Flood Risk andClimate Change
Hokkaido
WP1 Climate change

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Flood Risk andClimate Change
Hokkaido
WP1 Climate change
Draf t report

Authors
Hoshino, Tsuyoshi (Hokkaido Unversity)
Hegnauer, Mark (Deltares)
Shim izu, Keita (Hokkaido Unversity)
Yam ada, Tom ohito (Hokkaido Unversity)

PR3983.10
December 2021

Table of contents
1

Introduction

1

2

Risk-Based Approach for Future Flood Management with Huge
Ensembles of Climate Projections in Japan3

2.1

Flood risk assessment and social m ovement in Japan

3

2.2

Concept of risk-based approach using ensemble clim ate data

4

3

Data and Methodology

7

3.1

Large ensemble clim ate data

7

3.2

Estim ation method for peak-discharge distribution for each return period

83.2.1Return period of rainfall

9

3.4 Runoff simulation of 2016 flood

15

4

Results19

4.1

Probability distribution of Rainfall with target return period

19

4.2

Rainfall-Peak discharge relation

20

4.3

Peak discharge return period245Summary27

6Reference28

1

Introduction

The increasing risk of heavy rainfall due to clim ate change is of great im portance and is a challenge
for countries of the world. In recent years, the assessment of heavy rainfall risk considering climatechange has been realized by using future projections based on clim ate m odels (e.g., Yam ada et al.,2018, Yam ada, 2019). Various m odels have been used to assess the risk of heavy rainfall, such as
the clim ate projection m odel, rainfall-runoff m odel, levee breaching m odel, flooding m odel, anddam age assessment m odel for loss of life and economic damage. However, there are various
uncertainties inherent in these m odels because the num ber of disaster cases that we haveexperienced is lim ited. The development of these m odels and the m ethodology of risk assessment
are being studied in m any countries. In order to develop objective and reliable risk assessmentm ethods, it is necessary for countries around the world that are m aking advanced efforts in heavy
rainfall risk assessment and flood control m eas ures to compare their risk assessment m ethodology
with each other and develop them jointly. For this reason, a joint research project called "Flood risk
and clim ate change Hokkaido" has been started by research groups in Japan and the Netherlands.
The joint research project is based on the ensemble approach for clim ate change projection andrisk assessment led by Japan (e.g., Yam ada et al., 2018, Hoshino et al., 2020). In this approach, a
5km -resolution large ensemble clim ate dataset was used to estimate th e physical characteristics of
extrem e rainfall under past and future clim ate conditions. Results from flood risk assessment basedon the ensemble climate data clarified that extreme heavy rainfall in future clim ate could occur more
frequently compare to the past clim ate and extrem e rainfall will be stronger in the future clim ate andm ore intense both in temporal and spatial scales, and flood risk will be higher in the future (Yam ada,2019). The flood risk assessment considered the variability of the clim ate system which caused
extrem e heavy rainfall in both past and future clim ate using thousands years of clim ate simulations.
In addition, the ensemble inform ation derived from huge m odel calculations is also supported bystatistical theory (Shim izu et al., 2020). Our history represents only one outcome of a range ofclim ate variability. One of the im portant concepts from the risk-based m ethodology based on
ensemble information is that it proceeds under “two wheels” constructed fro m physics and statisticaltheory (Figure-1). In recent years, these achievements are now used to discuss future flood control
policy considering clim ate change at a national level in Japan, although flood control m anagement
was traditionally based on historical inform ation in Japan.
On the other hand, the Netherlands has pioneered the social im plem entation of risk-based flood
control m easure. Specifically, the Netherlands adopted a risk-based approach that integrated flood
dam age and its occurrence probability (J. Van, 2016). Their flood control m easure has assumed
evacuation rates for residents by each region (B. Kolen, 2013) and quantified the hum an andeconomic damage associated with levee breaches (S.N. Jonkm an, 2007, B. Maaskant, 2009). Basedon this risk-based approach, a policy decision has been m ade to reduce the risk of m ortality fromflood inundation to less than 10 -5 by 2050 for the entire Netherlands (KNMI and Deltares, 2015; The
Ministry of infrastructure and Environm ent and The Ministry of Economic Affairs, 2015). Under the
support of “Partner for waters”, a collaborate research with Academ ia, Industry and Government
prom oted by Netherlands Enterprise Agency, has been started to integrate knowledge from the twocountries.
In the field of flood control, hydrological m odels (rainfall-runoff, river flow and inundation m odel)that describe the processes from rainfall to inundation play a fundam ental role in planning the design
river discharge and flood damage. However, the m odel param eters are subject to uncertainty due tothe lim ited num ber of observed flood events. In “working package 1 (WP1)” of this joint research,

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the relationship between rainfall and peak discharge expressed by each m odel is considered as an
im portant factor to clarify uncertainty inherent in a m odel itself. The advantage of quantifying rainfallpeak discharge relation is the estimation of uncertainty included in different hydrological m odels andparam eter settings. In addition, future changes in the spatio-tem poral characteristics of extreme
rainfalls with low frequency can be incorporated in the peak discharge estimation by introducing therainfall-peak discharge relation expressed by m odels. The purpose of WP1 is to construct a rationalfram ework to m ake probability inform ation of peak discharge incorporating uncertainty of rainfallrunoff m odel and spatio-temporal rainfall pattern. The output of WP1 is a probability distribution ofpeak discharge of each return period. Derivation of probable inform ation of peak discharge requires
two types of inform ation. One is a frequency distribution of rainfall volum e of each return period,
and another is the rainfall-peak discharge relation derived from rainfall-runoff m odels. The advantageof this m ethodology is that the estim ated peak discharge can be updated in accordance with thedifferences and sophistication of clim ate m odels and rainfall-runoff m odels. This technical report
describes the m ethodology and results developed in WP1.
Figure 1 The concept of risk-based approach constructed from “two wheels” (Yamada et al., 2018)

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2

Risk-Based Approach for
Future Flood Managementwith Huge Ensembles of
Climate Projections in Japan

