Essay: Identifying a good site for groundwater exploitation in hard-rock terrains

Abstract
Identifying a good site for groundwater exploitation in hard-rock terrains is a challenging task. In Sinai, Egypt, groundwater is the only source of water for local inhabitants. Interpretation of satellite data for delineation of lithological units and weathered zones, and for mapping of lineament density and their trends, provides a valuable aid for the location of groundwater promising areas. Complex deformational histories of the wide range of lithological formations add to the difficulty. Groundwater prospect mapping is a systematic approach that considers the major controlling factors which influence the aquifer and quality of groundwater. The presented study aims to delineate, identify, model and map groundwater potential zones in arid South Sinai using remote sensing data and a geographic information system (GIS) to prepare various hydromorphogeological thematic maps, such as maps of slope, drainage density, lithology, landforms, structural lineaments, rainfall intensity and plan curvature. The controlling-factor thematic maps are each allocated a fixed score and weight, computed by using a linear equation approach. Furthermore, each weighted thematic map is statistically computed to yield a groundwater potential zone map of the study area. The groundwater potential zones thus obtained were divided into five categories (very poor, poor, moderate, good and very good) and were validated using the relation between the zone and the spatial distribution of productive wells and of previous geophysical investigations from a literature review. The results show the groundwater potential zones in the study area, and create awareness for better planning and management of groundwater resources.
Keywords: Remote sensing, Geographic information systems, Groundwater exploration, Modelling, Egypt
1. Introduction
Groundwater is a vital natural resource for sustainable-development planning around the world, particularly for agricultural and industrial activities. In addition, the need to develop groundwater resources to the maximum possible extent has gained importance. There is a corresponding need to identify, delineate, map and assess the factors controlling groundwater accumulation, in order to predict and explore the probable exploitation sites, thus supporting effective groundwater planning and development. Arid regions suffer from severe water scarcity. Groundwater resources are crucially important in arid and semi-arid regions, since the rainfall and, subsequently, the surface water availability are very unpredictable. Although the extractable amounts of groundwater are directly related to precipitation, the seasonal storage of groundwater is much higher than for surface water, and groundwater is often necessary as a back-up during the dry season or dry spells. Many arid and semi-arid areas are underlain by hard rocks in which the primary porosity and hydraulic conductivity are low and the groundwater resides mainly in fractures or weathered zones of the bedrock, generally referred to as secondary porosity and secondary hydraulic conductivity (Gustafsson 1994). In Sinai, Egypt, groundwater is the only source of water available to local inhabitants. From a groundwater exploration approach, the term ‘hard rock’ can comprise all rocks without sufficient primary porosity and conductivity for feasible groundwater extraction. In such areas, exploration is directed towards finding areas of increased transmissivity and storativity caused by secondary effects such as fracturing and weathering. In semi-arid regions, weathering concentrates along lines of structural weakness, such as faults, fracture zones and dykes, which often appear as linear features on satellite images, generally referred to as lineaments (Caruthers et al. 1991, Greenbaum 1992; Arnous et al., 2011, Arnous and Sultan 2014). In the last several decades, Egypt has been subjected to a very arid climate, where there is a notable increase in average temperature and decrease in rainfall intensity all over the country. The water levels of the different aquifers have decreased with time and the springs of the different hydrographic basins have stopped flowing, for example at Wadi Hebran (Aggour, 2006).
The hard rock of the Wadi Feiran basin (WFB) is described as a fractured basement aquifer. Exploration and utilization of groundwater specifically in fractured basement terrains, requires thorough understanding of the geology, geomorphology, geological structures, climatic conditions and land-use/land-cover of an area which directly or indirectly control the terrain characteristics (Mukherjee, 1996; Ravindran and Jeyram 1997; Pradeep 1998; Kumar et al., 1999; Oh, et al., 2011; Elewa and Qaddah, 2011; Pothiraj and Rajagopalan 2013; Mohamed et al., 2015).
