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Creation of a hazard index : Overview of the Hotspots methodology. Piet Buys pbuys@worldbank.org. Project Objectives. Identification of natural disaster risk hotspots at sub-national scales Initial focus: Drought, floods, tropical cyclones, earthquakes, volcanoes, landslides

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creation of a hazard index overview of the hotspots methodology
Creation of a hazard index:Overview of the Hotspots methodology

Piet Buys

pbuys@worldbank.org

5Th EM-DAT

Technical Advisory Group Meeting

project objectives
Project Objectives
  • Identification of natural disaster risk hotspots at sub-national scales
  • Initial focus:
    • Drought, floods, tropical cyclones, earthquakes, volcanoes, landslides
  • Where do they occur?
  • Where might damage be most severe (mortality and economic)

5Th EM-DAT

Technical Advisory Group Meeting

project objectives1
Project Objectives
  • Prioritization for local vulnerability assessments and risk reduction in highest-risk areas
  • Support Bank efforts to engage clients in hazard management activities (Turkey Earthquake Insurance, CAS, ...)

5Th EM-DAT

Technical Advisory Group Meeting

ingredients for disaster hotspots identification
Ingredients for Disaster Hotspots Identification

Hazard information / event probabilities

at a given location, including probable magnitude, duration, timing

Elements at risk

people, infrastructure and economic activities/assets that would be affected if the hazard occurred

Vulnerability of the elements at risk

how damaged they would be, if they experienced a hazard event of some level

5Th EM-DAT

Technical Advisory Group Meeting

global hazard data

Hazard

Hazardousness Parameter

Period

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*

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 Hazard Data

5Th EM-DAT

Technical Advisory Group Meeting

global data on elements 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 Risk

5Th EM-DAT

Technical Advisory Group Meeting

global data on elements at risk1
Global Data on elements at risk
  • Focused on two in this study
    • Population / mortality (shown below)
    • GDP per unit area / economic losses (not shown)

5Th EM-DAT

Technical Advisory Group Meeting

global data on vulnerability of the elements at risk
Global Data on Vulnerability of the Elements at Risk
  • Vulnerability estimates guided by past events
  • EM-DAT has records ofmortality, persons affectedand direct economic damage
    • http://www.em-dat.net/
  • epidemiological approach based on mortality rate (extension to economic loss is straightforward)

5Th EM-DAT

Technical Advisory Group Meeting

slide9

Mortality rates

  • compute mortality rates using EM-DAT cumulative number of persons killed by a given hazard and divide by the total population in the area exposed to that hazard
  • e.g. globally, for storms :
    • 240,000+ fatalities between 1981 and 2000
    • 1,312 million people in exposed area in 2000
    • 16.6 fatalities per 100,000 population (note time periods)
  • we can apply this rate to the population grid in areas exposed to the hazard to produce an estimate of expected fatalities over a 20 year period

5Th EM-DAT

Technical Advisory Group Meeting

slide10

Geographic variations in mortality

  • but: 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 vulnerability:

building codes, emergency response, education, topography, geology

  • many of these are related to the wealth of a country
  • Country data in EM-DAT is noisy

5Th EM-DAT

Technical Advisory Group Meeting

slide11

Geographic disaggregation

  • => use regionally specific mortality rates

WB regions classified into four income groups

  • geographically and hazard specific mortality rates provide a better estimate of potential vulnerability

5Th EM-DAT

Technical Advisory Group Meeting

slide12

Geographic disaggregation

World Bank regions by income group

5Th EM-DAT

Technical Advisory Group Meeting

slide13

Incorporating hazard severity

  • mortality rates will be higher in areas where severity measures are larger
  • some indication of how severely different areas are affected within exposed area
  • measures of severity: estimates of frequency or probability, frequency by wind strength, expected potential peak ground acceleration for earthquakes
  • use severity as a weight to adjust mortality rates

