Try et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00353-z(2020) 7:34

Progress in Earth andPlanetary Science

PAPER WITH FULL DATA ATTACHEDOpen Access

Assessing the effects of climate change on
flood inundation in the lower Mekong
Basin using high-resolution AGCM outputs
Sophal Try1,4* , Shigenobu Tanaka2, Kenji Tanaka2, Takahiro Sayama2, Giha Lee3 and Chantha Oeurng4

Abstract
Climate change currently affects the resilience and aquatic ecosystem. Climate change alters rainfall patterns whichhave a great impact on river flow. Annual flooding is an important hydrological characteristic of the Mekong River
Basin (MRB) and it drives the high productivity of the ecosystem and biodiversity in the Tonle Sap floodplain and
the Mekong Delta. This study aims to assess the impacts of climate change on river flow in the MRB and floodinundation in the Lower Mekong Basin (LMB). The changing impacts were assessed by a two-dimensional rainfallrunoff and inundation model (RRI model). The present climate (1979–2003) and future projected climate (2075–
2099) datasets from MRI-AGCM3.2H and MRI-AGCM3.2S models were applied with a linear scaling bias correction
method before input into the RRI model. The results of climate change suggested that flood magnitude in the LMB
will be severer than the present climate by the end of the twenty-first century. The increment of precipitation
between 6.6 and 14.2% could lead to increase extreme flow (Q5) 13–30%, peak inundation area 19–43%, and peak
inundation volume 24–55% in the LMB for ranging of Representative Concentration Pathways (RCP) and sea surface
temperature (SST) scenarios while there is no significant change on peak flood timing.
Keywords: Climate change, River discharge, Flood inundation, Lower Mekong Basin

Introduction
The impacts of climate change became one among the
global concerns threatening the environments and natural resources. The Intergovernmental Panel on Climate
Change (IPCC) AR5 reported the increase of global average temperature from 1.0 °C in the lowest emission scenario to 3.7 °C in the highest emission scenario by 2100
(IPCC 2014). Climate change has an impact on thehydrological system by altering the hydrological cycle
and precipitation patterns (Wang et al. 2013). The
spatial and temporal patterns of precipitation are themain factors affecting flow regimes and climate conditions (Beyene et al. 2010; Wu et al. 2016). The variation* Correspondence: trysophal001@gmail.com
1
Graduate School of Engineering, Kyoto University, Gokasho, Uji 611-0011,
Japan
4
Faculty of Hydrology and Water Resource Engineering, Institute ofTechnology of Cambodia, Russian Conf. Blvd, Phnom Penh 12156, Cambodia
Full list of author information is available at the end of the articleof precipitation patterns would disturb the water systemin the entire catchment. In Southeast Asia, the precipitation was projected to increase from 1 to 8% at the endof the twenty-first century (Oeurng et al. 2019). The future hydrologic system will be severely affected by climate change in the Mekong Rivere Basin (MRB) (Lauriet al. 2012; Perera et al. 2017) and its sub-basins: theTonle Sap Lake (Oeurng et al. 2019) and the Sekong,Sesan, and Srepok Rivers (Oeurng et al. 2016; Shrestha
et al. 2016). The increment of river discharge is expectedduring flood season, while the severe droughts are plausible to happen in the dry season (Oeurng et al. 2019).
The seasonal inundation and water interchange between
the Mekong River and Tonle Sap Lake of Cambodia
produce high productive biodiversity, agriculture, andfisheries (Arias et al. 2013; Uk et al. 2018). The MekongRiver supports about 70 million people from four countries (Cambodia, Laos PDR, Thailand, and Vietnam) in© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Try et al. Progress in Earth and Planetary Science(2020) 7:34

the Lower Mekong Basin (LMB) where most of the areas
are prone to flooding (Try et al. 2019). The ecosystem of the lake and its floodplain are prone to be affected by altering the hydrological cycle in the
Mekong River (Arias et al. 2012).
It is a challenge to predict the change of flow regime(Bates et al. 2008). The IPCC AR5 published climatechange scenarios known as the Representative Concentration Pathways (RCP) (IPCC 2014). The RCP scenarios
incorporate four emission scenarios of greenhouse gas: astringent mitigation scenario (RCP2.6), two intermediate
scenarios (RCP4.5 and RCP6.0) and one very high GHG
emission scenario (RCP8.5). The future projections given
by IPCC reports were based on general circulation
models (GCMs) outputs. GCMs are generally capable toproduce a coarse resolution that might consist of large
uncertainty. Generally, downscaling approaches weretaken: statistical downscaling and dynamical downscaling to produce the regional climate condition. The uncertainties of GCMs were dominantly caused by coarse
resolution (Li et al. 2012). To overcome this uncertainty,
this study used the outputs from two high-resolution atmospheric general circulation models (AGCMs), namely
as MRI-AGCM3.2S and MRI-AGCM3.2H with 20 km
and 60 km spatial resolution respectively. The model
performed a historical climate experiment using observed sea surface temperature, and they showed the improvement in heavy monthly mean precipitation aroundthe tropical pacific region confirmed by numerical skill
score (Mizuta et al. 2012).
A hydrological model is an effective tool that cansimulate hydrological characteristics under different scenarios. Various hydrological models were developed andapplied to investigate the hydrological processes in the
MRB and its tributaries: Soil and Water Assessment
Tool (SWAT) model (Arnold et al. 1998), Vmod model(Koponen et al. 2010), 2-D Local Inertial Equation (2-D
LIE) model (Tanaka et al. 2018), and Rainfall-RunoffInundation (RRI) model (Sayama et al. 2015). Try et al.
(2020) used the RRI model for comparison of gridded
precipitation products for flood inundation modeling in
the MRB. Perera et al. (2017) applied the same model toassess the climate change impacts on hydrological analysis in the MRB using outputs from atmospheric general circulation models (AGCM) based on RCP8.5
scenario considering different sea surface temperature(SST) boundary condition; however, their study did notconsider different emission of greenhouse gas scenarios.
To further understand hydrological study of climatechange impacts in the MRB, this study aims to predictthe changes of extreme river discharge in the MRB and
aspects of extreme flood inundation such as inundationarea, inundation volume, peak inundation duration andtime, and inundation probability in the LMB by using

Page 2 of 16

present climate (1979–2003) and future projected climate (2075–2099) from high and super high-resolution
AGCM models.

