Climate change unlikely to increase malaria West …eltahir.mit.edu/wp-content/uploads/2016/07/Yamana_et_al...This method also neglects changes to the West African monsoon, which have

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE3085

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1 1

Climate change unlikely to increase malaria burden in West AfricaSupplementaryInformation

SupplementaryMethods

EnvironmentaldatainputsforHYDREMATSsimulations

EnvironmentaldatainputsusedforthisstudyaresummarizedinSupplementary

Table4.Weusedhigh-resolutionsatelliteobservationsofrainfallfromtheClimate

PredictionCenterMorphingTechnique(CMORPH)Version1.0dataset,whichgives

~8kmresolutionrainfalldataevery30minutes1,andhasbeenfoundinmultiple

comparisonstudiestobeoneofthemostskilledsatelliterainfallproductscurrently

available2.CMORPHdataiscreatedbycombiningimagesfrommultiplepassive

microwavesensors,andinterpolatingthemforwardandbackwardinspaceand

timebasedoncloudadvectionvectorscalculatedfromgeostationaryinfrared

sensors1.

Likemostsatelliteproducts,CMORPHisknowntohaveapositivebiascomparedto

raingaugedatainWestAfrica,primarilyduetooverestimationofhigh-intensity

rainfall3.However,afterasimplebiascorrection,CMORPHcanbeusedasaninput

toHYDREMATStoaccuratelysimulatethehydrologicalvariablesrelevanttomalaria

transmission4.Weusedaprobabilitymatchingtechnique5sothatthecumulative

distributionfunction(CDF)ofcorrectedhourlyCMORPHdatamatchedthatofthe

groundobservations.Variationsofthistechniquehaverecentlybeenusedto

correctbiasesinCMORPHusingraingaugedata6,7.

Wedownloadedtemperature,windspeed,winddirection,andradiationdataforthe

HYDREMATSsimulationsfromtheERAInterimdataset8(downloadedfrom

http://www.ecmwf.int/en/research/climate-reanalysis/era-interim).Weassumed

uniformclimaticconditionswithinthe0.75degreeERAgridcell.Weconverted

ERA-interimwindspeeddatafroma10-meterelevationtoa2-meterelevationfor

useinHYDREMATSbyusingalogarithmicprofile.Also,welinearlyinterpolatedthe

Climate change unlikely to increase malaria burden in West Africa

© 2016 Macmillan Publishers Limited. All rights reserved.

http://dx.doi.org/10.1038/nclimate3085

2

ERA-Interimdatafromtheprovided3-hourresolutiontoa1-hourresolutionas

requiredbyHYDREMATS.

WeassignedthedominantvegetationtypeateachlocationusingtheUniversityof

MarylandLandCoverClassification9,andassignedsoilpropertiesusingthe

HarmonizedWorldSoilDatabase10.Werepresentsoilcrustinginthemodelusinga

thinlayeroflow-permeabilitysoilasistypicalinWestAfricaundercultivated

conditions11.Becausethisstudyfocusesonclimatevariables,weheldtopography

constantbetweenlocationsandassumedthatBanizoumbou,Nigertopography

representstypicaltopographicalconditionsandhousingpatternsintheWestAfrica

Sahel12.

Downscalingandbiascorrectionofclimatechangeprojections

ThemethodologythatweusetodownscaleGCMspredictionsofchangesinrainfall

andtemperatureisbasedontwoassumptionsthatarerootedintheconclusionsof

previousstudies:

(A)TheGCMshavelimitedaccuracyinsimulatingthecurrentandfutureclimateof

WestAfrica13-15.InparticulartheGCMshavepooraccuracyinsimulatingthe

temporalpatternsofrainfallatthedailytimescale16.Asaresult,weonlyusethe

GCMssimulationstoextractinformationaboutfuturechangesin(i)annualrainfall

magnitude;andin(ii)seasonaldistributionoftemperature;

(B)Thetemporalrainfallpatterns,attimescalesfromminutestoweeks,are

criticallyimportanttotheformationofthewaterpools,themainbreedingsitesfor

mosquitos17.Thephysicalandbiologicalrelationshipsthatshapepoolformation,

andmosquitopopulationdynamicsarehighlynon-linear.Itisnottrivialhowto

averagethesestronglynon-linearrelationshipsformsmallscales(1hour,10m)to


3

largescales(months,10kilometers)18.Thisiswhyinthesimulationspresentedin

thispaper,ourmalariatransmissionsimulationsarecarriedattheresolutionsof1

hour,10meters.Asaresult,thepatternsofrainfallusedtoforcethemodelhastobe

realistic,consistentwithobservations,andwecannotsimplyusethetypically

erroneoustemporalpatternssimulatedbyclimatemodels.

Basedontheseassumptionsanddiscussion,weapplythefollowingmethodologyin

describingtheforcingofoursimulations:

(1)Themagnitudeoftheaveragechangeindailytemperatureisestimatedfromthe

GCMsimulationsforeachmonthoftheyearandforeachlocation(x1,y1).This

estimatedchangeisthenaddeduniformlytotheobservedtimeseriesfromERA

interimreanalysisdataset,with3-hourtemporalresolutionand2.5degreesspatial

resolution,at(x1,y1),asastraightforwarddeltamethodapproach19-21.The

resultingtimeseriesisusedtorepresentthefuturetemperatureat(x1,y1).

(2)Therelativechangeinmagnitudeoftheannualrainfall,underfutureclimate

conditions,isestimatedfromtheGCMsimulationsforeachlocation(x1,y1).This

relativechangeismultipliedbytheactualobservedannualrainfallat(x1,y1),in

ordertoestimatetheprojectedfutureannualrainfallat(x1,y1).Asavariationon

thedeltamethod,ratherthanmultiplyingourdailyorhourlyprecipitationtime

seriesbythedeltavalue,weinsteadchosetheclosestlocation(x1,y2)wherethe

annualrainfallunderthecurrentclimate,basedonobservedsatellitedatathathas8

kmresolution,isequaltotheprojectedfuturerainfallat(x1,y1)isidentified.The

satellite-basedtimeseries,at30minutesresolution,at(x1,y2)isassumedto

representthefuturerainfalltimeseriesatlocation(x1,y1).Formostcases,(x1,y2)

islocatedwithinabout100kilometersnorthorsouthof(x1,y1),ie.withinthesame

ERAinterimgridpoint.

