Integrated LULCC modeling and impact assessment

Who and where from ? Center for Development Research (ZEF), University of Bonn

Research on integrated land use planning, participatory planning processes, integrated modelling and (ES) assessment (including trade-off analyses)

Research & scientific network

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

ELI – European Land-use Institute + adjoint partners; Nodal Office of GLP for „Integrated Land Management, Planning and Policy“

Research & scientific network

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

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

Land (use) systems

Actors

Impact

(natural) Bio-geo-physical conditions

Anthroposcene (individuals / communities / society)

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

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

GISCAME

Fürst et al., 2010 a, b

GISCAME

Fürst et al., 2010 a, b

actors

scenarios

relative benefit of single scenarios

GISCAME

Frank et al., 2011; 2013.; Fürst et al., 2013; Koschke et al., 2012

„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

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

restrictions („experts“ => planning / policy interface)

environmental attributes (suitability / risks) – forbidden / punished LUC

proximity effects (mutual impact) – forbidden / punished LUC

legal frame / regulations

LULCC scenarios

laymen stakeholders

Management scenarios

experts stakeholders

nested scenarios

laymen stakeholders

Frank et al., subm.; Fürst et al., 2011, 2012, 2013

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

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

How much forest area do we need?

What kind of management is favourable??

Case study Middle-Saxony

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

Results at regional scale

Fürst et al., 2013

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

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.)

§  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

§  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

The modeling dilemma, acc. Mohren, 2003

Some lessons learnt

… the perfect time to stop is…

… if you feel it takes you off

Thanks for your attention!