Predictions in Ungauged Basins: PUB Kick-off (Proceedings of the PUB Kick-off meeting held in Brasilia,
20–22 November 2002). IAHS Publ. 309, 2007.

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A distributed runoff model for flood prediction inungauged basins
TOSHIHARU KOJIMA, KAORU TAKARA &YASUTO TACHIKAWA
Disaster Prevention Research Institute, Kyoto University, Kyoto, Japankojima@green.gifu-u.a.cjp

Abstract Lumped parameter models are quite difficult to apply to ungauged
or information-poor basins, because such models need model parameters
calibrated using historical hydrological data. On the other hand, distributedparameter runoff models are suitable for ungauged basins, because they havethe ability to define model parameter values based on spatially distributed datasuch as topographic data, land-use data, radar rainfall and remote sensing
imagery. This paper introduces the development of the grid-cell based
distributed Kinematic Wave Runoff (KWR) model proposed by the authors in
1997, and describes its applications to two basins: the Yada River (Yadagawa) and the Shonai River (Shonai-gawa) basins in Japan. Since simulation
without model parameter optimization showed good results, the distributed
runoff model could be very useful for ungauged basins. The authors also
expanded this model into a physically-based distributed rainfall–sediment–runoff model for flood and sediment movement at a catchment scale. They
confirmed its usefulness for prediction of sedimentation in the upper Brantas
River basin, East Java, Indonesia.
Key words DEM; grid-cell based kinematic wave runoff model;
rainfall–sediment–runoff model

INTRODUCTION
Lumped parameter models are often used for flood runoff prediction and flood plain
management, because of their simple concept and relatively short computation time.
However, lumped models need model parameters calibrated using historicalhydrological data. Therefore lumped models are quite difficult to apply to ungauged orinformation-poor basins. Distributed parameter runoff models are suitable for ungauged basins because they have the ability to define model parameter values withspatially distributed data such as topographic data, land-use data, radar rainfall andremote sensing imagery. The authors developed the grid-cell based distributed
Kinematic Wave Runoff (KWR) model in 1997 (Kojima, 1997; Kojima et al., 1998).
This distributed runoff model needs geographical data such as flow direction, slopeand land cover on each grid-cell for developing the hydrological modelling. And themodel parameters, and equivalent roughness coefficients are defined using land
cover/use based on either the National Land Information developed by GeologicalSurvey Institute (GSI) or remote sensing imagery.
This paper introduces the development of the grid-cell based distributed KWR modeland describes its applications to two basins: the Yada River (Yada-gawa) and the Shonai
River (Shonai-gawa) basins, Japan. The authors also expanded this runoff model into aCopyright © 2007 IAHS Press

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physically-based distributed rainfall–sediment–runoff model for flood and sediment
movement at the catchment scale in the upper Brantas River basin, East Java, Indonesia.

GRID-CELL BASED KINEMATIC WAVE RUNOFF (KWR) MODEL
The KWR model has the following features:– A d-m square area on a node point of a d-m DEM is considered as a sub-basin,
which is called a grid-cell (cell). The whole river basin consists of many squarecells.
– A DEM produces drainage paths. A drainage path connects each upper cell withlower cells. Discharge from an upper cell to the lower cells flows to only one
direction of eight neighbourhood cells.
– In each cell, the total amount of outflow from the upper cells is the inflow to thecell, and the outflow from the cell is calculated using the kinematic wave modelwith the inflow and the rainfall over the cell.
Figure 1 shows the concept of the grid-cell based distributed KWR model. The
topographic properties such as slope angle and slope length are produced with a DEMand drainage paths are based on the DEM. The model needs the equivalent roughnessof each cell. The authors investigated the optimum values of the equivalent roughnessof each land use category.

Fig. 1 Illustration of a grid-cell based distributed runoff model.

APPLICATION OF THE KWR MODEL TO THREE BASINSThe Yada River basin
The Yada River basin is about 110 km2, and located at Aichi, Japan. The basin is in theSeko upper area of the Shonai River basin (see Fig. 5). The basin is almost whollyurbanized, but has a forest area in the upper basin. The Yada River basin is classified
into seven land-use classes such as forest, urban, grass, paddy field, bare soil, river and269

A distributed runoff model for flood prediction in ungauged basins

outlet012km

Fig. 2 Drainage paths with 250-m resolution at the Yada River basin.

(a) 50-m resolution withradar rainfall(b) 250-m resolution with radarrainfall(c) 250-m resolution with spatiallyaveraged rainfall

Fig. 3 Comparison of simulated and observed hydrographs.