ThisapproachhastheadvantageofremovingGCMbias,whileretainingfine-scale

spatialandtemporalvariabilityofhistoricalrainfallandtemperature19.The


4

disadvantageisthatthemethoddoesnotaccountforchangesinrainfalland

temperaturedistributionsaroundthemean.Thismaybeproblematic,especiallyin

thecaseofextremeeventssuchasfloodsordroughts,whichareimportantfor

waterpoolformation,andveryhightemperatures,whichaffectmosquitosurvival.

ThismethodalsoneglectschangestotheWestAfricanmonsoon,whichhavebeen

showninrecentstudiestooccurinclimatemodelsasaresultofseveralcompeting

physicalresponsestoclimatechangeincludinganenhancedconvectivebarrierin

thepre-monsoonmonths,enhancedmonsoonflowduetogreaterdifferentialin

land-seatemperatures,andvegetationfeedbacks22-25.Theeffectsofthese

mechanismsleadtoprojecteddecreasesinrainfallduringthemonsoononset,

followedbyincreasedprecipitationlaterinthewetseason22-25.Thetimingofthe

switchbetweenthenegativetopositiverainfallanomalywasshowntodetermine

theoverallchangestototalrainfallandlengthofrainyseason,andremainshighly

uncertain23.Sucheffectscanimpactmalariatransmission,particularlyiftheyalter

thedurationoftherainyseason,asthiswouldinturnalterthelengthofthemalaria

transmissionseason.Changestothefrequencyandintensityofrainfallevents

withintherainyseasoncouldalsohaveimportantimpacts,astheseaffectwater

poolpersistenceandplayalargeroleindeterminingthemalariaresponseto

rainfall17.


5

SupplementaryDiscussion

Comparisonofsimulatedvariablestoobservationaldata

HYDREMATShaspreviouslyundergoneextensivevalidationintwovillagesin

SouthernNiger26-28.Datasamplinglocationsinoneofthevillages,Banizoumbou,

areshowninSupplementaryFig.2.Environmentaldatacollectedincludedone-

hourresolutionmeteorologicalvariables,spatiallydistributedsoil,vegetationand

topographyvalues,andtime-varyingmeasurementsofsoilmoisture,andthedepths

andtemperaturesofselectedrecurringwaterpools.Entomologicalvariables

collectedincludedadultmosquitoescapturedinCDClighttrapsandmosquito

larvaecollectedusingastandarddippingmethod26,27.Bimonthlybloodsamples

weretakenfromchildrenagedonethroughfiveyearsold28.Wedemonstratedthe

model’sabilitytosimulatehydrology,entomologyandmalariaprevalenceinthis

region26-28.

Here,wepresentadditionalmodelvalidation,throughacomparisonofsimulations

toobservationsfromthreeothersources:theBeierdatasetonEIRandmalaria

prevalence29,malariaprevalenceestimatesfromtheMalariaAtlasProject30,and

entomologyandmalariaprevalencedatafromtheGarkidistrictinNigeria31.

ComparisontoBeierdata

Beieretal.29compiledpaireddatafrom31locationsacrossAfricaanddevelopeda

relationshipbetweentheentomologicalinoculationrate(EIR),andmalaria

prevalence.Thesepaireddatapointsoriginatedfrommultiplecountriesincluding

Kenya,Ethiopia,Tanzania,RepublicofCongo,BurkinaFaso,andSenegal,spanninga

widerangeofclimatezones.Thepaireddatapointswereselectedbyconductinga

literaturesearchwiththefollowinginclusioncriteria:entomologicaldatacollected

overatleastoneyearwithaminimumofmonthlysamplingoverthetransmission

season,standardmethodsforestimatingmosquitodensitiesandsporozoiterates,

andnovectorcontrolinterventions.Inclusioncriteriafortheprevalencedata


6

includeduseofstandardbloodsmeartechniquesandreportingbytimeperiodand

agegroup.Ininstanceswheretheoriginalmalariaprevalencedatawerereported

formultipletimeperiodsandagegroups,thesinglehighestprevalencevaluewas

selected.Theanalysisshowedalinearrelationshipbetweenprevalenceandthe

logarithmofannualEIR.Thislog-linearrelationshippersistedwhendatawere

stratifiedbyecologicalzoneaswellasbetweenEastandWestAfrica,indicatingthat

thisisafundamentalrelationshipandindependentofclimate.Thesedatatherefore

provideatestforthehumanimmunityandmalariatransmissioncomponentof

HYDREMATS,whichhasbeenlessextensivelytestedagainstobservationsthanthe

hydrologyandentomologycomponents.

BecauseBeieretal.usedthemaximumprevalencevaluesampledovermultipleage

groupsandtimeperiods,wecomparedthedatatothemaximumsimulated

prevalenceforthe2to10yearagegroupfromourequilibriumsimulations.

HYDREMATSwasabletoreproducetheobservedlog-linearrelationshipbetween

EIRandpeakmalariaprevalence(Figure2,maintext).Oursimulationresultsand

observationaldataagreeverywellforawiderangeofepidemiologicalconditions,

from0toover300infectiousbitesperpersonperyear.

ComparisontodatafromtheMalariaAtlasProject

TheMalariaAtlasProject(MAP),basedattheUniversityofOxford,producesmaps

showingglobalestimatesofvariousmeasuresofmalariarisk.MAPcompilesand

maintainsadatabaseofroutinemalariaprevalencesurveys.Thecurrentmapsuse

over22,000geo-referenceddatapointsofmalariaprevalencemeasuredbetween

1985and2010fromacrossthe85malariaendemicnationsandstandardizedtothe

2to10yearoldagegroup30.Temperatureandariditymasksareusedtopredictthe

limitsofstablemalaria.Withinthepredictedlimitsofstablemalariatransmission,

theseprevalencedataaremappedtoaglobalsurfaceata5kmx5kmresolution

usingBayesianinferenceandgeospatialmodeling30.TheMAPestimateofmalaria


7

prevalenceinchildrenaged2to10yearsisshownasthecoloredsurfacein

SupplementaryFig.3,toppanel.Thecolorsoftheoverlayingcirclesshowthe

prevalenceduringthepeakmalariatransmissionseasonsimulatedbyHYDREMATS

intheequilibriumsimulations.

