Annual Journal of Hydraulic Engineering, JSCE, Vol.50, 2006, February

APPLICATION OF DISTRIBUTED HYDROLOGICALMODEL TO ANALYZE BRIDGE-COLLAPSESCENARIOS IN THE ASUWA RIVER 2004 FLOOD
1

2

3

NMNS Bandara NAWARATHNA , Kazuo ISHINO Yasuto TACHIKAWA and4
Nobuyuki TAMAI
1Member of JSCE, Dr. of Eng., Taisei Technology Center (344-1, Nase-cho, Totsuka-ku, Yokohama, Japan)
2Member of JSCE, Dr. of Eng., Taisei Technology Center (344-1, Nase-cho, Totsuka-ku, Yokohama, Japan)
3Member of JSCE, Dr. Eng., DPRI, Kyoto University, (Gokasho, Uji, 611-0011, Japan)
4Fellow of JSCE, Dr. of Eng., Kanzawa University (Kakuma, Kanazawa 920-1192, Japan)

In July 2004, torrential rain induced by baiu–front caused devastating floods in the Asuwa Riverbasin damaging five railway bridges between Fukui and Miyama stations on the JR Etsumi Hoku line.
The Asuwa River basin has only one river gauging and two AMeDAS ground rainfall measuring stations.
Those limited data are not sufficient to forecast the heavy floods caused by unprecedented downpours. A
physically based distributed TOPMODEL with Muskingum-Cunge flow routing method and radar
derived rainfall intensities are used to simulate hydrological processes of the basin. Results show good
agreements with observed discharges at Tenjinbashi gauging river station. Simulated distributed
discharge values are used to compute the bending stresses of the collapsed railway bridge piers andcompared with respective bearing capacities. The main objective of this paper is to introduce the state ofart of distributed hydrological modeling with radar derived rainfall inputs to identify the river structures
along the river channel that are vulnerable to floods.
Key Words: Fukui heavy rainfall in July 2004, distributed hydrological model, bridge collapse, radar rainfall

1. INTRODUCTION
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-front. Among these the heaviest
torrential rainfall was caused by the active seasonalrain front and ravaged Niigata, Fukushima andFukui prefectures.
On July 18th, torrential rains in Fukui prefecturecaused devastating floods and mudslides in the
Asuwa River basin. The observed peak discharge at
the Tenjinbashi (2320m3/s) was almost two times
larger than the previously recorded peak discharge1).
Six hour and daily accumulated rainfall on 18th at
Kidouchi was 314mm and 338mm respectively.
These unprecedented river discharges resulted inbursts of riverbank and spilling the levees, partly
inundating Fukui city. Five people were drowned orwent missing. More than 14,000 houses were
flooded. Five bridges between Fukui and Miyama
stations on the JR Etsumi Hoku Line were washedaway. Consequently train services were suspended
Ishino et al.(2005) has studied bridge collapsingmechanism using a constant river discharge (2400m3/s) along the Asuwa River2). In this paper weapply a physically based distributed TOPMODEL
coupled with Muskingum-Cunge flow routing
method and radar derived rainfall intensities tosimulate hydrological processes of the Asuwa river
basin to identify river structures that are vulnerable
to floods. It is found that the simulated peakdischarge values of sections of collapsed railway
bridges varies from 2323 m3/s to 2651 m3/s. In
addition, we discuss the possibility of extending thisapproach to analyze the risk involved with
non-collapsed bridges.

- 67 -

2. ASUWA
RIVER
BASIN
CHARACTERISTICS

0.

