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Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014. 1 Pascal Peduzzi, PhD Gregory Giuliani, PhD Andrea de Bono, PhD Christian Herold Bruno.

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Presentation on theme: "Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014. 1 Pascal Peduzzi, PhD Gregory Giuliani, PhD Andrea de Bono, PhD Christian Herold Bruno."— Presentation transcript:

1 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Pascal Peduzzi, PhD Gregory Giuliani, PhD Andrea de Bono, PhD Christian Herold Bruno Chatenoux GEO Ministerial and Plenary Meetings – Side Event 13 January 2014 Data access and interoperability. GAR and PREVIEW Global Risk Data Platform Generating and sharing risk data UNEP / GRID-Geneva

2 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, The PREVIEW Global Risk Data Platform Presentation plan Global level risk analysis Who are we? GAR 2013: new developments

3 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, UNEP DEWADEPIDRCDGEFDCPIDTIE DEWA UNEP/ GRID-Geneva Global Change & Vulnerability Unit (ex Early Warning) Dr P. Peduzzi C.Herold Dr G.Giuliani Global Change & Vulnerability Unit Swiss Env. Agency University of Geneva B. Chatenoux Global Change & Vulnerability: a unit of the UNEP/GRID-Europe Dr A. De Bono

4 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Field data collection Image analysisStatistical analysis Spatial analysis (GIS) Global Change & Vulnerability Unit Maps & Info PREVIEW Data (SDI)

5 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Contribution to 12 UN reports on risk & global change G A R Scientific papers

6 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Global analysis

7 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Exposure Hazards Vulnerability Natural variability Anthropogenic Changes Climate Environment DEVELOPMENT Disaster Risk Management Adaptation Disaster GHG emissions, deforestation,… DISASTER RISK How to generate risk data

8 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Who generates the data? Global Flood ModelUNEP/GRID-Geneva and CIMA Foundation Global Tropical CyclonesUNEP/GRID-Geneva Probabilistic TC modelCIMNE Global LandslidesNorwegian Geotechnical Institute (NGI) Global TsunamiNorwegian Geotechnical Institute (NGI) Tsunami eventsNOAA Volcanic eruptionSmithonian Institute Flood eventsDartmouth Flood Observatory (now at Colorado Uni) Earthquakes shakemapsUSGS Forest firesESA Drought ModelIRI EarthquakesGSHAP, CIMNE, (GEM coming) GDPWorldbank Population distributionLandscan Global Exposure ModelUNEP/GRID-Geneva

9 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Tectonic Hazards New Global Hazard Datasets created for GAR 2009

10 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, New Human & Economic exposure datasets (1 x 1 km Population and GDP distribution Models made for every years from 1970 to 2010

11 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Past floods as detected by satellite sensors

12 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Compilation of Past Earthquakes ShakeMaps 5686 events downloaded over the period

13 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva,

14 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, >6000 tropical cyclones events were processed Global coverage for the period 1970 to Using central pressure Maximum windspeed Latitude … Individual past hazardous events modeling

15 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Nargis 2 May 2008 Myanmar Extraction of exposure and other parameters

16 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Date Iso3 Killed Est. damages FootprintsPop. exp. GDP exp. Pop.Urb exp. GDP Urb. exp Category 1 10,500,00043,000,0004,800,00032,500, ,500,000 3,500,0001,400,000525, , ,000375,000150,000 Country: Myanmar Iso3: MMR Date: 02 May 2008 Preview Tropical Cyclones Database EM-DAT, CRED Database Date Iso3 Killed: 138,366 Vulnerability Database 43 indicators Damages: 4,000 US$ millions GDPcap: 1,227 US$ Voice & acc.: Governance efficiency : Radio/inhabitant: 99.68% HDI: … Urban growth: 2.55% … Date Iso3 GDPcap Voice & acc. Governance efficiency Radio/inhabitant HDI … Urban growth

