207

The Flood and Drought Risk Assessment due to Global Warming
Kazunori WADA
Center for Disaster Management Informatics Research, Ehime University
3 Bunkyo-cho, Matsuyama-city, Ehime 790-8577, Japane-mail: wada@dmi.ehime-u.ac.jp

Abstract
In order to implement policy measures on practical water management related to floods to combat
the influences of variations in rainfall characteristics arising from global warming, there is a need forquantitative indicators of future regional-based flood risk. In this study, changes in regional-based floodrisk arising from global warming were analyzed based on records of observed precipitation obtained inJapan over the past 100 years. The effectiveness of the risk assessment was then evaluated using the results of projection of regional climate change over the next 50 and 100 years by the RCM20 regional
climate model developed by the Japan Meteorological Agency (JMA) and the Meteorological ResearchInstitute (MRI). Based on that changes in regional-based flood risk were forecasted. This study was
based on a joint study between the National Institute for Land and Infrastructure Management (NILIM)
and JMA/MRI under the Global Warming Research Initiative of the Council for Science andTechnology Policy, Cabinet Office of Japan.
Key words: flooding, global warming, rainfall characteristics, regional climate model

1. Introduction

Frequency of occurrence (%)

A general picture of global warming in the second
half of the 20th century has emerged in recent years
along with understanding of its possible consequences.
The effects of global warming include not only
temperature and sea level rises, but also changes inprecipitation in Japan, encountered in the form of
frequent occurrence of abnormally high or low rainfall.
For example, Yamamoto (2003) investigated the timesof occurrence of the four highest annual maximum
daily precipitations observed at 56 rain gauge stationsin Japan over the past 100 years and showed that all ofthem occurred in or after 1940 (Fig. 1).
The average annual precipitations at 51 locations









2,400
2,200
2,000
1,800
1,600
1,400
1,200
1,000



Fig. 1 Time of occurrence of 100-year annual maximum
daily precipitation. (Yamamoto, 2003)
Global Environmental Research
10(2)/2006: 207-218
printed in Japanin Japan have shown slightly decreasing trends andconsiderable differences between wet years and dry
years. This tendency began to appear in the mid-1960s,
and the frequency of droughts has shown a tendency
to increase (Fig. 2).
Global warming estimates used as inputs in impact
prediction studies conducted thus far, however, haveproblems associated with the temporal or spatial
scales required to accurately predict flow regime

Fig. 2 Changes over time in annual precipitation in Japan.

©2006 AIRIES

208

K. WADAchanges on the scale of river basins in Japan (e.g., theassumption of uniform temperature rises in Japan or
the use of monthly prediction values based on 60 km
grids).
The enormous increases in computing power andadvances in simulation technology have provided theability to enhance time and space scale resolution. In
the Japan area, it is now possible to conduct impact
assessment using 20 km grids and daily prediction
results. In their study, Wada et al. (2004) attempted toconstruct a flood and drought risk assessment processusing results obtained from a regional climate model(MRI-RCM20-Ver. 1) developed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency (JMA), but the study was unable toadvance beyond an analysis mainly of eastern Japan,
where precipitation can be modeled with good accuracy.
MRI-RCM-Ver. 1 then underwent a number of improvements, mainly in precipitation modeling accuracy. In September, 2004, the JMA released computation results for the present climate (1981-2000) andfuture climate (2081-2100) obtained from MRI-RCMVer. 2 (hereafter referred to as “RCM20”). Ishihara
et al. (2004) verified the accuracy of its climate
modeling and showed that the accuracy of precipitation estimates was good and that flood and droughtrisk assessment performed using RCM20 was useful.
Wada et al. (2005a) conducted a study using 100-year
prediction results obtained from RCM20 and showedthat there are areas mainly in northern Japan and on
the Japan Sea side of the eastern part of Japan’s mainisland, Honshu, where annual maximum daily precipitations of 100-year probability will almost double in
100 years. However, in order to assess flood risks using the probable precipitation values thus obtained, it
will be necessary to address challenges such as conducting an even more detailed study based on understanding of the limits of model applicability andobtaining results at intermediate stages (less than 100
years later).
The predictive calculations contain a degree of uncertainty because they are based on many assumptions.
Nevertheless, predicting the impact of climate changes
in Japan on water resources caused by global warming
and assessing flood risks with a clear understanding of
the limits of applicability of the prediction results are
essential for deciding on measures to be taken in the
coming years and useful in making policy decisions.
In view of these, this study was conducted to evaluatechanges in rainfall characteristics due to global warming and the corresponding flood risk.
The basic risk assessment process used in thisstudy is the same as the one reported by Wada et al.
(2005b). In this study, the accuracy of probability
distribution equations is verified for different scales,and estimates are presented which were obtained on
the scale at which the most stable accuracy can beachieved.

