Journal of Japan Society of Civil Engineers, Ser.
B1urn
(HydraulicEngineering),
Vol.
No.Vo
4,l.5
I_115-I_120,
2015.
Annual Jo
al of Hydrau
lic Engineering, 71,
JSCE,
9, 2015, Febru
ary

NUMERICAL STUDY ON FLUSHING CHANNELEVOLUTION, CASE STUDY OF DASHIDAIRARESERVOIR, KUROBE RIVER
Taymaz ESMAEILI1, Tetsuya SUMI2, Sameh A. KANTOUSH3, Yoji KUBOTA4
and Stefan HAUN5
1Member of JSCE, Ph.D. Student, DPRI, Kyoto University (Gokasho, Uji, Kyoto 611-0011, Japan)
2Member of JSCE, Professor, DPRI, Kyoto University (Gokasho, Uji, Kyoto 611-0011, Japan)
3Associate Professor, DPRI, Kyoto University (Gokasho, Uji, Kyoto 611-0011, Japan)
4Hydro-soft Technology Institute Co., Ltd. (Nakanoshima, Osaka 530-6126, Japan)
5Post-Doctoral Fellow, Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart(Stuttgart 70569, Germany)

Due to the high sedimentation rate in reservoirs located along the Kurobe River (e.g. Dashidairareservoir), sediment flushing with water level drawdown is employed to preserve the effective storage
capacity. During a flushing operation with full drawdown, the incoming flood erodes a flushing channel
into the deposited sediment. The flushing channel evolution procedure is a complex phenomenon
especially in steep reservoirs owing to the dynamic interaction between the unsteady flow field and bed
variations. The flushing channel formation and evolution procedure were investigated in meanderingchannel of Dashidaira reservoir utilizing field measurements and numerical simulations. A 3D numericalmodel which applies the Finite Volume Method (FVM) in combination with a wetting/drying algorithm
was used together with different bed load transport formulas. Numericaloutcomes disclosed a reasonableagreement with the field measurements. In addition, a further insight in the correlation between the
flushed out sediment discharge and the water discharge as well as the water level was found.

Key Words: Dashidaira reservoir, 3D numerical model, drawdown flushing.

1. INTRODUCTION
The loss of reservoir storage volume due to thesediment deposition reduces the dam’s effective life
time and also diminishes the reservoir function forflood control purpose, hydropower generation,
irrigation and water supply which represents a
substantial economic loss1),2). Various measures
including sediment dredging, sediment routing,bypassing, flushing, sluicing and upstream sediment
trapping have been used to control the progressive
sedimentation in reservoirs. Of all the mentioned
measures, flushing plays a major role in reservoir
storage capacity restoration since it is an efficienthydraulic technique for sediment removal3).
Drawdown flushing involves a complete loweringof the water level by opening the low-level outlet totemporarily establish riverine flow (i.e. free flow)
towards the outlet. The accelerated flow erodes achannel through the deposits and flushes out bothfine and coarse sediement1)~3). However, detailed
explanation of flushing channel formation andevolution in prototype reservoir delta is scarce.
In Japan, generally the sediment yield by rivers ishigh due to the geologically young mountains, steep
slope, and flashy flow regimes as well as wide
spread landslides. Consequently, incoming sedimentloads to the reservoirs fed by these rivers are high(e.g. Kurobe River located in Toyama prefecture).
Kurobe River originates from the mountainous areawith 3000m height and flows into Toyama bay inJapan Sea. The Catchment area of the river is 682
km2 and the length of the river is 85km. The bed
slope is steep and variesbetween 1% and 20%.The
average rainfall and total sediment yield per year are
4000mm and 1.4×106m³/year, respectively, that are
bothone of the highest in Japan4).
For optimizingthe sediment management strategies

