integrated lulcc modeling and impact assessment
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Integrated LULCC modeling and impact assessment
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Who and where from ? Center for Development Research (ZEF), University of Bonn
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Research on integrated land use planning, participatory planning processes, integrated modelling and (ES) assessment (including trade-off analyses)
Research & scientific network
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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
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ELI – European Land-use Institute + adjoint partners; Nodal Office of GLP for „Integrated Land Management, Planning and Policy“
Research & scientific network
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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
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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 regions National 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
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Land (use) systems
Actors
Impact
(natural) Bio-geo-physical conditions
Anthroposcene (individuals / communities / society)
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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
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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
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GISCAME
Fürst et al., 2010 a, b
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GISCAME
Fürst et al., 2010 a, b
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actors
scenarios
relative benefit of single scenarios
GISCAME
Frank et al., 2011; 2013.; Fürst et al., 2013; Koschke et al., 2012
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„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
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scenario + analytical tools (experts)
drivers and system interactions
inheritable
attribute dependent scenarios
risks (mass movement / water
erosion)
management scenarios (forestry)
landscape structural analysis
(LUT/attribute depen-dent) probabilities
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restrictions („experts“ => planning / policy interface)
environmental attributes (suitability / risks) – forbidden / punished LUC
proximity effects (mutual impact) – forbidden / punished LUC
legal frame / regulations
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LULCC scenarios
laymen stakeholders
Management scenarios
experts stakeholders
nested scenarios
laymen stakeholders
Frank et al., subm.; Fürst et al., 2011, 2012, 2013
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Temporal variability of specific ES (indicators) (are there hidden trade-
offs or benefits over time?)
laymen experts stakeholders stakeholders
General trends in LULCC and essential ES
(are decision alternatives broadly acceptable?)
Frank et al., subm.; Fürst et al., 2011, 2012, 2013
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Change & impact of landscape structure / land-use pattern (are there hidden trade-offs or
benefits for structural diversity?)
laymen experts stakeholders 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
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How much forest area do we need?
What kind of management is favourable??
Case study Middle-Saxony
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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
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Results at regional scale
Fürst et al., 2013
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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
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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.)
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§ 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
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§ 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
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The modeling dilemma, acc. Mohren, 2003
Some lessons learnt
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… the perfect time to stop is…
… if you feel it takes you off
Thanks for your attention!