Hydrol. Earth Syst. Sci., 16, 3101–3114, 2012www.hydrol-earth-syst-sci.net/16/3101/2012/doi:10.5194/hess-16-3101-2012© Author(s) 2012. CC Attribution 3.0 License.

Hydrology andEarth System

Sciences

Water resources trends in Middle East and North Africatowards 2050

P. Droogers1, W. W. Immerzeel1, W. Terink 1, J. Hoogeveen2, M. F. P. Bierkens3, L. P. H. van Beek3, and B. Debele4

1FutureWater, Wageningen, The Netherlands2FAO, Water Development and Management Unit (NRL), Rome, Italy3Utrecht University, Dept. Physical Geography, The Netherlands4World Bank, Middle East and North Africa Region, Washington, D.C., USA

Correspondence to:P. Droogers (p.droogers@futurewater.nl)

Received: 20 March 2012 – Published in Hydrol. Earth Syst. Sci. Discuss.: 3 April 2012Revised: 25 July 2012 – Accepted: 18 August 2012 – Published: 3 September 2012

Abstract. Changes in water resources availability can beexpected as consequences of climate change, populationgrowth, economic development and environmental consid-erations. A two-stage modeling approach is used to explorethe impact of these changes in the Middle East and NorthAfrica (MENA) region. An advanced, physically based, dis-tributed, hydrological model is applied to determine the in-ternal and external renewable water resources for the currentsituation and under future changes. Subsequently, a water al-location model is used to combine the renewable water re-sources with sectoral water demands. Results show that to-tal demand in the region will increase to 393 km3 yr−1 in2050, while total water shortage will grow to 199 km3 yr−1 in2050 for the average climate change projection, an increaseof 157 km3 yr−1. This increase in shortage is the combinedimpact of an increase in water demand by 50 % with a de-crease in water supply by 12 %. Uncertainty, based on theoutput of the nine GCMs applied, reveals that expected watershortage ranges from 85 km3 yr−1 to 283 km3 yr−1 in 2050.The analysis shows that 22 % of the water shortage can beattributed to climate change and 78 % to changes in socio-economic factors.

1 Introduction

Water resources are being altered due to changes in cli-mate, population, economic development and environmentalconsiderations. The Middle East and North Africa (MENA)region can be considered as the most water-scarce region

of the world. According to FAO AQUASTAT (WRI, 2005)the average renewable water resources per capita for 2005are about 47 000, 20 000, 11 000, 4000, 6000 and 1500 m3

per year for South America, North America, Europe, Sub-Saharan Africa, Asia and the Middle East, and North Africa(MENA) regions, respectively. Moreover, water availabilityis highly variable within the MENA region. For example,within MENA the per capita water availability is currentlyless than 200 m3 per year in Yemen and Jordan. The 4th As-sessment Report of the IPCC (IPCC, 2007) projects strongchanges in climate across the MENA region. Temperaturesare expected to increase while at the same time substantialdecreases in precipitation are projected. These elevated tem-peratures will result in higher evapotranspiration demandsand this will, in combination with decreases in precipita-tion, severely stress the water resources in the region. Theimpact of these changes is assessed by various other stud-ies (e.g. Hanasaki et al., 2008; Elshamy et al., 2009; Witand Stankiewicz, 2006; Legesse et at., 2003) indicating anincreasingly large water deficit in the future.

However, for many of the 22 MENA countries, climatechange is not the only challenge that the water sector faces.Population growth and economic development, with associ-ated increases in irrigation, domestic and industrial water re-quirements, might even be a bigger challenge (Falkenmarkand Lannerstad, 2005; Rosegrant et al., 2009). One of themajor challenges in the MENA countries is to increase agri-cultural production to sustain the fast growing population.The “Agriculture towards 2030/2050” study of FAO (FAO,2006) shows that, on a global scale, agricultural production

Published by Copernicus Publications on behalf of the European Geosciences Union.

3102 P. Droogers et al.: Water resources trends in Middle East and North Africa towards 2050

can grow in line with food demand. However in the MENAregion the situation differs as high population growth ratesare expected and water is a crucial constraint. The FAO studyestimates that 58 % of the renewable water resources in theMENA will be used for food production by 2030 and far-fetching efficiency measures are required.

The World Bank study “Making the Most of Scarcity:accountability for Better Water Management Results in theMiddle East and North Africa” (World Bank, 2007) asks thequestion of whether countries in MENA can adapt to meet allthese combined challenges. The study argues that they haveto, because, if not, the social and economic consequenceswill be enormous. The study continues that the MENA coun-tries are insufficiently equipped to meet the above challengesand adaptation is essential to face the otherwise unavoid-able social, economic and budgetary consequences. Still, thisstudy lacks clear numbers and potential options to addressthese issues across the entire MENA region.

A study by Trieb and Muller-Steinhagen (2008) focusingon the options desalination might offer to overcome watershortages estimated substantial increases in water demandin the MENA region. This study was based on FAO statis-tics and assumptions on growth rates in population. The pro-jected increase in total water demand is from 270 km3 in2000 to 460 km3 in 2050. The study also projected that thedemand-supply gap will increase from 50 km3 in 2000 to150 km3 in 2050.

However, a complete analysis on water demand and watershortage over the coming 50 yr based on a combined use ofhydrological and water resources models, remote sensing andsocio-economic changes has never been undertaken for theMENA region. Studies published so far have not been able toreveal the full picture as their focus has been on a limitednumber of aspects only. Outstanding issues with previousstudies include the following: (i) they focus on only climateor agriculture; (ii) they are based on statistics rather than ona full hydrological approach; (iii) they are based on annualor monthly approaches rather than on the required daily ap-proach to capture hydrological processes; (iv) they use only alimited amount of GCM realizations, (v) they model the hy-drology with coarse spatial resolution; and (vi) they do notinclude socio-economic aspects. In this study these issuesare addressed by integrating various data sources, differentmodel concepts and an integration of various projections forthe future.

