Yamamoto et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00386-4(2021) 8:5

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Impact of climate change on floodinundation in a tropical river basin inIndonesia
Kodai Yamamoto1, Takahiro Sayama2*and Apip3

Abstract
Climate change will have a significant impact on the water cycle and will lead to severe environmental problems
and disasters in humid tropical river basins. Examples include river basins in Sumatra Island, Indonesia, where thecoastal lowland areas are mostly composed of peatland that is a wetland environment initially sustained byflooding from rivers. Climate change may alter the frequency and magnitude of flood inundation in these lowland
areas, disturbing the peatland environment and its carbon dynamics and damaging agricultural plantations.
Consequently, projecting the extent of inundation due to future flooding events is considered important for river
basin management. Using dynamically downscaled climate data obtained by the Non-Hydrostatic Regional Climate
Model (NHRCM), the Rainfall-Runoff-Inundation (RRI) model was applied to the Batanghari River Basin (42,960 km2)
in Sumatra Island, Indonesia, to project the extent of flood inundation in the latter part of the twenty-first century.
In order to obtain reasonable estimates of the extent of future flood inundation, this study compared two bias
correction methods: a Quantile Mapping (QM) method and a combination of QM and Variance Scaling (VS)methods. The results showed that the bias correction obtained by the QM method improved the simulated flow
duration curve (FDC) obtained from the RRI model, which facilitated comparison with the simulated FDC usingreference rainfall data. However, the high spatial variability observed in daily and 15-day rainfall data remained as
the spatial variation bias, and this could not be resolved by simple QM bias correction alone. Consequently, thesimulated extreme variables, such as annual maximum flood inundation volume, were overestimated compared to
the reference data. By introducing QM-VS bias correction, the cumulative density functions of annual maximum
discharge and inundation volumes were improved. The findings also showed that flooding will increase in thisregion; for example, the flood inundation volume corresponding to a 20-year return period will increase by 3.3
times. River basin management measures, such as land use regulations for plantations and wetland conservation,
should therefore consider increases in flood depth and area, the extents of which under a future climate scenario
are presented in this study.
Keywords: Climate change, Flooding, NHRCM, Peatland, Quantile mapping, RRI model, Sumatra Island, The
Batanghari River basin, Variance scaling* Correspondence: sayama.takahiro.3u@kyoto-u.ac.jp
2
Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji, Japan
Full list of author information is available at the end of the article© The Author(s). 2021 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/.

Yamamoto et al. Progress in Earth and Planetary Science(2021) 8:5

1 Introduction
Climate change will have a significant impact on waterrelated disasters and environmental problems inIndonesia. The country is one of the regions in which
extreme monsoon-related rainfall is projected to increase(Hijioka et al. 2014), and such increases are expected to
expose a larger proportion of the population to floods(Hirabayashi et al. 2013). In Sumatra and the Kalimantan
Islands of Indonesia, coastal lowland areas are mostly
comprised of peatland. Peatland is a wetland environment that is initially maintained by flooding from rivers
and which has, in recent years, been extensively developed for oil palm and other plantations. Changes in thefrequency and magnitude of flood inundation due to climate change may disturb the peatland environment andits carbon stores (Hirano et al. 2012), as well as cause
damage to the agricultural plantations in the region.
The use of General Circulation Models (GCMs) is indispensable for projecting future climate change. However, GCMs have limitations in reproducing reasonable
rainfall patterns in humid tropics, especially in areas
with many islands such as those in the so-called Indonesian Maritime Continent (MC) (Ramage et al. 1968),
which is surrounded by the Indian and Pacific oceans(Neale and Slingo 2003). These limitations can be attributed to the imperfect parameterization of physical processes (e.g., convection, cloud generation, local land sea
breeze circulation, and regional variations in the diurnal
cycle) (Gianotti et al. 2012; Ulate et al. 2014), as well as
the coarse resolution of topography (Neale and Slingo
2003; Schiemann et al. 2014; Hertwig et al. 2015). Given
the high levels of uncertainty associated with GCMs,
they are considered to be poorly suited for projecting future climate signals in and around the MC (Hijioka et al.
2014). On the other hand, Regional Climate Models(RCMs) are capable of representing local climate systems
and therefore have potential for realistically reproducing
rainfall events. Several studies have shown that RCMs
can successfully reproduce local rainfall patterns inspace and time in the MC (Juneng et al. 2016; Cruz and
Sasaki 2017; Rashid and Hirst 2017; Ratna et al. 2017;
Kang et al. 2018).
Despite an abundance of studies on rainfall projections, studies on the impact of rainfall projections onriver discharge are still limited in Indonesia (Emam et al.
2016; Marhaento et al. 2018). Moreover, very few studies
have focused on flood inundation due to climate change
at a river-basin scale, which is important for assessing
the impacts on flood damage (Iwami et al. 2017) andwetland environmental conditions.
Even though RCMs are potentially better for hydrologic impact assessment in the MC, there is still a needto correct the bias associated with RCMs (Christensen
et al. 2008). Among the various bias correction methods

