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Global projections of changing risks of floods anddroughts in a changing climate
a

b

c

b

YUKIKO HIRABAYASHI , SHINJIRO KANAE , SEITA EMORI , TAIKAN OKI & MASAHIDEKIMOTOdaInterdisciplinary Graduate School of Medicine and Engineering, University of
Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi, 400-8511, Japan E-mail:bInstitute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku,
Tokyo, 153-8505, Japanc

Atmospheric Environment Division, National Institute for Environmental Studies, 16-2
Onogawa, Tsukuba, Ibaraki, 305-0053, JapandCenter for Climate System Research, The University of Tokyo, 5-1-5, Kashiwanoha,
Kashiwa-shi, Chiba, 277-8568, Japan
Version of record first published: 18 Jan 2010

To cite this article: YUKIKO HIRABAYASHI, SHINJIRO KANAE, SEITA EMORI, TAIKAN OKI & MASAHIDE KIMOTO (2008): Global
projections of changing risks of floods and droughts in a changing climate, Hydrological Sciences Journal, 53:4, 754-772
To link to this article: http://dx.doi.org/10.1623/hysj.53.4.754

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754

Hydrological Sciences–Journal–des Sciences Hydrologiques, 53(4) August 2008

Global projections of changing risks of floods and droughts in achanging climate
YUKIKO HIRABAYASHI1, SHINJIRO KANAE2, SEITA EMORI3,
TAIKAN OKI2 & MASAHIDE KIMOTO4

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1 Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu,
Yamanashi 400-8511, Japanhyukiko@yamanashi.ac.jp
2 Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505, Japan
3 Atmospheric Environment Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba,
Ibaraki 305-0053, Japan
4 Center for Climate System Research, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba 277-8568,
Japan

Abstract Simulated daily discharge derived from a relatively high-resolution (approximately 1.1-degree)
general circulation model was used to investigate future projections of extremes in river discharge underglobal warming. The frequency of floods was projected to increase over many regions, except thoseincluding North America and central to western Eurasia. The drought frequency was projected to increase
globally, while regions such as northern high latitudes, eastern Australia, and eastern Eurasia showed adecrease or no significant changes. Changes in flood and drought are not explained simply by changes inannual precipitation, heavy precipitation, or differences between precipitation and evapotranspiration.
Several regions were projected to have increases in both flood frequency and drought frequency. Suchregions show a decrease in the number of precipitation days, but an increase in days with heavy rain. Several
regions show shifts in the flood season from springtime snowmelt to the summer period of heavy
precipitation.
Key words drought; flood; global warming; river discharge

Projections globales des changements dans les risques de crues et de sécheresses liés auchangement climatique
Résumé Les débits journaliers simulés à partir d’un modèle de circulation générale d’une résolution
relativement haute (approximativement 1.1 degré) ont été utilisés pour étudier les projections futures desextrêmes des débits de rivières dans le contexte du réchauffement global. Une augmentation de la fréquence
des crues est projetée pour de nombreuses régions, à l’exception des régions incluant l’Amérique du Nord etl’Eurasie centrale à occidentale. L’augmentation de la fréquence des sécheresses est projetée globalement,
alors que les régions des hautes latitudes boréales, de l’Australie orientale et de l’Eurasie orientale
présentent une décroissance ou une absence de changement significatif. Les changements pour les crues et
les sécheresses ne s’expliquent pas simplement par des changements dans les précipitations annuelles, lesfortes précipitations ou les différences entre précipitation et évapotranspiration. Les projections de plusieurs
régions présentent des augmentations à la fois dans la fréquence des crues et dans celle des sécheresses. De
telles régions présentent une décroissance dans le nombre de jours de précipitation, mais une augmentation
des jours de forte précipitation. Plusieurs régions présentent des décalages de la saison des crues, de lapériode printanière de fonte nivale vers la période estivale de fortes précipitations.
Mots clefs sécheresse; crue; réchauffement global; débit de rivière

1

INTRODUCTION

Several recent studies have reported trends over the past several decades in the frequency of heavy
to extreme hydrological events. A common and popular topic for such analyses has been extreme
precipitation events (e.g. Kunkel et al., 2003; Kanae et al., 2004), because long-term continuous
gauge observations are available for several decades for many stations around the globe. Future
extreme daily precipitation projections have also been estimated and discussed based on various
climate change simulations (e.g. Emori et al., 2005). However, there have been insufficient studies
of future projections of extremes in global river discharge in climate change simulations. This is
primarily because of the limitation of spatial resolution in general circulation models (GCMs).
Milly et al. (2002) investigated changes in flood extremes using monthly river discharge datafor both gauge observations and GCM simulations. They found that the risk of great floods
increased during the 20th century, mainly over the northern high latitudes, and that this increase
Open for discussion until 1 February 2009

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Global projections of changing risks of floods and droughts in a changing climate755