During the heavy rainfall observed over Fukui
area on 18 July 2004, baiu-front extending fromJapan Sea to Fukui prefecture became active
forming thick nimbus clouds over the region.
Localized, band-shaped torrential rainstorm brought
87 mm of maximum hourly rainfall and 338 mm ofdaily rainfall to the area centering the Asuwa Riverbasin (Fig. 1). This heavy rainfall was a result of an
intensification of convective instability over the
Baiu frontal zone, induced by the inflows of
low-level humid air and middle-level dry air. The
middle-level air was considerably warm, not colder
than the surrounding air resulting heavy rainfall3).
In this flood there was a 3 to 4 hour time lag
between basin peak rainfall and peak observed
discharge at Tenjinbashi (Fig. 2). This lead time wassufficient to advice nearly 40,000 households toevacuate to safer places in advance. However, thelead time may not be sufficient to evacuate residents
from the smaller watersheds without proper rivermanagement practices. Considering these facts, it isimportant to develop an advanced and proper water
related disaster management practices to small-scalewatersheds.

0

136 15’STORM

0136 20’136 25’0

136 30’

Fukui (AMeDAS). Fukui
Tenjinbashi ◘Miyama360 00’Ono

KidouchiItagakiKanemitani

0

35 55’. Imatate
Aiki
Simbo
KumogawaDam

035 50’

Seto
1360 15’1360 20’1360 25’

1360 30’

Fig.1 Ground rainfall measuring stations of the study area

30

14:00

12:00

10:00

Time

8:00

6:00

4:00

0
2:00

Radar AMeDAS Precipitation (RAP) program
which was developed by Japan Meteorological
Agency was used to extract rainfall intensities fromthe AMeDAS digital radar rainfall data sheets.
Excess rain water from 354 km2 catchment drains to
the Tenjinbashi gauging station along the Asuwa
River and its tributaries. There are 72 radar gridsthat falls fully or partly inside the drainage basin.
Rainfall at 9:00 shows uniform distribution
throughout the basin. Maximum rainfall wasrecorded at 10:00.
Two interpolating techniques namely; nearest
neighborhood (nnm), weighed distance average(wdm) and mean of the recorded rainfall were used
to estimate basin rainfall distribution from groundmeasured values. Hourly rainfall records at Miyama,
Ono, Itagaki, Simbo, Kidouchi, Aiki, Kanemitani
and Kumogawa dam stations were utilized toestimate the spatial distributions and compared withestimates from radar derived rainfall. Both nearest
neighborhood and weighted distance averagemethods give similar values with subtle differences.

Radar
nnm
wdm
Average
Hydrograph

0:00

3. COMPARISON OF BASIN GROUNDMEASURED RAINFALL WITH RADARDERIVED RAINFALL

Hourly Rainfall (mm)60

Fig.2 Spatially averaged Asuwa River basin rainfall
estimates by different interpolation methods

Fig. 2 shows the spatially averaged rainfall of thebasin estimated by four different methods. Radarderived estimated rainfall shows the highest
deviation from other three methods at 9:00. If therouting effect and the rainfall losses are not taken
into account the graph would be the floodhydrograph. But there are losses as well as time
taken to route the locally generated runoff values todown stream. The amount of peak discharge
reduction and lag time between peak in rain and- 68 -

discharge depend on physical characteristics ofbasin and the spatial and temporal distribution of
rainfall. To capture those differences, we applied
TOPMODEL and Muskingum-Cunge flow routing
method.
It was found that spatially averaged radar derived
14 hours cumulative rainfall is about 259.9 mm,
which is about 93 % of the basin averaged
cumulative recorded rainfall, estimated using
nearest neighborhood interpolating method (278.8mm). However differences in spatial and temporal
rainfall distributions estimated by different
techniques greatly influence the simulated
discharges time series. A distributed hydrologicalmodel could be the best tool to capture the input
effect of different rainfall distribution estimation
techniques on simulated discharges.

4. HYDROLOGICAL SIMULATION
Modeling of hydrological processes is not aneasy task even in the physically based distributed
approach 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 index4). The BTOPMC5) model
developed in Yamanashi Univeristy, Japan
adopted a block wise concept to the TOPMODEL
together with Muskingum-Cunge flow routing
method6). The model was extended introducing
distributed parameters and applied it to a part of the
Mekong river basin7).
(1) Model structure
In this paper, we used sub-basin TOPMODEL
with Muskingum-Cunge flow routing method tosimulate the hydrological processes of the Asuwa
River basin for the 2004 July flood event.
The Watershed is divided into 45 imaginary
sub-basins and local saturation deficit which
controls the depth to the local saturation zone iscalculated with respect to the sub-basin average
saturation deficit value. The local saturation deficit
is determined from local soil-topographic index
relative to its sub-basin average value (Eq. 2). Bothoverland flow and base flow depend on local
saturation deficit. Thus, the soil-topographic index
which depends on topography and soil type is thecritical controlling factor in runoff generation.
Over a sub-basin, an average saturation deficit
S(t+1) is determined from Eq. (1).
S(t+1) = S(t) - Qv(t) + Qb(t)(1)

