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

Development of a real-time distributed flood prediction system
in a flow regulated river basin
T. Sayama1, Y. Tachikawa1, K. Takara1, Y. Ichikawa2
1
Disaster Prevention Research Institute, Kyoto University, Japan;
2
Department of Urban and Environmental Engineering, Kyoto University, Japan
sayama@flood.dpri.kyoto-u.ac.jp
Abstract: A real-time distributed flood prediction system (RDFPS) is developed for theYodo River basin (7,281 km2) in Japan. The system predicts river discharges at 1,707
locations along the river channel network. It predicts also inflows, outflows and waterlevels of eight multi-purpose dams located in the basin. A distributed hydrologic model
used for this system is composed of 1) distributed rainfall-runoff models with 250 mresolution, 2) kinematic wave river routing models, and 3) dam operation models.
Rainfall forecasts used for driving the simulation model have six hours of the lead timewith 2.5 km resolution (5 km resolution for three to six hours of lead time). The
performance of the developed system is presented here with the initial results of ahindcast for a flooding event in September 2004.
1. Introduction
Steep mountainous landscapes and wet climate conditions in Japan feature thevulnerability to a flash flood, which is extremely dangerous because of its sudden nature.
In 2004, for example, a series of severe flash floods killed more than 230 people in Japan.
A noticeable feature of the disasters was the concentrations of their damages in tributary
river basins, where the flood safety levels are relatively low due to fewer investments onriver works and no flood control by dam reservoirs. In addition, not enough information
associated with river discharge is available for issuing accurate flood warnings or fortaking relevant counter measures.
Recent advances in numerical weather forecast and hydrologic simulation techniqueswith a distributed parameter model may enable significant improvement to predict flash
floods. More accurate and finer flood predictions in terms of magnitudes, locations, andtimings, compared to current operational ones with a lumped parameter model may bepossible by converging state-of-the-art technologies in hydrology, meteorology andcomputer sciences. In this study, we develop a real-time distributed flood prediction
system in the Yodo River basin with aims at constructing a robust system conducting dataacquisitions, simulations, predictions, data storing, and interactive displaying of predictedresults.
2. A distributed hydrologic model incorporating dam operation models
2.1 Study area
The study area of this research is the Yodo River basin in Japan. The total area of theYodo River basin (upper Hirakata) is 7,281 km2 (Fig. 1). The origin of the Yodo River isLake Biwa (670 km2), whose catchment area is 3,848 (km2). The outflow of from LakeFig. 1 Map of the Yodo River basin

Biwa is controlled by Seta weir and flows into the Uji River, whose catchment area is 506
km2. The Uji River runs through Amagase dam and joins the Kizu River and the Katsura
River. The Kizu River basin (1,596 km2) has five multi-purpose dams including
Takayama dam, and the Katsura River basin (1,100 km2) has one multi-purpose dam
called Hiyoshi dam. The lower main Yodo River flows from the confluence of the threerivers and it flows into Osaka bay through Hirakata. The catchment area of the lower
main Yodo River (upper Hirakata) is 231 km2. The mean annual precipitation of theYodo River basin is about 1,800 mm. The mean annual discharge at Hirakata is 273 m3/s.
2.2 Structure of the distributed hydrologic model
A distributed hydrologic model for the Yodo River basin is developed (Sayama et al.
2005, 2006) based on “Object-oriented Hydrological Modelling System (OHyMoS)”. The
following three kinds of element models compose the whole structure of the model.
1) River element model: The kinematic wave model is applied to a river segment, which
is prepared from digital river channel network data and the location information of Lake
Biwa. The length of each river segment is set to be about 3 km.
2) Sub-catchment element model: A digital elevation model with 250 m resolution is used
to calculate the flow direction and to delineate a sub-catchment for each river segment. A
stage-discharge relationship simulating saturated, unsaturated subsurface and surface
rainfall-runoff (Tachikawa et al. 2004) is applied to each grid-cell.
Figure 2 (a) shows a schematic diagram of the soil layer of the model. In this figure,the soil depth is D (m), the water depth corresponding to the water content is ds (m), andthe water depth corresponding to maximum water content in the capillary pore is dc (m).
Figure 2 (b) shows the stage-discharge relationship. Let kc and ka be saturated hydraulicconductivities in capillary pore and in non-capillary pore, respectively, and vc = kci, va = kai(i: slope), then the relationship between the discharge per unit width q (m2/s) and the waterFig. 2 Schematic diagram of (a) the soil layer and (b) the stage-discharge
relationship of saturated-unsaturated subsurface and surface runoff model