may continue. Limitations in the spatial resolution of their GCM allowed the analysis of monthly
averaged discharge over 29 river basins larger than 200 000 km2 in area, where the coarse spatial
resolution of the GCM can resolve the basin and where the annual maximum monthly mean flows
are correlated with annual maximum instantaneous flows.
Dai et al. (2004) used temperature and gauge-based precipitation data to estimate the Palmer
drought severity index (Palmer, 1965) for the USA. The method of Dai et al. (2004) is simple andhas great potential for projecting future drought, viewed mainly as a shortage of precipitation.
However, low river flows (streamflow droughts) must also be considered, because many regions
are irrigated with river water. Andreadis et al. (2005) used a land surface model (LSM) driven byhistorical atmospheric data on a 1/2° grid to estimate water budget in the 20th century over the
USA and found that the shortage of storages in soil moisture and in runoff were not always
coincident in space and time. This study implies that low river flows may not be estimated simply
from vertical water and energy balances, such as soil moisture or precipitation minus evapotranspiration. This indicates the importance of global estimation of low river flows.
To date, computer technology has allowed long-term global estimates of hydrologicalcomponents using a GCM containing ocean, atmosphere and land surface sub-modules (hereafterreferred to as “coupled ocean–atmosphere–land” GCM) with relatively high spatial resolution(T106, approximately 1.1 degree) for historical and future climate change scenario forcing. The
estimated river discharge from the high-resolution GCM named the Model for Interdisciplinary
Research on Climate (MIROC) can be used to predict future local hydrological extremes for risk
assessments. Kimoto et al. (2005) showed that long-term global climate simulated by the MIROC
implemented herein shows a more realistic geographical distribution and more reasonable numbersof days with precipitation than do simulations with coarser resolutions using the same model. Thissuggests that our projection of future extremes provides state-of-the-art results based on the highresolution GCM. Because extremes in river discharge are strongly affected by the spatial distribution of the precipitation intensity within a basin, an atmospheric sub-module in a high-resolution
GCM may be indispensable for the accurate estimation of extremes in river discharge.
The aim of this study was to demonstrate future projections of extremes in global riverdischarge, i.e. flood and drought, estimated using the daily modelled discharge by the MIROC.
The global warming simulation with relatively high spatial resolution of the MIROC will provide a
detailed picture of future extremes in river discharge, even over small basins that have not been
distinguished by Milly et al. (2002). It is essential to show projections of changes in extreme
discharge under global warming using daily discharge data, since previous studies discussed future
projections on water-related disasters mainly in terms of changes in monthly means ofprecipitation or in discharge. At present, the MIROC is the only GCM used in the global warming
experiment with relatively high resolution. Therefore, we focus on the results of a single GCM
projection, while future studies with other high-resolution GCMs are expected.
The global validation of flood characteristics in the GCM is attempted using available gauge
observations and a long-term off-line simulation. Because the method for estimating drought uses
a relative threshold value of discharge, the characteristics of droughts in the 20th century
simulation in the GCM cannot be validated directly. The definitions of flood and drought are based
upon severe flood and drought disasters recorded in a disaster database, so that statistically defined“extremes” in the time series of daily river discharge replicate historical disastrous floods anddroughts. Finally, the relationship of future changes in atmospheric forcing to changes in flood anddrought over large river basins is discussed. The discussion and the methodology used to validate
extremes in the GCM discharge here can be applied to other GCMs.
2

DATA

Three long-term daily river discharge data sets are described below: future projections of flood anddrought from daily discharge estimated by the MIROC; daily gauge observations; and daily discharge
estimation by off-line simulation are used to validate the extreme discharge estimated by the MIROC.
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2.1 GCM output (1901–2100) by the MIROCThe GCM output analysed was obtained from a climate change simulation using the MIROC. The
MIROC is a coupled ocean–atmosphere–land GCM developed collaboratively by the Center for
Climate System Research (CCSR) at the University of Tokyo, the National Institute forEnvironmental Studies (NIES, Japan), and the Frontier Research Center for Global Change(FRCGC) at the Japan Agency for Marine-Earth Science and Technology. A complete descriptionof the model is provided by K-1 model developers (2004). Aerosol distribution is prescribed in the
MIROC, not calculated interactively, and the second indirect effect of aerosols is suppressed. The
MIROC has T106 spectral truncation in the horizontal plane (320 × 160 grid for the globe) and56 atmospheric layers. The ocean resolution is 1/4° × 1/6° with 48 levels. Sub-modules for land,sea ice, and five soil layers also use the T106 grid. The MIROC includes the Minimal Advanced
Treatments of Surface Interaction and Runoff (MATSIRO) LSM (Takata et al., 2003). The
MATSIRO model incorporates spatially-distributed soil properties and vegetation coverage, andcomputes vertical fluxes of energy and water for each grid cell. Vegetation processes in
MATSIRO are derived mainly from the Simple Biosphere 2 (SiB2) model (Sellers et al., 1996);
these include the multilayer canopy model by Watanabe (1994), and runoff generation processes
as suggested by Famiglietti & Wood (1991) and Stieglitz et al. (1997), that are based on theoriginal topography-based TOPMODEL (Beven & Kirkby, 1979).
Milly et al. (2005) showed that annual runoff estimated by the MIROC matches observations
well. Runoff at each T106 spectral grid of MIROC was assigned to a 1° grid using the nearestneighbour method; these were then integrated for the river basin using the total runoff integrating
pathways (TRIP) model (Oki & Sud, 1998) with a constant discharge velocity of 0.5 m/s. The
MIROC simulation periods included the retrospective simulations of the 20th century for 1901–
2000 and the SRES A1B (IPCC, 2000) future scenario for 2001–2100. The SRES A1B assumes afuture world of very rapid economic growth and a mix of technological developments and fossil
fuel use. Emori et al. (2005) and Kimoto et al. (2005) have discussed the future projection of
precipitation by this simulation. The global warming experiment of the MIROC showed thehighest temperature transient among the Intergovernmental Panel on Climate Change (IPCC)
Fourth Assessment Report (AR4) climate models due to its large ice-albedo feedback and lower
ocean heat uptake. Because the MIROC shows better agreement with the observed factors on icealbedo feedback and ocean heat uptake than the GCM with lower horizontal resolution, the future
projection of temperature in the MIROC is not considered to be unrealistic (Yokohata et al., 2007).
Since higher temperature rise normally leads to higher acceleration of the water cycle, theprojected changes in extremes in the water cycle in the MIROC are expected to be higher thanthose of other GCMs.
Figure 1 shows future changes with respect to 20th century values of annual precipitation,
annual evapotranspiration, the intensity of the annual fourth largest daily precipitation event(approximately 99th percentile), and the annual mean surface temperature. The MIROC yielded
increased annual precipitation under the future scenario over most northern high latitudes, eastern
Asia, and central to eastern Africa. The region of increased evapotranspiration nearly overlaps theregion of increased annual precipitation. Heavy precipitation increases globally, except for theregions including the western coasts of the USA, parts of Chile and the Arabian Peninsula. Surface
temperature increases globally in the 21st century, most notably over the northern high latitudes.
2.2 Gauge observations compiled by the GRDC
Gauge-based daily discharge observations collected by the Global Runoff Data Center (GRDC)
were used to validate discharge extremes estimated by the MIROC. The analysis used daily
discharge observations at 724 stations of the GRDC that spanned at least 30 of the 60 years from
1941 to 2000, for areas >100 000 km2. When estimating drought indices from the data, Student’s ttest to detect the artificial control of river discharge, e.g. reservoir operation, was applied to theannual mean discharge.
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Fig. 1 Future (2071–2100) changes from present-day (1901–2000) values of: (a) annual precipitation,(b) annual evapotranspiration, (c) intensity of annual fourth largest daily precipitation, and (d) annual
mean surface temperature.