2.1

Flood risk assessment and social movement in Japan

This chapter explains the latest clim ate change projection data and risk assessment m ethods inJapan and concept of the role of ensemble climate data in risk assessment under clim ate change
conditions.
In August 2016, three typhoons landed in the Hokkaido region, Japan, within a week. Typhoon
No. 10 approached this region after the landing of these typhoons. These typhoons caused recordbreaking rainfall in m any parts of Hokkaido, resulting in river flooding, landslides, road and bridge
washouts, and extensive damage to land devoted to agriculture, which is the m ain economic
activity in the region (Ministry of Land, Infrastructure and Transport (MLIT), 2017). The m em bersof the Japanese side in this joint research project conducted large -scale climate simulations using a
regional clim ate m odel under the past and warm er clim ate condition by using the domestic
supercomputer (earth sim ulator) to develop a 5-km spatial resolution large ensemble climate
dataset (e.g., Yam ada et al., 2018, Yam ada, 2019, Hoshino et al., 2020). The advant age of using
this data is that the probability of occurrence and intensity of possible extreme rainfall events canbe estim ated for each river basin under the past and future clim ate condition. The annualm axim um rainfall and hourly rainfall estim ated from the 5-km resolution large ensemble dataset
are consistent with observed data for m ultiple basins in Hokkaido as well as the Chikugo Riverbasin in the Kyushu region and the Tone River basin in the Kanto region , Japan. In addition, thevalidity of the rainfall volum e and occurrence probability of the huge number of sim ulated heavy
rainfalls were dem onstrated by a statistical theory (Shim izu et al., 2020).
The ensemble data enables us to estimate flood risk considering rainfall volum e and spatio tem poral rainfall patterns. The spatio-temporal rainfall pattern highly affected peak discharge andflood dam age for river basins in Japan; however, the rainfall patterns were not consideredsufficiently because the experienced rainfall events were lim ited. On the other hand, ensemble data
included m any rainfall patterns both sim ilar to experienced and not experienced rainfall patterns.
The flood inundation dam age were estimated by using the thousands of rainfall patterns. The
flood risk which consists of human and e conomic damage and probability of occurrence wasestim ated under past and future clim ate conditions using the ensemble clim ate data, hydrologicalm odel and flood dam age estimation m odel (Yam ada, 2019). This study has contributed to a shift
from the traditional hazard-based flood control planning based on past rainfall observations to newrisk-based planning for the potential large floods under the future clim ate. Such detailed risk
analysis using ensemble climate data is one of the m ost advanced efforts worldwide and has been
selected as a representative disaster prevention research at the United Nations Framework
Convention on Clim ate Change (UNFCCC Bonn Clim ate Change Conference) (Yam ada, 2019).
From a social point of view, im m ediately after a series of typhoon tracks in August 2016, the
Hokkaido Development Bureau of the Ministry of Land, Infrastructure and Transport (MLIT) and thePR3983.10 • December 2021

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Hokkaido Governm ent held an expert committee to review the disaster and consider the risk of
unprecedented heavy rainfall and future flooding (MLIT, 2016). The expert's committee
sum marized that ”im pacts of clim ate change should be assessed scientifically, and future flood
control m easures should be taken based on the risk assessment result”. In FY2017, the researchgroup of Hokkaido University conducted a dynam ical downscaling to the regional experiment of
Database for Policy Decision Making for Future Clim ate Change (d4PDF) (Mizuta et al., 2017) todevelop the dataset of 5-km horizontal resolution (Hoshino et al., 2020). This data is capable ofrepresenting highly localized rainfall phenomena, such as topographic rainfall, with high accuracy.
In FY2017, clim ate change im pact projection and flood risk were quantified based on the d4PDF
with 5-km horizontal resolution. In 2018, the MLIT held m eetings to discuss “flood control plans inchanging clim ate” and issued the conclusion in October 2019 (MLIT, 2019). Their analysis using
d4PDF estim ated that heavy rainfall volum e in future clim ate will be 1.15-fold higher in all of the
Hokkaido region relative to heavy rainfall volum e under the past climate condition and 1.1-foldhigher in all other regions (MLIT, 2019). These evaluations are based on the assumption ofreaching a 2 K warm er clim ate condition compared to pre-industrial clim ate. Note that global
tem peratures had already risen by about 1 K at the beginning of the 21st century. In 2019,
another committee was held by the Hokkaido Development Bureau of MLIT and the Hokkaido
Governm ent and the committee assessed the risk of heavy rainfall and resultant flooding
associated with clim ate change at Tokachi river and Tokoro river basin in Hokkaido, Japan. The
com mittee also considered effective adaptation measures based on the risk assessment. In thistechnical committee, the rainfall data m ade by the clim ate m odel were considered as "physicallypossible rainfall data, so-called quasi-observation data."

2.2

Concept of risk-based approach using ensemble climate data

The detailed contents and im portance of the risk-based approach with huge ensembles of clim ate
projections in Japan are following. Current flood control plans in Japan are based on projected
future rainfall (e.g., 100-year return period), which is derived deterministically from observed
rainfall data over the past several decades after the consideration process of rejecting outliers.
However, a single data set from the past few decades is only a partial representation of possible
rainfall phenomena, given the variability of the clim ate system. In other words, even if a steadystate is assum ed, there is always a range of uncertainties that depend on the tim escale. Using an
ensemble approach with d4PDF, we were able to assess the uncertainty such as T-year rainfall on agiven tim escale with a confidence interval, which concept is introduced in chapter 3. Precisely, it
equals the realization of uncertainty evaluation considering the degree of freedom of the clim ate
system. Meteorological phenomena occur at different frequencies governed by arbitrary boundary
conditions, and the prim ary boundary condition for the clim ate system is sea surface temperature(SST). Past experim ents of d4PDF used ensemble calculations with m ultiple initial condition s,
including perturbations. The clim ate m odel developed by the Meteorological Research Institute(MRI), Japan were used with observed SSTs as the bottom boundary condition. Past clim ate data
are available for a total of 3000-years, including 50 initial perturbations over a 60-year period. In
addition, the ensemble climate sim ulation were also applied for future clim ate conditions based on
global m ean tem peratures rising between 2–4 K relative to the pre-industrial period. This clim ate
experim ent used the six SST patterns from the global-scale coupled atm ospheric-oceanic m odels(two of which are from Japan) which registered in the Fifth Coupled Model Intercomparison Project(CMIP5). The ensemble data for these six m odels are generate d from m ultiple initial conditions
with perturbations. The d4PDF covers the global scale with a spatial resolution of 60 km and theEast Asia region with a spatial resolution of 20 km .

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One of the risk assessment results is shown following. Figure 2 indicates the risk of potential
flooding based on a probability assessment using data from a past experiment, a 2 K warm er
experim ent, and a 4K warm er experiment. Rainfall patterns of all ensemble m embers (past: 3000
events; 2K warm er: 3240 events; 4K warm er: 5400 events) were inputted to the runoff m odel,
one-dim ensional unsteady river flow m odel, levee breach model, and two-dim ensional inundationm odel. This inform ation allows us to quantitatively discuss the risk of flood dam age in the future.
Furtherm ore, fatalities can be estim ated from thousands of rainfall events based on the inundation
depth, flow velocity, and rate of water level rise. Also, a tim e development of a F-N curve
representing the relationship between the number of fatalities and its occurrence probability isshown in Figure 3. By extending this F-N curve to the tim e axis, it m ay be used for discussing
future adaptation m easures such as advancements in hardware and software m easures (Figure 3).
Also, other types of regional risk related to m ajor infrastructure facilities and rates of soil runoff
have been evaluated using this risk-based approach. In the relevant local communities, discussions
regarding these risks have been started.
There are two m ajor advantages of using ensemble information in assessing flood risk underclim ate change. First, the data enables us to evaluate the range of uncertainty in observed or
predicted rainfall and to allow consideration of the degree of freedom in the clim ate system. By
applying this data to past clim ates and future clim ates, it is possible to assess the im plications for
future flooding risks. Another advantage is the ability to assess flooding risk based on thespatiotemporal pattern of projected rainfall. Ensem ble data include spatiotemporal distributions ofrainfall, which have profound effects on the extent of flood dam age and are therefore im portant toconsider when assessing flood risk. This is also essential when developing countermeasures toflooding because it enables the prediction for other potential dam age. Thus, the introduction oflarge ensemble data sets adds a new perspective to flood risk assessment. It will im prove decisionm aking processes. The abovementioned risk-based approach in Japan com bines deterministic
inform ation from past observations with probabilistic inform ation from ensemble data. The essenceof the idea originates from a debate between Einstein and Bohr.
Figure 2 Occurrence probability of overflow and levee failure in per year for past experiments, and 4 K warmer
experiments (4K future experiment) (inundation depth: 3.2 m) (Yamada, 2020)

