Journal of Japan Society of Civil Engineers, Ser.
B1 (HydraulicEngineering),
Vol.JSCE,
68, No.
4, I_97-I_102,
2012.
Annual Journalof HydraulicEngineering,
Vol.56,2011, February

Past and future hydrological simulations of Chao
Phraya river basin
Tetsuya Ogata1, Oliver C.SaavedraValeriano2, Chihiro Yoshimura3,
Winai Liengcharernsit4and Yukiko Hirabayashi51Student Member of JSCE, Graduate student, Department of Civil Engineering, Tokyo Institute of Technology(2-12-1 Oookayama, Meguro-ku, Tokyo 152-0033, Japan)
2Member of JSCE, Dr. Eng., Associate Professor, Department of Civil Engineering, Tokyo Institute of Technology(2-12-1 Oookayama, Meguro-ku, Tokyo 152-0033, Japan)
3Member of JSCE, Dr. Eng., Associate Professor, Department of Civil Engineering, Tokyo Institute of Technology
4(2-12-1 Oookayama, Meguro-ku, Tokyo 152-0033, Japan)
Not Member of JSCE, Dr. of Eng, Associate Professor, Faculty of Engineering, Kassetsart University(50 Phaholyothin Road, Bangkok 10900, Thailand)5
Member of JSCE, Dr. Eng., Associate Professor, School of Engineering, The University of Tokyo(2-11-16, Yayoi, Bunkyo-ku, Tokyo 113-8656)

Predictions of variations in global and regional hydrological cycles and their response to changes inclimate and the environment are key problems for future human life. Therefore, basin-scale hydrologicalforecasts, along with predictions regarding future climate change, are needed in areas with high flood
potential. This study forecasts hydrological process scenarios in the upper Chao Phraya River basin using a
distributed hydrological model (DHM) and precipitation scenarios from three general circulation models
modified by the H08 model. First, discharge was simulated by the DHM using the observed rainfall from2007 to 2009 and then, future discharge scenarios from2010 to 2040 were forecast.

Key Words: Chao Phraya river, hydrological scenario, flood forecast, GCM

Large unexpected floods occurred in 2010 and2011. In the 2010 flood, 51 prefectures suffered flood
damage and 260 people died.5) In 2011, which was
similar to 2010, continuous rainfall from May to
October caused the worst flooding in Thailand’s
history; approximately 600 persons perished, and by
early November, 58 provinces were affected.
These two floods were attributed to continuous,
intense precipitation due to tropical depressions
occurring on the upper Chao Phraya River basin
before the rainy season. Thus, unexpected rainfall
patterns caused these serious floods. These previous
instances indicate that hydrological projections thattake into account various precipitation scenarios areimportant. In addition, since the basin morphology
might change owing to flooding, predictions of
sediment erosion and transport are necessary.
In the past decade, many studies of the Chao
Phraya River basin have been conducted. In 2000,
river discharge and water levels in the entire Chao
Phraya basin were simulated, including reservoir
operation, using a distributed hydrological model(DHM) at a 5-km computing resolution.2) In 2004,

1. INTRODUCTION
Most of the heavy precipitation events inSoutheast Asian countries occur during the monsoon
season. This rainfall in these humid regions allows
the production of many crops. In particular, Thailand
is the world’s biggest rice exporter, shipping about
10 million tons of milled rice annually, which
accounts for about 34.4% of all exports worldwide.1)
However, seasonal differences in precipitation andover cropping may cause problems such as flooding,inundation, subsidence, sediment erosion, anddeterioration of water quality.
Over the past 30 years, the number and impact offlood disasters across Asia have increased.3)
Moreover, the IPCC 4th Assessment Report statesthat the frequency of precipitation is likely todecrease, but rainfall intensity could increase.4)
These changes in precipitation patterns could causeseasonal changes in the problems mentioned above,
especially flooding and sediment erosion. Therefore,
river network discharge and sediment transportation
predictions are necessary for watershed management.
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future hydrological scenarios were forecast by the
SiB2 scheme considering the effects of land
use/coverchanges.6)
In this study, we forced the DHM using three
general circulation model (GCM) outputs. First, we
validated the DHM using the observed rain gauge
precipitation. Next, we predicted stream flow
scenarios over the next 30 years using the GCM
outputs. The qualitative precipitation predictions
were modified using the global daily meteorologicaldataset H08.8)