I_115

in reservoirs where the occurring conditions changes
from shallow parts to deep parts and also sandbar
areas, numerical models other than 1D should be
used5). Advanced 2D numerical models have been
employed for practical problems in rivers (e.g. rivermeandering)6). However, 2D and quasi 3D
numerical models are not able to directly simulate acomplex 3D flow field including secondary
currents, whereas they have strong contribution innatural sediment transportation processes7). Thus,
utilizing of 3D numerical models are essential whenthe velocity variation over the flow depth, i.e.
helical flow, plays a major role (e.g. in channelbends). In case of sediment flushing from reservoirs,
3D numerical model had a better performancecompared to 2D one in simulating the bed
deformation notably in channel bends 8).
In the present study, a fully3D numerical model was
employed and the bed evolution trend along with thesurface velocity field was simulated in Dashidairareservoir during 2012 flushing operation. The
results were compared with available fieldmeasurements. Beside this, also a variation of
flushed out sediment discharge, in different stagesof flushing operation, was assessed quantitatively.

III
II

I

Dam

Fig. 1 Measured bed topography of Dashidaira reservoir
before flushing operation in June 2012 along with the locationof cross-sections A-F for bed evolution assessment. Area I, II
and III are segments with different bed material type.

middle area (i.e. area II), shown with a red line
rectangle,cannot be removedeffectively. Information
about the flushing channel evolution in this area canbeusefulfor implementingproper measurestoenhance
the sediment erosion and the flushing efficiency as
well. Table 1 shows the average sediment size
distribution, in different cross sections, before theflushing operation. Seven sediment sizes, ranged
between 316mm and 0.37mm were considered as
the representativegrainsizes.Fig. 2 shows the inflow
discharge and water level changes during the 2012
flushing event in which the major sediment inflow
was wash load. Preliminary drawdown was from 8 to
24 and free-flow state was from 24 to 38 hours after
starting the operation.

2. MATERIALS AND METHODS(2) Numerical model
The numerical model solves the massconservation equation (Eq. 1) together with the
Reynolds-averaged Navier-Stokes equation (Eq. 2)
to compute the water motion for turbulent flow:(1) Study case description
Dashidaira dam with 76.7m height has been
constructed in 1985 by Kansai Electric Power
Company across the Kurobe River. The gross andeffective storage capacity of the dam reservoir is
9.01 and 1.66×106m3 respectively.Sediment flushing
operation has been performed since 1991 throughthe bottom outlets3,4). From June 2001 a coordinatedsediment flushing in both Dashidaira and Unazuki
reservoir, which is located 7 km downstream ofDashidaira dam, is conducted. This type of flushingis done every year during the first major flood eventin the rainy season, to reduce the negativeenvironmental impacts downstream of thereservoirs.
Fig. 1 illustrates the measured bed levels before
flushing operation of the reservoir that was
conducted in June 2012 along with the location ofcross-sections (A-F) for a further quantitative
assessment of bed evolution pattern. Almost 2km
length of the study case has been divided into threeareas, namely, area I, II and III to analyze the study
outcomes based on the type of the bed material exist
in each area. In area I, bed material type is coarse
while in area III and the latter half of area II, theychange into fine type. The total volume of theeroded sediment was equal to 408,700m3≈409KCM
in the study zone. In this reservoir, almost finesediment deposited along the left bank of the wide

U i
0
xi
U i
U i 1 
U j
 P ij   uiu jtx j  x j

Table 1.

(1)



(2)

Average sediment size distribution in the reservoir
entrance and specified cross-sections.

Location
Sediment
size (mm)
316
118.3
37.4
11.8
3.7
1.2
0.37

Reservoir
entrance

A-A

B-B

D-D

F-F

0.08
0.36
0.29
0.23
0.01
0.01
0.02

0
0
0.73
0.07
0.14
0.04
0.02

0
0
0.69
0.13
0.12
0.02
0.04

0
0
0.01
0.04
0.15
0.19
0.61

0
0.03
0.02
0.01
0
0
0.94

Fig. 2 Water level and discharge rates during the flushing
operation in June 2012.

