Key Global Agendas

SendaiFrameworkon Disaster
Risk
Reduction

Investment

Sustainable
Development
Goals

December 2015

Opening Remarks

Governance
September 2015

March 2015

Understanding

EW/BBB

Paris
Agreement(COP 21)

Concerted Actions are Required
Toshio Koike
Director, International Centre for Water Hazard and Risk Management (ICHARM)
Professor Emeritus, the University of Tokyo
Council Member, Science Council of Japan (SCJ), Cabinet Office of Japan
1
Chair, River Council of Japan

HLPW Panel members

Reducing
Current Risk

Preventing
Future Risk
Building Resilience

Adaptation& Recovery

Sustainable Development(as of 3/21/2016)

Special Advisors to the Panel

Kevin Rutte

János ÁderEmomalii RahmonovPrime Minister, Netherlands

President, Hungary

President, Tajikistan

Dr. Han Seung‐soo
Former prime
Minister, South Korea
Manuel Pulgar‐Vidal
Minister, Peru

`

`

`

Macky Sall
President, Senegal
Co‐chairs

Abdullah Ensour``

Enrique Peña Nieto

Prime Minister, Jordan`

President, Mexico``

President, Mauritius

Jacob Zuma

Sheikh Hasina
Prime Minister, Bangladesh

Ameenah Gurib‐Fakim``

Malcolm Turnbull

President, South Africa

Co‐convened by:Prime Minister, Australia

UNITED NATIONS
3

Ban Ki‐moon

Jim Yong Kim

Secretary General, United Nations

President, World Bank Group

4

5

6

in collaboration with
National Graduate Research Institute for Policy Study (GRIPS)
Platform on Water and Disasters
STIPS

DMP
Community ofPractice

Policy-making

Societal Change(land use , population)

past

Monitoring
Identification

Integrated Risk Assessment
1. DataArchiving

Socio-

DamageHazardEconomic

Capacity Building

Interdisciplinary

2. ModelDevelopment

Prediction

present

MasterCourseHigh-Level Practitioner

futureClimate Change

PhD
CourseHigh-Level Policy-Maker

Transdisciplinary

3. Societal
BenefitCreation

Short
CourseICHARM

International Cooperation

78

Support for effective flood management in Sri Lanka• Background/ History
 Large‐scale flood disaster occurred in Sri Lanka in late May
2017, leaving over 300 people dead or missing.
 The Government of Japan dispatched the Japan Disaster
Relief (JDR) Expert Team to help emergency efforts, towhich PWRI has contributed.
 ICHARM and EDITORIA will continuously provide useful
information for flood management though newly
developed Website on the DIAS, and will conduct capacity
development for effective use of information.

2nd Plenary Session for the Platform on
Water Resilience and Disasters in Sri Lanka

Review of the 1st Plenary Session
for the Platform on Water and Disasters
on August 24, 2017
Tetsuya IKEDA
Chief Researcher Plenary Session was held on August 24 with the DGsfrom ID, MD, DMC, NBRO, Ministry of Megapolis & WD

E-mail: te-ikeda@pwri.go.jp

International Centre for Water Hazard and Risk Management
under the auspices of UNESCO (ICHARM)

March 28, 20181

• Expected Outcomes This will lead to human damage reduction and efficient
emergency recovery by disseminating effective flood
forecasts and early evacuation alerts.

2

1st Plenary Session
for the Platform on Water and Disasters”

Support Menu for effective flood management in Sri Lanka(EDITORIA, ICHARM, JAXA‐SAFE)

Time: August 24, 2017
Venue: Auditorium, Irrigation Department, Colombo, Sri Lanka
Co‐Chair:Prof. Srikantha Herath, Ministry of Megapolis & Western Development
Prof. Toshio Koike, ICHARM
Agenda:• Lessons and actions from 2017 flood disaster (Report from Sri Lankan
institutions and Japan Disaster Relief Expert Team)• Presentation on ICHARM’s activities• Discussion on concept and framework of Platform on Water & Disasters• Targeted Actions to be undertaken

1) Rainfall forecasting information
 Hourly 3 days prediction, possibly 16 days ahead at maximum2) Rainfall observation data Ground observation data through rain gauge installation
 Satellite observation data
- GSMaP_NOW (0 hour delay from observation)- GSMaP_NRT corrected data by using real time in-situ data

