GIS and Remote Sensing-Based Spatial Analysis of Hydrogeochemical Degradation in the Darb El-Arbaein Aquifer System, Egypt

1  Introduction

The quality of the aquifer is influenced by rock-water interactions [1,2], streams [3], and climate factors [4–6]. Rapid urbanization and intensive agriculture have significantly affected groundwater supply and quality through over-pumping and wastewater discharge. According to the World Health Organization (WHO), water is among the main causes of many human diseases and infections [7–9]. The degradation of the aquifer results from activities such as agriculture and farming [10], irrigation and canal systems [11,12], urbanization [13,14], soil salinity [15,16], and industrialization [10,17–19]. The hydrogeochemistry assessment involves understanding historical aquifer degradation over various periods. This method helps identify potential pollution sources that threaten aquifer resources, offering an effective tool for early contamination detection—crucial in desert regions.

Aquifer quality was assessed through TDS concentration [20,21]. Urbanization and routine water use have led to increased irrigation. Salt buildup in soil diminishes permeability, blocking water from reaching roots and resulting in poor agricultural yields. Regular monitoring of irrigation water is vital to support the sustainable expansion of agricultural zones in desert areas. When aquifers are contaminated, pollutants are no longer confined to sediments and must be removed or recovered, which incurs costs. Continuous monitoring of new desert aquifers in terms of time and space is essential for their sustainability. Consequently, aquifer assessment plays a key role in managing and mitigating contamination. Water resources in desert regions are limited and depend mainly on rainfall and surface water.

The ArcGIS model uses the spatial distribution of the hydrogeological parameters of the Nubian aquifer (investigated area). Currently, aquifer status can be evaluated using GIS techniques [22–24], which identify the most promising areas for aquifer potential [25–27]. Remote sensing (RS) satellite maps and vulnerability maps are integrated to delineate aquifer potential zones [28–31]. The unconfined aquifer attracts population density and is affected by anthropogenic sources [32,33]. Aquifer management needs an evaluation of hydrogeological parameters to develop effective water strategy plans. The hydrogeomorphological characteristics of the aquifer greatly influence aquifer recharge [34]. The aquifer salinity increases in the new investment area (study area) due to urbanization and irrigation. Aquifer deterministic modeling was applied to identify the most promising areas for groundwater exploration and exploitation.

This paper employs the ArcGIS aquifer model to analyze aquifer flow, offering detailed insights into natural transport processes within the lithostratigraphic column. While the model provides valuable insights, it requires high-resolution spatial input data, which was collected through extensive fieldwork and funded accordingly. Despite this, the modelling of hydrogeological transport and flow processes remains quite limited. The model depends on spatial hydrogeological parameters and can simulate regional aquifer flow, helping assess groundwater sustainability, including storage, discharge, protection, and management. Studying aquifer quality over time, identifying hydrogeochemical reactions, and simulating resource availability all support effective groundwater management, fostering sustainable development and protecting public health [35].

2  Study Area

Darb El Arbaein gets its name from the historic Darb El Arbaein Route (see Fig. 1), which means the Forty Days Route. The agency responsible, GARPAD, has conducted numerous studies on agricultural projects in the area, integrating urban development with agricultural practices.

Figure 1: Map of the middle Darb El Arbaein area.

The aquifer, which is the main resource for this study, has boreholes installed at depths of about 450 m. Middle Darb El Arbaein is situated between North Darb El Arbaein to the north, South Darb El Arbaein to the south, Toshka Depression to the east, and East Owinat to the west (see Fig. 1). The annual rainfall here averages less than 5 mm, and the average temperature ranges from 13.1°C to 39.3°C monthly [36]. This project aims to analyze fluctuations in potentiometric levels and total dissolved solids in the Nubian aquifer across different periods, and to explore the effects of hydrogeological parameters on water quality. Groundwater flow patterns and optimal recharge zones were identified using ARCGIS modelling techniques.