Materials and methods
Study area

The MRB is the largest river in Southeast Asia travelingacross China, Myanmar, Laos PDR, Thailand, Cambodia,
and Vietnam (Fig. 1). Annual flooding is an importanthydrological characteristic of the MRB where it drives
the high productivity of ecosystems and biodiversity(Lamberts and Koponen, 2008), particularly the Tonle
Sap floodplain and the Mekong Delta (Kummu et al.
2006). The total basin area of the MRB is 795,000 km2,
and its average discharge is 14,500 m3/s (MRC 2005).
The MRB is located in the tropical monsoon climate
with two seasons: rainy season (May–October) and dry
season (November–April) (MRC 2005). The flood season, accounting 80–90% of annual flow, is exclusively
important for maintaining the aquatic ecosystem in thebasin (MRC 2010). The Tonle Sap Lake in the LMB has
a unique hydrologic system of two-directional flows. In
the dry season, the water flows from the Tonle Sap Laketo the Mekong River, while the water level of the river ishigher than the water level in the lake causing the waterflowing backward to the Tonle Sap Lake in the rainyseason. The surface area of the lake expands from 2600
km2 in the dry season to 12,000km2 in the wet season
resulting in the inundation from the floodplain (Oeurng
et al. 2019).
Climate change datasets

The Meteorological Research Institute (MRI) developed
a high-resolution (MRI-AGCM3.2H, 60 km) (Mizuta
et al. 2012) and a super high-resolution atmospheric climate models (MRI-AGCM3.2S, 20 km) (Kitoh andEndo, 2016; Mizuta et al. 2014) with hourly temporalscale. The climate parameters (precipitation and evapotranspiration) were used as input for the RRI model fortwo 25-year periods: present climate covering of 1979–
2003 and the future climate (2075–2099). The MRIAGCM3.2H model was projected from present (HPA_
m01) to future climate based on degree of GHG emission ranking from low emission (RCP2.6) to high emission (RCP8.5), namely as HFA_rcp26, HFA_rcp45,
HFA_rcp60, and HFA_rcp85, respectively. The MRIAGCM3.2S model was simulated for the present climate(1979–2003) using observed boundary condition of SST(SPA_m01). The different SST patterns for future projection under RCP8.5 scenario were characterized byusing SST as output from 28 GCMs in Coupled Model
Inter-comparison Project Phase 5 (CMIP5) (Taylor et al.
2012). The future climate experiments (2075–2099) weregrouped with SST distributions into four clusters: 8-

Try et al. Progress in Earth and Planetary Science(2020) 7:34

Page 3 of 16

Fig. 1 Location of the Mekong River Basinmodel average (uniform warming in the northern andsouthern hemispheres), 14-model average (El Ninolike pattern with a larger warming belt in the central
equatorial Pacific), 6-model average (a larger warmingin the northern hemisphere than in the southernhemisphere), and total 28-model average labeled as
SFA_rcp85_c1, SFA_rcp85_c2, SFA_rcp85_c3, andSFA_rcp85, respectively. GPCC was determined to be
an accurate precipitation product in the MRB region(Try et al. 2020). Therefore, this precipitation wasused as a reference for bias correction as the following linear scaling method:

PBC
hr ¼

Pobs
agcm
mon
 Phr
Pagcm
mon

ð1Þ

agcm
where Pobs
mon : average monthly GPCC precipitation, Pmon :agcm
average monthly AGCM precipitation, Phr : hourly
AGCM precipitation, and PBC
hr : hourly bias-correctedTry et al. Progress in Earth and Planetary Science(2020) 7:34

Page 4 of 16

AGCM precipitation. The correction factor calculated
from the present climate was applied to future climate
experiments.
The different climate change scenarios would lead togenerate different evapotranspiration (ET) in the future.
For example, when precipitation increases in the future,and ET may also increase due to the increase of
temperature increase. In this case, the surface runoff
might not change following only precipitation change. In
order to eliminate this uncertainty for future projection,
the correction of future ET was calculated by extracting
ET from present and future climates of AGCMs as thefollowing equation.
ET fut ¼ ET JRA − 55 þ △ET fut

ð2Þ

where ETfut is ET for each future scenario; ETJRA − 55 isevapotranspiration from JRA-55 dataset; and ΔETfutis
the change of ET for each future scenario comparing totheir present climates.
RRI model simulation

The rainfall-runoff-inundation (RRI) model was used tosimulate runoff and flood inundation for the MRB. The
RRI model is a two-dimensional distributed hydrodynamic model that is able to simulate rainfall-runoff
and inundation processes simultaneously (Sayama et al.
2012, 2015). The slope grid cells receive rainfall and flow
based on 2D diffusive wave equations, while the inchannel flow is calculated with 1D diffusive equations.
Topographic information (digital elevation model(DEM), flow direction, and flow accumulation), obtainedfromtheMulti-Error-Removed-Improved-Terrain(MERIT DEM, Yamazaki et al. 2017), were used as input
to the RRI model. The river geometry was available from
the Mekong River Commission, and land use was from
MODIS (product: MCD12Q1) for 2000 (Friedl et al.
2010). The simulation setting was separated into twoparts. First, the model was set up with 2.5′ resolution(approx. 5 km) for the whole MRB for assessing riverdischarge. Then, the model was set up with 1.5′

resolution (approx. 2.7 km) for the LMB for predicting
more accurate flood inundation. Try et al. (2020) evaluated the performance of five various gridded precipitation datasets for rainfall-runoff and inundation modelingover the MRB. The result showed that the Global Precipitation Climatology Centre (GPCC) product was suitable for long-term hydrological modeling in the MRB
comparing to other precipitation products: APHRODITE, TRMM-3B42V7, PERSIANN-CDR, and GSMaP.
Based on retrospective experiment, this study conducteda simulation of 25 years (1982–2007) using GPCC precipitation for model verification. The surface evapotranspiration was available from the Japanese 55-year
Reanalysis dataset (JRA-55) with 3-hourly and 0.5625°
resolution (Kobayashi et al. 2015). The calibrated RRI
model and its parameter setting (Table 1) were retrieved
from Try et al. (2020). The performance of river discharge and flood inundation was compared between theobservation and simulation results. After verification of
the hydrodynamic model, the same parameter setting
was used to simulate river discharge and flood inundation using the climate change dataset.
To evaluate the performance of the model, we used indicators as the following: Nash-Sutcliffe model efficiency(NSE), coefficient of determination (R2), and root meansquare error (RMSE):PðQ ðt Þ − Qobs ðt ÞÞ2
NSE ¼ 1 − P  sim
2
Qobs ðt Þ − Qobs

