THA 2017 International Conference on“ Water Management and Climate Change Towards Asia's Water
Water-Energy-Food Nexus ”
25 - 27 January 2017,, Bangkok, Thailand
Thailand.

Prediction of extreme floods and risk curve development
under a changing climate
Yasuto TACHIKAWA, Tomohiro
mohiro TANAKA, Hiromasa HAYASHI, Kazuaki YOROZU,
and Yutaka ICHIKAWA

Abstract To estimate probabilistic characteristics of
extreme floods and to predict the magnitude of largestclass floods under a changing climate are key issues for
impact assessment studies on water resources
resources. In thisresearch, a non-stationary hydrologic frequency analysis
was introduced to future extreme rainfall GCM outputs
to evaluate the change of extreme rainfall and flood
characteristics. Then, a physically--based method toestimate a probable largest-classclass flood was shown.
Finally, a probabilistic method to evaluate economicalloss due to flood inundation was proposed tto assess
adaptation measures to cope with flood disasters
disasters.
Keywords Climate change; non-stationary
stationary hydrologic
frequency analysis; largest-classclass flood; flood risk curve

Introduction
To estimate probabilistic characteristics of extreme
floods and magnitudes of largest-classclass flood
floods under achanging climate is a key issue for building up an
adaptation strategy. To evaluate the change of extreme
rainfall and flood characteristics,, a non-stationaryhydrologic frequency analysis was applied to future
extreme rainfall outputs projected by MRI
MRI-AGCM 3.2S,
20km resolution atmospheric general circulation model
developed by Metrology Research Institute in Japan(Mizuta et al., 2012).
). The results showed that themagnitudes of the annual maximum short
short-term rainfall
increase for the near future in most parts of Japan andthis tendency was intensified in the end of the 21st
century (Hayashi et al., 2014).

Yasuto TACHIKAWA, Tomohiro TANAKA
Kazuaki YOROZU, Yutaka ICHIKAWA
Department of Civil and Earth Resources Engineering,
Kyoto University
Kyoto, Japantachikawa@hywr.kuciv.kyoto-u.ac.jp
Hiromasa HAYASHI
Ministry of Land, Infrastructure, and Transportation
Tokyo, JapanAs well as a non-stationary
stationary hydrologic frequency
analysis, the magnitudes of largest-classclass flood
floods causedby typhoons under a changing climate scenario were
examined by using numerical typhoon simulation
outputs. The rainfall data used for the flood simulation
was developed by a physically-based
physicallycourse ensemble
typhoon experiment for the Ise Bay Typhoon in 1959
under a pseudo global warming condition (Takemi etal., 2013). The simulated rainfall data was given to a
distributed rainfall-runoff
runoff model considering flood
regulation with dam reservoir operations
operation and flood
control basins. It was revealed that the largest flood
discharge at the Hirakata station, located at the upper
part of the Osaka City area happened at the flood
simulation using the typhoon course closest to the best
track of the Ise-Bay
Bay Typhoon, which means the actual
Ise Bay Typhoon course was the worst course for flood
disaster in the Osaka area; the peak discharge in thepseudo global warming experiment showed 10%
increase than the control experiment.
Finally, a method to construct a flood risk curve, arelation between the annual maximum damage due toflood inundation and its exceedance probability,
probability wasdeveloped for a probabilistic assessment of economic
loss by flood disasters (Tanaka et al., 2015, 2016;
Tanaka, 2016). The flood risk curve was obtained byintegrating the exceedance probability of the annual
maximum rainfall that causes a given inundation
damage for all historical spatio-temporal rainfall
patterns. A flood risk curve wasas constructed
constrin the Yodo
River basin using the latest climate change projection
database, d4PDF, the database for Policy Decision
making for Future climate change (Mizuta et al., 2016).
Non-stationary
stationary hydrologic frequency analysis
Non-stationary hydrologic frequency models
model with timedependent parameters (Coles, 2001)
2001 were introduced toassess the change of probabilistic characteristics of
extreme rainfall (Hayashi et al.,al 2014). The nonstationary hydrologic frequency models used here werea non-stationary
stationary GEV model, a non-stationarynon
Gumbelmodel, a non-stationary
stationary SQRT-ET
SQRT
model, and a nonstationary lognormal model. A cumulative distributionfunction (CDF) and a probabilistic distribution function(PDF) for these models are represented as below:
1) CDF of the non-stationary
stationary GEV model

THA 2017 International Conference on“ Water Management and Climate Change Towards Asia's Water
Water-Energy-Food Nexus ”
25 - 27 January 2017,, Bangkok, Thailand
Thailand.