The conventional methods used to identify, delineate and map the groundwater potentiality (GWP) zones are mainly based on ground surveys using geophysical, geological and hydrogeological tools in addition to the geospatial technologies including remote sensing (RS) and geographic information system (GIS) investigation tools. The existing methods of groundwater exploration using geophysical and geo-electrical techniques are expensive and time consuming hence there is a need to exploit new technologies of RS and GIS in the exploration of groundwater (Sener et al., 2005 and Mohamed et al., 2015). Geospatial technologies, with their advantages of spatial, spectral and temporal availability and manipulation of data covering large and inaccessible areas within a short time, have become very handy tools in deciphering, accessing, monitoring and conserving groundwater resources. Additionally, the geospatial tool is a rapid and cost-effective tool in producing and modelling valuable data in various geoscience and environmental research fields such as geology, geomorphology, and the determination of structural lineaments, slope, land-use and land-cover. This helps in identifying, eliminating and mapping the probable sites of GWP. Geospatial technologies have been used successfully for the mapping and eliminate of surface structures and therefore represent an integral part of applied geomorphology (Verstappen, 1977; Butler and Walsh, 1998; Bocco et al., 2001; Bubenzer and Bolten, 2008; Dar et al., 2010; Arnous 2011, Arnous and Green, 2011; Arnous et al., 2011, Kuria, et al. 2012; Elmahdy, 2012; Pothiraj and Rajagopalan, 2013; Arnous and Sultan 2014; Nampak, et al., 2014; Mallick et al., 2015; Arnous and Green 2015). There are a number of works where GWP has been estimated using geospatial technologies. Rao et al. (2009) carried out hydrogeological mapping coupled with hydrogeological investigations for evaluating GWP in Madhurawada, India, using GIS. Elewa and Qaddah (2011) mapped GWP zones in Sinai, Egypt, utilizing RS and GIS watershed based modelling. Kamaraju et al. (1996) performed an evaluation of GWP of a district in India using GIS approaches. Shahid et al. (2000) used GIS in the analysis of hydrogeological data acquired from RS and surface geophysical techniques in the assessment of the groundwater condition of a soft rock terrain in Midnapur District of India. Concerning extensive studies like the detection of landforms, the analysis of feature distribution or land cover investigations, and visual interpretation of aerial photos has proved to be very effective, and its advantages for surveying are well documented (Van Zuidam, 1985; Drury, 2001; Servenay and Prat, 2003).
With the advent of RS and GIS technologies, the mapping of GWP zones within each geological unit has become an easy procedure (Jain, 1998; Singh and Prakash, 2003; Nag and Ghosh, 2013). The groundwater conditions vary significantly depending upon many factors such as the slope, depth of weathering, rock type, presence of fractures, drainage density, plan curvature, elevation and land forms. These factors can be interpreted or analyzed in GIS using RS data to indicate the GWP zones in qualitative terms. Many studies have used GIS and RS tools to prepare and analyze different thematic maps such as landforms, geomorphology, surface water bodies, land-use, drainage pattern, lineament, lithology, slope and soil (Krishnamurthy et al., 1996; Chowdhury et al., 2009; Jaiswal et al., 2003; Prasad et al., 2008; Saha et al., 2010; Saraf and Choudhury, 1998; Solomon and Quiel, 2006; Pareta and Pareta; 2011, Nag and Ghosh, 2013).
The present study aims to delineate, identify and map GWP zones in arid terrain regions using RS data and a GIS approach to prepare various hydromorphogeological thematic maps such as slope, drainage density, lithology, landforms, structural lineaments, rainfall intensity and curvature using ArcGIS. Additionally, groundwater controlling factors will be assessed by combining RS, global digital elevation model (GDEM) and field study data with various thematic maps in a GIS modelling environment to create a groundwater prospect map of Wadi Feiran basin (WFB), South Sinai.
2. Study area
WFB is one of the principal drainage basins in the south Sinai which drains into the Gulf of Suez (Fig. 1). It trends nearly in an E–W direction (Moneim 2005). Rainfall represents the main source of water supply in WFB, which covers an area of approximately 1868 km2. Through its course from east to west, it dissects a variety of terrains ranging in age from Precambrian to Quaternary. One of the south Sinai highways, specifically the Feiran – Saint Catherine City road (Wadi Feiran road), is located in the mountainous section, which passes through Wadi Feiran. The length of the highway is about 105 km (Fig. 1) and is heavily used by thousands of tourists annually, which underlines its significance in connecting this rugged landscape to the lowland, as well as the people who live in some oases along Wadi Feiran road which is the only access to the St. Catherine area from the western side. The climate of the area is characterized by hot summers and warm winters. The minimum and maximum mean temperatures at the Catharine Weather Station are 13oC and 34oC, respectively. The annual relative humidity varies between 46 and 54%. The average annual rainfall is between 1.6 and 21.5 mm. The highest recorded average annual rainfall in the studied area is 76.2 mm (Egyptian Meteorological Authority, 2006).
