Seawater desalination is a practical technology for providing fresh water to coastal
arid regions. Indeed, the use of desalination is rapidly increasing due to
growing water demand in these areas and decreases in production costs due to
technological advances. In this study, we developed a model to estimate the
areas where seawater desalination is likely to be used as a major water
source and the likely volume of production. The model was designed to be
incorporated into global hydrological models (GHMs) that explicitly include
human water usage. The model requires spatially detailed information on
climate, income levels, and industrial and municipal water use, which
represent standard input/output data in GHMs. The model was applied to a
specific historical year (2005) and showed fairly good reproduction of the
present geographical distribution and national production of desalinated
water in the world. The model was applied globally to two periods in the
future (2011–2040 and 2041–2070) under three distinct socioeconomic
conditions, i.e., SSP (shared socioeconomic pathway) 1, SSP2, and SSP3. The
results indicate that the usage of seawater desalination will have expanded
considerably in geographical extent, and that production will have increased
by 1.4–2.1-fold in 2011–2040 compared to the present (from
2.8
Water is vital to society. However, due to population increases, economic growth, and climate change, there is growing concern over the sustainability of water use around the world. There have been a number of studies regarding present and future worldwide water availability and use. Initially, the mean annual runoff (i.e., river discharge) was regarded as the primary renewable water resource (Vörösmarty et al., 2000; Oki et al., 2001, 2003). With increasing sophistication of global hydrological models (GHMs), the sources of water have been subdivided into various categories, such as rivers, reservoirs, lakes, groundwater, and others (Hanasaki et al., 2008a, b; Wada et al., 2011; Wada and Bierkens, 2014; Döll et al., 2014).
In this study, we focused on desalinated water derived from seawater, which
has not been explicitly represented as a water source in GHMs, except for a
few cases. Desalination is a technique for removing high concentrations of
minerals and salts from saline and brackish water. Although it accounts for
a marginal fraction at present (5.2 km
Desalinated water has been included in some previous global water resource
assessments based on simulations. Oki et al. (2001) presented a global water
resource assessment incorporating reported national desalinated water
production for various countries. Oki et al. (2003) conducted future global
water projections assuming that the present volume of desalination
production would remain unchanged. As production of desalinated water is
increasing rapidly, however, this assumption underestimates the future
contribution of desalination. Wada et al. (2011) incorporated desalinated
water as a water source in their GHM PCR-GLOBWB by spatially distributing
the national volume of usage for areas within 40 km of the seashore. They
assumed that the volume would increase in proportion to the population. The
above examples represent reasonable simplifications for GHMs, but further
refinement is necessary because desalination is increasing rapidly and is
strongly connected to climate and socioeconomic conditions. Independently of
GHMs, a few studies have projected future growth of desalination on a global
scale. Bremere et al. (2001) projected the desalination capacity required
for 10 water-scarce countries in 2025 to meet their growing municipal water
demands. They assumed that water use per person would remain constant and
that growth in municipal water demand would be proportional to population
increase. Fichtner GmbH (2011) reported a detailed future projection of
desalinated water production in the Middle East and north Africa (MENA). The
projection shows marked increases in desalinated water production based on
the assumption of rapid and widely distributed uptake of concentrating solar
power desalination technology throughout the MENA region. Ouda (2014)
projected future water supply and demand in Saudi Arabia under three
scenarios, taking into account desalinated water and treated wastewater. Kim
et al. (2016) proposed an economic approach to project future desalinated
water production globally. The authors assumed that supply and demand of
desalination is determined by price mechanisms, and reported that total
desalinated water would reach 250 km
In this study, we developed a model to infer the geographical distribution
and production of seawater desalination. The model was designed to be
incorporated into GHMs that are applicable for state-of-the-art global
long-term projections. Because of the low specificity of GHMs (e.g., the
typical spatial resolution is 0.5
There were two key questions. First, what are the climatic and socioeconomic conditions where seawater desalination is implemented on a large scale around the world? Identification of such conditions would facilitate the development of a model to explain the present geographical distribution and production of seawater desalination. Second, what would be the production of desalinated water in the future under various socioeconomic scenarios? The model was developed and validated utilizing newly available global datasets. Future projections were conducted based on a comprehensive global water assessment (Hanasaki et al., 2013a, b). Both the distribution and production of seawater desalination were assessed for three socioeconomic scenarios to meet increasing industrial and municipal water requirements.
