Study on Flood Risk Reduction measures based onrecent flood disaster cases in JapanMay 30, 2019
Osamu ITAGAKI, Flood Disaster Prevention Division, River Department
National Institute for Land and Infrastructure Management, MLIT*
Photo：Akatani-gawa River in Fukuoka Prefecture（Aug. 4, 2017）

Contents
Ⅰ What is NILIM
Ⅱ Background of Study
Ⅲ Climate Change Adaptation study for FRR
Ⅳ Simplified Flood Hazard Mapping
Ⅴ Urban Flood Prediction System
Ⅵ How to promote in-advance evacuation
Ⅶ Human Damage Macro Evaluation Method forCatastrophic Flood Disasters
Ⅷ What I learned during my stay in the Philippines

1

Ⅰ What is NILIM
National Institute for Land and InfrastructureManagement (NILIM) is a research institute in Ministryof Land, Infrastructure, Transport and Tourism.
As the only national research organization in theinfrastructure/ housing field, it works to create an
attractive society that is safer, more secure, and morevigorous, by conducting engineering research anddevelopment, and disseminating the results so as tocontribute to society now and in the future, by
preventing and mitigating disasters, promoting
formulating favorable environments, and by utilizing,
maintaining, and improving roads, rivers and harbors,
etc.

Ref. http://www.nilim.go.jp/english/about/nilim2017e.pdf.
2

Ⅰ What is NILIM

Tachihara Office

Asahi Office

You are here

Yokosuka Office3

Ⅱ Background of Study
Catastrophic flood disasters are frequently occurring in Japan these years.

Flood disaster by Typhoon No.18 (Etau) in September 2015

Photos: From website of Joso City government, Ibaraki Prefecture.

20 fatalities, 82 injured, 81 residential houses completely destroyed, 7,090
residential houses half destroyed, 384 residential houses partially destroyed,
2,523 residential houses flooded above the floor, 13,259 residential houses
flooded below the floor, 37 public buildings damaged, other 1,685 buildings
damaged. (As of Oct. 18, 2017, Fire and Disaster Management Agency)

4

Ⅱ Background of Study
Flood disaster by Typhoon No. 10 (Lionrock) in August 2016

Photo: Geospatial Information Authority of Japan http://www.gsi.go.jp/BOUSAI/H28.taihuu10gou.html

26 fatalities, 3 missing, 14 injured, 518 residential houses completely destroyed,
2,281 residential houses half destroyed, 1,174 residential houses partially destroyed,
279 residential houses flooded above the floor, 1,752 residential houses flooded
below the floor, 17 public buildings damaged, other 2,500 buildings damaged (As of
Nov. 8, 2017, Fire and Disaster Management Agency)
5

Ⅱ Background of Study
Flood disaster by seasonal rain front and Typhoon No.3
(Nanmadol) in July 2017.

Photo: Geospatial Information Authority of Japan http://www.gsi.go.jp/BOUSAI/H29hukuoka_ooita-heavyrain.html

42 fatalities, 2 missing, 39 injured, 338 residential houses completely destroyed, 1,101
residential houses half destroyed, 89 residential houses partially destroyed, 223
residential houses flooded above the floor, 2,113 residential houses flooded below thefloor, 9 public buildings damaged, other 1,407 buildings damaged. (As of Oct. 31, 2018
Fire and Disaster Management Agency)
6

Ⅱ Background of Study
Flood disaster by Torrential rainfall and Typhoon No.12(Jongdari) in July 2018237 fatalities, 8 missing, 466 injured, 6,767 residential houses completely destroyed,
11,248 residential houses half destroyed, 4,199 residential houses partially destroyed,
7,173 residential houses flooded above the floor, 21,337 residential houses flooded
below the floor, 159 public buildings damaged, other 2,423 buildings damaged. (As ofJan. 9, 2019, Fire and Disaster Management Agency)

7

Ⅲ Global Climate Change Adaptation study for Flood
Risk Reduction

8

Ⅲ GCC Adaptation Study for FRR
IPCC Assessment Report 5 （2014）

http://ipcc.ch/report/ar5/syr/Ⅲ GCC Adaptation Study for FRR
IPCC Assessment Report 5 （2014）

http://ipcc.ch/report/ar5/syr/*Red circle and line added.

Ⅲ GCC Adaptation Study for FRR
Global Circulation Model mesh size around Tokyo (MRI-GCM20* (left),and MRI- RCM5* (right))* GCM20: Atmospheric GCM with the horizontal mesh size of about 20km,
RCM5:Regional Climate Model with the horizontal mesh size of about 5km

Tone-gawa River basin (Largest river basin in Japan)* NILIM (2013) and retouched.

11

Ⅲ GCC Adaptation Study for FRR

Number of A-class River
Basins in Japan

River Basin Scales in Japan and the Philippines
e.g. Tone-gawa:16,840 (km2)
cf. Cagayan:27,280, Rio Grandede Mindanao:23,169,
Agusan:10,921, Agno:5,952,
Pasig:570* From Wikipedia.

River basin area km2
A-class River: 109 rivers managed by the central government of Japan.
High resolution prediction of extreme rainfall is essential for predicting theprobability distribution of Floods in each River Basin.
Adopted GCM20 (20km mesh Global Circulation Model), RCM5 (5km
mesh Regional Climate Model) by Meteorological Research Institute (MRI). 12

Ⅲ GCC Adaptation Study for FRR
Framework for CCA against Water-Related Disasters*
Current capacity ofhard measures

Disaster Scale for planning/
designing hard measures

Maximum assumed
Disaster scale

Scale of rainfall, flood
Smaller
Againstcomparatively
small scale offlood, hardmeasures areplanned andconstructed.

