Groundwater saving and quality improvement by reducing water footprints of crops to benchmarks levels

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ContentslistsavailableatScienceDirect

Advances

in

Water

Resources

journalhomepage:www.elsevier.com/locate/advwatres

Groundwater

saving

and

quality

improvement

by

reducing

water

footprints

of

crops

to

benchmarks

levels

Fatemeh

Karandish

a,∗

_,

_Arjen.Y.

_Hoekstra

b,c

_,

_Rick

_J.

_Hogeboom

b

a Water Engineering Department, University of Zabol, Zabol, Iran

b Twente Water Centre, University of Twente, P.O. Box 217, Enschede 7500AE, The Netherlands

c Institute of Water Policy, Lee Kuan Yew School of Public Policy, National University of Singapore, 259770, Singapore

a

r

t

i

c

l

e

i

n

f

o

Keywords:

Water footprint assessment Groundwater scarcity Groundwater quality Nitrate pollution Benchmarking Water saving Crop production

a

b

s

t

r

a

c

t

Theformulationofwaterfootprint(WF)benchmarksincropproduction– i.e.identifyingreferencelevelsof reasonableamountsofwaterconsumptionandpollutionpertonneofcropproduced– hasbeensuggestedas apromisingstrategytocounterinefficientwateruseandpollution.Thecurrentstudyisthefirsttoshowhow settingWFbenchmarksmayhelpalleviategroundwaterscarcityandpollution,inacasestudyforIran.We ad-vancethefieldofWFassessmentbydevelopingWFbenchmarklevelsforcropproduction,whichwesuccessively usetoassesspotentialgroundwatersaving,qualityimprovementandeconomicwaterproductivitygains.First, wecalculateclimate-specificWFbenchmarklevelsforbothtotalbluewaterfootprintsandnitrogen-relatedgrey groundwaterfootprintsfor26crops,forallyearsintheperiod1980–2010,at5×5′spatialresolution.Second, weestimatethewatersavingpotentialfortotalbluewaterresourcesandforgroundwaterresourcesspecifically, aswellasthegreygroundwaterfootprintreductionpotential.Finally,wecomparemeaneconomicwater pro-ductivitiesofcropproductioninthepastwithproductivitiesifWFsarereducedtobenchmarklevels.Wefind thatgroundwatercomprisesupto83%oftotalbluewaterconsumptionofirrigatedcrops,withthehighestshare inaridareasandincereals.Aquifersareundersignificanttoseverestress,exceptinthedrysub-humidzone, whereirrigationmainlyreliesonsurfacewater.ReducingWFsofcropsto25thpercentilebenchmarklevelscan save32%ofgroundwatercomparedtothereferenceyear2010,andlowerthenitrogen-relatedgreygroundwater footprintby23%.Moreover,itwouldincreaseaverageeconomicgroundwaterproductivityinIranby20%for cereals,and59%fornuts.WeconcludethatreducingWFstoclimate-specificbenchmarklevelsinawater-stressed countryisapromisingwaytoalleviateoverexploitationofaquifersandincreasenationalfoodsecurity.

1. Introduction

Apromisingstrategytosavewaterandreducewaterpollutioninthe agriculturalsectoristoformulatebenchmarklevelsforwaterfootprints of cropproduction(Zwartetal., 2010;Braumanetal., 2013; Hoek-stra,2013;MekonnenandHoekstra,2014;Zhuoetal.,2016b;Chukalla etal.,2017).Awaterfootprintreferstothevolumeof waterthatis consumedorpollutedtoproduceatonneofproduct.Theblue surface-waterfootprint(blue SWF)referstotheconsumptionof surface wa-ter,thebluegroundwaterfootprint(blueGWF)totheconsumptionof groundwater,andthegreenWFtotheconsumptionofrainwater.The greysurface-waterfootprint(greySWF)andgreygroundwaterfootprint (greyGWF)aremeasuresofsurfacewaterandgroundwaterpollution, respectively(Hoekstraetal.,2011).TheblueandgreenWFcomponents togetherformthetotalconsumptiveWF,whilethegreyWFisalsocalled thedegradativeWF.Benchmarkingwaterproductivity(kg/m3 )or

wa-∗_{Corresponding}_author.

E-mailaddress:f.karandish@uoz.ac.ir(F.Karandish).

terfootprints(m3 /kg)impliesdeﬁningwhatisareasonableamountof waterappropriationfortheprocessathandgivenenvironmental con-ditionsandtechnicalpossibilities.Benchmarksmayvarywith environ-mentalfactorslikeclimateandsoil(Hoekstra,2013;Hoekstra,2014). Consumptionbeyondthebenchmarklevelindicatesineﬃcientresource use.Benchmarkscanbeformulatedbasedongoodorbestavailable tech-nologiesandmanagementpractices(Chukallaetal.,2015,2017,2018) ortheycanbesetbyconsideringthespreadofactualWFsinacertain regionandtakingtheWFlevelthatisnotbeingexceededbythebest 10%,20%or25%ofthetotalproductioninthatarea(Mekonnenand Hoekstra,2014;Zhuoetal.,2016b).

Althoughstillinitsinfancy,theideaofdevelopingbenchmarklevels hasbeenelaboratedinafewpreviousstudies.Inaglobalassessment, Mekonnen andHoekstra(2014) developedbenchmarklevelsfor con-sumptiveandgreyWFsforvariouscropsthroughananalysisataspatial resolutionof5×5′andfoundthatWFbenchmarklevelsmaybelowerin atemperatethaninatropicalclimate.Theyfoundthatifallproducers

https://doi.org/10.1016/j.advwatres.2018.09.011

Received20February2018;Receivedinrevisedform15September2018;Accepted17September2018 Availableonline18September2018

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globallywouldachieveaconsumptiveWFsimilartoorlowerthanthat ofthebest25%ofproduction,globalbluewatersavingswouldsum upto40%ofthetotalwaterconsumptionincropproduction.Ifgrey WFsincropproductionarereduced,worldwide,tothelevelofthebest 25%ofcurrentglobalproduction,waterpollutionwouldbereducedby 54%.Chukallaetal.,(2015,2017)studiedthereductionpotentialof blueandgreenWFsforcerealsbydevelopingbenchmarksfordifferent alternativeirrigationtechniques,irrigationstrategies,anddifferent al-ternativemulchingpractices.Theirresultsdemonstratethatintegrating dripirrigationwithdeficitirrigationandsyntheticmulchingmaycause a29%reductioninconsumptiveWFcomparedtoconventionalfarming practices.Zhuoetal.,(2016b) carriedoutastudyforconsumptiveWF benchmarksofwheatinChina,addressingtheimportantquestionofthe needtodifferentiatebenchmarksbasedonenvironmentalfactors. Con-sideringrain-fedversusirrigatedcroplands,wetversusdryyears,warm versuscoldyears,fourdifferentsoilclassesandtwodifferentclimate zones,theyconcluded thatitis justifiedtodifferentiatebenchmarks basedonclimatezones.

