International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

Flood prediction for large scale catchment with atmospheric model
outputTACHIKAWA YASUTO
Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto 611-0011, Japane-mail: tatikawa@rdp.dpri.kyoto-u.ac.jp
SHRESTHA ROSHAN K.
Graduate School of Civil Engineering, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japane-mail: roshan@rdp.dpri.kyoto-u.ac.jp,
TAKARA KAORU
Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto 611-0011, Japane-mail: takara@rdp.dpri.kyoto-u.ac.jp

Abstract A distributed hydrological model linked with an atmospheric simulation model is anindispensable tool to predict floods, droughts and future water resources for densely populated but
less gauged river basins. In this study, a development of a macro-scale grid based distributedhydrological model and its application to the Huaihe River basin in China (132,350km2) withatmospheric model outputs are presented. The model simulates river discharge properly by including
a channel routing model with a digitized channel network data set. The simulated river discharges
show better accuracy depending on the grid resolution of input forcing data. Here, the effect ofchannel routing to estimate river discharge for a large river basin and the effect of different input
resolutions for river discharge simulations are studied. Three different grid resolution input dataobtained from HUBEX-IOP EEWB data and GAME reanalysis product are used to simulate riverdischarges and they are compared against the observed one. As a result, GAME reanalysis
1.25-degree resolution data is found quite satisfactory for larger basins; HUBEX-IOP EEWB
10-minute resolution data is better for small catchments; while GAME reanalysis 2.5-degree
resolution data did not give good results. Simulated results by using spatially interpolated data arerather worse than using original data. Through this experience, data and information needs for flood
forecasting are discussed.
Key word flood forecasting; macro scale hydrological modeling; GAME reanalysis product

INTRODUCTION
Hydrologists often practice discharge simulation not only to know the flow pattern but also to use theflow data information in several decision-making and strategy formulation processes. However,correct prediction of river discharge is still a challenging job. Macro scale hydrological models aretools to predict the river discharge. However their results largely depend on the quality of input dataand grid resolution. Very high-resolution data may not be necessary especially on the verge ofcatchment size as it adds cost. Using minimum data to obtain better result is always highly prioritized.
Suitability of grid resolution to a certain range of catchment size may be a matter of discussion. In
this study, effects of grid resolution of forcing data in river discharge simulation are investigated.
368

International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

Also the performance of the channel routing model developed here is examined, because channelrouting has a dominant role on forming a hydrograph especially for large basins.
Basin and sub-basins of the Huaihe River in China are taken as the case to study. Grid precipitation
and actual evapotranspiration data are referred from (1) HUBEX IOP EEWB data with 5-minutespatial and 1 hour temporal resolution, (2) GAME reanalysis product (Version 1.1) with 1.25-degree
spatial and 6 hour temporal resolution and (3) GAME reanalysis product (Version 1.1) with
2.5-degree spatial and 6 hour temporal resolution for the period from May 1, 1998 to August 31,
1998. The effects of data interpolation in distributed hydrologic simulation are also presented. A brief
description of the model and findings for the information needs for flood forecasting of large
catchments are presented.

MODEL DESCRIPTION
A macro-scale grid based distributed hydrological model (Tachikawa et al., 2000a, 2000b, 2001) isused here. It simulates water movement following automated procedures like basin partitioning,
sub-basin hydrological modeling and linking sub-basin models together to produce a total runoff
simulation model with the use of OHyMoS, Object-oriented Hydrological Modeling System(Ichikawa et al., 2000). Main features of the model are as follows:(a) The model is able to consider exact location and linkage of river segments even within a grid-cellon physical basis to give better flow routing, which has dominant effect on hydrograph.
(b) Dividing watershed basin into grid-cells according to an atmospheric model, this model
incorporates the atmospheric model output into hydrological model effectively. For each grid-cell,a runoff process and a flow routing process are modeled separately before constructing a total
runoff simulation system.
In addition, this model considers the channel width in accordance to the catchment coverage.
River network arrangement
Figure 1 shows the channel network data for the Huaihe River above Bengbu, the outlet of the study
area (132,350km2). The channel network data is generated from the 1:500,000 scale TPC, Tactical
Pilotage Chart, series and the GLOBE, Global Land One-km Base Elevation, data sets.

