Integrated assessment of global water scarcity over the 21st century under multiple climate change

25 Pages·2014·4.37 MB·English

Hydrol.EarthSyst.Sci.,18,2859–2883,2014
www.hydrol-earth-syst-sci.net/18/2859/2014/
doi:10.5194/hess-18-2859-2014
©Author(s)2014.CCAttribution3.0License.
Integrated assessment of global water scarcity over the 21st century
under multiple climate change mitigation policies
M.I.Hejazi1,J.Edmonds1,L.Clarke1,P.Kyle1,E.Davies2,V.Chaturvedi1,M.Wise1,P.Patel1,J.Eom1,and
K.Calvin1
1JointGlobalChangeResearchInstitute,PacificNorthwestNationalLaboratory,CollegePark,Maryland,USA
2DepartmentofCivilandEnvironmentalEngineering,UniversityofAlberta,Alberta,Canada
Correspondenceto:M.I.Hejazi([email protected])
Received:31January2013–PublishedinHydrol.EarthSyst.Sci.Discuss.:13March2013
Revised:11June2014–Accepted:16June2014–Published:6August2014
Abstract. Water scarcity conditions over the 21st century demands than the amount of available water in a year (i.e.,
both globally and regionally are assessed in the context of thewaterscarcityindex(WSI)>1.0).Whencomparingthe
climatechangeandclimatemitigationpolicies,byestimating climatepolicyscenariostothebaselinescenariowhilemain-
both water availability and water demand within the Global tainingthesamebaselinesocioeconomicassumptions,water
ChangeAssessmentModel(GCAM),aleadingcommunity- scarcitydeclinesunderaUCTmitigationpolicybutincreases
integratedassessmentmodelofenergy,agriculture,climate, withaFFICTmitigationscenariobytheyear2095,particu-
and water. To quantify changes in future water availabil- larly with more stringent climate mitigation targets. Under
ity,anewgriddedwater-balanceglobalhydrologicmodel– the FFICT scenario, water scarcity is projected to increase,
namely, the Global Water Availability Model (GWAM) – is drivenbyhigherwaterdemandsforbio-energycrops.
developedandevaluated.Globalwaterdemandsforsixma-
jordemandsectors(irrigation,livestock,domestic,electricity
generation, primary energy production, and manufacturing)
aremodeledinGCAMattheregionalscale(14geopolitical 1 Introduction
regions, 151 sub-regions) and then spatially downscaled to
0.5◦×0.5◦ resolution to match the scale of GWAM. Using Struggling to understand the implications of global climate
a baseline scenario (i.e., no climate change mitigation pol- change,societyhasdevotedconsiderableattentiontoassess-
icy) with radiative forcing reaching 8.8Wm−2 (equivalent ing the potential consequences of climate change on dif-
totheSRESA1Fiemissionscenario)andthreeclimatepol- ferent human and Earth systems, and to devising adapta-
icy scenarios with increasing mitigation stringency of 7.7, tionandmitigationpoliciestominimizeanypotentiallydan-
5.5, and 4.2Wm−2 (equivalent to the SRES A2, B2, and gerous anthropogenic consequences. To clarify the longer-
B1 emission scenarios, respectively), we investigate the ef- termnatureofconnectionsbetweenhumanandnaturalEarth
fects of emission mitigation policies on water scarcity. Two systems,severalhigh-resolution,interdisciplinaryglobalin-
carbontaxregimes(auniversalcarbontax(UCT)whichin- tegrated assessment (IA) models have emerged and con-
cludes land use change emissions, and a fossil fuel and in- tributedsignificantlytotheIntergovernmentalPanelonCli-
dustrial emissions carbon tax (FFICT) which excludes land mateChange(IPCC)climatechangeassessmentreports.IA
use change emissions) are analyzed. The baseline scenario modelscurrentlyfocusonenergy,agriculture,landuse,and
results in more than half of the world population living un- climate.However,allassessmentstodatehaveimplicitlyas-
der extreme water scarcity by the end of the 21st century. sumed that emissions mitigation and adaptation to climate
Additionally,inyears2050and2095,36%(28%)and44% changewillbeunaffectedbywaterscarcity–yetwateravail-
(39%)oftheglobalpopulation,respectively,isprojectedto ability may pose a significant constraint on our ability to
liveingridcells(inbasins)thatwillexperiencegreaterwater adapt to or mitigate climate change. As an example, global
emission mitigation policies can significantly alter land use
PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
2860 M.I.Hejazietal.:Integratedassessmentofglobalwaterscarcityoverthe21stcentury
patterns, both by limiting land use change emissions, and al.,2007;Vivirolietal.,2007;Shenetal.,2008;Wadaetal.,
byincreasingbioenergyproduction.Thefutureofbioenergy 2011;Hayashietal.,2013)withestimatesofaboutone-third
crops, an important component of many technology strate- of world population currently living in water-scarce coun-
gies to reduce greenhouse gas emissions, may depend crit- tries(WMO,1997).Severalstudieshavealsoassessedwater
ically on water (Berndes, 2008; Varghese, 2007; Gerbens- scarcity conditions in the future, with water demands mod-
Leenes et al., 2009; Berndes, 2002). Thus, water availabil- eled as a function of socioeconomic assumptions, and wa-
ity could impose severe limitations on both emissions mit- teravailabilityderivedusingclimatemodels(Arnell,1999a,
igation and climate adaptation, making the ability to assess 2004;Vörösmartyetal.,2000;Alcamoetal.,2003b,2007).
theimplicationsofchangingsuppliesanddemandsforwater However,onlyonestudyhasinvestigatedtheimplicationsof
–stemmingfromasimultaneouslyevolvinghumanpopula- climatepolicyonglobalwaterscarcity(Arnelletal.,2011).
tion,economicsystem,technology,andclimate–animpor- Arnell et al. (2011) compares the percentage of the popula-
tantscientificdevelopment. tionlivinginscarceareasunderabaselinescenario(nocli-
Withrapidshiftstosocioeconomicsystems,especiallyin mate policy) and a climate mitigation of policy of 450ppm
the developing world, and potentially large-scale but vary- CO e(equivalentcarbondioxide)by2100.Inthispaper,we
2
ingimpactsofclimatechangeonglobalandregionalhydro- followasimilarapproachbystudyingtheeffectofdifferent
logicalcyclesoccurringsimultaneously,anintegratedassess- types and levels of climate change mitigation policies. In-
mentframeworkisexpectedtoproviderichinsightsintoal- stead of linking results from a set of models like Arnell et
ternative future global water demand and supply scenarios. al.’s(2011)study–whichusedtheIMAGEmodelandShen
Thus,anyquantificationofclimatechangeimpactsonwater et al.’s (2008) water withdrawals – we use linked water de-
scarcityisincompletewithoutaccountingforchangesinboth mandandsupplymodulesincorporatedwithinanIAmodel
water availability and demands for humans. Several studies to ensure consistency and the ability to pinpoint the system
have assessed the impact of climate change on hydrology componentsresponsibleforanyimportantobservedchanges.