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Figure 3 F-N curve on the time axis. The magnitude and shape of the F-N curve are determined by rainfall
increment associated with climate change, various adaptive measures, and other factors. (Yamada, 2020)

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3

Data and Methodology

3.1

Large ensemble climate data

In recent years, a large-ensemble climate sim ulation database (d4PDF) (Mizuta et al., 2017) has
been created and utilized by the research project of the Ministry of Education, Culture, Sports,
Science, Japan (SOUSEI, TOUGOU, SI-CAT, DIAS) and Japan Agency for Marine-Earth Science andTechnology (JAMSTEC) Earth Sim ulator Special Prom otion Projects. Using d4PDF and its extensive
data on past, current, and future clim ate, it is possible to assess the frequency of extreme weather
phenomena that leads to disasters. The d4PDF consists of sim ulations based on the atm osphericgeneral circulation m odel (AGCM) called the Meteorological Research Institute AGCM, version 3.2(MRI-AGCM3.2) (Mizuta et al., 2012) with a horizontal resolution of approxim ately 60 km (d4PDF60km ), and dynam ical downscaling (DDS) from d4PDF-60km to a horizontal resolution of 20 km
using the regional clim ate model (RCM) targeted over east Asia, included Japan (d4PDF-20km ).
The experim ental settings of d4PDF consist of a past clim atic condition (p ast experiment; 50
ensembles×60 years (1951–2010)) and a 4 ℃ warm er clim atic condition (4K warm er experiment;
six sea surface temperature patterns×15 ensembles×60 years), which has a 4 ℃ warm er global
m ean air tem perature than the pre-industrial period. The sea surface temperature (SST) used in
the past experim ent was obtained from the Centennial Observation-Based Estimates of SST,
version 2 (COBE-SST2) (Hirahara et al., 2014). Sm all perturbations based on SST analysis errors
were added to the initial conditions of the SST to m ake the ensemble m embers. The SST used in
the +4K experim ent consisted of six patterns based on RCP8.5, conducted under phase 5 of theCMIP5 (Taylor et al., 2012). The details of the experimental settings of the d4PDF are presented in
Mizuta et al.(2017). Moreover, dynam ical downscaling (DDS) was applied to convert annualm axim um rainfall events from d4PDF-20km to a horizontal resolution of 5 km (Yam ada et al.,2018). The target rainfall event was defined as an event in d4PDF-20km for each year of d4PDF20km , between June 1 and December 1, where rainfall am ounts reached the m aximum value in
72-hour over the Tokachi river basin (upper reach of the Obihiro reference point). This study
defined the rainfall events as annual m axim um rainfall events. For DDS, we em ployed the NHRCM(Sasaki, 2008), which was used to m ake the d4PDF-20km. We set the target area of the DDS to
800 x 800 km around Hokkaido. Figure -4 shows the process of dynam ical downscaling.
Furtherm ore, we set the number of calculation grids to 161 x 161 in the horizontal direction and 50
in the vertical direction. The Kain-Fritsch convective parameterization scheme (Kain et al., 1993)
was used for the DDS. The other physical schemes, such as m icrophysics, land surface, andboundary layer schemes, were the same as those used in Kawase et al. (2018). The grid -m ean
topography was used in this study, while Kawase et al. (2018) used envelope -type mountains. This
study utilized values from the d4PDF-20km to set the initial and boundary conditions for the
calculation. The target period for the DDS was set to 15 days, including the annual m axim um
rainfall events extracted from the d4PDF-20km. The DDS was perform ed for a total of 3000 events
for the past experiment and 5400 events for the +4K experiment.
Figure 4 Process of the downscaling calculation to make d4PDF-5km (e.g., Yamada et al., 2018)

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3.2

Estimation method for peak-discharge distribution for each
return period

The estim ation method for the peak-discharge frequency for each return period consists of threesteps, as shown in Figure -5. The first step is the estim ation of the rainfall volum e frequency foreach return period using ensemble clim ate datasets with the resampling technique. By using thisprocess, the frequency distribution of rainfall with an arbitrary return period, which is a range ofarbitrary probability distributions, is defined as a confidence interval. Specifically, the confidence
interval includes the population param eter θ with a probability of (1 − p), the interval [LC.I.(X), U
C.I.(X)] is constructed with an upper confidence lim it value and a lower confidence lim it value
based on available data. Here, p is the significance level (0 < p < 1). Int erval (LC.I.(X), U C.I.(X)]is defined as the 100(1 − p)% confidence interval. The second step is the estimation of therelationship between rainfall volum e and peak discharge using the rainfall-runoff m odel. Rainfallrunoff sim ulations for several rainfall patterns included in the ensemble clim ate dataset wereconducted to calculate the peak discharges at target points. Next, quantile regression was applied
to the relationship between rainfall volum e and peak discharge to obtain the relative frequency ofpeak discharge as a function of rainfall volum e. The third step is the estimation of the peakdischarge frequency for each return period. The occurrence frequency of the peak-discharge
caused by a given rainfall am ount corresponds to the conditional distrib ution. Therefore, by taking
a weighted average of the conditional distribution of peak discharge with T-year rainfall
distribution, we derived the probability distribution of the T-year peak discharge.

Figure 5 Estimation method for peak-discharge frequency for each return period

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3.2.1Return period of rainfall
The design rainfall in the current flood control m anagement of Japan is generally calculated by
the following steps. First, the observed annual m axim um rainfall was fitted to som e probability
distributions, which are candidates for the estimation of design rainfall. Then, a probability
distribution that has the highest level of stability and goodness of fit is adopted. The design rainfall
is then obtained as a quantile value corresponding to the target annual exceedance probability “p”
in this adopted distribution. A probable rainfall corresponding to exceedance probability “p” m eansthat the occurrence probability of rainfall that exceeds this probable rainfall in a year becomes “p.”In addition, the return period is defined as the reverse inverse number of exceedance probability in