where, S(t) is the sub-basin average saturation
deficit at the start of time step, Qv(t) and Qb(t) are

Table 1 Land Use distribution of the Asuwa River basin

Land UseArea (km2)%

Paddy Field

17.9

5.1

Agricultural Lands

1.1

0.3

Forest

321.6

90.8

Waste Land

3.9

1.1

Building Sites

5.1

1.4

Roads

0.4

0.1

Bare Lands

1.5

0.4

Water Bodies

2.7

0.8

input to saturation zone storage from infiltration
zone and groundwater discharge at the sub-basin
outlet during the simulating time step.
The local saturation deficit S(i,t) at grid cell i isestimated with respect to the sub-basin average
saturation deficit S(t), and the magnitude of local
soil topographical index relative to its sub-basin
average value γ. In the TOPMODEL both γ andS(t) are calculated for the whole basin4).
S(t)+ ( γ - m ln(a/T0 tan β))S(i,t) =(2)

where, m is the decay factor that controls the rateof change of conductivity with depth in the soil
profile. The value γ can be given as follows:γ=

1A∑ mi ln T0,i tani β i
a

(3)

i

The root zone (interception store) first receives
rainfall on the ith grid cell and root zone storage
Srz(i,t) changes over time as follows
Srz(i,t)=Srz(i,t-1)+R(i,t)-E(i,t)(4)

Where R is rainfall and E is the potential
evapotranspiration. If available water in theinterception zone is not enough to meet
evapotranspiration demand, model takes water frominfiltration zone. The excess of root zone storage(Srz(i,t)- Srzmax(i,t)) overflows into the infiltration
zone and its storage Suz(i,t) can be given as
Suz(i,t) = Suz(i,t-1)+ Srz(i,t)-Srzmax(i,t)(5)

Overland flow from grid cell I, qof(i,t) can begiven as follows
qof(i,t) = Suz(i,t)-S(i,t)(6)

The decline of local transmissivity withdecreasing storage in the soil profile has been
approximated by an exponential function1).

Hydraulic gradient is assumed parallel to the- 69 -

ground surface. Groundwater discharges fromsub-basin outlets are determined from Eq. (7).
qb(i,t) = T0 e-S(i,t)/m tan β(7)The overland flow from each grid and subsurface
flow calculated at sub-basin outlets are routed toTenjinbashi gauging station. Total discharge fromthe cell and surface run on to the cell is routed to thedown stream along the drainage network determinedfrom 50 m resolution digital elevation model
distributed by Geographical Survey Institute whichis under the Ministry of Land, Infrastructure andTransport using Muskingum-Cunge flow routing
method.
(2) Parameterization
Four model parameters namely, lateraltransmissivity under saturated conditions, decay
factor, maximum root zone storage, and flood plain
Manning’s coefficient, are assigned as functions ofland use (Table 1). The Ministry of land
infrastructure and transport published the land coverdetails of Japan in regular intervals. The data
published in 1997 was used to detect the land use
distribution of the study area.
Ministry of Land Infrastructure and Transport
distribute digital data sets of soil of Japan at 45"
longitude and 30" latitude resolution. Surface
geology distribution of the study basin is depicted inFig. 3. Because of the difficulties in finding highresolution reliable soil property databases which cancorrelate T and m of each grid, it is assumed that soil
properties are largely correlated to land use. This
assumption is likely to be valid for a basin that has
not significant human interference on its naturalenvironment like the Asuwa River basin.
The Manning’s roughness coefficient along themain stream and tributaries are assigned as afunction of slope and the best Manning’s coefficientvalue at the most downstream location5). Thresholds
for effective drainage area values were defined todifferentiate the mainstream, tributaries and flood
plains. Average Manning’s coefficient values foundfor both the main river and tributaries are 0.022 and
0.030 respectively. Flood plain Manning's
coefficients were assigned based on land use type of
the pixel.
(3) Results
The distributed hydrological model was applied
to simulate hydrological processes of the Asuwa
River basin. High flows spilled the river banks
before passing Tenjinbashi and observed discharges
are controlled by the hydraulic structure near to thegauging station. These happenings can not be
simulated by the hydrological model. Therefore theGraniteGreen Tuff
Sand Stone
Sand Stone + (mudstone, tuff, shale)
Unconsolidated gravel
Unconsolidated sedimentFig. 3 Distribution of surface geology of the study area

