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Hydrological Sciences Journal
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Impact of climate and land-use changes on hydrological
processes and sediment yield—a case study of the Be
River catchment, Vietnam
a

Dao Nguyen Khoi & Tadashi Suetsugi

b

a

Faculty of Environmental Science, University of Science, Vietnam National University, Ho
Chi Minh City, Vietnam
b

Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi,
Kofu, Yamanashi 400-8511, Japan
Accepted author version posted online: 04 Jul 2013.Published online: 29 Apr 2014.

To cite this article: Dao Nguyen Khoi & Tadashi Suetsugi (2014) Impact of climate and land-use changes on hydrological
processes and sediment yield—a case study of the Be River catchment, Vietnam, Hydrological Sciences Journal, 59:5,
1095-1108, DOI: 10.1080/02626667.2013.819433

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Hydrological Sciences Journal – Journal des Sciences Hydrologiques, 59 (5) 2014
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1095

Impact of climate and land-use changes on hydrological processes and
sediment yield—a case study of the Be River catchment, Vietnam
Dao Nguyen Khoi1 and Tadashi Suetsugi2
1

Faculty of Environmental Science, University of Science, Vietnam National University, Ho Chi Minh City, Vietnam


2

Downloaded by [NUS National University of Singapore] at 22:40 03 June 2014

Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Kofu, Yamanashi 400-8511, Japan

Received 9 June 2012; accepted 8 May 2013; open for discussion until 1 November 2014
Editor Z.W. Kundzewicz; Associate editor Q. Zhang
Citation Khoi, D.N. and Suetsugi, T., 2014. Impact of climate and land-use changes on hydrological processes and sediment yield—a
case study of the Be River catchment, Vietnam. Hydrological Sciences Journal, 59 (5), 1095–1108.

Abstract The impact of climate and land-use changes on hydrological processes and sediment yield is investigated in the Be River catchment, Vietnam, using the Soil and Water Assessment Tool (SWAT) hydrological
model. The sensitivity analysis, model calibration and validation indicated that the SWAT model could reasonably
simulate the hydrology and sediment yield in the catchment. From this, the responses of the hydrology and
sediment to climate change and land-use changes were considered. The results indicate that deforestation had
increased the annual flow (by 1.2%) and sediment load (by 11.3%), and that climate change had also significantly
increased the annual streamflow (by 26.3%) and sediment load (by 31.7%). Under the impact of coupled climate
and land-use changes, the annual streamflow and sediment load increased by 28.0% and 46.4%, respectively. In
general, during the 1978–2000 period, climate change influenced the hydrological processes in the Be River
catchment more strongly than the land-use change.
Key words climate change; hydrology; land-use change; sediment yield; SWAT model; Be River catchment, Vietnam

Impact des changements climatiques et de l’utilisation des terres sur les processus hydrologiques
et la production de sédiments—étude de cas du bassin versant de la rivière Be, Vietnam
Résumé L’impact des changements du climat et de l’utilisation des terres sur les processus hydrologiques et
l’apport de sédiments dans le bassin versant de la rivière Be (Vietnam) a été étudié en utilisant le modèle
hydrologique SWAT. L’analyse de sensibilité, l’étalonnage et la validation des modèles indique que le modèle
SWAT peut raisonnablement simuler l’hydrologie et la charge sédimentaire dans le bassin versant. C’est donc
avec cet outil que les réponses de l’hydrologie et des sédiments au changement climatique et au changement
d’utilisation des terres ont été étudiées. Les résultats indiquent que la déforestation a augmenté l’écoulement
annuel (1,2%) et la charge sédimentaire (11,3%), et que le changement climatique a également augmenté de

manière significative le débit annuel (26,3%) et la charge sédimentaire (31,7%). Sous l’impact couplé du
changement climatique et du changement d’utilisation des terres, l’écoulement annuel et la charge de
sédiments ont respectivement augmenté de 28% et 46,4%. En général, le changement climatique a eu une
influence plus importante sur les processus hydrologiques que le changement d’utilisation des terres dans le
bassin versant de la rivière Be durant la période 1978–2000.
Mots clefs changement climatique ; hydrologie ; changement d’utilisation des terres ; production de sédiments ; modèle SWAT ;
bassin versant de la rivière Be, Vietnam

1 INTRODUCTION
The principal influences on hydrological processes and
soil erosion include not only climate change but also
land-use/land-cover change. Climate change is likely to
affect the hydrological cycle with changes in temperature and precipitation, and this may lead to changes in
© 2014 IAHS Press

water availability, as well as the transformation and
transport characteristics of pollutants (Tu 2009).
Changes in land use as a result of deforestation,
agricultural expansion and urbanization have altered
surface runoff generation, and have then affected the
hydrological processes and the transport of pollutants.


