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Multi criteria analysis for groundwater management using solar energy in Moghra Oasis, Egypt
The Egyptian Journal of Remote Sensing and Space Sciences xxx (xxxx) xxx
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Multi criteria analysis for groundwater management using solar energy in Moghra Oasis, Egypt Ebtehal Sayed a,b,⇑, Peter Riad b, Salwa Elbeih c, Mona Hagras b, Ahmed A. Hassan b a
Higher Technological Institute (HTI), 10th of Ramadan City, Egypt Irrigation and Hydraulics Dept., Faculty of Engineering, Ain Shams University, Cairo, Egypt c Engineering Applications Department, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt b
a r t i c l e
i n f o
Article history: Received 2 December 2018 Revised 16 March 2019 Accepted 12 April 2019 Available online xxxx
a b s t r a c t Egypt is facing a lack of water resources in addition to some diesel fuel supply problems which makes it necessary to rely more on renewable energy. Renewable energy is the most promising source of energy and is one of the most important growing industries around the world. Solar energy based on Photovoltaic panels, is being frequently used as a source of energy for groundwater pumping. The objective of this study is to investigate the feasibility of using solar energy in exploiting the groundwater reserves in Moghra oasis, Western Desert of Egypt. A multi-criteria analysis model based on the Geographic Information Systems technique was applied. The applied multi-criteria layers include depths to groundwater, salinity (water use), solar radiation, topography, accessibility and land-use. The output results of the model showed that the best location for Photovoltaic panels is close to the Nile Delta and outside the boundary of Qattara Depression, Moghra Lake and oil fields restricted areas. In these areas the groundwater salinity is less than 5000 ppm and depth to groundwater is less than 100 m. It is recommended in areas with high groundwater salinity to study the possibility of groundwater desalination. Ó 2019 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B. V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
1. Introduction In remote areas in the Western Desert of Egypt there is usually no electricity grid, so farmers use diesel-driven pumps to pump groundwater for irrigation purposes. Although the diesel pumps have the advantage of lower capital cost, the PV pumps are preferred regarding the low costs of maintenance and operation (Shouman et al., 2016). Furthermore, PV pumps avoid the fluctuations in availability, cost of diesel fuel, depletion of fossil fuel reserves and more environment-friendly.
Abbreviations: TDS, Total Dissolved Solids; GIS, Geographic Information Systems; MCA, Multi criteria analysis; GW, Groundwater; NDVI, Normalized Difference Vegetation Index; a-Si, Amorphous Silicon; BOS, Balance of system; CAPEX, Capital Expenditure; CdTe, Cadmium Telluride; CIS, Copper-Indium-Selenide; CIGS, Copper-Indium-Gallium-Diselenide; c-Si, Crystalline silicon; CSP, Concentrated Solar Power; DOE, Department of energy; GSR, Global Solar Radiation; KWh, Kilo Watt per hour; KWp, Kilo Watt Peak; LCOE, Levelized Cost of Energy ($/kWh); mcSi, multi-crystalline silicon; OPEX, Operational Expenditure; PV, Photovoltaic. Peer review under responsibility of National Authority for Remote Sensing and Space Sciences. ⇑ Corresponding author. E-mail address: [email protected] (E. Sayed).
Photovoltaic cells are simpler than diesel generators as they are composed of less number of components and easier to service and maintain. Accordingly, PV cells are more reliable and have a longer lifetime (EU Delegation to Egypt, 2015). Using PV technology, solar radiation can be transformed to a sustainable electric power source. When compared with conventional energy sources, PV systems have many environmental benefits and advantages. However, the large required area may cause unfavourable impacts on both the land use and landscape (Castillo et al., 2016). International and regional examples for using PV in other countries include Saudi Arabia, USA, South Africa, India, Spain, Europe, Morocco and Tunisia, (Sahin and Rehman, 2012; Phillips, 2013; Hernandez et al., 2013; Aliaga et al, 2016, Al Garni and Awasthi, 2017; Castillo et al., 2016 and Merrouni et al, 2018). Geographic Information Systems (GIS), as a spatial decision support tool, is one of the tools used to select suitable sites for using solar energy (Salim, 2012). Aliaga et al. (2016), studied the effects of agricultural pumping using solar energy in both Spain and Morocco, with different factors according to climate conditions, energy mix and energy policies. GIS was proposed to analyse the temporal and spatial variability of the solar resource through real data of both sites and to study groundwater resources.
https://doi.org/10.1016/j.ejrs.2019.04.001 1110-9823/Ó 2019 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: E. Sayed, P. Riad, S. Elbeih et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j. ejrs.2019.04.001
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Techno economic feasibility studies, of producing energy, were carried out using PV pumping systems in Saudi Arabia. The capital cost of PV pumping was considered for a well with total dynamic head (TDH) 50 m. The cost was found between 2 and 3 $/m3 for water pumping, (Sahin and Rehman, 2012). Solar powered pumping systems were already used in Toshka Project in Western Desert of Egypt. Many factors were considered in the design of solar power; crop type, size of planting area, number of peak sun hours and the solar array efficiency, (Abo-Khalil and Ahmed, 2016). The direct coupled PV water pumping system sizing and performance prediction under climate conditions is developed in Toshka, Egypt. This system includes PV array, pump controller and submerged pump (Gad and El-Gayar, 2011). In 2017, 25 wells were drilled in Dakhla oasis in Egypt and designed to pump GW using solar energy with a life time of about 20 years. PV systems produce electricity rates estimated by the capital cost (CAPEX), operational costs (OPEX), solar irradiation level and efficiency of solar cells. The PV system’s capital cost consists of the cost of both PV modules and Balance of system (BOS), while, the total costs consist of the PV module costs, costs of BOS, and installation costs (IRENA, 2012). The U.S. Department of Energy (DOE) chose the levelized Cost of Energy (LCOE) as a main parameter for evaluating PV systems. With the LCOE method, the $/Watt can be transformed into $/kWh, which is a more significant parameter in the power industry, (EL-Shimy, 2012). The continuous drops in prices of PV module and total installation costs intended that these values had already been decreased (IRENA, 2013). The PV prices depend on the forecasted PV life time. For solar PV system the life time has a lifetime range from 20 to 25 years, (Wohlgemuth and Solar, 2003; Sherwani et al., 2010). Rapid decreases in installed costs and increases of capacity factors have made solar PV more economic than other energy resources. The market of PV has grown in the last decade for installed cost, Operation and Maintenance costs (O&M), and levelized Costs of Electricity (LCOE) Costs for installed solar PV module decreased by 83℅ while module costs decreased by 80℅ (from 2010 to 2016). At last, LCOE of PV plants have fallen by 73% between 2010 and 2017 with average 40–75% depending on the country, (IRENA, 2018). The PV efficiency depends on the cell utilized; CdTe (Cadmium Telluride), a-Si (Amorphous silicon, triple junction), CIGS (Cupper/Indium/Gallium/Selenide), poly-Si (polycrystalline silicon), Mono-Si (Monocrystalline silicon). The max efficiency according to the cell type was found 10.07, 8.26, 11.33, 17.59 and 19.62%, while the minimum efficiency was found 4.86, 1.81, 6.6, 6.07, and 7.48%, (Hussein, 2016). Groundwater mapping were obtained using remote sensing and GIS as one of the main tools for the groundwater resources development. These maps are useful for strategic groundwater assessment and planning to be used by engineers, planners and decision makers for groundwater management, (Elbeih, 2016). GIS is operated by collecting appropriate and available data, overlaying the thematic layers on a base map for a given area and processing it into an optimal Model, (Abdelaziz et al., 2012). Janke (2010) applied multi-criteria analysis using GIS modelling to determine the land-cover classes and identify the suitable area which are affiliated with solar potential and high wind and in Colorado. The reclassification of Land-cover, renewable potential, population density, distance to roads, transmission lines, and cities were according to their suitability. Each weight was based on their relative importance to one another. The main objective of this work is to produce a potentiality map for the solar energy in the area of Moghra oasis in the Western Desert of Egypt applying a multi-criteria analysis (MCA) supported by (GIS). This potentiality map can be used to define the most
