Hirabayashi et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-021-00431-w(2021) 8:36

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Anthropogenic climate change haschanged frequency of past flood during
2010-2013
Yukiko Hirabayashi1* , Haireti Alifu1, Dai Yamazaki2, Yukiko Imada3, Hideo Shiogama4 and Yuki Kimura5

Abstract
The ongoing increases in anthropogenic radiative forcing have changed the global water cycle and are expected to lead tomore intense precipitation extremes and associated floods. However, given the limitations of observations and model
simulations, evidence of the impact of anthropogenic climate change on past extreme river discharge is scarce. Here, a large
ensemble numerical simulation revealed that 64% (14 of 22 events) of floods analyzed during 2010-2013 were affected byanthropogenic climate change. Four flood events in Asia, Europe, and South America were enhanced within the 90%
likelihood range. Of eight snow-induced floods analyzed, three were enhanced and four events were suppressed, indicatingthat the effects of climate change are more likely to be seen in the snow-induced floods. A global-scale analysis of flood
frequency revealed that anthropogenic climate change enhanced the occurrence of floods during 2010-2013 in wide area ofnorthern Eurasia, part of northwestern India, and central Africa, while suppressing the occurrence of floods in part ofnortheastern Eurasia, southern Africa, central to eastern North America and South America. Since the changes in theoccurrence of flooding are the results of several hydrological processes, such as snow melt and changes in seasonal andextreme precipitation, and because a climate change signal is often not detectable from limited observation records, large
ensemble discharge simulation provides insights into anthropogenic effects on past fluvial floods.
Keywords: Floods, Event attribution, Large ensemble numerical simulations

1 Introduction
Flooding is a common major natural hazard in many areasof the world, and its frequency and magnitude are expected to increase with climate warming. Previous studies
indicated that flood frequency is non-stationary (Millyet al. 2008; Villarini et al. 2009), although many water
management designs have assumed that flood occurrenceor magnitude is stationary based on patterns observed in
the past. Therefore, it is important to analyze changes in
the frequency and magnitude of flooding to develop adequate adaptation and mitigation strategies. For this purpose, flood changes associated with climate change havebeen projected in the near (e.g., 2030-2050) and far* Correspondence: hyukiko@shibaura-it.ac.jp
1
Department of Civil Engineering, Shibaura Institute of Technology, 3-7-5
Toyosu, Koto-ku, Tokyo 135-8548, Japan
Full list of author information is available at the end of the article(-2100) future, driven by anthropogenic climate change
scenarios (Dottori et al. 2018; Hirabayashi et al. 2013; Hirabayashi et al. 2021); often, the results of the warmest scenario (e.g., RCP8.5) have been used to capture a
prominent climate change signal.
The effect of climate change on flooding can also be
evaluated by detecting and attributing past changes inflooding in historical data. Due to smaller signals in thelower level of warming in the recent past compared tofuture projections, many climate simulations with andwithout anthropogenic climate change since the industrial revolution have been compared to evaluate theeffects of past anthropogenic effects on climate change.
For example, the effects of human-induced global warming on past heavy precipitation events (Imada et al. 2020;
Li et al. 2021) and mean and extreme streamflow (Gudmundsson et al. 2021) have been investigated using large© The Author(s). 2021 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 the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence 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 this licence, visit http://creativecommons.org/licenses/by/4.0/.

