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Impact assessment of climate change
on water-related disasters for
building up an adaptation strategy
Yasuto TACHIKAWA
Hydrology and Water Resources Research Lab.
Dept. of Civil & Earth Resources Engineering, Kyoto University
Flood and Inundation Disaster
in the Kinu River
by Typhoon 18
on September 10, 2015
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After Construction Ministry of Japan
Flood and inundation simulation
under a changing climate
1. Probabilistic evaluation of magnitude
and frequency on flood and
inundation under a changing climate;
and
2. Physical prediction of a probable
largest-class flood and inundation
under a changing climate.
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Climate Change Research
Projection of extreme
events and water resources
Latest high-resolution General
Circulation Models, and
Regional Climate Models.
Evaluation and reduction of
prediction uncertainty
Ensemble projection,
Typhoon course ensemble
prediction,
Dynamical downscaling,
Statistical downscaling,
Bias correction.
Adaptation
GCM/RCM
Modeler
Climate
Projection
Impact
Assessment
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SOUSEI 創生 Program
(2012-2017)
Imminent global
climate change
(AORI, U Tokyo)
Climate
variability and
change
Integrated
prediction
system
Stabilization
target setting
(JAMSTEC)
Risk Information
(MRI-JMA)
Long-term
projection
Probabilistic
climate
projection
Large-scale
variations
Producing a
standard
climate
scenario
GCM modeler
Impact
assessments
(DPRI, Kyoto U,
U Tokyo,
Tohoku U)
Natural
Disaster
Water
Resources
Ecosystem
Water resources
engineering
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Climate Change Research Program
Climate Change
Projection
(Metrological Research Institute,
Japan, Univ. of Tokyo)
Probabilistic
climate projection
Impact Assessments
(DPRI and Dept. of Eng. at
Kyoto Univ., Univ. of Tokyo,
Tohoku Univ., ICHARM, etc.)
Natural Disaster
Water Resources
Producing a
standard climate
scenario
Climate change projection PI:
Dr. Takayabu (MRI, JMA)
Ecosystem
Impact assessment PI:
Prof. Nakakita (DPRI, Kyoto Univ.)
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Future climate projection data using General
Circulation Model developed by MRI, Japan
Present climate
experiment: 1979-2004
Near future climate
experiment: 2015-2039
Future climate
experiment: 2075-2099
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MRI GCM simulation
20km spatial resolution,
hourly
Precipitation (mm/h)
Sea Level Pressure (hPa)
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GCM rainfall projection for the end of 21st century
Precipitation (mm/hr)
Provided by Dr. Oku at DPRI, Kyoto University
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Large number of GCM ensembles
by MRI, JMA
Different SST settings
YS
20km AGCM by MRI
60km AGCM by MRI
Cluster 3
Cluster 2
AS
Cluster 1
Ensemble
mean Present
climate
2.6
4.5
6.0
8.5
60km AGCM by ME
RCP Scenario
KF
Different cumulus
convection
parameterization
PPE
Flood and inundation simulation
under a changing climate
Probabilistic evaluation of magnitude
and frequency on flood and inundation
under a changing climate; and
Worst case scenario simulation on
flood and inundation under a changing
climate.
Future extreme event projection
on flood and inundation
Probabilistic evaluation of magnitude
and frequency on flood and inundation
under a changing climate
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Method of Analysis
20km resolution GCM output
1km resolution distributed
hydrologic model
for all Japanese catchments
for 75 years runoff
simulations
Examine the changes of flood risks, drought risks,
and the change of water resources.
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Hydrologic Flow Modeling
A flow direction map with 1 km spatial resolution is
developed. Then, runoff is routed according to the
flow direction map using one dimensional kinematic
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wave flow model .
River flow
simulation
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Runoff projections using MRI-3.1S
Present climate
experiment 1979-2003
Near future climate
experiment 2015-2039
Future climate
experiment 2075-2099
Ishikari River
Hokkaido
region
Mogami River
Northern
Japan
Abukuma River
Northern
Japan
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Change of mean of annual maximum hourly discharge
Flood Risk
Far future climate experiment
/Current climate experiment
Near future climate experiment
/Current climate experiment
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Change of standard deviation of annual maximum
hourly discharge
Flood Risk
Future climate experiment
/Current climate experiment
Near future climate experiment
/Current climate experiment
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Change of flood risk?
