Journal of Japan Society of Civil Engineers, Ser.
Annual JournalB1of(HydraulicHydraulicEngineering),
Engineering,Vol.
JSCE,
Vol.4,58,
2014, February
70, No.
I_115-I_120,
2014.

CHANGES IN RIVER DISCHARGE IN THE
INDOCHINA PENINSULA REGION PROJECTEDUSING MRI-AGCM AND MIROC5 DATASETS
Duc Toan DUONG1, Yasuto TACHIKAWA2, and Kazuaki YOROZU3
1Student Member of JSCE, Dept. of Civil and Earth Resources Eng., Kyoto University (Kyoto 615-8540, Japan)
2Member of JSCE, Prof., Dept. of Civil and Earth Resources Eng., Kyoto University (Kyoto 615-8540, Japan)

3Member of JSCE, Assist. Prof., Dept. of Civil and Earth Resources Eng., Kyoto University (Kyoto 615-8540, Japan)

A distributed flow routing model with kinematic wave flow approximation (1K-FRM) was applied toproject river discharge in the Indochina Peninsula region. The input data for flow routing model 1K-FRM
were the generated-runoff data at 3-hourly time step from the latest versions of the MeteorologicalResearch Institute atmospheric general circulation model (MRI-AGCM3.2S, MRI-AGCM3.2H) and theModel for Interdisciplinary Research on Climate (MIROC5) for three 25-year periods: 1979-2003(present climate), 2015-2039 (near future climate), and 2075-2099 (future climate). Simulated discharge
data for the near future climate and the future climate were compared with those for the present climate toevaluate the changes in flow in the region under a changing climate. The statistical significance of riverdischarge changes in the Indochina Peninsula region was also analyzed.
Key Words: Indochina Peninsula, river discharge, climate change, 1K-FRM, MRI-AGCM, MIROC5

1. INTRODUCTION
In the Fourth Assessment Report (AR4)1)
published in 2007, the Intergovernmental Panel on
Climate Change (IPCC) concluded that theanthropogenic increasing of greenhouse gas
concentrations is the main cause of the current
global warming trend. The global average surface
temperature was reported to increase about 0.74
degree Celsius over the last 100 years. Increases insurface temperature are likely to have many impacts
on the hydrological cycle and associated extremes
such as floods and droughts.
Currently, general circulation models (GCMs) are
the most effective tools for simulating present
climate and projecting future climate. The outputs ofGCMs with various spatial resolutions can be used
as inputs to hydrological model simulations toassess the impact of climate change on hydrology
and water resources.
In this study, the impact of climate change onriver discharge in the Indochina Peninsula region(see Fig.1) and statistical significance of riverdischarge changes were investigated by conducting

distributed hydrological simulations using threedifferent GCM datasets: the MRI-AGCM3.2S2), theMRI-AGCM3.2H2), and the MIROC53). Outputs ofthese GCMs were obtained from the multi-model
dataset archive of the Coupled Model
Intercomparison Project Phase 5 (CMIP5).

2. METHODS(1) Hydrological modelRiver discharge in the Indochina Peninsula region
was projected using flow routing model 1K-FRM.
1K-FRM is a distributed hydrological model withkinematic wave flow approximation4). It wasdeveloped by Hydrology and Water Resources
Research Laboratory of Kyoto University(http://hywr.kuciv.kyoto-u.ac.jp/products/1K-DHM/1K-DHM.html).
1K-FRM was based on a catchment topographymodel. The topographic information used for 1KFRM in this study was generated from processing
the scale-free global stream-flow network dataset,
which was provided by Masutani et al.5), with aspatial resolution of 5 arc minutes. The flow
direction is defined using 8-direction method, which

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(3) Relative change in river discharge
The ratio of annual mean discharge, mean of
annual maximum daily discharge, and mean of
annual minimum daily discharge of the near futureclimate and the future climate to the one of thepresent climate were calculated and analyzed to
examine the change in river discharge in the
Indochina Peninsula region under climate change.