2. Precipitation Trends
A recent tendency is often pointed out toward ahigher frequency of extremely heavy localized rains.
This is happening because, as shown in Fig. 3, since1940, annual maximum daily precipitation has gradually increased, with increasingly greater amplitude, atmany locations from the precipitation figures observed in the first half of the 1900s. At such locations,the precipitation probability data compiled in, for example, 1950 could differ considerably from theprecipitation probability data compiled in 2000.
Long-term precipitation trends, therefore, were investigated using long-term observation data provided by
JMA’s meteorological stations.
The data used in the analysis were provided by atotal of 51 JMA meteorological stations. Of those, 40
stations have annual maximum precipitation data for1901 to 2000. Figure 4 and Table 1 show the locationsof these meteorological stations.
First considered was the 100-year probability
maximum daily rainfall, which was used as an indicator of flood risk and as the basis for flood control
planning for many Class A rivers, and its trends were
investigated. To calculate probable precipitation values, Gumbel distributions were applied, and trends inpast changes were determined by the method shown in
Fig. 5. Figure 6 shows trends at different locations.
The 100-year probability maximum daily precipitation
values showed an increasing trend at 28 locations andFig. 3 Changes over time in annual maximum daily
precipitation.

Fig. 4 Locations of meteorological stations used for the analysis.

The Flood and Drought Risk Assessment due to Global Warminga decreasing trend at twelve. Thus, the number oflocations at which an increasing trend was observed
was more than double the number of locations at
which a decreasing trend was observed. Locations
with increasing trends were concentrated in areas tothe north of Aomori, in Hokkaido, from Kanto to
Niigata, from Kansai to Nagoya, and in the middle
and western parts of Kyushu. The rates of increase
were particularly high in the middle and western partsof Kyushu.
Table 1 Meteorological stations used for the analysis.
1 Asahikawa 14 Utsunomiya 27 Hamamatsu 40 Oita*
2 Abashiri
15 Fukui
28 Tokyo
41 Nagasaki
3 Sapporo
16 Takayama* 29 Yokohama
42 Kumamoto
4 Obihiro
17 Matsumoto 30 Sakai
43 Kagoshima
5 Nemuro
18 Maebashi
31 Hamada
44 Miyazaki
6 Suttsu
19 Kumagaya 32 Kyoto
45 Matsuyama
7 Akita
20 Mito33 Hikone
46 Tadotsu
8 Miyako*
21 Tsuruga
34 Shimonoseki 47 Kochi*
9 Yamagata* 22 Gifu
35 Kure48 Tokushima
10 Ishinomaki* 23 Nagoya
36 Kobe49 Naze
11 Fukushima* 24 Iida*
37 Osaka
50 Ishigakijima*
12 Fushiki
25 Kofu
38 Wakayama* 51 Naha*
13 Nagano
26 Tsu39 Fukuoka
Note) Stations marked * don’t have anual maximum precipitation data.

Fig. 5 Method for calculating probable precipitation trends.

