1. RAPID URBAN IMPACT APPRAISAL
Fast urban impact assessments for data-scarce, large urban agglomerations of the South
Matthias Lüdeke, Oleksandr Kit, Bin Zhou, Sebastian Schubert et al.
RD2/CCD: Climate proof cities and infrastructure

2. RAPID URBAN IMPACT APPRAISAL
Fast urban impact assessments for data-scarce, large urban agglomerations of the South
Matthias Lüdeke, Oleksandr Kit, Bin Zhou, Sebastian Schubert et al.
Motivation: why
- urban CC impact assessments?
still insufficient coverage by existing studies regarding:
spatial coverage & impact paths; quality varies greatly due to diverse methods
no reliable global overview
most cities‘ adaptation planning lacks a solid impact analysis
- large urban agglomerations of the South?
- fast methods?
- approaching data-scarcity?
RD2/CCD: Climate proof cities and infrastructure
MKBL/OK

3. RAPID URBAN IMPACT APPRAISAL
Fast urban impact assessments for data-scarce, large urban agglomerations of the South
Matthias Lüdeke, Oleksandr Kit, Bin Zhou, Sebastian Schubert et al.
Motivation: why
- urban CC impact assessments?
still insufficient coverage by existing studies regarding:
spatial coverage & impact paths; quality varies greatly due to diverse methods
no reliable global overview
most cities‘ adaptation planning lacks a solid impact analysis
- large urban agglomerations of the South?
quantitatively: most future urbanization will happen there
qualitatively: specific development properties (informality, rapidness, etc)
- fast methods?
- approaching data-scarcity?
RD2/CCD: Climate proof cities and infrastructure
MKBL/OK

4. RAPID URBAN IMPACT APPRAISAL
Fast urban impact assessments for data-scarce, large urban agglomerations of the South
Matthias Lüdeke, Oleksandr Kit, Bin Zhou, Sebastian Schubert et al.
Motivation: why
- urban CC impact assessments?
still insufficient coverage by existing studies regarding:
spatial coverage & impact paths; quality varies greatly due to diverse methods
no reliable global overview
most cities‘ adaptation planning lacks a solid impact analysis
- large urban agglomerations of the South?
quantitatively: most future urbanization will happen there
qualitatively: specific development properties (informality, rapidness, etc)
- fast methods?
major obstacle: presently a comprehensive, “from the scratch” CC-impact study
for an urban area takes years
- approaching data-scarcity?
RD2/CCD: Climate proof cities and infrastructure
MKBL/OK

5. RAPID URBAN IMPACT APPRAISAL
Fast urban impact assessments for data-scarce, large urban agglomerations of the South
Matthias Lüdeke, Oleksandr Kit, Bin Zhou, Sebastian Schubert et al.
Motivation: why
- urban CC impact assessments?
still insufficient coverage by existing studies regarding:
spatial coverage & impact paths; quality varies greatly due to diverse methods
no reliable global overview
most cities‘ adaptation planning lacks a solid impact analysis
- large urban agglomerations of the South?
quantitatively: most future urbanization will happen there
qualitatively: specific development properties (informality, rapidness, etc)
- fast methods?
major obstacle: presently a comprehensive, “from the scratch” CC-impact study
for an urban area takes years
- approaching data-scarcity?
major impediment for impact assessment – choice of cases according to data
availablilty -> poor global coverage
RD2/CCD: Climate proof cities and infrastructure
MKBL/OK

6. RAPID URBAN IMPACT APPRAISAL
Fast urban impact assessments for data-scarce, large urban agglomerations of the South
Matthias Lüdeke, Oleksandr Kit, Bin Zhou, Sebastian Schubert et al.
Basic idea:
Step 1 – Filtering cities:
Identifying urban agglomerations where a specific Climate Change impact path
is relevant or even the dominant one.
For a specific case study -> choice of impact path to be studied with priority
For a global overview > global distribution of cities showing a specific
impact path
Step 2 – targeted, fast quantitative Impact Assessment :
Urban remote sensing oriented toolbox to quantify impacts along the
chosen relevant impact path
RD2/CCD: Climate proof cities and infrastructure
MKBL/OK