LargerAgainst the extreme/ larger flood
than current capacity of hardmeasures, the portfolio approach is
crucial for DRR.
Besides, there are some types of flood, e.g.
riverine floods, urban floods, coastal floods.

* From the report on the Framework of Climate Change Adaptation against Water-Related
Disasters submitted by Council for Infrastructure Development, MLIT, 2015.

13

GCM20-1

Median among A-class rivers**** “Value in climate condition at the end of 21st
century”/ “Value in current climate condition”

Macro estimation of future Flood Risk in Japan*
GCM20-2Upper end
MedianLower end

End of 21st Century
4.4
times

RCM5-1
RCM5-2

1.16 times
In average

1.24 times
in average

1.95 times
In average

2.87 times
in average

1.8
times

Rain fallFlooddischarge

Amount of FRR
works

Probability ofFlood* By Kakushin Project, NILIM conducted the analysis based on the result of climate change
simulations by MRI Japan. Current：1979～2003, End of 21st Century：2075～2099. 4 Red lines areaverages of Median estimates among A-class rivers for 4 models.
8* NILIM (2013) and retouched14

Ⅳ Simplified Flood Hazard Mapping (for Small andMedium Rivers with LP data)
1. Recent Flood Disasters in Small and Medium
Rivers2. Challenges
3. Simplified Flood Hazard Mapping method
4. Test application examples
5. How to use Simplified Flood Hazard Map
6. Remaining Challenges

15

1. Recent Flood Disasters in Small and Medium Rivers(1) Flood Disaster in Omoto-gawa River by Typhoon No. 10(Lionrock) in August 2016

Photo: Taken by Researcher Yamaji (then) of Water Cycle Div. NILIM on Sept. 2, 201616

1. Recent Flood Disasters in Small and Medium Rivers(1) Flood Disaster in Omoto-gawa River by Typhoon No. 10(Lionrock) in August 2016

Photo: Taken by Researcher Yamaji (then) of Water Cycle Div. NILIM on Sept. 2, 201617

1. Recent Flood Disasters in Small and Medium Rivers(2) Flood Disaster in Akatani-gawa River during Northern Kyusyu
Heavy rainfall in July 2017

Photo: In Asakura City, Fukuoka Pref. on Aug. 4, 201718

1. Recent Flood Disasters in Small and Medium Rivers(2) Flood Disaster in Akatani-gawa River during Northern Kyusyu
Heavy rainfall in July 2017

Photo: In Asakura City, Fukuoka Pref., taken by Researcher Nishi (then) on Aug. 4, 201719

1. Recent Flood Disasters in Small and Medium Rivers(2) Flood Disaster in Akatani-gawa River during Northern Kyusyu
Heavy rainfall in July 2017

Photo: In Asakura City, Fukuoka Pref., taken by Researcher Nishi (then) on Aug. 4, 2017 20

1. Recent Flood Disasters in Small and Medium Rivers(2) Flood Disaster in Akatani-gawa River during Northern Kyusyu
Heavy rainfall in July 2017

Photo: In Asakura City, Fukuoka Pref., taken by Researcher Nishi (then) on Aug. 4, 201721

2. Challenges
Table 4-1 Length of rivers in Japan
Managed by theManaged byCentral Government in Prefecture in
thousand km
thousand km

Total
in thousandkm

A class river

11

77

88

B class river

0

36

36

Total

11

113

124

Created based on MLIT（http://www.mlit.go.jp/common/001139145.pdf, http://www.mlit.go.jp/common/001139148.pdf）

Huge number/ length of rivers are managed by Prefecture. But Prefectures
are in general much facing difficulties with the shortage of budget andengineers/ staffs for managing the rivers.
Difficulty in surveying the cross sections of the rivers, and creating the flood
hazard maps in each river.
Low cost Flood Hazard Mapping method is necessary to provide the Flood
Hazard Maps of Small and Medium Rivers.
23

2. Challenges
The Flood Safety Level of Small and Medium Rivers (managed by Prefecture
Government) are relatively lower than that of Large Rivers (managed by the Central
Government).
The Central Gov. Managing Rivers（Mostly colored blue）
LegendFlood Safety Level for morethan 30 years return period.
Flood Safety Level from 10
to 30 years return period.

Prefecture Gov.
Managing Rivers（Mostly colored redor yellow）

Flood Safety Level for lessthan 10 years return period.

