing water demand, where resource optimization is essential. The spatial heterogeneity reflected in the map underscores the need for localized investigations and emphasizes the potential of using multi-criteria geospatial models in arid-zone groundwater studies. 4.4.2 Validation of the groundwater potential mapping output To assess the reliability of the developed groundwater potential zones (GWPZ), a validation analysis is conducted using the spatial distribution of 390 existing groundwater wells across the study area as shown in Fig. 8 . These wells are overlaid on the classified GWPZ map produced through the GIS-AHP model. The results revealed that 2% of wells are located in high potential zones, 26% in moderate zones, 65% in low zones, and 7% in very low zones. At first glance, the large proportion of wells (72%) located in low or very low potential areas might seem inconsistent with the groundwater potential classification. However, this distribution can be logically explained when taking into account the hydrogeological nature of the region and the structure of the model itself. The AHP framework used in this study placed the highest weight on precipitation, which reflects active recharge under current climatic conditions. As such, the resulting map is optimized to identify areas that are currently favorable for natural recharge processes for shallow aquifers. However, many aquifers in the Eastern Desert are known to be fossil or non-renewable, recharged during wetter climatic epochs such as the Holocene pluvial period. These aquifers, while no longer receiving substantial recharge, continue to store large volumes of groundwater accumulated over thousands of years. As confirmed by Abdel-Shafy & Kamel, ( 2016 ), much of Egypt’s groundwater, particularly in desert regions, is stored in deep, confined aquifers that have not been significantly replenished in modern times. These systems are not easily detected through models based on surface conditions, as their recharge is historical, not active, and their yields are supported by large paleo-reserves rather than ongoing hydrological processes. In addition, the model did not account for proximity to the Nile River, which plays a critical role in the hydrogeological behavior of adjacent zones. Shallow aquifers along the Nile Valley may receive recharge from lateral seepage or irrigation return flow, maintaining higher water tables even in areas of low rainfall. Therefore, wells located near the Nile may appear in low potential zones based on rainfall, slope, and drainage patterns, while still benefiting from human-influenced or river-connected recharge. In summary, the validation process supports the model’s ability to accurately delineate zones of current recharge potential, while also acknowledging that many productive wells are located in non-renewable aquifers, or in areas influenced by proximity to the Nile. These findings underscore the need to integrate both modern spatial modeling and historical hydrogeological knowledge in groundwater management. 5. Conclusion This study demonstrates the effectiveness of integrating remote sensing data, thematic geospatial layers, and the Analytical Hierarchy Process (AHP) within a GIS framework for delineating groundwater potential zones (GWPZ) in the Eastern Desert of Egypt. By combining seven hydrogeologically relevant factors—precipitation, lithology, slope, drainage density, soil type, land use/land cover, and lineament density—this multi-criteria approach successfully identified areas with varying groundwater potential across a complex and arid landscape. The resulting GWPZ map classifies the region into four groundwater potential categories: high, moderate, low, and very low. Notably, no zones are classified as "very high" due to prevailing environmental and structural limitations. The spatial analysis showed that the majority of the study area (71%) falls within the low potential class, with high potential zones occupying only 2%, mainly in the northern and southeastern areas where favorable geological, structural, and climatic conditions converge. These findings reflect the natural constraints of the region—steep topography, impermeable lithologies, and extremely limited precipitation. The model is further validated using 390 well locations, which revealed that most existing wells fall within low to moderate potential areas. This aligns with the notion that many of the current wells tap into non-renewable or paleo-recharged aquifers, emphasizing the importance of historical rainfall events and structural control in groundwater occurrence. Overall, this research provides a robust, scalable framework for groundwater exploration in hyper-arid environments. The GWPZ map can serve as a critical tool for decision-makers and water resource planners, offering guidance for well-siting, recharge project planning, and sustainable water development. Future research may benefit from incorporating more detailed aquifer parameters and groundwater quality data to enhance model precision and water resource management strategies. Declarations Author Contribution I have made all the work required in the manuscript including writing also. Data Availability All data generated or analysed during this study are included in this published article References Abdalla, F. (2012). Mapping of groundwater prospective zones using remote sensing and GIS techniques: A case study from the Central Eastern Desert, Egypt. Journal of African Earth Sciences , 70 , 8–17. Abdalla, F., Moubark, K., & Abdelkareem, M. (2020). Groundwater potential mapping using GIS, linear weighted combination techniques and geochemical processes identification, west of the Qena area, Upper Egypt. Journal of Taibah University for Science , 14 (1), 1350–1362. Abdel-Shafy, H. I., & Kamel, A. H. (2016). Groundwater in Egypt issue: resources, location, amount, contamination, protection, renewal, future overview. Egypt J Chem , 59 (3), 321–362. Allafta, H., Opp, C., & Patra, S. (2020). Identification of groundwater potential zones using remote sensing and GIS techniques: A case study of the Shatt Al-Arab Basin. Remote Sensing , 13 (1), 112. Alrawi, I., Chen, J., & Othman, A. A. (2022). Groundwater potential zone mapping: integration of multi-criteria decision analysis (MCDA) and GIS techniques for the Al-Qalamoun region in Syria. ISPRS International Journal of Geo-Information , 11 (12), 603. Arulbalaji, P., Padmalal, D., & Sreelash, K. (2019). GIS and AHP techniques based delineation of groundwater potential zones: a case study from southern Western Ghats, India. Scientific Reports , 9 (1), 2082. Das, B., Pal, S. C., Malik, S., & Chakrabortty, R. (2019). Modeling groundwater potential zones of Puruliya district, West Bengal, India using remote sensing and GIS techniques. Geology, Ecology, and Landscapes , 3 (3), 223–237. El-Sayed, H. M., & Elgendy, A. R. (2024). Geospatial and geophysical insights for groundwater potential zones mapping and aquifer evaluation at Wadi Abu Marzouk in El-Nagila, Egypt. Egyptian Journal of Aquatic Research , 50 (1), 23–35. Elewa, H., & Qaddah, A. (2011). Groundwater potentiality mapping in the Sinai Peninsula, Egypt, using remote sensing and GIS-watershed-based modeling. Hydrogeology Journal , 19 (3), 613–628. Fouad, S. S., Heggy, E., Ramah, M., Abotalib, A. Z., Palmer, E. M., Jomaa, S., & Weilacher, U. (2023). Egypt’s waterways conservation campaigns under growing intrinsic demand and Nile upstream damming. Journal of Hydrology: Regional Studies , 50 , 101537. Ghanim, A. A. J., Al-Areeq, A. M., Benaafi, M., Al-Suwaiyan, M. S., Aghbari, A. A. Al, & Alyami, M. (2023). Mapping groundwater potential zones in the Habawnah Basin of Southern Saudi Arabia: an AHP-and GIS-based approach. Sustainability , 15 (13), 1–19. Harris, I., Osborn, T. J., Jones, P., & Lister, D. (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data , 7 (1), 109. Heath, R. C. (1998). Basic ground-water hydrology (Vol. 2220). US Department of the Interior, US Geological Survey. Hengl, T., de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., & others. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS One , 12 (2), 1–40. Ketkat, A. M., El Shenawy, A. M., Zarif, F. M., Afify, W. E., El Kaliouby, H. M., & Mansour, N. M. (2024). Groundwater Potential Assessment Using Analytic Hierarchy Process (AHP), Remote Sensing, and GIS: A Case Study from the Zaafarana Region, Western Coast of the Gulf of Suez, Egypt. Journal of Basic and Environmental Sciences , 11 (4), 626–653. Khan, M. Y. A., ElKashouty, M., & Tian, F. (2022). Mapping groundwater potential zones using analytical hierarchical process and multicriteria evaluation in the Central Eastern Desert, Egypt. Water , 14 (7), 1041. 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 Resources Management , 2 , 419–430. Megahed, H. A., Farrag, A. E.