Tenjinbashi
RB 3

RB 5

RB 2

RB 4
RB 1

RB 6

RB 7

Fig. 4 Locations of the bridges subjected to sever flooding

model calibration was carried out considering higher
observed peak discharge than the recorded. This
assumption enhanced the upstream ungauged
simulated discharges before spilling the river banks.
The distributed river discharges at different points
were simulated along the mainstream and tributariesof the Asuwa River basin. The simulated riverdischarges shown in Fig. 7 and 10 are used to
discuss the bridge collapses in the section.

5. ANALYSES OF COLLAPSE BRIDGES
Ishino et al.(2005) studied collapsing mechanismof railway bridges along the Asuwa Riverconsidering constant river discharge, 2400 m3/s 2).

- 70 -

Table 2 Comparison of improved bending stresses with bearing capacities of the concrete pears of the collapses bridges

River RiverRB # Width Bed
(m)
Slope
1
3
5
7

108
75
74
58

Height between FlowσoldΣM
Deck and
Velocity(ton.m) (N/mm2)
foundation (m)
(m/s)

1/200
1/87
1/370
1/270

11.5
11.6
12.3
12.4

2.9
4.8
3.8
4.6

184
484
342
645

0.17
0.45
2.86
4.75

They have compared bending stresses with bearing
capacities at 7 railway bridge (RB) piers. As shownin the Fig. 4, all seven analyzed JR Etsumi Hoku
Line railway bridges are located within 1.5
kilometers from the Tenjinbashi gauging station of
the Asuwa River.
Computed values of the bending stress and thebearing capacity of the collapsed railway bridges areshown in Table 2. The calculation was madeexactly as shown in Ishino et al.2). Improvements
were made in the section of calculating bending
stress with computed distributed river discharges
Drag force (Fd) which is proportional to bending
stress (σ), is proportional to the square of flow
velocity. Flow velocity (V) is proportional to riverdischarge (Q). Hence multiplication factor (MI) is
estimated based on following scenarios.

σ ∝ Fd ∝ V 2 ∝ Q 2(8)MI = (Q new / 2400) 2(9)

σ new = σ old × MI

(10)

σca
2

(N/mm )

σnew
Peak River
3
Flow (m /s) (N/mm2)

0.18〜2.6
0.18〜2.6
0.18〜2.6
0.31〜3.3

2650
2425
2368
2323

0.19
0.45
2.82
4.60

Fig. 5 Flood situation of the RB1 at 11:24(source Fukui Newspaper)

where σnew and σold are the bending stresses
calculated using simulated river discharges and2400 m3/s respectively.

