Hydrology in a Changing World: Environmental and Human Dimensions
Proceedings of FRIEND-Water 2014, Montpellier, France, October 2014 (IAHS Publ. 363, 2014).

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Statistical analysis of river discharge projected using the MRIAGCM3.2S dataset in Indochina Peninsula
D. T. DUONG, Y. TACHIKAWA, M. SHIIBA & K. YOROZU

Department of Civil and Earth Resources Engineering, Kyoto University, C1-116, Nishikyo-ku, Kyoto 615-8540, Japan
duong@hywr.kuciv.kyoto-u.ac.jp

Abstract To evaluate the impacts of climate change on river discharge in the Indochina Peninsula region, a
distributed flow routing model (1K-FRM) with kinematic wave flow approximation was applied. Generated
runoff data from the MRI-AGCM3.2S dataset corresponding to the present climate experiment (1979–2008),
the near future climate experiment (2015–2044) and the future climate experiment (2075–2104) were fed
into the flow routing model 1K-FRM to project river discharge in the Indochina Peninsula. The MRIAGCM3.2S is the latest version of the super-high-resolution atmospheric general circulation model(AGCM) with a horizontal grid size of about 20 km, which was jointly developed by the MeteorologicalResearch Institute (MRI) and the Japan Meteorology Agency (JMA). In this study, the impacts of climatechange on river discharge in the Indochina Peninsula region were investigated by comparing projected riverdischarge of the present climate, the near future climate and the future climate experiment. From the results,
clear changes of annual mean discharge, annual maximum daily discharge and annual minimum daily
discharge were detected with the degree of changes differing according to location. In addition, the statisticalsignificance of river discharge changes in the region under climate change was examined by performing
statistical analyses on the projected river discharge.
Key words Indochina Peninsula; river discharge; 1K-FRM; climate change; general circulation model; statistical test

INTRODUCTION
According to the Intergovernmental Panel on Climate Change (IPCC), global average temperature
has increased roughly 0.74°C over the last century. The main reason is the anthropogenic increaseof greenhouse gas concentrations (IPCC, 2007a). The IPCC also reported, with high confidence,that hydrological systems are being affected by temperature increases. The frequency andmagnitude of hydrological extremes such as flood and drought events may also be influenced bychanging climate.
One approach to estimate the effects of global warming on hydrology and water resources is
to use outputs of general circulation models (GCMs) as inputs for hydrological models. Many
GCMs have been developed to simulate the present climate and have been used to project futureclimate change. They serve as a powerful tool for studying global climate and its variability. The
climate simulations from the GCMs can be converted by hydrological models to relevant water
resources variables for evaluating the hydrologic and water resources effects of climate change.
In this study, river discharge in the Indochina Peninsula region was simulated using a superhigh-resolution atmospheric general circulation model (AGCM) with 20 km spatial resolution anda distributed flow routing model with kinematic wave approximation. The AGCM runoff
generation data for the present climate experiment (1979–2008), the near future climate
experiment (2015–2044), and the future climate experiment (2075–2104) under the SRES A1B
scenario were used as input data for the flow routing model.
The objective of this study was to investigate the changes of river discharge in the Indochina
Peninsula region under climate change. The statistical significance of river discharge changes was
also analysed to locate possible hotspot basins in the region where significant changes related tofloods, droughts, and water resources could occur.
METHODS
Flow routing model
The flow routing model 1K-FRM is a distributed hydrological model with kinematic wave flow
approximation (Hunukumbura et al., 2012) applied to investigate the impacts of climate change onriver discharge. It was developed by Hydrology and Water Resources Research Laboratory of
Copyright  2014 IAHS Press