suitable locations for installing solar plants to generate the required power for pumping groundwater. The used multicriteria parameters are depths to groundwater, GW salinity, solar radiation, topography, area accessibility and land-use. 2. Study area Moghra Oasis is one of the land reclamation areas in the Western Desert of Egypt with an area of about 630 km2 (about 150,000 acres) of desert land. It lies in the NorthEastern extension of Qattara Depression between Longitudes 28° 100 and 29° 100 E and Latitudes 29° 500 and 30° 410 N, as shown in (Fig. 1). Its climate is characterized to be hot in summer and short warm in winter with low rainfall and high evaporation rates (Youssef et al., 2012). The average temperature is ranging between 23 °C and 39 °C and the annual rainfall is between 25 and 50 mm (Mohamaden et al., 2017). The study area includes El-Dabaa, AlMoghra Lake, Aghar Oil Field and El-Alamein petroleum fields. El-Moghra aquifer lies in a well identified geological unit dominated by the Lower Miocene Formation, composed of sand and sandstone with clay and siltstone intercalations. It extends from the western boundaries of the Nile Delta to Qattara Depression and southwards to El-Fayoum Depression with a total surface area of about 50,000 km2 with a thickness ranging from 50 to 250 m. In general, the groundwater flow in El-Moghra aquifer is directed towards Qattara Depression in the west (Dawoud et al., 2005). (Fig. 2) shows the hydrogeological map of the study area with the main aquifers, flow directions and piezometric contour lines. There are various sources that recharge El-Moghra aquifer; (i) seepage from the western part of the Nile Delta aquifer, (ii) flow from the overlying Miocene limestone aquifer, (iii) upward leakage from the deep Nubian Sandstone Aquifer System (NSAS) and (iv) minor contribution from rainfall (EU Delegation to Egypt, 2015). Aquifer discharge takes place through evaporation in Qattara and Wadi El Natroun depressions and by lateral seepage into carbonate rocks of the western part of Qattara Depression. The hydraulic gradient within the aquifer is less than 0.2 m/km and its base slopes from ground level near Cairo in the east to 1000 m below the Mean Sea Level at Alexandria in the west. The current annual extraction from this system is approximately 200 Mm3/year. Where Moghra aquifer’s water is a mixture of renewable and fossil water (El-Tahlawi et al., 2008). El-Moghra Aquifer shows a wide variation of transmissivity (from 61 to 6500 m2/day). The Total Dissolved Solids (TDS) ranges between 289 and 31,000 ppm. The geographical distribution of TDS indicates fresh water in the vicinity of Wadi El-Natroun and highly saline water in the vicinity of Qattara Depression (Abdel Mogith et al., 2012). According to the (EU Delegation to Egypt, 2015), for GW salinity of 2000 ppm, the Moghra aquifer can be used for cultivating some crops such as wheat, barley, maize and sorghum with a yield reduction of 0%, 0%, 25% and 50% respectively, compared with those irrigated by fresh water. While their yield has a reduction of 25%, 10%, 100%, and 100% for water salinity of 5000 ppm respectively. For water salinity more than 5000 ppm, water can be used for industrial purposes such as; salts extraction, oil industry and washing of mineral materials, (Abdel Mogith et al., 2012). 3. Materials and methods 3.1. Data collection To fulfil the requirements of the present study, various sets of data have been collected, processed, analysed and integrated into a GIS database. These data sets include:
Please cite this article as: E. Sayed, P. Riad, S. Elbeih et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j. ejrs.2019.04.001
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Fig. 1. Location map of the study area.
Fig. 2. Hydrogeological map of the study area, (Modified after: RIGW, 1988).
3.1.1. Remote sensing data 3.1.1.1. Shuttle radar topography mission (SRTM) data: SRTM data, acquired by space shuttle Endeavour in 2001 by C-band SAR interferometry instrument, was needed to model land elevation, land slope, aspect angles and area solar radiation maps using ArcGIS10 spatial analyst modules, as shown in Fig. 3.
3.1.1.2. Sentinel-2 satellite image: ESA, as part of the Copernicus Programme, developed Sentinel-2 to be an Earth observation mission for performing terrestrial observations. Sentinel-2 is utilized significantly in the applications related to Earth land surface. A Sentinel-2 image with a 10 m spatial resolution, acquired in 2018, was used for:
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was the used method for extracting cultivated areas using the well-known Eq. (1) (Tucker et al., 1985). The output of the classification was enhanced using Google Earth images for small cultivated areas.
NDVI ¼
ðqNIR qRedÞ ðqRed þ qNIRÞ
ð1Þ
where: qNIR is the Reflectance in the near infrared band and qRed is the Reflectance in the red band. The value of this index ranges from 1 to 1 where the range for green vegetation is known to range from 0.2 to 0.8. 3.1.2. Groundwater data The present study used some data from previous researches including: The piezometric contour map of El-Moghra aquifer (Modified after: Ezzat, 1984; RIGW, 1988, and Salem and El-Bayumy, 2016) as shown in (Fig. 2). Chemical analyses of selected water samples from El-Moghra aquifer and wells tapping El-Moghra Aquifer (Modified after: Abdel Mogith et al., 2012).
Fig. 3. Digital Elevation Model (DEM) of the study area.
Digitalizing the main roads of the study area Determining boundaries of Moghra Lake, Oil fields, and surrounding buildings.
3.1.1.3. Multi-spectral landsat image: Landsat data created by the U. S Geological Survey was obtained in a geographic Tagged Image file format (Geotiff). The data type is level 1 requiring both radiometric and atmospheric corrections. The multi-Spectral OLI image (UTM zone 35) was acquired in March 2018. The cultivated and reclaimed lands were extracted from multi-spectral Landsat image. ENVI 5.4 software was used to produce a map for Normalized Difference Vegetation Index (NDVI) and a land use map. NDVI
The salinity of the Moghra aquifer, at a depth of about 50 m, differs from one area to another. In other words, it is highly saline in the vicinity of Qattara Depression, while in the vicinity of Wadi El-Natroun, it is fresh water (Abdel Mogith et al., 2012), (Fig. 4). The data were geometrically corrected using the ArcGIS program and were matched with the Landsat satellite image. 3.2. Applied methods Design parameters of the weighted overlay model using GIS include the following:
Solar Radiation (Kwh/m2/day) Topography of the terrain and obstacles Accessibly to Roads Depth to groundwater GW Salinity and water use Land use type
Fig. 4. Salinity contour map of El-Moghra groundwater aquifer, (Modified after: Abdel Mogith et al., 2012).
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Fig. 5. A flow chart for the required Layers of the MCA.