and water availability with a general consensus that runoff This capability can provide greater insight about the impli-
increasesinhigherlatitudesanddecreasesinaridandsemi- cationsofclimatepoliciesonthedynamicsofnaturalEarth
aridregionswithmorefrequentextremesduetoanenhanced systemsandhelptoidentifyimportanttechnologicalinvest-
watercyclethroughwarming(Millyetal.,2005).Yet,much mentandadaptationmeasures.
uncertaintyremains,especiallywithregardtoGCMsimula- The primary goal in this study is to quantify, in an inte-
tionsusedtoforcethehydrologicsystem,duethelargedif- grated framework, the effects of climate mitigation policies
ferencesinrainfallpredictions. on global and regional water scarcity estimates, while ac-
Several global water management models coupled with countingforuncertaintyarisingfromusingdifferentGCMs
globalhydrologicmodelsarealsocapableofproducingwa- and following different types and constraining levels of cli-
ter use estimates generally categorized in domestic, indus- mate policies on water scarcity. Toward that goal, it is es-
trial, and agricultural sectors. However, there is potentially sential to model water demand and supply in a unified and
aninfinitenumberofunderlyingassumptionsthatcanrepli- internally consistent integrated assessment framework that
cateaparticularemissionscenariobutinducevastlyorsome- captures the inter-linkages and feedbacks with other natu-
what different water demand projections. Future global wa- ral and human systems, which can be exceptionally use-
teruseestimatesaregenerallydrivenbysocioeconomicand ful to assess the current and future picture of both global
technological change assumptions that reflect the storyline andregionalwaterscarcitymoreaccurately.Thisintegrated
of the emission scenario producing the GCM climatic forc- modelingframeworkfacilitatesestimationofwaterresources
ings (Alcamo et al., 2003b; Shen et al., 2008; Vörösmarty demands and supplies globally and regionally by explicitly
etal.,2000;Arnell,2004)orbyasetofassumptionsrepre- modelingtheeffectsandfeedbacksofbothnaturalprocesses
senting a wide range of socioeconomic pathways (Hejazi et (climatemodel,cropmodel,andlandusemodel)andhuman
al.,2013b).However,futurewaterdemandscanbeaffected systems(anthropogenicforcing,landusechange,energyde-
byclimatechangemitigationpolicies(i.e.,policiesdesigned mandsandtechnologies,socioeconomics,andmarket-based
to reduce emissions of greenhouse gases (GHGs)), which economies). Thus, in this study, we incorporate a new grid-
could feed back to affect the land and energy systems, and ded water-balance global hydrologic model (GWAM) as a
subsequentlyglobalwatersupplyanddemands(Falkenmark, newcomponenttotheGCAMwatersystem,andcombineit
1999;Vörösmartyetal.,2000;Jacksonetal.,2001).Inthis withspatiallydownscaledrepresentationsofalltheexisting
paper,wequantifytheeffectofvariousclimatechangemit- waterdemandsectors(Hejazietal.,2013a,b;Chaturvediet
igation policies on global and regional water demands and al., 2013; Davies et al., 2013) in GCAM to produce a dy-
scarcityconditions. namic,high-resolutionviewofglobal,annualwaterscarcity.
Several authors have assessed water scarcity conditions Next, we briefly describe GCAM. Then we present the
both globally and regionally at various spatial (e.g., grid, methods of modeling global water availability and de-
county,basin,country)andtemporal(e.g.,monthly,annual) mands in GCAM, with special attention to the downscaling
scales(Okietal.,2001;AlcamoandHenrichs,2002;Islamet techniques.
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M.I.Hejazietal.:Integratedassessmentofglobalwaterscarcityoverthe21stcentury 2861
3 GlobalWaterAvailabilityModel(GWAM)
3.1 Background
Forassessmentofclimateimpactsonwater,generalcircula-
tion models (GCMs) and their representation of the hydro-
logiccycleareoftenusedtoprovidespatiallyandtemporally
explicit quantification of changes in the water system over
the upcoming century. The first such numerical parameteri-
zation of the hydrologic cycle within a GCM was done by
Manabe(1969).However,theinitiallysimplisticandcoarse
representation of the hydrologic cycle within a GCM re-
sulted in poor hydrologic predictive skill (Kuhl and Miller,

Figure 1: Schematic of the GCAM systems with links to the water system 1992; Miller and Russell, 1992). To alleviate this short-
Figure1.SchematicoftheGCAM systemswithlinkstothewater
coming, GCMs have utilized land surface models (LSMs)
system.
with hydrologic capabilities to improve the representation
of runoff. Oki et al. (1999, 2001) argued that current LSMs
2 GCAM can simulate monthly river runoff quite well, provided that
theprecipitationandotherclimateforcinginputdataforthe
TheGlobalChangeAssessmentModel(GCAM)hasplayed LSMs are sufficiently accurate. However, many LSMs still
a central role in the United States and international assess- remain too coarse to capture some underlying hydrologic
ment processes since its inception (Edmonds and Reilly, processes.Thus,agroupofsub-gridparameterizationLSMs
1985; Clarke et al., 2007; Kim et al., 2006; Brenkert et al., have emerged (e.g., VIC (Liang et al., 1994, 1996), SIM-
2003).GCAMisadynamic-recursivemodelcombiningrep- TOP(Niuetal.,2005),andGBHM(Yangetal.,2001)).This
resentationsoftheglobaleconomy,theenergysystem,agri- groupofmodelsgenerallyrepresentsthesub-gridvariability
cultureandlanduse,andclimate.Exogenousinputsinclude (in soil moisture storage capacity, topography, and/or vege-
(amongothervariables)presentandfuturepopulation,labor tation)asaspatialprobabilitydistribution.
