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Integrated LULCC modeling and impact assessment

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Presentation delivered by Christine Fürst from the University of Bonn during the LSE seminar series on 30 October 2014

Publicada em: Ciências
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Integrated LULCC modeling and impact assessment

  1. 1. Integrated LULCC modeling and impact assessment
  2. 2. Who and where from ? Center for Development Research (ZEF), University of Bonn
  3. 3. Research on integrated land use planning, participatory planning processes, integrated modelling and (ES) assessment (including trade-off analyses) Research & scientific network
  4. 4. Research on integrated land use planning, participatory planning processes, integrated modelling and (ES) assessment (including trade-off analyses) Research & scientific network How to make use of the ES concept in SEA to improve territorial planning Support of integrated land and water management strategies – conflict solutions Integrative strategies for adapted land management under GC / CC; establishing planning processes Support of regional planning & development by using the ES concept; conflict solutions related to private goods and public services
  5. 5. ELI – European Land-use Institute + adjoint partners; Nodal Office of GLP for „Integrated Land Management, Planning and Policy“ Research & scientific network
  6. 6. Francis Mwambo Energy efficiency, emergy and foot- print analyses to assess sustainable regional resource use Research in Africa WASCAL: Research on integrative modelling and impact assessment BioMassWeb: Integrated modelling (agriculture & forestry), energy efficiency based assessment of the performance of regional solutions Gülendam Baysal Statistically based land use / land cover change scenarios and their impact on ES provision HongMi Koo Stakeholder based develop- ment and assess- ment of future agricultural land use and management options Janina Kleemann Expert-knowledge based land use / land cover change scenarios and their impact on ES / landscape services provision Justice Inkoom Landscape metrics and land-use interactions – mathematical approaches to assess bio-geo- physical interactions Regional Level: West-Africa (Sub-Sahara/Sudanian zone) (+ East-Africa, Ethopia) Local Level: Sub-regions / Catchments National level: Ghana (North), Burkina Faso, Benin, Côte d´Ivoire, Nigeria, Ethiopia Marcos J.-Jimenez Embedding LU models (forestry, agriculture, agro- forestry) to assess temporal fluctuations in ES Maurice Ahouansou Modelling and assessing hydrological ES and related trade- offs for agriculture Mitra Ghotbi Functional diversity of bacterial com- munities in agri- cultural soils – role of management for ES provision
  7. 7. Dr. Katrin Pietzsch Coordination programming and software architecture of GISCAME R&D in EU and S-America Dr. Susanne Frank Biomass provision and trade-offs for ES; landscape structural aspects in ES provision Lars Koschke Multi-criteria evaluation of ES provision – reliable indicators and stakeholder involvement Daniel Rozas Vasquez Connecting SEA, ES-concept and territorial planning – a policy implementation framework Frank Pietzsch Chief programmer GISCAME Regional Level: Europe (transect) Local Level: Model regionsNational level: Chile, (China adj.), Finland, Germany, Sweden, Slovenia Rene Schulze Data base management, webservices Thomas Gumpert Senior programmer GISCAME Martin Schultze Clustering approaches to derive Ecological- Hydrological Response Units RegioPower: Integrated modelling of spatial / temporal variability of ES provision ELI / INTECRE: knowledge base and knowledge integration frameworks
  8. 8. Land (use) systems Actors Impact (natural) Bio-geo-physical conditions Anthroposcene (individuals / communities / society)
  9. 9. objectives, TOR planning structures, process, responsibilities resource / services demands & conflicts alternatives & preferences suitability trade-offs land-use plan implementation governance, adaptation mechanisms data preparation / processing Bio-geo-physical conditions Anthroposcene
  10. 10. objectives, TOR planning structures, process, responsibilities resource / services demands & conflicts alternatives & preferences suitability trade-offs land-use plan implementation governance, adaptation mechanisms data preparation / processing Bio-geo-physical conditions Anthroposcene knowledge integration participation / consensus building IT support