- 71 -

Fig. 6 Collapsed RB 1
RainfallSimulated Discharge
Observed Discahrge

3000

40

20: 00:00

19: 18:00

19: 12:00

19: 06:00

160
19: 00:00

0
18: 18:00

120

18: 12:00

1000

18: 06:00

80

18: 00:00

2000

Fig. 7 Flood Hydrograph at Tenjinbashi (RB 1)

Rainfall (mm/hour)

04000
Discharge (m3/s)

Flood situation of the collapsed railway bridge 1
is shown from Fig. 5 to Fig. 7. High river flow has
submerged the railway bridge before its piers werecollapsed. According to the Flood hydrograph at
Tenjinbashi the maximum observed flow was 2320
m3/s. However as discussed earlier, resulted highflows exceeded river levee height and over flowed
to the flood plains resulting smaller observed riverflows at Tenjinbashi. Also river flow controlling
structures upstream of Tenjinbashi blocked the freeriver flow, and back-water propagation had been
observed. Simulated discharge at the Tenjinbashi is2651 m3/s. According to the simulated hydrograph,the falling limb of the hydrograph has very steep
slope. However observed hydrograph lags the highflows in falling limb due to returns flows of spilledwater.
Fig. 8 shows the collapsed bridge at the RB 3.

Peak flow at RB 3 was 2425 m3/s. In the case ofRB7, where river width is 58 m, peak discharge is2323 m3/s inducing higher bending stress (4.75N/mm2) and totally washed away the railway bridge.
Fig. 9 and Fig. 10 show the collapsed bridge and theflood hydrograph of the section.

7. CONCLUSION REMARKS
Sub-basin TOPMODEL and Muskingum Cunge
flow routing with distributed parameterization using
land use distribution was used to simulatehydrological processes of the Asuwa River flood
disaster in July 2004.
Radar derived rainfall at 96 locations were used
as input to the model. Analysis was made to study
the importance of using radar derived rainfall
instead of limitedly available ground measured
rainfall. It was found that the basin average values
estimated from different interpolating techniques
and the radar derived rain have subtle differences.
Nevertheless spatial distribution of rainfall which
ultimately controls river discharges of medium tolarge-scale basins shows significant difference
during the storm.
Bending stresses of the damaged railway bridgepiers were compared with the bearing capacity of
the construction materials at the time of collapse.
The distributed hydrological model can be used to
study the bridge collapsing scenarios due to highflows at any location of the basin. This methodology
can be used to understand the vulnerability of river
structures in ungauged river watershed.

Fig. 8 Collapsed RB 3

Fig. 9 Collapsed RB 7
Rainfall

40

1800

60
1200

80

20: 00:00

19: 18:00

19: 12:00

19: 06:00

120
19: 00:00

0
18: 18:00

100
18: 12:00

600
18: 00:00

REFERENCES

20

Simulated Discharge

Rainfall (mm/hour)2400

18: 06:00

Run off (m3/s)

ACKNOWLEDGMENT: The authors would liketo acknowledge the River Environment Fund
(Principle Researcher: Prof. N. Tamai).
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K.:Estimation of Heavy Flood Discharge on Fukui Rainfall
Disaster 2004 and Some Recommendations on Flood Control
Planning for small scale catchments, Annuals of Disas. Prev.
Res. Inst., Kyoto Univ., No. 48 B, 2005, in printing.
2) Ishino, K., Umeda, S. and Tamai, N.: Study on the causes ofcollapsed bridges during the disaster in Fukui 2004.7 andinvestigation on extending life of bridges, Advances in River
Engineering, JSCE, Vol.11, pp.157-162, 2005.
3) Kato, T. and Aranami, K.: Formation factors of 2004
Niigata-Fukushima and Fukui heavy rainfalls and problems
in the predictions using a cloud-resolving model, SOLA,Electronic Journal of the Meteorological Society of Japan,
Vol.1, pp.1-4, 2005.
4) Beven, K.J. and Kirby M.J.: A physically based variable
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5) Takeuchi, K, Tianqi, A. and Ishidaira, H.: Introduction ofblock-wise of TOPMODEL and Muskingum–Cunge
method for hydro-environmental simulation of large

0

3000

Fig. 10 Flood Hydrograph at RB 7
ungauged basins. Special Issue of Hydrological science,Vol. 44, no 4, 633-645.1999.
6) Cunge, J.A.: On the subject of a flood propagation
computation method (Muskingum Method). Journal of
Hydraulic Research, Vol.7, pp.205-230, 1969.
7) 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.

- 72 -(Received September 30, 2005)