Ly et al.
Progress in Earth and Planetary Science(2023) 10:55
https://doi.org/10.1186/s40645-023-00586-8

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open AccessIntegrated impact assessment of climatechange and hydropower operation
on streamflow and inundation in the lower
Mekong Basin
Steven Ly1* , Takahiro Sayama1 and Sophal Try1,2

Abstract
Water resources are key to economic development of the Mekong River Basin, but are threatened by climate change
and affected by hydropower development. Knowledge of these drivers’ integrated impact on future hydrologicalalterations is limited, especially with respect to flood inundation in the lower basin. This study assesses streamflow
and flood extent alterations by reservoir operations and climate change using the latest climate projections. A distributed hydrologic model is used to generate discharge and flood extent. Our findings indicate substantial changes
in seasonal and annual peak discharge due to reservoir operations. Under the future hydropower scenario, the discharge at Kratie will change by + 28% ( − 10%) during the dry (wet) season. While the effects of hydropower operations vary by season, climate change tends to increase river discharge overall. Under the high-emission scenario,
the wet seasonal flow at Kratie will increase by + 7% in the near-future (2026–2050), but change by -5% under integrated impact of climate change and reservoir operations. In the far-future, the wet seasonal flow at Kratie under climate change only (integrated impact) will increase by + 33% (+ 19%). Although climate change is the dominant driverof flow alterations, hydropower development is critical for reducing discharge and flood magnitude. Nonparametricstatistical testing shows significant changes in the inundated area by up to + 37% during the projected periods.
Keywords Hydropower, Lower Mekong Basin, River discharge, RRI model
1 Introduction
The Mekong River Basin (MRB), which originates fromthe Tibetan Plateau, is the largest transboundary river inSoutheast Asia. With a length of approximately 4,800 km,
the Mekong River travels from its source in China andflows through Myanmar, Laos, Thailand, and Cambodia,*Correspondence:
Steven Ly
steven.ly70@gmail.com
1
Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji,
Kyoto 611‑0011, Japan
2
Faculty of Hydrology and Water Resources Engineering, Instituteof Technology of Cambodia, Russian Conf. Blvd., Phnom Penh 120404,
Cambodia

before finally reaching its delta and discharging into theSouth China Sea in Vietnam (MRC 2005). Commonly
known as the most fertile ecosystem, more than 70 million inhabitants rely on this river system through fisheryand agriculture (Varis et al. 2012). The water resources ofthe MRB not only provide food and water to its dependents but also contribute significantly to the region’s economic development. During the wet monsoon season,
the unique flow reversal from the Mekong River to the
Tonle Sap floodplain creates the most productive ecosystem, delivering fish and other biodiversity, sediments,
and nutrients to Tonle Sap Lake (Arias et al. 2012). In
addition, substantial economic development for member countries comes from hydropower development, as
explained in the Strategic Plan and Basin Development© The Author(s) 2023. 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 theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence 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 thislicence, visit http://creativecommons.org/licenses/by/4.0/.

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

of the Mekong River Commission (MRC) (2019a). The
potential benefits generated from the hydropower sector
alone are almost equivalent to the three major sectors offisheries, agriculture, and navigation. In addition to electricity supply, hydropower developments could potentially expand agricultural activities during the dry season,
function as flood protection during the high-flow season,
attract foreign investment, and improve navigation systems (MRC 2019b).
Nonetheless, climate change, along with massive
developments in hydropower at a rapid rate, has drastically changed the flow regime of the MRB. According tovarious studies, climate change is expected to alter temperature and rainfall patterns throughout the region,
jeopardizing the hydrology of the basin. Using multiple
general circulation models (GCM) from the Coupled
Model Intercomparison Project Phase 5 (CMIP5) climate
projections, Hoang et al. (2016) analyzed the MekongRiver flow under a changing climate. Seasonal and annual
river discharge was found to increase (between + 5%
and + 16%), but the degree of changes depended on
location. In addition, they suggested that the selectionof GCM, as well as different versions of climate experiments, influenced the results of the flow changes. Try
et al. (2020b) used high-resolution atmospheric general circulation model (AGCM) outputs to estimate theriver flow alterations and hydrological extremes in the
MRB due to changing climate. Their study estimateda + 14% increase in annual precipitation and high flow
(Q5) increased up to + 30% at Kratie in the downstreamof MRB under the high-emission scenario of representative concentration pathway (RCP8.5). Moreover, underthe 4 K increasing scenario from the database for Policy
Decision-Making for Future Climate Change (d4PDF),
Try et al. (2020a) found that increasing precipitation
contributed to the intensity of future flood events, resulting in an increase in the flooding area and volume bynearly + 40%. In addition to climate change, the MRB’s
water resources have been strained by the hydropower
construction. Despite its benefits, such rapid developments are expected to impact water resource management and the seasonality of the flow regime. Manyhydropower projects are being constructed and proposed
throughout the basin, including 11 hydropower projects
along the Lower Mekong Basin (LMB). Most will be completed in the next 10–20 years (Hecht et al. 2019). Several studies (Piman et al. 2013a; Liu et al. 2016; Räsänen
et al. 2017; Li et al. 2017; Do et al. 2020) have studied theeffects of hydropower construction in the MRB from various perspectives. The degree of impact differs from one
study to another based on the study periods, dam scenarios, and reservoir operation rules. However, they shared
the same conclusion that reservoir operations alter riverPage 2 of 20

flow by increasing dry seasonal flow and decreasing wet
seasonal flow. Moreover, hydropower can reduce peakflow and delay timing by up to one month (Pokhrel et al.
2018; Shin et al. 2020).
Traditionally, hydropower development and climatechange have been analyzed separately; however, acknowledging their interdependence is crucial for effective decision-making and sustainable water resource development(Wang et al. 2017; Hoang et al. 2019; Yun et al. 2020).
Unfortunately, the fundamental aspect of the integrated
impact stemming from these drivers is often overlooked
and inadequately studied, especially the impacts on floodinundation. Some studies prefer to focus on limited
impacts of hydropower in only certain regions for casestudies, such as the effects of hydropower constructionin the Sesan, Sre Pok, and Sekong (3S) river basins (Wildand Loucks 2014; Arias et al. 2014; Piman et al. 2016) andthe effect of climate change and reservoir operations in
the Upper Mekong Basin (UMB) (Räsänen et al. 2017;
Han et al. 2019; Zhong et al. 2021). Motivated by thisknowledge gap, this study aims to investigate the combined effects of changing climate and hydropower operations (both existing and future hydropower projects) onflow alterations and flood inundation in the MRB using
the Coupled Model Intercomparison Project Phase 6(CMIP6) projections. This integrated impact assessment
not only enhances our comprehension of hydrologicalchanges but also provides valuable insights into the intricate interaction between climate change and reservoir
operation in the MRB. This study adopted a distributed
rainfall–runoff–inundation (RRI) model coupled with asimple storage model (for reservoir operations) to simulate streamflow and inundation simultaneously for present and future climates.