productivity,energyandagriculturaltechnologycharacteris- An alternative is the emergence of a family of global
tics, and resource availabilities. In its current implementa62- water-balancemodels(WBM/WBMplus(Vörösmartyetal.,

tion, GCAM has the following 14 geopolitical regions: the 1998, 1989), WATBAL (Kaczmarek, 1993; Yates, 1994,
United States, Canada, western Europe, Japan, Australia & 1996),WaterGAP(Alcamoetal.,2003a,1997),macroPDM
New Zealand, former Soviet Union, eastern Europe, Latin (Arnell, 1999b), IMPACT-WATER (Cai and Rosegrant,
America, Africa, Middle East, China [& Asian reforming 2002),FAO’smodel(Bruinsma,2003),LPJ/LPJmL(Gerten
economies],India,SouthKorea,andtherestofSouth&East etal.,2004;Rostetal.,2008),GEPIC(Liuetal.,2007,2009;
Asia. The model is calibrated to historical energy, agricul- Liu and Yang, 2010), WASMOD-M (Widén-Nilsson et al.,
tural, land, and climate data through the 2005 time period, 2007), WATERSIM (de Fraiture, 2007), H08 (Hanasaki et
and runs in 5-year time steps to 2095, establishing market- al., 2008a, b), and PCR-GLOBWB (Sperna Weiland et al.,
clearing prices for all energy, agriculture, and land markets 2010) that improve the ability to simulate water availability
such that supplies and demands of all modeled markets are onaglobalscalefromGCMclimaticforcings.Thosemodels
inequilibrium.GCAMtraditionallyincludesrepresentations have evolved from a vertical water balance at the grid scale
oftheeconomic,energy,andlandusesystems,andusesthe (typically0.5×0.5),toroutingwaterspatiallyfromonegrid
Model for the Assessment of Greenhouse-gas Induced Cli- celltoanother,incorporatingcropgrowthmodels,andmod-
mate Change (MAGICC) (Raper et al., 1996; Wigley and eling global water management components (e.g., reservoir
Raper, 1992, 2002) as its reduced-form representation of management,humanwateruse).However,notallsuchmod-
the atmosphere, ocean and climate systems. The water sys- elsincorporateallofthesedetails,andtheyvarywithregard
temincludingbothdemandandsupplyareincorporatedinto totemporalandspatialscale,runoffmechanism,cropgrowth
GCAMasshowninFig.1.InthecurrentGCAMform,water modelingscheme,andtherepresentationofhumansystems.
doesnotholdanymonetaryvalue,andwhendemandexceeds In general, global water balance models compute the water
thewateravailability,additionalsupplyisassumedtocome fluxesandrelatedprocesses,andemploysoilwaterbalances
fromnon-renewablegroundwater. basedonclimate,landcover(cropland,pasture,naturalveg-
etation) and soil information on a grid cell basis, typically
at 0.5◦ resolution. In several of the models that focus on
agriculture,soilmoisturethresholdsaresetaccordingtothe
irrigation triggered. Some models, such as GEPIC, extend
further to include nutrient cycling, tillage and agronomics,
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2862 M.I.Hejazietal.:Integratedassessmentofglobalwaterscarcityoverthe21stcentury
simulating the effects of different agricultural management tion of 0.5◦×0.5◦, and a monthly time step. Water routing
options (Hoff et al., 2010). Some models (GEPIC, LPJmL) capabilities and reservoir operation rules are not included.
internallycalculatecropyields,whileothermodels(GCWM, The water supply model is first evaluated against observa-
WBMplus)use(mostlycountry-based)cropproductiondata tionaldataandothermodels,andthensimulatedintothefu-
fromagriculturalstatistics.Inthisstudy,thelatterapproach turetoprovideestimatesoftotalwatersupplyuptotheend
isused. ofthe21stcentury.
Waterbalancemodelstypicallyusemonthlyclimatedata Theglobalwateravailabilitymodelprovidesestimatesof
from the Climate Research Unit (CRU, University of East renewable freshwater resources in the form of surface flow.
Anglia)tosimulatethehydrologicprocesses,butsomemod- Thisindicatesthemaximumtheoreticalamountofwaternat-
els temporally downscale the climate input data to daily urally available in a year for each of the basins. In reality,
scale to better model crop growth and irrigation water de- some of the runoff flows too quickly to saline water bod-
mand. Land use is generally based on the Ramankutty et iesoroccursinremoteareaswherethereisnopotentialfor
al.(2008)distributionandextentofcroplandandpermanent peopletouseit.Thus,almostallrecentstudieshaveassessed
pasture. Areas equipped for irrigation are generally taken waterscarcityconditionsusingwaterscarcityindicessuchas
from Siebert et al. (2007) and crop types from Monfreda thatofFalkenmark(Falkenmark,1989)andRaskin(Raskin
et al. (2008) or Portmann et al. (2008). Generally, land use et al., 1997). Falkenmark (1989) assumes that a country or
is assumed constant over the duration of the simulation pe- regionexperiencessomewaterstresswhenannualwatersup-
riod. Furthermore, different approaches for calculating po- pliesdropbelow1700cubicmetersperpersonperyear,and
tential evapotranspiration (PET) are typically used in dif- faces water scarcity when they drop below 1000 cubic me-
ferent models, e.g., Penman–Monteith (GCWM),Priestley– ters per person per year. Raskin et al. (1997) compare total
Taylor (GCWM, IMPACT, LPJmL, WaterGap), Hargreaves waterdemandtothetotalamountofrenewablewateravail-
(GEPIC), Hamon (WBMplus), or bulk formulas (H08). able,anddefineextremewaterscarcityinanyregionasde-
Zomer et al. (2006) applied five methods of estimating po- mand in excess of 40% of total water availability; Wada et
tential evapotranspiration to South America and Africa and al. (2011) provide a detailed summary of previous studies
found that the Hargreaves method (Hargreaves et al., 1985) thathaveusedthedefinitionofRaskin.Inregionswherethe
comparedfavorablywiththeFAOPenman-Monteith,butre- totalwaterdemandsexceedthetotalstableflowofrenewable
quired less parameterization, and with improved robustness water,humanshavetappedintoothersourcesthatareeither
to error in climatic inputs (Hargreaves and Allen, 2003). quiteexpensive(desalinationofbrackishandsalinewater)or
Thus, to determine agricultural water consumption in this unsustainable(fossilgroundwaterabstractions)(Wadaetal.,
study,weusetheHargreavesmethod. 2010; Gleeson et al., 2012). In this paper, we only consider
