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Correction notice Nature Clim. Change http://dx.doi.org/10.1038/nclimate1911 (2013).

Global flood risk under climate change Yukiko Hirabayashi, Roobavannan Mahendran, Sujan Koirala, Lisako Konoshima, Dai Yamazaki, Satoshi Watanabe, Hyungjun Kim and Shinjiro Kanae

In the version of this Supplementary Information file originally published online, the error bars in Supplementary Fig. S1 were incorrectly plotted. A correction to the observational data set requires changes to the text in section S1. The third and fourth sentences in the second paragraph should have read “The annual discharge compared well with observations, with a bias of <50% in 19 out of 32 selected basins. The mean annual maximum daily discharge had a slightly larger spread than the mean annual discharge but was also within an acceptable range, with 17 basins having a bias of <50%”. In the same paragraph, the overall bias for all basins for the annual, annual maximum and 100-year discharges should have been <22%, <38% and <43%. These errors have been corrected in this file 5 July 2013.

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Global flood risk under climate change

Yukiko Hirabayashi, Roobavannan Mahendran, Sujan Koirala, Lisako Konoshima, Dai Yamazaki, Satoshi Watanabe, Hyungjun Kim and Shinjiro Kanae

Supplementary Information

S1. Model validation against observations

For validation of the historical model simulations, discharges in 32 selected river basins were compared to Global Runoff Data Centre observations. Only river basins larger than 150,000 km2 and with at least 20 years (within the period 1970– 2000) of observed daily discharge were selected. Because of the limited availability of such long observational datasets worldwide, 16 of the 32 basins were in North America, 2 were in Europe, 10 were in the former USSR, but only 1 each were in Africa, South America, Asia, and Oceania. Nonetheless, the comparison with observations provided a general overview of the model’s performance globally. As for the simulations, the observation-based discharge corresponding to the 100-year return period was also computed using the Gumbel distribution (see Supplementary Information S2).

A comparison of observation-based and simulated multi-model means of annual discharge, annual maximum daily discharge, and discharge corresponding to the 100-year return period in 20C (20C 100-year flood) is presented in Fig. S1 (note the logarithmic scale). All values are means from the data period when observations were available. The annual discharge compared well with observations, with a bias of < 50% in 19 out of 32 selected basins. The mean annual maximum daily discharge had a slightly larger spread than the mean annual discharge but was also within an acceptable range, with 17 basins having a bias of < 50%. The overall biases for all basins, for multi-model means of annual, annual maximum, and 100-year discharges were < 22%, < 38% and < 43%, respectively. The simulated multi-model mean discharge corresponding to the 100-year return period was slightly under-predicted compared to observations in most of the selected basins. The simulation accuracies for all of these discharge variables were similar to those attained in a similar study for Europe by ref. 12.

The likely sources of bias in the model simulations were the relatively low intensity of precipitation and weaker interannual climate fluctuations in the AOGCMs22, the representation of runoff processes in the AOGCMs, and the anthropogenic regulation of river flow in the observations. Attribution of the effects of these factors to discharge was not performed here. However, the AOGCM simulations were not corrected for bias. Furthermore, the CaMa-Flood river routing model does not consider the effects of reservoir operations and, hence, cannot reproduce anthropogenic flow regulation. Even though biases should exist in precipitation and other climate variables in the AOGCMs and hence in runoff, we assumed that the change in frequency of extremes, such as floods, with a relatively long return period could be illustrated without correcting for bias. In addition, there is no guarantee that an analysis with an artificial bias correction would be better for illustrating the changes in flood frequency associated with climate change.

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Global flood risk under climate changeYukiko Hirabayashi, Roobavannan Mahendran, Sujan Koirala, Lisako Konoshima, Dai Yamazaki, Satoshi Watanabe, Hyungjun Kim and Shinjiro Kanae


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S2. Probability distribution and goodness-of-fit test

Flood events were described as T-year floods. These are defined as river discharges with probability of exceeding 1/T in any given year. In this study, future changes in the frequency of floods corresponding to the magnitude of discharge with a 100-year return period in 20C (1971-2000) were estimated.

