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

PREDICTION OF THE LARGEST EVER FLOOD:
CASE STUDY ON TYPHOON RUSA IN 2002
AT THE GAMCHEON BASIN, KOREA
Sunmin KIM 1, Yasuto TACHIKAWA2 , Giha LEE3 and Kaoru TAKARA4
1Member of JSCE, Dr. Eng., Research Associate, DPRI, Kyoto University(Gokasho, Uji 611-0011, Japan) E-mail:sunmin@flood.dpri.kyoto-u.ac.jp
2Member of JSCE, Dr. Eng., Associate Professor, Dept. of Urban and Env. Eng.,
Kyoto University (Nishikyo-ku, Kyoto 615-8540, Japan)
3Student Member, Graduate student, Dept. of Urban and Env. Eng., Kyoto University (Kyoto 615-8540, Japan)
4Fellow of JSCE, Dr. Eng., Professor, DPRI, Kyoto University (Gokasho, Uji 611-0011, Japan)

This study focuses on a prediction of the largest ever flood, which caused by Typhoon Rusa in 2002 at
the GamCheon Basin, Korea, with a distributed hydrologic model. Using hydrologic events data earlier
than the typhoon flood, the model was calibrated with the SCE optimization algorithm under various
conditions, such as different event size and duration, integrating evaporation information and adopting
additional model parameters for distributed land use. As more hydrologic data (evaporation and land use)
integrating, the model shows improved simulation behavior for long-term events. However, parameter
sets calibrated with short term events provides stable and efficient prediction on the Rusa flood event.
Key Words: Typhoon Rusa, distributed hydrologic model, model calibration, SCE algorithm

1. INTRODUCTION
Typhoon Rusa (No. 0215) hit South Korea on
31st August 2002, which is the strongest typhoon to
strike Korea since Typhoon Sarah (1959). Morethan 200 people were deceased or missing and up to
870 mm/d rainfall was reported in some areas.
Damage, mostly by severe floods, was estimated tomore than 750 million USD.
To predict this kind of heavy flood before it
occurs is one of the main topics to hydrologists, andmany models have developed to meet this demand.
After a model is composed and calibrated for acertain basin using available hydrologic data, themodel is possible to simulate any future flood withpredicted rainfall input. At this moment, calibrated
parameter sets are believed to represent inherent
properties of the hydrologic system sufficiently.
However, since the representation is not perfect
yet with any model and any parameter, calibrated
parameters are also believed to represent a specificcondition of the event that used for the calibration,such as event size and duration, seasonal or climate
difference. If the model confront with very different
type of events to the prior hydrologic records thatwere used for the model calibration, there will beconsiderable uncertainty on the model performance.
This study examines a prediction performance ofa distributed hydrologic model, CDRMV31), on thelargest ever flood, which happened in a basin of
Korea during Typhoon Rusa in 2002. Parameters
were calibrated only with typical flood events datausing the SCE algorithm2) under various conditions.
The various conditions for the calibration were (1)variant flood sizes and durations, (2) integrating
evaporation data for giving further hydrologic data
into the model and (3) allowing more model
parameters to consider spatially variant land use.
Variant parameter sets that were calibrated underthese conditions were tested to predict the largest
flood event of the study basin.
The organization of this paper is as follow:
Section 2 illustrates the scopes of this study
including an introduction to the distributed model,
optimization algorithm, the study basin and thehydrologic data for the model calibration. Section 3
shows the procedure and the results of the parameter
optimization. Section 4 provides the application
results of the calibrated parameter sets on the Rusa
flood event and discuss with the results. Finally,
Section 5 concludes the paper by summarizing thisstudy.