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an adopted probability distribution. It is an im portant index in flood control m anagement. Floodcontrol m anagement of the m ain river basins in Japan adopted a return period of 100 –200 years as
the design level.
However, the observation period of hydrological quantity in m ajor river basins of developed
countries ranges from a few decades to m ore than a hundred years. This shows that theobservation period is shorter than the design return period of the flood prevention facilities. In
other words, when using the annual m axim um data, the num ber of observed extremes ranges
from a few dozen to a few hundred at m ost, so that the estimation of the design external force inthe conventional hydrological frequency analysis contains uncertainty (estimation error). Because
ensemble clim ate data provide many sam ples of rainfall based on dynam ical m odels that verify t hephysical feasibility of the calculated m eteorological phenomenon, the uncertainty of the probablerainfall can be quantified as a frequency distribution. In this study, resam pling of the ensemble
data was conducted to quantify the uncertainty of rainfall for each return period.
The data in d4PDF-5km was resam pled to clarify the frequency distribution of the T-year rainfall
am ount. The d4PDF past experim ent is the output of a clim ate m odel in which the boundary
conditions are the observed SSTs from 1951 to 2010. For this purpose, one annual m aximum 72hour rainfall value from the 50 ensemble members in 1951, and one annual m axim um rainfall
value from the 50 ensemble m embers in 1952, were repeatedly extracted until 2010. The annualm axim um 72-hour rainfall values for the 60 years from 1951 to 2010 were defined as one sample.
In addition, six different SSTs were used in the +4K warm er experiment. In this study, theprobability of occurrence of each SST was assum ed to be equivalent, and the annual m axim um 72 hour rainfall for 60 years was defined as one sam ple, by extracting one annual m axim um rainfall
from 90 ensemble m embers for each year from 2051 to 2110. By applying the aforem entioned
resam pling method, 100,000 samples were generated for both clim ate experiments. The Gum bel
distribution was fitted to each of the 100,000 resampled samples from the past experiment and the+4K warm er experiment to derive the frequency distribution of the T-year probable rainfall. In
other words, we estimated 100,000 Gum bel distributions fitted to the annual m axim um 72-hour
rainfall for 60 years, which yielded 100,000 sets of T-year probable rainfall for a given return
period and enabled the estimation of its frequency distribution. Because of the use of the clim ate
m odel, the introduced resampling m ethod can be interpreted as a physical Monte Carlo m ethod.
The cum ulative distribution function of the Gum bel distribution is shown in Equation (1).

  x −   FX ( x ) = exp  − exp −      
(1)where FX(x) is the cum ulative density distribution of the Gum bel distribution, x is the annualm axim um rainfall, μ is the location param eter, and σ is the scale param eter.

3.2.2 Rainfall-peak discharge relation
In this study, the frequency distributions of peak discharge as a function of rainfall volum e were
estim ated using quantile regression. The peak discharges have a range even if the rainfall volum eis the sam e because of its spatiotemporal pattern. The relationship between rainfall volum e andpeak discharge is represented by the following function. The peak discharge range was quantified
as percentile values using quantile regression, and each quantile value from the 1st to 99th
percentile was extracted for each rainfall volum e. The obtained 99 discharges were used todetermine the frequency distribution of the peak discharge. By applying quantile regression, thePR3983.10 • December 202110

conditional probability distribution of the peak discharge caused by an arbitrary am ount of rainfall
can be calculated.

3.2.3Return period of peak discharge
The frequency distributions of peak discharge for each return period were estimated through thefollowing process. The frequency distribution of rainfall volum e for each return period obtained instep 1 (Figure-5), and the frequency distribution of the peak discharge under each rainfall volum eobtained in step 2 are used to calculate the frequency distribution of the peak discharge for each
return period. Step 3 shows that each bin (section in the histogram) of the rainfall volum e has afrequency distribution of peak discharge. The frequency distribution of the peak discharge isexpressed by Equation (2).

fQp ( q p , T ) =  fQp ( q p | r , T ) f R ( r , T ) dr(2)r

where T is the target return period based on rainfall am ount, Qp is the peak discharge, R is therainfall volum e, fQp(qp|r,T) is the conditional probability density function of the peak dischargethat can occur under a given rainfall volum e r, fR(r, T) is the probability density function of T-year
rainfall am ount, and fQp(qp) is the probability density distribution of the peak discharge.
In this study, the Gum bel distribution was fitted to a set of probable rainfall values derived fromthe afore-described resampling m ethod to construct a continuous distribution of a probable rainfallfR(r,T) for any given return period T. The Gum bel distribution was also applied to a set of
calculated peak-discharge values obtained for a given total rainfall value r, which can be derived
from the relationship between the total rainfall in an arbitrary period and the calculated peakdischarge, to construct the conditional distribution of peak discharge, fQp(qp|r,T). Equation (2)expresses the probability distribution of the peak discharge fQp(qp,T) under the total rainfall for agiven return period T as fR(r,T). Here, fQp(qp,T) is the weighted average of the conditional
distribution of peak discharge under a given rainfall value fQp(qp|r), where the distribution of
rainfall fR(r,T) is derived from d4PDF-5km .
The afore-described method for the probability peak discharge integrates the conditional
distribution (expressed by the relationship between the total rainfall and peak discharge obtained
from the rainfall-runoff m odel) and the distribution of the probable rainfall from the ensemble
clim ate data. In other words, the m ethod is capable of updating the probability distribution of theoutput peak discharge, as the clim ate m odel and rainfall-runoff m odel differ or become moresophisticated.

3.3 Hydrological modelThis section describes the rainfall-runoff m odels adopted by the two countries in working package
1. The characteristics of rainfall and peak discharge represented by these physical m odels allow us
to evaluate the uncertainty of the m odel calculation.

3.3.1 wflow_sbm
Wflow_sbm is a conceptual bucket-style hydrologic m odel based on simplified physicalrelationships, and it uses a kinem atic wave surface and subsurface routing for lateral transport.

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The wflow_sbm m odel is a fully distributed hydrological m odel that allows the use of highresolution spatial input data. This m akes the m odel well suited for the use of high -resolution
clim ate m odel output, as presented in Section 3.1. A schematic of the m ost relevant vertical an dhorizontal processes in the wflow_sbm concept is shown in Figure 6.
The wflow_sbm m odel for the Tokachi River basin was set up based on globally available data at aresolution of approximately 1 × 1 km 2. The river network, river length, and slope param eters were
derived from the state-of-the-art hydro-MERIT dataset (Yam azaki et al., 2019). The hydrologicalparam eters for the soil were derived from the GLOBCOVER land cover m ap and SoilGrids in the
250m soil database. The param eters were derived and scaled to m odel resolution using point-scale(pedo)transfer functions in combination with m ultiscale parameter regionalization (MPR)
techniques.

Figure 6 Schematic of the wflow_sbm concept showing the vertical and horizontal processes for a specific gridcell (left) and the river network of the wflow_sbm model (right).

The wflow_sbm m odel for the Tokachi river basin was set up using globally available data at a~1x1 km 2 m odel resolution. The river network, river length and slope param eters are derived fromhydro-MERIT dataset (Yam azaki et al. (2019)), using the state -of-the-art upscaling techniques topreserve information from the high-resolution base date (Eilander et al., 2021). The hydrologicalparam eters for the soil are derived from the VITO land cover m ap and the SoilGrids250m soil
database. The param eters are derived and scaled to the m odel resolution using point -scale transfer
functions in combination with Multiscale Param eter Regionalization (MPR) techniques (Im hoff et al.,2020).

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12Figure 7 3D visualization of the wflow model of the Tokachi river basin.