depth h (m) is described as follows:β⎧⎛ ⎞⎪v c d c ⎜ h ⎟ ,(0 ≤ h ≤ d c )⎜d ⎟⎪⎝ c⎠⎪(d c < h ≤ d s )q = ⎨v c d c + v a (h − d c ),⎪m⎪v c d c + v a (h − d c ) + α(h − d s ) , (d s < h )⎪⎩where α equals to i1/2/n; n is Manning roughness coefficient (m-1/3/s-1). A non dimensionalparameter β is introduced to describe the reduction of hydraulic conductivity in capillarypore as the water content decreases. β equals to ka/kc so as to keep the continuity of thedepth–discharge relationship between the capillary pore and the non-capillary pore layers.
Combining this depth–discharge relationship and the continuity equation, we simulate
rainfall–runoff from each grid-cell. The water flow is routed until it reaches a riversegment.
3) Dam element model: By formulating the dam operation rules and decision-making
processes of dam operators, a dam operation model is developed (Ichikawa, 2001). Itpredicts the outflow and water levels of a dam with the input information of inflow,
average rainfall in the dam catchment, and operation status of other related dams. The
dam operation model is applied to eight multi-purpose dams in the Yodo River basin.
Although each dam has different operating rules, we can categorize all the flood
control operations into the following six common operation processes: Ordinaryoperation; Operation under flood warning; Preliminary release operation; Peakattenuation operation; Flood release operation; and Post flood operation. Each dam isalways under one of the six operations, and we formulate the conditions to shift from one
operation to another with if-then equations.
3. Development of a Real-time Distributed Flood Prediction System (RDFPS) withthe distributed hydrologic model
3.1 Outline of RDFPS
A Real-time Distributed Flood Prediction System (RDFPS) is developed based on theFig. 3 Flow chart of the real-time flood prediction