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water. In this basin, two drainage paths were produced with a 50- and 250-m DEM.
Figure 2 shows the drainage paths defined by the 250-m DEM resolution. Two gridcell based distributed KWR models were developed with 50- and 250-m resolution
drainage paths. Figure 3 shows the comparison of the observed and simulatedhydrographs. Figure 3(a) and (b) show the simulated hydrographs with the 50-m resolution model and the 250-m one, respectively. Comparison of Figs 3(a) and (b) showsthat the 250-m model has a tendency to give earlier peak time and larger peakdischarge than the 50-m model.
Radar rainfall observation is one of the solutions to prediction in ungauged basin,because it does not need many raingauges. Radar rainfall data was used with the gridcell based distributed KWR model in the Yada River basin. The radar rainfall data areabout 2-km grid spatially distributed data (see Fig. 4). The grid-cell based distributed
KWR model can take into account these spatially distributed rainfall data.
Hydrographs were simulated using the radar data and its spatially averaged rainfall
data, and the effects of the spatial distribution of rainfall on the simulated hydrographwere investigated. Figures 3 (b) and (c) show the comparison of the hydrographs using
two kinds of rainfall data. The simulated hydrograph with the spatially averaged
rainfall estimates a larger peak discharge than the hydrograph with radar rainfall.
Figure 4 shows how the greatest rainfall intensity zone moved from the lower site to
the upper site. The movement of the rainfall area affects the peak discharge volume.
The optimum equivalent roughness coefficient of forest class in this basin is 0.5.
This is within the range of values conventionally used in Japan, which is 0.4 to 0.8(River Bureau, 1998).

(a) At 3:00 am(b) At 3:20 am(c) At 3:40 am(d) At 4:00 am

Fig. 4 Distribution of rainfall. The rainfall rate is greater in the lighter areas (thelightest corresponds to about 60 mm/hour), and less in the darker areas.

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The Shonai River basin
The Shonai River basin is about 1010 km2. Its lower basin is almost all urbanized area,
while 70% of its upper basin is forest, 15% is urbanized and 15% is other land-useareas. Figure 5 shows the location of the precipitation stations (raingauges) and thedischarge sites (gauging stations) in the Shonai River basin. Discharge is observed at
Shidami, Tajimi, Toki and Mizunami. The test basin in this study is about 532 km2 at
Shidami upper site in the Shonai River basin. Figure 6 shows the variation of the threeevaluation indices, the Correlation Coefficient between observed and simulated hydrographs during the runoff event (COR), Mean Relative Error between observed andsimulated hydrographs during the runoff event (MRE), and Relative Root Mean
Square Error between observed and simulated hydrographs during the runoff event(R.RMSE), when the value of the equivalent roughness of the forest class is varied.
Figure 6 shows that the optimum roughness coefficients for the forest class at Shidami,Tajimi and Mizunami are 1.5, 1.0 and 0.5 respectively.

Fig. 5 The Shonai River basin.

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DISCUSSION
Assigning the equivalent roughness coefficients to each grid-cell based on the land
cover class, one can develop the KWR runoff model without calibration usinghydrological data. The conventionally used values of roughness coefficients depend on
the land-use category of the basin. However, the optimum values cannot be definedusing only land-use category. At the upper site in the Shonai River basin, such as at
Toki and Mizunami, the optimum values of the forest class are 0.5. At the lower site
such as Shidami, the optimum value of the forest class is 1.5. Other parameters such as
topographic and geological properties of the local areas in the basin may affect theoptimum values of the roughness coefficients.

RAINFALL–SEDIMENT–RUNOFF MODEL
A physically-based distributed rainfall–sediment–runoff model
A physically-based distributed rainfall–sediment–runoff model for flood and sediment
movement at the catchment scale was also developed. Dealing with spatial hydrological and geographical information, this type of model is useful not only forprediction of sedimentation in reservoirs and riverbeds but also for understanding of
the severe sedimentation problems caused by land-use change and deforestation. A
grid-cell based distributed KWR model considering surface and subsurface flows isconstructed here. Rainfall–sediment–runoff is modelled on each grid-cell defined by aDEM with 250-m resolution. The transportation capacity of overland flow on eachgrid-cell is calculated based on the unit stream power (USP) theory (Yang, 1996) tomodel sediment yield and sediment deposition processes physically. Sediment
generated in slope cells moves in the flow direction derived from the DEM to rivergrid-cells. Bed load and suspended load in the channel are calculated in river gridcells.