The Nubian aquifer consists of Palaeozoic-Mesozoic sandstone beds and Upper Cretaceous layers, including Taref and Kiseiba formations. It sits above basement rock and is covered by the Dakhla bed, an aquitard. The stratigraphic sequence from bottom to top includes (Figs. 2 and 3): the Pre-Cambrian basement, Palaeozoic-Mesozoic sandstone and shale—mainly coarse-grained sandstone with grey shale layers—and the Upper Cretaceous layers, which comprise Taref (sandstone and shale), Qusier (sandstone, conglomerates, siltstone, and claystone), and Kiseiba (sandstone and ferruginous sandstone with shale). The Kiseiba and Taref formations constitute the Nubian sandstone aquifer. Above these are Tertiary layers, including the Kurkur Formation (limestone with sandy shale) at the bottom, and the Dakhla Formation (shale and marl, interbedded with siltstone and sandstone) at the top.

Figure 2: Geological map of the middle Darb El Arbaein area (modified after [37]).

Figure 3: Subsurface cross-section in the middle Darb El Arbaein area (modified after [31]).

3  Materials and Methods

Hydrogeological and hydrogeochemical data were collected from 2000 to 2012. These data were systematically organized, analyzed, contoured, and interpreted. Using ArcGIS (Version), spatial distribution patterns of aquifer quality and hydrogeological parameters were better understood by matching them with contour maps. This approach clarifies the influence of hydrogeology and points and nonpoint pollution sources on aquifer quality. The data were imported into ArcGIS to generate contour maps, with raster surfaces interpolated from points using inverse distance weighting (IDW). ArcGIS was also used to calculate the groundwater volume balance residual and the seepage velocity vector (direction and magnitude) for steady flow conditions (Fig. 4). They are created using Spatial Analyst Tools, groundwater option. The groundwater sections include Darcy flow and velocity.

Figure 4: Flow chart of the ARCGIS model of the Nubian sandstone aquifer.

GIS spatial analyst and groundwater tools calculate Darcy flow and groundwater velocity, along with the underlying rules and assumptions discussed earlier.

The specific discharge (q), expressed as volumetric flow rate per unit area, indicates groundwater movement through connected pores. Therefore, Darcy’s (q) reflects superficial velocity, also called velocity (v) [38].

q=Ki

K = permeability, i = hydraulic gradient.

Q=KAi

Q = Volumetric flow rate (L3/T).

v=Ki/n

n = effective porosity.

v=q/n

The groundwater analysis used spatial analyst tools to evaluate groundwater volume balance residuals and average velocity. Key parameters, including saturated thickness (ST), transmissivity (T), effective porosity (EP), hydraulic conductivity (HC), and drawdown (DD), were reclassified in ArcGIS based on their contributions to aquifer potential. These thematic layers, prepared using Geographic Information System (GIS), were combined to produce a Groundwater Favorability Index (0–10) as per Kamaraju et al. [39]. The assigned weights are 35% for ST, 20% for T, 15% for EP, 15% for HC, and 14% for DD. Using Spatial Analyst Tools and the Map Algebra option in GIS, these layers were integrated to delineate areas with the highest and lowest groundwater potential. The potential ranges from low to high aquifer recharge zones. Two ETM+ images from Landsat-8 (path 176/row 43, scene ID C81760432016336LGN00), each with 11 bands, were used. These images underwent mosaicking, radiometric and atmospheric correction, and were subsetted to the study area. The geological map was scanned and georeferenced to align with the satellite images. It was digitized into distinct rock units based on satellite image features. Maximum likelihood classifications (ML) aimed to distinguish different exposed lithologies using the spectral information from multiple bands. The ML classifier is essential for lithological classification where residual uncertainty exists in overlapping classes. Using the geological maps, all categories were linked to existing features. To gather statistics describing each rock unit’s spectral response, 101 supervised training areas or AOIs were selected and controlled to represent five categories (rock types). Texture, shadow, tonal, and pattern differences for each class were considered based on the number of AOIs. The spectral signatures of the selected AOIs were analysed, and statistical parameters were calculated for each. The final product was a digitally classified geological map.

4  Results and Discussion

4.1 Hydrogeological Parameters

The elevation ranges from 129 m (southeast) to 163 m (west) (Fig. 5a), with basement surfaces all below sea level (Fig. 5b).

Figure 5: (a) DEM, (b) basement elevation, (c) saturated thickness, and (d) effective porosity of the Nubian sandstone aquifer.

