Simulations of Land Use ChangesSimulations of Land Use Changes

-- AGENTAGENT--LUC Model LUC Model

K S RajanInternational Institute of Information Technology, HyderabadInternational Institute of Information Technology, Hyderabad

rajan@iiit.ac.inrajan@iiit.ac.in

March 29March 29thth, 2007, 2007

International Workshop on

URBANIZATION, DEVELOPMENT PATHWAYS AND CARBON IMPLICATIONS

28-30 March 2007, Tsukuba, Japan

Source: Fukui, 1993

Land Use Change and Related FactorsLand Use Change and Related Factors

For 3 villages in Central Thailand

Villages that were Surveyed Per capita acreage of Paddy Land

Proportion of Population

commuting to WorkProportion of Upland Area

in Total Area

Land Use Change and Spatial FactorsLand Use Change and Spatial Factors

Earth (Environment )

Resource System(Land/ Water, Ecosystem)

Agricultural Land Use(Crop Choice)

Urban Land Use

Pastures/Grassland

Other Land uses

Farmer

Land Owner

Micro-sphere of Decision Making

Market Dynamics Cumulative Changes

in Environment

Changes in

Life Style

Macro-sphere of Decision Making

Policy Directions Migrations

Short termLong Term

Water Supply

Multiple Levels of Decision Making

Environment / Resource

System

MICRO

Sub-Models

SPATIAL URBAN

EXPANSION

MODEL

BIO-PHYSICAL

CROP MODEL

AGRO-ECONOMIC

Sub-Model

Behavioral

Models

Land UserLand Use Conversion - within Agriculture

MigrationAgent Decision Sub-Model

PopulationPrice Supply

Regulations

National Scenario Crop Demand Estimation MACRO

Sub-Model

International Market

Model Structure of AGENTModel Structure of AGENT--LUCLUC

Major Components are Major Components are --

�� Agent Decision ModelAgent Decision Model

�� Agricultural Income ModelAgricultural Income Model

�� BioBio--Physical Crop ModelPhysical Crop Model

�� Agricultural Cost Estimation ModelAgricultural Cost Estimation Model

�� Spatial Urban Expansion ModelSpatial Urban Expansion Model

�� Limited focus: Limited focus:

�� agricultural land useagricultural land use

�� simplified urban expansion.simplified urban expansion.

�� no forestry or industrial development.no forestry or industrial development.

�� Shifting Cultivation (in Laos version)Shifting Cultivation (in Laos version)

shorter

longer

Time

horizon

Decision Table

(Age, Education Level, Food Reserves)

Land Use Decision

Migration Decision

- No change

- Crop change

- Forest to Agriculture

- Agriculture to Urban

- Forest to Urban

- New lands

- To Nearest Urban Area

- Major Urban Center

Expected Increase

of Income (Grid-based)

Expected Risk

Age, Educational level

Expected Increase of Income

← Age

← Educational Level

Agent Decision ModelAgent Decision Model

Spatial Urbanization Model (SUM)Spatial Urbanization Model (SUM)

Industrial /Service Sector

GDP per capita

Urban GDP Share

Land Needs

Expansion of Urban Area

Current Urban Area

Transport

Infrastructure

Land Use Conversion

Potential

Rural to Urban

Migration

Population Growth

and Readjustments

Pull factorsTopography

Model Results Model Results –– An Example of Nan Province in ThailandAn Example of Nan Province in Thailand

Elevation Maps (L: Thailand; R: Nan Province)

Land Use Map of Nan Land Use Map of Nan

(1980)(1980)

Income Map of Nan Province

Legend

Examples of the MicroExamples of the Micro--Simulation Model Results [1]Simulation Model Results [1]

Urban Centre

No. of Households in Each Grid : 600

LU: Paddy(4); Maize(1,6,7,8); Paddy+Maize(rest)

Examples of the MicroExamples of the Micro--Simulation Model Results [Simulation Model Results [22]]

Income Graph (Around Urban Center)

-400000

-200000

0

200000

400000

600000

800000

1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11

in Bahts

Revenue Gen_Cost On_Farm_Inc Off_Farm_Inc Gross_Inc

Income Graph (Around the Urban Center)Income Graph (Around the Urban Center)

No. of Households in Each Grid : 83(1,2); 117(rest)

LU: Paddy(all grid points)

