Estimation of Possible Damages Due to Catastrophic Flooding for Long-term Disaster Mitigation Planning

8/3/2019 Estimation of Possible Damages Due to Catastrophic Flooding for Long-term Disaster Mitigation Planning

1/22

Saqib Ehsan, M. Sc.

Universitt StuttgartInstitut fr Wasserbau

Lehrstuhl fr Wasserbau undWassermengenwirtschaft

Prof. Dr.-Ing. Silke Wieprecht

Risk and Planet Earth Conference 2009, Leipzig

Estimation of possible damagesdue to catastrophic flooding

for long-term

disaster mitigation planning


2/22

Risk and Planet Earth Conference, Panel 2 for Junior Scientists, 4 th March 2009, Leipzig

Contents

- Introduction

- 1D-Hydrodynamic modeling with MIKE 11

- Development of an improved method for loss of

life (LOL) estimation

- Loss of life (LOL) estimation for different scenarios

- Conclusions and Suggestions


3/22


Introduction

- Role of climate change in disaster management

- Possible extreme changes in climate as guidelinesfor the development of new concepts for disaster

mitigation

- Drastic weather change

- Heavy rainfall- Catastrophic flooding downstream of the dam

- Risk to people and property


4/22


5/22


Introduction contd

- Jhelum river valley downstream of Mangla dam in Pakistan

- One of largest earth and rock-fill dams in world

- Main dam height ~125 m high above riverbed

(by Google earth)


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Introduction contd

Gross storage (original) 7.25 E+9 m3

Net storage (original) 6.59 E+9 m3

Catchment area of reservoir (original) 33,360 km2

Water surface area of reservoir (original)

(at maximum conservation level)

253 km2

Power generation 1,000 MW

Crest length of main dam 2,561 m

Design capacity of main spillway 28,583 m3/s

Design capacity of emergency spillway 6,452 m3/s


7/22


1D-Hydrodynamic modeling with MIKE11

Chenab River

Upstream Trimmu Barrrage

Jhelum Bridges

Rasul BarrageMalikwal Bridge

Khushab Bridge

Confluence Point

SuketarNallahBandar Kas

Jabba Kas

Kahan River

Mangla dam

Bunha River

-Project Reach: about 329km

-Different Hydraulic

structures

-Five tributaries between

Mangla and Rasul Barrage;

No gauges are existing there

-1D-modeling for unsteady

flow conditions


8/22


1D-Hydrodynamic modeling with MIKE11contd

Maximum Discharges

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

55000

60000

65000

70000

0 50000 100000 150000 200000 250000 300000 350000

Downstream chainage (m)

Max.Q

(m3/s)

40000 m3/s (withbridges)

40000 m3/s (withoutbridges)

50000 m3/s (withbridges)

50000 m3/s (withoutbridges)

MDF (61977 m3/s: with

bridges)

MDF (61977 m3/s:without bridges)


9/22


1D-Hydrodynamic modeling with MIKE11contd

Rasul Barrage

High Flooding Scenarios (maximum water level)

150

160

170

180

190

200

210

220

230

240

250

260

270

280

290

0 30000 60000 90000 120000 150000 180000 210000 240000 270000 300000


Max.waterlevel(m) 40000 m3/s (with bridges)

50000 m3/s (with bridges)

MDF (61977 m3/s: with bridges)

40000 m3/s (without bridges)

50000 m3/s (without bridges)

MDF (61977 m3/s: without bridges)


10/22


1D-Hydrodynamic Modeling with MIKE11contd

Dam break Flood Routing (maximum discharges)

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

220000

240000

260000

280000

300000

320000

0 30000 60000 90000 120000 150000 180000 210000 240000 270000 300000


Max.

Q(m3/s)

Case1 (with bridges)



Case1 (without bridges)




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Rasul Barrage

1D-Hydrodynamic Modeling with MIKE11contd

Dam break FloodR

outing (maximum water level)

150

160

170

180

190

200

210

220

230

240

250

260

270

280

290

300

310

0 30000 60000 90000 120000 150000 180000 210000 240000 270000 300000


Max.waterlevel(m)








12/22


Development an improved LOL estimation method

LOLi= PARi x FATBASE x Fsv x Fage x Fmtx Fstx Fh x Fwar x Fev

LOLi=loss of life at a particular location i`` downstreamof the dam

PARi = Population at risk at a particular location i``downstream of the dam

FATBASE = Base Fatality rate of0.15 (worst case of medium

severity) (Graham, 1999), assuming an average value of1.0 forall other factors with average conditions.


