1

Impact of Climate Change on River Flow Regimes in Japanese Catchments using GCM
Hydrologic Projections and a Distributed Rainfall-Runoff Model
Yasuto TACHIKAWA
Department of Urban and Environmental Engineering, Kyoto University, Kyoto 615-8540, Japan

1

INTRODUCTION

Global warming will give us a serious impact on our life. Frequencies and magnitudes of floods andsedimentation disasters are predicted to increase due to the change of precipitation extremes. Storm surges andinundation disasters at the coastal and low-land areas are likely to increase due to sea level rise and the changesof typhoon magnitudes. The IPCC, the Intergovernmental Panel on Climate Change, 4th assessment reportpublished in 2007 describes increase of global average surface temperatures, increase of global average sea level,
potential increase of frequency of heavy rainfall, and so on based on long term observations. The report also
shows the projections of climate change according to several greenhouse gas emission scenarios and the impactsof climate change on water-related disasters and water resources.
To cope with water-related disasters induced by climate change, both mitigation measures and adaptation
measures are essential. Mitigation measures are efforts to reduce greenhouse gases, and adaptation measures areactions to avoid/mitigate disasters under global warming. Research institutes specializing disasterprevention/mitigation and governmental organizations have responsibility to predict future climate and possible
water-related disasters, and to make planning and implementation for adaptation measures. To establish
adaptation measures, the following are quite important themes to be studied together across the related fields:
1) Projection of the future extremes, especially precipitation and typhoon developments, using advanced
atmospheric models.
2) Impact analyses of climate change on water-related disasters. Analyses of the changes in frequency
and magnitude of water-related disasters such as floods, sedimentations, storm surges, inundations anddroughts using future climate projection data are essential to establish adaptation maesures.
3) Planning and implementation of adaptation measures based on the impact analysis studies of climatechange on water-related disasters.
Projection of future climate is the area of climate and meteorology researchers and its related institutes. In
Japan, MRI (Meteorological Research Institute, Japan Meteorology Agency) and CCSR (Center for Climate
System Research, the University of Tokyo) develop their own GCMs (General Circulation Models), and provide
future climate projections under a greenhouse gases scenario. Impact analyses of climate change on water-related
disasters are the research fields of hydrology, water resources engineering, river engineering, and coastal
engineering.
To effectively promote the research for better projection under climate change and its potential impact onnatural disasters, MEXT, the Ministry of Education, Culture, Sports Science and Technology Japan, launched a
5-year (FY2007-02011) initiative called the Innovative Program of Climate Change Projection for the 21stCentury (KAKUSHIN Program). In the KAKUSHIN Program, MRI and CCSR have a mission to enhance
prediction abilities of their GCMs and provide projection data to the research communities. Disaster Prevention
Research Institute and Engineering Department at Kyoto University etc. have responsibility for the impact
studies on water-related disasters, which will contribute to the planning and implementation of adaptation
measures.
In the KAKUSIN Program, DPRI and Engineering Department of Kyoto University have organized theresearch project entitled “Integrated assessment of climate change impacts on watersheds in a disasterenvironment (PI: Prof. Eiichi Nakakita)”, making up a team with MRI to contribute disaster environment
assessment research. The research topics include the following:

Rainfall extremes: Integrated analysis of rainfall extremes for Japan and over the globe in various
time and space scales; Development of statistical and physical down scaling methods to obtainregional scale precipitation.
Floods and water resources: Development of distributed hydrologic models and application to major
Japanese catchments to assess the impact of climate change on flood and drought risk; Development

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of integrated dam operation strategies to cope with floods and draughts by fully utilizing the existing
structures.
Inundations: Analysis of potential changes of inundation disasters in frequency and magnitude at
Japanese major cities such as Tokyo, Nagoya, Kyoto, Osaka and Fukuoka.
Sedimentations: Analysis of potential changes of sedimentation disasters in frequency and magnitude
at various Japanese mountainous catchments.
Strom surges: Risk assessment of storm surges and tidal waves at Tokyo Bay, Ise Bay, and Osaka Bay
using a stochastic typhoon model; Reliability analysis of bank protection for extreme typhoons
simulated by a regional climate model.
Strong winds: Development of a strong wind disaster assessment model which predicts the damage
ratio of buildings and structures; Estimation of the damage ratios at local authority level over Japanfor possible typhoons under climate change.