Basement levels vary from −358.2 m in the southeast to −208.6 m in the northeast. Saturated thickness ranges from 174 m in the east to 241 m in the northwest, southeast, and southwest (Fig. 5c). Effective porosity varies from 9.2% northeastward to 49.5% in the southwest (Fig. 5d). Hydraulic conductivity and transmissivity are consistent (Fig. 6a,b), suggesting permeability influences aquifer properties. Permeability is less than 4 m/d, mainly due to high fines content in sandstone deposits (Fig. 2). Changes in aquifer facies affect the hydrogeological regime, especially because fine deposits reduce hydraulic conductivity. Transmissivity increases toward the southwest, aligning with effective porosity. According to Georhage [40], the aquifer has moderate potential with transmissivity below 500 m2/d.

Figure 6: (a) Hydraulic conductivity, (b) transmissivity, and piezometric surface within (c) 2000 and (d) 2005 of the Nubian sandstone aquifer.

During 2000, groundwater flows toward the southeastern area (Fig. 6c). Recharge zones are identified from two high-pressure zones—southwest and northeast—moving towards the low-pressure region in the southeast (Fig. 6c). In 2005, the flow shifts to a high-pressure zone in the northwest, moving toward the southeast (Fig. 6d). The two high-pressure zones merge in the northwest, primarily because of intensive pumping, a sharp decrease in the piezometric head, and the aquifer’s non-renewable nature. Groundwater flows from the center toward the northeast and southwest (see Fig. 7a). Between 2000 and 2005, the piezometric head declined by 22–26 m in the northwest and by 35–40 m in the east and south (see Fig. 7b). The overall decline was more pronounced across the zone, mainly due to high discharge rates and low hydraulic conductivity (<4 m/d). The smallest head drops occurred where permeability, transmissivity (see Fig. 6a,b), and effective porosity (see Fig. 5d) were highest. From 2005 to 2012, the largest head decreases of 12–19 m were observed in the northeast and southwest regions (see Fig. 7c), caused by low transmissivity and hydraulic conductivity. Meanwhile, by 2005, the potentiometric surface increased by 6 m in the central region—from 2 to 6 (see Fig. 7c)—likely due to local rainfall. Overall, from 2000 to 2012, the maximum head decline of 21–50 m occurred, mainly driven by the low transmissivity and hydraulic conductivity of the aquifer (see Fig. 7d).

Figure 7: Piezometric surface, drop in piezometric heads within different periods of the Nubian sandstone aquifer, (a) piezometric head, (b) drop in plezometric head, (c) piezometric head difference (2005–2012), (d) piezometric head difference (2000–2012).

Throughout 2000, the TDS (total dissolved solids) concentration ranged from 877 to 1983 ppm, with an increase observed in the southeastern zone (1679–1983 ppm) and decreases in the northeast (877–1220 ppm) and northwest (1291–1389 ppm) (Fig. 8a). This rise in TDS is mainly due to the dissolution of shale, clay, carbonate, and both fine and coarse sandstone as groundwater flows (Fig. 6c). TDS levels tend to increase when groundwater interacts with surrounding rocks. There was a clear correlation between hydrogeology (groundwater flow, Fig. 6c) and hydrogeochemistry (TDS concentration, Fig. 8a). The higher TDS concentration in the southeast correlates with thicker Dakhla shale layers [41], which contribute to aquifer salinity through shale diffusion. The southeast area, having the highest TDS, is characterized by the absence of fault planes (profile C-D, Fig. 3). Upward leakage of fresh groundwater from Nubian sandstone is minimal here, unlike other zones (Fig. 3a,c,d), where fault planes enable more upward leakage and groundwater dilution. Before pumping, water depths ranged from 9 m in the southeast (highest TDS) to 27 m in the northwest (lower TDS). Post-pumping, water depths increased to 47–56 m, raising TDS levels and potentially damaging borehole equipment, such as submersible pumps. Between 2007 and 2012, TDS levels followed a similar upward trend as in 2005 but indicated higher salinity. TDS increased by 32–213 ppm in the NE and SW regions (Fig. 8d) in 2007 compared to 2000, while decreasing in other areas.

Figure 8: TDS concentration within different periods of the Nubian sandstone aquifer, (a) TDS (2000), (b) TDS (2007), (c) TDS (2012), (d) TDS (2007–2000).

