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

Predictions of Heavy Floods in 2004 and 2005 in Japan and PUB
Y. Tachikawa, G. Lee, N. R. Pradhan, S. Furuichi, K. Kobayashi, T. Sayama, K. Takara
Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto 611-0011, Japantachikawa@flood.dpri.kyoto-u.ac.jp
Introduction
In 2004, record-high ten typhoons and severe rainfall fronts caused heavy rainfall
disasters with 232 casualties in Japan. In 2005, a severe typhoon, which brought
historical rainfall with more than 1,000 mm in two days in the eastern part of the Kyushu
region, claimed 19 lives. The heavy rainfall events caused slope failures in many parts ofmountainous areas and inundations along rivers, which mainly occurred at tributarycatchments with several hundreds square km. River managements of these catchments
are usually conducted by prefectural governments. In most situations, the river
improvements are hard to realize in the near future.
In these catchments, to assess safety level at the current state of river basins is the basis todesign a river development program for the future. To develop a real-time flood runoff
prediction system to issue a flood warning is also an urgent task to save lives. To achievethese purposes, hydrologic predictions by a reliable rainfall-runoff model are
fundamental. However especially for small-scale catchments with several hundreds
square km organized by local governments, accumulations of hydrologic data to develop
and validate hydrologic models are quite insufficient. Generally, floods at small-scale
catchments is sensitive to space and especially time distributions of rainfall patterns.
Therefore, flood runoff predictions for small-scale catchments need more detailedhydrologic information rather than for large-scale catchments with more than several
thousand square km. In addition, flood data with a magnitude of a design flood level orabove the level does not exist in most situations. Thus, rainfall-runoff models for smallscale size (several hundreds square kilometers) should be closely examined theirpredictive performances especially for historical largest-ever floods under limitations ofavailable hydrologic data.
In this paper, we test a physically based distributed rainfall-runoff model for historicallarge floods, which is based on topographic representations by grid based DEMs andkinematic flow routing (Shiiba et al., 1999; Ichikawa et al., 2001; Tachikawa et al., 2004).
The hydrologic model is applied to the upper part of the Asuwa River basin (351km2) toexamine the predictability of the distributed runoff model for the 2004 Fukui flood. The
same model is also applied to the Kamishiiba Dam basin (210 km2) for the Kyushu heavy
rainfall in 2005. Then, we analyze the causes of the differences between predicted andobserved floods; discuss the sources of flood prediction uncertainty and a direction to
improve flood discharge predictions.
Physically based distributed rainfall-runoff model
Figure 1 illustrates the catchment topographic model using DEMs processed with thealgorithms by Shiiba et al. (1999). A slope segment is represented by a rectangle formedby the adjacent two grid points determined to have the steepest gradient. In Figure 1,
three flow lines (two inflows and one outflow) connect to the grid point A and two flowlines (one inflow and one outflow) connect to the grid point B; thus 1/3 of the grid area A
and 1/2 of the grid area B are allocated to form the area of the slope segment AB. The

slope width is determined by dividing the area by the slope length AB. The catchment
topography is represented as a set of these slope segments. Figure 2 shows the
topography model for the Maruyama River basin (1,115km2) using a DEM with 250m
grid resolution. According to the flow directions shown in Figure 2, the slope flow isrouted one dimensionally. Then the slope discharge is given to the river flow routing
model, and finally the river flow is routed to the catchment outlet.
In each slope segment, the slope is assumed to be covered by a soil layer composed of acapillary soil layer and a non-capillary soil layer above bed rocks (Fig. 3). In the capillarysoil layers, sub-surface flow is modelled as saturated-unsaturated Darcy flow withvariable hydraulic conductivity, and saturated Darcy flow is assumed in the non-capillarysoil layer. If the depth of water exceeds the soil water capacity, overland flow happens.
These processes for each slope segment are represented with a kinematic wave modelusing a function of the discharge-stage relationship (1) as illustrated in Fig. 3 (Tachikawa
et al., 2004) and the continuity equation (2):

A

A
B

B
slope segment= (1/3 of grid area A)+(1/2 of grid area B)

Fig. 1 Catchment topography modeling by Shiiba et al. (1999).

