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

River Discharge Assessment under a Changing Climate in the Chao
Phraya River, Thailand by using MRI-AGCM3.2S
Supattana WICHAKUL1, a *, Yasuto TACHIKAWA1, b, Michiharu SHIIBA1, c and Kazuaki YOROZU 1,dAbstract In recent years, General Circulation Model(GCM) has been widely used in climate change impact
studies. It is an effective tool for well understanding of achange in climate behavior in a long term. Nowadays,
more than twenty GCMs have been developed in many
research institutes around the world. We selected thelatest version of GCM developed by MeteorologicalResearch Institute (MRI), Japan Meteorology Agency,
MRI-AGCM3.2S. The model has a horizontal resolutionof triangular truncation 959 (TL959), and the transform
grid uses 1920 x 960 grid cells, corresponding toapproximately a 20-km grid interval with 64 verticallayers (top at 0.01 hPa).We used a regional distributedhydrologic model based on the concept of the variableinfiltration capacity to generate runoff intensity and akinematic wave model including effects of dam operationand inundation to simulate river discharge. The C.2
gauging station at Nakhon Sawan was selected to monitorchanges in the river discharge.

Input data of the distributed hydrological model, GCM
precipitation and evapotranspiration, were corrected toremove biases using the quantile-quantile bias-correction
method for precipitation and the different factor biascorrection method for evapotranspiration. The results ofthe experiment in projecting discharge of the Chao
Phraya River under the near future climate (2015-2043)
and the future climate (2075-2103) by using the biascorrected GCM data set showed that 1) the mean annual
discharge tends to increase in both near future and future
projection periods, 2) During a dry season the tendency
of low flow in the near future period tends to decrease.
However, the flood frequency analysis using Generalized
Extreme Value distribution (GEV) indicates that floodrisk in the future will have more severities and damages
to the country as the result of the analysis shows that, inthe near future the magnitude of 80-year return period
flood is greater than the devastating 2011 Thai flood. .
Keywords Climate change, Discharge projection, Chao
Phraya River, Distributed hydrological model

S. Wichakul (supattana.w@gmail.com)
Research assistant at department of civil and earth
resources engineering, Kyoto University, Japan
Y. TACHIKAWA
Professor
Department of civil and earth resources engineering,
Kyoto University, Japantachikawa@hywr.kuciv.kyoto-u.ac.jp
M. SHIIBA
Professor
Department of civil and earth resources engineering,
Kyoto University, Japanshiiba@hywr.kuciv.kyoto-u.ac.jp
K. YOROZU
Assistant professor
Department of civil and earth resources engineering,
Kyoto University, Japanyorozu@hywr.kuciv.kyoto-u.ac.jp

Introduction
Prediction of the Chao Phraya River discharge has been
conducted for water resources assessment of the basin byutilizing outputs of the MRI-AGCM3.2S (20 km
resolution) without bias correction. The result showedthat water availabilities in the CPRB increase all year
round, both in the wet and dry seasons in the futureclimate experiment, and during dry season of the near
future climate trends of projected discharge considerably
reduced (Wichakul et al., 2014). However, direct usageof hydrological variables of the GCM does not provide
reliable information on scales below about 200 km(Maraun et al., 2010). Therefore, for reliable and realistic
prediction result of the river discharge situation of the
Chao Phraya River, we introduced several bias correction
methods to the MRI-AGCM3.2S outputs, precipitationand evapotranspiration. Bias in the GCM precipitation
distribution was removed by the empirical distribution
and quantile-quantile correction methods. For the GCM
evapotranspiration, the multiplicative factor and different
factor correction methods were applied.