Fig.1 The study area, Indochina Peninsula region.

assigns flow from each grid cell to one of its 8neighbors, either adjacent or diagonally, in thedirection with the steepest downward slope. Eachslope element determined by flow direction isrepresented by a rectangle formed by the twoadjacent nodes of grid cells. Then the runoff isrouted according to the flow direction informationby applying the kinematic wave flow model to all
slope elements.
(2) General circulation models
The climate models used in this study are MRIAGCM3.2S, MRI-AGCM3.2H and MIROC5. The
MRI-AGCM3.2S (the 20-km model) and MRIAGCM3.2H (the 60-km model) are the latest
atmospheric GCMs based on a model jointly
developed by the Japan Meteorological Agency(JMA) and the Meteorological Research Institute(MRI). The MIROC5 (the 140-km model) is anatmospheric-ocean GCM developed jointly by the
Atmosphere and Ocean Research Institute (AORI),
University of Tokyo, the National Institute forEnvironmental Studies (NIES), and the Japan
Agency for Marine- Earth Science and Technology(JAMSTEC).
The generated-runoff data at 3-hourly time stepfrom three GCMs mentioned above were used as
input for flow routing model for three 25-year
periods: 1979-2003 (present climate), 2015-2039(near future climate), and 2075-2099 (futureclimate). The data used for future projection werebased on the Special Report on Emissions Scenarios(SRES) A1B scenario (MRI-AGCM3.2S, MRIAGCM3.2H) and the Representative Concentration
Pathway 6 (RCP6) scenario (MIROC5). The SRESA1B scenario and the RCP6 scenario are more orless equivalent in term of CO2 concentration.

(4) Statistical significanceThe statistical significance of river dischargechange in the Indochina Peninsula region wasassessed by comparing annual mean values ofprojected river discharge data in the near futureclimate and the future climate with those in thepresent climate. It is to evaluate whether thechanges in river discharge are statisticallysignificant or just occur by chance.
The null hypothesis H0 for the statisticalsignificance testing of river discharge change wasdefined as follows: there is no significant difference
between annual mean discharge/mean of annual
maximum daily discharge/mean of annual minimum
daily discharge of the near future climate/the futureclimate and the present climate.
The approach for comparing those statistics ischosen based on the distribution of projected riverdischarge data. If the data are normally distributed, aparametric approach can be employed to determine
whether the difference in the means is statisticallysignificant or not. In cases where the data are notnormally distributed, non-parametric approaches
can be used. Non-parametric tests are generally lesspowerful than parametric tests because they arebased on fewer assumptions.
In this study, the Shapiro-Wilk W test6) wasapplied to test for normality of the projected data at
each model grid cell. The parametric Welchcorrection t-test7) was performed to test for
statistical significance of normally distributed riverdischarge data. And the non-parametric MannWhitney U test6) was applied for the data whichhave a non-normal distribution. A significance levelof 5% (or confidence level of 95%) was selected.
The map of statistical significance of riverdischarge change in the Indochina Peninsula wasmade by combining the results from statistical tests
mentioned above.

3. RESULTS(1) Changes in annual mean discharge
The relative change and statistical significancedifferences between annual mean discharge of thenear future climate and the present climate areillustrated in Fig.2 and Fig.3.

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(a)

(b)

(a)

(c)
Fig.2 Ratio of annual mean discharge of the near futureclimate to the present climate simulated using MRI-AGCM3.2S(a), MRI-AGCM3.2H (b), and MIROC5 (c).

(c)
Fig.4 Same as Fig.2, but for the future climate.

(a)
(a)

(b)

(b)

(b)

(c)
Fig.3 Statistical significant differences between annual meandischarge of the near future climate and the present climate
simulated using MRI-AGCM3.2S (a), MRI-AGCM3.2H (b),and MIROC5 (c).