5Th EM-DAT

Technical Advisory Group Meeting

slide14

In summary

  • mortality rate
  • weighted cell mortality
  • adjustment
  • multi-hazard

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

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

5Th EM-DAT

Technical Advisory Group Meeting

hurricane severity and intensity
Hurricane Severity and Intensity

5Th EM-DAT

Technical Advisory Group Meeting

uniform global mortality rate
Uniform Global Mortality Rate

log of mortality

5Th EM-DAT

Technical Advisory Group Meeting

region specific mortality rate
Region Specific Mortality Rate

log of mortality

5Th EM-DAT

Technical Advisory Group Meeting

region specific mortality weighted by hazard severity
Region Specific Mortality Weighted by Hazard Severity

log of mortality

5Th EM-DAT

Technical Advisory Group Meeting

slide19

Global results

  • although the model output presents an estimate of predicted cumulative mortality from all hazards over a twenty year period, we interpret it as a notional index (lowhigh)
  • hazard specific mortality-weighted indexes
  • combined, multi-hazard hotspots index
  • the same methodology can be applied to economic losses (globally /proportion)

5Th EM-DAT

Technical Advisory Group Meeting

slide20

Estimated Mortality rates

  • highest mortality rates:
    • droughts: AFR low income
    • earthquakes: ECA low middle income
    • floods: LAC upper middle income
    • storms: SA low income
    • landslides: EAP upper middle income
    • volcanoes: LAC low middle income
  • given the limited time period and quality of input data => relative risk levels / deciles:

5Th EM-DAT

Technical Advisory Group Meeting

drought mortality risk hotspots
Drought mortality risk hotspots

5Th EM-DAT

Technical Advisory Group Meeting

identification of areas affected by multiple hazards
Identification of areas affected by multiple hazards

5Th EM-DAT

Technical Advisory Group Meeting

all hazards mortality risk hotspots
All hazards mortality risk hotspots

note Africa vs. Europe

5Th EM-DAT

Technical Advisory Group Meeting

all hazards total economic loss risk hotspots
All hazards total economic loss risk hotspots

note Africa vs. Europe

5Th EM-DAT

Technical Advisory Group Meeting

all hazards prop economic loss risk hotspots
All hazards Prop economic loss risk hotspots

note Africa vs. Europe

5Th EM-DAT

Technical Advisory Group Meeting

slide26

Conclusion

  • impact-weighted multi-hazard hotspots index combines information on hazard extent, exposed elements and vulnerability (based on historic impacts)
  • Scope for refinement
    • Better weights / response function (feasible?)
    • narrower definition of exposed area (hazards maps)
    • better (more complete) damage estimates (EM-DAT)
    • better definition of exposed economic assets

5Th EM-DAT

Technical Advisory Group Meeting

thank you
Thank you

5Th EM-DAT

Technical Advisory Group Meeting

slide28
5Th EM-DAT

Technical Advisory Group Meeting

slide29

Statistical determination of weights

  • consider hazard severity as the dose and hazard impacts as the response
  • requires ability to link specific hazard events (e.g., hurricanes) to their impacts (fatalities, economic damage)
  • statistical estimation also yields measures of accuracy
  • e.g., Mh = βo + β1Hh + β2Xh+ εwhereMh = damage (mortality) from disaster event hHh = characteristics of the hazard leading to disasterXh = exposure and vulnerability characteristics of area affectedβ1 = an estimate of severity weight W

5Th EM-DAT

Technical Advisory Group Meeting

slide30

Statistical determination of weights

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

hazard impact

hazard severity

5Th EM-DAT

Technical Advisory Group Meeting

slide31

Estimated mortality rates

fatalities 1981-2000 per 100,000 inhabitants in 2000

5Th EM-DAT

Technical Advisory Group Meeting

slide32

Caveats

  • this is an intuitive approach and relatively easy to implement (but: it builds on many years of diligent data development!)
  • main problem: weighting is ad hoc and deterministic – need to know:
    • what should be the cutoff for exposed area?
    • at what level of severity does damage occur?
    • how does damage vary with changes in severity?

5Th EM-DAT

Technical Advisory Group Meeting

slide33

Mask areas of low pop, non-ag

55 % of area, 99 % of population remains

5Th EM-DAT

Technical Advisory Group Meeting