Spatio-temporal variations in climate, primary productivity and efficiency of water and carbon use of the land cover types in Sudan and Ethiopia

2018·5.1 MB·English

by  Muhammad Khalifa

ScienceoftheTotalEnvironment624(2018)790–806
ContentslistsavailableatScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Spatio-temporal variations in climate, primary productivity and
efficiency of water and carbon use of the land cover types in Sudan
and Ethiopia
MuhammadKhalifaa,b,⁎,NadirAhmedElagiba,LarsRibbea,KarlSchneiderb
aInstituteforTechnologyandResourcesManagementintheTropicsandSubtropics(ITT),TechnischeHochschuleKöln-CologneUniversityofAppliedSciences,Cologne50679,Germany
bDepartmentofGeography,UniversityofCologne,Albertus-Magnus-Platz,D-50923Cologne,Germany
H I G H L I G H T S G R A P H I C A L A B S T R A C T
• Interactionbetweenclimateandvege-
tationecosystemsiscomplexandstill
unclear.
• Correlatingclimatevariabilityandvege-
tationproductivityprovidesusefulin-
sights.
• Spatio-temporalvariationsinclimate,
NPP,WUEandCUEusingremotesens-
ingdata
• NPP,WUEandCUEvarywidelyamong
landcoversindifferentclimatecondi-
tions.
• Thisvariationshouldbeconsideredin
policy-making for water and food
security.
a r t i c l e i n f o a b s t r a c t
Articlehistory: TheimpactofclimatevariabilityontheNetPrimaryProductivity(NPP)ofdifferentlandcovertypesandthere-
Received9October2017 actionofNPPtodroughtconditionsarestillunclear,especiallyinSub-SaharanAfrica.Thisresearchutilizespub-
Receivedinrevisedform7December2017 lic-domaindatafortheperiod2000through2013toanalyzetheseaspectsforseverallandcovertypesinSudan
Accepted7December2017 andEthiopia,asexamplesofdata-scarcecountries.Spatio-temporalvariationinNPP,wateruseefficiency(WUE)
Availableonline27December2017 andcarbonuseefficiency(CUE)forseverallandcoverswerecorrelatedwithvariationsinprecipitation,temper-
atureanddroughtatdifferenttimescales,i.e.1,3,6and12monthsusingStandardizedPrecipitationEvapotrans-
Editor:D.Barcelo
pirationIndex(SPEI)datasets.WUEandCUEwereestimatedastheratiosofNPPtoactualevapotranspirationand
Keywords: NPPtoGrossPrimaryProductivity(GPP),respectively.ResultsofthisstudyrevealedthatNPP,WUEandCUEof
NetPrimaryProductivity thedifferentlandcovertypesinEthiopiahavehighermagnitudesthantheircounterpartsinSudan.Moreover,
Remotesensing theyexhibithighersensitivitytodroughtandvariationinprecipitation.Whereassavannahrepresentsthe
Climatevariability mostsensitivelandcovertodrought,croplandsandpermanentwetlandsaretheleastsensitiveones.The
Wateruseefficiency inter-annualvariationinNPP,WUEandCUEinEthiopiaislikelytobedrivenbyadroughtoftimescaleof
Carbonuseefficiency threemonths.NostatisticallysignificantcorrelationwasfoundforSudanbetweentheinter-annualvariations
Drought intheseindicatorswithdroughtatanyofthetimescalesconsideredinthestudy.Ourfindingsareusefulfrom
theviewpointofbothfoodsecurityforagrowingpopulationandmitigationtoclimatechangeasdiscussedin
thepresentstudy.
©2017ElsevierB.V.Allrightsreserved.
⁎ Correspondingauthorat:InstituteforTechnologyandResourcesManagementintheTropicsandSubtropics(ITT),TechnischeHochschuleKöln-CologneUniversityofApplied
Sciences,Cologne50679,Germany.
E-mailaddress:[email protected](M.Khalifa).
https://doi.org/10.1016/j.scitotenv.2017.12.090
0048-9697/©2017ElsevierB.V.Allrightsreserved.
M.Khalifaetal./ScienceoftheTotalEnvironment624(2018)790–806 791
1.Introduction theAfricancontinent(Williamsetal.,2007).Monitoringthevariation
invegetationproductivity,WUEandCUE,andcorrelatingthisvariation
NetPrimaryProductivity(NPP)isdefinedastheamountofatmo- withclimatevariabilityforlargeareasisachallenge,particularlyagainst
sphericcarbonthatiscapturedbyplantsandtransformedintobiomass thebackdropofthegivenlimitationsofgrounddata,especiallyinthe
(ZhaoandRunning,2010).Thetotalamountcorrespondingtothepho- countriesofSub-SaharanAfrica,forinstance,wheregroundweather
tosynthesisprocessiscalledGrossPrimaryProductivity(GPP).Thedif- stationsarefewandsparselydistributed.
ferencebetweenNPPandGPPisreferredtoasrespiration(Ardö,2015), WUEandCUEareusefulindicatorsfortheassessmentofthepattern
whichistheamountofcarbonpreviouslyassimilatedbytheplantand ofwateruseandcarbonsequestrationbyplants.WUEisdefinedas“The
subsequentlyusedformaintenanceofthebiomassorforgrowth.Mon- rateofcarbonuptakeperunitofwaterlost”(Tangetal.,2014).Itcanbe
itoringofthevariabilityinprimaryproductioniscriticalduetothefact calculatedbydifferentapproachesaccordingtothepurposeofinvesti-
thatNPPprovidesvitalservicesforhumansurvival(Ardö,2015).Re- gation(ItoandInatomi,2012).Here,WUEisdefinedastheamountof
ductioninNPPpotentiallyjeopardizesfoodsecurityandmayincrease waterevaporatedforeverygcarbon/m2ofNPPproduced,i.e.NPP/ET,
a
globalwarmingsinceareductioninNPPmightdecreasetheavailable where ET is theactual evapotranspiration (Kuglitschet al.,2008).
