Preparing to Speak at Staples, Inc. TEAM 6 Ocean University of China Disaster Prevention Research...

Preparing to Speak at Staples, Inc. TEAM 6Ocean University of China

Disaster Prevention Research Institute Disaster Prevention Research Institute

Prof. Liu DefuProf. Liu Defu


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.

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.

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.

Historical review of the typhoon disaster

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Typhoonname

Maximum

Wind (m/s)

Influenced Provinces

InfluencedAgriculture

Area(thousand

hectare)

InfluencedPopulation

(million)

Death &lost

population

EconomicalLoss(RMB)

(billion)

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

Prapiroon

35 Guangdong,Guangx,

Hainan

569.43 11.11 66+9 8.23

Saomai 75.8 Fujiang, zhejiang,Jiangxi, Hubei

223.16 5.99 459+111 19.49

Tab. 2006 Typhoon disaster detail

Ocean University of China



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.


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

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.

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.

The detail of these typhoons is following:

Tab. The joint probability of samples


Surge(cm)

Return level of storm surge

(a)*

Joint probability of typhoon

**(a)

5612# 420 501 179

7413# 175 8 4.8

9711# 233 18 7.0

Fictitious typhoon

448 714 327


Fig. The tracks of typhoon

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.

Tab.3 Comparison of storm surge at Zhapu station with return period*and joint return period**


The track of typhoon Nina

Typhoon Nina


Historical reviews show that typhoon Ninatyphoon Nina in 1975 with duration 101 hours, induced Banqiao dam in the inland province Henan collapse and lead to 62 downstream dams collapse . typhoon Bilistyphoon Bilis in 2006 from landfall to dissipation persisted for 120 hours.

So typhoon durationtyphoon duration from landfall to dissipation (t) must be considered for extreme event prediction.

Typhoon duration from landfall to dissipation (t)


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.

Double layer nested multi-objective probability model


The double-layer nested multi-objective probability model

The first layer

Typhoon occurring frequency λ

Maximum central pressure difference (ΔP)

Radius of maximum wind speed (Rmax)

Moving speed of typhoon center (s)

Minimum distance betweenTyphoon center and target

site (δ)

Typhoon moving angle (θ)

Typhoon duration from landfall

to dissipation (t)

Joint probability analysis by using P-ISP

Stormsurge

Heavy rain

FloodStrong wind

Huge wave

Joint probability analysis using MCEVD

Typhoon disaster zoning and protection criterion

Select the target site

The second layer


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.

Theory of Multivariate Compound Extreme Value Distribution (MCEVD)


The MCEVD is derived based on the theory measure and order statistics by compounding a discrete distribution with multivariate distribution as follows:

111

11

11111111

111

111

1

111

111

),,()(

)|,3,2,,,,(..

)|,...,(

),...,(

,...,,...,

1

inn

ix x

i

jnni

i

ikj

ijnn

i

ki

nni

nnn

duduuuguGip

iNijxxPip

iNMaxxXxXPp

iNxXxXP

xXxXPxxF

n

111

111 ),,()(),,(

1

inn

ix x

in duduuuguGipxxFn

This can be proved as follows:

（ 1）


)),,(1(),,;( 2121)(

21 nnxF

n dxdxdxxxxfeexxxP

In which, -- mean value of the annual typhoon frequency; -- joint probability domain; -- probability density function and cumulative function;

-- stochastic variables such as ΔP, Rmax, s, δ, θ, t, and so on.

Ff ,

nxxx ,, 21

!i

eP

i

i

Therefore, Eq. (1) is proved. As mentioned above, the frequency of typhoon (hurricane, winter storm) occurrence

can be fitted to Poisson distribution ：


In the formula CEVD instead of ,the following formulas can be used for PNLTCED:

However, it also can be solved by P-ISP.

321

1

3

333

1

2

222

1

1

111

321

111exp

,,

xxx

xxxF

nn xxxfxxxF ,,,, 2121 ，


• 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

Engineering applications

1. Prediction of the disaster in New Orleans induced by hurricane Katrina


SPH and PMH are only close to CEVD 30~40yr and 120yr return values, 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 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 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 billion in the city of New Orleans and destroyed more than 110 platforms in the Gulf of Mexico. Gulf of Mexico.