The wflow m odel is used worldwide to simulate rainfall-runoff processes. The wflow m odel can beused for event-based modeling, as well as for continuous simulations. For this, an advanced soil
water accounting scheme is included in the m odel. This also m akes the m odel very suited for
drought situations. And since the m odel is physically based, the m odel can also be used to sim ulate
the effects of clim ate change on the hydrological response.
All relevant rainfall-runoff processes for flood peak estimation are included in the m odel. The m odel
includes snow and glacier processes, for cases where rapid snowmelt contributes to the existenceof floods, a soil water accounting scheme, and routing of surface and (rapid) subsurface water.

3.3.2 MATSIROThe Minim al Advanced Treatm ents of Surface Interaction and Runoff (MATSIRO) (Takata et al.,2003) was also used in this study. MATSIRO can sim ulate the exchange of water vapor, energy,
and m omentum between the land surface and atm osphere on a physical basis. The interaction
between the atm osphere and land surface can be sim ulated using this m odel with a GCM. MATSIRO
has been used for various im pact assessments of the hydrological cycle, such as the im pact of
hum an activities (Pokhrel et al., 2017) and groundwater im pacts (Koirala et al., 2014). Recently, asystem for estimating the conditions of land surfaces and rivers in real-tim e has been developed
and operated (Today's Earth). This system consists of MATSIRO and the river routing m odel CaMaFlood (Yam azaki et al., 2011). Meteorological forecast and observation data were used as input
data for the system to estimate the risk of water-related disasters on a global scale.
The schematic diagram of MATSIRO is shown in Figure 8. MATSIRO represents the canopy as asingle layer and illustrates the sim ulation of the energy and heat exchange between the groundsurface and the atm osphere. MATSIRO solves the surface and subsurface runoff by considering thefollowing four runoff types: the base flow, saturation excess runoff (Dunne runoff), infiltration
excess runoff (Horton runoff), and overflow of the uppermost soil layer. The first three runoff types
were calculated by applying a sim plified TOPMODEL (Beven and Kirkby, 1979), which assumes a
subgrid-scale slope profile. The assumed slope profile was used to estimate the saturation fraction
required for the calculation of the first three runoff types.

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In this study, we perform ed offline simulations using MATSIRO, which was decoupled from the
GCM. We did not use hum an activities and groundwater m odules of MATSIRO to focus on a sim ple
rainfall-runoff process, sam e as the other runoff m odel. The near-surface atm ospheric data ofd4PDF-5km were used as the boundary condition of MATSIRO to calculate the runoff of each grid
point within the target basin. The obtained runoff was fed to the Rainfall-Runoff-Inundation (RRI)m odel under non-infiltration conditions to calculate the river discharge.
Figure 8 Schematic diagram of MATSIRO. The symbols of the variables are the prognostic variables and heatand vapor fluxes in MATSIRO, which are listed in the right table (Takata et al., 2003).

3.3.3 RRI model
The RRI m odel was developed by the International Center for Water Hazard and Risk Managem ent(ICHARM). The RRI m odel was employed to sim ulate flood inundation and identify flood-prone
areas. The RRI m odel is a two-dim ensional m odel that is capable of sim ulating rainfall-runoff andflood inundation sim ultaneously (Sayam a et al., 2012, Sayam a et al., 2015). At a grid cell in whicha river channel is located, the m odel assumes that both the slope and river are positioned within
the sam e grid cell. The channel was discretized as a single line along the centerline of the overlying
slope grid cell. The flow on the slope grid cells was calculated using the 2D diffusive wave m odel,
whereas the channel flow was calculated using the 1D diffusive wave m odel. The RRI m odel
sim ulates the lateral subsurface, vertical infiltration, and surface flows to represent flood
characteristics better. The lateral subsurface flow, which is m ore im portant in m ountainous regions,
was treated in term s of the discharge–hydraulic gradient relationship, including both saturated
subsurface and surface flows, whereas the vertical infiltration flow was evaluated using the Green –Am pt m odel. The details of the m odel are presented below (Sayam a et al., 2012). Figure-9 shows
the m odel element in RRI.

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14Figure 9 Schematic of the RRI model (https://www.pwri.go.jp/icharm/research/rri/index.html)

3.4 Runoff simulation of 2016 flood
3.4.1 wflow_sbm
The wflow_sbm m odel was calibrated for the 2016 event. This event includes 3 consecutive flood
peaks within the m onth of August. The param eters that were used in the calibration are the Mparam eter which is linked to the decrease of hydraulic conductivity of the soil with depth, and theKSatHorFrac param eter which is the factor linking the horizontal to the vertical hydraulicconductivity of the soil.
The calibration was done by com paring simulated discharges with observed discharges for theselected event for 4 stations in the basin: The Tokachi dam inflow, the Satsunai dam inflow, theSatsunai point and the Obihiro points. The locations are indicated on the m ap below.

Figure 10 Overview of the Tokachi catchment and the selected calibration stations.

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In figure 11, the results of the sim ulation are shown for different stations in the basin. As can beseen, the m odel is capable of capturing the discharge at Obihiro reasonably well. The m odel slightly
overestimates the first two peaks, but underestimates the last peak. This m ight have to do with therainfall data, or the effect of the dam s, which are not well captured in the m odel yet. It can also beseen that the underestimation is m ost likely com ing from underestimating the flow in the Satsunai
river. Especially the inflow into the Tokachi dam is very well sim ulated, whereas the inflow to theSatsunai dam is largely underestimated. The reason for the underestimation could be that thisbasin is hydrologically very different, resulting in different param eter values needed to capture thepeaks, or, m ore likely, that the rainfall is underestimated.

Figure 11 Results for the selected parameter set, showing the discharge time series for different observation
stations.

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In figure 12 the tim e series for the cumulative volume are shown. The figures indicate that for
Tokachi dam and Obihiro point, the am ount of water in the m odel is in line with observations, but
how the discharge is distributed over tim e, is not always the sam e. Especially the inflow of theSatsunai reservoir is underestimated. This could be explained by underestimation of the rainfall in
the Satsunai basin.

Figure 12 Results for the selected parameter set, showing the cumulative discharge time series for different
observation stations.

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3.4.2 RRI model
The calibration was conducted as targeting the 2016 flood event. The sim ulations with the fourm odel param eters and three different param eter settings were conducted. The target param etersand conditions of sensitivity analysis are shown in Table 1. Finally, three land cover types were set
up for the target basin, and param eters were set for each land cover type.
Table 1 Parameter setting for sensitivity analysis

Figure13 Result of sensitivity analysis of inflow to the Tokachi dam (Influence of (a) Manning’s roughness onriver cells, (b) Manning’s roughness on slope channel, (c) Soil depth and (d) Lateral saturated hydraulicconductivity)

