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Toshio KoikeDirector, International Centre for Water Hazard and Risk Management (ICHARM)
Professor Emeritus, the University of TokyoCouncil Member, Science Council of Japan (SCJ), Cabinet Office of Japan
Chair, River Bureau of Japan
Innovative Science and Technology for Reducing Water-related Disaster Risk
1
Building Resilience
Mar
ch 2
015 Sendai
Framework on Disaster Risk Reduction
Sept
embe
r 201
5 Sustainable Development Goals
Dec
embe
r 201
5 Paris Agreement(COP 21)
Concerted Actions are RequiredPreventing Future Risk
Reducing Current Risk
Adaptation& Recovery
Sustainable Development
Understanding Governance Investment EW/BBB
Key Global Agendas
Quantifying uncertainty
Climate models
Multi-model
ensemble (MME)
Down-scaling
Basin-scale
prediction of
quantity & quality
Water quantity
and quality prediction
flood
ordinary water
drought
ground water
Information
Storage
Treatment
Current facility, plan, management
Flood control system
Water allocation &
cost
Environment
Human life
Industry
HumanBehavior
EconomicBehavior
Drought Simulation
Flood SimulationIm
pact assessment
Filed survey
Early warning
Allocation policy
Land use
Adaptation options
Innovative technology- Flood control
- quality control
Decision m
aking
Monitoring evaluation
implem
entation
IntegratedObserved Data Sets
ProcessStudy
Scientific approach Engineering Approach Socio-economical approach
3
End to End Approach on Climate Change Adaptation
Quantifying uncertainty
Climate models
Multi-model
ensemble (MME)
Down-scaling
Basin-scale
prediction of
quantity & quality
Water quantity
and quality prediction
flood
ordinary water
drought
ground water
Information
Storage
Treatment
Current facility, plan, management
Flood control system
Water allocation &
cost
Environment
Human life
Industry
HumanBehavior
EconomicBehavior
Drought Simulation
Flood SimulationIm
pact assessment
Filed survey
Early warning
Allocation policy
Land use
Adaptation options
Innovative technology- Flood control
- quality control
Decision m
aking
Monitoring evaluation
implem
entation
IntegratedObserved Data Sets
ProcessStudy
4
Scientific approach Engineering Approach Socio-economical approach
End to End Approach on Climate Change Adaptation
MODEL SELECTION: Precipitation
(May-November)
Underestimation of extreme rainfallExtreme rainfall correction
too many low drizzle/ tiny rainfall No rainfall correction
Large Diversity Low Seasonal variation
Normal rainfall correction
Bias in GCM
GCM raw seasonality (1986-2000)
0
5
10
15
20
Jan
Feb
Mar Ap
r
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
rain
fall
mm
/day
Bandaunginmcm30csiro_mk3_5gfdl_cm20gfdl_cm21miroc32hire
Ranking Raw rainfall (1986-2000)
0
50
100
150
200
rain
fall
mm
/day
Bandaunginmcm30csiro_mk3_5gfdl_cm20gfdl_cm21miroc32hire
1500 2000 2500 3000 3500
2
4
6
8
10
12
14
16
18
20
22
rank
rain
fall
mm
/day
Long drizzle rainy day problem
Bandaunginmcm30csiromk35gfdlcm20gfdlcm20miroc32hire
7
Insitu-station Corrected GCMavg Future Corr_GCMavg Future - Past
Probability:10year
Probability:100year
Quantifying uncertainty
Climate models
Multi-model
ensemble (MME)
Down-scaling
Basin-scale
prediction of
quantity & quality
Water quantity
and quality prediction
flood
ordinary water
drought
ground water
Information
Storage
Treatment
Current facility, plan, management
Flood control system
Water allocation &
cost
Environment
Human life
Industry
HumanBehavior
EconomicBehavior
Drought Simulation
Flood SimulationIm
pact assessment
Filed survey
Early warning
Allocation policy
Land use