2

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in which i=1, 2, 3 is the representative of three
directions; whereis the Reynolds-averaged
velocity over time t,
is the spatial geometricalscale,
is the water density,
is theReynolds-averaged pressure,is the Kronecker
delta and   ui u j are the turbulent Reynolds stresses.
The finite volume approach is employed, as
discretization scheme. The convective term in the
Reynolds-averaged Navier-Stokes equations issolved by a second order upwind scheme. The
turbulence is modeled by the standard k-ε model,using the constant empirical values9). The grid is
non-orthogonal, unstructured and adaptive whichmoves vertically with changes in the bed andfree-water surface. Thus, just the water body ismodeled. The grid is regenerated after each time
step and an employed wetting/drying algorithm
enables the model to have a varying number of gridcells (e.g. in vertical direction) with respect to thechanges in water and bed level. The wetting/drying
algorithm eliminates cells with a smaller water
depth than a defined boundary10).
The Dirichlet boundary condition for the water
inflow (logarithmic velocity distribution) was used
whereas for the water and sediment outflow azero-gradient boundary condition was assumed. For
the boundary condition at bed and walls, in where
there is no water flux, wall laws were used.
Bed forms, namely dunes and ripples effect, arealso taken into account through an empiricalformula11). The sediment transport computation forsimulating the morphological changes is divided
into suspended and bed load transport. Suspended
load is calculated by solving the transientconvection-diffusion equation formula and bed load
can be simulated by Meyer-Peter-Müller formula(Eq. 3) (that is called MPM hereafter) or
alternatively by Van Rijn formula12) (Eq. 4). MPM
formula is more appropriate for steep rivers whichmainly transport the coarse sediment close to thebed. Recently, application of Van Rijn’s formula
also showed a satisfactory performance insimulation of flushing operation in specificsegments of some cases13).

1   w grI  0.047 g (  s   w )d 50 
qb ,i  2g
1     3w
0.25  w 3  s s 

qb ,i
di

1 .5

(s  w )g
w

 0.053   c ,i   c ,i 

1 .5

0.3  (    w ) g  d i  s2  w

(4)
0 .1

where
is the diameter of the ith fraction, τ is theshear stress, τc,i is the critical shear stress for diwhich was calculated from the Shield’s curve, and ν
is the kinematic viscosity. The thickness of theupper active layer that can be eroded during a single
time step as well as the lower inactive layer
thickness that supplies the sufficient sediment for
the active layer is introduced to the model.
Model accounted for side slope effect utilizing a
reduction function of critical shear stress forincipient motion (i.e. Brooks formula) together witha sand slide algorithm14). Sand slide algorithm willcorrect the bed slope when it exceeds the angle ofrepose of the bed material. If there is no cohesion
influence,
sediment
particles
canmove
independently from each other on the side slopes
and bank erosion can be modeled appropriately.

3. RESULTS AND DISCUSSIONS(1) Model calibration
The computational grid was made based on themeasured bed levels before the flushing presented inFig. 1. The mesh cell size in streamwise andtransversal direction was 10-20m and 5-10m
respectively. The bed material density assumed to be
2650 kg/m3. The water levels and inflow discharge
fluctuations, employed as the hydrodynamic
boundary conditions in the simulations, are shownin Fig. 2. In addition, non-uniform bed material size
distribution within the reservoir was also introduced
to the model utilizing the seven representative
sediment sizes. A bed material size distribution
within the reservoir area was obtained based on thesize interpolation of the available bed material
samples. Since the wash load basically assumed to
be transported without deposition in the reservoir, its
effect on the bed evolution was neglected incomputations.
To disclose the effect trend of parameters that are
mainly empirical (e.g. active layer thickness, watercontent of the bed material as well as the criticalangle of repose for the sand slide algorithm), first, areference case was established with assuming thegeneral values for the mentioned parameters. Then,assumed values have been changed and the total
Volume of Flushed out Sediment (that is called
TVFS hereafter) was computed and compared withthe measured one (i.e. 409×103) for calibrating themodel. Table 2 shows the sensitivity analysis of the(3)

where , is sediment transportation rate for theth fraction of bed load per unit width, g is thegravity acceleration, ρs is the density of sediment, ρw
is the density of the water, d50 is the characteristic
sediment size, r is the hydraulic radius and I is theslope of the energy line.

