THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

Impact assessment of climate change on water-related disasters forbuilding up an adaptation strategy
Yasuto TACHIKAWA

Abstract To estimate probabilistic characteristics of
extreme floods and to predict the magnitude of a largestclass flood under a changing climate are a key issue forbuilding up an adaptation strategy. In this research, aphysically-based method to estimate a probable largestclass flood and a flood damage assessment method
considering probabilistic flood characteristics areintroduced. Based on the future flood prediction
techniques, an adaptation strategy to cope with flood
disasters is discussed.
Keywords river discharge prediction, climate change, alargest-class flood, flood risk curve

Introduction
Flood predictions are classified into three categories: theflood magnitude prediction in terms of the flood
frequency such as the annual maximum 100-year flood,
the largest-class flood prediction such as a probablemaximum flood (PMF), and the real-time flood
prediction. To improve these flood prediction techniques
is a key engineering issue to cope with flood disasters.
In this study, a largest class flood prediction and aprobabilistic assessment of flood damage occurrence arefocused.
To estimate a largest-class flood, a physicallybased flood prediction method using a multi-track
ensemble numerical typhoon simulation (Ishikawa et al.,2012; Oku et al., 2014) is proposed. The method wasapplied to the historical large typhoon, the Ise-Bay
Typhoon in 1959 under a present climate condition anda pseudo global warming condition (Takemi et al.,2013). The estimated precipitation data was given to a
distributed rainfall-runoff simulation model (1K-DHM,Tachikawa and Tanaka) to predict river discharge. The
estimated flood magnitude under a pseudo globalwarming condition is a central issue with respect to anadaptation measure to avoid catastrophic damage.
Yasuto TACHIKAWA
Department of Civil and Earth Resources Engineering,Kyoto University
Kyoto, Japantachikawa@hywr.kuciv.kyoto-u.ac.jp

Secondly, a method to develop a flood risk curve ispresented to assess flood damage probabilistically. Aflood risk curve is a relation between flood inundationdamage and its exceedance probability. A procedure todevelop a flood risk curve is below: 1) a probability
distribution of annual maximum rainfall is obtained
from historical record; 2) the relations between the Tyear annual maximum rainfall and the maximum
inundation water depth are obtained through rainfallrunoff and inundation simulations for different spatiotemporal rainfall patterns; 3) economic damage isestimated for each rainfall-runoff and inundation
simulation; and finally 4) the relation between economic
damage and its exceedance probability is obtained byintegrating the exceedance probability of the annual
maximum rainfall that causes the inundation damage forall spatio-temporal rainfall patterns. To cope with huge
rainfall-runoff and inundation simulations, a nesting
rainfall-runoff-inundation method is newly developed toreduce computational burden.

Largest-class flood prediction
Takemi et al. (2013) developed a heavy rainfall dataset
based on an ensemble simulation of the historicalextreme typhoon, the Ise-Bay Typhoon (1959) using amesoscale 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 al., 2012; Oku et al., 2014). Fig. 1 shows
the typhoon tracks simulated by the PVI method for theIse-Bay Typhoon in 1959. The ensemble simulation
approach was also applied to the typhoon under apseudo global warming condition by setting a different
sea surface temperature. The difference of the sea
surface temperature was given based on the differenceof the monthly mean SST in September between the end
21st century climate experiment (2075-2099) and thepresent climate experiment (1979-2003) simulated by
MRI-AGCM3.2 (Mizuta et al., 2012).

THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

(a) Simulated flood hydrographs for the present climatecondition.

Fig. 1 Virtual shifting of typhoon’s initial position for
the Ise-Bay Typhoon in 1959 (Takemi et al., 2013).
(b) Simulated flood hydrographs for the pseudo global
warming condition.
Fig. 3 Simulated flood hydrographs at the Hirakatastation (7,281km2) for different conditions.

Fig. 2 Spatially distributed river flow for the Central andKansai regions in Japan simulated by a distributedhydrologic model, 1K-DHM.
The simulated rainfall data was given to a
distributed rainfall-runoff model (1K-DHM) developed
for the Central and Kansai regions in Japan (Fig. 2). The
simulated discharge for each typhoon track was storedwith about 1km grid resolution. Fig. 3 shows thesimulated hydrographs at the Hirakata station(7,281km2) in the Yodo River basin for various typhoon
tracks. For each grid cell in the study region, thetyphoon track which causes the maximum discharge wasanalyzed. Fig. 4 shows a spatial distribution of thetyphoon track number that caused the maximum
discharge for each grid cell. The flood runoff simulation
results for the present climate condition shown in Fig. 3
underestimate the observed data, however, the ones forthe pseudo global warming condition clearly shows theincrease of flood flow.