Distribution of annual maximum daily rainfall
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Change of the 100-year annual maximum
hourly discharge
Future climate
/Current climate
Quintiles of river discharge with 100-year return period
estimated using GEV distribution.
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Unsteady frequency analysis
T-year
hydrologic
variable
year
Annual maximum daily rainfall
at Osase, Japan（1939 - 2012）
Characteristics of population
will change with time.
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Unsteady hydrologic frequency analysis model
Unsteady GEV distribution (Coles, 2001)
Estimate parameters using the method of likelihood.
ξis constant.
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Unsteady Gumbel distribution
Unsteady SQRT-ET distribution
Unsteady Lognormal distribution
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Akaike Information Criteria, AIC
竹内情報量規準が小さいほど良いモデル
最大化すべき目的関数：
目的関数の漸近不偏推定量：
特に
赤池情報量規準が小さいほど良いモデル
が成り立つ場合には
となるから
となる．
これは赤池情報量規準に他ならない．
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Comparison of 100-year annual maximum
daily rainfall at 1962 and 2012
Annual maximum daily rainfall
at Osase, Japan（1939 - 2012）
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Comparison of 100-year annual maximum daily
rainfall at 1993 and 2089 using MRI GCM
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Future extreme event projection
on flood and inundation
Physically prediction of a probable
largest-class flood and inundation under
a changing climate
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Design flood discharge
for dam reservoir construction
Maximum discharge which flows through spillway
Kuzuryu Dam in Japan
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Design flood discharge
for dam reservoir construction
Maximum discharge which flows through spillway
Kuzuryu Dam in Japan
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Design flood discharge
for dam reservoir construction
Maximum discharge which flows through spillway
Kuzuryu Dam in Japan
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Maximum runoff height (m3/sec/km2)
Design flood discharge for dam reservoir construction
Kanto region
Experimental equation
for probable maximum flood
Technical Report of PWRI Japan, no. 1247, 1976
土木研究所資料 第1247号,1976
Catchment Area (km2)
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Maximum discharge estimation
Current climate experiment
(1979-2004)
Maximum runoff height (m3/sec/km2)
Experimental equation
for probable maximum flood
Catchment Area (km2)
Kanto region
Experimental equation
for probable maximum flood
Maximum discharge estimation
Near future climate experiment
(2015-2039)
Catchment Area (km2)
Maximum runoff height (m3/sec/km2)
Maximum runoff height (m3/sec/km2)
GCM Maximum flood discharge at Kanto region
Experimental equation
for probable maximum flood
Maximum discharge estimation
Far future climate experiment
(2075-2099)
Catchment Area (km2)
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How water-related disasters will change
if typhoon takes different tracks?
hPa
(Ishikawa et al. 2013)
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Virtual shifting of typhoon’s initial
position- for making a worst scenario NHM-5km
Virtual Shifting of typhoons
initial position by keeping
potential vorticity same
(a vorgas method)
AGCM20
Dynamic
downscale by RCM
Worst case impact assessment on
• Land： extreme wind and rainfall
• Ocean： storm surge and wave height
Ishikawa et al (2009)
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Virtual Shifting of typhoon’s initial position for the
historical typhoon case (Isewan Typhoon, 1959)
Control run
pseudo-global warming experiment
Oku et al., 2013
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Historical flood at Yodo River (Hirakata)
Discharge at Hirakata
Plan
Record
Rainfall(mm)
Design flood
Design flood with
flood control works
Improved target
1位
2位
3位
Discharge(m3/s)
261mm/24h
Remarks
17500
12000
222
10700
If no inundation,
12800
1953年9月25日
台風13号
249
7800
Seta weir was closed.
1956年9月21日
台風15号
176
5025
1958年8月27日
台風17号
171
3990
1959年8月14日
前線、台風7号
272
6800
1959年9月27日
伊勢湾台風、15号
215
7970
1960年8月30日
台風16号
179
3775
1960年6月
前線
1961年10月28日
前線
251
7206
Seta weir was closed.
1965年9月17日
台風24号
203
6868
Seta weir was closed.
1972年9月17日
台風20号
200
5228
Seta weir was closed.
1982年8月2日
台風10号
231
6271
2013年台風18号
台風18号
269
9500
Seta weir was closed.
If no inundation,
10100
Seta weir was closed.
Seta weir was closed.
Seta weir was closed.