a
carbonsinks(ZhaoandRunning,2010).Whereasthespatialvariation CUEisdefinedasaratioofNPPtoGPP.Theearliestconceptionofthe
of NPP depends on vegetation type, soil, climate conditions and CUE is that it ideally equals 0.5 (DeLucia et al., 2007; Zhang et al.,
humanactivities,thetemporalvariationofNPPdependsmostlyon 2009).However,CUEshouldnotbeconsideredasaconstantvalue
thevariabilityofclimaticfactors(Lietal.,2016).Severalclimatefactors since the driving processes of photosynthesis and respiration are
controltheNPP,suchastemperature,precipitationandshortwavesolar nonlinearlygovernedbydifferentenvironmentaldrivers;thus,the
radiation(Lietal.,2016).Temperatureandprecipitationhavemorein- ratioofthesefluxesvaries.Photosynthesisandrespirationareprimarily
fluenceonNPPinaridandsemi-aridareaswhereassolarradiationisthe governedbytheabsorbedphotosyntheticallyactiveradiation(APAR)
main controllingfactorin humid andsemi-humid areas(Liu et al., and temperature, respectively. Several researchers noted that CUE
2015).TemperatureplaysaroleinraisingNPP(ZhaoandRunning, mightvarydependingonclimatefactors(e.g.precipitationandtemper-
2010).Althoughtheperiodfrom2000through2009wasthewarmest ature)andgeographicallocation(Zhangetal.,2009).Plantsareconsid-
decadeintherecordssince1880(NOAA,2016),ZhaoandRunning eredcarbonsinks.ExaminingthevariabilityofCUEisthusimportantfor
(2010)foundthatglobalNPPhasdeclinedby0.55petagramcarbon climatechangeandCO emissionsstudies(Chenetal.,2013).Also,a
2
(PgC)duringthesamedecade.Theysuggestedthatadryingtrendin betterunderstandingofWUEandCUEmayleadtoabettermanage-
thesouthernhemispherewasthemaindriverforthisreduction.There mentofecosystems(Tangetal.,2014;Zhangetal.,2009).
hasbeenadebateregardingthesefindingsastowhethertherewasa Variabilityinprimaryproductivityaffectsfoodavailabilityandfood
decreaseinNPPorwhetherthisdecreasewasduetoartifactsfrom security.Atthesametime,themagnitudeofprimaryproductionstrong-
theappliedmodel(Samantaetal.,2011;ZhaoandRunning,2011).If lyaffectsthecarboncycle(Zhaoetal.,2005).However,lackofcontinu-
NPPisaffectedbyclimateassuggestedinthatmodel;then,NPPshould ousgroundobservationhindersthelong-termanalysisofthedynamics
havedecreasedduringthisperiod(Medlyn,2011). ofvegetationdevelopment.Luckily,manyremotesensingormodel
Ecosystemsdifferintheirresponsestoclimatevariability(Knapp basedpublic-domaindatasourcesnowadaysprovidecontinuousspatial
andSmith,2001).Differentplantspeciesresponddifferentlytodrought climateobservationsandotherkeyenvironmentalvariables(e.g.LAI,
conditionsbasedontheirphysiologicalandstructuralcharacteristicsin biomass,soilmoisture).Thegeneralavailabilityofthesepublic-domain
ordertopreventlossofwater(VanDerMolenetal.,2011).Understand- datasourcesprovidesauniqueopportunityforexaminingtheclimate-
inghowlandcovertypesrespondtodroughtandtoclimatevariability plantproductivityrelationship,whichisessentialforareproducible
canpromotemoreefficientmanagementoftheselandcoversandcan, analysisoftheimpactofclimatevariationonprimaryproductivity.
inturn,assistsignificantlyinsecuringwaterandfoodinthefuture. However,incomparisonwithclimaticvariables,onlyfewpublic-do-
Manystudieshavebeenconductedonthismatter,yetmostofthem maindatabasesprovidedataonprimaryproductivity.MODISdata,
havefocusedontheclimatedriveroftheNPPvariationonaglobalscale suchastheprimaryproductivity(MOD17)product,areoftenusedto
(e.g.Huangetal.,2016;Liuetal.,2015).Recentanalysesshowedthat detectthevariabilityofprimaryproductivityaswellasanalyzingthe
semi-aridareasareamaincontrolofglobalNPPvariations(Huanget WUEandCUEoflandcovertypesorentireecosystemsandtheirassoci-
al.,2016;Ahlströmetal.,2015).However,itisimportanttoexamine ationwithclimateconditions.Forasummaryofthemostimportant
whetherthesamepatternofNPPfoundbyZhaoandRunning(2010) studies,refertoTable1.
onaglobalscalealsoappliesonregionalandlocalscales(Chenetal., Tothebestofourknowledge,apartfromthestudyofPengetal.
2013).Asthescalingoftherelevantprocessesistypicallynon-linear,dif- (2017)whostudiedtheimpactofdroughtonNPPonacountryscale
ferentpatternsarelikelytoemergeattheregionalorlocalscale.Drought forthewholeglobe,nootherstudywasconductedtoanalyzethere-
isexpectedtobemoresevereinthefuture(Aultetal.,2014).Therefore,it sponseofNPP,WUEandCUEofdifferentlandcovertypestodrought
isimportanttoinvestigatetheeffectofdroughtonprimaryproductivity andclimate variability in Sub-SaharanAfricabasedupon generally
andefficiencyofthelandcovertypesintermsofwaterandcarbonuse. availabledatasets.Inthisstudy,inter-annualvariationsinclimatecon-
Analysisoftheseinteractionsatregionalandnationallevelsisuseful ditionsanddroughtondifferenttimescalesfortheperiodfrom2000
andprovidesessentialinformationforlandcovermanagementandcli- through 2013 were correlated with inter-annual variation in NPP,
matepolicy-making(Pengetal.,2017;Liuetal.,2015).Recently,acoun- WUEandCUEinvariouslandcovertypesinSudanandEthiopia.The
try-scaleanalysisoftherelationshipbetweenNPPanddroughtwas twoselectedcountriesareexamplesofSub-Saharancountrieswithse-
publishedbyPengetal.(2017).ThedatausedintheirworkwereModer- veredata-scarcityandhighvulnerabilitytoclimatevariationandfood
ateResolutionImagingSpectroradiometer(MODIS)NPPandthedrought insecurity. Understanding the spatio-temporal variability of NPP,
StandardizedPrecipitationEvapotranspirationIndex(SPEI).Theyfound WUE,andCUEisessentialtoachieveabetterlandmanagementin
thatcountriesshowdifferenttrendsinNPPfortheperiod2000to2014, thesecountriesandtoimprovefoodsecurity.
andonly35countriesaccountedforN90%oftheglobalNPP.
Onthecontinentalscale,AfricahaswitnessedanincreaseinNPP 2.Materialsanddata
duringthesameperiod,i.e.from2000through2009,by0.189PgC.
Thiswasmostlyduetoadecreaseinvaporpressuredeficit(Zhaoand 2.1.Studyareaanditsimportance
Running,2010).Africanecosystemsproducearound20%ofthetotal
globalNPP(Ciaisetal.,2011),andalargefractionoftheinter-annual EastAfricaisoneofthemostchallengingareasformanagingnatural
variabilityintheglobalcarboncycleisduetoecologicalprocesseson resourcesduetomanyfactors.Itisaregionhighlyvulnerabletoclimate
792 M.Khalifaetal./ScienceoftheTotalEnvironment624(2018)790–806
Table1
ReviewofsomeresearchusingMOD17dataofNPPandGPP.