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4

Results

4.1

Probability distribution of Rainfall with target return period

Figure 14 shows the confidence intervals of the probable rainfall in the Tokachi River basin for the
past experim ent and the +4K warm er experiment. In this figure, the black dots represent theobserved annual m axim um basin-averaged 72-hour rainfall, and the solid blue line is the Gum bel
distribution that these observations are assumed to follow. In addition, the blue and red areas
represent the confidence intervals of the probable rainfall for the past and future clim ates,
respectively. The confidence intervals for past and future clim ates were constructed using thephysical Monte Carlo m ethod described in Section 3.2.1. The num ber of calculated annualm axim um rainfall in each resam pled sample is described in Section 3.2.1. was the sam e as theobservation. The existence of the purple overlap area between the two intervals indicates thatheavy rainfall events with the sam e return period, but with different frequencies, can physicallyoccur in both clim ates, where the global average temperature differs by 4 K. The results of theprobability assessment based on ensemble climate data were also supported by statistical theory,and research results were obtained to ensure scientific validity (Shim izu et al., 2020). In thisstudy, we adopted the probability lim it m ethod (Moriguti, 1995), which allows us to derive thestatistical threshold of rainfall that can occur under the assumed probability distribution of annualm axim um rainfall, and constructed confidence intervals based on this test. The blue and red dottedlines in Figure 14 show the width of the confidence interval based on this theory. The figure showsthat the confidence intervals based on the ensemble clim ate data and the confidence intervals
based on the probability lim it m ethod coincide very well for both past and future clim ates. The
coincidence of the confidence intervals constructed independently from the physical Monte Carlocalculations, and the probability lim it m ethod supports the m athematical validity of the present
study.
Figure14 Confidence interval of probable rainfall derived from d4PDF-5km (Shimizu et al., 2020)

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In this study, the Gum bel distribution was fitted to the frequency distribution of the annualm axim um 72-hour rainfall in a given return period, derived from d4PDF-5km . A 150-year return
period was used as an exam ple, and the probability density function of the Gum bel distribution forthe past and 4K warm er experim ent is shown in Figure 15. From this figure, Gum bel distribution
fitted well with the frequency distribution of 150-year annual m axim um rainfall derived fromd4PDF-5km . By functionalizing the probable rainfall distribution with a continuous distribution, theprobability distribution of the T-year peak discharge, which is discussed in the next section, can becalculated analytically.
Figure 15(a) Frequency distribution of 150-year annual
maximum 72-hour rainfall in past experiments (bluebins) and the probability density function f R(r,150) of
the Gumbel distribution fitted to the frequency
distribution (solid blue line)

4.2

Figure 15(b) Frequency distribution of 150-year
annual maximum 72-hour rainfall in 4K warmer
experiments (red bins) and the probability density
function f R(r,150) of the Gumbel distribution fitted
to the frequency distribution (solid red line).

Rainfall-Peak discharge relation

In this section, a m ethod for calculating the relationship between total rainfall and associatedpeak discharge is presented. Figure 16 shows the relationship between peak discharge in the riverand the annual m axim um 72-hr rainfall over the Tokachi River Obihiro reference point basin. The
large circles in the figure represent observed results; the small circles are the results from pastexperim ents (blue) and 4K warm er experim ents (red). The peak discharges in both experiments
were obtained through the runoff and channel routing m odels used for river planning in the sam ebasin. In Figure 16, the observed data show that peak discharge increases with increasing m eanbasin rainfall, which is also confirm ed by the results of past experiments.
Furtherm ore, in the 4K warm er experiment, the same trend in peak discharge occurs while
covering a larger range of rainfall. Given that the sam e runoff param eters are used in this study, it
is evident that the difference in peak discharge for the sam e am ount of rainfall depends on thespatiotemporal distribution of that rainfall. Incidentally, the m axim um observed value was during
the flooding and landslide disaster in August 2016, suggesting that these rainfall events
significantly raised the peak discharge. A sim ilar analytical approach m ay be applied to river basinsthroughout Japan, which will allow us to discuss the im portance of spatiotemporal variation in
rainfall and its influence on peak flow.
In this study, the quantile regression curve was fitted to the relationship between the annualm axim um 72-hour rainfall and the peak discharge in each experiment, and the extrapolation of thepeak discharge was calculated for a given annual m axim um 72-hour rainfall. The quantile
regression curve used is shown in Equation (6). The relationship between the annual m axim um 72hour rainfall and the peak discharge for each experiment and the estimated quantile regression
curves are shown in Figure 18(a,b). The conditional frequency distribution of the peak discharge

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that can occur for a given rainfall can be calculated. In this study, the continuous distribution f
Qp(Qp|r,T)

was calculated by applying the Gum bel distribution to this conditional frequency

distribution. Figure 18(a,b) shows the conditional probability density function of the peak discharge
for each experiment at a given rainfall value of 300 m m .

Qp = aR 2 + bR

(6)

where Qp is the peak discharge; R is the annual m axim um72hour rainfall; and a and b areparam eters of the quantile regression curve. The extreme large discharge events are lim itedthough using the large ensemble clim ate dataset. However, using the function of the rainfall andpeak discharge can cover the area of extrem e large discharge. Note that extreme large discharge issensitive to the formula of the relationship between rainfall volum e and peak discharge. For thatreason, the type of the form ula should be considered based on the characteristics of the target
river basin.
The relationship between the rainfall volum e and the peak discharge is determined based on
spatio-temporal rainfall patterns and differences of runoff m odels and those parameters. The
scatter plots of rainfall volum e and peak discharge derived from the integration of describedhydrological m odels in Section 3.3 are shown in Figure 19. Note that the param eter of the runoff
m odels described in 2.3 need to be adjusted for practical use. The target rainfall events of three
additional m odels are the top 50 rainfall volum e events from d4PDF (Past) and the top 100 rainfall
volum e events from d4PDF (+4K). The results of all m odels show that a wide range of peakdischarges can be taken for large rainfall events. The proposed m ethod can estimate peakdischarge distribution at each return period in consideration of m ultiple runoff m odels by applying
the aforem entioned regression curve to the rainfall-peak discharge relationship for each runoff
m odel or for the com bining result of runoff m odels. In the following part, the return period of thepeak discharge is estimated according to the relationship between rainfall volum e and peakdischarge of a single m odel.

Figure16 Relationship between annual maximum 72-hour rainfall and peak discharge at the reference point in
the Tokachi River basin

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Figure 17(a) Relationship between annual maximum
72-hour rainfall and peak discharge at the samereference point and quantile regression curve in thepast experiment.

Figure 17(b) Relationship between annual maximum
72-hour rainfall and peak discharge at the samereference point and quantile regression curve in the
4K warmer experiment

Figure 18(a) Frequency distribution of probable peak Figure 18(b) Frequency distribution of probable peakdischarge (blue bins) and probability density function of discharge(red bins) and probability density function f
the Gumbel distribution f Qp(q p|r=300, 150) fitted to Qp(q p|r=300, 150) of the Gumbel distribution fitted
the distribution (solid blue line) when the maximum 72- to the distribution (solid red line) when thehour rainfall in a 150-year probability year is 300 mm maximum 72-hour rainfall value for a 150-year
in the past experiment.

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probability year in the 4K warmer experiment is 300
mm.

