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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)
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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. 10 November 2018
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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). 10 November 2018
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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 10 November 2018
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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 10 November 2018
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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 10 November 2018
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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. 10 November 2018
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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). 10 November 2018
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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 10 November 2018
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Results Baseline Problemetic Hotspots:
Southern european countries have the highest impacts. Eastern european present lower adaptive capacity. 10 November 2018
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Results Almost complete coverage by very high impact in southern european 10 November 2018
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Results 2040 - 2070 Very high impacts are expected in central Europe
10 November 2018
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Results Change Baseline–2041-70
Higher changes in Central Europe and Poland 10 November 2018
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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) 10 November 2018
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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 10 November 2018
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Thank you for your attention!
10 November 2018
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