3.2.1. The conceptual model A number of processes were performed for preparing the thematic layers to be used as input data in a weighted overlay model. In order to achieve the objectives of the study, a flow chart (Fig. 5) was prepared to illustrate the operational steps and processes of the Multi-Criteria Analysis (MCA). As shown in Fig. 5, six parameters were selected to be the main thematic layers to be analysed within the weighted overlay model. Each parameter was reclassified and a corresponding weight value was assigned affecting the final decision of the model. The different layers are categorized based on the following ratings: 1 (Best), 2 (Good), 3 (fair) 4 (Poor) and 5 (Worse and Unsuitable). 3.2.2. Preparing thematic layers for GIS analysis - The DEM was used to obtain slope (Fig. 6), aspect (Fig. 7) and solar radiation (Fig. 8). - Groundwater Salinity and depth to groundwater were obtained from previous studies (Figs. 9 and 10). - ArcGIS was used for converting vector layers to raster maps then interpolated using spatial analysis. - The road map layer and cultivated areas were obtained from the Landsat satellite images, as shown in (Fig. 11). All maps have been reclassified to integer values to be used as inputs in the weighted model where the reclassified layers ranged from 1 to 5. A value of 1 was assigned to the best class and 5 to the unsuitable class. 4. Results and discussion
and the slopes more than 5° are considered as unsuitable (Effat and El-Zeiny, 2017). 4.1.1.2. Aspect (As): The study area has aspect angles ranging from 0 to 360°, Fig. 7. Aspect angle of 0°, referring to flat lands, and aspect angles between 112.5° and 247.5°, matching with the South, Southeast and Southwest directions, are given a class value of 1. The rest of aspect angles are given a class value of 5. 4.1.2. Global solar radiation (GSR) Solar Radiation (Solar insolation) is the solar irradiance amount that is measured over a specified period of time. It is usually quantified at peak sun hours, which is the equivalent number of hours per day when the average solar radiation is 1 kWh/m2, (Morales and Busch, 2010). The area-solar grid is a global radiation equal to the sum of total direct and indirect radiation. The GSR raster for the whole year was converted from Wh/m2 to kWh/m2/day, (Fig. 8). The study area is exposed to high GSR in summer and lower GSR in winter. GSR for winter and summer were also estimated using the SRTM data where, in summer, GSR ranged from 3.24 to 5.63 kWh/m2/day, and in winter from 2.0 to 3.3 kWh/m2/day the annual GSR per day is divided into two classes: >3.5 Kwh/m2 and rated with a high class value (1) and the values <3.5 kWh/m2 areas are rated with a low class value (5). 4.1.3. Depth to Groundwater (DG) The GW depths in the study area range from 20 to 308 m and are classified into 5 classes as shown in Fig. 9. The depth to GW, between 20 and 0, do not need PV pumps, so it is a restricted area. On the other hand, the middle parts which have with DG less than 50 m are given a class of 1 while the north western parts with more than 200 m are given a class of 5.
4.1. Reclassification of thematic layers 4.1.1. Topographic parameters 4.1.1.1. Land slope (LS): El-Moghra Oasis is almost flat with slope ranging from 0° to 46° as shown in Fig. 6. Land slopes less than 5° are given a class value of 1 as the best for PV location
4.1.4. Salinity (SL) The GW Salinity of the Moghra Aquifer ranges from 1,827 to 21,118 ppm as shown in Fig. 10. So, the groundwater could be used for both agricultural and industrial purposes, (Abdel Mogith et al., 2012). SL is classified into two classes, less than 5,000 ppm is suit-
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Fig. 6. Land slope LS (in degrees). Fig. 8. Global Solar radiation GSR (kWh/m2/day).
Fig. 7. Aspect AS (in degree).
able for irrigation and more than 5,000 ppm is unsuitable for irrigation. Fig. 9. Depth to GW (Modified after Ezzat, 1984; Salem and El-Bayumy, 2016).
4.1.5. Land use class (LU) The study area is divided into five LU classes: Desert, Moghra Lake, cultivated lands, Oil fields and buildings. Most of the Moghra Oasis is desert, Fig. 11. Desert is rated with a high class value 1 and cultivated lands are classified by a relatively lower class of 2. On the other hand, Moghra Lake, Oil fields and buildings are undesirable because the PV stations cannot be placed in these areas so they are restricted in the model.
4.1.6. Distance to roads (DR) The layer of roads was digitized from the Sentinel-2 satellite image using ArcGIS 10.1 Software. Using the Spatial Analyst in ArcGIS, the distance to roads map was created by calculating the distance from the roads (Euclidean Distance). Near-distances roads are given values higher than the Far-distance roads to allow connection with high-voltage electric power in the future, Fig. 12.
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Fig. 12. Distance to roads (DR) in meters. Fig. 10. Salinity SL (ppm) (modified after Abdel Mogith et al., 2012). Table 1 Criteria weights for the input criteria. Rank
Criteria
Weight (n rj + 1)
Normalized weight wj
1 2 3 4 5 6 7 Sum
GW Salinity Depth to GW Land-cover Distance to road Slope Aspect Solar radiation
7 6 5 4 3 2 1 28
0.25 0.21 0.18 0.14 0.11 0.07 0.04 1
Depth to GW, the third priority rank assigned to the Distance to road, the fourth priority rank assigned to Land-cover, the fifth priority rank assigned to Slope, the sixth priority rank assigned to Aspect and the seven priority rank assigned to Solar radiation. The method was used for weight assignment using the following equation (2), (Malczewski, 1999) and Table 1 shows the method applied to get the Criteria weights for each factor.
wj ¼ n rj þ 1 =SUMðn rk þ 1Þ
ð2Þ
where, wj: the normalized weight for the j factor, n: the number of factors, rj is the rank position of the factor and rk is the factor number.
Fig. 11. The land use map.
4.2. Weighted overlay model The rank-sum method was applied for obtaining the criteria weights of the factors. (Malczewski, 1999) and (Effat and ElZeiny, 2017) explained the method of rank sum and used multicriteria decision analysis (MCDA) applications. GW salinity was assigned the first priority, the second priority rank assigned to
The GIS-Multi criteria analysis (MCA) is used, where all factors were merged into one layer in order to calculate the overall suitably of each site (pixel). After the GIS-MCA, the resulting raster layer defines the appropriate PV systems locations. Results range from 5 (unsuitable) to 1 (most suitable) and Eq. (3) calculates the suitability of each pixel (Malczewski, 2000; Riad et al., 2011; Castillo et al., 2016), Table 2 shows the different criteria, attributes and ratings assigned.
ri ¼
X
wj v j
ð3Þ
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Table 2 Solar Radiation, topography, distance from roads, depth to water, land use and salinity themes, criteria, factors attributes and rating. No.
Criteria
Factor
Attributes
Rating Classification
1
Topography
Slope (degrees)
<5 >5 0 [flat] 0–22.5 [North] 22.5–67.5 [Northeast] 67.5–112.5 [East] 112.5–157.5 [Southeast)] 157.5–202.5 [South] 202.5–247.5 [Southwest] 247.5–292.5 [West] 292.5–337.5 [Northwest] 337.5–360 [Northwest] 3.5 <3.5 1827–5000 5000–21,118 0–6000 6000–12,000 12,000–24,000 24,000–48,000 48,000–56,284 (20)–(0) (1) – (50) (51) – (100) (101) – (150) (151) – (200) (201) – (308) - Moghra lake - Cultivated Lands and planned for cultivation - Oil Fields and surrounding buildings - Desert
1 5 1 5 5 5 1 1 1 5 5 5 1 5 1 5 1 2 3 4 5 Restricted 1 2 3 4 5 Restricted 2 Restricted 1
Aspect (azimuth)
2
Solar Radiation
3
GW salinity (ppm)
4
Locational
5
Depth to GW (m)
6
Land-use
Solar Radiation (kWh/m2/day)
Distance to Main Roads (m)
Land-cover class
where: ri: is the overall appropriate level of each site (pixel) i P wj:is the weight of each factor j with Wj = 1 vj: is the appropriate value in each factor j. A percentage weight of each raster is assigned according to its effects and the sum of weight influence percentage must be equal to 100%. Each layer is multiplied by the percentage of their effect and is added to generate the output raster. All thematic layers were integrated into one layer in ArcGIS 10.1 software to produce a map of the appropriate sites of the PV location. All thematic layers were integrated into one layer in ArcGIS 10.1 software to produce a map of the appropriate sites of the PV location. 4.3. Suitable PV locations The developed model was run using the collected data and Fig. 13 show the results. It can be noticed that the most suitable zones for Photovoltaic stations are close to the River Nile and away from the oil fields and Qattara Depression. The output of the model confirms that Using PV for groundwater extraction has a number of economic and environmental limitations; groundwater salinity, depth to groundwater, pumping cost and abstraction (Salim, 2012). Fig. 13. Output suitability map for groundwater extraction using solar energy.