-H. A., Mohamed, A. A., D’Antonio, P., Scopa, A., & AbdelRahman, M. A. E. (2023). Groundwater recharge potentiality mapping in Wadi Qena, Eastern Desert basins of Egypt for sustainable Agriculture Base using geomatics approaches. Hydrology , 10 (12), 237. Morgan, H., Hussien, H. M., Madani, A., & Nassar, T. (2022). Delineating groundwater potential zones in Hyper-Arid regions using the applications of remote sensing and GIS modeling in the Eastern Desert, Egypt. Sustainability , 14 (24), 16942. Moshe, A., & Beza, M. (2024). Assessment of groundwater potential zones using GIS-based multicriteria decision analysis: a case study on Enemor and Ener Woreda, Central Ethiopia Region. H2Open Journal , 7 (3), 286–302. Moumane, A., Enajar, A. A., Ghazali, F. E. El, Khouz, A., Karmaoui, A., Al Karkouri, J., & Batchi, M. (2024). GIS, remote sensing, and analytical hierarchy process (AHP) approach for rainwater harvesting site selection in arid regions: Feija Plain case study, Zagora (Morocco). Applied Geomatics , 16 (4), 861–880. Needs, A., & Indicator, F. (2021). Progress on level of water stress. Progress on the Level of Water Stress . Saaty, T. L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill International, New York, NY, USA. Said, R. (2012). The geological evolution of the River Nile . Springer Science \& Business Media. Shangguan, W., Dai, Y., Duan, Q., Liu, B., & Yuan, H. (2014). A global soil data set for earth system modeling. Journal of Advances in Modeling Earth Systems , 6 (1), 249–263. Takaku, J., & Tadono, T. (2017). Quality updates of ‘AW3D’global DSM generated from ALOS PRISM. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , 5666–5669. United States Geological Survey. (2002). Surficial geology of africa (geo7_2ag) . Venter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., & Singh, G. (2022). Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sensing , 14 (16), 4101. https://doi.org/10.3390/rs14164101 Zayed, M. S., & Aly, M. M. (2023). Regional overview potential zones for groundwater recharge in Wadi Hodein, south Eastern Desert of Egypt. Water Science , 37 (1), 290–303. Zhu, Q., & Abdelkareem, M. (2021). Mapping groundwater potential zones using a knowledge-driven approach and GIS analysis. Water , 13 (5), 1–24. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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08:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6618206/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6618206/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83767661,"identity":"08053ec5-7f1d-42e8-a6b1-34cacb94f51e","added_by":"auto","created_at":"2025-06-02 11:32:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":901978,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area of the Eastern Desert of Egypt\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6618206/v1/3c28c22eed55326a8414d28b.png"},{"id":83767395,"identity":"bf675506-9926-4557-b3ea-47f330ba170c","added_by":"auto","created_at":"2025-06-02 11:24:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130636,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the methodology used for Groundwater Potential Zones Mapping\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6618206/v1/d8a4df3c592922f747891174.png"},{"id":83767403,"identity":"906f7cce-ff9d-45db-ad2a-755afeba40e0","added_by":"auto","created_at":"2025-06-02 11:24:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":500941,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Precipitation rates, and (b) Lithology of the study area\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6618206/v1/7009cd43d893964faea805a6.png"},{"id":83767404,"identity":"e0a5a152-8350-4f87-8bda-3072869de602","added_by":"auto","created_at":"2025-06-02 11:24:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":945339,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Slope, and (b) LULC of the study area\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6618206/v1/a5dfa68fead0f6fdb023eb2c.png"},{"id":83767401,"identity":"51d169a9-0a7d-4f56-ad0e-4906d6c47d69","added_by":"auto","created_at":"2025-06-02 11:24:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":746480,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Stream network and orders, and (b) Drainage density of the study area\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6618206/v1/d060035adfa133f6a1cf9793.png"},{"id":83767399,"identity":"dae04db2-9663-4deb-85da-4c7f84048120","added_by":"auto","created_at":"2025-06-02 11:24:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":481358,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Soil type, and (b) Lineament density of the study area\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6618206/v1/d8bf35b62df55fd0cc27da50.png"},{"id":83767400,"identity":"1abfd0bb-8a08-44ee-a12a-a004a2b0d9ae","added_by":"auto","created_at":"2025-06-02 11:24:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":300137,"visible":true,"origin":"","legend":"\u003cp\u003eGroundwater potential map for Eastern Desert of Egypt\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6618206/v1/1a58a0eee425be7b6e2f5b60.png"},{"id":83767406,"identity":"c0e8f26a-35ba-463b-a72f-6ea4f8885998","added_by":"auto","created_at":"2025-06-02 11:24:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":708439,"visible":true,"origin":"","legend":"\u003cp\u003eExisting wells in Eastern Desert of Egypt\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6618206/v1/dd9e4300edb37dcef8e4d0e5.png"},{"id":91444750,"identity":"8a23284b-37ad-4ae7-ae13-9935cb8dd5f4","added_by":"auto","created_at":"2025-09-16 14:32:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5204716,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6618206/v1/dcd6fe77-81ad-4806-944e-384451dc3f5c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Remote Sensing and Multi-Criteria Decision Analysis for Groundwater Zoning in the Eastern Desert of Egypt","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eEgypt, situated within the hyper-arid belt of North Africa, faces one of the most pressing water scarcity challenges in the world. With over 95% of the country receiving less than 100 mm of rainfall annually, the nation relies heavily on the Nile River as its primary freshwater source in addition to groundwater as a second source of water (Abdel-Shafy \u0026amp; Kamel, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the sustainability of the Nile is increasingly under threat due to rapid population growth, rising agricultural demands, industrial development, and transboundary pressures. Groundwater, therefore, represents a critical supplementary resource, particularly in regions distant from the Nile Valley, such as the Eastern Desert. According to the Food and Agriculture Organization (FAO), Egypt\u0026rsquo;s annual renewable water resources per capita have fallen well below the absolute scarcity threshold of 500 cubic meters, classifying it as a country experiencing absolute water scarcity (Fouad et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Needs \u0026amp; Indicator, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Compounding this crisis are the growing uncertainties posed by climate change, including elevated evapotranspiration, erratic rainfall, and declining upstream inflows, which collectively call for alternative and sustainable water sources.\u003c/p\u003e \u003cp\u003eGroundwater constitutes a vital natural resource, particularly in arid and semi-arid regions where surface water availability is scarce and often insufficient to meet the growing demands of human populations and ecosystems (Heath, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The Eastern Desert of Egypt, with its hyper-arid climate characterized by minimal rainfall and high evapotranspiration rates, faces significant challenges in securing adequate water supplies, making the identification and sustainable management of groundwater resources paramount. Accurate and up-to-date mapping of groundwater potential zones (GWPZ) is crucial for supporting informed decision-making in water resource management and for guiding future development activities in this region (Heath, 1983). Traditional methods of groundwater exploration, often relying on costly and time-consuming ground-based surveys, can be limited in their spatial coverage and efficiency (Abdalla et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Heath, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent decades, the integration of Geographic Information Systems (GIS) and remote sensing technologies has emerged as a powerful and cost-effective approach for assessing and mapping natural resources, including groundwater potential (Abdalla et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Allafta et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Das et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Elewa \u0026amp; Qaddah, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Morgan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Remote sensing provides valuable spatial data on