Fig. 4 Spatial distribution of the maximum discharge
caused by different typhoon tracks.
New typhoon ensemble simulations using otherboundary conditions and for other historical typhoons
are ongoing. Improvement of the distributed hydrologic
model including introduction of the dam reservoir
operations and readjustment of model parameters arealso underway. An inundation hydraulic simulation
model will be combined with the hydrologic simulation.
The method proposed here provides information on
physically-based largest-class floods under a changing
climate, which is basic information for building up anadaptation strategy.

THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

Fig. 5 Schematic explanation of the translation of a
distribution function of the D-day annual maximum
to a distribution function of inundation
rainfalldamage m through various rainfall patterns (Tanaka etal., 2015).

runoff and inundation simulation; and 4) the economic
damage m is estimated for each maximum inundation
depth caused by the T-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. The schematic
instruction of the development of the flood risk curve isshown in Fig. 5. This method requires many rainfallrunoff and inundation simulations, thus a nesting runoffinundation simulation method to reduce computationalcosts was also developed.
The method was applied to the Yura River basin(1,882 km2, Fig. 6) in Japan. Rainfall-runoff simulation
was applied to the entire basin using a distributedhydrologic model 1K-DHM with about 1km spatialresolution. Then, inundation simulations were applied tothe gridded area located at the lower part of the basin.
The estimated spatial distributions of indention depth
were used to calculate the indention damage amountusing a guideline to estimate the economic damage(MLIT, 2005).

Fig. 6 Yura River basin (1,882 km2) in Japan. A
distributed hydrologic model 1K-DHM was applied tothe entire basin and inundation simulations were applied
to the gridded area. The black and red marks represent
rainfall observation stations and the yellow marks
represents river stage gauging stations.

Probabilistic assessment of flood damage using floodrisk curve
To manage flood disasters flood risk control based on
appropriate risk assessment is essential. To realize an
integrated economic risk assessment by flood disasters,
a flood risk curve plays an important role. A flood risk
curve provides a relation between flood inundation
damage and its exceedance probability. A method toobtain the flood risk curve considering the uncertaintyof spatio-temporal rainfall distribution is newly
proposed (Tanaka et al., 2015).
A flood risk curve is generated from a probability
distribution function of the annual maximum rainfall
distribution through the following processes (Fig. 5): 1)
to prepare typical extreme rainfall patterns ; 2) toobtain a probability distribution of the annual maximum
from the historical data; 3) to obtainrainfallrelations between T-year annual maximum rainfall andthe maximum inundation water depth through a rainfall-Fig. 7 Relation between the 2-day basin average rainfall
and inundation damage estimated by rainfall-runoff andinundation simulations. The lines P1 to P5 show therelation for each rainfall pattern. Solid and dashed linesrepresent case studies with and without dam operation(Tanaka et al., 2015).
Fig. 7 shows the estimated relations between the 2-day
basin average rainfall at the Yura River basin andinundation damage obtained by rainfall-runoff andinundation simulations. The lines indicated by P1 to P5
show the relation for each rainfall pattern. Solid anddashed lines represent case studies with the dam
operation at the Ono dam and without the dam operation.
Each risk curves reflect the magnitude of rainfall and adifference scenario of flood works.

THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

ensemble numerical typhoon simulations was also
acknowledged.

References

Fig. 8 Estimated flood risk curve at the Yura River basin(Tanaka et al., 2015).
Fig 8 show the integrated flood risk curve which
was derived by integrating the relation shown in Fig. 7.
The flood risk curve considers the spatio-temporal
rainfall patterns and a distribution function of themagnitude of rainfall which cause inundation disasters
at the Yura River basin. This information reflects theprobabilistic occurrence of heavy rainfall and flood
under various scenarios, which provides key information
to cope with water-related disasters.
Summary and conclusions
In this study, predictions of a largest-class flood and aprobabilistic assessment of flood damage occurrence areintroduced. To provide a physically-based prediction ofa largest-class flood, a rainfall-runoff simulation withthe multi-track ensemble numerical typhoon simulation
under a pseudo global warming condition wasdemonstrated. Then, a development of a flood risk curve
was presented to assess flood damage probabilistically.
The proposed methods provide essential information todevelop an adaptation strategy to cope with waterrelated disasters
Acknowledgement
This research was conducted under the framework of the"Precise Impact Assessments on Climate Change"
supported by the SOUSEI Program of the Ministry ofEducation, Culture, Sports, Science, and Technology.
Prof. Takemi for providing the dataset of the multi-track

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