Noneofthepreviousstudiesquantifiedtheeffectthatsetting bench-marksmayhaveonalleviatingscarcityorpollutionlevelsfor ground-waterspecifically,whichwasnotpossiblebecausenoneofthem differ-entiatedblueWFsintoblueSWFsandblueGWFs.Inaddition,the eco-nomicbenefitsassociatedwithmoreefficientwaterconsumptiondueto benchmarkinghavenotbeenquantifiedbefore.Thecurrentstudyaims toadvance thefieldofwaterfootprintassessmentby(1)developing climate-specificbenchmarklevelsforbothblueandgreyWFs,inacase studyconsidering26cropsinIranovertheperiod1980–2010,(2) esti-matingwatersavingandwaterpollutionreductionpotentialforIran’s groundwaterresources,and(3)comparingtheeconomicwater produc-tivitiesofactualcropproductionwithproductivitiesifWFsarereduced tobenchmarklevels.Thiscomprehensiveaimprovidesamuch-needed widerperspectiveoninefficientwateruse incropproductioninIran inparticular,andonthestrategyofbenchmarkingWFsingeneral.Iran hasbeenchosenasanexemplarycaseofheavyrelianceongroundwater resources,ahighdegreeofaquiferoverexploitation,andmuchroomfor moreefficientwateruse.

2. Methodsanddata 2.1. Casestudy

Iranspansanareaof1,640,195km2 andisdividedinto30provinces (Fig.1a).Thelong-termnationalaveragesofminimum(T_min)and max-imum(T_max) temperaturesare12°Cand25°C,respectively.The an-nualaverageprecipitationis244mm,butthesouth-easternpartsofthe country(SistanandBalouhestanprovinces)receivemuchless precipita-tion(104mm)andthenorthernparts(Gilan,MazandaranandGolestan provinces)muchmore(1033mm).Overtheperiod1980–2010, refer-enceevapotranspirationvariesintherangeof858–2374mmy− 1 within thecountry,withthelowestandhighestvaluesinthehumidand hyper-aridzones,respectively(KarandishandMousavi,2016).Basedonthe De-Martonneclassificationmethod,Irancanbeclassifiedintofive cli-matezones(KarandishandMousavi,2016):hyper-arid,semi-arid,arid, humidanddrysub-humid,witharidandsemi-aridbeingthe predomi-nantclasses(Fig.1a).Despitetheirlowfreshwateravailability,thearid andsemi-aridzonesareresponsibleforabout70%ofthetotalirrigated cropproductioninthecountry(IMAJ,2016).

Iranissufferingfromunprecedentedwaterscarcityofbothsurface waterandgroundwaterresources.Thisscarcityhasatleastthreemain causes:rapidpopulationgrowthcombinedwithanunevenpopulation distributionwithinthecountry;aninefficientagriculturalsector;and mismanagementandthirstfordevelopment(Madani,2014). Agricul-tureisbyfarthelargestwateruser:97%ofthetotalnetbluewater abstractionin Iranrelatestoirrigatedcropproduction(Hoekstraand Mekonnen,2012).Theimpact of theinefficientagriculturebecomes visible through the partial disappearance of lakes, like Urmia Lake

(Ghaleetal., 2017),the dryingup of rivers, like theZayandehroud River (Madani, 2014),fallinggroundwatertables(Rahnemaand Mi-rassi,2014),pollutionofwater(Karandishetal.,2017a),anddamageto ecosystemsandlocallivelihoods(Madani,2014).Moreover,ineﬃcient wateruseleadstothelossofpotentialeconomicbeneﬁtsthatcouldbe derivedfromincreasedyields.

Rapiddepletionofthecountry’saquifersduetoexcessive groundwa-terabstractionstoproducecropsisarguablythemostcriticalchallenge ofIran’sirrigatedagriculture.Over60%ofthecountry’sirrigation de-pendsonnon-renewableorrenewablegroundwaterstocks(FAO,2016). Iran’sgroundwaterdepletion,embeddedinfoodproduction,was28.4 billionm3 y− 1 in2000andincreasedto33.3billionm3 y− 1 in 2010 (Dalinetal.,2017).Waterabstractionsforirrigationhaveexpanded be-yond regionalwateravailabilitylevels.Nationalstatistics revealthat about500,000wellsareoperatedbylocalfarmers,withoutalicenseor permissionformanyofthem(WRM,2016).Inadditiontoincreasedrisks tonationalwatersecurity,seriousenvironmentaldegradationisinduced andenvironmentalﬂowrequirementsareviolated(Wadaetal.,2012). Landsubsidenceandseawaterintrusionareamongthemostsubstantial secondaryenvironmentalimpactsofthisunsustainablegroundwateruse (Bouwer,1977;KonikowandKendy,2005).

Thespatiallyunevendistributionofagriculturallandscontributesto thementionedchallenges.Over70%oftheagriculturallandsarelocated inaridandsemi-aridzones.Here,groundwatercontributesover50%to totalwateruseinagriculturalfoodproduction(WRM,2016).The sta-plecropsrequiredtofeedIran’spopulationaresourcedfromprovinces wheregroundwaterisbeinghighlydepleted.Dalinetal.(2017)showed thatIranisinthetopfourcountriesoftheworldexposedtoglobalfood andwatersecurityrisksduetoproducingandimportingfoodirrigated fromrapidlydepletingaquifers.

2.2. Green,blueandgreyWFsofcropproduction

Thegreenwater,totalbluewater,bluegroundwater,blue surface-waterandgreygroundwaterfootprintsofcropproductionwere calcu-lated percrop at aspatialresolution of 5×5′foreach growing sea-sonintheperiod1980–2010,basedontheaccountingframeworkof Hoekstraetal.(2011).ThegreenandtotalblueWFsofacrop(m3 t−1 ₎ werecalculatedastheactualseasonalgreenandblue evapotranspira-tion(ET,m3 ha− 1 )dividedbythecropyield(Y,tha− 1 ).ETandYwere simulatedusingFAO’scropwaterproductivitymodelAquaCrop,using adailytimestep(Hsiaoetal.,2009;Raesetal.,2009;Stedutoetal., 2009).Themodelwasinitializedthrougha5-yearrain-fedfallowland simulationpriortotheplantingdate,inordertodampenouteﬀectsof beginningconditionsonthesoilmoisturecomposition,aswasproposed bySiebertandDöll(2010)andZhuoetal.(2016a).Themodelevaluates adailysoilwaterbalanceoftherootzonetocalculateET:

𝑆[ 𝑡] =𝑆[ 𝑡−1 ] +𝑃[ 𝑡] +𝐼[ 𝑡] +𝐶𝑅[ 𝑡] −𝑅𝑂[ 𝑡] −𝐸𝑇[ 𝑡] −𝐷𝑃[ 𝑡] . (1) inwhichS_[_t_]andS_[_t₋_1]arethesoilwatercontentattheendofdayt andt-1,respectively,P_[t] theprecipitationondayt,I[ t] theirrigation waterapplied,CR_[t] thecapillaryrisefromthegroundwater,RO[ t] the surfacerunoff,ET_[_t_]theevapotranspiration,andDP_[_t_]thedeep perco-lation.Alltermsareinmm.Capillaryriseisnotconsideredheresince mostgroundwatertablesareassumedtobedeeperthanonemeterbelow therootingzone.ThegreenandtotalbluewaterfractionsofROwere calculatedeachdaybasedontherelativesharesofPandIinadayin thesumofP+I.Thefractionsgreenandtotalbluewaterinthesoil wa-tercontentovertimewerecalculatedfollowingChukallaetal.(2015), Zhuoetal.(2016a) andKarandishandHoekstra(2017).Thismethod isbasedontheassumptionthatthegreenwatercontentinthesoil in-creaseswhenrainfallinfiltratesinthesoilandthatthetotalbluewater contentincreaseswhenprecipitationinfiltrates.Thefractionsgreenand totalbluewaterinthetotalsoilwatercontentattheendoftheprevious daywereusedtocalculatethegreenandtotalbluefractionsinETand

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Fig. 1. Provinces,municipalitiesandclimaticregionsofIran,withthedominantclimaticconditionperprovince.

ThetotalblueWFineachlocationandyearismadeupoftwo com-ponents:abluegroundwaterfootprint(GWF)andabluesurface-water footprint(SWF).Wedistinguishedthesecomponentsbasedondataon thefractionsgroundwaterandsurfacewaterinirrigationper munici-palityasprovidedbyWRM(2016).

The grey WF is an indicator of appropriated pollution assim-ilation capacity. Following the Global Water Footprint Standard (Hoekstraetal.,2011),wecalculatedthegreygroundwaterfootprint (inm3 t− 1 )relatedtotheapplicationofnitrogen(N)fertilizeras fol-lows:

𝐺𝑟𝑒𝑦𝐺𝑊𝐹 =𝛼𝐴𝑅∕ (

𝑐𝑚𝑎𝑥−𝑐_𝑛𝑎𝑡)

𝑌 (2)

whereARis theNfertilizer applicationratetotheﬁeld(kg ha− 1 _y-,

𝛼 thefractionofthisleachingtothegroundwater,Cmaxthemaximum

acceptableNconcentrationinthegroundwater(kgm− 3_),_C

natthenatural

Nconcentrationinthegroundwater(kgm− 3_),_and_Y_the_crop_yield_(t

ha− 1 ).Theproduct𝛼AR representstheNloadtogroundwater.Based onthesuggestedvaluesbyFrankeetal.(2013),weassume𝛼 =0.1.A maximumacceptableNconcentrationof50mgnitrateL− 1 (or11.3mg NL− 1 )isadopted,basedonEUNitratesDirective(Monteny,2001).We didnotconsidergreyWFsforsurfacewaterinthisstudy.

2.3. BlueandgreyWFbenchmarklevelsandpotentialblueandgreyWF reduction

Percrop,perclimatezoneandperyear,wedeterminedbenchmark levelsforthetotalblueWFandthegreyGWF.FollowingMekonnenand Hoekstra(2014)andZhuoetal.(2016b),WFbenchmarklevelswere de-terminedbyrankingthegrid-levelWFvaluesfromsmallesttolargest, andplottingthesesortedfootprintsagainstthecorresponding cumula-tivepercentageoftotalcropproduction.AWFbenchmarkforacertain crop,climatezoneandyear isthensetbytakingtheWFatagiven percentileofproduction,e.g.the10th,20thor25thpercentileof pro-duction.The10thpercentilebenchmarklevelthusreferstotheWFof thatisnotexceededbythebest10%ofthetotalproductionvolume

(with‘best’referringtothecropscomingfromtheplaceswithsmallest WFs).WedidthisforbothtotalblueWFsandgreyGWFs.Subsequently, weestimatedwatersavingpotentialifWFsarereduced– bothtotalblue WFsandgreyGWFs– tothesebenchmarklevels.

To estimate the potential reduction of groundwater scarcity, we firstcalculatedgroundwaterscarcityperclimatezone,bydividingblue groundwater footprints of the 26 crops considered in this studyby groundwater availabilityfor the referenceyear 2010 (Schynset al., 2015;SchynsandHoekstra,2014).Wetookgroundwateravailability (recharge)datafromWRM(2016).Subsequently,wecomputed ground-waterscarcityforthescenariosinwhich,percropandperclimatezone, actualtotalblueWFsarereducedtotheclimate-specificbenchmark lev-els setby the10th,20th and25th productionpercentiles.Following Hoekstraetal.(2012)andHoekstraandMekonnen(2016),weclassified scarcityintofourgroups:low(<20%),moderate(20%−30%), signifi-cant(30%−40%)andsevere(>40%)scarcity.Thedifferencebetween thereferenceyearandreducedWFscenariosgivesanindicationofthe potentialreductioningroundwaterscarcityperclimatezone.

ToestimatethegreyGWFreductionpotential,wefirstcalculated thewaterpollutionlevel perclimatezonebydividingthetotalgrey GWFbygroundwateravailabilityforthereferenceyear2010.Next,we calculatedgroundwaterpollutionlevelsforthescenariosinwhich,per cropandperclimatezone,actualgreyGWFsarereducedtothe bench-marklevelssetbythe10th,20thand25thproductionpercentiles.When thewaterpollutionlevelequals1,thecompletegroundwaterflowin theconsideredregionisrequiredtoassimilatetheloadof pollutants (Hoekstraetal.,2011).Thedifferencebetweenthereferenceyearand reducedWFscenariosgivesanindicationofthepotentialreductionin groundwaterpollution.

2.4. Economicwaterproductivity

FollowingAldayaetal.(2010)andHogeboomandHoekstra(2017), meaneconomicwaterproductivity(EWP,in$m− 3 )wascalculated,per cropclass,perclimatezone,andperyear,bydividingtheproducerprice ($ton− 1 )bythetotalconsumptive(i.e.greenplusblue)WF(m3 ton− 1 ).

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Table 1

Overviewofdatasources.

Input variable Source Spatial resolution ∗ _{Temporal resolution} _Remarks

Irrigated area IMAJ (2016) Municipal per crop Annual 1980–2010 Crop-speciﬁc municipal level irrigated area were downscaled to the 5 × 5 ′ resolution based on spatial distributions of crop-speciﬁc irrigated areas by Siebert et al., 2013 . Irrigation water resources

(ground and surface water)

WRM (2016) Municipal Crop-speciﬁc municipal level irrigated amounts and recharge were downscaled to the 5 × 5 ′ resolution based on spatial distributions of irrigated areas by Siebert et al. 2013 Applied N fertilizer in irrigated

areas

IMAJ (2016) municipal annually Spatial distribution of applied N fertilizer within the irrigated areas was derived based on the ratio of crop irrigated area within in a 5 × 5 ′ unit to the crop irrigated area in the corresponding municipal unit as a whole

Weather data (Tmin, Tmax, RH, WS, n) ∗∗

IRIMO (2016) 52 synoptic stations daily Weather stations are shown in Karandish and Hoekstra (2017) . Raster maps were resampled in GIS to the 5 × 5 ′ spatial resolution.