2 Materials and methods
2.1 Study area

The MRB covers a catchment area of 795,000 ­km2 withan annual mean discharge of 14,500 ­m3/s (Fig. 1). The
climate is governed by the Asian Southwest monsoon,
which brings two distinct wet and dry seasons (MRC2005). The MRB basin consists of two major parts: the
UMB in China (so-called Lancang Jiang) and LMB. The
MRB has unique and complex hydrological, climatic, andphysiographic features. Flood inundation is one of themost important characteristics of the basin because it
creates remarkable biodiversity, particularly in the Tonle
Sap floodplain and the Mekong Delta (Lamberts 2006;
Arias et al. 2012; Hoang et al. 2019; Try et al. 2020b).
Its extensive wetlands and floodplains provide the mostinland fisheries of 2.6 million tons annually, and otheranimals are valued at up to 7 million USD (Hortle 2007).

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

Page 3 of 20

Fig. 1 Location of the Mekong River Basin

2.2 RRI model simulation

The study used the hydrologic RRI model to generatedischarge and flood inundation. The two-dimensional
distributed RRI model can simultaneously simulate
runoff and flood inundation (Sayama et al. 2012, 2015).
The model deals with slopes and river channels separately. The hydrological process in the slope was determined by a two-dimensional diffusive wave model, while
a one-dimensional model was adopted in the channel
flow. The flow interaction between the two is estimatedbased on different overflowing formulae, depending onwater-level and levee-height conditions. Moreover, theadoption of diffusive wave models enables considerationof water exchange interactions between grid cells in all
directions (i.e., backwater effect). The adaptive time step
Runge–Kutta algorithm was adopted to solve the twodimensional equations. To prevent simulation instability,
the algorithm continues monitoring estimated errors andadjusting simulation time steps until errors were minimized to an appropriate level. A time step of 600 s was
assigned for slope-river interactions, whereas the initial time step for river calculation was 60 s. To conduct
the long-term simulation, evapotranspiration was taken
into account. The RRI model was calibrated from 2000
to 2003 and validated from 2004 to 2007 for the entire
MRB in a previous study by Try et al. (2020c). The Shuffled Complex Evolution (SCE-UA) global optimization
algorithm, developed by the University of Arizona, wasintegrated into the RRI model to calibrate the sensitive
parameters (Duan et al. 1994). This calibration process
specifically targeted five key parameters: river Manning’s coefficient, soil surface porosity, lateral hydraulicconductivity, unsaturated porosity, and coefficient forunsaturated hydraulic conductivity. Utilizing the SCEUA algorithm, the optimization model iterated 500 times
to identify the optimal values for these parameters within
their designated range of low and high parameter values.
Subsequently, the RRI model adopted these optimized
parameters to conduct a series of 500 simulations. Before
being applied in the long-term simulation of this study,
particularly for climate change studies, the RRI model
was re-validated from 1982 to 2012. Table 1 presents the
RRI model configuration used in this study.
In this study, the simulations were conducted in two
steps. Initially, the model was prepared to perform anentire MRB simulation and to assess river discharge at
a 2.5’ resolution (≈ 5 km). Subsequently, the discharge
simulated by the first model served as the boundary
condition for the finer-resolution model, which was
configured with a higher resolution of 1.5’ (≈ 2.7 km).
This finer-resolution model was employed to accurately

Table 1 RRI model configuration used in the study
Parameters

ForestAgriculture

Floodplain

Manning’s roughness on slope cell

ns

–

0.4

0.15

0.015

Soil surface porosity

ϕa

–

0.6

0.6

0.6

Vertical hydraulic conductivity

Kv

cm/s

–

–

0.06

Suction at the wetting front

Sf

m

–

–

0.273

Lateral hydraulic conductivity

Ka

m/s

0.25

0.25

–

Unsaturated porosity

ϕm

–

0.6

0.6

–

Coefficient of unsaturated hydraulic conductivity

β

–

4.0

4.0

–

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

Page 4 of 20

simulate flood inundations in the LMB (i.e., Cambodia
and Vietnam floodplain). For the climate change modeling, it required approximately 7 days to complete asimulation for the entire MRB and roughly 12 days tocomplete a flood inundation simulation in the LMB
on a computer (CPU: Intel Xeon E5-2694, 2.1 GHz, 2
core/4 threats, RAM: 7 GB) after it was parallelizedwith OpenMP. The model input data included precipitation, topography, land use, evapotranspiration, andriver geometry (Table 2). Precipitation data from the
Global Precipitation Climatology Centre (GPCC) wereused for simulations (Ziese et al. 2018). It was a reanalysis product based on rain gauge data from 67,200 stations around the world. Topographic data were received
from the Multi-Error-Removed-Improved-Terrain
digital elevation model (Yamazaki et al. 2017). Landuse data were derived from the Moderate Resolution
Imaging Spectroradiometer dataset (Friedl et al. 2010).
The evapotranspiration was taken from the Japanese
55-year Reanalysis database (Kobayashi et al. 2015).
River geometry, river depth D [m] and river width W[m], was approximately estimated from the following
equations (Try et al. 2018):
0.7491

D = 0.0015 × A(1)

W = 0.0520 × A0.7596(2)

The model performance was evaluated by four indicators, namely NSE, PBIAS, ­R2, and RSR. NSE quantifies the proportion of residual variance in the simulated
data relative to the observed data, indicating how
closely the hydrograph fits the actual data (Nash andSutcliffe 1970). NSE = 1 indicates the perfect simulation
result. PBIAS metric provides insight into whether thesimulated data tend to be biased toward overestimationor underestimation when compared with the observed
data (Gupta et al. 1999). PBIAS = 0% is the perfectprediction, while positive and negative values signify
underestimation and overestimation bias, respectively.
­R2 quantifies the level of collinearity between the simulation and observation, with perfect collinearity corresponding to a value of 1 (Wright 1921). RSR is the error
index that includes the benefits of error index statistics

Table 2 Summary of data adopted in the study
Parameter

Resolution

SourcePrecipitation

1° (≈ 120 km)

Ziese et al. (2018)

Topography

3 arc sec (≈ 90 m)

Yamazaki et al. (2017)Land use

500 m

Friedl et al. (2010)

Evapotranspiration

0.5625° (≈ 55 km)

Kobayashi et al. (2015)

and a scaling/normalization factor, where RSR = 0
denotes perfect model simulation (Singh et al. 2005).

