NACLIM Annual Meeting 2014 (Berlin)

1. NACLIMAnnual Meeting 2014 (Berlin) WP4.2 - Extraction of city morphology indicators for urban climate modeling: a case study for Antwerp, Berlin and Almada Bart Thomas (GIM)

2. Impact on urban societies CMIP5 Observations socio-economic City data Urban morphology UrbClim UHI prediction Heat stress exposure maps

3. Use case cities • Different morphologies density, city layout, vegetation abundance, topography, proximity to the sea, etc. • Almada • Mediterranean • KöppenCsa/Csb • Antwerp • Maritime temperate • KöppenCfb • Berlin • Warm-SummerContinental • KöppenDfb

4. Urban morphology indices • Relevant land surface parameters • PAI: planar area index • FAI: frontal area index • AVG_H: average building height • STD_H: variation building height • SVF: sky view factor • F_VEG: fraction vegetation cover • LULC: land use / cover (incl. vegetation) type • F_ULU: fraction urban land use • Calculated on a spatial grid • PAI- planar area index = the ratio of the plane area occupied by buildings to the total ground area • FAI- frontal area index = the ratio of the frontal area (i.e. area of buildings exposed to a given wind direction) of buildings to the total ground area (averaged over all wind sectors)

5. Source data • 3D City models • Individual buildings  PAI • Building blocks  FAI • Conversion to 2D + attributes • Including / excluding roofs 3D city model (input) Blocks (output, no roofs) Buildings (output, no roofs)

6. Methodology • Reference dataset  degree of soil sealing (EEA) • Determine area of interest (AOI) of 3D city model • Calculations on the analysis grid • Determine optimal spatial resolution  reduce scatter & local effects  1km • Calculate index (e.g. PAI, FAI, …) per grid cell (analysis grid) • Calculate average EEA SSL per grid cell (analysis grid) • Scatter plot & regressionbetween index and average EEA SSL for all grid cells inside AOI (analysis grid) • Calculations on the urban climate model grid • Finer resolution than analysis grid  250m • Typically larger extent than AOI 3D city model • Exact calculation of index inside AOI • Extrapolation outside AOI using relationships with EEA SSL • Attention for discontinuities on the boundary of the AOI

7. Resolution analysis grid • Literature (Bohnenstengelet al., 2011) • Reduce scatter due to local effects • Resolution EEA SSL (100m) • Resolution model grid • Stable relationships • Sample size (no. grid cells) • Example: PAI (Berlin) • Coarser resolution • Similar fit • Reduced scatter • Increasing R2  Choice = 1km 100m (R2lin = 0,54) 200m (R2lin = 0,66) 1km (R2lin = 0,81) 500m (R2lin = 0,76)

8. Urban morphology indices • Antwerp • inside AOI  exact calculation • outside AOI  extrapolation (except LULC)

9. Urban morphology indices • Almada • inside AOI  exact calculation • outside AOI  extrapolation (except LULC)

10. Urban morphology indices • Berlin • inside AOI  exact calculation • outside AOI  extrapolation (except LULC)

11. Planar area index (PAI) • PAI versus average EEA SSL (1km analysis grid)

12. Planar area index (PAI) • PAI (all cities, 250m model grid)

13. Frontal area index (FAI) • FAI versus average EEA SSL (1km analysis grid)

14. Frontal area index (FAI) • FAI (all cities, 250m model grid)

15. Transferability equations • PAI & FAI versus average EEA SSL (all cities, linear & quadratic fit)

16. Conclusions • Promising results to extract urban morphology indices from generic European datasets in absence of detailed city models • Modelling community  accessto relevant model input parameters for all cities in Europe • Flexible approach • Exact calculation when 3D city models are available  calibration on local data • Extrapolation / estimation from average EEA SSL for other regions • Transferability relationships depends on urban morphology index • Possibility for downscaling urban climate simulations to higher resolution

17. Future / ongoing work • Extraction urban morphology indices • Other scenarios per city (e.g. based on existing urban master plans) • Scenarios to be confirmed during 4th end-user meeting (16-Oct) • Other explaining variables (e.g. population layer EEA)? • Heat stress exposure mapping • Process UrbClim model output • Time scales: present 1986-2005, near future 2026-2045, far future 2081-2100 • Various scenarios / themes • Various cities • Identification variables linked to heat stress • UHI effect • Number of heat waves / heat wave days • Heat wave duration • Heat wave intensity • Combine results with socio-economic data  focus on: • Population (age class) • Vulnerable institutions (schools, hospitals, rest homes, child/day care facilities) • Relevant results to be consolidated during 4th end-user meeting • Collaboration NACLIM – EUPORIAS • Additional use case city? • Demonstrate UHI mapping approach

18. Exposure mapping

19. Exposure mapping

20. Exposure mapping

21. Exposure mapping

22. Exposure mapping

23. Exposure mapping

24. Questions? Don’t hesitate to ask us! GIM nv/sa ResearchparkHaasrode 1505 Interleuvenlaan 5 B-3001 Leuven (Heverlee) tel: +32 16 40 30 39 fax: +32 16 40 69 39 e-mail: info@gim.be website: www.gim.be

25. The research leading to these results has received funding from the European Union 7th Framework Programme (FP7 2007-2013), under grant agreement n.308299 NACLIM www.naclim.eu