Soil data (i.e. texture data and the total soil water holding capacity)

Batjes (2012) 5 × 5 ′ resolution –

Soil hydraulic characteristics (PWP, FC, TS, Ks) ∗∗∗

Steduto et al., 2009 Not applicable Raster maps at 5 × 5 ′ resolution were prepared based on 5 × 5 ′ soil texture map

Climatic zones 5 × 5 ′ Climatic zones were determined based on the method by

De-Martonne, based on weather data (see above, available at 5 × 5 ′ resolution and upscaled to the provincial level)

∗_During_the_study_period,_Iran_had₃₀_provinces._Each_province_was_further_divided_into_municipal_units_based_on_national_rules_established_by_the_Ministry_of

theInterior(KarandishandHoekstra,2017).

∗∗_Tmin:_minimum_air_temperature,_Tmax:_maximum_air_temperature,_RH:_relative_humidity,_WS:_wind_speed,_n:_sunshine_hour).

∗∗∗_PWP:_permanent_wilting_point,_FC:_soil_moisture_at_ﬁeld_capacity,_TS:_soil_moisture_at_saturation,_Ks:_soil_saturated_hydraulic_{conductivity.}

Basedonthe30-yeartimeseriesofWFs(1980–2010),EWPwas calcu-latedforboththeactualconditionsandthescenariosinwhichthe con-sumptiveWFsarereducedtotheclimate-speciﬁcbenchmarklevelsat thebest10%,thebest20%,orthebest25%ofIran’scropproduction. Usinglesssurfacewaterandgroundwater,whilemaintaining produc-tionlevels,increasestheeconomicoutputperunitofwaterallocatedto thepurposeofcropgrowing.Weestimatetheincreaseineconomic wa-terproductivity(EWP,$m− 3 )ifproducersweretoadheretobenchmark levelscalculatedearlierinthisstudy.

2.5. Data

Allrequireddatahadtobeobtainedpercropandperyearforthe studyperiod1980–2010,atthe5×5″resolution.Anoverviewofthe datasourcesused,includingdownscalingandresampling procedures appliedincaseofdeviatingresolutions,isgiveninTable1.Whileall analysesaredoneatthe5×5′gridlevel,resultsareaggregatedtoand shownattheclimatezonelevelforclarity.

Dailyweatherdataat5×5′resolutionwerederivedfrom observa-tionsat52weatherstations(KarandishandHoekstra,2017)locatedin fiveclimatezones(IRIMO,2016).ET_owascalculatedbasedontheFAO– Penman–Monteithequation(Allenetal.,1998).Soiltexturedataand thetotalsoilwaterholdingcapacitywereobtainedfromBatjes(2012). Forhydrauliccharacteristicsforeachtypeofsoil,theindicativevalues providedbyAquaCropwereused.Weconsidered26cropscommonly growninIran,classifiedintoeightcropcategoriesbasedontheFAO classification(Allenet al.,1998):cereals (wheat; barley;rice),roots andtubers(potato),sugarcrops(sugarbeet;sugarcane),pulses(bean; pea;lentil),nuts(pistachio;walnuts;almond;hazelnut),oilcrops (cot-tonseed;soybean;canola),vegetables(tomato;onion)andfruits(apple; banana;date;grape;andcitrusfruitsincludinglime,lemon,tangerine, orangeandgrapefruit).WheatinIranisallwinterwheat.Agricultural datafortheirrigatedandrain-fed crops,includingcrop sowingarea (ha),irrigatedarea(ha),cropplantingandharvestingdates,cropyield (kgha− 1 ),andN-applicationrateswerecollectedpercropperprovince peryearfromIran’sMinistryofAgricultureJihad(IMAJ,2016).Dataon thefractionsgroundwaterversussurfacewaterforirrigationat munici-pallevelwereobtainedfromWRM(2016).DataonIran’sinternational

tradepercrop(ty−1 ₎_were_taken_from_FAO₍₂₀₁₇₎_._Data_on_annual_crop priceswereretrievedfromFAO(2017).

3. Results

3.1. BlueandgreyWFbenchmarklevels

TableS-1showsthetotalblueWFbenchmarksofdifferentcrops,at differentproductionpercentilesforeachclimatezone.Whenweshow, forexample,thatthe25thpercentileofthetotalblueWFofwheatin thesemi-aridzoneis1067m3 t− 1 ,wemeanthatthebest25%ofwheat productioninthisclimatezone(‘best’intermsof‘havingthesmallest totalblueWFs’)hasatotalblueWFof1067m3 t− 1 orless.Significant differencesbetweenthebenchmarksfordifferentclimatezonescanbe observed.Regardlessof croptype,totalblueWFbenchmarksforthe hyper-arid,aridandsemi-aridzonesarehigherthanforthehumidand drysub-humidzones.Exceptforcitrusanddate,thehighesttotalblue WFbenchmarklevelsareobserved inthehyper-aridzone,while the lowestoccurinthehumidzone.ThehighertotalblueWFbenchmarks in thedrier(semi-arid,aridandhyper-arid)zonesarecausedbythe relativelyhighET₀andactualETandgreaterfractionoftotalbluewater inthetotalwaterconsumption.Theresultsconfirmthefindingsfrom previousstudiesthatthetotalblueWFofcropsisnegativelycorrelated withprecipitation,andpositively withET₀(Zwartetal.,2010;Zhuo etal.,2014).

TableS-2showsthegreyGWFbenchmarksofdiﬀerentcropsat dif-ferentproductionpercentilesforeachclimatezone.Thevariationacross climatezonesissmallerthaninthecaseofthetotalblueWF bench-marks,becausegreyGWFbenchmarksdon’trelatetoET,butratherto fertilizerapplicationratesandyields.

Sinceatthenationalscalewheatisresponsibleforarelativelyhigh fractionofbothtotalblueWFandgreyGWF,weshowthespatial dis-tributionoftotalblueWFsandgreyGWFsofwheatproductionforthe year2010inFig.2.Withinthedrierregions,totalblueWFsbelowthe 25thpercentilebenchmarklevelweremostlylocatedinEsfahan(arid), Tehran(arid),Yazd(hyper-arid),Chaharmahal(semi-arid)and Kerman-shah(semi-arid)provinces.Theseprovinceshavearelativelyhigh irri-gationdensity.Inthewaterabundantregions,WFsbelowthe25th per-centilebenchmarklevelareobservedinGilanprovince(humidzone),

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Fig. 2. Spatialdistributionofthetotalbluewaterfootprint(a)andgrey ground-waterfootprint(b)ofwheatInIran,classiﬁedbasedontheWFsatthediﬀerent productionpercentilesperclimatezone.Spatialresolution:5×5′.Year:2010.

whereET₀islowerthanintheotherplaces.Thenationalaveragetotal blueWFbenchmarkforwheatinIranasawholereﬂectsthetotalblue WFbenchmarksforaridandsemi-aridzones,whichcanbeexplainedby thefactthatmostofthecropproductioninIranoccursinthesezones.