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

The research aims to project discharge of the Chao
Phraya River and to evaluate tendency of flood anddrought risks under a changing climate by using the biascorrected GCM precipitation for a reliable result.
Input data and study area
We used the MRI-AGCM3.2 variables from three (3)different climate experiments: are the present climate
experiment (1979–2008), the near future climate
experiment (2015–2044), and the future climate
experiment (2075–2104). The GCM precipitation is
rainfall reaching to soil layer (PRCSL) and the GCM
evapotranspiration is a summation of evaporation frombare soil (EVPSL) and transpiration from root zone soil
(TRNSL). The GCM variables covering the CPRB wereextracted total 1,120 grids resolution 0.1875 (Cols=28
and Rows=40), which is defined as being between
Latitude = 12.094 - 19.406 N and Longitude = 98.060 –103.123 E.
To prepare the input data for the distributed hydrologicalmodel, the GCM variables were processed to remove
biases by quantile-quantile bias-correction method for
precipitation and the different factor bias-correction
method for evapotranspiration. In the bias correction
process, APHRODITE precipitation and the reference
evapotranspiration (ETo) were used to simulate thereference as observed discharge. In our study, we
obtained a reference crop evapotranspiration (ETc)calculated by the Royal Irrigation Department ofThailand (RID) using the FAO Penman-Monteith method(Allen et al., 1998) with recorded climatology data for the
30 years from 1981 to 2010 to be the truth reference data.
Due to limitation of APHRODITE data which wereavailable for 1979-2007, the bias correction was piloted
for only a period of 29 years. Therefore our hydrologicalsimulation was simulated for 1979-2007, 2015-2043, and2075-2103 periods.
Study area is the Chao Phraya River basin, Thailand. The
discharge monitoring is at C.2 located about 5 km
downstream of the Ping River and Nan River confluence,
beginning of the Chao Phraya River (15°40N and
100°06E). The location of the C.2 station can represent
the overall situation of the Chao Phraya River.

Modeling approach
The regional distributed hydrological model composes ofrainfall-runoff model and flow routing model including
dam operation. The rainfall-runoff named the Simplified
Xinanjiang model (SXAJ). It was established based on
the concept of the variable infiltration capacity (Wichakul
et al., 2013a). 1K-FRM is a 1 kilometer resolution flowrouting using a kinematic wave equation. In part of flowrouting, the 1K-FRM was additionally developed toinclude the inundation effect and reservoir operation

model to improve predicted discharge for the Chao
Phraya River Basin. (Wichakul et al., 2013b). Fig. 1
shows the framework of model simulation.
The bias-corrected precipitation and evapotranspiration
were input to the SXAJ model to generate runoff intensity
represented 1120 (28 columns and 40 rows) grid cells
covering the CPRB. Then, the runoff intensity was input
to the 1K-FRM represented by 288,000 (480 columns and
600 rows) computational grid. The predicted discharge
was extracted at the C.2 grid.

Fig. 1 Framework of the distributed hydrological model
including inundation model by using the GCM output.
Result and discussion
River discharge assessment under a changing climate
By using the bias corrected precipitation andevapotranspiration as input to the regional distributedhydrological model, we generated series of daily
discharge for twenty night (29) year in each climate
experiment period. Mean monthly discharge at themonitoring station, C.2 station, has been calculated todetect trend of stream flow change under a changing
climate as plotted in Fig. 2. It shows mean monthly
discharge at for three climate experiments. For the futureclimate, comparison of the discharge of the present
climate (1979-2007) and the future climate (2075-2103)
shows significant increases about 10% to 41% of themean monthly discharge in the present climate. For thenear future, most of the months the mean monthly
discharge shows a bit increase about 10% to 25% of themean monthly discharge in the present climate. On theother hand, the discharge of the near future climate
extremely rises up in October at the same rate of increaseas the next three decades. Only in May, the meandischarge does not much change. According of theThailand climatology, May is the end of summer season
and beginning of rainy season. That is why effect ofchanges of temperature (or evapotranspiration) andrainfall intensity have less effect to the mean discharge inthis month.
One of important tools to characterize the response of theriver to a changing climate is flow duration curve. The

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

probability of exceedance (P) of mean annual flow
magnitude of each climate experiment periods areillustrated in flow duration curves. Fig. 3 presents meanannual flow duration curves with standard deviations forthe three climate experiments. It clearly shows that themagnitudes of river flow considerably rise up for P < 0.6
in both the near future and future climate experiments.
The magnitude of river discharge in the future period has
a tendency to increase for all occurrence time.
For P > 0.6, there is no a significant signal of changes inmean annual flow for the near future and future period.
According to the dam operation model embedded in theflow routing model for generating stream flow, dischargeduring low flow season was influenced by the dam model(Wichakul et al., 2011). It means that future inflow into
dams dose not much change, so the dam operation model
still operates and releases flow from storage water todownstream as operating in the current climate. However,to enlarge the low flow section, Fig. 4 compares the flow
duration curves constructed based on daily discharge of aperiod-of-record of each climate experiment at the low
flow section. Therefore, it is clear that the low flow values
tend to decrease in the near future experiment whichmight result in increased drought risk in the CPRB.
Conversely, for the 21st century the low flow values tend
to increase roughly 15 % of the flow in the present
climate period. For the flood risk assessment, thefrequency analysis of the extreme events is generally
applied to evaluate to risk that we discuss in the next
paragraph.

