dynamic coupling of multiscale land change models: interactions and feedbacks across regional and...

Dynamic coupling of multiscale land change models:

interactions and feedbacks across regional and local deforestation models in the

Brazilian Amazonia

Amazônia em Perspectiva: Por uma Ciência IntegradaConferência Científica Internacional LBA, GEOMA & PPBio

17 a 20 de novembro, Manaus - Amazonas - Brasil

Evaldinolia Gilbertoni Moreira (INPE / CEFET-MA) Advisors: Dra. Ana Paula Aguiar (INPE)

Dr. Gilberto Câmara (INPE).. Collaboration: Sérgio Costa (INPE)

Motivation: understand intra-regional interactions of market pressure, connectivity, policies and institutional aspects

“It is impossible today, more than ever, to understand what happens in one place without considering the interests and conflicting actions at different geographical scales”

[Becker (2005)]

Actors, processes and differentiated uses

Differentiated local conditions:

• biophysical, • cultural, • agrarian structure, • production chain nodes,• market connections

Public policies and differentiated scenarios

Differentiated modeling approaches as appropriated to different sites and scales

Understand intra-regional interactions

Site B

Amazonia: market pressure for land,

national and regional politics,

migratory patterns

Site A Site C

context

feedbacks

Multi-scale, multi-locality, multi-approach modeling

Multi-scale, multi-locality analysis

DeforestationForestNon-forestClouds/no data

INPE/PRODES 2003/2004:

(continuous)

(discrete)

(landscape)

(farms)

Objective

• Propose a conceptual framework for dynamic coupling of land change models at different spatial and temporal scales, including top-down and bottom-up feedbacks

....

Model

Scale 1Inputs Outputs

Model

Scale 2Inputs Outputs

Model

Scale 3Inputs Outputs

Schematic representation of the multiscale coupling mechanism

....

Model

Scale 1Inputs Outputs

Model

Scale 2Inputs Outputs

Model

Scale 3Inputs Outputs

CONTEXT

CouplerTop-down

CouplerTop-down

Schematic representation of the multiscale coupling mechanism

Schematic representation of the multiscale coupling mechanism

....

Model

Scale 1Inputs Outputs

Model

Scale 2Inputs Outputs

Model

Scale 3Inputs Outputs

CONTEXT

CouplerTop-down

CouplerTop-down

FEEDBACK

CouplerBottom-up

CouplerBottom-up

Conceptual framework

• Each individual model should be designed to

clearly distinguish analytical, spatial and

temporal dimensions

• Model Couplers to define links between

models: Analytical and Spatial

• Specify a Scheduler that establishes the

combined temporal execution of the models

Model Couplers: Spatial Couplers

Application : Interactions and feedbacks across regional and

local deforestation models in the Brazilian Amazon

Study area: Amazônia and São Felix do Xingu

(a)

(c)(b)

PA 279 area, which is the connection to the local study area (Iriri/Terra do

Meio), including the municipalities of São Felix do Xingu, Tucumã, Ourilândia

and the southeast of Pará State

Macro model: Brazilian Amazonia

Local model: Iriri/Terra do Meio

MACRO Scenarios(A) - High pressure for new land (B) - Low pressure for new land

LOCAL Scenario(A) - No forest law enforcement

Top-down influence analysis

MACRO Scenarios(A) - High pressure for new land (B) - Low pressure for new land

LOCAL Scenario(A) - No forest law enforcement

Top-down influence analysis

Increase - 55%

Increase - 15%

the projected deforested area 263%

the projected deforested area 143%

Conclusions: top-down interactions

• This show the relevance of nesting scale models

• The amount of pressure at different sites in a large region such as Amazonia depends not only on local conditions, but also on processes that act at higher hierarchical levels

• The high pressure for change in São Felix/Iriri is related to its higher suitability for cattle expansion when compared to other areas in Amazonia (due to climatic, soils and market conditions)

Scenarios: Bottom-up influence analysis

MACRO(A) - High pressure for new land

LOCAL(A) - forest law enforcement(B) - No forest law enforcement

Scenarios: Bottom-up influence analysis

MACRO(A) - High pressure for new land

LOCAL(A) - forest law enforcement(B) - No forest law enforcement

Conclusions: bottom-up interactions

• In this exercise, the amount of deforestation resulting from the simulation depends on the local scenario conditions and agent’s behavioral rules

• When the finer scale model rejects the demand projected by the macro model, the bottom-up feedback mechanism corrects the projected areas at the macro scale and changes the suitability of the upper scale cells

• The top-down and bottom-up interactions show effects not easily detectable by single scale models

Finals Remarks

• This work is a first step towards more detailed studies on the balance between regional and local interactions, using nested studies

• Our aim is to continue to improve such models and use them to explore multiscale policy scenarios in Amazonia

• Similar approaches can be applied to many

other situations and parts of the world

Finals Remarks

• The conceptual framework we propose

contributes to answer such complex

questions:

– Which local measures could prevent the

projected macro scenario of aggressive forest

conversion to pasture?

– Are local actions enough?

– How would other regions – with heterogeneous

socio-economic and biophysical conditions - be

affected?

Obrigada!