2. Modelling Human-Environment Interactions: Theories and Tools Gilberto Câmara Licence: Creative Commons ̶̶̶̶ By Attribution ̶̶̶̶ Non Commercial ̶̶̶̶ Share Alike http://creativecommons.org/licenses/by-nc-sa/2.5/

3. We need cooperation at a global level… By 2050... 8,5 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. source: Guy Brasseur

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

5. In the last 10,000 years, Earth’s average temperature has not varied more than 1 0 C

6. Will the Artic become an ice-free ocean? graphics: The Economist

7. Are we crossing planetary boundaries? Stockholm Resilience Centre

8. Negative consequences of the green revolution? graphics: The Economist

9. Good consequences of green revolution graphics: The Economist

10. Global hunger index graphics: The Economist

11. Forests and food production: potential conflicts graphics: Nature

12. Brazil: Do biofuels cause indirect land change?

13. Brazil: Projected direct land change from biofuels (2020) source: Lapola et al (PNAS, 2010)

14. Brazil: Projected indirect land change from biofuels (2020) source: Lapola et al (PNAS, 2010)

16. There is an urgent need for the international scientific community to develop the knowledge that can inform and shape effective responses to these threats in ways that foster global justice and facilitate progress toward sustainable development goals.

17. ICSU “Grand challenges” Develop, enhance and integrate the observation systems needed to manage global and regional environmental change. Improve the usefulness of forecasts of future environmental conditions and their consequences for people. Determine what institutional, economic and behavioural changes can enable effective steps toward global sustainability.

18. The known unknowns (limits of our knowledge) 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

19. What do we know we don’t know? Science explains how nature works by proposing laws Science explains how society work by describing interactions

20. Geoinformatics enables crucial links between nature and society Nature: Physical equations Describe processes Society: Decisions on how to Use Earth´s resources

21. Geoinformatics enables crucial links between nature and society Nature: Physical equations Describe processes Society: Decisions on how to Use Earth´s resources

22. 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

23. Slides from LANDSAT Aral Sea 197319872000 images: USGS Modelling Human-Environment Interactions How do we decide on the use of natural resources? What are the conditions favoring success in resource mgnt? Can we anticipate changes resulting from human decisions?

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

25. Land change: A quintessential spatial problem Loggers Competition for land Soybeans Small-scale Farming Ranchers Source: Dan Nepstad (Woods Hole)

26. © GEO Secretariat Source : WASDE december 2009 18% Food Security Wheat 31% Rice 20% Maize 35% Others 14% Stock : 457 Mt = 2.5 mois Trade : 265 Mt = 12% /utilis.

27. Changes in dietary patterns: Meat consumption FAOSTAT 2007

28. Food challenge: prices are increasing

29. Where is the food coming from and going to? graphics: The Economist

30. The food challenge: search for new land graphics: The Economist

31. The food challenge: technology gaps source: Nature

32. The Chinese are coming… to Africa Trade between China and Africa surpassed $120 billion in 2010. China gives more loans to poor African countries than the World Bank. graphics: The Economist

33. simplified representation of a process Model = entities + attributes + interactions + change rules What is a Model? Deforestation in Amazonia in 2020? graphics: INPE, Pesquisa FAPESP

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

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

36. (Getty Images, 2008) (PRODES, 2008) source: Espindola, 2012 A typical spatial model: What causes tropical deforestation?

37. 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)

38. T now - 20 Calibration Validation Projection t now + 10 Dynamic Spatial Models graphics: Cláudia Almeida T now – 10 T now

39. Which is the better model?

40. TerraLib: spatio-temporal database as a basis for innovation Visualization (TerraView) Spatio-temporal Database (TerraLib) Modelling (TerraME) Data Mining(GeoDMA) Statistics (aRT)

41. SGBD Tools for integration

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

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

44. Deforestation in Amazonia ~230 scenes Landsat/year

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

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

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

48. 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

49. 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 assess changes resulting from human decisions? What computational tools are needed to model human- environment decision making?