The time series of annual maximum daily discharge for both 20C and 21C (2071–2100) from each AOGCM were first arranged in ascending order and then fitted to the Gumbel probability distribution function19. Using the Gumbel distribution, the cumulative distribution function (CDF), F(x), of river discharge (x) can be expressed as

( )x

eF x eξ

α− −

−= (S1) where ξ (location) and α (scale) are the parameters of the Gumbel distribution. The parameters are estimated from the probability-weighted moments20 (PWMs), which were calculated as

100

1

1101

1

1 11

N

iiN

ii

M QN

iM QN N

=

=

=

−= ⋅

−

∑

∑ (S2)

where Qi is the data value, N is the sample size (number of years), and i is the rank. The L-moments were then calculated from the PWMs as

1 100

2 110 1002L ML M M== ⋅ −

(S3)

Finally, using the L-moments, the α and ξ parameters were calculated as,

2

log 2Lα = (S4)

and

1 .Lξ α υ= − (S5) where ν is Euler’s constant (0.57721).

Finally, using the CDF for the Gumbel distribution, the return period corresponding to any given discharge was calculated as

11 ( )

Return PeriodF x

=+

(S6)

The probability plot correlation coefficient (PPCC) for the Gumbel distribution21 was determined to test the goodness of fit of the annual maximum daily discharge of historical simulation with the Gumbel distribution. The PPCC test was first developed by ref. 23. It assumes that the observations fit the distribution well if the correlation coefficient between the ordered observations and the corresponding fitted quantiles is

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close to 1.0. This method has better rejection performances than other tests, such as the Cramer von Mises test and Kolmogorov-Smirnov tests24. A PPCC score of ~1 indicates that the Gumbel distribution hypothesis explains the distribution of the simulated annual maximum daily discharge by the AOGCM. Ref. 21 suggested that the critical point/score of the PPCC test for a sample size of 30 (as in this study) at the 95% level of significance is ~0.96.

The PPCC values were computed for the simulated historical annual maximum daily discharge from all the AOGCMs (Fig. S2). As shown previously3, the majority of grids with lower PPCC values were located in arid regions, where the extreme flood discharge has a much lower importance than extreme drought. It can thus be implied that the Gumbel distribution can approximate the distribution of flood discharge simulations by the model in most global regions. On average, for all the AOGCM simulations, 76 ± 5% of the global land area excluding dry regions (< 0.01 mm day-1 in the annual discharge of a retrospective simulation13) was found to have a PPCC > 0.96. The minimum and maximum values were 68% and 83%, respectively.

S3. Relationships between changes in floods and changes in other hydrological components

Relationships between changes in floods and changes in other hydrological components (e.g., precipitation, annual discharge) are complex3,8. To illustrate possible relationships between changes in floods and changes in other hydrological components, our projected changes in multi-model flood frequency (Fig. 1) were compared with projected multi-model mean changes in annual precipitation, extreme (annual 4th largest) precipitation, annual runoff and annual discharge from the same set of AOGCMs (Fig. S3). In addition, for reference, the magnitude of 100-year flood discharge for the past and for the future, and the difference and the ratio between them, are also shown (Fig. S4) because Fig. 1 only shows changes in return period. In most regions in the central part of Africa, South Asia, Southeast Asia and Northeast Eurasia, frequency of flood, annual precipitation, annual runoff, and annual discharge were all projected to increase in 21C. These regions also showed relatively high consistency among the AOGCMs. Similarly, flood frequency, annual precipitation, annual runoff, and annual discharge were all projected to decrease in many regions in Central America, northern and southern South America, the Mediterranean basin, and the northern Middle East.

In contrast, flood frequency was projected to decrease in several regions including Alaska and Northeast Europe even though annual precipitation, annual runoff, and annual discharge were projected to increase. A potential reason for this may be changes in peak discharge related to snowmelt. Despite increased annual precipitation and resulting annual runoff, the peak discharge in these regions, which occurs during snowmelt, did not increase because a warmer, shorter snow season results in reduced snow accumulation in winter and subsequently reduced snowmelt during the spring peak. Flood frequency, however, was projected to increase in some regions (e.g., regions around Niger) even though annual precipitation and annual runoff were projected to decrease. This may be related to increases in extreme precipitation.