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Kyoto University (http://hywr.kuciv.kyoto-u.ac.jp/products/1K-DHM/1K-DHM.html). 1K-FRM
was based on a catchment topography model. The topographic information used for the flowrouting model 1K-FRM was generated from processing the scale-free global streamflow network
dataset (Masutani et al., 2006) with a spatial resolution of 5 arc minutes. The flow direction isdefined using an 8-direction method, which assigns flow from each grid cell to one of its 8neighbours, either adjacent or diagonally, in the direction with the steepest downward slope. Eachslope element determined by flow direction is represented by a rectangle formed by the two
adjacent nodes of grid cells. Then the runoff is routed according to the flow direction information
by applying the kinematic wave flow model to all slope elements.
Climate change data
The AGCM used in this study is MRI-AGCM3.2S, which was jointly developed by theMeteorological Research Institute (MRI) and the Japan Meteorological Agency (JMA). It is the latest
version of 20 km spatial resolution AGCM provided by MRI and JMA (Kitoh et al., 2009). Outputs
from MRI-AGCM3.2S included various atmospheric and hydrologic variables for three climate
experiments: the present climate (1979–2008), the near future climate (2015–2044), and the futureclimate (2075–2104). The runoff generation data from the MRI-AGCM3.2S dataset was fed into theflow routing model to project river discharge in Indochina Peninsula under climate change.
Statistical analysis
Relative change in river discharge To examine the changes in river discharge in the
Indochina Peninsula region under climate change, the ratio of annual mean discharge, mean of
annual maximum daily discharge, and mean of annual minimum daily discharge for the near futureclimate and the future climate to the one for the present climate were calculated and analysed(Duong et al., 2013).
Statistical significance The statistical significance of river discharge changes in the
Indochina Peninsula region was assessed with a comparison of means of projected river discharge
data for the near future climate experiment and the future climate experiment with those for thepresent climate experiment, to evaluate whether the changes in river discharge are statisticallysignificant or just occur by chance.
The approach for comparing those statistics is chosen based on the distribution of projectedriver discharge data. If the data are normally distributed, a parametric approach can be employed
to determine if the difference in the means was statistically significant. In cases where the data are
not normal distributed, non-parametric approaches can be used.
In this study, the Shapiro-Wilk W test (Gilbert, 1987) was applied for the data normality test.
The W test is performed by testing the following null hypothesis, H0: the sample data are normally
distributed. The results of the test for normality are shown in the section of Test for normality.
Based on those results, statistical significance testing methods were chosen.
The parametric Welch correction t-test (Zar, 2010) was applied for the river discharge data
which have a normal distribution. The non-parametric Mann-Whitney U test (Gilbert, 1987) was
performed to test for the statistical significance of non-normal distribution river discharge data.
The null hypothesis H0 for the statistical significance testing can be defined as follows: there is no
significant difference between annual mean discharge/mean of annual maximum daily
discharge/mean of annual minimum daily discharge for the near future climate/the future climate
and the present climate (µnear future = µpresent, µfuture = µpresent).
RESULTS AND DISCUSSION
Changes of river discharge in the Indochina Peninsula under climate change
The ratio of annual mean discharge, mean of annual maximum daily discharge, and mean of
annual minimum daily discharge for the near future climate and the future climate to those for theStatistical analysis of river discharge projected using the MRI-AGCM3.2S dataset

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Fig. 1 Map of the study area (left); ratio of annual mean discharge for the near future climate to thepresent climate (middle), and the future climate to the present climate (right).

Fig. 2 Ratio of mean of annual maximum daily discharge for the near future climate to the present
climate (left), and the future climate to the present climate (right).

Fig. 3 Ratio of mean of annual minimum daily discharge for the near future climate to the present
climate (left), and the future climate to the present climate (right).

present climate in the Indochina Peninsula region were calculated and are shown in Figs 1, 2, and3. The scale bar indicates the ratio range.
Figure 1 shows that the change in annual mean discharge in the near future is not so clear. Mostof the region has a ratio between 0.9 and 1.1. However, in the future climate, the area with changes
in annual mean discharge and ratio range become larger, especially in the Irrawaddy River basin andthe upper most parts of the Salween River basin and the Mekong River basin. A decreasing trend ofannual mean discharge was also detected at the Chi-Mun sub-basin of the Mekong.