NSE = 1 −PBIAS =



(Qsim − Qobs )2
Qobs − Qobs

2

(Qobs − Qsim ) × 100

Qobs


2
Qsim − Qsim Qobs − Qobs
R = 
2  
2
Qobs − Qobs
Qsim − Qsim
2

(3)

(4)

 

(5)



(6)

(Qobs − Qsim )2
RSR =  2Qobs − Qobs

where Qobs and Qsim are the flow observation and simulation at time t, and Qobs and Qsim are the mean of flow
observation and simulation, respectively.
2.3 Hydropower development scenarios

The hydropower database, obtained from the MRC(MRC 2009a, b) and the Asian Development Bank (ADB
2004), includes existing/proposed hydropower in the
LMB and Chinese dams in the UMB. Figure 2 illustrates
the geographical distribution and active storage capacity of hydropower within the MRB. The majority of thesefacilities have a storage capacity of less than 0.5 ­km3, with
Nuozhadu hydropower having the largest capacity of 12.3
­km3. Taking into account existing, under-construction,and proposed hydropower projects, the basin’s total storage capacity reaches 107.7 ­km3, equivalent to about 23%of its annual discharge volume. This estimation falls wellwithin the range of early projections, varying from 17 to
23% (Hoanh et al. 2010; Kummu et al. 2010; MRC 2011;
Hecht et al. 2019). Additionally, the specific total storage
capacity in the upstream area of Chiang Saen, Luang Prabang, Pakse, and Kratie hydrological station corresponds
to 6%, 8%, 15%, and 22% of the MRB annual discharge
volume, respectively. These ratios could serve as a valuable metric to quantify the potential anthropogenic disturbance on terrestrial water cycle resulting from reservoir
construction (Vörösmarty et al. 1997). Two hydropower
development scenarios were prepared for hydrologicalanalysis in this study. The present development scenario
consisted of 98 hydropower projects in the MRB. The
future development scenario included 126 hydropower
projects, including 23 mainstream dams. Since detailed
operation rules were not available in the database, weestimated the general optimized patterns of the dam reservoir outflow for each dam using a simple storage modelLy et al. Progress in Earth and Planetary Science(2023) 10:55

Page 5 of 20hydropower generation considering the local flow
regimes. The simple storage model was then integrated
into the RRI model to perform hydrological simulations.
See Ly et al. (2021) for further details of the simple storage model adopted in this study. General information of
the hydropower project, including its name, purpose, andcapacity, is provided in Additional file 1: Table S1.
2.4 Climate change scenarios

Fig. 2 Location and storage capacity of hydropower in the MekongRiver Basin

General circulation models have been widely developed
for climate change studies over time. The newly developed GCMs from CMIP6 promised some improvements
and less bias than previous models from CMIP5, particularly in the historical simulations (Eyring et al. 2016;
Try et al. 2022). The outputs of eight GCMs from CMIP6
were selected for this study (Table 3). Two Share Socioeconomic Pathways (SSP) scenarios were adopted: SSP2-4.5(middle of the road) and SSP5-8.5 (fossil-fueled development). The present period represented 1980–2014, andthe future period (2026–2100) was separated into three
25-year intervals: near-future, mid-future, and far-future.
GPCC precipitation was shown to be the most suitable
and reliable precipitation product for long-term hydrological modeling in the Mekong region (Try et al. 2020c).
Thus, it was chosen as the benchmark rainfall for correcting bias in selected GCMs using the following linear scaling method:gcm

BC= Pdaily ×
Pdailyproposed by Ly et al. (2021). Similar approach has also
been adopted in various studies where reservoir operation rule was not available (Räsänen et al. 2012; Lauriet al. 2012; Hoang et al. 2019). The simple storage model
estimated the optimum reservoir outflow pattern foreach dam using basic information such as river inflow,
reservoir storage, and turbine flow capacity. The objective function of the model was to maximize production
outflow (i.e., outflow through turbines), thus maximizing

obs
Pmon
gcm

Pmon

(7)

BC
where Pdailyis the daily bias-corrected GCM precipitagcm
obs is thetion, Pdaily is the daily GCM precipitation, Pmon
gcm
average monthly GPCC precipitation, and Pmon is theaverage monthly GCM precipitation.

2.5 Statistical Test
2.5.1 Nonparametric Kolmogorov–Smirnov test

For better representations of the variation in flood
extent during the study periods, a statistical test

Table 3 List of the GCMs adopted in the study
Model name

Developing research institute

Resolution

ACCESS-CM2

Australian Community Climate and Earth System Simulator Coupled Model version 2.0

1.9° × 1.3°

CNRM-CM6-1

Centre National de Recherches Météorologiques Coupled Model Sixth Generation

1.4° × 1.4°

GFDL-CM4

Geophysical Fluid Dynamics Laboratory Coupled Model version 4.0

1.3° × 1.0°

IPSL-CM6A-LR

Institut Pierre-Simon Laplace Climate Model for Low Resolution

2.5° × 1.3°

MIROC6The Sixth Version of the Model for Interdisciplinary Research on Climate

1.4° × 1.4°

MPI-ESM1-2-LR

Max Planck Institute for Meteorology Earth System Model for Low Resolution version 1.2

1.9° × 1.9°

MRI-ESM2-0

The Meteorological Research Institute Earth System Model version 2.0

1.1° × 1.1°

NorESM2-MM

Norwegian Earth System Model version 2.0 for Medium Resolution of Both Atmosphere–Land
and Ocean–Sea Ice

1.3° × 0.9°

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

Page 6 of 20

Kolmogorov–Smirnov (K–S) was used (Massey 1951).
The null hypothesis H0 states that there is no significant difference in the cumulative distribution function
between the two sample (i.e., flood extent in the present and future). The test is defined using the following
equation:

Dn,m = sup |Fn (x) − Fm (x)|(8)

xThe null hypothesis H0 is rejected when the likelihoodof the two sample’s different distributions exceeds a significance level (α):

n+m
(9)
Dn,m > c(α)
nm


S−1

 √var(S) if S > 0
if S = 0Z= 0 √S+1 if S < 0var(S)(14)

The positive value of Z indicates increasing trends,
while the negative value represents decreasing trends.
The null hypothesis H0 states that there are no significant
trends in the time series. At a significance level (α), H0
is rejected when |Z| > Z1−α/2. According to the standard normal distribution table, the value of Z1−α/2 corresponds to 2.576 and 1.96 at a significance level of 1% and5%, respectively.
2.5.3 Sen’s slope estimator

where in Eq. (8), empirical distribution functions aredenoted by Fn (x) and Fm (x) and supremum function isdenoted by sup, and in both Eqs. (8) and (9), sample sizes
are denoted by n and m. The value of c(α) is 1.36 at a significance level of 5%.

