1. Jan Geletič, Petr Dobrovolný
Empirical and modelling approaches for heat risk assessment in Urban Climate under Climate Change (a case study for Brno, Czech Republic)
Jan Geletič, Petr Dobrovolný
Global Climate Change Research Center and Department of Geography Masaryk University

2. Outline Motivation and basic terms Study area and compiled database
Main results of empirical studies
intensity of urban heat island from mobile temperature measurements
intensity of urban heat island analyzed from satellite data
Urban climate modelling
Model MUKLIMO and cuboid method
First results of modelling approach
Outlook for a near future

3. Motivation
High temperatures in cities may be problem under global climate change
Development of urban heat island (UHI) belongs to the most characteristic expressions of urban climate
The factors that affect variability in spatiotemporal air temperature and UHI intensity are known
However, the role of any one of these factors may vary from city to city with respect to geographical position, size, number of inhabitants, and more.
Main aim is to quantify the effects of a number of factors on the air temperature field in Brno area and to estimate UHI intensity
Besides various types of direct measurements numerical modelling offers an alternative for urban climate analysis

4. UHI types
Boundary layer UHI
Canopy layer UHI
Surface UHI

5. Study area Brno as an example of medium- sized city Complex terrain
Database of factors affecting temperature spatial variability

6. Atmospheric UHI intensity estimation
Mobile measurements
Air temperature variability along mobile transects: a) 8 July 2011; b) 31 January 2012; c) elevation profile; d) distribution of main land use categories.

7. Atmospheric UHI intensity estimation

8. Surface UHI intensity estimation

9. Summary of empirical studies
Atmospheric UHI clearly develops during night hours and also around noon while in morning and evening hours the temperature differences between urban and rural stations are close to zero in all seasons.
From regression analysis it follows that air temperatures and UHI intensity depend especially on amount of vegetation fraction represented by NDVI values
Air temperatures interpolated from mobile measurements and NDVI values indicate that the mean UHI intensity in the early night in summer is at its highest (approx. 5°C) in the city center and decreases towards the suburban areas.
The highest LST values typically occur in industrial and commercial areas, which contribute significantly to surface UHI intensity that reached 4.2 °C and 6.7 °C
The amount of vegetation cover (NDVI) is the main factor forming LST spatial variability (56–67% of explained variance); the degree of urbanization as represented by density of buildings explains 37–40% of LST variance.

10. Urban Climate Modelling
Numerical modelling – simulation of atmospheric flow fields in urban areas
MUKLIMO_3 = Mikroskaliges urbanes Klima-Modell, 3-dim.
Model developed at DWD (Deutscher Wetterdienst)
Simulates atmospheric conditions with pronounced influence of local land-use properties
Cloud processes and precipitation are not considered

11. MUKLIMO_3 model system

12. CUBOID method

13. First results: T CUBOID 101 (Tmax, Rhmin, vmax)
CUBOID 100 (Tmax, Rhmin, vmin)
CUBOID 101 (Tmax, Rhmin, vmax)

14. First results: AUHI
CUBOID 101 (Tmax, Rhmin, vmax); time steps: 9h, 12h, 18h

15. Outlooks
Model output validation - the model reproduces the spatial variability of air temperature well, but values of some temperature characteristics are overestimated
Local Climate Zones definition – input parameter that may significantly improve results
Implementation od 3D building model
International cooperation - Central European 2020 found (Vienna, Szeged, Bratislava and Krakow)
Cooperation with Brno authorities

16. Thank you for your attention