GreyGWFsbelowthe25thpercentilebenchmarklevelarelocated inEsfahan(arid),Fars(arid), Qom(arid),Hormozgan(arid), Khuzes-tan(arid),North-Khorasan(arid),Yaz(hyper-arid),Chaharmahal (semi-arid),Hamedan(semi-arid),Lorestan(semi-arid) andMarkazi (semi-arid)provincesandMazandaranprovince(humidzone).

3.2. Groundwatersavingpotential

Fig.3 showsthetemporalvariabilityofthegroundwatersavingthat wouldbeachievedifthetotalblueWFsofthevariouscropswere re-ducedtotheclimate-speciﬁctotalblueWFbenchmarklevelssetbythe 25thpercentiles ofproduction,foreach climatezoneandwithinthe wholecountry.Thesavingofgroundwaterspeciﬁcallywhenreducing overallbluewaterconsumptiontothebenchmarkleveldependsonthe groundwaterfractioninthetotalbluewatervolumeused.Thepotential groundwatersavingwhenloweringtotalblueWFsdowntothe25th per-centilebenchmarklevelwere3.2billionm3 y− 1 in1980and10.2billion m3 y− 1 in2010.Potentialgroundwatersavingtogroundwater consump-tionratiosvariedbetween30%and34%duringtheperiod1980–2010. Thehighestgroundwatersavingsovertheperiod1980–2010were pos-sibleinthearid(1.7–5.4billionm3 y− 1 )andsemi-arid(1.4–3.6billion m3 y− 1 )zones,whereirrigatedagriculturemainlyreliesongroundwater resourcesandwheremostofthecropproductiontakesplace.

Thepronouncedincreaseingroundwatersavingpotentialovertime isattributedtosigniﬁcantlyhighergroundwaterconsumptionin2010 relativeto1980.Thisincreaseiscausedbyarapidgrowthofareaunder irrigationinparticularlythewater-scarcearidandsemi-aridregionsof

thecountry.Previousstudiesconﬁrmsuchsubstantialincreaseinoverall bluewaterconsumptioninirrigatedagricultureinIranovertheperiod 1980–2010(KarandishandHoekstra,2017).

Fig.4 showsthatthereisanexpresseddiﬀerencebetweenvarious cropclassesintermsoftheabsolutegroundwatersavingpotentialupon loweringtotalblueWFsdowntothe25thpercentilebenchmarklevel. Thelargestgroundwatersavingcanbeachievedincerealproduction (1.7–4.5billionm3 y− 1 ),especiallywheat,whilerootsandtubers(0.04– 0.16billionm3 y− 1 )showthelowestabsolutepotential.Savingpotential acrosscropclassesis– asexpected– correlatedwiththeareaofirrigated landattributedtoeachclass.

Table 2 shows groundwaterscarcitycausedbygroundwater con-sumptionforthereferenceyear2010.Whilegroundwaterresourcesare underseverestressinthehyper-arid,aridandsemi-aridzones,thedry sub-humidzoneexperiencesalowpressureontherenewable groundwa-terresources.Thiscanbeexplainedbythefactthatirrigatedagriculture inthelatterclimatezonemainlyreliesonsurfacewater.Aquifersinthe humidzonearesignificantlywaterstressedundercurrentagricultural managementpractices.Themainreasonisthatricegrownhererequires largeamountsofmostlygroundwater(cf.Karandishetal.,2017a).As showninTable2,lowerscarcitylevelsmaybeachievedwhenWFsare reducedtothebenchmarklevelsinallclimatezones.Theexceptionis inthehyper-aridzone,whereaquiferswillremainunderseverewater stressinanyscenario.Atnationallevel,a32%reductioningroundwater scarcityispossiblecomparedtothereferenceyear2010whenreducing waterconsumptiontothe25thpercentilebenchmarklevels.Thenstill, Iran’saquiferswillremainundersignificantwaterstress-especiallyin aridandhyper-aridzones-indicatingthattoomuchwaterisbeingused intotal,evenifthewaterwouldbeusedmuchmoreefficiently.

3.3. Groundwaterpollutionreductionpotential

Iran’s aquifersaremainlylocated in areaswithintensive agricul-ture,andsufferfromdiffusenitratepollutionfromtheexcessiveuseof Nfertilizers,particularlyincerealfields(Fig.5).Intensiveagriculture inpredominantlyaridandsemi-aridzonesresultsinthelargest ground-waterpollutionbynitrateintheseareas.LoweringgreyGWFsdownto climate-specificbenchmarklevelssetbythe25thpercentileof produc-tionwillreducethegreyGWFofagriculturalproductionby23% com-paredtotheactualgreyGWFin2010(11%inthedrysub-humidand 38%inthehyper-aridzone).Inabsolutesense,thebiggestreductionin waterpollutionatnationalscalecanbeachievedincerealproduction.

Table3 showsthewaterpollutionlevelofaquifersforeachclimate zoneforthereferenceyear2010andforthescenariosinwhichgrey GWFswouldbereducedtothebenchmarklevelssetbythe25th,20th and10thproductionpercentiles.Theaveragewaterpollutantlevelsper climatezoneremainsmallerthan1inthereferenceyear,indicatingthat thewasteassimilationcapacityhasnotyetbeenfullyreached. Never-theless,nitratepollutionissevereinaquiferslocatedwithinthe semi-aridandhumidzones,where50%and70%ofgroundwaterisrequired, respectively,toassimilatethepollutantload.

3.4. Increasingeconomicwaterproductivity

Fig.6 showstheeconomicwaterproductivity($m− 3 )foreachcrop classandclimatezone,bothmeanEWPin2010andEWPsforthe10th, 20th and25th productionpercentileinthesameyear.EWPofcrops variedacrossthecountryfollowingvariationsinbothpriceandwater consumption,withthelargestabsolute EWPvaluesin thehumid re-gion.Fruits(1.6–3.2$m− 3 ),vegetables(0.9–1.8$m− 3 )androotsand ﬁbres(1.3–1.7$m− 3 )generatedthehighesteconomicvalueperdrop, whilepulsesandoilcrops– themostwaterintensivecrops– yielded thelowestEWP.In2010,cerealshadthelargestshareinnationalwater consumption,but,forIranasawhole,productionyielded62.3%,32.2% and26.3%lessvalueperdropthanproductionoffruits,vegetablesand

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Fig. 3. Groundwatersaving(bluegroundwaterfootprintreduction)andgroundwaterpollutionreduction(greygroundwaterfootprintreduction)foreachclimate zoneandIranasawholeovertheperiod1980–2010,iftotalblueWFswereloweredto25thpercentilebenchmarklevels.