South Sinai is a part of the Arabo-Nubian massif which is made up of the igneous and metamorphic rocks. This massive shield extends to the eastern part of the Eastern Desert of Egypt across the Gulf of Suez. It consists of many faulted blocks whose geomorphology is closely controlled by the geological structures. This massif shield is dissected by numerous incised wadis (drainage lines) with steep sides. A few of these drainage lines are smooth concave, but most of them are steep. Their floors consist of bare rock, and their longitudinal profiles are obstructed by knick points in dry waterfalls and cataracts. Drainage in the horst block of south Sinai is towards the Gulf of Suez and Gulf of Aqaba. Wadi Feiran is one of the main drainages which dissects south Sinai and drains westwards towards the Gulf of Suez. In its eastern part, it drains a relatively large catchment area of high-relief granitic rocks through three main tributaries called Wadi El Akhdar, Wadi El Sheikh, and Wadi Solaf which join at Feiran Oasis.
Geomorphologically, the study area can be categorized into two distinctive zones, each of which has its own geomorphological and geological features. The upper zone represents the main catchment area along the upstream side. It is characterized by high-relief mountains such as Gabal (G.) Saint Catherin, G. Mussa, G. Serbal, G. Banat, G. Tarboush, and G. Mukattab; some of them reach 2,640 m asl (Fig.1). This zone is mainly composed of granitic rocks, migmatites, gneisses, older granitoids, and gabbrodiorite intrusions that are intruded by younger granititoids. Minor fabric such as folding lineation and foliations are observed. The lower zone is characterized by low-relief mountainous terrain and gentle wide tributaries. Along the downstream side, near the main wadi trunk, a huge succession of sedimentary rocks of Phanerozoic age is observed. Wadi Feiran discharges its water to the Gulf of Suez. Some villages along the main course of WFB have been subjected to flash floods once or twice annually, especially in the spring and autumn. Recently, the frequency of flash floods increased and caused significant damage to the infrastructure and other facilities in the area. Some of these flood events occurred in 1994 and 2005, and caused severe damage to the economy, cultivated and urban areas, infrastructural facilities, and roads (Youssef, et al, 2011).
3. Materials and methods
3.1 Data used and image processing
RS is considered as a set of tools making possible the acquisition of information from space in a comprehensively visual way. Usually, RS data are used to provide information on terrain surface and suspicious signatures, as well as in monitoring approaches. GIS is a supporting tool to RS. It has the capability to manipulate and store data in digital forms. These two technologies were employed in this research. RS and GIS tools play important roles in the spatial integration of various datasets including geological map, Landsat satellite images and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. GDEM data were used in this study to identify and map the GWP zones and their environmental impact by using ERDAS Imagine and ArcGIS software. Almost all the images have been acquired in the summer and are of good quality with no effective clouds or image defects such as striping. The OLI Landsat 8 images, dated 2014 for Path 175 and Row 40 and Path 174 and Row 40, covering the investigated area, have been geometrically and radiometrically corrected in UTM projection WSG84, with spatial resolutions of 28.5 and 14.25 m, respectively. Topographic maps at scales of 1:50,000 and 1:100,000, a geological mapping by Conoco (EGPC/Conoco 1987) and the Geological map of Sinai by the Egyptian Geological Survey and Mining Authority (EGSMA, 1994) are used as reference maps and have been digitally processed. A DEM was created from the topographic maps and the ASTER GDEM to analyze features of the terrain such as slope, aspect, 3-D view and contour maps as well as to investigate the geomorphological characteristics of the investigated area. Digital processing of the RS data generated several products ranging from single-band images to stack image bands. False colour composite images, principal component analysis (PCA), ratio images, brightness inversion, directional filtering, IHA (Intensity, Hue and Saturation) and contrast stretching techniques were all used for detecting and identifying the hydrogeomorphological potential parameters in the study area. The tabular data of rainfall (Tropical Rainfall Measuring Mission TRMM data) in the study area are used to support the processes of flood generation and groundwater recharge. The groundwater prospect map is based on multiple criteria such as drainage texture, geomorphology, lithology, steepness of slope, frequency of lineaments, etc.The process of using all of these criteria at the same time is commonly known as multi-criteria evaluation and was used in the present study.