renewablewaterresources(i.e.,annualflowofrivers)inthe
3.2 Modeloverview waterscarcitycalculations.
In this study, a global hydrologic model, GWAM, is con- 3.3 Modelstructure
structed and evaluated. GWAM is designed to retain con-
sistency with the approaches used in current state-of-the- TheGlobalWaterAvailabilityModel(GWAM)isagridded
art hydrological models, but with several modifications and monthlywaterbalancemodelwitharesolutionof0.5◦×0.5◦
simplifications in order to allow incorporation into GCAM. (Fig. S1 in the Supplement). It requires gridded monthly
Components that are excluded in GWAM (as compared to precipitation, temperature, and maximum soil water stor-
mostexistingglobalhydrologicmodels)areriverandreser- age capacity (a function of land cover), and computes the
voir routing, and a crop growth model that is simulated at amountsofevapotranspirationtotheatmosphere,runoff,and
thesametemporalandspatialscaleasthehydrologymodel. soilmoistureinthesoilcolumn.Themodelstructureiscon-
The former component is generally an important feature to sistent with existing global water balance models, and with
achieve accurate monthly estimates of runoff, but it is less the FAO’s model formulation for modeling water resources
crucial when accounting for water on an annual basis and in Africa (FAO, 2001). GWAM tracks the fraction of rain-
oververylargefirst-orderbasinsthatdraindirectlyintonon- fallthatfeeds intothesoilcolumn (green water)andrunoff
freshwaterbodies.Thisstudyincorporatesacropmodelsim- (bluewater)atamonthlyscale.Themodelaccountsforthe
ilartotheFAOapproach(FAO,2001)sincetheagricultural monthly green water storage and estimates the fraction of
water demand module is repeatedly calculated as GCAM green and blue water that is evaporated back to the atmo-
solves all the markets in a particular time period, thus re- sphere through evapotranspiration from vegetation and cul-
ducingthecomputationalburdentremendously. tivated lands and evaporation from bare soil or water bod-
GWAMreproduceshistoricalstreamflowobservationsand ies.Themaximumsoilmoisturestoragecapacity(S )with
m
simulates the future availability of freshwater under both a a resolution of 0.5◦×0.5◦ is obtained from the soil map
changingclimateandanevolvinglandscapewithcompeting of the world and soil properties (FAO, 1998, 2003). Infor-
water users. The model is grid-based, with a spatial resolu- mation with regard to the maximum soil moisture storage
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M.I.Hejazietal.:Integratedassessmentofglobalwaterscarcityoverthe21stcentury 2863
capacity in mmm−1 is derived from the derived soil prop- whereγ isaunitlessdryingfunctionofthesoilcolumnfol-
erties of the Digital Soil Map of the World, which contains lowingtheWBMformulation(Vörösmartyetal.,1998).
rasterinformationonsoilmoistureindifferentclasses(FAO,
1998, 2003). Maximum available soil moisture is estimated 1−e−αSSt−m1 
fromestimatesofrootdepth,fieldcapacity,andwiltingpoint γ = , (6)
1−e−α
values(typicallyrangesbetween15–350mmm−1).Theroot
depth estimate is itself a function of land cover and water
whereαisanempiricalconstant(setto1.0)andγ isthesoil
stressconditions.Inthisstudy,astaticS mapovertimeis
m
and vegetation-dependent available water capacity (Vörös-
assumed.
martyetal.,1998).
Aswithanywaterbalancemodel,theconservationofmass
Finally,havingestimatedAET andS andgiventheval-
is observed following Eq. (1). That is, the amount of stor- t t
age in the soil column (S ) is the sum of initially available uesofSt−1(estimatedinprevioustimestep)andPt (aninput
t
variable),Eq.(1)canberearrangedtocomputeQ .
waterinstorage(St−1)plustheamountofprecipitationthat t
fallsduringthemonth,minustheamountofwaterthatevapo-
Qt =St−1+Pt−AETt−St (7)
ratesbacktotheatmospherethroughevaporationfromwater
bodies or the soil, or through transpiration from vegetation
Thenthestoragevalueofeachgridcellisupdated,thetime
(AET )andtheamountofrunoff(Q )duringthemontht.
t t index is incremented by 1 month, and the calculation pro-
cedure is repeated until the end of the simulation period.
St =St−1+Pt−AETt−Qt (1)
Figure S2 in the Supplement shows a detailed flowchart
More specifically, for any given month and grid cell, the schematicofthemodelcalculationalgorithm.Detailsofcal-
amountofwateravailableinanymontht isafunctionofthe culatingmonthlyPET(Hargreavesmethod)aresummarized
amount of new precipitation P , the amount of water avail- inAppendixA.
t
ableinstorageSt−1,andpotentialevapotranspirationPETt. TosimulateGWAMbothhistorically(1901–2002)andin
However,theamountofwateractuallyreturnedtotheatmo- thefuture(2001–2100),griddedmonthlyclimaticinputvari-
sphereisgenerallylessthantheamountpotentiallyavailable ables are taken from the Climate Research Unit (CRU TS
duetowaterstresses;i.e.,theamountofwateravailableinthe 2.0(Mitchelletal.,2004)),andfromtheTyndallCentrefor
soilcolumnislessthantheestimatedPET valueinapartic- ClimateChangeResearch(TYNSC2.1(MitchellandJones,
t
ulargrid(aneffectparticularlyapparentindesertandsemi- 2005)),respectively,bothattheUniversityofEastAnglia.
aridregions).Thus,theamountofactualevapotranspiration
3.4 Modelevaluation
(AET )ismodeledintheoryasafunction(Eq.2)ofthepo-
t
tentialevapotranspirationasderivedfromclimaticforcings,
Prior to simulating GWAM into the future, the model abil-
adjusted based on the water availability in a particular grid
itytoreproducehistoricalvaluesisevaluatedagainstobser-
foraparticularmonth.Anonlinearequation(Eq.3)isused
vations, statistical assessments, and other global hydrologic
todetermineactualevapotranspirationasafunctionofPET models.Globally,themeanannualrunoffof38587km3yr−1
andtherelativesoilmoisturestateinagridcell,accordingto
issimilartopreviousdata-basedandmodel-basedestimates,
Kaczmarek(1993).
as shown in Fig. S3 in the Supplement. Figure S4 in the
AET =β·PET (2) Supplement shows a comparison between our average an-
t t
nualrunoffatthecontinentalscalewithotherdata-basedand
modelestimates.Thesimulatedglobalandcontinentalmean
5·(cid:16)St−1(cid:17)−2·(cid:16)St−1(cid:17)2 annual runoff ranges correspond to the ranges of previous
β= Sm Sm  (3) studies.Therangebararoundourestimatesdenotesthemax-
 3  imumandminimumannualrunoffduringthehistoricalsim-
ulationperiod(1901–2002)fortheglobal(Fig.S3intheSup-
plement)andcontinental(Fig.S4intheSupplement)scales.