The objective of this study is to assess water demandand supply for the 22 MENA countries up to the year2050, taking into account changes in climate, population andeconomic development.

Fig. 1. Spatial domain of the hydrological model (red box). MENAcountries are shaded. Red dots show the location of the GRDC sta-tions used for calibration.

2 Materials and methods

2.1 Study area

The MENA region (Middle East and North Africa) is locatedbetween longitudes 13◦ W and 60◦ E and between latitudes15◦ N and 40◦ N covering a surface area of about 11.1 mil-lion square kilometers or about 8 % of the area of the world(Fig. 1). Because of the prevailing arid conditions in the re-gion, about 85 % of the area is desert. The 21 countries1 inthe MENA region have many similarities, although differ-ences in environments, resources and economies exist. TheMaghreb sub-region (North Africa countries) extends fromthe Mediterranean climate zone to the arid zone. Rainfall oc-curs in the winter season with a clear and dry summer season.There are differences in the climate within the sub-region be-tween the Maghreb countries. The Maghreb climate shows adrying and warmer gradient from north to south and a di-vided and dispersed hydrography with some average-sizedrivers only in Morocco. Egypt has an arid climate and asimple hydrography with very limited internal resources andonly one river, the Nile River, entering the country from Su-dan. The sub-region of the GCC (Gulf Cooperation Coun-tries= Middle East) has a complete desert climate that isvery hot in the summer and relatively cold in the winter withvery scarce rainfall. Finally, the Mashreq region (countrieslike Iran, Iraq, Lebanon, and Syria) has a milder and wetterclimate compared to the GCC countries.

MENA is the home for about 300 million people, or about5 % of the world’s population, with an average annual popu-lation growth rate of 1.7 % (World Bank, 2005). About 60 %of the total population lives in urban areas, but this percent-age is on the rise as people migrate to urban areas in searchfor better economic opportunities.

1 In the analysis we consider 22 regions (referred to as countries)given the separated locations of Gaza Strip and West Bank.

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MENA is the driest and most water-scarce region in theworld, and this is increasingly affecting the economic andsocial development of most countries of the region. MENAhas about 0.7 % of the world’s available freshwater resources(CEDARE, 2006; based on FAO-AquaStat). Today, the aver-age per capita water availability in the region is slightly abovethe physical water scarcity limit at about 1076 m3 yr−1 (com-pared to the world average of about 8500). However, countryfigures vary significantly from about 2000 m3/capita/year ormore in Iran and Iraq to less than 200 m3/capita/year in Jor-dan, the West Bank and Gaza, Yemen and many Gulf coun-tries. The exact percentage of the area that experiences physi-cal water scarcity is unknown as so far data are only availableper country. Generally, the Mashreq region is the richest inwater resources, whereas the GCC countries are the poorestin the region. Surface water still constitutes a main resourcein the region. More than two-thirds of the 360 km3 averagetotal annual renewable water resources in MENA come fromsurface resources (rainfall, rivers, springs and lakes) accord-ing to FAO (2006). Rainfall is highly variable in MENA, bothtemporally and geographically. Overall, average annual rain-fall is less than 100 mm in 65 % of the region, between 100and 300 mm in 15 % of the region and more than 300 mm inthe remaining 20 % of the region (Allam, 2002).

The main permanent rivers in MENA are the Nile inMaghreb and the Tigris and Euphrates in the Mashreq. TheGCC region has hardly any rivers of importance. The av-erage annual Nile flow at Aswan is reported to be about84 km3 yr−1 (CEDARE, 2006), out of which more than80 % occurs between August and October. Abu-Zeid and El-Shibini (1997) reported that this 84 km3 yr−1 is the averagenatural flow (for the period 1901–1950) and that this numberwas used in the design of the High Aswan Dam and to estab-lish a treaty between Egypt and Sudan in 1959. This treaty in-cludes allocating 55.5 km3 yr−1 to Egypt and 18.5 km3 yr−1

to Sudan, while the remaining 10 km3 yr−1 are considered tobe lost to evaporation from Lake Nasser. However, it shouldbe noticed that there is an ongoing debate about the ac-tual flows at Aswan (Molden, 2007). The Euphrates passesthrough Syria and then Iraq with average annual flows of 26and 30 km3 as it enters Syria and Iraq respectively (Abu-Zeidet al., 2004). The Tigris and Euphrates join together in Iraq toform Shatt al-Arab which eventually drains into the ArabianGulf.

2.2 Methods

A two-stage modeling approach is used in this study. Firstan advanced, physical based, distributed, hydrological modelis applied to determine the internal and external renewablewater resources for the current situation and under futurechanges (climate, socio-economics). Second, a water alloca-tion model is used to analyze the linkage between the renew-able water resources and the sectoral water demands. Thewater allocation model includes groundwater, surface water

and reservoirs as sources of water that are used to sustainthe sectoral water demands. The allocation model links sup-ply and demand for each country, sector and supply sources.The hydrological model runs on a daily time step and a spa-tial resolution of 10 km, and aggregated monthly time seriesof surface water and natural groundwater recharge serve asinput to the water allocation model.

The two models are set up for a period of 50 yr (2001–2050), where the period 2001–2010 is based on actual dataon climate and water requirements (see hereafter). For the pe-riod 2011–2050, projections on climate and water demandswere included and the overall water resources availabilityand demand are assessed.