Adaptation options
Innovative technology- Flood control
- quality control
Decision m
aking
Monitoring evaluation
implem
entation
IntegratedObserved Data Sets
ProcessStudy
9
Scientific approach Engineering Approach Socio-economical approach
End to End Approach on Climate Change Adaptation
ICHARM Hydrological Simulation SystemIntegrated Flood Analysis System(IFAS) Rainfall-Runoff-Inundation (RRI)
Flood Hazard Analysis by RRI Simulation
Flood Hazard Assessment in Pampamga River Basin
Using IfSAR DEM
Inundated area (>0.5m depth)= 77,396 ha
Inundated area (>0.5m depth)= 103,376 ha
25-Year Flood 50-Year Flood 100-Year FloodInundated area (>0.5m depth)= 127,008 ha
Different Flood Scale
Maximum Inundation Depth
Interferometric Synthetic Aperture Radar (IfSAR) provided by National Mapping and Resource Information Authority (NAMRIA)
450m x 450m grid
11
ICHARM Hydrological Simulation SystemIntegrated Flood Analysis System(IFAS) Rainfall-Runoff-Inundation (RRI)
Ensemble Rainfall
Prediction
12
Water andEnergy Budget
DistributedHydrological
Model(WEB-DHM)
Energy-Water
Balance
River Flow
Soil MoistureDynamics
Lateral Flow
Flood
Obs. River Discharge
EnsembleMean
SinglePrediction
270
280
290
300
310
320
330
340
350
1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec
Wat
er L
evel
, m
Sameura Dam Water Level: Past
19811982198319841985198619871988198919901991199219931994199519961997199819992000Average
270
280
290
300
310
320
330
340
350
1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec
Wat
er L
evel
, m
Sameura Dam Water Level: Future
20462047204820492050205120522053205420552056205720582059206020612062206320642065Average
Maximum Water Level
*Assumption here is 1981 and 2046 have the same initial condition (dam water level, discharge and volume of reservoir)Maximum Water Level
High Water Demand Season
High Water Demand Season
Snow & Glacier
Clean Glacier Debris covered glacier
Snow
Spatial distribution of snowfall
Fully Physical Model Upper Indus
Coupling with Climate Models
Seasonal variation of LAI by the Coupled Model (above) and MODIS (below)
River Discharge (obs. & simulated) LAI (obs. & simulated)
Sawada, Koike, et al. WRR (2014)
- Agricultural Drought Index -
Drought indices (SA index)Green:simulated annual peak LAI and Orange:nationwide crop production
The drought index calculated from the model-estimated annual peak of leaf area Index correlates well with the drought index from nationwide annual crop production.
Severe droughts (food shortage) in 1988-1989 and 1994-1995 are reported on FAO report [FAO, 2005]
R =0.89
Dro
ught
Sawada, Koike, et al. WRR (2014)
+
Land surface model Dynamic Vegetation Model
AMSR-E(Aqua)
AMSR2(Shizuku)
Yang, Koike, et al. JMSJ (2007)
Electronic-MagneticWave
Sawada & Koike, JGR (2014)
Data AssimilationCoupled
18
GPM-Core
GCOM-W1
Sawada & Koike, JGR (2016)
Immediately after the flood Before the flood
Channel change in the middle reach of Akadani Debris flow deposition in the upstream of Akadani basin 19
Sediment Disaster Simulation: Debris Flow, Sediment Transport, Flood
20
Integrated Activity: Sediment Disaster Simulation in Northern Kyushu
Quantifying uncertainty
Climate models
Multi-model
ensemble (MME)
Down-scaling
Basin-scale
prediction of
quantity & quality
Water quantity
and quality prediction
flood
ordinary water
drought
ground water
Information
Storage
Treatment
Current facility, plan, management
Flood control system
Water allocation &
cost
Environment
Human life
Industry
HumanBehavior
EconomicBehavior
Drought Simulation
Flood SimulationIm
pact assessment
Filed survey
