ExtendingABMapproachestonationalandcontinentalscales

MarkRounsevell

InstituteofGeography&theLivedEnvironmentSchoolofGeosciencesUniversityofEdinburgh

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Theuptakeofenergycrops(miscanthus andShortRotationCoppicing)

Howquicklyareclimatemitigationpoliciesadopted?

Alexander,P.,Moran,D.,Rounsevell,M.D.A.&Smith,P.(2013).Modellingtheperennialenergycropmarket:theroleofspatialdiffusion.JournaloftheRoyalSocietyInterface,10,20130656doi:10.1098/rsif.2013.0656

Nationalscalemodelling:Agent-BasedModels(ABM)

Simulationoftheuptakeofthebioenergycropsmiscanthus andshortrotationcoppicing

Alexanderetal.(2013).JournaloftheRoyalSocietyInterface,10,20130656

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EnergycropareaintheUK(1000ha)

OilseedrapeareainEnglandandW

ales(1000ha)

Yearssincebaseline

ObservedOilseedRape

Modelledperennialenergycrops

Timelagsinadaptation- historicoilseedrapedataforEnglandandWales,againstabaselineyearof1966,andmeanmodelledperennialenergycropareas,usingabaselineyearof2010

Alexander,P.,Moran,D.,Rounsevell,M.D.A.&Smith,P.(2013).Modellingtheperennialenergycropmarket:theroleofspatialdiffusion.JournaloftheRoyalSocietyInterface,10,20130656doi:10.1098/rsif.2013.0656

Arneth,A.,Rounsevell,M.D.A.&Brown,C.(2014).Globalmodelsofhumandecision-makingforland-basedmitigationandadaptationassessment.NatureClimateChange,4,550–557

Arneth,A.,Rounsevell,M.D.A.&Brown,C.(2014).Globalmodelsofhumandecision-makingforland-basedmitigationandadaptationassessment.NatureClimateChange,4,550–557

ModellingAdaptation toGlobalChangeintheSwedishforestrysector

CRAFTY-SwedenModel

Society

ModellingAdaptation toGlobalChangeintheSwedishforestrysector

CRAFTY-SwedenModel1km2 resolution

ForestOwnerFunctionalTypes

(Blancoetal.2015)

MappingandmodellingLandOwnerTypes

PineSpruceBorealBroadleafNemoralBroadleafPine– SprucePine– BorealBroadleaf(≥30%)Pine– BorealBroadleaf(<30%)Spruce– BorealBroadleaf(≥30%)Spruce– BorealBroadleaf(<30%)

ArtificialSurfaceAgricultureProtectedAreaNon-productiveForestSemi-naturalVegetationOpenSpaceWetlandWaterBodyUnmanaged

UnavailableFarmer(Commer/Non-commer +Cereal/Meat)

UnavailableUnavailableUnmanagedUnavailableUnmanagedUnavailableUnmanaged

Productionist PineProductionist SpruceProductionist Pine– SpruceProductionist BorealBr.Multi-objectivePine– SpruceMulti-objectivePine– BorealBr.Multi-objectiveSpruce– BorealBr.Multi-objectiveBorealBr.RecreationalistPine– SpruceRecreationalistBorealBr.RecreationalistNemoralBr.ConservationistBorealBr.ConservationistNemoralBr.PassivePine– BorealBr.PassiveSpruce– BorealBr.PassiveBorealBr.PassiveNemoralBr.

Land Use

Land Owner Types

CompetitivenessofForestOwnerTypes

Num

bero

fAgents

Year

Regionalland-usechanges(2010-2100)

EcosystemServiceProvision(2010-2100)

CopingAbilityofForestOwnerTypes

Coping Ability:

degree to which an

owner type can be at

least as competitive

under a global change

future (defined by the

scenarios) as under

present conditions

SwedishForestryInstitutions

ConceptualmodelofgenericinstitutionsintheSwedishforestrysector

InstitutionalTypes

InstitutionalActionConceptualModel

LandplannerpreferenceweightsforscenariosScenario Description ServiceSupply-DemandDifferences

Timber Biodiversity Recreation

TIMBERPROFUSION

Available forest land is managed primarily fortimber production while other services aretreated as secondary. Timber supply is very highto the point of substantially going beyond thedemand. The supply of biodiversity associatedwith production-oriented forests is low. Undersuch circumstances some recreation isprovided, but it does notmeet demand.

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ENVIRONMENTALEDEN

A large proportion of the forest land is managedwith nature conservation as a primary objective.Supply of timber does not meet demand, whilebiodiversity is oversupplied. Recreation, beingpartly associated with levels of biodiversity, isalso supplied slightly beyond the demand.

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PERFECTEQUILIBRIUM

Forest land management seeks multi-functionality. Production levels of all threeservices are equal, but they do not meet thedemand.

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Example:supplydemanddifference(SDD)

Institutions asemergentstructures?

The CRAFTY model: continental scale applications

A1scenario

Waysforward?

• Buildingthenextgenerationofhumandimensionsmodelsthatarebasedon:– Bettertheory– Morecompleteprocessrepresentation,e.g.individualdecisionmaking,adaptivelearning,agentevolution,institutionalemergence,…

– Multi-scalardynamics– Buildingfromthebottom-up,ratherthanthetopdown

• Doweneedtypologies,ormodelsof8billionagents?

Modelsinalandusechangeinter-comparisonstudy

Uncertainties in global scale land use models

Global coefficient of variation and variance components

Landusechangeinahypothetical regionwithglobalandregionalised demand

Arneth,A.,Rounsevell,M.D.A.&Brown,C.(2014).Globalmodelsofhumandecision-makingforland-basedmitigationandadaptationassessment.NatureClimateChange,4,550–557

Economicmodeltype(CGEvsPE)forcropland

Source:PeterAlexander,UniversityofEdinburgh