The magnitude of the trend (change per unit time) in thetime series was estimated using Sen’s slope procedure(Sen 1968). The trend magnitude is calculated as follows:
xj − xi,j > iβ = Median
(15)
j−i

2.5.2 Mann–Kendall Test

where xi and xj are the data values at time j and i,
respectively.

The nonparametric, Mann–Kendall, was employed to
identify trends in observed discharge used during thevalidation period. The Mann–Kendall test (Mann 1945;
Kendall 1975) has been widely used to detect monotonic
trends of variables in hydro-climatic time series such as
streamflow, precipitation, and temperature. The Mann–
Kendall test statistics is calculated as follows:

S=

n−1 
n


i=1 j=i+1


sgn xj − xi(10)where n is the number of data points, xi and xj are thedata valuesin time series i and j j > i , respectively, andsgn xj − xi is the sign function as: 1 if x > 0
sgn(x) = 0 if x = 0(11)
 −1 if x < 0When n ≥ 10, the standard normal statistics S isapproximately normally distributed with the following
mean and variance:(12)

E(S) = 0
var(S) =n(n − 1)(2n + 5) −p

j=1 tj18



2.5.4 Pettitt’s test

Pettitt’s test (1979) adopted to identify the significant
change-point in the annual streamflow time series during
the validation period. The nonparametric test statistics
can be described as follows:KT = maxUt,T (16)

Ut,T =

i=1 j=t+1


sgn xj − xi(17)where xi and xj are the data values at time j and i,
respectively.
The change-point of the time series occurs at KT , giventhat the statistics is significant. The null hypothesis H0 of
no change-point is rejected when p is smaller than 0.01
and 0.05 at a significance level of 1% and 5%, respectively.
Thus, the associated probability is given as follows:


−6KT2
p = 2 exp
(18)
T3 + T2tj − 1 2tj + 5 3 Results(13)
where p is the number of the tied groups in the data set
and tj is the number of data points in the jth-tied group.
The standard test statistics Z is computed as follows:

t 
T


3.1 Performance of model simulation

Four performance indexes, NSE, PBIAS, ­R2, and RSR,
were calculated at the four gauging stations along themain river from upstream to downstream: Chiang Saen,
Luang Prabang, Pakse, and Kratie during the validation

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

period. Figure 3 shows the observed and simulated daily
discharge during the validation at the selected gauging stations. The observation and simulation of monthly
discharge were also compared during the validation
period since this study analyzed the flow alterations
at monthly and seasonal scales (Fig. 4). Kratie had thehighest model performance in both daily (monthly) discharge with NSE = 0.88(0.93), PBIAS = −3.70% (−3.70%),

Page 7 of 20

R2 = 0.88(0.93), and RSR = 0.35(0.27) during the validation (Table 4). The model performance index of monthly
discharge at the most upstream station Chiang Saen was
NSE = 0.59, PBIAS = −32.9%, R2 = 0.81, and RSR = 0.64.
The four index values of monthly discharge at Luang Prabang were 0.72, −25.2%, 0.82, and 0.53. For the station
located in the middle of the LMB at Pakse, the evaluation indicators of monthly discharge were as high as

Fig. 3 Observed and simulated daily discharge during the validation period (1982–2012) at Chiang Saen, Luang Prabang, Pakse, and KratieLy et al. Progress in Earth and Planetary Science(2023) 10:55

Page 8 of 20

Fig. 4 Observed and simulated monthly discharge during the validation period (1982–2012) at Chiang Saen, Luang Prabang, Pakse, and Kratie

Kratie with NSE = 0.91, PBIAS = −7.50%, R2 = 0.91, and
RSR = 0.30. The underestimations observed in low flow
and peak flow at the upstream stations (Chiang Saen andLuang Prabang) can be attributed to various factors. One
significant contributing factor was the unaccounted flow
regulation caused by the upstream hydropower, as discussed by Hoang et al. (2016) and Räsänen et al. (2012).
Multiple studies (Lauri et al. 2012; Sridhar et al. 2019; Tryet al. 2022), utilizing different hydrological models such
as SWAT and VMod, have also reported similar underestimations for these upstream stations, further supporting aforementioned explanation. Another factor was thelower accuracy of the GPCC precipitation data, owing
to the orographic effects and a relatively sparse distribution of rain gauge in this region. Moreover, the exclusionof groundwater dynamics in our simulations may have

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

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NSE

PBIAS
(%)

R2

RSR

Daily

0.52

−32.900.70

0.69Monthly

0.59−32.90

0.81

0.64

Daily

0.65

−25.300.74

0.59

Monthly

0.72−25.20

0.82

0.53

Daily

0.86

−7.50

0.87

0.38

Monthly

0.91

−7.50

0.91

0.30

on discharge simulations in the study area. The monthly
pattern of GPCC precipitation served as a reference forbias correcting the selected GCMs. The objective of thismethod was to align the seasonality of selected GCMs
with that of GPCC data while preserving the long-term
trend of the original GCMs. Figure 5 illustrates the performance of simulated daily discharge before and after
bias correction. Prior to bias correction, substantial variation was observed among different GCMs in their simulated discharge. However, following bias correction, thesevariations were significantly reduced, resulting in a closeralignment with the simulated discharge derived from
GPCC data.