various environmental factors that influence groundwater occurrence, such as precipitation intensities, lithology, surface slopes, lineaments, drainage patterns, vegetation cover, and land surface characteristics. To effectively integrate these diverse factors in assessing groundwater potential, multi-criteria decision analysis (MCDA) methods, such as the Analytical Hierarchy Process (AHP), have proven to be highly suitable (Alrawi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Arulbalaji et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ghanim et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Moshe \u0026amp; Beza, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Moumane et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The AHP method, developed by (Saaty, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), provides a structured framework for evaluating and weighting various criteria based on their relative importance in influencing a particular phenomenon, such as groundwater potential. Previous studies in Egypt have focused on mapping groundwater potential zones in specific areas of the Eastern Desert, such as the Wadi Qena Basin (Abdalla et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Megahed et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the Wadi Hodein area (Zayed \u0026amp; Aly, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the central Eastern Desert (Ketkat et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Morgan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu \u0026amp; Abdelkareem, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these advancements, there remains a significant knowledge gap regarding the groundwater potential of the Eastern Desert. However, most of the studies have focused on relatively smaller watersheds or specific regions within the Eastern Desert, with few efforts aimed at large-scale, multi-factorial groundwater assessments in the basement-dominated desert regions. No study has comprehensively mapped the entire Eastern Desert of Egypt, despite its vast size and critical importance for future water resource management and sustainable development. Given the region\u0026rsquo;s tectonic complexity, heterogeneous lithology, and episodic hydrological inputs, a tailored GIS-AHP framework is essential for developing an effective and practical groundwater potential map.\u003c/p\u003e \u003cp\u003eIn this context, the selection of appropriate thematic layers is paramount. Based on hydrological theory, regional experience, and previous modeling efforts, this study considers seven critical factors: precipitation, lithiology, slope, land use/land cover (LULC), drainage density, soil type, and lineament density. Each of these parameters is known to significantly influence groundwater recharge, storage, or movement. For instance, rainfall provides the primary input for recharge, while geology and lineament density govern subsurface permeability and storage potential. Slope and drainage density regulate runoff and infiltration, whereas soil and land cover modulate infiltration rates and surface retention.\u003c/p\u003e \u003cp\u003eThe main objective of this study is to identify and map groundwater potential zones in the Eastern Desert of Egypt using an integrated GIS-AHP model. The approach involves reclassifying and standardizing seven thematic datasets, followed by applying AHP to derive factor weights, which are then combined using weighted overlay analysis. The resulting map aims to support sustainable groundwater exploration, water resource planning, and policy development, especially in rural and infrastructure-scarce regions of Egypt. In doing so, the study contributes to Egypt\u0026rsquo;s broader water security goals and provides a methodological framework applicable to other arid regions facing similar challenges.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Eastern Desert of Egypt extends from the Nile Valley in the west to the Red Sea in the east, and from the borders of Sudan in the south to the outskirts of the Nile Delta in the north (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It encompasses a diverse landscape characterized by rugged mountains, arid plains, and numerous wadis (dry riverbeds). The climate of the Eastern Desert is predominantly hyper-arid, with extremely low and erratic rainfall, high temperatures, and significant potential for evaporation (Said, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These harsh climatic conditions result in a scarcity of perennial surface water bodies, making groundwater the primary source of water for human consumption, agriculture, and other developmental activities.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe topography of the Eastern Desert is dominated by the Red Sea Hills, a chain of rugged mountains running parallel to the Red Sea coast, reaching elevations of over 2,000 meters above sea level. Westward, the terrain gradually slopes down towards the Nile Valley, forming plateaus and undulating plains dissected by wadis that drain towards the Nile or the Red Sea. The geomorphology of the region plays a crucial role in influencing surface runoff and groundwater recharge. The socio-economic context of the Eastern Desert is characterized by scattered settlements and economic activities primarily centered around mining, tourism along the Red Sea coast, and limited agriculture in areas where groundwater is accessible. The increasing demand for water due to population growth and developmental projects necessitates a comprehensive understanding of the available groundwater resources and their potential.\u003c/p\u003e \u003cp\u003eThe Eastern Desert of Egypt is underlain by three primary groundwater aquifer systems: the Nubian Sandstone Aquifer, the Fissured Hard Rock Aquifers, and the Coastal Aquifers (Abdel-Shafy \u0026amp; Kamel, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Due to the region's arid conditions, natural recharge is minimal and occurs primarily through episodic flash floods and wadi infiltration. Nevertheless, structurally favorable zones, particularly those with high lineament density, exhibit promising groundwater potential. Understanding and delineating these zones is essential for supporting long-term water security and guiding sustainable development in one of Egypt\u0026rsquo;s most water-scarce regions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"3. Material and Methods","content":"\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThis study employs an integrated geospatial methodology that combines remote sensing, multi-source geodata, and the Analytical Hierarchy Process (AHP) within a Geographic Information System (GIS) environment to delineate groundwater potential zones (GWPZ) in the Eastern Desert of Egypt. This approach enables the systematic evaluation of multiple hydrogeological and environmental parameters, each of which contributes to the spatial variability of groundwater occurrence and recharge.\u003c/p\u003e\n \u003cp\u003eThe methodology is structured into three major phases: (1) data acquisition and preparation, (2) thematic map generation and classification, and (3) multi-criteria integration using AHP-based weighted overlay analysis. A total of seven key thematic layers\u0026mdash;precipitation, geology (lithology), slope, drainage density, soil type, land use/land cover (LULC), and lineament density are selected based on their relevance to groundwater dynamics in arid environments.\u003c/p\u003e\n \u003cp\u003eGeospatial datasets are first standardized through georeferencing and projection into a common spatial framework (UTM). Derived products such as slope and lineament density were extracted from high-resolution Digital Elevation Models (DEMs), while other layers are prepared using satellite imagery, geological surveys, and global datasets. Each thematic map was then reclassified into five groundwater potential categories ranging from 1 (very high potential) to 5 (very low potential), based on their hydrogeological characteristics.\u003c/p\u003e\n \u003cp\u003eIn parallel, the AHP method is applied to derive the relative importance (weights) of each factor through pairwise comparison, normalization, and consistency verification. These weights are then applied in the weighted overlay analysis, which resulted in a composite GWPZ index map. The final map is further classified into five classes, from very high to very low groundwater potential zones. The overall methodology is summarized in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, which illustrates the flow from dataset processing to GWPZ map generation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Parameters for Groundwater Potential Mapping\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe selection of appropriate thematic parameters is a fundamental step in developing accurate groundwater potential zone (GWPZ) maps, particularly in arid and structurally complex regions such as the Eastern Desert of Egypt. This study incorporates seven geospatial layers: precipitation, lithology, slope, drainage density, land use/land cover (LULC), soil type, and lineament density. These parameters are selected based on their hydrogeological significance, spatial availability, and proven application in previous studies in the Eastern Desert.