roles of science and technology - world...
TRANSCRIPT
Toshio Koike
小池俊雄
International Centre for Water Hazard and Risk Management (ICHARM)
水災害リスクマネジメント国際センター
The University of Tokyo
東京大学
Global Challenge for Flood Disaster Risk Reduction
世界の洪水被害軽減に向けて
Roles of Science and Technology科学技術が果たすべき役割
1984-2013, 5yr averagedAnnual Human Loss
1984-2013, 5yr averagedAnnual Economic Damage
Water-related Disaster
Mar
ch 2
015 Sendai
Framework on Disaster Risk Reduction
Sep
tem
ber
2015 Sustainable
Development Goals
Dec
ember
2015 Paris
Agreement
(COP 21)
• The world knows that countries cannot act in
isolation to address these risks.
• Concerted Action is Required
Three Key Global Agendas in 2015
IPCC/AR4(2007) IPCC/AR5(2013)
Warming of the climate system is unequivocal.
Climate Change Impacts of Water Cycle ExtremesAR4(2007), SREX(2011), AR5(2013)
Monsoon
Monsoon
global
RCP2.6, RCP4.5, PCP6.0, RCP8.5
RCP2.6, RCP4.5, PCP6.0, RCP8.5 Psd: standard deviation of inter-annual variability in seasonal average precipitation
Wet-Dry Contrast(water storage)
R5d: seasonal maximum 5-day precipitation total
Large Flood(wide coverage of rainfall observation)
IPCC/AR5 (2014)
Quantifying
uncertaintyClimate
models
Multi-model
Ensemble
(MME)
Down-scaling
Basin-scale
Prediction of
quantity & quality
End to End Approach on Climate Change Adaptation
Integrated
Observed
Data Sets
Process
Study
Water
quantity &
quality
prediction
Flood
Ordinary
Water
Drought
Ground
Water
Informa-
tion
Storage
Treat-
ment
Current facility, plan, management
Flood
Control
System
Drought
Disaster Potential
Flood
Disaster Potential
GPCP
Basis of Spatial comparison
Rainfall (June and July) Climatology (1981-2000)
Derived from Satellite Observations
Obs.
Evaluation for relative distribution：Correlation coefficient(CC)
Evaluation of absolute value:
RMSE
ScoringCC and RMES are more than all GCM averaged value ：1CC or RMES are more than all GCM averaged value ：0CC and RMES are less than all GCM averaged value ：‐1
モデル計算値補正前Observed
Bias-correction and Down-scaling in Yoshino River（20 years average of monthly rainfall in September)
0
2000
4000
6000
8000
10000
1 3 5 7 9 11 13 15 17 19
CSIRO_5PAST
CSIRO_5FUTURE
0
2000
4000
6000
8000
10000
12000
1 3 5 7 9 1113151719
INGV PAST
INGVFUTURE
0
2000
4000
6000
8000
10000
12000
1 4 7 10 13 16 19
MIROC_HIRES PAST
MIROC_HIRES FUTURE
0
2000
4000
6000
8000
10000
12000
1 4 7 10 13 16 19
MIROC_MEDRES PAST
MIROC_MEDRES FUTURE
0
2000
4000
6000
8000
10000
1 3 5 7 9 11 13 15 17 19
CSIRO_0PAST
CSIRO_0FUTURE
0
2000
4000
6000
8000
10000
12000
1 3 5 7 9 1113151719
IAP PAST
IAP FUTURE
0
2000
4000
6000
8000
10000
12000
1 3 5 7 9 1113151719
GFDL PAST
GFDLFUTURE
0
2000
4000
6000
8000
10000
12000
1 3 5 7 9 1113151719
MRI PAST
MRI FUTURE
Top 20 Large Floods at the Ikeda, Yoshino Riverpast(1981-2000)ーfuture(2046-2065) (㎥/s)
Severer FloodsVery Likely
270
280
290
300
310
320
330
340
350
01-Jan 01-Feb 01-Mar 01-Apr 01-May 01-Jun 01-Jul 01-Aug 01-Sep 01-Oct 01-Nov 01-Dec
Wat
er
Leve
l, m
Sameura Dam Water Level: Past
19811982198319841985198619871988198919901991199219931994199519961997199819992000Average
270
280
290
300
310
320
330
340
350
01-Jan 01-Feb 01-Mar 01-Apr 01-May 01-Jun 01-Jul 01-Aug 01-Sep 01-Oct 01-Nov 01-Dec
Wat
er
Leve
l, 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
MODEL SELECTION: Precipitation
(May-November)
14
15
Insitu-station Corrected GCMavg Future Corr_GCMavg Future - Past
Probability:10year
Probability:100year
17 Changes of Flood in Angat Dam Basin
Quantifying
uncertaintyClimate
models
Multi-model
Ensemble
(MME)
Down-scaling
Basin-scale
Prediction of
quantity & quality
Water
quantity &
quality
prediction
Flood
Ordinary
Water
Drought
Ground
Water
Informa-
tion
Storage
Treat-
ment
Current facility, plan, management
Flood
Control
System
Wate
r Allo
catio
n
& C
os
t
Environ-
ment
Human
life
Industry
Human
Behavior
Economic
Behavior
Drought
Disaster Potential
Flood
Disaster PotentialIm
pact
Assessm
en
t
Filed
Survey
End to End Approach on Climate Change Adaptation
Integrated
Observed
Data Sets
Process
Study
Early
Warning
Allocation
Policy
Land Use
Adaptation
Options
etc.
