Modelagem Dinâmica com TerraME: A Gentle Introduction Dezembro/09 Pedro Ribeiro de Andrade DSA/CCST/INPE

Motivação É crescente a demanda por modelos dinâmicos que permitam a avaliação e o prognóstico dos impactos causados por mudanças ambientais e que considerem de forma integrada fatores sociais, econômicos, hidrológicos, climáticos e ecológicos. –Projeto Land Use and Cover Change - LUCC –Projeto GEOMA ? Fonte: (Carneiro, 2006)

Processo cognitivo no qual os princípios de uma ou mais teorias são aplicados para se produzir um modelo de um determinado fenômeno, sistema ou processo real. Desmatamento ? Teoria de Modelagem If (... ? ) then... Fonte: (Carneiro, 2006)

Causa e Efeito

rain N Itacolomi do Itambé Peak Lobos Range Fonte: (Carneiro, 2006)

Área de estudo – ALAP BR 319 e entorno ALAP BR 319 Estradas pavimentadas em 2010 Estradas não pavimentadas Rios principais Portos new road

Data models but not process models Geographic Information Systems (GIS)

Simulação Computacional Caixa Preta

Source: (Carneiro, 2006) TerraME:Terra Modelling Environment

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

Methodology: TerraME architecture & applications Fonte: (Carneiro, 2006)

Basic concepts The Earths environment … can be represented as a synthetic environment… … where analytical entities (rules) change the space properties in time. Several interacting entities share the same spatiotemporal structure. Fonte: (Carneiro, 2006)

TerraMEs way: Modular components Spatial structure 1:32:00Mens :32:10Mens :38:07Mens :42:00Mens return value true 1. Get first pair 2. Execute the ACTION 3. Timer =EVENT 4. timeToHappen += period Temporal structure Newly implanted Deforesting Slowing down latency > 6 years Iddle Year of creation Deforestation = 100% Rules of behaviourSpatial relations Source: [Carneiro, 2006]

Source: (Aguiar, 2003;; Carneiro, 2006) Cell CellularSpace Trajectory Neighbourhood (, ) Space

Cellular Space AnariCells = CellularSpace { dbType = "ADO", host = "localhost", database ="c:\\terralibDB\\mas_lucc.mdb", user = "", password = "", layer = "celulas_500_Anari_1985_pasto", theme = "celulas_500_Anari_1985_pasto", select = {"object_id", "cover", "owner} }

r = Trajectory{cellularspace, function(cell) return cell.height > 50 end, function(c1, c2) return c1.height > c2.height end } (, ) Trajectory

Source: (Aguiar, 2003) Which objects are closer?

Source: (Aguiar, 2003) Which objects are closer?

Euclidean SpacesOpen NetworkClosed Network GPM – strategies for building relations

GPM as a graph To Agent Cell a b a b c c Cell Agent From

Vizinhança createMooreNeighborhood(cs) cs:loadTerraLibGPM("name") cs:loadGALNeighborhood("file.gal") Source: (Aguiar, 2003)

Behaviour Agent Automata Fonte: (Carneiro, 2006)

Desmatamento > 70% recém Implantada desflorestando saturada Latência > 6 anos Evolução Desflorestamento - Fazenda recessão crescimento normal time = 1997 time = 1988 time = 1994 Sistema Econômico Hybrid Situated Behaviour – State Machines

State{id = "MoveDown", Jump{ function(event, agent) return input == "raise" end, target = "MoveUp" }, Jump{ function(event, agent) return gate.y == 0 end, target = "Closed" }, Flow{ function(event, agent) gate.y = gate.y - 9 if gate.y <= 0 then gate.y = 0 end end} } State Machine