Fig. 3 Mean annual flow duration curves with standard
deviation of the present climate (SPA), near futureclimate (SNA), and future climate (SFA) experiments.

Fig. 4 Low flow section of the flow duration curves
constructed based on daily discharge of a period-ofrecord of each climate experiment.

Frequency analysis of extreme events

Fig. 2 Mean monthly discharge at the C.2 station for thepresent (SPA), near future (SNA) and future climate
experiments (SFA).

Frequency of occurrence of the extreme events wasanalyzed by fitting the probability distribution function(PDF) of extreme values with three families of
distribution functions, Square-root exponential typemaximum distribution (SqrtET), Generalized extreme
value distribution (GEV), and Gumbel distribution. The
following are the cumulative distribution functions(CDFs) of each distribution.
The
Square-root
exponential
type
maximum
distribution’s CDF is as:𝑥 ≥ 0,
𝐹(𝑥; 𝛽, 𝜆) = {−𝜆{(1 + √𝛽𝑥) 𝑒𝑥𝑝(−√𝛽𝑥)}0
𝑥 < 0.
(1)
In which β is the scale parameter, and λ is the frequency
parameter (Etoh et al., 1987). The generalized extremevalue (GEV) distribution has a cumulative distributionfunction with a parameter k ≠0 as:

𝐹(𝑥; 𝑐, 𝑎) = 𝑒𝑥𝑝 [− (1 − 𝑘

𝑥−𝑐 1⁄𝑘
𝑎

)

]

(2)

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

where x is an annual maximum daily discharge; a is apositive scale parameter; c is a location parameter and k
is a negative shape parameter. In case of k = 0, the GEV
distribution is equivalent to the Extreme Value Type I
distribution or the Gumbel distribution. The cumulative
distribution function of Gumbel distribution is shown as:
𝐹(𝑥; 𝑐, 𝑎) = 𝑒𝑥𝑝 [−𝑒𝑥𝑝 (−

𝑥−𝑐
𝑎

)]

(3)

For the Gumbel distribution, x is unbounded (-∞<x< ∞)(Takara, 2009).
The annual maximum series of river flow at the C.2
station was extracted from three periods of simulation, to
fit with the distribution function as mention above. Fig.
5, 6, and 7 illustrate the CDFs of twenty night (29) valuesof annual maximum river flow fit with different functionsfor SPA SNA, and SFA, respectively.
Standard least-squares criterion (SLSC) is a criterion weused to evaluate goodness of fit of each distribution to theannual maximum daily discharge. It was proposed by
Takara and Takasao (1998). Afterward, Tanaka and
Takara (1999) proved that SLSC < 0.04 is acceptable toriver discharge frequency analysis in Japan. However, theChao Phraya River Basin topographic condition is very
different to river basins in Japan. Therefore, we evaluatedthe acceptable SCSC by the smallest value among eachprobability distribution function. Table 1 shows
comparison of the goodness-of-fit for the annual
maximum daily discharge at the C.2, Y.16, N.67 and P.17
stations for present climate (1979-2008) near futureclimate (2015-2043) and future climate (2075-2013).

Fig. 5 Cumulative distribution functions of the annual
maximum daily discharge at C.2 station for present
climate.