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Precipitation\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eRainfall is a key driver of natural groundwater recharge; particularly where other sources are absent. In hyper-arid environments such as Egypt\u0026rsquo;s Eastern Desert, precipitation is both limited and erratic; however, occasional intense rainfall events, especially in mountainous areas, can lead to substantial infiltration through wadis, fractured bedrock, and ephemeral channels. Regions that receive relatively higher rainfall are generally more favorable for groundwater accumulation. In this study, historical rainfall data spanning the period from 2010 to 2020 is collected to assess spatial variability across the study area as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea. The dataset is derived from the CRU-TS-4.06 gridded product by the Climatic Research Unit, University of East Anglia, and downscaled using WorldClim 2.1 to improve spatial resolution and apply bias correction (Harris et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Annual averages are computed to represent mean rainfall depths across the region.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 Lithology\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eGeological formations play a fundamental role in controlling the subsurface storage and movement of groundwater. Lithologies such as fractured basement rocks and porous sedimentary units, particularly sandstone and limestone, tend to possess higher permeability and porosity, making them more conducive to groundwater accumulation. In contrast, compacted or crystalline formations, including shale and igneous rocks, typically act as barriers to groundwater flow due to their low hydraulic conductivity. In this study, geological formations, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb, are delineated and classified using geological maps provided by United States Geological Survey, (\u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) based on their anticipated influence on groundwater occurrence and transmissivity.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3 Topographic Data (Slope)\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eSlope controls the partitioning between infiltration and surface runoff. Gentle slopes favor water retention and percolation, thereby enhancing recharge, whereas steep slopes promote rapid runoff and erosion, reducing the potential for infiltration. Slope gradients are derived from a Digital Elevation Model (DEM) using standard terrain analysis techniques and classified according to recharge potential. The Digital Elevation Model (DEM) for Egypt\u0026apos;s Western Desert has a resolution of 1 arc-second (about 30 meters) and is geo-referenced to UTM WGS84 Zone 36 North. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea shows how this Digital Elevation Model (DEM) is made using ArcGIS and the Spatial Analyst Extension to get data from the ALOS World 3D \u0026minus;\u0026thinsp;30m (AW3D30) version 2.1 (Takaku \u0026amp; Tadono, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.4 Drainage Density\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eDrainage density is a widely used hydrological parameter that serves as an indirect indicator of surface permeability and infiltration capacity. It is defined as the total length of drainage channels per unit area and provides insight into the terrain\u0026rsquo;s ability to absorb or shed surface water. Generally, areas with low drainage density indicate higher infiltration potential due to more permeable surface materials, while high drainage density often corresponds to compacted or impermeable substrates that promote surface runoff and limit groundwater recharge.\u003c/p\u003e\n \u003cp\u003eIn this study, drainage density is derived through hydrological modeling in a GIS environment using a high-resolution Digital Elevation Model (DEM). The DEM is first preprocessed by applying flow direction and flow accumulation algorithms to delineate the drainage network. Stream segments are classified according to the Strahler stream order system to distinguish between primary and higher-order channels (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). This hierarchical classification allowed for the identification of major flow paths and their distribution across the landscape. Once the drainage network is fully extracted and validated, the total stream length within each spatial unit is computed, and drainage density is calculated using a raster-based method. The resulting drainage density map offers a spatially continuous representation of infiltration potential across the study area, with distinct variations correlating to terrain features and geological controls. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb displays the drainage network overlaid with stream orders and the derived drainage density classes, highlighting zones with differing hydrological responses and recharge capacities.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.5 Land Use / Land Cover (LULC)\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eLand use and land cover (LULC) play a critical role in shaping surface hydrology and influencing infiltration rates. Vegetated areas, such as agricultural fields and shrublands, enhance infiltration by reducing surface runoff and promoting soil moisture retention, thereby supporting groundwater recharge. In contrast, impervious surfaces like urban developments and exposed barren lands restrict percolation, leading to increased runoff and reduced recharge potential. In this study, LULC data are sourced from the Environmental Systems Research Institute (ESRI) global land cover map, a widely recognized and reliable dataset in GIS-based environmental assessments (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). ESRI is a global leader in geospatial software and provides high-quality spatial data that are essential for accurate hydrological and land surface analysis (Venter et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.6 Soil Type\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eSoil characteristics, particularly texture and structure, play a vital role in determining infiltration capacity and soil water retention (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). Well-drained soils such as sandy and loamy types typically exhibit high permeability, allowing for greater vertical percolation and enhancing groundwater recharge. In contrast, clay-rich soils have lower permeability and tend to retain surface water, limiting infiltration into deeper layers. In this study, soil data are obtained from globally validated sources, including the works of Hengl et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Shangguan et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), which provide high-resolution, harmonized soil property datasets suitable for spatial hydrological modeling.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.7 Lineament Density\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eLineaments\u0026mdash;such as faults, fractures, and shear zones\u0026mdash;play a critical role in facilitating groundwater movement, particularly in crystalline and hard rock environments where primary porosity is minimal (Yeh et al., 2016). In structurally complex regions like Egypt\u0026rsquo;s Eastern Desert, these features significantly enhance secondary porosity and serve as preferential pathways for infiltration and subsurface flow. Lineament patterns are extracted through remote sensing techniques and geological interpretation of satellite imagery, then processed in ArcGIS to generate a lineament density map. This map quantifies the concentration of structural features per unit area, offering a valuable proxy for fracture connectivity. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb illustrates the spatial distribution of lineament density across the study area.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Analytical Hierarchy Process (AHP) for Groundwater Potential Mapping\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe Analytical Hierarchy Process (AHP) is a structured decision-making technique introduced by (Saaty, \u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e) that enables the integration of multiple spatial and thematic parameters by assigning relative weights based on expert judgment and pairwise comparison. In the context of groundwater potential mapping, AHP has become a widely accepted method for synthesizing hydrogeological, topographical, climatic, and environmental data into a coherent spatial model.