□ less than 2 mm/yr, ○ –2 - 1 mm, △ –1 - 0 mm,▲ 0 - 1 mm, ● 1 - 2 mm, ■ more than 2 mm/yr

Fig. 6 Distribution of trends in 100-year
probability maximum daily precipitation. (Wada, 2004)

209

3. Global Warming Model
3.1 Overview of the global warming modelA climate model enables calculation of various climate changes using a physical method and develops
realistic climate change scenarios based on different
greenhouse gas emission scenarios. The type of meteorological phenomena that can be calculated and theaccuracy of modeling differ from model to model. It isbelieved, however, that if these problems and the limits of applicability of different models are taken into
consideration, useful information can be obtained concerning, for example, year-to-year changes, seasonalchanges and extreme meteorological phenomena.
In this study, the regional climate model RCM20of the MRI was used as a model for predicting future
flood risks. RCM20 is a model that uses boundary
conditions derived by downscaling the results obtained by the MRI from its Second Coupled General
Circulation Model (CGCM2). The model is similar to
the JMA’s numerical weather model called “RSM”(regional spectral model) used for daily weather
forecasting. The model is described in detail in Reference (JMA, 2000).
For the downscaling, two-stage nesting (a method
in which a region-specific high- resolution model isgradually operated using the calculation results from alarger-region model as its boundary conditions) is
performed as shown in Fig. 7. In the nesting, noise isgenerated near the lateral boundaries by phasedifferences between the models. The wavenumber
space connection method in which the long-wave
parts of the global model are connected to the shortwave parts of the regional model is used to match thephases of the two models, minimize noise and achievesmooth connection at the boundaries (Sasaki et al.,2002). Table 2 (JMA, 1997; 2000) and Table 3 summarize data on RCM20 and CGCM2, respectively. As
the conditions for future climate calculation (greenhouse gas emission scenarios), the global climate
model uses two (A2 and B2) out of the four narrative
scenarios (A1, A2, B1, B2) described in the IPCC
Special Report on Emissions Scenarios. With CGCM2,1971 values were used as the initial values in calculating the years up to 2100. Calculation using RCM20
was performed by the JMA under a global warming
research initiative by using the calculation results
under the A2 scenario, which basically assumes
regional economic development based on self-relianceand the preservation of local identities, for 1981-2000
and 2081-2100. Predictive calculation for 2031-2050
was performed jointly by the National Institute forLand and Infrastructure Management (NILIM) and the
MRI as part of their joint research. This study
calculated results for a total of 60 years (1981-2000,
2031-2050, 2081-2100).

210

K. WADACGCM2 (resolution: 280 km)

RCM60 (resolution: 60 km)

RCM20 (resolution: 20 km)

Fig. 7 Nesting of RCM20 in CGCM2.

Table 2 Data on MRI-RCM20.
Base model
Lateral boundary
conditions
Horizontal resolution
Number of grids in thehorizontal direction
Total number in thevertical direction
Precipitation process
Radiation process
Vertical diffusion
Surface boundary layer
Surface and undergroundtemperature
Sea surface temperature
Snowfall

RSM (Regional Spectral Model)
Estimates from the Asian region
climate
20 km
129×129
36
Arakawa-Schubert + large-scale
Long-wave radiation + short-wave
Mellor-Yamada level 2 closure model
Monin-Obukhov similarity theory
Forecasting by 4-layer modeling
To be calculated from the global
Diagnostic calculation

Table 3 Data on MRI-CGCM2.

Fig. 8 Model output verification areas (1-12).

Atmosphere model
Base model
Horizontal resolution
Number of grids in thehorizontal direction
Total number in thevertical direction
Precipitation process
Radiation process
Vertical diffusion
Land surface process

GSM9603
280 km
128×6430
Arakawa-Schubert + large-scale
Long-wave radiation + short-wave
Mellor-Yamada level 2 closure model
SiB (plant sphere model)

Ocean model
Horizontal resolution
Total number in verticaldirection
Eddy mixing
Vertical diffusion

2.5°×(2 – 0.5)°
23
Uniform-density surface mixing
Mellor-Yamada level 2 closure model