ð3Þ


2
P 
Qsim ðt Þ − Qsim Qobs ðt Þ − Qobs
R ¼ P
2 P 
2
Qsim ðt Þ − Qsim
Qobs ðt Þ − Qobs

ð4Þ2sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃPðQsim ðt Þ − Qobs ðt ÞÞ2
RMSE ¼n

ð5Þ

where Qsim(t) and Qobs(t) are the simulated and observed
discharge at time step t. Qsim and Qobs are the simulated
and observed average discharge, and n is the number ofdata.

Table 1 The values of parameter setting of the RRI model used in the study
Parameters−1/3

Manning’s coefficient for slopen(mSoil depth

d

(m)

s)

Mountains

Plains

0.4

0.015

2.0

–

Lateral saturated hydraulic conductivity

ka

(m/s)

0.1

–

Parameter of unsaturated hydraulic conductivity

β

–

9.0

–

Vertical hydraulic conductivity

kv(cm/h)–

0.06

Soil porosity

φ

–

–

0.6

Wetting front soil suction head

Sf

–

–

0.273

Manning’s coefficient for rivernriver(m−1/3s)

0.03

Try et al. Progress in Earth and Planetary Science(2020) 7:34

Page 5 of 16

To evaluate the performance of flood extent, two of
the following indices including true ratio (TR) and hit
ratio (HR) were used:

Assessing climate change impacts

ICobs ∩ICsim
TR ¼
ICsim

ð6Þ

ICobs ∩ICsim
IAobs

ð7Þ

HR ¼

temporal resolutions are 250 m and 3 days for global
scale (Nigro et al. 2014).

where ICsim and ICobs are the number of inundated cellfrom simulation and observation. The observed inundation maps were available from NASA MODIS nearreal-time global flood mapping product (https://floodmap.modaps.eosdis.nasa.gov/) whose spatial and

In addition to assessing the impacts of climate change
on extreme river flow and flood inundation in the MRB,
the change of precipitation was also examined. The
monthly precipitation of the projected future climate
was compared with that of the present climate for theentire MRB. Next, the mean annual flow (Qm) and thehigh flow exceeded 5% of the time (Q5) were calculated.
Further, the changes of inundation area and volume,
flood probability, inundation peak time, and duration
were also evaluated from the present period (1979–

Fig. 2 Monthly simulated and observed discharges at Luang Prabang, Pakse, and Stung Treng for the period of 1982–2007. Model performancefor river discharge was evaluated by Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and root mean square error (RMSE)Try et al. Progress in Earth and Planetary Science(2020) 7:34

Page 6 of 16

Table 2 The statistical indices of model performance of flood
extent true ratio (TR) and hit ratio (HR)
2000

2001

2002

2003

2004

2005

2006

2007

Avg.

TR

0.82

0.74

0.65

0.42

0.58

0.46

0.49

0.47

0.58

HR

0.85

0.80

0.87

0.89

0.76

0.88

0.94

0.89

0.86

2003) to the future period (2075–2099). Moreover, thestatistical Kolmogorov-Smirnov (K-S) test, a nonparametric test of two samples, was used to check thevariation of flood inundation between present and futureclimates. The maximum difference of the cumulative
distribution function of the two samples is defined by:
Dn;m ¼ sup j F n ðxÞ − F m ðxÞjx

ð8Þwhere Fn and Fm are the empirical distribution functionsof the two samples, and sup is supremum function. The
null hypothesis, H0, assumes that two samples have nosignificant difference in CDF. When the likelihood of thedifferent distribution of the two samples exceeds asignificance level, the null hypothesis is rejected. Two
samples have different distribution if
rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
nþmDn;m > cðαÞnm

ð9Þ

where n and m are the sample sizes. At the significancelevel 5% used in this study, c(α) is equal to 1.36.

Results
Performance of the model simulation

The RRI model simulated for 1982–2007 considering as
model verification period, and the performance indicesof NSE, R2, and RMSE were calculated at three stations
along the mainstream of the MRB: Luang Prabang,
Pakse, and Stung Treng. These three stations were selected according to the availability of observed discharge,
and their locations vary in the upper part of the LMB(i.e., Luang Prang is located near the outlet from theFig. 3 Comparison of flood extent from simulation result (top) and satellite observation (bottom)

Try et al. Progress in Earth and Planetary Science(2020) 7:34

Upper Mekong in China), middle part (Pakse), anddownstream of the LMRB (Stung Treng). Figure 2
showed the simulated and observed monthly discharge
at the three locations. In general, the hydrographs of observation and simulation had similar pattern of all threestations; however, the model predicted underestimationin the low flow season in Luang Prabang. The evaluation
statistics were NSE = 0.79. R2 = 0.85, and RMSE = 1371
m3/s. At Pakse, the performance indices were NSE =0.90, R2 = 0.92, and RMSE = 3433 m3/s. The prediction
at Stung Treng was high performance (NSE = 0.91, R2 =0.92); however, large flow at this location produced moreerror value (RMSE = 3990 m3/s) than other locations.
The performance of annual maximum flood inundation was determined using true ratio (TR) and hit ratio(HR). The observed flood extent was obtained from theNASA MODIS flood observation dataset (Nigro et al.
2014) for the period of 2000–2007. The threshold of inundation depth of 0.5 m was selected following the previous studies (Sayama et al. 2012 and 2015; Try et al.
2018) to identify the flood and non-flood areas. The statistic indices of observation and model prediction flood
extent were listed in Table 2 (see Fig. 3 for the comparison map of simulation and observation). The spatial performance index TR ranged from 0.42 to 0.82 during
2000–2007 where the average accuracy was 58% (avg.
TR = 0.58). The HR indicator varied from 0.76 to 0.94,
and its mean accuracy was 86% (avg. HR = 0.86). The
factors which reduced inundation accuracy would comePage 7 of 16

from two main sources. First, the satellite observation
would not be able to detect flooded area at the mangrove forests on the banks of the Tonle Sap Lake while
the simulation might correctly identify these areas underinundation. Secondly, the simulation was not able to detect inundated area in some low parts of the Mekong
delta where might be influenced by saltwater intrusion.
This effect was not considered in this study due tounavailability of observed information.
Precipitation changes