Catchment Outlet

40
(km)

30

20

10

0
0

10

20

30

40

(km) 50
Fig. 2 Watershed model with 250m grid DEM for the Maruyama River basin (1,115km2).

q
q = vmdm + va(h-dm)+α(h-da)

Surface flowm

Non-capillary poreq = vmdm + va(h-dm)
Saturated flowdaD

Capillary poreq = vmdm(h/dm)dm

βUnsaturated flow
Soil
(a)

0

dm

da

h

(b)

Fig. 3 Slope soil model and discharge stage relationship (Tachikawa et al., 2004).

⎧v m d m (h / d m ) β , 0 ≤ h < d m⎪(1)
q = ⎨v m d m + v a (h − d m ), d m ≤ h < d a⎪m⎩v m d m + v a (h − d m ) + α ( h − d a ) , d a ≤ h∂q ∂h+= r ( 2)∂x ∂hwhere q is discharge with unit width; h is flow depth; r is rainfall intensity; vm = kmi, va =kai, km = ka/ β , α = i / n ; i is gradient of slope segment; km is saturated hydraulicconductivity for the capillary soil layer; ka is hydraulic conductivity for the non-capillarysoil layer; n is surface roughness coefficient; dm is the capacity of water depth for thecapillary soil layer; and da is the capacity of water depth including capillary and noncapillary soil layers. The model parameters to be determined are n (m-1/3s), ka (m/s), da(m), dm (m), and β . For channel flow routing, surface flow with rectangular cross section
is assumed for kinematic wave approximation.
Discussion on Fukui 2004 flood simulations and predictions (Tachikawa et al, 2006)
The nine floods with more than 400 m3/s peak discharge were selected from the discharge
data since 1978. For each flood, spatial distributions of hourly rainfall with 3 km gird
resolution were generated from the ground gauged rainfall measurements using thenearest neighbor method; then the five model parameters above mentioned that define thestage-discharge relationship are determined. The initial water depth at each slope segment
is determined from the initial river discharge at the outlet assuming a steady state
condition. Table 1 summarizes the results of parameter identifications. To evaluate
appropriateness of the simulated discharges, the peak discharge ratio and the NashSutcliffe efficiency are used. The evaluation results suggest that the identified model
parameter sets are classified into three groups: group 1 with the 1993, 1981 and 1982
flood; group 2 with the 1985, 1983 and 1979 flood; and group 3 with the 1989, 1990 and2004 flood. The parameter sets in the group 1 have tendency to overestimate and thegroup 3 to underestimate floods; the group 2 shows both tendencies. The estimated peakdischarges are widely distributed from 2500 to 4200 m3/s, and it is larger than theestimated peak discharge 2400 m3/s obtained by the Construction Ministry fromhydraulic river flow simulations with the high flood stage marks.

Table 1 Model parameter values fitted to each year flood and the characteristics of eachyear heavy rainfall and flood discharge.
Properties
Parametersn (m-1/3s)ka (ｍ/s)da (ｍ)dm (ｍ)
da-dm (ｍ)β (-)
Peak discharge(m3/s)
Initial discharge(m3/s)
6 hours rainfall
R6h (mm)
2 days rainfall
R2d (mm)
Rainfall ratio
R6h/R2d
Station numberGroup 1:(overestimating peakdischarge)
1993
1981
1982
0.4
0.4
0.4
0.01
0.03
0.01
0.25
0.4
0.2
0.18
0.35
0.15
0.07
0.05
0.05
24
24
12

Group 2:(over/underestimating
peak discharge)
1985 1983
1979
0.4
0.4
0.4
0.01
0.03
0.01
0.2
0.6
0.17
0.1
0.15
0.1
0.1
0.45
0.07
8
12
4

Group 3:(underestimating peakdischarge)
1989 1990
2004
0.4
0.4
0.4
0.01
0.01
0.01
0.25
0.325 0.26
0.18
0.2
0.16
0.07
0.125 0.10
4
8
4