3.2 Model accuracy
3.2.1 Accuracy of calculated precipitation
Model accuracy was examined for RCM20 using
the results for the computation of the present climate
period (1981-2000). Accuracy was verified at thetwelve CGCM2 grid points (spaced 280 km apart) in
the Japan area shown in Fig. 8 for comparison with
CGCM2 in which RCM20 was nested.
The accuracy of the calculated values of annual
and seasonal precipitation from 1981 to 2000 was
examined. As shown in Figs. 9(a), (b), (c), the RCM20
is generally accurate in reproducing values of annual

and seasonal precipitation except some areas.
The coefficient of correlation between the calculated values and measured values in each verification
area was calculated from (1) the CGCM2 calculation
result, (2) the average of the values obtained from
AMeDAS (Automated Meteorological Data Acquisition System) observation and (3) the average value ofthe calculation results obtained from the regional
climate model included in the grids. Figure 10 shows
the coefficients of correlation calculated for different
verification areas. And Fig. 11 shows the result ofcomparison in terms of the monthly averages during
the 20-year period from 1981 to 2000. As shown in
Fig. 10, RCM20 results show high coefficients ofcorrelation (around 0.9) for many areas, while
CGCM2 results tend to show relatively weak correlations for the Hokkaido and Tohoku regions. As shownin Fig. 11, the calculated amounts of precipitation insummer in Area 1 (eastern Hokkaido) tend to be larger
than the measured values, and, conversely, the calculated amounts of precipitation in summer in Area 8(Kinki) tend to be smaller than the measured values.
In spite of these tendencies, however, the RCM20 isgenerally accurate in reproducing changes in monthly
precipitation. The reason RCM20 outputs are greaterratioThe Flood and Drought Risk Assessment due to Global Warming

Area Number

ratio

Fig. 9(a) Ratio between calculated values and observed
values of annual precipitation. (Sasaki et al., 2000)

Area Number

ratio

Fig. 9(b) Ratio between calculated values and observed
values of winter/spring precipitation.

Area Number

Fig. 9(c) Ratio between calculated values and observed
values of supper and autumn precipitation. (Sasaki
et al., 2000)

upper: CGCM2
lower: RCM20

Fig . 10 Accuracy (coefficient of correlation) ofmonthly precipitation in different verification areas. (Sasaki et al., 2000)

211

than the measured values in eastern Hokkaido is that
there was insufficient distance between the easternpart of Hokkaido and the RCM20 calculation area, so
the calculation results were affected by the boundary
conditions. CGCM2 showed low levels of accuracy inmodeling monthly changes for Areas 1 to 3. The
calculated results for Areas 10 and 12 were considerably smaller than the measured values.
3.2.2 Accuracy of calculated annual maximum
precipitation
To evaluate the applicability of the model to floodrisk evaluation, the accuracy of the calculated valuesof annual maximum daily average values of AMeDAS
observation results for precipitation from 1981 to
2000 was examined. Comparison was made between
the values located in the CGCM2 grids and the average values of the calculation results located in the
RCM20 grids directly over the AMeDAS stations.
The results are shown in Fig. 12. As indicated, theCGCM2 results indicate that the calculated extreme
values are only 10% to 20% of the measured values.
In contrast, the RCM20 results show that although thecalculated values are somewhat greater than the measured values, regional variation is small and the overall
results can be deemed fairly good. The RCM20 isvalid for meteorological disturbances on a scale corresponding to about three times the horizontal resolution,that is, on a scale of the order of about 60 km(Yonetani et al., 2002). It can be inferred, therefore,that phenomena contributing to the occurrence ofheavy rains, such as low pressure systems, fronts andtyphoons, were modeled with fair accuracy. Anotherlikely reason for the high accuracy of the calculated
extreme values is that because the horizontal resolution of the RCM20 (20 km) is by far higher than that(280 km) of the CGCM2, the distribution of precipitation intensities reflecting the ground surface configuration has been reproduced realistically.
3.2.3 Comparison of extreme value distribution
The preceding section showed that RCM20 provides high accuracy in calculating extreme values. In
flood control planning, it is fairly common practice touse probable precipitation values that can be obtainedby fitting the extreme value data for a continuous
period of several decades to the extreme value
distribution. Since RCM20 requires vast amounts ofcomputation time and computer resources, themaximum continuous computation period used for
future prediction is limited to 20 years. In order toenhance the reliability of 100-year precipitation
estimates based on the 20-year data, it is important to
pay attention to not only the degree of agreement of
the extreme values during the period, but alsosimilarity in extreme value distribution patterns.
Comparison was made, therefore, of the distribution
patterns obtained by fitting the annual maximum
values to Gumbel distributions for the 20 years from1981 to 2000.