SupplementaryFig.3,bottompanel,comparessimulatedpeakprevalencetothe

MAPestimate,withresultsoftheequilibriumsimulationontheright,andthemean

ofthemultiyearsimulationontheleft.Forthemajorityofthetwelvelocations,

simulatedresultsmatchwellwiththeMAPestimate.Oneoutlierintheequilibrium

resultsisthepointS1,locatedinSenegal.Thiscouldbeexplainedinpartby

Senegal’smalariacontrolactivities,whichcauseobservedprevalencetobe

substantiallylowerthanwhatwouldhavebeenexpectedgiventheenvironmental

conditions.Oursimulationsdidnotaccountforvaryinglevelsofmalariacontrol

betweenlocations.Inter-annualvariabilityislikelyplayingaroleaswell,asthe

MAPestimatesdonotaddressseasonalandinter-annualvariabilityinresponseto

climate.AnotherdiscrepancybetweensimulationsandMAPestimatesarethethree

locations(N1,N2andM3)wheresimulatedprevalencewas0%,whileMAP

estimateslevelsbetween10and30%.Thereareseveraladditionalexplanationsfor

thisdiscrepancy.OneisthatinthesparselypopulatedregionsofnorthernNiger

andMali,thereareveryfewobservationsofmalariaprevalence.Intheseregions,

MAPestimatesrelyonstatisticaltechniquesusingenvironmentalcovariates

includingrainfall,temperature,landcover,andruralversusurbanclassification.

WethereforehavelessconfidenceinMAPestimatesathighlatitudes.Another

explanationforthediscrepancyisthatwhiletheequilibriumsimulationpredicted

R0lessthanoneleadingtoeliminationoftheparasite,allthreeoftheselocations

hadatleastsomeyearswithR0greaterthanone.Transmissionisthereforepossible

intheselocationsiftheparasiteispresentinthepopulationduringyearswithR0

greaterthanone.Athirdpossibilityforthediscrepancyisthattheselocationsmay

havehadsomeformofpermanentwatersourcenotaccountedforinour

simulationsthatcouldserveasmosquitobreedinghabitatintheabsenceofrain-fed


8

waterpools,suchasoases.ThisislikelytobetrueofM3,whichliesonthebanksof

atributarytotheNigerRiver.

ComparisontodatafromtheGarkiProject

TheGarkiProjectwasamajorefforttostudymalariatransmissionandcontrolby

theWorldHealthOrganizationandtheGovernmentofNigeriafrom1969to1976.

ThestudylocationcorrespondswithsiteNA1showninFig.1inthemaintext.The

goalsoftheprojectweretostudytheepidemiologyofmalariatransmissioninthe

SudanSavannaclimatezone,testinterventionmethods,anddevelopamodelof

diseasetransmission31.Thestudyincludedfourtiersofvillages.Malariacontrol

interventionswereappliedtotheinnervillages.Villagesoutsideoftheintervention

zoneweremonitoredasnon-interventioncomparisonlocations.Mosquitoeswere

capturedevery5weeksduringthedryseasonandevery2weeksduringthewet

seasonusinghumanlandingcatches.Humanbaitcollectorswerestationedat

indoorandoutdoorlocationsforthedurationofthenightandcollectedmosquitoes

attemptingabloodmeal.Mosquitoeswerealsocapturedusingpyrethrumspray

collectionsandlighttraps.Capturedmosquitoeswerecountedandanalyzedin

ordertoestimatethemosquitobitingrate,EIR,mosquitoage,andthesporozoite

rate(proportionofmosquitoesinfectedbytheplasmodiumparasite).Age-specific

malariaprevalencewasmeasuredforselectedvillagesevery10weeksusing

standardbloodsmearmethods.Seroimmunologicalsurveyswereconductedevery

6monthstotestforthepresenceofantibodiestoPlasmodiumfalciparumandother

formsofthemalariaparasitewithinthehumanpopulation.

SupplementaryFig.4showsacomparisonbetweendataonmosquitobitingrates

collectedfromKwaruvillageoutsideoftheinterventionareaandthecorresponding

variablesimulatedbyHYDREMATSinour15-yearbaselinesimulation.Becausethe

high-resolutionenvironmentaldatasourcesrequiredforHYDREMATSsimulations

didnotoverlapwiththeGarkiProjecttimeperiod,wecomparedtheresultstothe

rangeofvaluessimulatedinour15-yearsimulation.Asshowninthefigure,the


9

modelreproducesthegeneralcharacteristicsandtimingoftheseasonalcycle.Our

simulationresultsshowmosquitobitingactivitytoberestrictedtotheperiod

betweenMayandearlyNovember,correspondingtotheavailabilityofrain-fed

waterpoolsrequiredformosquitobreeding.Theobservationsshowoccasional

mosquitobitesoutsideofthisperiod;thesemayhavebeenmosquitoesthathad

takenrefugeinsideofhomes,orwereengaginginlow-levellocalizedbreedingina

permanentwatersourcenotrepresentedinourmodel.Oursimulationsalso

includelowlevelsofmosquitoestosustainthemosquitopopulationoverthedry

season;however,thesemosquitoesareassumedtoenteranaestivationstateanddo

notengageinbloodseekingactivityuntilthebeginningofthefollowingwet

season32.

ThetoppanelinSupplementaryFig.5showsprevalencebyageforacross-section

ofthepopulationinthenon-interventionvillagesmeasuredinOctober,1971,atthe

endofrainyseason.Theobservations,inred,showacharacteristicageprofileof

malariaprevalence.Youngchildrenbecomeinfectedathighratesuntiltheybegin

todeveloppartialimmunitytodisease.Thisexplainsthesharpdecreasein

prevalencebetweenthefirstfewyearsoflifeandearlyadolescence.Whiletheexact

shapeandmagnitudeoftheprevalenceprofilevariesbyyearandbylocation,

HYDREMATSsucceedsinsimulatingthebasiccharacteristicsoftheageprofile.In

thissimulation,childrenappeartodevelopimmunityseveralyearsfasterthanthe

observedpopulation.