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For the mean of the annual maximum daily discharge, there are significant changes in the near
future climate in the upper part of the Irrawaddy River basin, the Salween River basin, andnorthwestern Vietnam (see Fig. 2). The changes which appeared in the near future climate become
more visible in the future climate. The Irrawaddy River basin, the Salween River basin, and the
Red River basin showed a noticeable increase in the mean of annual maximum daily discharge inthe future climate. The ratio range in some areas is larger than 2.5. It means that flooding risk inthose areas could increase.
From Fig. 3 it can be seen that there is an increasing trend in the upper most part of the
Mekong River basin and the Salween River basin in the near future climate. However, in the
middle part of the Mekong River basin and southern part of Vietnam, the mean of annual
minimum daily discharge tended to decrease. In the future climate, these trends become clearer.
Test for normality
The results of the data normality test using Shapiro-Wilk W test are shown in Figs 4, 5 and 6.
Those figures show the W statistic values of annual mean discharge, mean of annual maximum
daily discharge, and mean of annual minimum daily discharge data for the present climate, thenear future climate, and the future climate.
In the Shapiro-Wilk W test, if the calculated W statistic is smaller than the critical W statistic
value, the null hypothesis is rejected. The critical W statistic value can be obtained from a table ofW critical values based on data sample size n and significant level α. In climate science, thesignificant level of 0.05 or 5% is often selected. With a data sample size of 30, the statistic table

Fig. 4 W test statistic of annual mean discharge data for the present climate (left), the near futureclimate (middle), and the future climate (right).

Fig. 5 W test statistic of mean of annual maximum daily discharge data for the present climate (left),the near future climate (middle), and the future climate (right).

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Fig. 6 W test statistic of mean of annual minimum daily discharge data for the present climate (left), thenear future climate (middle), and the future climate (right).

provides a critical value of Wαcr= 0.05 = 0.927 . Therefore, the sample data are not normally
distributed when calculated W statistics are less than 0.927 (see Figs 4, 5, and 6).
Test for statistically significant differences between two means
Based on the results of data normality test, the test for statistical significance was carried out. If
both river discharge data being compared are normally distributed, the parametric Welchcorrection t-test is used. In other cases, the non-parametric Mann-Whitney U test is applied. The
results of statistical significant test are shown in Fig. 7, Fig. 8 and Fig. 9.
From Fig. 7, it can be seen that, in the near future climate, the change in annual meandischarge in the Indochina Peninsula region is statistically significant only in the main channel ofthe Irrawaddy River basin. In the future climate, the change in annual mean discharge isstatistically significant in middle and upper part of the Irrawaddy River basin, the main channel ofthe Salween River basin and upper part of the Mekong River basin’s main channel.
For the mean of annual maximum daily discharge, the change is statistically significant in themain channel of the Irrawaddy and the Red River basin in the near future climate (see Fig. 8). The
statistical significance of river discharge change appeared in the future climate at the Irrawaddy
River basin, lower part of the Salween River basin’s main channel, main channel of the MekongRiver basin in the territory of Lao PDR, and the Red River basin.
Figure 9 shows that the change in mean of annual minimum daily discharge is statisticallysignificant at the upper most part of the Salween and the Mekong River basin in the near futureclimate. In the future climate, the change in river discharge was found statistically significant at
the upper most part of the Salween and the Mekong River basin, left tributary of the Chao Phraya
River basin, and the lower most part of the Mekong River basin’s main channel.

Fig. 7 Statistical significant differences between annual mean discharge for the near future climate andthe present climate (left); and for the future climate and the present climate (right).

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Fig. 8 Statistical significant differences between mean of annual maximum daily discharge for the near
future and the present climate (left); and for the future and the present climate (right).

Fig. 9 Statistical significant differences between mean of annual minimum daily discharge for the near
future and the present climate (left); and the future and the present climate (right).

CONCLUSIONSThis study investigated the change in river flow under climate change in the Indochina Peninsula
region using the distributed flow routing model 1K-FRM and MRI-AGCM3.2S dataset. Statisticalanalysis was also carried out to examine the statistical significance of river discharge changes in
the region. A clear change of river flow was detected and found statistically significant in the
Irrawaddy and the Red River basin, and in some parts of the Salween and the Mekong River basin.
For future further work, different GCMs’ data will be used to evaluate the uncertainty inclimate projection. A detailed distributed hydrological model will be developed for hotspot basinsin the region with significant changes in river flow for more comprehensive impact assessment.
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