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RAPID URBAN IMPACT APPRAISAL

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. RAPID URBAN IMPACT APPRAISAL

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RAPID URBAN IMPACT APPRAISAL

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  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 – Filteringcities: 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. Climatic Stimulus: pluvial fluvial coastal … Heat waves ... Flooding: 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 … … Impact type: People affected … Property loss Break down ... Temperature extremes … RD2/CCD: Climate proof cities and infrastructure MKBL/OK

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

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

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

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

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

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

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

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

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

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

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

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

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

  22. 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 RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example Zhou, 2012 MODIS analysis RD2/CCD: Climate proof cities and infrastructure BZ/MKBL

  23. 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 RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example Zhou, 2012 MODIS analysis RD2/CCD: Climate proof cities and infrastructure BZ/MKBL

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

  25. 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 RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example Zhou, 2012 MODIS analysis RD2/CCD: Climate proof cities and infrastructure BZ/MKBL

  26. 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 RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example Zhou, 2012 MODIS analysis RD2/CCD: Climate proof cities and infrastructure BZ/MKBL

  27. 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 RAPID URBAN IMPACT APPRAISAL – Step 1: filtering 2nd example Zhou, 2012 RD2/CCD: Climate proof cities and infrastructure BZ/MKBL

  28. 1st example:Pluvial flooding Slum areas People affected Slum dwellers affected by future flooding (mid 21stcent.) • Trend-based slum dev. scenarios RAPID URBAN IMPACT APPRAISAL – Step 2: fast impact quantification a) c) b) Hyderabad/India RD2/CCD: Climate proof cities and infrastructure OK/MKBL

  29. 2nd example: Heat waves Urban settlements Extreme temperatures (UHI) ? Berlin: COSMO-CLM (CCLM) Schubert & Grossman-Clarke (2012) RAPID URBAN IMPACT APPRAISAL – Step 2: fast? impact quantification Relations between local UHI, urban fraction, building height and street width 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., 2012. 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., 2012. Texture-based identification of urban slums in Hyderabad, India using remote sensing data. Applied Geography 32, 660-667 p. Schubert, S.; Grossman-Clarke, S., 2012. 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. 2012. Climate Change Scenarios for Hyderabad: integrating uncertainties and consolidation. Emerging megacities V1/2010, ISSN 2193-6927, pp 3-37 Book chapters Kit, O.; Lüdeke, M. K. B.; Reckien, D. 2011. 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 35-44 p. Reckien D, Lüdeke M, Reusswig F, Kit O, Meyer-Ohlendorf L, Budde M, 2011. 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 152-154 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

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