In this presentation, floods and water resources are focused, and the recent research results of possible
changes of river discharge regimes under climate change are presented. In section 2, a basic knowledge of thekinematic wave theory for rainfall-runoff simulation is presented. In section 3, a high resolution distributedhydrologic model to assess future hydrologic change in catchment scale is described. Then, in section 4, thelumping method of the distributed hydrologic model to reduce the computational burden is presented. In section
5, the hydrologic model ODIS-LEM (OHyMoS based distributed hydrologic model with lumped element
models) is applied to the Mogami River basin (7,040km2) and the Yoshino River basin (3,750km2) in Japan forriver discharge simulations using high resolution climate change projections with 20km and 1hr space and time
resolution for current climate (1979 to 2004), near future (2015 to 2039) and future climate (2075-2099)
developed by MRI in Japan. The hydrologic model ODIS-LEM simulates water flows based on Digital Elevation
Models, channel networks and land use, which enables long term hydrologic simulations with hourly time
resolution to assess the change of water resources and extreme events in catchment scale.
The Mogami River basin is a typical catchment with heavy snowfall in winter season and the snow melting
is important water resources for rice planting in spring. The Yoshino River basin is located at the south part ofJapan having heavy rainfall caused by rainy front and typhoons in summer season. It is revealed at the Mogami
River basin that the differences of annual and monthly precipitations between present-day and future climate aresmall; but the differences of annual and monthly snowfall between present-day and future climate is quite large
and snowfall in future climate decreases drastically. The simulated river discharge show that monthly peakdischarge which happens from March to May in the present climate shifts to January to March in the futureclimate; and river discharge in April and May in the future climate much decreases. At the Yoshino River basin,
the magnitude and frequency of severe rainfall would increase and peak flood discharge would increase more
highly in the future climate (Tachikawa et al., 2009).

2

KINEMATIC WAVE MODEL

The kinematic wave model was originally developed for river routing, then, it was applied to catchment
rainfall-runoff modeling. Several variations of the kinematic wave model were developed to improve theprediction accuracy of rainfall-runoff from catchments. These models realize various flow mechanisms,
characteristics of soil and land use, and these spatial distributions within a catchment.
2.1 Basic theory
The basic form of the kinematic wave equation isA Q q L ( x, t )
t x

(1)

Q  Am(2)

where A(x, t) is the flow cross-sectional area, Q(x, t) is the flow discharge, qL(x, t) is the lateral inflow discharge
per unit length, and  and m are constants.

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Equation (1) is the continuity equation and is derived from the principle of mass conservation within acontrol volume. Equation (2) is derived from Manning's or Chezy's laws, which are flow resistance laws of open
channel uniform flow. Manning's law is described as:v

1 1/ 2 2 / 3
I R
n

(3)

in which v is the average flow velocity, n is Manning's friction coefficient, R is the hydraulic radius, and I is thefriction slope. By assuming that R can be generally expressed as z1Az2 (z1 and z2 are constants), we can easily
obtain the expression of Equation (2).
2.2 Kinematic wave model for shallow surface water flow on a rectangular plane
A catchment is often modeled as a rectangular plane or a set of rectangular planes, and rainfall-runoff from acatchment is modeled as shallow water flow on a rectangular plane (or rectangular planes). The hydraulic radiusof shallow water flow can be approximated by the water depth h. Then, Equation (3) is written as:v

1 1/ 2 2 / 3
I h
n

(4)

Therefore the discharge per unit width q is expressed as:

q  vh 

1 1/ 2 5 / 3
I h  h mn

(5)

in which   I / n , m = 5/3. This equation is the kinematic wave equation for shallow water flow on arectangular plane. By dividing both sides of Equation (1) by the width of a rectangular plane B, the continuity
equation bocomesh q r  qL / B
t x

(6)

where, r is rainfall intensity. The value of I is usually approximated as the slope gradient of the rectangular plane,
and the value of n is calibrated from observation or determined empirically.
2.3 Kinematic wave model for surface-subsurface saturated flow (Takasao & Shiiba, 1988)
The kinematic wave model described above is applied to infiltration excess overland flow (Hortonian overland
flow). To extend the discharge-depth relationship to represent saturation excess overland flow, Takasao andShiiba (1988) developed a new discharge-depth relationship which includes both surface and subsurface flow
and its interaction. They assumed that hillslope surface was covered by a soil layer with high surface infiltration
capacity and permeability, which is illustrated in Figure 1. In this figure, D is the depth of the soil layer, L is thelength of the hillslope, H is the depth of water flow, and r is the rainfall intensity. Let  be the porosity of thesoil layer and be spatially constant. Then the depth of the effective pore of the soil layer, d, becomes D . Also,
the actual water depth (water volume in height), h, becomes H (0 < H < D) or H-D+ D (H > D). Takasao
and Shiiba (1988) derived the following relation between h and q from Darcy's law and Manning's law:

ah,
q
m
 (h  d )  ah,

0hd
hd

(7)

where a  k sin /  is the velocity of water which flows in the soil layer, k is the hydraulic conductivity, and  sin / n and m=5/3, respectively. The upper relation given in Equation (7) is used for the case where only

4subsurface flow occurs, and the lower relation in Equation (7) is used for the case where saturation excess
overland flow occurs.

