1. What if? prospects based on Corilis Alex Oulton, Manuel Winograd Ronan Uhel & Jean-Louis Weber LAND QUICK SCAN INTERFACE: Challenges and needs Internal workshop EEA, Fontana Room, 8 July 2009

2. What if? prospects based on Corilis Dialogue on impacts/options based on common representations; versatile tool; incremental/ –‘Urgent’ questions, quick overview Highlight (scan, prospect, 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, probabilities of conversion instead…) –Additive layers  simple calculations possible (additions, subtractions, ratios…)

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 2000Urban temperature 2010 – prospect 1

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

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

14. Next on urban Validation with 1990-2000-2006 Corilis time-series Comparisons between What ifs? constant values increase (as in the example), % increase, others (change smoothing radius of Corilis…) Test attraction effects of transport infrastructures Change in UMZ green quality of life (as sum of Green Landscape Index at 1ha and Green Urban Areas); effect of urban sprawl, agriculture intensification… New What ifs? (exposure to flood risk… ) Compare results to existing land use models, scenarios…

15. Areas prone to agriculture intensification (driven by agro-fuel demand) b a 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 agriculturePasture and agriculture mosaics

16. 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 20%  Map of areas prone to conversion according to the demand for arable land -100 +100

17. 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…) -100 +100 XX X

18. -100 +100 Effect of agriculture intensification over landscape polarity

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

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

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

22. 10 40 10 40 Highest potential of conversion to cropland [4] As of 2000 + 5% increase of arable land

23. 10 40 10 40 Highest potential of conversion to cropland [4] As of 2000 + 10% increase of arable land

24. 10 40 10 40 Highest potential of conversion to cropland [5] As of 2000 + 20% increase of arable land

25. 10 40 10 40 Highest potential of conversion to cropland [6] As of 2000 + 50% increase of arable land

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

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

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

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

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

31. Case of fallow land/set aside 1990-2000 at risk of re-intensification, 1st test maps: Areas prone to intensification (in GREY) Fallow land / set aside mapped from land cover flow 1990-2000 LEAC lcf41 (in GREEN). Fallow land/set aside at risk of re-intensification are in RED. (RED = GREY+GREEN) At this stage, this is a distribution map, not yet a statistical quantification

32. Areas prone to intensification (in GREY) AND fallow land set aside mapped from land cover flow 1990-2000 LEAC lcf41 (in GREEN).

33. Fallow land/set aside at risk of re-intensification are in RED. (RED = GREY+GREEN)

34. Potential impact on Natura2000 sites

35. The European view

36. Next on agriculture intensification: 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 land use models; integrate? Prepare an interactive tool for users dialogue