Hirabayashi et al. Progress in Earth and Planetary Science(2021) 8:36

ensemble climate simulations. Despite evidence of a general increase in extreme precipitation events in the past
(Hartmann et al. 2013; Min et al. 2011), the observedchanges in flooding vary with catchment sizes or hydroclimatological characteristics (Do et al. 2017; Sharma
et al. 2018). Small catchments often show changes similar to those of precipitation (Do et al. 2017), whereas
other changes associated with warming, such as decreases in soil moisture and snow melt, might be dominant in larger catchments (Bloeschl et al. 2019; Sharma
et al. 2018). Historical human intervention could alter
natural flood changes. Due to the difficulties in attribution because of the complexity of drivers of change inriver discharge (Madsen et al. 2014; Trenberth et al.
2015), large-scale perspectives on potential impacts ofanthropogenic activities on the probability of historicalflood events that include these regions could have significant policy implications. As most climatic attribution
studies have focused on single-step direct output of climate models (e.g., temperature and precipitation), few
studies have investigated event attribution to river flooding. Since studies of flood projection (Hirabayashi et al.
2013; Hirabayashi et al. 2021; Sharma et al. 2018) havedemonstrated that the direction of the changes (wet ordry) in precipitation and flooding is not consistent inspatiotemporally because of several hydrological processes, event attribution of river discharge will enhance
our understanding of the effects of climate change on
flood risk.
This study evaluated the impact of anthropogenic climate change on the probability of historical large flood
events based on river discharge derived from large ensemble climate experiments to investigate whether possible
mechanisms of the past changes in floods due to climatechange have already been revealed. For this purpose, we
analyzed 22 flood events occurring during the target
period at a global scale, rather than focusing on specificflood events, to provide an overview of the effects of past
anthropogenic climate change on flood events.

2 Data and methods
2.1 Event attribution of historical floods between 2010
and 2013

Modeled discharges obtained from a global river and inundation model—the Catchment based Macro-scale Floodplain(CaMa-Flood) model (Yamazaki et al. 2011), driven by runoff
data from a large-ensemble climate experiment with (hereafter referred to as “ALL”) and without (“NAT”) anthropogenic radiative forcing (Shiogama et al. 2013) were used forevent attribution of historical flood from 2010 to 2013
(Table 1). A large-ensemble climate experiment was conducted using an atmospheric general circulation model(AGCM) MIROC5 (Watanabe et al. 2010) driven by sea

Page 2 of 9

surface temperature (SST), sea ice, and historical external
forcing factors.
Three types of experiment were performed in thisstudy (Table 1). ALL-LNG was a ten member ensemble
run of the AGCM driven by observed SST, sea ice, andhistorical anthropogenic and natural external forcing between 1949 and 2013. The 100-member ensemble runsof ALL were produced with the same anthropogenic andnatural external conditions as the ALL-LNG runs. NAT
was a 100-member ensemble run produced under natural external conditions. To analyze the uncertainties inremoving anthropogenic signals from SST and sea ice
data, two NAT experiments (NAT-dtr and NAT-cmip5)
were used for the analysis (Shiogama et al. 2014).The
main difference between the experiments is SST without
historical climate change used as a boundary condition(See Supporting Information S1 for the details of theexperiments).
The daily runoff output of the experiments with aspatial resolution of T85 (approximately 133 km at theequator) was simply interpolated and then integrated toriver discharge through a high-resolution (15′ × 15′)
global river network map from the CaMa-Flood model.
Subsequently, the annual maximum daily discharge wasobtained for each experiment. The annual maximum
daily discharge data calculated using ALL-LNG (total,
650 members from ten ensembles annually between
1949 and 2013) were used to define flood events corresponding to a 10-year flood, i.e., floods with probability> 0.1 in any given year for each 15′ × 15′-grid cell, by
fitting the data to the Gumbel probability distributionfunction using the L-moment method (Supplementary
Information S3).
Twenty-two historical flood events that occurred during the period were selected from the Emergency Events
Database (EM-DAT n.d.), news media, or streamflow records (Supporting Information S2). Events were selected
only if the flood probability under the historical simulation of ALL at the specific location and year, corresponding to the selected flood events, was higher thanthat of long-term experiments (ALL-LNG). This outcome indicated that the flooding for a particular location
in a particular year did not occur purely by chance. A
flood was defined as when the return periods of the annual maximum daily discharge in all samples of ALLLNGs were longer than 10 years— (i.e., 10-year floods).
The fraction of attributable risk (FAR) can quantify
the human influence on the occurrence of individual recent historical events, such as heat waves, droughts(Lewis and Karoly 2013; Otto et al. 2012; Shiogama et al.
2013; Stott et al. 2005), heavy precipitation (Min et al.
2011; Sippel and Otto 2014), and floods (Pall et al.
2011). We calculated FAR as (PA−PN)/PA, where PA andPN represent the probability of a flood exceeding theHirabayashi et al. Progress in Earth and Planetary Science(2021) 8:36

