1. 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 Integrada Conferência 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)

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

3. “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)]

4. 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

5. Multi-scale, multi-locality analysis Scale 1 Amazonia 100 km Scale 2 Central Amazonia 25 km Scale 3 São Felix/Ourilândia 2.500 m Scale 3 Santarém/Belterra 2.500 m Scale 4 Laboratory 500 m based on CLUE based on CLUE-S Representative agents Individual agents Conventional allocation models: Continuous (based on CLUE) Discrete (based on CLUE-S) Multi-agents models: Representative agents (landscape) Individual agents (farms) Top-down coupling: quantity of change. Goal: analyze detailed projected patterns at finer scales. Bottom-up coupling: to be defined. Scale 1 Amazonia 100 km Scale 2 Central Amazonia 25 km Scale 3 São Felix/Ourilândia 2.500 m Scale 3 Santarém/Belterra 2.500 m Scale 4 Laboratory 500 m based on CLUE based on CLUE-S Representative agents Individual agents Deforestation Forest Non-forest Clouds/no data INPE/PRODES 2003/2004: Conventional allocation models: Continuous (based on CLUE) Discrete (based on CLUE-S) Multi-agents models: Representative agents (landscape) Individual agents (farms) (continuous) (discrete) (landscape) (farms)

6. 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

7. .... Model Scale 1 InputsOutputs Model Scale 2 InputsOutputs Model Scale 3 InputsOutputs Schematic representation of the multiscale coupling mechanism

8. .... Model Scale 1 InputsOutputs Model Scale 2 InputsOutputs Model Scale 3 InputsOutputs CONTEXT Coupler Top-down Coupler Top-down Schematic representation of the multiscale coupling mechanism

9. .... Model Scale 1 InputsOutputs Model Scale 2 InputsOutputs Model Scale 3 InputsOutputs CONTEXT Coupler Top-down Coupler Top-down FEEDBACK Coupler Bottom-up Coupler Bottom-up

10. 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

11. Model Couplers: Spatial Couplers

12. Application : Interactions and feedbacks across regional and local deforestation models in the Brazilian Amazon

13. Study area: Amazônia and São Felix do Xingu 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

15. 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

16. 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

17. Increase - 55%

18. Increase - 15%

20. the projected deforested area 263% the projected deforested area 143%

21. 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)

22. Scenarios: Bottom-up influence analysis MACRO (A) - High pressure for new land LOCAL (A)- forest law enforcement (B)- No forest law enforcement

23. Scenarios: Bottom-up influence analysis MACRO (A) - High pressure for new land LOCAL (A)- forest law enforcement (B)- No forest law enforcement

28. 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

29. 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

30. 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?

31. Obrigada!