modelling human-environment interactions: theories and tools

Modelling Human-Environment Interactions: Theories and Tools

Gilberto Câmara

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Vespucci Summer School 2010

By the Year 2050…

9 billion people: 6 billion tons of GHG and 60 million tons of urban pollutants.

Resource-hungry: We will withdraw 30% of available fresh water.

Risky living: 80% urban areas, 25% near earthquake faults, 2% in coast lines less than 1 m above sea level.

The fundamental question of our time

fonte: IGBP

How is the Earth’s environment changing, and what are the consequences for human civilization?

from Jackie McGlade (EEA)

Source: Carlos Nobre (INPE)

Can we avoid that this….

Fire...

Source: Carlos Nobre (INPE)

….becomes this?

source: Global Land Project Science Plan (IGBP)

Global Land Project•What are the drivers and dynamics of variability and change in terrestrial human-environment systems?

•How is the provision of environmental goods and services affected by changes in terrestrial human-environment systems?

•What are the characteristics and dynamics of vulnerability in terrestrial human-environment systems?

Impacts of global land change

More vulnerable communities are those most at risk

Global Change

Where are changes taking place?How much change is happening? Who is being impacted by the change?What is causing change?

Human actions and global change

photo: A. Reenberg

photo: C. Nobre

Deforestation in Amazonia

~230 scenes Landsat/year

simplified representation of a processModel = entities + relations + attributes + rules

What is a Model? Deforestation in Amazonia in 2020?

Computational models

If (... ? ) then ...

Desforestation?

Connect expertise from different fieldsMake the different conceptions explicit

Computational models

Territory(Geography)

Money(Economy)

Culture(Antropology)

Modelling(GIScience)

Connect expertise from different fieldsMake the different conceptions explicit

Modelling and Public Policy

System

EcologyEconomyPolitics

ScenariosDecisionMaker

Desired System

State

ExternalInfluences

Policy Options

Atmospheric Physics/Dynamics

Tropospheric Chemistry

Global Moisture

Ocean Dynamics

MarineBiogeochemistry

Terrestrial Ecosystems

Terrestrial Energy/Moisture

Climate Change

Pollutants

CO2

CO2

Soil

Land Use

Physical Climate System

Biogeochemical Cycles

Human Activities

(from Earth System Science: An Overview, NASA, 1988)

Earth as a system

Slides from LANDSAT

Aral Sea 1973 1987 2000

images: USGS

Modelling Human-Environment Interactions

How do we decide on the use of natural resources?Can we describe and predict changes resulting from human decisions? What computational tools are needed to model human-environment decision making?

Nature: Physical equations Describe processes

Society: Decisions on how to Use Earth´s resources

We need spatially explicit models to understand human-environment interactions

f ( It+n )

. . FF

f (It) f (It+1) f (It+2)

Dynamic Spatial Models

“A dynamical spatial model is a computational representation of a real-world process where a location on the earth’s surface changes in response to variations on external and internal dynamics” (Peter Burrough)

tp - 20 tp - 10

tp

Calibration Calibration tp + 10

ForecastForecast

Dynamic Spatial Models

Source: Cláudia Almeida

Which is the better model?

Limits for Models

source: John Barrow(after David Ruelle)

Complexity of the phenomenon

Un

cert

ain

ty o

n b

asic

eq

uat

ion

s

Solar System DynamicsMeteorology

ChemicalReactions

HydrologicalModels

ParticlePhysics

Quantum Gravity

Living Systems

GlobalChange

Social and EconomicSystems

How do we decide on the use of natural resources?

Loggers

Competition for Space

Soybeans

Small-scale FarmingRanchers

Source: Dan Nepstad (Woods Hole)

Human-enviromental systems

[Ostrom, Science, 2005]

Types of goods

Source: E Ostrom (2005)

Institutional analysis

Old Settlements(more than

20 years)

Recent Settlements(less than 4

years)

Farms

Settlements 10 to 20 anos

Source: Escada, 2003

Identify different actors and try to model their actions

Institutional arrangements in Amazonia

Cells (objects)

Question #1 for human-environment models

Fields

What ontological kinds (data types) are required for human-environment models?