3

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TVFS to the major selected empirical parameterscompared with the reference case.
As can be seen, the TVFS was enhanced withincreasing the active layer thickness and the watercontent whereas it was reduced with increasing thecritical angle of repose. When a larger amount of
bed material can be eroded in one time step (i.e.
thicker active layer), a higher volume of erosion
from deposits is expected. Assuming a higher watercontent in the sediment depositions (e.g. 50%),
which decreases the submerged density of the bed
material, leads to a higher sediment entrainment. A
higher critical angle of repose keeps a steeper sidebank of the flushing channel after each time stepthat results in a further deepening of the channel
which is not an efficient approach for increasing the
TVFS. In contrast, a lateral development of theflushing channel, that is favorable for increasing theTVFS, can be achieved by a lower critical angle ofrepose. Beside the TVFS, the final simulated bed
topography pattern was compared qualitatively withthe measurements to provide information about theerosion around the lower part of area III.
Afterwards, the reference case was modified andvalidated with the updated values (e.g. active layer
thickness, water content and critical angle of repose
was set to 1m, 40% and 33 degrees respectively).

Table 2 Sensitivity analysis of reference case to the selected
empirical parameters (1KCM=1×103 m3).
Active layer
thickness (m)Parameter

TVFS
(KCM)

Water content of
the bed material

Critical angle ofrepose (degree)

0.3

0.45

0.85

0.50

0.43

0.40

33

34

35

261

290

300

369

306

313

311

302

284

flushing channel compared to the observations
regardless to the used bed-load sediment transport
formulae type. This can be attributed to the complex
flow field formed by secondary currents and reverse
flow pattern in the wide bending area close to thedam. Bed forms (i.e. dunes and ripples) which aredeveloped in this area will increase the complexityof the flow and subsequently the sedimenttransportation pattern. The empirical formula in themodel to take into account the bed forms effect has
been developed for small Froude numbers.
Moreover, in this area the sediment transportcapacity is reached to its highest level that would besmaller than the original value in the prototype.
Thus, bed levels are different than measured ones.
Since bed material type and water head varies indifferent segments of the reservoir, each bed-loadtransport formula (i.e. MPM and Van Rijn) may(2) Simulation of flushing channel evolution
The numerically simulated bed levels after the
flushing event in June 2012 were validated with themeasured ones after the flushing. During the waterlevel lowering, a distinctive flushing channel with
free-flow condition was observed in the numericalmodel with similar characteristics as in the
prototype. Fig. 3 demonstrates the computational
grid adjustment at the beginning of the preliminary
drawdown and also within the free-flow condition
with a low water head in the reservoir. Cells with alower water head than a specified value will be
removed from the computational domain due to theemployed wetting/draying algorithm.
Fig. 4 illustrates the simulated final bed
topography after flushing by employing MPM and
Van Rijn formula (Fig. 4(a) and 4(b) respectively)
versus the measured one (Fig. 4(c)). As can be seen,
application of Van Rijn’s formula resulted in lower
erosion in area III while the erosion was
overestimated in area II. The computed TVFS was
313.14 KCM when MPM formula was used whereas
it was 333.91 KCM by utilizing Van Rijn’s formula.
When MPM formula is employed, the erodedcoarse material from area III may be deposited again
in area I in where the flow velocity would be
reduced due to a larger water depth. In addition, inarea I, the results of the simulation show a narrower

(b)

(a)
600.0 m600.0 mLevel 2

Level 2

Fig. 3 Computational grid (a) at the beginning of simulation;(b) within the free-flow state (t=32hrs).