Table 2

Groundwaterscarcityastheratioofbluegroundwaterfootprinttogroundwateravailability(annualrecharge),perclimatezone,for thereferenceyear2010andincasethetotalblueWFsin2010wereloweredtodiﬀerentbenchmarklevels(pertainingtothe25th, 20thand10thproductionpercentiles).

Climate zone Blue groundwater footprint (10 9 m 3 y −1 )

Groundwater availability (10 9 m 3 y −1 )

Ground water scarcity Level of water scarcity

Reference year 2010 Hyper-arid 4.7 6.7 0.7 Severe

Arid 15.1 24.4 0.6 Severe

Semi-arid 9.1 15.2 0.6 Severe

Humid 0.4 1.0 0.4 Signiﬁcant

Dry sub-humid 0.5 2.2 0.2 Low

Iran 29.8 49.5 0.6 Severe

25th percentile Hyper-arid 3.6 6.7 0.5 Severe

Arid 10.1 24.4 0.4 Signiﬁcant

Semi-arid 6.0 15.2 0.4 Signiﬁcant

Humid 0.4 1.0 0.4 Signiﬁcant

Iran 20.4 49.5 0.4 Signiﬁcant

Semi-arid 5.6 15.2 0.4 Signiﬁcant

Humid 0.3 1.0 0.3 Moderate

Semi-arid 4.8 15.2 0.3 Moderate

Humid 0.3 1.0 0.3 Moderate

rootandﬁbres,respectively,causedbytheirlowyieldandhigh wa-terconsumption.ReducingtheWFsto25thpercentilebenchmark lev-elsimprovesfarmeconomics,throughincreasingaverageEWPinIran by19.5%forcereals,55.1%forvegetables,25.2%forrootsandﬁbres, 31.1%forpulses,21.4%forsugarcrops,58.8%fornuts,29.3%foroil crops,and36.7%forfruits.ThehighestincreasesinEWParegenerally foundintheprovinceslocatedinthearidandsemi-aridzones.

4. Discussion

ThisstudyintothedevelopmentandapplicationofWFbenchmarks forcropproductioninIranincludesvariouslimitationsand uncertain-ties. First, we used the AquaCrop model to estimate ET and yield, using default parametersper crop.Calibration andvalidatingmodel

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Table 3

Groundwaterpollutionlevelastheratioofgreygroundwaterfootprinttogroundwateravailability,per climatezone,forthereferenceyear2010andincasethegreygroundwaterfootprintsin2010werelowered todiﬀerentbenchmarklevels(pertainingtothe25th,20thand10thproductionpercentiles).

Climate zone Grey groundwater footprint (10 9 m 3 y −1 )

Groundwater availability (10 9 m 3 y −1 )

Water pollution level

Reference year 2010 Hyper-arid 0.5 2.0 0.2

Arid 3.6 9.3 0.4 Semi-arid 2.8 6.1 0.5 Humid 0.4 0.6 0.7 Dry sub-humid 0.3 1.7 0.2 Iran 7.7 19.7 0.4 25th percentile Hyper-arid 0.3 3.1 0.1 Arid 2.7 14.3 0.2 Semi-arid 2.2 9.2 0.2 Humid 0.4 0.6 0.6 Dry sub-humid 0.3 1.8 0.2 Iran 5.9 29.1 0.2 20th percentile Hyper-arid 0.3 3.2 0.1 Arid 2.6 14.1 0.2 Semi-arid 2.2 9.6 0.2 Humid 0.4 0.7 0.6 Dry sub-humid 0.3 1.8 0.2 Iran 5.8 29.3 0.2 10th percentile Hyper-arid 0.3 3.5 0.1 Arid 2.5 14.7 0.2 Semi-arid 2.1 10.4 0.2 Humid 0.3 0.7 0.5 Dry sub-humid 0.3 1.8 0.1 Iran 5.5 31.1 0.2

parametrizationstothelocalcontext,basedonlocalorﬁelddata,would improvetrustintheoutcomes(KarandishandŠimůnek,2018).

Theuseofdatasourcesofdifferingspatialscalescancause inconsis-tenciesintheresults.Mostoftheavailabledatawerereportedatthe mu-nicipallevelandsubsequentlydownscaledtoa5×5′resolution,thereby potentiallyaffectingtheresults.Acomparisonwithapreviousstudyby MekonnenandHoekstra(2011)inFig.7showsthatourresultsdifferin therangeof±40%fortotalblueWFs,to±60%forgreyWFs,whichcan beexplainedbydifferencesinmodelsanddatasourcesused,butalso bytheinputdataresolutionused.Mostofourinputdatawerereported atthemunicipallevel,whileMekonnenandHoekstra(2011)used pre-dominantlynationalleveldata.

Wedeterminedthebenchmarklevelsspecificallyforvariousclimatic zones,ratherthanconsideringotherenvironmentalfactorssuchassoil typeandslope,followingZhuoetal.(2016).Includingtheeffectofsoils, slopesandotherlocal environmentalfactorson ETandyieldswhen formulatingbenchmarkscouldpossiblyrefinetheresultsofthisstudy, butbytakingbenchmarksatthe25thpercentileofbestproductionwas areattheconservativesidewhenestimatingpotentialwatersavingsand pollutionreduction.

Inorder toexplorethesensitivityofourresults,wecarriedouta sensitivityanalysisforselectedmodelparametersforasimulationfor wheat.WeanalysedthemostsensitiveparametersaccordingtoHui-Min etal.(2017).Intheiraglobalassessmentforwinterwheat,theyfound thateightparameterstobemostsensitive:growingdegreedaysfrom sowingtoﬂowering(FLO),upperthresholdofsoilwaterdepletion fac-torforcanopysenescence(PSEN),totallengthofcropcycleingrowing degree-days(MAT),cropcoeﬃcientwhencanopyiscompletebutprior tosenescence(KCB),normalizedwaterproductivity(WP),harvestindex (HI),maximumcanopycoverinfractionsoilcover(MCC),andgrowing degreedaydecreaseincanopycover(CDC).Weassessedtherelative changein model-simulatedwheat yieldandcropwater consumption ifeachoftheselectedeightparameterswereadjustedby±20%alone. Table4showsthattheoutputparameterschangedintherangeof−70%

to21%and−16%to3%,respectively.Sensitivitiesvariedperclimatic region.Such adjustmentintheselectedeightparametersmay conse-quently resultin a −17%to178% change in theestimated ground-watersavinganda−17%to219%changeinthereducedgreyGWF. Table4 alsoshowsthatMATisthemostsensitiveparameter,causing thelargestrelativechange incropyieldandwaterconsumption,and consequentlyingroundwatersavingandgreyGWFreduction.