Author(s) Scale Time Mainobjective(s) Mainfindings
period
Huanget Global 2000–2013 ToexaminetheimpactofdroughtonNPP NPPishighlycontrolledbydrought,andsemi-aridecosystemsplaythemost
al.,2016 importantroleintheinter-annualvariabilityontheglobalscale.
Lietal., Global 2000–2014 TostudytheclimatefactorsaffectingNPPvariabilityand NPPiscorrelatedpositivelywithETo,anditrespondsdifferentlyinthe
2016 feedbackofthisvariabilityonactualevapotranspiration northernandsouthernhemispheresaccordingtodominantclimatefactorsin
(ETo). eachhemisphere.
Ahlström Global Tofigureouttheroleofsemi-aridecosystemsinthe ThetrendandvariabilityofthegloballandCO2sinksarelargelyderivedby
etal., trendandvariabilityoflandCO2sinks. variationintemperatureandprecipitationvariationoccurringoversemi-arid
2015 ecosystems.
Ardö,2015 Africa 2000–2010 Tocompareprimaryproductiondatafromremote GPPestimationsderivedfromremotesensingdata(i.e.MOD17)arehigher
sensinganddynamicvegetationmodels. thanthosederivedfromdynamicvegetationmodels,whileNPPestimations
arelower.Whenvalidatedagainstground-baseddata,bothestimationsshow
significantpositivecorrelation.
Liuetal., China 2000–2011 ToassesstheWUEofecosystemsandtheirresponseto DroughthasanimpactonWUE,andtheresponseofWUEtodroughtvaries
2015 drought amongecosystemtypes,geographiclocationsandclimateconditions.
Abdietal., Sahel 2000–2010 ToestimateandanalyzethesupplyanddemandofNPP ThedemandofNPPincreasedonanannualrateof2.2%,butwithanear
2014 region, intheSahelcountries. constantsupply.Themajorincreaseindemandisforfoodrequirement.
Africa
Tangetal., Global 2000–2013 ToinvestigatetheWUEofdifferentecosystemsandto WUEvariedgreatlyamongecosystemtypesandamongecosystemslocatedin
2014 studytheirvariationandtrends. differentclimatezones.Recentchangesinlandcoverledtodeclineinglobal
WUE.
Zhanget Lower 2000–2011 Toassesstheeffectofdroughtonvegetation Droughtswithvariedintensitieshavedifferentimpactsonecosystems,which
al.,2014 Mekong productivity showvariationinresponsetodrought.
Basin
Chenetal., Global 1997–2009 ToanalyzetheimpactofdroughtonNPP NPPanddroughtarepositivelycorrelatedinaridregions,whereasboreal
2013 (sub-arctic)areasshownegativecorrelation,andsomeareasshowno
correlation.
Zhaoand Global 2000–2009 TodetectthetrendofNPPanditsrelationtodrought AglobaldecliningtrendintheaverageNPPisdetectedduringthe
Running, investigationperiod.Themaindrivingforceofthisdeclineisdrought.
2010
Zhanget Global 2000–2003 ToinvestigatethepatternofCUE(GPP/NPP)indifferent CUEvariesconsiderablybasedonecosystemtype,geographicallocationand
al.,2009 ecosystems,geographicallocationsandclimate climateconditions.
conditions.
Turneret Global 2000–2004 ToevaluatetheperformanceofMOD17productsacross MOD17productsprovidegooddatatodetectthegeneraltrendofprimary
al.,2006 differentbiomesandcompareitto9Eddycovariance productivity,butshowsoverestimationsinlowproductivitylocationsandvice
fluxtowersdata. versa.
Fig.1.LocationmapofEastAfricashowingtheboundariesofthetwocasestudies(SudanandEthiopia)andthedifferentlandcovertypeslocatedintheregion.Landoverdatainthismap
arethatofMCD12Q1product.
M.Khalifaetal./ScienceoftheTotalEnvironment624(2018)790–806 793
changeimpacts(Abebe,2014).Moreover,mostofthecountriesinthis fromMODISsatellite.MODISisoneofthesensorsonNASA'sEarthOb-
regionareconsideredamongtheleastdevelopedcountries,witha servingSystem(EOS)satellites.Itprovidescontinuousglobalmonitor-
highandrapidlyrisingpopulation(UNECA,2016),consequentlyput- ingdataofprimaryproductivitywithaspatialresolutionof1kmand
tingmorepressureonthenaturalresourcesinthefuture.Withatotal at a temporal resolution of 8-day, monthly and annual intervals.
areaofabout3millionkm2,SudanandEthiopiaareagoodexampleof MOD17version055dataofannualestimatesofNPPandGPPwere
landcovertypestypicalforthisregion(Fig.1).Thetwocountriesto- downloaded from the Numerical Terradynamic Simulation Group
getherarecharacterizedbyagreatdiversityinlandcovers,includingsa- website.Invalidvalueswereremovedfromtherasterfiles;then,each
vannah, permanent wetlands, croplands, shrublands, and forests. rasterfilewasmultipliedbyascalefactorof0.1torestoretheoriginal
Accordingly,theyshowsignificantspatialvariationinclimatecondi- NPPandGPPvalues,asinstructedinthemetadatafileofthisdataset.
tions,rangingfromhyper-aridinnorthernSudantothehumidcondi- Lastly,using“extractbymask”toolinArcGIS,separaterastertimeseries
tionsinsomepartsoftheEthiopianhighlands.Thesefeaturesmake ofNPPandGPPwereproducedforeachofthetwocountries,i.e.Sudan
thetwocountriesparticularlysuitableforourresearch.Mostofthe andEthiopia.
area in Sudan is bare or sparsely vegetated land (62% of the total WithlimitedavailablegrounddataincomparisonwithMOD17data,
area).Theseareasaremainlylocatedinthenorthernhalfofthecountry validationofMODISdataisachallenge(Zhaoetal.,2005).Numerous
(Fig.2).InSudan,grasslandisthedominantlandcovertype,covering studieswereconductedtocomparetheMOD17datawithfieldmea-
around20%ofthetotalarea.InEthiopia,openshrublandsrepresent surements.Adetailedoverviewoftheperformanceofthisproductisbe-
thelargestlandcovertype,coveringapproximately27.6%ofthetotal yondthepurposeofthisresearch.Nevertheless,itisworthmentioning
area.Woodysavannah,grasslandsandcroplandsalsorepresentimpor- forinstancethatTurneretal.(2006)usedEddyfluxtowerstovalidate
tantlandcoversinEthiopiaintermsofarea(Fig.2). MOD17 dataandfound that MOD17datasetsshow no overall bias
whencomparedwithtowersdata.Theyfound,however,thatMOD17
2.2.Dataandmethods datatendtooverestimateprimaryproductivityinthelowproductivity
areasandunderestimateitinhighproductivityareas.Zhaoetal.(2005)
Usingdataacquiredfrompublic-domainsourcesofferasolutionfor mademanyenhancementsinthemaininputsofthisdatasetandre-
suchadata-scarceregion.Alongwithlandcoverdata,timeseriesofthe ported on correlation analyses between MOD17 and ground-based
NPP,GPP,NormalizedDifferenceVegetationIndex(NDVI),precipita- data. Ardö (2015) compared MOD17 NPP with Aboveground NPP
tion,evapotranspiration,temperatureandStandardizedPrecipitation (ANPP) data collected from ground measurements in 35 sites in
EvapotranspirationIndex(SPEI),wereusedinthisanalysis(Table2). Sudan.Whiletheyfoundastrongcorrelationbetweenthemulti-year
Allofthesedatawerederivedfromthepublic-domainsourcesfora averageMOD17NPPandtheANPP(r=0.80,RMSE=135g),they
timeframeextendingfrom2000through2013.Mostoftheremote alsoreportedasystematicover-estimationofMOD17NPP,whichis
sensingdatasetsusedinthisstudyarerecentproductsandavailable attributed to the fact that the ANPP only considers aboveground
onlyfortheyearsafter2000,forinstance,theNPP,GPPandET from biomass.