Figure19 Relationship between annual maximum 72-hour rainfall and peak discharge estimated by thedifferent runoff models at the reference point in the Tokachi River basin※ Note that the initial condition and parameters of the RRI, wflow and MATSIRO need to be adjusted forpractical use

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4.3

Peak discharge return period

Figure 20 and Figure 21 show the derivation process of the probability distribution of the 150 year peak discharge in the previous experiment and the +4K warm er experim ent. Figure 22 shows
a com parison of the probability density function of the 150-year peak discharge for the pastexperim ent and the +4K warm er experiment. It can be observed that the shape of the probability
distribution gets shifted under different climatic conditions. The m ultiplier of future change is 1.88
for the expected value, and 2.04 for the 95% upper co nfidence lim it of the +4K warm er
experim ent and the past experiment, respectively.
The calculation process is shown in (Figure 20 and Figure 21). By setting the target probability
scale T of the design rainfall (150 years in this section), the horizontal axis of the annual m axim um
peak flow relationship is the 150-year probability rainfall, and the vertical axis is the peakdischarge that can occur under the 150-year probable rainfall. The probability distribution of thepeak discharge that occurs for a given rainfall r, fQp(qp|r, 150), are weighted and averaged withthe 150-year probability annual m axim um 72hour rainfall distribution f R(r, 150) to obtain theprobability distribution of the 150-year peak discharge fQp(qp, 150). It is evident from both figuresthat the relationship between the annual m aximum 72-hour rainfall and peak discharge increases
m ore in m agnitude under the future climate as compared with the past clim ate, owing to thespatiotemporal concentration of rainfall (Hoshino and Yam ada, 2018). Figure 23 shows therelationship between the return period of annual m axim um rainfall and peak discharge in both pastand future clim ate conditions. From this diagram, the proposed m ethod enables the estimation of
uncertainty in T-year peak discharge.
The 150-year peak discharge based on this method was calculated at the Obihiro, Satsunai,
Bisei, and Otohuke River confluence points. These probable peak discharges can be used as astarting point for the evaluation of water levels caused by flo ws equivalent to the planned
probability scale, the evaluation of the probability of overtopping and levee breaching, and thequantification of hum an and economic risks.
Figure20 Process of deriving the T-year probability peak discharge under the past climate condition

Figure21 Process of deriving the T-year probability peak discharge under the 4K warmer climate condition

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Figure22 Comparison of the probability density function of the 150-year probability peak discharge for the pastexperiment and the 4K warmer experiment

Figure 23(a) Relationship between return period ofannual maximum rainfall and peak discharge in pastexperimentBlue solid curve; mean value
Blue dotted curve; 95% confidence limit line

PR3983.10 7 December 2021

Figure 23(b) Relationship between return period ofannual maximum rainfall and peak discharge in 4K
warmer experiment
Red solid curve; mean value
Red dotted curve; 95% confidence limit line

25

Table 2 Comparison of mean and 95% confidence interval of 150-year peak discharge distribution
corresponding to the past experiment and 4K warmer experiment

Statistics in 150-year
peak discharge
Mean
95% upper confidence
limit value
95% lower confidence
limit value

PR3983.10 • December 2021

Past experiment3960.0 [m3 /s]
7901.1 [m3 /s]

4 K warmer
experiment7462.1 [m 3 /s]
16171.3 [m 3/s]

Change ratio
4 K warmer / Past
1.88
2.04

1858.0 [m3 /s]

2993.9 [m 3 /s]

1.61

26

5

Summary

It is necessary to quantify the uncertainty of the design rainfall and establish a projection m ethod
for heavy rainfall in order to adapt to changing clim ate. In this joint research, the uncertainty ofthe design rainfall, which was derived from the observation of rainfall over the past several
decades via traditional hydrological frequency analysis, was evaluated, and its future change wasestim ated by utilizing the huge am ount of clim ate dataset. In particular, the ensemble clim ate data
allowed us to quantify the uncertainty of the design rainfall and its confidence intervals due to thefinite sam ple size of observations, as well as to evaluate the characteristics of the spatiotemporal
rainfall pattern and the occurrence risk of large -scale heavy rainfall under clim ate change. In
addition to the evaluation of the uncertainty in the total am ount of rainfall, it is necessary toincorporate the spatiotemporal distribution of rainfall into flood control, because the shape of thehydrograph and peak discharge are affected by the distribution of rainfall pattern, even for thesam e volume of rainfall. Therefore, we have developed a fram ework for understanding the rainfall–peak discharge relation and evaluating its im pact on the probable discharge.
In flood control m anagement, flood control facilities such as levees and retarding basins areplanned by considering the design peak discharge and water level, which are calculated through ahydrological m odel using the hyetograph of design rainfall. In working package 1, a fram ework to
estim ate the probability distribution of peak discharge with a target design return period that can
incorporate the spatiotemporal distribution of rainfall is proposed. Specifically, the probability
distribution of rainfall corresponding to the target return period is transformed into the probability
distribution of peak discharge through the relationship between total rainfall and peak discharge,
which is defined by rainfall characteristic and rainfall-runoff m odel. The distribution of the probablerainfall derived from the ensemble climate data and the total rainfall–peak discharge relation
specified by the rainfall-runoff m odel were used as inputs to calculate the probable peak discharge.
Therefore, the m ethodology can be used to update the probability distribution o f peak discharge
with the target design return period, in accordance with the differences and sophistication ofclim ate m odels and rainfall-runoff m odels; this can contribute to the realization of flood risk
assessment in response to future advances in science and technology.

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6Reference

1. Bas Kolen and Ira Helsloot, 2012: Time needed to evacuate the Netherlands inthe event of large-scale flooding: strategies and consequences.
2. Beven, K.J., M.J. Kirkby, N. Schofield, A.F. Tagg, 1984: Testing a physicallybased flood forecasting model (TOPMODEL) for three U.K. catchments, J.
Hydrol, Volume 69, Issues 1–4, Pages 119-143, ISSN 0022-1694,
https://doi.org/10.1016/0022-1694(84)90159-8.
3. David Samuel Williams, María Máñez Costa, Dmitry Kovalevsky, Bart van den
Hurk, Bastian Klein, Dennis Meißner, Manuel Pulido-Velazquez, Joaquín
Andreu, Sara Suárez-Almiñana, A method of assessing user capacities for
effective climate services, Climate Services, Volume 19, 2020, 100180, ISSN
2405-8807, https://doi.org/10.1016/j.cliser.2020.100180.
4. Kain, J.S.; Fritsch, J.M. 1993: Convective Parameterization for Mesoscale
Models: The Kain-Fritsch Scheme. In The Representation of Cumulus
Convection in Numerical Models; Emanuel, K.A., Raymond, D.J., Eds.;
Meteorological Monographs; American Meteorological Society: Boston, MA,
USA; pp. 165–170.
5. Kawase, H.; Sasai, T.; Yamazaki, T.; Ito, R.; Dairaku, K.; Sugimoto, S.;
Sasaki, H.; Murata, A.; Nosaka, M. , 2018: Characteristics of Synoptic
Conditions for Heavy Snowfall inWestern to Northeastern Japan Analyzed by
the 5-km Regional Climate Ensemble Experiments. J. Meteorol. Soc. Jpn., 96,
161–178.
6. Hoshino, T., Yamada, T. J., and Kawase, H., 2020: Evaluation for
Characteristics of Tropical Cyclone Induced Heavy Rainfall over the Sub-basinsin The Central Hokkaido, Northern Japan by 5-km Large Ensemble
Experiments. Atmosphere, 11(435), 1–11.
7. Hirahara, S. Ishii, M. Fukuda, Y., 2014: Centennial-Scale Sea Surface
Temperature Analysis and Its Uncertainty. J. Clim., 27, 57–75.
8. Hiroyasu YASUDA, Masami SHIRATO, Chiaki GOTO and Tadashi YAMADA,
2003: Development of Rapid Numerical Inundation Model for the Levee
Protection Activity, Journal of Japan society civil engineering, No. 740/II-64,
1-17.
9. Intergovernmental Panel on Climate Change 2021. Sixth Assessment Report.
10. Jonkman, S.N., 2007: Loss of life estimation in flood risk assessment; theory
and applications, TU, Delft, library.
11. Khaing, Z.M.; Zhang, K.; Sawano, H.; Shrestha, B.B.; Sayama, T.; Nakamura,
K.,2019: Flood hazard mapping and assessment in data-scarce Nyaungdon
area, Myanmar. PLoS ONE 2019, 14. DOI:10.1371/journal.pone.0224558.
12. KNMI, Deltares.,2015: Wat Betekenen de Nieuwe Klimaatscenario’s voor deRivierafvoeren van
Rijnen
Maas?.
p.
15.,
Available online:
http://publications.deltares.nl/1220042_004.pdf (accessed on 4 September2020).