5. Conclusions and recommendations Moghra Oasis is one of the promising areas for using solar energy in different applications. Groundwater extraction using renewable energy, especially solar energy, represents one of the future development driving forces. The remotely sensed data and Geographic Information Systems (GIS) were used to develop a MCA model that was applied to Moghra Oasis area to produce a
map of the optimum sites for exploiting solar energy in GW extraction. The produced map shows that the most suitable locations for applying the PV systems are close to the Nile Delta with GW salinity less than 5000 ppm and depth to GW is less than 100 m. In addition, these best locations are away from Moghra Lake, Qattara Depression, oil fields, buildings and areas with Groundwater salinity more than 5000 ppm.
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It is recommended to rely more on solar energy since it is ecofriendly and a renewable source of energy and to consider wind energy as an alternative of renewable sources in the study area. Also, in areas of high groundwater salinity, it is possible to study the possibility of desalination and to establish fish farms as a source of protein. A cost analysis is recommended to be implemented taking into account the Capex and Opex for the best areas between Qattara Depression and Moghra Lake in addition to the good areas from North east to south west of the study area. Conflict of interest There is no conflict of interest. References Abdel aziz, A.Y., Mekhamer, S.F., Mohamed, A.B., 2012. Geographic Information Systems (GIS) application in wind farm planning. Online J. Power Energy Eng. 3 (2), 279–283. Abdel Mogith, S.M., Ibrahim, S.M.M., Hafiez, R.A., 2012. Groundwater potentials and characteristics of el-moghra aquifer in the vicinity of Qattara Depression. Egyptian J. Desert Res. 62 (63), 1–20. 2012/2013. Abo-Khalil, A.G., Ahmed, S.S., 2016. Water-pumping using powered solar system more than an environmentally alternative: the Case of Toshka. J. Energy Natural Resour. 5 (1-1), 19–25. Al Garni, H.Z., Awasthi, A., 2017. Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia. Appl. Energy 206 (2017), 1225–1240. Aliaga, Á.R., Lozano, J.M.S., Cascales, M.S.G., Benhamou, M., García, A.M., 2016. GIS based solar resource analysis for irrigation purposes: rural areas comparison under groundwater scarcity conditions. Solar Energy Mater. Solar Cells 156 (2016), 128–139. Castillo, C.P., Silva, F.B., Lavalle C., 2016. An assessment of the regional potential for solar power generation in EU-28. Energy Policy88 (2016) 86–99. Dawoud, M.A., Darwish, M., El-kady, M., 2005. GIS-based groundwater management model for western Nile Delta. Water Resour. Manage. 2005 (19), 585–604. Effat, H.A., El-Zeiny, A., 2017. Modeling potential zones for solar energy in Fayoum, Egypt, using satellite and spatial data. Model. Earth Syst. Environ. 2017 (3), 1529–1542. EL-Shimy, M., 2012. Analysis of Levelized Cost of Energy (LCOE) and grid parity for utility-scale photovoltaic generation systems Alexandria, Egypt. 15th International Middle East Power Systems Conference (MEPCON 12). Elbeih, S.F., 2016. Mapping of Groundwater in Egypt Using RS/GIS: Case Studies, Areas Surrounding Nile Valley and Its Delta. In: Negm, A. (Ed.), The Nile Delta. The Handbook of Environmental Chemistry. Springer, Cham, p. 55. El-Tahlawi, M.R., Farrag, A.A., Ahmed, S.S., 2008. An environmental overview Groundwater of Egypt. Environ. Geol. 2008 (55), 639–652. EU Delegation to Egypt, 2015. Pre-Feasibility Study for New Programmes in Application of Irrigation Based on Groundwater in Western Egypt. Final Report, EuropeAid/132633/C/SER/multi. Ezzat, M.A., 1984. Qattara Hydro Energy project Side Effects on groundwater Aquifers. The Ministry of Electricity. Qattara hydro and renewable Energy Project authority: Publ., Cairo. Gad, H.E., El-Gayar, S.M., 2011. Using photovoltaic array for solar water pumping in Toshka Region, Egypt. Fifteenth International Water Technology Conference. IWTC, Alexandria, Egypt.
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Hernandez, R.R., Easter, S.B., Mariscal, M.L., Maestre, F.T., Tavassoli, M., Allen, E.B., Barrows, C.W., Belnap, J., Hueso, R., Ravi, S., Allen, M.F., 2013. Environmental impacts of utility-scale solar energy. Renew. Sustain. Energy Rev. 29 (2014), 766–779. Hussein, T.A.M., 2016. Design and performance Analysis of PV-Powered Irrigation Water Pumping Systems. AIN SHAMS UNIVERSITY FACULTY OF ENGINEERING, Electrical Power and Machines Engineering. Master thesis. IRENA, 2013. Renewable Power Generation Costs in 2012: An Overview. Abu Dhabi: International Renewable Energy Agency. IRENA, 2012. Renewable energy technologies: Cost Analysis Series- Solar Photovoltaics. Abu Dhabi: International Renewable Energy Agency. IRENA, 2018. Renewable power generation costs in 2018. Abu Dhabi: International Renewable Energy Agency. Janke, J.R., 2010. Multicriteria GIS modeling of wind and solar farms in Colorado. Renew. Energy 35, 2228–2234. Malczewski, J., 1999. GIS and Multicriteria Decision Analysis. Wiley, New York. Malczewski, J., 2000. On the use of weighted linear combination method in GIS: common and best Practice approaches. Trans. GIS 4 (1), 5–22. Merrouni, A.A., Elalaoui, F.E., Mezrhab, Ah., Abdel, Mezrhab, Ghennioui, A., 2018. Large scale PV sites selection by combining GIS and Analytical Hierarchy Process Case study: Eastern Morocco. Renew. Energy 119 (2018), 863–873. Morales, T.D., Busch, J., 2010. Design of Small Photovoltaic (PV) Solar-Powered Water Pump Systems. United States Department of Agriculture. Mohamaden, M.I.I., El-Sayed, H.M., Mansour, S.A., 2017. Combined application of electrical resistivity and GIS for groundwater exploration and subsurface mapping at northeast Qattara Depression, Western Desert, Egypt. Egyptian J. Basic Appl. Sci. 4 (2017), 80–88. Phillips, J., 2013. Determining the sustainability of large-scale photovoltaic solar power plants photovoltaic solar power plants. Renew. Sustain. Energy Rev. 27 (2013), 435–444. Riad, P.H.S., Billib, M., Hassan, A.A., Abdel-Salam, M., El Din, M.N., 2011. Application of the overlay weighted model and Boolean logic to determine the best locations for artificial recharge of groundwater. J. Urban Environ. Eng. 5 (2), 57– 66. RIGW, 1988. Hydrogeological map of Egypt. 1:2,000,000. Sahin, A.Z., Rehman, S., 2012. Economical Feasibility of Utilizing Photovoltaics forWater Pumping in Saudi Arabia. Hindawi Publishing Corporation. Int. J. Photoenergy 2012, 9. Salem, Z., El-Bayumy, D., 2016. Hydrogeological, petrophysical and hydrogeochemical characteristics of the groundwater aquifers east of Wadi El-Natrun, Egypt. NRIAG J. Astronomy Geophys. 5 (1), 124–146. Salim, M.G., 2012. Selection of groundwater sites in Egypt, using geographic information systems, for desalination by solar energy in order to reduce greenhouse gases. J. Adv. Res. 2012 (3), 11–19. Sherwani, A.F., Usmani, J.A., 2010. Varun. Life cycle assessment of solar PV based electricity generation systems: a review. Renew. Sustain. Energy Rev. 14, 540– 544. Shouman, E.R., El Shenawy, E.T., Badr, M.A., 2016. Economics analysis of diesel and solar water pumping with case study water pumping for irrigation in Egypt. Int. J. Appl. Eng. Res. 11 (2), 950–954. Tucker, C.J., Vanpraet, C.L., Sharman, M.J., and van Ittersum, G., 1985, Satellite remote sensing of total herbaceous biomass production in the Sengalese Sahel— 1980–1984: Remote Sensing of Environment, v. 17, p. 233–249, Natural Resources Conservation Service. Technical Note No. 28/PORTLAND, OREGON. Wohlgemuth, J.H., Solar, B. P., 2003. Long term photovoltaic module reliability. National Center for Photovoltaics and Solar Program Review Meeting; Denver, CO, USA p. 179-83. Youssef, T., Gad, M.I., Ali, M.M., 2012. Assessment of Groundwater Resources Management in Wadi El-Farigh Area Using MODFLOW. IOSR J. Eng. 2 (10), 69– 78.