The disaster certified that using SPH as flood-protective standard was a The disaster certified that using SPH as flood-protective standard was a main reason of the catastrophic results.main reason of the catastrophic results.

Comparison between NOAA and CEVD Comparison between NOAA and CEVD ZoneZone NOAA (hPa)NOAA (hPa) CEVD (hPa)CEVD (hPa) HurricaneHurricane

(hPa)(hPa)

AA SPHSPHPMHPMH

941.0941.0890.5890.5

50-yr50-yr1000-yr1000-yr

910.8910.8866.8866.8

KatrinaKatrina902.0902.0

11 SPHSPHPMHPMH

919.3919.3885.4885.4

50-yr50-yr1000-yr1000-yr

904.0904.0832.9832.9

RitaRita894.9894.9


MethodsMethods MCEVDMCEVD(2006)(2006)

Cole et al. Cole et al. (2003)(2003)

Casson & Casson & ColesColes

(2000)(2000)

Georgion et Georgion et alal

(1983)(1983)

100yr Wind speed 100yr Wind speed (m/s)(m/s)

70.670.6 46.046.0 38.038.0 39.039.0

Comparison among MCEVD and other methods Comparison among MCEVD and other methods


• 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.


2 Design water level for disaster prevention in ShanghaiShanghai 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


43.3

The number of tropical storm in one year

Total years/Total number

frequency

Shanghai0 1 2 3 4 5 6 7

Year of occurrence

0 4 3 3 6 2 2 1 72/21

Tab. Typhoon frequency in Shanghai (1962-1987)

Probability plot

empirical

model

0.0 0.2 0.4 0.6 0.8 1.0

0.2

0.4

0.6

0.8

1.0

Quantile Plot

empirical

model

1.0 1.2 1.4 1.6 1.8 2.0 2.2

0.5

1.0

1.5

2.0

Return Level Plot

Return period

Retu

rn le

vel

0.1 1.0 10.0 100.0 1000.0

1.0

1.5

2.0

1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Density

x

f(x)

Probability plot

empirical

mod

el

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Quantile Plot

empirical

mod

el

0.05 0.10 0.15 0.20 0.25 0.30

0.0

50

.15

0.2

5

Return Level Plot

Return period

Re

turn

lev

el

0.1 1.0 10.0 100.0 1000.0

0.0

50

.15

0.2

50

.35

0.0 0.1 0.2 0.3

01

23

45

6

Density

x

f(x

)

Fig . Diagnostic check of spring tide. Fig. Diagnostic check of flood.


Joint return Joint return period period (years)(years)

Flood Flood surgesurge(m)(m)

Storm Storm surgesurge(m)(m)

Spring-tideSpring-tide(m)(m)

Design Design water water level level (m)(m)

100 0.43 1.32 4.14* 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.

Probability plot

empirical

model

0.0 0.2 0.4 0.6 0.8 1.0

0.2

0.4

0.6

0.8

1.0

Quantile Plot

empirical

model

0.2 0.4 0.6 0.8

0.0

0.2

0.4

0.6

0.8

Return Level Plot

Return period

Retu

rn le

vel

0.1 1.0 10.0 100.0 1000.0

0.0

0.4

0.8

1.2

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.5

1.0

1.5

Density

x

f(x)

Fig. Diagnostic check of storm surge.


Furthermore, more than 37 nuclear power plants along coast of

South-east 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.

3. Discussion on Coastal Nuclear Power Plant Safety Regulations of China


Design Basic Flood (Design Basic Flood (DBFDBF), Probable Maximum Typhoon (), Probable Maximum Typhoon (PMTPMT), ), Standard Project Typhoon (Standard Project Typhoon (SPTSPT) and Probable Maximum Storm ) and Probable Maximum Storm Surge (Surge (PMSSPMSS) will be discussed.) will be discussed.

According to “According to “HAD101/11HAD101/11”, PMSS should be obtained based on ”, PMSS should be obtained based on PMT. So aiming at PMT with different combinations of typhoon PMT. So aiming at PMT with different combinations of typhoon characteristics, some sensitive factors should be selected as control characteristics, some sensitive factors should be selected as control factors and substituted into procedure of GUA and GSA. The PMSS factors and substituted into procedure of GUA and GSA. The PMSS corresponding to PMT of different sea areas can be derived by corresponding to PMT of different sea areas can be derived by repeated calculations.repeated calculations.



