Land Use Change Modelling in the ROBIN project: a multi-scale idea
1. Land Use Change Modelling
in the ROBIN project;
a multi-scale idea
Michiel van Eupen,
Alterra Wageningen University
Technical & Networking Session 1.1
Supporting landscape scale planning for REDD+;
How useful are land use change models?
UNEP, WCMC, IIASA, Warsaw, 16 November 2013
3. Role of Land Use Modelling in
ROBIN
Continental
Main Objective:
further use by vegetation
models like LPJm /JULES
+ ESS modelling at
continental scale
Entire South- + Meso-America:
1km scale resolution, limited
amount of land use classes
equal in each country
Based on available (world )
datasets
Focussing on major (generic) socio-
economic pathways+ knowledge
Using CLUE-s land use change
model
Regional (=landscape?)
Main Objective:
regional policy evaluation
Finding relevant thresholds at
regional scale policies
to see to what level up- and
downscaling is relevant
Different regional scales: Amazone,
country (Mexico), detailed case stud.
extended land use classes,
regionally specific
Using regional, specific data
Specific regional policy scenarios
Using CLUE –s/Dinamica & Participative
modelling tools
4. Continental: CLUE model structure
CLUE will provide:
• yearly output maps of
future land use and
hence, ageing
Y Xβ ε , ~ N(0, 2 )
8. Workflow combining Maps & Rules
Map and compare alternatives
Output Map
Rules used
Trace back to
Rules used
Statistics
per adm.unit
9. QUICKScan changes land cover Pantanal
Effects of floodings due to
upstream erosion & climate conditions
10. How useful are land use change
models on the land scape scale?”
Focus on policy development
current limiting factors land-use planning?
CLUE approach is „needed‟ generic method to translate
demands/changes in to patterns. link with existing models
etc.
On the land scape scale models like CLUE needs to be linked to
the policy + planning issues not easy
Policies are made by people (policy makers....not modellers):
“Useful for who?” The Policy maker or the scientist?
The policymaker needs to TRUST the model output.
Model s should be transparent enough to judge the effect of
the assumptions