Resilience

Concepts for spatial dynamical models

Events and processes

degradation


vulnerability

Human-environmental models need to describe complex concepts (and store their attributes in a database)

and much more…

biodiversity


sustainability

What models are needed to describe human actions?


Clocks, clouds or ants?

Clocks: deterministic equations

Clouds: statistical distributions

Ants: emerging behaviour

Statistics: Humans as clouds

Establishes statistical relationship with variables that are related to the phenomena under study

Basic hypothesis: stationary processesExample: CLUE Model (University of Wageningen)

y=a0 + a1x1 + a2x2 + ... +aixi +E

Fonte: Verburg et al, Env. Man., Vol. 30, No. 3, pp. 391–405

Spatially-explicit LUCC models

Explain past changes, through the identification of determining factors of land use change;

Envision which changes will happen, and their intensity, location and time;

Assess how choices in public policy can influence change, by building different scenarios considering different policy options.

Underlying Factorsdriving proximate causes

Causative interlinkages atproximate/underlying levels

Internal drivers

*If less than 5%of cases,not depicted here.

source:Geist &Lambin (Université Louvain)

5% 10% 50%

% of the cases

What Drives Tropical Deforestation?

Driving factors of change (deforestation)

Category VariablesDemographic Population Density

Proportion of urban populationProportion of migrant population (before 1991, from 1991 to 1996)

Technology Number of tractors per number of farmsPercentage of farms with technical assistance

Agrarian strutucture Percentage of small, medium and large properties in terms of areaPercentage of small, medium and large properties in terms of number

Infra-structure Distance to paved and non-paved roadsDistance to urban centersDistance to ports

Economy Distance to wood extraction polesDistance to mining activities in operation (*)Connection index to national markets

Political Percentage cover of protected areas (National Forests, Reserves, Presence of INCRA settlementsNumber of families settled (*)

Environmental Soils (classes of fertility, texture, slope)Climatic (avarage precipitation, temperature*, relative umidity*)

source: Aguiar (2006)

Linear and spatial lag regression modelswhere:Y is an (n x 1) vector of observations on a

dependent variable taken at each of n locations,

X is an (n x k) matrix of exogenous variables,

is an (k x 1) vector of parameters (estimated regression coefficients), and

is (n x 1) an vector of disturbances.

),N(~,ε 20 XβY

XβWYY

W is the spatial weights matrix, the product WY expresses the

spatial dependence on Y (neighbors),

is the spatial autoregressive coefficient.

Statistics: Humans as cloudsMODEL 7: R² = .86

Variables Description stb p-level

PORC3_ARPercentage of large farms, in terms of area 0,27 0,00

LOG_DENS Population density (log 10) 0,38 0,00

PRECIPIT Avarege precipitation -0,32 0,00

LOG_NR1Percentage of small farms, in terms of number (log 10) 0,29 0,00

DIST_EST Distance to roads -0,10 0,00

LOG2_FER Percentage of medium fertility soil (log 10) -0,06 0,01

PORC1_UC Percantage of Indigenous land -0,06 0,01

Statistical analysis of deforestation

source: Aguiar (2006)

CLUE modeling framework

Demand scenarios

0

5000

10000

15000

20000

25000

30000

35000

40000

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

Year

Rat

e (k

m2/

year

)

Decreasing

Baseline

Increasing

25 x 25 km2

100 x 100 km2

100 x 100 km2

Scenario exploration: linking to process knowledge

Cellular databaseconstruction

Exploratory analysisand

selection of subset of variables

Porto Velho-Manaus

BR 163Cuiabá-Santarém

São Felix/Iriri

ApuíHumaitáBoca do Acre

SantarémManaus-Boa Vista

Aripuanã

Scenario exploration

Scenarios for deforestation in Amazonia (2020)

Agents as basis for complex systems

Agent: flexible, interacting and autonomous

An agent is any actor within an environment, any entity that can affect itself, the environment and other agents.