global land use map (Global Land CoverCharacteristics Data Base Version 2.0, Simple
Biosphere 2 Modelhttp://edc2.usgs.gov/glcc/tabgeo_globe.php.). This map classifies land use into 10
types; the dominant land use types in the study area
were broadleaf evergreen trees (40%), mixed
broadleaf and needle leaf trees (23%), andagricultural land (17%). Information on the soil
water properties was collected from the 2002 global
soil type map Digital Soil Map of the World on
CD-ROM, based on the Food and Agriculture
Organization (FAO)/UNESCO Soil Map of theWorld. The spatial reference for both land cover andsoil types is 1km. For discharge calibration, observed
discharge data from the Thai Royal Irrigation
Department were used (Royal Irrigation Department,
hydro-1 center in Chainmail, URL: http://www.
hydro-1.net/).
Meteorological data were obtained from the Royal
Irrigation Department. According to the data quality,
the daily rainfall data of four years (2007 to 2010) at
80 gauges were selected for the hydrologicalsimulation. We also used satellite image rainfall datafrom the Tropical Rainfall Measuring Mission(TRMM; URL: http://www.eorc.jaxa.jp/TRMM/
index_j.htm). TRMM, which was launched by the
US National Aeronautics and Space Administration
and Japanese Aerospace Exploration Agency in
1997, provides three-hourly rainfall data in tropicalareas as a global dataset. The spatial resolution is
0.25°.

2. STUDYAREA AND DATA
(1) STUDY AREAThe Chao Phraya River originates in the northernhigh mountains, drains through the central plain, andfinally discharges into the Gulf of Thailand. The
entire basin covers approximately 160,000 km2,
which is one-third of the country. The river basin canbe divided geographically into upper, middle, andlower reaches from north to south. Most agriculturalland exists in the middle basin (latitude: about
14°–17.5°). In the upper basin (latitude: 17.5°–20°),
most land is covered by forest or bare soil. The lower
basin (latitude: 13.5°–14°) consists mainly ofagricultural and urban areas. The climate is tropicalmonsoon. The monsoon season runs from May to
October, within which about 90% of the annual
rainfall occurs. The annual precipitation isapproximately 1500 mm.
The simulated basin set appears inFig.1. It consistsof the Ping and Wang river basins (influent rivers of
the Chao Phraya River). This area is mountainous,
and thus, sudden rainfall is more likely to occur thanon the plains. The simulated basin covers an area of
36,285 km2. In the lower basin, the largest dammed
reservoir, created by the Bhumipole Dam (drainage
area: 26,386km2, maximum water storage: 13,462
million m3) appears. Discharge points are simulated
throughout this area. We need to consider thedifference between the hydrographs of points in theupper and lower basins of the reservoir. In the basin
below the Bhumipole Dam, the hydrograph should
be moderate because this large dam correctly reduces
flood peaks.
(2) DATA AVAILAVILITY
The basin topography was simulated by a digital
elevation model having a 90-m resolution. The
original elevation data were obtained from the
Shuttle Radar Topography Mission (URL: http://www2.jpl.nasa.gov/srtm/dataprod.htm).
Landcover information was collected from a 1996 USGS

Fig.1 Location of target basin
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computational units having this feature. The module
is divided into four parts: storage of precipitation in
the soil; precipitation storage in the canopy; waterexchange between the saturated layer, unsaturated
layer, and the surface; and evapotranspiration fromthe canopy and the soil. Richard’s equation was used
to calculate the water exchange between the saturated
and unsaturated layers. Darcy’s law was used to
calculate the water flow in the saturated zone. To
simulate transpiration, the Normalized Difference
Vegetation Index14)was used to obtain the canopy
cover ratio in each computational unit. The landuse
type and soil type from the USGS and FAO data
determined each parameter of each hill-slope
equation, and each parameter was calibrated for eachtype of soil and land use.
The simulated discharge flowing from eachcomputational unit was accumulated into interval
flows, which were identified according to thePfafstetter
Basin
Numbering
System15).The
accumulated discharge was routed from the upper tothe lower basin according to the Pfafstetter
numbering scheme by using the kinematic wave
module. Thus, we obtained the discharge and waterlevels at each control point.

Fig.2 Spatial distribution of rainfall observation points

For future rainfall scenario was taken from future
GCM experiments: MIROC3.29), MPI ECHAM10),
CCSM3.0.11)As climate scenario Special Report on
Emission Scenarios (SRES) A1B12)indicating in thehigh economical growth rate was chosen.
The distribution of the available rainfall data isshown in Fig.2. All of the collected meteorological
data were adjusted to 1-km resolution for thecomputation units. To interpolate the rain gauge datain particular, we used the Thiessen polygon method
to determine the distance between each pair of rain
gauge points.