Figure 1. Schematic drawing of surface-subsurface flow on a hillslope.

2.4 Kinematic wave model for surface-subsurface saturated-unsaturated flow (Tachikawa et al., 2005)
The kinematic wave models described in 2.2 and 2.3 are basically used to simulate flood runoff which occurs inheavy rainfall events where the gravity force is dominant as a driving force of water flow. However thesedischarge-stage relations can not be used in drier conditions because the capillary force of soil particles can not
be negligible in comparison with the gravity force in such conditions. To realize long term runoff simulations
from dry to humid seasons continuously, Tachikawa et al. (2005) further extended the discharge-stage relation torealize both high flow and low flow simulation introducing an unsaturated flow mechanism in capillary pore ofhillslope soil layer.

q(x)vm d m  va ( h  d m )   ( h  d a ) m

q = g(h)vm d m  va ( h  d m )dadmSurface flow
Non-capillary pore
Saturated flow
Unsaturated flow

D

Soil

qa
qm

vm d m ( h / d m ) F(q(x))
dm

da

h(x)

Figure 2. Schematic drawing of saturated-unsaturated flow and the f, g, and F relationships in section 4.

The discharge depth relation realizes three lateral flow mechanisms including 1) subsurface flow throughcapillary pore; 2) subsurface flow through non-capillary pore; and 3) surface flow on the soil layer. At a slope
segment, when the water depth is lower than the equivalent water depth for unsaturated flow (0 < h < dm), flow issimulated by Darcy law with an unsaturated hydraulic conductivity km. If the water depth exceeds the equivalent
depth for unsaturated flow, the exceeded water flows as saturated subsurface flow that is simulated by Darcy law
with saturated hydraulic conductivity ka. Once the water depth is greater than the soil layer D times effective
porosity  ( d a  D ), the water flows as surface flow, which is simulated by the Manning’s equation. These
three flow processes are represented by the following single set of stage-discharge relationship represented in
Figure 2 and Equation 8) and the continuity equation (Tachikawa et al., 2004; Sayama and McDonnel, 2009):

5

vm d m (h / d m )  ,0  h  dmq  vm d m  va (h  d m ),dm  h  dam
vm d m  va (h  d m )   (h  d a ) , d a h(8)

 m  k m i ,  a  k a i , km  ka /  ,   i / nvm (h / d m )  1 ,q 
 va ,
c* 
h 
m 1
va  m (h  d a ) ,0  h  dm
dm  h  da(9)d a hwhere q is discharge per unit width, h is water depth, i is the slope gradient, km is the saturated hydraulicconductivity of the capillary soil layer, ka is the hydraulic conductivity of the non-capillary soil layer, dm is thedepth of the capillary soil layer, da represents the depths of the capillary and non-capillary soil layers,  is the
exponent constant of unsaturated flow, vm and va are the flow velocities of unsaturated and saturated subsurface
flows, respectively, and n is the Manning’s roughness coefficient which varies as a function of land use type.
Model parameters in the stage-discharge relationship are dm, km, da, ka, n, and  (= ka/ km). The kinematic wave
celerity for each layer is formulated in Equation 9.

3

DISTRIBUTED HYDROLOGIC MODEL WITH KINEMATIC WAVE THEORY

3.1 Open-book type catchment modeling and Kinematic wave flow
The simplest application of the kinematic wave model is to apply the model to an open-book catchment model.
Figure 3 illustrates a schematic representation of an open-book catchment model. As its name implies, an
open-book catchment model consists of two rectangular planes and a stream. Rain water flows on the planes anddrains into the stream. The streamflow drains out of the catchment outlet. The flows on the rectangular planes
and the stream are routed using the kinematic wave model. This is the simplest approximation of catchmenthydrology using a kinematic wave model. A more complex structure of a catchment can be modeled as a cascadeof the open-book catchment model.

Figure 3. Schematic drawing of an open-book catchment modeling.

