Journal of Japan Society of Civil Engineers, Ser.
Annual Journalof HydraulicEngineering,
JSCE,
Vol.57,2013, February
B1 (HydraulicEngineering),
Vol.
69, No.
4, I_43-I_48,
2013.

DEVELOPING A REGIONAL DISTRIBUTEDHYDROLOGICAL MODEL FOR WATERRESOURCES ASSESSMENT AND ITS APPLICATION
TO THE CHAO PHRAYA RIVER BASIN
Supattana WICHAKUL 1, Yasuto TACHIKAWA 2, Michiharu SHIIBA 3,
and Kazuaki YOROZU 4
1Student Member of JSCE, Graduate Student, Dept. of Civil and Earth Resources Eng., Kyoto University, Kyoto
615-8540, Japan
2Member of JSCE, Assoc.Prof., Dept. of Civil and Earth Resources Eng., Kyoto University, Kyoto 615-8540, Japan
3Member of JSCE, Prof., Dept. of Civil and Earth Resources Eng., Kyoto University, Kyoto 615-8540, Japan
4Member of JSCE, Assist. Prof., Dept. of Civil and Earth Resources Eng., Kyoto University, Kyoto 615-8540, JapanThe Thailand's Great Flood in 2011 resulted in the great calamity causing tremendous losses impacting
livelihood, social and economic of the nation. A better understanding of the basin hydrological processes
is necessary for studying and predicting a future flood. Consequently, this study aims to develop a
regional distributed hydrological model for water resources situation prediction. The regional hydrologic
model was composed of a runoff generation model with a concept of the variable infiltration capacity anda flow routing model using the kinematic wave equation. The effects of dam control were also included in
the flow routing model. The model was applied to the Chao Phraya River basin to reproduce floods in
1995, 2008, 2010, and 2011. By using the model, the effect of the existing dams operations and the new
dam construction on flood control is numerical evaluated.
Key Words: Thailand, Chao Phraya River, runoff prediction, regional hydrologic modeling

1. INTRODUCTION
Due to continuous and intense precipitation
occurring in the upper part of the Chao Phraya River
Basin (CPRB), the unforeseen devastating flood
occurred most significantly in the lower part of thebasin from July 2011 until the end of the year.
There are many losses in term of human, social andeconomic losses. Thai Ministry of Interior revealed
that 815 people were killed during the inundated
period, as of January 20, 2012. The World Bank has
estimated 1,425 billion baht (US$ 45.7 Bn) in
economic damages and losses due to flooding, as ofDecember 1, 2011. This is the worst recorded
damage in Thailand. Thus, it is a quite important
issue to develop a regional hydrologic model toevaluate the vulnerability of the existing river
system for water-related disasters and water
resources, and to assess a future river plan, such as a
new dam construction under a changing climate.
Recently, the future of hydrological situation

under climate change in the CPRB has gained more
attention. For example; Hunukumbura andTachikawa1) projected future river discharge todetect hotspots on rivers discharge in the CPRB.
Jayawardena et al.2) applied several versions of thevariable infiltration capacity (VIC) models3)4) topredict river flow in the Mekong and Chao Phraya
basins using general circulation model output.
The main objective of this paper is to develop a
regional distributed hydrological model which is
up-to-date and can reproduce historical floods in the
CPRB. The model is applicable to assess a riverplan under a changing climate. Using this developed
regional hydrologic model, we examine the effect ofexisting dams on reducing flood and a new dam
construction on flood control.