2.2.1 Hydrological model

Current and future water availability are assessed using a re-vised version of the PCR-GLOBWB (PCRaster Global WaterBalance) hydrological model (Van Beek and Bierkens, 2009;Van Beek et al., 2011). PCR-GLOBWB can be described asa physical-based, dynamic and distributed model written inthe metalanguage of the PCRaster GIS package (Wesselinget al., 1996). The PCR-GLOBWB concepts are comparableto HBV-model (Bergstrom, 1995), with the main differencethat PCR-GLOBWB is fully distributed and implemented ona regular grid. Within this grid, variations in soil, land coverand topography are taken into account by parameterizingsub-grid variability (Van Beek and Bierkens, 2009). Such agrid-based approach is, over large areas, often preferred overthe traditional sub-basin approach (Meigh et al., 1999).

To our knowledge, this is the first time that a hydrologicalmodel has been developed capturing the entire MENA regionat such high spatial and temporal resolutions. Various mod-eling approaches for single river basins have been developedsuccessfully (e.g. Conway, 1993; Elshamy, 2008). However,most of these modeling study objectives were to focus onstream flows only. For this study all components of the com-plete water balance were required including land processessuch as evapotranspiration by crops and natural vegetation,groundwater recharge, and runoff. Obviously, model perfor-mance should be considered in the context of the applicationof the modeling results (Moriasi et al., 2007).

Originally, PCR-GLOBWB was set up as a global modelwith a spatial resolution of 0.5◦ (∼ 50 km). More recently,the model was applied at a higher resolution in Asia with theaim to assess future water availability in large Asian riverbasins in relation to food security (Immerzeel et al., 2009,2010). For this study, it has been downscaled to a spatial res-olution of 10 km, with inclusion of the sub-grid variabilityin vegetation cover. This resolution is the optimum trade-off between required detail for hydrological processes, dataavailability and calculation times (Wada et al., 2011; SpernaWeiland, 2012).

The study focuses on the 21 countries in the MENA (Mid-dle East and North Africa) region. However, to assess the

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availability of water resources in the MENA region, it isnecessary to include the upstream river basins of all MENAcountries in the model domain. To identify the upstream ar-eas, an overlay was made using a map with major drainagebasins derived from the Hydro1K database (USGS, 2012).The model domain extends relatively far to the south toinclude the entire Nile basin boundary (Fig. 1). The sizeof the model domain is 8860 km× 5250 km. Details of thehydrological model development can be found elsewhere(Immerzeel et al., 2010, 2012).

2.2.2 Water assessment model

The PCR-GLOBWB model is used to determine changes inwater resources availability as a result of changes in climateand irrigation demands. The linkage between water resourcesand water demand requires a different type of model, andhere the WEAP modeling framework (SEI, 2005) is used.WEAP is considered to be amongst the best tools to under-take integrated analysis of different scenarios (e.g. Droogersand Perry, 2008).

WEAP (SEI, 2005) operates on the basic principles of awater balance. WEAP represents the system in terms of itsvarious supply sources (e.g. rainfall, rivers, groundwater, andreservoirs); withdrawal, transmission and wastewater treat-ment facilities; ecosystem requirements, water demands andpollution generation. WEAP is applicable to many scales:municipal and agricultural systems, single catchments orcomplex transboundary river systems. WEAP calculates awater balance for every node in the system. Water is allocatedto meet instream and consumptive requirements, taking intoaccount demand priorities, supply preferences, mass balanceand other constraints (Yates et al., 2005).

The conceptual base as built using the WEAP model (re-ferred to as the MENA Water Outlook Framework, in shortMENA-WOF) is shown in Fig. 2. It is assumed that withineach country the following objects are present: streams,reservoirs, groundwater, irrigation demand, domestic de-mand and industrial demand. These objects are intercon-nected with each other, and per country a lumped approach istaken. Details for each of these objects include the following:

– Streamsrepresent all the surface water within a coun-try. The inflow into the surface water originates fromthe PCR-GLOBWB results. Water can be extracted fordomestic, industrial and irrigation needs; water can bestored in the reservoir; and additional outflow to the sea(or any other outlet point of the country) can occur.

– Reservoirsare represented by one single lumped objectand present total storage capacity in a specific country.Reservoirs can receive water from the streams, and wa-ter can be released to support the demand.

– Groundwateris, similar to the reservoir object, one sin-gle lumped object and represents total groundwater stor-age in a specific country. Groundwater receives water

Fig. 2.Conceptual framework of the MENA Water Outlook Frame-work (MENA-WOF).

from natural recharge as calculated by PCR-GLOBWBand additional return flows from irrigated areas. Wateris abstracted from the three demand nodes (irrigation,domestic and industry).

– Irrigation represents all the water requirements for irri-gation in a country. Water is obtained from the surfacewater and the groundwater. Return flows by drainageand surplus irrigation applications can return to thegroundwater or to the surface water.

– Domestic represents all water required for domesticsupply. Water is obtained from the surface water andthe groundwater. Return flows can return upstream inthe stream (so can be reused) and/or downstream in thestream (so no reuse).

– Industryrepresents all water required for industrial sup-ply. Water is obtained from the surface water and thegroundwater. Return flows can return upstream in thestream (so can be reused) and/or downstream in thestream (so no reuse).

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2.3 Data

2.3.1 Spatial and temporal resolution

As discussed by Wada et al. (2011) and Sperna Wei-land (2012), selection of model and thus data resolution is theoptimum tradeoff between required detail for hydrologicalprocesses, data availability and calculation times. Moreover,the objective of the study, e.g. scoping, feasibility, detaileddesign, etc., should be considered as well. For this particularanalysis, the nature of the study is scoping. For the hydrolog-ical model PCR-GLOBWB, all data are converted to a 10-km spatial resolution and a daily temporal resolution. Sincevegetation is analyzed at sub-grid level, vegetation data wereused at 1 km. Data for the water assessment model MENA-WOF are aggregated to country level and the temporal res-olution to a monthly base. Details of the individual datasetsare provided hereafter.