Page 2 of 15(Teutschbein and Seibert 2012), the Quantile Mapping(QM) method has been widely applied to correct rainfall
inputs for hydrologic models (Huang et al. 2014;
Marhaento et al. 2018; Lee et al. 2019). However, the applicability of the QM method to correct for bias in the
MC region, particularly to assess flood inundation at theriver-basin scale, has not been thoroughly investigated.
Our previous study showed that inundation is more sensitive to rainfall than it is to runoff (Sayama et al. 2015).
Therefore, it is important to investigate appropriate bias
correction methods to assess the impact of flood inundation in a humid tropical river basin. Specifically, thisstudy focuses on the effects of spatial variations of rainfall on flood inundation. The Variance Scaling (VS)method (Teutschbein and Seibert 2012) applied in thisstudy is one of the potential approaches that can be used
to match both the mean and the spatial variance of adata series with observed data.
The objective of this study was to assess the impact ofclimate change on flood inundation in a river basin on
Sumatra Island. This study applied dynamically downscaled RCM rainfall data as the input for the RRI modeland identified appropriate bias correction methods forflood simulations. The specific objectives are:– What is the impact of non-/bias-corrected rainfall
output of the RCM on river discharge and floodinundation?
– What is the most suitable method to correct rainfall
bias when using the RCM to perform river discharge
and inundation simulations?
– What are the projected future changes in rainfall,streamflow, and flood inundation in the studied
region on Sumatra Island?

2 Methods
2.1 Dataset

This study uses climate data produced by the NonHydrostatic Regional Climate Model (NHRCM) that wasdeveloped by the Japan Meteorological Research Institute (MRI). The NHRCM is based on the NonHydrostatic Model (Saito et al. 2006), and the Kain andFritsch scheme is used to parameterize cumulus convection (Kain and Fritsch 1993). The MRI-JMA Simple Biosphere Model is used to describe land surface processes(Hirai et al. 2007). The NHRCM uses a boundary condition of the Atmospheric General Circulation Modeldeveloped in ver. 3.2S (AGCM) at a 20 km resolution (Mizuta et al. 2012) for downscaling to a 5 km
resolution (Sasaki et al. 2008; Cruz and Sasaki 2017).
The future boundary condition is based on changes of
the CMIP3 ensemble mean sea surface temperature(SST) under the Representative Concentration Pathways
8.5 scenario (RCP8.5) (Endo et al. 2012). Downscaling

Yamamoto et al. Progress in Earth and Planetary Science(2021) 8:5

was performed for our study area in Sumatra Island (−3.32° S–0.41° S; 99.65° E–105.25° E). The model is run
for 21 years of present climate (1980–2000) and for 20
years of future climate (2079–2098) under the RCP8.5
scenario.
As reference rainfall data, this study uses Global Satellite Mapping of Precipitation (GSMaP) Reanalysis ver. 6(Shimada and Kazumasa 2017) from 2001 to 2013 (13years) with a resolution of 0.1°. The GSMaP Reanalysis
product is satellite rainfall data that were calibratedusing Japanese 55-year Reanalysis (JRA-55) data. In
data-scarce regions such as the river basins of Sumatra
Island, rain gauge data are typically too limited to accurately represent spatial patterns in rainfall data. Consequently, the availability of GSMaP data with a temporal
resolution of 1 h on any grid is well suited for hydrologic
modeling. However, it should be noted that the reference data also contain uncertainty. For example, compared with other reanalysis datasets, some issues havebeen identified in JRA-55 data over tropical regions,such as weaker equatorial waves and Madden-Julian oscillation due to the characteristics of the forecastingmodel used for JRA-55 with a resolution of 60 km(Harada et al. 2016). In this study, the quality of the
GSMaP rainfall data is assessed based on gauged rainfall
at 23 locations in the Batanghari River Basin.
The climatic data used for estimating evapotranspiration, such as wind speed, temperature, surface pressure,specific humidity, and downward long- and shortwave
radiation fluxes, were obtained from WATCH Forcing
Data based on ERA-Interim (Weedon et al. 2014) with a
0.5-degree resolution. Land surface variables, such as theLeaf Area Index, surface roughness, and albedo, wereobtained from ECOCLIMAP (Champeaux et al. 2005).
Based on the data, potential evapotranspiration was estimated by using the Penman-Monteith equation to reference present climate conditions. The actual
evapotranspiration is estimated by the RRI model, andthe details are described in the “Rainfall-Runoff-Inundation Model” section.

2.2 Bias correction and validation

This study compares two methods to correct for bias in
NHRCM rainfall data, i.e., the QM method and a combination of QM and VS methods (hereafter, the QM-VS
method). The QM method used in this study employs
equiratio cumulative distribution function (CDF) matching developed by Wang and Chen (2014). The method
assumes that a ratio between the observed and modeled
values at the same percentile is preserved in the projection period. Specifically, the bias between modeled andobserved values in the reference period is quantified by
the ratio of the observed quantile value ( F o−−1c ) to the

Page 3 of 15

modeled quantile value ( F m− −1 c ) at the same percentile ina future projection period ( F m − p ðxm − p Þ). The statisticaltransformation of bias correction is shown in Eq. (1),xim − p:QM: ¼ xim − p

F o−−1c ð F m − p ðxm − p ÞÞ
F m− −1 c ð F m − p ðxm − p ÞÞ

ð1Þ

where Fm − p is a CDF of RCM outputs in a future projection, F o−−1c is an inverse CDF corresponding to theobservations, F m− −1 c is an inverse CDF of RCM variablesin the reference period, and xim − p is a quantile of a variable in a future projection at a grid i. xm − p represents
the spatial average over the basin of xim − p . In this study,
a CDF is constructed based on basin-averaged daily rainfall of observations and NHRCM data. The quantiles ofthe observation or model during the reference period at
a given percentile of future rainfall are estimated by linear interpolation to compensate for differences in thesize of the model and observation datasets.
The second method is a combination of the QM
method and the VS method, which was described by
Teutschbein and Seibert (2012). After calculating the differences in xim − p:QM:2 from the spatial mean xm − p:QM: asshown in Eq. (2) on a daily basis, the VS method calculates the standard deviations of the spatial pattern σm − c.
QM. ∣ j depending on the average rainfall categories represented as j (j = 1, …, 21). Specifically, the categories include basin-averaged rainfall at a percentile ( xperm − p:QM: )in an interval of five percentiles between 2.3 and 99.
Note that when j equals 21, the category includes rainfall
exceeding x99
m − p:QM: . The spatial standard deviation isthen adjusted according to Eq. (3), based on the spatial
standard deviation of the observed rainfall (σo − c ∣ j) for
each category. Finally, the corrected quantiles xim − p:QM:3
are shifted back to estimate the bias-corrected resultsxim − p:QM − VS: using Eq. (4).
xim − p:QM:2 ¼ xim − p:QM: − xm − p:QM:
xim − p:QM:3 ¼ xim − p:QM:2