Finally,thebottompanelofSupplementaryFig.5showstheseasonalcycleof

malariaprevalenceintheGarkidistrict.Theredandbluelinesshowobserved

prevalencesampledintwonon-interventionvillagesoverfouryears(1971-1974),

andsimulatedprevalenceinthesameareabetween1998and2012.Whilethe

specificprevalencevaluevariesfromyeartoyear,HYDREMATScorrectlysimulates

thetimingoftheseasonalpeakaroundearlyOctober,andsimulatesreasonable

levelsofprevalence.


10

Thesimulationsshowedmuchhigherinterannualvariabilitythantheobservations.

However,theobservationsspanonlyfouryears,withrelativelylowandstable

rainfall(between300-600mm/year).Bycontrast,meanannualrainfallbetween

1998-2012was765mm,withastandarddeviationof168mm,andasmuchas586

mmdifferencebetweenconsecutiveyears.Itisthereforenotunreasonablethatwe

shouldseesuchhighvariabilityinsimulatedmalariaprevalenceoverthistime.Our

simulationsalsotendtounderestimatemalariaprevalenceinthedryseason.One

possibleexplanationforthisisthatoursimulatedpopulationhadhigherimmunity

levelsthantheobservedpopulation,causinginfectionstoclearmorerapidly.There

wasalsoevidenceoflowlevelsofmalariatransmissionoccurringduringthedry

season,whichgenerallydidnotoccurinthemodelsimulations31.

Regressionrelationshipsbetweenclimateandmalariatransmissionindices

Wedevelopedalinearregressionmodelforeachofthemalariatransmission

indices.Webrokeannualrainfallintosegmentsthataccountedforthresholdeffects

andothernonlinearitiesbetweenrainfallandpredictorvariables.ForR0,theslope

oftheleast-squaresregressionlinewasgreaterforannualrainfallvalueslessthan

690mmthanitwasforhighervalues.Inyearswithrainfalllessthan690mm,

mosquitoeswereconstrainedbytheavailabilityandpersistenceofsufficiently

persistentdevelopmentalhabitats.Additionalrainfallmadeitmorelikelythata

waterpoolwilllastlongenoughforlarvaetoemergeasadults,thusincreasingR0.

Inyearswithheavyrainfall,mosquitoeshadmanywaterpoolsavailablefor

breeding,sotheabundancewaslesssensitivetoincreasesinrainfall.Excess

rainfallcanleadtopoolsthataretoodeepforAnophelesgambiaes.l.mosquitoes,

whichprefertobreedinshallowareas.Larvalmosquitoabundanceisalso

regulatedbythecarryingcapacityofdevelopmentalhabitatsinyearswithample

rainfallanddensity-dependentmortality.


11

TherelationshipbetweenrainfallandEIRhadtwodistinctsegments.Rainfallwas

linearlycorrelatedtothelogarithmofEIRforannualrainfallupto950mm.Beyond

thisthreshold,thetwowerenolongercorrelated,bothbecauseofthewaning

influenceofrainfallonmosquitonumbersathighrainfall,andalsobecause

prevalencedidnotincreasewithrainfallbeyondthispoint.

Theinfluenceofrainfallonprevalenceandtheimmunityindexhadthreedistinct

segments.Forrainfallvalueslessthan415mmperyear,environmentaland

entomologicalconditionsweregenerallyinsufficienttosustaintransmission,

resultinginnear-zerovaluesofEIR.Therewasalooselinearrelationshipbetween

rainfallandmalariaindicesforannualtotalsbetween415mmand950mm.There

wasalmostnocorrelationbetweenrainfallgreaterthan950mmandmalaria

transmission,duetothefinitenumberofsusceptibleindividuals,theupperlimitof

acquiringimmunity,andthedecreasedsensitivityofmosquitoestohighlevelsof

rainfall.

Sensitivityofresultstoclimatechangeprojections

Weinvestigatedthesensitivityofourresultstothechoiceofclimatechange

projectionsby1)extendingourmodelingstudytoincludetwoadditionalGCMs:

CanESM2andMIROC5and2)demonstratinghowFig.4(maintext)canbeusedto

estimatethesensitivityofresultstodifferencesinclimateprojections

1)InadditiontothetwoGCMsusedinourstudy(CCSM4andMPI-ESM-MR),we

conductedsimulationsusingtwoadditionalGCMstoinvestigatethesensitivityof

ourresultstothechoiceofclimateprojection.Thefourmodelsshowageneral

patternofdryinginthewesternportionofourstudyarea,andwettinginthe

easternandsouthernareas(SupplementaryFig.7).Themagnitudeofthese

changes,aswellastheextentoftheareawithpredicteddrying,variesbymodel.

ThispatternofrainfallchangeisconsistentwithasubstantialmajorityofCMIP5


12

modelsandhasbeenidentifiedasarobustfeatureoffutureclimateinthisregion14,24,33,34.

Temperaturesgenerallyincreasemoreinthewestthanintheeast,consistentwith

thespatialextentofpredictedchangesinprecipitation(SupplementaryFig.8).The

four-modelmeanshowstemperatureincreasesbetween2.5and5.8degreesCelsius

ofourstudyarea.MIROC5andCCSM4aresubstantiallycoolerthanthemean,while

CanESM2projectstemperatureincreasesover8degreesCelsiusinthewestern

Sahel.

MIROC5isknowntoproduceoneofthecoolestandwettestprojectionsoverthe

Sahel(Subregionsiiandiii)withintheCMIP5ensemble;mostCMIP5models

projecthighertemperatures,andrainfallwithin30%ofcurrentvalues14.Inthis

regard,oursimulationsresultsfromMIROC5canbeseenasaworst-casescenarioin

termsofpotentialincreasesinmalariatransmission.Bycontrast,CanESM2showsa

considerablystrongerdryingandwarmingsignalthantheotherthreemodels.The

resultsofoursimulationsusingallfourclimatemodelsareshowninTable4.