Inpractice,weestimateAET followingEq.(4).
t
Notethat,globally,data-basedestimatesarehigheronaver-
AET = βP·EPTEtTt 0≤SStt−−11++PPtt−−PβE·TPtE≥TtS<mSm (4) athgeegthloabnaml aonddelc-obnastiendenetsatilmscaateles.sTcahneaorbisseervdeudedtoiffvearreinatcieonast
t  S0t.−1·1P+ETPt St−1+Pβt−≤β0·.1PETt <0 inthesimulationperiodandmodelingapproach.Discrepan-
t
ciesmayalsoariseduetoinconsistencyofspatialcoverage.
Once AET is estimated, the current amount of storage can For example, data-basedestimates generallycover smallis-
t
becomputedfollowingEq.(5). lands,whilemodel-basedestimatesignorethoseregionsdue
to lack of appropriate spatial resolution, as is the case with
( Sm St−1+Pt−PETt ≥Sm GWAM’sscaleof0.5◦×0.5◦,whichisgenerallytoocoarse
St = γ·St−1+0Pt−AETt γ·St−1o+thPetrw−isAeETt <0 , (5) tomodelsmallislands.Furthermore,somestudies,including
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2864 M.I.Hejazietal.:Integratedassessmentofglobalwaterscarcityoverthe21stcentury

FFiiggureu 2:r Ceom2par.isoCn oof tmhis sptuadyr’si rsunoofnf vootlhuemf ceostu thnot rFiyAs aOn dsa nGtduC oAtdhMeyr rme’gosidoenlra-lbu sacsnaeldeo sesftifmavteso (el.gu., mWBeMs, WtBoMc,F anAd WOGHaMn) adt
othermodel-basedestimates(e.g.,WBM,WBMc,andWGHM)at
thecountryandGCAMregionalscales.
ours, do not include Greenland or Antarctica in the globa6l3

andcontinentalestimates.Oneadditionalsourceofvariation
at thecontinental scalemay be dueto how different studies
split Russia between Asia and Europe. Table 1 summarizes
theglobalandcontinentalestimatesfrom theliterature.
Figure3.Comparingsimulatedrunoffforthemajorbasinsofthe
Next,wecompareourresultsinFig.2Fwigiuthret h3o: sCeoomfpseavri-ng simulated runoff for the major basins of the world against observed values
worldagainstobservedvalues(fromGRDC).
eral other global hydrologic models (e.g., WBM (Fekete et (from GRDC)
al., 2000), WBMc (Fekete et al., 2000), and WGHM (Döll
and Fiedler, 2008)), and with the FAO AQUASTAT esti- tionisextensive(especiallyagriculturalwaterconsumption)
mates of renewable water resources by country, aggregated relativetowatersupply.
by GCAM regions in the year 2005 (AQUASTAT, 2012).
SpecificestimatesforthecountryandGCAMregionalscales 3.5 GWAMfuturesimulations
are summarized in Tables 1 and S1 in the Supplement, re-
GWAM is simulated over the entire 21st century using the
spectively. Table 2 presents estimates of the models’ depar-
SRES(2000)A1Fi,A2,B1,andB2emissionscenariosand
ture from FAO’s estimates at the country scale (only coun-
data from four GCMs (HadCM3, CSIRO2, CGCM2, PCM)
triesforwhichallfourmodelshavevaluesareused)andthe
(TYN SC 2.1; Mitchell and Jones, 2005) to result in 16 in-
GCAMregionalscale;modeldepartureismeasuredinterms
dividualsimulationsofGWAM(4GCMs×4emissionsce-
ofrootmeansquarederror(RMSE).Overall,allmodelsex-
narios). In this study, we use the GCM ensemble mean an-
hibitasimilarrangeofgoodness-of-fit(Table3).Variations
nual runoff to establish the amount of maximum theoreti-
amongthemodelsmaybeduetovariationsintherepresenta-
callyavailablerenewablewateratanygridcellinanymonth.
tionofrainfall-runoffgenerationandhowPETiscomputed.
GCM data are also used to quantify the effect of climate
Finally, we compare our simulation results of historical
model uncertainty on the amount of water availability, and
average annual runoff for 29 major basins of the world
subsequently,waterscarcityconditions.Nextwedescribethe
against observed values from the Global Runoff Data Cen-
globalwaterdemandmodel.
tre(GRDC,1999)–seeTablesS2andS3intheSupplement
for a complete summary of our results in comparison with
severalotherdata-basedandmodel-basedestimates.Figure3
4 GCAM–GlobalWaterDemandModel(GWDM)
showsascatterplotofthebasinscaleresultsincomparison
toseveralothermodelresults.Overall,modelstendtoover- 4.1 Modeloverview
estimate small annual runoff values for the selected basins.
Variations again could arise due to several factors. Delin- Recently, Hejazi et al. (2013b) explicitly incorporated sec-
eateddrainageareasaresomewhatinconsistentandareadif- toralwaterdemandmoduleswithintheframeworkofGCAM
ferencesarerelativelylargeforsomebasins(thisisnotacon- to estimate the amount of freshwater demanded on an an-
64
cernforthecomparisonwithWBMandWBMcbecausethey nual basis. The water demand modules account for water

usethesamedrainageareas).Second,thetimeofrecordand use for irrigation, livestock, domestic purposes, electricity
simulationperiodsdifferacrosssources.Third,thisstudyas- generation, primary energy production, and manufacturing.
sumesnaturalconditionswhileobservedrunoffdatacapture The modules have been constructed, calibrated, and eval-
otherfactors(e.g.,humanactivities).Thus,GWAMgenerally uated (Hejazi et al., 2013b). Future agricultural water de-
overestimateswateravailability(i.e.,annualrunoff)ascom- mands are driven by crop production from GCAM and the
pared with observed data in basins where water consump- share of crop production that takes place on irrigated lands,
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M.I.Hejazietal.:Integratedassessmentofglobalwaterscarcityoverthe21stcentury 2865
Table1.Summaryofliteratureestimatesofglobalmeanannualrunoffusingbothmodelsanddata-basedestimatesatboththeglobaland
continentalscales.