σ o − cj j
ð j ¼ 1; …; 21Þ
σ m − c:QM:j j

xim − p:QM − VS: ¼ xim − p:QM:3 þ xm − p:QM:

ð2Þ
ð3Þ
ð4Þ

2.3 Rainfall-runoff-inundation model

The RRI model is a two-dimensional, distributed hydraulicand hydrological model that is capable of simulating
rainfall-runoff and flood inundation simultaneously. The
river channel is expressed as a single vector on a single gridcell, which also has a slope. Interaction between a river andthe slope is calculated based on different overflowing

Yamamoto et al. Progress in Earth and Planetary Science(2021) 8:5

formulae that vary according to levee height, river, andslope water depth. On a slope grid cell, the model can deal
flexibly with rainfall-runoff processes and runoff-generation
mechanisms, subsurface flow and saturated excess overland
flow, vertical infiltration and excess overland flow, andcomplete surface flow. In addition, groundwater flow canbe combined with subsurface flow and vertical infiltration
processes (Sayama et al. 2012). In the RRI model, actual
evapotranspiration is calculated by first subtracting surface
water, before subtracting additional water from a cumulative infiltration amount estimated by the Green-Amptmodel until it meets the potential evapotranspiration.
When the value approaches 0, there is no water to evaporate and actual evapotranspiration at that time is less thanthe potential evapotranspiration.

Page 4 of 15

2.4 Study area and application of the RRI model

The Batanghari River Basin is the second largest river
basin in Indonesia (42,960 km2) (Fig. 1a). The river originates in the Barisan Mountains before merging with theTembesi River as it runs eastward towards the coast.
Downstream, the river is diverted into the Kumpeh Riverand merged again. At Simpang, the river is diverged into
the Berbak River (Fig. 1b).
The climate is classified as humid tropical, andmonthly rainfall typically exceeds 100 mm (Chang and
Lau 1993). The average air temperature is within therange of 22.2 ± 0.2 °C (upstream) and 26.8 ± 0.2 °C(downstream) based on climate stations maintained by
the Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) from 2001 to 2013

Fig. 1 Maps of the study area. a Indonesian Maritime Continent and b the Batanghari River Basin in Sumatra Island

(2021) 8:5

Yamamoto et al. Progress in Earth and Planetary Science(Yamamoto et al. 2020). Despite high monthly rainfall,
there is a distinct wet season and dry season that isstrongly influenced by two monsoons. The wet monsoon
originates in the northern hemisphere and causes rainfall
to increase from November to March, and the dry monsoon originates in the southern hemisphere and causes a
decrease in rainfall from May to September. The wet
monsoon has two clear peaks: one in December and another in April (bimodal rainfall). In the wet season, fluvial flooding can occur in the Batanghari River Basin. In
some extreme cases, these floods can affect Jambi city
located in the downstream river reaches.
The topography of the area is comprised of mountains
that range in height from 1000 to 3700 m in the upstream river reaches, and wetlands in the low-lying flat
areas. Forest soils are thick, typically deeper than 1.5 m
on mountainous slopes, and have high infiltration rates.
Tropical peatlands occur along the eastern coast of Sumatra Island.
For the application of the RRI model, Manning’s
roughness for river and land were set to 0.03 m−1/3s and
0.3 m−1/3s, respectively, based on a previous study(Sayama et al. 2012). The soil depth and parameters of
the groundwater model, which affect discharge, were adjusted for the calibration period (2001–2006) and used
for the validation period (2007–2013). The details of themodel parameters and model set up were the same as
those of Yamamoto et al. (2019). The performance of
the RRI model with GSMaP Reanalysis rainfall input wasevaluated based on observed river discharge for the calibration period and the validation period. In addition, themaximum inundation distribution was estimated for 4
months (December 2016 to March 2017) using the RRI
model. The estimates were compared with sentinel satellite images from four flood events (December 6, 2016,
and January 23, February 27, and March 24, 2017). The
sentinel images were developed using the radar reflection intensity before and after flooding, and the maximum extent of flood inundation during these periods
was estimated for comparison with our simulations.