2)Theuncertaintyinclimateprojectionswasamotivatingfactorinour

developmentoftherelationshipslinkingrainfallandtemperaturetomalaria

transmissionindices,whichusesimulationsdrivenbyoveronethousand

combinationsofannualclimatedatatoexploreawiderangeoftemperatureand

rainfallconditionsthatmaybeobservedinthisregion.Theresultingplotsshownin

Fig.4,aswellastheregressionequationsoutlinedintheSupplementarymaterials,

canbeusedtoestimatetheeffectsofdifferentclimatechangescenariosonmalaria

transmissionthroughoutthisregion.

InSupplementaryFig.10,weshowthecurrentandfuturevaluesofmeanannual

rainfallandwet-seasontemperature,superimposedonthescatterplotsofmalaria

transmissionindicesinrainfall-temperaturespaceshowninFig.4.Theyellow

pointsshowthecurrentclimate,andthegreenpointsshowthefutureclimateas


13

projectedbyeachofourfourclimatemodels.Thesensitivityofourresultsto

climateprojectionscanbeestimatedbyconsideringthespreadofthegreenpoints,

andthevaluesofRoandmalariaprevalenceintheunderlyingscatterplots.This

methodcanalsobeusedtoestimatetheclimatechangeimpactsonmalaria

transmissionforadditionallocationswithinstudyarea,aswellastheimpactsof

differentclimateprojections.Mostsignificantwouldbechangesthatcausethe

movementfromthehightransmission(red)tolowtransmission(blue)regionsof

rainfall-temperaturespace.Itisimportanttonotethatwhileeachlocationisshown

asasinglepoint,inrealitywewouldobserveacloudofpointssurroundingthe

meanduethehighinterannualvariabilityinrainfallandtemperature,asshownin

Fig.3band3c.

InSub-regioni,bothincreasedtemperatureanddecreasedrainfallworktoreduce

malariatransmission.Afutureclimatethatwassubstantiallywettercould

potentiallyleadtoincreasesinmalariaprevalence;however,suchafutureis

unlikelygiventhenear-consensusofcurrentgenerationclimatemodelsofadryer

futureinthisregion14.

InSub-regionii,anyfuturewarmingleadstoconditionsthatexceedthelimitsof

mosquitosurvival;noamountofadditionalrainfallcouldmakethisareasuitablefor

malariatransmission.

Asstatedinthemaintext,futuremalariatransmissionismostuncertaininSub-

regioniii,duetothecompetingforcesofincreasedmosquitobreedingthrough

higherrainfallandshortermosquitolifespanthroughincreasedtemperatures.

Furthermore,thisregionsitsclosetothethresholdofmalariatransmission;small

changescanpushthesysteminoroutoftheepidemicregion.Thisregionis

thereforethemostsensitivetotheselectionofclimateprojections.Inour

simulationresults,therelativelycoolandwetfuturepredictedbyMIROC5ledto

increasedprevalenceatsiteN2.


14

Finally,Sub-region4iscurrentlydeepinthehigh-transmissionareaofrainfall-

temperaturespace.Modestchangesinclimatearenotsufficienttosignificantly

changefrequencyorseverityofepidemics.Howevertheverystrongdryingand

warmingsignalpredictedbyCanESM2wasenoughtocauseasignificantdecreasein

malariaprevalence.


15

SupplementaryFigure1Schematicoftheprocesseslinkingclimatechangetomalariatransmission.Rainfallprimarilyaffectsmosquitobreeding,whiletemperatureaffectsmosquitolongevityandparasitedevelopmentrate.


16

Supplementary Figure 2 Data sampling locations in Banizoumbou, Niger. Reprinted from Bomblies et al26. The hydrology component of HYDREMATS was rigorously compared to field observations in Bomblies et al26, showing that the model very closely reproduced observed volumetric water content is soil, as well as location, depth and persistence of water pools.


17

Supplementary Figure 3 Top: Malaria prevalence in children aged 2-10 in 2010 estimated by Malaria Atlas Project 30 (colored surface) and peak equilibrium prevalence simulated by HYDREMATS (colored circles). Bottom left: Simulated prevalence in children aged 2-10 for equilibrium simulation compared to MAP estimate. Each point refers to one of the twelve locations in the top panel. Bottom right: Simulated prevalence in children aged 2-10 over a 15-year simulation. Error bars on for the 15-year simulation show one standard deviation from the mean.


18

Supplementary Figure 4 Mosquito biting rate in Kwaru, Nigeria, one of the non-intervention comparison villages in the Garki project31. Colored lines show captured mosquitoes for 3 consecutive years. Grey lines show simulation results.


19

Supplementary Figure 5 Top: Malaria prevalence by age group in October, 1971. The observations show that malaria prevalence peaks in childhood and gradually decreases through adolescence, with dramatically lower prevalence rates in adults. This is the result of semi-protective immunity, which is gradually acquired through repeated malaria infections. The blue bars show a cross-section of the simulated model population, showing a similar pattern of prevalence vs. age as older individuals are protected by higher levels of immunity. Bottom: Observed and simulated seasonal cycle of malaria prevalence in the Garki district. Grey lines show simulated malaria prevalence between 1998-2012. The red and blue lines show observed malaria prevalence sampled at two control villages over the period 1971-1974.


20

Supplementary Figure 6 Mean annual rainfall [mm] during the period 1930-2005. The top-left panel shows observational data from CRU TS3.21. The remaining panels show rainfall simulated in the historical runs of the four climate models used in this study.


21

Supplementary Figure 7 Predicted change in rainfall by 2070-2100 as a percentage of 1975-2005 mean rainfall under RCP8.5. Predictions are shown for the following CMIP5 models: CCSM4, MPI-ESM-MR, CanESM2 and MIROC5. The bottom-center plot shows the percent change averaged between CCSM4 and MPI-ESM-MR models, and the bottom-right plot shows the average prediction over all 4 CMIP5 models.