(a)

(b)

(c)
Fig. 4 Simulated bed levels after flushing employing (a) MPM
formula; (b) Van Rijn formula versus (c) the measured one.

4

I_118

360
355
350
345
340
335
330
325

Elevation (m)provide a more reasonable prediction of bed
evolution pattern in a specific segment. Thus, final
bed levels simulated by MPM and Van Rijn’s
formulas have been compared with the measured
one at cross-section A-A and E-E in Fig. 5.
Subsequently, MPM formula was selected forfurther quantitative assessment of the numericaloutcomes. The numerically simulated bed evolution
pattern in cross-sections A-A, B-B, D-D and F-F
using MPM formula is also presented in Fig. 6.
In area III, where the bed material is coarser, thesediment erosion has been underestimated and thesimulated flushing channel width and depth was
smaller than the measurements (Fig. 6(a) and 6(b)).
Owing to the complex velocity profile and effectivehydrodynamic forces on the coarse bed material,
especially during the free-flow state with low water
head, model could not simulate the erosion patternproperly in this area. Another reason is eliminationof the cells with shallow water head (i.e. drying up),from the computational domain, along the left bankof lower part of area III in where noticeable bed
degradation has been measured (Fig. 6(b)). In area
ӀӀ, which is the wide middle segment of thereservoir, the flushing channel shape and location
obtained by application of MPM formula isgenerally consistent with the observations.
However, bed degradation has been over estimatedalong the right embankment in numerical outcomes(Fig. 6(c)). The flushing channel characteristics inarea II have a considerable contribution inpreserving the reservoir storage capacity as well as
enhancing the flushing efficiency. During thefree-flow flushing operation the narrow flushingchannel location is deflected to the right bank regionof area II and consequently there is a low chance for
erosion of the large amount of deposited sediment in
the left bank zone. It also should be taken into
account that the sediment flushing is conducted in therainy season in Dashidaira reservoir and bed levels
measurement is not conducted right before and after
the flushing event. Therefore, uncertainties would
be possible in the measured bed levels. Moreover,there is also no sediment size distribution
measurement with high spatial resolution along theinactive layer while it may affect the quality of thenumerical model outcomes remarkably.
Fig. 7 shows the water velocity with secondary
current vectors during the free-flow condition. In the
flushing channel, velocities rose up to 4.5m/s andsuper critical flows emerged in several zones (Fig.
7(a) and 7(b)). 3D model also captured thecharacteristic of bed development in the channel
bend (i.e. erosion at the outside of the bend anddeposition at the inside) and reproduced thenonsymmetrical velocity profile over the width as

Measured bed level_before flushing
Measured bed level_after flushing
Simulated bed level_after flushing_MPM
Simulated bed level_after flushing_Van Rijn(a)

Elevation (m)

0

20

40

60

80

100

120

140

160

Measured bed level_before flushing
Measured bed level_after flushing
Simulated bed level_after flushing_MPM
Simulated bed level_after flushing_Van Rijn

347
342
337
332
327
322
317
312

(b)
0

20

40

60

80

100 120 140 160 180 200 220

Distance (m)Elevation (m)Elevation(m)Fig. 5 Final bed level obtained by MPM and Van Rijn
formula at cross section (a): A-A; (b): E-E.
360
355
350
345
340
335
330
325

Me. bed level_before flushing
Si. bed level_t=26 hrs
Si. bed level_after flushing

Si. bed level_t=16 hrs
Si. bed level_t=32 hrs
Me. bed level_after flushing

(a)
0

20

40

60

80

100

120

140

160

Elevation (m)

(b)
350
345
340
335
330
325
320
315

Me. bed level_before flushing
Si. bed level_t=26 hrs
Si. bed level_after flushing(c)0

Elevation (m)Si. bed level_t=16 hrs
Si. bed level_t=32 hrs
Me. bed level_after flushing

345
340
335
330
325
320
315
310
305

20

40

60

80

100

Me. bed level_before flushing
Si. bed level_t=26 hrs
Si. bed level_after flushing

120

140

160

180

200

220

Si. bed level_t=16 hrs
Si. bed level_t=32 hrs
Me. bed level_after flushing

(d)
0

20

40

60

80

100

120

140

160

Distance (m)Fig. 6 Bed evolution pattern for (a) cross section A-A; (b) crosssection B-B; (c) cross section D-D and (d) cross-section F-F.
Me. & Si. Are abbreviations for Measured and Simulated.

