Proc. of the 3rd APHW Conference on "Wise Water Resources Management
towards Sustainable Growth and Poverty Reduction", October 16-18, 2006, Bangkok

Application of Distributed Hydrological Modeling to Identify River Structures
Vulnerable to Extreme Floods
1

B. Nawarathna1 , K. Ishino1, Y. Tachikawa2 and N. Tamai3
Civil Engineering Research Institute, Taisei Technology Center, Japan
2
Disaster Prevention Research Institute, Kyoto University, Japan3
Department of Civil Engineering, Kanazawa University, Japanrdpnava@yahoo.com

Abstract: Japan has experienced several unprecedented precipitations in last two
years. These extremes have resulted in catastrophic floods in different parts of thecountry. In this study, flooding in, the Asuwa River in July 2004 due to torrential
rains caused by baiu-front, the Miya River in October 2004 caused by typhoon 0423
and the Gokase River in September 2005 due to typhoon 0514 are modeled using
physically-based distributed TOPMODEL with Muskingum-Cunge flow routing
method. The Asuwa, the Miya and the Gokase river basins have two, four and five
AMeDAS rainfall recording stations respectively. Those limited data are notsufficient to model floods caused by unprecedented downpours. Therefore radar
derived rainfall intensities were used to determine spatial and temporal distributionsof rainfall during the storm periods. The worst damage was reported in the Asuwa
river basin due to localized intensified rainfall. The basin maximum hourly and fivehour rainfalls were 95 mm and 317 mm. A continuous 48 hour downpour resulted insevere flooding in the Gokase River basin. The recorded maximum hourly and 2-day
rainfalls were 80 mm and 1441 mm. In the Miya river basin, 57 mm of hourly and
220 mm of eight-hour maximum rainfall were recorded. Incidentally, the typhoon
followed the Miya River meandering path resulting severe damages in downstream
areas. Several river structures, mainly railway bridges were washed away by heavy
river flows. The hydrological model was applied to simulate discharges at different
locations of the three river basins. Simulated discharges at damaged bridge locations
were used to calculate drag forces acted on bridge piers and hence to compute
experienced maximum bending stresses of the collapsed bridge piers. Results arecompared with their bearing capacities of the collapsed locations. The maximum
rainfall regions of baiu-front, typhoon 0423 and 0514 were identified and thosevalues exported to the other two river basins to simulate perceptual hydrologicalprocesses. It was found that peak runoff values resulting from typhoon 0514 are thelargest in three basins. Similar kind of studies can be used to understand river
structures in ungauged basins that are vulnerable to floods due to different types ofstorms.
Keywords: Floods, Distributed Hydrological Models, River Structures, Japan

1. INTRODUCTION
Several regions of Japan have experienced catastrophic storms due to baiu-fronts or
typhoons during last couple of years. Those storms have resulted in record breaking
intensified precipitations in short duration or longer durational large rainfalls. In 2004,
heavy rainfall related natural disasters caused more than 230 casualties in Japan.
Those heavy rainfalls were induced by 10 strong typhoons and intensified baiu-fronts.
In early September, 2005 the typhoon Nabi (0514) hit Japan, causing 26 deaths andbringing a record breaking three-day precipitation in western Japan, which wasapproximately three times as much as its normal monthly rainfall of September.
These unprecedented torrential rains have caused severe damages due to floods andland slides. Larger river flows resulted in bursts of riverbank and spilling the levees
disturbing the livelihoods of the inhabitants of the regions.

A number of river structures mainly railway bridges have been washed away by theseheavy river flows. Five bridges on the JR Etsumi Hoku Line in the Asuwa river basin,
two railway bridges in the JR Takachiho line in the Gokase river basin and threerailway bridges in JR Takayama line in the Miya river basin were washed away by
the flood forces. Ishino et al.(2005) has studied bridge collapsing mechanism using a
constant river discharge along the Asuwa River1). Nawarathna et al.(2006) applied aphysically based distributed hydrological model using radar derived rainfall
intensities to simulate hydrological processes in the Asuwa river basin. Estimateddischarge values were used to calculate bending stresses of the collapsed bridge
sections2). In this paper different bridge collapsing scenarios of both the Asuwa andthe Gokase river basins are being discussed.
In Japan approximately 50% of the population lives in alluvial plains. Short and steep
Japanese rivers are often subjected to short durational flash floods. During floods
river water levels are higher alluvial plain. As a result, people will suffer from floods
unless river banks are properly constructed. It is an uttermost important to investigate
maximum possible river flows along the river for proper river management practices.
In this study each of the three storms (baiu-front, typhoon 0423 and typhoon 0514)
are applied to other two basins to study possible flood situations.
2. BACKGROUND
2.1 Study Area
Three Japanese river basins (Fig. 1) namely the Asuwa River in Fukui prefecture, theGokase River in Miyazaki prefecture and the Miya River in Gifu prefecture wereselected for the study. Watershed physical properties are shown in Table 1.
Table 1: Watershed physical characteristics of the study basins
Name of theRiver