\u003c/p\u003e\n \u003cp\u003eIn this study, AHP is applied to evaluate and prioritize seven parameters influencing groundwater occurrence in the Eastern Desert of Egypt. These parameters include: precipitation, lithology, slope, drainage density, land use/land cover (LULC), soil type, and lineament density. The weights assigned to each parameter are derived from a normalized pairwise comparison matrix to compare each criterion against the others based on a 1\u0026ndash;9 scale (1\u0026thinsp;=\u0026thinsp;equal importance, 9\u0026thinsp;=\u0026thinsp;extreme importance). These comparisons are used to calculate weights for each criterion, representing their relative influence on the goal. The process ensures consistency through the Consistency Ratio (CR), which should be less than 0.1 for reliable results. In this study, AHP is applied to assign weights to seven factors influencing groundwater potential in the Eastern Desert of Egypt, based on insights from existing literature and the specific hydrogeological characteristics of the study area.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Development of thematic maps\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo effectively implement the Analytical Hierarchy Process (AHP) using a weighted overlay in ArcGIS, the dataset is classified each into five categories (very high potential, high potential, moderate potential, low potential, and very low potential). This classification ensures a structured evaluation of multiple factors that influence spatial analysis, such as precipitation, lithology, slope, drainage density, land use/land cover (LULC), soil type, and lineament density. By standardizing these parameters, it is possible to systematically assign weights and rank their influence on the study area, facilitating a more data-driven decision-making process.\u003c/p\u003e \u003cp\u003eEach dataset is classified based on its specific characteristics. For instance, precipitation values are categorized according to rainfall intensity, while slope classification followed terrain steepness. Similarly, geological formations, land cover types, soil classes, and lineament density are grouped based on their impact on hydrological and environmental processes. Below, we discuss the classification of each dataset in detail.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Precipitation classification\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePrecipitation data, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, is categorized into five classes based on daily rainfall intensity (mm/day). The lowest category, 0\u0026ndash;5 mm/day, represents arid or semi-arid regions with minimal rainfall. Areas receiving between 6\u0026ndash;10 mm/day fall into the second category, indicating slightly wetter conditions. Moderate precipitation zones, ranging from 11\u0026ndash;20 mm/day, form the third class. Higher rainfall intensities of 21\u0026ndash;30 mm/day characterize the fourth category, while the highest precipitation class, more than 31 mm/day, corresponds to regions experiencing significant daily rainfall. This classification helps assess the potential influence of precipitation on surface runoff and erosion.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Lithology classification\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe geological formations in the study area, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, are reclassified based on their hydrogeological properties\u0026mdash;primarily porosity, permeability, and their capacity to facilitate groundwater recharge and storage. Lithological units such as Quaternary (undivided) and Holocene deposits are categorized as having very high groundwater potential, due to their unconsolidated nature and high infiltration capacity. Formations like the Tertiary, Tertiary-Cretaceous, and Carboniferous units are assigned high potential status, as they generally possess moderate porosity and may contain secondary permeability through fracturing or weathering. Sedimentary units including the Cretaceous, Lower Cretaceous, and Jurassic are considered to have moderate potential, reflecting their ability to store groundwater primarily in fractures or bedding planes. More compact formations such as the Cretaceous-Jurassic and Cretaceous-Carboniferous sequences are classified as having low potential, due to their typically reduced permeability. Finally, very low groundwater potential is attributed to Precambrian basement rocks, Mesozoic Igneous, and Tertiary Igneous formations, which are generally impermeable and lack sufficient secondary porosity except where structurally disrupted. This classification scheme reflects both regional hydrogeological knowledge and established principles regarding lithologic influence on groundwater occurrence. It supports the broader AHP-based groundwater modeling by providing a geologically informed thematic layer for integration with other spatial parameters.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Slope classification\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSlope is a key topographic factor influencing surface runoff and infiltration, both of which are critical in determining groundwater recharge potential. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, the slope of the study area is derived from a high-resolution Digital Elevation Model (DEM) and classified into five categories based on gradient. These categories are then reinterpreted in the context of groundwater favorability. Areas with gentle slopes (0\u0026ndash;5%) are classified as having very high groundwater potential, as they favor water accumulation and infiltration with minimal runoff. Zones with moderate slopes (5.1\u0026ndash;10%) are assigned high potential, reflecting their suitability for moderate infiltration while still maintaining limited runoff. Slopes ranging from 10.1\u0026ndash;20% are considered to have moderate potential, as water retention begins to diminish and runoff increases. Steeper slopes between 20.1% and 30% are classified as low potential, due to rapid runoff and limited infiltration time. Finally, areas with very steep slopes (more than 30.1%) are assigned very low potential for groundwater recharge, as these terrains facilitate fast surface water discharge and are typically erosion-prone. This classification ensures that slope-induced hydrological processes are accurately reflected in the overall groundwater potential model.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.1.4 Drainage density classification\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDrainage density is a crucial indicator of the infiltration capacity and surface permeability of a landscape, which directly influences groundwater recharge potential. The drainage density are classified into five categories, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, and interpreted in terms of groundwater potential. Areas with low drainage density (0\u0026ndash;0.29) are classified as having very high groundwater potential, indicating well-drained, permeable surfaces that promote infiltration and reduce surface runoff. Zones with moderately low drainage density (0.30\u0026ndash;0.42) are assigned high potential, still reflecting favorable infiltration conditions. Drainage densities ranging from 0.43 to 0.52 are categorized as moderate potential, suggesting a balance between runoff and recharge. Regions with higher drainage density (0.53\u0026ndash;0.65) are considered to have low potential, typically associated with less permeable surfaces and increased surface flow. Finally, areas with very high drainage density (0.66\u0026ndash;0.93) are assigned very low groundwater potential, as these are usually compact or rocky terrains where rapid runoff limits infiltration. This classification ensures the drainage network\u0026rsquo;s influence is accurately incorporated into the groundwater potential assessment.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.1.5 Land use/land cover (LULC) classification\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLand use and land cover (LULC) conditions have a significant influence on groundwater recharge, primarily through their impact on infiltration rates and surface runoff. In this study, the LULC map is reclassified into groundwater potential categories based on the permeability and hydrological behavior of each land cover type. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, areas covered by water bodies and flooded vegetation are assigned a very high groundwater potential classification, as these surfaces support direct recharge and high moisture retention. Similarly, croplands and bare ground are categorized as high potential zones due to their relatively permeable surfaces, which allow moderate infiltration under certain conditions. Rangelands and tree-covered areas are assigned a moderate potential, reflecting their mixed capacity to promote infiltration depending on vegetation density and soil structure. In contrast, urban and built-up areas are classified as having very low groundwater potential due to their impervious surfaces that inhibit infiltration and significantly increase runoff. Snow and ice-covered areas, although limited in the study region, are also considered very low potential, given their limited contribution to groundwater recharge in arid climates. This classification ensures that the LULC layer accurately reflects the spatial variability in surface permeability and its influence on groundwater occurrence across the Eastern Desert.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.1.6 Soil type classification\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSoil characteristics, particularly texture and permeability, are essential in determining the rate and volume of groundwater infiltration. In this study, the soil map is reclassified into categories of groundwater potential based on the physical and hydrological properties of each soil type. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, Fluvisols and Arenosols are classified as having very high groundwater potential due to their high porosity and excellent infiltration capacity. These soils are typically found in alluvial environments or sandy terrains that facilitate rapid percolation. Regosols and Andosols are assigned a high potential rating. These soils are well-drained and generally formed from unconsolidated material, allowing for moderate to high infiltration. Soils such as Umbrisols, Lixisols, Kastanozems, and Calcisols are considered to have moderate groundwater potential, reflecting intermediate permeability and variable water retention characteristics. In contrast, Podzols and Ferralsols are classified as having low groundwater potential, as they tend to be compacted, rich in iron and aluminum oxides, and often exhibit poor drainage properties that hinder infiltration. This classification approach ensures that the soil layer used in the AHP model accurately represents the variability in infiltration capacity and its influence on groundwater recharge across the study area.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.1.7 Lineament density classification\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLineaments are critical in controlling groundwater occurrence in hard rock terrains, where primary porosity is minimal and groundwater movement relies heavily on secondary structural pathways. This lineament density map is reclassified into five classes to assess their relative contribution to groundwater potential, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb. Areas with very high lineament density (0.1\u0026ndash;0.11) are assigned very high groundwater potential, as they represent structurally complex zones that facilitate recharge through enhanced secondary porosity and permeability. Zones with high density values (0.08\u0026ndash;0.09) are categorized as having high potential, indicating well-connected fracture systems. Moderate potential is assigned to areas with lineament densities between 0.05 and 0.07, where structural influence on recharge is present but less intense. Low potential areas are identified in regions with densities ranging from 0.03 to 0.04, where fewer fractures limit water movement. Finally, zones with very low lineament density (0\u0026ndash;0.02) are considered least favorable for groundwater recharge, as they lack significant structural conduits. This classification ensures that fracture-controlled groundwater movement is adequately represented in the overall potential mapping, particularly important in the crystalline terrains of the Eastern Desert.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.2 AHP calculations and weights\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe AHP analysis yielded the normalized weights for each factor, reflecting their relative importance based on expert knowledge. The derived weights from the AHP process are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The consistency ratio (CR) calculated for the pairwise comparison matrix equals to 0.011 which is well below the acceptable threshold of 0.1, indicating a consistent and reliable weighting scheme. The final weights derived for the seven parameters are as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAHP weights for the different parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLithology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.89%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrainage Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.37%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLineament Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThese weights reflect a balanced consideration of both hydroclimatic drivers and structural-geological controls that shape groundwater dynamics in the Eastern Desert of Egypt. Rainfall, assigned the highest weight (32.31%), reflects its critical role as the primary recharge mechanism, despite the region\u0026rsquo;s hyper-arid nature. Although rainfall is sporadic, episodic precipitation events\u0026mdash;particularly in highland zones\u0026mdash;can lead to significant localized recharge through infiltration in wadis and fractured terrains.\u003c/p\u003e \u003cp\u003eLithology, ranked second (17.89%), strongly influences groundwater storage and movement through lithological properties such as porosity and permeability. In this region, fractured Precambrian basement rocks and porous sedimentary formations serve as key aquifer hosts. Factors such as slope (12.63%), drainage density (10.60%), soil type (10.60%), and lineament density (10.60%) are assigned nearly equal weights due to their interlinked roles in governing surface runoff, infiltration efficiency, and subsurface flow dynamics. Notably, lineament density is emphasized due to the dominance of fracture-controlled aquifers in the crystalline terrains of the Eastern Desert.\u003c/p\u003e \u003cp\u003eIn contrast, land use/land cover (LULC) received the lowest weight (5.37%), consistent with its relatively localized influence on groundwater processes in the study area. Given the sparse vegetation and limited anthropogenic land cover, its impact is minimal compared to the more dominant physical and structural factors.\u003c/p\u003e \u003cp\u003eThe weighting scheme developed in this study aligns with and builds upon a range of published AHP-based groundwater assessments across Egypt. Rainfall weights in the literature vary widely\u0026mdash;from 6.8% (Khan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to 30% (Elewa \u0026amp; Qaddah, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) depending on the climatic setting and emphasis on recharge. Our assignment of 32.31% is consistent with studies in hyper-arid regions where recharge is largely episodic but remains critical for groundwater sustainability. The lithology weight of 17.89% falls within the commonly cited range of 3\u0026ndash;20%, (El-Sayed \u0026amp; Elgendy, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Elewa \u0026amp; Qaddah, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), reinforcing its central role in subsurface water behavior .\u003c/p\u003e \u003cp\u003eSimilarly, our equal weighting of drainage density, soil type, and lineament density (10.6% each) reflects a balanced approach where all three contribute meaningfully to recharge potential. The final weights assigned in this study fall well within the ranges reported in similar groundwater potential mapping studies across Egypt. According to the literature, drainage density weights have ranged from 0.25\u0026ndash;23% (Abdalla et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; El-Sayed \u0026amp; Elgendy, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with higher values generally reported in studies where runoff control is a dominant factor, such as in Wadi Abu Marzouk and the Central Eastern Desert. Soil type weights vary between 5% and 18.2% (El-Sayed \u0026amp; Elgendy, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Morgan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), reflecting the differing significance of soil permeability and retention properties across study areas. Similarly, lineament density weights span a broad range from 2\u0026ndash;23% (Abdalla, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; El-Sayed \u0026amp; Elgendy, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with the highest values observed in tectonically complex regions where fracture-controlled aquifers prevail. The nearly equal weights assigned to these three factors in our model reflect their interrelated contributions to groundwater recharge in the structurally diverse and geomorphologically varied terrain of the Eastern Desert, while remaining well-aligned with published methodologies. LULC weights reported in literature typically range from 4.5\u0026ndash;7% (El-Sayed \u0026amp; Elgendy, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Morgan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our allocation of 5.37% fits this trend, acknowledging its secondary role compared to hydrogeological parameters.