7. RAPID URBAN IMPACT APPRAISAL
Fast urban impact assessments for data-scarce, large urban agglomerations of the South
Climate change impact paths:
Exposure unit: …
Traffic
Slum areas
Critical infrastructure
Residential settlements
Flooding:
Climatic Stimulus:
pluvial
fluvial
coastal …
Heat waves
...
Impact type:
People affected …
Property loss
Break down
...
Temperature extremes
RD2/CCD: Climate proof cities and infrastructure
MKBL/OK

8. Residential settlements
RAPID URBAN IMPACT APPRAISAL – Step 1: filtering
Fast urban impact assessments for data-scarce, large urban agglomerations of the South
Climate change impact paths: two examples
Exposure unit: …
Traffic
Slum areas
Critical infrastructure
Residential settlements
Flooding:
Climatic Stimulus:
pluvial
fluvial
coastal …
Heat waves
...
Impact type:
People affected …
Property loss
Break down
...
Temperature extremes
RD2/CCD: Climate proof cities and infrastructure
MKBL/OK

9. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 1st example
Pluvial flooding
Slum areas
People affected
Large urban agglomerations >1000km2
RD2/CCD: Climate proof cities and infrastructure
OK/MKBL

10. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 1st example
Pluvial flooding
Slum areas
People affected
Climate zones with high-intensity rainfall
RD2/CCD: Climate proof cities and infrastructure
OK/MKBL

11. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 1st example
Pluvial flooding
Slum areas
People affected
Climate zones with high-intensity rainfall
RD2/CCD: Climate proof cities and infrastructure
OK/MKBL

12. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 1st example
Pluvial flooding
Slum areas
People affected
Close to watersheds and distant to coasts
RD2/CCD: Climate proof cities and infrastructure
OK/MKBL

13. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 1st example
Pluvial flooding
Slum areas
People affected
Close to watersheds and distant to coasts
RD2/CCD: Climate proof cities and infrastructure
OK/MKBL

14. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 1st example
Pluvial flooding
Slum areas
People affected
Orography: hilly urban landscape
RD2/CCD: Climate proof cities and infrastructure
OK/MKBL

15. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 1st example
Pluvial flooding
Slum areas
People affected
Orogaphy: hilly urban landscape
RD2/CCD: Climate proof cities and infrastructure
OK/MKBL

16. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 1st example
Pluvial flooding
Slum areas
People affected
High probability of urban slum settlements
RD2/CCD: Climate proof cities and infrastructure
OK/MKBL

17. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 1st example
Pluvial flooding
Slum areas
People affected
High probability of urban slum settlements
RD2/CCD: Climate proof cities and infrastructure
OK/MKBL

18. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 1st example
Pluvial flooding
Slum areas
People affected
Filtered urban agglomerations in India
RD2/CCD: Climate proof cities and infrastructure
OK/MKBL

19. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example
Heat waves
Urban settlements
Extreme temperatures (UHI)
Large urban agglomerations >40km2
RD2/CCD: Climate proof cities and infrastructure
BZ/MKBL

20. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example
Heat waves
Urban settlements
Extreme temperatures (UHI)
Day-time and night-time UHI increases with
increasing temperature of the urban periphery
Zhou, 2012
MODIS analysis
RD2/CCD: Climate proof cities and infrastructure
BZ/MKBL

21. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example
Heat waves
Urban settlements
Extreme temperatures (UHI)
Day-time and night-time UHI increases with
increasing temperature of the urban periphery
Zhou, 2012
MODIS analysis
RD2/CCD: Climate proof cities and infrastructure
BZ/MKBL

22. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example
Heat waves
Urban settlements
Extreme temperatures (UHI)
Day-time and night-time UHI increases with
increasing temperature of the urban periphery
rank correlation coefficient (day/night) > 0.4/0.3
Zhou, 2012
MODIS analysis
RD2/CCD: Climate proof cities and infrastructure
BZ/MKBL

23. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example
Heat waves
Urban settlements
Extreme temperatures (UHI)
Day-time and night-time UHI increases with
increasing temperature of the urban periphery
rank correlation coefficient (day/night) > 0.4/0.3
Zhou, 2012
MODIS analysis
RD2/CCD: Climate proof cities and infrastructure
BZ/MKBL

24. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example
Heat waves
Urban settlements
Extreme temperatures (UHI)
Day-time and night-time UHI increases with
increasing temperature of the urban periphery
rank correlation coefficient (day/night) > 0.6/0.5
Zhou, 2012
MODIS analysis
RD2/CCD: Climate proof cities and infrastructure
BZ/MKBL

25. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example
Heat waves
Urban settlements
Extreme temperatures (UHI)
Day-time and night-time UHI increases with
increasing temperature of the urban periphery
rank correlation coefficient (day/night) > 0.6/0.5
Zhou, 2012
MODIS analysis
RD2/CCD: Climate proof cities and infrastructure
BZ/MKBL

26. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example
Heat waves
Urban settlements
Extreme temperatures (UHI)
Day-time and night-time UHI increases with
increasing temperature of the urban periphery
rank correlation coefficient (day/night) > 0.7/0.55
Zhou, 2012
MODIS analysis
RD2/CCD: Climate proof cities and infrastructure
BZ/MKBL

27. RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example
Heat waves
Urban settlements
Extreme temperatures (UHI)
Day-time and night-time UHI increases with
increasing temperature of the urban periphery
rank correlation coefficient (day/night) > 0.7/0.55
Zhou, 2012
RD2/CCD: Climate proof cities and infrastructure
BZ/MKBL

28. RAPID URBAN IMPACT APPRAISAL – Step 2: fast impact quantification
1st example: Pluvial flooding
Slum areas
People affected
Analyzing
SRTM data
Slum dwellers affected by
future flooding (mid 21stcent.)
Trend-based slum dev. scenarios
Numerical
Analysis of QuickBird
scenes
Hyderabad/India
Screenshot of the “Climate Assessment Tool for Hyderabad“ (CATHY, PIK 2012)
Kit et al., 2012
RD2/CCD: Climate proof cities and infrastructure
OK/MKBL

29. ? RAPID URBAN IMPACT APPRAISAL – Step 2: fast? impact quantification
2nd example: Heat waves
Urban settlements
Extreme temperatures (UHI)
Relations between local UHI, urban fraction,
building height and street width
Berlin: COSMO-CLM (CCLM)
Schubert & Grossman-Clarke (2012)
local
QuickBird ?
MODIS
agglomeration
- Published papers directly related to the Rapid Urban Impact Appraisal-activity -
ISI-journals
Kit, O.; Lüdeke, M. K. B.; Reckien, D., Defining the bull's eye: satellite imagery-assisted slum population assessment in Hyderabad/India. Urban Geography, in press
Kit, O.; Lüdeke, M. K. B.; Reckien, D., Texture-based identification of urban slums in Hyderabad, India using remote sensing data. Applied Geography 32, p.
Schubert, S.; Grossman-Clarke, S., The influence of green areas and roof albedos on air temperatures during extreme heat events in Berlin. Meteorologische Zeitschrift, accepted
Other journals
Lüdeke, M. K. B.; Budde, M.; Kit, O.; Reckien, D Climate Change Scenarios for Hyderabad: integrating uncertainties and consolidation. Emerging megacities V1/2010, ISSN , pp 3-37
Book chapters
Kit, O.; Lüdeke, M. K. B.; Reckien, D Assessment of climate change-induced vulnerability to floods in Hyderabad/India using remote sensing data. In: Resilient Cities - Cities and Adaptation to Climate Change Ed.: Otto-Zimmermann, K. Dordrecht : Springer p.
Reckien D, Lüdeke M, Reusswig F, Kit O, Meyer-Ohlendorf L, Budde M, Hyderabad, India, infrastructure adaptation planning. In Rosenzweig C, Solecki WD, Hammer SA, Mehrotra S: Climate Change and Cities – First Assessment Report of the Urban Climate Change Research Network, Cambridge University Press, pp
Zhou, B. (2012): Urban Heat Islands: A study based on a vast number of urban agglomerations. MSc. in Geography of Global Change, Univ. of Freiburg
RD2/CCD: Climate proof cities and infrastructure
SS/SGC/MKBL