2. STUDY AREA AND INPUT DATAThe Chao Phraya River originates in the northern
region of Thailand. There are two parts of the CPRB,

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Fig.2 The distribution of runoff and infiltration as afunction of grid wetness and infiltration capacity.

over the study area vary due to variations in
topography, soil, and land cover (refer to Fig. 2), theinfiltration capacity i over an area can be
represented as the following equation,

Fig. 1 Diagram of The Chao Phraya River basin of Thailand

upper and lower parts comprising of a total area of
157,925 km2. The upper part of the basin consists offour principal sub-basins, the Ping, Wang, Yom andNan river basins. The confluence of the Ping Riverand Nan River at Nakornsawan province is the
beginning of the Chao Phraya River.
Rainfall data was collected from 26 stations
covering the CPRB. The average of 2011 annual
rainfall in those sub-basins is approximately 36%
larger than the annual rainfall average over 30 years(1980-2009), whereas in the Lower CPRB theaverage of 2011 annual rainfall is slightly (2%)
higher than the average over 30 years. Therefore, inthis study we focus on the runoff generated in theupper sub-basins of the CPRB by observing mainly
at the C.2 station (15°40′N and 100°06′E). Fig. 1
illustrates a diagram of the CPRB including thesatellite image of inundated area during flood 2011.

3. MODELING

0

0
1

1

1

1

1

where im represents the maximum infiltration
capacity, A is the fraction of the cell area for the
infiltration capacity and takes values between 0 and
1, Ai is the portion of direct runoff generation areasin the cell, and b is an empirical parameter showing
a shape of the storage water capacity curve.
By integrating the function of the infiltration
capacity i (Eq.1) from Ai to 1, the maximum tension
water storage of the cell Wm can be expressed as
1(2)

Therefore, the current soil moisture W
corresponding to a current infiltration capacity i0 is
obtained by the following equation.

APPROACH

Principally, a distributed hydrological model
consists of a hydrologic model and a flow routing
model. In this study both hydrologic and flowrouting models were founded as a grid-based model.
In order to reproduce the realistic runoff situation in
the CPRB, a dam operation model has beencombined in the flow routing model.
(1) Hydrological Model
To develop the hydrologic model, we simplified
the Xinanjiang (XAJ) model5) by reducing a numberof parameters and modifying sub-layers in themodel for surface and subsurface runoff
generations. Additionally, a concept of the modified
XAJ model, tension water storage variation andaquifer condition proposed by Nirupama et al.6)
were adapted in this study.
Based on assumption that infiltration capacities

1

0

1

(3)

According to Fig.2, during the precipitation
event, rainfall r and evapotranspiration e are taken
as input to the model. From the impervious area, thedirect runoff depth Wd is generated which is shown
as
∆
(4)
∆
where ∆ is time interval, and Qd is the direct runoff
intensity. From the pervious area, surface runoff
depth Wp is calculated using the following
relationships;∆
Case 1: If(i.e., severe rainfall occurs and/or soil is saturated)
∆

1

∆

(5a)

Case 2: If∆t(i.e., normal rainfall occurs and/or soil is unsaturated)

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∆

∆

1

∆

1

(5b)

where Qp is the surface runoff intensity provided by
the infiltration capacity concept in pervious area.
As show in Fig.2, due to the shallow aquifer
underneath in some part of the CPRB7), we included
the effect of the groundwater component into theupper layer of the model to separate some amount of
infiltrated water for recharging to the shallow
aquifer. The soil water storage contributes to thegroundwater
expressed as the function of anon-linear reservoir relationship. The equation
presents as(6)where kg is the groundwater coefficient (hr) and pg is
the empirical parameter of aquifer storage.
Thus, the updated soil moisture W is determinedaccording to water balance in the upper layer of themodel by the following equation,
∆

∆

∆

(7)

Remark that the values W vary between 0 to Wm. By
referring to Eq. 3, the W is a function of i0. Hence,the values i0 can be solved as well.
The surface runoff Qp infiltrates to be inputs ofthe subsurface runoff (base flow) component of themodel as shown in the Fig.2 (lower layer). The
subsurface runoff component of the model isapproximated by the relationship of a non-linear
reservoir and continuity equation conveyed by
,

s

(8)

where Qs represents the subsurface runoff, s is the
subsurface storage, ks is the subsurface coefficient(hr), and ps is the empirical parameter of subsurface
storage. Finally, total runoff Q produced for a cell isobtained as
Q = Qd + Qs
(9)The simplified Xinanjiang model has sevenparameters in total, Ai, Wm, b, ks, kg, ps, and pg. They
were identified in the process of model calibration.
The model was applied for the CPRB at the 1/4
degree resolution and the model represents about
560 (20 columns and 28 rows) computational gridcells covering the basin, and 1-hr time step of thecalculation. Hereafter, we would refer to thesimplified Xinanjiang Model as the SXAJ model.
The outputs from the SXAJ model obtained as total
discharge depth (millimeter per hour) at eachcomputational grid cell were used as inputs to a flowrouting model.