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Multi criteria analysis for groundwater management using solar energy in Moghra Oasis, Egypt Ebtehal Sayed a,b,⇑, Peter Riad b, Salwa Elbeih c, Mona Hagras b, Ahmed A. Hassan b a
Higher Technological Institute (HTI), 10th of Ramadan City, Egypt Irrigation and Hydraulics Dept., Faculty of Engineering, Ain Shams University, Cairo, Egypt c Engineering Applications Department, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt b
a r t i c l e
i n f o
Article history: Received 2 December 2018 Revised 16 March 2019 Accepted 12 April 2019 Available online xxxx
a b s t r a c t Egypt is facing a lack of water resources in addition to some diesel fuel supply problems which makes it necessary to rely more on renewable energy. Renewable energy is the most promising source of energy and is one of the most important growing industries around the world. Solar energy based on Photovoltaic panels, is being frequently used as a source of energy for groundwater pumping. The objective of this study is to investigate the feasibility of using solar energy in exploiting the groundwater reserves in Moghra oasis, Western Desert of Egypt. A multi-criteria analysis model based on the Geographic Information Systems technique was applied. The applied multi-criteria layers include depths to groundwater, salinity (water use), solar radiation, topography, accessibility and land-use. The output results of the model showed that the best location for Photovoltaic panels is close to the Nile Delta and outside the boundary of Qattara Depression, Moghra Lake and oil fields restricted areas. In these areas the groundwater salinity is less than 5000 ppm and depth to groundwater is less than 100 m. It is recommended in areas with high groundwater salinity to study the possibility of groundwater desalination. Ó 2019 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B. V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
1. Introduction In remote areas in the Western Desert of Egypt there is usually no electricity grid, so farmers use diesel-driven pumps to pump groundwater for irrigation purposes. Although the diesel pumps have the advantage of lower capital cost, the PV pumps are preferred regarding the low costs of maintenance and operation (Shouman et al., 2016). Furthermore, PV pumps avoid the fluctuations in availability, cost of diesel fuel, depletion of fossil fuel reserves and more environment-friendly.
Abbreviations: TDS, Total Dissolved Solids; GIS, Geographic Information Systems; MCA, Multi criteria analysis; GW, Groundwater; NDVI, Normalized Difference Vegetation Index; a-Si, Amorphous Silicon; BOS, Balance of system; CAPEX, Capital Expenditure; CdTe, Cadmium Telluride; CIS, Copper-Indium-Selenide; CIGS, Copper-Indium-Gallium-Diselenide; c-Si, Crystalline silicon; CSP, Concentrated Solar Power; DOE, Department of energy; GSR, Global Solar Radiation; KWh, Kilo Watt per hour; KWp, Kilo Watt Peak; LCOE, Levelized Cost of Energy ($/kWh); mcSi, multi-crystalline silicon; OPEX, Operational Expenditure; PV, Photovoltaic. Peer review under responsibility of National Authority for Remote Sensing and Space Sciences. ⇑ Corresponding author. E-mail address: [email protected] (E. Sayed).
Photovoltaic cells are simpler than diesel generators as they are composed of less number of components and easier to service and maintain. Accordingly, PV cells are more reliable and have a longer lifetime (EU Delegation to Egypt, 2015). Using PV technology, solar radiation can be transformed to a sustainable electric power source. When compared with conventional energy sources, PV systems have many environmental benefits and advantages. However, the large required area may cause unfavourable impacts on both the land use and landscape (Castillo et al., 2016). International and regional examples for using PV in other countries include Saudi Arabia, USA, South Africa, India, Spain, Europe, Morocco and Tunisia, (Sahin and Rehman, 2012; Phillips, 2013; Hernandez et al., 2013; Aliaga et al, 2016, Al Garni and Awasthi, 2017; Castillo et al., 2016 and Merrouni et al, 2018). Geographic Information Systems (GIS), as a spatial decision support tool, is one of the tools used to select suitable sites for using solar energy (Salim, 2012). Aliaga et al. (2016), studied the effects of agricultural pumping using solar energy in both Spain and Morocco, with different factors according to climate conditions, energy mix and energy policies. GIS was proposed to analyse the temporal and spatial variability of the solar resource through real data of both sites and to study groundwater resources.
