landscapemodelling.net flute: @jamesmillington | james
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FLUTE: Food & Land Use Telecoupling ModelJames MillingtonKing’s College London
@jamesmillington | [email protected]
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Collaborators and Funding● Ramon Felipe Bicudo da Silva STATE UNIVERSITY OF CAMPINAS, Brazil
● Mateus Batistella EMBRAPA, Brazil
● Steve Peterson DARTMOUTH COLLEGE, USA
● Jem Woods IMPERIAL COLLEGE LONDON, UK
● Valeri Katerinchuk KING’S COLLEGE LONDON, UK
● Yue Dou VRIJE UNIVERSITEIT AMSTERDAM, Netherlands
● Daniel de Castro Victoria EMBRAPA, Brazil
● Hang Xiong HUAZHONG AGRICULTURAL UNIVERSITY, China
● Many others at Michigan State University and elsewhere
Supported by the UK Natural Environment Research Council (NE/M021548/1) and São Paulo Research Foundation FAPESP (14/50628-9, 15/25892-7 and 18/08200-2)
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Telecoupling Modelling ChallengesMillington et al. (2017) [LAND]
● Argued that hybrid computational models would beneficial● Highlighted challenges of data requirements and uncertainty assessment
Liu et al. (2018, p.64) [Current Opinion in Environmental Sustainability]
“Combining and synthesizing quantitative methods to examine scenarios of change will be particularly important for understanding spillover effects and options for future sustainability.”
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CRAFTY-Brazil
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Murray-Rust et al. (2014) [Environmental Modelling & Software]
● Climate● Transport Access● Conservation Value● Technological Inputs● Protected Areas
Capitals
● Individual Services ● Soy-Maize Double Crop ● Pristine ‘Nature’● Secondary ‘Nature’
Agents
Cobb-Douglas Production Function: ● Soy
● Maize ● Beef
Services
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CRAFTY-Brazil
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Simulation of 10 Brazilian States
(4 million km2, annually) based on observed
land use and production data
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CRAFTY-Brazil Results
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Model appropriately reproduces observed time-series of:
(a) land use (b) production
Mod: ModelledObs: Observed
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CRAFTY-Brazil Results
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Model is not as accurate at reproducing spatial distribution of land uses
Difference: Municipality proportional difference from observed
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CRAFTY-Brazil Results
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Time-series output from counter-factual scenarios (below)
Model is more sensitive to yields than climate (or demand)
YieldA: high increase rateYieldB: low increase rate
RCP45: med GHG emissions RCP85: high GHG emissions
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CRAFTY-Brazil Results
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Spatial output from counter-factual scenarios (below)
Widespread change for yield scenarios vs localised for climate
YieldA: high increase rateYieldB: low increase rate
RCP45: med GHG emissions RCP85: high GHG emissions
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FLUTE
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Coupling STELLA (BioLUC) with CRAFTY-Brazil
Warner et al. (2013)[Environmental Research Letters]
Global Annual Commodity Demand
Total Brazil Annual Production
STELLA - BioLUC18 Global Regions
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FLUTE: Testing Results
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Time-series output from counter-factual scenarios
Testing model behaviour and comparing global regions for land area
CHIHKG : China, Hong KongS S AFR: Sub-Saharan Africa
DC: Soy-Maize double crop
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FLUTE: Testing Results
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Export Flows (Gigagrams)
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FLUTE: Example Application
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2019 Swine Flu in ChinaMarked decrease in soy imports & significant increases in pork and beef imports
Scenarios (next decade)1. Observed changes are permanent and the new demand levels continue 2. Gradual return to per capita demand levels in China for soy and pork 3. Decline in per capita consumption of pork and beef over next 10 to 20 years
Outputs● Land and Production impacts for regions around the world (incl. spillovers)● Brazil: focus on changes at margin for soy production and de/re-forestation
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Telecoupling Modelling ChallengesCoupling Existing Models● Pros: Existing, verified code base for faster development (theoretically)● Cons: Constraints on representation (e.g agent memory in CRAFTY)
Data Requirements ● Analysis and assumptions needed to produce inputs
○ e.g. downscaling municipality production/yield data to pixel-level ○ e.g. assume demand was equal to consumption
Uncertainty Assessment ● Reproducing temporal trends easier than spatial allocation● Spatial and temporal variation in parameters (e.g. yields)● [Beauty | Verification] is in the eye of the [Beholder | Stakeholder]?
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Thanks!
James MillingtonKing’s College London
@jamesmillington | [email protected]