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Table 1 Summary of event attribution climate experiments. SST, sea surface temperature; ALL, large-ensemble climate experimentwith anthropogenic radiative change; ALL-LNG, ALL experiment forcing long period runs; NAT, large-ensemble climate experiment
without anthropogenic radiative forcing
Ensemble members Time period External forcing

SST and sea ice cover

ALL-LNG10

1949-2013

All historical forcing Observationa

ALL

100

2010-2013

All historical forcing Observationa

NAT (NAT-dtr and NAT-cmip5) 100

2010-2013

Natural forcing

Anthropogenic signals are removed from observed dataa(Rayner et al. 2003)

magnitude of the flood (10-year flood in ALL-LNG experiment) for the ALL and NAT experiments, respectively. The occurrence of floods was enhanced(suppressed) by human influence if FAR was positive(negative). Due to lower warming levels in the past records, the probability of change shown by FAR enables
us to attribute the effects of climate change on the occurrence of flooding. The anthropogenic influence on
floods remained uncertain when NAT-dtr and NATcmip5 experiments yielded FAR values with opposite
signs. To evaluate the uncertainty of FAR, we conducted
uncertainty analysis based on a bootstrap method; thesignificance of the FAR signal (positive or negative) wastested using nonparametric bootstrap samples (Efron
and Tibshirani 1993). A random member generator wasused to sample a 50% subset of the annual maximum
daily discharge from the 100 ensembles when and where
the flood event was observed. This procedure was repeated 1000 times, giving 1000 estimates of FAR foreach experiment (ALL, NAT-dtr, and NAT-cmip5). To
indicate the spread of the FAR values, we provided two
estimates of the FAR value, those exceeding 50% (bestestimate; FAR50) and 90% (very likely; FAR90) of thevalues in the bootstrapped FAR distributions. FARvalues are reported as here as FAR50 and FAR90. The
value of the 10th (90th) percentile of FAR (Table S1) isshown as FAR90 when FAR50 is positive (negative). The
same signal of FAR50 and FAR90 values (positive or
negative) indicate that past anthropogenic climatechange has affected the occurrence of analyzed flood
events with a 90% probability. Compared with standard
climate projection studies, event attribution and assessment using FAR imposes the effects of anthropogenic
climate change on past flood events with uncertainty of
the attribution. We interpreted FAR50 values (positive or
negative) between the different NAT experiments(NAT-dtr and NAT-cmip5) to indicate that the effect ofclimate change on the event was unclear within the uncertainty of past simulations.
To evaluate changes in the occurrence of flooding forthe period 2010–2013 at global scale, we calculated theaverage FAR50 values for all sub-catchments of large
river basins. The probability of a flood exceeding a 10year flood among 100 samples was calculated at theoutlet of each sub-catchment for each year and thenaveraged for the entire period. Finally, the average FAR50
for NAT-dtr and NAT-cmip5 were calculated andpresented.
2.2 River discharge data

