2011 iscram summer school tom de groeve

1ISCRAM Summer School 2011

Developing disaster alert and impact systems

Lecture at the ISCRAM Summer School19 August 2011

Tom De Groeve

Joint Research Centre of the European Commission


Red earthquake alert

SMS Fax Voice Email

6156 286 148 14062

6

Global Disaster Alert and Coordination System (GDACS)

• GDACS: system for international disaster response community– Information gap in the initial

response phase Monitoring Impact / risk analysis Information integration

– 15000 active users of 212 countries– Secretariat: OCHA

– Open access, standards OGC, RSS GLIDE number

• JRC’s role: alert and monitoring system– Earthquakes and tsunamis

13 scientific partners Tsunami modelling Impact modelling

– Tropical cyclones 2 scientific partners Wind modelling Impact modelling

– Floods 16 scientific partners Detection Impact modelling

– Extra-tropical windstorms

– Volcanoes

Expert meeting on Early Warning Systems, 28 April 2011

7

Japan tsunami

• 20 minutes: Orange tsunami alert (2.1m waves, M7.9)• 42 minutes: Red tsunami alert (8.6m waves, M8.8)• Alerts sent to 15000 users

– Only global system sending alerts based on tsunami wave heights• Later, JRC released several manual reports on the risk in Japan and

the Pacific



Lecture overview

Developing disaster alert and impact systems– Introduction

Humanitarian assistance, response and GDACS

– Natural hazards Basic physics

– Consequence analysis GIS data and models

– Community Remote Sensing Use of Social media


Joint Research Centre of the European Commission

IRMM – Geel, Belgium- Institute for Reference Materials and Measurements

IE – Petten, The Netherlands- Institute for Energy

ITU – Karlsruhe, Germany - Institute for Transuranium elements

IPSC - IHCP - IES – Ispra, Italy - Institute for the Protection and the Security of the Citizen - Institute for Health and Consumer Protection - Institute for Environment and Sustainability

IPTS – Seville, Spain- Institute for Prospective Technological Studies

7 InstitutesRELEX ECHO ENV JRC...

13

Global Security and Crisis Management

• Global Security and Crisis Management Unit– Preparedness– Response– Recovery– Prevention and risk reduction

• Technology– Text mining (open source

intelligence)– Image mining (remote sensing)– Data mining (statistics)

– Physical / epidemic modelling– System integration (GIS, ICT)

Humanitarian & Disaster Response Technologies, Cape Town, 17 Sept 2010

14

International Emergency Management

Natural disasters, pandemics, conflict

Geospatial technologies

(remote sensing)

Text mining, Visual analytics

Crisis Room Processes

and Technology

Partner organisations– European Union– United Nations

– World Bank– African Union

Social Networking Technologies for Emergency Management, October 27, 2010, Washington

International Humanitarian and

Emergency Response


Global Disaster Alert and Coordination System

for more effective and efficient humanitarian response

www. .org

an example of a disaster alert and impact system for international

humanitarian assistance


International humanitarian aid

• A complex system with many stakeholders– No “Command and Control

Centre”

• Help is based on scarce information on the disaster– What, when, how, who?

• Decisions must be made very quickly (within 72h)

Humanitarian Aid Flow

Victims

DonorsECHO, etc.

LocalGovernment

UNWFP,HCR…

Local NGOs

Charity

Int. NGOsIFRC, MsF

Coo

rdin

atio

n: U

N O

CHA


EvacuationSearch and rescue

EvacuationSearch and rescue


Refugee managementRefugee camp, Lukole, Tanzania

Refugee managementRefugee camp, Lukole, Tanzania


Inefficiencies in humanitarian response

• Monitoring disasters– 24/7 monitoring capacity is expensive– Many heterogeneous sources of natural hazard

monitoring hard to keep up to date• Response can be delayed because

– Not alerted / monitored– Affected government does not appeal– Not sure if others respond

• Size and type of response must be needs driven (Madrid Declaration 1995)– Size of disaster can be under/overestimated– Information on needs can be incomplete, vague,

lacking

What are the needs??