212

K. WADA(month)(month)(month)(month)(month)(month)(month)(month)(month)(month)(month)(month)Fig. 11 Comparison of monthly precipitation.

The probability density function of a Gumbel distribution can be expressed as,f (X) = exp {−exp [−(X−ξ) / β ]−(X−ξ) / β } / β (1)β = Sx 6 / π
ξ = Xm − γ βupper: CGCM2
lower: RCM20

Fig. 12 Accuracy of calculated annual maximum
daily precipitation.

(2)

(3)
where f (X) is a probability density function for theoccurrence of X; Sx, the standard deviation of X; Xm,
the average of X; γ : Euler’s constant (=0.57721…); ξ,a location parameter indicating the peak location; andβ, a scale parameter indicating the spread of distribution.
Figure 13 shows the results of a comparison ofGumbel distributions of annual maximum daily precipitation at major locations in Japan. While there aresome locations such as Sapporo and Tokyo that show
relatively similar extreme value distribution patterns,
there are other locations such as Osaka and Fukuoka
that show very different distribution patterns. In thecase of Osaka, the values of β in the Gumbel distribu-

The Flood and Drought Risk Assessment due to Global Warming

tion to which the calculation results from RCM20
have been fitted are nearly three times as large, indicating that the annual extreme values obtained from
RCM20 vary more widely from year to year than themeasured values. A similar comparison was made fortwo-day precipitation. The results are shown in Fig. 14.
As indicated, in eastern Japan the β ratio tends to berelatively low, but the probable precipitation values
calculated from these results are excessively large
compared with the probable precipitation values calculated from the measured values.
To solve this problem, we prepared a set of statistical samples by standardizing data obtained frommultiple observation stations and then we analyzed thesamples by the method proposed by Sakurai et al.
(1998). Although there are no clearly defined criteria
for standardizing data obtained from two or more
observation stations, Kunitomo et al. (2003) zoned
Japan on a scale comparable to that of climate zoning
and investigated the probability distribution in eachFig. 13zone. In this study, since the smallest scale of meteorological disturbances that could be reproduced by
RCM20 is about 100 km, standardization was performed for 100 km × 100 km areas, and a comparisonof distribution patterns was made for 42 areas.
As an example, Fig. 15 shows the results of a comparison of extreme value distribution patterns of annual maximum daily precipitation. Extreme value
distributions derived from the model and the measured
values show mostly similar distribution patterns. Thus,
the extreme differences revealed in the locationby-location comparison have now been eliminated.
Therefore, it has been shown that data from different
observation stations can be used for the evaluation ofprobable precipitation values by standardizing the dataon a certain space scale. The processed data were examined to investigate changes in the degree of similarity between the measured values and RCM20 outputs.
The results are shown in Fig. 15.
Comparison with Fig. 15 shows that evaluating onDistribution of measured and calculated (RCM20) extreme values.

㪦㫅㪼㪄㪻㪸㫐㩷㫉㪸㫀㫅㪽㪸㫃㫃㪫㫎㫆㪄㪻㪸㫐㩷㫉㪸㫀㫅㪽㪸㫃㫃

Fig. 14 Distribution of measured and calculated (RCM20)
extreme values.
Comparison of the β ratio among different locations andprecipitation durations.

