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Measuring the Giant
(or, How Big is the Big One?) - Managing Natural Hazard Risk Before, During, & After Claims
Keith Porter, PE PhD Principal SPA Risk LLC
Research Professor University of Colorado, Boulder
© SPA Risk
Property Insurance Claims Group Conference
May 14th 2015
The British Library Conference Centre
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You already live with the giant
0.0001
0.001
0.01
0.1
1
0.0001 0.01 1
Exce
edan
ce r
ate,
yr-1
Loss, fraction of portfolio value
1/250-year loss
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PROPERTY VULNERABILITY &
THE IP VALUE OF LOSS DATA
So let’s start quantifying him...
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Vulnerability
Relationship between
environmental input (like
shaking) and loss (like
repair cost)
Vulnerability data can be
valuable IP
0.00
0.25
0.50
0.75
1.00
0 0.5 1 1.5 2
S a (1.0 sec, 5%)
Dam
age
fact
or
Y
E[Y |S a =s ]
f Y |S =1g(y )
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Empirical vulnerability functions
x = ground motion (windspeed, ...) at property i
y = loss at property i
c = attributes of property i (age, material, height...)
Wesson et al. (2004)
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Empirical method: data to get
• Address or lat & lon (for shaking, windspeed, ...)
• Replacement cost new by coverage
• Repair costs by coverage (better: Xactimate est.)
• Structural material
• Structural system (frame, shearwall, ...)
• Number of stories
• Year built
• Occupancy (residential, etc.)
• Plans, photos of damage, ....
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Analytical vulnerability functions
• Design several realistic specimens
• Estimate structural response, damage, & repair
cost at many levels of shaking
• Combine them probabilistically
0.00
0.25
0.50
0.75
1.00
0.00 0.50 1.00 1.50
Mea
n d
am
ag
e fa
cto
r o
r C
OV
Sagm(1 sec, 5%), g
1.4 · sample COV
Typical
Poor
Superior
MDF
X
Y
Z
XY
Z
MODE- 1 : 0.43261 (sec)
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Analytical method: data to get
• Shaking (windspeed...)
instrumental records
• Structural and
architectural drawings
• Detailed costs (e.g.,
Xactimate)
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Aside: analytical (multi-site) BI modeling
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(Multi-site) red tagging
X direction capacity spectrum
0
1
2
3
4
5
6
7
0 0.5 1 1.5 2 2.5 3
Sd (in)
Sa (
g)
Red tagging often doesn’t contribute much to BI risk
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(Multi-site) equipment failure
0.00
0.25
0.50
0.75
1.00
0 1 2 3
Base acceleration, units of gravity
Fai
lure
pro
bab
ilit
y
Best conditions
No bracingNo spacers
No battery
restraints
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(Multi-site) operational failure
Legend
Or gate: the event connected
above occurs if any event
connected below occurs
And gate: the event
connected above occurs if all
events connected below occur
Transfer symbol: tree
continues elsewhere
Event: something
undesirable occurs
Basic event: an event whose
probability is quantified
without lower events
Primary Facility
Fails
Equipment
Systems Fail
Building FireData Processing
Equipment Fails
Building Support
Systems Fail
2 3 4
Red
tagged
Backup Facility
Fails
1
Operations Fail
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(Multi-site) system failure
0.00
0.25
0.50
0.75
1.00
0.00 0.50 1.00 1.50 2.00
Peak ground acceleration (g)
Fai
lure
pro
bab
ilit
y Current conditions,
correlated
Red tag
probability
Mitigate
generator
equipment Mitigate all
(a)
Current conditions,
uncorrelated
0.00
0.25
0.50
0.75
1.00
0.00 0.50 1.00 1.50 2.00
Peak ground acceleration (g)
Fai
lure
pro
bab
ilit
y
Current conditions,
uncorrelated
Red tag probability
Mitigate tape silos
Mitigate all
(c)
Current conditions,
perfect correlation
Primary Backup
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(Multi-site) BI risk
As-is
(5 yr)
Fix these weak links* Get
(5 yr)
Operations 0.8% Generator equipment 0.3%
Backup site 3.2% Generator equipment, fans,
EQSL, computers, raised
access floors, tape silos
0.1%
Ops & backup 0.1% With periodic evaluation ~10-6
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Expert opinion vulnerability
• Experts judge vulnerability
• We treat experts like data
• Useful when we lack data
• Inexpensive
• Long tradition of use
But:
• Underestimates uncertainty
• Least credible, verifiable 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0
Pro
babili
ty o
f C
olla
pse
0.2-Second Spectral Acceleration (g)
EXP2EXP3EXP7EXP9EXP11EXP12EXP13
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HOW BIG IS THE BIG ONE?
SHAKEOUT
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ShakeOut
• M7.8 hypothetical SoCal earthquake imagined in granular detail
• A science-based, community-oriented earthquake exercise
• 300 scientists, engineers, operators, and others created it
• 25 million people in 26 states, regions, countries participated in 2014
• Pamphlets, reports, journal articles, reports, games, YouTube vids, etc.