Fig. 4-1 Flood Safety Level Rough Assessment based on LP data
conducted by NILIM (as of 2007)（http://www.nilim.go.jp/lab/rcg/newhp/seika.files/lp/index.html）

24

2. Challenges
It has been pointed out that flood damage is likely to occur in low places
along the river.→ How to share this simple risk information in society?
Low places along the river

River

HousesFig.4-2 Cross sectional image with terrace

Low places along the riverRiver

Fig.4-3 Cross sectional image without terrace25

3. Simplified Flood Hazard Mapping method
Because of the huge length of the small and medium rivers, and the limitation of thebudget and the number of the staffs, it is in general difficult to provide flood hazard
maps in small and medium rivers in Japan.
For this reason, Simplified Flood Hazard Mapping method has been developed by
NILIM, and notified by MLIT in Dec. 2018.
① Considering the wide range of the scale of possible Flood and GCC, provision ofrelative Flood Hazard zone was prioritized. It is different from the current general
Flood Hazard Map based on a single Flood scale/ scenario.
② Tried to reduce the amount of the necessary data and the time/ resources forcreating the FHM through simplifying the flood hazard zone assessment calculation.
③ Tried to contribute to Laver saving by using the existing LP data and “Small andMedium Rivers Flood Safety Assessment System developed by NILIM”(http://www.nilim.go.jp/lab/rcg/newhp/seika.files/lp/abst.html).

Fig.4-4 LP survey

Fig.4-5 Example of presumed river cross section from LP data26

3. Simplified Flood Hazard Mapping method（２）Deriving H-Q equation at each river cross section
12LP横断図
10mDEM

10

標高（m）

8
6
4
2
0

0

100

200

300

400

500

600

700

800

900

1000

1100

1200

X方向（m）
435
434
433

標高（m）

432
431
430
429
428
427

Fig.4-7 Presumption of river cross section from LP data

426

LP横断図

425

10mDEM

424

0

20

40

60

80

100

120

140

X方向（m）

Fig.4-8 Comparison between 10mDEM and LP data・Roughness coefficient of river channel is always assumed to be 0.033.
・The river water level was calculated with assuming the fixed width of river flowing channel.
・Each H-Q equation is derived from one-dimensional non-uniform flow calculation result with multiple Qs.
(River cross section interval is about 0.1 km, but flexible.)・”Small and Medium Rivers Flood Safety Assessment System” developed by NILIM（http://www.nilim.go.jp/lab/rcg/newhp/seika.files/lp/abst.html） was used for laver saving.

28

3. Simplified Flood Hazard Mapping method（３）Estimation of Flood water level for each heavy rainfall scale

Fig.4-9 Example of
distribution of rain gauge
stations in a river basin

PWRI（https://www.pwri.go.jp/jpn/results/offer/amedas/sample.htm）

Fig.4-10 Rainfall intensity- Annual Exceedance Probability assessment
program provided by PWRI (for rain gauge stations in Japan only)②
AssumedFlood flow
width

Fig.4-11 River cross sectional image for
estimating flood water level and flood prone area29

3. Simplified Flood Hazard Mapping method（４）Delineation of Flood Prone Area for each Flood Scale②

Legend*Rainfall intensity in flood arrival time(about 2 hours).

AssumedFlood flow
width

: 50mm (AEP 1/10): 70mm (AEP 1/30): 90mm (AEP 1/100)※Note
This figure is created based on roughestimation of Flood Prone Area based on LP
data. The colored areas are relatively floodprone areas, and non-colored areas are notnecessarily non-flood-prone areas. Besides,
other disasters’ prone areas, such as
sediment disaster prone areas, are notpresented in the figure.

④Delineation of Floodprone area by smoothly
connecting the points of③.

Fig.4-12 Delineation of Flood Prone Area

Fig.4-13 Example of Simplified
Flood Hazard Map30

4. Test application examples
Comparison between the estimated flood prone area, actual flooded area in July 2017, and theValley floor by Terrain classification*.
* From Terrain Classification Map（Mar. 1986 Fukuoka Pref.）
140

130

Valley floor*

120

130
120

110

Flooded area
in July 2017100

Flooded area
in July 2017

110
100

90

90

80

80

70
60

Valley floor*

70
050 100 150 200 250 300 350 400 450 500 550 600 650 700① No.26 cross section

0

50 100 150 200 250 300 350 400 450 500 550 600 650 700② No.30 cross section

Legend：Cross section after Flood（as of Aug. 2017)：Cross section before Flood（as of Mar. 2017）：Estimated flood water level（with rainfall 137mm/hr）：Flooded area（roughly estimated from aerial photo）：Valley floor*P32

5. How to use Simplified Flood Hazard Map
e.g. To avoid flood prone areas when selecting the location of evacuation site and the route.

Flood scale of AEP 1/3
Flood scale of AEP 1/10
Flood scale of AEP 1/50

Fig.4-14 Simplified Flood Hazard Map image with the locationsof candidate locations of evacuation sites and routes

Relative proneness of flooding of candidate locations for evacuation sites is as follows.
site A<site B<site C<site D
Therefore, site A is relatively less flood prone among those 4 locations.
With assuming the gaps between the road surface and the ground surface are almost the same,
relative proneness of flooding of candidate evacuation route is as follows.
route α<route β
Therefore, route α is less flood prone than route β.
35

6. Remaining Challenges
In order to provide Simplified Flood Hazard Map for river reaches where there is a risk ofsevere sediment deposition during flood event, it is necessary to research and develop
methods for considering the flood water level rise caused by the severe deposition.
Flood water level may be higher by 1m or more if sediment depositionchanges the river cross section profile at the moment of the flood peak.

Cross section ①
Flood water level may be higher by 2m or more if
sediment deposition changes the river cross section
profile at the moment of the flood peak.

NILIM is conducting the
research and developing
to consider the influenceof sediment deposition
when creating Simplified
Flood Hazard Map.