The bias correction performance was checked bycomparing the precipitation and simulated dischargeof historical AGCMs before and after bias correction.
Figure 4 shows average daily precipitation and simulated discharge comparing between GPCC and
AGCMs before and after bias correction. The raw
precipitation and discharge had seasonal bias (i.e.
overestimation) at the beginning of the wet season.
The performance of average daily AGCM precipitation before and after bias correction was improved
with R2 from 0.85 to 0.94 for SPA_m01 and from
0.87 to 0.93 for HPA_m01, respectively. Moreover,the performance discharge was improved from R2 =0.89 and 0.96 to R2 = 0.99 and 0.98 for SPA_m01
and HPA_m01, respectively.
Figure 5 showed the bias-corrected monthly precipitation from MRI-AGCM3.2H and MRI-AGCM3.2S
datasets in the present (1979–2003) and future

Fig. 4 Comparison of raw and bias corrected average daily precipitation and discharge at Stung Treng for historical AGCMs (SPA_m01 and
HPA_m01) with GPCC simulation

Try et al. Progress in Earth and Planetary Science(2020) 7:34

Page 8 of 16

Fig. 5 Bias corrected monthly precipitation in present and future climate experiments of MRI-AGCM3.2H and MRI-AGCM3.2S models. The bottom andtop of the box show first and third quartiles, and the flat line inside the box is median. The whiskers represent the minimum and maximum values(2075–2099) climates. The basin average precipitation
was expected to increase for RCP and SST scenarios
during the rainy season (May–October). During thelate rainy season in September, the precipitation of
future scenarios of HFA_rcp45, HFA_60, and HFA_
rcp85 significantly raised up while less increase could
be observed in HFA_rcp26. Three future SST patterns(SFA_rcp85, SFA_c2, and SFA_c3) provided higher increase of precipitation than SFA_rcp85_c1 in rainyseason comparing to their present climate SPA_m01.
The annual precipitation would increase by 6.6%,
8.0%, 9.5%, and 14.2% for four RCP scenarios(RCP2.6–RCP8.5), and four SST pattern scenarios had
less diversified increasing (9.9–12.5%).
Effects of climate change on river flow

The future changes in river discharge were assessed bysimulation of RRI model using precipitation and evapotranspiration projected by MRI-AGCM3.2H and MRI-AGCM3.2S models from present climate (1979–2003) tothe future climate (2075–2099) for four greenhouse gas
emission scenarios (RCP2.6, RCP4.5, RCP6.0, and
RCP8.5) and four SST scenarios (Fig. 6). The flowchange was examined at four locations along the mainstream of the MRB. Table 3 showed the mean annual
discharge (Qm) and extreme river flow exceeding 5% of
the time (Q5). Overall, Qm and Q5 increased for all future scenarios. The Qm of the upstream stations (Chiang
Saen and Vientiane) was predicted to increase 5–10%
while the downstream stations (Pakse and Kratie) raised
up a higher increment of 10–15% for three climatechange scenarios (RCP2.6, RCP4.5, and RCP6.0). The
Qm for all stations significantly escalated for RCP8.5(23–26%) and SST scenarios (10 ± 3%–29 ± 3%). At theend of the twenty-first century, high flow (Q5) showed
increases of 5–14% at Chiang Saen and Vientiane and
11–18% at Pakse and Kratie for the three low emission
scenarios (RCP2.6, RCP4.5, and RCP6.0). The Q5

Try et al. Progress in Earth and Planetary Science(2020) 7:34

Page 9 of 16

Fig. 6 Average monthly discharge of present and projected future climate experiments of RCP and SST scenarios

Table 3 Changes (%) of annual mean discharge (Qm) and flow
exceeded 5% of the time (Q5) for each RCP and SST scenarios
comparing with present climate. The values for SST scenarios
show mean (μ) ± standard deviation (σ)
Station
Chiang Saen

Vientiane

Pakse

KratieScenario

Qm

Q5

RCP2.6

6%

9%

RCP4.59%

10%

RCP6.05%

14%

RCP8.523%

27%

SST (μ ± σ)

10% ± 3%

10% ± 4%

RCP2.6

7%

5%

RCP4.5

10%

8%

RCP6.07%

10%

RCP8.523%

21%

SST (μ ± σ)

11% ± 2%

10% ± 4%

RCP2.6

10%

11%

RCP4.5

13%

11%

RCP6.013%

17%

RCP8.526%

29%

SST (μ ± σ)

25% ± 3%16% ± 4%

RCP2.6

12%

14%

RCP4.513%

13%

RCP6.015%

18%

RCP8.525%30%

SST (μ ± σ)

29% ± 3%18% ± 3%

momentously stepped up to 21–30% for four observations stations for RCP8.5 while it ranged between 10 ±4% and 18 ± 3% for the SST scenario. The peak discharge took place in September (Fig. 6), and thisremained the same for all future scenarios at Vientiane,
Pakse, and Kratie except Chiang Saen where the peakdischarge from SST somehow happened earlier inAugust.
Effect of climate change on flood inundation