Google “USGS SAFRR”
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To answer two questions
1. What could happen in a large
earthquake near me?
2. How can I prepare for it?
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Stakeholders wanted a scenario
1 outcome
No probability
An earthquake
everyone should
be ready for
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150 year return period; 300 yr since last rupture
ShakeOut rupture unzips SE to NW, taking 90 seconds;
Shaking lasts >3 min.’s in LA & Ventura Northridge 1994
over in 30 sec’s
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Fault offset
Landers, California M 7.3 - 1992 earthquake; ~1 meter (3 feet) offset
Wairarapa fault, New Zealand - 1855 earthquake; ~18 meter (54
feet) right-lateral offset
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Palm
Springs
Los
Angeles
Bakersfield
Compare with
1994 Northridge
earthquake:
• 50x smaller
• Magnitude 6.7
• 57 deaths
• $40 billion
Shaking
Compare with
2008 Chino Hills
earthquake:
• 5000x smaller!
• Magnitude 5.4
• 0 deaths
• Minimal damage
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Damage to the building stock
• 300,000 significantly damaged
(1 in 16)
– Significant: repairs cost at least 10%
of replacement cost
• 45,000 complete losses (1%)
• Most dangerous:
– Brick
– Older concrete
• Most numerous:
– Older wood
• Less dangerous:
– Steel buildings built before 1994
1933 Long Beach, CA
1994 Northridge CA
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Older reinforced concrete
ShakeOut study by UCLA:
• 50 collapses
• 5,000 – 10,000 people in
collapsed buildings
• 100 red tagged buildings
1995 Kobe
1994 Northridge
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Woodframe buildings
• Most California
housing is wood
• Extensive damage
in past earthquakes
• Scenario: 175,000
wood buildings
significantly
damaged (1 in 25)
5/2/83
M6.5 Coalinga
1992 Mendocino
1989 M7.1
Loma Prieta
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Highrise steelframe buildings
• Connections fractured in
Northridge (1994)
• In Kobe (1995), 10s of
steel buildings collapsed
• No US buildings have
collapsed, but ShakeOut
is longer, stronger, and
richer in damaging motion
than Northridge.
• 600+ such buildings in
study area
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Highrise steelframe buildings
• Rupture-to-rafters analyses
• 5 collapses, 11-15 stories,
200,000-300,000 sq ft, 1,000
occupants each
• 10 red tags, 20 yellow
• Expert panel:
– “The possibility of some
collapses is quite credible.”
Kobe, Japan 1995
Mexico City, 1985
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Electric power
10 experts from 5 agencies find:
• Immediate loss of power throughout
region
• Collapse of some high-tension towers,
damage to transformers on poles
• Generators offline for inspection
LA, Riverside, & San Bernardino Counties:
• 30-50% of service restored in 24 hrs
• 75-90% restored in 3 days
• ~100% restored within 1-4 months
Transmission lines & power plants
1971 San Fernando
Earthquake
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Water & sewer
• Older pipe susceptible to
shaking, liquefaction, ...
– Some LA pipe 100+ yr old
• Aqueducts, tunnels cross
the fault several places
• Groundwater supply
needs power, pumps,
tanks, and other
damageable equipment
1994 Northridge Earthquake
1971 San Fernando Earthquake
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Water supply
19 reps from 8 agencies:
• Within 10 miles of fault & isolated areas:
damage impairs supply for up to 6 months
• Throughout much of study area, 1/2 of
customers lose service for up to 1 week
– Loss of power, damage to pumps, tanks, etc.
• In LA, Riverside, San Bernardino Counties,
5% of customers lose service 1-8 weeks
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Deaths & injuries
• Study by UCLA:
• 50,000 injured (to ERs)
• 1,800 killed
– 900 from fires, 900 from shake-
related building and
transportation damage
– Vs. 8,300 injured, 33 killed in
1994 Northridge Earthquake
• Up to 2/3 of hospital beds
unavailable in some counties Olive View Medical Center
1971 San Fernando earthquake
Evacuation of Sherra Cox, 1989 Loma
Prieta earthquake
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Fire following earthquake
2 largest peacetime
conflagrations: 1906 SF,
1923 Tokyo because:
• Numerous ignitions
• Degraded fire-resistive building
features
• Damaged water mains
• Saturated communications
• Traffic impacts
• More fires than firefighting
capabilities
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Fire following earthquake
• 1,600 ignitions requiring a fire engine
• 1,200 exceed capability of 1st engine
• Orange County & LA basin: dozens of
large fires merge into conflagrations
destroying 100s of blocks
• 200 million square feet burnt
≈ 133,000 single family dwellings
• Property loss: $65 billion
• No Santa Ana winds, not worst case
1989 Loma Prieta
1994 Northridge
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Property Damage: $113 billion
Fire-Damaged
Contents
($25b)
Highway
($0.4b)
Pipeline (water,
sewer, gas;
$1.1b)
Shake-
Damaged
Buildings
($32.7 billion)
Shake-
Damaged
Contents
($10.6b)
High-Rise
($2.2b)
High-Rise
Content ($0.7b)
Fire-Damaged
Buildings
($40b)
Business Interruption: $96 billion
Non-High-Rise
Buildings ($8
billion)
Fire ($22.4b)
Transportation
($0.5b)
Power ($7.3b)
Water ($53b)
Gas ($0.8b)
Ports ($1b) High-Rise
Buildings
($3.2b)
Monetary bottom line
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Important variables
• Demand surge could add 20%
• Fatalities are highly uncertain
• Few are insured; vast outmigration & long-term
economic malaise are possible
• Seismic retrofit can save up to $8 per $1 spent,
prevent loss of operations in critical facilities
• Above-code design & accelerated replacement of
obsolete infrastructure are the long-term fixes
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HOW BIG IS THE BIG ONE?