Daily

0.88

−3.70

0.88

0.35

3.3 Impacts of hydropower on river discharge

Monthly

0.93

−3.70

0.93

0.27

The impacts of hydropower were assessed by the RRI
model using GPCC precipitation from 1982 to 2016 with
two hydropower development scenarios. Two hydrological stations along the MRB mainstream, Chiang Saen andKratie, were selected to analyze the flow changes. Due to
its location at the UMB outlet, the streamflow analysis
at Chiang Saen reflected the direct effect of hydropower
operations and hydrological changes in the upper MRB.
On the other hand, the analysis of streamflow at Kratie
provided insight into the general hydrological patternsof the MRB, as well as flood alteration in the LMB, witha particular focus on the Cambodian floodplain and theTonle Sap Lake, given that this station monitored allhydropower development activities in the MRB. The analysis at both stations provided useful information on theoverall water resources and flood conditions in the MRB,
which was beneficial for policymakers and hydropower
operators. Figure 6 shows the relative changes in monthly
average discharge at Chiang Saen and Kratie over thelast three decades under present and future hydropower

Table 4 Model performance of river discharge during thevalidation period (1982–2012)
Station
Chiang Saen

Luang Prabang

Pakse

Kratieplayed a contributory role in these underestimations.
Despite some underestimations at the upstream station, the results generally indicated favorable agreement
between the simulated and observed discharge, particularly in the downstream region. Thus, flood simulations in the LMB under climate change and hydropower
operations were further assessed by the RRI model. For
the upper Mekong area, we mostly looked at flow alterations at monthly and seasonal scales, as the model performance in simulating monthly discharge was better.
3.2 Performance of bias correction

To order to enhance the performance of precipitation
projections, the bias correction of CMIP6 precipitation
was carried out using the linear scaling method. Thisapproach aimed to improve the accuracy and reliabilityof the projected precipitation and its subsequent impact

Fig. 5 Daily discharge at Kratie before and after bias correction of CMIP6 GCMs

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

Page 10 of 20

Fig. 6 Relative changes of monthly discharge under different hydropower scenarios

scenarios. Under the present hydropower scenarios (98dams), the relative changes in monthly discharge at Chiang Saen and Kratie ranged from −22 to + 102% and −7%
to + 55%, respectively. The newly proposed dams in the
future hydropower scenario further altered the monthly
average discharge at both stations. Under the futurehydropower scenario, significant changes were observed
at Kratie rather than at Chiang Saen, for instance, relativechanges in February discharge doubled from the present
scenario. A similar tendency was also observed in the wetmonths, including August and September. Figure 7 presents the relative changes in the average discharge during
the dry season (November–April), wet season (May–
October), and annual peak. At Kratie, dry seasonal flow
increased by + 14% while wet seasonal flow decreased by-5% under the present hydropower scenario, but it doubled to + 28% and −10% in the future scenario, respectively. At Chiang Saen, dry seasonal flow increased

by + 36% and wet seasonal flow decreased by -17% underboth hydropower scenarios (i.e., there was no significant
difference in discharge changes between the hydropower
scenarios at Chiang Saen). In addition, future hydropower reduced annual average peak discharge by −29% at
Chiang Saen and −13% at Kratie.
3.4 Impacts of climate change on river discharge

To assess the effects of climate change on flow alterations, eight GCMs from CMIP6 projections were utilized as listed in Table 3. The model adopted SSP2-4.5
and SSP5-8.5 scenarios to simulate river discharge forthe present climate (1980–2014) and future climate(2026–2100). Figure 8 presents the simulated seasonal
discharge under climate change scenarios at Chiang
Saen and Kratie. For a better representation of temporalchanges in discharge, the future period was divided into
three timeframes near-future (2026–2050), mid-future

Fig. 7 Relative changes of flow characteristics (seasonal flow and peak discharge) under different hydropower scenariosLy et al. Progress in Earth and Planetary Science(2023) 10:55

Page 11 of 20

Fig. 8 Simulated discharge in the dry season (top) and wet season (bottom) under present climate and projected future climate scenarios(2051–2075), and far-future (2076–2100). The results
showed a substantial increase at both upstream (Chiang Saen) and downstream (Kratie). Despite the generalincreasing trend, the SSP5-8.5 scenario had more variation between GCMs, especially in the far-future. Thechanges in seasonal discharge became more significant
in the far-future at all stations during both the dry andwet seasons. Under the SSP5-8.5, the dry season discharge at Chiang Saen and Kratie increased from + 4%
and + 10% in the near-future to + 20% and + 27% in
the far-future, respectively. Discharge changes wereslightly greater during the wet season than those in thedry season. It increased from + 7% (+ 7%) in the nearfuture to + 50% (+ 33%) in the far-future at Chiang

Saen (Kratie). Overall, the degree of climate change
impacts increased with the future timeframe (i.e., nearfuture period < mid-future period < far-future period).
On the other hand, peak discharge was computed andanalyzed on the annual timescale for both present andfuture conditions. The time series of the simulated
annual peak discharge under climate change scenarios(SSP2-4.5 and SSP5-8.5) from 1980 to 2100 is presented
in Fig. 9. There was a noticeable increase in peak discharge in the far-future, especially under the SSP5-8.5
scenario. In comparison with the present condition,
the peak discharge in the far-future at Chiang Saen
and Kratie increased by an average of + 50% and + 43%,
respectively. These increases in peak discharge would
lead to a surge in flooded area in the LMB.

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

Page 12 of 20

Fig. 9 Time series of simulated annual peak discharge from 1980 to 2100 under present climate and projected future climate scenarios

3.5 Integrated impact of hydropower and climate change
on river discharge

The integrated impact of hydropower and climate change
on flow alterations were evaluated on a seasonal timescale using the RRI model. Future changes were analyzed
in three different timeframes: near-future, mid-future,and far-future. Overall, the seasonal discharge showed
noticeable changes at all stations, but the direction anddegree of changes differed between the seasons. At
Chiang Saen, no significant changes in discharge weredetected between the two hydropower development scenarios due to the fact that there was no newly proposed
dam in the upper part of this station. Figure 10 shows thesimulated seasonal discharge under reservoir operations
and climate change (SSP5-8.5) at Chiang Saen and Kratie.
Discharge increased gradually from time to time during
the dry season. The impacts can be seen clearly at both
stations, where reservoir operations (especially the future
dam scenario) further increased the discharge in addition to climate change. Under future hydropower andclimate change (SSP5-8.5 scenario), the relative changes
in average dry seasonal flow at Chiang Saen and Kratie
in the far-future increased by up to + 60% and + 61%,
respectively. On the contrary, during the wet season, climate change substantially increased the discharge, while
reservoir operations tended to reduce the effect of climate change by decreasing the discharge. Nevertheless,reservoir operations have not been able to fully diminish the effects of climate change. Consequently, it was
still noticeable at both stations. Under climate change
only (SSP5-8.5 scenario), the relative changes in average
wet seasonal flow at Chiang Saen and Kratie in the farfuture increased by up to + 49% and + 33%, respectively.