The flood plain and agricultural land are the majority inthe LMB, so the threshold was selected at 0.5 m of water
depth to classify the inundated and non-inundated area.
According to the simulation results, flood magnitudes
were expected to increase for all future scenarios.
Figure 7 showed the 25-year average inundation extent in the present and future climatic conditions.
The ratios of mean and variance of the inundation
extent of the future and present were 1.19–1.43 and
2.08–3.92 for MRI-AGCM3.2H and 1.26–1.32 and
1.12–2.13 for MRI-AGCM3.2S (Table 4), respectively.
Figure 8 showed boxplot comparison of peak inundation time (days of the year (DOY)) and inundation
volume in the LMB. There was no significant changeof peak flood time of future climate compared to thepresent climate (i.e., the variation of the median waswithin ± 5 days). The K-S test showed result of no significant difference for all future scenarios (RCP and SST) at
significant level 5% (p value = 0.2370–0.8774). The peak
inundation volume took place in October for the present
climate and all projected future scenarios. However, there
was a huge variability in peak inundation volume. The firstTry et al. Progress in Earth and Planetary Science(2020) 7:34

Page 10 of 16

Fig. 7 25-year average of annual maximum inundation extent for the present and the future climate experiments

quartile values of the LMB peak inundation volume (theboxplot in the lower row of Fig. 8) for all future scenarios(100–124 km3 for HFA and 99–118 km3 for SFA) weregreater than the third quartile value of their present climates (99 km3 and 98 km3 for HPA and SPA). Median ofpeak inundation volume for HFA increased between 15%
for RCP2.6 and 42% for RCP8.5 and 28–41% for SST. The
relative ratio of the mean of inundation volume varied between 1.24–1.55 and 1.29–1.41, and the relative ratio ofinundation volume variance changed 2.30–4.60 and 1.66–
2.09 (Table 3) for RCP and SST scenarios, respectively.
The result of K-S test of peak inundation volume revealed
significant difference for all RCP and SST scenarios (i.e.,null hypothesis was rejected at significant level of 5% with
p value less than 0.002).
Figures 9 and 10 showed the flood inundation probability, the difference of probability of flood inundation
Table 4 The ratio (F) of mean (μ) and variance (σ2) ofinundation area and volume for future climates comparing theirpresent climate
Model
MRI-AGCM3.2H

MRI-AGCM3.2S

ScenarioInundation area

Inundation volume

F(μ)

F(μ)

F(σ2)

F(σ2)

HFA_rcp26

1.19

2.28

1.24

2.68

HFA_rcp45

1.26

2.08

1.32

2.30

HFA_rcp60

1.32

3.92

1.41

4.60

HFA_rcp85

1.43

3.37

1.55

3.96

Avg.

1.30

2.91

1.38

3.38

SFA_rcp85

1.32

1.12

1.41

1.66

SFA_rcp85_c1

1.26

2.13

1.29

1.98

SFA_rcp85_c2

1.32

1.67

1.38

2.09

SFA_rcp85_c3

1.27

2.08

1.30

1.89

Avg.

1.29

1.75

1.34

1.90

from the present to future (dP), and the K-S test result foreach climate model. The dP clearly indicated the positive
value for all projected future scenarios. For MRIAGCM3.2H, the dP ranged from one less increasing(HFA_rcp26), two medium increase (HFA_rcp45 andHFA_rcp60), and one large increase (HFA_rcp85). Amongthe area of significant difference of inundation probability(dP ≥ 0.05 in Figs 9 and 10), the proportion for area dP ≥0.3 was just only 12% (i.e., area of proportion 0.05 ≤ dP <0.03 was 88%) for RCP2.6, and this proportion raised up
to 48%, 40%, and 56% for HFA_rcp45, HFA_rcp60, and
HFA_rcp85, respectively. On the other hand, the same
proportion value of dP ≥ 0.3 had less diversification (i.e.,
54–62%) for ranges of SST scenarios. The spatial K-S test
indicated almost not significant in HFA_rcp26 and partial
significant in HFA_rcp45 and HFA_rcp60 while large area
was found significant in HFA_rcp85. The K-S test for SST
patterns determined significant at most area in SFA_rcp85
following by SFA_rcp85_c3, and SFA_rcp85_c2 and SFA_
rcp85_c1 had less area of significant.
The inundation duration, change of duration, and its
spatial K-S test results were illustrated in Figs. 11 and 12
for MRI-AGCM3.2H and MRI-AGCM3.2S, respectively.
The variation of inundation duration per 25-year in thepresent and future between − 1 and 1 day was considered
as no change in this study. Overall, the longer durations ofinundation were observed for the LMB for both RCP and
SST scenarios. The area with high flood inundation probability commonly experienced long flood durations while
the low probability place corresponded with a shorter duration of inundation. Similarly, the spatial K-S test showedsignificant at most of area regardless of RCP and SST scenarios. The increasing of flood duration was observed at
the floodplain except inside the Tonle Sap Lake where thewater exists for the whole year.

Try et al. Progress in Earth and Planetary Science(2020) 7:34

Page 11 of 16

Fig. 8 Peak inundation time (upper row) and peak inundation volume (lower row) in the LMB resulted from MRI-AGCM3.2H model (left column)
and MRI-AGCM3.2S model (right column). The boxplot explanation is the same as Fig. 5

Fig. 9 Spatial distribution of inundation probability (upper row), difference between present and each future RCP scenarios (middle row), and K-S
test (lower row) for MRI-AGCM3.2H model

Try et al. Progress in Earth and Planetary Science(2020) 7:34

Page 12 of 16

Fig. 10 Spatial distribution of inundation probability (upper row), difference between present and each future SST scenarios (middle row), and KS test (lower row) for MRI-AGCM3.2S model

Discussion
Benefits of high-resolution data

This study used two high-resolution models MRIAGCM3.2H and MRI-AGCM3.2S with spatial resolutionof 60 km and 20 km. To see benefits of fine spatial-scale,
two other coarse-resolution GCMs (MRI-CGCM3 and

MRI-ESM1) from the CMIP5 were examined to compare their performance without bias correction for 25year in the historical climate (1979–2003). The grid resolutions of MRI-CGCM3 and MRI-ESM1 are 125 km ×
125 km. Figure 13 showed the comparison of spatial distribution of annual average precipitation of GPCC and