Agent-Based Modelling

Goal

Environment

Representations

Communication

ActionPerception

Communication

source: Nigel Gilbert

Agents: autonomy, flexibility, interaction

Synchronization of fireflies

Bird Flocking

No central authority: Each bird reacts to its neighbour

Not possible to model the flock in a global manner. Need to necessary to simulate the INTERACTION between the individuals

Requirement #2 for human-environment models

Models need to support both statistical relations (clouds) and agents (ants)


What types of spatial relations exist in nature-society models?

Rondonia

1975 1986

Natural space is (usually) isotropicSocietal space is mostly anisotropic

Which spatial objects are closer?

Societal spaces are anisotropic

Which cells are closer?

[Aguiar et al., 2003]

Euclidean space Open network Closed network

D2

D1

Requirement #3 for human-environment models: express anisotropy explicitly

[Aguiar et al., 2003]


How do we combine independent multi-scale models with feedback?

Models: From Global to Local

Athmosphere, ocean, chemistry climate model (200 x 200 km)

Atmosphere only global climate model (50 x 50 km)

Regional climate model (10 x 10 km)

Hydrology, VegetationSoil Topography (1 x 1 km)

Regional land use changeSocio-economic adaptation (e.g., 100 x 100 m)

National level - the main markets for Amazonia products (Northeast and São Paulo) and the roads infrastructure network;

Regional level - for the whole Brazilian Amazonia, 4 million km2;

Local level - for a hot-spot of deforestation in Central Amazonia, the Iriri region, in São Felix do Xingu, Pará State

25 x 25 km2

1 x 1 km2

Human-enviroment models should be multi-scale, multi-approach

[Moreira et al., 2008]

Nested grids are not enough!

Environmental Modeler [Engelen, White and Nijs, 2003]

CLUE model [Veldkamp and Fresco, 1996]

Multi-scale modelling: hierarchical relations need to be described

Requirement #4 for human-environment models: support multi-scale modelling using explicit relationships

Express explicit spatial relationships between individual objects in different scales [Moreira et al., 2008]

[Carneiro et al., 2008]


Small Farmers Medium-Sized Farmers

photos: Isabel Escada

How can we express behavioural changes in human societies?

When a small farmer becomes a medium-sized one, his behaviour changes

Old Settlements(more than

20 years)

Recent Settlements(less than 4

years)

Farms

Settlements 10 to 20 anos

Societal systems undergo phase transitionsIsabel Escada, 2003

[Escada, 2003]

Requirement #5 for human-environment models: Capture phase transitions

Newly implanted

Deforesting

Slowing down

latency > 6 years

Deforestation > 80%

Small Farmers

Iddle

Year of creation

Deforestation = 100%

Deforesting

Slowing downIddle

Year of creation


Deforestation > 60%

Medium-Sized Farmers

photos: Isabel Escada

TerraME: Computational environment for developing human-environment models

Cell Spaces

Support for cellular automata and agents

Modular modelling tool[Carneiro, 2006]

Spatial structure in TerraME: Cell Spaces integrated with databases

TerraME´s approach: Modular components

Describe spatial structure

1:32:00 Mens. 11.

1:32:10 Mens. 32.

1:38:07 Mens. 23.

1:42:00 Mens.44.. . .return value

true

1. Get first pair 2. Execute the ACTION

3. Timer =EVENT

4. timeToHappen += period

Describe temporal structure

Newly implanted

Deforesting

Slowing down

latency > 6 years

Iddle

Year of creation


Describe rules of behaviour Describe spatial relations

[Carneiro, 2006]

Spatial Relations in TerraME

Spatial relations between entities in a nature-societal model are expressed by a generalized proximity matrix (GPM)

44434241

34333231

24232221

14131211

wwww

wwww

wwww

wwww

W

[Moreira et al., 2008]

TerraME: multi-scale modelling using explicit relationships

44434241

34333231

24232221

14131211

wwww

wwww

wwww

wwww

W

Generalized proximity matrices express explicit spatial relationships between individual objects in different scales

up-scaling

Scale 1

Scale 2

father

children

[Moreira et al., 2008][Carneiro et al., 2008]

To

Ag

en

t

Cell

a

b

a

b

c

c Cell Agent

FromGPM: Relations between cells and agents

[Andrade-Neto et al., 2008]