3.2 Catchment modeling using Digital Elevation Models and Kinematic wave flow
Digital elevation models are available at any catchments with a high resolution enough to describe thetopography of catchments. Figure 4 is an example of a flow direction map derived from a grid-based DEM using
the 8-direction method. After deriving the flow direction map, it is easy to define a rectangular plane formed by
two adjacent grid points as shown in Figure 5. Catchment topography is represented by a set of slope units. For
each slope unit, its area, length and gradient are easily calculated. The kinematic wave model is applied to all
slope units and rainfall is routed according to the flow direction mapping. The outflow discharge from thecatchment was used as the lateral inflow condition of a river routing model.

6

Figure 4. Flow direction mapping using DEM at the Maruyama Basin in Japan.

Figure 5. Schematic drawing of a catchment modelling using DEM. Left: Connection of slope unit.
Right: Calculation of the area, length, gradient of a slope unit.

4

LUMPING OF DISTRIBUTED RAINFALL-RUNOFF MODELS

Distributed runoff models are based on physical hydrological processes. It is a strong point of distributedmodels; however they require much computational time and computer resources. For long time simulation suchas discharge simulation under climate change, a lumping method of distributed models is essential to reduce thecomputational burden. In this section, a lumped kinematic wave runoff model is derived by spatially integrating
the kinematic wave model described in 2.4. The derived lumped model has a physical basis and the parametervalues are determined using topographic variables obtained from DEMs.
The lumping method is based on Ichikawa and Shiiba (2002). The fundamental assumption is that therainfall-runoff process of the catchment system reaches a steady state with spatially uniform rainfall input. From
this assumption, discharge from the catchment is expressed as a product of rainfall intensity and the upslope
contributing area. Then, the relationship between the total amount of stored water in the catchment and theoutflow discharge from the catchment is theoretically derived by integration of spatially distributed equations
assigned to each sub-catchment over the entire catchment.
From the stage-discharge relationship in Equation (8), a general kinematic wave equation can be expressed
as q = g(h), and it is assumed q is possible to integrate with h. Furthermore, it is assumed h is explicitly
expressed by q as the equation of h = f(q). As shown in Figure 6, the discharge per unit width in each rectangularslope is given at the steady state assumption as:

7

r (x)

h

h(x)
h(0)
q(0)

q(x)

s(x)

x

0

L

Figure 6. Relation between flow variables and storage in a slope unit.

xq ( x )  q (0) / w  r  ( x ) dx  r U/w  r x

(10)

0

where is r the rainfall intensity, U is the upslope contributing area, x is the horizontal distance (slope length)from the upstream end of a slope unit, and w is the width of the slope unit. According to Figure 6, the stored
water volume of a slope unit, s, is given as a function of cross-sectional area A, which can be derived by the
integration of h from 0 to x as follows:xxs   A( x ) dx  w h( x ) dx
0

(11)

0

By substituting the variable of integration from x to q using the relationship given by Equation (10), dq = r dx,
and the relationships h = f(q) and q = g(h), then Equation (11) becomes
q( x)

w
w
f ( q ) dq  F ( q ( x ))  F ( q (0)) srr q (0)(12)

As mentioned above, h is assumed to be a function of q. However, f cannot always be solved explicitly since q isalso a function of h. In this case, F cannot be analytically quantified. Figure 2 schematically displays therelationships between f, g, and F. This figure assumes that g is a function of h and that it can be analyticallyintegrated with h; the figure gives a solution for how to obtain F.
According to Figure 2, if the value of q is known from Equation (10), and h is numerically obtained using
Equation (8), then F(q(x)) can be calculated. The equation s to obtain the value of F(q(x)) is as follows:
For an unsaturated flow condition (q(x) less than or equal to qm), qm  Vm d m :h x h x  h F qx   q x h x    g h dh  q x h x    Vm d m   dh dm 
00
and it is reduced toF q  x   q  x h x  

Vm h  1(13)

  1 d m  1

For a saturated flow condition (q(x) less than or equal to qa), qa  Vm d m  Va d a  d m  :h x  h F q x   q x h x    Vm d m   dh   Vm d m  Va h  d m dh
 dm 
0dm
dm

and it is reduced to2Vm d m 0.5Va h 2  d m 2  d m h  d m Vm  Va  1
For a saturated overland flow excess condition (q(x) greater than qa), qa  Vm d m  Va d a  d m  :F q x   q  x h x  

(14)