Fig. 11 Spatial distribution of inundation duration (upper row), difference between present and future RCP scenarios (middle row), and K-S test
(lower row) for MRI-AGCM3.2H model

Try et al. Progress in Earth and Planetary Science(2020) 7:34

Page 13 of 16

Fig. 12 Spatial distribution of inundation duration (upper row), difference between present and future SST scenarios (middle row), and K-S test
(lower row) for MRI-AGCM3.2S modelFig. 13 Spatial distribution of annual precipitation of GPCC and four climate models (MRI-AGCM-3.2S, MRI-AGCM3.2H, MRI-CGCM3, and MRI-ESM1)

Try et al. Progress in Earth and Planetary Science(2020) 7:34

the four models over the MRB region. The two highresolution AGCMs (MRI-AGCM3.2H and MRIAGCM3.2S) used in this showed a similar distribution
while the two coarse resolution models (MRI-CGCM3
and MRI-ESM1) indicated large error, particularly in thearea close the coast. This was the advantage of using observed SST as boundary condition in AGCM; however,
GCMs were not able to consider this effect.
In addition, basin monthly precipitation was also compared between GPCC and four climate models withoutbias correction. Figure 14 showed the similarity of violin
shapes between GPCC and two high-resolution AGCMs(MRI-AGCM3.2H and MRI-AGCM3.2S). The meanvalues range between 126–131 mm for AGCMs and
119–113 mm for GCMs comparing to 126 mm of
GPCC. The MRI-CGCM3 and MRI-ESM1 models produced precipitation at more spread density (i.e., their
median is at very low value, and top height of violin isoverestimation) comparing to GPCC. Overall, two highresolution AGCMs used in this study performed better
than two GCMs in CMIP5 in term of spatial andmonthly precipitation in the MRB region.
Implementation of climate change impact

The results from this study clearly indicated that theriver discharge and flood inundation in the MRB weresignificantly affected by climate change impacts. The
simulation results of climate change revealed that floodinundation magnitude in the future in the LMB would
be severer than the present climate. The increasing ofannual precipitation (6.6–14.2%) could force to enlarge
the extreme high flow (Q5) at the LMB (increasing 13–
30% at Kratie) and lead to increase excessive inundation

Page 14 of 16

in the LMB up to 19–43% for inundation area and 24–
55% for inundation volume. Lauri et al. (2012) determined the change in discharge at Kratie from − 10.6 to+ 13.4% for A1B scenario (comparable to RCP6.0) andbetween − 6.9 and + 8.1% for B1 scenario (comparable
to RCP4.5) using five GCMs from baseline (1982–1992)
to projected period (2032–2042). Västilä et al. (2010)
projected annual maximum flooded area in the LMB
flood pulse for 2010–2049 by changing between − 3%
and 14% in for A2 emission scenario (comparable to
RCP8.5). Perera et al. (2017) found out the increasing
discharge volume of 25% at Kratie in RCP8.5 scenario.
Shrestha et al. (2016) analyzed the uncertainty sourcesof climate change on river flow in the Sekong, Sesan,and Srepok (3S) Rivers, one of the main tributary of the
MRB, using three GCMs (GISS, GFDL, and IPSL) andrevealed the results that peak flow was likely to increase
ranging from 54.1% for RCP2.6 to 78.9% for RCP8.5 forthe 2060s. The assessment results of climate change impacts on hydrological extreme flows in 11 sub-basins of
the Tonle Sap Lake by Oeurng et al. (2019) using three
GCM models (GFDL-CM3, GISS-E2-R-CC, and IPSLCM5A-MR) for three projected time horizons (2030s,
2060s, and 2090s) under RCP6.0 scenario revealed thatmost sub-basins of the Tonle Sap basin would face more
extreme drought than flood.
In addition to findings of Perera et al. (2017) who
studied flood inundation under SST patterns in the
LMB, this study found the significant increment of floodinundation area and volume on various projected futureclimate change including 4 RCP scenarios and 4 different SST patterns scenarios. More importantly, further
analysis of increasing changes in flood inundation

Fig. 14 Violin plot and boxplot of monthly basin precipitation of GPCC and four climate models. The red circle and black dots represent mean
and medianTry et al. Progress in Earth and Planetary Science(2020) 7:34

duration and probability was significantly determined inthis study while the flood peak time would be no substantial variation.

Limitations
However, the limitation of this study was assessing only
output from MRI-AGCM3.2H and MRI-AGCM3.2S.
Considering more high-resolution GCMs would provide
more reliability for future prediction of flood inundation.
Plus, due to the limited capacity of long-term and largescale simulation, the spatial resolution of the inundation
simulation in this study was taken 1.5′ (approx. 2.7 km);therefore, the finer resolution was able to provide moreaccurate results. Further, the water for urban water supply, irrigation, and power generation purposes was not
carried out in this study. The land-use map was takenfrom MODIS (year: 2000) considered as static from thepresent to the future. The rapid development would
affect the uncertainty in the prediction. The potential effect of land-use change should be considered in futurestudies in the MRB.
Conclusions
This paper presented the effects of climate change on
flow in the MRB and inundation in the LMB for thepresent (1979–2003) and the future period (2075–2099).
This study compared the extreme river flow, peak inundation area, peak inundation time and volume, floodprobability, and inundation duration. The analysis was
conducted based on bias corrected precipitation andevapotranspiration which were outputs from two highresolution atmospheric models (MRI-AGCM3.2H 60 km
and MRI-AGCM3.2S 20 km). The result indicated thatthe extreme river flow and extreme flood inundation will
be severer and higher magnitude at the end of the twentyfirst century for all future scenarios (RCP and SST). However, flood peak time was observed with no significant
variation. The area with long flood duration correspondedwith a high probability of flood inundation. This study
provided additional information about climate change impacts on flood inundation for further understanding andpreparing for climate change adaptation as well as flood
damage reduction strategies in the LMB.
Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s40645-020-00353-z.
Additional file 1: Supplement A. Simulated and observed discharge,
Supplement B. rain basin GPCC, Supplement C. rain basin AGCM20km,
Supplement D. rain basin AGCM60km.
Abbreviations
2-D LIE: Two-dimensional local inertial equation; AGCM: Atmospheric generalcirculation model; APHRODITE: Asian Precipitation–Highly-Resolved