Data on the observed daily discharge were obtained fromthe Global Runoff Data Centre (https://www.bafg.de/
GRDC/EN/Home/homepage_node.html). Since the spatial
resolution of climate models cannot resolve small river basins, only relatively large river basins (area > 100,000 km2)
with at least 30 years of observed daily discharge data(within the period 1975–2015) were selected. We useddata only when data were missing for fewer than 73 daysin the year (20% of the year).
Global daily discharge reanalysis for the same period
was obtained from CaMa-Flood, which simulates floodplain inundation dynamics in a realistic manner and improves the predictability of river discharge at hightemporal and spatial resolutions (Yamazaki et al. 2011).
Daily runoff input for the simulation was obtained froma land surface model, Minimal Advanced Treatment ofSurface Interaction and Runoff (MATSIRO) simulation(Koirala et al. 2014; Takata et al. 2003), which is forced
by a bias-corrected global reanalysis, i.e., S14FD (Iizumi
et al. 2017). The annual maximum daily discharge of thereanalysis had a slightly large spread (Fig. S2), but wasalso reasonable correlation at many basins (Fig. S3).
Among 136 selected catchments, 46 (34%) show correlation higher than 0.6 and 65 (48%) show correlation
higher than 0.5. Large bias occurred for catchments withaveraged observed discharge less than 2400 m3/s. The
proportion of gauges with less than 50% bias increased
from 41% in the entire group to 82% for the group withlarge discharges. The proportion of bias less than 20%
increased from 20 to 43%. Possible reasons of the discrepancies in peak discharge is human interventions ormodel’s limitations including quality of climate forcing
and hydrological processes such as freezing in cold rivers(Hirabayashi et al. 2013). It should be noted that ourmodel chain does not take into account human regulation of rivers, such as reservoir operation and irrigation
intake, or land-use changes. Hence, the modeled daily
discharges of the event attribution experiment andHirabayashi et al. Progress in Earth and Planetary Science(2021) 8:36

reanalysis differ from the observed discharge under intensive human water management.

3 Results and Discussion
3.1 Evaluation of large ensemble climate experiments

We evaluated the model performance by comparing observed and simulated discharges. As the limited numberof ensemble climate experiments does not necessarily
produce observations matching time series, the ranges often ensemble long-term historical experiments (ALLLNG) and their ensemble mean are shown (Fig. S4 andS5). The long-term daily discharge reanalysis (S14FD) isalso plotted. Comparisons of the annual maximum daily
discharge derived from observations and long-term historical experiments (ALL-LNG) indicated that theclimate model output and our modeling framework haveseveral potential biases. For the 12 basins for whichobservations were available (red lines in Fig.S3), five (theMississippi, Amazon, Danube, Volga, and Fitzroy Basins)
had similar magnitudes and seven (the Mackenzie,
Peace, Fraser, Saskatchewan, Red, Missouri, andOkavango Basins) overestimated the annual maximum
daily discharge. Since discharge reanalyses also showed
overestimation and had magnitudes similar to ALL-LNG
in all these basins except for the Fraser and Okavango,
the overestimation is thought to be the effect of humanactivities, such as reservoir operations or irrigation intake, which are not implemented either the reanalyses or
ALL-LNG simulations. The overestimations for the Fraser and Okavango Rivers may be caused by bias in the
precipitation output of climate experiments.

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The cumulative distribution function (CDF) of the annual maximum daily discharge of flood events suggested
that the observed distribution of the annual maximum
daily discharge was within the probable range of the longterm event attribution experiment (ALL-LNG) in five basins (Mississippi, Amazon, Volga, Yalu, and Fitzroy) of the
12 basins for which observations were available (blacklines in Fig. S5). The remaining seven basins (the Mackenzie, Peace, Fraser, Saskatchewan, Red, Missouri, andOkavango) showed overestimation of CDF. Most of thesebasins also showed smaller annual fluctuations in the annual maximum daily discharge, perhaps due to humanwater management. Despite the biases in magnitude inthese basins, the ALL-LNG experiment reasonably reproduced the fluctuations in the annual maximum daily discharge in most basins (Fig. S2 and S5).
3.2 Anthropogenic radiative effect on the occurrence of
past floods

