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FORECOM Project meeting May 2014 Forest cover change modelling: future scenarios.

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Presentation on theme: "FORECOM Project meeting May 2014 Forest cover change modelling: future scenarios."— Presentation transcript:

1 FORECOM Project meeting May 2014 Forest cover change modelling: future scenarios

2 FORECOM Project meeting May 2014 Map current forest/land cover Identify over-arching processes – Climate change – Population growth/demographic change – Economic growth – Political change new energy policy conservation policy, etc Define storylines for future scenarios Identify drivers of cover change – Land abandonment – Policy/planning – Urban sprawl Quantify future ‘demands’ for land use/ forest Forest cover change scenarios – general approach

3 Current land cover Forest cover change scenarios Climate Change Agricultural change -Land abandonment, marginal open areas to forest -Agricultural intensification Urbanisation -new settlements Maps of land cover/forest cover change scenarios Initial State Land use demand Dyna-CLUE Modelling framework (P. Verburg, University of Amsterdam) Land use suitability Environmental Data/explanatory variables Land-use/forest suitability

4 FORECOM Project meeting May 2014 CLUE (Conversion of Land Use and its Effects) model one of the most frequently used land use models worldwide is based on the spatial allocation of demands for different land use types to individual grid cells

5 FORECOM Project meeting May 2014 Overview of modeling procedure - CLUE Verburg 2010

6 Dyna-CLUE – allocation procedure Verburg & Overmars 2009

7 FORECOM Project meeting May 2014

8 Current land use scenario modelling in Switzerland Focus on land use change in general (not only forest) – 6 land use classes Whole of Switzerland 5 future scenarios to 2035 Largely focusing on population and agricultural drivers – no climate change

9 FORECOM Project meeting May 2014 Land use/land cover classes Closed Canopy Forest Pasture Agriculture Arable Agriculture Urban Areas Open Forest/ Scrub Overgrown Areas

10 Self-sufficiency, Regionally centered development, high ecological concerns (B2) ¨ Driving forces Population Economy …. More regionalMore global More intervention Less intervention Globalisation, High global economic growth but low Swiss growth (A1) Globalisation but emphasis on services, high ecological concerns. Low Swiss growth (B1) Heterogeneous world, regionally centered growth, (comparatively) high economic growth for Switzerland (A2)

11 FORECOM Project meeting May 2014 Population growth scenarios defined by the Swiss Federal Statistics Office Per capita urban demand (Swiss Federal Statistics Office) Agricultural demand related to population and level of imports Land cover change restrictions representing policy and planning – Conversion restrictions – Spatial restrictions Common to all scenarios – Forests and current National Parks/protected areas are ‘sacred’ Quantification of Scenarios

12 1985 1997 2009 Swiss land use statistics Land-use suitability 1ha resolution Land cover change scenarios Agricultural change -Land abandonment, marginal open areas to forest -Agricultural intensification Urbanisation - high density housing - new settlements Maps of land-use change scenarios Initial State Land use demand Dyna-CLUE Modelling framework (P. Verburg, University of Amsterdam) Land use suitability Environmental data

13 FORECOM Project meeting May 2014 Adaptation to forest cover change scenario modelling for FORECOM Same general approach Re-focus on over-arching processes of relevance to forest cover change, and in study area (including climate change) Adapt scenario storylines to reflect processes and drivers important for forest cover change

14 FORECOM Project meeting May 2014 Adaptation to forest cover change scenario modelling for FORECOM Suitability include long-term forest cover change perspective Incorporate findings from TASK 6 Storylines – new aspects Climate change Renewable energy – bio energy and/or infrastructure construction Tourism – urbanisation/infrastructure

15 FORECOM Project meeting May 2014 Adaptation to forest cover change scenario modelling for FORECOM ‘Interventions’ Changes to forest protection laws Conservation protections – Alpine Agriculture or ‘old growth’ forest

16 FORECOM Project meeting May 2014 Implementation of the Dyna-CLUE model in the Polish Carpathians: STEPS tests of simple scenarios of future forest change with Dyna- CLUE (variables like elevation, radiation, distance to roads, distance to built-up areas, population, employment, distance to forest boundary, neighbourhood) development of full storylines for possible future forest cover change with full list of variables – 5 to 6 possible scenarios comparison with Swiss models/storylines simplification of the Polish models (data availability) – 2-4 final scenarios implementation of scenarios in Dyna-CLUE

17 FORECOM Project meeting May 2014 Questions? Thanks Swiss Federal Institute for Snow, Forest and Landscape research, WSL

18 FORECOM Project meeting May 2014

19 Biogeographical (Static, 1ha) Continentality indexCSD/DEM25 (Zimmermann & Kienast 1999) Yearly moisture index CSD/DEM25 (Zimmermann & Kienast 1999) Yearly direct solar radiation CSD/DEM25 (Zimmermann & Kienast 1999) Precipitation average growing seasonCSD/DEM25 (Zimmermann & Kienast 1999) No. of summer precipitation daysCSD/DEM25 (Zimmermann & Kienast 1999) ElevationDEM100 SlopeDEM100 Sine of aspect (east)DEM100 Cos of aspect (north)DEM100 Soil permeabilitySoil suitability maps BLW 2012 Soil stoninessSoil suitability maps BLW 2012 Soil suitability for agricultureSoil suitability maps BLW 2012 Socio-economic (temporally variable, per Gemeinde) Taxable income per tax paying residentFederal Office for Statistics Percentage inhabitants employed in primary sectorFederal Office for Statistics Public Transport accessibilityFederal Office for Spatial Planning Infrastructure (temporally variable, 1ha) Distance to major roadsVector25 Distance to access roadsVector25 Neighbourhood variables No. of neighbours in classes (Urban, closed forest, agriculture) Distance to forest Explanatory variables


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