3.2 Preparation of the geo-spatial thematic modelling and GWP maps
In order to identify, assess and generate the GWP map for the WFB, the main factors considered are slope degree, drainage density, lithology, landforms, structural lineaments, structure density, rainfall, curvature, and altitude. The thematic maps of these factors were prepared and then converted into vector or raster form so that they could be easily integrated using the GIS tool. Each of these factor thematic maps has been assigned a suitable weight. These weights are based on the work carried out by researchers such as Krishnamurthy et.al., 1996; Saraf and Choudhary, 1998; Srinivasan and Jugran, 2003; Sikdar et al., 2004; and Naghibi, et. al. 2015.
The thematic maps were then integrated using GIS tools to delineate GWP zones that are assigned knowledge-based hierarchy using the Spatial Analyst tool of ArcGIS. This required three steps: spatial database building, spatial data analysis and data integration. All geo-spatial thematic maps were re-classified and assigned suitable weights (Table 1). Multi-criteria evaluation was applied: the constructed geo-spatial maps of these parameters were assigned individual theme weights and their class weights (Eastman, 1999; Arnous, 2013). In this model, the thematic maps are ranked from 1 to 5 depending on their suitability to hold groundwater. The rank of each layer has been converted into map weight by dividing each map ranked by the total number of ranks. Similarly, the map classes in each input map are assigned different categories and ranked by a numeric scale (1 to 5 where 5 is the most favorable and 1 is the least) depending on the capability to store and transmit water. The rank of each inter-map class is divided by the total classes to generate the capability values (CVi), as shown in Table 1. For example, CVi of the drainage density thematic layer for ranks one, two and three equals 1/6, 2/6 and 3/6 respectively (Table 1). These capability values are multiplied by the respective map weight in each thematic map to initiate the GWP zones map (Table 1). The procedure of linear combination dominates in raster files in the GIS software, and these files are assigned to produce a combined map (Eastman et al. 1995; Eastman, 1999). The individual theme weight (Table 1) was multiplied by its respective class weight and then all the geo-spatial thematic coverage data were combined in the weighted linear combination method and computed by the ArcMap GIS (Raster Calculator) module as a function of the Spatial Analyst tool to integrate it as given here:
Wi= Spw + Ddw + Ltw + Ldw + Crw + Tew+ Lmw + Rfw …………………………..(1)
Where the Wi is map weight, Sp is the slope, Dd is the drainage density, Lt is the lithology, Ld is the lineament density, Cr is plan curvature, Te is the topographic elevation, Lm is the landform and Rf is the rainfall. The subscript w indicates the normalized weight of the individual features of each thematic layer.
GWP = ∑ Wi × CVi ……………………………………………………………………(2)
Where the GWP is groundwater potential, Wi is map weight and CVi is capability value (weight of inter-map class). Finally, the resultant GWP map is classified into very good, good, moderate and poor GWP zones.
Validation of the constructed GWP map is the most important process of modeling in that without validation, the models will have no scientific significance. The validation was carried out by using water production rates from groundwater wells and geophysical data.
4. Results and discussion
4.01 Lithological potential mapping
Water bearing formations of the earth’s crust act as conduits for transmission and as reservoirs for storing water. Surface runoff is typically high in areas of compact formations and very low in areas of loose and more porous rocks, where the infiltration of the surface runoff water is controlled by grain size and the porosity of the top soil. These in turn are controlled by weathering and the erosion mechanisms, rock types, age, rock hardness and fracture spacing. Moreover, the presences of interconnected fractures, cracks, joints, crushed zones such as fault or shear zones or solution cavities allow rainwater to easily percolate and contribute to groundwater (Todd, 1980). In the current study, the lithological map of WFB area has been prepared with the help of OLI Landsat 8 satellite images and other published ancillary geological data.