This paper is structured as follows. Section 2 describes the data, model, and simulation settings. In Sect. 3, the results of the model and simulation are presented along with a discussion regarding its performance in reproducing the present distribution of desalination plants. The results of future projections are also shown. Section 4 concludes the paper.
We first collected country-based information on desalination production for nine countries with the largest seawater desalination capacity in the world from the AQUASTAT database: United Arab Emirates (UAE), Saudi Arabia, Kuwait, Spain, Qatar, Libya, Bahrain, Israel, and Oman (hereafter “major countries”). We selected the year 2005 as the base year mainly due to the availability of socioeconomic data as discussed below. In addition, we collected municipality-based information for Saudi Arabia (KICP, 2011) and the UAE (RSB, 2013). In spite of our efforts, subnational information for other countries was not found.
To obtain spatially detailed data, we used DesalData (
Since the objective of this study was to develop a model that would be
applicable to the global domain for long-term water scarcity projections, we
focused on information regarding active major plants using seawater as
source water. Currently, seawater is the primary source of desalination,
amounting to 19.2 km
To determine the distances between desalination plants and major cities of
selected countries, the geographical data of major cities were collected. We
referred to the GeoNames database (
At present, major plants are located mostly in arid regions. This makes
sense because alternative water sources are limited in such conditions and
costly seawater desalination becomes a practical option. To express aridity,
the ratio of precipitation to potential evapotranspiration (hereafter
aridity index, AI) was calculated globally. We used the WATCH Forcing
Dataset (Weedon et al., 2011) as a global meteorological dataset for the
historical period in question. This dataset covers the whole globe at a
spatial resolution of 0.5
For the future period, we used the climate projections obtained with a global climate model (GCM), namely MIROC4-ESM-CHEM. The selection and combination of the GCM and climate scenarios and the methods of downscaling and bias correction were identical to those reported previously (Hanasaki et al., 2013a, b).
For population and GDP, we referred to the shared socioeconomic pathways
(SSPs) database provided by IIASA (
For nationwide water use data, including desalinated water production, we relied primarily on the AQUASTAT database for the historical period in question. This database includes municipal and industrial water use data for 200 nations from 1960 to 2010 at 5-year intervals. Data are available for most countries in 2000 or 2005 but are largely missing for other years. For the future period, we used the projections of Hanasaki et al. (2013a). They estimated water withdrawal for irrigation, industrial, and municipal use over three periods (2011–2040, 2041–2070, and 2071–2100, centered on 2025, 2055, and 2085, respectively). Projections of industrial and domestic water withdrawal were obtained with a semi-empirical model using electricity production and population as primary driving forces and scenario-based parameters of improvements in water use efficiency. The parameters were set to be compatible with the storylines of the SSPs (e.g., slow improvements in SSP3, rapid improvements in SSP1). Consequently, as shown in Figs. 12 and 14 of Hanasaki et al. (2013a), the projected future water withdrawal was largest in SSP3 and smallest in SSP1. Note that these projections are demand based without specifying water sources. The key socioeconomic factors are summarized in Table 1. In this study, we focused on three scenarios, i.e., SSP1, SSP2, and SSP3.
Global future scenarios in 2055 (extracted from Hanasaki et al., 2013a, b).
In this subsection, the collected data are analyzed to model seawater desalination around the world. We quantitatively investigate desalination production and usage at the national and subnational levels. Because seawater desalination is mainly used in coastal arid regions, aridity conditions and distances from the seashore to plants and cities are also examined.
Table 2 summarizes desalinated water capacity and production in the major
countries. The countries accounted for 85 % of the seawater desalination
plant capacity in 2005. UAE and Saudi Arabia produced the largest volumes of
desalinated water, which accounted for more than half of the global total.
The major countries share two characteristics. First, all are located in the
Middle East or on the coast of the Mediterranean Sea and have arid or
semi-arid climates. Second, their income is relatively high: GDP per person
exceeds 14 000 USD PPP person
Major countries producing desalinated seawater in 2005.
