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Munich Re NatCatSERVICE

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1 Munich Re NatCatSERVICE
Disaster loss data handling & the data landscape Angelika Wirtz Munich Re Geo Risks Research June 2013

2 World Map of Natural Hazards We know and understand risk – it is our business
Munich Re founded 1880 – world leading reinsurance company branch offices in 60 countries NatCatSERVICE established 1985 – before paper archive

3 Chief Editor „Topics Geo“
Munich Re Head of NatCatSERVICE Chief Editor „Topics Geo“ ICSU-IRDR Chair of Project „DATA – Disaster Loss Data and Impact Assessment“ ICSU-CoData Co-Chair of Task Group „Linked Open Data for Global Disaster Risk Research” WMO World Weather Research Programme Member of SERA (Working Group on Societal and Economic Research and Applications) Title of presentation and name of speaker

4 Expert on global loss data
Munich Re Head of NatCatSERVICE Chief Editor „Topics Geo“ ICSU-IRDR Chair of Project „DATA – Disaster Loss Data and Impact Assessment“ ICSU-CoData Co-Chair of Task Group „Linked Open Data for Global Disaster Risk Research” WMO World Weather Research Programme Member of SERA (Working Group on Societal and Economic Research and Applications)

5 The NatCatSERVICE database
Technical Workshop on Standards for Hazard Monitoring, Databases, Metadata and Analysis Techniques to Support Risk Assessment The NatCatSERVICE database Global database – analyses examples Methodology The worldwide data landscape Goals of this workshop © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

6 Global vs. local database
Munich Re, Swiss Re, CRED Em-Dat >300 identified country databases Local databases © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

7 Natural disasters © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

8 Natural catastrophes worldwide 1980 – 2012 Number of events with trend
NatCatSERVICE Natural catastrophes worldwide 1980 – 2012 Number of events with trend Number Meteorological events (Storm) Hydrological events (Flood, mass movement) Climatological events (Extreme temperature, drought, forest fire) Geophysical events (Earthquake, tsunami, volcanic eruption) © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE – As at January 2013

9 Natural catastrophes worldwide 1980 – 2012 Overall and insured losses with trend
(bn US$) Trend insured losses Trend overall losses Overall losses (in 2012 values) Insured losses (in 2012 values) © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE – As at January 2013

10 Natural catastrophes worldwide 2012 Overall losses US$ 165bn - Percentage distribution per continent
70% 13% 16% 2012 14% 42% 40% <1% 1% <1% 3% Continent Overall losses US$ m America (North and South America) 115,000 Europe 21,000 Africa 1,000 Asia 26,000 Australia/Oceania © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE – As at January 2013

11 Natural catastrophes worldwide 1980 – 2011 Losses as a ratio of GDP
(% of GDP affected ) High income economies High income economies (GNI > 12,476 US$) Upper middle income economies (GNI 4,036 – 12,475 US$) Lower middle income economies (GNI 1,026 – 4,035 US$) Low income economies (GNI < 1,025 US$) Income Groups 2012 (defined by World Bank): © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

12 Upper middle income economies
Natural catastrophes worldwide 1980 – Losses as a ratio of GDP (% of GDP affected ) Upper middle income economies High income economies (GNI > 12,476 US$) Upper middle income economies (GNI 4,036 – 12,475 US$) Lower middle income economies (GNI 1,026 – 4,035 US$) Low income economies (GNI < 1,025 US$) Income Groups 2012 (defined by World Bank): © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

13 Lower middle income economies
Natural catastrophes worldwide 1980 – Losses as a ratio of GDP (% of GDP affected ) Lower middle income economies High income economies (GNI > 12,476 US$) Upper middle income economies (GNI 4,036 – 12,475 US$) Lower middle income economies (GNI 1,026 – 4,035 US$) Low income economies (GNI < 1,025 US$) Income Groups 2012 (defined by World Bank): © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

14 Natural catastrophes worldwide 1980 – 2011 Losses as a ratio of GDP
(% of GDP affected ) 11,5% Low income economies High income economies (GNI > 12,476 US$) Upper middle income economies (GNI 4,036 – 12,475 US$) Lower middle income economies (GNI 1,026 – 4,035 US$) Low income economies (GNI < 1,025 US$) Income Groups 2012 (defined by World Bank): © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

15 Global databases in dialog
CRED UNDP Asia Disaster Reduction Center DesInventar UN-ISDR © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

16 Disaster loss data NatCatSERVICE EM-DAT Sigma
Overview of global databases – entry criteria CRED NatCatSERVICE EM-DAT Sigma Criteria*: Property damage People killed People injured Criteria*: ≥10 people killed ≥100 people affected Declaration of a state of emergency/ Call for international assistance Criteria*: >20 people killed >50 people injured >2,000 homeless Insured losses **: >US$ 14m (Marine) >US$ 28m (Aviation) >US$ 35m (all other losses) Overall losses **: >US$ 70m * Criteria for a disaster to be entered into the databases. (At least one of the criteria has to be fulfilled.) ** Entry criteria of losses are adjusted to inflation every year. © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