2.3.2 Digital elevation data

To determine the distribution of elevation, the HYDRO1Kdatabase was used (USGS, 2012). HYDRO1k is a geographicdatabase developed to provide comprehensive and consis-tent global coverage of topographically derived datasets, in-cluding streams, drainage basins and ancillary layers derivedfrom the USGS 30 arcsecond digital elevation model (DEM)of the world (about 1× 1 km). It is known that the originalDEM has inaccuracies in flat areas like the Sudd, and theHYDRO1K dataset provides therefore hydrologically correctDEMs along with ancillary datasets for use in continental-and regional-scale modeling and analyses.

2.3.3 Vegetation

PCR-GLOBWB requires information on the fraction of talland short vegetation for each grid cell, monthly crop factors(required to convert reference evapotranspiration to poten-tial evapotranspiration), monthly fractional vegetation coversand monthly maximum interception storage. This informa-tion is derived from the Global Land Cover Characterization(GLCC) database (USGS, 2008). The US Geological Survey(USGS), the University of Nebraska-Lincoln (UNL), and theEuropean Commission’s Joint Research Centre (JRC) havegenerated this 1-km resolution global land cover character-istics database for use in a wide range of environmental re-search and modeling applications. The data have been sub-jected to a formal accuracy assessment (USGS, 2008).

2.3.4 Soils

Soil physical properties for PCR-GLOBWB are derived fromthe FAO gridded soil map of the world (FAO, 1998). Data areavailable at a 1-km spatial resolution. The most prominentfeatures that are required are depth of the soil layers, sat-

Fig. 3. Projected population in the MENA region for the 22 coun-tries. Only some selected countries are shown in the legend (Source:CIESIN, 2002).

urated and residual volumetric moisture contents, saturatedhydraulic conductivity and total storage capacities.

2.3.5 Irrigation

The map with irrigated areas as developed by FAO, in coop-eration with the Center for Environmental Systems Researchof the University of Kassel, and the Johann Wolfgang GoetheUniversity Frankfurt am Main (Siebert et al., 2005), is usedas input to define irrigation in PCR-GLOBWB. The first ver-sion of this map was developed in 1999, but it has been up-dated continuously. In this study version 4.0.1 is used, whichis the most recent version and was released in 2007. Dataare available at a spatial resolution of 5 arcminutes (about10× 10 km). Irrigated areas in 2030 and 2050 were basedon projections published in the study “Agriculture towards2050” (FAO, 2006).

2.3.6 Population

Population data and projections originate from the Center forInternational Earth Science Network (CIESIN) of ColumbiaUniversity (CIESIN, 2002). Figure 3 shows that the entireMENA population is projected to grow enormously from 316million in 2000 to 697 million in 2050. Egypt and Yemenshow the largest increases in population.

2.3.7 Domestic water demand

Current domestic water requirements are taken from FAO’sAQUASTAT database (FAO, 2007). Projections on future do-mestic demands are not available and were derived thereforeusing the FAO approach (Bruinsma, 2009). This approach

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Fig. 4.Relationship between per capita domestic water withdrawalsand GDPP.

assumes that there is a correlation between gross domesticproduct per capita (GDPP) and current water demand percapita. Figure 4 shows that there is generally a clear rela-tionship with Iraq and Bahrain as outliers. Iraq’s GDPP hasdrastically reduced because of the war and political instabil-ity while domestic water withdrawals have remained more orless constant. Bahrain has a small population but is a popu-lar tourist destination in the region explaining the relativelyhigh domestic demand. Based on this relationship and futureGDPP projections, domestic water requirements up to 2050are estimated and used in the models.

2.3.8 Industrial demand

Data on industrial water withdrawals during the reference pe-riod are taken from FAO’s AQUASTAT database. Future pro-jections of industrial water requirements are assessed assum-ing that these depend on gross domestic product (GDP) andGDP per capita (GDPP) according to the following equation(Bruinsma, personal communication and 2009):

IWWyr = IWWyr-1 ∗ GDPyr/GDPyr-1 ∗ GDPPyr-1/GDPPyr

(1)

where IWWyr is the industrial water withdrawal for year(km3 yr−1). The rationale for this equation is that, if a coun-try produces more GDP, but it does not get richer per per-son (constant GDPP), industrial water demands will changeproportionally with GDP. However, if the country also getsricher per person, it is more inclined to save water. GDP pro-jections are based on the CIESIN data (CIESIN, 2002).

2.3.9 Current climate

For the period 2001–2010, climate data were obtained fromvarious public domain sources. Precipitation is based onthe TRMM (Tropical Rainfall Measuring Mission) satel-lite data. These data were made available by the God-dard Earth Sciences Data and Information Services Cen-

tre of NASA (National Aeronautics and Space Administra-tion, http://www.nasa.gov/). For temperature and evapotran-spiration data, the NCEP/NCAR Reanalysis 1 surface fluxeswere taken (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.surfaceflux.html). These data are used be-cause (i) the temporal resolution is daily, and (ii) it coversthe entire current climate from January 2000 through De-cember 2009. Reference evapotranspiration was calculatedwith the daily average, daily maximum and daily minimumtemperature, using the method of Hargreaves (Droogers andAllen, 2002). This is a well-known method for the calcula-tion of reference evapotranspiration, if only average temper-ature, maximum temperature and minimum temperature areavailable.