Next, we focused on water use in the major countries, as shown in Table 2. More than 3 times the volume of water was used by municipalities than by industry, except in Spain. Globally, 99 % of major plants were installed for combined municipal and industrial use, with 90 % for municipal water use. Seawater desalination was seldom used for agricultural water supply. The production to capacity ratio varied considerably among nations, ranging from 53 to 110 %; the ratio sometimes exceeded 100 %, probably due to inconsistencies in the data and the year of report.
Figure 1 shows the geographical distributions of major plants in the
Mediterranean and Middle East regions where the majority of plants are
concentrated. Note that the plants were aggregated into gridded data with a
spatial resolution of 0.5
Actual locations of major desalination plants in the Mediterranean
and Middle East in 2005. Boxes and circles represent plants (in 1000 m
Figure 2 shows the AI of the present climate. Among the major countries, countries located in the Arabian peninsula, namely UAE, Saudi Arabia, Kuwait, Qatar, Bahrain, and Oman, are located in a hyper-arid climate, where the AI is below 0.15. The countries on the coast of the Mediterranean Sea, namely Spain, Libya, and Israel, are located in more humid climates than the above-mentioned countries, with AIs typically between 0.15 and 0.5. Note that the desalination plants in Spain are only located on the southeastern coast and some islands where the AI is substantially lower than that in the remaining regions. The fraction of desalinated water production to total industrial and municipal water withdrawal is shown in Table 2. The fraction for the countries in the Arabian peninsula exceeds 50 % except for Saudi Arabia, while that for the Mediterranean countries falls below 20 %. This indicates that desalinated water is a major water source for industrial and municipal water in countries in the Arabian peninsula, while it is supplemental in the countries on the Mediterranean Sea.
We plotted the relationship between the AI and plant capacity in each grid
cell (Fig. S2), which shows a remarkable difference in the
distribution of capacity by AI. We applied the segmented regression method
and estimated the break point at AI
Simulated aridity index under the present climate (ratio of precipitation to potential evapotranspiration).
Figure 3 shows the distances of the major desalination plants from the
seashore and the primary cities of the major countries. Desalination plants
are typically located on the seashore supplying water to cities at close
range. Therefore, we considered that the summation of the distance between
the seashore and a plant, and that from the plant to the closest major city
was the distance that seawater was transferred. We calculated the distance
using the Clark Lab's IDRISI Taiga Release 16.05 GIS platform software
(Eastman, 2009). Note that due to technical limitations of the software, the
individual plants were first aggregated into 5 arcmin
For further geographical breakdown, Table 3 shows the water supply of the
major cities in UAE and Saudi Arabia. The two largest cities in UAE, Abu
Dhabi and Dubai, depend heavily on desalinated water. Municipal and
industrial water for Abu Dhabi is supplied entirely by desalinated water
(EAAD, 2012) and their plant capacity reaches 1.18
Cumulative capacity of desalination water and the distance of
plants from the coastline
We developed a seawater desalination model (SDM) to estimate the areas where
desalinated water is used and the volume of desalinated water production. As
the model is intended to be incorporated into the global water resources
model H08 (Hanasaki et al., 2008a, b), for consistency, its spatial
resolution was set at 0.5
SDM consists of a set of climatological, geographical, and socioeconomic conditions to estimate the spatial extent of where seawater desalination is likely to be used. SDM identifies the grid cells in which all of the conditions given below are met. We called these grid cells area utilizing seawater desalination (AUSD) cells. In the AUSD grid cells, fresh water is produced by seawater desalination technology and used to meet local water demands under several assumptions. By combining the gridded map of AUSD cells and requirements for water withdrawal (i.e., potential water demand), which is standard input/output data of global hydrological models, we can estimate the global production of seawater desalination. Note that AUSD cells indicate where desalinated water is used and, hence, they are not identical to the location of individual desalination plants. It is expected that the spatial extent of AUSD cells includes the locations of major seawater desalination plants, but desalination plants are not necessarily located at every AUSD grid cell because desalinated water can be transferred to surrounding grid cells through the pipeline network. The conditions and assumptions of SDM were determined based on analyses of the national and subnational statistics of desalinated water production and the geographical distribution of desalination plants, as described in Sect. 2.4.