Basedonthese limitationsanduncertaintiesweconsiderthe cur-rent studyasexplorative.FormulatingWFreductiontargetsforcrop production,asageneralnationalpolicy,andtodownscaletargetsper climatezonetospecifictargetsatfarmlevelstillawaitpractical imple-mentation.Thefieldisstillinitsinfancy,withonlyafewearlierstudies availablefortotalblueWFbenchmarks(Hoekstra,2013;Mekonnenand Hoekstra,2014;Chukallaetal.,2015,2017;Zhuoetal.,2016b)orgrey WFbenchmarks(MekonnenandHoekstra,2014;Chukallaetal.,2018). Furtherstudies,usingdifferentmodelsandremotesensing,and validat-ingfindingsbasedonfielddata,willbenecessarytoassessuncertainties inmoredetail,andtestthefeasibilityofloweringtheWFsofcropsto benchmarklevelsatlargescale.

Regardingtheresults,wefoundthatreducingWFs tobenchmark levelshasthelargestwatersavingeﬀectwhenappliedforcereals,nuts andfruitsandinthearidandsemi-aridzones.Ariskofwatersaving isthatfarmersincreasetheirproductionvolumeoncetheyrequireless waterperunitofcropproduction,therebyundoingtheoriginalsaving (Hoekstra,2013).Besides,whileanoverallalleviationofgroundwater scarcitymaybeachievedbyreducingcropWFstothebenchmark lev-els,regionalaquiferswillstillremainunderseverestress,particularly inthehyper-aridzone.Hence,benchmarkingWFsofcropneedstobe integratedwiththeotherpossiblegroundwatermanagementsolutions toachievesustainableagriculture.

Unsustainablegroundwaterconsumptionwilllimitfuture groundwa-teravailability,therebyposingaseriousthreattofoodsecurity.Hence, newpoliciesshouldaimatslowingdowngroundwaterdepletionto pro-tectnationalfoodsecurity.Giventhatunderclimatechangeirrigation

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Fig. 4. Temporalvariationofgroundwatersavingforvariouscropclasses,for eachclimatezoneandIranasawholeovertheperiod1980–2010,iftotalblue WFswereloweredto25thpercentilebenchmarklevels.

waterrequirementsareexpectedtoincreaseinIran(Karandishetal., 2017b;KarandishandMousavi,2016),groundwaterwillbeamore valu-ableresourcestill.Additionally,rain-fedagricultureislikelyto experi-enceincreasedperiodsofdrought,asindicatedbyasigniﬁcantincrease ingreen waterdeﬁcitsunderglobalwarminginIran (Karandishand Mnousavi,2016).Undersuchcircumstances,groundwaterbecomes a valuablesupplementalwatersourcein rain-fedcultivation, tosecure adequatecropyieldsinthefuture.

Economic water productivity (EWP) is another factor that deci-sionmakersshouldtakeintoconsiderationwhenallocatingwater re-sources(Mekonnenetal.,2012;Hoekstra,2013;Schynsetal., 2015; HogeboomandHoekstra,2017).OurresultssuggestthatIran’s ground-watercan beusedmoreeconomicallyeﬃcientifactualWFsofcrops arelowereddowntotheclimate-speciﬁcbenchmarklevels.The poten-tialincreaseinproductivityishigherinthearidthaninthehumidzone. Whilenutspotentiallyhavethehighestincreaseinvalueperdropwhen theconsumptiveWFsofcropsarelowereddowntobenchmarklevels, thehighestnationalincomeincreaseinabsoluteterms($y− 1 )canbe

Fig. 5. Temporalvariationofgreygroundwaterfootprintreductionforvarious cropclasses,foreachclimatezoneandIranasawholeovertheperiod1980– 2010,ifactualgreygroundwaterfootprintswereloweredto25thpercentile benchmarklevels.

achievedwheneﬃcienciesareimprovedincerealproductionbecause ofthevolumeofcerealproduction.

Marstoneetal.(2015)proposedpayingapremiumon groundwater-irrigatedcrops,tobeusedtostoregroundwaterforfutureuse,a so-lution that maybe considered as a payment for ecosystem services (Naeem et al., 2015) and may be well-received by policy makers (Qureshietal.,2012).Thiswouldalsoprovideapricesignalofwater scarcitytoconsumers. Anyincreaseintheprice ofdomestically pro-ducedcrops,however,mayleadconsumerstobuyimportedfood com-moditiesatcheaperrates(Marstoneetal.,2015).Thiswouldincrease thedependencyonothercountries’waterresources,therebypotentially posinganotherrisktofoodsecurity.Anotheradaptationsolutionto re-ducethepressureonaquifersistoreconsiderwhichcropscanbestbe grownwhere,basedonwaterresourcesavailabilityperclimatezone, andtheirrigationrequirementsandeconomicwaterproductivityof dif-ferentcrops.

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Fig. 6. Meaneconomicwaterproductivity(EWP)(inUS$m−3₎_for_various_crop_classes_in_2010,_per_climate_zone,_as_well_as_EWP_for_the_10th,_20th_and_25th

productionpercentile.

Thecurrentstudyhasbeenabletoquantifytheeffectoflowering waterfootprintstocertainbenchmarklevelsonalleviating groundwa-terscarcityandpollution.Besides,wequantifiedtheeconomicbenefits associatedwithmoreefficientwaterconsumptionduetobenchmarking.

Ourmethodisnovelinawaythatthebenchmarklevelsforthetotalblue WFsaredevelopedfordifferentclimaticregions,allowingspatially dis-aggregatedquantificationofpotentialwatersavings,thusbetter reflect-ingobservedregionalWFdifferences.Unlikepreviousstudiesthatonly

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Table 4

RelativechangeinAquaCrop-simulatedyieldandwaterconsumptionofwheatinresponsetoa±20%changeinsensitivecropparametersfordiﬀerentclimatic regions,andtheconsequentchangeingroundwatersavingandgreygroundwaterfootprintreductioniftotalblueWFsareloweredtothebenchmarklevels.