The results of FAR50 indicated that ongoing climatechange affected the occurrence of 64% (14 of 22) of historical floods from 2010 to 2013 (Fig. 1, Table 2); they
were enhanced in eight basins and suppressed in six.
Anthropogenic radiative forcing increased the occurrence of eight floods in Asia, Europe, North America,and South America within the 90% likely range (Table 2).
Of these, the cause of five flood events (Magdalena in
2011, Amazon in 2012, Amur in 2013, Songhua in 2013,
and Indus in 2010) was heavy rainfall, corresponding tothe observed significant increases in heavy precipitation
on a global scale (Dunn et al. 2020), which has beenshown to be the result of anthropogenic changes (Min

Fig. 1 Effects of anthropogenic radiative forcing on selected historical floods between 2010 and 2013. The locations of enhanced (blue) andsuppressed (red) flooding due to anthropogenic radiative forcing, and locations (gray) with non-significant changes. Changes were considered
non-significant when the FAR50 between ALL and the two natural forcing experiments (NAT-cmip5 and NAT-dtr) showed different directions ofchange. Numbers on the map correspond to the flood events listed in Table 2

Hirabayashi et al. Progress in Earth and Planetary Science(2021) 8:36

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Table 2 Summary of selected flood events between 2010 and 2013. The effects of anthropogenic climate change on flood events(E, enhanced; S, suppressed; N/A, no significant effect) were judged as E (S) when both fraction of attribution (FAR50) scores
between ALL and NAT_dtr (dtr) and those between ALL and NAT_CMIP5 (CMIP5) show the same signal. The value of the 10th
(90th) percentile of FAR (Table S1) is shown as FAR90 when FAR50 is positive (negative)
River

RegionYear

Causes

FAR

Effectdtr
50th/90th percentile

CMIP5
50th/90th percentile

1

Mackenzie

NA

2012

Snow melt, heavy rain

0.44/0.17−0.17/0.24

N/A

2

Peace

NA

2011

Heavy rain

0.14/−0.42−0.29/0.18

N/A

3

FraserNA

2012

Snow melt

0.36/0.08

0.01/−0.52

E4

Saskatchewan

NA

2013

Heavy rain

0.11/−0.34−0.11/0.35

N/A

5

Red

NA

2011

Snow melt−0.92/−0.26−0.49/0.01

S

6

MissouriNA

2011

Snow melt−0.30/0.17−0.07/0.37

S7

Mississippi

NA

2011

Snow melt, storm−0.29/0.27−0.46/0.13

S8

Magdalena

SA

2011

Heavy rain

0.46/0.16

0.30/−0.02

E9

AmazonSA

2012

Heavy rain

0.45/0.24

0.48/0.24

E

10

Sava

EU

2010

Snow melt, heavy rain

0.09/−0.54

0.36/−0.17E11DanubeEU

2013

Heavy rain

−0.01/0.38

0.28/−0.19

N/A

12

Kuban

EU

2012

Storm

0.65/0.31−0.24/0.28

N/A

13

Volga

EU

2012

Snow melt

0.27/0.01

0.35/0.12

E

14

Lena

AS

2010

Snow melt−0.07/0.32−0.12/0.24

S

15

Amur

AS

2013

Heavy rain

0.54/0.25

0.45/0.15

E16

SonghuaAS

2013

Heavy rain

0.63/0.37

0.48/0.17

E

17

Yalu

AS

2010

Heavy rain

0.16/−0.34−0.14/0.32

N/A

18

Indus

AS

2010

Heavy rain

0.20/−0.27

0.17/−0.31

E19

Fitzroy

OC

2010

Heavy rain

−0.09/0.64−0.39/0.47

S

20

Murrumbidge, Murray

OC

2012

Heavy rain

0.35/−0.14−0.33/0.17

N/A

21

Niger

AF

2010

Heavy rain

−0.15/0.37−1.84/−0.72

S22

Okavango

AF

2011

Heavy rain

0.61/0.22−0.17/0.37

N/A

Note: AF Africa, AS Asia, EU Europe, SA South America, NA North America, OC Oceania, N/A not available

