Ocean
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Inc.
TEAM 6
Prof. Liu Defu
Disaster Prevention Research Institute
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TEAM 6
Historical review of the typhoon disaster
Hurricane Katrina in
2005 induced the most
catastrophic failure of
an engineering system
in the history of United
States: approximately
2000 people died as a
result of this disaster in
New Orleans. Direct
costs are estimated to
approach 400 billions
dollars.
At the end of April, 1991,
the tropical cyclone induced
storm surges led to140 ,000
people deaths and
economical damage was
over three billion dollars
in Bangladesh.
In 2008, Cyclone
Nargis made landfall
with sustained winds
of 130 mph in Burma
and caused a huge
tidal surge to sweep
inland. The death toll
from this storm
reached more than
ten thousands.
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Tab. 2006 Typhoon disaster detail
Influenced
Agriculture
Area
(thousand
hectare)
Influenced
Population
(million)
Death &
lost
population
Economical
Loss(RMB)
(billion)
Typhoon
name
Maximu
m
Wind
(m/s)
Influenced
Provinces
Chanchu
45
Guangdong,Fujian,
Zhejiang
368.96
11.06
30+5
8.56
Bilis
30
Fujian,
Guangdong,
Hunan, Guangxi,
Zhejiang, Jiangxi
1170.38
29.85
655+194
32.99
Kaemi
40
Fujian,
Guangdong,
Hunan, Guangxi,
Zhejiang,Jiangxi,
Anhui, Hubei
397.56
8.42
29+35
5.89
Prapiroo
n
35
Guangdong,Guang
x,
Hainan
569.43
11.11
66+9
8.23
Saomai
75.8
Fujiang, zhejiang,
Jiangxi, Hubei
223.16
5.99
459+111
19.49
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The typhoon characteristics are usually
described by using maximum central
pressure difference (ΔP), radius of
maximum wind speed (Rmax), moving
speed of typhoon center (s), minimum
distance between typhoon center and
target site (δ ), and typhoon moving
angle (θ ). But one of the chief
advantages lies in taking the annual
typhoon frequency (λ) into account as a
discrete random variable in the new
model derivation.
TEAM 6
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Different combinations of typhoon characteristics
led to corresponding disasters
In order to test the calculation errors of the model, typhoon 5612#,
7413#, and 9711# are taken as the samples.
The detail of these typhoons is following:
 In 1956, typhoon 12# made landfall with sustained winds of 130 mph and 923hPa
atmospheric pressure. The storm surge caused by this super typhoon reached 4.2m.
 Typhoon 7413# landed with 968hPa atmospheric pressure and the maximum wind power
reached Force 12.The water level caused by typhoon 7413# exceeded history record.
 Typhoon 9711# which landed on August 18 is the most influential typhoon in north and
middle regions of Zhejiang Province in history.
Tab. The joint probability of samples
No.
ΔP (hPa)
δ(km)
S (m/s)
Rmax(km)
5612#
87
146
20.6
36.09
7413#
35
220
19.8
66.25
9711#
50
188
28.8
52
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In this model, the long term
probability characteristics of
typhoon factors such as the
typhoon occurring frequency (λ),
drop of central pressure (ΔP),
radius of maximum wind speed
(Rmax), typhoon moving speed
(S), minimum distance between
typhoon center and certain area
(δ) are considered.
Fig. The tracks of typhoon
Tab.3 Comparison of storm surge at Zhapu station with return period*
and joint return period**
Surge
Return level of
Joint probability of
(cm)
storm surge (a)*
typhoon **(a)
5612#
420
501
179
7413#
175
8
4.8
9711#
233
18
7.0
Fictitious typhoon
448
714
327
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Typhoon duration from landfall to dissipation (t)
Historical reviews show that
typhoon Nina in 1975 with
duration 101 hours, induced
Banqiao dam in the inland
province Henan collapse and
lead to 62 downstream dams
collapse .
typhoon Bilis in 2006 from
landfall to dissipation persisted
for 120 hours.
So typhoon duration from
landfall to dissipation (t) must be
considered for extreme event
prediction.
Typhoon Nina
The track of typhoon Nina
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Double layer nested multi-objective probability model
Double layer nested multi-objective probability model (DLNMOPM) is
proposed, in which the joint probability prediction of different
typhoon characteristics (such as the typhoon occurring frequency ,
drop of central pressure, radius of maximum wind speed, typhoon
moving speed, minimum distance between typhoon center and
certain area, typhoon moving angle and duration from typhoon land
to dissipation) are taken as the first layer and typhoon induced
disaster factors (such as strong wind, storm surge, huge wave,
heavy rain, flood, and so on) are taken as the second layer.
The new model will be used to establish typhoon disaster
zoning and the prevention criteria system.
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The double-layer nested multi-objective probability model
Select the target
site
Typhoon occurring
frequency λ
Maximum central pressure
difference (ΔP)
Radius of maximum wind speed
(Rmax)
Joint probability analysis by using
P-ISP
Storm
surge
Heavy
rain
Flood
Strong
wind
Moving speed of typhoon center
(s)
Minimum distance between
Typhoon center and target site
(δ)
Typhoon moving angle (θ)
Joint probability analysis using
MCEVD
Typhoon duration from landfall
to dissipation (t)
The first layer
The second layer
Typhoon disaster zoning and
protection criterion
Huge
wave
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Theory of Multivariate Compound Extreme
Value Distribution (MCEVD)
Since 1972 Rita typhoon attacked on Dalian Port and induced severe catastrophe,
we were studied on statistical prediction model of typhoon induced wave height
and wind speed.
The first publication in US (J. of Waterway Port Coastal & Ocean Eng. ASCE, 1980,
ww4 ) proposed an new model “Compound Extreme Value Distribution” used for
China sea, then the model was used in “Long term distribution of hurricane
characteristics for Gulf of Mexico &Atlantic coasts, U.S.(OTC.1982).
During the past few years, CEVD has been developed into Multivariate Compound
Extreme Value Distribution (MCEVD) and applied to predict and prevent typhoon
induced disasters for offshore and coastal areas.
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The MCEVD is derived based on the theory measure and order statistics by
compounding a discrete distribution with multivariate distribution as follows:

F ( x1 ,, xn )   pi  i    
i 1
xn
x1


i 1
G1 (u ) g (u1 ,, un ) du1 dun （1）
This can be proved as follows:
F x1 ,..., xn   P X 1  x1 ,..., X n  xn 

 P( X 1  x1 ,..., X n  xn  N  i)
i 1

i
i 1
k 1


  pi P(  X 1  x1 ,..., X n  xn  Max 1 j  1k | N  i )
1 j i

  pi ..i  P(11  x1 , ,  n1  xn , 11  1 j , j  2,3, i | N  i )
i 1

  pi  i    
i 1
xn
x1


i 1
G1 (u ) g (u1 ,, un ) du1 dun
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Therefore, Eq. (1) is proved. As mentioned above, the frequency of
typhoon (hurricane, winter storm) occurrence can be fitted to Poisson
distribution：
e   i
Pi 
i!
P(; x1 , x2 , xn )  e (1     e

F ( x )
f ( x1 , x2 , xn )dx1dx2  dxn )

In which,
 -- mean value of the annual typhoon frequency;
 -- joint probability domain;
f , F  -- probability density function and cumulative function;
x1 , x2 , xn -- stochastic variables such as ΔP, R , s, δ, θ, t, and so on.
max
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In the formula CEVD instead of F x1 , x2 , xn ，f x1 , x2 , xn  ,the
following formulas can be used for PNLTCED:
F x1 , x2 , x3  


 
1  
1
1

 


1
2




x3  3  3  
x1  1
x2   2


  


exp   1  1
 1   2
 1   3


1 
2 
3   
 



 
 