The Y.16 is located at 16°45'N and 100°07'E on the Yomriver upstream of the N.67 station (15°52'N and
100°15'E). Other monitoring location is at 15°56'N and
99°58'E on the Ping River named P.17 station. From theTable 1, it shows that among these probability
distribution functions, GEV distribution function
provides the best goodness-of-fit to the annual daily
maximum discharge of the projection periods for all
locations. Therefore, we selected the GEV for evaluatingchange in extreme floods and assessing flood risk.
Fig. 6 Cumulative distribution functions of the annual
maximum daily discharge at C.2 station for near futureclimate.
Table 1 Goodness-of-fit criteria for each probability distribution function. Standard Least Squares Criterion, SLSC

PDF
Gumbel
SqrtEtGev

C.2

Standard Least Squares Criterion, SLSC
Y.16
N.67

P.17

SPA

SNA

SFA

SPA

SNA

SFA

SPA

SNA

SFA

SPA

SNA

SFA

0.071

0.036

0.043

0.035

0.056

0.058

0.047

0.031

0.054

0.036

0.087

0.120

0.090

0.038

0.067

0.046

0.051

0.059

0.059

0.043

0.073

0.054

0.098

0.122

0.092

0.037

0.029

0.035

0.050

0.058

0.034

0.030

0.040

0.020

0.060

0.078

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

Changes in return period of flood for different periods areillustrated in Fig. 8 for four different locations; (a) for C.2
station, (b) for Y.16 station, (c) for N.67 station and (d)for P.17 station. The assessment has been conducted bycomparing the return period of the 5-year, 10-year, 20year, 30-year, 50-year, 80-year, 100-year, 150-year, and200-year. The short return periods (shorter than 50-year
return period) are relevant to flood related design
structure according the impacts of climate change, suchas irrigation structure, urban drainage and bridge. Refer
to Fig. 8a, in 21st century the magnitude of extreme
events at the C.2 station significantly increase for the
return period shorter than 50-year and also it is larger thanthe magnitude of the flood events in the near futureclimate (2015-2143). For the long return periods (form80-year return period), the magnitude of discharge isextremely high in the near future climate. That change in
the C.2 station mostly corresponds to change in flood
magnitude in the P.17 and N.67 stations for both near
future and future periods, referring to Fig. 8c and 8d.
Fig. 7 Cumulative distribution functions of the annual
maximum daily discharge at C.2 station for futureclimate.

Fig. 8 Maximum daily discharge corresponding to different return periods for present climate (SPA),
near future climate (SNA) and future climate (SFA) for each location.

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

Flood frequency analysis of the Y.16 station showed
different pattern from other location as illustrated in Fig.
8b because location of the Y.16 station is far for thelocation of those another three stations. Flow from theY.16 station merges with the Nan river at the confluence
located upstream of the N.67 station.
The corresponding discharge of the C.2 station (5,034
m3/s) at 80-year return period of the near future is larger
than the peak discharge of Thai’s flood 2011 (4,686m3/s). Consequently, overall trend shows the flood risk isincrease in the future that will cause more damage to the
CPRB.
Conclusion
Four sets of bias-corrected outputs of the MRIAGCM3.2S, precipitation and evapotranspiration, wereapplied to the Chao Phraya River for projection riverdischarge in the present climate (1979–2007). The
regional distributed hydrological model including theeffect of dam operation and inundation simulated theriver discharge. Simulated long term hydrographs fromdifferent sets of the input data were compared with thereference observed hydrograph. Result shows that thehydrograph simulated by using the bias-corrected
precipitation by the quantile-quantile method with thebias-corrected evapotranspiration by the different factor
achieved a well fit with the reference observed discharge
at the C.2 station.
Consequently, the bias-corrected GCM data have been
conducted for the near climate future and the futureclimate experiments. Changes in the projected riverdischarge at the C.2 station, Nakorn Sawan province canbe concluded that 1) the mean annual discharge tends toincrease in both near future and future projection periods,
2) during a dry season the tendency of low flow in thenear future period leads to decrease. These findings of ourstudy are also compatible with previous studies of
Hunukumbura and Tachikawa (2012), and Kure andTebakari (2012); but it is contradictory with the study
result of Champthong et al., (2013). Furthermore, theGEV was applied for flood frequency analysis whichindicates that flooding frequency has increased, leading
high flood risk in the future. Flood in the basin will havemore severity; especially in the near future (2015-2043)
the magnitude of 80-year return period flood (5,034 m3/s)
is greater than the devastating 2013 Thai flood (4,686m3/s). Therefore, adaptation measures to protect damagesof flood in the country are critical to accomplish.
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