3) Calculated hourly information on flood & inundation forecasting4) Cloud images from Himawari No.8 satellite5) Information on large-scale inundation area from an emergency
satellite observation by ALOS-2
6) DIAS: Real-time provision of flood hazard information
7) Capacity development for effective flood management34Outcomes of the 1st Plenary Session

Global Forum on Science and Technology
for Disaster Resilience 2017

Platform Participating Organizations:
•
•
•
•
•
•
•

Irrigation Department (ID)
Meteorology Department (MD)Survey Department (SD)
Disaster Management Center (DMC)
National Building Research Organization (NBRO)
Ministry of Magapolis and Western Development (MMWD)
Ministry of Mahaweli Development & Environment (TBD, MMDE)

Date: November 23 - 25, 2017 Venue: Tokyo, Japan
Organizers: UNISDR, ICSU, IRDR, SCJ, ICHARM, NIED
Participants: 228 from 42 countries(including Dr. Asiri, DG of NBRO and Ms. Anoja, Director of DMC)
Objectives:
1) Guidelines for strengthening DRR national platforms and coordination
mechanisms through enhanced contribution of science and technology
2) Periodic synthesis reports on the state of science and technology for
reducing disaster risk.

Platform Target Actions and Coordinating Bodies
1. Early Warning: rainfall, flooding, landslide:
2. Adaptation Planning for Global Change:(such as Climate Change, Urbanization)
3. Economic Effect of Disasters:
4. Contingency Planning and Mainstreaming DRR:

ID, MD, NBRO
ID, MMDE, MMWDA
MMDE, DMC
DMC

Demonstration Sites of Target Actions
1. Kalu River Basin (as rural basin)
2. Kelani River Basin (as urban basin)
3. Malvathu River Basin (as arid basin)

5

6

IFI technical session at the World Bosai Forum

3rd Asia-Pacific Water Summit Thematic Session

Platform on Water and Disaster– ICT, Economy, Community, Dynamics –WATER AND DISASTERS
IN THE CONTEXT OF CLIMATE CHANGE‐ From the Mountains to the Islands‐

Date: November 28, 2017
Venue: Sendai, Japan
Objective:
To discuss how the Platforms can contribute to reduction of waterrelated disaster damage from diverse perspectives, and to discuss about
the international trend and the effort by individual countries
Outline:• Keynotes from different expertise of ICT, economy, community, dynamics• Presentations by four government officials from Philippines, Sri Lanka,
Pakistan and Brazil

7

Time & Date: 13:30 – 17:00, December 11, 2017
Venue: Yangon, Myanmar
Co-organizers: ICHARM, ICIMOD, SPC, HELP
Session Framework:
Part 1: Keynote Speeches by High-Level Leaders (3 speakers)
Part 2: Country Presentations(from 7 countries and 10 presenters)
Part 3: Panel Discussion (5 panelists)

8

End to End Approach on Climate Change Adaptation
Scientific approach

Engineering ApproachSocio-economical approach

Quantifying
uncertainty
ClimatemodelsFloodcontrolsystemgroundwater

Storagedrought

Treatment

Environment

Human
Behavior

Human
life

Filed
surveyIndustry

Economic
Behavior

Adaptation
options
Early
warningInnovativetechnology‐ Flood
control‐ quality
control
Allocation
policyLand use

Drought Simulation
Monitoring
evaluation

Data & Information

Toshio Koike
Director, International Centre for Water Hazard and Risk Management (ICHARM)
Professor Emeritus, the University of Tokyo
Council Member, Science Council of Japan (SCJ), Cabinet Office of Japan
1
Chair, River Council of Japan

VolumeVeracity

Working Together

MODEL SELECTION:
Precipitation(May‐November)

Insitu‐station

Velocity

Platform

Corrected GCMavg

Variety

Visualization

Capacity Building

Future Corr_GCMavg

2

Future ‐ Past

Probability:
10yearProbability:
100year3

implementation

Basin‐
scale
predictionofquantity& quality

Information

ordinary
water

Flood Simulation

DecisionmakingIntegrated
Observed
Data Sets

Down‐
scaling

Current
facility, plan,management

Impact assessment

Process
Study

Multi
‐modelensemble
(MME)