Wartershed Maximum Mean Maximum
Mean
Mean Topographic
Slope
Slope Elevation Elevation
Index
Area (km2)% ofForest

Asuwa

354

1.47

0.48

1447.0

433.9

6.42

90.8

Gokase

1048

2.80

0.47

1699.0

639.8

6.48

76.6

Miya

1155

2.36

0.45

3012.0

959.7

6.60

89.0

The topographies of three watersheds are dominated by steep, heavily forested
mountains. Average values of slopes and topographic index are approximately equal.
Drainage area of the Asuwa river basin up to Tenjinbashi gauging station (Fig. 1 a) is
about 354 km2. And it has the smallest drainage area among the three study basins.
Therefore hydrological respond to intensified local heavy rainfalls per unit catchment
area is large. Other two basins may require comparatively longer durational storms tocause heavy floods. Minimum flood plain elevation of the Asuwa, Gokase and Miya
river basins are 19 m, 6 m and 220 m respectively. Paddy field covers 5.1, 4.5 and 5.7
percentages of the Asuwa, Gokase and Miya river basins. Scrub forest and farm land
covers 11.3 and 5.4 percentage of the Gokase river basin. That is the main differencein land cover types among these three watersheds. There are no dam reservoirs inboth the Asuwa and the Gokase river basins. The Miya river basin has a series ofsmall capacity dams in addition to the large Shimokotori dam (3.5 MCM capacity).
2.2 Storm characteristics
On July 18th,2004 torrential rains induced by baiu-front in Fukui prefecture causeddevastating damages due to floods and mudslides mainly in the Asuwa River basin.
The observed peak discharge (2320m3/s) at the Tenjinbashi was almost twice larger
than the previously recorded peak discharge2). Six hour and daily accumulated rainfall
on 18th at Kidouchi measuring station was 314 mm and 338 mm respectively.

1360 20’1360 10’1360 25’(a)1360 20’▲(b)

Tenjinbashi▲

1360 30’

Inotani●●360 00’360 20’●▲
Shimokotori Dam

360 10’350 50’(c)
320 50’●:Collapsed Bridges

Asuwa River basin

320 40’Miya River basin
Tokyo

Gokase River basin

●

●

▲

Miwa
1360 15.0’1310 30.0’

Fig. 1 Study river basins; a) Asuwa, b) Miya, C) Gokase
Record breaking 10 typhoons struck Japan in 2004. The last typhoon of the year,
TOKAGE (0423), left severe damages in many regions in Japan; more than 90 people
were killed making it the deadliest storm to hit Japan in 25 years. The typhoon hit
Japan’s main island of Honshu on 20th October 2004. Tokage caused mudslides andflash floods while the strong winds uprooted trees. Some 480 mm of rain fell in somepart of Kyushu Island in 24 hours. The storm left over one million homes without
power and forced around 20,000 people to evacuate.
Typhoon Nabi (0514) which hit Japan in first week of September, 2005 exposed her
to danger for many hours. The maximum hourly rainfall was not so great, but morethan 500 mm of three day continuous rainfall caused severe damages. This typhooncaused incidences of 30 landslides. On account of that, 19 people were killed in
Kagoshima, Miyazaki and Yamaguchi Prefecture. The Gokase river basin in Kyushu
Island experienced recorded downpours and as a result severe flooding caused severe
damages to river structures and hampering livelihood of thousands of people.
Basin averaged cumulative radar derived rainfall with storm duration is shown in Fig.
2. These three graphs clearly differentiate the spatially averaged storm characteristics
in the study basins. Fig. 3 depicts the radar derived maximum rainfall within thebasin against storm duration. Both typhoon 0514 and rain induced by baiu-front

produced nearly equal amount of maximum cumulative rainfall in first five hours.
Hourly maximum rainfall is about 95.0 mm in the Asuwa river basin. Five hour time
averaged maximum rainfall is about 63.4, 71.2 and 33.6 mm/hr in the Asuwa, Gokase
and Miya river basins respectively.