35

34

33

32

31

111

112

113

114

115

116

117

118

Figure 1 Channel network for the Huaihe River. Solid lines show the channel network of the HuaiheRiver for the upper part of Bengbu.

369

International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

Figure 2 Division of river network

This model rearranges the river network before simulation. 10-minute grid-cell frames divide theexisting river network (Figure 1) into piecewise sub-networks and assigns new identity defining an
independent sub-network within a grid-cell. Figure 2 displays the process schematically. Existing
network data intersects grid-cell frames at points A, B, C, D and E, which is rearranged by dividing at
A, B, C, D and E to produce sub-networks within individual grid-cells.

Runoff process model (RPM)
A simple hydrological model (Tachikawa et al., 2001) based on Xinanjiang model (Zhao et al., 1992)
is used for runoff simulation within a grid-cell, which assesses hydrological processes in view ofspatial distribution of soil water capacity. The runoff appears only after soil water storage exceeds its
capacity to hold. The runoff process model has three model parameters to be identified byhydrological data sets.

Flow routing model (FRM)
A lumped stream kinematic wave model (Shiiba et al., 1996) computes river flow within eachgrid-cell. It assumes river discharge varies linearly along each river segment within a grid-cell at eachtime step instead of computing discharge for each computational cross-section. If we assume that ashape of cross sections of river segments is a wide rectangle, model parameters of the flow routing
model are Manning's roughness coefficient and channel width.

Constructing total simulation process
A total hydrological simulation model (Figure 3) is constructed using functions of OhyMoS (Ichikawa
et al., 2000) to connect element models with Data sending port (DSP) and Data receiving port (DRP).
For each grid-cell, the runoff process model (RPM) receives grid-mean input data (precipitation andevapotranspiration) and calcurates grid-discharge. The flow routing model (FRM) receives theinformation through its DRP from the RPM and adjacent upper element models (EMs) to compute
river discharge to the lower EMs (see Figure 4).

370

International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

Catchment

Area (km2)

Suiping

2093

Wangjiba

29844

Bengbu132350hment area of test basins.

Figure 3 Schematic drawing of total model.

Figure 4 Structure of element model.

F
Figure 5 Location of discharge stations.

371

Tabl
e 1
Catc

International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

Before exchanging information between EMs, the model automatically accumulates the calculated
values and transfers that to lower EMs only on request. The flow path of river network arrangement
defines the upper and lower EMs. They are linked in accordance with the network connection
relationship to transfer the computed outputs. Total runoff simulation model is thus a combination of
EMs.
BASIN AND SUB-BASINSThe Huaihe River Basin and two sub-basins are taken for this study. Three points namely Suiping,
Wangjiba and Bengbu (see Figure 5) are adopted to compare the model output against the observed
discharge. The largest catchment is Bengbu and smallest is Suiping (see Table 1).
INPUT DATA DESCRIPTION
The input hydrological data is grid-mean precipitation and actual evapotranspiration. Original data,
after extraction, is fed into the model with resolution conversion. The simulation model accepts
10-minute spatial and 1-hour temporal resolution data. Therefore data resolution is converted as thesimulation model’s requirement. Following two kinds of data are used for discharge simulation andthe results are compared.
HUBEX-IOP EEWB data
A five-minute grid resolution data set created for Estimation of Energy and Water Budget (EEWB) in
the Huaihe River Basin, China (Kozan et al., 2001) is used in this study. The data set is generated
based on the data observed during Huaihe River Basin Experiment - Intensive Observation Period(HUBEX-IOP) in 1998. Grid-mean precipitation data is produced by interpolation of the groundobservation points. This interpolation considers both distance and direction of each observation
station. Hourly precipitation data is interpolated from 12 hour accumulated values. Grid-mean
evapotranspiration data is generated from the observed surface meteorological data with the use a land
surface model, SIBUC model (Tanaka et al., 1998).
GAME reanalysis product
Using 2-dimensional forecast data of GAME (GEWEX Asian Monsoon Experiment) reanalysis
product (Version 1.1) with 1.25 and 2.5-degree resolutions (Meteorological Research Institute, Japan
Meteorological Agency, 2000), the precipitation and actual evapotranspiration data are extracted for
all the grids lying in the basin. These data sets were produced using JMA 4DDA (4 dimensional data
assimilation) system in a cooperative study of MRI/JMA and EORC/NASDA. The data sets have an
advantage of including the GAME special observation of off-line radio-sonde data set. The temporal
resolution of these data is 6-hour.
DISCHARGE SIMULATION
Discharge simulation by using HUBEX-IOP EEWB data
HUBEX-IOP EEWB data is converted to 10-minute spatial resolution by averaging the adjacent grid
data before feeding into the hydrologic model. It gives quite satisfactory result while simulating thedischarge with HUBEX-IOP EEWB data input at Suiping and Wangjiba catchment (see Figures 6 and
7). However, the simulation remains good at Bengbu only for initial 60 days, and after then the runoff
is predicted below observed values (see Figure 8). The water budget (Figure 9) clearly reflects this
distinct difference between observed and simulated values of accumulated discharge, showing
significant mismatch after 180th days.