22

Supplementary Figure 8 Predicted increase in July-August-September mean temperature between 1975-2005 and 2070-2100, in degrees Celsius, under RCP8.5. Predictions are shown for the following CMIP5 models: CCSM4, MPI-ESM-MR, CanESM2 and MIROC5. The bottom-center plot shows the percent change averaged between CCSM4 and MPI-ESM-MR models, and the bottom-right plot shows the average prediction over all 4 CMIP5 models.


23

SupplementaryFigure9A)Thecolorsonthemapindicatesthechangesinaveragerainfallbetweencurrent(1975-2005)andfuture(2070-2100)conditions,averagingpredictionsbetweenthetwoclimatemodels.Thelabeledrectanglesgroupourstudyareabyresponsetoclimatechange.BandC)DetailedresultsforobservedchangesinR0andmalariaprevalenceareshownfortwosites,M7(Fig3Sb)inthesouthandN1(Fig.3SC)inthenorth.Thetoprowsshowlog10(R0)andthebottomrowshowsmalariameanprevalenceinchildrenaged2-10.Opencirclesindicatemodelresultsinthecurrentclimate,crossesshowresultswithfutureclimateconditionsaspredictedbyCCSM4andtrianglesindicateresultsusingclimatepredictionsfromMPI-ESM-MR.TheleftpanelsaremodelingresultsfromthedetailedHYDREMATSsimulations.TherightpanelsapplyaregressionrelationshiptoannualrainfallandmeanwetseasontemperaturedatafromCRUTS3.1.


24

SupplementaryFigure10Migrationofstudysitesthroughrainfall-temperaturespaceunderdifferentprojectionsoffutureclimate.Thetriangularmarkersineachsubplotshowthelocationofastudysiteinrainfall-temperaturespaceundercurrent(yellow)andfuture(green)conditionsunderprojectionsfromfourclimatemodels:CCSM4,MPI-ESM-MR,CanESM2andMIROC5.Themarkersareoverlaidonplotsoflog(R0)(leftcolumn)andmalariaprevalenceinages2-10(rightcolumn).Thespreadbetweengreenmarkersestimatestheuncertaintyinfuturemalariatransmissionduetodifferencesinclimateprojections.


25

Supplementary Table 1: HYDREMATS simulation results for baseline period (1975-2005) and future conditions (2070-2100). We show results for simulations using climate projections from the two GCMs selected for our study (CCSM4 and MPI-ESM-MR), as well as two additional models as a sensitivity analyses (CanESM2 and MIROC5). Red values indicate statistically significant decreases from baseline while green values indicate statistically significant increases. ProportionofyearswithR0>1(95%CI)

Sub-region Site Base CCSM4 MPI-ESM-MR CanESM MIROC5i M3 0.20(0.04,0.48) 0.00(0.00,0.22) 0.00(0.00,0.22) 0.00(0.00,0.22) 0.27(0.08,0.55)i S1 0.73(0.45,0.92) 0.27(0.08,0.55) 0.07(0.00,0.32) 0.00(0.00,0.22) 0.40(0.16,0.68)i M4 0.87(0.60,0.98) 0.53(0.27,0.79) 0.33(0.12,0.62) 0.00(0.00,0.22) 0.93(0.68,1.00)ii M1 0.00(0.00,0.22) 0.00(0.00,0.22) 0.00(0.00,0.22) 0.00(0.00,0.22) 0.00(0.00,0.22)ii M2 0.00(0.00,0.22) 0.00(0.00,0.22) 0.00(0.00,0.22) 0.00(0.00,0.22) 0.00(0.00,0.22)ii N1 0.13(0.02,0.40) 0.07(0.00,0.32) 0.00(0.00,0.22) 0.00(0.00,0.22) 0.00(0.00,0.22)iii N2 0.53(0.27,0.79) 0.67(0.38,0.88) 0.07(0.00,0.32) 0.40(0.16,0.68) 1.00(0.78,1.00)iii N3 1.00(0.78,1.00) 0.93(0.68,1.00) 0.33(0.12,0.62) 0.53(0.27,0.79) 1.00(0.78,1.00)iv M5 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00)iv M6 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00)iv NA1 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00)iv M7 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00) 1.00(0.78,1.00)

Log_10(R0)(95%CI)

Sub-region Site Base CCSM4 MPI-ESM-MR CanESM MIROC5i M3 -0.3(-0.7,-0.0) -2.1(-2.4,-1.7) -1.5(-1.8,-1.1) -2.4(-2.7,-2.0) -0.2(-0.4,-0.0)i S1 0.7(0.2,1.1) -0.5(-0.9,-0.0) -1.5(-2.0,-1.0) -2.8(-3.0,-2.5) -0.2(-0.6,0.1)i M4 1.0(0.7,1.4) -0.0(-0.5,0.5) -0.3(-0.7,0.1) -2.4(-2.7,-2.1) 0.8(0.5,1.0)ii M1 -1.9(-2.4,-1.4) -2.8(-3.0,-2.6) -3.0(-3.0,-3.0) -3.0(-3.0,-3.0) -2.5(-2.8,-2.2)ii M2 -2.2(-2.7,-1.7) -2.7(-3.0,-2.4) -2.8(-3.0,-2.6) -2.9(-3.1,-2.8) -1.9(-2.2,-1.6)ii N1 -0.7(-1.1,-0.4) -1.3(-1.7,-0.9) -2.2(-2.6,-1.7) -2.2(-2.6,-1.7) -0.8(-1.0,-0.6)iii N2 0.0(-0.3,0.3) 0.2(-0.2,0.6) -0.8(-1.1,-0.5) -0.1(-0.3,0.1) 1.0(0.8,1.3)iii N3 1.0(0.7,1.3) 0.9(0.6,1.1) -0.2(-0.5,0.0) -0.1(-0.4,0.1) 1.2(1.1,1.4)iv M5 1.6(1.5,1.8) 1.4(1.2,1.6) 1.2(0.9,1.4) 1.2(0.9,1.5) 1.5(1.3,1.6)iv M6 2.0(1.9,2.2) 2.1(1.9,2.2) 1.9(1.8,2.0) 1.5(1.4,1.6) 2.3(2.2,2.4)iv NA1 1.6(1.3,1.8) 1.7(1.6,1.9) 1.4(1.1,1.7) 1.7(1.5,1.9) 2.0(1.9,2.1)iv M7 1.9(1.8,2.0) 2.1(2.0,2.3) 1.9(1.8,2.1) 1.6(1.5,1.6) 2.4(2.2,2.5)

