Annual Journal of Hydraulic Engineering, JSCE, Vol.52, 2008, February
Annual Journal of Hydraulic Engineering, JSCE, Vol.52, 2008, February

IMPROVEMENT OF INTERNAL BEHAVIOR IN
DISTRIBUTED HYDROLOGICAL MODEL
Hunukumbura P.B1, Yasuto TACHIKAWA2 and Kaoru TAKARA3
1Student Member, Graduate student, Dept. of Urban and Environmental Eng., Kyoto University(Kyoto 615-8540, Japan) E-mail: hunu@kyoto.mbox.media.kyoto-u.ac.jp
2Member of JSCE, Dr. Eng., Associate Professor, of Urban and Environmental Eng., Kyoto University(Kyoto 615-8540, Japan)
3Fellow of JSCE, Dr. Eng., Professor, DPRI, Kyoto University (Gokasho, Uji 611-0011, Japan)

Distributed hydrological models are capable of predicting the internal behavior at any point within astudy basin. However, most cases, models are calibrated only at the catchment outlet and prediction
accuracy at ungauged points inside is not assured. In this study, we investigate the capability of aphysically based distributed hydrological model for predicting internal behavior by calibrating only using
discharge observations at the basin outlet. It is found that the prediction ability for internal discharges isdifferent depending on the initial condition; and especially the prediction accuracy of the floods with dry
initial condition is low. This is due to a spatially uniform parameter setting for satisfying the calibration at
the catchment outlet. Incorporating spatial heterogeneity of the parameter value for easily generating
surface flow along the channel network improves the discharge prediction inside for different initialconditions.
Key Words: internal behavior of a basin, distributed hydrological model, flood prediction, ungauged
basins

1. INTRODUCTION
Predicting floods at stream flow gauging stations
can be achieved by a calibrated hydrological modelusing observed flow data. However, availability ofstream flow data for model calibration is limited andmaking prediction not only at the stream flow
gauging stations but also at strategically importantungauged places inside a basin is necessary to takebetter actions before and during the disaster for
reducing the risk and the damage. Therefore,
utilizing available measured data at a gauging
station and making better prediction at ungauged
locations inside a basin is vital.
Modeling the actual hydrological response of abasin is a difficult task as the actual basin processes
are complicated and basin properties change
temporally as well as spatially. However, thisdifficulty can be addressed to some extent by using
distributed hydrological modeling approach, whichis capable of incorporating the basin heterogeneity
to model the basin hydrological responses. The main
objective of distributed hydrological modeling is torepresent the characteristics of a basin spatially andtemporally. Difficult problem is a lack ofmeasurement technology at the scale required by themodel and the approximations made in the processmodeling. Therefore, calibration of the model
parameters using observed hydrological response isrequired 1),2).
In general, distributed hydrological models use
conservation equations to describe the flow at eachslope element due to rainfall and then routed
through the channel network. Therefore,
theoretically it is possible to predict stream flow at
any interior location. However, as the model
calibration and validation is done at the basin outlet,
there is no guarantee of the prediction accuracy at
inside locations. Therfore, in the application of a
distributed hydrological model, a question arises
whether it is possible to use the calibrated model atthe basin outlet to predict the internal behavior atany place within the basin, in other words, howaccurately predictions can be made at ungauged
sub-basins inside, using a distributed hydrologicalmodel. Hence, it is important to study the reliabilityof a distributed hydrological model prediction at
different ungauged places within a gauged basin
outlet. This fact was highlighted in the distributedmodel intercomparison project (DMIP) as well 3).
Ajima et al.1) applied a semi distributed
sacramento soil moisture accounting model for the
Illinois River basin in USA and analyzed theinternal prediction accuracy of the model for- 49 -