2.3 Off-line hydrological estimation by MATSIRO LSM (1901–2000)
A global off-line simulation for 1901–2000 using the MATSIRO LSM, which is incorporated into
the MIROC model as a land surface sub-module, was used to validate the 20th century simulationby the MIROC at regions lacking long-term discharge observations with which to estimate
extremes.
Hirabayashi et al. (2005) described the design of the off-line simulation in detail. The
MATSIRO model was driven by atmospheric forcing obtained from available observations andseveral statistical and empirical methods, because there were not sufficient atmospheric data todrive the model for relatively longer periods. The monthly observations of precipitation, numberof rainy days, and maximum and minimum temperature provided by the Climate Research Unit
(CRU; Mitchell & Jones, 2005) were used to derive diurnal to sub-diurnal atmospheric forcing
using a statistical weather generator and several empirical equations suggested by Nijssen et al.
(2000). Several parameters required in the methods to obtain the sub-daily atmospheric forcing
data were derived from available global daily data sets for the last several decades of the 20th
century, which were assumed to be constant, and applied over the entire 20th century. Runoff ateach grid was integrated using the TRIP model. The estimation by the off-line simulation showed
good correlations for long-term observed variation in river discharge, snow area and soil moisture.
A GCM simulation using soil moisture and snow estimated from the off-line simulation as aboundary, showed an increase in the predictability of boreal summer precipitation (Kanae et al.,2006), which indirectly indicates that the long-term hydrological components estimated in the offline simulation are reasonable. The ability of the off-line simulation to estimate extremes indischarge is validated in the next section.
To eliminate discharge estimation driven by atmospheric forcing based on poorly gauged
precipitation observations, the discharge data at poorly gauged grid locations in the off-line
simulation were screened out by the density of gauge observations for precipitation of upper basinsof the grid cells. This study used 30 gauges per 106 km2, as suggested by Oki et al. (1999), as thecritical density for the screening procedure. This screening of poorly gauged grid locations
resulted in fewer plots in the off-line simulation than available gauge observations, especially forstations at low latitudes.

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FLOOD

To describe flood events, we analysed 100-year floods, defined as river discharge that has aprobability of being exceeded in any given year of 0.01. The time series of annual maximum daily
discharge was fitted using the moment fitting method for a Gumbel distribution (Gumbel, 1960) at
each grid. The fitting method uses two moment parameters, a and b. The probability distributionfunction (PDF) of annual maximum daily discharge x is defined as:

f x ( x) = a ⋅ e − a ( x −b ) − e

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σx =

ν
a

− a ( x −b )

(1)

π
6a

where fx(x) is the PDF of x, ν is Euler’s constant (= 0.57721), and μx is the average and σx thestandard deviation of x. The moment parameters (a and b) were estimated from the average andstandard deviation of x in each grid as shown in equation (1).
3.1 Flood validation

The MIROC was validated using gauge data for locations where long-term gauge observations
were available. The off-line simulation was also used to validate the MIROC for regions where
gauge observations were not available. First, a statistical test was used to check whether the annual
maximum daily discharge in the MIROC was well approximated by the Gumbel distribution. The
Gumbel distribution hypothesis using the moment fitting method for the annual maximum daily
discharge in MIROC was examined using the probability plot correlation coefficient (PPCC;
Vogel, 1986). When the PPCC is close to 1.0, the Gumbel distribution hypothesis explains the
distribution of the maximum daily discharge estimated by MIROC. Vogel (1986) indicated that thecritical point of the PPCC at the 90–95% significance level is approx. 0.98 when the size of thetest sample is 100, and approx. 0.96 when the size of the test sample is 30. Figure 2 shows the
PPCCs estimated from the annual maximum daily discharge by MIROC for 100 years from 1901
to 2000 and for 30 sample years from 1971 to 2000 at each grid. In both cases, the PPCCs were
low in dry regions such as northern Africa, the Arabian Peninsula and Australia. However, forthese dry regions, disasters due to high extreme discharge may be less important than otherdisasters such as drought. The PPCCs for 1971–2000 (Fig. 2(b)) were lower than those for 1901–
2000 (Fig. 2(a)), mainly because of the shorter sample time. The averaged PPCCs over the 50
largest river basins globally for the two samples showed that 32 basins exceeded the significancelevel (0.98) for the 100-year sample and 43 basins exceeded the significance level (0.96) for the
30-year sample. These results indicate that the Gumbel distribution hypothesis using the moment
method can be applied to these large river basins. The PPCC was used to screen out regions forwhich the Gumbel distribution hypothesis could not be applied by MIROC to project flood.
In a second step, the distributions of the return periods estimated by the method in the off-line
simulation were compared to those in the observations. In order to investigate whether thestatistically estimated extremes in discharge represent actual disastrous flood, the return periods ofthe annual maximum daily discharge when severe flood disasters occur are examined. The
disastrous flood information was extracted from the statistical disaster archives for large flooding
events of the Dartmouth Flood Observatory (Brakenridge et al., 2003) and the Emergency
Disasters Data Base (EM-DAT) of the Office of US Foreign Disaster Assistance / Centre for
Research on Epidemiology of Disasters International Disaster Database (available online at
http://www.em-dat.net). Because these disaster databases registered floods where a certain level ofdamage to goods and human lives were reported, the floods that are obtained statistically from thedaily discharge data sets are sometimes not included in the databases, especially over times andregions where the population is low, or for regions where damage due to disasters is not well
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Global projections of changing risks of floods and droughts in a changing climate

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1901–2000(b)
1971–2000

Fig. 2 Probability plot correlation coefficient (PPCC; 0-1, dimensionless) for Gumbel distribution
hypothesis on the annual maximum daily discharge by MIROC for sample years: (a) 1901–2000 and(b) 1971–2000.