Cross section ②
LegendRed/ Blue lines：Cross section After/ Before the Flood by LP data.
Orange line：Estimated flood water level with “After Flood” cross section
Navy blue line：Estimated flood water level with “Before Flood” cross section* Scale interval：Vertical=1m・Horizontal=10m
Fig.4-16 Examples of river bed rise and estimated flood water level rise during Flood in July 201736

Ⅴ Urban Flood Prediction System
1. Necessity of Urban Flood Prediction System
2. Outlines of Urban Flood Prediction System
3. Outlines of Social Experiment
4. Examples of Urban Flood Prediction37

1. Necessity of Urban Flood Prediction System
There are frequent urban floods caused by torrential rainfall, and it is crucial to continue
developing rivers and sewerage systems. But it is difficult to develop them enough against
such torrential rainfall events. Considering the impact of GCC, it is necessary to provide andutilize Early Warning System against Urban Flood necessary for emergency response such as
evacuation from the underground floor, damage prevention/reduction activities.
NILIM has developed a system which can promptly predict urban flood area/ depth of inundation
based on monitored/ predicted rainfall data etc., and distribute the urban flood alert mail.

Preparation for preventing flood
water from flowing into thesubway station.
Inflow of urban flood water into the underground
shopping area in Fukuoka city in June 1999.
Photo: Kyusyu Regional Development Bureau, MLIT

Flooded Underpass Signage.
Photo: From website of Kanto Regional
Development Bureau, MLIT

38

The Urban Flood Prediction information is up to 1 hr. ahead from the time of therainfall forecast. After receiving necessary data for the Urban Flood Prediction,i.e. rainfall and river water level observed/ predicted data, it takes 10 min. forthe calculation and the alert mail distribution.
In地下管理者のthe case ofシステム活用undergroundイメージ
facility
managerWithusing this
浸水予測情報new system…
があれば・・・

豪雨開始
Startof torrential
12:30
rainfall(12:30)（0分）

地下浸水開始地上浸水開始of underground 1時間先Startof flooding Start
13:10
13:00
13:30
flooding (13:10)(13:00)（+40分）（+30分）（+60分）

Torrential rainfall
豪雨開始
浸水察知
豪雨察知 ofPrediction
Receiving
12:10
12:00
Urban Floodtorrential rainfall（-20分）（-30分）
Alert(12:00)
予測雨量で
豪雨察知

【現状】
Withoutusing
Installation of waterproof board in 40 min.
監視カメラ等で 防水板の設置（40分）
this new system

Urban
Flood
浸水予測
Prediction

10分間でCalculationof
1時間先の浸水
Urban
Flood
を予測

地上浸水開始Flooding on the ground

地上浸水察知地下浸水開始Underground flooding
防水板設置中に地下浸水が始まる

RT浸水予測情報配信で浸水察知

Underground flooding will
start before completing
the installation ofwaterproof board.
地下浸水開始の20分前にInstallation of waterproof board in防水板設置完了
40 min.
→地下浸水なし
防水板の設置（40分）
Underground flooding will be prevented.

39

2. Outlines of Urban Flood Prediction System
The system is capable of delivering Urban Flood Warning from 40 to 50 minutes
before. Social experiment has been conducted in an area of about 100 km2 in Tokyo.
Observation and forecast data is taken into thesystem and Urban Flood Prediction
information is distributed at 10-minute intervals.

統一河川情報等
気象業務支援センター（XRAIN、水位等） （高解像度降水ﾅｳｷｬｽﾄ）

フェイズドアレイ
レーダ（予定）Predicted flood depth of each 25 m mesh is displayed.

Current

1 hr. later

予測、観測データ取得

Current

1 hr. later

Rainfall distribution

Closing up registered‘Monitoring Point' andcheck rainfall, flood
depth information.

Prevent/ reduce
Urban Flood damage
Residents

Emergency
responders40

2. Outlines of Urban Flood Prediction System

氾濫モデル
Groundsurface model

Pump
ポンプ所 station

河川モデル
River model

25ｍ size mesh人孔
manholes

水再生センター地表面
Groundsurface
雨水調整池
下水道へ

溢水

雨水吐室

河川及び下水道モデル
Riverand sewer model

雨水貯留管

下水道モデル
Sewer
model
Sewer pipes with diameter of 600 mm or larger are modeled.
41

3. Outlines of Social Experiment
Purpose： To verify Urban Flood Prediction accuracy, Urban Flood Damage Reduction
effect, Usability of the System.
Participants: Persons in charge of Flood Response Activities in LGU, Facility managers,
etc.. Approximately 50 people (as of the end of March 2019) in Kanda-gawa River basinin Tokyo.
Experiment Period：From FY 2016
What we learned…
① The contents and accuracy required for Urban Flood Prediction Information differ
depending on the his/her position, such as residents, officer in charge of Flood
Response in LGU, and facilities management users.
② Some residents prefer to receive information (including evacuation decisions)through the LGU rather than directly receiving the detailed Urban Flood Prediction
information.
③ Rainfall forecast accuracy greatly influences on the Urban Flood Prediction accuracy.
④ Even if people received the Urban Flood alert emails, most of the people did not
check the relevant information on the web of this system.

42

4. Examples of Urban Flood Prediction (Aug.
19, 2017)
Rainfall mm/10min.
Urban Flood was predicted, and thealert mails were distributed.

