Chen et al.
Progress in Earth and Planetary Science(2024) 11:16
https://doi.org/10.1186/s40645-024-00618-x

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open AccessRegional event‑based flood quantile
estimation method for large climate projection
ensembles
Jiachao Chen1* , Takahiro Sayama2, Masafumi Yamada2 and Yoshito Sugawara2

Abstract
Emerging large ensemble climate datasets produced by multiple general circulation models and their downscaling products challenge the limits of hydrodynamic models because of the immense data size. To overcome thisnew challenge and estimate the discharge quantiles corresponding to different return periods at all river sectionsin an entire region, this study proposes an event-based regional approach that uses a nationwide distributed rainfall–
runoff model as well as large climate projection ensembles. This approach addresses the high computational burden
associated with continuous simulations and solves the problem of conventional event-based simulations serving
only a single outlet of a basin. For our analysis, we extracted 372 annual maximum 48 h rainfall events that cover
the entirety of Shikoku Island and its eight major river basins. Peak discharges were estimated using a 150 m resolution rainfall–runoff–inundation model. These discharges were then screened using either the peak-over-threshold(POT) method or block maxima (BM) method, and frequency curves were subsequently constructed and evaluated.
The primary reason for the necessity of POT or BM was to avoid interference from extraneous low discharges. The
POT-based frequency curves showed good accuracy when using peak discharges in the range of the top 10–50%,
and the results remain stable within this threshold range. The BM method, employing block sizes of 2–5 years, cangenerate relatively accurate frequency curves, but the choice of block size introduces significant variations in results
among certain basins. Generally, the accuracy of results based on the POT method surpasses that of the BM method.
Considering the accuracy, computational cost, and result stability, the POT method is preferred. The error introducedby the regional approach was acceptable with more than half of the relative root-mean-square errors remaining
within 10% and basically all of the results are within 20%. The results of the regional approach exhibited good accuracy across climate scenarios and provided consistent information regarding future flood quantiles. This study serves
as the foundation for high-resolution future flood risk assessment.
Keywords Large ensembles, Peak over threshold, Flood frequency analysis, Climate change*Correspondence:
Jiachao Chenchen.jiachao.52h@st.kyoto-u.ac.jp
1
Graduate School of Engineering, Kyoto University, Katsura, Nishikyo‑ku,
Kyoto 615‑8530, Japan
2
Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji,
Kyoto 611‑0011, Japan© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/.

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

1 Introduction
Floods, whose risk is influenced by climate change, havesubstantial economic and social ramifications (Hirabayashi et al. 2013; Winsemius et al. 2018; Tellman et al.
2021). Flood intensity and frequency have been reported
to increase due to climate change (IPCC 2021). Thesechanges necessitate proactive risk-management strategies for human settlements, urban planning, and socioeconomic development.
Typical flood hazard assessments rely on extensive datacollection, detailed hydraulic engineering surveys, andhydrological and hydrodynamic modeling (Martínez et al.
2018; Zeng et al. 2022). Such approaches (e.g. Institute ofHydrology 1999) often adopt statistical methods, such as
intensity–duration–frequency or depth–area–duration
methods, to design rainfall intensity for a specified return
period (Gellens 2002; Sun et al. 2019) and rainfall pattern(Yan et al. 2020). However, because of the variability in precipitation, the impact to different river sections of an event
is different, while statistical method selects events by specifying an outlet first, which means the simulation result
cannot be used for investigating other river sections inthe same basin (e.g., precipitation events that cause a 100year flood in a particular basin may not necessarily resultin floods with the same return period in its sub-basins).
Moreover, preparing designed rainfall and conductinghydrologic simulations for all river sections, including subbasins, over a large region is impractical (Packman and
Kidd 1980; Watt and Marsalek 2013). Another issue is thatthe return period of rainfall and floods has no unified correspondence; thus, results obtained from the design storm
may not represent the actual river flood intensities for the
return periods (Breinl et al. 2021).
To achieve reliable national- or global-scale floodrisk assessment, several approaches have been proposed, including analysis-based and cascade modelingapproaches (Dottori et al. 2016; Hoch and Trigg 2019).
The analysis-based modeling approach utilizes regional
flood frequency analysis (FFA) with observed discharge
datasets to estimate peak discharge and employs cluster algorithms to extend the results to ungauged basinsbased on catchment descriptors (Sampson et al. 2015;
Smith et al. 2015; Wing et al. 2017, 2021). The cascade
modeling approach, exemplified by the utilizations of
GloFAS (Dottori et al. 2016), GLOFRIS (Winsemius et al.
2013), and ECMWF (Pappenberger et al. 2012), typically employs a land surface model with a river routing
model such as CaMa-Flood (Yamazaki et al. 2013) or
LISFLOOD (Bates et al. 2010) to compute flood peaks
over large domains based on long-term simulations. The
simulated flood peak discharges at each river section
can then be statistically analyzed to compute those withcertain frequencies. The estimated discharges are used

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as inputs to high-resolution hydrodynamic models. For
example, Alfieri et al. (2014) employed LISFLOOD togenerate a continuous 21-year daily discharge sequence
for the pan-European region at a resolution of 5 km andmapped flood hazards at a resolution of 100 m. However, these approaches have difficulties in handling large
climate projection ensembles. The analysis-based modeling approach relies on an observed discharge dataset,
which is unavailable for future scenarios. Thus, the cascade modeling approach is more applicable for climatechange impact assessment. Nevertheless, even with
low-resolution cascade simulations, the computational
requirements are significant because of the numerous
climate projection ensembles spanning several decades.
Moreover, errors and uncertainties are associated with
discharge results from simulations at low resolutions(Meade et al. 1991; Alfieri et al. 2014; Zhao et al. 2017).
Sampson et al. (2015) suggested that its applicability inregions with low-quality topographic data is yet to be
demonstrated and quantified. Therefore, considering new
approaches for large-scale flood simulations under climate change and obtaining accurate peak discharges at
specific return periods for local flood hazard mapping arenecessary.
In this study, we propose an event-based approach toobtain the peak discharge in all river sections. To distinguish this approach from the conventional event-based
approach (called the basin approach in this study), which
focuses only on a single-basin outlet, the new approachis hereafter referred to as the regional approach. The
regional approach aims to reduce the computational burden with relatively low errors for large climate projection
ensembles.
Event-based simulations, including the regional
approach, confront the challenge of precipitation variability. Some events lead to high discharge in specificriver sections and low discharge in others. These cases
ultimately result in severe errors in the FFA. To reducethese impacts and obtain accurate peak discharges foreach river section at specific return periods, we further
examine screening methods using block maxima (BM)and peak over threshold (POT). The problems addressedin this study are as follows:
1. Which method–BM or POT–is more suitable forfrequency analysis based on the result using regional
approach? How should its corresponding parameters
be selected?
2. How much error will be introduced by the regional
approach?
3. Will the approach be valid under future climate conditions?