(2) Flow Routing Model
Generally, excess rainfall is easily routed by
lumped approaches, such as unit hydrograph, flow
isochrones or linear reservoir modeling incomputational of overland flow and channel flow,
however it is difficult to represent land cover andtopography as spatially distributed on a basin scale.
Hence, the 1-km distributed flow routing model,
1K-FRM (http://hywr.kuciv.kyoto-u.ac.jp/products/
1K-DHM/1K-DHM.html), was chosen for routing inthis study.
A digital elevation model (DEM) was applied todefine the flow direction of the catchment, assumingthat the flow direction 1-dimensionally to steepest
downward slope to an immediately neighboring cell.
The topographic data used in the 1K-FRM were the
30 arc-second DEM and flow direction stored in
HydroSHED.
The flow model is based on the one-dimensional
kinematic wave model. According to the flow
direction, each cell has a routing order fromupstream to downstream. Then runoff generated by
the SXAJ model becomes river discharge. The
one-dimension kinematic wave equation for eachcell is given by(10)
where t is time, x is the distance from the top of therectangular grid, A is cross section area on theregular grid, Q is discharge, and q(t) is the lateral
inflow per unit length of channel unit given as
runoff generated by the SXAJ model. The Manning
relation type of the discharge and cross-sectional
area was joined with the continuity equation to routethe water for each cell. There are two types of thecross sections used in this study, rectangular andquadratic shapes. The relationship of the discharge
to the cross section area is given as follows;(11)
For a rectangular cross-section shape (m = 1.67),) (m =
and a quadratic function shape (1.44), respectively;
, and

(12)

where g0 is slope; n is the manning roughness
coefficient; B is the width of flow; and a iscross-section parameter. The quadratic function wasapplied to flooded area where cross-section of theriver was accordingly changed with the over bankflow. The criterion to distinguish the type of thecross section is set by the number of upstream grids.
When the number is larger than 35,000 (about
35,000 km2) the quadratic cross section is adopted

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for representing the inundated areas. The 1K-FRM
parameters are n, B and a. In this study, we used thevalues of n and B same as the original model, n =0.03 m-1/3s and 11.0 m-1/3s for channel and slope
flow, respectively. The value of B is equal to
1.06C0.69; where C is catchment area at the points.
These two values were determined and used in theJapanese catchment1). To reproduce the inundation
phenomena of the flood 2011 in Thailand, we
assumed the quadratic cross section shape. The
cross section parameter a is set to 0.00012 toreproduce a flood discharge properly.
(3) Dam Operation Model
As mentioned in the study area, flow in the Chao
Phraya River is significantly influenced by the dams
operation. Therefore, dam operation models for the
Bhumibol dam and Sirikit dam were embedded intosome particular grids of 1K-FRM where the dams
locate. An algorithm to develop a general reservoir
operating rule is a flexible function that can be
adjusted for different dam features.
The kinds of information, which are required for
input to the dam operation model, are spillway
capacity, downstream requirement, active storage,
min/max storage, and upper/lower rule curves. The
monthly operation basis of the dam model is to store
water in wet season (May-December) and to releasewater in dry season (January-April).
Finally, an overall framework of the distributedhydrological model to achieve the simulated
discharge in the river at each focused point can be
schematized as in Fig. 3.