548

1117

676

542

758

622

608

447

2400

11

65

18

86

36

37

44

17

25

60

73

42

43

54

84

67

56

265

116

163

136

116

169

103

174

127

297

0.52

0.45

0.31

0.37

0.32

0.82

0.39

0.44

0.89

10

4

7

10

4

6

10

10

12

Within a group, the values of model parameters are close, and floods simulated with any
parameter sets show good scores of the peak ratio and the Nash efficiency. The clear
difference among the groups is that the value of β is larger and the capacity of noncapillary layer da-dm is smaller in the group 1 as compared to the group 3. The differenceof parameter values represents the difference of hydrologic characteristics. In the group 1,
rainfall stored in soil layer flows quite slow, therefore at the beginning of floods, riverdischarge is insensitive to rainfall intensity. Then the soil layer is easily saturated andonce the water depth at slope segments exceeds the capacity of the capillary soil layer,
river discharge rises up suddenly. On the contrary, the parameter sets in the group 3 tends
to show opposite characteristics that a hydrograph rises up from the beginning of rainfall
and its peak discharge is smaller when the same rainfall is given to the runoff model withparameter sets in the group 1. The group 2 shows the middle feature of the group 1 and 3.
The question is why the difference is observed in the same catchment. Each group
includes various scales of floods and there are no distinguished characteristics in therainfall and discharge data to form three groups. One of clear difference is the number ofrainfall observatories. All floods in the group 3 were observed with more than 10 rainfall
stations. It is inferred that the accuracy of rainfall observations affects the value of thetuned model parameters and makes large prediction uncertainty. Another possible reason
that makes the large prediction uncertainty is the setting of initial condition for prediction
simulations. The groups 1 and 2 have a flood observed with 10 rainfall stations. The
difference among the floods with more than 10 rainfall observations is the initialdischarge. The 1993 flood has the smallest initial discharge; while the 1985 flood has thelargest initial discharge in these floods. For the 1985 flood, the recession of riverdischarge is clearly observed. This implies it was not correct for the 1985 flood to assume
the steady state condition at the beginning of simulation. Rainfall is spatially distributed
and the distributions are memorized in the spatial distribution of soil moisture, therefore

if the initial condition setting is inappropriate the resultant model parameter values are
obtained wrongly.
For the 1993 flood, a sudden rising up of river discharge followed dry condition. To
simulate the flood with the runoff model used here, the value of parameter β is needed to
be set in a large value to keep water in soil layer for lasting small discharge at the outlet.
If the low flow observation in 1993 is correct, the improvement of model structure
including the refinement of the discharge stage relationship and the initial condition
setting are the key to improve the flood runoff prediction for the rainfall-runoff model.
Discussion on Kamishiiba 2005 flood simulations and predictions
The peak discharge of the 2005 heavy rainfall at the Kamishiiba Dam site (Fig. 4, 210km2) is estimated about 1,800 m3/s by Kyushu Electric Power Co., Inc. The estimatedpeak discharge value is the highest record after the dam was constructed in 1955, which
is more than 1.5 times larger than the floods recorded in recent ten years. The catchment
mean rainfall in two days is 831 mm and the highest hourly rainfall is 45 mm. We applied
the same distributed hydrologic model to predict the floods in this catchment. The rainfall
data used is observed by radar with 2.5 km spatial resolution calibrated by ground gauged
rainfall measurements. The discharge data was estimated at the Kamishiiba Dam site withthe relation between the water stage at the dam reservoir and the dam release.

27500

25000

27500
7500

22500

20000

17500

15000

25000
5000

12500

10000

7500

5000

22500
2500
0

2500

5000

7500 10000 12500

2500

5000

Fig. 4 Watershed model for the Kamishiiba basin (210 km2). The location is specified
using UTM coordinate with m unit. The right figure shows enlarged illustration.

Table 2 Model parameter values fitted to floods and the characteristics of each year
heavy rainfall and flood discharge at Kamishiiba catchment.
Parametersn (m-1/3s)ka (ｍ/s)da (ｍ)dm (ｍ)
da-dm (ｍ)β (-)
Peak discharge(m3/s)
Initial discharge(m3/s)
Total amount of
rainfall (mm)Total amount ofdischarge (mm)
Runoff ratio

Sept. 1997

June 1999

Aug. 1999
0.3
0.01
0.55
0.45
0.10
4

Sept. 1999

Sept. 2005

1203

210

489

644

1840

33

59

82

34

11

496

463

473

339

831

415

238

237

256

780

0.84

0.51

0.50

0.77

0.94

(a) 1997 September flood(b) 1999 June flood(c) 1999 August flood(d) 1999 September flood

