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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$156 bil
Bret.89
Top 10 by Damage: Top 10 by DI:
ResultsDamages vs. DI: no covariates
Methodology II: physical covariates
Want to capture physical characteristics of individual storms thatare relevant to its capacity to cause damage
Methodology II: physical covariates
http://myfloridapa.com/type%20of%20claims.html
Wind Storm surgeSensitive to:- Wind speed (Vmax)- Size (R34)
Sensitive to:- Wind speed (Vmax)- Size (R34)- Bathymetry (seff)- Translation speed- Landfall angle
Causes of damage
See Irish et al. (2008)
Methodology II: physical covariates
• Wind speed Vmax: HURDAT Best Track 1900-2004
• Storm size R34: Extended Best Track (CSU) 1988-2005• Bathymetry: gridded 1-min res altimetry data
100 km
seff
ResultsDamage: with covariates
Damages
(42pts) 5$ 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
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