In this study, we developed SDM and its variant (hereafter SDM2). SDM2 has an identical model structure to SDM, but it consists of different objectives and sets of conditions and assumptions. SDM estimates AUSD cells where industrial and municipal water are mostly dependent on seawater desalination. As seen in Sect. 2.4, countries such as UAE and Qatar are typical cases. SDM2 estimates AUSD cells where industrial and municipal water are at least partly dependent on seawater desalination. Countries on the Mediterranean Sea are typical cases.
Major cities that use desalinated water.
SDM extracts AUSD cells or the grid cells meeting the following three conditions: Nations are included whose GDP exceeds 14 000 USD PPP person AI is below 8 %. Cells are located within three consecutive grid cells (approximately 165 km along the
equator) of seashore. In general, the desalination plants and the regions using their production
are located in arid coastal zones in relatively high-income countries. The
globally uniform thresholds were determined by the analyses given in Sect. 2.4.
Table 2 shows that GDP exceeded 14 000 USD PPP person SDM estimates desalinated water production under the following assumptions: Seawater desalination is used for municipal and industrial
purposes, not for irrigation. All municipal and industrial water withdrawal in AUSD cells is
supplied by seawater desalination. The production-to-capacity ratio, which shows the fraction of
the seawater desalination production of a country relative to the total
plant capacity is 30–80 %. Combining these assumptions, AUSD cells, and grid-based requirement of water
withdrawal, the volume of desalinated water production can be estimated,
which is directly transferable to global hydrological models. The rationale
of Assumption A is that desalinated water is not considered to be affordable
for irrigation (e.g., Bremere et al., 2001). Note the production cost is
approximated as 0.86–3.21 USD m SDM2 is a variant of SDM. SDM2 replaces Condition B of SDM with an AI below
50 % and adds a new Condition D, which is that the
withdrawal-to-water-resources ratio (WWR) is above 40 %. WWR is the ratio
of annual total water withdrawal of all sectors to the mean annual renewable
water resources typically substituted by annual runoff. Both AI and
threshold WWR were devised by Raskin et al. (1997), and are widely accepted
and used in quantitative macroscale water resource assessments (e.g.,
Vörösmarty et al., 2000; Oki et al., 2001). With these new
conditions, SDM2 is able to identify the coastal regions with modest aridity
and high water stress where actual major desalination plants are currently
located. Unlike SDM, it is difficult for SDM2 to maintain Assumption B. Outside
hyper-arid climatic areas, in many cases, several water resource options are
available, and seawater desalination only provides supplemental water. To
estimate the desalination production, it is necessary to first identify how
much of water withdrawal is assigned to seawater desalination. This could be
partly achieved if SDM2 and GHMs such as H08 were fully coupled, but such
modeling and simulations require considerable additional work and are beyond
the scope of this paper. Therefore, SDM2 was only used to estimate AUSD cells, and
not the volume of desalination.
Four simulations were performed using SDM and SDM2. The first was a historical simulation for 2005, which was used for model validation. The results were used to validate the locations and production volumes of desalinated water. The remaining simulations were simulations of SSP1, SSP2, and SSP3 for three periods (2011–2040, 2041–2070, and 2071–2100), which were consistent with the projections of Hanasaki et al. (2013a, b). The results were used to assess future desalinated water use.
Historical simulations were performed to validate SDM and SDM2. First, we validated the geographical extent of the simulated AUSD cells. Validation was conducted in two steps. First, we checked whether AUSD cells existed in the major countries shown in Table 2. Next, we checked whether the AUSD cells included the grid cells with desalination plants shown in Fig. 1. The extent of AUSD cells is expected to be greater than the area of desalination plants, because desalinated water produced in a grid cell can be transferred to multiple cells through the pipeline system.
Figure 4a shows the AUSD cells derived from SDM. Recall that the objective of SDM was to identify the regions dependent, to a significant extent, on seawater desalination. The AUSD cells included the location of major plants in the Middle East (Fig. 1), spreading along the seashore in Kuwait, Saudi Arabia, UAE, Bahrain, Qatar, and Oman. Yemen was not included because it fell below the income threshold, which agreed well with Table 2 indicating that Yemen produces little desalinated water. Southern Oman was indicated as suitable for desalination, but this area had no desalination plants in 2005. This does not represent an overestimation of desalinated water production as the region is sparsely populated, and therefore water withdrawal is quite limited compared to the northeastern part of the country. Few AUSD cells are found in the Mediterranean except for a part of Libya, which is consistent with the findings in Sect. 2.4 that the fraction of desalination production to water withdrawal was relatively low in this region.