17 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, AIDS estimated deaths, aged 0-49 (% of tot. pop.) 2 non GLC2000 bare land 3 Arable and Permanent Crops - % of non GLC2000 bare land 4 Motor vehicles in use - Passenger cars (thousand) 5 Motor vehicles in use - Commercial vehicles (thousand) 6 Physical exposure to conflicts 7 Corruption Perceptions Index (CPI) 8 Arable and Permanent Crops - Total 9 Arable and Permanent Crops - Percent of Land Area 10 Control of Corruption 11 Deforestation rate 12 % of population with access to electricity 13 Forests and Woodland (% of Land Area) 14 Gross Domestic Product - Purchasing Power Parity per Capita 15 Gross Domestic Product - Purchasing Power Parity 16 inequality (Gini coefficient) 17 Human Induced Soil Degradation (GLASOD) 18 Government Effectiveness 19 Human Development Index (HDI) 20 Per capita government expenditure on health (PPP int. $) 21 # of hospital beds per 100,000 habitants # of doctors 22 infant mortality and malnutrition (though are also factored into HDI) 23 Improved Drinking Water Coverage - Total Population 24 telecommunications (phone density per 100,000 habitants) 25 Political Stability 26 Population (Persons (in Thousands)) 27 Urban Population (% of Total Population) 28 Radio receivers (per thousand inhabitants) 29 Regulatory Quality 30 Rule of Law 31 School enrollment, primary (total) 32 % of urban population living in slums / squatter settlements 33 Physicians density (per population) 34 Under five years old mortality rate 35 Undernourished (% of total population) 36 Urban Population Growth on past 3 years 37 Voice and Accountability 38 Motor vehicles in use - Passenger cars (per inhabitant) 39 Motor vehicles in use - Commercial vehicles (per inhabitant) 40 School enrollment, primary (per inhabitant) 41 Population growth on 3 past years 42 income-consumption poverty (from WB poverty calculator also from MDG project) 43 Transport 43 indicators on: Economy, Demography, Environment, Development, Early Warning, Governance, Health, Education, … List of vulnerability parameters considered

18 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, From hazardous events to frequency and exposure

19 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Aggregation of human exposure at country level

20 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Aggregation of economical exposure at country level

21 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Landslides risk

22 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, About 2.2 million people are exposed to landslides worldwide. 55% of mortality risk is concentrated in 10 countries, which also account for 80% of the exposure. Comoros, Dominica, Nepal, Guatemala, Papua New Guinea, Solomon Islands, Sao Tome and Principe, Indonesia, Ethiopia, and the Philippines Landslides (modelled for both precipitation and earthquakes)

23 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Tropical cyclones risk Multiple Risk 23

24 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Multi Mortality Risk Index (MRI)

25 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Floods Mortality Risk Index (MRI) Cyclones Mortality Risk Index (MRI)Earthquakes Mortality Risk Index (MRI) Landslides Mortality Risk Index (MRI)

26 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, PREVIEW Global Risk Data Platform

27 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, The Global Risk Data Platform preview.grid.unep.ch Used by: in GEOSS portal UNEP UNISDR (For GAR). World Bank UNHCR Inform (EU/JRC) WRI (UNU) OCHA Mapplecroft And many others Users can visualise, interrogate, download data related to disaster risk (hazard, exposure, risk).

28 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Fully Open Source OGC & ISO compliant Based on: PostgreSQL/PostGIS, PHP, Geoserver, GeoNetwork, OpenLayers & GeoExt. Analysis of geospatial data: ESRI ArcInfo & ArcGIS GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective

29 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, ~220’000 visitors ~3’200’000 pages ~7’100’000 maps produced ~300 GB of data downloaded Access x4 after Sichuan and Haiti events Access x10 after Fukushima GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective

30 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective PreView Mobile Web-based Multiplatform Access all layers in WMS Zoom IN/OUT, Pan Multi-touch control Search location: GeoNames GPS

31 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective

32 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, GAR 2015: new developments

33 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, GAR probabilistic approach? Yes NoRemarque Earthquakes Based on GSHAP 1:475 years Landslides (Eq) Based on GSHAP 1:475 years Tsunamis Based on GSHAP 1:475 years CC. Floods Based on 100 years returning period Trop. Cyclones Based on 1970 – 2009 detected events Landslides (Pr)Based on 1960 – 2000 precipitations Forest fires Based on 1997 – 2010 detected events Drought Based on 1960 – 2000 precipitations