Examples of the MicroExamples of the Micro--Simulation Model Results [Simulation Model Results [3]3]

Income Graph (Far from Urban Center)

-400000

-200000

0

200000

400000

600000

800000

1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11in Bahts

Revenue Gen_Cost On_Farm_Inc Off_Farm_Inc Gross_Inc

Fixed Cost

Variable Cost

- Land Based

- Yield Based

Income Graph (Mainly Rural Area)Income Graph (Mainly Rural Area)

Simulated Land Use Map (Zoomed view)Simulated Land Use Map (Zoomed view)

Model Simulated Total Migration in the Period 1980Model Simulated Total Migration in the Period 1980--9090

Model Simulation Model Simulation

of Land Use for of Land Use for

Thailand Thailand

19801980--19901990

Validation: 60-85% depending on

the Provinces

Model Simulation Model Simulation

of Land Use for of Land Use for

Thailand Thailand

19801980--19901990

Further Applications of Further Applications of the Modelthe Model

�� Application to BangladeshApplication to Bangladesh

��New multiNew multi--season approach to crop modellingseason approach to crop modelling

�� Involving such seasonal changes in decision makingInvolving such seasonal changes in decision making

�� Application to LaosApplication to Laos

��Shifting Cultivation Shifting Cultivation –– spatial and temporal changesspatial and temporal changes

��Forest land use changesForest land use changes

�� Application to IndiaApplication to India

�� Nature Conservation Nature Conservation vsvs Human Needs Human Needs

�� Water resourcesWater resources

�� Forest reserveForest reserve

Some ObservationsSome Observations

•• Rural areas are no more predominantly Rural areas are no more predominantly

Agriculture orientedAgriculture oriented

•• Spatial Location of the Rural landscape is Spatial Location of the Rural landscape is

important to understand the different Economic important to understand the different Economic

structuresstructures

•• Highly data intensive on SocioHighly data intensive on Socio--Economic Economic

characteristicscharacteristics

�������� Urbanization Modelling Urbanization Modelling --

SUMSUM

Vulnerability AssessmentVulnerability Assessment

-- Flood Impacts and SocioFlood Impacts and Socio--

Economic ConsequencesEconomic Consequences

Urbanization Model Urbanization Model

Simulations of Urban Land use & PopulationSimulations of Urban Land use & Population

19901990--21002100•• ObjectivesObjectives

–– To Develop Tools that forecast the To Develop Tools that forecast the Spatial extentSpatial extent of the of the Urban AreasUrban Areas

–– Incorporating the Economic growth, Migration and sprawl Incorporating the Economic growth, Migration and sprawl effecteffect

•• Simulation Tools Help understand the Impact Simulation Tools Help understand the Impact –– Shift of Population and Changing Dimensions of Shift of Population and Changing Dimensions of

•• NEEDNEED

–– Water and other resourcesWater and other resources

–– Economic sphere of influenceEconomic sphere of influence

•• DISASTERS/VulnerabilityDISASTERS/Vulnerability

–– Increased Populations at RiskIncreased Populations at Risk

•• Approach Approach –– Urban Agglomeration BasedUrban Agglomeration Based

3232

4848

3434

9999

60**60**

3737

7979

3737

5454

5353

4747

100100

4242

4141

BangladeshBangladesh

ChinaChina

FijiFiji

Hong KongHong Kong

India**India**

IndonesiaIndonesia

Korea, RepublicKorea, Republic

MalaysiaMalaysia

MyanmarMyanmar

PhilippinesPhilippines

PakistanPakistan

SingaporeSingapore

Sri LankaSri Lanka

ThailandThailand

Estimated Urban GDPEstimated Urban GDP

(%)(%)Country/TerritoryCountry/Territory

An Estimate of GDP of Urban Areas as An Estimate of GDP of Urban Areas as

Percentage of National GDPPercentage of National GDP

Source: "State of the Environment in Asia and the Pacific - 1990" Bangkok: The UN Economic and

