Water scarcity in Africa: Analysis of seawater solar desalination applicability by GIS
By Fulya Aydin-Kandemir, Ege University Solar Energy Institute, İzmir, Turkey
In the last century, global water use has increased at twice the rate of population growth. The water shortage due to excessive consumption and pollution of water resources has become a global environmental issue. Water scarcity today affects over 3 billion people on a global scale; nearly 2.5 billion people lack access to clean and reliable drinking water [1, 2]. Hence, a continuous supply of safe and reliable water is a leading issue in most of the urban areas, especially in arid and semi-arid regions [3, 4, 5].
Seawater solar desalination as a solution
Desalination (seawater and brackish groundwater) is a reliable and efficient water supply option in many countries in arid and semi-arid areas (especially in the MENA region). However, desalination technologies have high operational costs, mainly due to the use of fossil fuels . While the unit costs for desalination processes have fallen considerably over the last three decades, the rapid increase in costs of conventional water systems has enhanced the applicability of desalination technologies. Although the difference between conventional water systems and desalination has been reduced 2 to 3 times today, high costs prevent the desalination from becoming widespread as a water supply method in many countries.
Especially in Africa, seawater solar desalination (SWSD) systems, desalination system powered by solar energy, can be an opportunity to reduce the future concerns of the countries regarding the increasing water demand [7 – 11]. However, many environmental, economic, demographic, and climatic factors should be taken into consideration in the planning of the areas where SWSD facilities will be suitable.
The research question of this project was how the applicability (according to which factors) of SWSD systems is determined and in which countries SWSD is more suitable. This project aimed to develop a model using geographic information systems (GIS) and multi-criteria decision analysis (MCE) to determine the SWSD suitability potential in Africa.
Finding suitable locations
The potential areas for SWSD were determined based on factors such as sea surface temperature, sea surface salinity, solar insolation, aridity, population, water tariffs, human development, groundwater depth, groundwater productivity, and groundwater storage. In order to analyse SWSD suitability, various datasets were compiled.
Sea surface temperature (SST), sea surface salinity (SSS), and solar insolation (Solarlns) data were obtained from Nasa Earth Observations (NEO). The SST product has 1 km resolution collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. The SSS product has a 50 km spatial resolution from the Aquarius instrument, and the Solarlns dataset has 25 km spatial resolution from the Clouds and the Earth’s Radiant Energy System (CERES) flying aboard Terra and Aqua satellites. For this project, the layers were extracted by polygon, which covers Africa, using the Extract by Mask tool in ArcGIS Pro 2.5.
Global-aridity datasets were obtained from CGIAR Consortium for Spatial Information (CGIAR-CSI) in standard Arc/Info grid format, at 30 arc seconds (~1 km at the equator) as the annual average over the 1970 to 2000 period. These datasets are based on modelling and analyses by Antonio Trabucco, with the support of the International Water Management Institute and the International Centre for Integrated Mountain Development, and are provided online by the CGIAR-CSI with the support of the International Center for Tropical Agriculture. To derive these datasets, the methods of Zomer et al are used [12, 13]. In this project, the layer was extracted by polygon, which covers Africa, via Extract by Mask tool of ArcGIS Pro 2.5. Here, Aridity Index (Al) values increase for more humid conditions and decrease with more arid conditions.
Coastline vector data were attained from Natural Earth Data. For this project, the Euclidean Distance tool was used to generate the distance to coastline layer. Here, the values in decimal degree format were converted to km by the Raster Calculator tool of ArcGIS Pro.
Depth to groundwater, groundwater productivity, and groundwater storage data were obtained from the British Geological Survey for 2012. The data type is a xyzASCII file, and the spatial resolution of the GeoTIFF is 5 km. The data were saved as point shapefile, and then converted to raster by using the Point to Raster tool of ArcGIS Pro.
2020 water tariff data were collected from the International IBNET Benchmarking Network for Water and Sanitation Utilities (IBNET) for 253 cities in Africa. The values of water tariff of municipalities as water consumption per month in $/m3 were collected to create tabular data. The point shapefile was also generated by using the tabular data. The Inverse Distance Weighting tool was used to create a water tariff density raster based on the point shapefile.
The World Resources Institute’s Aqueduct database was used for baseline water stress along with country populations data for 2019. The tabular baseline water stress data was downloaded for 1547 cities and of populations for all the African countries. The baseline water stress layer was generated using the Inverse Distance Weighting tool in the GIS software. The country population layer was also created using the Polygon to Raster tool.
Human development index data based on mortality, education level, and gross national income were attained from the United Nations Development Program for 2018. The data was tabular, and the raster was generated based on the African countries, again using the Polygon to Raster tool.
Combining datasets and factors
Factors were standardised by the suitability intervals for factors under the Fuzzy technique. The suitability intervals for factors were developed based on the literature about desalination with the environment, water usage, demography, geography, and climatology [6, 14 – 19]. The standardisation of these factors was applied with the Fuzzy Membership tool, with the consideration of the suitability intervals identified in the range of 0-1, with increasingly suitable from 0 to 1; decreasingly suitable from 1 to 0; not suitable (0) and suitable (1) recasting values that assigned the factors. For example, the suitability increases from 0 to 1 with increasing seawater temperature, but the suitability decreases from 1 to 0 with increasing groundwater productivity.
The fuzzy maps of factors were overlaid by the equally weighted overlay method to generate a suitability map.
The use of systems such as SWSD for alternative water sources is also essential for the protection of people’s right to access water, especially in the regions where people face drought. This project was based on many different criteria, regardless of whether only the cities close to the coastline or the areas with high solar insolation are suitable for SWSD. As a result, the entire coastline of Africa is not suitable for SWSD. Different criteria affect SWSD suitability.
Suitable areas for SWSD systems are shown in the resulting map with suitability values between 0 – 1, with the visualised values classified between the lowest (0 – 0.20), low (0.20 – 0.40), medium (0.40 – 0.60), high (0.60 – 0.80) and the highest (0.80 – 1) suitability.
As seen on the SWSD suitability map, the countries suitable for the SWSD installation include South Africa, Mozambique, Zimbabwe, Tanzania, Ethiopia, Somalia, Djibouti, Eritrea, Morocco, Algeria, Egypt, and Nigeria. The most suitable regions for SWSD systems are in South and East African coastal countries where groundwater use is low. These are also among the countries that are struggling with drought today and will experience water scarcity in the future. North African countries are also suitable for SWSD systems, which may be an alternative to decreasing clean water resources due to future climate change and intensive groundwater use.
This study has developed an effective solution proposal for a current problem based on criteria identified in domain literature. For further studies, the accuracy assessment of this model will be studied, and factors will be weighted by the pairwise comparisons based on the judgments of different experts. This project does not cover validation and weighting studies due to time and cost constraints.
This model has been developed and operated on a continental level. According to the model’s results, large-scale modelling studies and feasibility projects which considers local criteria (facility capacity, local populations, water sources of local communities, etc.), are recommended for the areas where SWSD suitability is determined.
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Note: The project received a Esri Young Scholar 2020 award.