2.3.10 Climate change

Climate change data are derived from nine general circula-tion models (GCMs). These GCM results cannot be used di-rectly for two reasons. First, the resolution of GCMs is onthe order of several hundreds of kilometers, which is toocoarse for the detailed hydrological assessment required forthe study. Second, GCM time series for the past climate showdifferent patterns than observed climate records. Therefore, itwas required to downscale GCM output (precipitation, min-imum and maximum temperature) using a statistical down-scaling approach.

From the various emission scenarios, this study uses theA1B GHG emission scenario. This scenario is chosen be-cause it is widely used and adopted by the IPCC. The A1Bscenario is considered as the most likely scenario, becauseit assumes a world of rapid economic growth, a global pop-ulation that peaks in mid-century and rapid introduction ofnew and more efficient technologies. The A1B scenario canbe seen as an intermediate between the B1 (low GHG emis-sions) and A2 (high GHG emissions) scenario (IPCC, 2000).

Shongwe et al. (2009, 2011) evaluated the performanceof all IPCC GCMs in different regions of Africa by com-paring their outputs from 1960–1990 with the observed cli-mate as collected in the CRU TS2.1 dataset (New at al.,2000). The nine best performing GCMs, in terms of sim-ulating historic observed monthly rainfall (Shongwe et al.,2011), were selected to be used in this study. This numberof nine models was selected as a trade-off between includ-ing sufficient variation and having conceivable calculationtimes. Selection was based on the performance as describedby Shongwe et al. (2011). This has been explained in themanuscript. A statistical downscaling procedure was usedin which, for temperature, the difference between observedand GCM-simulated historic values (delta correction) weasused to correct the GCM future projections. For precipita-tion the same procedure was used but, instead of the absolutedifference as correction factor, the fractional difference wasused (ratio correction). Downscaling to a spatial resolutionof 10 km was performed using spline interpolation (Mitasova

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Fig. 5.Long-term average annual observed and simulated flow.

and Mitas, 1993). Details of the downscaling process can befound elsewhere (Immerzeel et al., 2010; Terink et al., 2012).

3 Results

3.1 Model performance

The original PCR-GLOBWB model has been demonstratedto perform well globally (Van Beek et al., 2011). Althoughdetailed model calibration is not the objective of the cur-rent study, the performance of the fine-scaled model as de-veloped for this particular study was assessed separately. Fora number of major rivers, the model results were comparedto observed data as stored in the Global Runoff Data Cen-tre (GRDC). Given limited data in the study area, also riversincluding in the model domain, but not in the MENA re-gion, are included (e.g. Volta). Due to the absence of recentriver flow data, the long-term average discharge was used toassess the performance of the model assuming that, if thelong-term average hydrology is simulated well, the modelcan be trusted to assess future changes in water availabil-ity. Moreover, it has been proven that relative model ac-curacy (difference between current situation and scenario)is higher than absolute model accuracy (difference betweenmodel output and observations) (Bormann, 2005; Arabi etal., 2007; Droogers et al., 2008).

Figure 5 shows the results of the performance on streamflows. There is a very good match between observed and sim-ulated flow, and therefore it was concluded that the model isable to accurately simulate the average hydrological condi-tions. In the original model, there was however one excep-tion for the river Nile at the El Ekhsase gauge. The simulatedflow in El Ekhsase (2600 m3 s−1) was substantially higherthan the observed river flow (1250 m3 s−1), while the simu-lated flows in the Blue and White Nile in Khartoum in Su-dan agree well with the observed flows. The fact that BlueNile, White Nile and Atbara tributaries are simulated well isunique as most model studies have severe problems in accu-rately simulating these rivers (Mohamed et al., 2005, 2006).The difference in observed and simulated river flow in the

Nile at El Ekhsase can be explained by the following. ELEkhsase lies on the main Nile in Egypt upstream of the delta,and therefore irrigation abstractions in the delta itself cannotbe a reason for this mismatch. The main reason is probablyabstractions along the Nile from Khartoum to El Ekhsase inSudan and Egypt. These abstractions are probably not cap-tured fully in the model given limitations in the irrigated areadataset and the small size (but large in number) abstractions.Moreover, there is a significant water loss from Lake Nasser(Aswan dam) on the order of 10 km3 yr−1 which is also notfully captured in the model. The model was adjusted by in-cluding these additional abstractions and evaporation fromLake Nasser.

Obviously, further model improvement might be possibleby a more formal calibration process. However, the Nash-Sutcliffe efficiency criterion (NSE), the most commonly usedperformance indicator to evaluate watershed models, is 0.97for the final model. Before adjusting the model, as explainedin the previous section, NSE was 0.67. The final NSE of0.97 is considered as very good since NSE values between0.75 and 1.00 are considered as the highest performance thatcan be reached in validating models (Moriasi et al., 2007).A more detailed description of the model performance, cali-bration and validation has been reported to be very good andapplicable to the current study (Wada et al., 2011; SpernaWeiland, 2012; Van Beek et al., 2011).

Further calibration of the model was assumed not to benecessary, given the current performance of the model asshown in Fig. 5 and the objectives of the current study. Im-portant to realize is, despite the fact that model performancewas based on annual flows, the model runs on a daily baseto ensure that hydrological processes are captured correctly.Without such a daily modeling approach, model performancewould be probably much lower. Moreover, the model hasbeen demonstrated to capture seasonality very well over var-ious spatial scales and areas (Immerzeel et al., 2009, 2010;Immerzeel and Bierkens, 2009).