(a)

(b)

Fig. 3 Return period (year) of the annual maximum daily discharge over the USA in 1993 estimatedfrom: (a) gauge observation and (b) off-line simulation.

summarized and reported. Nevertheless, most severe floods registered in the database can bedetected from the daily discharge data. In Fig. 3, the estimated return periods of the annual
maximum daily discharge are compared with those from available gauge stations over the USA in
1993, when severe flooding occurred along the Mississippi River. The dots represent stations withrelatively long-term gauge observations, in which the Gumbel approximation to estimate the return
period of 100-year floods could be applied. The parameters of the Gumbel distribution were
estimated from all available observation periods and the return period of the annual maximum
daily discharge in 1993 was estimated using these parameters. The Gumbel parameters were
estimated separately for observations and the off-line simulation by using the same maximum
available periods for the two data sets. Both the observations and off-line simulation showed
severe floods with a long return period (30-years or longer) in the middle to upper Mississippi

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Fig. 4 Return period (year) of the annual maximum daily discharge of seven flood disasters
summarized in Table 1.
Table 1 Return periods (year) of observed discharge (GRDC) and off-line simulation on severe flood
disasters.
1
2
3
4
5
6
7

Year
1940
1940
1975
1977
1983
1993
1993

River (GRDC station)
Donau (6742500)
Elbe (6340150)
Donau (6742500)
Brahmaputra (2151100)
St Lawrence (4243150)
Mississippi (4125801)
Rhein (6435060)

Return period GRDC
21.79
28.90
3.03
2.06
2.48
46.66
27.00

Return period off-line
18.40
33.07
1.12
3.99
2.00
158.501.24

River basin. Because the Dartmouth and EM-DAT database also show that the Mississippi flood in
1993 occurred mainly over the middle to upper basin area, it is evident that the off-line simulation
reproduced the occurrence and location of such a historical flood well. In Fig. 4 the off-line
simulation and observation of return periods are compared for seven selected flood disasters(Fig. 4, Table 1). All of the stations had more than 30 years of data, with PPCC > 0.9. If there weremore than two stations along a flooded river, the station with the largest upper basin area wasselected. Because the return period estimated by the Gumbel distribution changes exponentially,
the values were plotted on a logarithmic scale. The return periods in the off-line simulation
correspond to the observations, except for the case of the Rhein River flood. One of the main
reasons for this is that the annual variation in the maximum daily discharge in the off-line
simulation for the Rhein River was poorly correlated with the observations. An artificial control byreservoir operations, which was not implemented in the off-line simulation, may be responsible forthis poor correlation. Discharge in the off-line simulation shows lower correlation with observations over cold regions (Hirabayashi et al., 2005), and it may also cause the poor correlation for
the Rhein River. The reason for this may be inadequate distinction of solid precipitation, low
quality of precipitation forcing data due to low gauge density in mountainous regions, or theprocess of river freezing which is not included in the model. The overestimation of the return
period in the off-line simulation at stations 3, 4 and 6 is reasonable, because our model does notinclude a process of decrease in discharge when floodwaters overflow the river channel andinundate a basin area, such as frequently occurs in severe flood disasters.
The land-surface sub-module in the MIROC, i.e. the off-line simulation, captures the pastdisastrous flood events well. However, it is difficult to compare the simulated floods in the
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MIROC with historical floods, because the MIROC is a coupled ocean–atmosphere–land modeland its climate may be statistically similar to observed data, but not necessarily the same as thoseobserved each day as estimated from a weather prediction model. If significant changes in floods
have already appeared in recent decades compared to the past several decades of observation, it iseasy to simply check whether the same trends can be reproduced in the MIROC. For example,
Zhang et al. (2007) investigated the results of 14 GCMs and showed that the detection andattribution of human influence on the observed changes in zonal mean of annual mean terrestrial
precipitation during the 20th century is robust. However, the number of available discharge
observations required to investigate such significant changes in floods is limited. If the stations
that spanned at least 60 of the 80 years from 1901 to 1980 and at least 15 of the 20 years from1981 to 2000, are assumed to be sufficiently long to investigate a trend in annual maximum daily
discharge between 1901–1980 and 1981–2000, 48 stations are available from the 724 available
gauges. However, statistical jumps due to artificial controls are detected by the Student’s t test in
all but four of these stations. The result of the statistical test is reasonable, because most of thestations providing observations longer than several decades are commonly located near largedams. The trend analysis for the four stations using the Monte Carlo approach (explained in detailin the next sub-section) showed that three stations show the same results for observation and
MIROC; significant trends are seen for two stations and negative trend is detected for one station.
For one station, the data in the MIROC show positive trend but no significant trend is seen in theobservations. This inconsistency may be because the total area of the upper basin of this station isas small as the area size of one grid of the atmospheric sub-model, which leads to relatively highmodel sensitivity of discharge to changes in precipitation.
3.2 Projecting future flood events

The projection of flood events by the MIROC under the A1B global warming scenario is described
here. The return period was estimated of floods with future discharge equivalent to that of a 100year return period in the 20th century (1901–2000) simulation. Before estimating the change in the
return period, the statistical significance of changes in the annual maximum daily discharge under
the global warming simulation was tested using a Monte Carlo approach:(a) 1000 simulations, each performed using 30 random samples of the annual maximum daily
discharge from the 20th century simulation, were averaged, and the 1000 average values were
sorted in descending order; and(b) the annual maximum daily discharges in the projected future climate for each of 2001–2030
and 2071–2100 were averaged.
When the value of (b) is larger than the 50th sample of (a), the annual maximum daily discharge isincreased in the future climate with 95% significance. The annual maximum daily discharge isdecreased with 95% significance when the value of (b) is smaller than the 950th sample of (a).
Figure 5 shows the comparison of statistical significance of changes in the annual maximum daily
discharge between the first and last 30-year periods of the 21st century (i.e. 2001–2030 and 2071–
2100) estimated by the MIROC under the SRES A1B scenario with that of the 20th century(1901–2000). In 2001–2030, only a few regions showed widespread significant changes in theannual maximum daily discharge (Fig. 5(a)), whereas in 2071–2100, most regions showedsignificant changes (Fig. 5(b)). The increase over regions at low latitudes, such as central Africa
and India, is prominent in 2071–2100. The annual maximum daily discharge also increased
significantly over central to northern South America and Australia. In contrast, the annual
maximum daily discharge decreased over northern North America, western Eurasia except in the
Iberian Peninsula, and northern and southern Africa, as well as parts of South America.
The return period of 100-year floods with respect to the 20th century (1901–2000) simulation(hereafter referred to as “20C 100-year flood”) changed under the future climate as predicted by
the MIROC (Fig. 6). Regions with low PPCC (i.e. <0.9 during 1971–2000) were screened out.
Regions that had no significant change in the annual maximum daily discharge (Fig. 5) were also
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2001–2030(b)
2071–2100

