1. Assessment urban vulnerabilities to climate-related natural hazards Urban Heat Vulnerability Assessment
Ricardo Barranco, Tobias Lung
Land Use Modelling Group, Sustainability Assessment Unit (DG JRC)

2. Introduction
- A spatially explicit urban, indicator-based heat vulnerability assessment;
- This methodology is meant to be transferrable to other climate-related hazards
Spatial Cover
- E27 countries (plus Norway and Switzerland) but not Malta and oversees regions due to missing climate data.
Choice of Cities
Based on the revised and harmonised OECD-EC definition of cities in Europe E27 countries;
Main high density urban cluster (with over 250,000 inhabitants);
Exception to Paris, Milano and Barcelona which had 2;
Grouping of single urban high density cluster stretching across 2-3 larger urban zones: Solingen-Wuppertal and Leeds-Bradford-Kirklees;
Augsburg [DE], Toulon [FR], Venezia [IT], and Utrecht [NL] have 2 clusters each bellow 250,000 inhabitants. These were considered as one area.
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3. Data and Methods Choice of Cities (cont.)
To delineate the different single input indicators, three different spatial extents of the cities were used:
High density urban clusters - input indicators that aim to reflect a measure of only the densely built-up area (e.g. population density);
High density urban clusters buffered (25km) - input indicators with a coarse spatial resolution (e.g. the climate input data);
NUTS-2 or NUTS-3 region for each of the high density urban clusters - (e.g. for statistical data from EUROSTAT).
Data and Methods
- Built upon a framework of Lung et al (2013) on NUTS-2 level hazard-specific impacts and vulnerability, which is based on the definitions of climate change impact and vulnerability as specified by the IPCC (2007) and the concept of Füssel and Klein (2006).
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4. Data and Methods (cont.)
Impact is determined by exposure and sensitivity to climatic drivers;
Exposure describes ‘‘the nature and degree to which a system is exposed to significant climatic variations’’ while ‘‘the sensitivity of a system denotes the [...] dose–response relationship between its exposure to climatic stimuli and the resulting impacts”
Framework of urban heat vulnerability assessment
Adaptive capacity
Sensitivity
to climate change
Exposure
Indicators of financial
capital
Indicators of human capital
Indicators of technological capital
Climatic, morphological, human drivers
Social, economic, infrastructure drivers
Hazard-specific impact
Hotspots of vulnerability
Baseline (current)
Medium-term scenario
Short-term scenario
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5. Data and Methods (cont.)
Assessing heat impact
Climatic exposure represented by climate indicators from 5 different ENSEMBLES experiments:
“Summer Days”: (maximum temperature of above 25 ºC);
“Tropical nights” (minimum temperature not below 20 C);
Share of urban green space (cooling effect).
Simulation name
Institute
RCM
Driving GCM
C4I_RCA_HadCM3Q16
C4I
RCA3
HadCM3Q16
CNRM_ALADIN_ARPEGE
CNRM
ALADIN
ARPEGE
DMI_HIRHAM_ECHAM5
DMI
HIRHAM
ECHAM5
ETHZ_CLM_HadCM3Q0
ETHZ
CLM
HadCM3Q0
SMHI_RCA_BCM
SMHI
BCM
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6. Assessing heat impact (cont.)
Climate indicators: averaging across the results of multiple regional climate models (ENSEMBLES data, SRES A1B) for improved robustness
Name
Institution
RCA_HadCM3Q16
C4I
1.8
2.3
5.0
ALADIN_ARPEGE
CNRM
1.2
2.2
3.1
HIRHAM_ECHAM5
DMI
0.8
2.5
CLM_HadCM3Q0
ETHZ
1.5
2.6
3.5
RCA_BCM
SMHI
0.7
1.9
2.7
Ensemble
3.3
(Projected mean annual
temperature changes [in ˚C] from )
OBSERVED
ORIGINAL
CORRECTED
Dosio et al. (2012), JGR
 Statistical bias correction (‘quantile-mapping’ technique) for improved reliability
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7. Assessing heat impact (cont.)
Human sensitivity was covered employing data on population density and structure:
Share of elderly people of >= 75 years;
Elderly people living alone in single households;
Urban city centre population density.
Overall heat stress impact assessed by combining the indicators into a single composite heat indicator:
Following OECD and JRC procedures;
3 time windows (baseline, short-term , medium-term );
Exposure and sensitivity indicators were converted into a dimensionless unit using standardised z-scores centred on zero;
Geometric aggregation was applied to combine into a composite indicators (i.e. the product of equal weighted indicators);
Final composite indicator was classified into 5 impact categories (‘very low’, ‘low’, ‘medium’, ‘high’, ‘very high’) according to percentile thresholds.
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8. Assessing heat impact (cont.)
Adaptive capacity:
Lack of specific data on heat-related preparedness and institutionalised measures and systems (especially projected data);
Aiming at quantitative assessment of baseline;
3 components :
Awareness (educational attainment, internet use);
Ability (research & development expenditure, health infrastructures);
Action (GDP, government effectiveness index).
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9. Exposure and sensitivity indicators
Component
Description
Baseline
Scenario1
Scenario2
Data source
Heat exposure
Number of summer days with Tmax > 25°C in summer period [June, July, August]
ENSEMBLES-project, five RCMs
Number of tropical nights with Tmin > 20°C in summer period [June, July, August]
Share of green space within urban high density cluster [CLC classes 141, 311, 312, 313]
2006
---
refined CORINE 2006
Heat sensitivity
Percentage of elderly people > 75 years
2010
2025
2055
Eurostat (2025 & 2055: EUROPOP2010)
Percentage of households composed of one adult > 65 years
Eurostat
Population density [in people/km2]
EU-ClueScanner
Adaptive capacity indicators
Component
Parameter description
Data source
Awareness
NUTS-2 level educational attainment [people aged with tertiary education, ISCED L5-6], 2010
EUROSTAT
NUTS-2 level internet use [percentage of people with frequent use], 2010
Ability
NUTS-2 level research & development expenditure per capita, 2009
NUTS-2 level health infrastructure [physicians/doctors per capita], 2009
Action
NUTS-3 level gross domestic product GDP [in PPS per capita], 2010
NUTS-0 level government effectiveness index, average for years
World Bank
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10. Results Baseline Problemetic Hotspots:
Southern european countries have the highest impacts.
Eastern european present lower adaptive capacity.
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11. Results
Almost complete coverage by very high impact in southern european
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12. Results 2040 - 2070 Very high impacts are expected in central Europe
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13. Results Change Baseline–2041-70
Higher changes in Central Europe and Poland
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14. Results Change (%) Baseline–2041-70
Better perception of the overall changes even after reaching the highest class.
Problematic hotspots:
Southern and Eastern Europe (high change percentage, low adaptive capacity)
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15. Further developments
Development of the following hazards assessment following the previously described methodology:
River flood risk
Flood exposure
Percentage of flooded area, magnitude of a 100-year event flood
Mean water depth [in m] of flooded area, magnitude of a 100-year event flood
Flood sensitivity
Population density within areas affected by 100-year recurrence interval flood
Percentage of commercial & industrial areas affected by 100-year recurrence interval flood
Sea-level rise risk
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16. Thank you for your attention!
10 November 2018