\u003c/p\u003e \u003cp\u003eOverall, the weight distribution adopted in this study is both scientifically grounded and context-specific. It offers a balanced framework for evaluating groundwater potential in the Eastern Desert, where rainfall and geologic controls dominate recharge, but structural features and terrain characteristics significantly influence storage and movement.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Integration of AHP and Remote Sensing Data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe integration of Analytical Hierarchy Process (AHP) with remote sensing-derived thematic layers is performed within a Geographic Information System (GIS) environment to produce the final Groundwater Potential Zones (GWPZ) map. This integrated approach allowed for the objective combination of multiple geospatial datasets, each representing factors that control groundwater occurrence in the Eastern Desert of Egypt.\u003c/p\u003e \u003cp\u003eSeven thematic maps\u0026mdash;precipitation, lithology, slope, drainage density, soil type, land use/land cover (LULC), and lineament density\u0026mdash;are generated and reclassified into five groundwater potential classes (1\u0026ndash;5), where 1 indicates very high potential and 5 indicates very low potential. The reclassification is based on hydrological behavior, infiltration capacity, and geological properties, supported by field knowledge and published literature as mentioned before. Each thematic map is standardized to this scale to ensure compatibility in the subsequent overlay operation.\u003c/p\u003e \u003cp\u003eThe classified layers are then multiplied by their respective AHP weights and summed to produce a groundwater potential map (GWPZ) map, which indicates areas with varying potential for groundwater occurrence. The equation (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for the weighted overlay method, employed using GIS environment, in this study is:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{G}\\varvec{W}\\varvec{P}\\varvec{Z}=\\:\\sum\\:_{\\varvec{i}=1}^{\\varvec{n}}\\left({\\varvec{W}}_{\\varvec{i}}\\times\\:{\\varvec{F}}_{\\varvec{i}}\\right)$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eW: AHP-derived weights for each factor\u003c/p\u003e \u003cp\u003eF: Classified thematic map for each factor\u003c/p\u003e \u003cp\u003en: number of studied factors (7)\u003c/p\u003e \u003cp\u003eThe result is a composite raster map representing a continuous groundwater potential map across the region. This map is then classified into zones corresponding to their potential to groundwater. Lower values represent higher groundwater potential, consistent with the adopted classification scheme.\u003c/p\u003e \u003cp\u003eThis methodology leverages both expert-based decision weighting and high-resolution spatial data, enabling a more accurate and scalable assessment of groundwater resources in arid environments where field data are sparse or difficult to obtain.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Groundwater Potential Zone Mapping\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Spatial distribution and interpretation of GWPZ\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe integration of these weighted thematic layers in the GIS environment resulted in the generation of a GWPZ map for the Eastern Desert of Egypt is developed through the integration of seven spatially significant factors using a weighted overlay model driven by the Analytical Hierarchy Process (AHP). The resulting map categorizes the region into four groundwater potential classes: high, moderate, low, and very low. It is noteworthy that the \u0026ldquo;very high\u0026rdquo; potential class did not appear in the final output, likely due to the combined limitations of rainfall scarcity, impermeable geology, and rugged terrain. This classification enables an in-depth understanding of the hydrogeological setting of the region and its influence on groundwater recharge and storage.\u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the low potential zone dominates, covering approximately 71% of the study area. This is followed by moderate potential zones at 23%, very low potential zones at 4%, and high potential zones accounting for only 2%. These proportions highlight the hydrogeological constraints across most of the Eastern Desert, while also identifying localized areas where favorable conditions align for targeted groundwater development.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAreas of different groundwater potential zones\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZones\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (Km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of Total Area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Low Potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe low groundwater potential class dominates the landscape, covering approximately 71% of the study area. These zones are largely concentrated in the central belt and southwestern parts of the region, where Precambrian basement rocks and Mesozoic igneous formations prevail. These formations are known for their low primary porosity and poor permeability. Coupled with steep slopes and high drainage density, these geological settings facilitate rapid surface runoff and restrict infiltration. The limited rainfall in these areas, typically in the range of 0 to 10 mm/year, further reduces recharge potential, making groundwater development in these zones challenging.\u003c/p\u003e \u003cp\u003eThe moderate potential zones, accounting for 23%, are distributed across the northeastern and southeastern regions, particularly in areas where Cretaceous and Tertiary sedimentary units are present. These regions exhibit moderate lineament density, gentler slopes, and balanced drainage characteristics, all of which support partial recharge, especially when intersected by fractured zones or overlying moderately permeable soils such as Regosols and Lixisols. Rainfall in these areas ranges between 11 and 20 mm/year, contributing to seasonal recharge potential, especially in structurally controlled basins.\u003c/p\u003e \u003cp\u003eAlthough covering only 2% of the total area, high groundwater potential zones are of great significance due to their favorable combination of hydrogeological factors. These zones are primarily located in the northernmost tip and southeastern edge of the Eastern Desert. In these regions, precipitation intensities reach up to 75 mm/year, particularly near the Red Sea foothills and Gebel Elba area, driven by orographic rainfall. Additionally, these areas are underlain by unconsolidated Quaternary and Holocene deposits, which offer high permeability and shallow aquifer conditions. They also exhibit low drainage density, low slope gradients, and high lineament density, all of which enhance infiltration and subsurface water retention. These zones are considered the most promising for sustainable groundwater development and should be prioritized for further hydrogeological surveys and well installation.\u003c/p\u003e \u003cp\u003eIn contrast, very low potential zones, which constitute about 4% of the region, are mainly located in steep mountainous terrain along the eastern escarpments and isolated patches in the southwest. These areas are geologically dominated by crystalline basement rocks, have very steep slopes (often\u0026thinsp;\u0026gt;\u0026thinsp;30%), and are characterized by high surface runoff. Moreover, rainfall in these zones is minimal (often\u0026thinsp;\u0026lt;\u0026thinsp;5 mm/year), and the structural conditions offer limited pathways for infiltration. These zones are thus poorly suited for groundwater development without substantial intervention such as artificial recharge or engineered water harvesting systems.\u003c/p\u003e \u003cp\u003eThe distribution of groundwater potential classes emphasizes the dominance of less favorable zones, a pattern that aligns with the arid climate, hard-rock geology, and topographic constraints typical of the Eastern Desert. However, the spatial association between higher rainfall regions, permeable lithologies, and structural features such as faults and fractures highlights the importance of integrating multiple thematic layers for accurate groundwater targeting.