different calibration strategies. They found that themodel dose not predict well the stream flow atinternal locations. Andersen et al.2) applied amodified version of MIKE SHE model to the
Senegal River basin and they calibrated the modelfor one station and validated against eight internal
locations. They also found significant disagreementsof the predictions with observed data at some
upstream locations. Several studies1),4),5) concluded
that calibration against additional internal gauge
improves the prediction accuracy at some internal
locations. Vieux et al.6) observed that their
physically based hydrological model is capable ofmaking reliable predictions at some interior points.
However, it is not clear in what situations, thehydrologic model well predict or not well predict
internal hydrologic behavior.
In this study we investigate the prediction
accuracy of internal behavior of a distributedhydrological model, OHDIS-kwmss, OHyMos
based distributed hydrologic model with kinematicwave
methodincludingsurface-subsurface
runoff.(http://hywr.kuciv.kyoto-u.ac.jp/index.html#
products). Even though the model calibration andvalidation at a basin outlet gives good agreement fordifferent flood events it is observed that theprediction accuracy of internal locations is low forsome flood events. The main objective of this study
is to investigate in detail on the model internal
behavior for different flood events to clarify thehydrologic condition that internal prediction is
failed, and to improve the internal prediction
accuracy without using the internal observations formodel calibration.
Section 2 describes the distributed hydrologicalmodel used in this study. Details for the study basin
and the internal prediction accuracy of the model are
given in section 3. Section 4 presents the model
improvement and discussion.

2. MODEL DESCRIPTIONOHDIS-kwmss is based on the one dimensional
kinematic wave theory and developed by Ichikawa
et al. 7). The basin topography in the hydrologic
model is represented according to the methodology
described in Shiiba et al.8). In the model, it isconsidered that the basin consists of number ofrectangular slope elements which drains to thedeepest gradient of its surrounding (Fig.1). Eacharrow corresponds to rectangular slope element.
Slope Element

The area of each element depends on thenumber of connections it has. In Fig. 1, when thearea of grid cell is a, the slope element connected to
Node B is assigned 1/4 of a and element connected
to Node C is assigned 1/3 of a; thus the area of theslope element BC is 7/12 a according to Eq.(1).
Slope flow produced due to rainfall is routed one
dimensionally. Then it flows to the channel networkand routed to the basin outlet.

Area of grid cell = a,⎫Area of slope element assigned from Node B(a b ) = a/4⎪⎪ (1)Area of slope element assigned fromNode C( ac ) = a/3 ⎬⎪⎪⎭Area of the slope element BC =ab+ ac
It is assumed that the each slope element of thebasin is covered with a permeable soil layer and astage-discharge relationship for unsaturated,saturated and overland flow defined by the thresholddc and ds 9) is adopted. Figure 2 shows the flow
process and the stage discharge relationship used in
the OHDIS model given in Eq.(2).

Fig.2 Flow process and Stage-discharge relationship⎧Vc d c (h / d c ) β
0 ≤ h ≤ dc⎪
q = ⎨Vc d c + Va (h − d c )dc < h ≤ d s⎪m⎩Vc d c + Va (h − d c ) + α (h − d s ) d s < h(2)where, q is the discharge per unit width; h is thestage; Va and Vc are flow rates in the capillary poreand non capillary pore respectively.
OHDIS model demands detail channel network
information based on Japanese GIS data sets.
Unfortunately, detail channel network information is
not available for many countries in the world
especially in developing countries where the water
related problems hindered their development.
Therefore, a preprocessor for topographic
information, OHDIS-PreTOP10)(http://hywr.kuciv.
kyoto-u.ac.jp/hunu/) was developed to apply
OHDIS to any catchments on the globe only using
globally available digital elevation models and it
was used for this study.

3. INTERNAL BEHAVIOR OF
DISTRIBUTED HYDROLOGIC MODEL
Fig.1 Representation of the topography.

(1) Study basin and data
The Maruyama River basin in Japan having area

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of about 909 km2 was selected for this study andfour internal stream flow measuring stations wasused for analysis of internal hydrologic behaviors.
Figure 3 shows the stream flow gauging locations,rainfall measuring stations and sub-basins at eachinternal gauging location. Brief physical details
about each sub-basin are given in Table 1.

Fig. 3 Maruyama Basin at Fuichiba.
Table 1 Physical properties of sub basins.
Property2

Basin Name
Fuichiba Kamioda Kyoguchi Jyunisho Nakama

Area (km )

909

766

318

216

45

StreamLength (km)

240

202

82

55

12

Mean
Slope(deg)

13.80

14.19

13.51

15.54

17.05

According to the Japanese land use and land
cover data set 11), the basin covered with different
land use types comprise of 85% forest, 7 % rice, 2%buildings, 1% inland water and 5% other. To
represent the basin topography, the SRTM 90m
digital elevation model (http: //seamless. usgs.gov/)
was used. Considering the time taken for automatic
calibration of the distributed hydrological model,
the original digital elevation model (DEM) was
resampled to 200m x 200m grid size.
Table 2 Details of the selected flood events at Fuichiba.