3.2 Water resources

A typical example of the results of the PCR-GLOBWBmodel for the current situation is shown in Fig. 6. Renew-able water resources (water available for further use) basedon the full analysis using the model is a function of precipi-tation, actual evapotranspiration and the many state variableslike soil, groundwater, land cover, slope, climate, drainage,amongst others. The actual evapotranspiration is, besides theprecipitation, the biggest term of the water balance. Figure 7presents future water availability for the entire MENA re-gion based on PCR-GLOBWB for the period 2010–2050. Itis clear that the total internal renewable water resources andthe recharge show a significant decline. This is the combinedeffect of the changes in precipitation and evapotranspiration.It should be noted that, although groundwater declines aresevere, the contribution of groundwater compared to surface

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Fig. 6. Internal renewable water resources and actual evapotranspiration based on PCR-GLOBWB for the period 2000–2009.

water is relatively small for the entire MENA region. Obvi-ously, for some countries this decline in groundwater is oneof the main threats to sustainable water resources. The to-tal external renewable water resources show a very small in-crease for the entire region. This is mainly attributed to thefact that the majority of the external water resources are pro-vided by the Nile and an increase in precipitation is projectedby most GCMs in Eastern Africa where most Nile water isgenerated. However, increased abstractions by urban and in-dustry might be expected in this region, which is not includedin our analysis. However, additional abstractions for irriga-tions, the major consumer, are included in our analysis. Thecombined effect is that the total renewable water resourcesshow a negative trend aggregated over the entire MENA re-gion. The average total MENA renewable water resourcesfrom 2000 to 2009 equals about 250 km3, and this is pro-jected to decline by 0.6 km3 per year to 2050. The Fig. 7shows also that there is considerable variation between thedifferent GCMs and that the results should be interpretedwith care. Nonetheless, it is safe to conclude that an overalldecrease in water resources is likely to occur in the future.

There is great variation between the different MENAcountries in the hydrological response to climate change.Groundwater recharge shows a very sharp decrease in almostall countries. This decrease is generally much stronger thanthe projected decrease in precipitation, and this can be ex-plained by the increase in evapotranspiration and the non-linearity of hydrological processes. In relative terms someof the GCC countries (Oman, U.A. Emirates, Saudi Ara-

bia) show the largest decline. However, in some of the wet-ter countries, the decline is also very considerable (Morocco−38 %, Iraq−34 %, Iran−22 %) and this might lead to se-vere problems in the future. The internal and external renew-able water resources also show negative trends throughoutthe region with the exception of Egypt, Djibouti and Syria.The largest decreases are observed in Jordan (−98 %), Oman(−46 %), Saudi Arabia (−36 %) and Morocco (−33 %). InSyria the internal renewable water resources show an in-crease but the total renewable water resources show a de-crease, because the external inflow of the Euphrates intoSyria is projected to decrease by 17 %. More details on thewater resources assessment can be found elsewhere (Im-merzeel et al., 2010, 2011, 2012)

3.3 Water supply and demand

The overall water supply and demand analysis, as based onthe MENA-WOF model for the entire MENA region, is pre-sented in Table 1. Total demand will increase by 132 km3 peryear, while total water shortage will grow by 157 km3 peryear for the average climate change projection. This enor-mous increase in shortage is the collective impact of the in-crease in demand by 50 % combined with the decrease insupply by 12 %. What is interesting is that the changes in sup-ply are mainly attributed to a decrease in surface water avail-ability, while groundwater supply shows a relatively smalldecrease compared to total supply.

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P. Droogers et al.: Water resources trends in Middle East and North Africa towards 2050 3109

Fig. 7. Total gross recharge, internal, external and total renewable water resources from 2010 to 2050. The thick line is the average of thenine GCMs, and the thin lines show the second wettest and second driest GCM.

Table 1. Annual water supply and demand for the MENA regionfor current (2001–2010) and future (2041–2050) time frames. Fu-ture projections include ignoring climate change (NoCC), and sce-narios for average, dry and wet projections. All numbers in km3 peryear.

Current NoCC Avg CC Dry CC WetCC

DEMAND 261 380 393 412 374Irrigation 213 251 265 283 246Urban 28 88 88 88 88Industry 20 41 41 41 41UNMETDEMAND 42 164 199 283 85Irrigation 36 109 136 199 53Urban 4 37 43 56 20Industry 3 18 20 27 11SUPPLY 219 215 192 129 290Surface water 171 168 151 97 237Groundwater 48 47 41 31 53

The analyses are also undertaken for the driest and thewettest climate projections (Table 1). Logically, only the ir-rigation demand differs between these different climate pro-jections, while no impact on urban and industrial demands isseen in our analysis. Obviously, in reality some increase indemand might occur by some additional water need for cool-ing requirements and/or bathing. However, these amounts areconsidered to be minor compared to other impacts. In termsof supply, these climate projections have a rather big impact.What is interesting is that, even under the most positive pro-jection, water shortage is increasing from 42 km3 per yearcurrently to 85 km3 per year in 2050, despite the increasein water resources availability. The increase in demand by

socio-economic factors outbalances the increase in additionalwater resources availability as projected for the wet climateprojection.

The progression of water demand, supply and shortage upto 2050 is presented in Fig. 8. These figures show the de-mand for irrigation, domestic and industry, the water supply(split between groundwater and surface water) and total wa-ter shortage for the entire MENA region. These results areobtained by taking the sum of the 22 countries in the MENAregion on an annual base. It is important to realize howeverthat these results are based on daily calculations by the hy-drological model (and monthly for the water demand-supplymodel) to ensure that variations within a year are properlytaken into account. The figures show the year-to-year varia-tion too, which is especially noticeable in surface water avail-ability.

From these results it can be concluded that the currentwater shortage in the MENA region is around 42 km3 peryear (2000–2009). The annual variation is known as well andranges between 24 km3 (2004) and 64 km3 (2008). This is al-ready a substantial unmet demand, and a clear reflection ofthe conditions in the MENA region where water shortage isalready occurring in most of the countries.

