The document discusses land use scenario development for climate change adaptation and mitigation. It provides background on scenarios, describing them as plausible stories about how the future may unfold based on assumptions. Scenarios are not predictions or projections. The document then discusses different types of scenarios and their purposes from environmental and social science perspectives. It provides an example of the Millennium Ecosystem Assessment scenario process and outcomes. Finally, it discusses linking scenarios to models and participation.

1. Land use scenario development Workshop Regional changes of land use for climate change adaptation and mitigation 2 4-25 May 2011, Zamorano By Wilbert van Rooij Netherlands Environmental Assessment Agency (PBL) seconded to Aidenvironment

3. What is a scenario? Scenarios are credible , challenging, and relevant stories about how the future might unfold that can be told in both words and numbers. Scenarios are plausible descriptions of how the future may develop, based on a coherent and internally consistent set of assumptions about key relationships and driving forces. Scenarios are not forecasts , projections , or predictions .

6. Types of scenarios A Project goal - exploration vs decision support: I. Inclusion of norms? : descriptive vs normative II. Vantage point: forecasting vs backcasting III. Subject: issue-based, area-based, institution-based IV. Time scale: long term vs short term V. Spatial scale: global/supranational vs national/local B Process design – intuitive vs formal: VI. Data: qualitative vs quantitative VII. Method of data collection: participatory vs desk research VIII. Resources: extensive vs limited IX. Institutional conditions: open vs constrained C Scenario content - complex vs simple: X. Temporal nature: claim vs snapshot XI. Variables: heterogeneous vs homogenous XII. Dynamics: peripheral vs trend XIII. Level of deviation: alternative vs conventional XIV. Level of integration: high vs low

7. Scenarios, models, and participation Traditional approach Integrated approach

8. Example scenario for Global assessment 1: The Millennium Ecosystem Assessment (full Storyline-And-Simulation approach)

10. MA Conceptual Framework

15. Changes in direct drivers Crop Land Changes in crop land and forest area under MA Scenarios Forest Area

21. Approach to quantifying scenarios in IMAGE model IMAGE 2 model Global change WaterGAP model World water resources

25. Taking environmental constraints into account Source: Van Meijl et al., 2005

28. The MA is a multi-scale assessment with multiple layers of nesting e.g. Central America e.g. Honduras e.g. El Paraíso

29. Methodology of multi scaled assessment:Eururalis

30. Examples Sub Global Assessments (SGAs). Multi-scale assessments

31. Story of the present: Writing post-its

32. Discussing relationships between factors

33. Final product Climate Water Land use change Population, Migration Environmental education Regional Policies Agrarian Policies Desertification

34. Creating the scenarios

35. Presenting the scenarios

36. Backcasting exercise: Multifunctional sustainable agriculture (BiB)

37. Quick reference scenario exercise Phase 1 Phase 2 Phase 3 Phase 4 Scenario exercise Source: Ecosystems and human well-being: A manual for Assessment Practitioners Neville Ash et all, 2010

38. Phase 1: How to set up a scenario exercise Understand context, aim of scenario exercise Identify and agree on type of support to be given Agree on expected outcome in terms of process and product Define scope: Budget and time frame Geographical scale and time horizon Type of scenarios and analysis Set up a project team and environment: Establish authorising environment Decide who to involve in the process and when Define role of stakeholders 1 2 3 4 5

39. Phase 2: How to develop scenarios Identify main concerns and stakeholder questions and understand how past changes have come about Establish scenario development procedure and decide on method( inductive, deductive or incremental Analyse main drivers of change in future Discuss possible trends for each driver Identify the main uncertainties for the future Develop a set of scenario logics Describe scenario assumptions and story lines based on identified drivers and scenario Optional: use models to quantify main trends and assumptions Stage 1 2 3 4 1 Stage 2 Stage 3

40. Phase 3: How to analyze scenarios Determine whether, what and how to quantify. Check: Need and role quantitative information Availability of quantification tools Availability of budget and time Time horizon of analysis Which need to be assessed To what extent models need to be coupled Analyze implications of individual scenarios Optional: Quantify driving forces and impacts Assess ecosystems and human well being implications Optional: Analyze specific response options Analyze across the set of scenarios: Identify reasons for differences across scenarios Identify differing, similar and offsetting trends Optional: Analuze response options in scenarios 2 3 1 Policy options: e.g. climate adaptation and mitigation

41. Phase 4: How to use and communicate scenarios Map target audience and context conditions: Do a network analysis regarding actors, relationships, information needs and habits Map purpose to context: Check consistency ,credibility, saliency and legitimacy Assess what can reasonably be done with resources Develop outreach and communication strategy Develop clear success criteria Ensure steady high level support and backing Resent scenarios to target audience(s) Discuss implications, response options and lessons learnt Evaluate and monitor outreach action against your success criteria 1 2 3 4 5

42. Practical steps: Storyline And Simulation approach Narrative storylines Model runs

43. Practice: What are the scenario archetypes? IPCC SRES A1 GEO-3 Markets First OECD Reference MA Global Orchestration MedAction Big is Beautiful Solidarity/Pro-active Self-interest/Reactive Regional Global IPCC SRES B1 GEO-3 Sustainability First MA Techno Garden MedAction Knowledge is King IPCC SRES A2 GEO-3 Security First MA Order from Strength MedAction Big is Beautiful? IPCC SRES B2 MA Adaptive Mosaic