17 Structure – peril families
Family Main event Sub Peril Geophysical Earthquake Volcanic eruption Mass movement dry EQ Ground shaking Fire following Tsunami Meteorological Hydrological Subsidence Liquefaction Rockfall Landslide Climatological © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

18 Structure – peril families
Family Main event Sub Peril Geophysical Tropical cyclone Extra tropical cyclone (winter storm) Convective storms (thunderstorm, hail lightning, tornado) Local windstorm (orographic storm) Sandstorm/Dust storm Blizzard/Snowstorm Meteorological Storm Hydrological Climatological © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

19 Structure – peril families
Family Main event Sub Peril - examples Geophysical General / River flood Flash flood Storm surge Glacial lake outburst flood Meteorological Hydrological Flood Mass movement wet Climatological Subsidence Avalanche Landslide © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

20 Structure – peril families
Family Main event Sub Peril Geophysical Heat wave Cold wave / frost Extreme winter conditions Meteorological Hydrological Drought Climatological Extreme temperature Drought Wildfire Forest / grassland fire © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

21 Structure – peril families
Family Main event Sub Peril Associated Cascading Sub-sub peril Geophysical Heat wave Cold wave / frost Extreme winter conditions Meteorological Hydrological Drought Famine Climatological Extreme temperature Drought Wildfire Forest / grassland fire © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

22 Structure – peril families
Family Geophysical Meteorological Hydrological Climatological Biological Extra-Terrestrial © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

23 Meta information Loss and damage (monetary and human impact)
© 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

24 Meta information Scientific parameter Start and end day / duration
Geographic information (continent village.....addresse) etc. Title of presentation and name of speaker

25 Geocoding From global to national to footprint
TN KS KY IN NE OH Los Angeles © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

26 Multi-country event © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

27 Multi-country event Hurricane Ike USA Cuba Turks & Caicos Dom. Rep
Haiti Bahamas Region Details Damages Region Details Damages Region Details Damages Region Details Damages Region Details Damages Region Details Damages © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

28 Multi-peril event Typhoon Flood Landslide Tornado Affected region
Scientific details Damage Affected people Affected region Scientific details Damage Affected people Affected region Scientific details Damage Affected people © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

29 What exactly is disaster loss data
© 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

30 Disaster Loss Data EM-DAT

31 overlaps Disaster loss data Data providers Data collectors
Overview of stakeholders Data providers overlaps Data collectors Data platforms Data users © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

32 Disaster loss data Overview of data providers - examples Kind of data
Example Information Example Data Providers General informaion Description of event Media, satellite images, case studies Scientific information Precipitation, magnitude Scientific institutes (Weather services, USGS) Human impact People affected, injured, death, missing Aid organisations, like Relief Web, IFRC Monetary loss information - Economic loss Financial impact of disaster (direct loss, indirect loss, secondary loss) Different organisations (governments, World Bank,ECLAC, professional loss provider, etc) - Insured loss Regional, national, local loss Reinsurance, insurance associations, local insurance, professional loss provider Sector based national loss NFIP (flood), USDA (agro) Automatic generated information Region affected, people involved Joint Research Centre/GDACS, USGS-Pager © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

33 Disaster loss data Overview of data providers - examples Kind of data
Example Information Example Data Providers General informaion Description of event Media, satellite images, case studies Scientific information Precipitation, magnitude Scientific institutes (Weather services, USGS) Human impact People affected, injured, death, missing Aid organisations, like Relief Web, IFRC Monetary loss information - Economic loss Financial impact of disaster (direct loss, indirect loss, secondary loss) Different organisations (governments, World Bank,ECLAC, professional loss provider, etc) - Insured loss Regional, national, local loss Reinsurance, insurance associations, local insurance, professional loss provider Sector based national loss NFIP (flood), USDA (agro) Automatic generated information Region affected, people involved Joint Research Centre/GDACS, USGS-Pager © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

34 Disaster loss data Overview of data providers - examples Kind of data
Example Information Example Data Providers General informaion Description of event Media, satellite images, case studies Scientific information Precipitation, magnitude Scientific institutes (Weather services, USGS) Human impact People affected, injured, death, missing Aid organisations, like Relief Web, IFRC Monetary loss information - Economic loss Financial impact of disaster (direct loss, indirect loss, secondary loss) Different organisations (governments, World Bank,ECLAC, professional loss provider, etc) - Insured loss Regional, national, local loss Reinsurance, insurance associations, local insurance, professional loss provider Sector based national loss NFIP (flood), USDA (agro) Automatic generated information Region affected, people involved Joint Research Centre/GDACS, USGS-Pager © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