The projections in this study for the regions having high consistency among the 11 AOGCMs tend to correspond well with those of previous studies. For example, increases of flood frequency in South Asia, Southeast Asia, Oceania, and Central and





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South Africa have also been shown by other researchers2,3. However, several inconsistencies exist between our projection and those of previous studies. For example, flood frequency was projected to increase in the Danube by ref. 2 and ref. 14, but our estimation and those of ref. 3 and ref. 12 show an opposite future signal. A similar inconsistency occurs at high latitudes in several Arctic rivers, for which ref. 3 and this study projected flood frequency to decrease whereas ref. 2 and ref. 14

projected to increase. These inconsistencies may indicate the difficulty in projecting flood frequency from a single AOGCM.

Another possible reason for the inconsistency among previous studies may come from the timescale of the discharge data used to define a flood event. When annual maximum monthly discharge was used to calculate the return period of floods instead the annual maximum daily discharge, the flood frequency in several small rivers showed opposite signals because peak discharge in these small rivers may reflect timescales (e.g., daily to hourly) shorter than a month. Even for relatively large river basins, four rivers (the Mackenzie, Columbia, Yenisei and Zambezi) that had an increase signal in our study (Fig. 2b) showed a decrease signal (Mackenzie and Yenisei) or no consistency (Columbia and Zambezi) when annual maximum monthly discharge was used to calculate the return period of floods. The reason for the contradictory signals of all of the rivers except the Zambezi is the change in peak discharge during snowmelt season. In the Mackenzie and Yenisei, the peak in monthly discharge occurs during snowmelt season (approximately April–June). Warmer future temperatures would reduce snow amounts and result in seasonal to monthly discharge in these basins, but changes in peaks of annual maximum daily discharge could be affected by other hydrological mechanisms, such as changes in seasonal precipitation or extreme precipitation. In the Columbia, both annual daily and monthly peak discharges occurred during the snowmelt season (May–June) in 20C, but the timing of occurrence of the daily peak shifts to the winter wet season (December–February) in 21C due to an increase in precipitation. The monthly peak discharge, however, still occurs mainly during snowmelt season in 21C but with lower peaks due to reduced snow accumulation under a warmer climate. In the Zambezi, the change in the return period in 21C is very small (around the 20C 100-year flood discharge) for both daily and monthly peak discharges, possibly leading to opposite signals of increase or decrease between daily and monthly peak discharges.

S4. Uncertainty analysis with bootstrap

In this study, the uncertainty of projection is mainly expressed by the spread (sometimes called as consistency) of results from multiple AOGCMs as shown in Figs 1–3, recognizing 11 time series from 11 AOGCMs as 11 samples. However, even though the 30-year data closely fit the Gumbel distribution, the future projection of flood frequency may be biased by the selection of specific 30-year samples for past and future periods. Here, we also show another uncertainty analysis based on a bootstrap method; the significance of the future change in flood frequency was tested using nonparametric bootstrap samples25. A random number generator was used to sample a 30-year subset (hereafter referred to as a bootstrap subset) of annual maximum daily discharge from the 30-year samples for the past and the future, individually. The future return period of the 20C 100-year flood was then calculated from a bootstrap subset for the past and a bootstrap subset for the future. This procedure was repeated 1000 times, giving 1000 estimates of the return period in 21C





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for the 20C 100-year flood, at each grid cell, for each AOGCM. The 5th and 95th percentiles of the 1000 estimates of the return period were then compared each other, respectively. Fig. S5 shows grid cells where the future return periods of both the 5th and 95th percentiles show the same increase or decrease direction. The calculation was performed for the RCP8.5 scenario, similar to Fig. 1. The results in Fig. S5 indicate that the future change in flood frequency is globally significant for most AOGCMs. However, so far, we did not fully utilize the merit of multiple AOGCMs.

Thus, the multi-model mean probability of increase (or decrease) was calculated at each grid cell (Fig. S6). Firstly, the probability of increase (or decrease) at each grid cell for each AOGCM was computed by counting the number of samples showing increase (or decrease) out of 1000 bootstrap results. Then, the probability for all the AOGCMs was averaged at each grid cell. Although an analytical method might potentially be applied to the computation of uncertainty, we chose a numerical bootstrap method because we expect it can easily deal with multi-GCM outputs in a transparent manner. The overall spatial distribution of this multi-model mean probability of increase (or decrease) in Fig. S6 is similar to the overall spatial distribution of the consistency in Fig. 1b, although they are not perfectly the same. Because the consistency among AOGCMs is easy to understand and because a similar strategy was often adopted in major climate change studies, we mainly used the consistency, in general the spread of AOGCMs, to show the range of uncertainty in this study. But, the potential of using a bootstrap method for evaluating the significance is also introduced in the above.