3. PARAMETERS IDENTIFICATIONThe SXAJ model have seven parameters, i.e., theshape parameter of the soil water storage curve b,
the groundwater parameter kg, the baseflow
parameter ks, the parameter of groundwater storagepg, the parameter of sub-surface storage ps, themaximum soil moisture storage Wm, and the fractionof direct runoff generation areas Ai, to be calibrated
for each computational grid in the basin.
In put Data : Rainfall Data + Evapotranspiration

According to the CPRB size (large), its
non-homogenous geological, and its topographic
characteristics, the best combination of the model
parameter was estimated using the observed
discharge data of the year 2011 at the C.2 station,
Bhumibol dam and Sirikit dam. Also the observed
data of year 1995, 2008 and 2010 at those points
was compared with the simulated discharge toverify the model parameters. We, therefore,
separated the set of parameters to three sets
depending on the topographic and geologic
conditions. The first set of parameters was applied
to the lower part of the Yom and Nan River, thesecond set was proposed to the Ping River basin,
and the last set was used for remaining areas over
the CPRB.
To identify the parameters, the trial-and-error
method and the following procedures wereconducted for the model calibration in this paper: (a)setting initial values of the parameters, (b)comparing simulated and observed discharge, (c)adopting a coarse step-size and then a finer step-size
to identify the range of probable parameters andrefine values, respectively.
The optimized SXAJ model parameters are given
in Table 1. In the SXAJ model, a relationshipbetween the kg value and the effect of groundwater
was not in direct proportion (Eq. 6). Consequently,
the high values of kg were obtained in the general
grids and Ping River basin resulted from a lesser
effect of shallow groundwater in those areas. With
these sets of parameters, the SXAJ model generated
runoff as an input for the routing model, and initialcondition of the routing model was set accordingly
to the observed discharge. The comparisons ofsimulated and observed discharge at the Bhumiboldam, Sirikit dam, and C.2 Station are respectively
illustrated in Fig.4 for the model calibration and
Fig.5 for the model verification. The model wascalibrated by maximizing the Nash-Sutcliffe
efficiency (NSE) of the daily discharge, and someerror indicators, coefficient of determination R2, androot mean square error RMSE, were used to justify
the model performance. The summary of the model
performance indicators of the calibration andverification stages are given in the Table 2.
Parameters

Hydrologic Model :Simplified XNJ Model with variableinfiltration capacity concept

Ai
Wm
b
ks
kg
ps
pg

Re se r vo i r Ope r at i o n
Mo de l :Bhumibol Dam and Sirikit Dam

Flow Routing Mode l(1K-FRM)

Disc harge Prediction

Fig. 3 Framework of the distributed hydrological model.

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Table 1 The SXAJ model parameters.
GeneralLower
Ping River
grids
Yom&NanBasinRiver Basin
0.35
0
0.30
350
1500
400
1.5
0.2
1.5
150
400
200
1500
40
1500
0.6
0.6
0.5
0.6
0.6
0.6

(a)

(b)

(c)

(a)

(b)

(c)

Fig. 4 Comparisons of discharge at the Bhumibol dam, Sirikit dam and C.2 station for the model calibration, 2011.

(a)

(b)

(d)

(e)

(c)

(f)

Fig. 5 Comparisons of discharge at the Bhumibol dam and Sirikit dam for the model verification, 2010 2008 and 1995.
Table 2 Summary of model performance indicators.
PeriodLocation