35 Disaster loss data Overview of data providers - examples Kind of data
Example Information Example Data Providers General informaion Description of event Media, satellite images, case studies Scientific information Precipitation, magnitude Scientific institutes (Weather services, USGS) Human impact People affected, injured, death, missing Aid organisations, like Relief Web, IFRC Monetary loss information - Economic loss Financial impact of disaster (direct loss, indirect loss, secondary loss) Different organisations (governments, World Bank,ECLAC, professional loss provider, etc) - Insured loss Regional, national, local loss Reinsurance, insurance associations, local insurance, professional loss provider Sector based national loss NFIP (flood), USDA (agro) Automatic generated information Region affected, people involved Joint Research Centre/GDACS, USGS-Pager © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

36 Disaster loss data Overview of data providers - examples Kind of data
Example Information Example Data Providers General informaion Description of event Media, satellite images, case studies Scientific information Precipitation, magnitude Scientific institutes (Weather services, USGS) Human impact People affected, injured, death, missing Aid organisations, like Relief Web, IFRC Monetary loss information - Economic loss Financial impact of disaster (direct loss, indirect loss, secondary loss) Different organisations (governments, World Bank,ECLAC, professional loss provider, etc) - Insured loss Regional, national, local loss Reinsurance, insurance associations, local insurance, professional loss provider Sector based national loss NFIP (flood), USDA (agro) Automatic generated information Region affected, people involved Joint Research Centre/GDACS, USGS-Pager © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

37 Disaster loss data Overview of data providers - examples Kind of data
Example Information Example Data Providers General informaion Description of event Media, satellite images, case studies Scientific information Precipitation, magnitude Scientific institutes (Weather services, USGS) Human impact People affected, injured, death, missing Aid organisations, like Relief Web, IFRC Monetary loss information - Economic loss Financial impact of disaster (direct loss, indirect loss, secondary loss) Different organisations (governments, World Bank,ECLAC, professional loss provider, etc) - Insured loss Regional, national, local loss Reinsurance, insurance associations, local insurance, professional loss provider Sector based national loss NFIP (flood), USDA (agro) Automatic generated information Region affected, people involved Joint Research Centre/GDACS, USGS-Pager © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

38 Disaster loss data Overview of data providers - examples Kind of data
Example Information Example Data Providers General informaion Description of event Media, satellite images, case studies Scientific information Precipitation, magnitude Scientific institutes (Weather services, USGS) Human impact People affected, injured, death, missing Aid organisations, like Relief Web, IFRC Monetary loss information - Economic loss Financial impact of disaster (direct loss, indirect loss, secondary loss) Different organisations (governments, World Bank,ECLAC, professional loss provider, etc) - Insured loss Regional, national, local loss Reinsurance, insurance associations, local insurance, professional loss provider Sector based national loss NFIP (flood), USDA (agro) Automatic generated information Region affected, people involved Joint Research Centre/GDACS, USGS-Pager © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

39 Disaster loss data Overview of data collectors - examples Kind of data
Examples Data Collectors Comments Global multi peril EmDat, Munich Re, Swiss Re Regional multi peril La Red EEA European Environmental Agency In planning National multi peril UNDP (country databases after TS 2004), Sheldus Event based Dartmouth Flood Observatory CEDIM Center for Disaster Management and Risk Reduction Technology Flood Earthquakes, Landslides Sector based Ascend USDA (US Dept. of Agriculture) Aviation Agriculture © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

40 Disaster loss data Overview of data collectors - examples Kind of data
Examples Data Collectors Comments Global multi peril EmDat, Munich Re, Swiss Re Regional multi peril La Red EEA European Environmental Agency In planning National multi peril UNDP (country databases after TS 2004), Sheldus Event based Dartmouth Flood Observatory CEDIM Center for Disaster Management and Risk Reduction Technology Flood Earthquakes, Landslides Sector based Ascend USDA (US Dept. of Agriculture) Aviation Agriculture © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

41 Disaster loss data Overview of data collectors - examples Kind of data
Examples Data Collectors Comments Global multi peril EmDat, Munich Re, Swiss Re Regional multi peril La Red EEA European Environmental Agency In planning National multi peril Sheldus UNDP (country databases after TS 2004) Event based Dartmouth Flood Observatory CEDIM Center for Disaster Management and Risk Reduction Technology Flood Earthquakes, Landslides Sector based Ascend USDA (US Dept. of Agriculture) Aviation Agriculture © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