Early warning
Allocation policy
Land use
Adaptation options
Innovative technology- Flood control
- quality control
Decision m
aking
Monitoring evaluation
implem
entation
IntegratedObserved Data Sets
ProcessStudy
21
Scientific approach Engineering Approach Socio-economical approach
End to End Approach on Climate Change Adaptation
22
Flood duration= 1-2 days Flood duration= 3-4 days Flood duration= 5-6 days
Flood duration= 7 days Flood duration >7 days
0
20
40
60
80
100
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Perc
enta
ge o
f yie
ld lo
ss (%
)
Flood depth (m)
0
20
40
60
80
100
0 0.5 1 1.5 2
Perc
enta
ge o
f yie
ld lo
ss (%
)
Flood depth (m)
0
20
40
60
80
100
0 0.5 1 1.5 2
Perc
enta
ge o
f yie
ld lo
ss (%
)
Flood depth (m)
0
20
40
60
80
100
0 0.5 1 1.5 2
Perc
enta
ge o
f yie
ld lo
ss (%
)
Flood depth (m)
Vegetative Stage
Maturity Stage Ripening Stage Note: Green line and blue line are overlapped
Reproductive Stage
Flood Risk: Crop Damage Function
Developed based on flood damage matrix published by the Philippines Bureau of Statistics (2013) and considering height of rice plant
被害率
Flooded duration (day)
Life cycle Stage
Rice-Crops
37M Peso 1327M Peso 1952M Peso
Flood Damage Assessment in Pampanga River Basin
23
25-Year 50-Year 100-Year
Flood Contingency Scenario
24
Pampanga River
Labangan River
Angat River
Bag Bag River
Labangan River
Community Alert System using poles
Workshops for sharing results and discussing Barangay/Municipal Plan
Flood Contingency Planning
25Probability map of first flood inundation
Barangay Santa Lucia
Workshop at Municipality (2015.2)
Workshop at Barangays (2016.1)
Explanationof scenario
Discussionwith localresidents
Identification of necessaryaction for developing Barangay contingency plan(what they do? What they request)
Quantifying uncertainty
Climate models
Multi-model
ensemble (MME)
Down-scaling
Basin-scale
prediction of
quantity & quality
Water quantity
and quality prediction
flood
ordinary water
drought
ground water
Information
Storage
Treatment
Current facility, plan, management
Flood control system
Water allocation &
cost
Environment
Human life
Industry
HumanBehavior
EconomicBehavior
Drought Simulation
Flood SimulationIm
pact assessment
Filed survey
Early warning
Allocation policy
Land use
Adaptation options
Innovative technology- Flood control
- quality control
Decision m
aking
Monitoring evaluation
implem
entation
IntegratedObserved Data Sets
ProcessStudy
Scientific approach Engineering Approach Socio-economical approach
26
Volume Veracity Velocity Verity VisualizationData & Information
End to End Approach on Climate Change Adaptation
High Speed NetworkAnalysis Server
Extra-large volume data storage (25PB)
Base SystemICT Experts
Data Archive
Search / Download
Data ProcessingApplication Development
ICT Experts
Field Specialists R&D CommunityICT Experts
Field Specialists WaterDisaster
RiskReductionAgriculture
Urban
Economy
Biodiversity
Health Climate
Hydroelectric power
Social Implementation
Climate Change Adaptation
ASIAN Monsoon Year
International Contribution
DIAS/CEOSWater Portal
GEOSS/AWCI GEOSS/AfWCCI
Joint Research
S-8
CMIP5
GRENE-ei
DIAS-P
RECCA
Data Integration & Analysis System: Challenges to 5V
DIAS-ICHARM: Flood Information Sharing Support in Sri Lanka
In-situ rain gauge data (6 numbers)
Satellite precipitation data(GSMaP)
On-line Information provision on DIAS:In-situ rainfall, satellite rainfall, calibrated and forecast rainfall, inundation simulations
Implemented by EDITORIA and ICHARM on DIAS
ALOS © JAXA (2016)
Inundation analysis by using
RRI in DIAS