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

Based on the challenges faced when studying floods
under climate change, this study provides a novel
approach for large-scale and high-resolution flood simulations. Additionally, the cases selected in this study shedlight on the potential magnitude of errors and uncertainties introduced by the regional approach, which can
inform efforts for further improvement.

2 Methods
2.1 Regional approach

Figure 1 compares the key steps of the conventional basin
approach and the proposed regional approach. Bothapproaches extract extreme rainfall events. While thebasin approach extracts events based on basin average
precipitation, the regional approach extracts them basedon an entire region, in this case Shikoku Island, whichcontains multiple river basins. The former approach has
been adopted in Japan for estimating annual maximum
peak discharges corresponding to different return periods (i.e., flood quantiles) by simulating flood discharges
with a rainfall–runoff model. In case we use a distributed
rainfall–runoff model, we can simulate discharges even attributaries within the basin. However, the estimated discharges do not necessary represent the quantiles at eachriver section, except for the outlet of the basin, becauseof the spatial variability of precipitation of the extracted
events. In order to obtain a flood quantile map, which is
our objective here, we have to apply the basin approachfor each river sections even within a river basin. Obviously, that requires enormous computation, which is
not practically possible. As an alternative approach, we
introduce the regional approach. The method extracts
large number of rainfall events from a climate projection
ensemble based on the amount of rainfall over a region.
Then, we estimate flood peak discharges using a distributed rainfall–runoff model. Here we hypothesize thatdischarges with longer return periods at different river
sections have high probability to be affected by a seriesof extreme precipitation events. That means if we extract
sufficient number of events and adequately screen thepeak discharge data based on POT or BM, we can estimate the flood quantiles at all river sections. To evaluate
the accuracy, we can compare the one estimated by thebasin approach (at the outlet) and the regional approach.
Given the ample data provided by climate projection
ensembles spanning centuries, the criterion for extreme
events in this study was the annual maximum 48 h precipitation (48 h-AMS). Subsequently, a distributed
high-resolution hydrologic model, the rainfall–runoff–
inundation (RRI) model, was employed to simulate thedischarges for each river section. Because of the variability in precipitation, event-based simulation results may
contain low discharges, which necessitate the adoption ofPage 3 of 15

the POT and BM methods as potential solutions for estimating the frequency curves. The flood quantiles wereobtained by further screening the discharge and conducting FFA.
In this study, discharges from the basin outlets derived
via the basin approach were considered as references.
The criterion for extreme events in the basin approachremains the 48 h-AMS and computed for each basin
within the study area. A comparison was made between
the results obtained from the basin and regional
approaches.
A numerical experiment was conducted on Shikoku
Island (applying the regional approach) and its eight
major river basins (applying the basin approach as a reference). In particular, we evaluated the performance ofthe regional approach with different screening parameters. For the POT method, the screening parameteris a threshold above which they will be included in thefrequency analysis. For the BM method, the screening
parameter is an interval at which the maximum value isdetermined, referred to as block size (Zou et al. 2021).
In addition, we verified the performance of the regional
approach in the 2 K and 4 K scenarios (the Sea Surface
Temperature (SST) rises 2 K and 4 K, respectively) bycomparing the flood quantiles with those from the basin
approach. The effects of the frequency analysis method
and sources of error are discussed later in this paper.
2.2 RRI modelThis study used the RRI model, a two-dimensional distributed hydrodynamic model, to simulate the physicalprocesses involved in the transition from precipitation to
runoff (Sayama et al. 2012). It discretizes the study areaand divides it into land and river channel grids using twodimensional and one-dimensional diffusion wave equations
to describe water movement, respectively. River channel
grids contain information of levees, allowing the RRI model
to simulate runoff overflows and returns in the river grids.
In terms of runoff components, the RRI model considers
surface and lateral subsurface flows, which are importantin mountainous areas (Sayama and McDonnell 2009). The
Green–Ampt model (Green and Ampt 1911) was used tomodel infiltration. The fifth-order Runge–Kutta formula(Cash and Karp 1990) was used as the numerical scheme
to ensure efficiency and accuracy. The RRI model performs
well in Japan and Southeast Asian countries (Sayama et al.
2015, 2017, 2020; Bhagabati and Kawasaki 2017; Yamamotoet.al 2021; Try et al. 2022). Due to the good performance,
Rasmy et al. (2019) further developed a Water and Energy
Budget-based RRI (WEB-RRI) model in Sri Lanka for local
water resources and disaster management under a changing climate. Even though the WEB-RRI model is advantageous in terms of estimating long-term water and energy

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

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Fig. 1 Flow chart of the basin and regional approaches for this study

budget, which is essential for the applications of the model
to water resources management, the original RRI model isthought to be suitable for the present study because of thephysically sound and simple representations of the subsurface and surface runoff processes, essential for event-basedstorm runoff predictions even in ungauged basins. In a previous study, the RRI model had been well calibrated acrossJapan, and these model parameters were followed in thisstudy (Yamada et al. 2022; Yamakita 2023).
The peaks of the RRI model results based on the
48 h-AMS extracted for each basin are called the basinannual maximum flood series (AMFS), which was used as
the reference for this study and has a total of 372 (discharge
peaks) × 8 (basins) × 3 (scenarios) values. The simulation
results using 48 h-AMS , which were extracted for Shikoku
Island and then extracted at the outlet of each basin, arecalled the regional AMFS, which also has 372 × 8 × 3 values.

2.3 Frequency analysis method

Depending on the sample selection method, frequency analysis can be classified into the BM and
POT methods (Mostofi Zadeh et al. 2019). The BM
method divides a time series into equal-sized blocks
and extracts the maximum value for each block. The
most typical application of the BM method in hydrology is the annual maximum method. The L-moment
method is used to estimate parameters of each distribution (Hosking and Wallis 1993). For the BM method,the optimal distribution is ultimately determined to be
the Pearson Type III (PE3), as given by Eq. 1, among
those tested distributions including generalized logistic(GLO), generalized extreme value (GEV), generalized
normal (GNO), generalized Pareto distribution (GPD)and PE3. The results of the goodness-of-fit statistic Z,
used for selecting the optimal distribution, are detailedin Additional file 1: Tables S1–S3. However, according

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

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to the definition of block maxima, only one value canbe selected within a block. That means, in years with
abundant precipitation, multiple flood events may
occur, but the second-largest discharge cannot be chosen as an “extreme value”, even if it surpasses the maximum in a drought year. While in a drought year, nomatter how low the discharge was, it will be selected as
an “extreme value”. Due to the above-mentioned limitation, the BM method is possible to miss high values andselect low values, causing inaccurate frequency analysis
results (Ferreira and de Haan 2015). The POT method
selects all data above a certain threshold. The newly
created sequence theoretically follows the GPD (Eq. 2)(Lang et al. 1999).

