BACK TO THE FUTURE - UF/IFAS OCI | Homeconference.ifas.ufl.edu/aces14/presentations/Dec 08 Monday/1...
Transcript of BACK TO THE FUTURE - UF/IFAS OCI | Homeconference.ifas.ufl.edu/aces14/presentations/Dec 08 Monday/1...
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1ACES scenarios workshop December 8, 2014
BACK TO THE FUTURE: SCENARIO GENERATOR FOR ECOSYSTEM SERVICES ANALYSIS
WWF and Natural Capital Project, ACES Workshop
December 8, 2014
Amy Rosenthal Nasser Olwero Nirmal Bhagabati
Adam Dixon Emily McKenzie Gregory Verutes
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2ACES scenarios workshop December 8, 2014
SCENARIO GENERATORA new tool in the InVEST 3.1 software suite
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3ACES scenarios workshop December 8, 2014
SCENARIO MODELING TOOLS
• IDRISI Land Change Modeler
• Metronamica
• PoleStar
• IMAGE
• WaterGAP
• AIM
• GLOBIOM
• CLUE-S
• GTAP/MAGNET
• LandSHIFT
• International Futures Model
• Marxan
• Dinamica
• GeoMod
• Vensim
• MAGICC/SCENGEN
• IPAT Scenario Navigator
MANY OPTIONS, DIFFERENT STRENGTHS
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4ACES scenarios workshop December 8, 2014
SCENARIO MODELING TOOLS
• IDRISI Land Change Modeler
• Metronamica
• PoleStar
• IMAGE
• WaterGAP
• AIM
• GLOBIOM
• CLUE-S
• GTAP/MAGNET
• LandSHIFT
• International Futures Model
• Marxan
• Dinamica
• GeoMod
• Vensim
• MAGICC/SCENGEN
• IPAT Scenario Navigator
SPATIAL MODELS
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5ACES scenarios workshop December 8, 2014
SCENARIO MODELING TOOLS
WATER
• WaterGAP
CLIMATE
• MAGICC/SCENGEN
• AIM
• GTAP/MAGNET
• IMAGE
LAND USE PLANNING
• Metronamica
SYSTEM DYNAMICS
• Vensim
OPTIMIZATION
• Marxan
GLOBAL
• GLOBIOM
• IMAGE
• International Futures
SPECIFIC THEMES
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6ACES scenarios workshop December 8, 2014
WHY ANOTHER TOOL?
• Complexity of modelling
• Lack of scenario development expertise
• Data scarcity
• Time required
• Translating qualitative to quantitative
• Engaging and using stakeholder input
CURRENT CHALLENGES IN PRACTICE
@ Taylor Ricketts
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7ACES scenarios workshop December 8, 2014
WHY ANOTHER TOOL?
Experience in Tanzania• Sparse data
• Stakeholder engagement
• Few easy tools
• Many steps in GIS
• Difficult to estimate relative strength of drivers
SCENARIO GENERATOR
Swetnam et al. 2011
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8ACES scenarios workshop December 8, 2014
INVEST SCENARIO GENERATORConverting storylines into maps
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9ACES scenarios workshop December 8, 2014
• Primarily designed to incorporate – Storylines based on stakeholder input, often gathered in a workshop setting
– Inputs gleaned from scientific literature surveys, policy documents, etc.