a
MODIS.Theselectionoftheperiod(2000−2013)wasmainlycon-
trolledbytheavailabilityofthedatafromdifferentsourcesforthe 2.2.2.NormalizedDifferenceVegetationIndex(NDVI)
sametime period.Ontheotherhand,manystudiesdealtwiththe DataontheNDVIwereobtainedfromthewebsiteoftheFamine
samesubjectswereconductedforsomehowthesimilarperiod.We EarlyWarningSystemsNetworks(FEWSNET).Thisdatasetwasdevel-
choosetheperiodtobeconsistentwiththeseresearchesinordertofa- opedbytheU.S.GeologicalSurvey(USGS)EarthResourcesObservation
cilitatethecomparisonofthefindings.Moreover,asmentionedearlier, andScience(EROS)Center.Thedatausedhereinwere10-daycompos-
theperiodbetween2000–2009wasthehottestontheglobalrecord; itedatawithaspatialresolutionof250m.TherawNDVIimagespro-
therefore,theseyearsareveryinterestingforstudyingtheNPPpatterns. cessinginvolved:(i)eliminatingtheinvalidvalues,(ii)convertingthe
Inthisstudy,thedataprocessingwascarriedoutusingArcGIS10.3 digitalnumbers(DN)providedintherasterfilestoNDVIvalues,using
software. theformulaNDVI=(DN−100)/100,asperinstructionoftheproduct
documentation;(iii)using“extractbymask”tooltocreateseparate
2.2.1.Primaryproductivity NDVIdataforeachcountryfromtheoriginaltilesofEastAfrica;(iv)ag-
Primary productivity data were obtained from MOD17 product gregating10-daycompositedataintomonthlytimesteps,usingmaxi-
(Zhaoetal.,2005),whichprovidesNPPandGPPdata(ingcarbonm−2) mumvaluecomposite(MVC)method(Holben,1986),whichselects
1.4
Abbreviations
1.2
B Barren or sparsely vegetated MF Mixed forests
CS Closed shrublands OS Open shrublands
2m) 1 C/NV Cropland/Natural vegetation mosaic PW Permanent wetlands
k C Croplands S Savannas
n 0.8 DBF Deciduous broadleaf forests W Water
o
milli EGBF GEvraesrgsrlaenedns broadleaf forests UWS UWroboadny savannas
0.6
(
a
re 0.4 Ethiopia
A
Sudan
0.2
0
B CS C/NV C DBF EBF G MF OS PW S U W WS
Fig.2.TotalareaofeachlandcoverinSudanandEthiopiaasappearintheMDC12Q1product.
794 M.Khalifaetal./ScienceoftheTotalEnvironment624(2018)790–806
themaximumvalueforeachpixelfromthethree10-daycompositeim-
% d
28 dny an ages.IntegratedNDVI(INDVI)forthecultivationseasonwasusedasa
Performance fiDatadifferfromeldmeasurementsby CHIRPScorrelationswithgriddedgrounNprecipitationdataiswithR0.75inmaareasoftheworldfiCorrelationcoefcientbetweenMOD16towerdatais0.86(Muetal.,2011) w22cwtdp1hi.09ar2phiiot19ts.iia3Ihxt55cnr.ayh))iaePntt..fishrrItsooteeeoeprncpawaimcarrptwauceciioshtcsarianreultcteihntmarninhoetlccsStnulslyutolustaalhtladattuuenteietdoedddimdynob,anaaadwniigsonoangemafturgaues0aar,ugos.sl0Cemsawd5dHt(amii°FnIotC.RaiganPle.PitrlmsiTSodooehfacnae(eettsFtehonsouasfe,nHnlite.n,oahkixng1zreteia9rotgNrtah9fidcDn5eapsta;lV-rr.Gbl,PeIeyr1g2rfcooi-ii00nopmur-1cnipJdt5eauaa(s)InatEnykienvlfoacrtde–noagorRoiGOmsdbleioca,opditw2tnnooa0PabsG21rfrieoetI.d4r0Ser-,,,
oads/Global/CHIRPS%202.0 oads/East%20Africa/eMODIS%20NDVI ata.UDel_AirT_Precip.html#detail dis/modis_products_table/mcd12q1 wCbpmfi2(nrU.yHro2aoeaDm.Istn4AcRhegui.iPpeltoctTs)oSeiloeeUtirwmmmddapintnratppariioogtoseeteed.enlruprdiauBaarssdtcbtoSlteuueataitddrltnatriieuaisettidoycneatinepsnootsrgtGhgf,obirmiiteidesnrhdoeueciiudlnsmlocsesuvtgdeedodeiraddtcisiptneeeatafrivgslosgeseSeroescalutftitneopairp(apsovtiFrriitneenueolady.ycnngtUiiksw(opbeDUniyiesatneSattrdtliGtnShamaipuiSoletn.ra),danogU2tsatvfieo0nnoomit1drni,pavi5eSwssrne)susodr.whivdgsoiEiiaricndttitnhyhdsehdaiiaohiosnennfipdddgd-DihesaEmdeiv-,ttlqrehruoaoeulinwoousdatpupgalhaielihrttltadyyeast.