The increase of annual mean discharge iscommonly found in the middle part of the Irrawaddy
River basin and lower part of the Mekong Riverbasin. Only the simulation using MRI-AGCM3.2H
dataset shows a decreasing trend at the upper part of
the Red River basin (see Fig.2b). However, except
the simulation using MIROC5 dataset, the changes
in annual mean discharge in the near future were notstatistically significant.
For the future climate, the changes of annual
mean discharge and statistical significance are(c)Fig.5 Same as Fig.3, but for the future climate.

represented in Fig.4 and Fig.5. The pattern of riverdischarge changes in the future climate was similar
to the one in the near future climate but with larger
area and higher ratio. The increase of annual meandischarge in the future was statistically significant at
the Irrawaddy River basin and lower most part of
the Mekong River basin in all three simulations. The
simulation using MIROC5 dataset showed asignificant increase in annual mean discharge inalmost all over the study area.
(2) Changes in mean of annual maximum daily
discharge
For the mean of annual maximum discharge, thenear future increase of annual maximum daily
discharge appeared at the upper part of Irrawaddy

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(a)

(a)

(b)

(c)
Fig.6 Ratio of mean of annual maximum daily discharge ofthe near future climate to the present climate simulatedusing MRI-AGCM3.2S (a), MRI-AGCM3.2H (b), andMIROC5 (c).

(c)
Fig.8 Same as Fig.6, but for the future climate.

(a)
(a)

(b)

(b)

(b)

(c)
Fig.9 Same as Fig.7, but for the future climate.
(c)Fig.7 Statistical significant differences between mean of
annual maximum daily discharge of the near future climate
and the present climate simulated using MRI-AGCM3.2S(a), MRI-AGCM3.2H (b), and MIROC5 (c).

River basin and lower part of Mekong River basinin all three simulations, although the statisticallysignificant area was small (see Fig.6 and Fig.7). In
addition, both MRI-AGCM3.2S and MIROC5
simulations showed the statistical significance in theincreasing of annual maximum daily discharge at
the lower tributary of the Red River.
From Fig.8, it can be seen that the increasing
trend of annual maximum daily discharge in theIrrawaddy River basin and the Red River basin,

which was detected in the near future, become more
visible in the future. The relative change ofdischarge was higher than in the near future climate;
and the area with statistically significant change indischarge was also larger (see Fig.9).
In the future climate, the increase of annual
maximum daily discharge was also found statisticalsignificance at lower most part of the Mekong Riverbasin from the results of all three simulations (seeFig.9).
(3) Changes in annual minimum daily discharge
For the annual minimum daily discharge, Fig.10
and Fig.11 show opposite results from threesimulations. While simulation using MIROC5I_118

(a)

(b)

(a)

(c)
Fig.10 Ratio of mean of annual minimum daily dischargeof the near future climate to the present climate simulatedusing MRI-AGCM3.2S (a), MRI-AGCM3.2H (b), andMIROC5 (c).

(c)
Fig.12 Same as Fig.10, but for future climate.

(a)
(a)

(b)

(b)

(b)

(c)
Fig.13 Same as Fig.11, but for future climate.

(c)Fig.11 Statistical significant differences between meanof annual minimum daily discharge of the near future andthe present climate simulated using MRI-AGCM3.2S (a),MRI-AGCM3.2H (b), and MIROC5 (c).

dataset demonstrated a statistically significant
decreasing trend at the upper most parts of theSalween River basin and the Mekong River basin,
the others illustrated an increase of river discharge
with statistical significance in the near futureclimate.
In other parts of the Indochina Peninsula region,
except the result from MIROC5 simulation, thechanges in annual minimum daily discharge were
not statistically significant.

In the future climate, all three simulations
showed an increase in annual minimum daily
discharge at the upper most part of the Salween andthe Mekong River basin. This increase is also
statistically significant (see Fig.12 and Fig.13).
In the central part of Vietnam, a decreasing trendof annual minimum daily discharge in the futureclimate was found statistically significant fromsimulations using MRI-AGCM3.2S and MRIAGCM3.2H datasets. However, the simulation
using MIROC5 dataset displayed an opposite trend(see Fig.12c, Fig.13c). This opposition also occurs
at the Chao Phraya River basin and middle part of
the Mekong River basin.