PeakPrevalenceinchildrenaged2-10(95%CI)

Sub-region Site Base CCSM4 MPI-ESM-MR CanESM MIROC5i M3 2%(1,3) 3%(2,4) 2%(1,3) 3%(1,5) 3%(1,4)i S1 11%(2,20) 2%(1,2) 2%(1,2) 2%(2,3) 2%(1,2)i M4 46%(34,58) 9%(5,14) 4%(2,6) 9%(5,14) 17%(7,28)ii M1 1%(1,1) 1%(1,1) 1%(1,1) 2%(1,2) 1%(1,2)ii M2 2%(1,3) 2%(1,3) 2%(1,3) 3%(2,5) 2%(1,4)ii N1 2%(1,3) 2%(1,3) 2%(1,2) 2%(1,4) 2%(0,4)iii N2 2%(1,4) 4%(1,6) 2%(1,4) 4%(0,8) 19%(8,29)iii N3 23%(11,35) 13%(10,16) 4%(2,6) 4%(0,8) 26%(19,33)iv M5 42%(26,57) 43%(26,60) 30%(13,47) 27%(8,46) 29%(18,40)iv M6 66%(57,75) 67%(59,76) 64%(55,74) 33%(18,48) 69%(61,78)iv NA1 48%(29,67) 66%(52,80) 54%(38,70) 55%(38,71) 66%(51,80)iv M7 65%(55,74) 67%(57,78) 66%(57,76) 35%(19,51) 69%(59,79)


26

Supplementary Table 2: Coefficient of determination (R2) values for annual rainfall, TJAS and indices of malaria transmission

TJAS log10(R0) log10(EIR) Pr2-10 ImmAnnualrainfall 0.51 0.72 0.81 0.62 0.68

TJAS 0.67 0.64 0.52 0.42log10(R0) 0.87 0.51 0.53log10(EIR) 0.7 0.81Pr2-10 0.65

Allpairsofvariablesarecorrelatedwithsignificancelevelsp<<0.0001


27

Supplementary Table 3: Coefficients and statistics of regression model.

Regressionmodelstaketheformoff(TJAS,rain)=a+b*TJAS+c*rain/1000


28

Supplementary Table 4: Summary of data sources

Datasource Spatialresolution

Temporalresolution

Reference

BaselineClimatology

temperature CRUTS3.21 0.5x0.5degree

1month1901-2012

35

rainfall CRUTS3.21 0.5x0.5degree

1month1901-2012

35

MeteorologicalInputsforHYDREMATSSimulation

precipitation CMORPHversion1.0 ~8km 30min1998-present

1

temperature ERA-Interim .75x.75degree

3hour1979-present

8

windspeed ERA-Interim .75x.75degree

3hour1979-present

8

winddirection ERA-Interim .75x.75degree

3hour1979-present

8

surfaceradiation ERA-Interim .75x.75degree

3hour1979-present

8

Othermodelinputs

soiltype HarmonizedWorldSoilDatabase

~1km Static 10

vegetation UniversityofMarylandLandcover

1km Static 9

topography ComputedfromEnvisatsyntheticapertureradarandgroundsurvey

10m Static36

householdlocations Quickbirdimage 0.6m Static 26


29

Supplementary Table 5: Performance of CMIP5 Climate Models

ModelName RainfallScore TemperatureScore Total

Zone1 Zone2 Zone3 Zone1 Zone2 Zone3

BNU-ESM 1 1 1 1 1 1 6

MIROC5 1 1 1 1 1 5

MPI-ESM-MR 1 1 1 1 4

CANESM2 1 1 1 3

CCSM4 1 1 1 3

FGOALS-g2 -1 1 1 1 2

CESM1-CAM5 1 1 2

MIROC-ESM-CHEM 1 1

FIO-ESM 1 1

BCC-CSM1-1 1 1

CNRM-CM5 -1 1 1 1

CSIRO-Mk3-6-0 -1 -1 1 1 1 1

CMCC-CM 0

GFDL-CM3 0

GISS-E2-H -1 -1

HadGEM2-AO -1 -1

ACCESS -1 -1 -2

MRI-CGCM3 -1 -1 -1 -3

EC-EARTH -1 1 -1 -1 -1 -3

IPSL-CM5A-MR -1 -1 -1 -3

GFDL-ESM2M -1 -1 -1 -3

inmcm4 1 -1 -1 -1 -1 -3

HadGEM2-CC -1 -1 -1 -1 -1 -5


30

Supplementary Information References

1 Joyce,R.J.,Janowiak,J.E.,Arkin,P.A.&Xie,P.CMORPH:Amethodthatproducesglobalprecipitationestimatesfrompassivemicrowaveandinfrareddataathighspatialandtemporalresolution.JournalofHydrometeorology5,487-503(2004).

2 Serrat‐Capdevila,A.,Valdes,J.B.&Stakhiv,E.Z.Watermanagementapplicationsforsatelliteprecipitationproducts:Synthesisandrecommendations.JAWRAJournaloftheAmericanWaterResourcesAssociation50,509-525(2014).

3 Gosset,M.,Viarre,J.,Quantin,G.&Alcoba,M.EvaluationofseveralrainfallproductsusedforhydrologicalapplicationsoverWestAfricausingtwohigh‐resolutiongaugenetworks.QuarterlyJournaloftheRoyalMeteorologicalSociety139,923-940(2013).

4 Yamana,T.K.&Eltahir,E.A.B.Ontheuseofsatellite-basedestimatesofrainfalltemporaldistributiontosimulatethepotentialformalariatransmissioninruralAfrica.WaterResourcesResearch47,W02540(2011).

5 Wolff,D.B.etal.Groundvalidationforthetropicalrainfallmeasuringmission(TRMM).JournalofAtmosphericandOceanicTechnology22,365-380(2005).