Fig. 5 Statistical significance of changes in the annual maximum daily discharge of: (a) the first and(b) the last 30 years of the 21st century, by the MIROC against those in the 20th century (1901–2000).

(a)
2001–2030(b)
2071–2100

Fig. 6 Projected return period (year) of the 20C 100-year floods in the present-day (1901–2000)
simulation during: (a) 2001–2030 and (b) 2071–2100 estimated by the MIROC.
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screened out. A return period of less than 30 years, corresponding to that for a 20C 100-year floodin the retrospective climate, was projected in many low-latitude regions and in eastern Eurasia. In
contrast, the frequencies of the extreme discharge exceeding the amount of the 20C 100-year flood
decreased over central and northern North America, eastern Europe and western Russia, eventhough the annual precipitation and fourth largest precipitation event per year both increased inthese areas (e.g. see Fig. 1).
Figure 7 shows the month of annual maximum daily discharge in the 20th century simulation(1901–2000), in the future scenario simulation (2071–2100), and the difference between them.
Northern high latitudes showed a 1-month shift in the maximum discharge at most grid points.
Maximum discharges in these regions occurred in spring or early summer, and their shift occurredearlier, possibly as a result of earlier snowmelt under the warmer future climate. This earlier peakat northern high latitudes causes a lower peak discharge and lower frequencies of 20C 100-year
flood. Shifts in the peak by 1 month or more were evident in regions such as Eastern Europe,
central and northeastern Northern America, and northern China to Mongolia. In the retrospective
climate, these three regions have peak discharges during late winter to spring (February–April);the future shift moves these peak discharges by 3 or 4 months to July and August. Such a shift in(a) 1971–2000(b) 2071–2100(c) b minus a

Fig. 7 Month of annual maximum daily discharge during: (a) 1901–2000, (b) 2071–2100 and (c) the
difference between them estimated by the MIROC.

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Yukiko Hirabayashi et al.

peak discharge likely reflects a change in the flood season from springtime snowmelt to summertime heavy precipitation. The shift in flood season and the change in the type of flood may requirechanges in river management within these regions to protect against flood disasters in unexpectedseasons.

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4

DROUGHT

Drought events were defined by the number of days when daily discharge was lower than the tenth
percentile of all river discharge data from the 20th century simulation (Q90). The Q90 was estimatedfor each calendar day (1–365) using the variable threshold level method (Hisdal et al., 2001). The
minimum thresholds for drought conditions were determined by ranking discharge data fromlargest to smallest for each individual day of the year. The 21-day samples from a moving window
before and after the calendar day were used to increase the number of samples. For example,
21 days, from 1 January to 21 January, were used to obtain the Q90 on 11 January for 1901–2000(a total of 2100 samples).
Before estimating drought in the GRDC, a simple statistical test for annual mean discharge for
the GRDC data was used, because observed low flow can be affected by artificial discharge
controls such as large reservoirs. The time series of annual average gauge observations weredivided into two, and the two samples were tested using a one-sample Student’s t test to detectstatistical jumps in the time-series. This method can be used to detect the construction of largedams, such as the Grand Coulee Dam in 1942 on the Colorado River. Jumps in annual discharge
occurred at 352 stations from the 724 GRDC gauges. This method detected no statistical jumps
over regions including Europe, eastern Canada, or Australia. Three possible reasons could explain
the lack of jumps in these areas. First, most large dams were constructed before the first year ofavailable data. Second, gauge data in these countries were processed using a gap-filling method.
Third, the range of annual discharge has large natural variation. The first and the second reasons
may explain the lack of a statistical jump for Europe, whereas the third reason may account for thelack of a statistical jump in Australia. Stations with data jumps were not used in the estimation ofdrought.
4.1 Drought validation

As in the case of flood validation, annual drought days in the MIROC that were used to define
drought were also checked with the observations and off-line simulation. The number of annual
drought days in the off-line simulation was first validated against the number of observations byexamining the correlation coefficients (Fig. 8). The observed annual drought days were adequately
reproduced globally by the off-line simulation, suggesting that the land surface sub-module of the
MIROC has the potential to estimate drought frequency in the discharge record. As explained
above, the MIROC is a coupled ocean–atmosphere–land model and its climate may be statisticallysimilar to observed patterns, but not necessarily similar at daily time intervals. Consequently, thecharacteristics of drought days, rather than year-to-year correlation coefficients should be
evaluated. However, it is difficult to validate the characteristics of drought days estimated by the
MIROC because drought is defined by the relative Q90 from long-term data. Therefore, this study
assumed that the MIROC model with the MATSIRO land surface sub-module estimated therelative change in projected future drought conditions, because relative changes in the Q90 between
the 20th century and future simulations in the MIROC can be projected.
As in the case of flood, a drought day was evaluated whether it reflected historical disastrous
drought or not. Estimated annual drought days in the off-line simulation were compared with thosefrom the gauge observations for times for which disastrous droughts were recorded in the EMDAT disaster database. Because this study did not use the observed discharge data that had
artificial discharge controls for the drought validation, data availability was less than that for theflood validation, especially for developed countries. In contrast, available data from developing

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Fig. 8 Correlation coefficient of the number of annual drought days between gauge observation and offline simulation.

Fig. 9 Number of the annual drought days estimated from gauge observation (left) and off-line
simulation (right).