The simulated AUSD cells of SDM2 are shown in Figure 4b. AUSD cells were extensive not only in the Arabian peninsula but also in the Mediterranean region. These regions included the grid cells of the southeastern coast of Spain, western Mallorca, southern Sicily, and northwestern Libya, which agreed well with the actual distributions of major desalination plants (Fig. 1). In general, the AUSD cells of SDM2 were concentrated near the large cities, as compared to that of SDM. Although SDM2 performed well, particularly in the Mediterranean coastal regions, the AUSD cells of SDM2 differed from the actual situation in some cases. First, AUSD cells expand in large parts of the coastal regions of California in the USA (see Fig. S5a). Desalination plants exist in the region, but they produced marginal volume in 2005. Although the grid cells for California were categorized as a water-stressed area under the WWR index, in reality, water is effectively transferred long distances owing to California's long history of intensive water resource development. This implies that inter-grid water transfer should be taken into account when WWR is calculated. Another example is that, in reality, there are no plants at the southwestern edge of the Italian peninsula, but they do exist in the central northern coast of Algeria; however, the AUSD cells of SDM2 show the opposite. The geographical distributions of desalination plants reflect various local circumstances and it is difficult to estimate the AUSD grid by grid.
Simulated distribution of area utilizing seawater desalination
(AUSD) in the Mediterranean and Middle East in 2005.
Because the stand-alone SDM2 model is unable to estimate the volume of
production, the results from SDM are discussed hereafter. Table 2 includes
the global and national volumes of desalinated water production for 2005
estimated by SDM. First, global total production and capacity of seawater
desalination were estimated at 2.8
At this stage, SDM has been validated mainly based on the spatial extent of areas and total volume of desalinated water production in 2005. Although a systematic validation of the temporal dynamics of desalinated water was hampered mainly by the lack of access to long-term data on socioeconomic conditions and water use, a simple validation was performed and is shown in Fig. 5. Conditions A–C and Assumptions A–C in SDM indicate that AUSD cells vary over time, due to changes in municipal and industrial water withdrawal, because the other factors are unchanged during the time frame of a few decades. Figure 5 shows the relationship between national desalination capacity and municipal and industrial water withdrawal in AUSD cells for different periods for 1980–2005 (the methods' summary is given in the Supplement). In general, the plots are located near the diagonal line. This supports Assumption B in SDM that the growth in municipal and industrial water in AUSD cells is sustained by seawater desalination. Exceptions are the cases of Spain and UAE. As we have mentioned several times above, AUSD cells in Spain are not well reproduced in this study. Although desalination capacity substantially increased in UAE during the period, AQUASTAT reported little increase in municipal and industrial water withdrawal. Taking into account the marked economic growth in UAE during the period, we inferred that water withdrawal would have grown more than reported in AQUASTAT.
Comparison between national desalination capacity and municipal and industrial water withdrawal in AUSD cells. Each plot indicates one specific year. The shape of symbols indicate the data source of municipal and industrial water withdrawal in AUSD cells: circles for AQUASTAT and stars for MDPS (2016).
In summary, SDM was validated from three perspectives: geographical extent of AUSD cells (Fig. 4a), total desalinated water production in 2005 (Table 2 and Fig. S2), and temporal change in production (Fig. 5). The simulated regions that rely largely on seawater desalination were well reproduced, as compared to earlier studies referred to in the Introduction section. The regions are typically hyper-arid coastal regions where it is difficult to find alternative water sources. SDM also reproduced the reported volume of desalination production fairly well in the major countries. Because such countries dominate global water desalination, SDM also captured the total global volume reasonably well. Although SDM and SDM2 advance our resources for modeling global desalination, the results conveyed uncertainties, which can mainly be attributed to the limited availability of data and the simplicity of the model structure, similar to the future simulations described below. SDM2 was validated only for the geographical extent of AUSD cells. The performance was promising because it reproduced reasonably well the present distribution of major plants.