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Dao Nguyen Khoi and Tadashi Suetsugi

As a result, climate and land use are identified as key

factors controlling the hydrological and sediment behaviours of catchments (Elfert and Bormann 2010). It is
important to understand the hydrological and sediment
responses to these changes in order to develop strategies for land-use planning and water resource management. Studies of the hydrological and water quality
impacts of climate change and land-use change are
desirable (Tong et al. 2012).
Many studies have considered the impact of climate change and land-use change on hydrology (Li
et al. 2009, 2012, Ma et al. 2009, 2010, Mango et al.
2011, Zhang et al. 2011). However, few studies have
investigated changes in hydrological processes and
water quality as well as sediment yield under the impact
of climate and land-use changes on a basin scale (Ward
et al. 2009, Tong et al. 2012). To assess the hydrological
and sediment impacts of environmental change, the
common methods used are the paired catchment
approach, statistical analysis and hydrological modelling (Li et al. 2009, 2012). Among these approaches,
the hydrological method is an appealing option, because
it is most suitable to be used as a part of scenario studies.
There are numerous hydrological models, such as the
Water Erosion Prediction Project (WEPP), Hydrologic
Simulation Program Fortran (HSPF), the Soil and Water
Assessment Tool (SWAT) and the physically-based
distributed hydrological model Système Hydrologique
Européen TRANsport (SHETRAN), that could be used
in simulating the runoff and transport of sediment and
pollutants in the catchment. The SWAT model has been
selected for the current study because it is widely used
to assess hydrology and water quality in agricultural
catchments around the world (see the SWAT literature
database: />Another reason for its selection is its availability and
user-friendliness in terms of handling input data

(Arnold et al. 1998).
Vietnam has experienced climate changes,
including rising air temperature and more variable
precipitation (MONRE 2009). In addition, rapid agricultural and industrial development, as well as population growth have occurred in recent decades (Trinh
2007). These changes have affected soil erosion and
the availability of water resources in Vietnam.
However, no studies have investigated the effects of
climate change and human activities on hydrological
cycles and sediment yield in Vietnam. Moreover,
Wang et al. (2012) emphasized that the local impacts
of climate change and human activities on hydrology
and sediment yield vary from place to place and need
to be investigated on a regional scale.

The overall objective of this study was to quantify the impacts of past land-use change and climate
change on hydrological processes and sediment yield
in a case study of a catchment in Vietnam. The
specific objectives were: (a) to calibrate and validate
the SWAT model in terms of streamflow and sediment load in the Be River catchment; (b) to evaluate
the separate impacts of climate and land-use changes
on hydrology and sediment yield; and (c) to assess
the impacts of combined climate change and land-use
change on hydrological processes and sediment yield.
The results achieved through this study provide decision-makers with a comprehensive understanding of
the interactions among hydrological processes, landuse change and climate change, which are required to
assist with water resource planning efforts and sustainable development.
2 STUDY AREA
The catchment selected for study lies in the Dong Nai
River basin in south Vietnam between latitudes 11°10′–
12°16′N and longitudes 106°36′–107°30′E (Fig. 1). It is

located in Dak Nong, Binh Phuoc, Binh Duong and
Dong Nai provinces, and has a catchment area of
about 7500 km2. The altitude varies from 1000 m a.m.
s.l. in the highland area to 100 m a.m.s.l. in the plain
area, in a northeast to southwest and south direction. The
origin of the branched-tree drainage system of the Be
River lies in Tuy Duc on the international border
between Vietnam and Cambodia, in Dak Nong
province. The study area is located in the steep area.
The degree of slope can be divided into three levels:
slopes of 0% to 7% account for 45% of the total area,
slopes of 8–15% account for 33% of the area, and slopes
greater than 15% account for 22% of the area. The
climate is tropical monsoon. The annual rainfall varies
between 1800 and 2800 mm, with an average of
2400 mm year-1. The area has two seasons: the rainy
season and the dry season. The rainy season lasts from
May to November and accounts for 85–90% of the total
annual precipitation. The average temperature is about
25.9°C, the maximum temperature is 36.6°C and the
minimum temperature is 17.3°C. The area has relatively
fertile land (75% basalt soil), consistent with agricultural
development. The main land-use types in this catchment
are forest and agricultural lands. The total population in
2010 was approximately one million inhabitants. The
mean annual flow of the catchment is about
7.51 × 109 m3. Similar to the distribution of rainfall,
the flow is distinguished by two distinct seasons: the
flood season (accounting for 67% of the total annual

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Impact of climate and land-use changes on hydrological processes

Fig. 1 Location map of the Be River catchment.

flow) and the low-flow season (accounting for 33% of
the total annual flow). The Be River catchment has been
assessed as having the most abundant water resources in
the Dong Nai River basin and significant hydropower
potential.