https://doi.org/10.1016/j.ejrs.2019.04.001 1110-9823/Ó 2019 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: E. Sayed, P. Riad, S. Elbeih et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j. ejrs.2019.04.001
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Techno economic feasibility studies, of producing energy, were carried out using PV pumping systems in Saudi Arabia. The capital cost of PV pumping was considered for a well with total dynamic head (TDH) 50 m. The cost was found between 2 and 3 $/m3 for water pumping, (Sahin and Rehman, 2012). Solar powered pumping systems were already used in Toshka Project in Western Desert of Egypt. Many factors were considered in the design of solar power; crop type, size of planting area, number of peak sun hours and the solar array efficiency, (Abo-Khalil and Ahmed, 2016). The direct coupled PV water pumping system sizing and performance prediction under climate conditions is developed in Toshka, Egypt. This system includes PV array, pump controller and submerged pump (Gad and El-Gayar, 2011). In 2017, 25 wells were drilled in Dakhla oasis in Egypt and designed to pump GW using solar energy with a life time of about 20 years. PV systems produce electricity rates estimated by the capital cost (CAPEX), operational costs (OPEX), solar irradiation level and efficiency of solar cells. The PV system’s capital cost consists of the cost of both PV modules and Balance of system (BOS), while, the total costs consist of the PV module costs, costs of BOS, and installation costs (IRENA, 2012). The U.S. Department of Energy (DOE) chose the levelized Cost of Energy (LCOE) as a main parameter for evaluating PV systems. With the LCOE method, the $/Watt can be transformed into $/kWh, which is a more significant parameter in the power industry, (EL-Shimy, 2012). The continuous drops in prices of PV module and total installation costs intended that these values had already been decreased (IRENA, 2013). The PV prices depend on the forecasted PV life time. For solar PV system the life time has a lifetime range from 20 to 25 years, (Wohlgemuth and Solar, 2003; Sherwani et al., 2010). Rapid decreases in installed costs and increases of capacity factors have made solar PV more economic than other energy resources. The market of PV has grown in the last decade for installed cost, Operation and Maintenance costs (O&M), and levelized Costs of Electricity (LCOE) Costs for installed solar PV module decreased by 83℅ while module costs decreased by 80℅ (from 2010 to 2016). At last, LCOE of PV plants have fallen by 73% between 2010 and 2017 with average 40–75% depending on the country, (IRENA, 2018). The PV efficiency depends on the cell utilized; CdTe (Cadmium Telluride), a-Si (Amorphous silicon, triple junction), CIGS (Cupper/Indium/Gallium/Selenide), poly-Si (polycrystalline silicon), Mono-Si (Monocrystalline silicon). The max efficiency according to the cell type was found 10.07, 8.26, 11.33, 17.59 and 19.62%, while the minimum efficiency was found 4.86, 1.81, 6.6, 6.07, and 7.48%, (Hussein, 2016). Groundwater mapping were obtained using remote sensing and GIS as one of the main tools for the groundwater resources development. These maps are useful for strategic groundwater assessment and planning to be used by engineers, planners and decision makers for groundwater management, (Elbeih, 2016). GIS is operated by collecting appropriate and available data, overlaying the thematic layers on a base map for a given area and processing it into an optimal Model, (Abdelaziz et al., 2012). Janke (2010) applied multi-criteria analysis using GIS modelling to determine the land-cover classes and identify the suitable area which are affiliated with solar potential and high wind and in Colorado. The reclassification of Land-cover, renewable potential, population density, distance to roads, transmission lines, and cities were according to their suitability. Each weight was based on their relative importance to one another. The main objective of this work is to produce a potentiality map for the solar energy in the area of Moghra oasis in the Western Desert of Egypt applying a multi-criteria analysis (MCA) supported by (GIS). This potentiality map can be used to define the most
suitable locations for installing solar plants to generate the required power for pumping groundwater. The used multicriteria parameters are depths to groundwater, GW salinity, solar radiation, topography, area accessibility and land-use. 2. Study area Moghra Oasis is one of the land reclamation areas in the Western Desert of Egypt with an area of about 630 km2 (about 150,000 acres) of desert land. It lies in the NorthEastern extension of Qattara Depression between Longitudes 28° 100 and 29° 100 E and Latitudes 29° 500 and 30° 410 N, as shown in (Fig. 1). Its climate is characterized to be hot in summer and short warm in winter with low rainfall and high evaporation rates (Youssef et al., 2012). The average temperature is ranging between 23 °C and 39 °C and the annual rainfall is between 25 and 50 mm (Mohamaden et al., 2017). The study area includes El-Dabaa, AlMoghra Lake, Aghar Oil Field and El-Alamein petroleum fields. El-Moghra aquifer lies in a well identified geological unit dominated by the Lower Miocene Formation, composed of sand and sandstone with clay and siltstone intercalations. It extends from the western boundaries of the Nile Delta to Qattara Depression and southwards to El-Fayoum Depression with a total surface area of about 50,000 km2 with a thickness ranging from 50 to 250 m. In general, the groundwater flow in El-Moghra aquifer is directed towards Qattara Depression in the west (Dawoud et al., 2005). (Fig. 2) shows the hydrogeological map of the study area with the main aquifers, flow directions and piezometric contour lines. There are various sources that recharge El-Moghra aquifer; (i) seepage from the western part of the Nile Delta aquifer, (ii) flow from the overlying Miocene limestone aquifer, (iii) upward leakage from the deep Nubian Sandstone Aquifer System (NSAS) and (iv) minor contribution from rainfall (EU Delegation to Egypt, 2015). Aquifer discharge takes place through evaporation in Qattara and Wadi El Natroun depressions and by lateral seepage into carbonate rocks of the western part of Qattara Depression. The hydraulic gradient within the aquifer is less than 0.2 m/km and its base slopes from ground level near Cairo in the east to 1000 m below the Mean Sea Level at Alexandria in the west. The current annual extraction from this system is approximately 200 Mm3/year. Where Moghra aquifer’s water is a mixture of renewable and fossil water (El-Tahlawi et al., 2008). El-Moghra Aquifer shows a wide variation of transmissivity (from 61 to 6500 m2/day). The Total Dissolved Solids (TDS) ranges between 289 and 31,000 ppm. The geographical distribution of TDS indicates fresh water in the vicinity of Wadi El-Natroun and highly saline water in the vicinity of Qattara Depression (Abdel Mogith et al., 2012). According to the (EU Delegation to Egypt, 2015), for GW salinity of 2000 ppm, the Moghra aquifer can be used for cultivating some crops such as wheat, barley, maize and sorghum with a yield reduction of 0%, 0%, 25% and 50% respectively, compared with those irrigated by fresh water. While their yield has a reduction of 25%, 10%, 100%, and 100% for water salinity of 5000 ppm respectively. For water salinity more than 5000 ppm, water can be used for industrial purposes such as; salts extraction, oil industry and washing of mineral materials, (Abdel Mogith et al., 2012). 3. Materials and methods 3.1. Data collection To fulfil the requirements of the present study, various sets of data have been collected, processed, analysed and integrated into a GIS database. These data sets include:
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Fig. 1. Location map of the study area.
Fig. 2. Hydrogeological map of the study area, (Modified after: RIGW, 1988).
3.1.1. Remote sensing data 3.1.1.1. Shuttle radar topography mission (SRTM) data: SRTM data, acquired by space shuttle Endeavour in 2001 by C-band SAR interferometry instrument, was needed to model land elevation, land slope, aspect angles and area solar radiation maps using ArcGIS10 spatial analyst modules, as shown in Fig. 3.
3.1.1.2. Sentinel-2 satellite image: ESA, as part of the Copernicus Programme, developed Sentinel-2 to be an Earth observation mission for performing terrestrial observations. Sentinel-2 is utilized significantly in the applications related to Earth land surface. A Sentinel-2 image with a 10 m spatial resolution, acquired in 2018, was used for:
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was the used method for extracting cultivated areas using the well-known Eq. (1) (Tucker et al., 1985). The output of the classification was enhanced using Google Earth images for small cultivated areas.
NDVI ¼
ðqNIR qRedÞ ðqRed þ qNIRÞ
ð1Þ
where: qNIR is the Reflectance in the near infrared band and qRed is the Reflectance in the red band. The value of this index ranges from 1 to 1 where the range for green vegetation is known to range from 0.2 to 0.8. 3.1.2. Groundwater data The present study used some data from previous researches including: The piezometric contour map of El-Moghra aquifer (Modified after: Ezzat, 1984; RIGW, 1988, and Salem and El-Bayumy, 2016) as shown in (Fig. 2). Chemical analyses of selected water samples from El-Moghra aquifer and wells tapping El-Moghra Aquifer (Modified after: Abdel Mogith et al., 2012).