What is the damage??

Who will respond?

?

Is it a disaster??

How many

people??

What is offered??

time

What is needed now??


Needs-driven response: what are the needs?

• OCHA Cluster approach*– Camp Coordination and

Camp Management– Logistics– Early Recovery– Emergency

Telecommunications

– Emergency Shelter– Health– Nutrition– Protection– Water, Sanitation and

Hygiene

Information needs for responders

Relief needs for affected population(with information need, e.g. affected population)

* OCHA, 2006. Appeal for improving Humanitarian Response Capacity: Cluster 2006


Sources of situational information

1. Early warning and alert systems– Timely knowledge about the occurrence of a natural hazard– Geophysical, meteorological measurement systems

2. Automated consequence analysis– Modelling the likely impact

3. Social media– Timely source, not always reliable– Very hard to turn into useful information

4. International media – Rich source, very timely– but not always true and complete



5. Office for Coordination of Humanitarian Affairs (UN-OCHA): – Mandate to coordinate humanitarian response– Sends disaster assessment and coordination (UNDAC) teams, search

and rescue teams (through the INSARAG network) – Sets up an On Site Operations Coordination Centre (OSOCC),

humanitarian information centres (HIC) – Disseminates all information through ReliefWeb

6. Local government, with its local emergency management authority (LEMA):

– Main source for official information on the scale of the disaster



1. Alert systems

2. CAT

3. Social Media

4. Media

5. UN-OCHA

6. LEMA Reliability

Timeliness


Information needs versus sources

Early warning or alertAutomated consequence analysisMediaOCHALEMA

Situation

X Source contains information for need


Information needs versus sources

Need clusters

X Source contains information for need


Role of information systems

• Early warning or alert systems• Automated consequence

analysis – GIS based analysis, real-time

or based on scenarios• Media

– Automated intelligent monitoring

• OSOCC / LEMA– Web based “Virtual” OSOCC– Web Portal technology

Addressed (partially) by GDACS



a EU and UN initiative

• European Union– Humanitarian aid 2004Member states: € 867 millionEuropean Commission € 570m– 53% of official dev. Aid (ODA)

• Joint Research Centre

• United Nations Office for Coordination of Humanitarian Affairs


Sharing a global system for alerting and coordination?

– Global

– Disaster

– Alert

– Coordination

– System

– Is interested in disasters anywhere on Earth

– Intervenes if local authorities cannot cope

– Is not a homogeneous community: big players and small players

not all have similar information gathering capacity

– Is not coordinated on all levels: funding, deployment, reporting…

– Does not collect information systematically

• International humanitarian aid community



• GDACS provides a systematic approach for– Predictable information of– Predictable quality at– Predictable time

• Through– A network of computer systems and

Internet technology; Computer modeling Mainly task of JRC

– A network of disaster managers 24/7 duty; connected to authorities Mainly task of OCHA

Media analysis

Remote Sensing damage analysis

Field Missions (Search

& Rescue)

Alert

Model

results

time


Objective: “What are the latest disasters?”


Objective: “Is an event of humanitarian concern?”

• The objective is to distinguish between – large earthquake in

unpopulated or resilient regions

– smaller earthquake in highly populated and vulnerable regions

M 6.7

M 6.0


GDACS automatic and manual event analysis

DisasterLevel II Alert

DisasterLevel I Alert

Start of coordi-nation

Event Alerts

TsunamiWarningNetworks

FloodWatch

Networks

EarthquakeObservation

Networks

Trop. CycloneObservation

Networks

Automatic Evaluation of scale of disaster

Geographical,Socio-economic, population data

Alert

Manual Evaluation of scale of disaster

Coordination

Eye witness and Field information

Gov, IFRC, ECHO, NGO

Automatic information collection


Disaster alert: systematic impact analysis

• Event magnitude and affected area

• Collected from specialized sources through Internet technology

• Modelled if required

• People and vulnerability

• Critical infrastructure

Nuclear plantsnear New Orleans

http://dma.jrc.it/services/dmaexplorer/?Box=70:32.92:76:36.92


http://dma.jrc.it/services/dmaexplorer?mapservice=dma_infrastructure&box=-91:29.5:-90:30.5


