Climate Model Berlin - Planning Advices Urban Climate 2015

Download

Texts

  • Climate Model Berlin - Planning Advices Urban Climate as Word document

    DOCX-Document (8.6 MB)

  • Climate Model Berlin - Planning Advices Urban Climate as PDF document

    PDF-Document (1.9 MB)

  • Documentation Climate Model Berlin 2015 [only in German]

    PDF-Document

  • Accompanying document for the planning advice map 2015 [only in German]

    PDF-Document (13.5 MB)

Maps

  • 04.11.01 Planning Advices Urban Climate - Main Map

    PDF-Document (22.7 MB)

  • 04.11.02 Planning Advices Urban Climate - Additional Information

    PDF-Document (21.8 MB)

  • 04.11.03 Planning Advices Urban Climate - Measures

    PDF-Document

Figures

  • Fig. 1: Percentage distribution of evaluation classes for the overall thermal situation in the settlement areas (linking of day and night situation) of Berlin.

    JPG-Document (31.0 kB)

  • Fig. 1: Percentage distribution of evaluation classes for the overall thermal situation in the settlement areas (linking of day and night situation) of Berlin.

    XLSX-Document (167.7 kB)

  • Fig. 2: Spatial distribution of evaluation classes for the overall thermal situation in the settlement areas (linking of day and night situation ) of Berlin.

    GIF-Document (197.5 kB)

  • Fig. 3: Balancing of the overall thermal situation in the settlement area of 12 districts of Berlin

    JPG-Document (50.7 kB)

  • Fig. 3: Balancing of the overall thermal situation in the settlement area of 12 districts of Berlin

    XLSX-Document (167.7 kB)

  • Fig. 4: Percentage distribution of the evaluation classes for overall thermal situation on public roads, paths and places in Berlin.

    JPG-Document (32.2 kB)

  • Fig. 4: Percentage distribution of the evaluation classes for overall thermal situation on public roads, paths and places in Berlin.

    XLSX-Document (167.7 kB)

  • Fig. 5: Overall evaluation of the thermal situation on public roads, paths and places in Berlin.

    GIF-Document (201.2 kB)

  • Fig. 6: Balancing of the overall thermal situation on public roads, paths and places of the 12 districts of Berlin.

    JPG-Document (48.4 kB)

  • Fig. 6: Balancing of the overall thermal situation on public roads, paths and places of the 12 districts of Berlin.

    XLSX-Document (167.7 kB)

  • Fig. 7: Core zones of cold air pathways ("pathway corridors") in Berlin

    GIF-Document (79.1 kB)

  • Fig. 8: Summary effective area of the components of Berlin's air-exchange system in case of autochthonous weather conditions.

    GIF-Document (174.3 kB)

  • Fig. 9: Balancing of cold air effect on the settlement area in Berlin according to districts

    JPG-Document (115.1 kB)

  • Fig. 9: Balancing of cold air effect on the settlement area in Berlin according to districts

    XLSX-Document (167.7 kB)

  • Fig. 10: Percentage distribution of evaluation classes for climate-ecological worthiness of protection of open/green spaces in Berlin

    JPG-Document (29.0 kB)

  • Fig. 10: Percentage distribution of evaluation classes for climate-ecological worthiness of protection of open/green spaces in Berlin

    XLSX-Document (167.7 kB)

  • Fig. 11: Spatial distribution of evaluation classes for climate-ecological worthiness of protection of open/green spaces in Berlin

    GIF-Document (164.8 kB)

  • Fig. 12: Percentage distribution of the area categories with special urban-climatic drawbacks in Berlin.

    JPG-Document (29.0 kB)

  • Fig. 12: Percentage distribution of the area categories with special urban-climatic drawbacks in Berlin.

    XLSX-Document (167.7 kB)

  • Fig. 13: Balancing of areas with special urban-climatic drawbacks for the 12 districts of Berlin

    JPG-Document (41.4 kB)

  • Fig. 13: Balancing of areas with special urban-climatic drawbacks for the 12 districts of Berlin

    XLSX-Document (167.7 kB)

  • Fig. 14: Areas with special urban-climatic drawbacks in Berlin

    GIF-Document (64.2 kB)
    Image: Umweltatlas Berlin

  • Fig. 15: Demographic vulnerability as compared to thermal stress - Spatial analysis at the level of block areas in Berlin

    GIF-Document (193.3 kB)

  • Fig. 16: Demographic vulnerability as compared to thermal stress - Balancing at the level of Berlin's districts

    JPG-Document (48.2 kB)
    Image: Umweltatlas Berlin

  • Fig. 16: Demographic vulnerability as compared to thermal stress - Balancing at the level of Berlin's districts

    XLSX-Document (167.7 kB)

  • Fig. 17: Absolute number and relative percentage of aggregated sensitive uses in thermally stressed environment in the 12 districts of Berlin.

    JPG-Document (43.9 kB)
    Image: Umweltatlas Berlin

  • Fig. 17: Absolute number and relative percentage of aggregated sensitive uses in thermally stressed environment in the 12 districts of Berlin.

    XLSX-Document (167.7 kB)

  • Fig. 18: Absolute number and relative percentage of sensitive use types in thermally stressed environment in Berlin.