Fig. 3. Digital Elevation Model (DEM) of the study area.
Digitalizing the main roads of the study area Determining boundaries of Moghra Lake, Oil fields, and surrounding buildings.
3.1.1.3. Multi-spectral landsat image: Landsat data created by the U. S Geological Survey was obtained in a geographic Tagged Image file format (Geotiff). The data type is level 1 requiring both radiometric and atmospheric corrections. The multi-Spectral OLI image (UTM zone 35) was acquired in March 2018. The cultivated and reclaimed lands were extracted from multi-spectral Landsat image. ENVI 5.4 software was used to produce a map for Normalized Difference Vegetation Index (NDVI) and a land use map. NDVI
The salinity of the Moghra aquifer, at a depth of about 50 m, differs from one area to another. In other words, it is highly saline in the vicinity of Qattara Depression, while in the vicinity of Wadi El-Natroun, it is fresh water (Abdel Mogith et al., 2012), (Fig. 4). The data were geometrically corrected using the ArcGIS program and were matched with the Landsat satellite image. 3.2. Applied methods Design parameters of the weighted overlay model using GIS include the following:
Solar Radiation (Kwh/m2/day) Topography of the terrain and obstacles Accessibly to Roads Depth to groundwater GW Salinity and water use Land use type
Fig. 4. Salinity contour map of El-Moghra groundwater aquifer, (Modified after: Abdel Mogith et al., 2012).
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Fig. 5. A flow chart for the required Layers of the MCA.
3.2.1. The conceptual model A number of processes were performed for preparing the thematic layers to be used as input data in a weighted overlay model. In order to achieve the objectives of the study, a flow chart (Fig. 5) was prepared to illustrate the operational steps and processes of the Multi-Criteria Analysis (MCA). As shown in Fig. 5, six parameters were selected to be the main thematic layers to be analysed within the weighted overlay model. Each parameter was reclassified and a corresponding weight value was assigned affecting the final decision of the model. The different layers are categorized based on the following ratings: 1 (Best), 2 (Good), 3 (fair) 4 (Poor) and 5 (Worse and Unsuitable). 3.2.2. Preparing thematic layers for GIS analysis - The DEM was used to obtain slope (Fig. 6), aspect (Fig. 7) and solar radiation (Fig. 8). - Groundwater Salinity and depth to groundwater were obtained from previous studies (Figs. 9 and 10). - ArcGIS was used for converting vector layers to raster maps then interpolated using spatial analysis. - The road map layer and cultivated areas were obtained from the Landsat satellite images, as shown in (Fig. 11). All maps have been reclassified to integer values to be used as inputs in the weighted model where the reclassified layers ranged from 1 to 5. A value of 1 was assigned to the best class and 5 to the unsuitable class. 4. Results and discussion
and the slopes more than 5° are considered as unsuitable (Effat and El-Zeiny, 2017). 4.1.1.2. Aspect (As): The study area has aspect angles ranging from 0 to 360°, Fig. 7. Aspect angle of 0°, referring to flat lands, and aspect angles between 112.5° and 247.5°, matching with the South, Southeast and Southwest directions, are given a class value of 1. The rest of aspect angles are given a class value of 5. 4.1.2. Global solar radiation (GSR) Solar Radiation (Solar insolation) is the solar irradiance amount that is measured over a specified period of time. It is usually quantified at peak sun hours, which is the equivalent number of hours per day when the average solar radiation is 1 kWh/m2, (Morales and Busch, 2010). The area-solar grid is a global radiation equal to the sum of total direct and indirect radiation. The GSR raster for the whole year was converted from Wh/m2 to kWh/m2/day, (Fig. 8). The study area is exposed to high GSR in summer and lower GSR in winter. GSR for winter and summer were also estimated using the SRTM data where, in summer, GSR ranged from 3.24 to 5.63 kWh/m2/day, and in winter from 2.0 to 3.3 kWh/m2/day the annual GSR per day is divided into two classes: >3.5 Kwh/m2 and rated with a high class value (1) and the values <3.5 kWh/m2 areas are rated with a low class value (5). 4.1.3. Depth to Groundwater (DG) The GW depths in the study area range from 20 to 308 m and are classified into 5 classes as shown in Fig. 9. The depth to GW, between 20 and 0, do not need PV pumps, so it is a restricted area. On the other hand, the middle parts which have with DG less than 50 m are given a class of 1 while the north western parts with more than 200 m are given a class of 5.
4.1. Reclassification of thematic layers 4.1.1. Topographic parameters 4.1.1.1. Land slope (LS): El-Moghra Oasis is almost flat with slope ranging from 0° to 46° as shown in Fig. 6. Land slopes less than 5° are given a class value of 1 as the best for PV location
4.1.4. Salinity (SL) The GW Salinity of the Moghra Aquifer ranges from 1,827 to 21,118 ppm as shown in Fig. 10. So, the groundwater could be used for both agricultural and industrial purposes, (Abdel Mogith et al., 2012). SL is classified into two classes, less than 5,000 ppm is suit-
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Fig. 6. Land slope LS (in degrees). Fig. 8. Global Solar radiation GSR (kWh/m2/day).
Fig. 7. Aspect AS (in degree).
able for irrigation and more than 5,000 ppm is unsuitable for irrigation. Fig. 9. Depth to GW (Modified after Ezzat, 1984; Salem and El-Bayumy, 2016).
4.1.5. Land use class (LU) The study area is divided into five LU classes: Desert, Moghra Lake, cultivated lands, Oil fields and buildings. Most of the Moghra Oasis is desert, Fig. 11. Desert is rated with a high class value 1 and cultivated lands are classified by a relatively lower class of 2. On the other hand, Moghra Lake, Oil fields and buildings are undesirable because the PV stations cannot be placed in these areas so they are restricted in the model.
4.1.6. Distance to roads (DR) The layer of roads was digitized from the Sentinel-2 satellite image using ArcGIS 10.1 Software. Using the Spatial Analyst in ArcGIS, the distance to roads map was created by calculating the distance from the roads (Euclidean Distance). Near-distances roads are given values higher than the Far-distance roads to allow connection with high-voltage electric power in the future, Fig. 12.
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Fig. 12. Distance to roads (DR) in meters. Fig. 10. Salinity SL (ppm) (modified after Abdel Mogith et al., 2012). Table 1 Criteria weights for the input criteria. Rank
Criteria
Weight (n rj + 1)
Normalized weight wj
1 2 3 4 5 6 7 Sum
GW Salinity Depth to GW Land-cover Distance to road Slope Aspect Solar radiation
7 6 5 4 3 2 1 28
0.25 0.21 0.18 0.14 0.11 0.07 0.04 1
Depth to GW, the third priority rank assigned to the Distance to road, the fourth priority rank assigned to Land-cover, the fifth priority rank assigned to Slope, the sixth priority rank assigned to Aspect and the seven priority rank assigned to Solar radiation. The method was used for weight assignment using the following equation (2), (Malczewski, 1999) and Table 1 shows the method applied to get the Criteria weights for each factor.
wj ¼ n rj þ 1 =SUMðn rk þ 1Þ
ð2Þ
where, wj: the normalized weight for the j factor, n: the number of factors, rj is the rank position of the factor and rk is the factor number.
Fig. 11. The land use map.
