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What if? prospects based on Corilis

What if? prospects based on Corilis. Alex Oulton, Manuel Winograd Ronan Uhel & Jean-Louis Weber. Land Use Interface Workshop EEA, Copenhagen, 1-2 December , 2008. What if? prospects based on Corilis. Dialogue on prospects based on common representations; versatile tool; incremental

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What if? prospects based on Corilis

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  1. What if? prospectsbased on Corilis Alex Oulton, Manuel Winograd Ronan Uhel & Jean-Louis Weber Land Use Interface Workshop EEA, Copenhagen, 1-2 December , 2008

  2. What if? prospects based on Corilis • Dialogue on prospects based on common representations; versatile tool; incremental • Highlight (check, map, quantify) consequences of various assumptions  ideally defined with users • No real scenario, 3 to 5 assumptions at a time, maximum • Shows what it doesn’t deliver as well as what it delivers  formulation of variants, requirement for adjustments • Use of Corilis (smoothed Corine, fuzzy sets) properties: • Potentials in a neighbourhood  no need of complex topological analysis (no need to tell which pasture will be converted…) • Additive layers  simple calculations possible

  3. From Corine land cover to Corilis Ref.: EEA 2006, Land accounts for Europe 1990-2000

  4. CLC Urban areas and N2000 sites

  5. Processing urban areas in a grid…

  6. Smoothing CLC values, accounting for urban surface inside each cell + within a radius of 5 km (values of urban surface decreasing with the square of the distance to the centre of the grid cell)‏

  7. Urban “temperature” or “radiation” over N2000 (habitats) sites

  8. Note that not all the “temperature” is coming from large cities (here, agglomerations of pop>50 000 hab are in purple)‏

  9. An index of urban “temperature” of N2000 sites can be computed. Here, MEAN value per site, radius of 5 km

  10. CORILIS map of artificial land cover 2000

  11. 10 100 What if? prospect: when urban sprawl takes place in the neighbouring countryside… Baseline Data: Corilis / Urban Temperature 2000, scale of 0-100 // Average increase 2000-2010: 5%, even over Europe Prospect 1: a constant of 5 points is added up to Corilis values > 5 (below 5 corresponds to remote countryside) Urban temperature 2000 Urban temperature 2010 – prospect 1

  12. 10 100 What if? Prospect: when urban sprawl takes place in the countryside Corilis 2000 +3 points +5 points +10 points

  13. 10 100 What if? Prospect: when urban sprawl takes place in the countryside

  14. b a Areas prone to agriculture intensification driven by the agro-fuel demand Assessment based on Corilis, the computation in a regular grid of CLC values in and in the neighbourhood of each cell (in the application: radius of 5km) Broad pattern intensive agriculture Pasture and agriculture mosaics

  15. -100 +100 What if? prospect: where conversion to broad pattern intensive agriculture may take place? • Analysis of Corilis values of classes 2a and 2b • 2a = broad pattern intensive agriculture (clc21, 22 + 241) • 2b = pastures and mosaics (clc231, 242, 243 & 244) • Each cell of the grid is given a value of: Ι(2a-2b)Ι *(2a+2b) Positive values (more broad pattern intensive agriculture) are brown, negative values (more pasture and mosaics) are green, yellow meaning transition areas • Assumption 1: 2a+2b = UAA is constant (e.g. no deforestation)  Map of change in overall potential: the share of 2a within 2a-2b increases of 5, 10, 20 and 50% • Assumption 2: change may take place only when polarity < 80% AND when UAA > 20% Map of areas prone to conversion according to the demand for arable land

  16. -100 +100 X X X Highest potential of conversion to cropland [1] Landscape polarity: pixels in dark GREEN and dark BROWN are NOT prone to more change, as well as pixels in light YELLOW (urban, forests, lakes…)

  17. -100 +100 Effect of agriculture intensification over landscape polarity

  18. 10 40 Highest potential of conversion to cropland [2] RED: within transition areas dominated by arable land

  19. 40 10 Highest potential of conversion to cropland [3] BLUE: within transition areas dominated by pasture & mosaics

  20. 40 10 10 40 Highest potential of conversion to cropland [4] As of 2000

  21. 40 10 10 40 Highest potential of conversion to cropland [4] As of 2000 + 5% increase of arable land

  22. 40 10 10 40 Highest potential of conversion to cropland [4] As of 2000 + 10% increase of arable land

  23. 40 10 10 40 Highest potential of conversion to cropland [5] As of 2000 + 20% increase of arable land

  24. 40 10 10 40 Highest potential of conversion to cropland [6] As of 2000 + 50% increase of arable land

  25. Highest potential of conversion to cropland [7] And Natura2000 sites: distribution

  26. Highest potential of conversion to cropland [8] And Natura2000 sites: a first indicator PCZ = “Prone to Conversion Zones”

  27. Risks of soil erosion: The PESERA map by JRC

  28. Highest potential of conversion to cropland [9] And soil erosion risks (PESERA)

  29. Highest potential of conversion to cropland [10] NUTS2/3 prone to conversion

  30. Next: • Validate assumptions; differentiation according to countries, regions (e.g. important conversion of pasture is taking place in Ireland…) • Test new assumptions (taking into account roads, farming practices…), new scenarios • Work on change coefficients • Cross-check methodology and results with other models; integrate? • Prepare an interactive tool for users dialogue

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