Rainfall data at ten stations within and nearby
the Maruyama basin was used for the study. Threeflood events in 2004 and one event in 2003 wereselected and the details are given in Table 2. Event
1, 2 and 3 are occurred after a rainy period and theinitial status of the basin can be considered as wet.
Event 4 occurred after reasonably dry period (about
25 days) and therefore the status of the basin before
to the flood is considered as dry. Initial conditionof the basin was set up using the initial river flow
and assuming the steady state condition.
(2) Methodology
Overall objective of this study is to investigate
the accuracy of representation of the internal
behavior of a distributed hydrological model andimprove the internal prediction accuracy. Even
though distributed hydrological models are capableof predicting some internal state variables of thebasin, most of the cases, measured stream flow datais the only information for model calibration andvalidation due to lack of internal spatio-temporalobserved data of the other hydrological responses.
Here in this study also we used stream flow data formodel calibration, validation and to evaluate how
accurately the OHDIS model can predict the internal
behavior of the basin.
The Shuffled Complex Evolution Algorithm(SCE)12),13) is used for calibrating the model using
the observed stream flow data at the basin outlet atthe Fuichiba station. The SCE algorithm is a singleobjective global optimization method widely used tofind the best fitted parameter set for nonlinearhydrological models. Here, in this study, Nash
Sutcliffe coefficient (Nc) is used as the objectivefunction for model calibration. Calibration of a
distributed hydrological model with large number ofspatially distributed model parameters is time
consuming and computationally intensive 5). Even
though the OHDIS model is capable of representing
the heterogeneity of the basin land use and soil
properties, it is rather difficult for automatic
calibration of fully distributed parameters in thebasin. Therefore, it is assumed that the wholebasin has uniform properties and optimized for one
set of model parameters, β, dc , ds , Saturated
hydraulic conductivity and Manning’s coefficient.
At first, the OHDIS model was calibrated forEvent 1, which is the biggest recorded flood in thebasin in recent history, by using the observed data at
the basin outlet at Fuichiba. A best fit parameter set
for the basin was obtained with the help of the SCE
algorithm and the other three events were used formodel validation. After the model was verified, atthe outlet, the optimum parameter set was used tosimulate the flood hydrographs at each internalgauging station. Finally, the model simulated floodhydrographs and the observed flood hydrographs ateach internal gauging location were compared. The- 51 -Nash Sutcliffe coefficient was used for quantitative
comparison of the prediction accuracy for all flood
events at each internal stream flow gauging stations.
(3) Results and discussion
Table 3 shows the performance of the model atthe basin outlet for calibration and validation. It isclear that the optimized parameter for Event 1
predicts the other flood events to a reasonableaccuracy at the basin outlet. Notably, the flood event
starts after a dry period (Event 4) shows low
prediction accuracy than other two events in themodel validation. However, by looking at the model
prediction accuracy only at the outlet of the basin,
we can conclude that the uniform parameter set cancapture range of flood events to a reasonableaccuracy at the outlet.

setting uniform parameters for whole basin is not
sufficient to reproduce the internal behavior forfloods which begin especially after a dry period.
Heterogeneity of a natural basin (e.g. temporalgullies) produces faster flows even though therainfall intensity is weak. By considering all thesefacts and figures, we decided to include catchment
heterogeneity and investigate whether it is possible
to see any improvement.

Table 3 Nash Sutcliffe coefficient for model calibrationand validation.

Calibration
Event 1
0.99

Event 20.97

Validation
Event 3
Event 40.91
0.73

Fig.4 Internal prediction accuracy of the OHDIS model.

The values of Nash efficiency for four events
are shown in Figure 4 for Fuichiba at the catchment
outlet and internal stations Kamioda, Kyoguchi,
Jyunisho and Nakama. According to Figure 4, themodel gives good prediction accuracy at all internal
locations for Event 1, 2 and 3, where the events
starts with wet initial condition. Therefore, themodel calibrated for very high floods with wet
initial condition can even predict the internal
behavior of small flood with wet initial condition. It
is a good evidence to prove that the OHDIS modelis capable of predicting the internal behavior to areasonable accuracy for wide range of floods with
wet initial conditions. In contrast to this, the modelshows very low prediction accuracy at all internal
locations for Event 4 (medium flood after a dry
period) even though it gives fairly reasonableaccuracy at the outlet at Fuichiba.
Figure 5 shows the observed and model
predicted hydrographs for Event 4 at each internalgauging station. When we look at the predicted andobserved hydrographs, the model predictions are
slower than the observed hydrographs at all internal
locations. Having longer concentration time forflood events after a dry period is normal. However,in the case, the model is requested to generate fasterflow at all internal locations when compared toobserved flow. That is the main reason for giving
low prediction accuracy at these places. Thus themodel is requesting more fast flow. The parameterset used to predict the flow at these internal
locations is the best fit parameter set obtained forthe flood Event 1 at the basin outlet. Hence, there is
no chance to change the parameters to improve theprediction accuracy inside the basin. Therefore,