4.2. Weighted overlay model The rank-sum method was applied for obtaining the criteria weights of the factors. (Malczewski, 1999) and (Effat and ElZeiny, 2017) explained the method of rank sum and used multicriteria decision analysis (MCDA) applications. GW salinity was assigned the first priority, the second priority rank assigned to
The GIS-Multi criteria analysis (MCA) is used, where all factors were merged into one layer in order to calculate the overall suitably of each site (pixel). After the GIS-MCA, the resulting raster layer defines the appropriate PV systems locations. Results range from 5 (unsuitable) to 1 (most suitable) and Eq. (3) calculates the suitability of each pixel (Malczewski, 2000; Riad et al., 2011; Castillo et al., 2016), Table 2 shows the different criteria, attributes and ratings assigned.
ri ¼
X
wj v j
ð3Þ
Please cite this article as: E. Sayed, P. Riad, S. Elbeih et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j. ejrs.2019.04.001
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E. Sayed et al. / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
Table 2 Solar Radiation, topography, distance from roads, depth to water, land use and salinity themes, criteria, factors attributes and rating. No.
Criteria
Factor
Attributes
Rating Classification
1
Topography
Slope (degrees)
<5 >5 0 [flat] 0–22.5 [North] 22.5–67.5 [Northeast] 67.5–112.5 [East] 112.5–157.5 [Southeast)] 157.5–202.5 [South] 202.5–247.5 [Southwest] 247.5–292.5 [West] 292.5–337.5 [Northwest] 337.5–360 [Northwest] 3.5 <3.5 1827–5000 5000–21,118 0–6000 6000–12,000 12,000–24,000 24,000–48,000 48,000–56,284 (20)–(0) (1) – (50) (51) – (100) (101) – (150) (151) – (200) (201) – (308) - Moghra lake - Cultivated Lands and planned for cultivation - Oil Fields and surrounding buildings - Desert
1 5 1 5 5 5 1 1 1 5 5 5 1 5 1 5 1 2 3 4 5 Restricted 1 2 3 4 5 Restricted 2 Restricted 1
Aspect (azimuth)
2
Solar Radiation
3
GW salinity (ppm)
4
Locational
5
Depth to GW (m)
6
Land-use
Solar Radiation (kWh/m2/day)
Distance to Main Roads (m)
Land-cover class
where: ri: is the overall appropriate level of each site (pixel) i P wj:is the weight of each factor j with Wj = 1 vj: is the appropriate value in each factor j. A percentage weight of each raster is assigned according to its effects and the sum of weight influence percentage must be equal to 100%. Each layer is multiplied by the percentage of their effect and is added to generate the output raster. All thematic layers were integrated into one layer in ArcGIS 10.1 software to produce a map of the appropriate sites of the PV location. All thematic layers were integrated into one layer in ArcGIS 10.1 software to produce a map of the appropriate sites of the PV location. 4.3. Suitable PV locations The developed model was run using the collected data and Fig. 13 show the results. It can be noticed that the most suitable zones for Photovoltaic stations are close to the River Nile and away from the oil fields and Qattara Depression. The output of the model confirms that Using PV for groundwater extraction has a number of economic and environmental limitations; groundwater salinity, depth to groundwater, pumping cost and abstraction (Salim, 2012). Fig. 13. Output suitability map for groundwater extraction using solar energy.
5. Conclusions and recommendations Moghra Oasis is one of the promising areas for using solar energy in different applications. Groundwater extraction using renewable energy, especially solar energy, represents one of the future development driving forces. The remotely sensed data and Geographic Information Systems (GIS) were used to develop a MCA model that was applied to Moghra Oasis area to produce a
map of the optimum sites for exploiting solar energy in GW extraction. The produced map shows that the most suitable locations for applying the PV systems are close to the Nile Delta with GW salinity less than 5000 ppm and depth to GW is less than 100 m. In addition, these best locations are away from Moghra Lake, Qattara Depression, oil fields, buildings and areas with Groundwater salinity more than 5000 ppm.
Please cite this article as: E. Sayed, P. Riad, S. Elbeih et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j. ejrs.2019.04.001
E. Sayed et al. / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
It is recommended to rely more on solar energy since it is ecofriendly and a renewable source of energy and to consider wind energy as an alternative of renewable sources in the study area. Also, in areas of high groundwater salinity, it is possible to study the possibility of desalination and to establish fish farms as a source of protein. A cost analysis is recommended to be implemented taking into account the Capex and Opex for the best areas between Qattara Depression and Moghra Lake in addition to the good areas from North east to south west of the study area. Conflict of interest There is no conflict of interest. References Abdel aziz, A.Y., Mekhamer, S.F., Mohamed, A.B., 2012. Geographic Information Systems (GIS) application in wind farm planning. Online J. Power Energy Eng. 3 (2), 279–283. Abdel Mogith, S.M., Ibrahim, S.M.M., Hafiez, R.A., 2012. Groundwater potentials and characteristics of el-moghra aquifer in the vicinity of Qattara Depression. Egyptian J. Desert Res. 62 (63), 1–20. 2012/2013. Abo-Khalil, A.G., Ahmed, S.S., 2016. Water-pumping using powered solar system more than an environmentally alternative: the Case of Toshka. J. Energy Natural Resour. 5 (1-1), 19–25. Al Garni, H.Z., Awasthi, A., 2017. Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia. Appl. Energy 206 (2017), 1225–1240. Aliaga, Á.R., Lozano, J.M.S., Cascales, M.S.G., Benhamou, M., García, A.M., 2016. GIS based solar resource analysis for irrigation purposes: rural areas comparison under groundwater scarcity conditions. Solar Energy Mater. Solar Cells 156 (2016), 128–139. Castillo, C.P., Silva, F.B., Lavalle C., 2016. An assessment of the regional potential for solar power generation in EU-28. Energy Policy88 (2016) 86–99. Dawoud, M.A., Darwish, M., El-kady, M., 2005. GIS-based groundwater management model for western Nile Delta. Water Resour. Manage. 2005 (19), 585–604. Effat, H.A., El-Zeiny, A., 2017. Modeling potential zones for solar energy in Fayoum, Egypt, using satellite and spatial data. Model. Earth Syst. Environ. 2017 (3), 1529–1542. EL-Shimy, M., 2012. Analysis of Levelized Cost of Energy (LCOE) and grid parity for utility-scale photovoltaic generation systems Alexandria, Egypt. 15th International Middle East Power Systems Conference (MEPCON 12). Elbeih, S.F., 2016. Mapping of Groundwater in Egypt Using RS/GIS: Case Studies, Areas Surrounding Nile Valley and Its Delta. In: Negm, A. (Ed.), The Nile Delta. The Handbook of Environmental Chemistry. Springer, Cham, p. 55. El-Tahlawi, M.R., Farrag, A.A., Ahmed, S.S., 2008. An environmental overview Groundwater of Egypt. Environ. Geol. 2008 (55), 639–652. EU Delegation to Egypt, 2015. Pre-Feasibility Study for New Programmes in Application of Irrigation Based on Groundwater in Western Egypt. Final Report, EuropeAid/132633/C/SER/multi. Ezzat, M.A., 1984. Qattara Hydro Energy project Side Effects on groundwater Aquifers. The Ministry of Electricity. Qattara hydro and renewable Energy Project authority: Publ., Cairo. Gad, H.E., El-Gayar, S.M., 2011. Using photovoltaic array for solar water pumping in Toshka Region, Egypt. Fifteenth International Water Technology Conference. IWTC, Alexandria, Egypt.
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Please cite this article as: E. Sayed, P. Riad, S. Elbeih et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j. ejrs.2019.04.001