2. Modelling Human-Environment Interactions: Theories and Tools Gilberto Câmara, Tiago Carneiro Pedro Andrade Vespucci Summer School, 2010 Licence: Creative Commons ̶̶̶̶ By Attribution ̶̶̶̶ Non Commercial ̶̶̶̶ Share Alike http://creativecommons.org/licenses/by-nc-sa/2.5/

3. "We went to explore the Moon, and in fact discovered the Earth." Eugene Cernan photo: NASA

4. Deforestation cut by 300% (2005-09) 46% of energy is renewable Brazil: a natural knowledge economy Best technology in biofuels World leader in tropical agriculture

5. A Vision for INPE in the 21 st Century INPE will be a world-class centre in Space and Environment R&D for the tropical region Brazil will be an environmental power and the first developed nation in the tropics

6. Agriculture Energy Ecosystems Climate change Weather and natural disasters Space technology can add value to Brazil´s natural knowledge economy Megacities

7. INPE: CONVERTING DATA INTO KNOWLEDGE SATELLITES Earth observation, scientific, and data collection satellites GROUND SYSTEMS Satellite control, reception, processing and distribution of satellite data ANALYSIS AND MODELLING Space Weather, Weather Prediction and Earth System Science SOCIETAL BENEFITS Innovative products to meet Brazil´s needs

8. DETER: 15-day alerts of newly deforested large areas Monitoring Deforestation in Amazonia

9. Monday AMLecture: Modelling human-environment interactions Hands-on: Creating a cellular automata model in TerraME Monday PMLecture: Complex Systems and Emergence Hands-on: Game of Life, Drainage, Schelling´s Segregation Tuesday AMLecture: Game Theory, Evolution of Cooperation Hands-on: Iterated and Spatial Prisioner´s Dillema Tuesday PMLecture: Governing the commons Hands-on: Management of common pool resources Wed AM Groupwork: Conceptual models for management of common pool resources Outline

10. Runaway greenhouse :: No water cycle to remove carbon from atmosphere Earth is unique in our solar system in its capacity to sustain highly diversified life Our Earth is a Unique Planet in the Solar System Loss of carbon :: No lithosphere motion on Mars to release carbon Earth Harbor of Life from Guy Brasseur (NCAR)

11. 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.

12. The fundamental question of our time fonte: IGBP How is the Earth’s environment changing, and what are the consequences for human civilization?

13. Sustainability Science Core Questions  How can the dynamic interactions between nature and society be better incorporated in emerging models and conceptualizations that integrate the earth system, human development and sustainability?  How are long-term trends in environment and development, including consumption and population, reshaping nature- society interactions in ways relevant to sustainability?  What determines vulnerability/resilience of nature-society interactions for particular places and for particular types of ecosystems and human livelihoods? Source: Sustainability Science Workshop, Friibergh, SE, 2000

14. 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?

15. Impacts of global land change More vulnerable communities are those most at risk

16. 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

17. source: Global Land Project Science Plan (IGBP)

18. ESSL - The Earth & Sun Systems Laboratory GDP per Person in Western Europe (1-2000 AD) from Guy Brasseur (NCAR)

19. ESSL - The Earth & Sun Systems Laboratory Perturbations by humans are quasi-exponential from Guy Brasseur (NCAR)

21. from Jackie McGlade (EEA)

22. Source: Carlos Nobre (INPE) Can we avoid that this….

23. Fire... Source: Carlos Nobre (INPE) ….becomes this?

24. Deforestation in Amazonia ~230 scenes Landsat/year

25. simplified representation of a process Model = entities + relations + attributes + rules What is a Model? Deforestation in Amazonia in 2020?

26. Computational models If (... ? ) then... Desforestation? Connect expertise from different fields Make the different conceptions explicit

27. Computational models Territory (Geography) Money (Economy) Culture (Antropology) Modelling (GIScience) Connect expertise from different fields Make the different conceptions explicit

28. Modelling and Public Policy System Ecology Economy Politics Scenarios Decision Maker Desired System State External Influences Policy Options

29. Earth as a system

30. Slides from LANDSAT Aral Sea 197319872000 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?

31. We need spatially explicit models to understand human-environment interactions Nature: Physical equations Describe processes Society: Decisions on how to Use Earth´s resources

32. f ( I t+n ). FF f (I t )f (I t+1 )f (I t+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)

33. t p - 20 t p - 10 tptp Calibration t p + 10 Forecast Dynamic Spatial Models Source: Cláudia Almeida

34. Which is the better model?

35. Limits for Models source: John Barrow (after David Ruelle) Complexity of the phenomenon Uncertainty on basic equations Solar System Dynamics Meteorology Chemical Reactions Hydrological Models Particle Physics Quantum Gravity Living Systems Global Change Social and Economic Systems

36. How do we decide on the use of natural resources? Loggers Competition for Space Soybeans Small-scale Farming Ranchers Source: Dan Nepstad (Woods Hole)

37. Underlying Factors driving proximate causes Causative interlinkages at proximate/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?

38. Human-enviromental systems [Ostrom, Science, 2005]

39. Types of goods Source: E Ostrom (2005)

40. 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

41. Institutional arrangments in Amazonia

42. Prisoner’s Dilemma: Game Theory Did you lie to Congress about WMD in Iraq?

43. Cells (objects) Question #1 for Nature-Society models Fields What ontological kinds (data types) are required for nature-society models?