Several digital image processing techniques, including standard colour composites, contrast stretching, PCA, brightness inversion, HIS and de-correlation stretch were used to map rock types. The statistical technique adopted by Sheffield (1985) was employed to select the most effective three-best-band-colour composite image. The band combination 7, 5 and 3 is the best triplet and was used to create colour composites with OLI Landsat 8 bands 7, 5 and 3 in red, green and blue, respectively. PCA, IHS transformation and de-correlation stretch were also applied to the selected band combination in order to enhance the differences between rock types (Fig. 2). The lithological units of WFB were mapped by using ArcGIS and could be distinguished by distinct colour in the enhanced satellite images (Fig. 3). In the present study, ranging for lithological units is assigned based on mineral assemblage, alteration, fractures and weathering conditions. The rocks units that are highly fractured are more prone to weathering and have high infiltration and high runoff resistance, and hence, high rank values were assigned. Similarly, the rock units that are less fractured and less prone to weathering are assigned low rank values. The lithological map of WFB was divided into five categories: 1) metamorphic rocks (biotite gneiss, calc-silicate gneiss, hornblende biotite gneiss, migmatites and metagabbro–diorite complex), 2) intrusive rocks (older granitoids, younger granitoids (Phase II and III), 3) younger volcanic rocks (rhyodacite rutig, granophyre dyke-like intrusions), 4) Phanerozoic sedimentary sequences, and 5) Quaternary deposits. Surface runoff is typically high in areas with massive, younger rocks and very slow in areas of friable, porous rocks (EGPC/Conoco 1987; EGSMA, 1994). According to the runoff infiltration capability values of the lithological units of WFB, the study area is classified into five categories ranging from 1 for metamorphic rocks, old volcanics and granitoids to 4 for wadi deposits and 5 for sedimentary sequences (Table 1). Valley deposits consist mainly of thick alluvium loose gravel, sand and silt that have large pores between the grains that easily allow water to infiltrate through the subsurface strata, thus highly favorable for GWP in addition to the highly weathered and fractured rock units of WFB.
4.2 Hydrogeomorphological potential mapping
The geomorphology of an area is one of the most important features in evaluating the GWP (Kumar et al. 2008). In the present study, the hydrogeomorphological map is useful for the planning and execution of groundwater exploration in addition to clarifying controls on the movement and accumulation of surface water and groundwater. Topographic model parameters such as slope, elevation and curvature of the WFB were calculated from the ASTER GDEM and used for geomorphologic analysis. Moreover, the visual interpretation of the enhanced RS data is used to identify the different landform feature classifications of WFB. The geomorphological classification results were visually evaluated and re-classified into seven landform categories in order to construct the geomorphological map of WFB (Fig. 4). These classes include the highly rugged mountainous basement complex, highly dissected tableland, peneplained surfaces, inland alluvial fans, lacustrine deposits, drainage system and fault scarp landform classes (Table 1).
4.3 Structural lineaments potential mapping
Recently, RS has increasingly been used, in both regional and small-scale investigations, to identify the structural or linear geologic features (lineaments) and provide important information on surface and subsurface fracture systems (Hung et al. 2005; Arnous 2011; Arnous and Green, 2011). The large-scale linear features, which are surficial expressions of underlying structural features like faults or joints in hard rock areas, are considered as good potential water zones. They act as conduits for groundwater movement and zones of increased secondary porosity (Pradeep, 1998; Obi Reddy et al., 2000; Ganapuram et al., 2009). Areas up-gradient of dykes or other intrusions within a porous host rock often possess good water potential as the dyke/intrusive constitutes a barrier to groundwater flow. Lineaments have generally been used as an indicative tool for locating potential groundwater zones (Pradeep, 1998; Obi Reddy et al., 2000; Ganapuram et al., 2009), but with the present scenario of over-exploitation of the aquifer (El-Rayes 2004), characterization of the lineament becomes essential to ensure the possibility of locating new groundwater potentiality zones and managing over-exploited aquifers in hard rock areas. Lineaments are clearly obvious in all digitally processed colour composites of OLI Landsat 8 satellite images. Most of the linear features can be enhanced by using contrast stretching techniques. In addition, directional filtering was applied to various single band images along NE–SW and NW–SE directions. Although major lineaments can be detected in the raw image data, most of the finer details are more clearly recognizable in the filtered image particularly after directional filtering.
The GDEM was very useful in delineating regional scale lineaments related to geomorphologic features, mainly drainage channels. Therefore, the shaded relief model is considered key in lineament extraction because it is the most suitable terrain model for the recognition and interpretation of complex geomorphologic features (Jordan et al, 2005; El-Rayes et al., 2015). In the present study, the shaded relief map was constructed from the GDEM by varying the azimuth and elevation of simulated sun illumination (Singh and Prakash 2003; Soulakellis et al. 2006; Arnous 2011; Arnous and Sultan 2014; El-Rayes, et al., 2015). The enhanced contrast stretching OLI Landsat 8 image of WFB (7, 5 and 3 as assigned RGB) colour composite was overlaid on the shaded relief maps to improve the manual detection and extraction of structural lineaments. The final integrated lineament map was created by combining all interpretations and editing duplicates in each of the files by using ArcGIS (Fig. 5). The majority of lineaments, such as fractures and faults, are oriented along NE-SW trends, while N-S and NW-SE trends are intermediate in magnitude and are dissected by the NE-SW trends.