Water
quantityand quality
prediction
flood

Water allocation & cost

Innovative Science and Technology
for Reducing Water‐related Disaster Risk

4

ICHARM Hydrological Simulation System
Integrated Flood Analysis System(IFAS)

DIAS-ICHARM: Flood Information Sharing Support in Sri Lanka

Rainfall‐Runoff‐Inundation (RRI)

Implemented by EDITORIA and ICHARM on DIAS

500mm
ALOS © JAXA (2016)

Real-time Rain
Gauge Data

Ensemble
Flood
Rainfall
Prediction

Inundation
map bysatellite data
(ALOS‐2)

Water andEnergy Budget
Distributed
Hydrological
Model(WEB‐DHM)

Simulation andforecasting of riverdischarge, water level,
inundation extent

Bias-corrected
Satellite Rainfall

Inundation a nalysis results

Ensemble
Flood
Prediction

Lateral
Flow

5

72hr
11ensemble
every 24hr

Inundation

Issuedon May24

Issued6
on May25

Data Integration & Analysis System: Challenges to 5V

Rice-Crops
1327M Peso

Himawari‐8
cloud images

Inundation
analysis by using
RRI in DIAS

forecast rainfall, inundation simulations

Flood Damage Assessment in Pampanga River Basin

37M Peso

Ensemble
forecasting
rainfall for the
next 16 days
(max)

Concept of RRI model

Energy‐Water
Balance

50-Year

Real time
data (NOW)

On-line Information provision on
DIAS：In‐situ rainfall, satellite rainfall, calibrated andRiver Flow

25-Year

4 hr latency
data (NRT)Calibration

Soil
Moisture
Dynamics

River Discharge
 Obs.
Ensemble
MeanSinglePrediction

Satellite precipitation data（GSMaP）

In‐situ rain gauge
data (6 numbers)

GRENE‐ei

ClimateChangeAdaptation

Hydroelectric
power

RECCA

100-Year

DIAS‐P
S‐8

1952M Peso

HealthGEOSS/AWCI

DIAS/CEOS
Water Portal

CMIP5

GEOSS/AfWCCIASIAN Monsoon Year

SocialImplementation
Joint Research

ClimateInternational Contribution
ICT Experts

EconomyField Specialists

Urban

Data Archive

R&D Community

Biodiversity

Agriculture

ICT Experts

Application Development

Field
Specialists

Water

Disaster
Risk
Reduction

Data Processing

Search / Download

ICT Experts

7

Extra-large
volume data
storage (25PB)

Base System

8High Speed

Single and Ensemble prediction system
Single (Deterministic) prediction

Ensemble Rainfall prediction
Downscaling

Regional
model

GCM

Hydrologicalmodel

Hydrograph with observation

Ensemble prediction

Tomoki Ushiyama (ICHARM, PWRI）
Mar. 28, 2018 at Sri Lanka plenary meeting
Hydrograph with observation
Downscaling

Globalensemble

Regional
ensemble1

Numerical Weather Prediction in Sri Lanka

a. Ensemble prediction provide
Ensemble
forecast uncertainty.
flood forecast
b. Ensemble mean is better than
2
a single control run.

GSMaP vs. Forecast (single)
GSMaP‐NRT

20km

72h Flood forecasts from
18UTC are available
at 01UTC (single)
at 02UTC (ensemble)

4km

21:50

5/23
00 06

12

18

Single forecast

5/24
5/25
00 06 12 18
00 (UTC)
72hour single rainfall forecast
72hour single flood forecast

Rainfallforecast(takes 3h)
Ensemble forecast

Hydrologicalforecast(takes ?min)

5/26
18

Single Forecast
Issuing Floodalert

72hour ensemble rainfall forecast
72hour ensemble flood forecast

22:52

3

4

Rainfall forecast in 25May2017
GSMaP observation

Grell3D

OverestimatedEnsemble Forecast

Newer Tiedtke

Ensemble prediction
showed a possibility
to predict heavy
rainfall in May 2017
flood.
May21～

