A High Resolution Land use/cover Modelling Framework for Europe: introducing the EU-ClueScanner100 model
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The document describes the Land Use Modelling Platform (LUMP), which provides land use/land cover simulations for Europe. LUMP consists of three main modules: 1) interfaces with external models to define land use demands, 2) an endogenous land allocation model (EU-ClueScanner), and 3) impact assessment tools. EU-ClueScanner simulates land use change based on demands, suitability factors, and neighborhood effects. It outputs land use maps and indicators on themes like erosion and biodiversity. LUMP calibrates and validates EU-ClueScanner using historical data and allows impact assessment of policies on land and environment.
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A High Resolution Land use/cover Modelling Framework for Europe: introducing the EU-ClueScanner100 model
- 1. Land Use Modelling Platform Core : Eu-ClueScanner 100m Sarah MUBAREKA Land modelling team: Carlo Lavalle, Claudia Baranzelli, Carla Rocha Gomes Land Management and Natural Hazards Unit Institute for Environment and Sustainability Joint Research Center
- 12. Historical Land Use/Cover , U t = 1990;2000;2006 Land Demands: Urban fabric (residential areas) Historical Population , P (Eurostat) t = 1990;2000;2006 NUTS 2 regions i Historical population density , D
- 14. Historical statistics on agricultural commodity production from CoCo/CAPRI (1990-2005) Land Demands: Agricultural areas Trends in historical agricultural land use from Corine (1990-2006) EU-wide agriculture sector modelling system CAPRI ( forecast 2020 ) Minimum and maximum trends in agricultural land claims per crop group Currently being employed for the assessment of the new CAP Land demand for main crop groups per NUTS 2 region
- 15. Historical statistics on forest related products Land Demands: Forest areas Trends in historical forest land use from Corine (1990-2006) and Forest Map 2006 Wood/Non-Wood product economic modelling ( EFSOS, OECD, .. ) Harvest demand -> Minimum and maximum trends in land claims per main forest species/typologies In progress .. (Ref. AA with CLIMA) Land demand for main forest typologies per Country
- 18. Refinement of the Corine Land Cover 2006 Current land-use How does it work? Base map. CLC original CLC refined
- 21. Source: Verburg and Overmars, 2009 How does it work? Natural succession.
- 23. Erosion sensitive areas How does it work? Subsidies and taxes. Natura 2000 areas High Nature Value areas Soil/Crop/Vegetation properties Ecological corridors Flood prone areas Mean annual/seasonal temp. / precipit. Upstream parts of catchments
- 28. OVERALL SUITABILITY Location Specific Preference Addition (LOCSPEC) Factor Data Neighbourhood effect Conservation Premium + + + For each spatial (modelling) unit, the overall suitability is computed Where: i is the simulated land-use class i n is the total number of simulated land-use classes How does it work? Overall suitability.
- 30. DIRECT OUTPUT OF EUCS: Land use / land cover change RASTERS Eu-ClueScanner output 2005 2010 2015 2020 2025 2030 2005 2010 2015 2020 2025 2030 2005 2010 2015 2020 2025 2030 2005 2010 2015 2020 2025 2030
- 31. Impact analysis: Indicator models use information both derived from external models and the land allocation module to provide a balanced set of indicators focusing on the land-use and environmental domains. Eu-ClueScanner output
- 33. Hardcoded, quantitative indicator: Agricultural abandonment Agricultural abandonment hot spots:
- 36. Soft link between GUIDOS and EUCS: Habitat connectivity Links and nodes are classified according to their level of importance The best placement for new links to associate two different components can be proposed based on a series of criteria
- 37. Soft link between GUIDOS, R and EUCS: Urban morphology What we can derive from land use Composite indicator for urban compactness, calculated for 305 large urban zones for the year 2000 with inset for northern Italy. (The urban morphological classes are overlain.)
- 38. Soft link between IDL, Lisflood and EUCS: Water quantity Water quantity Summer Spring
- 41. From Pontius, Huffaker and Denman, 2004 Model Validation Proportion correct based on a pixel:pixel comparison of ref map : simulated map The other 4 expressions give proportion correct if the simulated map is adjusted for quantity and/or location (for better or for worse)
- 44. JRC models within Unit Dynamic Land Allocation Forestry Soil JRC models outside of Unit: Water Global Economic models Demographic models Demand for products and services Land allocation Wood/non-wood products demand Water demand & management Hydrological parameters Agriculture Transport Energy Regional Policy Alternatives* *Policy and the relevant indicators are sector-specific … Soil parameters Forest land availability Indicators* Assessment of policy alternatives Stakeholders Land availability Land demand
- 46. Impact Analysis module LUMP - Components and Workflow Integration with LISFLOOD for Water Quantity Management
Notes de l'éditeur
- Schematic representation. We started with simple schematic scheme, trying to include more and more complexity.
- Schematic presentation of the EUClueScanner model. I leave the definition of scenarios turned on because is something to be ware of all along the modelling process. Its exact configuration is case study specific while the overall modelling approach is generic and similar in various applications.
- Schematic presentation of the EUClueScanner model. NB Economy, as to say Economic sectors models
- Schematic presentation of the EUClueScanner model.
- The CLUE model is based on the dynamic simulation of competition between land uses. Its spatial allocation rules are based on a combination of empirical analysis of current land use patterns, neighbourhood characteristics and scenario-specific decision rules. It combines the top-down allocation of land use change at national/regional level for all EU Member States to regular either 1km×1 km or 100mx100m grid cells with a bottom-up determination of conversions for specific land use transitions.
- The rules depend upon the specific application.
- Schematic presentation of the EUClueScanner model.
- ..or emphasizing change through simple spatial statistics such as agricultural abandonment..
- Indicators are powerful visual and statistical tools, which we like to emphasize can always be refined with the collaboration of experts!
- Using proxies, we are testing indicators that may seemingly not be calculable from land use model output such as biodiversity, carbon sequestration, soil erodability, river discharge (flood risk) etc.
- Again with GUIDOS, we are able to create composite indicators and derive output very quickly for all questions related to morphology, even built up area spatial metrics can be incorporated!
- Throughout the various phases of the Lisflood model modifications, the river discharge was used as a parameter to measure sensitivity We look at different events (floods, water stress) and different seasons
- Schematic presentation of the EUClueScanner model. I leave the definition of scenarios turned on because is something to be ware of all along the modelling process. Its exact configuration is case study specific while the overall modelling approach is generic and similar in various applications.