THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

Improvement of a Kinemative Wave-based Distributed Hydrologic
Model to Predict Flow Regimes in Arid Areas
Tomohiro TANAKA1*, Soe THIHA2 , Yasuto TACHIKAWA3 and Kazuaki YOROZU4

Abstract Many studies have applied DHMs-KWSS
concept (Distributed Hydrologic Models based on a
Kinematic Wave approximation with Surface andSubsurface flow components) to predict river discharge
for mountainous catchments in Japan. Some studies
have showed that DHMs-KWSS represents well flow
regimes of river basins with temperate climaticconditions, however it is difficult to explain flow
regimes of arid basins. This study introduced the
Hortonian overland flow caused by high rainfall in thedry season into a DHM-KWSS to explain the highdischarge which happens at the beginning of rainyseason in arid basins. The improved model represented
high discharge in the beginning of rainy season better
than the original DHM-KWSS structure.
Keywords Distributed rainfall-runoff models, verticalinfiltration, the Ayeyarwady river basin, arid basin,
kinematic wave model
Introduction
To develop a suitable hydrologic model to forecast
river flows, dominant hydrologic processes in acatchment must be identified. In Japan, many river
basins have steep slopes and are located in mountainous
terrain, which causes rapid propagation of flood flows.
These slopes are mainly covered by forests in which
temperate climatic conditions keep soil moisture in thesurface layer. Rainfall-runoff processes in these climaticconditions have been represented by some DHMs1

Tomohiro Tanaka
Graduate School of Engineering, Kyoto University
Kyoto Japantanaka@hywr.kuciv.kyoto-u.ac.jp
2
Soe Thiha
Graduate School of Engineering, Kyoto University
Kyoto Japan
thiha@hywr.kuciv.kyoto-u.ac.jp
3
Yasuto Tacikawa
Graduate School of Engineering, Kyoto University
Kyoto Japantachikawa@hywr.kuciv.kyoto-u.ac.jp
4
Kazuaki Yorozu
Graduate School of Engineering, Kyoto University
Kyoto Japanyorozu@hywr.kuciv.kyoto-u.ac.jp

KWSS (Distributed Hydrologic Models based on a
Kinematic Wave approximation with Surface andSubsurface flow components) such as HydroBEAM(Kojiri et al. 2008) and OHDIS-KWMSS (Takasao et al.
1988, Sayama et al. 2006, Sayama and McDonnell
2009, and Kim et al. 2011).
Hunukumbura et al. (2012) used a DHM-KWSS todescribe short-term flood events in several river basinslocated in multiple countries, encompassing a range of
geographic and climatic conditions different from thosein Japan. The results showed that the model wasapplicable to basins with steep slopes and a temperate
climatic condition similar to those in Japan; however, it
has difficulty in describing hydrologic behaviors in arid
climates as well as those with mild topography. On theother hand, Tanaka and Tachikawa (2014) applied 1KDHM (Tachikawa and Tanaka 2013) which is one of
distributed hydrologic models with the DHM-KWSS
structure to two river basins for long-term flow regimes.
One has temperate climatic conditions as with river
basins in Japan, and the other has different ones fromriver basins in Japan. It clarified that the DHM-KWSS
model structure represents long-term flow regimes in atemperate climatic conditions and a large flood in aridbasins; however, it is unable to explain long-term flow
regimes in arid basins. The above two studies lead to theconclusion that the DHM-KWSS model structure isapplicable to river basins with temperate climaticconditions and difficult to explain rainfall-runoff in aridbasins.
Therefore, this study tries to improve 1K-DHM tobetter represent flow regimes in arid basins. In
particular, sensitive responses of river discharge torainfall in the dry season was targeted. The DHMKWSS model structure provides all of rain water tosubsurface flow because it is aimed at humid basins
where humid surface soil layers have a large value ofhydraulic conductivity. This characteristic does notmatch rainfall-runoff of arid basins, thus this study
considered the Hortonian overland flow due to higher
rainfall intensity than vertical infiltration capacity. The
improved 1K-DHM was applied to the AyeyarwadyRiver basin located in Myanmar and reproduced theobserved drastic change in river discharge in the
beginning and middle of a rainy season.
Model description
1K-DHM is a distributed hydrologic model based

THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

effective porosity; and ns is the Manning’s roughness
coefficient for saturation overland flow in a slope
component. Equation (2) represents the q-h relationshipfor surface and subsurface soil layer as shown in Fig. 1.
Runoff from a slope cell component is given to a river
channel component as lateral inflow. River discharge isthen calculated using the following kinematic wave
equation:Fig. 1 Discharge-storage function of slope cells
on a kinematic wave flow approximation that considers
subsurface flow. This model uses a kinematic wave
model that describes surface and subsurface flow using
a storage-discharge equation. Kinematic flow is applied
to each cell within 1K-DHM, where flow is calculatedusing rainfall input to the cell and discharge from upper
cells as the boundary condition. Flow direction isdetermined using topographical data provided byHydroSHEDS (http://hydrosheds.cr.usgs.gov).
Each cell has a slope component and a river channel
component. Rain water is provided to a slope
component, and runoff from a slope component iscalculated by the following two equations, which
consider both saturated and unsaturated subsurface flow
components (Hunukumbura et al. 2012):

hsub q sub r e
t
x

(1)



dckc hsub/ dc  i0 hsub dc qsub  
dckci  hsubda kaidc hsub da 
 i / n h d m  h d k i  d k i d h 
s sub
a
sub
a a
c c
a
sub


A Q

 q subt x(3)

Q   c Am

(4)where A is the cross-sectional area; Q is discharge; r
is rainfall intensity; e is evapotranspiration; and  c
and

m

are

determined asparameters.

These c  i / nc B

3 / 2parameters
and

are

m  3/5 ,assuming a rectangular cross section of each cell. The
parameters i , nc , and B represent the slope gradient,
Manning’s roughness coefficient, and the width of ariver channel. The value of B is determined by

B  bS c , where b = 1.06 and c = 0.69. These values
were determined by considering the behavior of manyobservation stations across Japan.
Introduction of vertical infiltration into 1K-DHM
To consider the Hortonian overland flow, verticalinfiltration component was introduced into a slope
component of 1K-DHM. 1K-DHM-VI (1K-DHM thatconsiders Vertical Infiltration) gives not all of effective
rainfall r  e but infiltration P to equation (1). P is
defined as bellow:

r  e ( r  e  f )P
(r  e  f )
 f

(5)

(2)
where h sub is water stage; q sub is discharge per unit
slope width; r is rainfall intensity; e is intensity ofevapotranspiration; d c is an equivalent water stage tothe maximum water content in the capillary pore; k c ishydraulic conductivity when the capillary pore issaturated;  is an exponent parameter that describes
the relationship between hydraulic conductivity andsaturation; k a is saturated hydraulic conductivity and=according to the continuity of the q-h
relationship Equation (2); d a is the water stage
equivalent to the maximum water content in thewhere, f is vertical infiltration capacity. Given larger
effective rainfall r  e than f , rain water that is unable
to infiltrate r  e  P contributes to the overland flow.
f is defined as:

k c hsub / d c   k 0
f 
kc  k0(hsub  d c )(hsub  d c )(6)

As shown in equation (6), f represents verticalhydraulic conductivity that is function of water stageh sub . To consider vertical infiltration through a crackon the extremely dry condition, minimal vertical

THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

The Mitkyina Station
The Monywa Station
Rainfall Station
Evaporation Station

28゜

3,800
26゜

3,800

Myitkyina

INDIA

CHINA

2,500

24゜

1,300
1,300
3,800

Monywa

1,300

22゜

650
750

LAOS
20゜

2,500

5,100

3,800

THAILAND
18゜

5,100

3,800

3,800
YANGON

50

92゜

94゜

96゜

0

50 100 150 km

16゜

3,800
98゜ 3,800

100゜

[mm]

Fig. 2 Spatial distribution of a mean annual
precipitation in the Ayeyarwady basin (The Kansai
Electric 2008)

Fig. 3 water-shed boundary of the Ayeyarwady Riverbasin and target stations (Myitkyina and Monywa)
Climatic and topographic conditions

hydraulic conductivity k0 is added. Overland flow in aslope component is calculated by the following
kinematic wave equation:

hsfc
t



q sfcx r e P

q sfc  i / n s h sfc

m

(6)

