Hydrological Research Letters 3, 6 9 (2009)
Published online in J-STAGE (www.jstage.jst.go.jp/browse/HRL). DOI: 10.3178/HRL.3.6

First estimate of the future global population at risk of flooding
Yukiko Hirabayashi1, 2 and Shinjiro Kanae3, 4Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi,
4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
2
Institute of Physical Geography, Frankfurt University,
P.O. Box 111932, 60054 Frankfurt am Main, Germany
3
Department of Mechanical and Environmental Informatics, Tokyo Institute of Technology,
2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan
4
Institute of Industrial Science, The University of Tokyo,
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.

1

Flooding is one of the major risks anticipated toincrease in association with anthropogenically induced
climate change which is likely to intensify the global
water cycle. Currently, 20 to 300 million people per year
are affected by floods that threaten both social security
and sustainable development. This study presents thefirst estimate of future populations at risk of flooding.
Results indicate that in the case of 3°C warming fromthe average of 1980 1999, approximately 300 million
people could be at risk even in years of relatively low
flooding; this number corresponds to the number ofpeople affected in a devastating flood year at present. If
the temperature increase is greater than 3°C, the floodaffected population would likely be even larger. We
suggest that approximately 2°C warming, rather than 3°C warming, should be considered the critical level oftemperature increase.

ticular cell at which river discharge is being computed.
This computation, called “river routing,” was neglectedin that study. Second, judgment of whether future flood
frequency is likely to increase was made at the riverbasin level (e.g., Mississippi, Mekong), and the entire
population of each basin was uniformly classified intoeither the affected or non-affected population category.
Further, Kleinen and Petschel-Held (2007) did not use
actual flood records for comparison or validation of thesimulation. Thus, although their study was a valuableattempt, the estimate of 20% should not be used without
noting these issues, particularly in publically distributed documents.
Given this background, we attempted to calculate
the first estimate of the flood-affected population worldwide in the 21st century within the context of global
warming. We recognize by ourselves that this study
presents a relatively rough estimate; nevertheless webelieve this result is worth to present for seeking our
future.

KEYWORDS critical level; flood; impact of climatechange; millions at risk.

CALCULATION OF FLOOD DISCHARGEINTRODUCTION
Several previous studies have estimated the numberof people who will live in regions of severe water
scarcity in the 21st century (e.g., Alcamo et al., 2003; Oki
and Kanae, 2006). Such estimates are necessary for identifying and attempting to avoid dangerous levels ofclimate change. On the other hand, only one estimate(Kleinen and Petschel-Held, 2007) of the future, global
flood-affected population was introduced in the latest
Intergovernmental Panel on Climate Change (IPCC)
report (4th Assessment Report: AR4). Kleinen andPetschel-Held (2007) estimated that “up to 20% of theworld’s population are likely to be affected by increased
flood hazard by the year 2080 in the course of global
warming.”
However, there are a few caveats that cannot be
overlooked in Kleinen and Petschel-Held’s (2007) study.
First, only monthly surface runoff was used for future
flood estimation where the river-routing process was
not incorporated; this method is not sufficient for evaluating changes in flood risk. To obtain river discharge ata cell in a global gridded distributed model, the surface
runoff derived from a climate model must be aggregated with time-delay over all cells upstream of the par-Global daily river discharge from 1901 2100 was
computed on a global river model with 1-degree horizontal resolution by inputting surface runoff derived
from a relatively high spatial resolution (approximately
1.1-degree) climate model; this was the highest-spatialresolution ocean-atmosphere coupled model presented
in the IPCC AR4. Details on the calculation of river discharge have been described by Hirabayashi et al. (2008).
As the future climate scenario, we used the scenario“A1B” from the IPCC’s Special Report on Emissions
Scenarios (SRES). Compared to the other SRES scenarios, A1B suggests moderate warming. Because of theavailability of climate model outputs with high spatiotemporal resolution for flood calculation, only thismodel and scenario set was chosen.
The river discharge corresponding to 100-year flood
was calculated from the time series of simulated riverdischarge in the 20th century at the same resolution as
described above, and the value was fixed throughout theassessment for the 21th century. Thus, the occurrence
probability of a “100-year flood” (calculated using data
for the 20th century) generally becomes much larger
than 1/100 in the 21st century, as also described indetail by Hirabayashi et al. (2008). The same calculation
procedure was applied for other flood probabilities (e.g.,
50-year flood). Here, river discharge of the so-called X-

Correspondence to: Yukiko Hirabayashi, Interdisciplinary
Graduate School of Medicine and Engineering, University of
Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan,
E-mail: hyukiko@yamanashi.ac.jp ©2009, Japan Society ofHydrology and Water Resources.