S5. Results of different base return periods: 10-year floods and 30-year floods

In almost the last part of “Methods: Fitting an extreme distribution”, we describe as follows. “Although the 30 years (1971–2000 and 2071–2100) of discharge data is a relatively short time period for making estimates of events with a return period of 100 year, when the same samples were fitted to the Gumbel distribution, the changes in multi model median return periods (21C–20C) of other return periods of 20C floods (e.g., 10, 30, and 50-year return periods) showed a very similar spatial distribution because of the same parameter values of the Gumbel distribution. We therefore analyzed 100-year floods for easier comparisons with previous studies.”

Here, we show Fig. S7 for 10-year floods and Fig. S8 for 30-year floods. Clearly, the distributions of future changes are similar to those of 100-year floods in Fig. 1. Hence, we selected 100-year as the base return period for Figs 1 and 2 for easier comparisons with previous studies.

S6. Multi-model median return period in 21C for RCP6.0, RCP4.5, and RCP2.6

Because Fig. 1a shows the result for RCP8.5 only, we herewith show a set of figures that is the same as Fig. 1a but for RCP6.0, RCP4.5, and RCP2.6 scenarios (Fig. S9). Also, Fig. S10 shows the same as Fig. S6 but for other RCP scenarios.

S7. Estimation of flood exposure

The population exposed to flooding (hereafter flood exposure) was calculated by overlaying the simulated inundation area onto a gridded population dataset using the





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concept developed by ref. 4. The “Gridded Population of the World (GPW) version 3” with global coverage at 2.5’ resolution26 was used as the population data. The simulated inundation area was downscaled following the very high resolution DEMs (SRTM3 DEM between 60°N and 60°S; GTOPO30 above 60°N), which were used to calculate the subgrid-scale floodplain elevation profile of each 15’ grid cell (see ref. 6 for a detailed explanation). The downscaled inundation area was then aggregated at the 2.5’ resolution to calculate the exposed population at each grid cell by multiplying the population by the fraction of inundation area. The fraction of inundation area for each return period at each 2.5’ grid cell was obtained from the simulated inundation area by a retrospective land surface simulation with CaMa-Flood (a detailed explanation of the retrospective simulation and the reason for its use is given below). A fixed population corresponding to that of 2005 was used for the entire period to highlight changes in flood exposure due to climate change.

Due to the inevitable biases, especially for weak extreme precipitation in the AOGCMs, the absolute (rather than relative) magnitude of runoff and hence absolute magnitude of extreme discharge calculated from the AOGCMs may not be able to replicate a reasonable inundation area under flooding for each return period. An inundation area associated with a specific return period was therefore calculated from a retrospective simulation of a land surface model, MATSIRO27, forced by climate variables obtained from gauges and reanalysis data13 between 1979 and 2010 with the CaMa-Flood river routing. The estimated discharge and other hydrological variables from the retrospective MATSIRO simulation showed reasonable values, as compared to observations13.

The annual maximum daily discharge of the retrospective simulation from 1980 to 2010 was first fitted to the Gumbel distribution. Then, the discharge magnitude corresponding to the return periods from 100 years to 1000 years was calculated for each grid cell. At the same time, the calculated time series of discharge and water level were fitted according to

bH a Q= ⋅ (S7) and the two empirical parameters (a and b) were obtained. Here Q is discharge (m3/s) and H is water depth (m). Using the obtained parameters, a water depth corresponding to the magnitude of discharge with return period n (Hn; n = 100–1000) was calculated. Finally, the inundation area corresponding to each Hn was calculated at each grid. Due to the small differences in the probability change at longer return periods, this calculation was performed for each 10-year bin of return periods (n = 100, 110, …990, 1000). The calculated inundation area for each return period was used to compute the flood inundation area of floods derived from the AOGCMs with CaMa-Flood.

Our estimated inundation area for a 50-year return period (using n = 50 in equation S7) showed a similar spatial distribution to that calculated by ref. 28, who introduced a similar method to calculate inundation area using CaMa-Flood.