GSMaP observation

Raingauge

Different color
shows ensemble
members

May23～

May24～

Forecast (based on ECMWF)
Grell3D
KF MS KF Modified Tiedtke
Newer Tiedtke

Local time difference: UTC+5.5h 5May22～

May25～

6

SYSTEM DEVELOPMENT & APPLICATIONS
GSMaP
Data

WRF
+Ensemble

Bias Correction

Hydrological
Model
Water Level,& Discharge,
Inundation

Hydrological
Model

Near
Real-time
RainfallMohamed Rasmy (Senior Researcher)International Centre for Water Hazards and Risk Management (ICHARM)

Flood
Forecasting

Public Work Research Institute (PWRI)

Short-term
Draught
Monitoring

Landslide/debris
flow
warning

Rainfall
Forecast
Geographical,
soil, and landuse data

Socio-economic Benefits:
fits:
Hazard Maps & Disaster Early Warning, Assessment of Risk and Damages

Technology upgrade for rainfall monitoring

2

Bias correction of satellite rainfall using ground observation
GroundGauge
Data

Dissemination through DIAS

JAXA-SAFE PROJECTREAL-TIME RAINFALL MONITORING &HYDROLOGICAL MODELING IN KALU RIVER BASIN

System Development & Integration

Gauge
Data

Error of rainfall area location(Geolocation error)
corrected by comparison of rainfall pattern

Bias of rainfall intensity
Corrected using information of real-time groundobservation
Bias corrected GSMaP

GSMaP_NRT
Bias correction*60D3,)

GGGGGG pG

25 May 2017, Kalu basin

4Rainfall – Runoff – Inundation (RRI) Model
Input

Water-Energy budget-RRI (WEB-RRI) Model
Energy andWater flux
Balance

Subsurface + Surface
1D Diffusion
in RiverRainfall

OutputVerticalSoil moisture
ProfileDischarge

DEM
River Routing

W. Level
Land
Cover
Cross Sec.

Surface flow

Inundation

2D Diffusion
in Catchment

River flow - 1D
Diffusion wave

Vertical Infiltration
Slope-RiverInteraction• Two-dimensional model capable of simulating rainfall-runoff and flood inundation simultaneously

GW-RiverInteraction

Lateral flow-2DDiffusion wave
GW-Soil MoistureInteraction• The model deals with slopes and river channels separately

Ground water Flow

• At a grid cell in which a river channel is located, the model assumes that both slope and river are
positioned within the same grid cell
Sayama, T. et al.: Rainfall-Runoff-Inundation Analysis of Pakistan Flood 2010 at the Kabul River Basin,
Hydrological Sciences Journal, 57(2), pp. 298-312, 2012.

Hydrological Modeling – Real--time modeling

ENSEMBLE FLOOD FORECAST
May24䡚

Inundation depth

From 24th May ~ 31st May

Hourly discharge at Kekula

Hourly discharge at Putupaula

Inundation depth
Need accurate DEM data for better resultsRiver Water LevelENSEMBLE FLOOD FORECASITNG

7

May25䡚

From 25th May ~ 1st JuneENSEMBLE FLOOD FORECASITNG
8Warning and evacuation for sediment disasters

A Method to Estimate Sediment Runoff due to
Heavy Rainfall in A Mountainous Watershed

Timingfor evacuation and warning
When

ICHARM Yusuke Yamazaki• The present study proposes a method for predicting sediment runoff tomountain streams during heavy rainfalls based on an estimation of landslides
and associated debris flows.
• We formulated developing and decreasing of debris flow along a stream using
a mass point system as well as an erosion and deposition formula of debris
flow.
• The method is applied to a mountainous watershed during heavy rainfall
event and it successfully reproduces the actual behavior of debris flow. The
result shows the method enables to estimate sediment production and runoff
spatially in a mountainous watershed.
1

Sediment runoff on river network
V21

V2

V20 V1

2

Locations of safe places and paths

1

debris fan

Required
Where information: spatial distributions of theoccurrences of shallow landslides, the runout path ofdebris flow and the debris fan.