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

a
 h 
F q x   q x h x    Vm d m   dh   Vm d m  Va h  d m dh
 dm 
dm0dmdh x 

  Vm d m  Va h  d m    h  d m  dhmda

and it is reduced to

Vm d m2
h  d a m 1
 0.5Va h 2  d m 
 1
m 1
 d m h  d m Vm  Va 

F q x   q x h x  

2

(15)

By using Equation (13), (14) and(15), the value of F(q(x)) is calculated and a storage of a slope unit s is obtained
through Equation(12). The stored water at the entire catchment, Sw, can be calculated by summing the s fromeach slope unit as follows:N

S w   si

(16)

i 1

where N is the total number of slope units for the entire catchment. Finally, q at the catchment outlet is linked to
Sw as a function of the effective rainfall intensity and the topographic and physical characteristics of each slope
unit, which are derived from DEMs and land use data.
Figure 7 shows study catchments to analyze the change of flow regimes under global warming in thefollowing section. The basins are subdivided into sub-catchments with about 10km2, and then the lumping
method is applied for each sub-catchment; and finally a distributed model composed of lumped sub-catchment
models are developed. Figure 8 gives the simulated hydrographs by the full distributed and the
distributed-lumped kinematic wave model. The observed discharges and simulated discharges using the full
distributed and the distributed-lumped kinematic wave model shows quite good agreements. By applying the
lumping method, computational time was reduced to half compared to the simulation using the full distributedmodel.

Figure 7. Left: Mogami River basin (7,040km2). Right: Yoshino River basin (3,750km2).

9Figure 8. Comparisons of simulated discharges for Mogami River basin (left) and Yoshino River basin (right).

5

STUDY ON THE IMPACT OF CLIMATE CHANGE ON RIVER FLOW REGIME

The impact of climate change on river discharge regime in the Mogami and Yoshino River basins are analyzed
by feeding future climate projection data into the lumped-distributed hydrologic model shown above. The
projection data consists of hourly precipitation, daily evapotranspiration and daily snow melting of the current
climate (1979-2004), the near future climate (2015-2028), and the future climate (2075-2099), which were
simulated by a high resolution general circulation model developed by the Meteorological Research Institute,Japan Meteorological Agency.
5.1 Projected hydrologic variables by super-high-resolution atmospheric model
The global atmospheric model (hereafter, AGCM20) performs at a triangular truncation 959 with the linear
Gaussian grid (TL959) in the horizontal, which means 1920×960 of grid cells of about 20 km size, and with 60
levels in the vertical (Mizuta et al., 2006; Kitoh and Kusunoki, 2007). For the projection simulation, future SST
was estimated from the previous AOGCM outputs simulated under the SRES A1B scenario. The AGCM20
output data used for this study are mainly related to hydrologic variables. Five variables as shown in Figure
9 were used as input data for the distributed hydrologic model and it was converted into river discharge
data. The PRCSL (rainfall reached to the soil layer, daily) provides effective rainfall amount, and SN2SL(snowmelt into the soil layer, daily) enables the hydrologic model to access the snowmelt amount directly.
For more accurate runoff simulations, the daily PRCSL and SN2SL data were downscaled in hourly
resolution using the time series of the PRECIPI (rainfall + snowfall, hourly) variables, and the EVPSL(evaporation from the soil layer, daily) and TRNSL (transpiration from the soil root zone, daily) values
were used as provided.

Figure 9. Hydrologic projection data provided by GCM20 used for river discharge simulation.

10

Figure 10.

River discharge simulations with GCM20 at Sagoshi (6,500km2) in the Mogami River basin.
Left: Current climate (1979-2004). Right: Future climate (2075-2099).

(a) Monthly mean discharge
Figure 11.

(b) Monthly rainfall and snowfall(c) Monthly snow melting

Change of monthly hydrologic variables at the Mogami River basin.

