extreme weather: envisioning ontario agriculture · 2016-04-28 · extreme weather: envisioning...
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Extremeweather:envisioningOntarioagriculture
ScottMitchell1,AnnaZaytseva1,DanMacDonald2,andRuthWaldick1,2
AdaptationCanada2016
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Purpose->constraints• “…createanddeliverinformationaboutprospectiveclimateextremesthatwillaffectOntario’sagriculturesectorandruralcommunities.Wewilldevelopadecisionsupportmodel(DSM)tocharacterizeriskandvulnerabilitiesassociatedwithclimatechangeandextremesinagriculture,allowinguserstoplanforandmitigaterisksbyevaluatingdifferentadaptationchoices.”
• spatialscenariomodellingframework– impactsoncropsandlivestock*• map-based,field-levelmapping;expectations• datarealities:weatherstations(time),GCMresolution• temporalscalesatwhichcansaymuchaboutfutureextremeeventsarehardtotranslatetoimpactstocropsandlivestock
• useofseasonal,phenology-linkedindiceswithlinkstospecificcrops
(some)Issueswithexistinginformation• (asyou’veheard)therearelimitationsinusinglimitedweatherdata,orclimatemodelprojections,tocharacterizeextremeweather• howextremesusuallyconsidered?(climatemodelvariability)• spatial-temporalresolutionissues/discrepancies• howarethoserelevanttofarm-scale/locallevelplanning?
• someoftheoptionswe’veconsidered• GCMoutput:customdownscaling,PCICdownscaling(tostationorgrid)• pastweatherdata:everythingavailable?“cleaned”data?
• station-basedorgrid(10kmregulargridusedbyAAFC,EC)?• temporalresolution:aggregatesummaries?Dailyvariability?
• scenarios:• GCM:AR4/AR5?Allmodels?Subset?• agriculture,demographics,economic(scale)
Whyfocusonscenariosandphenologicalimpactmodelling?• everyGCMmodelrunisascenario,notaprediction
• ecosystemresponseontopofthatimpactedbyrangeofpossiblereactions/adaptationfromallecosystemcomponents,includinghumans
• GCMslackspatialandtemporaldetail,but thereisdemandforinformationrelevanttolocallyevaluatinglevelsofriskandpotentialtradeoffs• finerresolutions(space&time)à assumptions&potentiallyveryhighdataneeds• usuallycan’tconfidentlyfillallthoseneeds,butcanexplorealikelyrange,considersetsoflikelyparametersunderfuturealternatescenarios
• cropmodellingtypicallyfocusesonyield,usingeitheraprocess-basedapproach(highuncertaintyinparameterizationacrosslargeregions)orempiricalmodels(usuallyassumingstationaryconditions)• phenologicalimpactmodellingallowsustoidentifytimeswhencropsareparticularlyvulnerabletoclimatologicalevents,andassignatypicalimpacttocropyield;concentrateonrelativeimpactsratherthanspecificphysiologicalprocesses
Indicesderivedfrom“just”weatherdata
• E.Ontarionotexpectedtobeahotspotofweatherextremes• buttypesofextremesofparticularrelevancein“regular”agriculturaloperationsarenotnecessarilywhatpeoplefirstthinkofas“extreme”
• “standard”indicesareavailabletoanalyse andcompareweather/extremes• usefultodescribegeneraltrends
• some,however,maskprocessesthatareimportanttoagriculture
Example:precipitation
CUMULATIVEDRYDAYS VERYWETDAYS
A.Zaytseva’s DRAFTM.Sc.Thesis(CarletonUniversity).
MORECROPRELEVANT:SEASONALPHENOLOGYINDICES• Corn(forexample):
A.Zaytseva’s DRAFTM.Sc.Thesis(CarletonUniversity).
Example:projectedseedingdelays
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2011 2012 2013 2014 2015 2016 2019 2020
Area"lost"tofallowduetoseedingdelays
541 543 544 545 546 547
Lessonsandfutureconsiderations:
• crop- andphenology-specific,scenarioimpact-basedapproachtoextremesallowsustohighlightrelativerisksof“subtle”butagriculturallyrelevantshiftsinclimate• relevance:impactonfarmoperations• potentialtoevaluateswitchingto(orneedtodevelop)differentvarieties
• scenariomodelling:usesfield-leveldecisionsbutdoesnotrelyonneedingtoconfidentlyparameterizefield-leveldetailswithaspecific“reality”• relevanttocategoriesoffarmingoperationsastheyexistinthisregion,withrealbiophysicalconstraints• allowsustomanageuncertainty,andconcentrateonscenariosthathaverelevancetoadaptationplanning