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2. METHODOLOGYα

Due to the distinctive flood characteristics ofJapan and Korea, which has very rapid change ofdischarge within a short time, it is still on-going
research to develop a hydrologic model applicableto various conditions. So far, it has not been easy tofigure out the general parameter set of a basin even
only for short-term flood type events.
However, extensive model testing under various
scenarios on a certain flood vulnerable basin candecrease the uncertainty on flood predictions. In thisstudy, a distributed hydrologic model, CDRMV3
has tested on the GamCheon basin, Korea to figureout its performance adaptability and to search thebest way to predict abnormal flood event during
Typhoon Rusa in 2002.
The model performance test was started frommodel calibration with variant event and conditions,such as different event size and duration, additionalhydrologic data and different model complexity.
Firstly, hydrologic data of the GamCheon Basinfrom June to September of the year 2000, 2001 and2002 was examined, and different size of six
short-term flood events (events last for several days)
were selected, and the model was calibrated withthose events. Secondly, four long-term flood events(events last for 30 to 40 days), which include theshort-term events within their event durations, wereselected for the second case of model calibration.
This case is for avoiding possible influences ofvariant soil wetness condition by any prior rainfallof the each short-term event. Thirdly, to give morehydrologic information for the long-term events
calibration, evaporation data has given into themodel, and the model was calibrated again. Lastly,
two types of parameter set were allowed for
spatially variant land uses of the basin, and more
complex model than earlier cases was calibrated foranother long-term events calibration.
Based on the illustrated four types of calibration
cases, automatic calibration using Shuffled Complex
Evolution (SCE) algorithm has carried out. The
calibrated parameter sets were applied on the Rusa
flood event to check and compare its adaptability.
(1) Distributed Hydrologic Model, CDRMV3
A distributed hydrologic model adopted for thisstudy is CDRMV31), (http://flood.dpri.kyoto-u.ac.jp/product/cellModel/cellModel.html). The Model
solves one-dimensional kinematic wave equations
for both sub-surface flow and surface flow using theLax-Wendroff scheme. Discharges and water depths
diffuse to the steepest downward adjacent cellaccording to a flow direction map generated from
DEM data. The CDRMV3 adopts specific stagedischarge relationship incorporating saturated andunsaturated subsurface as well as surface flow3).

βFig. 1 Relationship between unit width discharge andwater depth in the CDRMV3.

Fig. 2 Location of the GamCheon upper basin (413.3km2)
and the flow direction map for CDRMV3 model.
Korean basin map (left) source: WAMIS website.4)

This relationship is expressed by three equations
as shown in Fig. 1. Parameters to be optimized areroughness coefficient n, soil depths ds-dc and dc, andhydraulic conductivities ka and kc, which determine
the velocity of saturated subsurface flow, va andunsaturated subsurface flow vc, respectively. The
initial condition of CDRMV3 was determined using
an observed discharge at the basin outlet under thesteady-state assumption. More details for the
CDRMV3, refer to Kim et al. 5) 6)(2) GamCheon Basin, Korea
GamCheon is a tributary of the NakDong River,and there are two water-stage stations along theRiver; KimCheon and SunSan. The KimCheon
station is located in the middle reach of GamCheon,
and upper basin of the station has 413.3km2 ofcatchments area (see Fig. 2). The SunSan station is
located at the confluence of GamCheon and
NakDong River, and backwater effect makes it
difficult to adopt the point as a model simulation
outlet. In this study, upstream of the KimCheon
station was modeled for the Rusa flood event
simulation. During Typhoon Rusa, the middle reachof GamCheon was severely damaged by historic
heavy flood with much debris from the mountain.
Several rating-curves are available at KimCheon
station, and the rating-curve developed in 2002 wasadopted to convert water stage into discharge data.
Six rain gauge stations are around the GamCheon
upper basin; KimCheon, YoungDong, DaeGa,
MuPung, WungYang and HwnagGan. Spatiallydistributed rainfall was calculated from the stations’data using inverse distance weighting factor.

- 68 -

LT1

LT2
ST1

ST2

ST3

ST4

Fig. 3 Recorded rainfall (basin average) on the GamCheon upper basin and observed discharge at KimCheon gauge
station from June to September, 2000, and the selected flood event, LT1, LT2, ST1, ST2, ST3 and ST4.