wnl wnl d/d mo airtemperaturedatawithaspatialresolutionof0.5°forthewhole
Downloadsite http://www.ntsg.umt.edu http://www.ntsg.umt.edu http://earlywarning.usgs.gov/fews/datado http://www.ntsg.umt.edu/project/mod16 http://earlywarning.usgs.gov/fews/datado http://www.esrl.noaa.gov/psd/data/gridde http://www.sac.csic.es/spei https://lpdaac.usgs.gov/dataset_discovery/ gfwipo2pujnerlv.riso2coe.roeemt.aaAb5Odcr.ttes.e.Iihntt1oAshMw(els9nycpeWretOi0rtfus(sdoe1hDittEalwavul1lTattmtidohe6dniapoy)veAoed2un.ast2tp0Fbpstitgm1.eolioiaimTsrl4rtcnoevhr-.ateabdideDphonmaoeuselMaslamerpotsespiaarinotavdriutedotaivsaarnhsutfeptpailaruoUsoyonrssonratDsideEuraoset(aeT,trnlsEanlecao2,T(terpsfm0deaVsspr0)tar4ooihpwt1ran.daira0)gsoestut.1gheirvscT)oretltithyesdnouugipefodtsdasertfydetahfodm,eeastdiadMrsatptoueaadeutbdOcosoraseseaDntbiedaattny1runasgcv6fecrnaooaetAtiuvturnNw2iadeaopAelaralnpuaSsatsesrtacAvvtouhdeah/wadsErtaepecuaeOtgodogapcrrSteuetheorfspnarrweua.oinrtrlnosroamesddoys---
Spatialresolution 1km 1km 5km 1km 0.25km 50km 50km 0.5km MdalagOyo,DrmiItShosnmatthedllyelivtaeenl.doTphaenedndubaaytlaMtceoummepteoarianl.l(ar2e10so0kl7mu)tisaopnnad.tTiiahmlisrpeprsoroovldeuduticobtnyuasMensudaeanttEa8Tl-.
(2011) to calculate ET based on the Penman-Monteith equation
Temporalresolution Annual Monthly 10-daycomposite Monthly 10-daycomposite Monthly Monthly – (uMseoMdnhOteeDirt1eh6i,n1p.9rTo6hv5iis)d.persoadsupcaetcoiavleprrcoodmuecstftohrethperoNbilleembaesnincowuhnitcehreisdthbeyotnhee
Reference Zhaoetal.(2005) Zhaoetal.(2005) Funketal.(2015) Muetal.(2007);Muetal.(2011) – WillmottandMatsuura,2001 – trtcviheoamgelnuuesmelisadsaereajroMrnieerdsOderwDceeos1xets6croyelArsui2tcndergidemnaatgshtteaeiidnsnoevftrtohaisgrleoiiSdnfruaenvdlgoaEailtonTuncaeao.vsnnPadbrsluoiyEdcetereshersfiimsoonirpngomigvadi.toenhfsgeeMrtDthOsNe,D.wpS1ei6hxpAieca2lhrsariotasefwortnahdseeatseotearf
inthisstudy. Source MOD17 MOD17 CHIRPS MOD16 e-MODISNDVI,FamineEarlyWarningSystemsNetwork(FEWSNET)UniversityofDelaware(UDel) StandardizedPrecipitationEvapotranspirationIndex(SPEI)MCD12Q1 wSSdcEemoootTaoeenumtaraonTttai.erhdhtznFalaireAosasdtertrnfips,rroeideroiTndncoarasadedrsnotccy,auafghoMnncctesetdchoyrOtewsevsM,DaatafRPleoOr1s.aemiu6(Dvamnn2ans1mu0dolc6ibn1edeaielEdn5anoatT)ect-wneeoarMf.dtdenoeTsoauseulhtlinanns.isemrti(tdnieget2rwagiena0tatchg1oneagyr4spoamoe)bloeEuydeuordsTndttsdiwuadasyeergdbsdelecrhyeaaEoeosntnTev2acwmadaMi–bnrde7eflyiOtdaaniustmlDnttalexhoc1vmbfeamt6oeeotrrtflEewpdawstTueluaeeexrrvearreean8tstmnmndooednawMeepgaraaaraecOhyssorhrusuDausae.rrmnn1reeIisnndd6---.
d
e mentsinnorthandnorthwestChina.Validationstudiesofthisproduct
Table2fiSpecicationofthedataus Dataset NetPrimaryProductivity(NPP)GrossPrimaryProductivity(GPP)Precipitation(P) ActualEvapotranspi-ration)(ETaNormalizedDifferenceVegetationIndex(NDVI)Temperature(T) Droughtindex Landcover osfiSmo(eMuvveoreedlOvdrrraee-eDEnsdsa1uc.ts6aisTdmtelAhieAff2aefufwe)tyrleriaaceofm(taonpeuuaceornnrernbedasdgaetfbeiltroaohweetnnhsta.wctateAie,lrmeloposeZanavrnattoeyeaedtre)lhlurdtileehtcoeegtewg-tsibor,Gaoa(nlye.hsuae(zeinlt2igdr,ds0haMcE1)maIT6rOlEa)reeTDie,hagas1Msaatvu6vtimOaieroaleDuvanlmgtae1iSolso6eic.rdnnAhiDatt2mehtseemsimeadsptenhiMottdioneenOdneMtDcsdhoeOIueeSnfdDstotEir1hnbtTa6oge-al
M.Khalifaetal./ScienceoftheTotalEnvironment624(2018)790–806 795
product,thisproductprovidesessentialknowledgeonthewatercycle respectively(SuliemanandElagib,2012;Elagib,2013).Forthetwo
anditsinteractionwithenvironmentalchanges(Muetal.,2007).Itpro- years,10-daycompositeNDVIdataweresummedforthefivecultiva-
videseasilyaccessibleinformationforareaswithlimitedsurfacedata tionmonths(June–October),andtheseasonalINDVIwasusedasa
liketheregionofSub-SaharanAfrica. proxyofvegetationproductivityasexplainedbefore.Annualaverage
WUEandCUEwerecalculatedforeachlandcovertypeastheratiosof
2.2.6.Droughtindexdata NPPtoET andNPPtoGPP,respectively.SAIsofWUEandCUEwere
a
Becausedroughtisaslowphenomenonthatdevelopsoveralong alsocalculatedforthepurposeofinvestigatingthelinkagebetween
timewithoutprecipitation(Gillette,1950;WilhiteandGlantz,1985), themandSAIsoftheclimateelements.
droughtindicesthattakedifferenttimescalesintoaccountareveryuse-
fulfordroughtassessment.Incorporatingdifferenttimescalesintheas- 3.Results&discussion
sessmentofdroughtimpactiswidelyused(Potopováetal.,2015).In
the current investigation, we used the Standardized Precipitation 3.1.Climateconditionsduring2000–2013
EvapotranspirationIndex(SPEI)-developedbyVicente-Serranoetal.