Climatic regions Parameter ∗ _{Change in crop yield (%)} _{Change in water}

consumption (%)

Change in groundwater saving (%) Change in grey groundwater footprint reduction (%)

0.2 − 0.2 0.2 − 0.2 0.2 − 0.2 0.2 − 0.2

Hyper arid FLO − 4.9 3.6 0.5 − 1.2 5.7 − 4.6 5.2 − 3.5

PSEN 0.9 0.6 0.0 0.0 − 0.9 − 0.6 − 0.9 − 0.6 MAT − 60.5 − 9.5 − 14.2 − 14.8 117.2 − 5.9 153.2 10.5 KCB − 6.6 − 7.5 − 3.0 − 2.4 3.9 5.5 7.1 8.1 WP 20.9 − 19.4 0.0 0.0 − 17.3 24.1 − 17.3 24.1 HI 19.7 − 20.2 0.0 0.0 − 16.5 25.3 − 16.5 25.3 MCC − 11.2 − 11.2 − 3.8 − 3.8 8.3 8.3 12.6 12.6 CDC − 11.2 3.5 − 3.8 2.8 8.3 − 0.7 12.6 − 3.4 Arid FLO − 5.5 2.5 1.1 − 1.8 7.0 − 4.2 5.8 − 2.4 PSEN 0.1 − 0.1 0.0 0.0 − 0.1 0.1 − 0.1 0.1 MAT − 60.6 − 10.8 − 15.1 − 15.8 115.5 − 5.6 153.8 12.1 KCB − 4.9 − 9.6 − 3.1 − 2.7 1.9 7.6 5.2 10.6 WP 20.0 − 20.0 0.0 0.0 − 16.7 25.0 − 16.7 25.0 HI 20.9 − 20.8 0.0 0.0 − 17.3 26.3 − 17.3 26.3 MCC 0.1 − 21.3 − 3.5 − 3.7 − 3.6 22.4 − 0.1 27.1 CDC − 9.2 3.4 − 4.0 2.8 5.7 − 0.6 10.1 − 3.3 Semi-arid FLO − 6.2 3.8 0.5 − 0.9 7.1 − 4.5 6.6 − 3.7 PSEN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MAT − 68.6 − 11.4 − 12.8 − 13.6 177.7 − 2.5 218.5 12.9 KCB − 6.6 − 9.7 − 2.7 − 2.3 4.2 8.2 7.1 10.7 WP 20.0 − 20.0 0.0 0.0 − 16.7 25.0 − 16.7 25.0 HI 20.8 − 20.8 0.0 0.0 − 17.2 26.3 − 17.2 26.3 MCC 1.0 − 23.5 − 3.1 − 3.4 − 4.1 26.3 − 1.0 30.7 CDC − 13.5 3.9 − 3.4 2.2 11.7 − 1.6 15.6 − 3.8 Humid FLO − 4.7 2.3 1.6 − 2.0 6.6 − 4.2 4.9 − 2.2 PSEN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MAT − 56.5 − 11.9 − 13.7 − 14.4 98.4 − 2.8 129.9 13.5 KCB − 4.3 − 9.8 − 2.7 − 2.4 1.7 8.2 4.5 10.9 WP 20.0 − 20.0 0.0 0.0 − 16.7 25.0 − 16.7 25.0 HI 20.8 − 20.8 0.0 0.0 − 17.2 26.3 − 17.2 26.3 MCC − 0.4 − 21.4 − 3.6 − 2.6 − 3.2 23.9 0.4 27.2 CDC − 9.4 3.6 − 3.3 2.3 6.7 − 1.3 10.4 − 3.5

Dry sub-humid FLO − 4.9 2.2 1.2 − 1.7 6.4 − 3.8 5.2 − 2.2

PSEN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MAT − 56.9 − 10.7 − 15.1 − 15.7 97.0 − 5.6 132.0 12.0 KCB − 4.4 − 9.6 − 3.0 − 2.5 1.5 7.9 4.6 10.6 WP 19.9 − 20.0 0.0 0.0 − 16.6 25.0 − 16.6 25.0 HI 20.8 − 20.8 0.0 0.3 − 17.2 26.6 − 17.2 26.3 MCC − 0.5 − 21.3 − 3.5 − 3.2 − 3.0 23.0 0.5 27.1 CDC − 9.2 3.4 − 3.8 2.6 5.9 − 0.8 10.1 − 3.3

∗_FLO:_growing_degree_days_from_sowing_to_ﬂowering,_PSEN:_upper_threshold_of_soil_water_depletion_factor_for_canopy_senescence,_MAT:_total_length_of_crop

cycleingrowingdegree-days,KCB:cropcoeﬃcientwhencanopyiscompletebutpriortosenescence,WP:normalizedwaterproductivity,HI:harvestindex,MCC: maximumcanopycoverinfractionsoilcover,CDC:growingdegreedaydecreaseincanopycover

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focusedonalimitednumberofagriculturalcrops(Chukallaetal.,2015; Zhuoetal.,2016a),weconsideredawiderangeofcommonagricultural andhorticulturalcropscultivatedallovertheworld.

5. Conclusion

GroundwaterplaysacentralroleinIran’sirrigatedagriculture.Of allbluewaterconsumptionintheperiod1980–2010,upto83%was suppliedbygroundwaterresources,withthehighestcontribution typi-callyinthearidzonesofthecountry.WefoundthatIran’sgroundwater resourcesareunderseverestressinthehyper-arid,aridandsemi-arid zones,andundersignificantstressinthehumidzone(Table3). More-over,Iran’saquiferssufferfromseverenitratecontaminationwithinthe semi-aridandhumidzones.Ourfindingsrevealthatcereals,and espe-ciallywheat,makeupthemajorityofgroundwaterconsumption.

Theresultsshowthatsigniﬁcantgroundwatersavingsand ground-waterpollutionreductioncanbeachievedwhenfarmerswouldreduce totalblueWFsandgreyGWFsofcropproductiontocertainreasonable benchmarklevels.IfWFsoftheconsidered26cropshadbeenreduced tobenchmarklevelsasdeﬁnedbythe25thbestproductionpercentile, groundwaterconsumptioninIran’scropproductionwouldhavebeen 3.2billionm3 y− 1 lessthantheactualgroundwaterconsumptionin1980 and9.3billionm3 y− 1 lessin2010.Althoughweexpectthemtobe rel-ativelysmall,othergroundwaterconsumingactivitiesnotconsideredin thisstudy(e.g.minorcrops)mayaggravateourconservative ground-waterscarcityestimates.ReducingWFsto25thpercentilebenchmark levelswouldhaveresultedingroundwatersavingsbetween30%and 34%overtheperiod1980–2010.GreyGWFreductionwouldhavebeen 0.7billionm3 y− 1 in1980and1.9billionm3 y− 1 in2010.Overthe pe-riod1980–2010,greyGWFreductionasafractionofthetotalgreyWF wouldhavevariedbetween22%and24%.Thehighestpriorityshould begiventocerealproductioninthearidzone.

Withthis study,we providea narrativefor howtouse WFsand benchmarkingofWFstoassesspotentialwatersavings,potential wa-terpollutionreduction,andpossible economicwaterproductivity in-crease.AlthoughwetookIranasacaseanditsaquifersasanexample, webelievethemethodsputforthinthisstudycanbeappliedtoother regionsandsurfacewaterresourcesaswell.Thisresearchmayserve asanextsteptowardsactualuptakeofWFbenchmarkingintonational waterpolicy.

Acknowledgement

FatemehKarandishwouldliketothanktheUniversityofZabol for ﬁnancingtheproject(GrantNumber:UOZ_GR_9517_6).

Supplementarymaterials

Supplementarymaterialassociatedwiththisarticlecanbefound,in theonlineversion,atdoi:10.1016/j.advwatres.2018.09.011.

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