Cummulative Rainfall (mm)700
600Asuwa (Baiu-Front)

500

Gokase (Typhoon 0514)

400

Miya (Typhoon 0423)

300
200
100
0
0

12

24

36

48

60

72

Time (hr)Fig. 2 Basin averaged radar derived rainfall characteristics
Cummulative Rainfall (mm)

1500
1200
Asuwa (Baiu-Front)
Gokase (Typhoon 0514)

900

Miya (Typhoon 0423)

600
300
0
0

12

24

36

48

60

72

Time (hr)Fig. 3 Maximum localized radar derived rainfall characteristics with storm duration
3. HYDROLOGICAL SIMULATION
Modeling of hydrological processes is obscure task due to its complex governing
processes and loss of information in downscaling. TOPMODEL is a small-catchment
scale rainfall runoff model based on spatially distributed soil topographic index3). The
BTOPMC4) model developed in Yamanashi University, Japan adopted a block wise
concept to the TOPMODEL together with Muskingum-Cunge flow routing method.
Nawarathna et. al, extended the model by introducing distributed parameters andapplied it to a part of the Mekong river basin5).
3.1 Model structure
Sub-Basin wise, TOPMODEL and Muskingum-Cunge flow routine method2) wasused to simulate hydrological processes of the study basins. This model was modified
as follows to accommodate base flow contribution and saturations deficit.

qb (i, t ) = T0 e

− SD(i ,t ) / mtan β = ai * R = ai * e

⎛
mγ ⎞
⎛m ⎞
⎟
SDi = SD⎜ i ⎟ − mi ⎜⎜ γ i −
m ⎟⎠
⎝m⎠
⎝
Where Topographic Index,γ i = ln

⎛ SD mγ ⎞⎟−⎜+⎜ m m ⎟
⎠
⎝

(1)
(2)

ai
ai
1
and mγ = ∑ mi ln
T0,i tan β i
A i
T0,i tan β i

The watersheds are divided into number of imaginary sub-basins and local saturation
deficit which controls the depth to the local saturation zone is calculated with respect
to the sub-basin average saturation deficit. The local saturation deficit also depends
on local soil-topographic index relative and its sub-basin average value. Bothoverland flow and base flow depend on local saturation deficit. Overland flow fromeach grid and subsurface flow calculated at sub-basin outlets are routed to the down
stream along the drainage network which was determined from 50 m resolution
digital elevation model using Muskingum-Cunge flow routing method.
3.2 Parameterization
Four model parameters namely, lateral transmissivity under saturated conditions T0,
decay factor m, maximum root zone storage, and flood plain Manning’s coefficient,
are assigned as functions of land use. The Ministry of Land Infrastructure andTransport publishes the land cover details of Japan in regular intervals. The data
published in 1997 was used to detect the land use distribution of the study basins.
Because of the difficulties in finding high resolution reliable soil property databases
which can correlate T0 and m of each grid, it is assumed that soil properties arelargely correlated to land use. This assumption is likely to be valid for a basin that has
not significant human interference on its natural environment.

0

6000

30

Discharge (m3/s)

8000

Rainfall (Baiu-Front)
Rainfall (Typhoon 0514)
Rainfall (Typhoon 0423)
Obs. Q (Baiu-Front)
Sim. Q (Baiu-Frot)
Sim. Q (Typhoon 0514)
Sim. Q (Typhoon 0423)

4000

2000

60

90

0

Fig. 4 Simulate Hydrograph at Tenjinbashi of the Asuwa River

3/13 0:00

3/1 0:00

2/18 0:00

2/6 0:00

1/25 0:00

1/13 0:00

1/1 0:00

120

Rainfall (mm/hour)

3.3 Hydrological simulation results
The model was applied to simulate temporal and spatial distributions of hydrologicalprocesses and discharges of the basins. The Asuwa River spilled the river banks
before reaching Tenjinbashi and also observed discharges are controlled by thehydraulic structure near the gauging station. These controls can not be accurately
taken into account by the hydrological model. Therefore, calibration was carried out
considering higher observed peak discharge than the recorded (Fig. 4). Recorded
inflow to the Kamishiiba dam (211 km2) was used to calibrate a watershed model for
the Mimi River basin which is an adjacent basin of the Gokase River basin (Fig. 5).
Physical characteristics of both Gokase and Mimi watersheds are quite similar andthey are located in same climatic region. Calibrated watershed model parameters
were exported to the Gokase watershed model due to lack of observed discharges forcalibration. Simulated hydrograph of the Gokase river basin at Miwa gauging station
is depicted in Fig. 6. Miya watershed model was calibrated using recorded inflow to
Shimokotori dam (162 km2). Simulated hydrograph of Shimokotori dam location andMiya river basin’s most down stream location at Inotani are shown in Fig. 7 and Fig.
8.