2) CDF of the non-stationary
stationary Gumbel model3) CDF of the non-stationary SQRT-ET
ET modelhaving 20km spatial resolution for the present climate
experiment (1979-2003),
2003), the near future climate
experiment (2015-2044)2044) and the end of the 21st century
climate experiment (2075-2099)
2099) projected by MRIMRI
AGCM3.2S (Mizuta et al.,., 2012).
Figure 1 shows the spatial distribution of the best
fitted non-stationary
stationary frequency model evaluated by the
AIC for the annual maximum daily rainfall of each grid
having 20km resolution. Using the best fitted model, thechange of the 100-year
year annual maximum daily rainfall
was analyzed as shown in Figure 2.
24) PDF of the non-stationary
stationary lognormal mode
model

The time dependent model parameters for eachhydrologic frequency model are modeled using thepolynomial equation as

The coefficients of the polynomial equation wereidentified by using the maximum likelihood method.
The best fitted model was selected using the Takeuchi
information criterion TIC and the Akaike information
criterion AIC, which were analytically confirmed formodel selection criteria for non-stationary
stationary sequences.

Fig. 1 The best fitted non-stationary
stationary frequency modelfor the annual maximum daily rainfall projected by
MRI-AGCM3.2S with 20km spatial
al resolution. Red:
non-stationary GEV model;; Green: non-stationary
Gumbel model; Right Blue: non-stationary
stationary SQRTSQRT-ETmodel; and Orange: non-stationary
stationary lognormal mode
model.
The non-stationary four models were applied to GCM
rainfall time series to examine the future change of
extreme rainfall intensity. The GCM outputs used were
the time series of annual maximum daily rainfall data

Fig. 2 Change of the 100-year
year annual maximum daily
rainfall. Orange,, green, and right blue show increase, nochange, and decrease,, respectively. The above figure
shows the change between 2029 at the middle of thenear future experiment and 1993 at the middle of thepresent climate experiment.. The below shows thechange between 2089 at the middle of the end of the 21st
century climate experiment and 1993.
1993
As shown in Figure 2, we found
fthat the annual
maximum daily rainfall around Japan shows an increase
tendency and the increase tendency is intensified at theend of 21st century.

THA 2017 International Conference on“ Water Management and Climate Change Towards Asia's Water
Water-Energy-Food Nexus ”
25 - 27 January 2017,, Bangkok, Thailand
Thailand.

Largest-class flood prediction
A largest-classclass flood caused by a typhoon under aclimate change condition at the Yodo River basin is
examined by using rainfall data developed by aphysically-based
based course ensemble typhoon experiment
for the Isewan Typhoon in 1959 and a distributed
rainfall-runoff
runoff model including flood regulation withdam reservoir operation (Tachikawa et al., 2014,
Miyawaki et al., 2016).
Takemi et al. (2013) developed
eveloped a heavy rainfall
dataset based on an ensemble simulation of thehistorical extreme typhoon, the Ise-Bay
Bay Typhoon (1959)
using a meso-scale
scale meteorological model, the Weather
Research Forecasting (WRF) model version 3.1.1. The
ensemble simulation method realizes to generate
different typhoon tracks perturbed from the original
track of the typhoon by applying a potential vorticity
inversion (PVI) method (Ishikawa et aal., 2012). Figure 3
shows the typhoon tracks simulated by the PVI method
for the Ise-Bay
Bay Typhoon in 1959
1959. The ensemble
simulation approach was also applied to the typhoon
under a pseudo global warming
arming condition by setting a
different sea surface temperature. The difference of thesea surface temperature was given based on the
difference of the monthly mean SST in September
between the end 21st century climate experiment (2075(20752099) and the presentt climate experiment (1979(1979-2003)
simulated by MRI-AGCM3.2
AGCM3.2 (Mizuta et al., 2012).

Figure 5 shows the largest river discharge at the
Hirakata station (7,281km2) in the Yodo River basin foreach typhoon track.

Fig. 4 Spatially distributed river flow for the Central andKansai regions in Japan simulated by a distributedhydrologic model, 1K-DHM.

Fig. 5 The largest river discharge at the Hirakata station(7,281km2) in the Yodo River basin for each typhoon
track.

Fig. 3 Virtual shifting of typhoon’ss initial position for
the Ise-Bay
Bay Typhoon in 1959 (Takemi et al., 2013).
The simulated rainfall data was given to a
distributed rainfall-runoff model with 1km spatial
resolution, 1K-DHM (Tachikawa
Tachikawa and Tanaka, 2014
2014)
developed for the Central and Kansai regions in Japan asshown in Figure 4.. The simulated discharge for each
typhoon track was stored with about 1km grid resolution.

We found that the simulated typhoon track No. 9 whichcaused the largest river discharge at the Hirakata station
was the closest track to the best track of the Isewan
Typhoon in 1959, which means the Isewan typhoon took
the worst course
ourse for the Yodo River basin; and thelargest river discharge at the Hirakata station under apseudo global warming condition showed about 10%
increase. We also found that the peak flood discharge at
the Typhoon 18 in 2013 was larger than the largest peakdischarge estimated by the Isewan Typhoon course
ensemble simulation under a pseudo global warming
condition.

