Sokolniki, Poland, 20 May 2010(Reuters)

Laurens M. Bouwer

Overview of issues related to flood impact data availability

and interpretation

Data availability and interpretation

• Purpose of data collection?• Classification of flood impacts and costs• Availability of data:

– What type of floods?– What type of impacts and costs?– Comparison with other hazard types

• Interpretation

Costing typologies (1)

Source: Jonkman et al. 2008, JFRM

Costing typologies (2)(from FP7 ConHaz project)

Direct(including disruption of production)

Tangible

Indirect

IntangiblesCost of mitigation (risk reduction)

Flood types

• River flood• Flash flood• Coastal flood• Local inundation due to heavy rainfall/

drainage problems• (Dam break)

Approaches for direct costs(some findings from FP7 ConHaz project)

• Field data collection• Estimation methods

– Classification approaches (e.g. sectors, building types)– Exposure and asset assessment– Susceptibility analysis (stage-damage models)

Source: Kreibich and Bubeck in prep.

Data on direct damages(some findings from FP7 ConHaz project)

• Object specific databases– FHRC database for the UK (synthetic)– HOWAS 21– (primary) insurance data (insurance policy-specific)

• Event specific databases– EM-DAT– NatCatSERVICE– Swiss Re Sigma– Swiss flood and landslide damage database– DFO

Source: Kreibich and Bubeck in prep.

Data availability – availability of property insurance in Europe

Source: Bouwer et al. 2007 (data from CEA 2005)

Data availability – uptake of property insurance in Europe

Source: Bouwer et al. 2007 (data from CEA 2005)

Direct economic damages(some findings from FP7 ConHaz project)

• No common terminology across hazards• More attention to river floods, compared to

droughts, coastal floods, and alpine hazards• Limited availability of exposure data• Heterogeneous damage data• Lack of object specific data• Validation of models needed

Source: Kreibich and Bubeck in prep.

Data interpretation

• Feeding models for impacts (e.g. loss curves, models for indirect damages)

• Comparison and ranking of events• Spatial variability (different vulnerabilities across

Europe)• Temporal variability: time series analysis

– Identification of variations– Trends: climatic and socioeconomic drivers

Interpretation: Trend analysis of natural hazardsHazard Location Period Normalization Normalized loss Reference Bushfire Australia 1925-2009 Dwellings No trend Crompton et al. submitted Earthquake USA 1900-2005 Wealth, population No trend Vranes and Pielke 2009 Flood USA 1926-2000 Wealth, population No trend Downton et al. 2005 Flood China 1950-2001 GDP Increase since 1987 Fengqing et al. 2005 Flood Europe 1970-2006 Wealth, population No trend Barredo 2009 Flood Korea 1971-2005 Population Increase since 1971 Chang et al. 2009 Flood and landslide Switzerland 1972-2007 None No trend Hilker et al. 2009 Hail USA 1951-2006 Property, insurance market values Increase since 1992 Changnon 2009a Windstorm USA 1952-2006 Property, insurance market values Increase since 1952 Changnon 2009b Windstorm Europe 1970-2008 Wealth, population No trend Barredo 2010 Thunderstorm USA 1949-1998 Insurance coverage, population Increase since 1974 Changnon 2001 Tornado USA 1890-1999 Wealth No trend Brooks and Doswell 2001 Tornado USA 1900-2000 None No trend Boruff et al. 2003 Tropical storm Latin America 1944-1999 Wealth, population No trend Pielke et al. 2003 Tropical storm India 1977-1998 Income, population No trend Raghavan and Rajesh 2003 Tropical storm USA 1900-2005 Wealth, population No trend since 1900 Pielke et al. 2008 Tropical storm USA 1950-2005 GDP, population Increase since 1970;

No trend since 1950 Schmidt et al. 2009

Tropical storm China 1983-2006 GDP No trend Zhang et al. 2009 Tropical storm USA 1900-2008 GDP Increase since 1900 Nordhaus 2010 Weather (flood, thunderstorms, hail, bushfires)

Australia 1967-2006 Dwellings, dwelling values No trend Crompton and McAneney 2008

Weather (hurricanes, floods)

USA 1951-1997 Wealth, population No trend Choi and Fisher 2003

Weather (hail, storm, flood, wildfire)

World 1950-2005 GDP, population Increase since 1970; No trend since 1950

Miller et al. 2008

Source: Bouwer 2011, BAMS

Interpretation: Trend analysis of natural hazards

Source: Bouwer 2011, BAMS

Number of studies No loss trend Loss increase Loss increase due to human induced climate change

Human contribution to change in weather extreme

Wildfire 1 0 0 - Storm 6 2 ? ? River floods 3 2 ? - Tornado, thunderstorm, hail 2 2 ? - Various weather 3 0 0 - Total 15 6 ?

Most studies from USA, Asia; only two from Europe Few studies on floods

Interpretation: trend analysis of natural hazards

Source: Barredo 2009, NHESS

Some conclusions

• Consideration of purpose of cost estimations• Limited data available on floods

– Mostly event specific data(often unclear what costs are included)

– Little object specific (related to insurance cover)

– Most attention to river floods (compared to coast, flash floods, local inundation)

• Costing requires more information and analysis/modelling, beyond direct impact data

More information:

• Project websites:

conhaz.org

www.adaptation.nl

• E-mail: laurens.bouwer@ivm.vu.nl