• Converts these inputs into a transition likelihood that a given pixel will change to a different land cover in the future
INVEST SCENARIO GENERATOR
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10ACES scenarios workshop December 8, 2014
INVEST SCENARIO GENERATOR
• Econometric modeling tool• Forecasting model• Optimization tool• Regression-based• Highly complex
WHAT IT’S NOT
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11ACES scenarios workshop December 8, 2014
ILLEGAL GOLD MINING, PERUA CASE FROM SCENARIO GENERATOR
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12ACES scenarios workshop December 8, 2014
ILLEGAL GOLD MINING, PERUAN EXAMPLE
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13ACES scenarios workshop December 8, 2014
ILLEGAL GOLD MINING, PERUAN EXAMPLE
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14ACES scenarios workshop December 8, 2014
SCENARIOHUB.NET
Web-based forms to gather info from stakeholders & experts
Info converted to inputs in correct format for Scenario Generator
Under construction…
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15ACES scenarios workshop December 8, 2014
GLOSSARY
• Drivers• Storylines• Transition likelihood• Factors• Patch size• Constraints• Overrides
TERMS IN SCENARIO GENERATOR
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16ACES scenarios workshop December 8, 2014
DRIVERS
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17ACES scenarios workshop December 8, 2014
STORYLINES
From: McKenzie, E., A. Rosenthal et al. 2012. Developing scenarios to assess ecosystem service tradeoffs: Guidance and case studies for InVEST users. World Wildlife Fund, Washington, D.C
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18ACES scenarios workshop December 8, 2014
SPATIAL RULESIN SCENARIO GENERATOR
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19ACES scenarios workshop December 8, 2014
TRANSITION LIKELIHOODGOING FROM STORYLINES TO MAPS
Forest AgricultureGrassland
Built
72
13
1
Original
Forest AgricultureGrassland BuiltScenario
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20ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
LocationGA
IN
LOSS
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21ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
Quantity
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22ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
Quantity
Note: in the current version of the tool, you can only specify increases.
Decreases will be addressed in a future version
X
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23ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
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24ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
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25ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
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26ACES scenarios workshop December 8, 2014
FACTORS
• In the scenario tool, “factors" are rules that increase or reduce likelihood of a change in land cover
• E.g., – Deforestation may be higher close to roads and
cities
– Agriculture may occur only within certain ranges of elevation or slope
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27ACES scenarios workshop December 8, 2014
CONSTRAINTS
• Areas where a change cannot take place, or has a lower propensity to take place
• E.g., no-go or limited conversion zones, enforced protected areas
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28ACES scenarios workshop December 8, 2014
PATCH SIZE
• Minimum area threshold for a land cover or use
• E.g., new large-scale agricultural areas have to be at least 10 hectares
© Saudi Arabia Investment Group
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29ACES scenarios workshop December 8, 2014
OVERRIDES
• Areas where a land cover change will definitely take place
• E.g., a forest concession destined to convert to an oil palm plantation under BAU
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30ACES scenarios workshop December 8, 2014
MODEL FLOW
Current land cover
Factors
Transition matrix
Suitability
Patch size
Allocation
Constraints Proximity
Override Scenario
RulesRules
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31ACES scenarios workshop December 8, 2014
SCENARIOS QUIZWhat have you learned?
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32ACES scenarios workshop December 8, 2014
CONSIDERATIONS
• Accuracy depends on stakeholders
• Stakeholder-given values are best for near future
• Currently, users can input desired increase in a specific land cover, but not loss– This feature will be added in future releases
• Model assumes a cover type either increases or decreases but not both
• Assumes a single-step transition
CURRENT LIMITATIONS
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33ACES scenarios workshop December 8, 2014
USER NOTES
• Minimize number of transitions, select most important ones
• Iterative process
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34ACES scenarios workshop December 8, 2014
SCENARIOHUB.NET
Web-based forms to gather info from stakeholders & experts
Info converted to inputs in correct format for Scenario Generator
Under construction…
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35ACES scenarios workshop December 8, 2014
DEMO
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36ACES scenarios workshop December 8, 2014
LET’S RUN IT!Scenario Generator
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37ACES scenarios workshop December 8, 2014
SCENARIO GENERATORINVEST 3.1.0