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Flood and inundation simulations under
the largest-class typhoons
Various scenarios of the largest-class
typhoons under a changing climate
Flood and inundation simulations
The worst case impact assessment
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Rainfall-Runoff Model
rainfall
evapotranspiration
Water flow from
each slope
seepage
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Study Aarea
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Rainfall-runoff model for
historical simulation
(Isewan Typhoon
in 1959)
Kameoka
detention
area
Seta weir
Hirakata
Ueno
detention
area
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Maximum discharge analysis depending
on typhoon tracks
Typhoon tracks
Maximum peal discharge at
Hirakata for each typhoon track
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Simulated discharge hydrographs at Hirakata
(7,281km2) for the typhoon track which caused
the maximum discharge without dam reservoirs
Design flood
Control run
Pseudo global warming
simulation
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Rainfall-runoff model including
main reservoir flood controls and
inundation
(Present situation)
Hiyoshi
Dam
Seta weir
Kameoka
detention
area
Hirakata
Ueno
detention
area
Takayama
Dam
Amagase
Dam
Hinachi Dam
Shorenji Dam
Nunome
Dam
Murou Dam
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Simulated discharge hydrographs at Hirakata (7,281km2) for
the typhoon track which caused the maximum discharge
under a pseudo global warming condition
Design flood
without dams
with dams
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Flood by Typhoon 18, 2013
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After MLIT
Comparison of 2013 flood simulation by Typhoon 18
and 1959 Isewan Typhoon with a pseud global
warming condition at Hirakata
2013 Typhoon 18 flood simulation
1959 Isewan Typhoon flood simulation
with a PGW condition
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Wide-area inundation simulation model
Rainfall
Dyke broken,
overflow
over
flow
Sea
Main
river
Flood
water level
and
discharge
Urban river
Tokyo
See water level
Nagoya
Osaka
Kiso River, Shonai
River, Ise Bay
Tone River, Edo River,
Arakawa River,
Tokyo Bay
Yodo River,
Neyagawa River,
Osaka Bay
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Wide-area inundation simulation model
Osaka area
Water depth distributuion after 6 hours
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Evaluation of economic damage by flood
Rainfall-runoff model
Inundation simulation
Inundation
model
Estimation of inundation
damage from spatial
distribution of maximum
water depth
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Exceedance probability of hazard
Estimation of risk curve (relation between hazard
occurrence frequency and damage)
0.3
frequency of occurrence (medium damage)
Low frequency of occurrence (heavy damage)
0
Estimated economic damage
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Research topics of flood and inundation
simulation under a changing climate
Probabilistic evaluation of magnitude and frequency
on flood and inundation to evaluate a current design
level and future planning of structural measures for
water-related disasters;
Physically prediction of a probable largest-class flood
and inundation to cope with water-related disasters
exceeding a design level, and
Economical risk analysis of water-related disasters to
seek best combination of structural and non-structural
measures to cope with large-scale disasters.
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Future water resources projection
in the Southeast Asian Region
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Research Methodology
 Input data
Present climate experiment: 1979-2008
Near future experiment:
2015-2044
Future climate experiment: 2075-2104
20 km resolution future
climate simulation data,
MRI-AGCM3.2S made by Morological Research Institute, Japan.
 River flow routing model
for estimating river flow discharge
using the future climate data such as
rainfall, evaporation and so on.
 Analyzing
the changes river flow discharge for the assessment of
water resources, flood risks and drought risks.
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Topography Data
(Hydrological data and maps
based on SHuttle Elevation
Derivatives at multiple scale)
http://hydrosheds.cr.usgs.gov/index.php
Elevation (m)
using USGS HydroSHEDS
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109.5o E
34.0o N
Study Area
Salween
River basin
Irrawaddy
River basin
Red River
basin
Mekong River
basin
Chao Phraya
River basin
91.0o E
5.0o N
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Study area, data and hydrological model
• Topographic data:
_ Topographic information used in the study was generated from
processing the scale-free global streamflow network dataset with a
spatial resolution of 5 arc minutes.
• GCMs data:
_ MRI-AGCM3.2S (20-km resolution)
_ MRI-AGCM3.2H (60-km resolution)
_ MIROC5 (140-km resolution)
• Hydrological model:
_ Flow routing model with kinematic wave flow approximation,
1K-FRM
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River Flow Simulation
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Projection of river discharge
River discharge in the Indochina peninsula region
was projected by feeding 3-hourly runoff
generation data from MRI-AGCM3.2S dataset into
flow routing model 1K-FRM.