44. Practice: What are the scenario archetypes? Global Markets Global Sustainability Continental Barriers Regional Sustainability Solidarity/Pro-active Self-interest/Reactive Regional Global

45. Example: Characteristics of Global Markets scenario Main drivers Population growth: low increase quality of life Economic development: very rapid Technology development: rapid new inventions, but no magic Environmental attitude: reactive no environmental laws and policies Trade increase (globalisation) Institutional strength policies help economy State of environment very poor Main objective economic growth

48. Link with CLUE-s model

49. Demand: How much will be the change (in ha) of all major land uses? Spatial policies: New national parks, restricted areas, agricultural development zones Location characteristics: Infrastructure (new roads?) = change in accessibility Soil (soil degradation?) Population (migration because of globalisation?) Etc. Conversion settings Transition possibilities from one land use type to another Link with CLUE-s model

50. National scenarios – essential characteristics The scenario should be: 1. Consistent with the assumptions of a selected archetype scenario 2. Consistent with current national trends 3. Creative! (do not use archetype as straitjacket) 4. As specific as possible on policy options for conversation 5. Linked with CLUE-s where possible

51. National scenarios – essential elements Your scenario could have information on: Factors: Sectors: Actors: Economic development Agriculture * Government Population growth Tourism Businesses Consumption pattern Energy NGOs Technology Water Environmental Policies Forestry Scientists Protected area Institutions Urban area State of environment etc. (Biodiversity!) * Share intensive / extensive agr. area

54. Example FAO statistics

55. Example IMAGE simulation output

56. Wilbert van Rooij, May 2011 Bridging gap between numeric and geographical data: Baseline scenario 1: Extract numeric data from available resources: A: Global: FAO (website), Global Assessments (MA, OECD, GBO, IPCC) B: National: Development reports (agricultural + forestry department Outlooks (Vietnam: Agenda 21, MDG report, etc) Census data from statistical department Specialists (Socio-Economists. Agronomists, Forestry planners, Environmentalists, etc) 2: Aggregate land use classes So that you can compare spatial and numeric data and for which you have future data 3: Create trends, historical and for planned time horizon 4: Compare geographical areas aggregated land use classes of the land use map with the areas derived from non spatial sources 5: Interpret reasons for difference, adjust numeric data and use relative differences for creation of demand table

59. Forest scenario Vietnam used in Clue Heavily disturbed forest Slightly disturbed forest Regrowth shrub and bushes Shifting cultivation (ext.agr) Degraded lands Intensive agriculture Residential and urban land Year 2000 is the baseline derived from the current land use map. The rest are projections from a scenario Primary forest Plantation Nature Others Area 2000 0 26020 20763 36142 19691 13963 17846 47162 87488 11634 18229 28525 32,7463 2001 1 26124 21563 38226 21115 15830 15704 43870 86619 11657 18229 28525 32,7463 2002 2 26232 22411 40257 22375 17698 13820 40743 85493 11681 18229 28525 32,7463 2003 3 26344 23305 42238 23479 19565 12162 37772 84141 11704 18229 28525 32,7463 2004 4 26460 24244 44169 24434 21433 10702 34950 82590 11727 18229 28525 32,7463 2005 5 26581 25227 46052 25247 23300 9418 32269 80863 11751 18229 28525 32,7463 2006 6 26708 26253 47617 26254 24513 8288 30049 79254 11774 18229 28525 32,7463 2007 7 26839 27312 49143 27150 25727 7293 27939 77508 11798 18229 28525 32,7463 2008 8 26975 28404 50631 27940 26940 6418 25936 75644 11821 18229 28525 32,7463 2009 9 27117 29527 52081 28630 28153 5648 24032 73674 11845 18229 28525 32,7463 2010 10 27265 30682 53495 29225 29367 4970 22224 71612 11869 18229 28525 32,7463 2011 11 27418 31866 52255 29730 30580 4374 20506 72088 11893 18229 28525 32,7463 2012 12 27578 33013 51045 30148 31793 3849 18874 72492 11916 18229 28525 32,7463 2013 13 27743 34124 49865 30485 33007 3387 17324 72834 11940 18229 28525 32,7463 2014 14 27913 35200 48715 30745 34220 2981 15851 73120 11964 18229 28525 32,7463 2015 15 28089 36242 47594 30931 35433 2623 14452 73357 11988 18229 28525 32,7463 2016 16 28271 37250 46501 31047 36647 2308 13123 73551 12012 18229 28525 32,7463 2017 17 28457 38227 45435 31096 37860 2031 11860 73708 12036 18229 28525 32,7463 2018 18 28648 39171 44395 31083 39073 1787 10660 73831 12060 18229 28525 32,7463 2019 19 28844 40085 43382 31009 40287 1573 9520 73924 12084 18229 28525 32,7463 2020 20 29044 40970 42394 30878 41500 1384 8438 73993 12108 18229 28525 32,7463

60. Interpolation of land use area data Select regression type: liner, logarithmic, polynomial, etc. FRA2005 1990 1995 2000 2010 Prim. for 118 100 93 Sec. for 90 100 105

62. Scenario information is used for the geographical allocation of future land use and to determine its pressure on biodiversity + + + + Lu model + Globio MSA_lu2020 MSA_infr2020 MSA_frag2020 MSA_nitr2020 MSA_clim2020