Data access and interoperability. GAR and PREVIEW Global Risk Data Platform Generating and sharing risk data Pascal Peduzzi, PhD Gregory Giuliani, PhD Andrea de Bono,
Download ReportTranscript Data access and interoperability. GAR and PREVIEW Global Risk Data Platform Generating and sharing risk data Pascal Peduzzi, PhD Gregory Giuliani, PhD Andrea de Bono,
Slide 1
Data access and interoperability.
GAR and PREVIEW Global Risk Data Platform
Generating and sharing risk data
Pascal Peduzzi, PhD
Gregory Giuliani, PhD
Andrea de Bono, PhD
Christian Herold
Bruno Chatenoux
UNEP / GRID-Geneva
GEO Ministerial and Plenary Meetings – Side Event
13 January 2014
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
1
Slide 2
Presentation plan
Who are we?
Global level risk analysis
The PREVIEW Global Risk Data Platform
GAR 2013: new developments
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
2
Slide 3
Global
Change
& Vulnerability:
a unit of the
Global
Change
& Vulnerability
Unit
UNEP/GRID-Europe
UNEP
DEWA
DEPI
UNEP/
GRID-Geneva
DTIE
DRC
DCPI
Swiss Env. Agency
University of Geneva
Global Change
& Vulnerability
Unit
(ex Early Warning)
Dr P. Peduzzi C.Herold
B. Chatenoux
Dr A. De Bono Dr G.Giuliani
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
DGEF
Slide 4
Global Change & Vulnerability Unit
Spatial analysis (GIS)
Data (SDI)
Statistical analysis
Field data collection
Image analysis
Maps & Info
PREVIEW
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 5
Contribution to 12 UN reports on risk & global change2004
2008
2007
2006
2006
2005
2009
2010
+ 28 Scientific papers
A
G R
2013
2011
2012
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 6
1. Global analysis
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 7
How to generate risk data
Disaster
Vulnerability
Climate
Environment
Natural
variability
DEVELOPMENT
Hazards
Disaster Risk
Management
DISASTER
RISK
Anthropogenic
Changes
Adaptation
Exposure
GHG emissions, deforestation,…
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 8
Who generates the data?
Global Flood Model
UNEP/GRID-Geneva and CIMA Foundation
Global Tropical Cyclones
Probabilistic TC model
Global Landslides
Global Tsunami
Tsunami events
UNEP/GRID-Geneva
CIMNE
Norwegian Geotechnical Institute (NGI)
Norwegian Geotechnical Institute (NGI)
NOAA
Volcanic eruption
Flood events
Earthquakes shakemaps
Forest fires
Drought Model
Smithonian Institute
Dartmouth Flood Observatory (now at Colorado Uni)
USGS
ESA
IRI
Earthquakes
GDP
Population distribution
GSHAP, CIMNE, (GEM coming)
Worldbank
Landscan
Global Exposure Model
UNEP/GRID-Geneva
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
8
Slide 9
New Global Hazard Datasets created for GAR 2009
Tectonic Hazards
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
9
Slide 10
New Human & Economic exposure datasets (1 x 1 km
Population and GDP distribution Models made for every years from 1970 to 2010
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
10
Slide 11
1006 Past floods as detected by satellite sensors
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
11
Slide 12
Compilation of Past Earthquakes ShakeMaps
5686 events downloaded over the period 1973-2007
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
12
Slide 13
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
13
Slide 14
Individual past hazardous events modeling
>6000 tropical cyclones events were processed
Global coverage for the period 1970 to 2012.
Risk and Global Change
Using central pressure
Maximum windspeed
© Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Latitude …
14
Slide 15
Extraction of exposure and
other parameters
Nargis
2 May 2008
Myanmar
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
15
Slide 16
Footprints
Category
GDP exp.
Pop. exp.
Pop.Urb
exp.