THA 2017 International Conference on“ Water Management and Climate Change Towards Asia's Water
Water-Energy-Food Nexus ”
25 - 27 January 2017,, Bangkok, Thailand
Thailand.

inundation using a guideline to estimate the economic
damage (MLIT, 2005).

Fig. 6 Schematic explanation of the translation of a
distribution function of the D-day
day annual maximum
rainfallto a distribution function of inundation
damage m through various rainfall patterns (Tanaka etal., 2015).
Development of flood risk curve: pprobabilistic
assessment of economic loss due to flood
To realize an integrated economic risk assessment by
flood disasters, a flood risk curve plays an importantrole. A flood risk curve provides a relation between
economic loss due to flood disaster and its exceedance
probability. A method to obtain the flood risk curveconsidering the uncertainty of spatio
spatio-temporal rainfall
distribution is newly proposed (Tanaka et al., 2015,2016; Tanaka, 2016).
A flood risk curve is generated from a probability
distribution function of the annual maximum rainfall
distribution through the following processes (Figure 6):1) to prepare typical extreme rainfall patterns ; 2) toobtain a probability distribution of the annual maximum
rainfallfrom the historical data; 3) to obtain
relations between T-year
year annual maximum rainfall andthe maximum inundation water depth through a rainfall
rainfallrunoff and inundation simulation;; and 4) the economic
damage m is estimated for each maximum inundation
depth caused by the T-year
year annual maximum rainfall .
These procedures are conducted for each typicalextreme rainfall pattern. Finally, the relation between
economic damage m and its exceedance probability
is obtained by integrating the exceedance
probability of each inundation damage. This method
requires many rainfall-runoff
runoff and inundation
simulations, thus a nesting runoff-inundation
inundation simulation
methodd to reduce computational costs was also
developed.
The method was applied to the Y
Yodo River basin
in Japan (Figure 7). Rainfall-runoff
runoff simulation wasapplied to the entire Yodo River basin using a
distributed hydrologic model 1K-DHM
DHM with about 1km
spatial resolution. Then, inundation simulations wereapplied to the central part of the Yodo River basin
which is the most vulnerable area for flood inundation.
The estimated spatial distributions
utions of inundation depth
were used to calculate the economic loss due toFig. 7 Yodo River basin and an example of estimatedinundation at the central part of the Yodo River basin.
bas
To estimate the risk curve, the latest climate change
projection data, d4PDF (databasedatabase for Policy Decision
making for Future climate change (Mizuta et al., 2016)
was used. The d4PDF was simulated by using MRIMRI
AGCM3.2H and 3.2S, which includes the historicalexperiment from 1951 to 2011 with 50 ensemble
members which is equivalent to 3,000 years simulation.
Forr the future experiment, d4PDF assumes the four
degrees increase condition, which corresponds to theend of 21st century of the RCP8.5 scenario experiment.
The future data consists of 15 ensemble experiments
form 2051 to 2111 for six different SST patterns, whichmeans 9000 years simulation for each SST pattern.

Fig. 8 Estimated flood risk curve at the Yodo Riverbasin.

THA 2017 International Conference on“ Water Management and Climate Change Towards Asia's Water
Water-Energy-Food Nexus ”
25 - 27 January 2017,, Bangkok, Thailand
Thailand.

Figure 8 show a floodd risk curve at the central partof the Yodo River basin derived using the observed
rainfall data and d4PDF rainfall projection data. The
flood risk curve estimated by using observed rainfall
data well matched to the one using the d4PDF historical
data set. Figure 8 clearly shows that the predictedeconomic loss due to flood inundation becomes larger atthe end of the 21st century under the climate change
scenario.
Summary and conclusions
In this study, characteristics of extrem
extreme rainfall
under a climate change scenario was analyzed using a
non-stationary hydrologic frequency analysis
analysis. It is foundthat the annual maximum daily rainfall around Japanshows an increase tendency and the increase tendency isintensified in the end of 21st century. Then, a largestclass flood was estimated using a rainfall-runoff
simulation with the multi-track
track ensemble numericaltyphoon simulation under a pseudo global warming
condition. It is found that the Isewan typhoon took theworst course for the Yodo River basin and the largest
river discharge under a pseudo global warming
condition showed about 10% increase. Finally, aprobabilistic method to evaluate economical loss due toflood inundation was demonstrated using the latest
climate change projection data d4PDF
d4PDF.
Acknowledgement
This research was conducted under the framework of the"Precise Impact Assessments on Climate Change"
supported by the SOUSEI Program of the Ministry of
Education, Culture, Sports, Science, and Technology
Technology,
Japan. Prof. Takemi for providing the dataset of themulti-track
track ensemble numerical typhoon simulations
was also acknowledged.
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