44. Resilience Concepts for spatial dynamical models Events and processes

45. degradation Concepts for spatial dynamical models vulnerability

46. Human-environmental models need to describe complex concepts (and store their attributes in a database) and much more… biodiversity Concepts for spatial dynamical models sustainability

47. Question #2 for Nature-Society models What models are needed to describe human actions?

48. Clocks, clouds or ants? Clocks: deterministic equations Clouds: statistical distributions Ants: emerging behaviour

49. Statistics: Humans as clouds Establishes statistical relationship with variables that are related to the phenomena under study Basic hypothesis: stationary processes Example: CLUE Model (University of Wageningen) y=a 0 + a 1 x 1 + a 2 x 2 +... +a i x i +E Fonte: Verburg et al, Env. Man., Vol. 30, No. 3, pp. 391–405

50. 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.

51. Driving factors of change (deforestation) source: Aguiar (2006)

52. Linear and spatial lag regression models where: 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. W is the spatial weights matrix, the product WY expresses the spatial dependence on Y (neighbors),  is the spatial autoregressive coefficient.

53. Statistics: Humans as clouds Statistical analysis of deforestation source: Aguiar (2006)

54. CLUE modeling framework 25 x 25 km 2 100 x 100 km 2

55. Scenario exploration: linking to process knowledge Cellular database construction Exploratory analysis and selection of subset of variables Construction of alternative models for each group/partition/ land-use Alternative CLUE runs 1997 to 2020 Comparison to real data and new frontiers process knowledge Porto Velho- Manaus BR 163 Cuiabá-Santarém São Felix/ Iriri ApuíHumaitá Boca do Acre Santarém Manaus- Boa Vista Aripuanã Scenario exploration

56. Scenarios for deforestation in Amazonia (2020)

57. 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.

58. Agent-Based Modelling Goal Environment Representations Communication Action Perception Communication source: Nigel Gilbert

59. Agents: autonomy, flexibility, interaction Synchronization of fireflies

60. Bird Flocking No central authority: Each bird reacts to its neighbor Bottom-up: not possible to model the flock in a global manner. It is necessary to simulate the INTERACTION between the individuals

61. Requirement #2 for Nature-Society models Models need to support both statistical relations (clouds) and agents (ants) [Andrade-Neto et al., 2008]

62. Question #3 for Nature-Society models What types of spatial relations exist in nature-society models?

63. Rondonia 19751986 Natural space is (usually) isotropic Societal space is mostly anisotropic

64. Which spatial objects are closer? Societal spaces are anisotropic Which cells are closer? [Aguiar et al., 2003]

65. Euclidean space Open network Closed network D2 D1 Requirement #3 for Nature-Society models: express anisotropy explicitly [Aguiar et al., 2003]

66. Question #4 for Nature-Society models How do we combine independent multi-scale models with feedback?

67. Models: From Global to Local 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)

68. 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 grid of 25 x 25 km2 grid of 1 x 1 km2 Nature-Society models should be multi- scale, multi-approach [Moreira et al., 2008]

69. Not all multiscale models have nested grids Environmental Modeler [Engelen, White and Nijs, 2003] CLUE model [Veldkamp and Fresco, 1996] Multi-scale modelling: hierarchical relations need to be described

70. Requirement #4 for Nature-Society 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]

71. Question #5 for Nature-Society models 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

72. Old Settlements (more than 20 years) Recent Settlements (less than 4 years) Farms Settlements 10 to 20 anos Societal systems undergo phase transitions Isabel Escada, 2003 [Escada, 2003]

73. Requirement #5 for Nature-Society models: Capture phase transitions Newly implanted Deforesting Slowing down latency > 6 years Deforestation > 80% Small Farmers Iddle Year of creation Deforestation = 100% Deforesting Slowing down Iddle Year of creation Deforestation = 100% Deforestation > 60% Medium-Sized Farmers photos: Isabel Escada

74. TerraME: Computational environment for developing nature-society models Cell Spaces Support for cellular automata and agents TerraME: Modular modelling tool [Carneiro, 2006]

75. TerraME´s way: Modular components Describe spatial structure 1:32:00Mens. 1 1. 1:32:10Mens. 3 2. 1:38:07Mens. 2 3. 1:42:00Mens.4 4.... 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 Deforestation = 100% Describe rules of behaviourDescribe spatial relations [Carneiro, 2006]

76. Spatial structure in TerraME: Cell Spaces integrated with databases

77. Spatial Relations in TerraME Spatial relations between entities in a nature-societal model are expressed by a generalized proximity matrix (GPM) [Moreira et al., 2008]

78. TerraME: multi-scale modelling using explicit relationships 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]

79. 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

80. Hybrid automata: simple land tenure model STATEFlow ConditionJump ConditionTransition SUBSISTENCEDeforest 10% of land/yearDeforest > 60%CATTLE Extensive cattle raisingLand exhaustionABANDONMENT Forest regrowthLand revisionRECLAIM Public repossessionLand registrationLAND REFORM Land distributionFarmer gets parcels SUBSISTENCE Deforest 20%/year Farmer gets parcel deforest>=60% Land exhaustion CATTLE Extensive cattle raising ABANDONMENT Regrowth RECLAIM Public repossession Land revision LAND REFORM redistribution Land registration

81. TerraME Software Architecture TerraLib TerraME Framework C++ Signal Processing librarys C++ Mathematical librarys C++ Statistical librarys TerraME Virtual Machine TerraME Compiler TerraME Language RondôniaModelSão Felix Model Amazon ModelHydro Model [Carneiro, 2006]

82. 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

83. TerraME programming environment [Carneiro, 2006]