Received 22 October 2008
Accepted 21 January 2009

Abstract:―6―

FIRST ESTIMATE OF THE FUTURE GLOBAL POPULATION AT RISK OF FLOODING
year flood is defined as river discharge that has a probability of being exceeded in any given year of 1/X. If X
= 100, then 1/X is 0.01. The successful validation ofsimulated X-year flood was described in Hirabayashi
et al. (2008).

CALCULATION OF THE AFFECTEDPOPULATION
Actual flood-affected population and simulated
flood-affected population
The time series of the actual flood-affected population since the 1990s was taken from the International
Emergency Disasters Database (EMDAT; http://www.
emdat.be), and is shown in Figure 1 as a blue solid line.
Because the number of deaths, amount of economic
damage and other statistics stored in EMDAT can beaffected by many complicated and geographically andhistorically varying conditions, we examined theaffected population, which seems to be a more robust
index for climate change assessments, as the target of
estimation in this study. The actual flood-affected population recorded for the 1980s is several times smaller
than that for the 1990s. We assumed that the smaller
population in the 1980s reflected less information being
delivered to data centers at that time. Thus, our analysis
focused on the 1990s and later. Please note that the
future projections described later rely on the statistics
from 1990s in EMDAT which may still contain underestimation because of less information delivery.
The flood-affected population derived from climate
model outputs was computed for each year, at first, bysumming the population in cells where the maximum
flood-return period in a particular year exceeded 100
years. Population data used in this study is a gridded
product by Bengtsson et al. (2006). Then, the same wascarried out for the return period of 50 years. Both theresults are shown as black lines in Figure 1. Computed

Figure 1. The actual flood-affected population in thepast record (blue line) and the flood-affected population
derived from climate model outputs with temporally
changing population (black lines). Red lines show theflood-affected population derived from climate model
outputs in 1901 2000 (= 100-year difference in the horizontal axis) with the population fixed at the value
recorded in year 2000. Green lines show the floodaffected population derived from climate model outputs
in 2001 2100 with the population fixed at the year 2000
value. The change in global mean surface air temperature (from the average of 1980 1999 as in the IPCC
AR4) is also shown upside down.

population with uncertainty ranging from that of 50year floods to that of 100-year floods is called hereafteras the flood-affected population by 1/50~1/100 floods.
This uncertainty range is equal to the range between
the black solid line and the black dotted line in Figure 1.
The corresponding magnitude of floods with uncertainty range is hereafter called as 1/50~1/100 floods.
There is potential for a future extended study that
employs the calculation of flood-affected population
dependent on the magnitude of flood at each cell.
However, because of the lack of enough data for doingit, and thereby in order to avoid incorporating additional unknown parameters, this study applies thesimple threshold method as described above.
Note that the year 1998 in the climate model, forexample, cannot be intrinsically compared with actual
year 1998 because observed sea surface temperature is
not prescribed in a climate model in which the ocean
and atmosphere are interactively coupled. Thus, Figure
1 should be examined not in terms of the exact values
for each year, but rather in terms of the lowest andhighest values, the variability range, and the trend of
the time series for comparison.
The figure shows similarity between the actual
affected population (blue line) and the affected population derived from climate model outputs (black line)in the period of 1990 2006. It indicates that the overlapof simulated 1/50~1/100 floods and population data
provides a plausible measure of the flood-affected population. However, some peaks in the historical record(blue line), with values around 300 million, are not fully
reproduced in the time series of the affected population
derived from climate model outputs (black line) in 1990
2006. The time series of the actual flood-affected population varies from 20 to 300 million annually, while thetime series of the flood-affected population derived fromclimate model outputs is relatively smooth in 1990
2006. The next subsection further examines this issue.