PE3(x) =σξ(x − µ)ξ −1 exp (−σ (x − µ))Ŵ(ξ )GPD(x) =1 − 1 + ξ x−µσ
1 − exp − x−µσ−1/ξ

for ξ �= 0
for ξ = 0parameters, categorized as Model 1–6, are listed inTable 1. The extracted AMFS were nonstandard annual
series, the probability P corresponding to the standard
return period T must be calculated using Eq. 3, where thenpy refers to the number of discharge peaks per year.

P =1−

where ξ, μ, and σ are the shape, location, and scale parameter, respectively. All three parameters are estimated by
L-moment method. The block size and threshold are
the key parameters that must be artificially determined
for the BM and POT, respectively. Although many studies have been conducted on the choice of a reasonable
threshold for a POT, the aim is to find a low thresholdfor using as much data as possible (Langousis et al. 2016;
Mostofi Zadeh et al. 2019; Swetapadma and Ojha 2021).
In this study, because of the numerous climate projection
ensembles, we attempted to use high thresholds to balance efficiency and accuracy. Due to the spatial variability of precipitation, some of the regional 48 h-AMS may
result in low discharges in specific sub-basins. Therefore, using a high threshold is advantageous for filtering
low discharges. Furthermore, because the POT method
allows for more precise threshold control, it was chosen
as the frequency analysis method for the basin AMFS.
As the design floods of most infrastructures focus on areturn period longer than 10 years, the maximum threshold for the POT was set to the top 10% of the AMFS indescending order. Correspondingly, the maximum block
size of the BM was set to 10 years. A series of possible(3)

The FFA results were evaluated using the relative
bias (BIAS; Eq. 4) and relative root-mean-square error(RRMSE; Eq. 5).

BIAS =(1)(2)

1
T × npyye − yr
× 100%
yr

(4)

 
1
(ye∗ − yr∗ )2n
RRMSE =
1 
yr∗
n

(5)

where ye and yr are the quantiles corresponding to different return periods between 10 and 1000 years every
1 year. They are estimated based on the fitted curves tothe simulated peak discharges by the regional and thebasin approaches, respectively. The BIAS indicates how
the regional approach is biased compared to the basinone. On the other hand, RRMSE compares the simulated
peak discharges by the basin approach ( yr∗) and the fitted
curve by the regional approach denoted as ye∗, which arecorresponding to discrete return periods. Hence unlike
the BIAS index, the RRMSE indicates the degree of errors
more directly by the regional approach compared to thereference one without uncertainty associated to the curve
fitting to the yr∗. The model results can be regarded as
excellent if RRMSE ≤ 10%, good if 10% < RRMSE ≤ 20%,
fair if 20% < RRMSE ≤ 30%, and poor if RRMSE > 30% (dosSantos et al. 2016).

3 Study area and data
3.1 Study area

The study area of Shikoku Island, Japan, is locatedbetween 132–135° E and 32.5–34.55° N (Fig. 2). It is a relatively independent area separated from Honshu Islandby the Seto Inland Sea to the north and surrounded by
the Pacific Ocean on its three other sides. The area of
Shikoku Island is 18,785 ­km2, 70% of which is covered by

Table 1 Parameters of the POT and BM data selection methods
Models

1

2

3

4

5

6

POT (threshold)

10%

20%

30%

40%

50%

100%

BM (block size)

10 Years

5 Years

4 Years

3 Years

2 Years

1 Years

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

forests. Similar to other regions of Japan, it is mountainous and steep, with the highest peak on the island (with asummit of 2000 m) descending to sea level within 50 km.
Shikoku Island has an average annual temperatureof nearly 14 ℃, making it a major producer of early rice
crops in Japan (Yoshida et al. 2012). The spatial distribution of average annual precipitation varies considerably due to frontal storms and typhoons (Kamahori andArakawa 2018; Tada et al. 2018; Otaki et al. 2022), fromabout 1200 mm in the north to 2500 mm in the south and4000 mm in the mountains (Yao and Creed 2005).
The main reasons for selecting Shikoku Island as thestudy area were its relatively independent geographicalconditions, significant differences in precipitation, andflood-prone terrain. The morphological parameters of its
eight major river basins are listed in Table 2.
3.2 Data

The database for Policy Decision-making for Future climate change (d4PDF) is an open-access dataset used
to study climate change impacts and plan adaptation (Mizuta et al. 2017). The dataset was dynamicallydownscaled from the 60 km Meteorological ResearchInstitute Atmospheric General Circulation Model 3.2(MRI-AGCM 3.2), using a 20 km Non-Hydrostatic
Regional Climate Model (NHRCM) (Sasaki et al. 2011).
External forcing was derived from MRI-AGCM 3.2 andthe boundary conditions were derived from MRI-CM(Deushi and Shibata 2011) and MRI-CGCM3 (Yukimoto et al. 2012). The differences between simulations inthe same scenario were mainly caused by the differencein SST between current and future climate (ΔSST) andperturbations (δSST). The spatiotemporal distributions

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Table 2 Morphological parameters of the eight major river
basins of Shikoku Island
Basin

Area ­(km2)

Length (km)Elevation ofriver source(m)

Yoshino

3750

194

1897

Watari

2186

196

1336

Niyodo

1560

124

1982

Hiji

1210

103

460

Naka

874

125

1929

Monobe

508

71

1770

Shigenobu

445

36

1233

Doki

140

33

1059

of ΔSST were provided by six CMIP5 (Coupled Model
Intercomparison Project phase 5) models (Mizuta et al.
2017). Owing to the large number of ensembles thatfacilitate uncertainty reduction and good extreme event
reproducibility (Tanaka et al. 2018; Ishii and Mori 2020),
d4PDF has been widely used to study extreme precipitation, snowfall, drought, and water resource assessment(Kawase et al. 2016, 2020; Ohba and Sugimoto 2019; Hatsuzuka et al. 2020; Miyasaka et al. 2020; Ohba et al. 2022).
The data used in this study were further downscaled
using the 5 km NHRCM. This dataset is called dynamical
downscaling data for near-future atmospheric projection(from Tohoku to Kyushu) by the Social Implementation
program on Climate change Adaptation Technology(SI-CAT DDS5TK), which is funded by the Ministry of
Education, Culture, Sports, Science and Technology,
and covers the Tohoku to Kyushu regions of Japan. This
included three scenarios (current, 2 K ΔSST, and 4 K
ΔSST) with 31 years × 12 members (2 × 6 SST patterns)(Mizuta et al. 2017; Fujita et al. 2019). More specificparameter settings can be found in Sasai et al. (2019).
These 12 members were used as a complete dataset.

