US Hurricanes and economic damage: an extreme value perspective

Nick Cavanaugh, futurologistDan Chavas, tempestologist

Christina Karamperidou, statsinatorKaty Serafin, bathy queenEmmi Yonekura, landfaller

ASP 2011 Summer Colloquium Project23 June 2011

Outline

• Motivation• Previous work• Methodology and results– Economic data: absolute vs. relative damages– GPD without physical covariates– GPD with physical covariates– Application to GFDL current vs. future hurricanes

• Conclusions and future work

Motivation: society

Atlantic hurricane tracks (1900+)(NHC Best Track)

http://gecon.yale.eduhttp://gcaptain.com/wp-content/uploads/2010/09/Atlantic_hurricane_tracks.jpg

GDP: 1o x 1o

(Yale G-Econ)

63% of global insured natural disaster losses caused by US landfalling hurricanes(Source: Rick Murnane, last week)

Motivation: science

• Objectives:– Combine physical storm characteristics with

statistics of damages in an extreme value theory framework

– Reduce the sensitivity of statistical analysis of damage to economic vulnerability at landfall

Recent work

• Katz (2002), Jagger et al (2008,2011)• Jagger et al (2008,2011): Generalized Pareto

Distribution (GPD) is appropriate for modeling extreme events involving large economic losses

However, inclusion of physical characteristics of storms as covariates has not been tried

Methodology I: absolute vs. relative damage

Economic data: Pielke et al., 2008• Base year and normalized (2005$) economic damages

for 198 storms (pre-threshold) from 1900-2004

But are variations in damages representativeof the damage threat from a hurricane

or rather of the large variation in economicvalue along the coast?

Distribution of GDP (bil $) in 1o x 1o boxes along US coast

Methodology I: absolute vs. relative damage

Damage Index (DI)Fraction of possible damage [0,1]i.e. “damage capacity” of storm

EconomicPhysical

Goal: remove from our damage database the variability in damagesdue to variations in economic value along the coast

Physical characteristics of storms and economic value at landfall should be independent

corr = -.1

Neumayer et al. (2011)

*

Histogram of Total Damage: Histogram of Damage Index:

ResultsDamages vs. DI: histograms

Max = $150 bil Max = .89

Total Damage: (bil 2005$) Damage Index (DI): [0,1]

Great Miami$156 bil

Bret.89

Top 10 by Damage: Top 10 by DI:

ResultsDamages vs. DI: no covariates

ResultsDamages vs. DI: no covariates

Total Damage: (bil $) Damage Index (DI): [0,1]

ξ > 0 ξ ~ 0

ResultsDamages vs. DI: no covariates

Total Damage

Damage Index (DI)

Methodology II: physical covariates

Want to capture physical characteristics of individual storms thatare relevant to its capacity to cause damage

Hurricane Katrina8:15p CDTAug 28 2005

Hurricane Katrina8:15p CDTAug 28 2005

Eye

Hurricane Katrina8:15p CDTAug 28 2005

Eyewall

Hurricane Katrina8:15p CDTAug 28 2005

R34

Methodology II: physical covariates

http://myfloridapa.com/type%20of%20claims.html

Wind Storm surgeSensitive to:- Wind speed (Vmax)- Size (R34)

Sensitive to:- Wind speed (Vmax)- Size (R34)- Bathymetry (seff)- Translation speed- Landfall angle

Causes of damage

See Irish et al. (2008)

Methodology II: physical covariates

• Wind speed Vmax: HURDAT Best Track 1900-2004

• Storm size R34: Extended Best Track (CSU) 1988-2005• Bathymetry: gridded 1-min res altimetry data

100 km

seff

Methodology II: physical covariates

Bathymetry

Methodology III: GPD fit

PDF

With Multiple Possible Covariates

ResultsDamage: with covariates

Damages

(42pts) 5$ billionu

*Using 1900-2004 datar34 : not enough data

shape parameter left constant

Damage = f(Vmax)

)28(.62.

)009(.015.)05.1(58.ln max

V

Damage Index

pts) (41 06.0u

*Using 1900-2004 data

ResultsDI: with covariates

DI = f(seff, Vmax)

r34 : not enough data shape parameter left constant

Likelihood-ratio test

17.01.0

036.01.0005.001.064.065.2ln max

effsV

Methodology IV: Future Climate

• Statistical-Deterministic Hurricane model (Emanuel et al. 2006)

– downscaled from GFDL CM2.0 model: 1981-2000 and 2081-2100 (A1b) climates

• Modeled values of Vmax and seff => GPD

Results: Future ClimateGPD PDF of US Hurricane Damage Index

Add all PDFs and re-fit GPD for each climate

Results: Future ClimateLocal Distribution of Scale Parameter Change

Δσlocal =Δ exp( σ0 + σ1Vmax + σ2seff)

Conclusions

• Damage Index, which seeks to remove economic vulnerability from damages, appears to better capture role of physical characteristics of storm in causing damage than actual damages

• Bathymetry, wind speed found to be useful covariates whose relationships are consistent with physical intuition

• Changes in scale parameter in the future indicate a shift to higher probability of extreme damage events locally and globally, though we haven’t proven differences are statistically significant

Future work ideas

• Find means of relating back to actual economic damages

• Try rmax for size• Account for uncertainty• Try out a deterministic damage index and

apply GPD to that?

Thanks!Comments/suggestions welcome

ResultsDamages vs. damage index

DI = f(seff)

ResultsDamages vs. damage index

DI = f(Vmax)

Results: Future Climate

Top 10 by Wind Speed:

Example 1: Katrina vs. Camille

http://www.wunderground.com/hurricane/camille_katrina_surge.pnghttp://www.nhc.noaa.gov/HAW2/english/surge/slosh.shtml

Peak storm surge = 8.5 m Peak storm surge = 6.9 m

NOAA SLOSH model

KATRINA (2005) CAMILLE (1969)

…yet Katrina produced much higher storm surge because it was twice as large