(7)

where hsfc and q sfc are water stage and discharge perunit slope width of the overland flow. q sub and q sfc
contributes to a river channel component as lateral
inflow. Consequently, the continuity equation of a slope
component (1) and that of a river channel component (3)are changed as bellow:

hsub q sub

P
t
x

(8)

A Q

 q sub  q sfc
t x

(9)

The Ayeyarwady River basin is characterized by drastic
spatio-temporal variability of climatic conditions from
north to south of the basin. The spatial distribution of amean annual precipitation in Myanmar is shown in Fig.
2. Fig. 2 shows that a mean annual precipitation largely
varies from north to south. Affected by topography, amean annual precipitation in the southern and thewestern part of the basin amounts more than 3000 to5000 mm and that in the central area is less than 1000
mm. The whole area of the basin has clear dry and rainyseasons. The rainy season starts from middle of May
through October, and the dry season covers November
through early May.
This study simulated flow regimes at the
Myitkyina and Monywa stations in the AyeyarwadyRiver basin. The location of the two stations and watershed boundary of the Ayeyarwady River basin are
shown in Fig. 3. Comparing Fig. 2 and Fig. 3, we foundthat an upstream area of the Myitkyina station has muchrainfall, while the amount of annual precipitation in an
upstream of the Monywa station is smallest in the wholebasin.
Calibration framework

Applying 1K-DHM-VI to the Ayeyarwady Riverbasin

The parameters of 1K-DHM were calibrated toreproduce flow regimes at both the stations in 2001 byusing the SCE-UA algorithm (Duan et al. 1994). Daily
precipitation
at28
stations
andpotential

THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

Fig. 4 Hydrograph of observed (thick line) andsimulated discharge in 2001 at the Myitkyina station(effective
rainfallmeans
rainfallminus
evapotranspiration)

Fig. 5 Hydrograph of observed (thick line) andsimulated discharge in 2001 at the Monywa station(effective
rainfallmeans
rainfallminus
evapotranspiration)

Table 2: Calibrated parameter values for the Monywa
station and the Myitkyina station
The Myitkyina stationd c and  as those in Table 2, and the parameter k0 set
to 0.000001.

 (-)

Results and discussions

0.018
2.085
0.455
The Monywa station

9.989

The observed discharge and the simulated
discharge using 1K-DHM and 1K-DHM-VI at thens (m-1/3s)

k a (m/s)

d a (m)d c (m)

 (-)

1.626

0.013

1.627

0.335

7.497

Myitkyina station in 2001 are shown in Fig. 4. Fig. 4
indicates that 1K-DHM/1K-DHM-VI reproduced
general flow regimes in a rainy season; however, flow
regimes in the beginning of a rainy season and quick
response of river discharge to rainfall in the dry season(from January through the end of May) are notrepresented in 1K-DHM. 1K-DHM gives all rain water
into subsurface soil layers. Therefore, 1K-DHM isdifficult to explain quick response to rainfall in the dry
season.
On the other hand, simulated flow regimes by 1KDHM-VI well reproduced observed river discharge inthe beginning of a rainy season. In addition, 1K-DHMVI also well reproduced sensitive response to rainfall inthe dry seasonThis is attributed to the effect of the
Hortonian overland flow.
The calibrated result for the Monywa station in 2001
is shown in Fig. 5. It shows that flow regimes simulated
by 1K-DHM reproduced general flow regimes inobserved in the Monywa station. However, simulated
discharge using 1K-DHM does not the responses ofobserved discharge to high rainfall that occurred in May.
1K-DHM-VI reproduced the quick response of riverdischarge for the rainfall event. Since calibration of all
the six parameters by using 1K-DHM-VI was notconducted, the value of flood discharge is possibly
improved by the model calibration.
Figures 4 and 5 also indicated that the model is unable
to explain constant base flow in the dry season.

ns (m-1/3s)

k a (m/s)

0.703

d a (m)d c (m)evapotranspiration at 9 stations were used. The locationof each data is shown in Fig. 3. Observed riverdischarge data at Myithyina and Monywa stations wereused to calibrate the model parameters. The Nash
Sutcliffe coefficient in Equation (10) was used as anevaluation criterion for calibration: QN  1
 Qns

Qobs 

 Qsim,i 

2

i 1
n

obs ,ii 1

obs ,i Qobs1 n
 Qobs,in i 1



2

(10)