(b) Model Calibration
We calibrated the parameters mentioned in theprevious section by comparing the discharge
simulated using rain gauge data and the observed
discharge in 2007. The simulation time step was 1 h.
Because only daily precipitation observations wereavailable, they were transformed to hourly data on
the basis of the average time distribution in one day.
To include the effect of the dam in the simulation, thereleased flow was assumed to be the same as theobserved discharge at P.12 located about 7km
downstream than the dam (Fig.2).

3. METHOD(a) The hydrological model set-up
To simulate the current and long-term discharge
dynamics for the river basin, we used a DHM, thegeomorphology-based hydrological model (GBHM).
This model’s features were physically based on ahydrological model. That is, the water budget at eachcomputational unit was simulated by a hill-slope
module, and the lateral inflow was routed
downstream by a kinematic wave module. We thenobtained the discharge at each gauge point. We chose
1-km grids as computational units. Each
computational unit was viewed as a rectangularinclined plane with a defined length and unit width.
The inclination angle was given by the surface slope,
And the bedrock was assumed to be parallel to thesurface. We used the hill-slope element introduced
by Yang et al13).The hill-slope module was applied in(c) Improvement of GCM outputs
Daily future surface temperature and precipitation
determined by these introduced GCMs werecorrected against the 0.5° global daily meteorologicaldataset H08. The daily temperature change wascorrected by subtracting the differences between themonthly means for 1961–2000 in the H08 and the
GCMs. The daily precipitation was corrected by
employing the ratio of the H08 and GCM
precipitation intensities in eight different classes (1,5, 10, 15, 20, 25, and 30% largest daily intensities
and the remaining samples) for 1961–2000. The wet
day percentage in each H08 grid for 1961–2000 wasscaled by the ratio of the GCMs’ wet day percentage
for 1961–2000 and five 20-year future periods(2001–2020, 2021–2040, 2041–2060, 2061–2080,
and 2081–2100).
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We should be aware that each future climate by
GCM simulations does not necessarily show similar
characteristics of climate condition of observed
climate in short time scale, since scenario simulation
by GCMs reflect their own internal climate systems,
resulting different time series of SST, monsoon
activity and other seasonal to annual climate
oscillations.
The daily average precipitation from rain gauge,
TRMM and three modified GCM outputs is shown
from 2007 to 2009 in Fig.3. It can be seen that rain
gauge and TRMM are in close agreement. The threemodified model outputs can simulate rainy season
properly with some small difference among them.

4. RESULT AND DISCUSSION(a) DHM model set up
The simulated discharge calculated using rain
gauge data and TRMM data at P73 and W3A are
shown in Fig.4. The discharge at P2A is shown in
Fig.5. (Observed discharge data for P2A were notavailable for 2008.)
The discharge calculated using rain gauge data wasclose to the observed discharge at P73 and P2A.
However, the simulations generally tended tooverestimate, especially at W3A. The RMSE of thedischarge simulated using rain gauge data versus theobserved discharge was equal to 132(m3/s) at P73,
928(m3/s) at W3A, and 238(m3/s) at P2A.

Raingauge

0
1000

3

60

S im w ith T R M M
S im w ith R a in g au g e
O b se rva tio n

P 73

S im w ith T R M M
S im w ith R ain g a u g e
O b servatio n

W 3A

500

Discharge(m /s)

1000

1500

(d) Scenarios of future river discharge
We obtained simulated discharge from 2007 to
2040 with modified GCM outputs. Since simulated
discharge using modified GCM outputs cannot be
forecasted within short training period, our analysis
is based on the highest and average peak discharge
within three decades.

500

40
20
0
60
0

TRMM
40

Jan

Oct

Jan

A pr

J ul

O ct

Jan

A pr

2 00 8

Jul

O ct

Jan

2 009

Fig.4 Discharge simulation at P73 andW3A
1500

60
0

20
MPI

1000

40

P2A

Sim with TRMM
Sim with RG
Obervations

500

Discharge(m3/s)

miroc

0

40
20

Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan

2007

0
60
CCSM
2000

40
20
0
Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan

2007

2008

2009

Fig.5 Discharge simulation at P2A(data in 2008 was unavailable)

Discharge(m3/s)
5001000 1500

2008

2009

Fig.3 Average precipitation of entire target basin

Sim with CCSM
Sim with MPI
Sim with miroc
Obs

0

Precipitation(mm/day)