GDP Urb.
exp
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
Preview Tropical
Cyclones Database
Country: Myanmar
Iso3: MMR
Date: 02 May 2008
Date
Iso3
Killed: 138,366
Damages: 4,000 US$ millions
GDPcap: 1,227 US$
Voice & acc.: -2.16
Governance efficiency : -1.608
Radio/inhabitant: 99.68%
HDI: 0.592
…
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Urban growth: 2.55%
…
150,000
Vulnerability
EM-DAT,
CRED
Database 43
indicators
Date
Iso3
GDPcap
Killed
Voice
& acc.
Est. damages
Governance efficiency
Radio/inhabitant
HDI
…
Urban growth
16
Slide 17
List of vulnerability parameters considered
43 indicators on:
Economy,
Demography,
Environment,
Development,
Early Warning,
Governance,
Health,
Education,
…
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)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
17
Slide 18
From hazardous events to frequency and
exposure
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
18
Slide 19
Aggregation of human exposure at country level
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
19
Slide 20
Aggregation of economical exposure at country level
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
20
Slide 21
Landslides risk
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
21
Slide 22
Landslides (modelled for both precipitation
and earthquakes)
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
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
22
Slide 23
Tropical Risk
cyclones risk
Multiple
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
23
Slide 24
Multi Mortality Risk Index (MRI)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
24
Slide 25
Earthquakes
Cyclones
Mortality
Mortality
Risk
Risk
Index
Index
(MRI)
(MRI)
Floods
Mortality
Risk
Index
(MRI)
Landslides
Mortality
Risk
Index
(MRI)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
25
Slide 26
3. PREVIEW Global Risk Data Platform
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 27
The Global Risk Data Platform
Used by:
in GEOSS portal
UNEP
UNISDR (For GAR).
World Bank
UNHCR
Inform (EU/JRC)
WRI (UNU)
OCHA
Mapplecroft
And many others
http:// preview.grid.unep.ch
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Users can visualise,
interrogate,
download data
related to disaster
risk (hazard,
exposure, risk).
Slide 28
Fully Open Source
OGC & ISO compliant
Based on:
PostgreSQL/PostGIS,
PHP,
Geoserver,
GeoNetwork,
OpenLayers & GeoExt.
Analysis of geospatial data: ESRI ArcInfo & ArcGIS
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective - 13.01.2014
28
Slide 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
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective - 13.01.2014
29
Slide 30
PreView Mobile
http://preview.grid.unep.ch/mobile
Web-based
Search location:
Zoom IN/OUT, Pan
Multiplatform
GeoNames
Multi-touch control
Access all layers Risk
in and
WMS
GPS
Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective - 13.01.2014
30
Slide 31
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective - 13.01.2014
31
Slide 32
4. GAR 2015: new developments
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 33
GAR 2009-2011 probabilistic approach?
Yes
No
CC.
Remarque
Drought
Based on 1960 – 2000 precipitations
Earthquakes
Based on GSHAP 1:475 years
Floods
Based on 100 years returning period
Trop. Cyclones
Based on 1970 – 2009 detected events
Landslides (Eq)
Based on GSHAP 1:475 years
Landslides (Pr)
Based on 1960 – 2000 precipitations
Tsunamis
Based on GSHAP 1:475 years
Forest fires
Based on 1997 – 2010 detected events
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 34
GAR 2013: probabilistic approach ?
Yes
No
CC.
Remarque
Drought
Based on FEWS methodology (6 countries)
Earthquakes
Based on GEM
Floods
Based on 5 different returning periods
Trop. Cyclones
Based on synthetic tracks and stochastic approach
one global estimation of climate change impacts
Landslides (Eq)
Based on GSHAP 1:475 years
Landslides (Pr)
Based on 1960 – 2000 precipitations
Tsunamis
Based on GSHAP 1:475 years
Forest fires
Based on burnt areas 2000 - 2011
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 35
Hazards: GAR 2015
Yes
No
CC.