We calculated the flood exposure for floods with return periods longer than 100 years, to focus on the potential exposure under relatively large floods. The selection of large flood events avoids overestimation of exposure in rivers with moderate flood mitigation infrastructure, such as reservoirs and levees. In addition, global exposure for the case of 100-year floods is comparable as that of ref. 4 (see above). Hence, we adopted the 100-year return period. Nevertheless, we note that ref. 4 argued that for countries that do have sufficient protection against 1 in 100 year river discharge, this





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assumption entails that some of the modeled flood-prone areas will in reality not be flooded, leading to an overestimation of potential inundation areas

The annual flood exposure in 20C obtained from 11 AOGCMs was 5.6 ± 2.3 million, but that for 21C was 38 ± 11 million (scenario RCP4.5) and 77 ± 22 million (scenario RCP8.5), even with the population fixed at the 2005 level. In the RCP2.6 scenario (RCP6.0 scenario), the annual flood exposure of 8 (5) AOGCMs changes from 5.7 ± 2.4 million (5.8 ± 2.8 million) in 20C to 23 ± 7 million (43 ± 16 million). A significant percentage, more than 80%, of the total population exposed to river flooding is in Asia, which is followed by Africa, Central and South America, and Europe. The above ranges in exposure were derived from the spread of the AOGCMs.

The flood exposure calculated in this study was much smaller than the estimation of ref. 4. They calculated the total global population exposure to the 100-year floods as 805 million in 2010, by multiplying the population density in 2010 by the flood extent for the return period of 100 years created by ref. 29. Unlike our method of estimating the flood exposure for each year, the method of ref. 4 estimated the potential total global population exposed to 100-year floods assuming that the whole globe suffered from 100-year floods simultaneously. Considering the probability of occurrence of 100-year floods (0.01), our calculated flood exposure based on the flood discharge for each year seems reasonable compared to that of ref. 4. When we applied the same calculation for the 2010 population and counted the total global population in the inundation areas derived from the retrospective off-line simulation, we were able to obtain a similar value, 847 million. Note that river discharge and inundation calculations were completely different between the two studies; thus, the difference between 805 million and 847 million is acceptable.

The flood exposure is affected by the future return period calculated from 30-year samples for the past and the future. Hence, the uncertainty propagation of the return period to exposure could be given as the range of exposure among the bootstrap samples. Here, the applied bootstrap test is the same as the one in Supplementary Information S4. Table S3 shows the 21C flood exposure under the RCP8.5 scenario and those of the 5th, 50th and 95th percentile bootstrap samples for each AOGCM. The 50th percentile (median) bootstrap exposure and the originally calculated exposure used in Fig. 3 show similar values. On average, the global 21C flood exposure to the 100-year floods in the RCP8.5 scenario varied between 37 and 163 million at the 5% ~ 95% levels. We note, however, that the 5th percentile results may not occur simultaneously all over the land grid cells. The same is true for the 95th percentile. If the uncertainty represented by the bootstrap occurs randomly over the grid cells of the world, the expected global mean exposure could be the global exposure without bootstrap shown in Fig. 3. Thus, in the main manuscript, the range of exposure is shown as the spread among the AOGCMs without bootstrap.

S8. Limitations

The estimated future projection in this study contains several potential uncertainties. First, because runoff outputs from AOGCMs are inevitably biased, calculated annual maximum daily discharge inherits the bias of the climate models. We used direct output of runoff from the AOGCMs without correcting the bias related to the lack of gauge observations on a global scale. Even though biases in precipitation and other climatic variables should exist in the AOGCMs and hence in runoff, we assumed that





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the changes of frequency of extreme events, such as floods, with a relatively long return period could be illustrated without correcting the bias. Low spatial resolution of AOGCMs may cause limitation in replicating discharges in small river basins, even though its effect can be minimized via integration along the river network in relatively large river basins. Second, the selection of extreme distribution function may be another source of uncertainty. The limited data period is also a source of uncertainty in extreme distribution function analysis (Supplementary Information S4). Third, an inundation module in the river model, CaMa-Flood, makes the peak discharge lower when inundation occurs in the model. Because river width and storage capacity in CaMa-Flood were obtained through an empirical relationship from local topography and volume of discharge, projected inundation and hence flood peak include potential errors6. Also, the discharge modeled by CaMa-Flood may not be realistic in highly regulated rivers, such as those with river engineering, reservoir controls, or land use changes.





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Supplementary References (for references 1 to 21 see main text)

22 IPCC. Climate Change 2007: The physical science basis. Contribution of working

group I to the fourth assessment report of the intergovernmental panel on climate change. (Cambridge Univ. Press, 2007).