PR3983.10 • December 2021

28

13. Koirala, S., Yeh, P.J.F., Hirabayashi, Y., Kanae, S., Oki, T., 2014: Global-scaleland surface hydrologic modeling with the representation of water table
dynamics.
J.
Geophys.
Res.
119,
75–89.
https://doi.org/10.1002/2013JD020398
14. Maaskant, B. et al., 2009: Analysis slachtofferaantallen VNK-2 en voorstellenvoor aanpassingen van slachtooerfuncties,4-40.
15. Moriguti, S., 1995: Testing Hypothesis on Probability Representing Function—
Kolmogorov–Smirnov Test Reconsidered. Jpn. J. Stat. Data Sci., 25, 233–244.
16. Mizuta, R.; Yoshimura, H.; Murakami, H.; Matsueda, M.; Endo, H.; Ose, T.;
Kamiguchi, K.; Hosaka, M.; Sugi, M.; Yukimoto, S.; et al., 2012: Climate
Simulations Using MRI-AGCM3.2 with 20-km Grid. J. Meteorol. Soc. Jpn., 90,
233–258.
17. Ministry of Land, Infrastructure, Transport, and Tourism (MLIT)., 2017. FloodControl Technology Review Meeting Considering the Climate Change in
Hokkaido Region. Available online:
https://www.hkd.mlit.go.jp/ky/kn/kawa_kei/splaat000001offi.html
18. Ministry of Land, Infrastructure, Transport, and Tourism (MLIT). FloodControl Technology Review Meeting Considering the Climate Change in
Hokkaido Region. Available online:
https://www.hkd.mlit.go.jp/ky/kn/kawa_kei/splaat000001offi.html
19. Nguyen-Le, D., Yamada, T. J., 2017: Simulation of tropical cyclone 201610(Lionrock) and its remote effect on heavy rainfall in Hokkaido. Journal of Japan
Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 73(4), I_199I_204.
20. Nguyen T.T.; Nakatsugawa, M.; Yamada, T.J.; Hoshino, T.,2021: Floodinundation assessment in the low-lying river basin considering extreme rainfall
impactsandtopographic
vulnerability.
Water.,
13,
896-.
DOI:10.3390/w13070896.
21. Pokhrel, Y.N., Felfelani, F., Shin, S., Yamada, T.J., Satoh, Y., 2017: Modelinglarge-scale human alteration of land surface hydrology and climate. Geosci.
Lett. 4. https://doi.org/10.1186/s40562-017-0076-5
22. Sasaki, H.; Kurihara, K.; Takayabu, I.; Uchiyama, T., 2008: Preliminary
Experiments of Reproducing the Present Climate Using the Non -hydrostatic
Regional Climate Model. Sola, 4, 25–28.
23. Taylor, K.E.; Stouer, R.J.; Meehl, G.A., 2012: An Overview of CMIP5 and theExperiment Design. Bull. Am. Meteorol. Soc., 93, 485–498.
24. Sayama, T.; Ozama, G.; Kawakami, T.; Nabesaka, S.; Fukami, K., 2012:
Rainfall-runoffinundation analysis of the 2010 Pakistan flood in the Kabul Riverbasin. Hydro. Sci. J., 57, 298–312. DOI:10.1080/02626667.2011.644245
25. Sayama, T.; Tatebe, Y.; Iwami, Y.; Tanaka, S., 2015: Hydrologic sensitivity offlood runoff and inundation: 2011 Thailand floods in the Chao Phraya Riverbasin. Nat. Hazards Earth Syst. Sci. ,15, 1617–1630. DOI:10.5194/nhess-151617-2015.

PR3983.10 7 December 2021

29

26. Shimizu, K., Yamada, T. J., and Yamada, T., 2020: Uncertainty Evaluation in
Hydrological Frequency Analysis Based on Confidence Interval and Prediction
Interval. Water, 12(9):2554
27. Takata, K., Emori, S., Watanabe, T., 2003: Development of the minimal
advanced treatments of surface interaction and runoff. Glob. Planet. Change
38, 209–222. https://doi.org/10.1016/S0921-8181(03)00030-4
28. The Ministry of infrastructure and Environment and The Ministry of Economic
Affairs, 2015: National Water Plan 2016-2021.
29. Try, S.; Tanaka, S.; Tanaka, K.; Sayama, T.; Lee, G.; Oeurng, C., 2020:
Assessing the effects of climate change on flood inundation in the lower
Mekong Basin using high resolution AGCM outputs. Prog. Earth Planet. Sci, 7,
34. DOI:10.1186/s40645-020-00353-z.
30. Van alphen, J, 2016: The delta programme and updated flood risk
management policies in the Netherlands, 9.
31. Yamada, T. J., Hoshino, T., and Suzuki, A. 2021: Using a massive high‐resolution ensemble climate data set to examine dynamic and thermodynamic
aspects of heavy precipitation change. Atmospheric Science Letters, (July), 1–
11.
32. Yamada, T. J., 2019 : Adaptation Measures for Extreme Floods Using Huge
Ensemble of High − Resolution Climate Model Simulation in Japan. Summary
Report on the Eleventh Meeting of the Research Dialogue 2019 , 28 –30 ,UNFCCC Bonn Climate Change Conference, Bonn, Germany（June 19 2019 ）33. Yamada, T. J., 2020 : Ensemble Approach to Climate Change Projection andRisk Assessment, KASEN (RIVER), pp.77-81,Japan River Association.
34. Yamada, T.J.; Hoshino, T.; Masuya, S.; Uemura, F.; Yoshida, T.; Omura, N.;
Yamamoto, T.; Chiba, M.; Tomura, S.; Tokioka, S.; et al.,2018: The influenceof climate change on flood risk in Hokkaido. Adv. River Eng., 24, 391–396.
35. Yamazaki, D., Kanae, S., Kim, H., Oki, T., 2011. A physically based
description of floodplain inundation dynamics in a global river routing model.
Water Resour. Res. 47, 1–21. https://doi.org/10.1029/2010WR009726

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