Flood depth cm
Observed rainfall
Predicted flood depth
16:02 Alert
mails
distributed

Estimated peak flood depth
based on observed rainfall
data (126 cm at 17:30)

Estimated flood depth

At the site of ****, the flood depth may
exceed 30 cm in 27 min.. (Current timeis 16:02, Aug. 19, 2017)

Fig.5-1 Alert mail distributed(actual one was all in Japanese)

Fig.5-2 Observed rainfall and predicted/ estimated flood depth

Fig.5-3 Estimated flood depth distribution
based on observed rainfall

43

4. Examples of Urban Flood Prediction (Aug. 13, 2018)
Predicted Urban Flood based on the forecast rainfall data is compared withthat estimated based on observed rainfall.→No big difference, but…
Flood
depth m水位(m)0.50

Predicted flood depth distribution asof 15:00 based on forecast rainfall
data as of 14:50.

Estimated flood depth distribution
based on observed rainfall as of
15:00.

0.45
0.40
0.35
0.30
0.25
0.20
0.15m

0.15
0.10

0.12m

0.05
0.00
13:30 14:00 14:30 15:00 15:30 16:00

At Kugayama in Suginami city, Tokyo, 1 hour rainfall
amount from 2:40 pm was 60 mm. At around 2:50 pm at
Kugayama Station of Keio Inokashira Line, the vicinity of
the north entrance was flooded and the escalator to thestation building was stopped for safety. The station
worker placed sand bags around the entrance and tried
to prevent the inflow. Due to the rain, the Inokashira line
stopped driving当該箇所の浸水深ひげ図
from 3:27 to 3:56 pm. (From Asahi
ShimbunAugust2018)
16:30
17:00 Digital
17:30 on
18:00
18:30 13,
19:00
19:30 20:00

9

4. Examples of Urban Flood Prediction (Aug. 13, 2018)
Estimated flood depth at 15:00 on August 13 based on observed rainfall data.
Estimated flood depth distribution
at 15:00 based on observed rainfall.

LegendInundation depth

0.15m

0.15m flood depth estimatedNo color

Actual inundation based on the photo.

0.15～0.2m actual flood depth

出典：http://健康法.jp/archives/41780

出典：https://twitter.com/kitabatake46

In the vicinity of
Kugayama station,
approximately 15 ~20 cm depth ofUrban Flood wasestimated based on
the photo.

Estimated flood depth：About 15～20cm

104. Examples of Urban Flood Prediction (Aug. 27, 2018)
Urban Flood was predicted based on
rainfall forecast data as of 19:50, but…

Asagaya
Sta.

Flooding near Asagaya Sta.
2018.8.27 20:17

Kugayama
Sta.

pic.twitter.com/22QM8AYbwv

Urban Flood occurred near these stations around 20:10.
Predicted flood depth of 30 cm as of 20:50
based on the rainfall forecast data as of 19:50

Predicted flood depth of 70 cm as of 21:00
based on the rainfall forecast data as of 20:00.

久我山駅周辺のひげ図にさし替え

Flooding near
Kugayama Sta.
2018.8.27 20:07

https://www.businessinsider.jp/post-174098

30 cm depth of Urban Flood near
Kugayama Sta. as of 20:50 was
predicted based on the rainfall
forecast data as of 19:50.

70 cm depth of Urban Flood near
Asagaya Sta. as of 21:00 was
forecasted based on the rainfall
forecast data as of 20:00. 11

Ⅵ How to promote in-advance evacuation（Based on the cases during the WesternJapan Heavy Rainfall in 2018）

Photo: Mabicho District, Kurashiki City, Okayama Prefecture on July 26, 201847

July 2018 Flood Disaster in Western Japan
Heavy rainfall in Western Japan from June to July 2018.
2,281 mm rainfall was observed at Ebino rain gauge station in Miyazaki Prefecture,
Kyusyu, from 0:00 June 4 to 24:00 July 17.
More than 1,000 mm rainfall was observed at many rain gauge stations in Kyusyu
during the same period.
More than 500 mm rainfall was observed at many rain gauge stations in WesternJapan.
304 mm/day rainfall was observed at Ohmuta rain gauge station in Fukuoka
Prefecture, Kyusyu.
(297 mm/day is estimated to be 200 years return period rainfall at Fukuoka rain
gauge station in Fukuoka Prefecture (Japan Meteorological Agency))
150 mm/h rainfall was observed at Kousa rain gauge station in Kumamoto
Prefecture, Kyusyu.
(150 mm/h is roughly estimated to be around 780 years return period scale ofrainfall (based on PWRI AMeDAS rainfall probability estimation program(https://www.pwri.go.jp/jpn/results/offer/amedas/top.htm) )237 fatalities, 8 missing, 466 injured, 6,767 residential houses completely
destroyed, 11,248 residential houses half destroyed, 4,199 residential houses
partially destroyed, 7,173 residential houses flooded above the floor, 21,337
residential houses flooded below the floor, 159 public buildings damaged, other
2,423 buildings damaged. (As of Jan. 9, 2019, Fire and Disaster Management
Agency)48

July 26, 2018 Mabichou-District, Kurashiki-City, Okayama-Prefecture49

50

July 26, 2018 Mabichou-District, Kurashiki-City, Okayama-Prefecture

51

July 26, 2018 Mabichou-District, Kurashiki-City, Okayama-Prefecture

52

July 26, 2018 Mabichou-District, Kurashiki-City, Okayama-Prefecture

53

Flood Hazard Map revised by MLIT in April 2017

Estimated flooded area in July 2018 by Geospatial Information Authority, MLIT.
Actual flooded area was almost the same as that on the previously published Flood
Hazard Map, but…