(2010)-withaspatialresolutionof0.5°andamonthlytimestep.SPEI Ingeneral,precipitationinSudanismuchlessthaninEthiopia.Tem-
calculationrequiresprecipitationandtemperaturedatatoaccountfor porally,precipitationshowssomevariationfromyeartoyearinboth
thedifferencebetweenprecipitationandpotentialevapotranspiration countries(Fig.4a).TheyearwiththelowestprecipitationinSudan
(PET),i.e.asimplewaterbalance.SPEIisamulti-scalarindexthatallows was2004,recording180.3mm.Fortheperiod2000–2013,thearealav-
comparisonofdroughtseverityoverdifferenttimescalesandacross eragetotalannualprecipitationis227.9mmandthecoefficientofvar-
space.FortheSPEIscalerangesrefertoTable3.AsSPEIisastandardized iation(CV)is0.13forSudan.AsforEthiopiathecorrespondingvalues
variable,itcanbecomparedwithotherSPEIvaluesovertimeandspace. are808.8mmand0.08(Fig.4a).Theresultsonaverageprecipitation
Themostwidelyusedtimestepsare1,3,6,12and24months,denoted obtainedfromCHRIPSarecomparablewiththe1970–2000averages
bySPEI01,SPEI03,SPEI06,SPEI12andSPEI24,respectively(Chenetal., (i.e.225.5mmand799.1mmforSudanandEthiopia,respectively,ases-
2013).Inthecurrentinvestigation,weusedSPEI01,SPEI03,SPEI06 timatedusingWorldClim(FickandHijmans,2017)).Mostlyvegetated
andSPEI12. landsinSudanarefoundonaneast-westbeltlocatedinthesouthern
partofthecountrywhereascharacterizedbyprecipitationamountsbe-
2.2.7.Landcover tween250andrarelyabove1000mm.Largeareasofthenorthernpart
Inthecurrentresearch,MCD12Q1landcoverdataset(Friedletal., ofSudanreceiveb250mmperyear.AsforEthiopia,theannualprecip-
2010)providedbytheLandProcessesDistributedActiveArchiveCenter itationisonaverageN2000mm,withthehighestprecipitationoccur-
(LPDAAC)wasused.Thisproductprovidesannuallandcoverdatafor ringinthewesternpartwhereasthelowestprecipitationisrecorded
thewholeglobewithaspatialresolutionof1kmfortheperiodspan- inthenortheasternandsoutheasternportionsofthecountry.
ning2001to2012.Thelatestlandcoverdata(version051)wasused Inthestudyarea,itisessentialtounderstandthetimingofprecipi-
herein, with land cover classification scheme of the International tationasacrucialfactorinthevegetationdevelopment.SudanandEthi-
Geosphere-BiosphereProgramme(IGBP).Thegloballandcoverlayer opia are relying mostly on rainfed agriculture for domestic food
wasprocessedtogenerateseparatelayersoflandcoverclassesfor production.Therainyseasonandgrowingseasonarealmostidentical,
eachcountry. andtheyextendfromJunetoOctober.InSudan,eventhetimingof
themaingrowingseasonforirrigatedagriculturesynchronizesthe
2.2.8.Correlationofthevariablesandcalculationofwaterandcarbonuses rainyseason,especiallyinthelargeirrigatedschemes(e.g.Geziraand
efficiency Rahad)becausethewatersupplyintheseschemesishighlydependent
TheNPPandclimateelementswerefurtherstandardizedaccording ontheRiverNileflowwhichisinturnhighlyvariableduetotheseason-
toKraus(1977)toanalyzetheinter-annualvariabilityoftheStandard- alvariabilityinprecipitation.
izedAnomalyIndices(SAIs).Beforestandardization,thedataforallvar- Ontheaverage,mostofSudanshowsannualET ofb500mm.The
a
iablesweretestedfornormalityusingtheShapiro-Wilktest(Shapiro southernpartdisplaysET upto1500mm.Someoftheirrigatedagricul-
a
andWilk,1965;GhasemiandZahediasl,2012).Employinganonline turalschemes(e.g.Gezira)incentralSudanshowanaverageET be-
a
calculator(Dittami,2009),thedatawerefoundtobenormallydistribut- tween500mmand1000mm.Thecountry'sannualaverageofET is
a
ed. For each variable, the SAI was calculated as: {value − average 204.4inSudanand530.2mminEthiopia(Fig.4b).Spatialvariationin
(2000–2013)}/standarddeviation(2000–2013).Theannualaverage ET takesthesamepatternasthatofprecipitation.ThehighestET
a a
ofeachclimatevariableforeachlandcoverclasswascalculatedusing valuesareforwaterbodies,e.g.LakeTanainEthiopia(Fig.4b).
the functions of the GIS environment. Fig. 3 shows the procedure The14-yearannualaveragetemperatureis28.1°CinSudanand23.1
followedinthisstudytocorrelatethevariationintheannualNPPand °CinEthiopia.Duringthestudyperiod,thehighestaverageannualtem-
climateindices.Thisprocedurewasusedforalllandcoverclassesand peraturedetectedforeachcountrywas29.0°CinSudanin2010and
for each year using thenon-parametric Spearman's coefficients (ρ, 23.5°CinEthiopiain2009.Regionally,thecenterofSudanisthearea
rho),withtheaidofXLSTATV.19.4software(Addinsoft,2017).Crop- characterizedbythehighesttemperature(Fig.4c).
landsandgrasslandswereconsideredintheanalysisoftheimpactof Onamonthlytimescale,themostseveredroughtsintheregiondur-
climatevariabilityonfoodproductiononamonthlytimestepfortwo ingthestudyperiodoccurredin2004,2005and2009(Fig.S1).During
selectedyears(2007and2009)representingawetandadryyear, theseyears,Sudanwasaffectedbymoderatetoseveredroughtwhile
moderate drought conditionsprevailed during only few monthsin
these years in Ethiopia. Based on the annual data (Fig. 5a and b),
Table3
CategoriesoftheSPEIscale. Sudanwasaffectedbyamoderatedroughtin2009whereastherest
oftheyearswerenormal.Ethiopiaexperiencednormalmoisturecondi-
Class SPEIvalue
tionsduringthesameperiod.
Extremelywet ≥2.00
Severelywet 1.5to1.99 3.2.NetPrimaryProductivityduring2000–2013
Moderatelywet 1.00to1.49
Normal 0.99to−0.99
Moderatelydrought −1.00to−1.49 Spatially,thehighestNPPvaluesinSudanarefoundalongthesouth-
Severedrought −1.50to−1.99 easternandsouthwesternbordersofthecountrywheremostofthesa-
Extremedrought ≤−2.00
vannaharelocated(Fig.6a).Themajorityofthenorthernpartsofthe
796 M.Khalifaetal./ScienceoftheTotalEnvironment624(2018)790–806
Fig.3.Flowchartofthemethodologicalprocedurefollowedinthisstudytocorrelatetheinter-annualvariabilityinNPP,CUEandWUEandtheirresponsetoclimatevariabilityanddrought
conditions.