Simulation and forecasting of river
discharge, water level, inundation extent
Inundation analysis results
Concept of RRI model
Ensemble forecasting rainfall for the next 16 days (max)
Himawari-8 cloud images
Inundation map by satellite data (ALOS-2)
4 hr latency data (NRT)
Real time data (NOW)
Calibration
500mm
Bias-corrected Satellite Rainfall
Real-time Rain Gauge Data
Inundation
EnsembleFlood
Prediction
72hr11ensembleevery 24hr
Issued on May 24
Issued on May 25
Prediction
Water-Energy BudgetHydrological Model
(WEB-DHM)
River DischargeSoil MoistureGround WaterDam Storage
Crop Production
Satellite-based Land Data Assimilation
(CLVDAS)
Real Time Data Management System
DIAS Archives
NASA GLDAS Optimized LDAS ParametersAMSR2MODIS
GFDLAPCC
Hydromet Data Satellite Seasonal Forecast Global CLVDAS Outputs
Ensemble Rainfall Prediction
Ensemble Drought Prediction
Optimized RTM Parameters
NCDC Global Met
JMA Reanalysis
LDAS Reanalysis Statistics
River DischargeSoil Moisture, Ground Water
Dam StorageCrop Production
Rainfall Pattern 1Rainfall Pattern 2
Rainfall Pattern n
Prediction Accuracy EvaluationBias Correction
Weighting
Hydrometeorology-Agriculture Droughts Prediction System
Drought analysisWheat production
LAI anomaly from CLVDAS
2007 Morocco Drought
Morocco Algeria Tunisia
32
Quantifying uncertainty
Climate models
Multi-model
ensemble (MME)
Down-scaling
Basin-scale
prediction of
quantity & quality
Water quantity
and quality prediction
flood
ordinary water
drought
ground water
Information
Storage
Treatment
Current facility, plan, management
Flood control system
Water allocation &
cost
Environment
Human life
Industry
HumanBehavior
EconomicBehavior
Drought Simulation
Flood SimulationIm
pact assessment
Filed survey
Early warning
Allocation policy
Land use
Adaptation options
Innovative technology- Flood control
- quality control
Decision m
aking
Monitoring evaluation
implem
entation
IntegratedObserved Data Sets
ProcessStudy
Scientific approach Engineering Approach Socio-economical approach
33
Volume Veracity Velocity Verity VisualizationData & Information
Working Together Capacity BuildingPlatform
End to End Approach on Climate Change Adaptation
Additional slides for capacity building
Identification
Policy-making Community of Practice
1. Data Archiving
2. Model Development
3. Societal Benefit
Creation
Societal Change(land use , population)
Climate Change
Monitoring
Prediction
Tran
sdis
cipl
inar
y
Inte
rdis
cipl
inar
y
Cap
acity
Build
ing
Integrated Risk Assessment
future
present
past
International Cooperation
Hig
h-Le
vel P
ract
ition
erH
igh-
Leve
l Pol
icy-
Mak
er
Damage HazardSocio-Economic
35
Platform on Water and Disasters
Platform on Water and Disasters
Identification
Policy-making Community of Practice
1. Data Archiving
2. Model Development
3. Societal Benefit
Creation
Societal Change(land use , population)
Climate Change
Monitoring
Prediction
Tran
sdis
cipl
inar
y
Inte
rdis
cipl
inar
y
Integrated Risk Assessment
future
present
past
International Cooperation
Hig
h-Le
vel P
ract
ition
erH
igh-
Leve
l Pol
icy-
Mak
er
Damage HazardSocio-Economic
37
Cap
acity
Build
ing
in collaboration with National Graduate Research Institute for Policy Study (GRIPS)
MasterCourse
• Working at a national disaster managementorganization back home, he already had hadbasic knowledge and experience on socio-economic analysis and trans-boundary rivermanagement when he started the program. AtGRIPS and ICHARM, he improved his socio-economic understanding, and began to learn amethodology for assessing climate changeimpact on floods.