distributed hydrologic model for the Yodo River basin. RDFPS predicts river discharges
at 1,707 locations in the basin and the reservoir inflows, outflows and water levels at theeight multi purpose dams. It is operated automatically every an hour on a real-time basis
to simulate the current river discharges by forcing the model with radar and in-situ
composite rainfall data observed in the last an hour. Then RDFPS predicts riverdischarges for the next six hours by using rainfall forecast. Forecast rainfall is obtained
through Meteorological Information Comprehensive Online Service (MICOS) of Japan
Weather Agency (JWA). Observed river discharge at nineteen locations and observed dam
reservoir inflow, outflow, and water levels at the eight dams are also obtained from a
database maintained by Ministry of Land Infrastructure and Transport on a real-time basis.
The river discharge data is currently used only to check the simulation performance. The
detail of the rainfall data, the procedures, and a visualization system are presented in thefollowing sections.
3.2 Rainfall data used by RDFPS
1) Radar-AMeDAS composite precipitation (RAP): Automated Meteorological Data
Acquisition System (AMeDAS) maintained by Japan Meteorological Agency collects
precipitation amounts from more than 1,300 in-situ stations at an average interval of 17km throughout Japan. It is operated every ten minutes on a real-time basis. Weather radarobservation network of JMA consists of twenty C-band radars, which are also operated atten minute intervals. Radar-AMeDAS composite precipitation (RAP) is used as observed
rainfall data to simulate state variables of the distributed hydrologic model for the last anhour. The spatial and temporal resolutions of RAP are 2.5 km and 10 minutes,respectively.
2) JWA Ultra short-range forecast of precipitation (USRF): Ultra short-range forecast ofprecipitation is issued by Japan Weather Agency (JWA) to provide 10 minuteprecipitation forecast for the next three hours with 2.5 km resolution. The forecast
algorithm is based on a radar extrapolation. RDFPS uses this data to run the hydrologic
model for the next three hours.
3) JMA Very short-range forecast of precipitation (VSRF): A Very short-range forecast ofprecipitation is issued twice an hour to provide one-hour precipitation forecast for thenext six hours with 5 km resolution. This forecast is derived from the combination of theMesoscale Model (MSM) predictions and the extrapolation of RAP data. RDFPS uses
this data to run the hydrologic model from three hour ahead to six hour ahead.
4) JWA Mesh Forecast of Precipitation (MFP): A Mesh Forecast of Precipitation is issued
twice a day (at around 3 and 15 o’clock) to provide one-hour precipitation forecast for thenext 51 hours with 5 km resolution. This forecast is derived from a numerical weather
prediction model operated by JWA. The current version of RDFPS does not use this MFPdata and the maximum lead time of the current RDFPS is set to six hours.
3.3 Procedure for the real-time flood prediction
The procedure executed every an hour by RDFPS is illustrated in Fig. 3. The procedure iscomposed of the following main four steps: processing rainfall data, simulating currentconditions, predicting future conditions, and storing simulated and predicted results in adatabase. The detailed descriptions for each step are given here. For the clear illustrationof the procedure, let assume that it is now 10:15 and the current state variables of thehydrologic model represent the conditions at 9:00.
1) Processing rainfall data: RDFPS prepares two sets of rainfall data; one is observed
rainfall data from 9:00 to 10:00, and the other one is predicted rainfall data from 10:00 to16:00. As it is mentioned above, the current version of RDFPS uses RAP data asobserved rainfall, and USRF, VSRF as predicted rainfall from 10:00 to 13:00 and from13:00 to 16:00, respectively.
2) Simulating current conditions: RDFPS updates the state variables from 9:00 to 10:00
with the observed rainfall. It calculates also average rainfall in selected twenty nine subcatchments (among them, observed discharge data are available at the outlets of nineteencatchments). The calculated average rainfall data are used to draw hyetographs with avisualization system that is described below in 3.4. Average rainfalls over dam
catchments are also calculated because they are used for running the dam operation
models as well as for displaying the hyetographs.
The simulated state variables of the distributed hydrologic model are saved in files topredict river discharge for the next six hours from 10:00 to 16:00 with the predicted
rainfall and also to update the state variables from 10:00 to 11:00 with the observed
rainfall that will be available in the next time step.
3) Predicting future conditions: Using updated state variables as initial conditions,
RDFPS predicts river discharge and dam reservoir inflows, outflows, and water levels forFig. 4 Flood prediction visualization system

the next six hours (from 10:00 to 16:00). It calculates also predicted average rainfall over
the twenty-nine catchments and the dam reservoir catchments.
4) Storing simulated and predicted results in a database: Simulated and predicted results
are stored in a relational database, which is accessed by the visualization system. The
following items are stored with an hour time step interval.
- Observed discharge at nineteen locations- Observed average rainfall over twenty-nine catchments- Predicted average rainfall over twenty-nine catchments (1, 2, …, 6 hours lead time)- Simulated discharge at twenty-nine locations- Predicted discharge at twenty-nine locations (1, 2, …, 6 hours lead time)- Simulated discharge at 1,707 locations- Predicted discharge at 1,707 locations (1, 2, …, 6 hours lead time)- Observed inflows, outflows, water tables at the eight dams
- Simulated inflows, outflows, water tables at the eight dams
- Predicted inflows, outflows, water tables at the eight dams (1, 2, …, 6 hours lead time)
All the tables of the database have year, month, day, hour, and through time in second
from the beginning of a year. The time represents observed, simulated, predicted “target”time, for example, a six hour-ahead prediction obtained at 10:15 will be stored in thetable of six hour lead time, and the time appeared in the table will be 16:00.
3.4 Flood prediction visualization system
We developed an interactive visualization system to view the prediction results throughthe Internet. The contents of the visualization system are 1) the animation of current andpredicted rainfall and discharge distribution maps, 2) predicted hyetographs andhydrographs at the selected nineteen locations as well as at the eight dams. In additions to
the interactive visualization system, we developed an automatic renewal visualization
system, which is illustrated in Fig. 4. It shows the current and the next six hours