North South Data/
Reference Europe America Africa Asia America Oceania Global Modeltype
BaumgartnerandReichel(1975) 2564 5840 3409 12467 11039 2394 37713 Data
Korzunetal.(1978) 2970 8180 4600 14100 12200 2510 44560 Data
L’vovich(1979) 3110 5960 4225 13190 10380 1965 38830 Data
WMO(1997) 2900 7890 4050 13510 12030 2404 42784 Data
Shiklomanov(1997) 2900 7770 4040 13508 12030 2400 42648 Data
GRDC(2004) 3083 6294 3690 13848 11897 1722 40533 Data
WRI(2005) 6591 7461 12380 1693 43219 Data
FAO(AQUASTAT2010) 6548 6858 3931 12413 12380 892 43022 Data
Okietal.(2001) 2191 3824 3616 9385 8789 1680 29485 LSM-TRIP
Weilandetal.(2010) 2175 4803 5099 10572 10678 2371 35698 GCMensemble
Alcamoetal.(1997) 4333 4057 10471 33973 WaterGAP1
Cogley(1998) 42353 Data
Feketeetal.(2000) 2822 5396 5567 11425 11240 1308 37758 WBM
Feketeetal.(2002) 2772 5892 4517 13091 11715 1320 39307 WBMc
Dolletal.(2003) 2763 5540 3529 11234 11382 2239 36687 GWHM
Gertenetal.(2004) 40143 LPJ
Widén-Nilssonetal.(2007) 3669 7009 3738 13611 9448 1129 38605 WASMOD-M
DollandFiedler(2008) 3104 6493 4065 13168 11310 1272 39414 WGHM
Weilandetal.(2010) 2143 5249 5573 11461 11186 2633 38245 PCR-GLOBWB
Hanasakietal.(2010) 41820 H08
Thisstudy 3194 6108 5217 13066 9854 1148 38587 GWAM
by up to 18 agro-ecological zones (AEZ) within 14 geopo- However, the spatial resolution of these existing systems in
liticalregions(151sub-regions),andbycroptype(12types) GCAMisnotadequatefordetailedanalysisofthewatersys-
– see Chaturvedi et al. (2013) and Hejazi et al. (2013b) for tem; to link these systems, spatial downscaling of GCAM
greaterdetails.Futuremanufacturinganddomesticwaterde- output is necessary. In this paper, we separately downscale
mands (Hejazi et al., 2013a) are driven by scenario-specific irrigation water demands, livestock water demands, and all
socioeconomicassumptions(e.g.,population,GDP),among remainingwaterdemands(e.g.,municipal,electricitygener-
other factors. Water demands for primary energy are scaled ation, primary energy, and manufacturing water demands).
with the amount of each fuel produced, and water demands For the latter, we use population to downscale water de-
forsecondaryenergy(electricity,refinedliquidproducts)de- mandestimatesfromthe14GCAMregionstothegridscale
pend on the specific production technologies used. In the (0.5◦×0.5◦).UsingtheWWDR-IIpopulationdata(Elvidge
electricsectorinparticular,waterusedependsnotonlyonthe etal.,1997a,b;ESRI,1993;Tobleretal.,1995)andfollow-
generationtechnologymix,butalsoonthetypesofcooling ing the work of Wada et al. (2011), the population gridded
systems used, for which we make explicit assumptions (see mapisconvertedtopopulationdensitymaps,i.e.,thesumof
Davies et al., 2013). Thus, in this study, water demands are allgridswithineachGCAMregionaddsupto100percent.
modeled for the agricultural, industrial, and municipal sec- Assuming that population density maps remain static over
tors,usingtechnology-basedrepresentationswherepossible. time within each GCAM region, and using the population
Figure1showshoweachofthesixwaterdemandsectorsare projectionsforeachGCAMregion,aglobalmapofgridded
linkedtotheexistingsystemsinGCAM. population is generated for every GCAM modeling period
Next, we downscale the GCAM water demand results to (e.g., 2005, 2010, ..., 2095). Note that in this study, water
gridscaleusingavailablespatialassessmentsofpopulation, scarcityconditionsareassessedatthegridandbasinscales.
livestock,andirrigation. Notealsothatthedownscalingtechniquedoesnotallowfor
populationdiffusionintocurrentlyunpopulatedareas,anas-
4.2 Spatialdownscalingtogridscale sumption which may tend to overestimate water scarcity at
thegridscale.
Similarly, irrigation water demand is downscaled to a
A fundamental step in this study is to link the global wa-
0.5◦×0.5◦gridusingexistingglobalcoverageofgriddedin-
tersupplyanddemandmodelsdescribedabovetoGCAM’s
formationofcropareasequippedwithirrigationintheyear
existing energy, agricultural, and socioeconomic systems.
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2866 M.I.Hejazietal.:Integratedassessmentofglobalwaterscarcityoverthe21stcentury
Table2.Comparisonoftotalmeanannualrunoffineachofthe14GCAMregionswithFAOestimatesandtwootherglobalhydrologic
models(WBMandWGHM).