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38ACES scenarios workshop December 8, 2014
SCENARIO GENERATORINVEST 3.1.0
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39ACES scenarios workshop December 8, 2014
SCENARIO GENERATORINVEST 3.1.0
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40ACES scenarios workshop December 8, 2014
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41ACES scenarios workshop December 8, 2014
SAMPLE DATAGREATER VIRUNGA REGION, EAST AFRICA
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42ACES scenarios workshop December 8, 2014
VIRUNGAS EXAMPLE
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43ACES scenarios workshop December 8, 2014
TANINTHARYI, MYANMARScenario Generator case
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44ACES scenarios workshop December 8, 2014
• Biodiverse forests
• Infrastructure development
• Complex politics around land use, reform, resettlement
• Lessons for national-level planning
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45ACES scenarios workshop December 8, 2014
SCENARIO ASSUMPTIONS
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46ACES scenarios workshop December 8, 2014
CONSTRAINTS & OVERRIDES
Constraints Overrides
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47ACES scenarios workshop December 8, 2014
ADDITIONAL INPUTS
Footprint of reservoir due to proposed damOverride
Factor: roads
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48ACES scenarios workshop December 8, 2014
BASELINE & SCENARIOS
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49ACES scenarios workshop December 8, 2014
Baseline land cover (2013)
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50ACES scenarios workshop December 8, 2014
“Limited conversion” scenario
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51ACES scenarios workshop December 8, 2014
“More conversion” scenario
Dam reservoir
Mangrove loss
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52ACES scenarios workshop December 8, 20141/6/2015
Baseline
2013 land cover
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53ACES scenarios workshop December 8, 20141/6/2015
“Limited conversion”
Some deforestation around roads and population centers
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54ACES scenarios workshop December 8, 20141/6/2015
“More conversion”
More deforestation around roads and population centers
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55ACES scenarios workshop December 8, 2014
ECOSYSTEM SERVICE OUTCOMES UNDER TANINTHARYI SCENARIOS
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56ACES scenarios workshop December 8, 2014
CARBON EMISSIONS
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57ACES scenarios workshop December 8, 2014
EROSION
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58ACES scenarios workshop December 8, 2014
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59ACES scenarios workshop December 8, 2014
SCENARIO GENERATOR
• Turn storylines into maps
• Useful for projects with short timeline or in data-sparse environment
• Translation of qualitative & stakeholder inputs into scenario maps
• Replicable
• Good ‘what-if’ tool
• Particularly good for visions, explorations & interventions (in data-poor areas)
WHAT IT DOES
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60ACES scenarios workshop December 8, 2014
SCENARIO MODELING TOOLS
• Land Change Modeler
• CLUE
• LandSHIFT
• Marxan
• Dinamica
• GeoMod
• MAGICC/SCENGEN
• Metronamica
SPATIAL MODELS
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61ACES scenarios workshop December 8, 2014
SCENARIO GENERATOR KITWHAT IT DOES
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62ACES scenarios workshop December 8, 2014
SCENARIO GENERATOR KIT
Scenario Documenter
• Can be used in workshop setting with stakeholders
• Inputs from scientific literature, surveys & policy documents
Scenario Generator
• Converts inputs into transition likelihood that a given pixel will change to a different land cover in the future
• Based on total area allocated, user-defined constraints, proximity and access rules
Survey for Scenarios
• Gathers info from stakeholders & experts
• Info converted to kit-ready inputs for Scenario Hub and Generator
• (Under construction!)
WHAT IT DOES
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63ACES scenarios workshop December 8, 2014
Likelihood(+)Factors(+)Proximity (+)Constraints (x)
weighted
Aggregate transition probability
Rules
Scenario Documenter
Scenario Generator
Survey for Scenarios
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64ACES scenarios workshop December 8, 2014
SCENARIO GENERATOR
• Econometric modeling tool
• Projection or prediction model
• Optimization tool
• Regression-based
• Highly complex
WHAT IT’S NOT
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65ACES scenarios workshop December 8, 2014
SCHEMATIC
Allocation
Transition rate Priority
Override
Scenario
Quantity What goes first?
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66ACES scenarios workshop December 8, 2014
ALLOCATION
Suitability
Val (n) = 0 – 100Start from n = 100
Goal met?
Go to next cover
Yes
Suitable cells < goal?
No
Yes
Convert cells to Ag
y
Go to next lower
suitability
Suitable cells > 0?
No Group and select
Yes
No
Cells groupedTo minimum Patch size and selectedrandomly
In order ofpriority
(n = n-1)
While n > 0
No
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67ACES scenarios workshop December 8, 2014
PREPARING SUITABILITY LAYERS
Likelihood(+)
Factors(+)
Proximity (+)
Constraints (x)
weighted
Aggregate transition probability/suitability
Rules