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Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.1
Pascal Peduzzi, PhDGregory Giuliani, PhDAndrea de Bono, PhDChristian HeroldBruno Chatenoux
GEO Ministerial and Plenary Meetings – Side Event13 January 2014
Data access and interoperability. GAR and PREVIEW Global Risk Data Platform
Generating and sharing risk data
UNEP / GRID-Geneva
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.2
The PREVIEW Global Risk Data Platform
Presentation plan
Global level risk analysis
Who are we?
GAR 2013: new developments
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
UNEP
DEWA DEPI DRC DGEFDCPIDTIEDEWA
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
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Field data collection
Image analysisStatistical analysisSpatial analysis (GIS)
Global Change & Vulnerability Unit
Maps & Info
PREVIEW
Data (SDI)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
200520062006
Contribution to 12 UN reports on risk & global change2004
20072008
2009
2010
2011
2012
G
A
R2013
+ 28 Scientific papers
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
1. Global analysis
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Exposure
Hazards
Vulnerability
Natural variability
AnthropogenicChanges
ClimateEnvironment
DEVELOPMENT
Disaster RiskManagement
Adaptation
Disaster
GHG emissions, deforestation,…
DISASTERRISK
How to generate risk data
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.8
Who generates the data?
Global Flood Model UNEP/GRID-Geneva and CIMA Foundation
Global Tropical Cyclones UNEP/GRID-GenevaProbabilistic TC model CIMNEGlobal Landslides Norwegian Geotechnical Institute (NGI)Global Tsunami Norwegian Geotechnical Institute (NGI)Tsunami events NOAA
Volcanic eruption Smithonian InstituteFlood events Dartmouth Flood Observatory (now at Colorado Uni)Earthquakes shakemaps USGSForest fires ESADrought Model IRI
Earthquakes GSHAP, CIMNE, (GEM coming)GDP WorldbankPopulation distribution Landscan
Global Exposure Model UNEP/GRID-Geneva
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.9
Tectonic Hazards
New Global Hazard Datasets created for GAR 2009
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.10
New Human & Economic exposure datasets (1 x 1 kmPopulation and GDP distribution Models made for every years from 1970 to 2010
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.11
1006 Past floods as detected by satellite sensors
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.12
Compilation of Past Earthquakes ShakeMaps
5686 events downloaded over the period 1973-2007
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.13
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.14
>6000 tropical cyclones events were processedGlobal coverage for the period 1970 to 2012.
Using central pressureMaximum windspeedLatitude …
Individual past hazardous events modeling
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.15
Nargis 2 May 2008Myanmar
Extraction of exposure and other parameters
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.16
DateIso3KilledEst. damages
Footprints Pop. exp. GDP exp.Pop.Urb exp.
GDP Urb. exp
Category
1 10,500,000 43,000,000 4,800,000 32,500,000
2 1,500,000 3,500,000 1,400,000 525,000
3 400,000 800,000 375,000 150,000
Country: MyanmarIso3: MMRDate: 02 May 2008
Preview Tropical Cyclones Database EM-DAT, CRED
DatabaseDateIso3
Killed: 138,366
VulnerabilityDatabase 43 indicators
Damages: 4,000 US$ millionsGDPcap: 1,227 US$Voice & acc.: -2.16Governance efficiency : -1.608Radio/inhabitant: 99.68%HDI: 0.592…Urban growth: 2.55%
…
DateIso3GDPcapVoice & acc.Governance efficiencyRadio/inhabitantHDI…Urban growth
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.17
1 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 10 000 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
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.18
From hazardous events to frequency and exposure
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.19
Aggregation of human exposure at country level
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.20
Aggregation of economical exposure at country level
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.21
Landslides risk
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.22
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)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Tropical cyclones riskMultiple Risk
23
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.24
Multi Mortality Risk Index (MRI)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.25
Floods Mortality Risk Index (MRI)Cyclones Mortality Risk Index (MRI)Earthquakes Mortality Risk Index (MRI)Landslides Mortality Risk Index (MRI)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
3. PREVIEW Global Risk Data Platform
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
The Global Risk Data Platform
http:// preview.grid.unep.ch
Used by: in GEOSS portalUNEPUNISDR (For GAR).World BankUNHCRInform (EU/JRC)WRI (UNU)OCHAMapplecroftAnd many others
Users can visualise, interrogate, download data related to disaster risk (hazard, exposure, risk).