372

International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

3000

700
HUBEX-IOP EEWB
Observed

HUBEX-IOP EEWB
Observed

600

2500

Runoff (m**3/sec)

Runoff (m**3/sec)

500
400
300

2000

1500

1000

200

500

100
0
120

140

160

180
Time (day)

200

220

0
120

240

Figure 6 Simulation result at Suiping with HUBEX-IOP data.

7000

Accumulated hydrological values (mm)

6000
5000

Runoff (m**3/sec)

160

180
Time (day)

200

220

240

Figure 7 Simulation result at
Wangjiba with HUBEX-IOP data.
1000

HUBEX-IOP EEWB
Observed

4000
3000
2000
1000
0
120

140

140

160

180
Time (day)

200

220

240

Simulated discharge
Observed discharge
Evapotranspiration
Precipitation

900
800
700
600
500
400
300
200
100

0
120 130 140 150 160 170 180 190 200 210 220 230 240
Time (day)Figure 8 Simulation result at Bengbu with HUBEX-IOP data.

Figure 9 Water budget at Bengbu
with HUBEX-IOP data.

Discharge simulation by using GAME reanalysis product
GAME reanalysis data is rearranged to fit according to the need of the model input requirement.
While rearranging input data, all 10-minute grid cells, lying within 1.25-degree grid coverage, areassigned same value to maintain 1.25-degree resolution. The intersecting grid-cells are assigned
average values. The original 6-hour data is changed to 1-hour data assuming uniform distribution tomaintain 6-hour interval data. Thus the resolution of input data is maintained as 1.25-degree 6-hourdata. Similar practice is applied in the case of 2.5-degree resolution data as well.
1.25-degree resolution input
The simulation results at Suiping using GAME 1.25-degree data input are not satisfactory, as it fails
to attain the peak values several times (Figure 10). Also, it is hard to follow the observed discharge atmany places especially after 70th days. The simulation results at Wangjiba are nearly following theobserved hydrograph but it fails at many places (Figure 11). Distinct error is seen in attaining peakvalues and between 220th to 230th days, however trend is represented better. However, it gives goodresult at Bengbu (Figure 12). The observed low flow in between 165th and 180th days are notrepresented properly, but the peak flow is simulated quite better than that with HUBEX-IOP EEWB

373

International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

data. The recession limb of the hydrograph is still not simulated properly. Errors are present in further
periods too but the trend follows the observed hydrograph. Looking over the accumulated discharge,
it gives very good matching with observed one (Figure 13), and shows satisfactory agreement with thewater budget.
700

3000

GAME 1.25-deg
Observed
GAME 2.5-deg

600

GAME 1.25-deg
Observed
GAME 2.5-deg

2500

Runoff (m**3/sec)

Runoff (m**3/sec)

500
400
300
200

2000

1500

1000

100

500

0
120

140

160

180
Time (day)

200

220

0
120

240

Figure 10 Simulation result at Suiping with GAME data.

9000

6000
5000
4000
3000
2000
1000
140

160

180
Time (day)180Time (day)

200

220

240

Figure 11 Simulation result at
Wangjiba with GAME data.

200

220

Simulated Discharge
Observed Discharge
Evapotranspiration
Precipitation

800

600

400

200

0
120

240

Figure 12 Simulation result at Bengbu with GAME data.