The structural lineaments of WFB were converted into zones of different lineament density (lineaments per square km) and rated as very high, high, moderate and low according to their capability for runoff infiltration and their significance with reference to their GWP (Table 1). The lineament density map (Fig. 6) reveals that the younger crystalline rocks in WFB, are highly fractured. However, the older volcanic rocks have a lower fracture density value or subdued anomalies, indicating that runoff water may penetrate and flow through the highly fractured younger lithological units rather than older lithological units of WFB. Finally, the asymmetry of lineament densities along the main valleys reflects the deformation history of the crystalline rocks. The study area is mainly covered by brittle rock types which respond quickly to compression stresses acting on the regional scale by fracturing (Mohamed et al. 2015). The lineaments act as conduits for groundwater flow, and hence are hydrogeologically significant, with GWP decreasing with increasing distance away from lineaments. This implies that the best chances for groundwater targeting are close to lineaments particularly in the hard crystalline rocks of WFB. The groundwater recharge in the investigated area finds its way through sets of interconnected joints to feed the existing wells in the low-lying fault zones (El-Rayes, 2004) and also discharges as springs along the contact between the weathered, fractured topmost rocks and the underlying fresh granites (El-Rayes, 1992). According to this approach, the groundwater moves in a step-like downward flow until the fault zone is reached (El-Rayes, 2004). These jointed and fractured rocks are dissected by NE-SW dikes, which also play an important role in the groundwater occurrences in the fractured basement rocks and alluvium deposits (Abuo El-Magd, 2003; Aggour, 2006).
4.4 Slope potential mapping
Slope is a function of the topographic surface and is calculated in ArcGIS using surface analysis within the Spatial Analyst tool. The occurrence and movement of groundwater is governed strongly by slope which is an important factor in runoff; the infiltration is inversely related to slope (Surabuddin et al., 2007; Arnous, 2011, Arnous and Green 2011 and 2015). The slope map of WFB (Fig. 7) has been prepared from the GDEM data range from flat (0o) to vertical (90o). The area is categorized into four zones (i.e., 0–<5, 5–<10, 10–<15, and >=15). The slope map indicates that the surface runoff is typically slow in areas with gentle slope, allowing more time for rainwater percolation and promoting appreciable groundwater recharge. On the other hand, the steep slope areas promote fast surface runoff, allowing less time for rainwater to infiltrate and recharge local groundwater aquifers. The southeastern and central parts of the study area have poor groundwater prospects due to high slope gradients. However, most of the valley floor has good groundwater prospects due to gentle slopes. GWP weights assigned to the slope categories are shown in Table 1.
4.5 Curvature potential mapping
The curvature values that are extracted from GDEM analysis by using ArcGIS in the WFB varies from -537.144 to +555.138, indicating that the hilly nature of the study area is oriented east-west. A positive curvature indicates the surface is upwardly convex at that cell (flow dissipation zone). A negative curvature indicates the surface is upwardly concave (flow accumulation zone) and a value of zero indicates a flat surface and a flow transition zone (Fig. 8). In the results of WFB curvature, valleys and channels have the most negative values while ridges have the most positive values. A curvature map was classified into three numerical ranks (Table 1) with reference to GWP weight. The GWP is much higher in the flat and highly negative curvature areas (Subba, 2006; Elmahdy, 2012). The class with a value of zero for curvature will give more chance for groundwater accumulation. Moreover, areas showing the highly negative and flat values for curvature will exhibit low discharge of overland flow and high rates of infiltration, and have been assigned high GWP, whereas the positive value areas were considered poor for GWP.