ARKSTORM
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AR: Atmospheric River
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Great Floods in Santa Barbara Channel
40 40
0 500 1000 1500 2000
Year
212
440
603
1029
1418
1605
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K Street Sacramento, looking east
The 1861-1862 Floods
• 24 Dec 1861 – 21 Jan 1862:
nearly unbroken rains
• 300% to 500% of normal
precipitation in San Diego,
Sacramento, and SF
• Central Valley flooded 300
miles x 12 – 60 miles wide
• LA basin “generally
inundated”
• San Gabriel & San Diego
Rivers cut new paths to sea
K Street Sacramento, looking east
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ARkStorm Precipitation
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Rough estimates of
Flooding (drawing
from FEMA Flood
Insurance maps)
RUNOFF RETURN
PERIODS
PRECIPITATION
VIC
DFIRM
Estimating Flooding
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Flood Exercise Map
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Los Angeles & Orange Counties
45
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• Winds reach 125 mph
• 60 mph winds at Golden Gate, enough to close but not damage it
• Buildings & other structures do face damages.
Maximum wind speeds
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Highway damage: 100s of landslides
$100,000,000s in damage
Road closures of days to weeks
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24% of buildings flooded
Silicon Valley; SF Bayshore Sacramento
Orange & Los Angeles Counties Stockton
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Economics of the ARkStorm Property loss: ….........…………………….$350B
Property loss from flood: .........………….…97%
Demand surge: ………………….........…… 20%
Business interruption loss: ………...........$375B
Loss versus ShakeOut: ………………............3x
Probability versus ShakeOut: …………..........1x
CA sectors and counties affected: ............100%
Fraction insured: ……..................................12%
Long-term hit to the CA economy: ……...........?
Cost to enhance urban levees: …...........$25B?
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HOW BIG IS THE BIG ONE?
THE HAYWIRED SCENARIO
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Code’s surprising life-safety objective
The International Building Code does not aim
to provide earthquake-proof buildings. Its
goal for most buildings is life safety—limiting
collapse probability to ≤ 1% in 50 years—
with some damage reduction where
practical.
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After the Big One
International Building Code
aims to control the risk of this
But not this Both get this
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After the Big One
IBC does not aim to control this
Which gets this
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After the Big One
This, as scary as it might seem,
gets this
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To reduce BI, get fast inspections
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To reduce BI, get good inspectors
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To reduce BI, get efficient inspections
Google “FEMA ROVER” or “ROVER ATC-20”
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To reduce BI, preplan inspections
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New buildings in the Big One
Let’s call this “impairment”
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Code implication “Fraction impaired”
New buildings in the Big One
at ½ x code-mapped shaking
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New buildings in the Big One
60% impairment rate
over 3,300 km2
x 411 people/km2
≈ 1.4 million people
≈ 140,000 businesses
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3,300 km2 · 411 people/km2
≈ 1.4 million people
≈ 140,000 businesses
8,000 km2 · 203 people/km2
≈ 1.6 million people
≈ 160,000 businesses
New buildings in the Big One
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New buildings in the Big One
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Other lessons of HayWired
• Old water pipe is California’s Achilles’ heel
• Some lifeline operators have not thought
about aftershocks
• Power failure could trap up to 25,000
people in 5,000 elevators
• Some telecoms are well prepared
– 8hr + batteries; generators on 90% of towers
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DEMAND SURGE MAKES THE
GIANT BIGGER
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Evidence of demand surge
• Great storm of 1703
– Reed thatching price
up 150-200%
• 1886 Charleston
earthquake
– Construction labor
prices doubled
• 1974 Darwin cyclone
– Building costs doubled
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Demand surge evidence: Xactimate
• Olsen & Porter collected prices of 6 baskets of goods: (residential or
commercial) x (material, labor, both)
• 53 US cities, quarterly for 2002-2010 Atlantic hurricane seasons
• Number of storms & peak windspeed in each city
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Labor costs drive demand surge
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SUMMARY / CLOSURE
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What gets measured gets managed
If we can better measure him,
we can better manage him
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QUESTIONS?
+1-626-233-9758
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