140

160

180
Time (day)Simulated Discharge
Observed Discharge
Evapotranspiration
Precipitation

800

600

400

200

0
120

140

160

180
Time (day)

200

220

240

Figure 14 Water budget at Bengbu with GAME 2.5-deg data.

374

200

220

Figure 13 Water budget at Bengbu withGAME 1.25-deg data.

1000

Accumulated hydrological values (mm)

Runoff (m**3/sec)

7000

0
120

160

1000

GAME 1.25-deg
Observed
GAME 2.5-deg

Accumulated hydrological values (mm)

8000

140

240

International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

2.5-degree resolution inputThis data did not give good simulation results in either basin (Figures 10, 11, 12). In the water budget
at Bengbu, accumulated discharge shows better in initial 50 days, and then there is great mismatch(Figure 14).
Why so much variation in result?
The largest basin, Bengbu, touches seven 2.5-degree grid cells but only one lies completely within it.
Diverse local hydrological and meteorological behaviors are impossible to be reflected in coarse grid
information. The river basin’s diverse variability, which the present data does not reflect, may be
influencing factor in flow routing process. Hence the mean value of all the locations within its
coverage is believed as the major cause of failure to represent the true basin conditions. On the other
hand, the results are quite satisfactory with 1.25-degree data, where the same basin touches eighteen
1.25-degree grid-cells, among them ten grid-cells lie completely within it. This provides a base toaccept that the increased number of grid cell within the catchment improves the result.
Looking over the smallest study basin Suiping, the simulated results are almost similar for1.25-degree and 2.5-degree data, both not matching with observed one (Figure 10). The grid area islarger than the catchment area in both cases. Thus, the simulation model with uniform spatial data,
which does not represent the true site condition, produces much deviation in a simulation result than
the real one.
Effects of interpolation
GAME 1.25-degree data and the Bengbu catchment are chosen for observing the effects of datainterpolation, as it is the best simulated combination among the alternatives in previous section. Data
are separately interpolated in space and time, and then fed into the model to observe the effects indischarge simulation.
Spatial interpolation
Assuming that the original grid data refers to the grid intersection points, the bilinear interpolation isapplied before feeding the input data to the hydrological model. This experiment gave worse
simulation result at Bengbu than the original data unlike the expectation (Figure 17). The peak values
did not match, although it is able to represent the trend. The error is quite distinctive after 220th daysof simulation at Bengbu. In addition to that, the spatially interpolated data did not give better
simulation results in Suiping and Wangjiba (Figures 15, 16). Significant variations in simulation
results are observed with respect to original data as an effect of spatial interpolation.
Temporal interpolation
Using linear interpolation, the original data with 6-hour interval is changed into 1-hour time step data.
This data gave almost similar result as that of original data (Figures 15, 16, 17). The output values arechanged slightly but still they are not significant for all three basins.

Spatial and Temporal interpolation
Integrating both spatial interpolation and temporal interpolation, a new data with 10-minutespatial and 1-hour temporal resolution data is created. It gives almost similar result as that of
spatial interpolation only (Figures 15, 16, 17). This means that spatial interpolation has
dominant effect for discharge simulation.

375

International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

700
600

3000

Spatial & temporal interpolation
Observed
Spatial interpolation
Temporal interpolation
GAME 1.25-deg

Spatial & temporal interpolation
Observed
Spatial interpolation
Temporal interpolation
GAME 1.25-deg

2500

Runoff (m**3/sec)

Runoff (m**3/sec)

500
400
300

2000

1500

1000

200
500

100
0
120

140

160

180
Time (day)

200

220

0
120

240

140

160

180
Time (day)

200

220

240

Figure 15 Simulation result with interpolated data at Suiping. Figure 16 Simulation result with interpolated
data at Wangjiba.
10000

9000

Runoff (m**3/sec)

8000

Spatial & temporal interpolation
Observed
Spatial interpolation
Temporal interpolation
GAME 1.25-deg

7000
6000
5000
4000
3000
2000
1000
0
120

140

160

180
Time (day)

200

220

240

Figure 17 Simulation result with interpolated data at Bengbu.