Fig. 5 Observed and simulated hydrographs for the Kamishiiba basin in 1997 and 1999.
Table 2 summarizes the characteristics of the floods used and identified model
parameters. Figure 5 shows the observed and simulated floods in 1997 and 1999. The
model parameter set was determined using the flood in 1997 and it was successfully
applied to reproduce the floods in 1999. Figure 6 represents the observed and simulatedhydrograph for 2005 largest ever flood. For the discharge simulation, the ground-gauged
rainfall was used, because the radar rainfall was much underestimated the ground-gauged
rainfall and the spatial distribution pattern of the event was not significant. In contrast toRainfall (mm/hr)

the simulation results at the Asuwa River basin, one parameter set is applicable to predict
the floods with the peak discharges ranging from 210 to 1,800 m3/s and runoff ratio from0.50 to 0.94. The simulation results support that the model structure well reflects thecharacteristics of runoff properties for the mountainous catchment.
0

24

48

72

96

120

144

168

192

216

240

0
10
20
30
40
50
2000
simulated_discharge
observed discharge

1800

Discharge (cms)

1600
1400
1200
1000
800
600
400
200
0
0

24

48

72

96

120

144

168

192

216

240

Time (hr)Fig. 6 Prediction of the 2005 flood with identified parameter values.
Discussion on sources of prediction uncertainty and a direction to improve flood
discharge predictions
The state of the art physically based rainfall-runoff model shows large uncertainty to
predict the largest ever 2004 flood in the Asuwa River basin, while quite good prediction
is observed for the largest ever 2005 flood in the Kamishiiba basin. We also obtained
good simulation results applying the same model at the Yura River basin (1,860 km2,
Kobayashi et al, 2006) and the Maruyama River basin (1,115 km2, Tachikawa andFuruichi, 2006). Through the experiments of the hydrologic simulations in thesemountainous catchments, we think the cause of prediction uncertainty mainly comes fromthe insufficiency of the observed hydrologic data. At the Asuwa River basin, the numberof the rainfall observations would be a key factor to make better prediction; while at theKamishiiba basin, rainfall data is obtained by radar with ground measurement calibration,
and all the study floods are well reproduced by the same model parameter set.
If we use erroneous hydrologic observed data, we misunderstand the hydrologic
characteristics and it leads to develop a hydrologic model with an ill-chosen hydrologic
model structure. The identified model parameters also become erroneous, which resultsin large prediction uncertainty. We cannot avoid the input data insufficiency. Therefore,
the important research themes are:
1. Development of a method to diagnose observed and simulated data to identify input
data uncertainty; and2. Analysis of the behaviors of hydrologic models and prediction uncertainty throughuncertainty of identified model parameter and model structure that comes from input
data uncertainty.
Input data, model structures and model parameters are not free from errors. The
important is to access the prediction error ranges, to understand the causes of the errors
and to reduce the width of the error range. To make better predictions in ungauged basins,
we need to make efforts to use the knowledge obtained at gauged basins to transfer toungauged basins. The research topics to be addressed include:1. Development of a method to transfer model structures and parameters across scales
and regions;
2. Development of a method to diagnose input uncertainty;
3. Development of methods for estimating frequencies of hydrologic extreme events inungauged basins by using scaling, regionalization, and historical record analysis;
4. Development of assessing methods for hydrologic model performance through adevelopment of uncertainty evaluation indices;
5. Hydrological modeling considering interaction between natural variation andanthropogenic activities; and6. Downscaling of global hydrologic information for local scale watershed managements
in ungauged basins
Concluding remarks
In this paper, we have demonstrated the prediction uncertainty through the hydrologic
simulations at two mountainous catchments using a physically based distributedhydrological model. The simulation results suggest that input data insufficiency would bea main source of prediction uncertainty. Further works need to deal with a diagnostic way
to examine input data uncertainty and to analyze the hydrologic model behaviors thatcomes from input uncertainty through parameter and model structure uncertainty. In
addition, to develop transferable hydrologic models and estimating methods forhydrologic extremes across scales and regions are significant research themes.
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