Remarque
Drought
Based on FEWS methodology, more countries
Earthquakes
Based on GEM
Floods
Based on 5 different returning periods
Trop. Cyclones
Based on synthetic tracks and stochastic approach
Landslides (Eq)
Based on GEM
Landslides (Pr)
Based on stochastic approach
Tsunamis
Forest fires
Not yet discussed
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 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.
36
Slide 37
Scenarios on TC for 2030
As adapted from Knutson et al. (2010)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
37
Slide 38
Global Flood Model
NEW GLOBAL FLOOD MODEL
5 returning periods
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 39
NEW GLOBAL FLOOD MODEL
5 returning periods
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 40
GED - GLOBAL EXPOSURE MODEL
GAR13
Outputs
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
1
Low Income
S5
$
1
Low Income
C1
$ 7,449,689
1
Low Income
C1L
$ 7,449,689
1
Low Income
C2
$
1
Low Income
C2M
$ 2,483,230
1
Low Income
C3
$ 7,449,689
1
Low Income
M2
$
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
$
1
Middle Income
C1
$12,341,105
1
Middle Income
C1L
$12,341,105
1
Middle Income
C2
$
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
VALFIS
[USDX106]
496,646
496,646
993,292
822,740
822,740
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.
Slide 41
A
GAR 2013 / 2015
GED
Global Exposure Model
GAR13
Andrea de Bono (GRID) Miguel Mora (CIMNE)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 42
GED - GLOBAL EXPOSURE MODEL
GAR13
GED Thematic components
Built-environment
Demographic
Socioeconomic
People living in urban areas
Income, employment, health, education
Building type
Building structure class
(WAPMERR)
Assets value
Produced capital and urban land
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 43
GED - GLOBAL EXPOSURE MODEL
GAR13
Built-environment and urban population
Built-environment
1
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
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 44
GED - GLOBAL EXPOSURE MODEL
GAR13
Capital stock estimation
We use the World Bank’s “comprehensive wealth” methodology*.
•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.
* World Bank (2011). The changing wealth of nations :
measuring sustainable development in the new millennium
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.
GAR 2013
Slide 45
Than you
http://www.grid.unep.ch/GCV
http://preview.grid.unep.ch
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Data access and interoperability.
GAR and PREVIEW Global Risk Data Platform
Generating and sharing risk data
Pascal Peduzzi, PhD
Gregory Giuliani, PhD
Andrea de Bono, PhD
Christian Herold
Bruno Chatenoux
UNEP / GRID-Geneva
GEO Ministerial and Plenary Meetings – Side Event
13 January 2014
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
1
Slide 2
Presentation plan
Who are we?
Global level risk analysis
The PREVIEW Global Risk Data Platform
GAR 2013: new developments
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
2
Slide 3
Global
Change
& Vulnerability:
a unit of the
Global
Change
& Vulnerability
Unit
UNEP/GRID-Europe
UNEP
DEWA
DEPI
UNEP/
GRID-Geneva
DTIE
DRC
DCPI
Swiss Env. Agency
University of Geneva
Global Change
& Vulnerability
Unit
(ex Early Warning)
Dr P. Peduzzi C.Herold
B. Chatenoux
Dr A. De Bono Dr G.Giuliani
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
DGEF
Slide 4
Global Change & Vulnerability Unit
Spatial analysis (GIS)
Data (SDI)
Statistical analysis
Field data collection
Image analysis
Maps & Info
PREVIEW
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 5
Contribution to 12 UN reports on risk & global change2004
2008
2007
2006
2006
2005
2009
2010
+ 28 Scientific papers
A
G R
2013
2011
2012
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 6
1. Global analysis
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 7
How to generate risk data
Disaster
Vulnerability
Climate
Environment
Natural
variability
DEVELOPMENT
Hazards
Disaster Risk
Management
DISASTER
RISK
Anthropogenic
Changes
Adaptation
Exposure
GHG emissions, deforestation,…
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 8
Who generates the data?