Title

Table 1. Duration and peak discharges of selected flood events and the Rusa event
Short-Term Event
Long-Term Event
RainfallPeak Q
Rainfall
Duration
Title
Duration(mm)(m3/s)
(mm)

ST1
ST2
ST3
ST4

2000 July 21 ~ July 26
2000 Aug 02 ~ Aug 102000 Aug 23 ~ Aug 30
2000 Sep 10 ~ Sep 18

140.8
92.4
177.6
310.4

483.0
108.3
305.7
822.3

ST5

2001 June 22 ~ June 28

110.2

ST6

2002 Aug 05 ~ Aug 19

RUSA

2002 Aug 29 ~ Sep 03

Peak Q(m3/s)

LT1

2000 July 09 ~ Aug 11

434.2

483.0

LT2

2000 Aug 15 ~ Sep 18

664.8

822.3

179.4

LT3

2001 June 16 ~ July 22

359.4

179.4

466.4

518.8

LT4

2002 July 18 ~ Aug 22

517.4

518.8

316.4

2,572.5(303.6mm of rainfall was concentrated within 24hrs.)(3) Selected Flood Events
Table 1 presents the details of the selected flood
events, and Fig. 3 shows rainfall and discharge dataof the events in 2000. Each event is numbered as ST
for short-term event and LT for long-term event.
LT1 includes ST1 and ST2, and LT2 includes ST3and ST4; and so on for LT3 and LT4.
Observation data for rainfall and discharge has
one-hour time resolution and the simulation output
also has the same time resolution. Every event
includes more than one-day of dry period before its
rainfall started for model worming up.
(4) Automatic Optimization Algorithm, SCE-UA
The Shuffled Complex Evolution algorithm2)
was utilized for searching the best fitted parameterset on each calibration event. The SCE algorithm isa single-objective global optimization method, andhas been performed on a variety of models withoutstanding positive results2). The algorithm issynthesized by three concepts; combination of asimplex procedure with controlled random search
approaches, competitive evolution and complex
shuffling. Objective function adopted in this study
for the optimization algorithm is to maximize theNash-Sutcliffe Coefficient (NSC).

3. MODEL CALIBRATION UNDERVARIOUS CONDITIONS(1) Calibration with Short-Term Events
The five parameters of CDRMV3, n, ds-dc, dc, ka
and kc were optimized for each short-term flood
event, and the results are presented in Table 2.
Initial parameter ranges given to the optimization
algorithm are presented at the top of the results withbold characters. Although there are several values
that reach to the boundary of the given range, every
optimization was successfully converged and showshigh NSC value. As shown in Fig. 4, most of thecalibrated parameters give awesome match of thesimulated results to the observed hydrographs.

Fig. 4 Simulation results from the parameter optimization
ST5 event (left) and ST6 event (right)- 69 -

Type

Short
Term
Event

Table 2. Optimized parameter sets for each event and its Nash-Sutcliffe Coefficients (NSC)
Event
Parameters Optimized
Condition.
Title
n
ds-dc
dc
ka
kc

Single L.U.
/ No Evp

Single L.U.
/ No Evp

Long
Term
Event

Single L.U.
/ Evp

ST1
ST2
ST3
ST4
ST5
ST6
LT1
LT2
LT3
LT4

(0.00~300.0)(100.0~800.0)(0.005~0.030)(0.0001~0.003)

0.03980.03940.15500.05850.19110.100866.292
1.769
29.17854.462
6.053
26.980302.425497.241213.811126.917439.734
113.776

0.00500.01210.00760.02030.00650.01300.00020.00070.00130.00010.00080.0042(0.010~0.300)(0.00~300.0)(100.0~1500.0)(0.005~0.030)(0.0001~0.003)

0.08340.10140.1656
0.0525

1.342
0.044
0.015
21.8131499.682
1499.592
1499.811
1499.803

0.00530.01520.00500.03000.00020.00030.00050.0005

0.791
0.804
0.985
0.818
0.975
0.950
0.470
0.874
0.629
0.976

(0.010~0.300)(0.00~500.0)(100.0~1500.0)(0.005~0.030)(0.0001~0.003)