Performance of channel routing model
Figures 18, 19 and 20 show the effects of the river flow routing model on simulation resultswith the use of HUBEX-IOP EEWB data. In these simulations, the channel width isdetermined by using the relationship shown in Figure 21. The relationship is obtained using
the channel cross section data of the Huaihe River. In the simulated hydrographs, the solidline represents the hydrograph simulated with the river routing model and the broken line
shows the one without the river routing model. The solid hydrograph in Figure 20 and thesolid hydrograph in Figure 8 is the same hydrograph. This simulation shows that the effect ofriver routing to estimate river discharge for a large river basin is quite large and it is quite
important to incorporate the effect into the hydrological model effectively.

376

International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

Channel routing model is not included
Channel routing model is included

600

Channel routing model is not included
Channel routing model is included

4500
4000

500
Runoff (m**3/sec)

Runoff (m**3/sec)

3500
400
300
200

3000
2500
2000
1500
1000

100

500
0
0

20

40

60

80

100

0

120

0

20

40

Time (day)

80

100

120

Figure 18 Simulation result without routing at

Figure 19 Simulation result without routing at

Bengbu.

Wangjiha

25000

900

Channel routing model is not included
Channel routing model is included

Measured width
Area-width relationship, c = 0.3
Area-width relationship, c = 1.0

800

20000

700

600

15000

Width (m)Runoff (m**3/sec)

60
Time (day)

10000

500

400

300

200

5000

100

0

0
0

20

40

60

80

100

0

120

20000

40000

60000

80000

100000120000Catchment area (km2)Time (day)Figure 20 Simulation result without routing at
Suiping.

Figure 21 Relationship between catchment area andchannel width.

INFORMATION NEEDS FOR FLOOD FORECASTING
Forcing data resolution
The obtained results clearly indicate that the distributed hydrological model is very sensitive
with resolution of forcing data. Of course, the accurate higher resolution data gives better
solution. If we see over the ratio of input-resolution and catchment size (IC ratio), it indicatesthat there exists some ratio which provides the suitability of the forcing data resolution to
input in the distributed hydrological model with respect to catchment size. A summary of IC
ratios is presented in Table 2.
While the ratio of input-resolution and catchment size (IC ratio) is 1:13, it gave good result at
Bengbu. The results are found satisfactory with the IC ratio is 1:168 at Wangjiba and with the377International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

IC ratio 1:11 at Suiping. Simulation is not satisfactory with lower IC ratios. It may be too
early to draw some specific conclusion, but still this study suggests that the results of
distributed hydrologic models are better while IC ratio is more than that of 1:10. To set a
general acceptable IC ratio or to investigate the relationship of IC ratio with otherhydrological factors may be a good research topic in the coming days.
Table 2 Input-resolution-Catchment (IC) ratio.
Resolution
2.5-degree(GAME Re-analysis)
1.25-degree(GAME Re-analysis)
10-minute(HUBEX-IOP EEWB)

BengbuWangjiba

Suiping

1: 3.3

1: 0.75

1: 0.05

1: 13.24

1: 2.98

1: 0.21

1: 744.5

1: 167.9

1: 11.78

Forcing data interpolation
The results of interpolated data display that the effect of spatial interpolation is dominant in
distributed hydrologic simulation than the temporal interpolation. This means the spatial
distribution effect is very sensitive. It is obvious to have less effect of temporal distribution inlarge basins like Bengbu and Wangjiba because of larger travel time, and also it did not affectmuch in the simulation result of Suiping. This phenomenon is present in the result withspatially and temporally interpolated data as well.
The higher grid resolution is not necessarily good in hydrological modeling. Basically, thehigh-resolution data generation using interpolation techniques from the coarse grid intervals
may have poor performance than the original one for large catchments. Spatially interpolated
input data given to simulate river flow at Bengbu has shown this (Figure 17). The larger grid
data includes the average values within the grid cell but linear or bilinear spatial interpolation
does not reproduce the data that considers topographical variations, catchment boundaries
and hydrological characteristics exactly. Therefore, in distributed hydrological modeling, itcannot simulate the hydrological behavior of zonal variation by applying such interpolation
techniques.
Incorporation of river flow regulation by human activity into hydrological model
In the study basin area, large numbers of dams are operating but this model does not consider
the discharge manipulation due to dam operation. The obtained hydrographs display somehints about the effect of dam operation. It does not distinctly indicate hydrograph attenuationeven though recession parts are widened in observed hydrograph. To explain these behaviors
more specifically, it needs further studies. If the discharge variations are due to effects of dam
operation, then it will give a strong support to verify the suitability of GAME reanalysis
product in hydrological modeling processes.
Channel routing model
For river discharge simulation of large-scale catchments, the effect of channel routing has adominant role on forming a hydrograph. Thereby it is quite important to incorporate a378