Social Commission for the Asia Pacific. **FICCI estimates of 2005

Spatial Urbanization ModelSpatial Urbanization Model

Industrial /Service Sector

GDP per capita

Urban GDP Share

Land Needs

Expansion of Urban Area

Current Urban Area

Transport

Infrastructure

Land Use Conversion

Potential

Rural to Urban

Migration

Population Growth

and Readjustments

Pull factorsTopography

Relationships in the ModelRelationships in the Model

In-Migration to the Cities

Mt = β – ( γ . ln GDP pc,t )

where, β= 5.0846479; γ=0.4905977,

and GDPpc,t is the Per-capita GDP at time t

Population Growth

Pt = Po e(µ/ω).e(ωt - 1)

where, µ is the national population growth rate at initial time reference

t0 and ω is the exponential decreasing rate of national population growth

InIn--Migration FormulationMigration Formulation

•• Based on data of Japan since 1955Based on data of Japan since 1955

•• Considers Tokyo and the surrounding ProvincesConsiders Tokyo and the surrounding Provinces

•• Separates Natural Growth Rate with the Total Separates Natural Growth Rate with the Total Population changesPopulation changes

•• InIn--Migration is a proxy for all the regional and Migration is a proxy for all the regional and national population movementsnational population movements

•• Assumes that the Other Countries in Asia have Assumes that the Other Countries in Asia have similar Urbanization Patterns similar Urbanization Patterns –– Developmental Developmental PathwaysPathways

Land use Map of Bangkok and Land use Map of Bangkok and

Surrounding ProvincesSurrounding Provinces

Simulated Urban AreaSimulated Urban Area

Simulated Urban Area Simulated Urban Area

PPoopulation Changes 1980, 1990pulation Changes 1980, 1990--21002100Population in Bangkok and Surrounding Population in Bangkok and Surrounding

Provinces and its Ratio to ThailandProvinces and its Ratio to Thailand’’s total s total

PopulationPopulation

0

2

4

6

8

10

12

14

16

18

20

1980 1990 2000 2025 2050 2075 2100

Year

Population (in M

illions)

0

5

10

15

20

25

30

Percentages

Sim Total Popln (BKK+5 provinces) Ratio of (BKK+5 provinces) to Thailand

Urban PUrban Poopulation Changespulation Changes

1980, 19901980, 1990--21002100Share of Urban Population in Bangkok Share of Urban Population in Bangkok vsvs Total Total

Population in the RegionPopulation in the Region

0

2

4

6

8

10

12

14

16

18

20

1980 1990 2000 2025 2050 2075 2100

Year

Population (in M

illion)

0

10

20

30

40

50

60

70

80

90

Percentages

Sim Total Popln (BKK+5 provinces) Only Urban Popln % Urban Popln

Distribution of the Cities in Asia

Urbanization ModellingUrbanization Modelling

�������� Vulnerability AssessmentVulnerability Assessment

-- Flood Impacts and SocioFlood Impacts and Socio--

Economic ConsequencesEconomic Consequences

Where are We ?Where are We ?•• Most of the Major Cities are in the CoastsMost of the Major Cities are in the Coasts

•• Floods cause Human and Economic LossesFloods cause Human and Economic Losses

–– 5 fold in 30 years in Asia5 fold in 30 years in Asia

•• High rate of High rate of UUrbanization and population growth rbanization and population growth in coastal areas are likely to aggravate the situationin coastal areas are likely to aggravate the situation

What do we plan in this Research?What do we plan in this Research?•• Impact of floods in the Coastal CitiesImpact of floods in the Coastal Cities

•• Estimate Vulnerable Population and Risk COSTSEstimate Vulnerable Population and Risk COSTS

–– Spatial DistributionSpatial Distribution needed for Developing Responsesneeded for Developing Responses

Study Area : Hue City, VietnamStudy Area : Hue City, Vietnam

Characteristics of Hue CityCharacteristics of Hue City

•• Hue City of Hue City of ThuaThua ThienThien Hue province is Hue province is

located in the central part of Vietnam.located in the central part of Vietnam.

•• Old Capital of Vietnam Old Capital of Vietnam –– Now a Tourist Now a Tourist

AttractionAttraction

•• Total area of the province is 5,009 kmTotal area of the province is 5,009 km2.2.

•• Total population is 1,050,000 in 1999.Total population is 1,050,000 in 1999.

•• 70% of the total natural land is mountainous.70% of the total natural land is mountainous.

