19 August 2005 5 Th EM-DAT Technical Advisory Group Meeting Creation of a hazard index : Overview of...

19 August 200519 August 2005 55ThTh EM-DAT EM-DAT Technical Advisory Group MeetingTechnical Advisory Group Meeting

Creation of a hazard indexCreation of a hazard index::

Overview of the Hotspots Overview of the Hotspots methodologymethodology

Piet BuysPiet Buys

[email protected]@worldbank.org


Project ObjectivesProject Objectives

• Identification of natural disaster risk Identification of natural disaster risk hotspots at sub-national scaleshotspots at sub-national scales

• Initial focus: Initial focus:

Drought, floods, tropical cyclones, Drought, floods, tropical cyclones, earthquakes, volcanoes, landslidesearthquakes, volcanoes, landslides

• Where do they occur?Where do they occur?

• Where might damage be most severe Where might damage be most severe (mortality and economic)(mortality and economic)


Project ObjectivesProject Objectives

• Prioritization for local vulnerability Prioritization for local vulnerability assessments and risk reduction in assessments and risk reduction in highest-risk areashighest-risk areas

• Support Bank efforts to engage clients Support Bank efforts to engage clients in hazard management activities in hazard management activities (Turkey Earthquake Insurance, CAS, ...)(Turkey Earthquake Insurance, CAS, ...)


Ingredients for Disaster Hotspots Ingredients for Disaster Hotspots IdentificationIdentification

Hazard information / event probabilities Hazard information / event probabilities at a given location, including probable magnitude, duration, timing

Elements at riskElements at riskpeople, infrastructure and economic

activities/assets that would be affected if the hazard occurred

Vulnerability of the elements at riskVulnerability of the elements at riskhow damaged they would be, if they experienced a hazard event of some level


Global Hazard DataGlobal Hazard DataHazard Hazardousness

ParameterPeriod Resolution Source(s)

Storms Frequency by wind strength

1980-2000

30” UNEP/GRID-Geneva PreView, DECRG processing

Drought Precipitation less than 75% of median for a 3+-month period (WASP)

1980-2000

2.5° IRI Climate Data Library

Floods Counts of extreme flood events

1985-2003*

1° Dartmouth Flood Obs. World Atlas of Large Flood Events

Earthquake Expected PGA (10% prob. of exceedance in 50 years)

n/a sampled at 1’ Global Seismic Hazard Program

Freq. of earthquakes > 4.5 on Richter Scale

1976-2002

sampled at 2.5’

Smithsonian Institution

Volcanoes Counts of volcanic activity 79-2000 Sampled at 2.5’

UNEP/GRID-Geneva and NGDC

Landslides Estimated annual prob. of landslide or avalanche

n/a 30” Norwegian Geotechnical Institute


Global Data on Global Data on Elements at RiskElements at Risk

Exposure Parameter Period Resolution Source(s)

Land Land area 2000 2.5” GPW Version 3 (beta)

Population Population counts / density 2000 2.5” GPW Version 3 (beta)

Economic Activity

National / subnational GDP 2000 2.5” World Bank DECRG

Agricultural Activity

National agricultural GDP allocated to agricultural land area

2000 2.5” IFPRI

Road Density Length of major roads and railroads

c. 1993 2.5” VMAP(0)


Global Data on elements at riskGlobal Data on elements at risk

• Focused on two in this studyFocused on two in this study Population / mortality (shown below)Population / mortality (shown below) GDP per unit area / economic losses (not shown)GDP per unit area / economic losses (not shown)


Global Data on Vulnerability of Global Data on Vulnerability of the Elements at Riskthe Elements at Risk

• Vulnerability estimates guided by past eventsVulnerability estimates guided by past events

• EM-DAT has records ofEM-DAT has records of mortalitymortality, persons , persons affectedaffected and and direct economic damagedirect economic damage http://www.http://www.emem--datdat.net/.net/

• epidemiological approach based on mortality epidemiological approach based on mortality rate (extension to economic loss is rate (extension to economic loss is straightforward)straightforward)


Mortality ratesMortality rates• compute mortality rates using EM-DAT cumulative compute mortality rates using EM-DAT cumulative

number of persons killed by a given hazard and number of persons killed by a given hazard and divide by the total population in the area exposed to divide by the total population in the area exposed to that hazardthat hazard

• e.g. globally, for storms : e.g. globally, for storms : 240,000+ fatalities between 1981 and 2000240,000+ fatalities between 1981 and 2000 1,312 million people in exposed area in 20001,312 million people in exposed area in 2000 16.6 fatalities per 100,000 population 16.6 fatalities per 100,000 population (note time periods)(note time periods)

• we can apply this rate to the population grid in areas we can apply this rate to the population grid in areas exposed to the hazard to produce an estimate of exposed to the hazard to produce an estimate of expected fatalities over a 20 year periodexpected fatalities over a 20 year period


• butbut: mortality is not distributed uniformly : mortality is not distributed uniformly

e.g., earthquake of a given magnitude does more damage in India than in Japan

• social, economic and physical factors that reduce social, economic and physical factors that reduce vulnerability: vulnerability:

building codes, emergency response, education, topography, geology

• many of these are related to the wealth of a countrymany of these are related to the wealth of a country

• Country data in EM-DAT is noisyCountry data in EM-DAT is noisy

Geographic variations in mortalityGeographic variations in mortality


• => use regionally specific mortality rates=> use regionally specific mortality rates WB regions classified into four income groupsWB regions classified into four income groups