Disaster alert

• Automatic monitoring:– Earthquake, Tsunami,

Cyclone, Floods, Volcanoes• Automatic

– GIS consequence analysis– Classification:

• Alerting system– SMS, Fax, Email


Critical infrastructure: e.g. tropical cyclones

• Bottleneck: global databases– Now: Roads, airports, ports, nuclear plants, hydrodams– Near future: industrial plants (to some extent)

Collaboration with Joint UNEP/OCHA team on environmental risk


Consequence analysis: e.g. earthquakes

• Where?– Circle of 100km

• Affected people?– Sum up pixel values inside

affected area– Weight with indicators for

vulnerability and resilience• Damage? Secondary effects?

– List “critical infrastructure” in affected area

• Fast alerting is very important for earthquakes



Consequence analysis: e.g. tsunamis

• When?– Together with earthquakes– Tsunami propagation model

• Where?– Coastal areas, low elevation,

cities near coast• Affected people?

– Sum up pixel values inside affected area: timing


GDACS: timeline

• Near real-time– Event scraping: delay of ~20 min– Consequence analysis: max 5

min– Alerting (email, fax, SMS): 1500

SMS / 3 min– Web site: maps, analysis,

Google Earth

• Started upon event and ongoing– Model runs (e.g. tsunami wave

height model)– Media monitoring– Map creation / collection

• Situation and field information sharing– Virtual OSOCC portal

Information scraping

Virtual OSOCCAlert & CA

1h 1day 1week


GDACS Media monitoring

European Media Monitor

• Automatic collection of news from over 1000 on-line media sources– Fully Multilingual:

الن إلطالق أحاديا وقفا تعلن باكستانكشمير في ار

• Query interface: – “Show me news with the words

‘earthquake’ and ‘Iran’ from after the earthquake date”

• GDACS, for each disaster– automatically creates a query – keeps this updated for 3 weeks

http://emm.jrc.org/Archive/displayArticle?articleID=4316764

http://emm.jrc.org/Archive/displayArticle?articleID=4316764

http://emm.jrc.org/


GDACS disaster mapping

UNOSAT and JRC

• Maps from many organisations are catalogued automatically• GDACS users can request a new map (UNOSAT service)


GDACS Virtual OSOCCCoordination and information sharing

• Chat room: “what’s happening?”, “who’s going?”

• Structured information– Teams– Team status (monitoring,

deployed, mobilising…)– UNDAC reports– Relief items (in kind, pledges)

• Content moderation

• Started around 2000 and is now part of GDACS

• ~12000 professional users• Closed site with registration

– Trusted information– Trust in members

• Routinely used by many LEMA’s and Donor countries

• GDACS antenna offices in Tunesia, Fiji…


Evaluation of GDACS

• Overall– More effective or efficient

process?

• Outcomes– Usage statistics– Number of partnerships

• Components– Alert component: rate of

missed and false alerts


Usage

• Around 15000 users– Mostly from

international aid organisations• Donors / governments• OCHA• INGOs• Red Cross / Red Crescent

NGO’s– Some from

Local emergency management agencies / citizens

Media Insurance & commercial

companies

commercial

Travel, general interest


Usage

• Users by geographical area

Europe32%

Asia13%

North America10%

United Nations10%

5%

Unknown7%

Other37%

European Commission

16%

European Parliament

0%Africa

1%

Oceania3%

Middle East1%

Latin America2%


Alert component

• Missed events

• Missed aid $– EQ: 0.02%– TC: 50%– VO: 0%


Partnerships

• Early warning and alert– USGS, EMSC, WAPMERR,

GEOFON…– Hawaii University, Pacific

Disaster Centre– Dartmouth Flood Observatory– WFP (HEWSWeb)– PTWC– Global Volcanism Program– SWVRC/IntlVRC– IFA/SOLAR– Tropical Storm Risk

• Alert communication– UMTS (Norway)