Potential sources of errors and uncertainty in thesimulated flood-affected population
Because of the chaotic nature of the earth’s climate
system, actual climate variations and hence the population actually affected by flooding in 1990 2006 can beconsidered as a set of limited samples extracted from ahuge number of samples. A single climate simulation for1990 2006 may not have captured the actual variationsof climate in 1990 2006. Therefore, the flood-affected
population was calculated with a fixed population representing the year 2000 population and with variableclimate model outputs in 1901 to 2000 (red lines in
Figure 1). Here, we assumed that anthropogenicallyinduced climate change was negligible in the 20th
century. The variability range of the red lines in Figure
1 (also shown at the left in Figure 2 as vertical bars)
again shows that the overlap of simulated 1/50~1/100
floods and population data provides an acceptablemeasure of the flood-affected population.
Nevertheless, climate model-based high peaks shownin the red lines are seemingly less frequent, like as in theblack lines for 1990 2006, than high peaks of the actual
affected population shown in the blue line. There are a
few potential explanations for the incomplete representation of the affected population by this measure, especially for the less-frequent high peaks.
First, certain regions are more vulnerable to flood.
Further, the occurrence and magnitude of flood disaster
depends on numerous social factors, such as land use
and infrastructure development, in addition to hydrological extremes. However, as the inclusion of various
complicated factors would greatly lengthen the data―7―

Y. HIRABAYASHI AND S. KANAE

Figure 2. The flood-affected population and the change
in global mean surface air temperature in the 21st
century. For reference, the variability range of theactual flood-affected population in the past and the variability range of the flood-affected population withclimate model outputs in 1901 2000 and with the population fixed at the year 2000 value are also shown at theleft.
gathering and calculation processes, we adopted thesimple globally uniform measure, so that we could accomplish the goal of assessing the impact of climatechange on flood change. The uncertainty due to thisfirst potential factor is partially accounted for by ouruse of 1/50 and 1/100 and the range between 1/50~
1/100 in the analysis. Some regions may experience
flooding disasters due to floods of less than 1/50 probability, but the major results and conclusion of thisstudy are not affected by the selection of the upper
criteria (=1/50). For example, the use of 1/40 in place of
1/50 does not change the conclusion. The same is applicable to the change in the selection of the lower
criteria (=1/100).
Second, flooding disasters caused by tropical cyclones and associated storm surge are not necessarily
represented in our analysis framework and thus remaina source of error. In a future extended study, thesesources of errors and uncertainty should be better
treated.

FUTURE PROJECTION
Taking the variability range of the population
affected by 1/50~1/100 floods as a proxy, Figure 1
shows that the future flood-affected population is likely
to increase gradually in the 21st century. While thisincrease is partly due to the total global population
increase, the projected flood-affected population also increases even if the total global population is set at thevalue in year 2000 (green lines in Figure 1). In this particular model and scenario set, floods are predicted to
become devastating around 2060 and beyond. We mean
by devastating that even in those future years with relatively low flooding, more than 300 million people will beaffected, a number that is similar to maximum numbersin the past actual record. The future potential maximum
affected population may be several times larger than
300 million. The timing of “devastating” conditions
roughly corresponds to a 3°C rise in global mean surface
air temperature.
Previous studies (e.g., Allen and Ingram, 2002) have

Figure 3. The actual flood-affected population in thepast record (blue line), the flood-affected population
derived from climate model outputs (solid black line,
1/50 floods only), and the output of a Monte Carlo simulation (shading). Details of the Monte Carlo simulation
are described in the text. Dark shading indicates 95% of
the Monte Carlo results. Light shading shows the top
and bottom of the Monte Carlo results.