213

Fig. 15 Similarity of extreme value distributions on
different time scales.

214

K. WADAa certain space scale is an effective approach. In viewof the fact that the minimum scale of meteorologicaldisturbances that can be reproduced by RCM20 isabout 100 km, the 100 km grid averages of probablevalues were calculated using the mesh-specific probable values shown in Fig. 16, and the present values▽ ~ 0.8− 0.8~1.0 + 1.0~1.2• 1.2~1.4 ⋆ 1.4~

Fig. 16(a) Changes in 100-year probability maximum daily precipitation (50 years in
the future).

were compared with the corresponding values of 50
years and 100 years in the future. Figure 16 shows theresults of a comparison of 100-year probability maximum daily precipitation values. As indicated, 100year probability daily precipitation values of 50 years
in the future show national average increases of 10%
to 20%.
Some areas, particularly northern Hokkaido, Kanto,
Hokuriku and the Nansei Islands, show increases ofmore than 20%. After 100 years, the tendencies of 50
years in the future become even more pronounced,
and increases of 40% or more occur in many areas
including Hokkaido, northern Tohoku, Hokuriku andKanto. A likely reason increases in eastern Japan are
more pronounced than in western Japan is that, as
pointed out by Nishimori et al. (2003), high pressure
systems over the Pacific Ocean become stronger andmove northward during global warming as commonly
shown by many general circulation models. Thesetendencies resemble the phenomena observed during
the heavy rains of July 2004 in Niigata and Fukui, so
these results are interesting because they indicate thepossibility that the flood risk of the rivers in these areas is increasing.
As the evaluation scale becomes larger, the amplitude of variation in the region-specific β ratio tends todecrease so that the spread of extreme value distribution of observed values and that of RCM20 outputs
averagely approach each other. In Fig. 17, the β ratio
ranges from 0.8 to 1.5 at around 60 km. As the β ratios
converge toward the value of 1, the decision was madeto evaluate precipitation on a 60 km grid.

4. Influence of Changes in Rainfall Characteristics on Probable Rainfall▽ ~ 0.8− 0.8~1.0 + 1.0~1.2• 1.2~1.4 ⋆ 1.4~

Fig. 16(b) Changes in 100-year probability maximum daily precipitation (100 years in
the future).

Fig. 17 Ratio between observed values and RCM output(daily precipitation).

4.1 Probable rainfall distribution map based on
RCM20 outputs
Figure 18 (a) shows a distribution map of the
100-year probability maximum daily precipitation
under the present climate conditions based on the
RCM20 calculation results. Figure 18 (b) and (c) show
the distributions of the 100-year probability maximum
daily precipitation during 2031-2050 and 2081-2100,
respectively. The present-climate results show areas
with 600 mm or more daily precipitation in thesoutheastern part of Kyushu and the southeastern partof the Kii Peninsula. The calculation results showing
precipitation 50 years in the future show no significant
differences in intensity distribution patterns. The
calculation results do show, however, that the numberof land areas with maximum daily precipitations of
600 mm or more decreases, while in the Hokuriku
region probable rainfall increases. After 100 years, thetendency of probable rainfall to increase in the
Hokuriku and Niigata coastal regions is even stronger,
and there are even areas emerging with maximum
daily precipitations of 500 mm or more.

The Flood and Drought Risk Assessment due to Global Warming

Fig. 18(a) 100-year annual maximum daily precipitation determined from the calculation results for 1981-2000.

215

4.2 Changes in flood risk due to global warming
Section 3 showed that when evaluating probable
precipitation using RCM20, it was important to takeaverages on a certain space scale. In view of the factthat the minimum scale of meteorological disturbances that could be reproduced by RCM20 was about
100 km, the 100 km grid averages of probable values
were calculated using the mesh-specific probable values, and the present values were compared with thecorresponding values of 50 years and 100 years in the
future. Figures 19 (a) and (b) show the results of acomparison of 100-year probability maximum daily
precipitation values. As indicated, 100-year probability daily precipitation values of 50 years in the futureshow national average increases of 10% to 20%. Someareas, particularly northern Hokkaido, Kanto, Hokuriku
and the Nansei Islands, show increases of more than
20%. After 100 years, the tendencies of 50 years in the
future become even more pronounced, and increases

Fig. 18(b) 100-year annual maximum daily precipitation determined from the calculation results for 2031-2050.