Dolland
Feketeet Feketeet Fiedler(2008)
Area FAOEstimates al.(2000)(WBM) al.(2000)(WBMc) (WGHM) Thisstudy
GCAMregionname
1000ha km3yr−1 km3yr−1 km3yr−1 km3yr−1 km3yr−1
1 USA 964095 2825 2215 2303 2382 2259
2 Canada 998491 2850 2067 2494 2702 2751
3 WesternEurope 456723 2071 1564 1567 1591
4 Japan 37791 430 375 380 367 265
5 Australia&NZ 800977 819 520 545 1057 1016
6 FormerSovietUnion 2230727 4731 3286 4043 4920
7 China 1179691 3395 2126 2608 2267 2475
8 MiddleEast 559724 184 160 167 148
9 Africa 3031518 3966 5518 4469 5217
10 LatinAmerica 2051763 13575 12004 12459 10899
11 SoutheastAsia 637481 6,98 5,52 5,98 5,06
12 EasternEurope 116771 273 302 284 333
13 Korea 9,965 65 55 60 51 47
14 India 328726 1280 1264 1491 1435 1399
Total 13404443 43262 37008 38568 39414 38527
2000, as well as the corresponding fraction of coverage of 5 Waterscarcity
each grid area (both from Siebert et al., 2007) and by crop
type(fromPortmannetal.,2008).RecallthatGCAMcom- Next, we quantify the effect of climate change on water
putesirrigationwaterdemandattheAEZscale(151regions scarcitybothgloballyandineachoftheGCAMregions.To
globally). Also, the amounts of new irrigated lands in each estimatewaterscarcity,weadoptRaskin’sdefinitionofwater
crop(AEZ),GCAMregionandthecorrespondingtotalirri- scarcity(Raskinetal.,1997)astheratiooftotalwaterwith-
gationvolumeareknownfromGCAMforeach5-yearinter- drawal(TWW)tototalwateravailability(TWA);i.e.,WSI=
val period between 2005 and 2095. First, starting with the TWW.Thisdefinitionofwaterscarcityissometimesreferred
TWA
base year estimates, the fraction of irrigated land in each to in the literature as the water resources vulnerability in-
grid is scaled up linearly to account for the additional ir- dex(WRVI),thewithdrawal-to-availability(WTA)ratio,and
rigated lands. When the fraction of irrigated land in a grid the criticality ratio (Brown and Matlock, 2011). Following
cell reaches 100%, the remaining additional irrigated lands previouslysuggestedthresholdsforwaterscarcityconditions
aredistributeduniformlyacrosstheremaininggridcellsthat (Falkenmark,1999;Falkenmarketal.,2007),WSIisdivided
areclassifiedasequippedwithirrigation.Whenallgridcells into four categories: no scarcity (WSI<0.1) low scarcity
equippedwithirrigationarefullyirrigatedorwhenirrigation (0.1≤WSI<0.2), moderate scarcity (0.2≤WSI<0.4) and
emergesinsub-regionalAEZswherenogridcellsequipped severescarcity(WSI≥0.4).Tobeconsistentwiththetempo-
withirrigationcurrentlyexist,irrigatedlandsaredistributed ralandspatialscaleofGWAM,waterdemand(withdrawals)
uniformly across all arable lands. Using the density map of resultsaredownscaledfromtheGCAMoutputscale(GCAM
the share of irrigated lands and the required irrigation vol- 14regions,or151AEZscale)toestablishwaterscarcityfor
ume, and projecting them into the future, one can estimate eachgridonameanannualbasis.
theamountofirrigationdownscaledtothegridscale.
Todownscalelivestockwaterdemand,wefollowthework
of Alcamo et al. (2003a), Flörke and Alcamo (2004), and 6 Policyscenarios
Wadaetal.(2011)inutilizingthegriddedglobalmapsofes-
timatedlivestockdensityforsixmajorlivestocktypes(cattle, 6.1 Baselinescenario(noclimatepolicy)
buffalo, sheep, goats, pigs, and poultry) in year 2000 (Wint
andRobinson,2007).Projectingintothefuture,theamount The baseline scenario reflects a world of 14 billion peo-
ofwaterusedisscaledup,assumingthedensitymapsremain ple, slow technological progress, high energy demands,
staticwithineachGCAMregion. and no climate policy. More specifically, going from year
2005 to year 2095, population increases from 6.5 billion to
13.7 billion, GDP increases from 30 trillion to 384 trillion
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M.I.Hejazietal.:Integratedassessmentofglobalwaterscarcityoverthe21stcentury 2867
(USD1990), and per capita GDP increases from 4607 to orasetofGCMstomatcheverypolicyruncanbecomputa-
28136 (USD1990), while energy consumption globally in- tionallyexpensive.Onealternativeistheuseofdownscaling
creases from 458EJ to 2236EJ. This scenario is equivalent techniquestomatchexistingGCMrunstothepathwayofin-
to the radiative forcing pathway associated with the SRES terest,anapproachthatistypicallyadequatewhenperform-
A1Fiscenario.TheadoptedSRESA1Fiscenarioreflectsan ing pattern-scaling on temperature but that has known defi-
extremescenariowithnomitigationandissomewhatsimilar ciencies when applied to precipitation (Arnell et al., 2011;
to the POP14/MDG− scenario in Hejazi et al. (2013b). To Cabré et al., 2010). A second alternative is to use existing
exploretheconsequencesofclimatechangemitigationpoli- GCM runs and then force GCAM to reproduce the equiva-
cies on water scarcity, we employ the same socioeconomic lent radiative forcing trajectory through a particular climate
andtechnologicalchangeassumptionswithbothasetofpol- policy. Given that the focus of the study is to explore the
icy scenarios and the baseline scenario. This permits isola- implications of climate change mitigation policies on water
tionofhuman-inducedclimatechangefrompopulation-and scarcity, we follow the latter approach in this paper. Thus,
income-basedeffectsonwaterscarcity. wedeviseclimatepoliciesinGCAMthatreproducethefour
SRESradiativeforcingsscenarios,whereA1Fiisreferredto
6.2 Climatechangemitigationpolicyscenarios asourbaselinescenario(noclimatepolicy),andA2,B2,and
B1 are three climate policy scenarios with increasing mit-
In GCAM, policies designed to mitigate climate change igation stringency of 7.7, 5.5, and 4.2Wm−2 in year 2095,
are implemented in regions either as greenhouse gas emis- respectively.FigureS5intheSupplementshowstheradiative
sions prices, oras constraints on greenhousegas emissions, forcingtrajectoriesbasedoneachofthefourSRESemission
whereinthemodelsolvesfortheemissionspricesnecessary scenarios(A1Fi,A2,B2,andB1)andeachoftheUCTand
to meet the constraints. These emissions policies may price FFICT tax regimes to replicate SRES’s emission pathways.
terrestrialcarbondioxideemissionsortheymaynot:Wiseet Figure S6 in the Supplement shows the corresponding car-
al.(2009)usetwocanonicalcarbontaxregimes,(1)aUCT bonprice,CO2emissionandconcentration,andmeanglobal
regimewhichincludesallcarbonemissionsinallsectors(in- temperaturechangeassociatedwithboththeUCTandFFICT
cludinglanduseemissions)andallregionsoftheworld;and tax regimes and all four emission scenarios (A1Fi, A2, B2,
(2) an FFICT regime which includes only fossil fuel and and B1) when applicable. Note that the much lower reduc-
industrial emissions, but not land-use-related carbon emis- tions in emissions under the FFICT scenario, as compared
sions.Underbothregimes,thecarbontaxrisesovertimeto to the UCT scenario, is a result of the energy portfolios of
limit atmospheric CO concentrations to a prescribed stabi- eachpolicyregime,whereFFICTtypicallyresultsinamore
2
lizationlevel.However,thedifferenttypesofpoliciesleadto pronounced contribution of bioenergy to energy than UCT.