Schematic view of sediment hazards

Methodology
To obtain the above information, the following methods are proposed.
One method is to predict occurrences of shallow landslides and debris flows based on stability analysis for an infinite
slope, rainfalls, surface topography and soil properties.
Another method is to predict spatial distributions of sediment volume associated with debris flows based on an
estimation of developing and decreasing of debris flows produced by landslides.
2: longitudinal section area
: velocity

11：cell of landslide：drainage of 1st order channel：distance from landslide to stream

debris flow

Governing equations for debris flow (mass point system)

Sediment runoff volume of

L2

shallow landslide

Required information: a relationship between the rainfall
conditions and the occurrences of shallow landslide anddebris flow.

tan: sediment runoff volume at the top of
the second order stream: length/

Erosion rate⁄⁄

∗ : sediment concentration of bed

∗ tan

1
1

1

: equilibrium slope: mass density of mixture ofwater and fine sedimenttan: mass density of sediment: internal friction angle: sediment runoff volume at confluence point: longitudinal section area

: potential erosion depth
Schematic view of debris flow

Mass conservation ofdebris flowtan

: width at the end of the second order stream

Sediment runoff volume ofThis procedure is employed for thethird and larger order.
: width of land slide,, : composition rate ofcoarse and fine sediment: number of landslide: distance form landslide to stream: width of stream,: length of stream, : concentration ofcoarse and fine sediment

3

Mass conservation ofcoarse sediment

Erosion:∗ tan

Depotition:∗ tan

Erosion:
Mass conservation offine sediment

1

Depotition:

∗ tan

1

1

∗

tan

4

Stream order of Akatani River

B. Ave. 1h rainfall(mm)Heavy rainfall in northern Kyushu, July 201760

Length(L) , area(A), width(B), gradient(G)L
A
B
G(m ) (ha ) (m ) (°)
1st 6 31
1 0 162n d 2 04
4
1 0 12
3rd 4 0619
1 0 8 .9
4th 9 58
82
1 0 6 .4
5th 17 02 3 9 7 1 1 2 .3
6th 43 66 1 99 0 2 5 0 .9

40
20
0
6: 00

12 :00 18 :00
7/5

0: 00

6: 00

12 :00 18: 00
7/6

Temporal changes in basin average rainfall

7/5 20-21

7/5 9-7/6 8

Spatial distribution of 1‐hour rainfall and
24‐hour rainfall• The equilibrium gradient of the debris flow
becomes
8, when0.4.
• 1st and 2nd order river : erosion
3rd and larger order river: deposition• C‐band JMA• The maximum 1‐hour rainfall ranged from 5 mm to 96 mm.
• 24‐hour rainfall ranged from 190 mm to 480 mm within the basin.

5

Condition and results of simulation
Parameters

Value

Mesh size m

10×10: Soil layer depth m

0.6: Porosity: Infiltration rate ofsurface layer mm/h
: Saturated hydralicconductivity cm/s
: Equvalent roughness coefficient

0.4: Internal friction angle degrees

Simulated erosion and deposition
Otoishi

1.0∗ : Sediment concentratin

6

Spatial distribution of stream order(Horton‐Strahler )Akatani1

Produced

630,000 m3

Produced

420,000 m3

Deposited

280,000 m3

Deposited

220,000 m3

Runoff

350,000 m3

Runoff200,000 m3

200.0

Kogouchi
Produced

1.0
0.60

140,000 m3

Deposited

60,000 m3

Runoff80,000 m3

35.0: Cohesion kN/m

3.0

: Soil density kg/m

2650

: Water density kg/m

1000

Sediment runoff 630,000 m3

7
Simulated landslide and runout mark by aerial photo8

Evidence-based Flood Contingency Planning:
A Case of Calumpit Municipality in the Pampanga River Basin of the Philippines- Badri Shrestha, Senior Researcher

Aim of Flood Contingency Planning

International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute (PWRI), Japan

Calumpit Municipality:
General Features

 Calumpit Municipality is located in the
Pampanga River Basin of the Philippines

Pampanga River Basin (PRB)

1. Protect people/property/activity from damage
2. Quickly recover from damage
3. Reduce impacts due to disaster

Number of Barangays (Villages): 29
Population: 112,007 (based on Municipality data)
Households: 22,402
Area: 5,625 ha