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Yukiko Hirabayashi et al.

countries are very limited for the off-line simulation because of the poor quality of precipitation
input. As a result, the number of stations available for the drought validation was much more
limited than for the flood validation, and the number of available stations sometimes differed
between the off-line simulation and the observations. Figure 9 shows examples of drought
estimates when severe droughts were recorded in the EM-DAT. Because drought is a spatiallydistributed disaster and detailed information with which to specify the location (e.g. the rivername) is not implemented in the EM-DAT database, the geographic distribution of drought days
rather than a comparison of specific rivers was examined. In 1976, both the observations and offline simulations show more than 50 drought days in Europe. Drought spells longer than 50 days
also occurred in the regions of the Sahel in 1983, along the Mekong River in 1987, and on thenorthwestern coasts and the southeastern North America in 1988. Annual drought spells longer
than several tens of days seem to have some relation to the occurrence of disastrous drought.

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4.2 Projecting future drought

The statistical significance of changes in annual drought days was determined using a Monte Carloapproach (Fig. 10). Similar to the analysis for projecting change in future flood, 1000 multiple
Monte Carlo simulations were conducted using 30 random samples of annual drought days fromthe 20th century (1901–2000) simulation. The averages of the 1000 sets of the 30 samples werecompared with the average of annual drought days in the future climates (2001–2030 and 2071–
2100). Annual drought days were calculated based on Q90 discharge defined by daily discharge
data for 1901–2000 in every case.
Changes in drought are indicated by the ratio of annual drought days, e.g. the averaged annual
drought days in the future divided by the averaged annual drought days in the 20th century
simulation (Fig. 11). Regions that had no significant change in annual drought days (Fig. 10) werescreened out. A significant increase in drought frequency is projected as an increase in the number(a)
2001–2030(b)
2071–2100

Fig. 10 Statistical significance of changes in annual drought days of: (a) the first and (b) the last
30 years of the 21st century against those in the 20th century (1901–2000) estimated by MIROC.

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767

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2001–2030(b)
2071–2100

Fig. 11 Ratio of average number of annual drought days of: (a) 2001–2030 and (b) 2071–2100 topresent-day (1901–2000) simulation.

of drought days during the last 30 years of the 21st century over North and South America, central
and southern Africa, the Middle East, southern Asia from Indochina to southern China, and central
and western Australia. Some regions, including Eastern Europe to central Eurasia, inland China,
and northern North America, show increases in drought, but also show increases in annual
precipitation (Fig. 1). In contrast, wide areas over eastern Russia show a decrease in drought days.
These areas are the regions that have large increases in annual precipitation and marked increases
in flood flows (e.g. Figs 1 and 6).
5

REASONS FOR CHANGES IN EXTREMES IN LARGE RIVER BASINSGlobal analyses of flood and drought have suggested that the investigation of relationships
between changes in flood and drought frequencies and changes in climate forcing are complex. In
this section, a detailed analysis for global large river basins is described to illustrate possible
relationships between changes in flood and drought and changes in other hydrologicalcomponents.
Table 2 summarizes future changes predicted by MIROC simulations, over 30 large river
basins, as defined by the 1° × 1° TRIP model, shown in Fig. 12. Changes in flood, drought andannual discharge were estimated at the basin outlets; other values were averaged over wholebasins. Rivers in Fig. 12 were classified as high-latitude (nos 1–6), mid-latitude (7–20) and lowlatitude (21–30) rivers. The shaded cells in Table 2 indicate increases in flood frequency(expressed as a decrease in the return period defined for a 20C 100-year flood from 1901–2000)
and increases in drought frequency (i.e. the ratio of averaged drought days in 2071–2000 to that in
1901–2000 is >1.0). Negative differences in other components between the 21st century and 20th
century are also shaded. The value of the PPCC for 1971–2000 is also shown in Table 2. All
except two basins had PPCC values higher than 0.95. We also determined the time period at which
Copyright © 2008 IAHS Press

PPCC for1971–2000

Return period Ratio ofof 20C 100- droughtyear flood (a) days (b)Diff. in Diff. in
Diff. in
Diff. in rain Diff. inannual evapotrans- annual fourth days (c)P – E (c)P (c)piration (c) largest P (c)Diff. in Diff. in
Diff. in Period ofannual peak month T (c)
flood
Q (c)
of Q (c)change(year)(d)Period ofdroughtchange(-)(mm/d)(mm/d)(mm/d)(mm/d)(%)(month)

(K)

1
2
3
4
5
6

Yukon
MackenzieOb
Yenisey
Lena
Kolyma

0.98
0.98
0.97
0.97
0.98
0.97

351.9
341.0
162.3
32.7
5.6
6.4

1.44
2.72
1.91
0.20
0.02
0.22

0.66
0.41
0.29
0.41
0.35
0.52

0.24
0.27
0.23
0.22
0.14
0.14

5.22
3.62
3.04
4.06
4.51
5.09

22.89
16.21
11.78
18.89
13.26
21.35

0.41
0.14
0.06
0.19
0.22
0.38

25.7
16.3
16.6
39.0
40.5
36.0

0.01
–0.96
–1.10
–0.63
–0.41
–1.00

6.76
6.66
6.81
6.58
6.18
6.34

2060
2060
2045
2015
2015
2015

－
2075
－
－
2075
－

7
8
9
10
11
12
13
14
15
16
17
18
19
20

Rhein
Donau
DnieprVolga
Amur
Columbia
NelsonSt Lawrence
Mississippi
Colorado
Yellow
Yangtze
MurrayZambezi

0.97
0.98
0.98
0.96
0.98
0.97
0.98
0.98
0.97
0.96
0.97
0.97
0.94
0.97

328.7
292.2
2453.5
671.0
19.9
3.0
1.33 × 105
463.1
149.6
1.82 × 109
2.3
44.6
19.0
223.7