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4. DISCUSSIONS
From the simulation results presented above,
there are discrepancies on the ratio of riverdischarge change and area of statistically significant
increase or decrease among three simulations using
different GCMs datasets. These discrepancies may
come from the differences in GCMs structures,parameterization, and spatial resolution.
According to Giorgi et al.8), regional topographic
forcing due to orography, land-sea contrasts andvegetation characteristics will be described moreprecise with fine spatial resolution. Consequently,processes such as orographic precipitation andmonsoon circulations, which strongly forced bytopography, improve at increased resolution. And
the generated-runoff data used as input for flowrouting model are strongly related to projected
precipitation data. Therefore, we can see that theratio of river discharge change and area ofstatistically significant increase with the decrease of
spatial resolution, especially in the simulation using
MIROC5 dataset.
Factors other than spatial resolution may becontributors to the simulation biases of riverdischarge. It needs more future further work toinvestigate.
However, the results from those three simulations
also showed a consistent statistically significant
increase in river discharge in the Irrawaddy Riverbasin, especially the annual maximum daily
discharge in the future climate. It means thatflooding risk in this area could increase in thefuture.

5. CONCLUSIONSThis study investigated the change in riverdischarge in the Indochina Peninsula region under achanging climate using the distributed flow routing
model 1K-FRM and three different GCMs datasets(MRI-AGCM3.2S,MRI-AGCM3.2H,andMIROC5). Statistical analysis was also carried out
to examine the statistical significance of riverdischarge changes in the region. A clear change ofriver discharge was detected and found statisticallysignificant in the Irrawaddy River basin, especiallythe annual maximum daily discharge in the future.
For future further work, more GCMs’ data will be
used to evaluate the uncertainty in climate
projection and to investigate the factors contributed
to the simulation biases of river discharge.

ACKNOWLEDGMENT:We
acknowledge
Meteorology Research Institute, Japan Meteorology
Agency, Atmosphere and Ocean Research Institute,
University of Tokyo, the National Institute forEnvironmental Studies, the Japan Agency for
Marine-Earth Science and Technology, and World
Climate Research Program’s Working Group onCoupled Modeling for providing GCM data.
We also acknowledge Dr. Jun Magome (Universityof Yamanashi) for providing scale-free global
stream-flow networks datasets to use in thisresearch.
REFERENCES

1) IPCC: Climate Change 2007: Synthesis Report. Contributionof Working Groups I, II and III to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change[Core Writing Team, Pachauri, R.K and Reisinger, A.
(eds.)]. IPCC, Geneva, Switzerland, 104 pp, 2007.
2) Kitoh, A., T. Ose, K. Kurihara, S. Kusunoki, M. Sugi, andKAKUSHIN Team-3 Modeling Group: Projection ofchanges
infuture
weather
extremes
using
super-high-resolution global and regional atmosphericmodels in the KAKUSHIN Program: Results of preliminary
experiments. Hydrological Research Letters, 3, 49–53,
doi:10.3178/hrl.3.49, 2009.
3) Watanabe, M., Suzuki, T., Oishi, R., Komuro, Y., Watanabe,S., Emori, S., Takemura, T., Chikira, M., Ogura, T.,
Sekiguchi, M., Takata, K., Yamazaki, D., Yokohata, T.,
Nozawa, T., Hasumi, H., Tatebe, H., Kimoto, M.: Improved
climate simulation by MIROC5: Mean states, variability,and climate sensitivity, Journal of Climate, 23, 6312–6335,
2010.
4) Duong, D. T., Tachikawa, Y., Shiiba, M., Yorozu, K.: River
discharge projection in Indochina Peninsula under achanging climate using the MRI-AGCM3.2S dataset.
Journal of Japan Society of Civil Engineers, Ser. B1(Hydraulic Engineering), Vol. 69, No. 4, I_37-I_42, 2013.
5) Masutani, K., Akai, K., Magome, J.: A new scaling algorithmof gridded river networks, Japan Society of Hydrology andWater Resources, 19 (2), 139 – 150, 2006 (in Japanese).
6) Gilbert, R. O.: Statistical Methods for Environmental
Pollution Monitoring. Van Nostrand Reinhold Co., New
York, 320 pp, 1987.
7) Zar, J. H.: Biostatistical Analysis. 5th Edition. Pearson
Prentice-Hall, Upper Saddle River, NJ. 944 pp, 2010.
8) Giorgi, F. and Marinucci, M.: An investigation of thesensitivity of simulated precipitation to model resolution
and its implications for climate studies. Mon. Wea. Rev.,
124, 148–166, 1996.

I_120(Received September 30, 2013)