• Information– Maps: JRC, UNOSAT– Joint UNEP/OCHA PPER:

environmental impact reports– USGS Shakemaps

• And many for the Virtual OSOCC…


Conclusions

• For needs-based response, situational and other information is critical, in particular in the first 72h

• Various information systems can address large parts of the information needs in the early onset of a disaster

• GDACS was a UN/EU initiative to build such a system and is running successfully– Standards based– Community based (professionals, including LEMAs)

http://www.gdacs.org


Introduction to Natural hazards and disasters

Earthquakes, tsunamis, volcanoes, tropical cyclones, floods

51

Existing hazardmonitoring systems


Data collection Global aggregation

Standard dissemination

Affected area estimation

Impact estimation

Volcano eruptions ?

some partial

Earthquakes and tsunamis

mature mature?

multiple Mature (EQ) Mature (EQ)

Floods

developing

Tropical cyclones

mature mature lacking

Extra-tropical storms

(Europe)existing

?existing lacking

52

JRC’s role in GDACS Bridging gaps


Data collection Global aggregation

Standard dissemination

Affected area estimation

Impact estimation

Volcano eruptions ?

some partial

Earthquakes and tsunamis

mature mature?

multiple mature (EQ) mature (EQ)

Floods

developing

Tropical cyclones

mature mature lacking

Extra-tropical storms

(Europe)existing

?existing lacking


Natural disasters cannot be avoided

• Over 800 disasters affect near 300 million people yearly and kill hundreds of thousands

Source: EM-DAT Emergency Disasters Data Base, www.em-dat.net

http://www.em-dat.net/documents/Maps/EM-DAT-naturaldisastereventsbycountry1974-2003.jpg


More people are affected by disasters each year

But they are not distributed equally

Poor countries are affected more

Source: EM-DAT Emergency Disasters Data Base, www.em-dat.net

http://www.em-dat.net/documents/Maps/EM-DAT-naturaldisastervictimsperinhabitants1974-2003.jpg


Earthquakes


Earthquake mechanism

• Plate tectonics– Relative motion of plates

• Terminology Hypocentre and epicentre

(on surface) Magnitude: logarithmic

measure of energy Intensity: energy on surface

at given distance from epicentre

http://upload.wikimedia.org/wikipedia/en/0/0e/Fault_types.png

http://upload.wikimedia.org/wikipedia/en/1/1f/Oceanic-continental_convergence_Fig21oceancont.gif


Earthquake mechanism

• Energy propagates– P and S waves– Attenuation functions

Depends on local geology

• Energy shakes buildings– Earthquake engineering– Vulnerability curves


Earthquake occurrence

• Earthquakes, each year– 500 000 detectable– 100 000 can be felt– 100 cause damage

http://upload.wikimedia.org/wikipedia/en/d/db/Quake_epicenters_1963-98.png


Earthquake effects

• Shaking and ground rupture– damage to buildings or other

rigid structures. – Site or local amplification

(Mexico City effect): transfer of the seismic

motion from hard deep soils to soft superficial soils

• Landslides and avalanches

• Soil liquefaction – water-saturated granular

material temporally loses their strength and transforms from a solid to a liquid

– buildings or bridges tilt or sink into the liquefied deposits

• Tsunamis• Fires

– break of the electrical power or gas lines


Earthquake data

• Occurrence– Near real time (<15 min)– Location and magnitude, with

uncertainty

– USGS NEIC (US)– EMSC (Europe)– GEOFON (Germany)– JMA (Japan)– …

• Propagation– Shakemaps (USGS)– ESRC (Russia)

• Missing datasets– Building stock

Location, number, type of buildings

– Localized attenuation functions


Tropical cyclones

http://upload.wikimedia.org/wikipedia/commons/8/89/Cyclone_Catarina_from_the_ISS_on_March_26_2004.JPG


Tropical cyclone mechanism

• Mechanism– energy released by the

condensation of moisture in rising air causes a positive feedback loop over warm ocean waters