Fig. 19(a) The rate of change in 100-year annual
maximum daily precipitation 50 years in
the future.

Fig. 18(c) 100-year annual maximum daily precipitation determined from the calculation results for 2081-2100.

Fig. 19(b) The rate of change in 100-year annual
maximum daily precipitation 100 years
in the future.

K. WADA

of 40% or more occur in many areas including
Hokkaido, northern Tohoku, Hokuriku and Kanto.
A likely reason for the increase in Hokuriku has
been pointed out by Nishimori et al. (2003), high pressure systems over the Pacific Ocean become stronger
and move northward during global warming as commonly shown by many general circulation models.
These tendencies resemble the phenomena observed
during the heavy rains of July 2004 in Niigata andFukui, so these results are interesting because they
indicate the possibility that the flood risk of the rivers
in these areas is increasing.
4.3 Assessment of impact of changes in rainfall
characteristics on water resources
When assessing the impact of climate changes
such as global warming on water resources, it is necessary to evaluate likely changes in streamflow. The
prediction of streamflow requires not only highly accurate estimations of precipitation such as five-day or
daily precipitation estimations but also highly accuratehydrological runoff calculations to determine streamflow from precipitation. Section 3 has shown that annual maximum precipitation given by the global
warming model is accurate enough for use in floodrisk assessment. Accuracy, however, with respect tothe frequency of rainfall events of different intensities
and precipitation hydrographs, for example, has not
yet been fully verified. In addition, the degree of reliability of runoff analysis varies considerably, so it isdifficult at present to calculate drought risk fromstreamflow fluctuations. A realistic approach, therefore, is to estimate the impact of changing precipitation characteristics by using annual or monthly precipitation estimates.
4.3.1 Indicators for drought risk assessment
Drought risk is often discussed by comparing annual precipitation and historical droughts. If calendar
years are arranged in the ascending order of annual
precipitation as in the case of the Tone River basinshown in Table 4, it can be seen that droughts often
occur in wetter-than-normal years. These droughty but
wet years occur when a drought resulting from two or
three months of below-normal precipitation is preceded or followed by many months of above-normal
precipitation (Fig. 20). This means that droughts may
be missed if a time scale shorter than a year is used inconsidering precipitation for the purpose of droughtrisk evaluation. In this study, therefore, seasonal precipitation was used in addition to annual precipitation.
4.3.2 Estimated flood risk in the coming years
Annual precipitation and seasonal precipitation
were calculated from the RCM20 outputs for 19812000, 2031-2050 and 2081-2100, and their ratios were
examined. Figure 21 (a) shows the rates of change
in annual precipitation 50 years in the future, andFig. 21 (b) shows the rates of change in annual precipitation 100 years in the future. As shown, while therate of increase in annual precipitation 50 years in the

Table 4 Correspondence between annual precipitation at
Maebashi and droughts of the Tone River.
Year

Annualprecipitation(mm/year)

1996

815.5

1984
1978
1994
1980
1995
1997
1979
1985
Normal
year
value
1983
2000
1992
1977

816.5
847.5
970.5
1,060.0
1,075.5
1,078.5
1,100.0
1,140.5

Drought or not
Drought: Jan., Feb., March,
Aug., Sept.
Drought: Aug., Sept.
Drought: July to Sept.
Drought: July
Drought: Jan., Feb., March
Drought: July, Aug.

1,162.6—

1,163.0
1,163.0
1,174.0
1,186.0

1987

1,191.5

Drought: June to Aug.

1990

1,201.0

Drought: July, Aug.