KamiodaKyoguchiJyunishoNakamaFig.5 Observed and model predicted hydrographs at
Kamioda, Kyoguchi, Jyunisho and Nakama for
Event 4.

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4. IMPROVEMENT AND DISCUSSION
In general, area along the river has highmoisture content and the mountainous area has low
soil depth. Therefore both places tend to produce
quick flow during a rainfall event. The OHDISmodel structure is flexible and it is possible togenerate quick flow component easily by setting thethresholds dc and ds to zero. Here it is decided tocheck the effects on the internal prediction accuracy
by setting the threshold dc and ds to zero at two
different places within the basin,a. Along the river (Case II)
b. At the higher mountains (Case III)
Case (I) represents the situation in the previous
section as using only the optimized uniform
parameter set for the basin. We kept the total
modified area of nearly 10% of the total basin areafor both cases. Figure 6 shows the schematic
diagrams of the test modifications.

Case (II)Case (III)Fig.6 Schematic diagrams for modifications. The model
parameters dc and ds are set to zero for dottedslope elements. Both cases, the area is 10% of thetotal catchment area.

Figure 7 shows the model performance for each
modification case at each internal gauging location
for all flood events. According to the figure, model
performance at all the internal places for all events

Fuichiba

Jyunisho

was improved for Case (II), where the soil depth isset to zero near to the river. The improvement issignificant for the flood Event 4 at Kamioda,
KyoguchiandJyunishoandpercentage
improvements of these locations with respect toCase(I) are 94%, 75% and 41%, respectively. On
the other hand no significant improvement can beseen in Case (III) modification. In Case (II), theflow produced due to the modification directly
flows to the river and hence it is more effective thanthe Case (III) where the produced flow go throughslope elements with soil layer.
To investigate further on these findings, themodel was calibrated independently for the otherthree events (Event 2, 3, 4) using the data at thebasin outlet, Fuichiba. Then the best fit parameter
sets were obtained for each event and predictions atinternal locations were obtained for all three cases.
Nash efficiency values at Fuichiba and all internal
location for each parameter set for Case (I), (II) and(III) are shown in Figure 8. According to the figure
corresponding to the basin outlet at the Fuichiba
station, the model gives nice predictions for all
events regardless of the parameter set used. It meansthat the model calibrated for any event can
successfully use to predict the flow at the basin
outlet for all other events. However, when look at
the inside locations, Event 4 predictions shows low
Nash efficiency values than the other events for allparameter sets.
It is clearly noticed that the model performanceat internal locations improve significantly for theCase (II) modification while Case (III) gives little
improvement. This pattern can be observed for all
independent best fit parameter sets obtained at thebasin outlet for each flood event. It confirms that theinternal behavior of distributed hydrological model
can be improved by setting the soil depth parameterto zero to easily generate surface flow.

KamiodaKyoguchiNakamaFig. 7 Model performance at internal locations and the outlet at Fuichiba for Case (I), Case (II), and Case (III).

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Fuichiba

KamiodaKyoguchiNakamaJyunishoFig. 8 Model performance at internal locations and Fuichiba for different optimized parameter sets (Parameter sets,
1,2,3,4 corresponds to the optimum parameters obtained from SCE algorithm for event 1,2,3,4 respectively).

5. CONCLUSIONSCapability of a distributed hydrologic model
OHDIS for predicting the internal behavior wasstudied in detail. The findings are summarized
below:z The model is capable of predicting the internal
behavior of floods with wet initial conditions toa reasonable accuracy.
z The internal prediction accuracy of floods
occurs after a dry period is not good and it is
not possible to capture the internal behavior
only using a spatially uniform parameter set.
z Significant improvement can be achieved byincluding basin heterogeneity. The model
structure for easily generating quick flow along
the river improves the internal hydrologic
behavior.
z Setting out a saturated zone at arbitrary places
does not give significant improvement at theinternal locations.

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- 54 -(Received September 30, 2007)