J ul

20 07

20

60
0

Apr

Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan

Fig.6 Discharge simulation at P73with GCM outputs

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0
40
160 120

80

4000
0

2015

2020

2025

2030

2035

S im w i t h m ir o c
S im w i t h m p i
S im w i t h C C S M
P r e c m ir o cFig. 7Future hydro and hyetograph at P73(m 3 /s )4000(b) Future hydrological scenarios
Simulated discharge from 2007 to 2009 at P.73 withthree precipitation patterns from GCMs are shown in
Fig.6. Similar to Fig.3, we show this graph toevaluate the annual seasonality. The simulated
discharge was overestimated than observation.
Focusing on seasonality, the months with the highest
peak discharge simulated are between July andOctober.
Future hydrographs from 2010 to 2040 were
simulated at P73 as shown in Fig.7. In the part of thisfigure, hyetograph from MIROC output can be seen.
A consistent response of the hydrological model (i.e.
calculated discharge) in red line using MIROC data
can be noticed. Particularly、it is evident for years
2014, 2027 and 2032. Then, we calculated the annual
mean and highest discharge in four year groups(2007-2010, 2011-2020, 2021-2030, and 2031-2040)
at the stations P73 and W3A, see fig.8 and 9
respectively. From Fig.8 the mean annual discharge
at each decade does not show a high variation. On theother hand, Fig.9 showing the annual highest
discharge seems to increase for future decades,
especially from 2021 to 2030. Actually, simulations
with MIROC and CCSM output predict higher flood
peaks. On the other hand, calculated discharge with
MPI does show a clear increment.

3000

4000

W 3A
3000
2000
1000
0
0 7 -1 0

1 1 -2 0

2 1 -3 0

3 1 -4 0

Fig.9 Average of highest discharge at each decade

The frequency of the annual highest discharge atstations P73, W3A was counted as illustrated in
Fig.10 and Fig.11. From this figure, the peak timingfrom MIROC tends to be distributed before the rainyseason of each year. On the other hand, the result
from CCSM tends to be distributed to after rainyseason of each year. Finally, MPI output suggests theflood peak timing will become more concentrated in
September.
10
8
6

200

m ir o c
MPI
CCSM

2 0 1 1 -2 0 2 0

4

150

2

100

10

50

8

frequency

Discharge(m3/s)

P73

1000

P73

250

150

m ir o c
M PI
CCSM

2000

250

200

2040

Precipitation(mm/day)

8000

3
Discharge(m /s)

2010

W 3A

m ir o c
MPI
CCSM

2 0 2 1 -2 0 3 0

6
4
2
10

100

8

50

6

2 0 3 1 -2 0 4 0

40
0 7 -1 0

1 1 -2 0

2 1 -3 02

3 1 -4 00
M ar A pr M ay Jun

Fig.8 Mean annual discharge at each decade

Jul Aug Sep O ct N ov Dec

Fig.10 Frequency of the highest discharge at P73 station
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10
8
6

m ir o c
MPI
CCSM

Environmental engineering, faculty of engineering,
Kasetsart University. Dr. Komoriat University ofTokyo are also thanked for inviting us to IMPACT-T
project, additionally we appreciate RID and TMD
regional directors met at IMPAC-T project
conference for supplying meteorological data .

2 0 1 1 -2 0 2 042

frequency

1082 0 2 1 -2 0 3 0

REFERENCES
1)

6
4

2)

2
10
8

2 0 3 1 -2 0 4 0

6

3)

4
2
0
M ar A pr M ay Jun Jul A ug Sep O ct N ov D ec

4)

Fig.11Frequency of the annual highest discharge W3A station

5)

5. CONCLUSION
6)

In this paper, a distributed hydrological model andthe data simulated by modified GCM were applied to
Chao Phraya river basin. The overall performance of
the DHM model using observed precipitation
showed acceptable agreement with observed riverdischarge. The model overestimated discharge insome areas. Then, future river discharge patterns
were predicted forcing the DHM model using threemodified GCMs. The results suggest that the mean
highest peak discharge might increase within the
future three decades. In addition, the higher flood
peaks seems to be more concentrated in September inthe simulated basin. However, according to MIROC
and CCSM will tend to be more spread before andafter main rainy season.
In the future, more appropriate bias correction of
the GCM output to the river basin is necessary inorder to project future hydrology. Hence, long-term
comparisons of in situ data and these models’ output
are essential. Furthermore, to foresee the changes inbasin morphology due to future climate scenarios, wewill simulate sediment erosion and transportation byadding a sediment transport module to the existing
DHM. This information might be helpful forwatershed management in the Chao Phraya Riverbasin.

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ACKNOWLEDGMENT: This research was
supported by JSPS Asian core project. We are
grateful for support from members belonging to(Received September30, 2011)6

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