1986

1,240.5

1982

1,275.0

1981

1,283.0

2001

1,316.0

1993
1988
1991
1999
1998
1989

1,346.5
1,356.5
1,438.5
1,495.0
1,649.5
1,657.5

Cumulative precipitationin March to June: normal
year level −42%
Cumulative precipitationin May to Aug.: normal
year level −33%

Drought: July

Cumulative precipitationin Dec. to March: normal
year level −66%

Drought: Aug.

Cumulative precipitationin April to July: normal
year level −15%

500

Percentage to normal monthly

216

400
Black Symbol: drought period

300
200

100

0

1

Fig. 20

2

3

4

5

6

7

8

9

10

11

12

Monthly precipitation (percentage to normal year)
at Maebashi and drought months of the Tone River.
(Sasaki et al., 2000)

future is higher than 20% in many regions excluding
the Sea-of-Japan side of the country, the rate ofincrease 100 years in the future is not higher than 20%
in most regions. This means that precipitation will
increase until 50 years from now but will begin to decrease after that. Figures 22 and 23 show the rates ofchange in winter and spring precipitation.
According to the estimates shown in these figures,the increasing trend in precipitation observed 50 years
from now will be somewhat mitigated 100 years fromnow, and winter and spring precipitation will decreaseover wide areas except in Hokkaido. One of thecauses of this is thought to be that, as predicted bymany global climate models in the world including
CGCM2, cold air outbreaks from the Siberian conti-

The Flood and Drought Risk Assessment due to Global Warming

217

Fig. 21(a) Distribution of the ratio of annual
precipitation 50 years from now topresent annual precipitation.

Fig. 22(a) Distribution of the ratio of winterprecipitation 50 years from now topresent winter precipitation.

Fig. 21(b) Distribution of the ratio of annual
precipitation 100 years from now topresent annual precipitation.

Fig. 22(b) Distribution of the ratio of winterprecipitation 100 years from now topresent winter precipitation.

nent will become weaker under the influence of global
warming so that snowfall will decrease.

mum daily rainfall data for the past 100 years. As aresult, the probability of 100-year floods has increased at 28 locations, and flood risks have increased accordingly.
(b)The accuracy of CGCM2 and RCM20 was verified.
While the accuracy of CGCM2 in reproducing
monthly precipitation trends in northern Japan andsouthern Kyushu was inadequate in some respects,
RCM20 was highly accurate in reproducing normal year values of regional monthly precipitation.
(c) The accuracy of modeled extreme values was verified for flood risk evaluation. As a result, annual
maximum daily precipitation values obtained from
CGCM2 with a horizontal resolution of 280 km areabout 20% of the measured values. The regional
climate model RCM20 gives values 20% to 30%
greater than the measured values, but the model is

5. Conclusion
In this study, flood risk trends in different regions
under the influence of global warming were analyzed
on the basis of the results of observation of precipitation performed in Japan for the past 100 years. Global
warming estimates obtained from a climate model
developed by MRI of JMA were used to predict andanalyze flood risk trends in different regions. The
findings of this study were as follows:(a) Trends of 100-year probability maximum daily
precipitation were investigated using precipitation
data for the past 100 years at 40 meteorologicalstations in Japan that had collected annual maxi-

218

K. WADAwould be higher 50 years later throughout thecountry — more than 40% in the northern
Hokkaido, northern Tohoku and Hokuriku regions,indicating increased flood risks.
(f) The drought risk assessment using calculated results from RCM20 indicated the possibility that
under the influence of global warming, winter andspring precipitation in Japan excluding Hokkaido
would decrease, and the drought risk during thesnowmelt flood season would increase in some regions.
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Fig. 23(a) Distribution of the ratio of spring
precipitation 50 years from now topresent winter precipitation.

Fig. 23(b) Distribution of the ratio of spring
precipitation 100 years from now topresent winter precipitation.

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has been ascertained that the model can be used forflood risk evaluation.
(d) In order to calculate probable precipitation for use
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(e) Present and future probable precipitation amounts
were compared using estimates obtained from
RCM20. The comparison revealed that the
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