Land System Change Modelling - gms-eoc.org

Land System Change ModellingWhy, What and How?

Peter Verburg

2

2000190018001700

Anthromes 2How did the biosphere become anthropogenic?

Ellis et al. 2010

Urban Densesettlements

DenseSettlements

Woodlands Treeless& Barren

WildlandsSeminaturalRangelands

Residential PopulatedRemote

Croplands

Rice

Irrigated

Rainfed Pastoral

Villages

Irrigated Rainfed

ResidentialPopulated

Remote Treeless& Barren

ResidentialPopulated

Remote

Woodlands

SeminaturalUsed WildSeminaturalUsed WildSeminatural WildUsed Seminatural WildUsed

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Description of ‘model’

Simplified, idealized representation of a part of the real world

Learning tool

Experimental tool

Simulation tool

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Use of models

Tool to structure information, test hypothesis and validatenarrative models

• tool to provide more insights in the driving factorsand dynamics of LUCC (stakeholder: mainlyscientists)

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Complexity in land use studies

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Models for ‘learning’

Observations of landuse change Understanding of

land use change

Conceptual model

Simulation model

Model run for knownperiod

Validation

Learning

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Use of models



Ex-ante assessment• tool to evaluate impact of alternative land use

policies or indirect impacts of other policies Discussion support system

• tool to explore alternative futures to triggerdiscussion among stakeholders and createawareness of issues in land use and/or naturalresource management

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Policy cycle

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Explore ‘futures’ and ‘identify possible problems’

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A2 B2

B1A1Less

Intervention

Global

MoreIntervention

Local

V-A1

V-A2

V-B1

V-B2

Scenarios

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Scenarios

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Policy cycle

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Ex-ante assessment

Austria

Protecting ecologicalcorridors

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Policy cycle

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Ex-post assessments

Monitoring

Modelling based on observations to identify patterns (ofteneconometric models testing significance of findings/policyimpact) (e.g.: are protected areas effective)

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Use of models



Ex-ante assessment• tool to evaluate impact of alternative land use

policies or indirect impacts of other policies Discussion support system

• tool to explore alternative futures to triggerdiscussion among stakeholders and createawareness of issues in land use and/or naturalresource management

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Different roles of modelling connected

Observations andunderstanding

Model building andimplementation

Validation

Scenario analysis Alternative

policies/management options

Ex-ante analysis

Policy/magementimplementation

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Model types

Spatial vs. non-spatial

Dynamic vs. static

Descriptive vs. prescriptive

Deductive vs. inductive

Agent-based vs. pixel-based

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Model types


Dynamic vs. static




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Model types

• Non-spatial model:– How much land use

change?– Why does land use

change?• Spatial model:

– How much land usechange?

– Why does land usechange?

– Where will land usechange happen?

0.00

50000.00

100000.00

150000.00

200000.00

250000.00

2000

2004

2008

2012

2016

2020

2024

2028

built-up areanon-irrigated arablepastureforest/natureinland wetlandsstatic LUT'sirrigated arableabandoned land

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Model types


Dynamic vs. static




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Model types

Static model• Analysis of driving factors, linear extrapolation (no time steps)

Dynamic model• Explicit attention for temporal dynamics, feedback, path-

dependence (discrete time steps)

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Model types


Dynamic vs. static




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Types of land use models

Descriptive models aim at simulating thefunctioning of the land use system andgenerate spatially explicit explorations ofnear future land use patterns

Prescriptive models aim at the calculation ofoptimised land use configurations that bestmatch a set of goals and objectives

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Descriptive vs. Prescriptive models

020406080

100120140160

1980 1990 2000 2010 2020 2030 2040

Scen A

Scen B

Explorative

NormativeBack-casting

Policyintervention

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Scenario construction

Land use optimisation

Land evaluation

OptimizationModel

Interactive Multiple GoalLinear Programming

Objective functions

Land use options &Goal achievements

Data Maps

Policy views

Input/outputtables of

productionactivities

Constraints concerning resources:area, water, labour

Delineation of land units,resource assessment,

Land suitability analysis &Yield estimation

Prescriptive model (LUPAS: Roetter et al., 2005; Castella et al.,2007)

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Descriptive model(CLUE; Castella et al., 2007)

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Descriptive vs. Prescriptive models

Discussion and negotiation

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Model types


Dynamic vs. static




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Model types: Deductive vs. Inductive

Deductive:

This method of theory construction begins with a series ofconceptual relationships and through deductive reasoningand later empirical testing arrives at a conclusion or series ofconclusions