et al. 2011). Comparison of ensemble mean of precipitation showed increase in Songhua in 2013 and Indus in
2010, but decrease in other basins (Fig. S7), indicating
the effect of seasonal or extreme precipitation. The effects of anthropogenic warming on three snow-induced
floods were enhanced (the Fraser in 2012, Sava in 2010
and Volga in 2012) within a 90% likely range.
By contrast, flooding was suppressed in six flood events,
but the anthropogenic influence on the occurrence offloods was not clear, with a negative FAR50 and positive
FAR90. No significant signal was found in eight events.
Of the eight snow-induced flood events analyzed, threewere enhanced and four were suppressed, indicating that theeffects of climate change are more likely to be seen in thesnow-induced floods. Three snow-induced floods were enhanced (the Fraser in 2012, Sava in 2010, and Volga in2012), which is due to increased precipitation (Fig. S7). Twosnow-induced floods (the Red in 2011 and Missouri in 2011)were suppressed, despite the increased precipitation (Fig. S7),
may be due to decreased snow and earlier snowmelt peaks
due to warming (Fig. S9).
Corresponding to the above results, comparison of
the CDF and probability density function (PDF) of
the annual maximum daily discharge showed thatflood occurrence in a certain year was intensified byanthropogenic global warming, with the ALL experiments distributed to the right of the NAT-dtr and
NAT-cimp5 experiments (Fig. S6). The figure also
showed that floods were more likely to occur in eightrivers (Mackenzie in 2012, Peace in 2011, Fraser in
2012, Saskatchewan in 2013, Red in 2011, Missouri in
2011, Mississippi in 2011, and Magdalena in 2011)
due to natural internal fluctuations, without considering global warming since the CDF/PDF of NAT-dtr
and NAT-cmip5 are also distributed to the right of
the ALL-LNG experiment.

Hirabayashi et al. Progress in Earth and Planetary Science(2021) 8:36

3.3 Global analysis of anthropogenic effects on theoccurrence of flooding

The average FAR50 on a global scale shows clear regional
characteristics. The results showed that the occurrenceof floods was enhanced by anthropogenic radiative forcing during the target years (2010-2013) in wide areas at
northern high latitudes including in a wide area ofnorthern Eurasia, part of northwestern India and central
Africa, while the occurrence of floods was suppressed inpart of northeastern Eurasia, southern Africa, central toeastern North America and South America (Fig. 2). The
main reason for the enhancement was increased precipitation in ALL compared with the NAT experiments(Fig. 3, Fig. S7). Despite the increased annual precipitation in the ALL experiment compared to the NAT experiments, the occurrence of flooding was suppressed or
had small signals in northern USA and in Uruguay. One
possible reason is that the warmer temperature in ALL
reduced precipitation as snow and the peak snowmelt inthese regions, as indicated by the suppression of theflood events in the Red and Mississippi Rivers (Table 2).
In northeastern Russia, eastern Asia, southern USA, andeastern Australia, there were several basins with decreasing annual precipitation but enhanced flood occurrence,indicating that increasing heavy precipitation due towarming climate exacerbated flooding in these regions.
These inconsistencies between changes in precipitationand the occurrence of floods support the importance ofclimate change studies using river discharge, rather than
precipitation, to project future flood risk.