reported significantly positive correlations between
global mean surface air temperature change and water
cycle intensification; this relationship has apparently
not depended greatly on model and scenario differences.
Thus, our estimate of temperature change and the floodaffected population (Figure 2) should provide generalvalues that can be applied to other models and scenarios. Visual and subjective inspection of Figure 2
suggests that an approximately 2°C increase of temperature should be considered the “critical level”; theeffects of this rise in temperature do not reach the “devastating” level of the 3°C increase, which would not be
acceptable for global society. By the multi-model projections presented in the IPCC AR4, a 2°C increase roughly
corresponds to 2060~2070 in the A2 scenario and 2060~
2080 in the A1B scenario. The B1 scenario shows an approximately 2°C increase at the end of the 21st century.
However, even with warming less than 2°C, increased
flood risk is still likely to occur, as indicated in Figures
1 and 2. Countermeasures and plans must be developed
to adapt this future risk.
Finally, we investigated the geographical concentration/scatter of the projected floods using a Monte Carlosimulation. The Monte Carlo simulation process was asfollows. First, the number of cells with floods of 1/50 orlarger was counted for each year. Second, the same
number of cells was randomly selected for each year,
and the sum of the population in those cells was calculated. This random procedure was repeated 1000 times
for each year. The outputs of this Monte Carlo simulation can represent various hypothetical cases, such ascases in which floods mostly occur in highly populatedareas, and cases in which floods mostly occur in lesspopulated areas. The result of the Monte Carlo simulation is shown as shading in Figure 3. The interannual
variability range of the black line (i.e., the variability
range of the flood-affected population from climate
model outputs) generally corresponds to the range ofshading (indicating the Monte Carlo results) until approximately 2040. It appears that the interannual variability of affected people, and hence the interannual
variability of the geographical distribution of flood occurrence, is random until approximately 2040. On the―8―

FIRST ESTIMATE OF THE FUTURE GLOBAL POPULATION AT RISK OF FLOODING
other hand, about after 2040, the variability range of theblack line is located at the upper half of, or even beyond
the range of, the Monte Carlo shading. This implies thatthe geographical distribution of flood occurrence inthose years is likely to move away from random and toconcentrate on highly populated areas. Eventually, as
shown in Figure 6 of Hirabayashi et al. (2008), more
frequent floods under climate change are likely to occur
in Asia, Western Europe and tropical Africa and SouthAmerica where population density is relatively high.
This would represent a dire future for people worldwide
as well as in those areas. In this aspect, warming greaterthan 3°C would present an even more dangerous
scenario.

CONCLUSION
This study applied a simple proxy to a particularscenario and model set of future climate change. On thebasis of the results, we suggest that approximately 2°C
warming from the average of 1980 1999 should be considered the critical level. It is possible that human activities, if unchanged, may produce that 2°C warming 50
70 years from now.

ACKNOWLEDGMENTS

Society for the Promotion of Science (JSPS) Postdoctoral
Fellowships for Research Abroad, the Global Environment Research Fund of the Ministry of Environment(S-5), and the Innovation Program of Climate Change
Projection for 21st Century of the Ministry of Education, Culture, Sports, Science, and Technology of Japan.

REFERENCES
Alcamo J, D
oll P, Henrichs T, Kaspar F, Lehner B, Rosch T,
Siebert S. 2003. Development and testing of theWaterGAP 2 global model of water use and availability.
Hydrological Sciences Journal 48(3): 317 337.
Allen MR, Ingram WJ. 2002. Constraints on future changes inclimate and the hydrologic cycle. Nature 419: 224 232.
Bengtsson M, Shen Y, Oki T. 2006. A SRES-based gridded
global population dataset for 1990 2100. Population andEnvironment 28: 113 131.
EMDAT. The OFDA/CRED international disaster database,
University Catholic Louvain-Brussels, Belgium. http://
www.emdat.be. [January, 2009]
Hirabayashi Y, Kanae S, Emori S, Oki T, Kimoto M. 2008.
Global projections of changing risks of floods anddroughts in a changing climate. Hydrological Sciences
Journal 53(4): 754 772.
Kleinen T, Petschel-Held G. 2007. Integrated assessment ofchanges in flooding probabilities due to climate change.
Climatic Change 81: 283 312.
Oki T, Kanae S. 2006. Global hydrological cycles and world
water resources. Science 313: 1068 1072.

This study was partially supported by the Japan―9―