Statistical criterion

Calibration

2011

Verification

2010

2008

1995

Bhumibol Dam
0.62
310.610.63-0.28
397.420.56
0.2
217.340.75
0.53
192.460.67

NSE
RMSE (m3/s)
R2
NSE
RMSE (m3/s)
R2
NSE
RMSE (m3/s)
R2
NSE
RMSE (m3/s)
R2

Sirikit Dam
0.71
265.160.75
0.47
247.750.77
0.75
138.070.77
0.68
295.41
0.71

C.2
0.87
498.53
0.87
0.49
562.9
0.85
0.55
396.640.6
0.7
663.970.77

4. ASSESSMENT OF DAM FLOOD CONTROL(1) The effect of the Bhumibol and Sirikit dam
The Bhumibol (BB) dam was built in 1964 withthe capacity 13,420 billion m3 and the spillway
capacity 6,000 m3/s at the Ping River basin.
Afterwards, in 1974 the Sirikit (SK) dam was builtwith the capacity of 9,510 billion m3 and thespillway capacity of 3,250 m3/s at the Nan Riverbasin. The catchment areas of the BB and SK dams
are 26,400 km2 and 13,130 km2, respectively. In thesimulation shown in Fig. 4c, the dam operation of
two dams was embedded according to the actual
operation data to the 1K-FRM with the condition of
releasing water 200 m3/s and 250 m3/s during
January-April and 15% and 30% of natural inflow
during May-December for BB Dam and SK Dam,

respectively. The comparisons between simulated
and observed reservoir storage of these two dams
are shown in Fig.6. To assess the effect of dams on
the flood 2011, we have done a simulation of theyear 2011 using the runoff input generated by the
SXAJ model to the 1K-FRM without considering
the dam operation model, as illustrated in Fig.7. The
result shows the volume of simulated hydrographwithout two dams was 54,812 million m3, which
was as much as a 23% increase when compared tothe actual situation, focusing at the C.2 station
during April-December 2011. Moreover, the dams
facilitate water storage during the early stage of theflooding period by decreasing 15% of the peakdischarge compared to the value obtained with no dams.

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(b)

(a)

Fig.6 Reservoir Storage of the BB Dam SK Dam, Mar-Nov 2011.

(2) The effect of proposed dam construction
on the Yom River
Tentatively, the government of Thailand has
proposed to develop one more dam named Kang
Sue Ten (KST) in the Yom River basin to relieve
water resources problems. The catchment area of the
KST dam is 3,538 km2. The active storage is 1,125
million m3, and the spillway capacity is 5,355 m3/s.
In this paper, we considered the dam for flood
protection purpose only. The operation condition ofthis dam was made by optimizing the historicaldischarge data of 19 years (1992-2010) at the Y.20
station to figure out the suitable downstream releaseflow. Dam operation conditions of the KST dam arereleasing water 40 m3/s during Jan-Apr and 40% of
natural inflow during May-December. We assumed
that was what would happen to the flood 2011 if the
KST dam had already been built.
The results of this simulation show that there wasan insignificant effect on the overall water resources
situation in the CPRB. The volume of thehydrograph at the C.2 station was only a 1.5 %decrease as shown in Fig.8a. However, the KST hassignificant effect on a water resources situation of
the Yom River basin by increasing in dry season
flow and also reducing about 50% of peak dischargeduring the wet season as presented Fig. 8b.

(a)

(b)

Fig.8 Comparisons of simulated discharge between with and without
KST dam at the C.2 Station (a) and downstream of the dam (b).

5. CONCLUSION
In this study, we have successfully developed theregional distributed hydrological model embedding
the dam operation model to reproduce the flood

Fig.7 Comparison of simulated discharge between actual
situation, and without the BB and SK dams at C.2 station.

2011 in the CPRB and to investigate effects of dams
on a water resources situation of the CPRB. The
hydrological model was developed based on aconcept of the variable infiltration capacity
including the effect of shallow groundwater.
Overall, the agreement between observed discharge
and simulated discharge, and the water balance of
simulated and observed hydrographs were satisfied
by the NSE ranges from 0.62 to 0.87 and the R2
ranges from 0.63 to 0.87 for the calibration period.
As expected the NSE for the model validation issmaller than the model calibration. But the R2 forthe model validation, which ranges from 0.56 to
0.85, is almost the same range with the model
calibration. The dams in the upper part of the CPRB
were proved that they are useful for the flood
protection in the basin. Our future work tasks are to
predict a future water resources situation under achanging climate and to propose an adaptive
measure to cope with water related disasters.
ACKNOWLEDGMENT: We greatly thank theThai Royal Irrigation Department to provide therelevant data, rainfall data and observed discharge.
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