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