6 DeVera,A.&Terra,R.CombiningCMORPHandraingaugesobservationsovertheRioNegroBasin.JournalofHydrometeorology13,1799-1809(2012).

7 Guilloteau,C.etal.Impactsofsatellite-basedrainfallproductsonpredictingspatialpatternsoftheRiftValleyFevervectors.JournalofHydrometeorology(2014).

8 Dee,D.P.etal.TheERA-Interimreanalysis:configurationandperformanceofthedataassimilationsystem.QuarterlyJournaloftheRoyalMeteorologicalSociety137,553-597,doi:10.1002/qj.828(2011).

9 Hansen,M.C.,DeFries,R.S.,Townshend,J.R.G.&Sohlberg,R.Globallandcoverclassificationat1kmspatialresolutionusingaclassificationtreeapproach.InternationalJournalofRemoteSensing21,1331-1364(2000).

10 Nachtergaele,F.etal.Harmonizedworldsoildatabase.FoodandAgricultureOrganizationoftheUnitedNations(2008).

11 Morin,J.Soilcrustingandsealing.SoiltillageinAfrica:needsandchallenges.FAOSoilsBulletin(FAO)69,41-41-67(1993).

12 Yamana,T.K.&Eltahir,E.A.B.ProjectedimpactsofclimatechangeonenvironmentalsuitabilityformalariatransmissioninWestAfrica.EnvironHealthPerspect121,1179-1186(2013).

13 Cook,K.H.&Vizy,E.K.CoupledmodelsimulationsoftheWestAfricanmonsoonsystem:Twentieth-andtwenty-first-centurysimulations.JournalofClimate19,3681-3703(2006).

14 Roehrig,R.,Bouniol,D.,Guichard,F.,Hourdin,F.&Redelsperger,J.-L.ThepresentandfutureoftheWestAfricanmonsoon:aprocess-orientedassessmentofCMIP5simulationsalongtheAMMAtransect.JournalofClimate(2013).

15 Druyan,L.M.Studiesof21st‐centuryprecipitationtrendsoverWestAfrica.InternationalJournalofClimatology31,1415-1424(2011).

16 Crétat,J.,Vizy,E.K.&Cook,K.H.HowwellaredailyintenserainfalleventscapturedbycurrentclimatemodelsoverAfrica?ClimateDynamics42,2691-2711,doi:10.1007/s00382-013-1796-7(2013).

17 Bomblies,A.Modelingtheroleofrainfallpatternsinseasonalmalariatransmission.ClimaticChange112,673-685(2012).


31

18 Bomblies,A.Agent-basedmodelingofmalariavectors:theimportanceofspatialsimulation.ParasitVectors7,1-10(2014).

19 Mote,P.W.&SalatheJr,E.P.FutureclimateinthePacificNorthwest.ClimaticChange102,29-50(2010).

20 Horton,R.M.etal.ClimatehazardassessmentforstakeholderadaptationplanninginNewYorkCity.JournalofAppliedMeteorologyandClimatology50,2247-2266(2011).

21 Hay,L.E.,Wilby,R.L.&Leavesley,G.H.(WileyOnlineLibrary,2000).22 Seth,A.etal.CMIP5projectedchangesintheannualcycleofprecipitationin

monsoonregions.JournalofClimate26,7328-7351(2013).23 Biasutti,M.&Sobel,A.H.DelayedSahelrainfallandglobalseasonalcycleina

warmerclimate.GeophysicalResearchLetters36(2009).24 Biasutti,M.ForcedSahelrainfalltrendsintheCMIP5archive.JournalofGeophysical

Research:Atmospheres118,1613-1623(2013).25 Wang,G.&Alo,C.A.ChangesinprecipitationseasonalityinWestAfricapredictedby

RegCM3andtheimpactofdynamicvegetationfeedback.InternationalJournalofGeophysics2012(2012).

26 Bomblies,A.,Duchemin,J.B.&Eltahir,E.A.B.Hydrologyofmalaria:ModeldevelopmentandapplicationtoaSahelianvillage.WaterResour.Res44(2008).

27 Bomblies,A.,Duchemin,J.B.&Eltahir,E.A.B.Amechanisticapproachforaccuratesimulationofvillage-scalemalariatransmission.MalariaJournal8,223(2009).

28 Yamana,T.K.,Bomblies,A.,Laminou,I.M.,Duchemin,J.-B.&Eltahir,E.A.Linkingenvironmentalvariabilitytovillage-scalemalariatransmissionusingasimpleimmunitymodel.Parasites&Vectors6,226(2013).

29 Beier,J.C.,Killeen,G.F.&Githure,J.I.Shortreport:entomologicinoculationratesandPlasmodiumfalciparummalariaprevalenceinAfrica.TheAmericanJournalofTropicalMedicineandHygiene61,109-113(1999).

30 Gething,P.W.etal.Anewworldmalariamap:Plasmodiumfalciparumendemicityin2010.MalarJ10,1475-2875(2011).

31 Molineaux,L.&Gramiccia,G.TheGarkiProject.(WorldHealthOrganization,1980).32 Omer,S.M.&Cloudsley-Thompson,J.L.SurvivaloffemaleAnophelesgambiaeGiles

througha9-monthdryseasoninSudan.BulletinoftheWorldHealthOrganization42,319(1970).

33 Sultan,B.etal.RobustfeaturesoffutureclimatechangeimpactsonsorghumyieldsinWestAfrica.EnvironmentalResearchLetters9,104006(2014).

34 Lee,J.-Y.&Wang,B.FuturechangeofglobalmonsoonintheCMIP5.ClimateDynamics42,101-119,doi:10.1007/s00382-012-1564-0(2012).

35 Mitchell,T.D.&Jones,P.D.Animprovedmethodofconstructingadatabaseofmonthlyclimateobservationsandassociatedhigh-resolutiongrids.InternationalJournalofClimatology25,693-712,doi:10.1002/joc.1181(2005).

36 Toutin,T.&Gray,L.State-of-the-artofelevationextractionfromsatelliteSARdata.ISPRSJournalofPhotogrammetryandRemoteSensing55,13-33(2000).


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