• Movement– Steering winds; Coriolis effect

• Horizontal wind speed profile

24

22max

max RffRePP

R

RbRV

b

R

R

centreenv

b

h

http://upload.wikimedia.org/wikipedia/commons/a/a9/Hurricane_structure_graphic.jpg

http://en.wikipedia.org/wiki/Image:Hurricane_profile.svg

http://upload.wikimedia.org/wikipedia/commons/9/95/Ioke_2006_track.png


Tropical cyclone occurrence

1985-2005

http://upload.wikimedia.org/wikipedia/commons/2/23/Global_tropical_cyclone_tracks-edit2.jpg


Tropical cyclone effects

• High winds– people, mobile homes, unsound

substandard structures• Storm surge

– Abnormal rise in the water level caused by the wind and pressure forces

– 90% of death• Heavy rain

– Thunderstorm activity intense rainfall

– Rivers and streams flood, roads become blocked, and landslides can occur

• Tornado activity


Tropical cyclone data

• World Meteorological Organisation– Regional Specialized

Meteorological Centres Official advisories severe.worldweather.org/

rsmcs.html• Compilations at global level

– Pacific Disaster Center (Hawaii)– MetHaz of the University of Central

Florida (based on commercial data product)

– Tropical Storm Risk (http://tropicalstormrisk.com)

forecasting the risk modelled wind fields and

rainfall.

• Modelling data– Wind field equation

location central pressure lacking

– Storm surge Detailed coastal DEM lacking

• Rainfall– Available from radar observations

(e.g. TRMM)


Volcanic eruptions


Volcanic eruptions: mechanism

• A volcano is an opening in the Earth's surface

12: lava flow 15: ash cloud

• Plate tectonics

http://en.wikipedia.org/wiki/Image:Volcano_scheme.svg


Types of volcanoes

http://en.wikipedia.org/wiki/Image:Aa_channel_flow_from_Mauna_Loa.jpg

http://en.wikipedia.org/wiki/Image:FujiSunriseKawaguchiko2025WP.jpg

http://en.wikipedia.org/wiki/Image:Sunset_Crater10a.jpg


Volcanic eruptions: occurrence

• How many active volcanoes known? – Erupting now: perhaps 20 – Each year: 50-70 – Each decade: about 160 – Historical eruptions: about 550 – Known Holocene eruptions

(last 10,000 years): about 1300 – Known (and possible)

Holocene eruptions: about 1500


Volcanic eruptions: effects

• The different types of ("primary") eruptive events are:– Pyroclastic explosions– Hot ash releases– Lava flows– Gas emissions– Glowing avalanches (gas and ash

releases)• Secondary events are

– Melting ice, snow and rain accompanying eruptions are likely to provoke floods and hot mudflows (or lahars);

– Hot ash releases can start fires.

• Factors of Vulnerability– Topographic factors; – The proximity of a population to

the volcano; – Structures with roof not resistant to

ashes accumulations; – The lack of warning system and

evacuation plans

http://en.wikipedia.org/wiki/Image:Vesuvius1822scrope.jpg

http://en.wikipedia.org/wiki/Image:Stromboli_Eruption.jpg


Volcanic eruptions: data

• Global Volcanism Program– Smithsonian Institute– Weekly bulletins

• Local volcano observatories

• Volcano Ash Advisories (VAAC)

• Modelling– Local data needed

– Volcano types– Eruption types:

http://volcano.und.edu/vwdocs/vwlessons/kinds/kinds.html


Tsunamis


Tsunamis mechanism

• Tsunamis are giant sea waves that are produced by submarine earthquake or slope collapse into the seabed.

• Tsunamis can travel thousands of kilometers at 500-800km/h with very little loss of energy.

• Successive crests can arrive at intervals of every 10 to 45 minutes and wreak destruction for several hours.