Page 15 of 16

Observational Data Integration Towards Evaluation; CMIP5: Coupled Model
Inter-comparison Project Phase 5; DEM: Digital elevation model; DOY: Daysof the year; dP: Difference of probability; ET: Evapotranspiration;
GFDL: Geophysical Fluid Dynamics Laboratory; GISS: The Goddard Institutefor Space Studies; GPCC: Global Precipitation Climatology Centre;
GSMaP: Global Satellite Mapping of Precipitation; HFA: Future data for highresolution AGCM; HPA: Present data for high-resolution AGCM; HR: Hit ratio;
IPCC AR5: The Fifth Assessment Report of the Intergovernmental Panel on
Climate Change; IPSL: Institut Pierre Simon Laplace; JRA-55: Japanese 55-year
Reanalysis dataset; K-S: Kolmogorov-Smirnov; LMB: Lower Mekong Basin;
MERIT: Multi-error-removed-improved-terrain; MODIS: Moderate-resolution
imaging spectroradiometer; MRB: Mekong River Basin; MRC: Mekong River
Commission; MRI: Meteorological Research Institute; NASA: National
Aeronautics and Space Administration; NSE: Nash-Sutcliffe efficiency; PERSIA
NN-CDR: The Precipitation Estimation from Remotely Sensed Information
using Artificial Neural Networks-Climate Data Record; RCP: Representative
Concentration Pathways; RMSE: Root mean square error; RRI: Rainfall-runoffinundation; SFA: Future data for super high-resolution AGCM; SPA: Present
data for super high-resolution AGCM; SST: Sea surface temperature;
SWAT: Soil and Water Assessment Tool; TR: True ratio; TRMM: Tropical Rainfall
Measuring Mission
Acknowledgements
We thank Prof. Günter Meon for his advice and comment to improve thispaper. Try S. gratefully acknowledges the travel grant from AIDA Program(Kyoto University) to attend the 2nd World Congress on Climate Change inBerlin and short research stay at Technische Universitat Braunschweig,
Germany.
Authors’ contributions
Try S. proposed the topic and drafted the manuscript. Sayama T. supported
Try S. in the modeling process. Tanaka S. and Tanaka K. revised andsupervised the methodology. Oeurng C. supported the collection ofobserved hydrological data. GL supported discussion the main idea andrevised manuscript. All authors revised and approved the final manuscript.
Funding
This work was supported by AUN/NEED-Net (JICA) for the first author duringdoctoral scholarship program in Japan.
Availability of data and materials
The datasets supporting the conclusions of this article are included within
the article and its supplement files.
Competing interests
The authors declare that they have no competing interest.
Author details
1
Graduate School of Engineering, Kyoto University, Gokasho, Uji 611-0011,
Japan. 2Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji
611-0011, Japan. 3Department of Construction & Disaster Prevention
Engineering, Kyungpook National University, Gyeongsangdaero 2559, Sangju
742-711, Republic of Korea. 4Faculty of Hydrology and Water Resource
Engineering, Institute of Technology of Cambodia, Russian Conf. Blvd,
Phnom Penh 12156, Cambodia.
Received: 28 December 2019 Accepted: 19 July 2020

References
Arias ME, Cochrane TA, Norton D, Killeen TJ, Khon P (2013) The flood pulse as theunderlying driver of vegetation in the largest wetland and fishery of the
Mekong Basin. Ambio. 42(7):864–876
Arias ME, Cochrane TA, Piman T, Kummu M, Caruso BS, Killeen TJ (2012)
Quantifying changes in flooding and habitats in the Tonle Sap Lake
(Cambodia) caused by water infrastructure development and climate change
in the Mekong Basin. J Environ Manag 112:53–66
Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic
modeling and assessment part I: model development. JAWRA Journal of theAmerican Water Resources Association 34(1):73–89. https://doi.org/10.1111/j.
1752-1688.1998.tb05961.x