(a)Page 5 of 15

3 Results
3.1 Comparison of annual and 15-day rainfall patterns
and basin-averaged values estimated using the NHRCM
with GSMaP data and gauged data

We compared the spatial distribution patterns of annual
rainfall (Fig. 2) and annual maximum 15-day rainfall(Fig. 3) estimated using the NHRCM with reference
rainfall data (i.e., GSMaP and gauged data). Figure 2ashows that the GSMaP data indicate high annual rainfall
in the northern mountainous ranges of the basin, whichcorresponds with the gauged rainfall data. The detailed
values for the gauged rainfall data and the gridded GSMaP
rainfall data are shown in Figure S1 and Table S1. Figure
2a also shows that the rainfall patterns in the basin have a
diagonal pattern that is parallel with the mountain ranges
in the western part of the basin. We can visually distinguish that the rainfall in the basin is divided into several
parts, i.e., the mountainous region, the central region, thearea next to Jambi city (the narrowest part of the basin),and the coastal region. The NHRCM data shown in Fig.
2b, in general, also show a similar division of rainfall patterns. Based on the reference rainfall data, rainfall washigh in the northern part of the mountain ranges and low
in the central part of the mountain ranges. The NHRCM
data show consistently lower rainfall in the mountainous
areas. The rainfall of the reference data is relatively high inthe center of the basin, decreasing towards the west. The
rainfall decreases near Jambi city before increasing towards the coastal region. These rainfall patterns can also
be observed in the NHRCM data, although the rainfall ishigher in the central and eastern parts of the basin (approximately 3500 to 7500 mm) compared to the referencedata, as shown by the dark gray areas in Fig. 2b. Moreover,
the NHRCM data show higher spatial variability in annual
rainfall compared to the reference data. Figure 3ashows a comparison of the annual maximum 15-day
rainfall GSMaP data with gauged data. On the one
hand, the GSMaP data have a similar 15-day rainfall
pattern as the gauged data in the mountainous regions, the southwestern part of the basin, and(b)

LegendAnnual rainfall(mm/year)
445 - 835
835 - 1,300
1,300 - 1,800
1,800 - 2,000
2,000 - 2,200
2,200 - 2,400
2,400 - 2,600
2,600 - 2,800
2,800 - 3,000
3,000 - 3,485
3,485 - 5,000
5,000 - 7,751

Fig. 2 Comparison of average annual rainfall maps. a GSMaP with gauged rainfall (2001–2013) and b NHRCM (1980–2000)

(2021) 8:5

Yamamoto et al. Progress in Earth and Planetary Science(a)Page 6 of 15(b)

Legend15-day rainfall(mm/15days)

11 - 50
50 - 100
100 - 150
150 - 200(c)

200 - 250(d)

250 - 300
300 - 350
350 - 400
400 - 450
450 - 500
500 - 550
550 - 684
0 25 50

100 kmFig. 3 Comparison of average annual maximum 15-day rainfall maps. a GSMaP with gauged rainfall data (2001–2013), b original data, c QM biascorrected data, and d QM and VS-corrected NHRCM data. Note that the 15-day gauged rainfall data are estimated at the same time as the annual
maximum of the GSMaP data

downstream areas, such as the area around Jambi cityand the coastal area. On the other hand, the GSMaPdata tend to show higher rainfall in some parts ofmountainous regions in the western part of the basin.
The detailed values of gauged rainfall and the gridded
rainfall of the GSMaP are shown in Figure S2 andTable S2. Figure 3b shows that NHRCM data also
have higher spatial variability in terms of annual maximum 15-day rainfall compared to reference data (Fig. 3a),and it also has higher rainfall amounts (about 350 to 552mm) in areas such as the lowland areas.
We also compared basin-averaged values of the
NHRCM and the reference rainfall data with thegauged data. The average annual rainfall over thebasin was estimated to be 2321 mm by GSMaP and
2021 mm by the rain gauges (Yamamoto et al. 2019).
The basin-averaged annual rainfall values of the
NHRCM are higher than those of the reference rainfall data, which was 2913 mm. Figure 4 shows a comparison of the CDFs of annual maximum daily and
15-day rainfall of raw and QM bias-corrected NHRC
M and GSMaP data. The annual maximum basinaveraged daily rainfall of NHRCM data (average 29mm) is lower than that of GSMaP data (average 51mm) (Fig. 4a). Figure 4b also compares the annual
maximum 15-day rainfall (basin average)—cumulative
rainfall which is strongly correlated with peak discharge. NHRCM data generally have a lower annual
maximum 15-day rainfall (average 206 mm) compared
to GSMaP data (average 242 mm).

3.2 Effects of bias corrections (QM and QM-VS) on rainfall

In this study, we compared two bias correction methods,i.e., the QM method and the QM-VS method. The annual basin average rainfall was improved from 2913 to2316 mm by using the QM method and to 2328 mm byusing the QM-VS method, which is closer to the GSMaP
value (2321 mm). The findings of this study also showthat both methods result in similar CDFs for the annual
maximum daily and 15-day rainfall, as shown in Fig. 4.
Despite the similar results in the corrected basin average values, the application of both methods gives different results in the spatial variability of annual rainfall andannual maximum 15-day rainfall. The application of the
QM method lowers the spatial variability of annual rainfall, but does not improve the variability for the annual
maximum 15-day rainfall. Figure 3c shows the results of
the QM method after it was applied to the spatial variability observed in the annual maximum 15-day rainfall
data. Some areas, including the central parts of the basin
and the mountainous areas, showed that rainfall is corrected within the range of 250 to 400 mm, which moreclosely reflects the reference data (about 150 to 333mm). However, the rainfall values obtained in the lowland areas showed much higher values, ranging from
350 to 600 mm compared with the reference data (about
140 to 250 mm). On the other hand, the application of
the QM-VS method results in a lower spatial variability
for both annual rainfall and annual maximum 15-day
rainfall. Figure 3d shows the bias correction results obtained using the QM-VS method, which typically has