Tsunami mechanism

dgv

• Uplift of continental crust– Length of rupture: increases

with higher magnitude– Initial height: proportional to

length of rupture

• Shallow water


Tsunami wave propagation (SWAN code)

0

y

UHD

x

UHD

t

H yx

)(

)(

1

1

yx

yy

xx

y

xy

yy

xx

x

Ay

P

y

HgFU

y

UU

x

UU

t

U

Ax

P

x

HgFU

y

UU

x

UU

t

U

Mass conservation equation

Momentum conservation equations

Unknowns are H, Ux, Uy

The programme solves the equations in explicit form with a fixed time step, which depends on the size assumed for the bathymetry

Swan code by C. Mader used as basis


Tsunami occurrence and effect

• Continental coasts • Shallow water – Slows down wave– Amplifies wave height

http://www.ngdc.noaa.gov/seg/hazard/tsu.shtml


Tsunami data

• Real time– UNESCO/IOC– Pacific Tsunami Warning

Center (PTWC, US)– JMA (Japan)

• Relies on seismological data

• Historical– National Geophysical Data

Center, NOAA, US

• Modelling– Bathymetry

Rough: ok Detailed: not

– Run up: DEM needed


Floods


Flood mechanism

• Principle– Hydrology / hydraulics– Modelling is data intensive


Flood mechanism

• Types– Flash floods– River floods (mostly seasonal)– Coastal floods, associated with

tropical cyclones, tsunami, storm surges


Flood occurrence

• Floods cause major human suffering– 78% of population affected by

disasters– 46% of disasters are floods

• International aid for floods– 1/3 of all humanitarian aid– 93% of flood deaths in Asia

Figures from EM-DAT, OCHA, ECHO

http://www.em-dat.net/documents/Maps/EM-DAT-floodsbycountry1974-2003.jpg


Flood effects

• Direct effects: – Drowning– Injuries during evacuation

• Indirect effects:– Agriculture: loss of crops– Destruction of transport and

energy infrastructure– Contamination by toxic

chemicals

• Factors of Vulnerabilities– Location of settlements on

floodplains– Non resistant buildings and

foundations– Lack of warning system and

awareness of flooding hazard – Land with little capacity of

absorbing rain erosion due to deforestation concrete covering


Flood data

• Real time– Hydrographs– Met Offices

– Media– Dartmouth Flood Observatory– Satellite based…

• Historical– Dartmouth Flood Observatory– Disaster databases

• Modelling– Detailed DEM– Real time weather data


Conclusions

• Mechanisms of natural hazards are well known

• Occurrence of natural hazards– Geographical patterns– Random occurrence

• Data on natural hazards– Some data about occurrence

and location of disaster is available in near-real time

– Not all data needed for modelling hazard is available

• Effects of natural hazards on society depend on– Hazard– Affected area– Vulnerabilities

• Consequence analysis must take these into account– Limited by global data

availability


Consequence analysis

Near real time GIS for disaster management


GIS

• GIS = Geographic information system (or science)– Mapping


GIS

– Handling, storing geospatial data Coordinate in 2D or 3D space

special database techniques Spatial Reference System projection Imagery large volumes of data Most (>80%) data has geospatial component

– Manipulating, querying geospatial data Nearby point, line, polygon “In” area, “intersecting” with line Raster statistics sum of population in pixels


GIS systems: network enabled

• Web mapping• Web querying• Web processing

– Routing– Nearest objects– GIS Model

My system


GIS for disaster management

• Disaster management– Typical questions in early onset

Where? What is affected? Who is affected? How many people? How do we get there? What response capacity is nearby? Get me a map. Get me a BIG map! I need information for my briefing: SMALL maps!

– Detailed geospatial information is required Street level base data in Europe; less for Global Application specific data: transport, energy, health, vulnerability

Generating stationsSubstationsPower lines


Global datasets

• Population– Raster, 1km

• Digital Elevation– Raster, 90m

• Bathymetry– Raster, 2 arcmin (~3.6km)

• Topography– Vector, 1km– VMAP0, Global Discovery…– Roads, railways, rivers, populated

places, airports, mountains…• Land cover, land use

• Satellite coverage• Meteorological

– Clouds– Rainfall, winds…

• Not available– Hospitals, medical infrastructure– Energy infrastructure, industrial

plants– Critical roads, bridges– Detailed DEM (for flood, tsunami

modelling)– Building stock, urban areas


Why automating tasks?