• geographically and hazard specific mortality geographically and hazard specific mortality

rates provide a better estimate of potential rates provide a better estimate of potential

vulnerabilityvulnerability

Geographic disaggregationGeographic disaggregation


Geographic disaggregationGeographic disaggregationWorld Bank regions by income groupWorld Bank regions by income group


• mortality rates will be higher in areas where severity mortality rates will be higher in areas where severity measures are largermeasures are larger

• some indication of how severely different areas are some indication of how severely different areas are affected within exposed areaaffected within exposed area

• measures of severity: estimates of frequency or measures of severity: estimates of frequency or probability, frequency by wind strength, expected probability, frequency by wind strength, expected potential peak ground acceleration for earthquakespotential peak ground acceleration for earthquakes

• use severity as a weight to adjust mortality ratesuse severity as a weight to adjust mortality rates

Incorporating hazard severityIncorporating hazard severity


1. mortality rate

2. weighted cell mortality

3. adjustment

4. multi-hazard

where: h = hazard, i = grid cell, j = region_wealth

M = mortality (EM-DAT), P = population (GPW3), W = hazard severity weight

In summaryIn summary


Hurricane Severity and Intensity


Uniform Global Mortality Rate

log of mortality


Region Specific Mortality Rate

log of mortality


Region Specific Mortality Weighted by Hazard Severity

log of mortality


Global resultsGlobal results

• although the model output presents an estimate of although the model output presents an estimate of predicted cumulative mortality from all hazards over a predicted cumulative mortality from all hazards over a twenty year period, we interpret it as a notional index twenty year period, we interpret it as a notional index (low(lowhigh)high)

• hazard specific mortality-weighted indexeshazard specific mortality-weighted indexes

• combined, multi-hazard hotspots indexcombined, multi-hazard hotspots index

• the same methodology can be applied to economic losses the same methodology can be applied to economic losses (globally /proportion)(globally /proportion)


Estimated Mortality ratesEstimated Mortality rates

•highest mortality rates:highest mortality rates:droughts: droughts: AFR low income AFR low income

earthquakes: earthquakes: ECA low middle incomeECA low middle income

floods: floods: LAC upper middle incomeLAC upper middle income

storms: storms: SA low income SA low income

landslides: landslides: EAP upper middle incomeEAP upper middle income

volcanoes: volcanoes: LAC low middle incomeLAC low middle income

•given the limited time period and quality of given the limited time period and quality of

input data => relative risk levels / deciles:input data => relative risk levels / deciles:


Drought mortality risk hotspotsDrought mortality risk hotspots


Identification of areas affected by Identification of areas affected by multiple hazardsmultiple hazards


All hazards mortality risk hotspotsAll hazards mortality risk hotspots

note Africa vs. Europenote Africa vs. Europe


All hazards total economic loss risk All hazards total economic loss risk hotspotshotspots



All hazards Prop economic loss risk All hazards Prop economic loss risk hotspotshotspots



ConclusionConclusion• impact-weighted multi-hazard hotspots index impact-weighted multi-hazard hotspots index

combines information on hazard extent, exposed combines information on hazard extent, exposed elements and vulnerability (based on historic impacts)elements and vulnerability (based on historic impacts)

• Scope for refinementScope for refinement

Better weights / response function (feasible?)Better weights / response function (feasible?)

narrower definition of exposed area (hazards maps)narrower definition of exposed area (hazards maps)

better (more complete) damage estimates (EM-DAT)better (more complete) damage estimates (EM-DAT)

better definition of exposed economic assetsbetter definition of exposed economic assets


Thank youThank you


• consider hazard severity as the consider hazard severity as the dosedose and hazard impacts as the and hazard impacts as the responseresponse

• requires ability to link specific hazard events (e.g., hurricanes) to requires ability to link specific hazard events (e.g., hurricanes) to their impacts (fatalities, economic damage)their impacts (fatalities, economic damage)

• statistical estimation also yields measures of accuracystatistical estimation also yields measures of accuracy

• e.g., e.g., MMhh = = ββoo + + ββ11 HHhh + + ββ22 XXhh + + εε

wherewhere MMhh = damage (mortality) from disaster event = damage (mortality) from disaster event hh

HHhh = characteristics of the hazard leading to disaster= characteristics of the hazard leading to disaster

XXhh = exposure and vulnerability characteristics of area affected= exposure and vulnerability characteristics of area affected

ββ1 1 = an estimate of severity weight = an estimate of severity weight WW

Statistical determination of weightsStatistical determination of weights


Statistical determination of weightsStatistical determination of weights

hazard severity

haz

ard

imp

act

• ““dose-response function” could be any shape or dose-response function” could be any shape or formform


fatalities 1981-2000 fatalities 1981-2000 per 100,000 inhabitants in 2000per 100,000 inhabitants in 2000

Estimated Estimated mortality ratesmortality rates


• this is an intuitive approach and relatively this is an intuitive approach and relatively easy to implement (easy to implement (butbut: it builds on many : it builds on many years of diligent data development!)years of diligent data development!)

• main problem: weighting is ad hoc and main problem: weighting is ad hoc and deterministic – need to know:deterministic – need to know: what should be the cutoff for exposed area?what should be the cutoff for exposed area?

at what level of severity does damage occur?at what level of severity does damage occur?

how does damage vary with changes in severity?how does damage vary with changes in severity?

CaveatsCaveats


Mask areas of low pop, non-agMask areas of low pop, non-ag

55 % of area, 99 % of population remains55 % of area, 99 % of population remains

19 August 2005 5 Th EM-DAT Technical Advisory Group Meeting Creation of a hazard index : Overview of...

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