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Interestingly, the pattern of positive and negative FAR
values was not always in the same direction of change as
the past observed flood trends. However, both FAR andthe flood trend showed increasing signals in some rivers,
e.g., increasing in eastern Eurasia and northwestern Europe, but decreasing in central to eastern North America,corresponding to previous studies on observed extreme
discharge changes (Do et al. 2017).
The overall enhancement of the occurrence of flooding in northern Eurasia (Fig. 2) was consistent with theincrease in floods under future climate seen in previous
studies (Dankers et al. 2014; Hirabayashi et al. 2013),
implying the potential to detect the effects of climatechange on flood occurrence in these regions. By contrast, western Eurasia showed enhanced flooding during
2010-2013, inconsistent with the published projected
reduction in the probability of flood occurrence in the
future climate. The average FAR50 for 2010-2013 shows
the impact of anthropogenic radiative forcing on the recent climate, but is not necessarily consistent with future flood projections. One possible explanation for theinconsistency in eastern Europe is that the increased
precipitation (Fig. 3) associated with more vapor transferred to higher latitudes due to the warmer climate
currently dominates the effect of snowfall reduction
due to warming. In a previous far future (2071-2100)
projection (Hirabayashi et al. 2013), the effect of lesssnowfall due to warming may have dominated, and theresulting frequency of snowmelt flood was projected todecrease.

Fig. 2 The same as Fig. 1, but for the mean FAR50 during 2010-2013 at the lowest point of each sub-catchment. The average of the result fromtwo NAT experiments (NAT-dtr and NAT-cmip5) is plotted when FAR50 between these experiments shows the same effect direction. White shows
sub-catchments with different directions of change, while colors show small FAR50 values between −0.05 and 0.05

Hirabayashi et al. Progress in Earth and Planetary Science(2021) 8:36

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Fig. 3 Difference in the average annual precipitation (mm/day) between the ALL and NAT (average of NAT-dtr and NAT-cmip5) experimentsfor 2010–2013

Considering historical variability, the estimated timerequired to detect plausible changes in discharge is several decades (Hirabayashi et al. 2005; Ziegler et al. 2003).
Together with the limitations in the data length of insitu observations, the climate change signal can be detected later. Therefore, large ensemble simulation is a
powerful method to detect anthropogenic radiative forcing on flooding and bridge our knowledge gap before
the climate change signal can be detected.
The large ensemble climate was simulated using a single general circulation model; therefore, uncertainty associated with the specific climate model is expected. We
focused on relatively large river basins at the subcatchment scale, but the sensitivity of flood frequency to
climate change and the associated hydro-meteorologicalchanges would vary with basin sizes. A previous study(Sharma et al. 2018) indicated that the changes in evapotranspiration or soil wetness due to a warming climate
have greater effects on flood change in larger basins.
Since flood changes are more closely connected tochanges in extreme precipitation in smaller basins,
which our model could not resolve due to the limited
horizontal resolution of the models, analysis using finer
spatial scales is required to assess small catchments.
Overall, we analyzed the annual maximum daily discharge exceeding the magnitude of a 10-year flood as aproxy of flooding. Although a 10-year flood is statistically larger than normal conditions, the severity of thehazard varies with the basin characteristics, including
local flood protection standards.
Due to the limited period covered by the large ensemble experiment (2010-2013), we analyzed only 22 flood
events. Long-term experiments and a more comprehensive analysis of flood signals are required to confirm thedetectability of flood changes.

4 Conclusions
The event attribution of historical flood events showedthat anthropogenic climate change affected 64% (14 of
22) of the floods analyzed, with increased occurrence
caused by heavy rainfall, and increased or decreased
snowmelt flood events. The occurrence of all but one
event of snow-induced floods analyzed was affected byanthropogenic climate change, indicating that the effects
of climate change are more likely to be seen in thesnow-dominant rivers. A global-scale analysis of flood
frequency revealed that anthropogenic climate changeenhanced the occurrence of floods during 2010-2013 ina wide area of northern Eurasian, part of northwestern
India, and central Africa, while it suppressed the occurrence of floods in part of northeastern Eurasia, southern
Africa, central to eastern North America, and SouthAmerica over the same period. Moreover, the differencesin the occurrence of floods between climate experimentswith and without anthropogenic climate change are theresult of several hydrological processes such as snowHirabayashi et al. Progress in Earth and Planetary Science(2021) 8:36

melt and changes in seasonal and extreme precipitation
depending on the climate zone and basin size. Thus,
large ensemble discharge simulations can offer insight
into anthropogenic effects on fluvial floods.