Yamamoto et al. Progress in Earth and Planetary Science(2021) 8:5

Page 7 of 15

Fig. 4 Comparison of CDFs of annual maximum cumulative rainfall
for different durations. a Daily and b 15-day rainfall of raw data, QM
bias-corrected NHRCM and GSMaP data

lower spatial variability than the corrections obtainedusing the QM method. Unlike the results obtained using
the QM method, the rainfall values observed in the lowland areas are closer to those of GSMaP. The improvement in spatial variability due to bias correction is
confirmed by Fig. 5, which shows the relationship between spatially averaged daily rainfall and the corresponding spatial standard deviations over the basin. The
original NHRCM data have higher spatial standard deviations compared with the GSMaP data (Fig. 5a1). The
application of the QM method results in higher standard
deviations, particularly at high rainfall rates (Fig. 5a2);
conversely, the application of the QM-VS method successfully improved the standard deviations, making themcomparable with the standard deviations obtained using
GSMaP (Fig. 5a3).
3.3 Effects of bias corrections on the RRI simulation

The calibration and validation results of the RRI model
are shown in Fig. 6, which shows a comparison of theFig. 5 Scatter diagram of basin-averaged daily rainfall and corresponding
standard deviations in space of a1 raw NHRCM data, QM bias-corrected
a2 NHRCM data, and QM and VS bias-corrected a3 NHRCM data

simulated and observed monthly river discharges. The
model performance is considered satisfactory (Moriasi
et al. 2007) in that the Nash-Sutcliffe efficiency (NSE) is
0.57 in the calibration period (2001–2006) and 0.51 in
the validation period (2007–2013). To validate the simulated inundation patterns, we compared the maximum
inundation distribution from December 2016 to March2017 using the maximum flood extent inferred by sentinel satellite imagery (Fig. 7a). The simulated inundation
pattern (Fig. 7b) agrees well with the satellite image of
the Batanghari River and its tributaries.
Figure 8 shows the simulated flow duration curves(FDCs), annual maximum discharge, and inundation volume estimated using the original NHRCM, GSMaP, andbias-corrected rainfall data. For the reference discharge
and inundation volume (referred to as observed (Obs) in
Fig. 8), we use the GSMaP rainfall data as the input forYamamoto et al. Progress in Earth and Planetary Science(2021) 8:5

Page 8 of 15

Fig. 6 Comparison of simulated monthly discharge with observations at Muara Tembesi with basin-averaged daily rainfall from GSMaP

the simulation. The simulated FDC obtained using theoriginal NHRCM data shows that medium flows (Q20 toQ60) and low flows (Q70 to Q99) are overestimated, andhigh flows (Q0.1 to Q10) are underestimated (Fig. 8a); theranges of high, medium, and low flows are defined basedon Mohamoud (2008).
Table 1 summarizes the evaluation scores obtained forthe annual maximum discharge (Fig. 8b) and inundation(a)volume (Fig. 8c) simulated by raw/bias-corrected NHRC
M rainfall data based on relative root mean square error(RRMSE) and relative mean error (RME). For annual
maximum discharges, Table 1 shows that the QM
method had nearly the same accuracy, in terms of the
RRMSE (0.11), as the original NHRCM. However, compared to the raw data, the accuracy of the QM method
was lower for estimating annual maximum inundation

LegendRiver
Flood extent

Depth (m)0 - 0.1
0.1 - 0.2
0.2 - 0.3(b)

0.3 - 0.4
0.4 - 0.5
0.5 - 1.0
1.0 - 1.5
1.5 - 2.0
2.0 - 2.5
2.5 - 3.0
3.0 - 3.7

Fig. 7 Observation-based and simulated flood inundation maps for the downstream area. a Maximum flood extent captured by sentinel satellite
and b simulated maximum inundation depth distribution with GSMaP Reanalysis

(2021) 8:5

Yamamoto et al. Progress in Earth and Planetary Science

10,000

Obs
Raw
QM
QM-VS

8,000

Discharge (m3/s)

volumes. We confirmed that the application of the QM
and VS bias correction method improved the RRMSE(0.39) and RME (0.37) compared to the RRMSE (1.06)
and RME (1.13) obtained using the QM method alone.

(a)

9,000

7,000
6,000

3.4 Projection of rainfall, discharge, and inundation under
future climate conditions

5,000
4,000
3,000
2,000
1,000
0
0

Annual maximum discharge (thousand m3/s)

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

(b)
Obs
Raw
QM
QM-VS

9
8
7
6
5
4
3
2
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.8

0.9

1

Non-exceedance probability
Annual Maximum Inundation Volume (mm)Page 9 of 15

6

(c)
Obs
Raw
QM
QM-VS

5

4

3

2

1

Figure 9 shows the change in annual rainfall patterns between the present and future climate conditions. In general, annual rainfall will increase throughout the basin.
In some areas, including the central or lower parts ofthe basin, the projected increase in annual rainfall ismore than 650 mm. Figure 10 shows that the annual
maximum daily and 15-day rainfall will also increase forall return periods. For example, annual maximum daily
rainfall corresponding to a 20-year return period (i.e.,
non-exceedance probability equals 0.05) will increase
from 84 to 276 mm (Fig. 10a), and the corresponding increase in annual maximum 15-day rainfall will be from
350 to 666 mm (Fig. 10b).
Figure 11 shows the FDC and basin-averaged annual
maximum inundation volume under present and futureclimate conditions. The range of the FDC will increase
under future climate conditions (Fig. 11a). In particular,
the medium flow in the future climate is 2035 m3/s,
which is a 42% increase compared to the present climate. Future flood inundation volume will increase forall return periods (Fig. 11b). For example, flood inundation volume in the future climate corresponding to a 20year return period is 14.2 mm, which is an increase of
3.3 times compared to the present (4.3 mm). Annual
maximum inundation distributions under future climate
conditions (Fig. 12b) show that flood depth reaches amaximum of 4.8 m, and the total inundated area increases by 2.3 times, compared to that of the present climate (Fig. 12a). Most of the lowland areas are exposed
to flooding and flood depth ranges within 0.4 m and 4.8
m.

