vulnerability analysis for addressing …icfr2013.ex.ac.uk/papers/b1_ozaki.pdf · background and...

VULNERABILITY ANALYSIS FOR

ADDRESSING PLUVIAL FLOOD RISK IN

DENSELY URBANIZED AREA IN OSAKA

Kansai University: T. Ozaki, T. Ishigaki, N. Asano

Kyoto University : K. Toda １

International Conference on Flood Resilience:

Experiences in Asia and Europe

5-7 September 2013, Exeter, United Kingdom

Outline of presentation

Introduction

Background and Objectives

Methodology

Study area

Simulation model

Observed rainfall

Results & Discussion

Validating the model

Improving the model

Identifying new weak areas in urban area for flooding

Conclusion

Ozaki et al. 2 5-7 September 2013, Exeter, UK


Introduction


Methodology

Study area

Simulation model

Observed rainfall



Improving the model


Conclusion


Increase frequency of high intensity rainfall

& Increase rainfall intensity

Ozaki et al. 5-7 September 2013, Exeter, UK 4

Num

ber

of o

bse

rved 5

0m

m/h

r or

more

rain

fall

eve

nts

in Jap

an (

tim

es

/ ye

ar )

Damage occasioned by high intensity rainfall


26.08.2011 in Tokyo, (ref：FNN)

26.08.2011 (ref: TBS-i)

Pluvial (surface water) flooding

cause a traffic disruption

inconvenience for pedestrians

The rainfall amount was 80mm/hr in Tokyo.


In Osaka (ref：TBS-i)

(Ref：You Tube)

Pluvial (surface water) flooding

cause a detriment to the

commercial activities.

threaten the life of people,

ex) underground user

The next day, the same thing happened again.

Damage occasioned by high intensity rainfall

Objectives


Validating the pluvial flooding simulation model

Improving the model

Identifying the weak areas for pluvial floods in densely urbanized area

Validating Identifying Improving

To make a flood prevention and management plan

Save life, Save valuable property, prevent a urban activities


Introduction


Methodology

Study area

Simulation model

Observed rainfall



Improving the model


Conclusion


Study area

9

Tokyo Osaka

Japan

a lot of skyscrapers, mega-underground mall

some underground stations

Ozaki et al. 5-7 September 2013, Exeter, UK

Osaka Central St.

Total area: 1,215ha

2 Ground stations 5 Underground Stations 129 underground entrance

Study area


P

P P P

This area has combined sewer systems.

The capacity of discharging the rainwater is about 60mm/hr.

There are 4 pumping stations because ground level is almost under the sea level.

60m3/s

28m3/s

7m3/s 14m3/s

Pump capacity

Simulation model


By using the 1D-2D urban drainage/flood model

(InfoWorks CS)

Rainfall

Effective rainfall model

Rainfall runoff model

Conduit model

Inundation flow model

(Saint-Venant e.q.)

(Double Linear Reservoir Model) (Shallow flow e.q.)

(2D module) (Fixed PR Model)

PR: percentage runoff

1D-2D Model


1D model of Preissman Slot model considering pipes of

larger than 0.2 m diameter

Flow on roads was treated by using 2D shallow flow

model with unstructured meshs

Rainfall distribution (27 Aug, 2011)


Ref: Japan Meteorological Agency)

Daily rainfall amount Maximum hourly rainfall amount

12km

Hyetograph


Maximum hourly rainfall amount was 77.5mm/hr. (Highest

record in Osaka in the last 120 years)

Total rainfall was 88.0mm in 3 hours.

0

5

10

15

20

25

14:3

0

15:0

0

15:3

0

16:0

0

16:3

0

17:0

0

17:3

0

18:0

0

10分間雨量

(mm

/10m

in.)

27th August, 2011

Am

ou

nt

of

pre

cip

itat

ion

(

mm

/ 1

0 m

in.)

Rai

nfa

ll in

tensi

ty

(mm

/hr.)

0

30

60

90

120

150


Introduction


Methodology

Study area

Simulation model

Observed rainfall



Improving the model


Conclusion




Flood makers investigation

Field survey and interview

to store employees.

Rescue works investigation

Interview to fire-marshal

地下街

Station A

地下街0 500m100

Area-1

Area-3

Area-2

Area-4

10 - 30cm 30 - 50cm 50 - 70cmMaximum depth of simulation(m)

: Flooding and Survey Area

: Flood depth was observed more than 10 cm

: Entrance of underground space

UndergroundStation B

U. Station C

U. Station E

U. Station D

U. Station F



Station A

地下街0 500m100

Area-1

Area-3

Area-2


U. Station C

10 - 30cm

30 - 50cm

50 - 70cm

Maximum depth of simulation(m)

Area-1,

East-west orientation, there are good

agreement between the solutions of

the model and the data.

North-south orientation, there was

more than 20 cm disparity between

observed data and simulated data.

In this road section, some stores

were inundated. However the model

was not considering that situation.

Why

Real Model

Area-2,

the water depths were 30 – 40 cm in

the both results.



地下街0 500m100

Area-3

Area-4


U. Station C

U. Station EU. Station F

10 - 30cm

30 - 50cm

50 - 70cm

Maximum depth of simulation(m)

Area-3, 4

Simulation model could not

produce good results.

Observed water depths were

approximately 40 - 60cm,

simulated water depths were

around 30 - 40 cm.

Why Structural object on road such as

bridge pier or a central reserve

were not considered.

Therefore, the road width of

model was wider than real, and

the simulation water depth was

under estimate.

Improving the model


Before After Improved

• Considered the structural object on road such as bridge pier or a central reserve.

Identifying the weak areas


In the existing studies, researchers have indicated that

underpasses of road and/or railway and underground space were

vulnerable to inundation within urban area.

http://t.co/4EgxidnkbY



Narrow road between buildings or wall are vulnerable to

pluvial floods. Ordinary condition Inundated situation

Entrance to underground mall



Hollow shape of road is vulnerable to inundations.

①

②

① ②Car stuck

①

②



A basement floor of small building is vulnerable to floods.

Basement floor of small building

Conclusion


In this study, in order to validate our model, we

investigated the flood disaster and compare simulated

depth with observed depth.

We could obtain the good results to consider the

structural objects like a revetment, separating zone.

Regarding the identification of weak areas,

stakeholder have to consider the three vulnerable points;

i) Narrow streets between buildings or walls,

ii) Hollow shape of road,

iii) A basement floor of small building.


Thank you for your attention !

TOKYO SKYTREE

Results of Inundation analysis


Method of creating a model of road network

for 2D model


We can obtain the base map

(scale 1/2500).

By using GIS, we render the city

blocks polygon and railway

polygon

And subtract polygon rendering

map from base map

Base map

Polygon rendering of city blocks

road network

Parameter setting


Fixed Percentage Runoff Model

the runoff coefficient

0.85 (High density building and high quality paved roads area)

0.60 (other area)

Initial Loss value

0.00028 m (default)

Inundation flow model

Manning roughness value

0.043 (empirically)

Run parameter

Time step:

1 Second

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