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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)

Impact on urban societies

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UHI prediction

CMIP5

UrbClim

socio-economicUrban morphology Observations

Heat stress exposure maps

City data

Use case cities

• Different morphologies density, city layout, vegetation abundance, topography, proximity to the sea, etc.

• Almada– Mediterranean– Köppen Csa/Csb

• Antwerp– Maritime temperate– Köppen Cfb

• Berlin– Warm-Summer

Continental– Köppen Dfb

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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)

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Source data

• 3D City models– Individual buildings PAI– Building blocks FAI– Conversion to 2D + attributes– Including / excluding roofs

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3D city model (input)

Blocks (output, no roofs)Buildings (output, no roofs)

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 & regression between 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

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Resolution analysis grid

• Literature (Bohnenstengel et 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

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100m (R2lin = 0,54) 200m (R2

lin = 0,66)

1km (R2lin = 0,81) 500m (R2

lin = 0,76)

Urban morphology indices

• Antwerp– inside AOI exact calculation– outside AOI extrapolation (except LULC)

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PAI FAI H_AVG H_STD

SVF LULC F_VEG F_ULU

Urban morphology indices

• Almada– inside AOI exact calculation– outside AOI extrapolation (except LULC)

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PAI FAI H_AVG H_STD

SVF LULC F_VEG F_ULU

Urban morphology indices

• Berlin– inside AOI exact calculation– outside AOI extrapolation (except LULC)

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PAI FAI H_AVG H_STD

SVF LULC F_VEG F_ULU

Planar area index (PAI)

• PAI versus average EEA SSL (1km analysis grid)

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Planar area index (PAI)

• PAI (all cities, 250m model grid)

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Frontal area index (FAI)

• FAI versus average EEA SSL (1km analysis grid)

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Frontal area index (FAI)

• FAI (all cities, 250m model grid)

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Transferability equations

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

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Conclusions

• Promising results to extract urban morphology indices from generic European datasets in absence of detailed city models

• Modelling community access to 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

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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

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Exposure mapping

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Exposure mapping

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Exposure mapping

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Exposure mapping

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Exposure mapping

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Exposure mapping

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Questions?

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Don’t hesitate to ask us!

GIM nv/saResearchpark Haasrode 1505

Interleuvenlaan 5B-3001 Leuven (Heverlee)

tel: +32 16 40 30 39fax: +32 16 40 69 39e-mail: info@gim.be

website: www.gim.be

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

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