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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

SCENARIO GENERATORA new tool in the InVEST 3.1 software suite

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

• 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

• WaterGAP

CLIMATE

• MAGICC/SCENGEN

• AIM

• GTAP/MAGNET

• IMAGE

LAND USE PLANNING

• Metronamica

SYSTEM DYNAMICS

• Vensim

OPTIMIZATION

• Marxan

GLOBAL

• GLOBIOM

• IMAGE

• International Futures

SPECIFIC THEMES

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

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

INVEST SCENARIO GENERATORConverting storylines into maps

• 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

• Econometric modeling tool• Forecasting model• Optimization tool• Regression-based• Highly complex

WHAT IT’S NOT

ILLEGAL GOLD MINING, PERUA CASE FROM SCENARIO GENERATOR

ILLEGAL GOLD MINING, PERUAN EXAMPLE

SCENARIOHUB.NET

Web-based forms to gather info from stakeholders & experts

Info converted to inputs in correct format for Scenario Generator

Under construction…

GLOSSARY

• Drivers• Storylines• Transition likelihood• Factors• Patch size• Constraints• Overrides

TERMS IN SCENARIO GENERATOR

DRIVERS

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

SPATIAL RULESIN SCENARIO GENERATOR

TRANSITION LIKELIHOODGOING FROM STORYLINES TO MAPS

Forest AgricultureGrassland

Original

Forest AgricultureGrassland BuiltScenario

TRANSITION MATRIX

/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

TRANSITION MATRIX

/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

TRANSITION MATRIX

/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

TRANSITION MATRIX

/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

TRANSITION MATRIX

/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

TRANSITION MATRIX

/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

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

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

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

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

MODEL FLOW

Current land cover

Factors

Transition matrix

Suitability

Patch size

Allocation

Constraints Proximity

Override Scenario

RulesRules

SCENARIOS QUIZWhat have you learned?

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

USER NOTES

• Minimize number of transitions, select most important ones

• Iterative process

SCENARIOHUB.NET

Web-based forms to gather info from stakeholders & experts

Info converted to inputs in correct format for Scenario Generator

Under construction…

LET’S RUN IT!Scenario Generator

SCENARIO GENERATORINVEST 3.1.0

SAMPLE DATAGREATER VIRUNGA REGION, EAST AFRICA

VIRUNGAS EXAMPLE

TANINTHARYI, MYANMARScenario Generator case

• Biodiverse forests

• Infrastructure development

• Complex politics around land use, reform, resettlement

• Lessons for national-level planning

SCENARIO ASSUMPTIONS

CONSTRAINTS & OVERRIDES

Constraints Overrides

ADDITIONAL INPUTS

Footprint of reservoir due to proposed damOverride

Factor: roads

BASELINE & SCENARIOS

Baseline land cover (2013)

“Limited conversion” scenario

“More conversion” scenario

Dam reservoir

Mangrove loss

52ACES scenarios workshop December 8, 20141/6/2015

Baseline

2013 land cover

“Limited conversion”

Some deforestation around roads and population centers

“More conversion”

More deforestation around roads and population centers

ECOSYSTEM SERVICE OUTCOMES UNDER TANINTHARYI SCENARIOS

CARBON EMISSIONS

EROSION

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

• Land Change Modeler

• CLUE

• LandSHIFT

• Marxan

• Dinamica

• GeoMod

• MAGICC/SCENGEN

• Metronamica

SPATIAL MODELS

SCENARIO GENERATOR KITWHAT IT DOES

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

Likelihood(+)Factors(+)Proximity (+)Constraints (x)

weighted

Aggregate transition probability

Scenario Documenter

Scenario Generator

Survey for Scenarios

SCENARIO GENERATOR

• Econometric modeling tool

• Projection or prediction model

• Optimization tool

• Regression-based

• Highly complex

WHAT IT’S NOT

SCHEMATIC

Allocation

Transition rate Priority

Override

Scenario

Quantity What goes first?

ALLOCATION

Suitability

Val (n) = 0 – 100Start from n = 100

Goal met?

Go to next cover

Suitable cells < goal?

Convert cells to Ag

Go to next lower

suitability

Suitable cells > 0?

No Group and select

Cells groupedTo minimum Patch size and selectedrandomly

In order ofpriority

(n = n-1)

While n > 0

PREPARING SUITABILITY LAYERS

Likelihood(+)

Factors(+)

Proximity (+)

Constraints (x)

weighted

Aggregate transition probability/suitability

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