For future simulation, we mainly report the results of SDM because it
produced outputs of both AUSD cells and the volume of desalinated water
production. Figure 6 shows the estimated spread of AUSD cells at present and in
2055 under SSP1, SSP2, and SSP3 scenarios together with the actual
distribution of extra-large desalination plants (capacity
Global distributions of area utilizing seawater desalination
(AUSD) in
It is difficult to assess the validity of future projections, but comparing AUSD projections with the present distribution of desalination plants (shown in Fig. 6e, f) can provide some insights. Interestingly, although production in 2005 was not significant, several desalination plants were actually operational in southern Africa and northern Chile, which is consistent with our AUSD expansion. In contrast, although at least one extra-large plant was located in Algeria, Angola, northern China, southeastern India, Japan, Korea, Singapore, Thailand, Florida in the USA, and Venezuela in 2014, AUSD cells did not cover these regions in any scenarios. This was primarily attributable to Condition B in SDM, which excluded non-hyper-arid regions. (As mentioned above, AUSD projections using SDM2 are shown in Fig. S5.) AUSD cells of SDM2 expanded into Algeria, northern China, and southeastern India, which was consistent with the distribution in 2014. AUSD cells did not expand into other east and southeast Asian countries because their AI was too high.
Using SDM, the projected levels of desalinated water production in 2025 and
2055 are 1.4–2.1 and 6.7–17.3 times greater than present, respectively
(Table 4). The earlier estimates show a considerable range, but our results
are within the range of spread for the reported period of 2015–2025. Only
one earlier report was found with projections for the year 2050. Fichtner
GmbH (2011) reported that total desalination production would be as high as
96 800
Projected future production of desalinated water.
Further regional breakdown is illustrated in Table 5. We followed the
regional classification of the SSP scenarios, which subdivides the world
into 11 regions (see Table S4 for the list of countries). In 2005, 85 % of
the production was concentrated in MENA (including all major countries
except Spain). The second largest region of production was North America,
which requires caution in its interpretation. A total of 409
The economic cost of seawater desalination was estimated for each case
(Table 4 for worldwide, Tables S5 and S6 for regions). Adopting the method
of Lamei et al. (2008), we estimated the unit production costs at 0.40–3.78 USD m
Production of desalinated water by region (10
The future projection is sensitive to Condition A (threshold of 14 000 USD PPP person
The results of the sensitivity tests are shown in Fig. 7. First, a higher
threshold of GDP per capita decreases the total desalination production due
to reduction in AUSD cells. For the base year (2005), there is a gap between 0 and
7000 USD PPP person
Sensitivity of GDP per capita (GDPPC; Condition A) and the aridity
index (AI; Condition B) with respect to global total production of seawater
desalination. Black, blue, green, and red lines for base year (2005), SSP1,
SSP2, and SSP3 in 2055, respectively. Solid, broken, and dotted lines for AI
In 2005, the global total for industrial and domestic water withdrawal was
reported to be 1170 km
The SDM model estimates AUSD cells and the volume of desalinated seawater based on three conditions and three assumptions. The results demonstrate that these conditions and assumptions allow us to successfully simulate seawater desalination in accordance with the purpose of this study. Here, each condition and assumption is revisited and key uncertainties are discussed.
Conditions A and B, which set thresholds on national average income and aridity, explain the present distribution of major desalination plants and regions that are dependent on seawater desalination. It should be noted that countries meeting these conditions include major oil-producing countries. Seawater desalination has high energy consumption costs, even with the application of the latest technology. Hence, the price of energy is an important factor in the installation of desalination plants. Although we confirmed that non-oil-producing countries such as Spain and Israel had considerable desalination capacity, attention should be paid to the projected newly emerging AUSD cells in non-oil-producing countries under the SSP scenarios, such as Chile, Namibia, etc. The income threshold may differ for these nations compared to major oil-producing countries because of differences in energy prices. These regional biases cannot be completely eliminated from our model simulations and validation because the majority of desalination plants have, so far, been constructed in the Middle East. It should also be noted that we fixed the thresholds for income and aridity according to Conditions A and B throughout the study period. This corresponds to the assumption that technology and costs of production are essentially fixed at present. Advances in technology would likely further lower the production costs, which would alter the threshold for plant installation (e.g., Ghaffour et al., 2013; Ziolkowska et al., 2015).