However, it also can be solved by P-ISP.
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Engineering applications
1. Prediction of the disaster in New Orleans induced by
hurricane Katrina
• Study showed that the 50 yrs
and 1000yrs hurricane central
pressure P0 predicted by CEVD
were close to the Standard
Project Hurricane (SPH) and
Probable Maximum Hurricane
(PMH) proposed by NOAA
respectively in most of the
coastal areas,
• except Zone 1 of Florida coasts
and Zone A of Gulf coasts where
more severe and reasonable
results were obtained using
CEVD
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Comparison between NOAA and CEVD
Zone
NOAA (hPa)
CEVD (hPa)
Hurricane
(hPa)
A
SPH
PMH
941.0
890.5
50-yr
1000-yr
910.8
866.8
Katrina
902.0
1
SPH
PMH
919.3
885.4
50-yr
1000-yr
904.0
832.9
Rita
894.9
SPH and PMH are only close to CEVD 30~40yr and 120yr return values,
respectively. In 2005, hurricane Katrina and Rita attacked coastal area of the
USA, which caused deaths of about 2000 people and economical loss of $400
billion in the city of New Orleans and destroyed more than 110 platforms in the
Gulf of Mexico.
The disaster certified that using SPH as flood-protective standard was a
main reason of the catastrophic results.
Comparison among MCEVD and other methods
Methods
MCEVD
(2006)
Cole et al.
(2003)
Casson &
Coles
(2000)
Georgion et
al
(1983)
100yr Wind speed
(m/s)
70.6
46.0
38.0
39.0
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• After Hurricane Katrina2005, we reanalyzed hurricane
disaster along American coasts. Study shows, in Region
2,4,7,11, the predicted hurricane strength using our
model are close to those of other researchers, but in
Region1,3,6,8,10 our results are more greater, especially
significant in New Orleans area and Florida. The destroy
of New Orleans approved our theory is reasonable.
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2 Design water level for disaster prevention in Shanghai
Shanghai is located in the estuarine
area of the Yangtze River in China.
Historical observation data shows that
typhoon induced surges, flood peak
run-off from the Yangtze River and
astronomical spring tides have caused
significant losses of lives and
properties to Shanghai City.
The combined effects of storm surge,
upper river flooding and spring
tides on the coastal structures is the
prime factor for disaster prevention
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Probability plot
Quantile Plot
0.2
0.4
0.6
0.8
1.0
1.0
1.2
1.4
empirical
1.6
1.8
2.0
0.25
model
0.15
0.6
0.05
0.0
2.2
0.2
0.4
Density
0.8
1.0
0.05
0.15
0.20
Return Level Plot
Density
0.25
0.30
1.0
10.0
100.0
1000.0
1.0
1.5
Return period
2
0
0.05
1
0.2
0.0
0.1
3
f(x)
4
0.25
Return level
0.15
0.8
0.6
0.4
f(x)
1.5
1.0
Return level
2.0
5
1.0
0.10
empirical
0.35
Return Level Plot
0.6
empirical
empirical
6
0.0
0.4
model
0.5
0.0
0.2
0.2
1.0
1.5
model
0.8
2.0
1.0
0.8
0.6
0.4
model
Quantile Plot
1.0
Probability plot
TEAM 6
0.1
2.0
1.0
10.0
100.0
1000.0
0.0
0.1
0.2
Return period
x
Fig . Diagnostic check of spring tide.
x
Fig. Diagnostic check of flood.
Tab. Typhoon frequency in Shanghai (1962-1987)
The number of tropical storm in
one year
Shanghai
0
1
2
3
4
5
6
7
2
1
Total years/
Total number
frequency
72/21
  3.43
Year of occurrence
0
4
3
3
6
2
0.3
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Quantile Plot
0.6
0.2
0.4
model
0.6
0.4
0.0
0.2
model
0.8
0.8
1.0
Probability plot
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.6
empirical
empirical
Return Level Plot
Density
0.8
0.5
1.0
f(x)
0.8
0.4
0.0
0.0
Return level
1.5
1.2
0.0
0.1
1.0
10.0
100.0
1000.0
0.0
0.2
Return period
0.4
0.6
0.8
1.0
x
Fig. Diagnostic check of storm surge.
Joint return
period
(years)
100
Flood
surge
(m)
0.43
Storm surge Spring-tide
(m)
(m)
1.32
4.14*
Design
water
level (m)
5.89*
Tab. shows that the design water level for a 100-yr joint return period
event predicted by PNLTCEVD is close to the 1000-yr water level
predicted by the traditional univariate extrapolation method for Shanghai.
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3. Discussion on Coastal Nuclear Power Plant
Safety Regulations of China
TEAM 6
Furthermore, more than 37 nuclear power plants along coast of Southeast China Sea are constructing or in planning and designing state.
Adequate estimations of extreme high-water levels are very important for
coastal hazard mitigation because the coastal areas of the world are
becoming increasingly populated.
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Design Basic Flood (DBF), Probable Maximum Typhoon (PMT),
Standard Project Typhoon (SPT) and Probable Maximum Storm
Surge (PMSS) will be discussed.
According to “HAD101/11”, PMSS should be obtained based on PMT.
So aiming at PMT with different combinations of typhoon
characteristics, some sensitive factors should be selected as control
factors and substituted into procedure of GUA and GSA. The PMSS
corresponding to PMT of different sea areas can be derived by
repeated calculations.
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Typhoon characteristics
(λ、 ΔP、 Rmax、 s 、δ、 θ、 t)
Layer 1
Joint probability analysis
GUA&
GSA
Storm surge (SS) model
Max. SS?
Layer 2
No
Yes
Spring tide
PMSS
Wave height
Joint probability analysis
Design criteria for nuclear
engineering
Float chart of of DLNMPM
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According to the GSA results of existing data, annual typhoon
frequency (λ), maximum central pressure difference (ΔP), radius of
maximum wind speed (Rmax), moving speed of typhoon center (s)
and typhoon duration (t) are selected as control factors for PMSS
analysis.
For joint probability analysis, the marginal distribution parameters of
λ,ΔP, Rmax, s, t should be confirmed. Results show thatλfits to
Poisson distribution, while ΔP, Rmax, s and t can be described by
Generalized Extreme Value Distribution (GEVD). (See Table 4 and
Fig 2)
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0.35