60.0 m

x

(a)

(b)

Elevation (m)Bed level
Water level
Surface velocity

320
318
316

4
3
2

314
312
310

1

(c)
9

Velocity (m/s)

x
322

0
12

15

18

21

24

27

30

33

Distance (m)Fig. 7 (a) Simulated flow velocity field in the flushingchannel during the free-flow state (t=32hrs); (b) surface velocity
vector (black) and bottom velocity vector (green) showing thesecondary currents development in the channel bend; (c)cross-section x-x at the apex zone of the bend.

5

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well as tilting the lateral water surface in the apex of
the channel bend (Fig. 7(c)).

additional water discharge and water level loweringwithsmallfluctuations during the free flow condition
may increase the erosion possibility of sediment.

(3) Flushed out sediment discharge variation
Fig. 8 demonstrates the computed fluxes oferoded bed material that is flushed out in one hourintervals during the flushing process mainly as the
bed load. As can be clearly seen, an increase in the
average flow velocity and in the fluctuation of
turbulences, that is the case for the final portion of
the preliminary and the free-flow stage, tends toenhance the sediment mobility and subsequently
sediment entrainment.
Smaller sediment size tends to be eroded andflushed out earlier than larger sediment sizes. For
instance, sand sized sediment (i.e. with diameter of
3.7mm)experiencesa highconcentration immediately
after the flood peak at t=5hrs. The concentrations ofsmaller sediment sizes are sensitive to both waterlevel and also discharge fluctuations. On the other
hand, bigger sediment sizes are more sensitive towater level changes than to discharge variations.
Sedimentsize 118.3mm
Sediment size 11.8mm
Water level

14
12

REFERENCES
1)

2)

3)

4)

335

Sediment size 37.4mm
Sediment size 3.7mm

330
325

10

320

8

315

6

310

4

305

2

Water level (m)Sediment discharge(m³/s)

16

ACKNOWLEDGEMENT
The authors would like to give their sincere thanks
to Kansai Electric Power Co., Inc. and NEWJEC
Inc. and also Mr. Shuhei Minami for providing thefield data used in this study.

5)

300

0

295
0

4

8

12

16

20

24

28

32

36

40

44

48

Time (hr)6)

Fig. 8 Temporal variation of flushed out sediment discharge.

4. CONCLUSION
7)

The following outcomes were obtained from thepresent study:(a) A 3D Numerical model can properly simulate
the flushing channel evolutionary pattern under therealboundary
conditions(e.g.
unsteadyhydrodynamic conditions). Both Van Rijn and
MPM bed load sediment transport formulas canrepresent the volume of flushed out sediment during
the flushing process in a certain range. MPM
formula provides a better prediction of bed levelvariation in the upstream area covered by thecoarser materials. On the other hand, Van Rijn’s
formula showed a better performance in areas closer
to the dam in where the bed is mainly covered withthe fine material and also the reservoir is deeper.
Also, the helical flow and consequent erosion anddeposition pattern in channel bends could be identified
by the model, which represents an advantage over
2D modeling. However, more complex bank erosion
algorithms may need to be used if the bank material
is influenced by cohesion or vegetation.
(b) In terms of flushed out sediment dischargeduring the flushing, coarser bed material is mainly
flushed out at the end of the preliminary drawdown
and during the free-flow state. Thus, introducing an

8)

9)

10)

11)

12)

13)

14)

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