•• Flood and storms are the main disastersFlood and storms are the main disasters

–– Almost AnnuallyAlmost Annually

Topography around Hue CityTopography around Hue City

Hue

LanduseLanduse Map of the Study AreaMap of the Study Area

Flood Depth Map Flood Depth Map –– Base SimulationBase Simulation

Figure: Flood Depth Map at Peak Discharge in Kim Long (03.11.1999) for Base Condition

Acknowledge: APN Project 2004-05

Flood Depth Map Flood Depth Map –– 30cm Sea Level Rise30cm Sea Level Rise

Figure: Flood Depth Map at Peak Discharge in Kim Long (03.11.1999) for 30 cm Sea Level Rise

Flood Depth Map Flood Depth Map –– 88cm Sea Level Rise88cm Sea Level Rise

Figure: Flood Depth Map at Peak Discharge in Kim Long (03.11.1999) for 88cm Sea Level Rise

Difference in Flood DepthDifference in Flood Depth

with 100cm and 30cm Sea Level Risewith 100cm and 30cm Sea Level Rise

SocioSocio--Economic SimulationsEconomic Simulations

•• Urban Area and Population ChangesUrban Area and Population Changes

–– Economic developmentEconomic development

–– Population GrowthPopulation Growth

–– MigrationMigration

–– Increase of Floor area per capitaIncrease of Floor area per capita

Spatial Simulation is carried out based on IPCC Spatial Simulation is carried out based on IPCC

SRES B1 Scenario of Economic growth and SRES B1 Scenario of Economic growth and

Population changesPopulation changes

Simulation of Urban Land UseSimulation of Urban Land Use

Simulation of Urban Land UseSimulation of Urban Land Use

Simulation of Urban Land UseSimulation of Urban Land Use

Simulation of Urban Land UseSimulation of Urban Land Use

Simulation of Urban Land UseSimulation of Urban Land Use

Urban SprawlUrban Sprawl

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

2000 2025 2050 2075 2100

Year

Population (in M

illion)

0

1

2

3

4

5

6

7

8

Percentages

Sim Popln (Hue+4 districts) Share Ratio

Population in Hue and Neighboring Population in Hue and Neighboring

DistrictsDistricts

SocioSocio--Economic Impacts Economic Impacts –– Area Change in 2000, 2050 and 2100Area Change in 2000, 2050 and 2100

due to different levels of due to different levels of SeaLevelSeaLevel Rise Rise

0

50

100

150

200

250

300

350

Area-2000 Area-2050 Area-2100

Num

ber

of G

rids

Base in 1999 30cm Sea Level Rise 100cm Sea Level Rise

SocioSocio--Economic Impacts Economic Impacts –– Population affected in 2000, 2050 and 2100Population affected in 2000, 2050 and 2100

due to different levels of due to different levels of SeaLevelSeaLevel Rise Rise

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Popln-2000 Popln-2050 Popln-2100

Popu

lation

(in

Mill

ions)

Base in 1999 30cm Sea Level Rise 100cm Sea Level Rise

Impact Severity MapsImpact Severity Maps

-- Children (<6yrs)Children (<6yrs)

Impact Severity MapsImpact Severity Maps

-- Infrastructure (Roads)Infrastructure (Roads)

Summary of the Vulnerability Summary of the Vulnerability

Assessment of Floods in Coastal CitiesAssessment of Floods in Coastal Cities

•• Flood modelling indicates the Impacts are Flood modelling indicates the Impacts are nearly nearly

samesame irrespective of Sea Level Rise here.irrespective of Sea Level Rise here.

•• Urban Area Urban Area –– almost 7 times in 2050 and 13 times in almost 7 times in 2050 and 13 times in

21002100

•• Urban Population is going up by 300%Urban Population is going up by 300%

•• Impacted Population goes up by 350%Impacted Population goes up by 350%

•• Though the Physical Impact is similar to the present Though the Physical Impact is similar to the present

times, the Sociotimes, the Socio--Economic Impact is many fold.Economic Impact is many fold.

Ongoing and Future WorksOngoing and Future Works

•• SUM SUM –– Application to Indian Cities just started Application to Indian Cities just started –– Hyderabad City, Hyderabad City, PunePune

(National Urban Renewal Mission)(National Urban Renewal Mission)

•• Green Building Policy and Urbanization Green Building Policy and Urbanization ––Mainly Energy use scenariosMainly Energy use scenarios

•• ChallengesChallenges

–– Moving from a Single City to Multiple City Moving from a Single City to Multiple City Urbanization Urbanization –– interactions within the countryinteractions within the country

•• CautionCaution

–– National Projections are being used as Parameters National Projections are being used as Parameters

Thank youThank you