Application to the Lesti River basin
The model was applied to the Lesti River basin (625 km2) located in the upper Brantas
River basin (12 000 km2) which is the second largest river in Java Island, Indonesia.
The Lesti River basin is covered with volcanic soil due to the Mt Semeru eruption. The
Sengguruh dam, located at the junction of the Lesti and Brantas Rivers, wasconstructed in 1988 for water resources and power generation, but 85% of the grossstorage was filled with the sediment eight years after dam construction. Data sets suchas flow direction, slope and land cover on each grid-cell are necessary to develop
distributed hydrological models. In Indonesia, however, DEM and recent land-use
maps are not available, and so this research used GIS for generating a DEM andremote sensing for classifying land cover. Digitizing contour lines on a 1:50 000
topographical map of the area of interest, GIS generated a 250-m DEM. A remotesensing image was used to make a land-cover map with the maximum likelihood

273

A distributed runoff model for flood prediction in ungauged basinsclassifier. Equivalent roughness coefficient used for the KWR model was assigned toeach grid-cell based on the land cover class.

Model validation

Cumulative Sediment (m^3)
Cumulative Sed
Sediment
iment (m^3)
Rainfall(mm/hr)

A preliminary model study indicated that under the assumption of fixed slope
conditions at each grid-cell, regardless of sediment movement, the model predictedhuge deposition in some grid-cells. However, by updating elevations with sedimentyield and deposition at each time step, the model simulates deposition process at
confluences and makes riverbed change more realistic. Consequently, the model has
been improved by considering local geomorphological change caused by sediment.
The model performance was verified with historical hydrological data (hourlyrainfall and discharge sequences during a rainy season from November 1995 to April1996) and the sedimentation record in the Senggruh dam. Figure 7 shows the cumulative sediment runoff at the outlet, and the cumulative total sediment yield. Roedjito &Harianto (1995) reported that annual sedimentation at the Sengguruh dam reservoir isaround 1 × 106 m3, while simulated sediment runoff at the outlet in the rainy season isabout 0.65 × 106 m3. The simulation is relatively appropriate, if the following reasonsare considered. The simulated sedimentation does not include the sedimentation comingfrom the Brantas River. The lower panel in Fig. 7 shows the cumulative sediment yieldand deposition, indicating that a volume of 4.28 × 106 m3 of sediment had beengenerated and that 1.48 × 106 m3 had been deposited after the rainy season, in thewhole basin; therefore, about 3.00 × 106 m3 of sediment had entered the river channel.
The verification indicates that the model reproduces the sedimentation record. Further,
the volume of material eroded from the cultivated hillslopes of Mt Semeru is found to
be larger than at other places (e.g. forest areas), which is an analogous phenomenon to
the natural physical process of sediment erosion.
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Simulated sediment runoff at the outlet

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Simulated
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Simulated seSimulated sediment deposit
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96AprFig. 7 Rainfall and sediment runoff analysis in the rainy season from 1995 to 1996.

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SUMMARY
The distributed runoff model has the ability to define model parameter values withspatial data. The grid-cell based distributed KWR model was applied to two basins.
This paper shows that the distributed runoff model, which can take into account spatial
data such as remote sensing imagery, land-use data, radar rainfall and topographic data
is very useful for ungauged or information-poor basins. The grid-cell based distributed
runoff model was expanded into a rainfall–sediment–runoff model, and its usefulness
for prediction of sediment yield in a catchment scale was confirmed.

REFERENCES
Kojima, T. (1997) Application of remote sensing and GIS to hydrological analysis. Doctorate of Engineering Dissertation,
Kyoto University, Japan (in Japanese).
Kojima, T., Takara, K., Oka, T.& Chitose, T. (1998) Effect of resolution of raster spatial data on flood runoff simulation.
Ann. J. Hydraul. Engng, JSCE 42, 157–162 (in Japanese).
River Bureau (1998) Ministry of Construction: River/Sabo Technical Standard (Proposal) Investigation Section, Sankai-do(in Japanese).
Roedjito, D. M. & Harianto (1995) Controlling reservoir sedimentation in Sengguruh Reservoir, Brantas River basin. In:Reservoirs in River Basin Development (Proc. ICOLD Symposium, Oslo Norway), 281–292.
Yang, C. T. (1996) Sediment Transport—Theory and Practice. The McGraw-Hill Companies, Inc., New York, USA.