4 Results
4.1 Evaluation of the current scenario

Fig. 2 Overview of Shikoku Island

A primary objective of this study was to determine theoptimal combination of frequency analysis methods for
integration into the regional approach. Figure 3, using
the Watari river basin as an example, illustrates the frequency curves fitted by the POT and BM methods using
the six parameters from Table 1. It is crucial to note thatdifferent parameters can result in varying data samples,causing the x-axis’s starting position to be less than
10 years. However, since this study primarily focuses on
fitting beyond a 10-year return period, the calculation of
RRMSE and the display of fitting results only use results

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

exceeding 10-year return period. The RRMSE in the figure calculates the error between the predictions of theregional approach and the basin AMFS, and used forassessing the accuracy of the regional approach-basedfrequency curve. According to the RRMSE, models 1–5
using the POT method in the Watari Basin (i.e., employing the top 10–50% of basin AMFS as the threshold) yieldstable and satisfactory results.
To obtain robust conclusions, we examined theresults for eight major basins across Shikoku Island, andtheir RRMSE is summarized in Fig. 4. The blue histograms represent RRMSE based on the POT method, theorange histograms represent RRMSE based on the BM
method, and the grey line reflects the error of the fitted
curve under reference conditions (i.e., the RRMSE frombasin approach). The reference RRMSEs demonstrate
excellent goodness of fit of the basin approach, whichindicates the capacity of being reference, with values
consistently below 10%, meeting the criteria for excellence. Overall, the POT method appears to offer higher
accuracy. Specifically, models 1–5, corresponding to
the use of the top 10–50% of regional AMFS, demonstrate high accuracy across basins. Their results show
no significant difference in magnitude from the reference RRMSE (i.e., the grey line). Model 6, which utilizes 100% of the regional AMFS, tends to yield inferior
results. As analyzed in the issue of the limitations ofevent-based simulations, the simulation results of event
set in some river sections may include extremely low
discharges, which is caused by precipitation variability,
and thereby influence the fitting of the frequency curve.
This further underscores the necessity of employing
high thresholds to prevent these “non-extreme” datafrom frequency analysis.
For the BM method, models 1 and 6 (corresponding toblock sizes of 10 years and 1 year, respectively) typicallyexhibit the poorest performance. The results of models
2–5 vary by basin, making it challenging to determine
the optimal block size or provide a recommended range.
This variability is attributed to the inability of the BM
sampling method to effectively exclude “non-extreme”
discharges. Additionally, the order of occurrence of peakdischarges influences the sampling results, introducing considerable uncertainty. Therefore, considering theaccuracy and the robust of threshold, the POT method isrecommended.
Figure 5 shows the POT-based frequency curves forthe current scenario with threshold of 10%. According to
the RRMSE values, all the regional results have a good fit.
The distribution of data points in Fig. 5 shows that mostof the regional AMFS and basin AMFS overlap or havesmall differences, especially for extreme discharges withlong return periods, which confirms our hypothesis that

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most of the peak discharges in different river sections
originate from different events, but ultimately from an
extreme event group.
The Monobe River Basin presents a unique case, showing a poor overlap of discharge with long return periods and a large error in the maximum value. By manual
checking, we found that the maximum values of the basin
AMFS and regional AMFS originated from two different
precipitation events. This suggests that the maximum
value of the basin AMFS originated from a local extreme
precipitation event. Due to the spatial variability of precipitation, it was missed in the search for extreme precipitation across the entire region. The maximum valueof the regional AMFS corresponded to the second-largest value of the basin AMFS. Similar mismatches wereobserved in the subsequent AMFS in the Monobe River
Basin. Theoretically, the longer the return period of discharges, the greater the FFA model error caused by themismatch. This indicates that the regional approach
can be further improved by determining new extraction
approaches that can identify these crucial local extreme
precipitation events or alternative frequency analysis
methods that can reduce the sensitivity to ranking.
A relatively high BIAS was observed in the Doki river
basin, which has the smallest area among the eightbasins, consequently having low discharge in most years.
However, because of their topography and morphology,
they are more sensitive to precipitation, which leads to an
extremely high discharge from the largest precipitation
event, and therefore, the frequency curve fails to fit discharge with return period longer than 100 years.
Overall, the BIAS varied between ± 10% for different
return periods and the interval of BIAS variation was limited to 0 to − 5% in some basins. The regional approach
mitigates but does not entirely eliminate the influence of
precipitation variability.
4.2 Evaluation on future scenarios

In the 2 K and 4 K warming scenarios, the POT methodwith the top 10% AMFS yielded good outcomes. The
RRMSEs of each basin when employing the top 10%
AMFS are summarized in Table 3. In the current scenario, five basins were rated as excellent, whereas threebasins received good ratings, consistent with the resultspresented in Fig. 5. In the 2 K warming scenario, fourbasins were rated excellent, two good, and two fair. In
the 4 K warming scenario, five basins achieved excellent
ratings, and three received good ratings. Notably, noneof the basins were rated as poor in any of the three scenarios. Hence, we concluded that the constructed quantile estimation model performs well and is stable acrossthe three scenarios.

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

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Fig. 3 Accuracy comparison of applying the POT and BM methods using different parameters for Watari basin. Model 1–6 indicate the precipitation
data come from current scenario and the parameter corresponds to the value of Model 1–6 in Table 1. Blue points and lines represent the basin
AMFS and frequency curve, respectively. Red points and lines indicate the regional AMFS and frequency curve, respectively. Green dashed lines
show the BIAS between the basin and regional results

Figure 6 shows the estimation results of the discharge
quantiles for each basin with the POT method and 10%
AMFS in the 2 K warming scenario. In this scenario, we
observed a general underestimation in discharges fromregional approach. Manual checking revealed that thesedischarges originated from the same precipitation event,with peak times differing by several hours. This situation
arose mainly because the precipitation only extracted
for 48 h, whereas some extreme events lasted longer
than 48 h and exhibited variations in peak times among
the different basins. Consequently, in some basins, precipitation input ceased before the true peak was reached.