Global Flood Model
UNEP/GRID-Geneva and CIMA Foundation
Global Tropical Cyclones
Probabilistic TC model
Global Landslides
Global Tsunami
Tsunami events
UNEP/GRID-Geneva
CIMNE
Norwegian Geotechnical Institute (NGI)
Norwegian Geotechnical Institute (NGI)
NOAA
Volcanic eruption
Flood events
Earthquakes shakemaps
Forest fires
Drought Model
Smithonian Institute
Dartmouth Flood Observatory (now at Colorado Uni)
USGS
ESA
IRI
Earthquakes
GDP
Population distribution
GSHAP, CIMNE, (GEM coming)
Worldbank
Landscan
Global Exposure Model
UNEP/GRID-Geneva
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
8
Slide 9
New Global Hazard Datasets created for GAR 2009
Tectonic Hazards
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
9
Slide 10
New Human & Economic exposure datasets (1 x 1 km
Population and GDP distribution Models made for every years from 1970 to 2010
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
10
Slide 11
1006 Past floods as detected by satellite sensors
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
11
Slide 12
Compilation of Past Earthquakes ShakeMaps
5686 events downloaded over the period 1973-2007
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
12
Slide 13
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
13
Slide 14
Individual past hazardous events modeling
>6000 tropical cyclones events were processed
Global coverage for the period 1970 to 2012.
Risk and Global Change
Using central pressure
Maximum windspeed
© Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Latitude …
14
Slide 15
Extraction of exposure and
other parameters
Nargis
2 May 2008
Myanmar
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
15
Slide 16
Footprints
Category
GDP exp.
Pop. exp.
Pop.Urb
exp.
GDP Urb.
exp
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
Preview Tropical
Cyclones Database
Country: Myanmar
Iso3: MMR
Date: 02 May 2008
Date
Iso3
Killed: 138,366
Damages: 4,000 US$ millions
GDPcap: 1,227 US$
Voice & acc.: -2.16
Governance efficiency : -1.608
Radio/inhabitant: 99.68%
HDI: 0.592
…
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Urban growth: 2.55%
…
150,000
Vulnerability
EM-DAT,
CRED
Database 43
indicators
Date
Iso3
GDPcap
Killed
Voice
& acc.
Est. damages
Governance efficiency
Radio/inhabitant
HDI
…
Urban growth
16
Slide 17
List of vulnerability parameters considered
43 indicators on:
Economy,
Demography,
Environment,
Development,
Early Warning,
Governance,
Health,
Education,
…
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)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
17
Slide 18
From hazardous events to frequency and
exposure
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
18
Slide 19
Aggregation of human exposure at country level
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
19
Slide 20
Aggregation of economical exposure at country level
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
20
Slide 21
Landslides risk
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
21
Slide 22
Landslides (modelled for both precipitation
and earthquakes)
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
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
22
Slide 23
Tropical Risk
cyclones risk
Multiple
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
23
Slide 24
Multi Mortality Risk Index (MRI)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
24
Slide 25
Earthquakes
Cyclones
Mortality
Mortality
Risk
Risk
Index
Index
(MRI)
(MRI)
Floods
Mortality
Risk
Index
(MRI)
Landslides
Mortality
Risk
Index
(MRI)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
25
Slide 26
3. PREVIEW Global Risk Data Platform
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 27
The Global Risk Data Platform
Used by:
in GEOSS portal
UNEP
UNISDR (For GAR).
World Bank
UNHCR
Inform (EU/JRC)
WRI (UNU)
OCHA
Mapplecroft
And many others
http:// preview.grid.unep.ch
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Users can visualise,
interrogate,
download data
related to disaster
risk (hazard,
exposure, risk).
Slide 28
Fully Open Source
OGC & ISO compliant
Based on:
PostgreSQL/PostGIS,
PHP,
Geoserver,
GeoNetwork,
OpenLayers & GeoExt.