42 Disaster loss data Overview of data collectors - examples Kind of data
Examples Data Collectors Comments Global multi peril EmDat, Munich Re, Swiss Re Regional multi peril La Red EEA European Environmental Agency In planning National multi peril UNDP (country databases after TS 2004), Sheldus Event based Dartmouth Flood Observatory CEDIM Center for Disaster Management and Risk Reduction Technology Flood Earthquakes, Landslides Sector based Ascend USDA (US Dept. of Agriculture) Aviation Agriculture © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE – As at January 2013

43 Disaster loss data Overview of data collectors - examples Kind of data
Examples Data Collectors Comments Global multi peril EmDat, Munich Re, Swiss Re Regional multi peril La Red EEA European Environmental Agency In planning National multi peril Sheldus, UNDP (country databases after TS 2004) Event based Dartmouth Flood Observatory CEDIM Center for Disaster Management and Risk Reduction Technology Flood Earthquakes, Landslides Sector based Ascend USDA (US Dept. of Agriculture) Aviation Agriculture © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

44 Disaster loss data Overview of data users - examples Sector Examples
Science Research projects Trend analyses, IPCC, Global Assessment Report, GEM Decision makers Governments, NGOs Loss reduction purposes, risk reduction measurements Finance industry Insurance Risk calculation, development of new solutions, Microinsurance schemes, government schemes Alternative (monetary) risk transfers Cat Bonds, weather derivate Modelling companies (RMS, EQE Cat, AIR) Calibrate models Media © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

45 The ideal world of disaster loss data
Scientific data Global databases Local databases National and regional databases Literature: Forensic case studies Scientific analyses WORLD DATA ORGANISATION / PLATFORM with Meta-Data and links to specialized data provider GLIDE Funding organisations Local Decision Makers Governments Insurance industry Finance industry NGOs © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

46 Projects and initiatives
Examples ICSU – IRDR Project „DATA – Disaster Loss Data and Impact Assessment“ CRED – Harmonisation of human and economic loss indicators ICSU – CoDATA – Working & Task Force Group on disaster data European Commission – Standards and protocols for recording losses, recommendations for a European approach © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

47 IRDR – Integrated Research on Disaster Risk Science Plan
Political committees Clients Analysts, investors Media IRDR – Integrated Research on Disaster Risk Science Plan Objective 3: Reducing Risk and Curbing Losses Through Knowlede-Based Actions Disaster loss data are necessary to improve integrated disaster risk management

48 IRDR – Integrated Research on Disaster Risk
Project: DATA - Disaster Loss Data and Impact Assessment Objectives Identify what data and quality are needed to improve integrated disaster risk management Bring together loss data stakeholders and utilize synergies Have recognized standards, minimize uncertainty Education of users regarding data interpretation and data biases Ensure increased downscaling of loss data to sub-national geographies for policy makers Definition of "losses" and creation of a methodology for assessing it © 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

49 Objective of this workshop
Title of presentation and name of speaker

50 Challenge Bring different world together without a collission
Meteorological/hydrological/climate-related data Precipitation Water levels Soil conditions Wind speeds, gusts Storm tracks, landfall information Title of presentation and name of speaker

51 Challenge Bring different world together without a collission
Risk analysis data Hazard information Exposure to risk housing stock, capital stock, GDP, Population (per country/grid) Social Vulnerability information Reselience level Agriculture GDP /rural vs. urban population Etc. Title of presentation and name of speaker

52 Challenge Bring different world together without a collission
Damage and loss data Impact on people Damage on housings, property (cars, boats) Infrastructure / critical infrastructure Sectors (health, agriculture, small businesses...) Economic impact (direct/indirect/secondary loss) -currencies- Satelite images (before and after) Location - Geocoding Forensic studies / case studies / lessons learned Title of presentation and name of speaker

53 Challenge Bring different worlds together without a collission
Met-Offices Damage and Loss Risk analysis data Risk Analysis and Disaster Risk Assessment Goal: to minimize losses (human, monetary) to improve preparedness measurements to improve early warning to improve estisting infrastructure Title of presentation and name of speaker

54 Challenge Bring different worlds together without a collission
Different wordings / terminologies (i.e. hazard) Different users and requirements Different definitions (i.e. extreme event, natural event) Different hazard types Met-Data Damage and Loss Risk analysis data Risk Analysis and Disaster Risk Assessment Goal: to minimize losses (human, monetary) to improve preparedness measurements to improve early warning to improve estisting infrastructure Title of presentation and name of speaker

55 www.munichre.com/natcatservice/downloadcenter NatCatSERVICE
© 2013 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE – As at January 2013

56 NatCatSERVICE User NatCatSERVICE Analysts, investors Clients
Munich Re Group NatCatSERVICE Science General public Political committees Media © 2012 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

57 Thank you Angelika Wirtz
Geo Risks Research/Corporate Climate Centre Munich Re

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