0.3
For joint probability analysis, the
marginal distribution parameters
of λ,ΔP, Rmax, s, t should be
confirmed, results show thatλfits
to Poisson distribution.
probability density
0.25
0.15
0.1
0.05
0
Typhoon
characteristic

0.2
0
1
2
3
4
5
6
7
8
Annual occurance rate of typhoon
9
10
Marginal distribution parameters
Location
parameter
Scale
parameter
Shape
parameter
s
22.82
9.69
0.16
Rmax
30.28
12.15
0.52
t
10.45
4.54
-0.04
ΔP
15.16
8.71
0.16
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Probability plot
160
model
0.2
0.0
20
40
60
Return Level Plot
Density
10.0
100.0
1000.0
60
80
100
120
140
0.03
0.0
0.1
80
1.0
10.0
100.0
1000.0
0
50
R eturn period
100
150
x
Distribution diagnostic testing of Rmax
Probability plot
25
1.0
Quantile Plot
model
model
20
0.8
70
60
50
10
0.0
5
20
0.2
10
30
40
model
60
Density
f(x)
Return level
40
40
Return Level Plot
Quantile Plot
1.0
Probability plot
0.8
20
200
20
Distribution diagnostic testing of ΔP
0.6
1.0
empirical
x
0.4
0.8
0
0
R eturn period
0.2
0.6
empirical
0.6
1.0
0.4
0.01
0.04
0.02
0.0
0.01
f(x)
0.03
20 40 60 80 100
0.1
0.2
80
800
empirical
0.02
0
15
1.0
600
0.8
400
0.6
empirical
0.4
0.4
20 40 60 80
120
0.8
0.6
model
40
30
model
20
10
0.2
0
R eturn level
0.4
50
1.0
0.8
0.6
model
0.4
0.2
0.0
0.0
0.0
model
Quantile Plot
1.0
Quantile Plot
60
Probability plot
0.0
0.2
0.4
0.6
0.8
1.0
20
40
empirical
60
0.0
80
0.2
0.4
0.8
1.0
10
Return Level Plot
Density
15
20
25
empirical
Density
0.1
1.0
10.0
R eturn period
100.0
1000.0
0.06
0.04
f(x)
0.0
0.0
10
0.02
20
Return level
30
0.03
0.02
0.01
f(x)
20 40 60 80
Return level
120
0.08
Return Level Plot
0.6
empirical
empirical
20
40
60
80
100
x
Distribution diagnostic testing of s
0.1
1.0
10.0
R eturn period
100.0
1000.0
5
10
15
20
25
x
Distribution diagnostic testing of t
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Using DLNMPM, some combinations of typhoon characteristic factors with
different joint return period can be gotten
Joint return
period(yrs)
Typhoon characteristic factors
ΔP (hPa)
Rmax (km)
S (km/h)
T (h)
1000
95
198
35
72
500
85
185
48
60
100
76
147
54
40
50
70
112
67
24
Tab Extreme water level with different combination
Return period (a)
Variables
50
100
500
1000
Storm surge (m)
2.43
2.79
3.49
3.85
Spring tide (m)
1.99
2.14
2.19
2.75
Wave height (m)
6.3
6.6
7.3
7.9
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Tab. The comparison between CEVD and traditional DBF
Return period:1000a
CEVD
Traditional
DBF
water level (m)
3.85+2.75=6.6
6.35
Wave height (m)
7.9
6.6
It can be seen that 1000 years return values of storm surge, spring tide
(3.85+2.75=6.6m) and wave height (7.9m) should be more severe than
HAF0111 proposed DBI (6.35m) with 100 years return period wave height
(6.6m).
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Conclusions
The theory of MCEVD and DLNMOPM model is based on the
combinations of typhoon process maximum data sampling
and joint probability analysis of typhoon characteristics and
their corresponding extreme sea environments. It can be
widely used not only in climatologic disaster prediction, but
also for engineering project risk assessment.
The 1975 typhoon Nina and 2005 hurricane Katrina give the
most important lesson: they are only natural hazards, but
when natural hazards combined with human hubris, the
natural hazards should be become disaster, catastrophe
sooner or latter.
We hope: human hubris always out of decision making.
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Thank you for your
attention!
TEAM 6