Deductive Logic:All men are mortalSocrates is a manTherefore, Socrates is mortal

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Model types: Deductive vs. Inductive

Inductive: Inductive modelling relies on the use of a series of observations, in order to

reach a conclusion

Inductive Logic:The sun came up September 1The sun came up September 2The sun came up September 30The sun will come up tomorrow

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Example of a deductive model: the Von Thünen model

Economic theory:• People optimize profit• Land rent determines land use• Land rent is determined by the market price and transport

costs• Land use is therefore determined by market conditions and

the distance to the market

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Model types


Dynamic vs. static




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Pixel based modelling

Land is unit of simulation

Decision rules determine howdecision making influences the landunit

No explicit agents: 1 agent canmanage multiple land units ormultiple units can be managed by 1agent

Often attention for spatial interaction

Closely linked to spatial datacollection (remote sensing)

Pijanowski et al., 2001

Parker et al., 2003

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Agent-based modelling• Individuals or groups are units of

simulation• Explicit simulation of agent

decision making• Attention for interactions between

agents (‘emergent behavior’ –‘collective action’)

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Environment Objects

Representations

Goal

ActionPerception

Communication

Communication

Agent-based modelling

38http://www.cormas.cirad.fr/

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Model types


Dynamic vs. static




Scale

40(Alcamo et al., 1998)

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Scale

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Brown et al., 2013

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This project

Objective of modelling:Support land management/policy decisions at regional scaleEnable both scenario and ex-ante assessment of

policies/projectsAccount for the demand for multiple ecosystem services

Choice of model:• Pixel-based (scale / data requirements)• Dynamic modelling (account for complex land use

patterns/competing claims)• Validated/tested model environment

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CLUE model family

CLUE

CLUE-s

Dyna-CLUE

CLUMondo

1996

-200

020

01-2

010

Land cover (%)

Land cover (dominant)

Land systemsMultiple demand types

2012

-

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CLUE applications worldwide

Benin

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Land use demand France: A1 scenario

0.00

50000.00

100000.00

150000.00

200000.00

250000.00

2000

2004

2008

2012

2016

2020

2024

2028

time (years)

dem

and(

km2)

built-up areanon-irrigated arablepastureforest/natureinland wetlandsstatic LUT'sirrigated arableabandoned land

Scenario 2

0

10002000

30004000

5000

60007000

8000

2000 2005 2010 2015 2020

Year

land

are

a (h

a)

wet ricearablebananagrassforestwater bodiesfruit trees

• Wet Rice: +6%• Arable (corn): +1%• Banana: +1%• Grass: decreases• Forest: -1%• Fruit trees: 2001, +150 ha, other years+3%

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Characterization of location suitability

Land use is changed preferably at the location withthe highest ‘suitability’

‘Suitability’ is reflecting localconditions/preferences/land systems

‘Suitability’ cannot be expressed in monetary value orsolely by biophysical attributes

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Standard method based on regression(if no information on land use preferencesIs available)

Method if detailed information ofLocation preferences is available

Specification of suitability maps is dependent on study area andland use type

If neighborhood processes are important(Cellular Automata based method)

Calculation ofsuitability maps

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inniii

i XXXP

PLog ,,22,110 ......

1

Statistical analysis

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Variable Beta1 Exp(Beta)ConstantGeology

Ultramafic sedimentsUltramafic rock

Metamorphic rock DioriteErosion vulnerability Small MediumElevationDistance to roadDistance to townDistance to coastAspectPopulation density

-2.4701

1.42232.1128

-0.78891.3164

1.2066-0.53550.00220.00020.00010.00020.0023

-0.0065

4.14688.27150.45433.7302

3.34220.58541.00221.00021.00011.00021.00230.9936

Driving factors,e.g. population

Probability ofoccurrence ofland use type

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Decision rules / expert knowledge

‘Suitability’ for corn

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Conversion elasticity

Not all land use conversions are reversible:• Urban area and residential area• Deforestation of primary rain forest

Other conversions are very costly:• Fruit tree plantations

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Transition sequences

Spatio-temporal dynamics arise through:• Fixed land use conversion sequences• Fixed timing of land use practices throughtemporal feedbacks> Fallow periods with minimum length> Maximum length of cultivation in shifting

cultivation systems through soil depletion> Re-growth rate (non-forest => forest)

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Additional options:

Give minimum time under sameland use before conversionpossible (e.g. fallow; regrowth offorest)

Give maximum time under sameland use

Provide specific areas for certainconversions (e.g. protectedareas, buffer zones, projects)