\u003c/p\u003e \u003cp\u003eOverall, the resulting GWPZ map provides critical spatial insight for guiding groundwater exploration, land-use planning, and water management strategies. It is especially relevant in the context of climate change and growing water demand, where resource optimization is essential. The spatial heterogeneity reflected in the map underscores the need for localized investigations and emphasizes the potential of using multi-criteria geospatial models in arid-zone groundwater studies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Validation of the groundwater potential mapping output\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo assess the reliability of the developed groundwater potential zones (GWPZ), a validation analysis is conducted using the spatial distribution of 390 existing groundwater wells across the study area as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. These wells are overlaid on the classified GWPZ map produced through the GIS-AHP model. The results revealed that 2% of wells are located in high potential zones, 26% in moderate zones, 65% in low zones, and 7% in very low zones.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAt first glance, the large proportion of wells (72%) located in low or very low potential areas might seem inconsistent with the groundwater potential classification. However, this distribution can be logically explained when taking into account the hydrogeological nature of the region and the structure of the model itself.\u003c/p\u003e \u003cp\u003eThe AHP framework used in this study placed the highest weight on precipitation, which reflects active recharge under current climatic conditions. As such, the resulting map is optimized to identify areas that are currently favorable for natural recharge processes for shallow aquifers. However, many aquifers in the Eastern Desert are known to be fossil or non-renewable, recharged during wetter climatic epochs such as the Holocene pluvial period. These aquifers, while no longer receiving substantial recharge, continue to store large volumes of groundwater accumulated over thousands of years.\u003c/p\u003e \u003cp\u003eAs confirmed by Abdel-Shafy \u0026amp; Kamel, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), much of Egypt\u0026rsquo;s groundwater, particularly in desert regions, is stored in deep, confined aquifers that have not been significantly replenished in modern times. These systems are not easily detected through models based on surface conditions, as their recharge is historical, not active, and their yields are supported by large paleo-reserves rather than ongoing hydrological processes.\u003c/p\u003e \u003cp\u003eIn addition, the model did not account for proximity to the Nile River, which plays a critical role in the hydrogeological behavior of adjacent zones. Shallow aquifers along the Nile Valley may receive recharge from lateral seepage or irrigation return flow, maintaining higher water tables even in areas of low rainfall. Therefore, wells located near the Nile may appear in low potential zones based on rainfall, slope, and drainage patterns, while still benefiting from human-influenced or river-connected recharge.\u003c/p\u003e \u003cp\u003eIn summary, the validation process supports the model\u0026rsquo;s ability to accurately delineate zones of current recharge potential, while also acknowledging that many productive wells are located in non-renewable aquifers, or in areas influenced by proximity to the Nile. These findings underscore the need to integrate both modern spatial modeling and historical hydrogeological knowledge in groundwater management.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study demonstrates the effectiveness of integrating remote sensing data, thematic geospatial layers, and the Analytical Hierarchy Process (AHP) within a GIS framework for delineating groundwater potential zones (GWPZ) in the Eastern Desert of Egypt. By combining seven hydrogeologically relevant factors\u0026mdash;precipitation, lithology, slope, drainage density, soil type, land use/land cover, and lineament density\u0026mdash;this multi-criteria approach successfully identified areas with varying groundwater potential across a complex and arid landscape.\u003c/p\u003e \u003cp\u003eThe resulting GWPZ map classifies the region into four groundwater potential categories: high, moderate, low, and very low. Notably, no zones are classified as \"very high\" due to prevailing environmental and structural limitations. The spatial analysis showed that the majority of the study area (71%) falls within the low potential class, with high potential zones occupying only 2%, mainly in the northern and southeastern areas where favorable geological, structural, and climatic conditions converge. These findings reflect the natural constraints of the region\u0026mdash;steep topography, impermeable lithologies, and extremely limited precipitation.\u003c/p\u003e \u003cp\u003eThe model is further validated using 390 well locations, which revealed that most existing wells fall within low to moderate potential areas. This aligns with the notion that many of the current wells tap into non-renewable or paleo-recharged aquifers, emphasizing the importance of historical rainfall events and structural control in groundwater occurrence.\u003c/p\u003e \u003cp\u003eOverall, this research provides a robust, scalable framework for groundwater exploration in hyper-arid environments. The GWPZ map can serve as a critical tool for decision-makers and water resource planners, offering guidance for well-siting, recharge project planning, and sustainable water development. Future research may benefit from incorporating more detailed aquifer parameters and groundwater quality data to enhance model precision and water resource management strategies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI have made all the work required in the manuscript including writing also.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdalla, F. (2012). 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(2002). \u003cem\u003eSurficial geology of africa (geo7_2ag)\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eVenter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., \u0026amp; Singh, G. (2022). Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(16), 4101. https://doi.org/10.3390/rs14164101\u003c/li\u003e\n \u003cli\u003eZayed, M. S., \u0026amp; Aly, M. M. (2023). Regional overview potential zones for groundwater recharge in Wadi Hodein, south Eastern Desert of Egypt. \u003cem\u003eWater Science\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(1), 290\u0026ndash;303.\u003c/li\u003e\n \u003cli\u003eZhu, Q., \u0026amp; Abdelkareem, M. (2021). Mapping groundwater potential zones using a knowledge-driven approach and GIS analysis. \u003cem\u003eWater\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(5), 1\u0026ndash;24.\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":false,"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":"Sustainable Groundwater Management, Groundwater potential zones, Remote sensing, GIS, AHP method, Multi-Criteria Decision Analysis (MCDA), Egypt","lastPublishedDoi":"10.21203/rs.3.rs-6618206/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6618206/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Eastern Desert of Egypt is a hyper-arid region where groundwater represents the most viable freshwater source for sustainable development. This study presents a comprehensive geospatial analysis integrating remote sensing data and the Analytical Hierarchy Process (AHP) within a GIS environment to delineate groundwater potential zones (GWPZ). Seven thematic layers\u0026mdash;precipitation, lithology, slope, drainage density, soil type, land use/land cover (LULC), and lineament density\u0026mdash;are selected based on their relevance to groundwater recharge and availability. These layers are standardized, classified, and weighted using AHP, yielding a consistent and validated spatial model. The final resulting GWPZ map categorizes the region into four classes: high, moderate, low, and very low potential, with the low potential zone dominating 71% of the area, followed by moderate (23%), very low (4%), and high potential zones (2%). This research offers a scalable and reliable framework for groundwater exploration and supports strategic water resource planning in arid regions with limited surface water availability.\u003c/p\u003e","manuscriptTitle":"Integrating Remote Sensing and Multi-Criteria Decision Analysis for Groundwater Zoning in the Eastern Desert of Egypt","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 11:24:36","doi":"10.21203/rs.3.rs-6618206/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":"52b00e57-040d-479f-b608-1bc5efb71252","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49271604,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":49271605,"name":"Physical sciences/Engineering/Civil engineering"}],"tags":[],"updatedAt":"2025-09-16T14:23:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 11:24:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6618206","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6618206","identity":"rs-6618206","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}