0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Non-exceedance probability

Fig. 8 Effects of bias corrections on hydrological variables.
Comparison of a FDCs, b CDFs of annual maximum discharge, and c
CDFs of annual maximum inundation volume of raw data, QM andQM-VS bias-corrected NHRCM data, and GSMaP data

4 Discussion
4.1 Effects of bias corrections on precipitation, riverdischarge, and inundation volume

In terms of annual rainfall, the NHRCM rainfall data for
the Batanghari River Basin are higher than the reference
rainfall data (GSMaP and gauged data). In the same

Table 1 Evaluation of annual peak discharges and annual maximum inundation volume with a raw/bias-corrected NHRCM against
those with GSMaP
Bias correction method

Annual peak discharge

Annual maximum inundation volume

Raw

RRMSE 0.11, RME − 0.094

RRMSE 0.35, RME 0.048

QM

RRMSE 0.11, RME 0.093

RRMSE 1.06, RME 1.13

QM-VS

RRMSE 0.079, RME 0.047

RRMSE 0.39, RME 0.37

The notations of bias corrections are “Raw” for non-bias correction, “QM” for Quantile Mapping, and “QM-VS” for Quantile Mapping and Variance Scaling. The
shows relative root mean square error (RMSE) and relative mean error (RME)Yamamoto et al. Progress in Earth and Planetary Science(2021) 8:5

Page 10 of 15

LegendFuture-Pr esent(mm/year)-450 - -250
-250 - -50-50 - 00 - 5050 - 250
250 - 450
450 - 650
650 - 850
850 - 1,050
1,050 - 1,250
1,250 - 1,450
1,450 - 1,660

Fig. 9 Change of past (1980–2000) and future (2079–2098) bias-corrected NHRCM annual rainfall datatarget river basin, three RCMs from BMKG also overestimated annual rainfall (Handoko et al. 2019). This wet
bias may be caused by the boundary conditions associated with dynamical downscaling (Gianotti et al. 2012;
Cruz et al. 2016).
As in the reference data, the NHRCM (Fig. 2b) shows
similar differences in the rainfall patterns that developed
parallel to the mountain range (Fig. 2a). We find that
this is also true for the annual maximum 15-day rainfallof the NHRCM (Fig. 3b). However, differences between
the NHRCM and reference rainfall data were also observed. In the reference rainfall data, rainfall was highonly in the northern parts of the mountain ranges, but it
was low in the central parts of the mountains. On theother hand, the NHRCM rainfall data showed that rainfall was consistently lower in the mountainous areas. In
addition, the NHRCM rainfall data showed that rainfall
in the central and eastern parts of the basin was higher
than that measured using the reference data. Comparedto early GCM outputs, the representation of topography
and dynamical downscaling have improved the spatial
and temporal resolution of rainfall maps in recent years(Schiemann et al. 2014; Johnson et al. 2015). However,
our analysis shows that NHRCM exhibits high spatial
variability in both annual rainfall and extreme rainfall
data. This variability may be attributed to the rainshadow effect, i.e., rainfall is reduced on the leeward sideof a mountain and increased on the windward side
(Chang et al. 2005). Previous studies on the NHRCM
also reported that this effect may explain some of therainfall variability observed in the NHRCM data (KieuThi et al. 2016; Cruz and Sasaki 2017). The high variability of NHRCM data may also be one of the reasons forthe slightly higher annual maximum inundation volume,
which can be affected by spatially and temporally concentrated rainfall (Sayama et al. 2015).
Both of the bias correction methods resulted in similar
improvements for basin-averaged rainfall values (annual,Fig. 10 Projected CDF of present and future NHRCM annual maximum a daily and b 15-day rainfall dataYamamoto et al. Progress in Earth and Planetary Science(2021) 8:5

Fig. 11 Hydrological variables of bias-corrected present and future
data. a Flow duration curve and b CDFs of annual maximum
inundation volume

annual maximum daily, and annual maximum 15 days),which is expected because the additional VS method
does not change the average values, only the varianceover space. While the two methods reduced the spatial
variability of annual rainfall, they differed in the spatial
variability of the annual maximum 15-day rainfall; theQM method increased the spatial variability, while the
QM-VS method decreased it. The use of equiratio cumulative distribution function matching, as described in
the “Methods” section, may explain why the spatial variability increases after the application of the QM method.
Unlike the typical use case of the QM method, whichmatches the CDF at a grid scale, this study matches the
CDF of basin-averaged rainfall values to preserve thePage 11 of 15

