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Implemented by
WISC Exposure and Vulnerability Dr Elco Koks
Vrije Universiteit Amsterdam
pace23/06/2016, Introduction to WISC Indicators
Focus on the development of two indicators
Total sectorial insuredlosses
Total amount of losses per sectorwithin Europe due to the occurrence of wind storms.
Yearly / pan-European
Total windstorm risk per sector due to windstorms
Total windstorm risk, as a function of hazard, exposure and vulnerability per sector
Yearly/ pan-European
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pace23/06/2016, Introduction to WISC Indicators
Total windstorm risk per sector due to windstorms
Total windstorm risk, as a function of hazard, exposure and vulnerability per sector. Total losses are part of the risk
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Requirements
Hazard Data on the severity of the hazard (gust speed)
Exposure Data on exposed assets in wind-proneareas, preferabiliy per land-use
Vulnerability Data on the susceptibility of assets, preferability per land-use
Probability Probability of occurence for a given hazard
pace23/06/2016, Introduction to WISC Indicators
Methodology
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pace23/06/2016, Introduction to WISC Indicators
Exposure database 10mx10m resolution CORINE land cover data
http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-3
Openstreetmap data www.osm.org Freely available, allowing for more detail in exposure data. Very good
coverage for EU, shapefile format (will be rasterized when combined with CORINE) 80% coverage or so
Sectors considered similar to Perils: Agricultural Residential Commercial/Industrial (same class, but we know the percentage of each) Horticulture The greenhouses are in the building footprints, but its not a seperate class in
CORINE.
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pace23/06/2016, Introduction to WISC Indicators
Exposure data steps: Amsterdam Example
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pace23/06/2016, Introduction to WISC Indicators
Exposure data steps: Amsterdam Example
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pace23/06/2016, Introduction to WISC Indicators
Exposure data steps: Amsterdam Example
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pace23/06/2016, Introduction to WISC Indicators
Vulnerability database
Depth-damage curves will be developed and applied. A first set of curves is based on avaiable literature. E.g.:
Feuerstein et al. (2008) Heneka and Ruck (2008)
But, calibration with sample sets is required (and will be done)
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pace23/06/2016, Introduction to WISC Indicators
Vulnerability database
Per country we know the building type for urban residential, non-urban residential, urban non-residential and non-urban non-residential. This will improve the detail of the calculation. PAGER database (mainly used for earthquakes)
We have data on maximum reconstruction costs in Europe, scaled to GDP. But calibration is required (as stated before)
Almost all of the literature uses proxies to assess the building losses (because they do not have/use building footprint data). This would mean that this is the first study that does so! And that’s why the 10x10m resolution is wanted!
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pace23/06/2016, Introduction to WISC Indicators
Vulnerability database
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Vulnerability curves per land cover class and building type within Europe
Maximum losses per curve will rescaled based on GDP per NUTS3 (available via Eurostat)
Set of curves can be made country specific (e.g. wood vsconcrete vs brick buildings)
pace23/06/2016, Introduction to WISC Indicators
Total sectorial insured losses per year due to windstorms
Combine the storm data with the improved land-use data and use the curves to calculate the losses
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pace23/06/2016, Introduction to WISC Indicators
Greater Manchester example
V_crit of 25 (according to Heneka and Ruck, 2008) with the storm of March 11, 2008
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pace23/06/2016, Introduction to WISC Indicators
Thank you
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