dramatically different outcomes for bioenergy deployment, Alsonotethatthemuchhighercarbonpriceassociatedwith
landusechangeemissions,andconsequentlygreenhousegas the more stringent FFICT climate mitigation policies than
emissionsandclimatechange.TheFFICTcaseischaracter- thoseobservedincurrentcarbonmarketsresultsinasignifi-
ized by very high deployment of bioenergy and associated cantchangeingloballanduse(i.e.,biomassexpansion).The
land use change emissions, which lead to greater emissions A1Fiscenario(blacksolidline)reflectstheno-climatepolicy
and climate change than the UCT case (Wise et al., 2009). scenario(baseline).
However, in all previous results of various climate policy
regimes and stabilization levels, water was never included
as a potentially limiting resource for bioenergy production 7 Resultsanddiscussion
or any other activities. In this paper, with the assumption
of unlimited non-renewable groundwater and no monetary 7.1 Wateravailability
valueattachedtowateruseinGCAM,wedonotyetmodel
thefeedbacksthatresultwhenwaterdemandexceedsthere- To reflect the effect of climate change on water availability,
gional water availability. Instead, we simply assess the im- GWAM is simulated over the 21st century with four GCMs
pact of different target levels of climate change mitigation (PCM,CGCM2,CSIRO2,andHADCM3)andfouremission
policiesonwaterscarcitybothgloballyandregionally. scenarios (A1Fi, A2, B1, and B2), totaling 16 simulations.
To investigate how different climate change mitigation The use of multiple GCMs facilitates the quantification of
policies could impact future water scarcity conditions, we uncertainty associated with GCM results which feed in as
have two means to ensure consistency between the selected input to GWAM. However, results are generally presented
climatepolicyandthecorrespondingGCMforcingsusedto byconsideringtheirensemblemeanwithequalweights.The
simulateGWAM.Onewayistospecifytheemissionortem- four emission scenarios represent the baseline scenario and
peraturechangetarget–suchas450partspermillion(ppm) three climate policy scenarios with various levels of GHGs
CO eor2◦Cbytheendofthe21stcentury–andthentouse emission reduction targets. Figure 4 shows GWAM’s simu-
2
aGCMrunthatreflectsthesameradiativeforcingstrajectory lationsofglobalannualprecipitation,actualevapotranspira-
asinGCAM.ThiscanbeachallengesincerunningaGCM tion,andrunofffrom1901–2100;futurevaluesarefromthe
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2868 M.I.Hejazietal.:Integratedassessmentofglobalwaterscarcityoverthe21stcentury
Figure 4. Global annual precipitation, actual evapotranspiration, and runoff from 1901–2100; future values are from four GCM s (PCM,
CGCMF2ig,uCreS 4I:R GOlo2b,ala anndnuHaAl pDreCciMpit3at)ioann,d acftourale emvaispsoitorannsscpeirnatairoino,s an(Ad r1ufin,oAff 2fr,oBm1 1,9a0n1d – B2120)0.; future values are from four GCMs (PCM, CGCM2,
CSIRO2, and HADCM3) and for emission scenarios (A1fi, A2, B1, and B2)
four GCMs and four emission scenarios. Globally, on land,
the annual amounts of precipitation and actual evapotran-
spiration (ET) exhibit an upward trend for all the selected
GCMsandemissions.However,onlyPCMandCSIRO2ex-
hibit an upward trend in runoff as well, while CGCM2 and
65
HADCM3showaslightdecreasingtrendinrunoff.Tounder-

stand the magnitude of potential shifts in water availability
due to climate change and the level of variation from using
differentGCMsandemissionscenarios,wecompareannual
precipitation,actualET,andrunoffestimatesinyears2000,
2050, and 2095 (Fig. 5). These time periods are averaged
overthe1996–2002,2046–2055,and2091–2100periods,re-
spectively,tofilterouttheyear-to-yearvariations.Although
eachoftheemissionscenariosisassociatedwithaparticular
warming level, the global amount of water availability does

not show a consistent trend with temperature change across FFigiugreu 5:r Coemp5a.risCon oof mannpuaal prreicsipoitantioon, factauanl EnT,u anadl rupnorffe ecstiimpatietsa actriooss nGC,Mas cantdu emailssiEonT sce,naarniosd in ryeuarns 2o00f0f, 2050, and
theGCMmodels.Thelevelofuncertaintyarisingfromem- 2095; uncertainty across GCMs is larger than scenarios, and the uncertainty level increases with time, i.e., uncertainty is larger in 2100 than 2050
estimatesacrossGCMsandemissionscenariosinyears2000,2050,
66
ploying different GCMs and emission scenarios increases a nd2095;uncertaintyacrossGCMsislargerthanscenarios,andthe
over time; i.e., uncertainty is larger in 2095 than in 2050 uncertaintylevelincreaseswithtime,inotherwords,uncertaintyis
(Fig. 5). Also, the uncertainty across GCMs is larger than largerin2100than2050.
across scenarios. Hence, averaging across GCMs to reflect
aparticularemissionscenarioreducesvariabilityandresults
in a closer match to the historical average runoff, although Southeast Asia and Australia). Those regional patterns are
spatialvariationsremainhighinmanycases.Figure6shows consistentacrossallfouremissionscenarios,andsomewhat
thedifferenceinensemblemeanrunoffatthegridscalebe- similar to the finding of Arnell (2004), who found a reduc-
tweenyears2095and2000foreachoftheoffouremission tioninrunoffinmuchofEurope,theMiddleEast,southern
scenarios.Althoughgloballyhumansmayendupwithmore Africa, North America and most of South America, and in-
availablewaterthroughrunoffglobally,whenlookingatthe creasing runoff in high-latitude North America and Siberia,
spatial distribution of gains and losses in freshwater, there eastern Africa, parts of arid Saharan Africa and Australia,
are clear winners (Canada, northern Europe, Russia, India, andSouthandEastAsia.
andnorthernChina)andlosers(e.g.,mostofSouthAmerica,
easternhalfoftheUS,restofEurope,theMiddleEast,and
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