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14/22


Fst= Storey risk factor

Fst= 1 (for high severity and all house types)

Fst= 1- S % (for medium and low severity)

Where, S= % of more storey houses

Fh = Health risk factor; 3% disabled people

Fh = 1 *H % + 1.25*D % (general form)

Where, H= % of PAR with avg. health, D= % of disabled PAR



15/22


Fwar = Warning factor (Graham,1999)

Warning Flood Severity understanding FwarNo No 1Some (15-60min) Vague/unclear 0.7 Adequate (>60min) Precise/clear 0.3

Fev = Ease of evacuation factor

Warning Ease of evacuation Fev

No No 1Some (15-60min) Some 0.7 Adequate(>60min) Good



16/22


Loss of Life estimation

PAR downstream of Mangla dam (98-Census data)

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

06754

1818

9

26479

3190

4

4613

0

5038

2

5594

7

6365

4

6830

4

7370

3

7837

5

8345

4

9020

5

1027

11

1101

38

1158

56

1221

84

1352

57

1424

42

1525

75

164039

1786

97

1891

27

196388

206975

2169

66

2281

96

2379

66

246780

2565

48

266370

2809

16


PAR(No.ofPeopleatrisk)

PAR

Total PAR : 1178038

UrbanPAR : 37%

Rural PAR : 63%

Estimated PAR is related to the highest flood event in the past


17/22



Estimated Total Loss of Life downstream of Mangla dam (98-Census data)

0 5000 10000 15000 20000 25000 30000

1

2

3

4

5

S

electedScenarios

Total Loss of Life

LOL (MDF 61977 m3/s:without bridges)

LOL (MDF 61977 m3/s:

with bridges)

LOL (50000 m3/s:

without bridges)

LOL (50000 m3/s: with

bridges)

1- Warning Initiation

30min after Failure


15min after Failure

3- Warning Initiationat Failure


1hr before Failure


2hrs before Failure


18/22


%Total Loss of Life for Different Failure Cases

1

1.5

2

2.5

3

3.5

4

4.5

0 50000 100000 150000 200000 250000 300000 350000

Max. Discharge (m3/s)

%

TotalLOL(

%deadpeople)

%LOL (with bridges)

%LOL (without bridges)

Worst Case for Warning

Initiation:

30 minutes after Failure



19/22


Cumulative Loss of Life due to Dam Failure

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

0 25000 50000 75000 100000 125000 150000 175000 200000 225000 250000 275000 300000


CumulativeLOL

Failure Case1

Failure Case2

Failure Case3

% Cum. LOL up to 50Km:

about 80% ofTotal LOL



Total LOLWorst Case for Warning

Initiation: 30 minutes

after Failure





20/22


Conclusions and Suggestions

- Severe climate change can cause extreme flooding downstream of adam

- Estimation of possible damages is an important part of any dam

safety study

- Loss of life increases with the delay in warning initiation with respect

to dam failure

- For all dam failure cases, maximum LOL (~80%) occurs in first

50 km downstream of Mangla dam

- % total LOL for the worst case of Mangla dam failure is close to 4%

which seems to be very high


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Conclusions and Suggestions

- LOL results clearly show the need of improvement in existing riskReduction measures in order to reduce possible LOL due to Mangla

dam failure

- More research is required to estimate

- ease of evacuation- risks posed by age groups- very low strength houses and more storey houses

- Realistic estimation of possible LOL due to natural hazards likefloods helps in long-term disaster mitigation planning


22/22


THANKS FOR YOUR ATTENTION

QU

ESTIO

NS??

[email protected]

www.iws.uni-stuttgart.de

Lehrstuhl fr Wasserbau und Wassermengenwirtschaft

Institut fr Wasserbau, Universitt Stuttgart

Documents

Estimation of Possible Damages Due to Catastrophic Flooding for Long-term Disaster Mitigation Planning