23 Filliben, J. J. The probability plot correlation coefficient test for normality. Technometrics 17, 111-114 (1975).

24 Heo, J.-H., Kho, Y. W., Shin, H., Kim, S. & Kim, T. Regression equations of probability plot correlation coefficient test statistics from several probability distributions. J. Hydrol. 355, 1-14, http://dx.doi.org/10.1016/j.jhydrol.2008.01.027 (2008).

25 Efron, B. & Tibshirani, R. An Introduction to the Bootstrap. (Chapman and Hall, 1993).

26 CIESIN, Columbia Univ. & CIAT. Gridded Population of the World, Version 3 (GPWv3). NASA Socioeconomic Data and Applications Center (SEDAC), http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density (2005).

27 Takata, K., Emori, S. & Watanabe, T. Development of the minimal advanced treatments of surface interaction and runoff. Global Planet. Change 38, 209-222 (2003).

28 Pappenberger, F., Dutra, E., Wetterhall, F. & Cloke, H. L. Deriving global flood hazard maps of fluvial floods through a physical model cascade. Hydrol. Earth Syst. Sci., 16, 4143-4156, http://dx.doi.org/10.5194/hess-16-4143-2012 (2012).

29 Herold, C. & Mouton, F. Global flood hazard mapping using statistical peak-flow estimates. Hydrol. Earth Syst. Sci. Discuss. 8, 305-263, http://dx.doi.org/10.5194/hessd-8-305-2011 (2011).





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Supplementary Tables Table S1 | Summary of the AOGCMs selected for this study. The institution and model names were taken from http://cmip-pcmdi.llnl.gov/cmip5/availability.html. Size information was extracted from data headers.

Model Institution No. of grids

RCP scenario availability

(number of AOGCMs is given in brackets)

2.6 (8)

4.5 (11)

6.0 (5)

8.5 (11)

BCC-CSM1.1 Beijing Climate Center, China Meteorological Administration, China

128 × 64 Y Y Y Y

CCCma-CanESM2 Canadian Centre for Climate Modelling and Analysis, Canada 128 × 64 N Y N Y

CMCC-CM Centro Euro-Mediterraneo per I Cambiamenti Climatici, Italy 480 × 240 N Y N Y

CNRM-CM5

Centre National de Recherches Meteorologiques/Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique, France

256 × 128 Y Y N Y

CSIRO-Mk3.6.0

Commonwealth Scientific and Industrial Research Organisation in collaboration with the Queensland Climate Change Centre of Excellence, Australia

192 × 96 Y Y N Y

GFDL-ESM2G Geophysical Fluid Dynamics Laboratory, USA 144 × 90 Y Y Y Y

INM-CM4 Institute for Numerical Mathematics, Russia 180 × 120 N Y N Y

MIROC5

Atmosphere and Ocean Research Institute, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan

256 × 128 Y Y Y Y

MPI-ESM-LR Max Planck Institute for Meteorology (MPI-M), Germany 192 × 96 Y Y N Y

MRI-CGCM3 Meteorological Research Institute, Japan 320 × 160 Y Y Y Y

NCC-NorESM1-M Norwegian Climate Centre, Norway 144 × 96 Y Y Y Y





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Table S2 | Summary of multi-model means of flood exposure for each scenario with the standard deviations among AOGCMs.

Scenario and period (Number in brackets indicates the number of AOGCM used)

Flood exposure with fixed population in 2005 in millions and ratio of 20C to 21C (percentage to global population is given in brackets)

Flood exposure with a future medium population growth scenario16 in millions and ratio of 20C to 21C (percentage to global population is given in brackets)

RCP8.5 (11) 20C 5.6 ± 2.3 (0.09 ± 0.04) 4.2 ± 1.8 (0.09 ± 0.04) 21C 77 ± 22 (1.2 ± 0.35) 106 ± 31 (1.07 ± 0.32) 21C/20C 14 ± 10 25 ± 17

RCP6.0 (8) 20C 5.8 ± 2.8 (0.09 ± 0.04) 4.4 ± 2.2 (0.09 ± 0.05) 21C 43 ± 16 (0.67 ± 0.25) 62 ± 24 (0.63 ± 0.24) 21C/20C 7 ± 6 14 ± 11