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

51. 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)

52. 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.

53. 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

54. Cells (objects) Question #1 for human-environment models Fields What ontological kinds (data types) are required for human-environment models?

55. Resilience Concepts for spatial dynamical models Events and processes

56. degradation Concepts for spatial dynamical models vulnerability

57. 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

58. What models are needed to describe human actions? Question #2 for human-environment models

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

60. 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

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

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

63. Scenario exploration: linking to process knowledge Cellular database construction Exploratory analysis and selection of subset of variables Porto Velho- Manaus BR 163 Cuiabá-Santarém São Felix/ Iriri ApuíHumaitá Boca do Acre Santarém Manaus- Boa Vista Aripuanã Scenario exploration

64. Scenarios for deforestation in Amazonia (2020)

65. Amazonia: multiscale analysis of land change and beef and milk market chains with TerraME Deforestation Forest Non-forest Clouds/no data INPE/PRODES 2003/2004: São Felix do Xingu

66. Forest Not Forest Deforest River Change 1997-2006: deforestation and cattle

67. Create pasture/ Deforest Speculator/ large/small bad land management money surplus Subsistence agriculture Diversify use Manage cattle Move towards the frontier Abandon/Sell the property Buy new land Settlement/ invaded land Sustainability path (alternative uses, technology) Sustainability path (technology) Agents example: small farmers in Amazonia

68. Create pasture/ plantation/ deforest Speculator/ large/small money surplus/bank loan Diversify use Buy new land Manage cattle/ plantation Buy calves from small Buy land from small farmers Agents example: large farmers in Amazonia

69. Forest Not Forest Deforest River Observed deforestation from 1997 to 2006

70. Local scale Regional scale CATTLE CHAIN MODEL Flows: goods, information, etc.. Connections: Agents LANDSCAPE DYNAMICS MODEL - Front - Medium - Rear INDIVIDUAL AGENTS Large and small farmers Local farmers Frontier Region SCENARIOS

71. Land use Change model Beef and milk market chain model Small farmers Medium and large farmers Land use Change model Small farmers Medium and large farmers Landscape metrics model Pasture degradation model Several workshops in 2007 to define model rules and variables Landscape model: different rules for two main types of actors

72. 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 - 2006

73. Modeling results 97 to 2006 Observed 97 to 2006

74. Requirement #2 for human-environment models Models need to support both statistical relations (clouds) and agents (ants)

75. Question #3 for human-environment models What types of spatial relations exist in nature-society models?

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

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

78. Euclidean spaceOpen network Closed network D2 D1 Requirement #3 for human-environment models: express anisotropy explicitly [Aguiar et al., 2003]

79. Question #4 for human-environment models How do we combine independent multi-scale models with feedback?

80. Models: From Global to Local Athmosphere, ocean, chemistry climate model (200 x 200 km) Atmosphere only global climate model (50 x 50 km) Regional climate model (10 x 10 km) Hydrology, Vegetation Soil Topography (1 x 1 km) Regional land use change Socio-economic adaptation (e.g., 100 x 100 m)

81. 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 25 x 25 km 2 1 x 1 km 2 Human-enviroment models should be multi-scale, multi-approach [Moreira et al., 2008]

82. Nested grids are not enough! Environmental Modeler [Engelen, White and Nijs, 2003] CLUE model [Veldkamp and Fresco, 1996] Multi-scale modelling: hierarchical relations need to be described

83. Requirement #4 for human-environment 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]

84. Question #5 for human-environment 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

85. 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]

86. Requirement #5 for human-environment 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

87. TerraME: Computational environment for developing human-environment models Cell Spaces Support for cellular automata and agents http://www.terrame.org [Carneiro, 2006]

88. TerraME programming environment [Carneiro, 2006]

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

90. TerraME´s approach: 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]

91. 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]

92. 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]

93. To Agent Cell a b a b c c Cell Agent From GPM: Relations between cells and agents [Andrade-Neto et al., 2008]

94. 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

95. 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

96. 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