Climate change affects the characteristics and durations of extreme precipitation events. Therefore, in the
future, event extraction schemes that do not depend on
fixed durations must be considered. Overall, BIAS varied
between ± 20% and within ± 10% in some basins.
Figure 7 shows the estimation results of the discharge
quantiles for each basin using the POT method and
10% AMFS under the 4 K warming scenario. In this scenario, large RRMSE values were observed in the Yoshino
and Doki basins. From the perspective of precipitation
events, the maximum 48 h-AMS in the Yoshino basin,
whether extracted by the basin approach or the regional

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Fig. 4 RRMSEs of the eight basins using different methods and parameters under the current scenario. The grey dash line represents the RRMSE
between the predictions of the basin approach and the basin AMFS

approach, is associated with the same precipitation event.
However, due to the actual precipitation process exceeding 48 h and the event selection method being set with a
48 h time window, the start times of the basin 48 h-AMS
and the regional 48 h-AMS differ by 4 h. During thistime, the precipitation center shifts from the Yoshinobasin to another basin, resulting in a significant reductionin the discharge calculated by the regional method, ranking it 30th instead of the first. This further underscores
the need to develop an event extraction method with
non-fixed durations in future studies. In the Doki Basin,
the frequency curves fit well with the corresponding
points, indicating that the GPD distribution is capable
to fit the extreme discharges. While due to the limitation
on precipitation extraction method, there is a significant
difference between the regional AMFS and basin AMFS,
leading to large error.
Overall, increasing the threshold for the FFA can effectively improve the accuracy of the results by excluding low discharges. 10–50% of the AMFS were foundto be reasonable threshold. Considering the accuracy
and result stability, the POT method is the preferredfrequency analysis method. The spatiotemporal variability of precipitation remains the primary source of error,
and its impact may become more pronounced with climate change. However, this impact is expected to be
mitigated in the future by improving precipitation event
extraction methods. In summary, the regional approachyielded relatively accurate flood quantiles for each basin
at a relatively low computational cost.

5 Discussion
This study proposes a regional approach to estimate theflood quantiles of all river sections from finite extreme
precipitation events. This approach is expected to bevalid under diverse climatic conditions. In the regional
approach framework, a crucial step is aimed at further
screening discharges, offering two alternatives: POT and
BM. “Which option, accompanied by what parameters,
proves optimal? What is the magnitude of the error andhow does it perform in future scenarios?” These threefundamental questions have become the focal points ofthis study.

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

Page 10 of 15

Fig. 5 Estimation results of discharge quantiles for different basins using the POT methods with the top 10% AMFS. The meaning of elementsin the figure can refer to Fig. 3

Table 3 Performance of regional results in different scenarios
Scenarios

Excellent

Good

Fair

Poor

(RRMSE ≤ 10%)

(10 < RRMSE ≤ 20%)

(20% < RRMSE ≤ 30%)(RRMSE > 30%)

Current

5

3

0

0

2K

4

2

2

0

4K

5

3

0

0

The initial purpose of the POT method was to avoid
missing high discharges during rainy years, which likely
occurs when employing the BM method. To perform amore reliable FFA, the POT method typically sets a relatively low threshold to maximize the data utilization.
This study sets a high threshold mainly based on two
considerations. Firstly, it aims to eliminate the influenceof low values at short return periods, ensuring a robust
fit for extreme values at long return periods. Secondly,
the availability of multi-year data from climate ensembles, such as the 372 years of precipitation data provided by SI-CAT DDS5TK in this study, allows for theconstruction of reliable FFA even when employing ahigher threshold. Hence, a relatively high thresholdvalue was established. Notably, the selection of thethreshold constitutes a highly technical task within the
POT method as it affects data independence, distribution assumptions, and sample size (Lang et al. 1999;
Beguería 2005). Previous studies have predominantly
focused on threshold selection (Langousis et al. 2016;
Swetapadma and Ojha 2021). Nevertheless, the demandarises from attempts to employ a low threshold insteadof a high threshold. This is because the lower threshold
limit (more than once per year) for satisfying the GPD
is ambiguous, whereas it is tenable if the threshold is
adequately high. Independence was guaranteed because

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

Page 11 of 15

Fig. 6 Estimation results of discharge quantiles for different basins using the POT methods with the top 10% AMFS for the 2 K warming scenario

the discharges were from different precipitation events,
and a large ensemble ensured the data size.
The POT and BM methods have two primary distinctions: sampling technique and assumed distribution.
The divergent outcomes of the sampling techniques areshown in Fig. 8. After the screening process, the BM
method still retained discharges close to zero in certain
basins, such as Watari, Hiji, Shigenobu, and Doki. However, the POT method does not encounter this issue.
Due to its inability to entirely eliminate periodic fluctuations that may exist in the data (e.g., missing several flood
peaks in wet years and including low discharges in dry
years as extremes), the BM method may not be suitablefor filtering low discharges using the regional approach.
This limitation introduces uncertainty into the results.
Low discharges play a more significant role in estimating
the distribution curve parameters because they constitutea substantial proportion of the extremes. Consequently,
greater emphasis is placed on fitting short return periods,
whereas the accuracy of long return period predictions,which hold greater importance for decision-makers, isdisregarded. Theoretically, the GPD exhibits better adaptability to heavy-tailed distributions (Madsen et al. 1997).

Cunnane (1973) demonstrated that POT performed better when the average annual occurrence was higher than
1.6, and a similar conclusion was derived in other studies(Madsen et al. 1997; Bezak et al. 2014). According to theresults of this study, the POT method exhibits excellent
robustness and accuracy, making it a superior choice for
integration into the regional approach.
Errors in the quantile estimations can be classified into
two types. The first type comes from the gap between
the sample data and population distribution, which isunavoidable. This type of error can be considered as the
goodness of fit of the results from the basin approach(Fig. 3) when the basin approach is regarded as a reference. Generally, the first type of error accounted for overhalf of the total errors. The second type of error stems
from the differences in discharge when employing a
regional approach, which mainly caused by the precipitation variability.
The accuracy of the results provided two important insights. First, despite recent research indicatingthat storm temporal patterns under climate change will
become more acute and have shorter durations (Wasko
et al. 2021), the regional approach remains valid underChen et al. Progress in Earth and Planetary Science(2024) 11:16

Page 12 of 15

Fig. 7 Estimation results of discharge quantiles for different basins using the POT methods with the top 10% AMFS for the 4 K warming scenario

Fig. 8 Distribution of processed (POT and BM) and original regional AMFS with optimal selection parameters in the current scenario

future scenarios without a significant increase in error.
This suggests that the regional approach is compatible
with the potential changes in climatic conditions. Second,the error underscores the pivotal role of local extreme
precipitation events in future flood risk assessments,emphasizing the importance of utilizing detailed climate

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

projection ensembles. Recent studies on the dynamic
downscaling of climate data have focused on convectionpermitting models with higher resolution (National Institute for Environmental Studies 2022) because convection
is a potential mechanism of extreme precipitation thatvaries with climate warming (Pendergrass 2020) andcauses extremely severe precipitation (Qin et al. 2022).
To meet the emerging demand for handling high-resolution climate projection ensembles in flood research,developing event-based regional approach is crucial.
Future studies should prioritize the reduction of the second type of error. A potential direction could be developing event extraction methods that independent fromstatistical indicators, keeping natural evolution of precipitation and capturing local extreme precipitation.