54

Challenges of July 2018 Flood Disaster in Western Japan
Most of the people did not evacuate, e.g. 97% of people equal to or above 60 years old
did not evacuate (Research by CeMI). (Sept. 2, 2018 Yomiuri-shimbun newspaper)
48% of the people have not seen the Hazard Maps. (Research by Sompo Japan
property insurance company) (Aug. 26, 2018 Yomiuri-shimbun newspaper)
Only 0.5% people evacuated when received evacuation advise or evacuation instruction.
(Research by Fire and Disaster Management Agency) (Aug. 7, 2018 Sankei-shimbun
newspaper)

55

Challenges of July 2018 Flood Disaster in Western Japan
Reasons why one evacuated (Questionnaire survey in Hiroshima, Okayama, andEhime Prefectures by NHK Aug. 6, 2018)
Reason why evacuatedDegradation ofsurroundings
Calls by police and fire
department
Calls by neighbors
Calls by family/ relatives
Community Radio for
Disaster Prevention
TV/ Radio
Others“Calls” constitutes large proportion of the reasons of evacuation.

56

Challenges of July 2018 Flood Disaster in Western Japan
What information was considered when evacuated (Questionnaire survey inHiroshima, Okayama, and Ehime Prefectures by NHK Aug. 6, 2018)
Considered information for evacuation
Nothing special
Evacuation advisory
Evacuation instructions
River water level
information
Special warning
Warning
Sediment-related disasterwarning information
Others
Evacuation advisory/ instructions, warnings are not considered so much for
deciding the evacuation.

57

Proposal: Shifting from Flat structure to Hierarchy
structure for ensuring evacuation in each community so
as to prevent fatalities
Alert
Warning

Alert
Warning

We should shift to…

DRRM
leaders incommunity

Residentsincommunity“Flat structure”
Independent, decision making by individual,…
→Difficult to ensure the evacuation.

“Hierarchy structure”
Next to each-other , face-to-face,evacuate together, …
→It matches behavioral
characteristics in Japan. 58

Ⅶ Human Damage Macro Evaluation Method forCatastrophic Flood Disasters (with considering Distributionof Mid-to-High-Rise Buildings in Neighborhood)59• In Japan, not small number of residents
would start evacuation when flood inundation
was indeed imminent.
• We found that it was important to consider
the distribution of mid-to-high-rise buildingsin neighborhood when developing andexamining specific lifesaving
countermeasures against catastrophic flood
disaster.

60

Case study river, with maximum inundation area with
AEP 1/200 flood

Casestudy river

Estimated inundation depth [m]or more61

Case study districts
Case study districts
in a rural area

Case study districts
in a suburban area

: Area with estimated Inundation
depth of 5m or more

Case study districts inhighly developed area
62

Suggested modeling method of emergency evacuation
depending on distribution of temporary evacuation shelters andresidential houses.
Some of the family members could not run into the temporary evacuation shelter because
the distance from the house to the shelter was too far.
Possible running
into areafor evacuation
velocity of
24m/min
For 12m/min

Temporary evacuation shelter 1 Temporary evacuation shelter 2（Full）All of the family members
have run into the shelter.
Some of the family members
could not have run into theshelter, because the shelter was
full.

Some of the family members could nothave run into the shelter, because theelderly or infant members could notevacuate quickly.

* The number of the residents having evacuated in advance, for example evacuatedjust after the flood warning issued, are deducted in advance from this simulation. 66

Examples of simulation results in highly developed area

Left: Without using mid-tohigh-rise buildings as
temporary evacuation
shelter

Right: Using mid-to-high-risebuildings as temporary
evacuation shelter67Additional shelter plan in the highly developed area

New shelter(200m2) inthe park

🄫🄫Google 68

Examples of simulation results in suburban area

Left: Without using mid-tohigh-rise buildings as
temporary evacuation
shelter

Right: Using mid-to-high-risebuildings as temporary
evacuation shelter
69

New shelter plan in the suburban area

New shelter(884m2) inthe factory

🄫🄫Google

70

Examples of simulation results in rural area

Left: Without using mid-tohigh-rise buildings as
temporary evacuation
shelter

Right: Using mid-to-high-risebuildings as temporary
evacuation shelter71Additional shelter plan in the rural area

New stairs on
the slope of highway
Embankment.

🄫🄫Google72

Macro Assessment of FRR measures
Possibleevacuation
minutes
Highlydeveloped
area

Decreasing rate ofTemporary evacuation shelter Number of people not able
number of people not
to evacuate
case number
abel to evacuate

5155Rural area
15
5
SuburbanArea
15

Possible to make it
longer by warning,education, more
resilient defenses
etc..

1

14,700

-

2

3,130

79%

1

4,115

72%

2

1,799

88%

1

6,107

-

2

6,026

1%

1

5,314

13%

2

5,239

14%

1

11,789

-

2

9,382

20%

1

7,266

38%

2

3,375

71%

Possible to make thenumber of shelters
more by construction,
cooperation etc. in thearea.

Gradualcomprehensive
disaster risk
reduction. 73

Ⅷ What I learned during my stay in thePhilippines (from June 2015 to May 2017)74

Nice people and places in the Philippines.