TerraME uses hybrid automata to represent phase transitions

State A

Flow

Condition

State B

Flow

Condition

Jump condition

A hybrid automaton is a formal model for a mixed discrete continuous system (Henzinger, 1996)

Hybrid Automata = state machine + dynamical systems

Hybrid automata: simple land tenure model

STATE Flow Condition Jump Condition Transition

SUBSISTENCE Deforest 10% of land/year Deforest > 60% CATTLE

CATTLE Extensive cattle raising Land exhaustion ABANDONMENT

ABANDONMENT Forest regrowth Land revision RECLAIM

RECLAIM Public repossession Land registration LAND REFORM

LAND REFORM Land distribution Farmer gets parcels

SUBSISTENCE

SUBSISTENCEDeforest 20%/year

Farmer gets parceldeforest>=60%

Land exhaustion

CATTLEExtensive cattle raising

ABANDONMENTRegrowth

RECLAIMPublic repossession

Land revision

LAND REFORMredistribution

Land registration

TerraME Software Architecture

TerraLib

TerraLib TerraME Framework

C++ Signal Processing

librarys

C++ Mathematical

librarys

C++ Statistical

librarys

TerraME Virtual MachineTerraME Compiler

TerraME Language

RondôniaModel São Felix Model Amazon Model Hydro Model

[Carneiro, 2006]

Lua and the Web

Where is Lua?

Inside Brazil Petrobras, the Brazilian Oil Company Embratel (the main telecommunication company in Brazil) many other companies

Outside Brazil Lua is used in hundreds of projects, both commercial and academic CGILua still in restricted use

until recently all documentation was in Portuguese

TerraME Programming Language: Extension of LUA

LUA is the language of choice for computer games

[Ierusalimschy et al, 1996]source: the LUA team

TerraME programming environment

Eclipse & LUA plugin• model description• model highlight syntax

TerraView• data acquisition• data visualization• data management• data analysis

TerraLibdatabase

da

ta

Model source code

MODEL DATA

mod

el

• model syntax semantic checking• model execution

TerraME INTERPRETER

LUA interpreter

TerraME framework

TerraME/LUA interface

model d

ata

[Carneiro, 2006]

Amazonia: multiscale analysis of land change and beef and milk market chains with TerraME

Deforestation

Forest

Non-forest

Clouds/no data

INPE/PRODES 2003/2004:

São Felix do Xingu

Forest

Not ForestDeforest

River

Change 1997-2006: deforestation and cattle

Land use Change model

Beef and milk market chain model

Small farmersagents

Medium and largefarmersagents



Small farmersagents

Medium and largefarmersagents

Create pasture/Deforest

Speculator/large/small

bad land management

money surplus

Subsistenceagriculture

Diversify use

Manage cattle

Move towardsthe frontier

Abandon/Sellthe property

Buy newland

Settlement/invaded land

Sustainability path(alternative uses, technology)

Sustainability path (technology)

Agents example: small farmers in Amazonia

Create pasture/plantation/

deforest

Speculator/large/small

money surplus/bank loan

Diversify use

Buy newland

Manage cattle/plantation

Buy calvesfrom small

Buy landfrom small

farmers

Agents example: large farmers in Amazonia

Forest

Not ForestDeforest

River

Observed deforestation from 1997 to 2006

Local scale

Regional scale

CATTLE CHAIN MODEL Flows: goods, information, etc.. Connections: Agents

LANDSCAPE DYNAMICS MODEL - Front- Medium- Rear

INDIVIDUAL AGENTSLarge and small farmers

Loca

l far

mer

sFr

ontie

r Re

gion

SCENARIO

S



Small farmers

Medium and largefarmers


Small farmers

Medium and largefarmers

Landscapemetrics model

Pasture degradation

model

Several workshops in 2007 to define model rules and variables

Landscape model: different rules for two main types of actors

Landscape model: different rules of behavior at different partitions which also change in time

FRENTE

MEIO

RETAGUARDA

Forest

Not ForestDeforest

River

FRONT

MIDDLE

BACK

SÃO FÉLIX DO XINGU - 2006

Modeling results 97 to 2006

Observed 97 to 2006

modelling human-environment interactions: theories and tools

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