6 Conclusions
To fully leverage large climate projection ensembles
and provide discharges at specific return periods forlarge-scale flood hazard maps with limited computing
resources, this study proposes an event-based high-resolution simulation and analysis scheme called the regional
approach. Using Shikoku Island and its eight majorbasins as an example, the applicability of this approach
was verified using a 150 m resolution RRI model, and thesources of error were analyzed. This study contributes
to the development of big data processing techniques inhydrology. The conclusions are as follows:
1. The top 10–50% of AMFS for the POT method
are the reasonable combinations for the regional
approach. The POT method is more recommended
than the BM method.
2. Using the regional approach, the RRMSE of eachbasin was maintained within 20%. The main sourceof error was attributed to the fixed duration of 48 h
being unable to fully cover the peak occurrence timesof all basins. Moreover, using annual maximum 48 h
rainfall as the extraction criterion may miss extreme
precipitation events with short durations.
3. The regional approach designed in this study showed
good accuracy under current, 2 K warming, and 4 K
warming scenarios and can provide future flood
quantiles consistent with those obtained from thebasin approach.
Abbreviations
48 h-AMS	48 h annual maximum precipitation series
AMFS	Annual maximum flood seriesBM	Block maxima
BIAS	Relative bias
d4PDF	Database for policy decision-making for future climatechangePage 13 of 15

FFA	Flood frequency analysis
GPD	Generalized Pareto distribution
MRI-AGCM 3.2	Meteorological Research Institute Atmospheric General Circulation Model 3.2
NHRCM	Non-hydrostatic regional climate model
PE3	Pearson type III
POT	Peak over threshold
RRI	Rainfall–runoff–inundation
RRMSE	Relative root-mean-square error
SI-CAT DDS5TK	
Dynamical DownScaling data for near-future atmospheric projection (from Tohoku to Kyushu) by the Social
Implementation program on Climate change Adaptation
Technology
SST	Sea surface temperature
WEB-RRI	Water and energy budget-based RRI

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​024-​00618-x.
Additional file 1. Selection of the optimal distribution for block maxima
method.
Acknowledgements
We acknowledge MLIT of Japan for providing hydrologic and river data. We
acknowledge Assoc. Prof. Dai Yamazaki for providing J-FlwDir, which JRRI
model uses for flow direction. We acknowledge Mr. Ayato Yamakita for thedetailed validation of the model.
Author contributions
JC performed the data processing, analysis, and manuscript writing. TS proposed the topic, designed, and performed the simulations, and provided suggestions for this manuscript. MY and YS contributed to model development
and suggestions to this manuscript. All the authors have read and approved
the final version of the manuscript.
Funding
This work was supported by the MEXT Program for the Advanced Studies of
Climate Change Projection (SENTAN) [Grant Number: JPMXD0722678534],
Cabinet Office, Government of Japan, and Cross-Ministerial Strategic Innovation Promotion Program (SIP) (PI: Kusunoki). For the model development
phase, this work was supported by collaborative research Grants provided by
Kyoto Prefecture, Japan Society for the Promotion of Science (JSPS) KAKENHI[Grant JP19H02248 for PI: Sayama, JP21H04597 for PI: Yamori, and JP22H01600
for PI: Tachikawa].
Availability of data and materials
The simulation datasets created in this study are available from the corresponding author upon request.

Declarations
Ethical approval and consent to participate
Not applicable.
Consent for Publication
Not applicable.
Competing interest
The authors declare that they have no competing interest.
Received: 20 August 2023 Accepted: 15 March 2024

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

References
Alfieri L, Salamon P, Bianchi A, Neal J, Bates P, Feyen L (2014) Advances in panEuropean flood hazard mapping. Hydrol Process 28(13):4067–4077
Bates PD, Horritt MS, Fewtrell TJ (2010) A simple inertial formulation of theshallow water equations for efficient two-dimensional flood inundation
modelling. J Hydrol 387(1):33–45
Beguería S (2005) Uncertainties in partial duration series modelling of
extremes related to the choice of the threshold value. J Hydrol
303(1):215–230
Bezak N, Brilly M, Šraj M (2014) Comparison between the peaks-over-thresholdmethod and the annual maximum method for flood frequency analysis.
Hydrol Sci J 59(5):959–977
Bhagabati SS, Kawasaki A (2017) Consideration of the rainfall-runoff-inundation (RRI) model for flood mapping in a deltaic area of Myanmar. Hydrol
Res Lett 11(3):155–160
Breinl K, Lun D, Müller-Thomy H, Blöschl G (2021) Understanding the relationship between rainfall and flood probabilities through combined
intensity-duration-frequency analysis. J Hydrol 602:126759
Cash J, Karp A (1990) A variable order Runge–Kutta method for initial-value
problems with rapidly varying right-hand sides. Acm Trans Math Softw
16(3):201–222
Cunnane C (1973) A particular comparison of annual maxima and partial duration series methods of flood frequency prediction. J Hydrol 18(3):257–271
Deushi M, Shibata K (2011) Development of a meteorological research institute chemistry-climate model version 2 for the study of tropospheric andstratospheric chemistry. Pap Meteorol Geophys 62:1–46
Dottori F, Salamon P, Bianchi A, Alfieri L, Hirpa FA, Feyen L (2016) Development
and evaluation of a framework for global flood hazard mapping. Adv
Water Resour 94:87–102
Ferreira A, de Haan L (2015) On the block maxima method in extreme value
theory: PWM estimators. Ann Stat 43(1):276–298
Fujita M, Mizuta R, Ishii M, Endo H, Sato T, Okada Y et al (2019) Precipitation
changes in a climate with 2-K surface warming from large ensemble
simulations using 60-km global and 20-km regional atmospheric models.
Geophys Res Lett 46(1):435–442
Gellens D (2002) Combining regional approach and data extension procedure
for assessing GEV distribution of extreme precipitation in Belgium. J
Hydrol 268(1):113–126
Green WH, Ampt GA (1911) Studies on soil phyics. J Agric Sci 4(1):1–24. https://​doi.​org/​10.​1017/​S0021​85960​00014​41
Hatsuzuka D, Sato T, Yoshida K, Ishii M, Mizuta R (2020) Regional projection oftropical-cyclone-induced extreme precipitation around Japan based onlarge ensemble simulations. Sola 16:23–29
Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S,
Kim H, Kanae S (2013) Global flood risk under climate change. Nat Clim
Chang 3(9):816–821
Hoch JM, Trigg MA (2019) Advancing global flood hazard simulations byimproving comparability, benchmarking, and integration of global floodmodels. Environ Res Lett 14(3):034001
Hosking JRM, Wallis JR (1993) Some statistics useful in regional frequency
analysis. Water Resour Res 29(2):271–281
Institute of Hydrology (1999) Flood estimation handbook. Institute of Hydrology, Wallingford
IPCC (2021) Climate Change 2021: the physical science basis. Contribution ofworking group I to the sixth assessment report of the intergovernmental
panel on climate change. Cambridge University Press, Cambridge
Ishii M, Mori N (2020) d4PDF: large-ensemble and high-resolution climate
simulations for global warming risk assessment. Prog Earth Planet Sci
7(1):58
National Institute for Environmental Studies (2022) Bias corrected climate
scenarios over Japan based on NHRCM02 using monthly CDFDM Ishizaki
N. https://​doi.​org/​10.​17595/​20220​415.​001-e.​html. Accessed 14 Feb 2023
Kamahori H, Arakawa O (2018) Tropical cyclone induced precipitation overjapan using observational data. Sola Tokyo Meteorol Soc Jpn 14:165–169
Kawase H, Imada Y, Tsuguti H, Nakaegawa T, Seino N, Murata A, Takayabu I(2020) The heavy rain event of july 2018 in Japan enhanced by historicalwarming. Bull Am Meteorol Soc 101(1):S109–S114
Kawase H, Murata A, Mizuta R, Sasaki H, Nosaka M, Ishii M, Takayabu I (2016)
Enhancement of heavy daily snowfall in central Japan due to global
warming as projected by large ensemble of regional climate simulations.
Clim Chang 139(2):265–278