1.89
2.92
3.37
3.40
0.63
3.40
3.34
2.60
3.93
4.19
0.00
0.69
0.80
3.94

0.14
0.08
0.19
0.21
0.36
0.36
0.14
0.16
–0.16
–0.17
0.65
0.45
–0.01
–0.19

0.23
0.20
0.31
0.31
0.21
0.34
0.21
0.34
0.00
0.00
0.39
0.23
–0.07
–0.01

4.03
3.78
3.15
2.84
6.03
5.11
1.90
4.89
2.21
0.32
7.67
8.04
3.11
3.87

–5.97
–7.91
2.04
4.65
5.14
–4.31
5.28
–5.58
–9.42
–11.04
13.62
0.33
–7.79
–13.48

–0.09
–0.12
–0.12
–0.09
0.15
0.02
–0.06
–0.18
–0.16
–0.17
0.26
0.22
0.06
–0.18

–6.9
–6.5
–31.2
–14.9
29.3
–1.0
–17.8
–15.2
–22.0
–42.9
46.7
15.0
83.0
–21.4

0.35
2.50
–0.55
–1.13
0.59
–2.72
–0.89
0.68
0.27
1.42
0.14
–0.57
0.44
0.45

4.38
4.84
5.16
6.34
5.81
5.35
6.57
6.14
6.14
6.43
5.13
5.05
4.35
5.07

2085
2045
2045
2045
2030
2015
2045
2045
2060
2045
2015
2030
2015
2045

2075
2015
2015
2030
－
2015
2030
2030
2060
2030
2045
－
－
2060

0.95
11.9
2.59
0.22
0.12
11.56
–6.90
0.10
7.6
–1.40
5.10
2030
2015
Niger
0.97
89.7
2.80
0.03
0.27
10.84
–21.99
–0.24
–1.3
0.24
4.51
2075
－
Congo
0.95
15.7
0.07
0.25
0.04
7.44
–6.15
0.21
19.7
0.08
4.55
2030
－
Nile
0.93
3647.2
4.00
–0.22
–0.15
–0.58
–12.63
–0.07
–53.8
0.10
5.15
2045
2030
Euphrates
0.96
104.9
4.05
0.09
0.12
1.99
–2.87
–0.02
–12.5
0.28
6.02
2060
2015
Indus
0.97
26.1
1.17
0.23
–0.02
10.64
–8.19
0.25
17.6
0.40
5.07
2060
－
Ganges
0.97
3.8
0.15
1.91
0.30
26.53
6.41
1.61
32.4
–0.30
5.81
2015
2045
Brahmaputra
0.97
104.7
1.20
0.29
0.17
10.94
–14.23
0.12
10.6
–1.17
4.48
－
－
Mekong0.97
5367.0
3.82
–0.79
–0.01
5.92
–30.80
–0.79
–29.4
–0.99
5.18
2045
2015
Orinoco
238.2
0.98
3.31
–0.18
–0.10
10.64
–22.80
–0.08
–1.9
–0.99
5.44
2085
2030
Amazon(a)
Future (2071–2100) return period of 20th century 100-year discharge defined by annual maximum discharge during 1901–2000.
(b)
Ratio between drought days of 2071–2100 and drought days of 1901–2000.
(c)
Difference between values in 2071–2100 and 1901–2000 of: annual precipitation (P), annual evapotranspiration, annual fourth largest precipitation, number of annual rain days,
annual precipitation minus annual evapotranspiration, annual river discharge (Q), month of annual maximum daily discharge, and annual mean surface temperature.
The last two columns indicate time period when the projecting future changes in flood and drought are first detected.

21
22
23
24
25
26
27
28
29
30

Yukiko Hirabayashi et al.

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Table 2 Future changes projected by MIROC simulation, over 30 large river basins.

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Fig. 12 Selected 30 river basins.

the projected future changes in floods and droughts are first visible by indicating middle years of
30-year samples (e.g. 2015 indicates the period from 2001 to 2030) when the changes compared to
the values from 1901 to 2000 first become statistically significant.
Many basins experience an increase in heavy precipitation (the fourth heaviest annual daily
precipitation) and temperature in the future scenario (Table 2). High-latitude river basins also
experience increase in annual precipitation, evapotranspiration, precipitation minus evapotranspiration (P – E), and number of precipitation days in the future. Precipitation days are projected todecrease over low-latitude river basins, except for the Brahmaputra River.
The relationship between annual discharge and frequency of 20C 100-year flood varies; 11 of
the 13 rivers that show an increase in frequency of 20C 100-year flood also show an increase inannual discharge; the exceptions are the Columbia and Congo rivers (Table 2). Because return
period of a 20C 100-year flood on the Columbia River varied widely among basin grid points (notshown), the return period estimated at the basin outlet in the Table 2 does not represent the return
period of the basin. Thus, the Columbia River is considered an exception. The Congo River is also
treated as an exception because the change in the return period is relatively small (89.7 years), as is
the projected change in annual discharge (–1.3%). Rivers with an increase in frequencies of 20C
100-year flood also exhibit an increase in annual discharge, but rivers that show an increase inannual discharge do not necessarily show an increase in frequencies of 20C 100-year flood. Fourrivers (i.e. the Yukon, Mackenzie, Ob and Mekong) are predicted to have a decrease in frequencies
of 20C 100-year flood, even though the annual discharge is estimated to increase. Decreases in thefrequencies of 20C 100-year flood on the Yukon, Mackenzie and Ob rivers may result from earlier
and smaller maximum discharge at northern high latitudes because of decreased snow accumulation under the warming climate. In summary, river basins at mid- to low-latitudes in which
frequencies of 20C 100-year flood increase also show an increase in the annual discharge;
however, an increase in annual discharge does not always a signal of an increase in floods at somehigh-latitude rivers, where the annual maximum daily discharge occurs during snowmelt.
There was a relationship between changes in drought frequency and changes in P – E.
Changes in annual precipitation had a smaller effect than changes in P – E on changes in drought.
The Rhein, Donau, Dniepr, and Volga rivers in Europe; the Nelson and St Lawrence rivers innorthern North America; the Congo River; and the Indus River were all projected to have increases
in drought frequency, despite increases in annual precipitation. The increase in evapotranspiration
during 2071–2100 in these basins is much higher than the increase in precipitation (see Fig. 1),
perhaps because of the increase in surface temperature of 4 to 6°C during the same period.
Changes in drought over a river basin should therefore be predicted based on changes in P – E,
rather than changes in the annual amount of precipitation only.