0

2250

Basin Averaged Rainfall 20
Simulated Inflow

Rainfall (mm/hour)

Discharge (m3/s)

3000

40

Observed Inflow

1500

60
750

80
100
9/8 0:00

9/7 12:00

9/7 0:00

9/6 12:00

9/6 0:00

9/5 12:00

9/5 0:00

0

12000

80

2000

100

0

120
9/5 0:00

9/4 12:00

9/4 0:00

9/3 12:00

9/3 0:00

Discharge (m3/s)

8000

9/6 0:00

4000

10000

9/5 12:00

6000

0
Rainfall (Baiu-Front)
Rainfall (Typhoon 0514) 20
Rainfall (Typhoon 0423)
Sim. Q (Baiu-Frot)
40
Sim. Q (Typhoon 0514)
Sim. Q (Typhoon 0423)
60

Rainfall (mm/hour)Fig. 5 Simulate Hydrograph at Kamishiiba Dam catchment of the Mimi River

Fig. 6 Simulate Hydrograph at Miwa of the Gokase River
The recognition of the maximum possible flooding remains a major challenge forhydro-meteorologists. Floods arise as a consequence of heavy rainfall falling upon arapid response drainage basins or long durational rainfall falling on medium to large
river basins. Whilst the basin physical characteristics govern the specific timing,
location and depth of flooding, the amount and the spatial and temporal distributionof rainfall are prime factors in determining whether flooding is likely to occur at all.
0

3000

20

Discharge (m3/s)

Rainfall (Baiu-Front)
Rainfall (Typhoon 0514)
Rainfall (Typhoon 0423)
Sim. Q (Baiu-Frot)
Sim. Q (Typhoon 0514)
Sim. Q (Typhoon 0423)
Obs. Q (Typhoon 0423)

2000
1500
1000

40
60
80

10/21 0:00

10/20 12:00

10/20 0:00

10/19 12:00

120
10/19 0:00

0
10/18 12:00

100

10/18 0:00

500

Rainfall (mm/hour)

2500

Fig. 7 Simulated Hydrograph of the Shimokotori Dam
The maximum rainfall areas of baiu-front, typhoon 0423 and 0514 were identified
and exported to the other two river basins to simulate perceptual hydrologicalprocesses. Simulated hydrographs at Tenjinbashi, Miwa, Shimokotori dam and theMiya river basin are shown in Fig. 4, 6, 7 and 8. It was found that peak runoff values
resulting from typhoon 0514 are the largest in all basins. However, the possibility ofexperiencing similar long durational storm in the Asuwa and Miya River basins is inquestion. Nevertheless similar kinds of watershed models are good tools toinvestigate hydrological responds to storms of different nature.
Generalized procedures are needed for dealing with influence of different types of
uncertainties (ex: model and data uncertainty) in hydrological forecast. Initial soil
moisture condition or deficit (SMD) greatly controls the starting point floodhydrograph. In this study SMD of study basins kept similar for comparison.
0
Rainfall (Baiu-Front)
Rainfall (Typhoon 0514)
Rainfall (Typhoon 0423)
Sim. Q (Baiu-Frot)
Sim. Q (Typhoon 0514)

8000
6000

20
40
60

Sim. Q (Typhoon 0423)

120
10/20 0:00

10/21 0:00

0
10/20 12:00

100

10/19 12:00

2000

10/19 0:00

80

10/18 12:00

4000

10/18 0:00

Discharge (m3/s)

10000

Rainfall (mm/hour)