  11. 11. GISCAME Fürst et al., 2010 a, b
  12. 12. GISCAME Fürst et al., 2010 a, b
  13. 13. actors scenarios relative benefit of single scenarios GISCAME Frank et al., 2011; 2013.; Fürst et al., 2013; Koschke et al., 2012
  14. 14. „simple“ scenario tools (laymen / stakeholders) neighbored cells with the same LUT all cells of a LUT streets water bodies point shaped element with impact gradient water courses area focus cellwise freestyle „what if“? – change of observed pattern
  15. 15. scenario + analytical tools (experts) drivers and system interactions inheritable attribute dependent scenarios risks (mass movement / water management scenarios (forestry) landscape structural analysis (LUT/attribute depen- dent) probabilities
  16. 16. restrictions („experts“ => planning / policy interface) environmental attributes (suitability / risks) – forbidden / punished LUC proximity effects (mutual impact) – forbidden / punished LUC legal frame / regulations
  17. 17. LULCC scenarios laymenstakeholders Management scenarios expertsstakeholders nested scenarios laymenstakeholders Frank et al., subm.; Fürst et al., 2011, 2012, 2013
  18. 18. Temporal variability of specific ES (indicators) (are there hidden trade- offs or benefits over time?) laymen expertsstakeholders stakeholders General trends in LULCC and essential ES (are decision alternatives broadly acceptable?) Frank et al., subm.; Fürst et al., 2011, 2012, 2013
  19. 19. Change & impact of landscape structure / land-use pattern (are there hidden trade-offs or benefits for structural diversity?) laymen expertsstakeholders stakeholders Spatially explicit changes in ES provision potentials and risks (are there place-specific trade- offs that affect specific actors?) Frank et al., 2011.; Fürst et al., 2013; Witt et al., 2013
  20. 20. How much forest area do we need? What kind of management is favourable?? Case study Middle-Saxony
  21. 21. Integrated scenarios Basics: BAU, multifunctional / „economically motivated“ conversion Private concerns: ownership type specific simulation Public concerns: integrated scenarios comprising conversion & afforestation Visions: maximum scenarios (ecomax / lignomax) Fürst et al., 2013
  22. 22. Results at regional scale Fürst et al., 2013
  23. 23. initial situation BAU+2% aff. BAU+12% aff. max aff. initial situation BAU+2% aff. BAU+12% aff. max aff. initial situation BAU+2% aff. BAU+12% aff. max aff. agricultural areas / sparsely wooded areas loess-hill landscape (old grown cultural landscapes) mountain areas (Ore Mts.) Results for sub-regions Trend Trend based upon Frank et al., 2011
  24. 24. Trade-off analysis over time Yield Stocking Volume Fuel wood Sc1 Sc2 Sc3 Sc4 Sc5 Sc1 Sc2 Sc3 Sc4 Sc5 Sc1 Sc2 Sc3 Sc4 Sc5 Sc1 Sc2 Sc3 Sc4 Sc5 T10 T30 T50 T100 Sc1 – BAU Sc2 – Economic conversion Sc3 – BAU + Afforestation (2 %) Sc4 – BAU + SRC (2 %) Sc5 – Multifunctional conversion based upon Frank et al., subm. mountain areas (Ore Mts.)
  25. 25. §  State regional planning goal to achieve av. 30% of forest cover needs to respect subregional particularities §  In agricultural (sparsely wooded) areas, forest cover of ~30 % preferrably along green corridors most profitable; §  Resulting production and income losses over time can considerably reduced by replacing afforestation by planting SRC §  In structurally diverse areas / forest landscapes, noteable increase of the already higher ES provision potential could only be achieved by LULCC targets outside actor´s acceptance levels §  Here economic conversion is sufficient to increase biomass output Results summary
  26. 26. §  Integrated LULCC modeling and impact assessment needs to include different spatial (landscape – MPU) and temporal (now – future trade-offs) scales §  Problem is still incompatibility of landscape and land management models and large discrepancies in data quality and availability §  Combined qualitative-quantitative assessment and a fully nested approach in scenario design, modeling and evaluation is promising, but model errors tend to accelerate, uncertainties cannot be specified §  Conclusion: helpful for exploring trends and early awareness raising on potential problems, but not suitable for predictions! Some lessons learnt
  27. 27. The modeling dilemma, acc. Mohren, 2003 Some lessons learnt
  28. 28. … the perfect time to stop is… … if you feel it takes you off Thanks for your attention!

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