The TDS levels rose in 2012 compared to 2007, increasing by 33 ppm in the northwest to 820 ppm in the southwest (Fig. 9a). By subtracting the 2012 TDS content from that of 2000, the increase ranged from 12 to 148 ppm in the northwest and from 543 to 800 ppm in the southwest (Fig. 9b). Aquifer degradation was caused by discharge rates of 180–250 m3/h, poor hydrogeological conditions (permeability less than 4 m/d), high evapotranspiration, water interaction, and flow within the aquifer.

Figure 9: TDS concentration fluctuations over different periods in the Nubian sandstone aquifer, (a) TDS (2012–2007), (b) TDS (2012–2000).

4.2 Groundwater Deterministic Modeling

The overall drawdown in the investigation zone was significant, ranging from 41–52 m in the southeastern area to 17–24 m in the northern and southwestern zones (Fig. 10a). This results from low permeability, high evaporation rates, and a non-rechargeable aquifer. Well and formation losses ranged from 1.22 × 10−7 to 4.84 × 10−7 d2/m−5 and 2.03 × 10−3 to 6.25 × 10−3 d/m−2, respectively [42]. Losses from wells were lower than those from the formation, indicating geological and hydrogeological influences. The Darcy flow function assesses groundwater volume balance residuals, flow directions, and flow magnitude grids. Precise modeling relies on low residual values since it assumes steady-state flow within the aquifer. It estimates the volume balance residual under these conditions. All input rasters must share the same extent and cell size and be in floating-point format. The ArcGIS/GROUNDWATER tool requires rasters for the potentiometric surface, effective porosity, saturated thickness, and transmissivity of the Nubian sandstone aquifer. Darcy velocity represents the leakage velocity vector of the aquifer, including its direction and magnitude during steady flow. Effective porosity is the ratio of void volume contributing to fluid flow relative to total volume. Saturated thickness influences the volume of the flow system. The highest average linear seepage velocity ranged from 0.041 to 0.087 m/d in the southern and western zones, decreasing to 1 × 10−5 to 7 × 10−3 m/d in most study areas (Fig. 10c). Velocity (v) signifies aquifer pore or linear velocity and is higher than the superficial velocity (specific discharge), increasing as effective porosity decreases (Fig. 5a). The volume (q) and velocity (v) were expressed in terms of both direction and magnitude, with groundwater flow vectors shown at 360° in Fig. 10b.

Figure 10: Drawdown and aquifer deterministic modeling by ARCGIS/GROUNDWATER, (a) drawdown (2000), (b) direction of average linear velocity, (c) average linear velocity, (d) groundwater volume balance residual.

The steady-state residual aquifer volumetric balance ranged from −214.3 to 212.1 m3/d (Fig. 10d), indicating nonrenewable groundwater and a continuous decline in storage capacity. Positive values (>zero), shown as traces in the aquifer configuration, varied from 9 to 212 m3/d (Fig. 10d). The rasters for transmissivity, drawdown, permeability, effective porosity, and saturated thickness were reclassified in the ARCGIS model according to their contribution to the aquifer potential (Fig. 11). Zones with high aquifer potential were found in the SW and NE regions (Fig. 12a), owing to high permeability and transmissivity and low drawdowns.

Figure 11: Reclassify hydrogeological raster maps in the ArcGIS model.

Figure 12: Promising area (a) and remote sensing (b) of middle Darb El Arbaein.

4.3 Remote Sensing

The irrigated region in Middle Darb El Arbaein spans 33.2 km2, while the zone around each constructed well covers 0.54 km2 (see Fig. 12b). The total area used was calculated as 0.54 km2 × 27 wells, totaling 14.6 km2. This figure, reflecting the extent of aquifer degradation over 12 years from 2000 to 2012, shows a utilized area of 14.6 km2.