5 Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s40645-021-00431-w.
Additional file 1: S1. Event attribution experiments. S2. Selection ofhistorical flood events between 2010 and 2013. S3. Extreme distributionfunction. Table S1. Summary of selected flood events between 2010
and 2013. The effects of anthropogenic climate change on flood events(E, enhanced; S, suppressed; N/A, no significant effect) were judged as E(S) when both percentile of fraction of attribution (FAR50) scores
between ALL and NAT_dtr (dtr) and those between ALL and NAT_CMIP5(CMIP5) show the same signal. Fig. S1. Results of the evaluation of thefraction of attribution risk based on a bootstrap method. Fig. S2. Scatterplot of the annual maximum discharge derived from GRDC and global
daily discharge reanalysis based on the 136 GRDC stations. Geographic
location of the stations are shown in Fig. . Fig. S3. Correlation of annual
max discharge derived from GRDC and global daily discharge reanalysis
at 136 GRDC stations. Fig. S4. Comparisons of the annual maximum
daily discharge derived from observations (GRDC), discharge reanalysis(S14FD), and ALL-LNG experiments. Fig. S5. The cumulative distributionfunction of annual maximum daily discharge for 22 events derived fromobservations (GRDC), discharge reanalysis (S14FD), and ALL-LNG experiments. Shade indicates maximum and minimum range among 650 ALLLNG experiments. Fig. S6. The probability density function and cumulative distribution function of annual maximum daily discharge for 22
events derived from event attribution experiments (ALL, ALL-LNG, NATdtr, and NAT-CMIP5). The orange dot line indicates magnitude of 10-year
flood in ALL-LNG experiment. Fig. S7. Difference of average annual precipitation between ALL and mean of NAT (NAT-dtr and NAT-cmip5) experiments for selected flood events (mm/day). Fig. S8. Same for Fig 3
but the difference between ALL and NAT-cmip5 (a) and ALL and NAT-dtr(b). Fig. S9. Difference of average annual temperature (K). Difference between ALL and NAT-cmip5 (a), ALL and NAT-dtr (b), and ALL and NAT(average of NAT-dtr and NAT-cmip5) (c) experiments. Average for 20102013 are shown. Fig. S10. Difference of mean annual 5th largest daily
rainfall. Difference between ALL and NAT-cmip5 (a), ALL and NAT-dtr, andALL and average of NAT-dtr and NAT-cmip5 experiments for 2010-2013
are shown.

Acknowledgements
We thank A.S, A.T., and H.M. for their assistance in our experiments.

Authors’ contributions
H.A, Y.I, H.S., and Y.K. carried out numerical simulations and data analysis. D.Y.
carried out model development and evaluation. Y.H. designed the research
and wrote the paper. The authors read and approved the final manuscript.

Funding
This research was supported by the Environment Research and Technology
Development Fund (JPMEERF20202005) of the Environmental Restoration
and Conservation Agency of Japan, a JSPS Grant-in-Aid for Scientific Research(18H01540), the Integrated Research Program for Advancing Climate Models(TOUGOU) (JPMXD0717935457) from the Ministry of Education, Culture,
Sports, Science and Technology (MEXT), Japan, and MS&AD InterRisk Research & Consulting, Inc.

Availability of data and materials
The flood trend data in sub-catchments used for drawing the figures areavailable for research purpose by requests.

Page 8 of 9

Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Civil Engineering, Shibaura Institute of Technology, 3-7-5
Toyosu, Koto-ku, Tokyo 135-8548, Japan. 2Institute of Industrial Science, The
University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
3
Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052,
Japan. 4National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba,
Ibaraki 305-0053, Japan. 5Corporate Planning Department, MS&AD InterRisk
Research & Consulting, Inc., 2-105, Kanda Awajicho, Chiyoda-ku, Tokyo
101-0063, Japan.
Received: 30 October 2020 Accepted: 24 May 2021

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