Changes in water demand (Table 2) and water shortage(Table 3) are also presented per country. Changes in demanddo not clearly show specific country or regional trends, butare a result of the complex interactions between populationgrowth, economic development and changes in irrigation wa-ter demand. Overall, countries with high population growthand countries with a relatively low current water demand willexperience substantial growths in water demand in the future.An increase in water shortages is projected for all countries

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3110 P. Droogers et al.: Water resources trends in Middle East and North Africa towards 2050

Fig. 8.Water demand and supply MENA for the climate scenario AVG (top) and DRY (middle) and WET (bottom).

Table 2. Annual water demand for the 22 countries for the current(2001–2010) and future (2041–2050) periods. Future projections in-clude ignoring climate change (NoCC), and scenarios for average,dry and wet projections. All numbers in million m3 yr−1.

Current NoCC Avg CC Dry CC WetCC

Algeria 6356 11 912 12 336 12 818 11 878Bahrain 226 390 391 392 390Djibouti 28 84 84 85 82Egypt 55 837 85 281 87 681 90 381 85 235Gaza Strip 119 308 313 319 307Iran 74 537 93 111 97 107 103 461 90 949Iraq 50 160 81 622 83 803 87 415 80 336Israel 2526 4089 4212 4371 4047Jordan 1113 2219 2276 2349 2207Kuwait 508 1214 1216 1219 1212Lebanon 1202 1804 1869 1994 1746Libya 4125 5763 5982 6241 5727Malta 45 75 75 76 75Morocco 15 739 22 624 24 223 25 939 22 443Oman 763 1681 1709 1733 1668Qatar 325 385 395 405 382Saudi Arabia 20 439 25 945 26 633 27 424 25 857Syria 15 311 20 495 21 337 22 525 20 028Tunisia 2472 4150 4452 4808 4000U.A. Emirates 3370 3277 3389 3491 3212West Bank 341 689 709 741 679Yemen 5560 12 610 12 889 13 556 12 002

with the exception of Djibouti (Table 3). Countries with rel-atively high agricultural water consumption are expected toface a substantial increase in water shortage.

3.4 Climate change versus other changes

The results discussed above reflect the combination of pro-jections in socio-economic development as well as averageand extreme climate change projections. An interesting sci-entific, but also policy-related, question is what the contri-bution of only climate change would be. To this end, the en-tire modeling framework has been set up assuming climate in2050 would remain similar to current climate conditions, butsocio-economic changes would occur as expected. Table 1shows for the entire MENA region (under column “NoCC”)

Table 3. Same as Table 2, but here water shortage. All numbers inmillion m3 yr−1.

Current NoCC Avg CC Dry CC WetCC

Algeria 0 1082 3947 574 0Bahrain 195 379 383 389 378Djibouti 0 0 0 0 0Egypt 2858 31 332 31 648 61 867 0Gaza Strip 98 296 301 311 293Iran 8988 27 882 39 939 65 716 5262Iraq 11 001 48 748 54 860 68 529 38 181Israel 1660 3213 3418 3818 2946Jordan 853 1914 2088 2286 1808Kuwait 0 835 801 977 510Lebanon 141 732 891 1259 496Libya 0 3193 3650 3931 73Malta 0 14 36 51 16Morocco 2092 7369 15 414 19 554 8219Oman 0 1145 1143 1343 458Qatar 83 174 246 314 122Saudi Arabia 9467 20 045 20 208 22 717 17 136Syria 323 4135 7111 12 086 437Tunisia 0 0 837 2726 0U.A. Emirates 3036 3112 3189 3403 2851West Bank 210 580 624 696 539Yemen 1120 8285 8449 10 471 4838

the impact on water demand, unmet demand and water sup-ply. Results indicate that only 10 % of the change in waterdemand can be attributed to climate change, while the re-maining 90 % is a result from socio-economic changes (forthe average climate projection). Considering the dry climateprojection, 21 % can be attributed to climate change. Interest-ing to note is that, under the wet projections, water demandwill slightly decrease compared to the no climate change casedue to more precipitation.

The water shortage owing to only climate change is espe-cially interesting. Table 1 indicates that this water shortageis more strongly related to socio-economic factors. Consid-ering the average climate projection, 22 % of the water short-age can be attributed to climate change and 78 % to changesin socio-economic factors. Taking the dry climate projectionas reference, 49 % of water shortage is caused by climatechange and 51 % by socio-economic factors. Water shortage

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P. Droogers et al.: Water resources trends in Middle East and North Africa towards 2050 3111

Table 4. Contribution of socio-economic changes and climatechanges on total water demand in 2050.

Socio- Climate ChangeEconomics (%) Mean (%) Dry (%) Wet (%)

Algeria 93 7 14 −1Bahrain 99 1 1 0Djibouti 99 1 3 −3Egypt 92 8 15 0Gaza Strip 97 3 5 −1Iran 82 18 36 −13Iraq 94 6 16 −4Israel 93 7 15 −3Jordan 95 5 11 −1Kuwait 100 0 1 0Lebanon 90 10 24 −11Libya 88 12 23 −2Malta 100 0 4 0Morocco 81 19 32 −3Oman 97 3 5 −1Qatar 85 15 25 −5Saudi Arabia 89 11 21 −2Syria 86 14 28 −10Tunisia 85 15 28 −10Emirates 100 0 0 0West Bank 95 5 13 −3Yemen 96 4 12 −9MENA 90 10 21 −5

is smaller compared to the no climate projection under thewet climate projection.