Try et al. Progress in Earth and Planetary Science(2020) 7:34

Bates BC, Kundzewicz ZW, Wu S, Palutikof JP (2008) Climate change and water.
IPCC technical paper IV. Intergovernmental Panel on Climate Change,
GenevaBeyene T, Lettenmaier DP, Kabat P (2010) Hydrologic impacts of climate changeon the Nile River basin: implications of the 2007 IPCC scenarios. Clim Chang
100(3-4):433–461. https://doi.org/10.1007/s10584-009-9693-0
Friedl MA, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang X(2010) MODIS collection 5 global land cover: algorithm refinements andcharacterization of new datasets. Remote Sens Environ 114(1):168–182.
https://doi.org/10.1016/j.rse.2009.08.016
Intergovernmental Panel on Climate Change (IPCC) (2014) Sythesis report.
Contribution of Working Groups I, II and III to the Fifth Assessment Report of
the IPCC, Geneva, Switzerland
Kitoh A, Endo H (2016) Changes in precipitation extremes projected by a 20-km
mesh global atmospheric model. Weather Climate Extremes 11:41–52.
https://doi.org/10.1016/j.wace.2015.09.001
Kobayashi S, Ota Y, Harada Y, Ebita A, Moriya M, Onoda H, Onogi K, Kamahori H,
Kobayashi C, Endo H, Miyaoka K, Takahashi K (2015) The JRA-55 reanalysis:
general specifications and basic characteristics. J Meteor Soc Japan 93:5–48.
https://doi.org/10.2151/jmsj.2015-001
Koponen J, Lauri H, Veijalainen N, Sarkkula J (2010). HBV and IWRM watershed
modelling user guide. MRC Information and Knowledge Management
Programme, DMS—Detailed Modelling Support for the MRC Project.
Kummu M, Sarkkula J, Koponen J, Nikula J (2006) Ecosystem management of the
Tonle Sap Lake: an integrated modelling approach. Int J Water Resources
Development 22(3):497–519. https://doi.org/10.1080/07900620500482915
Lamberts D, Koponen J (2008) Flood pulse alterations and productivity of the
Tonle Sap ecosystem: a model for impact assessment. AMBIO: A Journal of
the Human Environment 37(3):178–184. https://doi.org/10.1579/00447447(2008)37[178:FPAAPO]2.0.CO;2
Lauri H, Moel HD, Ward P, Räsänen T, Keskinen M, Kummu M (2012) Futurechanges in Mekong River hydrology: impact of climate change and reservoir
operation on discharge. Hydrol Earth Syst Sci 16:4603–4619. https://doi.org/
10.5194/hess-16-4603-2012
Li G, Zhang X, Zwiers F, Wen QH (2012) Quantification of uncertainty in highresolution temperature scenarios for North America. J Clim 25(9):3373–3389.
https://doi.org/10.1175/JCLI-D-11-00217.1
Mizuta R, Arakawa O, Ose T, Kusunoki S, Endo H, Kitoh A (2014) Classification of
CMIP5 future climate responses by the tropical sea surface temperaturechanges. SOLA. 10:167–171. https://doi.org/10.2151/sola.2014-035
Mizuta R, Yoshimura H, Murakami H, Matsueda M, Endo H, Ose T, Kamiguchi K,
Hosaka M, Sugi M, Yukimoto S, Kusunoki S, Kitoh A (2012) Climate
simulations using MRI-AGCM3. 2 with 20-km grid. J Meteorol Soc Jpn 90:
233–258. https://doi.org/10.2151/jmsj.2012-A12
MRC (2005) Overview of the hydrology of the Mekong Basin vol. 82. MekongRiver Commission, Vientiane, Lao PDR
MRC (2010) State of the basin report 2010. Mekong River Commission, Vientiane,
Lao PDR
Nigro J, Slayback D, Policelli F, Brakenridge GR (2014). NASA/DFO MODIS nearreal-time (NRT) global flood mapping product evaluation of flood andpermanent water detection. Evaluation, Greenbelt, MD.
Oeurng C, Cochrane TA, Arias ME, Shrestha B, Piman T (2016) Assessment ofchanges in riverine nitrate in the Sesan, Srepok and Sekong tributaries of thelower Mekong River basin. J Hydrol Region Stud 8:95–111. https://doi.org/10.
1016/j.ejrh.2016.07.004
Oeurng C, Cochrane TA, Chung S, Kondolf MG, Piman T, Arias ME (2019)
Assessing climate change impacts on river flows in the Tonle Sap Lake Basin,
Cambodia. Water 11(3):618. https://doi.org/10.3390/w11030618
Perera E, Sayama T, Magome J, Hasegawa A, Iwami Y (2017) RCP8. 5-based future
flood hazard analysis for the lower Mekong river basin. Hydrology 4(4):55.
https://doi.org/10.3390/hydrology4040055
Sayama T, Ozawa G, Kawakami T, Nabesaka S, Fukami K (2012) Rainfall–runoff–
inundation analysis of the 2010 Pakistan flood in the Kabul River basin.
Hydrol Sci J 57(2):298–312. https://doi.org/10.1080/02626667.2011.644245
Sayama T, Tatebe Y, Iwami Y, Tanaka S (2015) Hydrologic sensitivity of flood
runoff and inundation: 2011 Thailand floods in the Chao Phraya River basin.
Nat Hazards Earth Syst Sci 15:1617–1630. https://doi.org/10.5194/nhess-151617-2015
Shrestha B, Cochrane TA, Caruso BS, Arias ME, Piman T (2016) Uncertainty in flow
and sediment projections due to future climate scenarios for the 3S Rivers inPage 16 of 16

the Mekong Basin. J Hydrol 540:1088–1104. https://doi.org/10.1016/j.jhydrol.
2016.07.019
Tanaka T, Yoshioka H, Siev S, Fujii H, Fujihara Y, Hoshikawa K, Ly S, Yoshimura C(2018) An integrated hydrological-hydraulic model for simulating surface
water flows of a shallow Lake surrounded by large floodplains. Water 10(9):1213. https://doi.org/10.3390/w10091213
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and theexperiment design. Bull Am Meteorol Soc 93(4):485–498
Try S, Lee G, Yu W, Oeurng C (2019) Delineation of flood-prone areas using
geomorphological approach in the Mekong River basin. Quat Int 503:79–86.
https://doi.org/10.1016/j.quaint.2018.06.026
Try S, Lee G, Yu W, Oeurng C, Jang C (2018) Large-scale flood-inundation
modeling in the Mekong River basin. J Hydrol Eng 23(7):05018011. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001664
Try S, Tanaka S, Tanaka K, Sayama T, Oeurng C, Uk S, Takara K, Hu M, Han D(2020) Comparison of various gridded precipitation datasets for rainfall-runoff
and inundation modeling in the Mekong River basin. PLoS One 15(1).
https://doi.org/10.1371/journal.pone.0226814
Uk S, Yoshimura C, Siev S, Try S, Yang H, Oeurng C, Li S, Hul S (2018) Tonle Sap
Lake: current status and important research directions for environmental
management. Lakes Reserv Res Manag 23(3):177–189. https://doi.org/10.
1111/lre.12222
Västilä K, Kummu M, Sangmanee C, Chinvanno S (2010) Modelling climatechange impacts on the flood pulse in the lower Mekong floodplains. J Water
Climate Change 1(1):67–86. https://doi.org/10.2166/wcc.2010.008
Wang S, Jiao S, Xin H (2013) Spatio-temporal characteristics of temperature andprecipitation in Sichuan Province, southwestern China, 1960–2009. Quat Int
286:103–115. https://doi.org/10.1016/j.quaint.2012.04.030
Wu F, Wang X, Cai Y, Li C (2016) Spatiotemporal analysis of precipitation trends
under climate change in the upper reach of Mekong River basin. Quat Int
392:137–146. https://doi.org/10.1016/j.quaint.2013.05.049
Yamazaki D, Ikeshima D, Tawatari R, Yamaguchi T, O'Loughlin F, Neal JC,
Sampson CC, Kanae S, Bates PD (2017) A high accuracy map of global terrain
elevations. Geophys Res Lett 44:5844–5853. https://doi.org/10.1002/
2017GL072874

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.