Page 14 of 15

Lang M, Ouarda T, Bobee B (1999) Towards operational guidelines for overthreshold modeling. J Hydrol 225(3–4):103–117
Langousis A, Mamalakis A, Puliga M, Deidda R (2016) Threshold detection for
the generalized Pareto distribution: review of representative methods
and application to the NOAA NCDC daily rainfall database. Water Resour
Res 52(4):2659–2681
Madsen H, Rasmussen PF, Rosbjerg D (1997) Comparison of annual maximum
series and partial duration series methods for modeling extreme hydrologic events: 1. At-Site Modeling. Water Resour Res 33(4):747–757
Martínez C, Sanchez A, Toloh B, Vojinovic Z (2018) Multi-objective evaluation ofurban drainage networks using a 1D/2D flood inundation model. Water
Resour Manag 32(13):4329–4343
Meade RH, Rayol JM, Da Conceicão SC, Natividade JRG (1991) Backwater
effects in the Amazon River basin of Brazil. Environ Geol Water Sci
18(2):105–114
Miyasaka T, Kawase H, Nakaegawa T, Imada Y, Takayabu I (2020) Future projections of heavy precipitation in Kanto and associated weather patterns
using large ensemble high-resolution simulations. Sola 16:125–131
Mizuta R, Murata A, Ishii M, Shiogama H, Hibino K, Mori N et al (2017) Over5,000 years of ensemble future climate simulations by 60-km global
and 20-km regional atmospheric models. Bull Am Meteorol Soc
98(7):1383–1398
Mostofi Zadeh S, Durocher M, Burn DH, Ashkar F (2019) Pooled flood
frequency analysis: a comparison based on peaks-over-threshold andannual maximum series. Hydrol Sci J 64(2):121–136
Ohba M, Arai R, Toyoda Y, Sato T (2022) Impact of weather regime on projected
future changes in streamflow in a heavy snowfall area of Japan. Clim Dyn
59(3):939–950
Ohba M, Sugimoto S (2019) Differences in climate change impacts between
weather patterns: possible effects on spatial heterogeneous changes infuture extreme rainfall. Clim Dyn 52(7):4177–4191
Otaki T, Fudeyasu H, Kohno N, Takemi T, Mori N, Iida K (2022) Investigation ofcharacteristics of maximum storm surges in japanese coastal regions
caused by typhoon Jebi (2018) based on typhoon track ensemble simulations. J Meteorol Soc Jpn 100(4):661–676
Packman JC, Kidd CHR (1980) A logical approach to the design storm concept.
Water Resour Res 16(6):994–1000
Pappenberger F, Dutra E, Wetterhall F, Cloke HL (2012) Deriving global flood
hazard maps of fluvial floods through a physical model cascade. Hydrol
Earth Syst Sci 16(11):4143–4156
Pendergrass AG (2020) Changing degree of convective organization as amechanism for dynamic changes in extreme precipitation. Curr Clim
Chang Rep 6(2):47–54
Qin H, Yuan W, Wang J, Chen Y, Dai P, Sobel AH, Meng Z, Nie J (2022) Climatechange attribution of the 2021 Henan extreme precipitation: impacts of
convective organization. Sci China Earth Sci 65(10):1837–1846
Rasmy M, Sayama T, Koike T (2019) Development of water and energy budgetbased rainfall-runoff-inundation model (WEB-RRI) and its verification in
the Kalu and Mundeni River Basins, Sri Lanka. J Hydrol 579:124163
Sampson CC, Smith AM, Bates PD, Neal JC, Alfieri L, Freer JE (2015) A highresolution global flood hazard model. Water Resour Res 51(9):7358–7381
dos Santos CM, Escobedo JF, Teramoto ÉT, da Silva SHMG (2016) Assessmentof ANN and SVM models for estimating normal direct irradiation (Hb).
Energy Convers Manag 126:826–836
Sasai T, Kawase H, Kanno Y, Yamaguchi J, Sugimoto S, Yamazaki T, Sasaki H,
Fujita M, Iwasaki T (2019) Future projection of extreme heavy snowfall
events with a 5-km large ensemble regional climate simulation. J Geophys Res Atmos 124(24):13975–13990
Sasaki H, Murata A, Hanafusa M, Oh’izumi M, Kurihara K. (2011) Reproducibilityof present climate in a non-hydrostatic regional climate model nested
within an atmosphere general circulation model. Sola 7:173–176
Sayama T, McDonnell JJ (2009) A new time-space accounting scheme to predict stream water residence time and hydrograph source components at
the watershed scale. Water Resour Res 45:W07401
Sayama T, Ozawa G, Kawakami T, Nabesaka S, Fukami K (2012) Rainfall-runoffinundation analysis of the 2010 Pakistan flood in the Kabul River basin.
Hydrol Sci J 57(2):298–312
Sayama T, Tatebe Y, Iwami Y, Tanaka S (2015) Hydrologic sensitivity of flood
runoff and inundation: 2011 Thailand floods in the Chao Phraya Riverbasin. Nat Hazards Earth Syst Sci 15(7):1617–1630