Flood impact with/without planning

Rate of Operation

100%

Philippines

Catchment Area of PRB:
10,434 km2
Calumpit
River Length: 260 km

Time Loss
SavingWith Planning
Without Planning

DamageMitigation

Time
1

Step 1: Understanding Current Conditions

Six steps of Contingency Planning
Assumedisasterscenario
Understandwhat will
happen2

1)2)(Formulation of Plan)
Step 1: Understanding current conditions
Step 2: Risk identification

Understand Natural Environment
Collect data on social environment(Barangay Profile: Population, Number of family, etc,)

3) Collect information on past flood damage

Step 2: Risk Identification
Flood depth due to High Flood (30 Years Flood)

Inundation Probability Map
Ex) Barangay Sta. Lucia

Ex) Barangay Sta. Lucia

Step 3: Impact analysis

6ft
4ft

Think how to
prepare forflood

Step 4: Developing coping strategy

2ft(Documentation and Sharing)
Step 5: Developing contingency plan
Step 6: Sharing and updating contingency plan

Reference: ISO22301 Societal Security– Business continuity management systems –.

High Flood (30 Year Flood) is similar to flood event of
Typhoon Pedring (September 2011)3

 Check water level at key facilities (Barangay
hall, evacuation center, schools etc.)
 Check water level at residential area bycomparing normal map and high flood map.

The figure shows the probability of inundation frommost frequent inundation areas (dark purple color) toareas of rare inundation (light purple color).

 Check frequently inundated areawith more than 2ft flood by using
inundation probability map.

4

Step 3: Impact Analysis

Step 3: Impact Analysis

 Result of household survey for measuring average floor height

Check lead time before inundation and duration of inundation by time-series inundation chart.

Ex) Barangay
Sta. Lucia

Ordinary flood(10yrs return period)

High flood(30yrs return period)

Ex treme flood(100yrs return period)

2011
Pedring and Quiel

One Story

Two Story

Yellow (less than 2ft)

Not inundated

Start inundated

Red (2‐4ft)

Start inundated

Inundated

Inundated

Inundated

Green(more than 4ft)

0.0‐0.17
0.17‐0.54

One StoryHouse

Two StoryHouse

Not inundated

Not
inundated

Not inundated

Start
inundated

0.54‐1.55

Inundated

1.55‐2.83

Inundated(Cannot use electricity)less than 2ft(<0.3048m)

Da y1

Day2

less tha n 4ft(<1.2192 m)

Day 3

D ay4



Identified
Problems

Based on Workshop in Jan. 2016

During Flood

Medical Treatment
Transportation
OthersAfter Flood Inform water level at “Colors of  Identify typical waterlevel,
duration,safety” regularly to MDRRMO
 Communicate with MDRRMO
source of flooding Inform obtained information to
Barangay people
 Use generator for charging

 Make residents evacuate quickly
to safer place Rescue residents
 Quantify affected individuals/families

0.00

0.26

0.32

0.34

0.32

0.28

0.20

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.15

0.33

0.40

0.43

0.45

0.45

0.44

0.43

0.40

0.33

0.25

0.13

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.26

0.42

0.58

0.65

0.70

0.74

0.76

0.76

0.73

0.69

0.62

0.52

0.42

0.38

0.33

0.28

0.23

0.15

0.00

0.00

0.00

0.00

0.00

0.00

0.15

0.32

0.40

0.43

0.44

0.44

0.44

0.43

0.42

0.43

0.44

0.45

0.43

0.42

0.39

0.36

0.30

0.16

0.00

This information can be considered for
preparation of relief goods, water, etc.

Flood Scale
Component
Information
Communication
Evacuation

Ordinary Flood

High Flood

Extreme Flood

What we
do/improve?

‐No telephone/mobile phone‐Lack of information about inundation /damage situation.
‐Fast speed of rising water during inundation.
‐Evacuation center doesn’t have enough capacity.

‐No electricity.

Others‐Difficulty in cleaning houses‐Need of more construction
materials for repairing houses.

‐Delay of relief goods‐Relief goods get wet.
‐Need of portable restroom‐Need of medical mission, providing medicines for leptospirosis,
fever etc.

What we request
to Municipality?