Typhoon characteristics(λ 、 ΔP 、 Rmax 、 s 、 δ 、 θ 、

t)

Joint probability analysis

Storm surge (SS) model

Max. SS?

PMSSSpring tide Wave height

Joint probability analysis

Design criteria for nuclear engineering

GUA&GSA

No

Yes

Layer 1

Layer 2


Float chart of of DLNMPM


According to the GSA results of existing data, annual typhoon According to the GSA results of existing data, annual typhoon frequency (λ), maximum central pressure difference (ΔP), radius of frequency (λ), maximum central pressure difference (ΔP), radius of maximum wind speed (Rmax), moving speed of typhoon center (s) maximum wind speed (Rmax), moving speed of typhoon center (s) and typhoon duration (t) are selected as control factors for PMSS and typhoon duration (t) are selected as control factors for PMSS analysis.analysis.

For joint probability analysis, the marginal distribution parameters of For joint probability analysis, the marginal distribution parameters of λ,ΔP, Rmax, s, t should be confirmed. Results show thatλfits to λ,ΔP, Rmax, s, t should be confirmed. Results show thatλfits to Poisson distribution, while ΔP, Rmax, s and t can be described by Poisson distribution, while ΔP, Rmax, s and t can be described by Generalized Extreme Value Distribution (GEVD). (See Table 4 and Generalized Extreme Value Distribution (GEVD). (See Table 4 and Fig 2) Fig 2)



Typhoon characteristic

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

0 1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Annual occurance rate of typhoon

pro

ba

bili

ty d

en

sityFor joint probability analysis, the For joint probability analysis, the

marginal distribution parameters marginal distribution parameters of λ,ΔP, Rmax, s, t should be of λ,ΔP, Rmax, s, t should be confirmed, results show thatλfits confirmed, results show thatλfits to Poisson distribution.to Poisson distribution.



Probability plot

empirical

model

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Quantile Plot

empirical

model

0 20 40 60 80

10

20

30

40

50

60

Return Level Plot

Return period

Retu

rn le

vel

0.1 1.0 10.0 100.0 1000.0

020

40

60

80

100

0 20 40 60 80

0.0

0.0

10.0

20.0

30.0

4

Density

x

f(x)

Probability plot

empirical

model

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Quantile Plot

empirical

model

20 40 60 80 100 120 140

20

40

60

80

120

160

Return Level Plot

Return period

Retu

rn le

vel

0.1 1.0 10.0 100.0 1000.0

0200

400

600

800

0 50 100 150

0.0

0.0

10.0

20.0

3

Density

x

f(x)

Probability plot

empirical

model

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Quantile Plot

empirical

model

20 40 60 80

10

20

30

40

50

60

70

Return Level Plot

Return period

Retu

rn le

vel

0.1 1.0 10.0 100.0 1000.0

20

40

60

80

120

20 40 60 80 100

0.0

0.0

10.0

20.0

3

Density

x

f(x)

Probability plot

empirical

model

0.0 0.2 0.4 0.6 0.8 1.0

0.2

0.4

0.6

0.8

1.0

Quantile Plot

empirical

model

10 15 20 25

510

15

20

25

Return Level Plot

Return period

Retu

rn level

0.1 1.0 10.0 100.0 1000.0

10

20

30

5 10 15 20 25

0.0

0.0

20.0

40.0

60.0

8

Density

x

f(x)

Distribution diagnostic testing of ΔP Distribution diagnostic testing of Rmax

Distribution diagnostic testing of s Distribution diagnostic testing of t



Using DLNMPM, some combinations of typhoon characteristic factors with Using DLNMPM, some combinations of typhoon characteristic factors with different joint return period can be gottendifferent 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


Variables

Return period (a)

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

Tab Extreme water level with different combination


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=6.6m6.6m) and wave height (7.9m7.9m) should be more severe than HAF0111 proposed DBI (6.35m6.35m) with 100 years return period wave height (6.6m6.6m).

Tab. The comparison between CEVD and traditional DBF



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.



Thank you for your Thank you for your attention!attention!

Preparing to Speak at Staples, Inc. TEAM 6 Ocean University of China Disaster Prevention Research...

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