Analysis of geospatial data: ESRI ArcInfo & ArcGIS
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective - 13.01.2014
28
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~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
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective - 13.01.2014
29
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PreView Mobile
http://preview.grid.unep.ch/mobile
Web-based
Search location:
Zoom IN/OUT, Pan
Multiplatform
GeoNames
Multi-touch control
Access all layers Risk
in and
WMS
GPS
Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective - 13.01.2014
30
Slide 31
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
GEO-X: Disasters Risk Reduction and Earth Observations, a GEO perspective - 13.01.2014
31
Slide 32
4. GAR 2015: new developments
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 33
GAR 2009-2011 probabilistic approach?
Yes
No
CC.
Remarque
Drought
Based on 1960 – 2000 precipitations
Earthquakes
Based on GSHAP 1:475 years
Floods
Based on 100 years returning period
Trop. Cyclones
Based on 1970 – 2009 detected events
Landslides (Eq)
Based on GSHAP 1:475 years
Landslides (Pr)
Based on 1960 – 2000 precipitations
Tsunamis
Based on GSHAP 1:475 years
Forest fires
Based on 1997 – 2010 detected events
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 34
GAR 2013: probabilistic approach ?
Yes
No
CC.
Remarque
Drought
Based on FEWS methodology (6 countries)
Earthquakes
Based on GEM
Floods
Based on 5 different returning periods
Trop. Cyclones
Based on synthetic tracks and stochastic approach
one global estimation of climate change impacts
Landslides (Eq)
Based on GSHAP 1:475 years
Landslides (Pr)
Based on 1960 – 2000 precipitations
Tsunamis
Based on GSHAP 1:475 years
Forest fires
Based on burnt areas 2000 - 2011
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 35
Hazards: GAR 2015
Yes
No
CC.
Remarque
Drought
Based on FEWS methodology, more countries
Earthquakes
Based on GEM
Floods
Based on 5 different returning periods
Trop. Cyclones
Based on synthetic tracks and stochastic approach
Landslides (Eq)
Based on GEM
Landslides (Pr)
Based on stochastic approach
Tsunamis
Forest fires
Not yet discussed
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 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.
36
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Scenarios on TC for 2030
As adapted from Knutson et al. (2010)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
37
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Global Flood Model
NEW GLOBAL FLOOD MODEL
5 returning periods
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 39
NEW GLOBAL FLOOD MODEL
5 returning periods
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 40
GED - GLOBAL EXPOSURE MODEL
GAR13
Outputs
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
1
Low Income
S5
$
1
Low Income
C1
$ 7,449,689
1
Low Income
C1L
$ 7,449,689
1
Low Income
C2
$
1
Low Income
C2M
$ 2,483,230
1
Low Income
C3
$ 7,449,689
1
Low Income
M2
$
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
$
1
Middle Income
C1
$12,341,105
1
Middle Income
C1L
$12,341,105
1
Middle Income
C2
$
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
VALFIS
[USDX106]
496,646
496,646
993,292
822,740
822,740
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.
Slide 41
A
GAR 2013 / 2015
GED
Global Exposure Model
GAR13
Andrea de Bono (GRID) Miguel Mora (CIMNE)
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 42
GED - GLOBAL EXPOSURE MODEL
GAR13
GED Thematic components
Built-environment
Demographic
Socioeconomic
People living in urban areas
Income, employment, health, education
Building type
Building structure class
(WAPMERR)
Assets value
Produced capital and urban land
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 43
GED - GLOBAL EXPOSURE MODEL
GAR13
Built-environment and urban population
Built-environment
1
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
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.
Slide 44
GED - GLOBAL EXPOSURE MODEL
GAR13
Capital stock estimation
We use the World Bank’s “comprehensive wealth” methodology*.
•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.
* World Bank (2011). The changing wealth of nations :
measuring sustainable development in the new millennium
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.
GAR 2013
Slide 45
Than you
http://www.grid.unep.ch/GCV
http://preview.grid.unep.ch
Risk and Global Change © Pascal Peduzzi, UNEP/GRID-Geneva, 2014.