4.6 Topographic potential mapping
Surface topography determines the flow direction of runoff water over the ground surface, and it has a great influence on groundwater occurrence, in that the steepness of slope increases runoff and therefore decreases potential recharge. The topographic conditions of the WFB area control the groundwater flow particularly in fractures and overland flow. The depth to water table in the existing hand dug wells varies widely due to the local topography (Ghodeif, 1995). The local slope affects mainly the quantity of infiltration and the rate of overland flow, since the steep slopes allow less time for infiltration and increase the surface runoff. Wadi Feiran ASTER GDEM was used in analyzing the topographic elevation zones in the study area. The GDEM analysis result revealed that the ground surface elevation ranges from 1 to 2607 m.a.s.l. The irregular variations between highest and lowest zones generate slopes that control the water movement towards the west and the Gulf of Suez, whereas the basement rocks occupy the highest topographic levels to the east. The elevation density map was constructed by classifying the GDEM into four numerical categories (i.e >=1700, <1700-1600, <1600-1500, <1500) based on their infiltration potentiality of runoff water (Fig. 9). The values range from low to very high based on their significance to groundwater recharge potential (Shendi and Abou El-Magd, 2004; Abou El-Magd, 2003). Ranks 1, 2, 3, and 4 assigned are low, moderate, high, and very high respectively (Table 1).
4.7 Drainage potential mapping
WFB represents the main drainage basin, in which the Wadi Feiran discharges its water into the Gulf of Suez. The drainage pattern of WFB and its watershed map has been extracted from the GDEM data by using the flow direction algorithm of Jenson and Domingue (1988). Flow direction will always be in the steepest down-slope direction and is used to determine the stream network. The flow direction map created from a raster shows that the direction of flow of WFB is from east to west to the Gulf of Suez. The raster of flow accumulation indicates that the low accumulation values represent ridge tops whereas higher accumulation values represent valleys and stream channels. The drainage patterns observed in the WFB are dendritic, trellis, rectangular and parallel, and the basin has a drainage density of 2–13 km/km2. Stream ordering is given by using DEM and drainage network in Hydrology module of ArcGIS (Fig. 10). The drainage network helps in the delineation of watersheds and for suggesting various water harvesting structures and soil conservation measures. The drainage density map was calculated from the GDEM-derived drainage channel network to define zones of different drainage density (Fig. 11), and the WFB area was divided into three numerical categories based on the capability of recharge infiltration (Table 1). Furthermore, the produced drainage density map revealed that the areas of high density reflected a low chance for groundwater recharge and vice versa.
4.8 Rainfall intensity potential mapping
Rainfall represents the main source of the groundwater recharge in WFB. At times, rain produces floods along the main streams or wadis that commonly flow towards the west direction. On the other hand, a considerable amount of the rainfall percolates into the basement terrain through open spaces and fracture systems. Water moves downward through the fractures by both hydraulic gradient and gravitational forces to recharge both Phanerozoic sedimentary rocks and the Quaternary alluvial deposits. Therefore, it is more useful to classify the water-bearing formations beginning with the fractured basement rocks followed by the Quaternary alluvial deposits and then the Pre-Cenomanian aquifer (Abou El-Magd 2003).
Today, Tropical Rainfall Measuring Mission (TRMM) data provide important information on rainfall impact along the drainage network systems globally. These data are helpful in predicting flash flood hazards, and estimating the runoff volume of surface water. The TRMM data on WFB are interpolated and classified into four categories; low, moderate, high and very high by using ArcGIS (Fig. 12). The area of highest rainfall anomaly is located in the east of WFB particularly at G. Saint Catherine. The classes are assigned weight factors of 1, 2, 3 and 4 that are low, moderate, high and very high respectively (Table. 1).
4.9 Spatial modelling and data integration of GWP zones
One of the most important applications of geo-spatial tools is the display and analysis of data to support the process of environmental decision-making. A decision can be defined as a choice between alternatives, where the alternatives might be different actions, locations, objectives, and the like. In the present study, the RS and GIS data of WFB are integrated to delineate the areas most suitable for groundwater prospecting in this arid region of hard rock terrain. The geospatial tools have been successfully used to identify and eliminate areas, and to create spatial distribution maps of the possibilities of groundwater accumulation, based on lithology, structure, slope, curvature, landforms, topography, rainfall and drainage network data analyses. Consequently, a groundwater prospect map was constructed by using a linear combination of these factors in an ArcGIS spatial analysis environment. The groundwater prospect map of WFB was classified into five categories from very high to very low probabilities of GWP zones (Fig. 13), using an analytical technique associated with the study of locations of geographic phenomena together with their spatial dimension and their associated attributes (like table analysis, classification, polygon classification, and weight classification). The various thematic maps as described above have been converted into raster form. These were then reclassified and assigned suitable weights following the methods used by Krishnamurthy et al. (1996), Saraf and Choudhary (1998) and Srinivasa and Jugran (2003),
The final cumulative groundwater prospect map was formed by applying the linear combination equations of the multi-criteria evaluation model with weight values ranging from .01 to 1.5. The very good and good areas occupied about 67.21% of the total area which represents valley deposits, sedimentary sequences and areas of younger basement rocks (Table 2). The deeply weathered metamorphic and older basement rocks, characterized by moderately to very poor zones, represent about 32.79% of the total area. From a hydrogeological point of view, none of the hard rocks have sufficient primary porosity for any significant groundwater storage. The rocks are, however, extensively tectonized and a large number of fracture zones intersect the terrain.