Fig. 5 Hindcast results at Hiyoshi dam (Oct. 19 – 23, 2004)

distributions of rainfall and discharge as well as hyetographs and hydrographs at thenineteen locations automatically.
4. Hindcast results with a flooding event in 2004
In order to investigate the performance of the developed system, we conducted a hindcast
for a large flood event caused by typhoon 23rd in 2004. During this typhoon event, asevere storm was observed in the Yodo River basin, particularly in the Katsura Riverbasin. The total rainfall amount in the Hiyoshi dam catchment located in the Katsura
River basin was 255 mm for the three days from October 19 to 21. Although thepresented results here are not obtained on a real-time basis since the actual operation ofRDFPS was started in April 2006, the conditions used here for this hindcast are the sameas those used for the real-time operation. Only a difference between this and the real-time
operation is that the prediction time horizon is limited up to three hours ahead in thishindcast since VSRF data was not available for the period. The used rainfall data,
therefore, are only RAP and USRF.
Figure 5 shows the observed and calculated hyetographs and inflows at Hiyoshi dam.
The hyetograph with the thicker line shows the rainfall from RAP, while the hyetographwith the thinner line shows three hours ahead forecast from USRF. Simulated hydrographwith RAP data reproduces the observed inflow reasonably, while the predicted peakdischarge with USRF is overestimated compared to the observed discharge due to theoverestimation in the rainfall forecast of USRF.
Figure 6 shows the simulated results at Kamo, which is located at the downstream ofthe Kizu River basin. The total rainfall amount over the Kamo catchment was 181 mm.
There is a discrepancy between simulated and observed peak discharges with the relative
error of 0.21. In addition, more significant discrepancy was found between the observed
and forecasted rainfalls. However, in spite of this significant error in the rainfall forecast,
the three hours-ahead discharge prediction with USRF data and the simulated discharge
with RAP data are close with the relative peak error of 0.07. It implies that a predicteddischarge whose catchment area is larger than a thousand square km is less sensitive tothe errors in rainfall forecast and depends more on the hydrologic simulations.

Fig. 6 Hindcast results at Kamo (Oct. 19 – 23, 2004)
5. Conclusions

This paper presented the development of a real-time distributed flood prediction system(RDFPS), which uses a distributed hydrologic model incorporating dam operation models.
Using radar in-situ composite rainfall data observed in the latest an hour, the model
updates the state variables representing soil moisture distributions, river discharges, anddam reservoirs’ conditions. The updated state variables are used as initial conditions of
the model to predict future conditions with rainfall forecast of six hours lead time.
Simulated and predicted results are stored in a database, so that the visualization system
can readily access to the simulated and predicted results. The hindcast test with a typhoon
event in 2004 indicates the predictability of large scale flood events.
Acknowledgements
A part of this study is supported by Grant-in-Aid for Young Scientists (B) 18760373 (PI:Takahiro Sayama, DPRI, Kyoto University) provided by MEXT, Japan, and River
Environmental Fund (1213-5, PI: Takahiro Sayama) provided by Foundation of River &Watershed Environment Management.
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