LT1
LT2
LT3
LT4

0.05180.05900.12820.0113

3.653
0.022
1.000
42.4601318.151
1296.715
909.810501.4970.03000.02840.02010.03000.00050.00040.00100.0001(forest/non forest)(0.01~0.300/ 0.01~0.300)(0.00~300.0/ 0.00~300.0)(1000~2000.0/100.0~1000.0)(0.005~0.030)(0.0001~0.003)

0.00650.0001

0.825

0.02950.00030.821

0.01010.00040.809

0.03000.00010.909

LT1
DoubleL.U.
/ Evp(0.010~0.300)

NSC

LT2
LT3
LT4

0.0145/ 0.0477
0.0100/ 0.0497
0.0111/ 0.09080.3000/ 0.0318

27.783/ 37.242
0.012/ 57.559
0.007/ 297.706
41.893/ 11.244

1186.014/ 999.943
1998.662/ 103.508
1002.531/ 997.184
1000.475/ 102.402

0.799
0.818
0.774
0.897

Fig. 5 Parameter optimization results of LT3 event. Consideration of evaporation loss improves the results well.

(2) Calibration with Long-Term Events
Four long-term events were also calibrated using
the SCE algorithm, and the results as well as thegiven ranges are presented in Table 2. Even thoughsome cases show good NSC values, every
optimization result shows that the results of ds-dc
and dc have the boundary values. The ranges for
ds-dc and dc had modified and tried several times fora proper result, but every optimization in this caseasks thicker soil layer. Thicker soil depth may give
better match for the low flow duration, but it also
gives dull responses on the peak.
Optimization results from the LT events show

dependant behavior on the rainfall pattern, such as
amount and its time distribution. In the case of LT2
event, as shown in Fig 3, it has large amount of
rainfall which is distributed within and between the
ST3 and ST4 events, and LT4 event also hasconcentrated rainfall within the duration of ST6.
While LT2 and LT4 show very good optimization
results with 0.874 and 0.976 of NSC values, theother events, LT1 and LT3, have more complex
rainfall pattern, and the model failed to catch thisdynamic behavior of the catchments. (See brownline in Fig. 5, which indicates the output with the
optimized parameters of LT3).

- 70 -

Fig. 6(a) Prediction of Rusa event with the parametersthat were calibrated using the ST event series

Fig. 6(b) Prediction of Rusa event with the parametersthat were calibrated using the LT event series

Fig. 6(c) Prediction of Rusa event with the parameters of
LT (Evaporation loss considered) event series

Fig. 6(d) Prediction with the parameters of LT (Evaporation loss and two types of land use considered)

The parameters n, ka and kc are within the given
ranges and do not show very different behavior forboth short-term and long-term events. Only theparameters ds and dc (especially dc) show significant
increase for the long-term events, and it might bebecause the model should keep much water in thesoil to produce low flow discharge continuously, fora dry period of the long-term events.

coefficient were adopted so that eight parameters
were optimized. With additional information on thecatchments and more detailed model structure, themodel may be able to properly describe low flow
parts and peak flow parts as well.
Table 2 shows the optimized parameter setsfrom the additional data integrated long-term
events. The NSC values show improved and morestable results as more information is available,
especially in the case of LT1 and LT 3 (see Fig. 5),
and most of the parameters were converged into thegiven ranges.
Adopting additional parameters for two types ofland uses does not produce significant improvement
after the evaporation loss consideration. When themodel tested with opposite case (no evaporation
loss but double land use considered), which is notdescribed in this paper, the model shows quite
similar behavior to the presented results; noticeable
improvement with the additional parameters, butslight improvement after that.
The optimization with the SCE algorithm in thisstudy has 800 ~ 1200 times of total searchingcombinations, and it takes about one day of running
time for the short-term events and two and half day
for long- term events on a Pentium desktop PC.