(11)

where Qobs is a mean of observed discharges. Q sim ,i issimulated daily discharge, and Qobs ,i is observed daily
discharge for the i-th day. The SCE-UA algorithm thensearches these parameter sets with the highest Nash
Sutcliffe coefficient. The calibrated parameters for thetwo stations are shown in Table 2. Simulation by 1KDHM-VI then used the same parameters ns , k a , d a ,THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

In particular, base flow in the dry season at the
Myitkyina station is largely underestimated. Chavoshin
et al. (2007) showed the same tendency, and MODIS
satellite data in Chavoshin et al. (2007) shows that baseflow in the day season at Myitkyina station is affectedby snow melt in an upstream area of the station.
Moreover, potential evapotranspiration was directly
used as evapotranspiration e , thus e in the dry season
is overestimated, which contributed to almost no flow inthe dry season in the model.
Conclusions
The DHM-KWSS model structure well represents
flow regimes of river basins with temperate climaticconditions; however, it is difficult to explain those inarid basins. To enhance the transferability of DHMKWSS models for arid basins, this study developed 1KDHM-VI that added the Hortonian overland flow
component on 1K-DHM. Application of 1K-DHM-VI to
the Ayeyarwady basin showed the improved
reproducibility of sensitive responses of river discharge
to rainfall in the beginning of a rainy season and in thedry season. The next step will calibrate all the six
parameters for one event and validate the results forother events.
Moreover, the application results also clarified thatmuch more improvement of estimation of actual
evapotranspiration and consideration of snow melting
are required. Future research must also focus on
estimation of meteorological force to capture hydrologic
processes in arid areas.
References
Chavoshian, A., Ishidaira, H., Takeuchi, K. andYoshitani, J., 2007, Hydrological Modeling ofLarge-scale Ungauged Basin Case Study of
Ayeyarwady (Irrawaddy) Basin, Myanmar, HRSD
UNESCO-IHP Southeast Asia and the Pacific.
Duan, Q., Sorooshian, S., Gupta, V.K., 1994, Optimaluse of the SCE-UA global optimization method forcalibrating watershed models, Journal of
Hydrology, 158:265-284.

Hunukumbura, P.B., Tachikawa, Y., Shiiba, M., 2012,
Distributed hydrological model transferability
across basins with different physio-climaticcharacteristics. Hydrological Processes, 26 (6), 793808.
Kojiri, T., Hamaguchi, T., Ode, M., 2008. Assessmentof global warning impacts on water resources andecology of a river basin in Japan. Journal of Hydroenvironmental Research, 1, 164-175.
Lee, G., Tachikawa, Y., Takara, K., Identification ofmodel structural stability through comparison ofhydrologic models. Annual Journal of HydraulicEngineering, Vol. 51, 49-54, 2007.
Sayama, T., Tachikawa Y., Takara, K., and Ichikawa,Y., 2006. Distributed rainfall-runoff analysis in aflow regulated basin having multiple multi-purpose
dams. IAHS Publication, 303, 371-381.
Sayama, T., McDonnell, J.J., 2009. A new time-spaceaccounting scheme to predict stream water
residence time and hydrograph source components
at the watershed scale, Water Resources Research,
45: W07401, doi:10.1029/2008WR007549.
Sunmin, K., Tachikawa, Y., Nakakita, E., Yorozu, K..,
Shiiba, M., 2011. Climate change impact on river
flow of the Tone river basin, Japan. Journal ofJapan Society of Civil Engineers, Ser. B1(Hydraulic Engineering), 67 (4), 85-90.
Tachikawa,Y.
andTanaka,T.,
2013.
http://hywr.kuciv.kyoto-u.ac.jp/products/1KDHM/1K-DHM.html [Accessed 15 November2014].
Takasao, T., Shiiba, M., 1988. Incorporation of theeffect of concentration of flow into the kinematicwave equations and its applications to runoff
system lumping, Journal of Hydrology, 102, 301322.
Tanaka, T., Tachikawa, Y., 2014, Testing theapplicability of a kinematic wave-based distributedhydrologic model in two climatically contrastingcatchments, Hydrological Sciences Journal. (thisissue)The Kansai Electric Power Company, 2008, Study
Report in Hydrology in Myanmar, 12.