Temporal model 1:32:00Mens :32:10Mens :38:07Mens :42:00Mens.4 4. Execute an agent over the cellular space regions Save the spatial data Draw cellular spaces and agents states Carrie out the comunication between agents... return value true 1. Get first pair 2. Execute the ACTION 3. Timer =EVENT 4. timeToHappen += period Fonte: (Carneiro, 2006)

time = Timer { Pair { Event{time = 1985, period = 1}, Message{ function(event) AgentRancher:execute(event) return 1 end} }, Pair { Event {time = 1988, period = 1}, Message { function(event) AgentSmall:execute(event) return 1 end} } Timer

myScale = Environment{ id = "MyScale", cs1 = CellularSpace{... }, csN = CellularSpace{... }, aut1 = Automata{... }, autN = Automata{... }, t1 = Timer{... }, tN = Timer{... }, sc1 = Environment{... }, scN = Environment{... }, } Environment

CellAgent ForEachAgentForEachCell ForEachNeighbor ForEachAgent CellularSpace Society GroupTrajectory DBMS

chuva N Pico do Itacolomi do Itambé Serra do Lobo Fonte: (Carneiro, 2006)

Autômatos Celulares DRY WET (soilWater > infCap) ? (soilWater <= infCap) ? Fonte: (Carneiro, 2006)

Simulation outcome fonte: Carneiro (2006)

STUDY AREA Parque Nacional Serra das Emas Fonte: (Almeida et al, 2008)

STUDY AREA Dados de Entrada do Modelo Fonte: (Almeida et al, 2008)

STUDY AREA Propagação do Incêndio Fonte: (Almeida et al, 2008)

Estimativa de Emissões por Desmatamento Fonte: (Aguiar et al, 2009)

Deforestation Forest Non-forest Deforestation Map – 2000 (INPE/PRODES Project) Federal Government induced colonization area (since the 70s): Small, medium and large farms. Mosaic of land use patterns. Definition of land units and typology of actors based on multi-temporal images (85-00) and colonization projects information (Escada, 2003). Deforestation in Rondônia Fonte: (Carneiro, 2006)

1985 to 1997 Source: adapted from (Carneiro, 2006) Deforestation in Rondônia

BASELINE SCENARIO – Hot spots of change (1997 a 2020) ALAP BR 319 Estradas pavimentadas em 2010 Estradas não pavimentadas Rios principais 0.0 – – – – – – – – – – 1.0 % mudança 1997 a 2020: Fonte: (Aguiar, 2006)

GOVERNANCE SCENARIO – Differences from baseline scenario ALAP BR 319 Estradas pavimentadas em 2010 Estradas não pavimentadas Rios principais Less: More: Differences: Protection areas Sustainable areas Fonte: (Aguiar, 2006)

STUDY AREA São Félix do Xingu Fonte: (Costa, 2009)

Landscape model: different rules of behavior at different partitions Forest Not Forest Deforest River FRONT MIDDLE BACK SÃO FÉLIX DO XINGU Observed deforestation in 1997 Fonte: (Costa, 2009)

Landscape model: different rules of behavior at different partitions which also change in time FRENTE MEIO RETAGUARDA Forest Not Forest Deforest River FRONT MIDDLE BACK SÃO FÉLIX DO XINGU Observed deforestation in 2006 Fonte: (Costa, 2009)

Modeling results 97 to 2006 Observed 97 to 2006 Real Data Simulation Fonte: (Costa, 2009)

Modelagem Multi-Escala Athmosphere, ocean, chemistry climate model (resolution 200 x 200 km) Atmosphere only global climate model (resolution 50 x 50 km) Regional climate model (resolution 10 x 10 km) Hydrology, Vegetation Soil Topography (e.g, 1 x 1 km) Regional land use change Socio-economic changes Adaptation (e.g., 100 x 100 m) Fonte: (Camara, 2009)

Acoplamento de Modelos Fonte: (Piribauer, 2009)

TerraME Hoje Versão 1.0 Beta prevista para fevereiro/ Extensão para acessar outros tipos de dados (pontos, linhas, etc.) como células TerraME-GIMS (Graphical Interface for Modeling and Simulation) TerraME-HPA (High Performance Architecture) Dificuldade para visualizar dinamicamente os resultados dos modelos