countryarebarrenareaswithtoolowNPPratescomparedtothevege- thereasonbehindthisconsiderabledropinNPP.Incomparisonwith
tatedareaswithinthecountry.InEthiopia,thehighestNPPvalueschar- Sudan,somelandcoversinEthiopiadisplayedpositiveNPPanomalies
acterizethemiddlepartofthecountrythatiscoveredmostlybywoody duringthefirstyearsofthestudyperiod,mainlyduetothehighprecip-
savannahandevergreenbroadleafforests(Fig.6b).Theareaswiththe itation.Theimpactofdroughtof2002insomelandcovertypes(e.g.
lowestNPPratesarelocatedinthenortheasternpartofthecountry closedshrublands,croplandsmixedforestandsavannah)wasnotable
which is mostly barren, sparsely vegetated or covered by open asNPPintheselandcoversshowednegativeanomalies.
shrublands.TheannualaverageNPPis87.24and501.92gcarbonfor Asmentionedearlier,manydriversregulatetheinter-annualvari-
SudanandEthiopia,respectively.Allof thelandcoversin Ethiopia abilityofNPP.Inaridandsemi-aridareas,suchasthestudyarea,pat-
showhigherNPPthantheircorrespondentsinSudan.Thiscouldbeat- terns of precipitation and temperature are likely to be the most
tributedtothehigherprecipitationandlargervegetatedlandsinEthio- importantclimaticfactors,buttheinteractionsoftheseclimaticfactors
piacomparedtoSudan.Onaverage,evergreenbroadleafforestsand withvegetationactivitiesarecomplex(Lietal.,2016).Fromtheanaly-
woodysavannahinEthiopiashowthehighestannualNPP,withNPP sis,itcanbenotedforEthiopiathatthevariabilityinNPPisinfluenced
valuesof1279.5and845.3gCarbon/m2,respectively. directlybythevariabilityinprecipitation(Fig.7).Thisismanifestby
the in-phase response of NPP to changes in precipitation and by
3.3. Variation of primary productivity and correlation with climate Spearman'sρ(Fig.S2).AmongalllandcovertypesinEthiopia,grass-
variability landsrevealedthestrongestcorrelationρvalue(ρ=0.82,p=0.05).
Thecorrelationwithclosedshrublands,croplands,deciduousbroadleaf
Throughouttheperiodfrom2000to2013,theNPPdisplayedhigh forests,grasslandsandpermanentwetlandsweresignificantwhereas
inter-annualvariabilityinbothcountries.InSudan,mostoftheland thecorrelationforevergreenbroadleafforests,mixedforests,open
covertypesshowednegativeanomaliesduringtheyears2000–2008 shrublands,savannahandwoodysavannahwerestatisticallyinsignifi-
(Fig.7a).Theyear2007wasanexception,probablybecauseitwasa cant.InSudan,thecorrelationwasstatisticallyinsignificantatp=
yearwitharelativelyhighprecipitation.Thelastfiveyears(2009– 0.05(Fig.S3).Zhangetal.(2014)listedmanybioticandabioticfactors
2013)witnessedanincreaseinNPPofalllandcovertypes.NPPforthe (e.g.soilproperties,nutrient,availabilityandtemperature)tobere-
year2002 wasassociatedwiththelargestdrop forall landcovers. sponsibleforthelackofimmediateresponseofNPPtothecurrent-
Droughteffectintwosuccessiveyears(2001and2002)islikelytobe yearprecipitation.Spatially,thehighestcorrelationbetweenNPPand
M.Khalifaetal./ScienceoftheTotalEnvironment624(2018)790–806 797
Fig.4.TemporalandspatialvariationinclimateelementsinSudanandEthiopia:(a)precipitation,(b)actualevapotranspirationand(c)temperature.
precipitationisdetectedintheeasternandsouthernEthiopia(Fig.8). et al. (2017) for many countries (e.g. Indonesia, Philippines and
TheSpearman'sρintheseareasisN0.6.Theseareasaredominatedby Malaysia). By correlating SPEI at different time scales (1, 3, 6, and
croplands,grasslandsandsavannah.Onlysmallspotsinthecentral 12 months) with NPP anomalies, we found the highest correlation
(GedarefandBlueNilestates)andwesternpartsofSudanexhibitedρ withtheannualNPPanomaliesforSPEI03inEthiopia.Thus,drought
valuesN0.6.Theformerischaracterizedbyextensivecroplands(both eventsonatimescaleof3monthslargelycontrolNPPinthiscountry.
irrigatedandrainfedagriculture).However,thecorrelationbetween Thishighdependencecanbedetectedspatially.LargeareasinEthiopia
NPPandprecipitationintheseareaswerestatisticallyinsignificant.Sta- showedstatisticallysignificantcorrelationbetweenNPPandSPEI03
tisticalanalysisshowednosignificantcorrelationbetweentheinter-an- comparedtodroughtindicesatothertimesteps(Fig.8).Savannah
nualvariationinmeanannualtemperatureandNPPforalllandcovers alsoshowedaverystrongpositivecorrelationbetweenNPPanomalies
inbothcountries.Severallandcovertypesshowedinsignificantcorrela- andSPEIatatimestepofthreemonths(ρ=0.93,p=0.05).Open
tionbetweentheinter-annualvariationintemperatureandNPP(Fig. shrublandsinEthiopiaseemtobemoresensitivetodroughtastheir
8).Lackofcorrelationbetweeninter-annualvariabilitiesintemperature NPPshowedstrongpositivecorrelationwithadroughtofonemonth
andNPPinaridandsemi-aridregionsweredetectedinsomepartsof (SPEI01)(ρ=0.84,p=0.05).TheNPPforcroplandsandpermanent
theworld,asreportedbyLiangetal.(2015)forChina.Thesefindings wetlandsdisplayedtheweakestrelationshipwithSPEI03(ρ=0.66
maysuggestthatthelandcovertypesinEthiopiaaremoresensitive and0.63respectively,p=0.05).Thisrelativelylowercorrelationsug-
tovariationinprecipitationthanthosecharacterizingSudan.However, geststhatthesetwolandcoversarelesssensitivetodroughtthanthe
sincethisanalysiswasconductedononlyanannualbasis,furtheranal- other land covers due to agricultural management (e.g. irrigation)
ysis on the varying seasonal effects on vegetation development is and/orsufficientwatersupplyfromtributariesorgroundwater.Ever-
deemedimperativetodrawamoresolidconclusion. greenbroadleafforestsseemtobequiteresistanttodroughtsince
theirinter-annualNPPanomaliesshowedamoderatecorrelationwith
3.4.Droughtimpactonprimaryproductivity SPEI(ρ=0.54withSPEI01,p=0.05).Deeperrooting(Songetal.,
2017) and access to larger water stores in soils and groundwater
DroughtaffectstheannualNPPinthelandcoversofSudanandEthi- couldbeareason.AllofthelandcovertypesinSudanshowednostatis-
opiadifferently.WhilealllandcovertypesinEthiopiashowapositive ticallysignificantcorrelationbetweentheirannualNPPanomaliesand
significantcorrelation(p=0.05)betweenNPPanddroughtseverity, SPEIatanyofthetimescales.
nostatisticallysignificantcorrelationwasdetectedforSudan.Lackof ThetotalNPPinthedryyear2009increasedby20.1%inSudanand
correlationbetweenannualNPPandtheSPEIwasalsofoundbyPeng decreasedin Ethiopia by11.4%from the14-yearcountry'saverage
798 M.Khalifaetal./ScienceoftheTotalEnvironment624(2018)790–806
Fig.5.TimeseriesofSPEIforinthestudyareawithdifferenttimesteps1,3,6and12months,(a)Sudanand(b)Ethiopia.