However, future hydropower could reduce the totalchanges to + 32% and + 19%, respectively. Although theeffects of climate change dominated reservoir operations in most scenarios, there were exceptional cases in
the near-future. Under climate change only (SSP5-8.5),
the average wet seasonal flow at Chiang Saen and Kratie
increased by up to + 7% (both stations) nonetheless, it
was reduced to −13% and −5%, respectively, under combined impacts (Table 5). In the annual peak discharge,
some compromise occurred between reservoir operations and climate change to some degree. A time series ofannual peak discharge under combined impacts at Chiang Saen and Kratie is presented in Fig. 11. Dependingon the scenarios, time frame, and location, the findings
indicated that hydropower construction could mitigate
climate change effects, as summarized in Table 5.
3.6 Integrated impact of hydropower and climate change
on flood extent

Flooding is one of the most important characteristicsof the MRB because its floodplain creates remarkablebiodiversity in the LMB. To further understand theeffects of reservoir operations and climate change projections on the flood extent in the LMB, simulations
were performed at a finer resolution of 1.5’ (≈ 2.7 km).
A water depth of 0.5 m was selected to distinguishbetween flooded and non-flooded area. Moreover, theK-S test was conducted to determine the significant
difference in flood variation during the study periods. The results indicated an increase in flood extent
in all scenarios, ranging from + 2 to + 37%, comparedwith the present condition (Table 6). Figure 12 shows
the changes in flood extent in the present and futureLy et al. Progress in Earth and Planetary Science(2023) 10:55

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Fig. 10 Simulated discharge in the dry season (top) and wet season (bottom) under reservoir operations and climate change (SSP5-8.5)

conditions under hydropower development scenariosand climate change projections (SSP5-8.5). The largest
relative changes occurred under the climate changeonly scenario in the far-future, up to + 37%. The smallest relative changes were observed under the combined
impacts in the near-future, at + 2%. The K-S test results
showed significant differences for most scenarios at
a 95% confidence level (p < 0.05), except for scenariosin the near-future, which showed no significant difference (p > 0.05) between present and future conditions.
Our findings indicated that hydropower played a significant role in reducing flood inundation in the LMB,
although the climate change effects remained to somedegree.

4 Discussion
This study investigated the seasonal flow and flood extent
changes caused by future climate change and reservoir
operations in the MRB using the RRI model with CMIP6
GCMs. Various bias-corrected GCMs with different
emission scenarios were considered to provide morerobust and less uncertain results.
4.1 Main findings

Our results suggested that reservoir operations substantially changed the seasonal flow in the MRB, particularly
under future hydropower development scenarios. The
simulation results of the reservoir operations indicatedthat the dry season flow would increase and the wetLy et al. Progress in Earth and Planetary Science(2023) 10:55

Page 14 of 20

Table 5 a Summary of relative changes of dry season discharge in the future under different scenarios b Summary of relative changes
of wet season discharge in the future under different scenarios c Summary of relative changes of annual peak discharge in the future
under different scenariosa
Dry season

Chiang Saen

Kratie

Dam
(%)

CC
(%)

Combined
(%)

Dam
(%)

CC
(%)

Combined(%)

SSP2-4.5

+ 44

+5

+ 52

+ 30

+9

+ 41

SSP5-8.5

+ 45

+4

+ 51

+ 29

+ 10

+ 42

SSP2-4.5

+ 44

+4

+ 50

+ 31

+6

+ 39

SSP5-8.5

+ 40

+ 11

+ 56

+ 27

+ 20

+ 53

SSP2-4.5

+ 39

+ 13

+ 56

+ 31

+ 11

+ 45

SSP5-8.5

+ 34

+ 20

+ 60

+ 27

+ 27

+ 61

Near-futureMid-future

Far-futureb
Wet season

Chiang Saen

Kratie

Dam
(%)

CC
(%)

Combined
(%)

Dam
(%)

CC
(%)

Combined(%)

SSP2-4.5

−19

+7

−13

−11

+9

−4

SSP5-8.5

−19

+7

−13

−11

+7

−5

SSP2-4.5

−17

+ 17

−3

−11

+ 14

+1

SSP5-8.5

−15

+ 26

+7

−11

+ 18

+6

SSP2-4.5

−15

+ 26

+6

−11

+ 20

+6

SSP5-8.5

−11

+ 49

+ 32

−10

+ 33

+ 19

Near-futureMid-future

Far-futurecPeak discharge

Chiang Saen

Kratie

Dam
(%)

CC
(%)

Combined
(%)

Dam
(%)

CC
(%)

Combined(%)

SSP2-4.5

−11

+ 10

−2

−11

+ 14

+1

SSP5-8.5

−12

+ 11

−2

−11

+ 16

+3

SSP2-4.5

−8

+ 20

+ 10

−11

+ 21

+8

SSP5-8.5

−7

+ 28

+ 18

−11

+ 23

+ 12

SSP2-4.5

−6

+ 27

+ 19

−11

+ 25

+ 11

SSP5-8.5

−4

+ 50

+ 44

−10

+ 43

+ 29

Near-futureMid-future

Far-future

The future hydropower development scenario was adopted in the calculationseason flow would decrease at the two investigated stations. Flow alterations were detected from the upstream(Chiang Saen) to the downstream (Kratie). Results fromthe monotonic trend analysis and change-point detection

are shown in Table 7. No significant trends were detected
at a 99% confidence level (|Z| < 2.576). Nonetheless, asignificant trend with a 95% confidence level (|Z| > 1.96)
was identified at a downstream station Kratie. Pettitt’sLy et al. Progress in Earth and Planetary Science(2023) 10:55

Page 15 of 20

Fig. 11 Time series of simulated annual peak discharge from 1980 to 2100 under reservoir operations and climate change (SSP5-8.5)

Table 6 Changes in flood inundation area and the K–S test results under climate change and integrated impact
Scenarios

Mid-future

Far-future

26,341 ­km(+ 13%)*

2

28,291 ­km(+ 21%)*

31,853 ­km2(+ 37%)*

Climate Change + Existing dam

24,720 ­km2(+ 6%)

26,813 ­km2(+ 15%)*

30,568 ­km2(+ 31%)*

climate change + future dam

23,762 ­km2(+ 2%)