Condition C, which sets the maximum distance from the seashore for AUSD cells, plays a crucial role in the simulated volume of desalinated water. Desalinated water can be used at large distances from the seashore if transportation costs are affordable. Assumption B, or 100 % dependence on desalination in AUSD cells, is probably the largest uncertainty in the SDM. Although surface water is unreliable under super-arid conditions in AUSD cells, the dependence is influenced by the availability of other water sources, typically rivers, groundwater, and sometimes long-distance water transfer (Lamei et al., 2008). Recycled water is another emerging source of freshwater in water-scarce regions. For example, Singapore has been strongly promoting recycled water usage, which is economically more efficient than seawater desalination due to Singapore's stringent waste water quality control and appropriate infrastructure (Tortajada, 2006). In contrast, major desalination plants are occasionally implemented in relatively wet regions, e.g., on islands that are characterized by relatively small catchment areas, limited storage capacities, and large temporal variations in water demand. Such regional details are beyond the capability of present global hydrological models, but may substantially affect the results in some places.
In this study, we used the future water use projection of Hanasaki et al. (2013a) based on empirical relationships between water use and population and electricity production excluding regional details, and therefore projections for individual nations include substantial uncertainty. The spatial distribution of water use is another source of uncertainty. Historical and future water use are obtained first at the national scale, and then converted into grid cells under various assumptions. This study adopted the assumption that the spatial distribution of municipal and industrial water use is proportional to that of population. We note that supply and demand are interconnected: seawater desalination relieves the availability of water constrained by hydrology. Increases in water availability may enhance water consumption. This may alter the future situation substantially from the historical past on which the water demand projections of Hanasaki et al. (2013a) were based. Water use projections substantially differ among models and a systematic model intercomparison is under way (Wada et al., 2016).
Finally, we summarize the limitations due to the preconditions of the SDM
model. First, SDM was designed to be incorporated into GHMs, typically
simulated at a spatial resolution of 0.5
We have developed two models to estimate the location and volume of desalinated water production. First, we identified climatic and socioeconomic conditions that are common to areas where seawater desalination is undertaken. Three typical conditions were found, i.e., relatively high income, aridity, and proximity to the seashore. We obtained common global parameters for each, and demonstrated that the present AUSD cells can be fairly well reproduced by the proposed model. Then, we assumed that municipal and industrial water in AUSD cells are fully supplied by seawater desalination, and estimated the production and plant capacity of seawater desalination. We achieved fairly strong agreement with independent data. Second, using the SDM and SSP socioeconomic scenarios, the future production of desalination water was projected globally. The results indicated that AUSD cells are expected to expand considerably in the 21st century. Income growth plays a primary role in the expansion of desalination plants.
Desalination is a practical engineering measure for meeting the growing water demand in arid regions and for adapting to climate change (Jiménez Cisneros et al., 2014). This study proposes one of the first models to express desalination in global water resource models supported by the available literature and technologies. Although further improvements are needed, the model provides a good starting point for dynamically incorporating information regarding desalination into global water resource assessment.
Data used in this paper is available upon request to the corresponding
author except for the data regarding the desalination plants database because redistribution is
prohibited by the data provider. Please contact Desal Data
(
This work was mainly supported by CREST, Japan Science and Technology
Agency. N. Hanasaki acknowledges the support of JSPS KAKENHI grant number
25820230 and the Environment Research and Technology Development Fund (S-14)
of the Ministry of the Environment, Japan. S. Yoshikawa, K. Kakinuma, and S. Kanae
acknowledge the support of JSPS KAKENHI Grant number 15H04047 and
16H06291. The authors are grateful to three anonymous reviewers, Yoshie
Maeda, and Yaling Liu for helpful suggestions. The present work was
partially developed within the framework of the Water Futures and Solutions
initiative at IIASA and the Panta Rhei Research Initiative of the
International Association of Hydrological Sciences (IAHS) by the Water
Scarcity Assessment: Methodology and Application working group. Map colors
are based on