spatial pattern of the NHRCM. Nevertheless, the variability observed in the raw NHRCM data changes after
multiplying the ratio uniformly by all rainfall values inthe basin; when the ratio < 1, the variability is reduced,
and when it is > 1, the variability is increased. When thebasin-averaged value is more than about 12 mm, the ratio is more than 1. The annual 15-day rainfall data aremostly a series of high rainfall values with a ratio > 1,
which causes the high variability. The additionalvariance-scaling step in the QM-VS method matches thevariance with the reference data, reducing the high variability through application of the equiratio CDF
matching.
We also investigated the impact of the bias correction
methods on hydrologic simulations. The simulation using
the QM bias-corrected rainfall data improved the FDC, as
has been demonstrated by several hydrological impact
studies that used QM bias-corrected GCM or RCM rainfall as an input for their hydrologic models (Teutschbein
and Seibert 2012; Huang et al. 2014; Lee et al. 2019). However, the simulation using QM bias-corrected rainfall leads
to overestimation of extreme variables (annual maximum
discharge and annual maximum inundation volume). Thismay be caused by the higher variability of rainfall. On theother hand, QM-VS bias correction improved basin average and spatial variabilities of the annual maximum 15day rainfall, producing reasonable extreme variables over
the target river basin.
Although correction of basin-averaged rainfall is not
typical, it is considered rational for river discharge simulations. According to Beven and Hornberger (1982),
assessing the correct volume of rainfall input from ahighly spatially variable pattern is considerably more important than a rainfall spatial pattern in predicting storm
flow hydrographs. However, bias correction methods
that match the CDF of basin-averaged rainfall withequiratio CDF matching may lead to higher spatial variability, particularly for extreme values; therefore, correcting the variance over the space improves the results.
4.2 Projection of rainfall, discharge, and inundation under
future climate conditions

The strongest and most consistent increase in seasonal rainfall broadly follows the Inter Tropical Convergence Zone, which lies above southern Indonesia
in December, January, and February and northernIndonesia in June, July, and August (Christensen et al.
2007). A recent study showed that RCMs, dynamicallydownscaled from three GCMs over the Western
Maritime Continent, consistently projected an increase in rainfall in December, January, and February
under RCP4.5 and RCP8.5 scenarios in parts of Sumatra Island, and a significant decrease in rainfall
during inter-monsoon seasons (Kang et al. 2018).

Yamamoto et al. Progress in Earth and Planetary Science(2021) 8:5

Page 12 of 15

Depth (m)0 - 0.1
0.1 - 0.2
0.2 - 0.3
0.3 - 0.4
0.4 - 0.5
0.5 - 1.0
1.0 - 1.5
1.5 - 2.0
2.0 - 2.5
2.5 - 3.0(a)

3.0 - 3.7
3.7 - 4.8
Depth (m)0 - 0.1
0.1 - 0.2
0.2 - 0.3
0.3 - 0.4
0.4 - 0.5
0.5 - 1.0
1.0 - 1.5
1.5 - 2.0
2.0 - 2.5
2.5 - 3.0(b)

3.0 - 3.7
3.7 - 4.8

Fig. 12 Projection of NHRCM annual maximum inundation depth distribution a under present and b in future climate conditions

In this study, the annual maximum 15-day rainfall intensity was projected to increase in the Batanghari River
Basin under RCP8.5 in the late twenty-first century. Thisagrees with IPCC AR5, which reports that the frequency
and intensity of extreme rainfall will likely increase over
wet tropical regions in the late twenty-first century(Stocker et al. 2013). Collins et al. (2013) also showed thatin most parts of Sumatra Island, the annual maximum 5day rainfall increased by more than 20% in the latetwenty-first century under RCP8.5 scenarios. In addition,
extreme events, such as 100-year annual maximum daily
rainfall, will occur more frequently in the late twenty-first
century under RCP8.5 scenarios (Stocker et al. 2013). The
findings of this and previous study (Muis et al. 2015) showthat the annual maximum inundation volume is projected
to increase in the Batanghari River Basin under RCP8.5 inthe late twenty-first century. Based on Muis et al. (2015),
using the global flood model combined with scenarios of 5
GCMs with 4 RCPs also projects an increase in inundation

volumes with a 100-year return period in parts of Sumatra
Island; however, there is considerable uncertainty acrossthe projections for the Indonesian region. Our results suggested that frequent expansion of the flood inundation
area in the downstream area of the basin will damage agricultural crops. According to a global data projection by
Fujimori et al. (2018), in one of the Shared Socioeconomic
Pathways (SSP) (Popp et al. 2017), particularly SSP3, cropland area in the Batanghari River Basin will increase continuously until 2100. Without implementing any
adaptation measures, the lowland areas of Sumatra Island
are likely to become more agriculturally developed due tothe large potential of unused areas, such as peatlands. The
findings suggest that the river basin should be managed
by implementing adaptation measures, such as land use
regulation of plantations, promoting wetland conservationin order to prevent damage due to flooding under futureclimate conditions, and protecting the unique environments of tropical peatland areas.

Yamamoto et al. Progress in Earth and Planetary Science(2021) 8:5

4.3 Limitations of the research

In some regions of Asia, shallow orographic rainfall occurs by low-level orographic lifting of maritime air leading to heavy rainfall (Shige et al. 2013; Shige andKummerow 2016). In humid areas that experience tropical rainfall caused by these shallow orographic rain systems, original satellite rainfall products tend to perform
poorly because they employ microwave radar algorithms(Kubota et al. 2007; Shige et al. 2013). To overcome theissue, the ability to distinguish between orographic andnon-orographic rainfall systems has been developed andimplemented in the GSMaP algorithm ver. 6 (Shige et al.
2013; Yamamoto and Shige 2015). Furthermore, for the
GSMaP Reanalysis data, the wind dataset from JRA-55 isused to detect orographic rainfall. Nevertheless, according to Nodzu et al. (2019), who assessed rainfall in areas
with very complex topography (including several mountainous ranges) in northern Vietnam, some bias remains
in GSMaP Reanalysis ver. 6; specifically, they observed
higher (lower) rainfall on the leeward (windward) side ofmountains in their case study. In our study, the coverageof the gauged data is still not sufficient to accurately assess the characteristics and potential biases of the
GSMaP product. With the limited data, the annual rainfall and 15-day rainfall were overestimated, particularlyin the leeward side of the Barisan Mountains. These uncertainties in the reference data may affect the model
calibration and other parts of our study.
It is essential that climate change impact studies
evaluate the uncertainty of future climate change impacts by comparing results with/without dynamical
downscaling using different RCP/SST scenarios andGCMs/RCMs. In this study, a dynamically downscaled
product of AGCM under one SST scenario was used
due to limitations imposed by computational resources and to retain the focus of this paper on bias
correction methods. As for the effects of different
SST scenarios, we ran the RRI model based on theoriginal AGCM outputs without dynamical downscaling. Figure S3 shows that the increasing rates of floodinundation volume range from 1.9 to 4.8 times without downscaling. Although the result of 3.3 times
based on the downscaling was within the range, we
realize that there are large uncertainties and thatthese are dependent upon SST patterns. In a future
study, it will be necessary to conduct more downscaling experiments with different SST patterns.
To focus on the flood analysis, this study did not consider future temperature changes in the Batanghari River
Basin. However, changes in temperature can increase
evapotranspiration, which can dry the land and promote
fires (Takahashi et al. 2003). According to the NHRCM,
the average monthly surface temperature (basin average)
increases by 3 °C in any season in the Batanghari River