Table 4 presents results for the individual countries inMENA. For all countries socio-economic development playsa much larger role compared to climate change in terms ofwater demand. There are however differences amongst coun-tries resulting from the complex interplay between differ-ent hydrological, socio-economic and country-specific char-acteristics. For countries that are already heavily water-stressed (Kuwait, Malta, Emirates), additional stress by cli-mate change is relatively low compared to socio-economicdevelopment. Countries where a considerable amount of wa-ter is allocated to irrigation (e.g. Iran, Morocco) are obvi-ously susceptible to changes in climate. What is interest-ing, in this respect is, for example, the difference betweenBahrain (no irrigation) and Oman and Saudi Arabia (someirrigation development).

4 Conclusions

The study presented here is unique in terms of combiningdifferent data, models and tools to come to an overall wateroutlook over a large area. The strength of the approach is aconsistent methodology over all countries so that intercom-parison is not affected by differences in approach. A draw-back of the presented approach is that calibration/validationis less detailed than what would be possible if smaller areas

and/or a more mono-disciplinary approach would have beenfollowed.

Uncertainty in the results presented originates mainly from(i) models, (ii) data, and (iii) projections. The hydrologi-cal model used here is based on the well-established PC-Raster framework and the PCR-GLOBWB implementation.This model is described extensively in literature and has beenvalidated over a range of different conditions. The watersupply-demand model is built in WEAP, again a very well-established and tested modeling framework. The data usedto feed these models originate from reliable and publishedsources (climate, land cover) and less-developed datasets(soils, reservoir operations). Finally, data on climate projec-tions are by definition uncertain. This is partly overcome byusing a selection of the nine best performing GCMs out ofa total of 21. These nine were all used in the hydrologicalmodel, and results from these analyses were used to feedthe average, the wettest and the driest projection into thesupply-demand model. The projections of population growthand especially of economic development are uncertain, butbased on rigorous analysis by CIESIN. At the same time,questions on the desired required accuracy of projections arebeing raised given uncertainties on policy that will be imple-mented over the coming decades (Dessai et al., 2009).

The hydrological model PCR-GLOBWB is developed asa “general-purpose” model. In the MENA region, hyper-aridareas can be found where rain falls in the form of intenseand short storms causing flash floods. In general, these shortstorms may not be well-captured by daily time step modelssuch as the PCR-GLOBWB as used in this study. However,total rainfall of these storms compared to overall rainfall overthe larger area considered in this study might be relativelylow. This is also demonstrated by the fact that the hydro-logical model is, over larger areas, able to mimic observedrunoffs quite well as demonstrated in this paper. However, adetailed analysis over a subset of the entire study area mightbe undertaken, but is beyond the scope of the current study.

Results from the study show clearly that, for the region un-der study, climate change is only a small factor in projectedwater shortage over the coming decades. However, given thealready enormous water shortage in the region, this climatecomponent should be taking into consideration in planningprocesses. The presented non-linearity in hydrological pro-cesses multiplies this even further: demand is increasing byabout 50 %, unmet demand by 370 %. In other words – theamount of unmet demand currently is relatively small com-pared to the future.

Comparing the results to other studies is complex giventhe unique nature of the approach presented here. The “2030Water Resources Group” (Addams et al., 2009) concludedthat, for the MENA region, the increase in demand wouldbe 99 km3 in 2030. The current study indicates that the in-crease in demand would be 74 km3 in 2030. It is howevernot completely clear what the “2030 WRG” study meansby “increase in demand”. The study sometimes refers to this

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3112 P. Droogers et al.: Water resources trends in Middle East and North Africa towards 2050

“increase in demand” when “water shortage” is meant, whichare obviously different terms. The current study makes aclear distinction between total demand in 2030 (335 km3), in-crease in demand in 2030 (74 km3), total unmet demandin 2030 (134 km3), and increase in unmet demand in 2030(91 km3).

A study by Wit and Stankiewicz (2006) estimated that cli-mate change will lead to a decrease in perennial drainagewhich will significantly affect surface water access across25 % of Africa by the end of this century. Their focus wasvery much on the nonlinear response of drainage to rainfall,and climate change will therefore most seriously affect re-gions in the intermediate, unstable climate regime. Our studypresented here took a more integrated approach instead of fo-cusing on drainage only.

The study “Economics of Adaptation to Climate Change(EACC)” (World Bank, 2010) concludes that developingcountries have to spend 0.12 % of their GDP in 2050 to over-come the negative impact of climate change. Although costsin 2030 will be lower compared to 2050, in terms of GDP thenumber is higher (0.2 %) as the economies are projected togrow substantially between 2030 and 2050. Actual amountsof changes in water demand and/or water shortages are notmentioned in the EACC study, while this is one of the mainoutcomes of the current study.

A comprehensive study on the options desalination mightoffer to overcome water shortage in the MENA basin in-cludes a water shortage analysis as well (Trieb and Muller-Steinhagen, 2008). The study projects an increase in totalwater demand from 270 km3 in 2000 to 460 km3 in 2050and that the demand-supply gap will increase from 50 km3

in 2000 to 150 km3 in 2050 for the region. The study was notbased on any hydrological modeling but on FAO statistics.Moreover, the study did not include climate change whichmight explain that their projected water shortages are lowerthan the results of the current study.

Further research work on these topics can go in two di-rections. First, a more detailed analysis can be undertakenif smaller areas, e.g. country level, would be studied usingthe same methodology (e.g. Droogers, 2009; Giertz et al.,2006). The second line of research to be undertaken is ananalysis on potential adaptation strategies and costs of theseadaptations using the developed framework (e.g. Agrawalaand Aalst, 2008; Droogers and Aerts, 2005).

Acknowledgement.We gratefully acknowledge comments andsuggestions that we have received from several anonymousreviewers and from the editor. The development of this paper hasbenefited substantially from their comments.

Edited by: S. Uhlenbrook

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