‐Difficult to go to center area because access road to theelevated road are inundated.
‐Damage of Rice field‐Delay in education in school

6Ex) Barangay Sta. Lucia

1) Organization Chart
2) Resource map
3) Early Warning System
Public Storm Warning System
Location of “Color of safety”, person in charge4) Coping Strategy
5) Sectoral Plan:
Describe “What we do
before/during/after flood”.
6) Needs for Future Improvement

Step 6: Sharing and Updating Contingency Plan
1) Support residents toclean houses forgetting back tonormal life quickly







Da y12

0.00

Step 5: Developing Contingency Plan

Housing

Water, Food, Relief
Goods

Day11

0.00

Medical Treatment

1) Discuss “What we do/improve” and “What we request”.

Evacuation

D ay100.00

Based on Workshop‐Only non‐elevated ‐Difficulty in cleaning houses
at Barangay Bulusan Housing
houses get flooded.
and Santa Lucia in
July 2015 and Jan.
water ‐No water supply.
Water, Food, Relief ‐No/less
2016
supply.
‐No electricity.

Prepare for evacuees
considering expected
inundation height.

Step 4: Developing Coping Strategy

Information
Communication

D ay9

0.00

Goods

Before Flood

Da y8

Step 4: Developing Coping Strategy

Population

Inundation depth (m)

Day7

0.00

Transportation

 Communicate
with
MDRRMO Identify which/where tokeep, remove or add
marker Update a list of contact
personsandphone
numbers
 Quantify
vulnerable
individuals /families
 Make residents evacuate
quickly to safer place

D ay6

Ordinary Flood: 2.5 days
High Flood: 6.5 days
Extreme Flood: 8.5 days

Inundation probability map means the area
frequently inundated above first floor level of
one/two story houses.

5

Flood Scale
Key Component

m ore than 4ft(>1.2192m)

Day5

Duration of Inundation:

 Population according to expected inundation height by High Flood
Inundation
Height (m)<Legend>Case

2)

Share plan with barangay
members.
Update plan periodically adding
new information and experience.

Plan

Act

Get/provide relief goods
Keep relief goods dry
Use generator for charging
Save children and elder people
Use Boat (Banka)

Do

Check
 Clean Barangay Hall
7

8

IFI ROAD MAP FOR 2020
Associated Events and IFI Milestones
9-18/7/2018

7-9/5/2018

20-29/6/2018

WMO Global Conference

11th HELP Meeting
Geneva, Switzerland

PLATFORM
ON WATER RESILIENCEAND DISASTERS

8 th World Water Forum
Brasilia, Brazil

2018
Jan.

Mar.

Kyoto, Japan

3-14/12/2018

COP24

16-20/7/2018

Geneva,Switzerland

3-4/5/2018

31/10-1/11/2018

GEO-XV Plenary

New York, USA

WMO Executive
Council

Geneva, Switzerland

18-23/3/2018

High-Level Political
Forum 2018

Katowice, Poland

7th Africa Water Week
Libreville, Gabon

11-15/6/2018

23 rd UNESCO-IHP
Intergovernmental Council
Paris, France

May

Jun.

Jul.

Side
Event

6-9/11/2018

8th Global FRIENDWater Conference
Beijing, China

Oct.

Nov.

August 2020

Mamoru Miyamoto

ICFM8International Centre for Water Hazard and Risk Management(ICHARM)

Iowa, USA

2019
Jan.

Dec.
Side
Event

Aug.

Dec.

Plenary
SessionIFI PLATFORM ACTIVITIESIN ASIAInternational Flood Initiative (IFI)International Flood Initiative (IFI) is a joint initiative incollaboration with such international organizations as
UNESCO-IHP, WMO, UNISDR, UNU, IAHS and IAHR since 2005.
ICHARM is the secretariat of IFI.

Side
Event

2020
Jan.

Global Agendas

GLOBAL TRENDS

Sendai Framework for
Disaster Risk Reduction
2015-2030

Paris
Agreement

IFI partners

Sustainable Development Goals(2016-2030)

SupportPlatform on Water Resilienceand Disasters
Objective components (6)••••Data integration•
Climate change
Economic assessment •Flood forecasting
Sediment disasterContingency planning

Dec.