    JPG-Document (40.3 kB)
    Image: Umweltatlas Berlin

  • Fig. 18: Absolute number and relative percentage of sensitive use types in thermally stressed environment in Berlin.

    XLSX-Document (167.7 kB)

  • Fig. 19: Spatial representation of areas with a special vulnerability as compared to the urban climate based on a shortage of green areas in Berlin

    GIF-Document (85.3 kB)

  • Fig. 20: Balancing of the vulnerability as compared to the urban climate based on a shortage of green areas in the 12 districts of Berlin.

    JPG-Document (42.2 kB)

  • Fig. 20: Balancing of the vulnerability as compared to the urban climate based on a shortage of green areas in the 12 districts of Berlin.

    XLSX-Document (167.7 kB)

  • Fig. 21: Schematic representation of the air-temperature-mortality relationship

    GIF-Document (14.9 kB)

  • Fig. 22: Daily deaths (all causes) and daily maxima of the Universal Thermal Climate Index (UTCI) in Berlin in 2010 compared to mean values based on 2000-2010

    JPG-Document

  • Fig. 23: Connection between thermal load (Universal Thermal Climate Index (UTCI), 2-day mean) and PM10 (2-day mean) as well as the total mortality (logarithmised relative risk) in Berlin. The bivariate response surface model has been adapted for trend, year and day of the week. A logarithmised relative risk of 0.2 corresponds to 22% more deaths

    GIF-Document (11.3 kB)
    Image: Burkart et al. 2013

  • Fig. 24: Connection between thermal load (Universal Thermal Climate Index (UTCI), 2-day mean) and PM10 (2-day mean) as well as the total mortality (logarithmised relative risk) in Berlin. The bivariate response surface model has been adapted for trend, year and day of the week. A logarithmised relative risk of 0.2 corresponds to 22% more deaths

    GIF-Document (12.5 kB)

  • Fig. 25: Total mortality (all causes), represented as deaths per 1 million inhabitants per day, and daily mean temperatures in the period of 2001-2010. The blue/red line represents the best fit line for the days with low/high air temperatures. The intersection of these lines marks the mean minimum of the mortality rate (21.5 deaths per 1 million inhabitants per day) under conditions of a daily mean temperature of 21 °C

    GIF-Document (40.3 kB)
    Image: Scherer et al. 2015

  • Fig. 26: Representation of the number of (a) hot days (Tmax ≥ 30 °C) and (b) tropical nights (Tmin ≥ 20 °C) per year for selected measurement stations in Berlin in the period of 2001-2010.

    JPG-Document

  • Fig. 27: Ratio of the monthly mean of the daily maximum of the air temperature (meteorological stations Tegel, Tempelhof, Schönefeld) to the monthly sum of the deaths (all causes) in Berlin for the month of July in the period of 2001-2010

    GIF-Document (15.2 kB)
    Image: Schuster et al. 2014

  • Fig. 28: Spatial pattern of the age-standardised heat-related excess mortality, calculated as relative risk (hot Julys of 2006 and 2010 compared to the cooler Julys of 2007-2009). Values greater than 1 indicate an increased risk. A value of 1.5 means that the risk in the respective planning area (PLA) is 1.5 times higher in proportion to the average of the relative risk of all PLAs

    GIF-Document (70.5 kB)

  • Fig. 29: Relative risks for patient admissions in hospitals for ≥ 65-year-olds with diseases of the respiratory system during the summer months of 2000-2009 in Berlin for the patients' places of residence (postcode areas). The red dots indicate the significant clusters with increased risk. Values greater than 1 indicate an increased risk. A value of 1.5 means that the risk in the corresponding cluster is 1.5 times higher than outside of the cluster. In addition, the risks of the postcode areas within the clusters are shown in quartiles

    GIF-Document (74.1 kB)
    Image: Scherber et al. 2014

Tables

  • Tab. 1: Categories and methods for identifying the areas having special urban-climatic drawbacks

    GIF-Document (17.4 kB)

  • Tab. 2: Recommendations for measures specific to spatial units as the third main level of the planning advice map for the urban climate of Berlin 2015

    GIF-Document (39.6 kB)

  • Tab. 3: Overview table of studies investigating the impacts of thermal load and air pollution on health in Berlin (as of 2015, sample)

    PDF-Document (99.5 kB)

  • Tab. 3: Overview table of studies investigating the impacts of thermal load and air pollution on health in Berlin (as of 2015, sample)

    XLSX-Document (13.7 kB)

  • Tab. 4: Annual overview regarding the heat-related excess mortality based on the heat-event-based risk model by Scherer et al. (2013). The number of heat waves (heat events) per year (N), the sum of heat wave days per year (days), the mean daily rate of the excess mortality (rate) and the number of excess deaths per year are listed

    XLSX-Document (10.8 kB)
    Document: Scherer et al. 2015

  • Tab. 5: Expected cases on day basis for fully inpatient admissions (PA) of the total of all age groups and for ≥ 65-year-olds as well as deaths (D) in hospitals for diseases of the cardiovascular (CVS) and respiratory system (RS) in Berlin in the mean of the summer periods (June-September) of 2001-2010 and 2021-2030, taking the influence factors population prognosis, air temperature projections and air temperature effects into account

    XLSX-Document (10.7 kB)
    Document: Scherber 2014