25,887 ­km2(+ 11%)*

29,649 ­km2(+ 27%)*

Climate change

Present

Near-future
2

23,299 ­km

2*Statistically significant at the 5% significance level

test indicated that most of the series were homogeneous,showing no significant changes. Except for Kratie, a significant shift with a 95% confidence level (p = 0.019) was
detected in 1993. It was confirmed by several studies thathydropower operations were the primary factor responsible for flow alterations during that period rather thanclimate change (Cochrane et al. 2014; Dang et al. 2016;
Binh et al. 2020). Under reservoir operations, the dry seasonal flow alterations started as early as December (early
dry season), while the largest relative changes occurredin February. These changes could reduce water shortage
issues and potentially increase agricultural activities for
local residents.
Climate change was estimated to affect flow regimes,
as reported in our study and several others (Ngo et al.
2018; Hoang et al. 2019; Han et al. 2019; Try et al.
2020a, b). In addition, water resources were changing
on a global scale, including the MRB. It was projected
to alter the intensity and pattern of precipitation andevaporation, thus affecting runoff at the local scale(IPCC 2007). Our findings from the eight CMIP6
GCMs suggested that climate change increased seasonal discharge and annual peak in all scenarios

throughout the century. Besides, reservoir operations
reduced the effect of climate change by decreasing wet
seasonal discharge and annual peak discharge acrossall scenarios and timeframes. The increased dry seasonal flow would provide additional water supply andextensive irrigation benefits to local residents. However, an increase in wet seasonal flow, especially peakdischarge, would result in excessive inundated area inthe LMB floodplain, affecting both the local residents’
livelihood and essential infrastructures. Although there
were some compromises between the two main drivers,
climate change remained the dominant factor of flow
alterations in the MRB.
Our findings indicated that climate change would trigger flood risk in the LMB under all scenarios by increasing the inundated area by up to + 37% at the end of the
century. However, hydropower development, especiallyfuture hydropower dams, could effectively reduce theflood magnitude in the flood-prone area of the LMB.
The study further evaluated the significance of flood risk
using the statistical K-S test. Except in the near-future
timeframe, the changes in the inundated area were shown
to be significant in most scenarios.

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

Page 16 of 20

Fig. 12 Comparison of inundated area under different hydropower development and climate change scenarios (SSP5-8.5)

4.2 Comparison to existing research

Various researches evaluated the impacts of hydropower operations on river discharge at Chiang Saen
and Kratie using different hydrologic models (Hoanh
et al. 2010; Lauri et al. 2012; Piman et al. 2013b). All
previous studies suggested considerable changes inseasonal discharge, which are consistent with our findings. Hoanh et al. (2010) used the SWAT and IQQM
models to analyze the impact of hydropower development in the MRB from 1985 to 2000. Their study
reported a -17% and -8% decrease in the high-flowseason and a + 60% and + 28% increase in the low-flow

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

Table 7 Results of the trend analysis and change-point
detection tests of annual discharge over the period 1982–2012
Station

ZMK

Sen’s slope

Pettitt test

Chiang Saen

0.034

0.159

NS

Luang Prabang−0.952−12.92

NS

Pakse

0.749

36.60

NS

Kratie2.416*

108.6*

NS Statistically not significant*Statistically significant at the 5% significance level**Statistically significant at the 1% significance levelseason at Chiang Saen and Kratie, respectively. Similarly, using the VMod model, Lauri et al. (2012) investigated the effect of hydropower from 1982 to 1992
and showed that wet seasonal flow would decrease by-19% and -10%, while dry seasonal flow would increaseby + 55% and + 44% at Chiang Saen and Kratie, respectively. The findings of Piman et al. (2013b), who evaluated the changes in average seasonal flow under futurehydropower scenarios from 1986 to 2000 at only Kratie,
are also in line with Lauri et al. (2012) and Hoanh et al.
(2010). Our findings concur with the directional discharge changes observed by their study, although there
are some differences in magnitude due to variations
in boundary conditions, hydrologic model selection,
operational conditions, and study duration. Comparedto other studies, this study has projected a relatively
modest rise in the dry season at Chiang Saen. Under
the future hydropower, our results suggested a -17%and -10% decrease in wet seasonal flow and a + 36%
and + 28% increase in dry seasonal flow at Chiang Saen
and Kratie, respectively.
Many studies also assessed the impact of climatechange on river hydrology in the MRB using different future climate change projections. The majorityof the studies focused on the flow variation at Kratie.
Hoang et al. (2016) adopted VMod hydrological model
along with the CMIP5 GCMs to study the effect of climate change on flow characteristics during the periodof 2036–2065, as compared to the baseline period of
1971–2000. Their results showed that river discharge
at Kratie was expected to increase in both dry and wet
seasons. Moreover, relative discharge increases weregreater during the dry season (up to + 40% in April)although absolute discharge increases were more substantial during the wet season. Using the regional climate data (RCM), Hoanh et al. (2010) studied theimpact of climate change (2010–2050) by the SWAT
model. Their findings indicated that the discharge at
Kratie would increase by + 23% in the dry season andby + 11% in the wet season, compared with the baseline