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.28
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 - 13.01.2014
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.29
~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 - 13.01.2014
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.30GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective -
13.01.2014
PreView Mobilehttp://preview.grid.unep.ch/mobile
Web-basedMultiplatformAccess all layers in WMS
Zoom IN/OUT, PanMulti-touch control
Search location:GeoNamesGPS
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.31GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective -
13.01.2014
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
4. GAR 2015: new developments
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GAR 2009-2011 probabilistic approach?
Yes No Remarque
Earthquakes Based on GSHAP 1:475 years
Landslides (Eq) Based on GSHAP 1:475 years
Tsunamis Based on GSHAP 1:475 years
CC.
FloodsBased on 100 years returning period
Trop. Cyclones Based on 1970 – 2009 detected events
Landslides (Pr) Based on 1960 – 2000 precipitations
Forest firesBased on 1997 – 2010 detected events
Drought Based on 1960 – 2000 precipitations
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GAR 2013: probabilistic approach ?
Yes No Remarque
Earthquakes Based on GEM
Landslides (Eq) Based on GSHAP 1:475 years
Tsunamis Based on GSHAP 1:475 years
CC.
FloodsBased on 5 different returning periods
Trop. Cyclones Based on synthetic tracks and stochastic approachone global estimation of climate change impacts
Landslides (Pr) Based on 1960 – 2000 precipitations
Forest firesBased on burnt areas 2000 - 2011
Drought Based on FEWS methodology (6 countries)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Hazards: GAR 2015
Yes No Remarque
Earthquakes Based on GEM
Landslides (Eq) Based on GEM
Tsunamis
CC.
FloodsBased on 5 different returning periods
Trop. Cyclones Based on synthetic tracks and stochastic approach
Landslides (Pr) Based on stochastic approach
Forest firesNot yet discussed
Drought Based on FEWS methodology, more countries
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.36
2.4 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.
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.37
Scenarios on TC for 2030
As adapted from Knutson et al. (2010)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Global Flood Model
NEW GLOBAL FLOOD MODEL
5 returning periods
NEW GLOBAL FLOOD MODEL
5 returning periods
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
NEW GLOBAL FLOOD MODEL
5 returning periods
NEW GLOBAL FLOOD MODEL
5 returning periods
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Outputs
GED - GLOBAL EXPOSURE MODELGAR13
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 [USDX106]
1 Low Income S5 $ 496,646
1 Low Income C1 $ 7,449,689
1 Low Income C1L $ 7,449,689
1 Low Income C2 $ 496,646
1 Low Income C2M $ 2,483,230
1 Low Income C3 $ 7,449,689
1 Low Income M2 $ 993,292
1 Low Income UFB3 $14,899,377
1 Low Income UCB $ 4,966,459
1 Low Income UNK $ 2,979,875
1 Middle Income S5 $ 822,740
1 Middle Income C1 $12,341,105
1 Middle Income C1L $12,341,105
1 Middle Income C2 $ 822,740
1 Middle Income C2M $ 4,113,702
1 Middle Income C3 $12,341,105
1 Middle Income M2 $ 1,645,481
1 Middle Income UFB3 $24,682,210
1 Middle Income UCB $ 8,227,403
1 Middle Income UNK $ 4,936,442
1 High Income S5 $ 45,026
1 High Income C1 $ 675,384 Capital stock distribution on a 5x5 km grid: map shows aggregate values for resident buildings.
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GEDGlobal Exposure Model
GAR13Andrea de Bono (GRID) Miguel Mora (CIMNE)
GAR 2013 / 2015
A
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GED Thematic components
GED - GLOBAL EXPOSURE MODELGAR13
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
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Built-environment and urban population
GED - GLOBAL EXPOSURE MODELGAR13
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
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Capital stock estimation
GED - GLOBAL EXPOSURE MODELGAR13
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
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Than youhttp://www.grid.unep.ch/GCV
http://preview.grid.unep.ch
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