34 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, GAR 2013: probabilistic approach ? Yes NoRemarque Earthquakes Based on GEM Landslides (Eq) Based on GSHAP 1:475 years Tsunamis Based on GSHAP 1:475 years CC. Floods Based on 5 different returning periods Trop. Cyclones Based on synthetic tracks and stochastic approach one global estimation of climate change impacts Landslides (Pr)Based on 1960 – 2000 precipitations Forest fires Based on burnt areas Drought Based on FEWS methodology (6 countries)

35 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Hazards: GAR 2015 Yes NoRemarque Earthquakes Based on GEM Landslides (Eq) Based on GEM Tsunamis CC. Floods Based on 5 different returning periods Trop. Cyclones Based on synthetic tracks and stochastic approach Landslides (Pr)Based on stochastic approach Forest fires Not yet discussed Drought Based on FEWS methodology, more countries

36 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Tropical cyclones global trends Peduzzi, P., Chatenoux, B., Dao, H., De Bono, A., Herold, C., Kossin, J., Mouton, F., Nordbeck, O. (2012) Tropical cyclones: global trends in human exposure, vulnerability and risk, Nature Climate Change, 2, 289–294.

37 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Scenarios on TC for 2030 As adapted from Knutson et al. (2010)

38 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Global Flood Model NEW GLOBAL FLOOD MODEL 5 returning periods NEW GLOBAL FLOOD MODEL 5 returning periods

39 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, NEW GLOBAL FLOOD MODEL 5 returning periods NEW GLOBAL FLOOD MODEL 5 returning periods

40 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Outputs GED - GLOBAL EXPOSURE MODEL GAR13 GED 2013 (Global Exposure Database): each record (exposed value) represents a certain building structural type of certain income level/sector in a certain urban area with a special point representation in the centroid of the 5x5 cell. Urban Area ID Income Level or Sector Building type VALFIS [USDX10 6 ] 1Low IncomeS5 $ 496,646 1Low IncomeC1 $ 7,449,689 1Low IncomeC1L $ 7,449,689 1Low IncomeC2 $ 496,646 1Low IncomeC2M $ 2,483,230 1Low IncomeC3 $ 7,449,689 1Low IncomeM2 $ 993,292 1Low IncomeUFB3 $14,899,377 1Low IncomeUCB $ 4,966,459 1Low IncomeUNK $ 2,979,875 1Middle IncomeS5 $ 822,740 1Middle IncomeC1 $12,341,105 1Middle IncomeC1L $12,341,105 1Middle IncomeC2 $ 822,740 1Middle IncomeC2M $ 4,113,702 1Middle IncomeC3 $12,341,105 1Middle IncomeM2 $ 1,645,481 1Middle IncomeUFB3 $24,682,210 1Middle IncomeUCB $ 8,227,403 1Middle IncomeUNK $ 4,936,442 1High IncomeS5 $ 45,026 1High IncomeC1 $ 675,384 Capital stock distribution on a 5x5 km grid: map shows aggregate values for resident buildings.

41 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, GED Global Exposure Model GAR13 Andrea de Bono (GRID) Miguel Mora (CIMNE) GAR 2013 / 2015 A

42 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, GED Thematic components GED - GLOBAL EXPOSURE MODEL GAR13 Produced capital and urban land Building structure class (WAPMERR) Demographic Socioeconomic Building type Assets value People living in urban areas Built-environment Income, employment, health, education

43 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Built-environment and urban population GED - GLOBAL EXPOSURE MODEL GAR13 Built-environment 4) populate “urban areas” extract Urban areas mask: from remote sensing (MODIS 500m) Population: number people per cell (Source Landscan) Urban population: nb. people per cell 1

44 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Capital stock estimation GED - GLOBAL EXPOSURE MODEL GAR13 GAR 2013 We use the World Bank’s “comprehensive wealth” methodology*. * World Bank (2011). The changing wealth of nations : measuring sustainable development in the new millennium Produced Capital using the Perpetual Inventory Method for machinery and structures, based on Gross Capital Formation data, and layers on urban land as a proportion of this. Capital stock data are at national level. The downscaling to cell is done using GDP at subnational scale as proxy

45 Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, Than you


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