Page 13 of 15

Basin (see Figure S4 showing the basin-averaged
monthly temperature for present and future simulations
using the original NHRCM). In order to assess the impact on the tropical peatland environment, it is necessary to use future temperature as an input for thehydrologic model.

5 Conclusions
This study compared the two bias correction methods,i.e., the QM method and a combination of the QM andVS methods, to examine how each method improves estimates of rainfall patterns and subsequently the simulated flood inundation by the RRI model in the
Batanghari River Basin, Indonesia. Originally, the dynamically downscaled NHRCM rainfall data showed
higher spatial variation in the 15-day rainfall and annual
rainfall compared to the reference data. While this does
not markedly influence the simulated FDC, it largely
overestimated the extreme values, such as the annual
maximum flood inundation volume. The combination of
QM and VS methods successfully decreased the rainfall
spatial variability and improved the estimations of the
FDCs and the extreme values.
Our analysis showed that the projected change of floodinundation is significant, particularly in the lowland
areas of Sumatra Island. Annual maximum flood inundation volume corresponding to a 20-year return period
would increase by 3.3 times. The annual maximum inundation map under a future climate scenario showedthat the inundation depths would increase and the inundated area would expand in tropical peatland areas.
These results suggest the need to consider future climatechange scenarios for river basin management, particularly to reduce the risk of flood damage and to sustainably maintain the unique ecosystem of tropicalpeatlands.
6 Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s40645-020-00386-4.
Additional file 1: Table S1. Comparison between annual rainfall valuesof gauged rainfall and gridded rainfall from GSMaP.
Additional file 2: Table S2. Comparison between 15-day rainfall valuesof gauged rainfall and annual maximum 15-day rainfall of gridded rainfall
from GSMaP.
Additional file 3: Figure S1. Identical to Figure 2a, but the station
numbers are shown.
Additional file 4: Figure S2. Identical to Figure 3a, but the station
numbers are shown.
Additional file 5: Figure S3. Projected CDFs of annual maximum
inundation volume based on present and future AGCM data with four
SST clusters (C0, C1, C2 and C3).
Additional file 6: Figure S4. Monthly average surface temperatures(basin average) under present climate (1980-2000) and future climate(2079-2098) conditions projected by NHRCM.

Yamamoto et al. Progress in Earth and Planetary Science(2021) 8:5

Abbreviations
AGCM: Atmospheric General Circulation Model developed by the Japan
Meteorological Research Institute ver. 3.2S; BMKG: Indonesian Agency for
Meteorological, Climatological and Geophysics; CDF: Cumulative Distribution
Function; FDC: Flow Duration Curve; GCMs: General Circulation Models;
GSMaP: Global Satellite Mapping of Precipitation; JRA-55: The Japanese 55year Reanalysis; MC: Maritime Continent; NHRCM: Non-Hydrostatic Regional
Climate Model; NSE: Nash-Sutcliffe Efficiency; QM: Quantile Mapping method;
RCMs: Regional Climate Models; RRI: Rainfall-Runoff-Inundation; SSP: Shared
Socioeconomic Pathways; SST: Sea Surface Temperature; VS: Variance Scaling
Acknowledgements
We thank Dr. Hidetaka Sasaki at the Japan Meteorological Research Institutefor providing a downscaled dataset of the NHRCM. We also appreciate theassistance of The TOUGOU project members, particularly Prof. Eiichi Nakakita
and Prof. Yasuto Tachikawa at Kyoto University, for supervising the research
project in Indonesia.
Authors’ contributions
KY analyzed the data and performed numerical simulations. TS designed thestudy and led the project. AA supported the field work and data collection.
The authors have read and approved the final manuscript.
Funding
This work was supported by the Integrated Research Program for Advancing
Climate Models (TOUGOU) Project (Theme D: PI: EN), JSPS KAKENHI Grant
Number 17H04586 (PI: TS), and JST Japan-ASEAN Science, Technology andInnovation Platform (JASTIP) (PI: YK, Leader of WP4: KT).
Availability of data and materials
The datasets supporting the conclusions of this article are currently notavailable in any public repository. However, data are available from thecorresponding author upon reasonable request.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Graduate School of Engineering, Kyoto University, Nishigyo Ward, Kyoto,
Japan. 2Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji,Japan. 3Research Center for Limnology, Indonesian Institute of Sciences (LIPI),
Jalan Raya Jakarta-Bogor Km 46, Cibinong, Bogor 16911, Indonesia.
Received: 3 March 2020 Accepted: 31 October 2020

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