(2000–2013).TheincreaseofNPPindryyearsisreportedformany yield(Bussmannetal.2016;Elagib,2015;Elagib,2013)andNPP.In
areasaroundtheworld,forexampleinNortheastChina(Liuetal., NortheastChina,autumndroughtwasfoundtoleadtolargerreduction
2015;Sunetal.,2016;Peietal.,2013).Generally,therearethreepoten- inNPPwhereasspringdroughthadinsignificantimpact(Liuetal.,
tialcausesofthisphenomenonasreportedbyYangetal.(2016),Liuet 2015).Frolking(1997)foundthatlatesummerdroughtincreasedNPP
al.(2015)andPeietal.(2013).Thesecausesare(i)theassociationofin- by about 20% due to reduced respiration. The present analysis of
creaseintemperaturewithdroughtconditions,(ii)thememoryeffect droughtcharacteristicsinSudanshowsastrongerintensitybutlesser
ofthepreviousyeardroughtonthecurrentyearNPPand(iii)thechar- spatialextentofdroughtin2009comparedtoastrongerdroughtin
acteristicsofdrought.Inthecaseofthedryyear2009inSudan,allthese otherdryyears(e.g.2002)duringthebeginningoftheseason(e.g.
factorsseemtohaveplayedaroleinincreasingtheNPP.Itcanbeex- July).ThesedroughtcharacteristicsresultedinarelativelyhigherNPP
plainedpartlybythenotableincreaseintheannualtemperaturein inmanylandcovers(e.g.croplandsandshrublands)inJulyin2009
thisyear(Fig.7),whichwasthehottestontherecordasreportedby whichislessby33%fromtheNPPofthesamemonthinthewetyear,
SuliemanandElagib(2012)foreasternSudan.Temperatureplaysa 2007.
keyroleintheplantrespirationprocess,thuscontributingtoincreasing
NPP (Zhao and Running, 2010). In particular, mild drought when 3.5.Intra-annualvariabilityofprimaryproductivityanddrought
coupledwithhightemperature,itmightoffsetthedecreaseofNPPin-
ducedbywaterdeficit,i.e.leadingtoanincreaseintheNPP(Sunetal. Results of monthly GPP and NDVI for croplands and grasslands
2016;Liuetal.2015).Incontrast,temperatureduringthedryyearof showedthesametemporalpatternsin2007and2009.GPPandNDVI
2004wasbelowthemulti-yearaveragewhichmighthavecontributed starttoincreaseremarkablyatthebeginningoftherainyseason(i.e.
tothedecreaseintheannualNPP.Additionally,thememoryeffectof June)asshowninFig.9.TheaverageNDVIshowedlowervaluesduring
droughtalsoimpactstheNPP(Yangetal.,2016),likelydecreasingthe thedroughtyear2009inbothcountriesandforbothtypesofland
NPPinthedryyearof2002duetocumulativedroughtoftheyears covers.AstrongcorrelationwasfoundbetweenthemonthlyNDVI
2001and2002.Contrarytothis,therelativelymilderdroughtcondition andmonthlyGPPforthetwolandcoversinbothcountries(Fig.S4).
in2008probablyweakenedthedroughtimpactof2009onNPP.The Thedroughtconditionof2009reducedboththemonthlyNDVIand
timingofdroughtduringtheyear,especiallythebeginningofthegrow- GPPduringtherainymonths.ThereductionintheNDVIwasmoreno-
ingseason,isalsoanimportantfactordeterminingthelevelsofcrop ticeableinSudancomparedtoEthiopia.Accordingly,thedeclinein
M.Khalifaetal./ScienceoftheTotalEnvironment624(2018)790–806 799
Fig.6.(a)SpatialvariationoftheaverageNPPinSudanandEthiopiaasmodeledbyMOD17.(b)Multiyearannualaverage(2000–2013)ofNPPforlandcovertypesinSudanandEthiopia.
iNDVIforcroplandsandgrasslandsin2009comparedto2007were16.9 H O)asreportedbyLiuetal.(2015),andforthesouthernUnitedStates
2
and14.9%inSudanand7.1and16.1%inEthiopia,respectively.Statisti- (0.71gCkg−1H O)asindicatedbyTianetal.(2010),buthighervalues
2
calcorrelationbetweentheintra-annualvariationsinGPPandintra-an- fortheglobalaverage (0.92 gCkg−1H O),asreportedbyItoand
2
nualvariationsinprecipitation,temperatureandSPEIrevealedweak Inatomi(2012).
correlationforcroplandsandshrublandsinbothcountries.Thehighest Variationsarenoticeableforthesamelandcovertypesunderdiffer-
correlation between GPP and SPEI03 was detected for Sudan with entclimateconditionsinSudanandEthiopia.Evidenceoflargevaria-
Spearman's ρ of 0.58 and 0.59 for croplands and shrublands, tionsinWUEwerealsopresentedbyotherresearchersfordifferent
respectively. landcovertypesduetodifferencesincarbonuptakeandwatercon-
sumption(Liuetal.,2015).Thisdissimilarityismainlyduetophysiolog-
3.6.Wateruseefficiency(WUE) icaldifferencesandclimateconditions(Tangetal.,2014).Generally,all
ofthelandcovertypesinEthiopiashowhigherWUEthantheircounter-
ThemagnitudeofWUEvariesdependingonthemagnitudesofNPP partsinSudan(Fig.10).Forinstance,thesavannahshowsanaverage
andevapotranspiration.ThenationalmultiyearaverageWUEforSudan WUEof0.31gCkg−1H OinSudanbut0.75gCkg−1H OinEthiopia.
2 2
is lower (0.24 g C kg−1 H O) as compared to that for Ethiopia InEthiopia,theevergreenforestshavehigherWUEthanthedeciduous
2
(0.74gCkg−1H O)duetolowerNPPandhigherevapotranspiration forests.Thesefiguresareinagreementwithresultsreportedforforests
2
fortheformer.LiteratureshowscomparablevaluesfortheEthiopianav- insimilarlatitudes(Tangetal.,2014).Amongallthelandcovertypesin
erageWUEandthenationalaverageWUEsforChina(0.79gCkg−1 theregion,evergreenbroadleafforestsandwoodysavannahexhibitthe