Chen et al. Progress in Earth and Planetary Science(2024) 11:16

Sayama T, Tatebe Y, Tanaka S (2017) An emergency response-type rainfall-runoff-inundation simulation for 2011 Thailand floods. J Flood Risk Manag10(1):65–78
Sayama T, Yamada M, Sugawara Y, Yamazaki D (2020) Ensemble flash flood
predictions using a high-resolution nationwide distributed rainfall-runoff
model: case study of the heavy rain event of July 2018 and Typhoon
Hagibis in 2019. Prog Earth Planet Sci 7(1):75
Smith A, Sampson C, Bates P (2015) Regional flood frequency analysis at the
global scale. Water Resour Res 51(1):539–553
Sun Y, Wendi D, Kim DE, Liong S-Y (2019) Deriving intensity–duration–frequency (IDF) curves using downscaled in situ rainfall assimilated withremote sensing data. Geosci Lett 6(1):17
Swetapadma S, Ojha CSP (2021) Technical note: flood frequency study using
partial duration series coupled with entropy principle. Hydrol Earth Syst
Sci 2021:1–23
Tada H, Uchiyama Y, Masunaga E (2018) Impacts of two super typhoons on the
Kuroshio and marginal seas on the Pacific coast of Japan. Deep-Sea Res
Part I 132:80–93
Tanaka T, Tachikawa Y, Ichikawa Y, Yorozu K (2018) Flood risk curve development with probabilistic rainfall modelling and large ensemble climate
simulation data: a case study for the Yodo River basin. Hydrol Res Lett
12(4):28–33
Tellman B, Sullivan JA, Kuhn C, Kettner AJ, Doyle CS, Brakenridge GR, Erickson
TA, Slayback DA (2021) Satellite imaging reveals increased proportion of
population exposed to floods. Nature 596(7870):80–86
Try S, Tanaka S, Tanaka K, Sayama T, Khujanazarov T, Oeurng C (2022) Comparison of CMIP5 and CMIP6 GCM performance for flood projections in the
Mekong River Basin. J Hydrol Reg Stud 40:101035
Wasko C, Nathan R, Stein L, O’Shea D (2021) Evidence of shorter more extreme
rainfalls and increased flood variability under climate change. J Hydrol
603:126994
Watt E, Marsalek J (2013) Critical review of the evolution of the design storm
event concept. Can J Civ Eng 40(2):105–113
Wing OEJ, Bates PD, Sampson CC, Smith AM, Johnson KA, Erickson TA (2017)
Validation of a 30 m resolution flood hazard model of the conterminous
United States. Water Resour Res 53(9):7968–7986
Wing OEJ, Smith AM, Marston ML, Porter JR, Amodeo MF, Sampson CC, Bates
PD (2021) Simulating historical flood events at the continental scale:observational validation of a large-scale hydrodynamic model. Nat Hazards Earth Syst Sci 21:559–575
Winsemius HC, Jongman B, Veldkamp TIE, Hallegatte S, Bangalore M, Ward PJ(2018) Disaster risk, climate change, and poverty: assessing the global
exposure of poor people to floods and droughts. Environ Dev Econ
23(3):328–348
Winsemius HC, Van Beek LPH, Jongman B, Ward PJ, Bouwman A (2013) A
framework for global river flood risk assessments. Hydrol Earth Syst Sci
17(5):1871–1892
Yamada M, Sayama T, Yamazaki D, Watanabe M (2022) Development of methodology for introducing surveyed river cross-sections into nationwide
distributed hydrological model throughout Japan, and its effect on waterlevel prediction accuracy. J Jpn Soc Civ Eng Ser B Hydraul Eng 78(1):7–22
Yamakita A (2023) Parameter regionalization of a nationwide hydrologic modeland its validation at multiple locations. Dissertation, Kyoto University
Yamamoto K, Sayama T, Apip (2021) Impact of climate change on flood inundation in a tropical river basin in Indonesia. Prog Earth Planet Sci 8(1):5
Yamazaki D, de Almeida GAM, Bates PD (2013) Improving computational
efficiency in global river models by implementing the local inertial
flow equation and a vector-based river network map. Water Resour Res
49(11):7221–7235
Yan Z, Xia J, Song J, Zhao L, Pang G (2020) Research progress on design hyetographs in small and medium-scale basins. Prog Geogr 39(7):1224–1235
Yao HX, Creed IF (2005) Determining spatially-distributed annual water balances for ungauged locations on Shikoku Island, Japan: a comparison of
two interpolators. Hydrol Sci J 50(2):245–263
Yoshida R, Iizumi T, Nishimori M, Yokozawa M (2012) Impacts of land-use
changes on surface warming rates and rice yield in Shikoku, westernJapan. Geophys Res Lett 39:L22401
Yukimoto S, Adachi Y, Hosaka M, Sakami T, Yoshimura H, Hirabara M, Tanaka
T, Shindo E, Tsujino H, Deushi M, Mizuta R, Yabu S, Obata A, Nakano
H, Koshiro T, Ose T, Kitoh A (2012) A new global climate model of the

Page 15 of 15

meteorological research institute: MRI-CGCM3—Model description andbasic performance. J Meteorol Soc Jpn Ser II 90A:23–64
Zeng Z, Wang Z, Lai C (2022) Simulation performance evaluation and uncertainty analysis on a coupled inundation model combining SWMM andWCA2D. Int J Disaster Risk Sci 13(3):448–464
Zhao F, Veldkamp TIE, Frieler K, Schewe J, Ostberg S, Willner S, Schauberger
B, Gosling SN, Schmied HM, Portmann FT, Leng G, Huang M, Liu X, Tang
Q, Hanasaki N, Biemans H, Gerten D, Satoh Y, Pokhrel Y, Stacke T, Ciais P,
Chang J, Ducharne A, Guimberteau M, Wada Y, Kim H, Yamazaki D (2017)
The critical role of the routing scheme in simulating peak river discharge
in global hydrological models. Environ Res Lett 12(7):075003
Zou N, Volgushev S, Buecher A (2021) Multiple block sizes and overlapping
blocks for multivariate time series extremes. Ann Stat 49(1):295–320

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