Future changes in river discharge in the region
were examined by comparing simulated discharge
in the near future climate experiment (2015-2044)
and future climate experiment (2075-2104) with
the one in the present climate experiment (19792008).
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Future changes and uncertainties in river discharge
projected using different ensemble experiments
Dataset
MRIAGCM3.2S
Resolution
20km
Cumulus Convection Scheme
Sea Surface Temperature
Run name
Present Climate
Observation
YS_20
Future Climate
CMIP MME
YS_CMIP_20
Present Climate
Observation
YS_60
Future Climate
CMIP MME
YS_CMIP_60
Present Climate
Observation
KF
CMIP MME
KF_CMIP
Cluster 1
KF_C1
Cluster 2
KF_C2
Cluster 3
KF_C3
Present Climate
Observation
MIROC5_P
Future Climate
CMIP
MIROC5_F
Yoshimura
Yoshimura
MRIAGCM3.2H
60km
Kain-Fritsch
Future Climate
MIROC5
150km
Chikira
* Observation: Observational data by Hadley Center of Met Office, United Kingdom
* CMIP MME: Coupled Model Intercomparison Project Multi-Model Ensemble
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Future changes in river discharge projected
using different ensemble experiments
Ratio of mean of annual maximum daily discharge in the future
climate to the one in the present climate
YS_CMIP_20
YS_CMIP_60
KF_CMIP
KF_C2
KF_C3
MIROC5
KF_C1
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Future changes in river discharge projected
using different ensemble experiments
Ratio of annual mean discharge in the future climate
to the one in the present climate
YS_CMIP_20
YS_CMIP_60
KF_CMIP
KF_C2
KF_C3
MIROC5
KF_C1
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Future changes in river discharge projected
using different ensemble experiments
Ratio of annual minimum daily discharge in the future climate
to the one in the present climate
YS_CMIP_20
YS_CMIP_60
KF_CMIP
KF_C2
KF_C3
MIROC5
KF_C1
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Statistical analysis of river discharge changes
The statistical significance of river discharge changes in the
Indochina Peninsula region was assessed by comparing
means of projected river discharge data in the near future
climate and the future climate with those in the present climate.
The test for statistical significance of river discharge changes is
chosen based on the distribution of projected river discharge
data.
 Shapiro-Wilk W test was applied for data normality test.
 The parametric Welch correction t-test was applied for the
river discharge data which have a normal distribution.
 The non-parametric Mann-Whitney U test was performed to
test for statistical significance of non-normal distribution river
discharge data.
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Testing for the difference between mean of annual
maximum daily discharge in the future and present
climate at the 5% significance level
YS_CMIP_20
YS_CMIP_60
KF_CMIP
KF_C2
KF_C3
MIROC5
KF_C1
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Testing for the difference between annual mean discharge in the
future and present climate at the 5% significance level
YS_CMIP_20
YS_CMIP_60
KF_CMIP
KF_C2
KF_C3
MIROC5
KF_C1
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Testing for the difference between mean of annual
minimum daily discharge in the future and present
climate at the 5% significance level
MRI_YS_CMIP_20
MRI_YS_CMIP_60
MRI_KF_CMIP
MRI_KF_C2
MRI_KF_C3
MIROC5
MRI_KF_C1
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Findings
 There are discrepancies on the ratio of river discharge change and area
of statistically significant increase or decrease among simulations using
different GCMs datasets.
 A clear change of river discharge was detected and found statistically
significant in the Irrawaddy River basin, especially the annual maximum
daily discharge in the future climate.
 In the central part of Vietnam, a decreasing trend of annual minimum
daily discharge in the future climate was found statistically significant
from simulations using MRI-AGCM3.2S and MRI-AGCM3.2H datasets.
However, the simulation using MIROC5 dataset displayed an opposite
trend. This opposition also occurs at the Chao Phraya River basin and
middle part of the Mekong River basin.
 For future further work, more GCMs data will be used to evaluate the
uncertainty in climate projection and to investigate the factors
contributed to the simulation biases of river discharge.
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Climate Change Research
Probabilistic evaluation of
magnitude and frequency
on water-related disasters;
Physically prediction of
a probable largest-class
water-related disasters,
Economical risk
analysis of waterAdaptation
related disasters to
seek best combination
of structural and nonstructural measures.
GCM/RCM
Modeler
Climate
Projection
Research
Impact
Assessment
6868
Thank you for your attention
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