5.2 Change of river flow regime at the Mogami River basin
Figure 10 shows the simulated river discharge at the Sagoshi station (6,500km2) in the Mogami River basinusing GCM20 projection data for the current climate (1979-2004) and the future climate (2075-2099). It isclearly observed that the peak discharges around April in the current climate disappear in the future climate andthe discharges from January to March increase. Figure 11 (a), (b) and (c) shows the monthly mean discharge
derived from the hourly runoff simulation, monthly rainfall and snow fall simulated by GCM20, and monthly
snow melting simulated by GCM20, respectively.
Figure 11(a) also includes the observed monthly mean discharge during 1979 to 2003. It is found thatmonthly change of the observed discharge and the simulated discharge is quite well matched though thesimulated discharge underestimates observed discharge. The possible reason of the discrepancy is due tooverestimation of the discharge observation or underestimation of the observed precipitation. The monthly
observed discharge in July is about 170mm in depth; for the same period, the monthly catchment mean
precipitation in about 210mm. Evapotranspiration of July is estimated around 100mm. No snowfall in July, thus
it is expected that the observed precipitation would be underestimated due to less observatories in themountainous area. Interesting is the monthly observed precipitation and the simulated precipitation by GCM20
shows quite good agreement in Figure 11(b). This suggests the simulated precipitation would be underestimatedthan the true precipitation value. Despite the differences, a quite interest finding is the monthly change pattern ofobserved and simulated river discharge shows a quite good much. This underpins the possible change of the riverdischarge regimes for the future climate under global warming.
Figure 11(c) shows the change of snowmelting simulated by GCM20. In the future climate, thesnowmelting in March, April and May drastically decreases, while it increases in January and February. As aresult, the river discharge for the future climate is smoothened. In addition, though monthly precipitation does
not change between the current and future climate, the snowfall in winter decreases and rainfall increases. These
are the reasons for the drastic change of river discharge. The change pattern could be observed widely in thenorthern part of Japan. The risk of water resources shortage, especially the risk for agricultural production, andadaptation measures should be analyzed.

11

Figure 12.

River discharge simulations with GCM20 at Iwazu (2,740km2) in the Yoshino River basin.
Left: Current climate (1979-2004). Right: Future climate (2075-2099).

(a)

(b)

(c)

Figure 13. Change of extreme rainfall characteristics at the Yoshino River basin. (a) Annual maximum
catchment mean 24 hours precipitation and peak discharge. (b) Mean and standard variation of theannual maximum 24 hours precipitation. (c) Frequency of one hour precipitation.

5.3 Change of river flow regime at the Yoshino River basin
Figure 12 shows the simulated river discharge at the Iwazu station (2,740km2) in the Yoshino River basin using
GCM20 projection data for the current climate (1979-2004) and the future climate (2075-2099). Based on the
discharge simulation, the annual maximum catchment mean 24 hours precipitation and the peak discharges forthe corresponding events are plotted in Figure 13(a). It is observed that the annual maximum 24 hours
precipitation increases in the future climate compared with the current climate, and the peak discharges becomes
higher. Figure 13(b) shows the mean and standard variation of the annual maximum 24 hours precipitation forthe periods of the current climate, the near future and the end of 21st century, respectively. The mean values ofsimulated precipitation by GCM20 are underestimated than observed values, however it is clearly observed theincrease tendency of the extreme values. Figure 13(c) shows the frequency of one hour precipitation. It is foundthat the number of one hour precipitation having more than 20mm clearly increase in the future climate. The
simulated precipitation is underestimated than observed values, but the increase tendency of extremes of shortterm precipitation is suggested. This tendency could be observed widely in the southern part of Japan. The riskof flood and sedimentation disasters and adaptation measures should be analyzed.

6

SUMARRY

A distributed hydrologic model and its lumping procedure are introduced. By using the lumped-distributedhydrologic model, the impact of climate change on river discharge regimes in the Mogami and Yoshino Riverbasins were presented by feeding future climate projection data into the lumped-distributed hydrologic model
shown above. The projection data we used consists of hourly precipitation, daily evapotranspiration and dailysnow melting of the current climate (1979-2004), the near future climate (2015-2028), and the future climate(2075-2099), which were simulated by a high resolution general circulation model developed by theMeteorological Research Institute, Japan Meteorological Agency.

12

The findings of the hydrologic simulation are as follows: 1) at the Mogami River basin, floods by snow
melting during March to May in the current climate shift to January to March and the flow pattern from January
to May would be smoothened in the future climate; 2) at the Yoshino River basin, the magnitude and frequencyof severe rainfall would increase and peak flood discharge would increase more highly in the future climate.
Further researches include analyzing all Japanese catchments to detect the effect of global warming, to conduct risk
analysis of flood and sedimentation disasters and water resources, to develop feasible adaptation measures such as
the change of dam operation rule, enhance the hydrologic prediction accuracy.

ACKNOWLEDGEMENTS
The river discharge simulation research is done under the framework of the "Integrated assessment of climatechange impacts on watersheds in a disaster environment (PI: Prof. Nakakita, DPRI, Kyoto University)”
supported by KAKUSHIN Program of MEXT. Hydrologic projection data used for the river flow simulations
was provided by the Meteorological Research Institute, Japan.

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