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River basins characterized by decreases in P – E in the 21st century show increases in drought,
but an increase in drought does not necessarily result in a decrease in P – E (Table 2). Drought
increased in six basins (i.e. the Yukon, Mackenzie, Ob, Columbia, Niger and Ganges basins) whenthe values of both P – E and annual discharge were projected to increase in the 21st century. Three ofthe six rivers are located at high-latitude (i.e. the Yukon, Mackenzie, and Ob rivers) and have only
small increases in the proportion of drought days. As is the case of flood, because the estimateddrought frequency on the Columbia River varied widely among the basin grids (not shown), and thechanges in P – E between the 21st and 20th centuries are small (+0.02 mm/d), the Columbia River
was again treated as an exception. The other two rivers (i.e. the Niger and Ganges) show increases inboth flood and drought, as also does the Congo River. These three river basins have a large increasein heavy precipitation (more than 10 mm/d) and a large decrease in precipitation days (10 days ormore). Such a change in the precipitation pattern may cause an increase in drought days even though
precipitation, discharge, and P – E increase.
In addition, an increase or decrease in flood frequency appeared before the middle of the 21st
century in 21 of the 30 basins (Table 2). In contrast, 14 basins showed a significant change indrought frequency in the first half of the 21st century; 12 of these 14 basins showed a significant
increase in drought days. This suggests that significant future increases in drought appear
relatively early under the projected climate, compared to significant decreases in drought.
6

CONCLUSIONS

Climate change is expected to accelerate global hydrological cycles. River discharge will increaseon a global scale because of the increased precipitation and reduced evapotranspiration. Studies ofextremes in hydrology at short time scales are considered indispensable (Oki & Kanae, 2006).
Previous studies have typically focused on averages of river discharge, and few studies show
global projections of extremes in river discharge. This paper considered the frequencies of future
floods and droughts as estimated by a relatively high-spatial-resolution coupled ocean–
atmosphere–land GCM (MIROC) and presents the global validation of extreme discharge in the
MIROC using 724 long-term daily gauge observations and a long-term off-line simulation by theland surface sub-model (MATSIRO). The results indicate that the MIROC has the potential toreproduce historical disastrous floods and droughts. Discharges that have a return period longer
than 30 years, and annual drought days of more than 50 days, were found to be related to several
past historical disasters, even though their values were roughly extracted from limited case studies
and further study is needed.
Floods and droughts projected for the 21st century show significant and large changes fromthose in the 20th century (i.e. from 1901 to 2000). The results indicate an increase in the frequencyof floods in many regions of the globe, except regions including North America and central andwestern Eurasia. Increases in the number of drought days during the last 30 years of the 21st
century are significant over regions such as North and South America, central and southern Africa,
the Middle East, central to western Australia, and Indochina to southern China.
Analyses over large river basins reveal the complexity of possible changes in future floods
and droughts. Changes in floods and droughts cannot be explained by changes in annual precipitation, annual evapotranspiration, or the difference between annual precipitation and annual evapotranspiration. Although rivers with an increase in floods also exhibit increase in annual discharge,
rivers with an increase in the annual discharge do not necessarily show an increase in floods inregions where the annual maximum discharge occurs during snowmelt. In contrast, river basinscharacterized by decreases in P – E in the 21st century show increases in drought, but an increase
in drought does not necessarily result in a decrease in P – E. The changes in precipitation patterns
may cause an increase in the number of drought days even though precipitation, discharge, and P –E increase. The comparison of flood and drought frequencies estimated from daily discharge istherefore important in predicting future discharge extremes. Several regions showed changes in theflood season. Many regions in the northern high latitudes show an earlier flood season mainly
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771

because of earlier snowmelt under the warming climate. Some regions show changes in the flood
season from the snowmelt season to the summer because of heavy precipitation.
The GCM in this paper is the only model with relatively higher spatial resolution at present,
which is comparable with daily discharge of gauge observation and global off-line simulations.
Given the non-linear uncertainty of GCMs, multi-model ensemble projections and analyses should
be indispensable. Because the IPCC data distribution centre (http://www.ipcc-data.org/) do notprovide daily discharge data, one possible way to estimate multi-model GCM projections in
extreme river discharge is to run a land surface model using atmospheric forcing by the multiGCMs. However, because of the current limitation of other GCMs in terms of spatial resolution,
direct use of discharge data by those models is not applicable to resolve extremes of daily riverdischarge, since the difference in meteorological features between higher (T106, approximately
1.1 degree) and middle (T42, 2.8-degree) GCMs is large (Kimoto et al., 2005). Moreover, themethod using off-line simulation loses land–atmosphere feedback information. It is also concerning that the inconsistency of land surface condition between a land surface model used for the
off-line simulation and land surface sub-modules of GCMs may affect the result. Future global
warming experiments by other GCMs with higher spatial resolution are therefore expected in the
future to obtain multi-model projection of daily discharge extremes.
The projected increases of frequencies in flood and drought in many regions globally indicatethat the global water cycle in MIROC is intensified due to radiative effects of anthropogenic
changes in atmospheric composition. Future increases or decreases in flood frequency wereapparent before the middle of the 21st century in 21 of the 30 basins selected. Twelve of thesebasins showed a significant increase in drought frequency in the early 21st century. The results
indicate that we have a potential to detect or attribute the changes in discharge extremes in the near
future, or the change may be already occurring and detectable from historical discharge, as wasdone for average precipitation by Zhang et al. (2007). However, trend analysis of the maximum
daily discharge is only available at four stations, because the number of available long-term daily
observations of river discharge is much smaller than that of precipitation, and because the artificial
water usage severely effects long-term variation of the observed discharge. The intensification indischarge extremes is therefore not easily detectable from observations or current GCMs without
considering anthropogenic water usage. Future development of GCMs to include human control
on discharge and intensive long-term daily discharge observations will help in further analyses.
Acknowledgements We thank the GRDC for providing daily discharge observations. This
research was supported by the JSPS Postdoctoral Fellowships for Research Abroad, the Global
Environment Research Fund of the Ministry of Environment (B-12 and 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.
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Received 1 June 2007; accepted 28 April 2008

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