Spatio-temporal dynamics

After 10 yearsgrassland

conversion toforest

possible

After 3 yearsof cultivation

the soil isdepleted, canno longer beagriculture

Logging onlyallowed indesignated

areas

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Grassland

Built-up area

Mixed cropping

Others

Forest

Arable land

Plantations

SimulatedLand use changestarting in 1990

Resolution:

2.5*2.5 km

Temporaldynamics

Verburg et al., 2004

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Indentificationof hot ‘zones’

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Scenario study of deforestation threat ofprotected areas

Verburg et al., 2006

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San Mariano MunicipalityIsabella ProvinceThe Philippines

Overmars et al., 2005

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Map

of p

redi

cted

agr

icul

tura

l exp

ansi

on in

to fo

rest

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Validation (?)In

tere

stin

g co

inci

denc

es a

nd d

iffe

renc

es w

ith

JRC’

s TR

EES

hot

spot

s ..

.

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Validation

Multiple years of (consistent) land use data areneeded

Performance depends on landscape and casestudy

Data quality major problem for validation

Multiple validation criteria needed

Performance at coarser scales generally better thanat fine scales

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0.6

0.65

0.7

0.75

0.8

0.85

0 20 40 60 80

Window Size

Fit

Validation

Klang-Langat watershed, Malaysia

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Validation exercise Pontius

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CLUMondo-from land cover to land system representations

-from food demand to demand for multipleecosystem services

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Representing land use:

FROM LAND COVER TO LAND SYSTEMS

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Landscape structure

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Land system change

Food/Feed/Fibre/Energydemand

Expansion of agricultural area

Intensification of land usesystems

Import from other areas

Change in consumptionpattern

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Land system change

Food/Feed/Fibre/Energydemand

Expansion of agricultural area

Intensification of land usesystems

Import from other areas

Change in consumptionpattern

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Forest transition: changing forest/population relations

Kauppi et al., 2006

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Land systems

Composition (fractional cover of different land cover/use types)

Intensity of human use (harvesting, input/output, disturbance)

Configuration (spatial structure, configuration of landscapeelements)

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Asselen & Verburg, 2012 GCB

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Shifting cultivation

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Results

Ornetsmeuller et al., in prep

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Modelling land system changes

Demand Supply Agric. commodities

Forest (wood/fuel)

Housing

Agriculture

Forest

Urban

Rest (nature)

Demand Supply Agric. commodities

Forest (wood/fuel)

Housing

Intensive Agricultural landscape

Mosaic landscape

Peri-urban mosaic

Nature

Extensive Agricultural lands.

88CLUMondo model, van Asselen and Verburg, GCB

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Model result: expansion/intensification vs land availability

Eitelberg et al., in press. Global Change Biology

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Multiple demands for ecosystem services

Biomass production for food, feed, fiber and energy

Wood production

Climate regulation (e.g., carbon storage)

Regulation of rivers and soil dynamics (e.g., flood regulation)

Tourism and recreation

Biodiversity

Land systems provide different supply of ecosystemservices

Demand for ecosystem services drives land change

Demand operates at different spatial scales

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What can the model simulate?

Scenarios with different pressures/competing claims

Scenarios with different spatial policies (protected area,regional incentives)

Spatial tradeoffs and displacement of impact within the studyregions

Changing location factors, e.g. improved accessibility,climate change impacts on land use pattern

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What can the model NOT simulate?

Macro-economic change

Global interactions through trade (link to global economicmodels)

Changes in demand for services

Impacts (link to impact models)

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Model Chain of the VOLANTE project

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Climate regulation

2000 2030 Reference 2030 Biofuel policies

CurrentC-sequestration

LULUFC

Method based on:

Schulp et al., 2008 Agriculture, Ecosystems and Environment

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Why model?

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Brown et al., in press

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Science-practice/policy linkage

Participation

Joint-learning

Trans-disciplinarity

Co-design

Co-production

Action-research

Cornell et al., 2013

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Roles in model building, implementation, evaluation

Scientists / Academic

Research / Policy support /Planning Support

Planners / Policy makers /NGO / other stakeholders

Spec

ifica

tion

of q

uest

ions

/sc

enar

ios

/ pol

icy

optio

ns

Modelbuilding

As

sess

men

t and

inte

rpre

tatio

n

Ev

alua

tion

Co-design

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Questions about modelling

Who to support?

Support with what?

What are the model requirements for the questions?

Can we model the questions?

What roles of what stakeholders in the modelling process?

What capacity is needed to enable the modelimplementation?

100

Thank you!!!

Land System Change Modelling - gms-eoc.org

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