2016-2017 Master’s Course Mr. GAMA Samuel Joseph, Malawi
38
In response to his presentation, UNDP has provided financial support amounting to US$ 16 million for my office to improve on the EWS through the “*Scaling-up the Use of Modernized Climate Information and Early Warning Systems Project (M-CLIMES)”* under the Global Climate Fund.
50 Years Return Period
9.1%
• A Brazilian student was from the National Departmentof Civil Protection and Defense. While studying atICHARM, he worked on research aiming to apply theseven Global Targets, defined in the Sendai Framework,to disaster management in Brazil, proposing how to usethese targets for achieving local disaster risk reduction.
2016-2017 Master’s Course Mr. Mikosz Lucas, Brazil
Relationship between SF indicator B(No. of affected people per 100,000 population) and expected change of basin average precipitation under RCP8.5 scenario
Indicator B(Flood)
Indicator B(Drought)
39
Platform on Water and Disasters
Identification
Policy-making Community of Practice
1. Data Archiving
2. Model Development
3. Societal Benefit
Creation
Societal Change(land use , population)
Climate Change
Monitoring
Prediction
Tran
sdis
cipl
inar
y
Inte
rdis
cipl
inar
y
Integrated Risk Assessment
future
present
past
International Cooperation
Hig
h-Le
vel P
ract
ition
erH
igh-
Leve
l Pol
icy-
Mak
er
Damage HazardSocio-Economic
40
STIPS DMP
ICHARM
Cap
acity
Build
ing
in collaboration with National Graduate Research Institute for Policy Study (GRIPS)
ShortCourse
MasterCourse
PhDCourse
Quantifying uncertainty
Climate models
Multi-model
ensemble (MME)
Down-scaling
Basin-scale
prediction of
quantity & quality
Water quantity
and quality prediction
flood
ordinary water
drought
ground water
Information
Storage
Treatment
Current facility, plan, management
Flood control system
Water allocation &
cost
Environment
Human life
Industry
HumanBehavior
EconomicBehavior
Drought Simulation
Flood SimulationIm
pact assessment
Filed survey
Early warning
Allocation policy
Land use
Adaptation options
Innovative technology- Flood control
- quality control
Decision m
aking
Monitoring evaluation
implem
entation
IntegratedObserved Data Sets
ProcessStudy
Scientific approach Engineering Approach Socio-economical approach
41
End to End Approach on Climate Change Adaptation
Volume Veracity Velocity Verity VisualizationData & Information
Working Together Capacity BuildingPlatform
STIPS
ICHARM DMP
Quantifying uncertainty
Climate models
Multi-model
ensemble (MME)
Down-scaling
Basin-scale
prediction of
quantity & quality
Water quantity
and quality prediction
flood
ordinary water
drought
ground water
Information
Storage
Treatment
Current facility, plan, management
Flood control system
Water allocation &
cost
Environment
Human life
Industry
HumanBehavior
EconomicBehavior
Drought Simulation
Flood SimulationIm
pact assessment
Filed survey
Early warning
Allocation policy
Land use
Adaptation options
Innovative technology- Flood control
- quality control
Decision m
aking
Monitoring evaluation
implem
entation
IntegratedObserved Data Sets
ProcessStudy
Scientific approach Engineering Approach Socio-economical approach
42
Volume Veracity Velocity Verity VisualizationData & Information
Working Together Capacity BuildingPlatform
End to End Approach on Climate Change Adaptation
coarse resolution
fine resolution
statistical model
Dynamical Downscaing:Localized climate information is generated using high resolution regional climate models (RCMs), driven by low resolution global climate models (GCMs), or using a variable resolution global model in which the highest resolution is over an area of interest.
observed large scale climate
Transfer function(statistical model)
observed small scale climate
predicted large scale climate
predicted small scale climate
Statistical downscaling
Down-scaling & Bias Correction