(3) Calibration with Long-Term Events- with Evaporation and Land Use Data
To search for more stable and physical parameter
sets for the long-term events, further hydrologic
data and higher model complexity has given.
Firstly, evaporation loss was considered into themodel. According to the Korea Climate Table of
KMA7), average of daily evaporation loss from Juneto September at the GamCheon area is equivalent to0.189 mm/hr. To consider this amount of the loss,
0.2 mm/hr was continuously extracted from the soil
moisture for the long-term simulations.
Secondly, spatially distributed parameters wereconsidered to reflect the variant land use type of theBasin. For forest area (62%) and non-forest area(38%), two types of soil depth and roughness- 71 -

Table 3. Application results of the optimized parameter
sets on the Rusa flood event
Type

Short
Term
Event

Cond.

SingleL.U./
NoEvp.

SingleL.U./
NoEvp.
Long
Term
Event

SingleL.U./
Evp.
Double
L.U./
Evp.

Event
Title

N.S.C

ST1
ST2
ST3
ST4
ST5
ST6
LT1
LT2
LT3
LT4
LT1
LT2
LT3
LT4
LT1
LT2
LT3
LT4

0.572
0.554
0.655
0.627
0.604
0.668
0.551
0.662
0.127
0.661
0.609
0.591
0.525
0.461
0.558
0.580
0.510
0.637

and double land use consideration, parameters fromthe LT3 event also shows improved results asshown in Fig. 6(c) and 6(d). As more hydrologic
information integrated, LT events’ parameter showsa tendency toward overestimation.

Evaluation
TP Diff.
QP Diff.
(QS-QO)
m3/s(TS-TO)hr+748.0+774.4-19.5+751.5-395.4+232.5-393.6+392.7-1941.8+401.6+527.1+683.2-654.1+824.5+595.1+748.9-609.6+518.8

-1
-1
+2
-1
+3
+1
+2
0
+4
0
0
-1
+3
-2
-1
-1
+2
-1

5. CONCLUSION

4. PREDICTION OF THE RUSA FLOOD
The optimized parameter sets from the four cases
were applied to the Rusa flood event. The Rusa
event at GamCheon has 2,572.5 m3 /s of peakdischarge, which is 4~25 times larger than the peakdischarges of the calibrated flood events. The mostinteresting point is whether the calibrated modelusing a normal size or small size of event can
successfully produce the largest ever flood event.
The application results of each case arepresented in Fig. 6. The ST events’ parameter sets
produce very reasonable simulation results as shownin Fig. 6(a). For the peak discharge difference, itvaries from -395.4 to +774.4 m3/s (see Table 3),
which is around 25% of peak discharge. For the
peak time difference, four of six parameter sets of
ST events show only one hour error; one hour is theminimum time step of the model output andobserved data as well.
After the peak of the Rusa event, the observed
discharge and every simulated result show very big
discrepancy. It seems that the observed discharges
include a certain level of observation error, sincemuch debris flow was observed during the flood,and river bed had severely changed at that time.
In the case of LT events’ parameters that were
calibrated without considering evaporation loss,
which is shown in Fig. 6(b), also gives reasonable
simulation results only except the parameter set
from the LT3 event. However, with the other twocases of parameter sets from the evaporation loss

This study examined a prediction performance ofa distributed hydrologic model, CDRMV3, to thelargest ever flood in the GamCheon Basin of Korea
during Typhoon Rusa in 2002. Model parameters
were calibrated with normal size flood data, and thecalibrated parameter sets were applied for the flood
prediction. The parameters were calibrated with the
SCE algorithm under various conditions, such as
different flood sizes and durations, integrating
evaporation loss information and giving moreparameters for two types of land use.
Results are summarized as follow:
1) For event based short-term flood simulations,calibration of CDRMV3 with a similar type ofshort-term event gives stable parameter set2) Even the parameter set that was calibrated with asmall size of event provides quite a good
simulation for the largest ever flood event3) Calibration for long-term events (30~40 days ofduration) is dependant on the event type, such as
rainfall amount and its time distribution4) Consideration of evaporation loss for a runoff
simulation more than one-month event duration
improves the model performance significantly
5) Adopting additional model parameters after
evaporation loss consideration provides only
slight improvement on the model performance.
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- 72 -(Received September 30, 2007)