75Summary of Main Issues and Comments
Main Issues

There are no systematic frameworks for Understanding andMonitoring DRR from concrete DRR implementation point of view.
• Less Incentive for Data Archiving, Sharing and Analyzing
for DRR.
• No general frameworks for Integrating Multiple DRR
measures among multiple stakeholders / agencies.
• Weak Implementation of Systematic DRR measures
based on Scientific Data from a long term point of view.
Comments

To overcome the issues above, New Systematic Framework /Method for DRR Understanding and Monitoring in locality
from a concrete execution point of view should be introduced. 76

Suggestions
Nationwide systematic mechanism using “Set ofHazard Maps with multiple scales ofpredominant Disaster” and “Disaster Risk
Graphs” (tentative name) should be introduced to
N/R/P/C/M/BDRRMC as appropriate forconcretely realizing DRR to reduce economic
damage from a long term point of view through“Mainstreaming DRR” and “Build Back Better“.

77To Understand Disaster Risk in the locality
Suggestion 1: Providing and sharing a Set of Hazard maps with multiple
scales of predominant type of disaster among stake holders.
Understanding DR in each area toward feasible DRR measures/Area BCM.
e.g. Storm Surge>100 yr. return period

50 yr. return
periodRare event
Appropriate for DRR planning against
catastrophic disaster situation, but
not appropriate for planning against
relatively frequent disaster events.
e.g. Good for evacuation planning.

30 yr. return period

10 yr. return period

Relatively frequent event

Appropriate for DRR planning against
relatively frequent disaster events.
e.g. Good for building foundation
elevation study for annual averagedamage reduction.
78

To Monitor DRR in the locality
Suggestion 2: Introducing Disaster Risk Graph (or Profile)* ineach area (e.g. LGU) as appropriate.
Example for flood
Effect of measures to lower thefrequency of the flood damage.

Effect of measures to mitigate the damage
被害の大きさを減じる適応策
causedby flood events. e.g. high standard
levee,
land use regulation in the flood risk area.
主に高規格堤防、流域に踏み出した対策

被害の大きさScale of flood damagee.g. river improvement work.

Consideration ofcatastrophic flood
disaster.

unavoidable
uncertainty
on the estimation

・Flood damage has multiple
aspects.
→Multiple axes for flood
damage estimation.

Mitigation of the flood
damagePrevent severe
increase offlood damage.

Scale / Return period of the flood
Ancient
condition

Current condition

With thecurrent plan

Flood damage mitigation
effects of some measures
have large uncertainty.
→Consideration of theuncertainty.
With additional measures* Tentative naming in English. Based on the research results of National Institute for Land and InfrastructureManagement, MLIT, Japan. e.g. http://www.nilim.go.jp/lab/kikou-site/data/info_data/2015_takenaka1.pdf

79

DRG framework is suitable to apply under GCC

Scale of flood damage

Evaluation based on2020 available data

As of 2015

As of 2020
(afterProject)

Evaluation based on2015 available data

As of 2015
As of 2020 (after
Project)
Changing the shapeof the curveWith downward shifting
project, the curve will
always shift downward.

Scale / Return period of the flood

80How to draw DRG with limited available data
(1) Collect the available data (e.g. Hazard Maps) in the target area.

Yolanda(>100 yr)

50 yr. return
period30 yr. return
period10 yr. return
period*Example
on
Storm
Surge
provided by
JICA
81

How to draw DRG with limited available data

Number of houses in inundated area

(2) Count the number of houses in the inundation area oneach Hazard Map.

10

30

50

100

Scale / Return period of Storm surge82How to draw DRG with limited available data

Number of houses in inundated area

(3) Judge the intersection with the horizontal axes depending on
past experiences or by engineering judgement.

10

30

50

100

Scale / Return period of Storm surge
83How to draw DRG with limited available data

Number of houses in inundated area

(4) Draw the curve smoothly connecting the points.

10

30

50

100

Scale / Return period of Storm surge
84How to draw DRG with limited available data

Number of houses in inundated area

(5) Clearly explain the limit of the DRG below the graph.

One of the
Characteristicsof the Storm
Surge Risk inthe target area

10

30

50

100

Scale / Return period of Storm surge* This graph is tentatively drawn by … depending on limited
available data. ….
85

How DRG shifts

Number of houses in inundated area

(1) After some relocation projects completed

10

30

50

100

Scale / Return period of Storm surge* This graph is tentatively drawn by … depending on limited
available data. ….

86How DRG shifts

Number of houses in inundated area

(2) After a new coastal embankment construction projectcompleted

10

30

50

100

Scale / Return period of Storm surge* This graph is tentatively drawn by … depending on limited
available data. ….

87How DRG shifts

Number of houses in inundated area(3) If the number of houses in high risk areas increased

10

30

50

100

Scale / Return period of Storm surge* This graph is tentatively drawn by … depending on limited
available data. ….

88How DRG shifts

Number of houses in inundated area(4) If the effect of climate change is assessed and considered

10

30

50

100

Scale / Return period of Storm surge* This graph is tentatively drawn by … depending on limited
available data. ….

89How to use DRG

Number of houses in inundated area

(1) To monitor the Disaster Risk in the target area.

As of 2020
As of 2015
Strike out measures
to shift the curve, even if
there is no actual disaster
happened these years.
Scale / Return period of Storm surge* This graph is tentatively drawn by … depending on limited
available data. ….