Page 17 of 20

period (1985–2000). Our estimations are consistent
with those of other studies, indicating a + 20% increasein the dry seasonal flow and a + 18% increase in the wet
seasonal flow at Kratie during the mid-future period(2051–2075). Based on the analysis of five GCMs from
CMIP5, Hoang et al. (2016) reported that the projectedannual discharge changes at Kratie ranged from + 3%
(CCSM4) to + 8% (ACCESS1-0) under the RCP4.5 and-7% (HadGEM2-ES) to + 11% (MPI-ESM-LR) under
the RCP8.5. In our study, using a more comprehensive ensemble of eight GCMs from CMIP6, a wider
range of changes has been observed, ranging from -4%(NorESM2-MM) to + 35% (GFDL-CM4) under the
SSP2-4.5 and from -12% (MPI-ESM1-2-LR) to + 37%(IPSL-CM6A-LR) under the SSP5-8.5. The enhanced
capability of CMIP6, particularly its broader equilibrium climate sensitivity, have contributed to thesemore robust estimations in our analysis. Notably, Try
et al. (2022) examined the performance of CMIP5 andCMIP6 projections in the MRB, finding that CMIP6
outputs demonstrated higher correlation and lower
error coefficients compared CMIP5 outputs, further
validating the improvements in the CMIP6 models.
Using the RRI model with AGCM outputs, Try
et al. (2020b) found that climate change (2075–2099)
increased the inundated area in the LMB between + 19%
and + 43%. Wang et al. (2017) used the GBHM modelwith CMIP5 GCMs to show significant increases in themean annual maximum flood and flood frequency in the
far-future (2070–2099) over the Mekong region, particularly in the lower basin. Perera et al. (2017) utilized the
BTOP model and RRI model with bias-corrected MRIAGCM to emphasize the future severity of flooding andagricultural damage (2075–2099) in the LMB. Thesestudies agree with our findings regarding the direction
and magnitude of changes. Results from our simulation
showed that peak discharge at Kratie would increase up
to + 43%, and inundated area in the LMB would increase
up to + 37% under climate change in the far-future. Apartfrom climate change, reservoir operations are anotheraspect to consider when analyzing flood extent in the
MRB. Yun et al. (2020) used the VIC model to analyze theimpact of both changing climate conditions and the construction of hydropower facilities on flood intensity andfrequency. Their study suggested some benefits of hydropower in eliminating flood risk, but their study period
for climate change was between 2008 and 2016. In addition to existing studies, this study analyzed the integrated
impact of reservoir operations and climate change to theend of the century and highlighted the important role ofhydropower in mitigating the effect of climate change.
Moreover, our study adopted the latest climate projections dataset from CMIP6, with different SSP scenarios.

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

4.3 Limitations and uncertainties

This study assessed the effects of changing climate conditions and hydropower operations using a hydrologic
model. Several aspects, including groundwater, irrigationwater withdrawal, and land-use change, were not considered in the simulations. For both current and futureclimates, it was assumed that land use would remain
constant during the study period. This study did notincorporate the possible alterations in evapotranspiration that may arise in the future. Instead, its average historical values were used as a baseline in simulating thefuture climate change scenarios. Future studies should
include these drivers to provide a broader perspectiveon the hydrological alterations of the MRB. In addition,a rectangular approximation for river geometry was
adopted in the study due to the limited data availability.
In general, such an approximation is not ideal for floodinundation analysis. However, the flood inundation in
the Mekong River is relatively large that the river is submerged under the inundation. In such a case, the rivercross section becomes less important. Therefore, a rectangular approximation was used to estimate the rivercross-section, where actual river geometry is unavailable
in this study. Nonetheless, actual river geometry would
provide a better result on flood prediction. Owing to thelimited information on reservoir operation rules, ourstudy used a simple storage model to estimate the general
optimized patterns of reservoir operations and assumed
that all hydropower was operated to maximize energy
production. In actual operations, hydropower may havemultiple functions, including drought relief, ecologicalsustainability, flood control, sediment control, and watersupply. Therefore, different reservoir operation scenarios
should be considered in future assessments. The simulations of flood inundation were carried out at a resolutionof 2.7 km owing to the computational capacity. A finer
resolution would provide more accurate predictions. On
the other hand, there were two main sources of uncertainties in this study: data input uncertainties induced
by different GCMs and model parameters uncertainties. Although this study applied bias correction to the
future projected climate data, the application of RCM or
downscaled GCM would reduce the model projections’uncertainties and provide better predictions of rainfall
patterns. Moreover, the consideration of multi-model
would be ideal to reduce the uncertainties of future riverdischarge predictions.

5 Conclusion
This study assesses changes in river discharge andflood inundation induced by hydropower facilities andfuture climate variation in the MRB using a hydrologic

Page 18 of 20

RRI model coupled with a reservoir model. This study
adopts bias-corrected CMIP6 GCMs to analyze thechanges in dry seasonal flow, wet seasonal flow, annual
peak discharge, and flood extent for the current period(1980–2014) and future period (2026–2100). Our
results indicate noticeable changes in seasonal flow andhighlight the important role of hydropower in reducing
annual peak discharge, thus mitigating the flood risk in
the LMB. Climate change has forcefully modified theflow regime from a monthly to an annual scale. The seasonal discharge and annual peak discharge increased
considerably in all climate change scenarios. The largest changes are observed in the far-future under thehigh-emission scenario (SSP5-8.5). During the wet season, discharge at Kratie increased by + 7% in the nearfuture and by + 33% in the far-future; nonetheless, theflow changes under integrated impact decreased to-5% and + 19%, respectively. Despite the effect of reservoir operations, climate change remains the dominant contributor to hydrological changes in the MRB.
However, the magnitude of the impact varies between
timeframes (i.e., near-, mid-, and far-future) and hydropower operations. This study provides concrete insights
and broader perspectives for understanding the futurehydrological alterations in the Mekong region.
Abbreviations
3S	Sesan, Sre Pok, and Sekong river basins
ADB	Asian Development Bank
AGCM	Atmospheric general circulation model
CMIP5	Coupled Model Intercomparison Project Phase 5
CMIP6	Coupled Model Intercomparison Project Phase 6
d4PDF	Database for Policy Decision-Making for Future Climate Change
GCM	General circulation model
GPCC	Global Precipitation Climatology Centre
K–S	Kolmogorov–Smirnov
LMB	Lower Mekong Basin
MRB	Mekong River Basin
MRC	Mekong River Commission
NSE	Nash–Sutcliffe efficiency
PBIAS	Percent bias
R2	Coefficient of determination
RCP	Representative concentration pathway
RRI	Rainfall–runoff–inundation
RSR	Root-mean-square error–observations standard deviation ratio
SCE-UA	Shuffled Complex Evolution-University of Arizona
SSP	Socioeconomic pathway
UMB	Upper Mekong Basin

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​023-​00586-8.
Additional file 1: Table S1. List of hydropower projects in the MekongRiver Basin.
Acknowledgements
The authors would also like to acknowledge the Mekong River Commission
for their assistance in providing the observed datasets.

Ly et al. Progress in Earth and Planetary Science(2023) 10:55

Author contributions
SL proposed the topic, designed the study, and drafted the manuscript. TS
revised and supervised the methodology. ST provided technical support forthe modeling process. All authors read and approved the final manuscript.
Funding
This work was supported by the Japanese Government (MEXT) for the firstauthor during his doctoral program in Japan.
Availability of data and materials
All data are available in the main text and the supplementary materials. Additional data may be available from the corresponding authors upon reasonable
request.

Declarations
Competing interests
The authors declare that they have no competing interest.
Received: 16 April 2023 Accepted: 29 August 2023

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