International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

channel routing model into an entire hydrologic simulation model. To build a river routing
model, channel network data sets having information of locations and connection relationshipof channel segments are indispensable.
CONCLUDING REMARKSIn this paper, a development of a macro-scale grid based distributed hydrological model andits application to the Huaihe River basin in China (132,350km2) using HUBEX-IOP EEWB
data and GAME reanalysis product were presented. The distributed hydrological model isvery sensitive with resolution of forcing data input. HUBEX-IOP EEWB data gave good
simulation results for Suiping and Wangjiba basins at Huaihe region. GAME reanalysis
2.5-degree data was not suitable to reproduce the river discharge, however, GAME reanalysis
1.25-degree data gave good simulation results at Bengbu of Huaihe River Basin. The results
obtained in this research are summarized as follows:• Better simulations are achieved while the IC ratio (Input-resolution-Catchment ratio) ismore than 1:10.
• Spatial distribution of data has significant effect than the temporal distribution in
distributed hydrologic modeling.
• The importance of a channel routing model and incorporation of the effect of dam
regulation into a hydrologic model is suggested.
ACKNOWLEDGMENT
Providing HUBEX-IOP EEWB data by Mr. K. Tanaka and Mr. O. Kozan at DPRI, Kyoto
University and GAME reanalysis product conducted by Meteorological Research Institute,Japan Meteorological Agency and Earth Observation Research Center/National Space
Development Agency of Japan are highly acknowledged. The authors are also grateful to the
Grants-in-Aid for Scientific Research, Encouragement of Young Scientists (A) 13750491
provided by Japan Society for the Promotion of Science.
REFERENCES
Ichikawa Y., Shiiba M., Tachikawa Y. and Takara K. (2000). “Object oriented hydrologicalmodeling system”, HydroInfromatics, CD-ROM, Cedar Rapids, Iowa.
Kozan O., Tanaka K. and Ikebuchi S. (2001). “Estimation of Energy and Water Budget in the
Huaihe River Basin, China”, Game publication, no. 28 pp. 32-35.
Meteorological Research Institute, Japan Meteorological Agency. (2000)
http://gain-hub.mri-jma.go.jp/4dda/GAME_reanal_1.html
Shiiba, M., Ichikawa, Y., Ikebuchi, S., Tachikawa, Y. and Takasao, T. (1996). “Scale-up of arunoff model using GIS and an object-oriented hydrological modeling system”, Proceedingsof HydroGIS 96: Application of Geographic Information System in Hydrology and Water
Resources Management, IAHS Publ., No. 235, pp. 63~70.

379

International Conference on Urban Hydrology for the 21st Century
14-16 October 2002, Kuala Lumpur

Tachikawa, Y., Takara, K., Ichikawa, Y. and Shiiba, M. (2000a). “Test Simulation of a macroscale distributed hydrological model for the Huaihe River Basin in China”, Proc. ofInternational GAME/HUBEX Workshop, Sapporo, pp. 139~146.
Tachikawa, Y., Takara, K., Ichikawa, Y. and Shiiba, M. (2000b). “Development of a macroscale distributed hydrological model using an object-oriented hydrological modeling system”,
HydroInformatics, CD-ROM, Cedar Rapids, Iowa.
Tachikawa, Y., Kawakami, T., Ichikawa, Y., Shiiba, M. and Takara, K. (2001). “Applicationof a macro grid based hydrological model to the Huaihe River Basin in China”, Annual
Journal of Hydraulic Engineering, pp. 127~132.
Tanaka, K. (1998). “Activities in Huaihe River Basin (HUBEX) study area”, Asia-Pacific
Network for Global Change Research, Report of 1997-1998, pp. 69-91.
Zhao, R. J. (1992). “The Xinanjiang model applied in China”, Journal of Hydrology, vol. 135
pp. 371~381, 1992.

380