90

How to use DRG

Number of houses in inundated area

(2) To share the effect of DRR by a proposed project.

current situation
after the project
Share the potential effectof the project and discuss
the priority of the project.
Scale / Return period of Storm surge* This graph is tentatively drawn by … depending on limited
available data. ….

91

How to use DRG

Number of houses in inundated area

(3) To discuss the priority of the DRR measures.

B Municipality
A Municipality

If the other conditions arealmost the same, DRR
measures in B have relatively
higher priority than A.
Scale / Return period of Storm surge* This graph is tentatively drawn by … depending on limited
available data. ….

92

How to use DRG

Number of houses in inundated area

(4) To discuss the adaptation measures against GCC.
projected climate
condition
current climate
condition

Share the potential future
condition of the Disaster Risk
and discuss how to adapt to it.
Scale / Return period of Storm surge* This graph is tentatively drawn by … depending on limited
available data. ….

93How to use DRG

Number of residents in hazard
area who cannot evacuate to
shelter(5) To monitor the effectiveness of ongoing/completed
projects. e.g. monitoring the effectiveness of ongoing
evacuation shelter project

Before the projectCurrent situation
Monitoring theeffectiveness ofthe project.
Scale / Return period of Storm surge* This graph is tentatively drawn by … depending on limited
available data. ….

94

How to use DRG

Number of houses in inundated area

(6) To conduct immediate Pre Disaster Risk Assessment*
Grasping roughly thescale of imminent /affecting flood.

Immediately
assessing scale ofdamages in the
area.

Contributing to
PDRA exercise.
* Assumed to be conductedby DRRMO as appropriate.

Scale / Return period of Flood* This graph is tentatively drawn by … depending on limited
available data. ….

95How to use DRG(7) To Understand Risk and Impact on a factory

Number of Employees who
cannot commute to their factory

Discuss/design DRR measure

Current situation

With measure (A-3), elevating mostof commuters’ roads (i.e. more parts
to be elevated than measure A-2)
With measure (A-2), elevating someparts of commuters’ roads (i.e.
more parts to be elevated than
measure A-1)
With measure (A-1), elevating
limited parts of commuters’ roads

Scale / Return period of Flood

Decide the scale of themeasure with PublicPrivate coordination.

* This graph should be drawn by integrating a set of Hazard maps,
employees’ house distribution, and road network and elevation by using GIS.96

How to use DRG
Application outlines

Developed
area

Houses /Residents
distribution

Inundation areawith levee

Inundation area by
1/10 Flood
Inundation area by 1/20
Flood

Inundation area
without levee

Inundation area by 1/30
Flood
Inundation area by
1/50 Flood

Integrating with GIS

97

Test application of DRG
Location of Agusan river in Mindanao
Agusan river

From Wikipedia (https://en.wikipedia.org/wiki/Agusan_River https://en.wikipedia.org/wiki/Mindanao )98

Test application of DRG

Project NOAH’s Flood Risk Map(http://www.noah.dost.gov.ph/#/section/geoserver/flood5)

Project NOAH’s Flood Risk Map was available on the web site. Based on the Map, the numberof residents in the potential inundation area was each calculated by using GIS.
For “Without (river improvement) Project” case, the potential inundation area on the Map wasused as it was, because the existence of the riverine levee was not considered when the Map
was drawn by Project NOAH.
99

Test application of DRG

Outside River

Outside River
Project NOAH’s Flood Risk Map ( http://www.noah.dost.gov.ph/#/section/geoserver/flood5) added by theauthor.

For “With Project” case, the potential inundation area on the Map outside the river (Agusan
River), i.e. the area farther than the riverine levee from the Agusan river channel, was taken to
be not potentially inundated area for 5-year and 25-year return period scale flood, because thesafety level provided by the river improvement project was 30-year return period scale.
An assumption of 1.5 m increment of inundation depth inside the river for “With Project” casecompared with “Without Project” case was adopted up to the design scale of the flood of theriver improvement project for considering the riverine flood water confining effect by theriverine levee constructed.
100Test application of DRG
With or without Lower Agusan River improvement projectThis graph is tentatively drawn by OCD-JICA Expert on DRRM Office on May 18,
2017, based on the limited available data of DPWH-RXIII, Project NOAH and PSA,
and Google Map’s image in the area of Butuan city and Magallanes municipality,
Agusan del Norte province, Mindanao, the Philippines. This graph was drawn for
the area of Butuan city and Magallanes municipality. In this graph, an assumptionof 1.5 m increment of inundation depth inside the river for “With Project” casecompared with “Without Project” case was adopted up to the design scale of theflood of the river improvement.
101

Test application of DRG
With or without Lower Agusan River improvement projectThis graph is tentatively drawn by OCD-JICA Expert on DRRM Office on May 18,
2017, based on the limited available data of DPWH-RXIII, Project NOAH and PSA,
and Google Map’s image in the area of Butuan city and Magallanes municipality,
Agusan del Norte province, Mindanao, the Philippines. This graph was drawn for
the area of Butuan city and Magallanes municipality. In this graph, an assumptionof 1.5 m increment of inundation depth inside the river for “With Project” casecompared with “Without Project” case was adopted up to the design scale of theflood of the river improvement.
102

Photo: Town in Agusan River (May 3, 2015)

Thank you for your kind attention.
I welcome your questions and comments.
itagaki-o92e8@mlit.go.jp

103