RCP4.5 (11) 20C 5.6 ± 2.3 (0.09 ± 0.04) 4.2 ± 1.8 (0.09 ± 0.04) 21C 38 ± 11 (0.60 ± 0.17) 49 ± 15 (0.50 ± 0.15) 21C/20C 7 ± 5 12 ± 8

RCP2.6 (5) 20C 5.7 ± 2.4 (0.09 ± 0.04) 4.3 ± 1.9 (0.09 ± 0.04) 21C 23 ± 7 (0.36 ± 0.12) 30 ± 10 (0.30 ± 0.10) 21C/20C 4 ± 3 7 ± 5

Table S3 | Global flood exposure for 100-year flood in millions. Average values in 21C (2071-2100) for the RCP8.5 scenario. Ranges of flood exposure in the bootstrap subset are also shown. The population is fixed at the level in the year 2005.

Model RCP8.5 5th percentile of bootstrap subset

50th percentile of bootstrap subset

95th percentile of bootstrap subset

BCC-CSM1.1 68.30 149.22 65.78 22.57

CCCma-CanESM2 106.32 204.17 110.81 56.31

CMCC-CM 108.89 210.77 113.74 54.97

CNRM-CM5 51.67 132.92 55.30 18.45

CSIRO-Mk3.6.0 62.44 135.34 63.65 31.76

GFDL-ESM2G 101.87 185.47 103.95 54.89

INM-CM4 41.62 108.48 44.91 16.42

MIROC5 68.46 145.40 70.63 34.16

MPI-ESM-LR 64.80 135.65 68.59 32.23

MRI-CGCM3 76.90 182.78 89.23 36.97

NCC-NorESM1-M 100.77 201.17 104.83 47.01

Multi-model mean 77.46 162.85 81.04 36.89





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Supplementary Figures

Figure S1 | Comparison of observed and simulated multi-model mean river discharges in 20C (1971–2000) at 32 selected river basins: (a) annual discharge, (b) annual maximum daily discharge, and (c) discharges with 100-year return periods. Error bars indicate the maximum and minimum values among the 11 AOGCMs.





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Figure S2 | PPCC test for historical AOGCM simulations.

Figure S3 | Percentage change of multi-model means of future (2071–2100) and past (1971–2000) divided by past mean annual precipitation (a), the 4th largest precipitation event (b), annual runoff (c), and annual discharge (d). The case for the RCP8.5 scenario is shown.





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Figure S4 | Multi-model means of discharge (mm day-1) corresponding to a 100-year return period derived from past (1971-2000) (a) and future (2071-2100) data (b). The difference between them in mm day-1 (c) and in % (d) are also shown. The case for the RCP8.5 scenario is shown.

Figure S5 | Significance test of future changes in the annual maximum daily discharge by a bootstrap method for each AOGCM. Red (blue) indicate grid cells where both the 5th and 95th percentile bootstrap subsets show the same increase (decrease) signal in future flood frequency as that of the AOGCM. The case for the RCP8.5 scenario is shown.





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Figure S6 | Multi-model mean probability of increase (or decrease) in flood frequency by a bootstrap method. Probability for all the AOGCMs was averaged. The case for the RCP8.5 scenario is shown. Percentages in the color bar are selected to be consistent with Fig. 1. For example, 7 samples out of 11 in Fig. 1 is consistent with 6.5/11 ~ 7.5/11 for the application to this figure. Thus, 59% ~ 68% is selected for the same color. Note that sample numbers in Fig. 1 are discrete; thereby, to be consistent with continuous values in this figure, 6.5 and 7.5 are selected, for example.

Figure S7 | Same as Fig. 1 but for 10-year flood.





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Figure S8 | Same as Fig. 1 but for 30-year flood.





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Figure S9 | Same as Fig. 1a but for RCP6.0 (a), RCP4.5 (b) and RCP2.6 scenarios (c).





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Figure S10 | Same as Fig. S6 but for RCP6.0 (a), RCP4.5 (b) and RCP2.6 scenarios (c). Note that the number of AOGCMs analyzed were 8 and 6 for the case of RCP2.6 and RCP6.0 scenarios, respectively.





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Figure S11 | Time series of global flood exposure for the 20C 10-year flood or above (a) and the 20C 100-year flood or above (b). Retrospective simulation (black dotted line), each AOGCM (colored lines) and the ensemble mean of AOGCMs (black thick line) for the RCP8.5 scenario are shown. The population is fixed at the level in the year 2005.





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