The water demand in the study area is high and many groundwater investigations have been carried out during the last few decades. In general, lineaments act as conduits for groundwater flow, and hence are hydrogeologically significant. The values given for lineaments were based primarily on the relation of well yields to proximity of lineaments. Accordingly four classes were defined based on distance from lineaments with decreasing values as the distance from lineaments increase, assuming that the GWP decreases with increasing distance away from the lineaments. This implies that the best chances for groundwater targeting are close to lineaments.
Areas showing negative values for curvature were assigned good potential whereas areas with positive values were considered poor for GWP. The modelling results of the present study makes it feasible to predicate locations of probable groundwater recharge in arid terrain regions. Ancillary geophysical analyses and field observations validated the GWP zones map. Furthermore, compared with the ancillary published groundwater well data, data that were collected from the field supports the conclusion of high yields particularly in the favorable zones that are extracted from RS and GIS integrated data. These favorable zones originated as valley beds where there is a thick accumulation of sand and gravel in addition to deeply fractured and weathered basement rocks.
4.10 Validation of the GWPZ map of WFB
Validation is considered to be the most important process of modelling. Without verification, models have no scientific significance (Chang-Jo and Fabbri, 2003). The verification of the GWP map of WFB area was confirmed by correlating the potential classes with the spatial distribution of the productive wells and sites (previously investigated) by using various geophysical tools. The groundwater potentiality data, such as topography, number of wells, well yield and groundwater depth, were collected from a literature review and were used for cross validation and confirmation of the GWP modelling results. Eighty three (83) individual groundwater wells and springs distributed in different water bearing formations of WFB were used to delineate the best potential zones for groundwater extraction. The groundwater data on the existing productive wells were collected from dug wells, drilled wells and springs, along with their geographic location. Production rates of wells were calculated based on transmissivity values that were obtained from pumping and recovery tests such as those of Haroun well at 109.8 m2/d; 1872.8 m2/d at El-Watia well and 330.5 m2/d at Feiran-2 well (El-Refaei, 1992; El-Rayes, 1992; Abou El-Magd, 2003).
The GWP map is overlaid with the spatial distribution of the groundwater wells and the cumulative percentage of the groundwater occurrence of the study area (Fig. 13). The overlaid maps show that there is a positive correlation between groundwater well locations and the GWP area, whereas the well locations with low productivity and areas without wells coincided with the sites falling into poor GWP classes. The groundwater well locations with high productivity coincided with the sites falling in the very good and good GWP classes particularly at W. Harqus, W. El-Sheikh, W. El-Furia, W. El-Akhdar and Saint Catherine sites. These wells were located through the valley alluvial deposits (Quaternary aquifer). Moreover, the bottom of these wells is close to barrier rocks like massif hard rocks, which hinder water infiltration to greater depths (El-Rayes, 1992). The yield of these wells has been found to vary from 11.5 to 1872.8 m3/day. The water quality is good or acceptable and the salinity ranges from 228 to 1071 ppm (Abou El-Magd, 2003). This variation of WFB salinity follows the groundwater flow direction from east to west along the main stream from the recharge to the discharge areas (El-Sayed et al., 2012) (Fig. 14). Local farmers have been using shallow groundwater from the Quaternary strata to irrigate several crops at the entrance of WFB particularly at Feiran and Tarfa Oases. Additionally, there are wells that fit the very good and good zones although they are located in the elevated areas. These pertain to other factors such as fractured rocks that act as conduits for the water infiltration and accumulation.
From the hydrogeological point of view, many geophysical studies have been carried out in WFB area such as Wachs et al., 1979; El-Shazly et al., 1985; Awad and Armanious 1985; Shendi, 1989; Hosney, 1991; Shendi, 1992; Shendi and El-Rayes, 1992; Abou El-Magd, 2003. Most of these studies have been carried out on different parts of WFB to study the GWP and by applying different methods including electrical resistivity, seismic refraction and magnetic techniques.