The digital number (DN) for each land cover in the study area is shown in Fig. 13a–e. The standard deviation, represented by error bars, was particularly high for irrigated areas (plants) in Fig. 13f, indicating considerable variability in spectral reflectance. The Dakhla shale exhibited the smallest error bar. The signature plot of the five classified geological units shows that irrigated areas (plants) have the lowest digital numbers across all bands, whereas chalcedony covers display the highest reflectance in bands 1–5. This implies that band 1 records the lowest mean values, mainly due to Fe adsorption and reflection. Multispectral supervised classification assigns pixels to different categories [43]. Lillesand and Kiefer [44] note that the maximum likelihood classifier is crucial for land cover classification. In this study, 101 areas of interest (AOIs) were chosen, each representing one of five geological units with unique spectral signatures.

Figure 13: Reflectance of different land covers in Middle Darb El Arbaein (a–f).

The geological distribution map in Fig. 14a provides a foundation for future research. The sand sheet rock unit makes up about 45% of the area, with Kurkur covering 4%. Chalcedony rocks account for roughly 26%, while Dakhla shale and irrigated areas (plants) constitute 19% and 6%, respectively (Fig. 14a). Band rationing aims to enhance spectral differences among bands and reduce topographical effects [45]. This process involves dividing one spectral band by another to produce an image that emphasizes relative band intensities. A composite image from bands B1 to B7 highlights five distinct land covers (Fig. 14b). Three pairs of ratio images were generated (Fig. 14c), with the hydrothermal band ratios 7/5, 3/1, and 4/3 (red, green, blue) shown in Fig. 14d, providing the most effective means of identifying and differentiating the key rock units within the study area. Land cover classification relies on tonal and textural features.

Figure 14: Geological map and selected band ratios of middle Darb El Arbaein (a–d).

5  Conclusion

Darb El Arbaein adds a new agricultural investment zone. The aquifer is the only water source for various uses. TDS levels and potentiometric fluctuations were reviewed over different timeframes. The Taref and Kiseiba formations form the confined Nubian aquifer. Due to high fines content, the aquifer’s hydraulic conductivity remains low (<4 m/d). Within the 2000–2012 period, the maximum piezometric head decreased by 31–50 m, driven by low transmissivity and conductivity. TDS levels rose from 12–148 ppm to 543–800 ppm during this interval. Groundwater modelling shows that average linear velocities range from 0.041 to 0.087 m/d to 10−5 to 7 ∗ 10−3 m/d, influenced by low permeability and transmissivity. The model indicates the most promising exploration and exploitation zones are in the southwestern and northeastern regions. Multispectral supervised classification maps five geological classes and generates a digital geological map. The recent research provided new insights:

1.    Reviewing historical data on the water table and TDS (Total Dissolved Solids) helps assess changes in aquifer quality over time.

2.    Hydrogeological parameters such as hydraulic conductivity, saturated thickness, and effective porosity influence drawdown and TDS levels.

3.    There is a relationship between the piezometric surface and the hydrogeochemistry of the aquifer.

4.    Digital geological maps are vital tools in modern geological and hydrogeological research, as they enhance accuracy, recharge and potential assessment, analysis capabilities, and decision-making.

5.    Aquifer recharge zones and potential areas (promising zones) should be delineated using current hydrogeological and geological parameters—including piezometric heads, saturated thickness, transmissivity, and effective porosity.

Acknowledgement: Not applicable.

Funding Statement: The authors received no specific funding.

Author Contributions: The authors confirm contribution to the paper as follows: Conceptualization, Mohd Yawar Ali Khan and Mohamed ElKashouty; methodology, Mohd Yawar Ali Khan and Mohamed ElKashouty; software, Mohamed ElKashouty and Samyah Salem Refadah; validation, Mohamed ElKashouty; formal analysis, Mohamed ElKashouty; investigation, Mohamed ElKashouty; resources, Mohamed ElKashouty; data curation, Mohd Yawar Ali Khan and Mohamed ElKashouty; writing—original draft preparation, Mohd Yawar Ali Khan and Mohamed ElKashouty; writing—review and editing, Mohd Yawar Ali Khan; visualization, Mohd Yawar Ali Khan and Mohamed ElKashouty; supervision, Mohd Yawar Ali Khan and Mohamed ElKashouty. All authors reviewed and approved the final version of the manuscript.

Availability of Data and Materials: The authors confirm that the data supporting the findings of this study are available within the article.

Ethics Approval: Not applicable.

Conflicts of Interest: The authors declare no conflicts of interest.

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