Side
Event

UN International Decade for Action:
Water for Sustainable Development(2018-2028)“Platform on
Water Resilienceand Disasters” isspecified in the
HLPW outcome
document.

High Level Panel on Water（UN/WB）
12 world leaders
issue clarion callfor accelerated
action on water
Outcome document (14 March 2018)4

CONCEPTUAL FRAMEWORKOF PLATFORM
Community ofPractice

Policy-making

High-Level Policy-Maker

Transdisciplinary

3. Societal
BenefitCreationInterdisciplinary

2. ModelDevelopment

Prediction

present
Societal Change(land use , population)

past

Monitoring
Identification

Integrated Risk Assessment
1. DataArchiving

DamageHazardSocioEconomy

Capacity Building

Climate Change

High-Level Practitioner

futureDATA LIST
An example of data list

HazardData

Source ofinformation

DEM

PLATFORM
IN SRI LANKA

Data

Source ofinformation

Data

Casualties &missing person

Population

Meteorological
data

AffectedpeopleAgriculture

Water level

Agricultural
damageIndustry

RainfallRiver flow
River crosssectionInfrastructure
Commerce
Drainagefacility

CriticalinfrastructuredamageTidal level
Inundation
depth

Source ofinformation

Land use

Housing
damageInformation

EconomicdamageDam operation

International Cooperation

Socioeconomy

Damage5

Objective
components
Data
Integration

7

INSTITUTIONALSTRUCTURE

Flood
Forecasting
ClimateChange Irrigation Department
 Disaster Management Center (DMC)

Chair National Building Research
Organization (NBRO) Meteorological Department

Sediment
Disaster
EconomicAssessment
Contingency
6
Planning

Leading
organization

Participatingorganization

 Survey Department
 Ministry of Megapolis and Western
Development
 Ministry of Mahaweli Development &Environment Sri Lanka Land Reclamation andDevelopment Corporation (SLLRDC)
8(8) Data management and archiving

Realtime archiving data on DIAS

Katsunori Tamakawa , Project researcher

MLIT

International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute (PWRI), Japan

DIAS: Real‐time data archiving, visualizing, and downloading interface.

/10 min.
Live Camera

NOAA,GMS,MTSAT,MODIS,AMSR2,
GMS8River Telemetry

Satellite Data

/1 min.
250m mesh
14areaC-band Radar X-band Radar/5 min.
1km mesh
All area/1 hour,
1300 points

Live
CameraImages/10min.

AMeDAS

Local government etc.

DIAS Core System/1 hour
0.1deg. mesh
Global(60S-60N)

Eiji Ikoma @ UT

1

DIAS: Real‐time data archiving, visualizing, and downloading interface.

GSMaP
JAXA

/1-3 hour/5 min.
91 points 84-264hr. forecast
0.2～1deg. mesh
Global, Japan area

Tidal
level

Japan Coast Guard

Eiji Ikoma @ UT

GPV 2JMA

DIAS: Real‐time data archiving, visualizing, and downloading interface.
Overlay visualization all real‐time data observation points, Cband and XRAIN data

MLIT Telemetry dataset[Precipitation] : 2649 stations

MLIT Telemetry dataset[Dam Data] : 343 stations

MLIT Telemetry dataset[Water Level] : 2350 stations

Japan Coast Guard[Tidal level ] : 91 stations
Eiji Ikoma @ UT

3

Eiji Ikoma @ UT

4

DIAS: Real‐time data archiving, visualizing, and downloading interface.

Japan Metrological Agency[AMeDAS] : 1504 stations

DIAS: Real‐time data downloading using API

Meta info and “sample”
download commandusing API is appeared.

Also the ascii data
set can download.

Eiji Ikoma @ UT

5

Application : Real‐time data for Ensemble Flood Prediction.

http://apps.diasjp.net/RR/api/amedas‐
data?acode=42091&element=51&begin=20180312&end=20180315&interval=60&
Eiji Ikoma @ UT
timefmt=24&api_key=1234567890

6

Application : Simulator of Dam Operation for Decision Making

Aimata dam

Fujiwara dam

Ikoma, Shibuo, Koike (2014)7

http://www.ktr.mlit.go.jp/tonedamu/tonedamu_index003.html

8Sonohara dam