12000

Fig. 8 Simulated Hydrograph of the Miya river basin at Inotani

4. ANALYSES OF COLLAPSE BRIDGES
Heavy floods have fully or partly washed away eight, four and three bridges in the
Asuwa, Gokase and Miya River basins. Bending stresses of the collapsed railway
bridge (RB) sections were calculated and compared with the bearing capacities of theconstruction materials as explained in Nawarathna et al.(2006). Fig. 9 and 10 depicts
collapsed RB# 3 of the Asuwa river basin and RB# 2 of Gokase river basin. The
results of the collapsed bridge piers in the Asuwa River and the Gokase River are
shown in the Table 2 and Table 3. Heavy river flows had submerged the railway
bridges before their piers were collapsed in the Asuwa River. However high flows
exceeded river levee heights in upstream and over flowed to the flood plains.
Therefore it is reasonable to say the maximum possible bearing stresses of river
structures were experienced during 2004 flood. It is necessary to study the stresssituation of the river structures in the Miya river basin for a storm similar to typhoon
0514.

Fig. 9 Collapsed RB 3 of the Asuwa River

Fig. 10 Collapsed RB 2 of the Gokase River

Table 2 Comparison of bending stresses with bearing capacities of the collapsed
bridge pier sections of the Asuwa RiverRB #DrainageRiverarea (km2) Width (m)

River
Bed
Slope

Height between Maximum
Maximum
σca
Peak River
Deck andFlood water
Flow Velocity σ (N/mm2)32
Flow (m /s)(N/mm)
foundation (m) height (m)
(m/s)

1

351

108

1/200

11.5

12.5

2650

2.9

0.19

0.18～2.6

3

332

75

1/87

11.6

12.5

2425

4.8

0.45

0.18～2.6

5

318

74

1/370

12.3

13.5

2368

3.8

2.82

0.18～2.6

7

288

58

1/270

12.4

14

2323

4.6

4.60

0.31～3.3

Table 3 Comparison of bending stresses with bearing capacities of the collapsed
bridge pier sections of the Gokase RiverRB #DrainageRiverarea (km2) Width (m)

River
Bed
Slope

Height between Maximum
Maximum
σca
Peak River
Flood water
Deck andFlow Velocity σ (N/mm2)
Flow (m3/s)(N/mm2)
foundation (m) height (m)
(m/s)

1

980

130

1/270

9.5

10

7320

5.6

1.40

0.18～2.6

2

875

85

1/94

14.2

15

7100

5.6

2.04

0.18～2.6

3

780

95

1/350

19.1

14

6050

4.5

0.51

0.18～2.6

5. CONCLUSION REMARKS
Sub-basin wise TOPMODEL and Muskingum Cunge flow routing method with
distributed parameterization using land use were used to simulate hydrologicalprocesses of the Asuwa, Gokase and the Miya River basins for the flood disasters inJuly 2004 due to baiu-front and typhoon 0423 and 0514. Bending stresses of thedamaged railway bridge pier sections were compared with the bearing capacity of theconstruction materials at the time of collapse. The hydrological model can be used to
study the bridge collapsing scenarios due to high flows at any location of the basin
due to different types of storms. This methodology can be used to understand thevulnerability of river structures in ungauged river watershed.
ACKNOWLEDGMENT: The authors would like to acknowledge the River
Environment Fund (Principle Researcher: Prof. N. Tamai).
REFERENCES:
1) Ishino, K., Umeda, S. and Tamai, N.: Study on the causes of collapsed bridges during thedisaster in Fukui 2004.7 and investigation on extending life of bridges, Advances in River
Engineering, JSCE, Vol.11, pp.157-162, 2005.
2) Nawarathna, B., Ishino K., Tachikawa, Y., and Tamai N.: Application of distributedhydrological model to analyze collapsed bridges in Asuwa River basis, Annual Journal of
Hydraulic Engineering, JSCE, vol. 50, pp. 67-72, 2006.
3) Beven, K.J. and Kirby M.J.: A physically based variable contributing area model ofhydrology. Hydrological science bulletin, Vol.24(1), pp.43-69, 1979.
4) Takeuchi, K, Tianqi, A. and Ishidaira, H.: Introduction of block-wise of TOPMODEL andMuskingum–Cunge method for hydro-environmental simulation of large ungauged basins.
Special Issue of Hydrological science, Vol. 44, no 4, pp. 633-645.1999.
5) Nawarathna, N.M.N.S.B., Ao, T.Q., Kazama, S., Sawamoto, M. and Takeuchi, K.:
Influence of human activities on the BTOPMC model runoff simulations in large-scalewatersheds, XXIX IAHR congress proceedings, Theme A, pp.93-99,2001.