• Disasters happen always at night, in the weekend or on Christmas

• It is always the same work– In early stages all crises have similar requirements

• Computers can pre-calculate things or make things according to a template– And they work faster than humans

• Automated things have limitations– Cannot handle unforeseen cases– Can break down over the weekend


Earthquakes

• Where?– Circle where ground motion

longer than 1 second– Circles with varying radius


affected area– Weight with indicators for

vulnerability and resilience• Damage? Secondary effects?


• Fast alerting is very important for earthquakes



Tsunamis

• When?– Together with earthquakes– Tsunami propagation model– Tsunami wave height model

• Where?– Coastal areas, low elevation


affected area, with timing• Damage? Secondary effects?


• Animation


Tsunamis

Play


Tsunamis

Time (min)

Seismic event

Event notification

0 +30

EWS detection

Quick analysis and reports (propagation time)

1’

Max 30’

First analysis (height and population affected)

Max 1h

More detailed analysis (run-up calculations) are not of our interest at the moment


Tropical cyclones

• Where?– Track, including forecast– Buffers for Saffir-Simpson

categories (wind speed)


affected area: past, future• Damage? Secondary effects?


• Early warning is possible• Animation

24

22max

max RffRePP

R

RbRV

b

R

R

centreenv

b

h

http://dma.jrc.it/services/dmaexplorer/?MapService=dma_weather&AddOn=EQ&labelText=WILMA%20Storm%20Eye&Box=-90:22.8:-50:46.8&Label=true

http://dma.jrc.it/services/dmaexplorer?mapservice=dma_infrastructure&box=-91:29.5:-90:30.5


Tropical cyclones

• Impact– List critical infrastructure– Population

• Risk (probabilities)


Volcanoes

• When?– Significant change in eruption

status• Where? Affected people?

– Can be pre-calculated– Real time ash cloud information

• Damage? Secondary effects?– List “critical infrastructure” in

affected area• Future

– Imagery…

http://earthobservatory.nasa.gov/NaturalHazards/Archive/Oct2005/galapagos_tmo_2005296_lrg.jpg


Volcanoes

• Ash plumes


Conclusions

• GIS can store and manipulate information useful for disaster management

• GIS is a good basis to implement models to calculate or infer information for disaster management

• Standards are essential for distributed systems– OGC, GLIDE, CAP, RSS

• Real time models depend on– Real time input data

Accuracy, timeliness– Available background data

Precision, Fit-for-use– Processing time

Distributed systems– Operational systems

Redundancy, resilience


System design

Operational alerting systems


Automating GIS

Scraper

Alerter

Models DMA

Queuer

ReporterWeb site

Input Output

GIS Analysis


In reality more complex

DMA: Spatial Data Infrastructure

TsunamiSWAN

Servers

SMS Server

Email Server

Fax Server

AsgardLite

GDACS

Development

SMS Server

Monitor


Alerting

• Technology– SMS

Individual messages (rate 10/sec)

Cell broadcast– Email– Fax– RSS, web

• Authority to Authority– Reliable– Training assumed; content can

be difficult

• Authority to Population– Reliable– Culture bound– Trust, authority, source


Operational system

• Reliable: – stable servers, not for development

• Monitoring– When is something down– Action plan to recover

• Redundancy: – copy of system and automatic switch


Developing disaster alert and impact systems

Conclusions


Conclusions

• Disaster alert and impact systems are a combination of– Hazard science

Geophysics Meteorology

– Modelling GIS models Physical models Mathematical models

– GIS Spatial data infrastructure Data collection

– Disaster management Requirements analysis Reporting

– Communication technology Alerting Web systems

– Operational systems Monitoring and recovery Maintenance


Some links

• http://www.gdacs.org– GDACS website

• http://www.gdacs.org/flooddetection– Global Flood Detection System

• http://dma.jrc.it/map– Mapping tool

http://www.gdacs.org/

http://www.gdacs.org/flooddetection

http://dma.jrc.it/map

2011 iscram summer school tom de groeve

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