Innovative
Technology
- Flood
- Quality
Control
Decis
ion
Makin
g
Monitoring
&
Evaluation
Imp
lem
en
tatio
n
4. Develop economic models to reproduce actual economic parameters.
Economic simulation
1. Develop of flood models to reproduce actual flood damage.
2. Demonstrate counter measure effects for reducing damage.
Flood simulation
3. Translate flood model outputs into economic model inputs
5. Simulate effect of the counter measures on economy and society with several scenarios.
Investment for Disaster Damage Reduction
(Catchment-based Macro scale Floodplain model, Yamazaki, 2012)
(Disaster Risk Reduction investment Accounts for Development (𝐷𝑅2𝐴𝐷), Yokomatsu, 2013)
2. Establish the levee as disaster prevention and calculate the effect on damage reduction
D. Yamazasi, 2012, Physically-based Modelling of Large-scale Floodingin Continental-scale Rivers of the World
Establishing LEVEE as Disaster Preventionin CaMa-flood and measure the effect of the levees on the damage reduction
middlelower
Building levee in mainstream of LOWER(LW) and MIDDLE(MD) basin
-150-125-100
-75-50-25
0255075
20
04
20
12
20
20
20
28
20
36
20
44
20
52
20
60
20
68
20
76
20
84
20
92
210
0
Gini coefficient reduction (%)
LW and MD levee against NO levee
(NO-LW)/NO (NO-MD)/NO
0.80
1.00
1.20
1.40
1.60
1.80
20
04
20
12
20
20
20
28
20
36
20
44
20
52
20
60
20
68
20
76
20
84
20
92
210
0
GDP comparisonLow levee/No levee
Middle levee/No levee
LW/NO MD/NO
LW: less GDP growth
MD: more GDP growth MD: increase equality
LW: decrease equality
Dam Operation Optimization System by Using
Rainfall Forecasting
Spatially distributed data
Minimization
scheme
Potential
Operational
Rule
&
Weighting
Optimization of dam network
Short-term
Coupled DHM & Dam Operation
Error Forecast
Integrated dam networkrelease schedule
Flood Peak Reduction
Effect Water Use
Weather forecast Output
Observed rainfall
0
100
200
300
400
500
600
700
800
900
1000
7/8.1z 7/9.1z 7/10.1z 7/11.1z 7/12.1z
2002
dis
char
ge
[m3/s
]
600
605
610
615
620
625
630
635
640
645
650
wat
erle
vel
[m
]
Sim outflow
Sim inflow
Sim water level
Obs water level
Flood reduction with GPV 7~12
0
500
1000
1500
2000
2500
3000
7/8.1z 7/9.1z 7/10.1z 7/11.1z 7/12.1z
2002
dis
char
ge
[m3/s
]
Optimized rules
Outflow eq. 0
Outflow eq. inflow0
100
200
300
400
500
600
700
800
900
1000
7/8.1z 7/9.1z 7/10.1z 7/11.1z 7/12.1z
2002
dis
ch
arg
e [
m3/s
]
470
480
490
500
510
520
530
540
550
560
570
wate
rlev
el
[m]
Sim outflow
Sim inflow
Sim water level
Obs water level
Flood peak reduction
Fujiwara dam
Sonohara dam
Iwamoto gauge
Peak created due to
water release from dams
Water is stored until
max capacity is reached
Water level increase due to storage
Optimized release
Quantifying
uncertaintyClimate
models
Multi-model
Ensemble
(MME)
Down-scaling
Basin-scale
Prediction of
quantity & quality
Water
quantity &
quality
prediction
Flood
Ordinary
Water
Drought
Ground
Water
Informa-
tion
Storage
Treat-
ment
Current facility, plan, managemen
t
Flood
Contro
l
System
Wate
r Allo
catio
n
& C
os
t
Environ-
ment
Human
life
Industry
Human
Behavior
Economic
Behavior
Drought
Disaster Potential
Flood
Disaster PotentialIm
pact
Assessm
en
t
Filed
Survey
Early
Warning
Allocation
Policy
Land Use
Adaptation
Options
etc.
Innovative
Technology
- Flood
- Quality
Control
Decis
ion
Makin
g
Monitoring
&
Evaluation
Imp
lem
en
tatio
n
End to End Approach on Climate Change Adaptation
Integrated
Observed
Data Sets
Process
Study
Scientific approach Engineering Approach Socio-economical approach
END to END
Data Integration and Analysis System
To create knowledge enabling us to solve the Earth environment
problems and to generate socio-economic benefits.
a legacy for Japan's contributions to GEOSS
Massive data on water, atmosphere, lands and oceans are collected, integrated and put into archive the Data Integration and Analysis System (DIAS).
It is also important for people to understand the meaning of disaster information they receive, and to take adequate measures such as prompt evacuations.
Second UN Special Thematic Session on Water and DisastersNovember 18th, 2015, The UN Headquarters, New York, the U.S.A.