8
À
Á < þ1 xj À xi > 0
0 xj À xi ¼ 0
sgn xj À xi ¼
:
À1 xj À xi < 0
nðn À 1Þð2n þ 5Þ À
varðSÞ ¼

3 METHODOLOGY

The Mann-Kendall test (Mann 1945, Kendall 1975)
is a non-parametric test for identifying trends in
hydro-meteorological time series. The MannKendall test statistic is calculated as follows:

S >0
S¼0
S <0

(1)

À
Á
sgn xj À xi

(2)

varðS Þ

where
S¼

nÀ1 X
n
X
i¼1 j¼iþ1

!
ti ðti À 1Þð2ti þ 5Þ

i¼1

18
(4)

3.1 Change detection in hydro-meteorological
data

8 SÀ1
pffiffiffiffiffiffiffiffiffi
>
< varðS Þ
0
Zc ¼
>
: pSþ1
ffiffiffiffiffiffiffiffiffi

m
P

(3)

where n is the length of the data set, xi and xj are the
sequential data values, m is the number of tied groups
(a tied group is a set of sample data with the same
value), and t is the number of data points in the mth
group. The null hypothesis H0 (there is no trend) is
accepted if –Z1–α/2 ≤ Zc ≤ Z1–α/2, where α is the
significant level. A positive value of Zc indicates an
increasing trend, and a negative value indicates a
decreasing trend.
In the Mann-Kendall test, the Kendall slope is
another very useful index that estimates the magnitude of the monotonic trend and is given by:



xj À xi
"i < j
β ¼ Median
jÀi

(5)


1098

Dao Nguyen Khoi and Tadashi Suetsugi

where 1 < i < j < n. The estimator β is calculated as
the median of all slopes between data pairs for the
entire data set.
The Pettitt test (Pettitt 1979) is a non-parametric
approach used for detecting the change point. There
are two samples (x1, x2, …, xt) and (xt+1, xt+2, …, xN)
that come from the same population (x1, x2, …, xN).
The test statistic Ut,N is given by:
Ut;N ¼

t X
N
X

À
Á
sgn xi À xj

(6)

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i¼1 j¼tþ1

À6K 2
p ¼ exp 3 t 2
N þN

À
Á0:56
sed ¼ 11:8  Qsurf  qpeak  areaHRU
 KUSLE  CUSLE  PUSLE  LSUSLE

The null hypothesis of the Pettitt test is the
absence of a change point. Its statistic Kt and associated probabilities are given as:


(7)
Kt ¼ maxUt;N 


method (Monteith 1965), the Priestley-Taylor method
(Priestley and Taylor 1972) and the Hargreaves method
(Hargreaves et al. 1985). Channel routing is simulated
using the variable storage coefficient method (William
1969) and the Muskingum method (Chow 1959).
The SWAT model uses the Modified Universal

Soil Loss Equation (MUSLE) to simulate the sediment yield for each HRU. The MUSLE (William
1995) is given as:


(8)

When p is smaller than the specific significance
level, the null hypothesis is not accepted. The time t
when Kt occurs is the change point time.
These methods have been commonly used to
detect changes in hydro-meteorological data (Ma
et al. 2008, Zhang et al. 2009, Zhang et al. 2011).
3.2 SWAT model
The SWAT model is a physically based, distributed,
continuous time model that is designed to predict the
effects of land management on the hydrology, sediment
and agricultural chemical yields in agricultural watersheds with varying soils, land-use and management
conditions (Arnold et al. 1998). In the SWAT model,
a catchment is divided into a number of sub-watersheds
or sub-basins. Sub-basins are further partitioned into
hydrological response units (HRUs) based on soil
types, land-use and slope classes that allow a high
level of spatial detail simulation. The model predicts
the hydrology at each HRU using the water balance
equation, comprising precipitation, surface runoff, evapotranspiration, infiltration and subsurface flow.
The SWAT model provides two methods for estimating surface runoff: the SCS curve number procedure
(USDA-SCS 1972) and the Green and Ampt infiltration
method (Green and Ampt 1911). SWAT calculates the
peak runoff rate using a modified rational method. The
potential evapotranspiration is estimated in the SWAT

model using three methods: the Penman-Monteith

(9)

 CFRG
where sed is the sediment yield on a given day (t),
Qsurf is the surface runoff volume (mm ha-1), qpeak is
the peak runoff rate (m3 s-1), areaHRU is the area of
the HRU (ha), KUSLE is the USLE soil erodibility
factor, CUSLE is the USLE cover and management
factor, PUSLE is the USLE support practice factor,
LSUSLE is the USLE topographic factor and CFRG
is the coarse fragment factor.
The channel sediment-routing model consists of
deposition and degradation, which operate simultaneously. In the channel, deposition or degradation
can occur, depending on the sediment loads from
upland areas and the transport capacity of the channel
network. If the sediment entering a channel is larger
than its sediment transport capacity, channel deposition will occur. Otherwise, channel degradation will
be the dominant process.
Further details of hydrological and sediment
transport processes can be found in the SWAT
Theoretical Documentation (Neitsch et al. 2011).
3.3 SWAT model set-up
The input data required for the SWAT model include
weather data, a land-use map, a soil map and a digital
elevation map (DEM), as listed in Table 1. The land-use
data were generated from Landsat satellite images—
Landsat Thematic Mapper (TM) image in 1990 and
Landsat Enhanced Thematic Mapper Plus (ETM +)

image in 2001—obtained from the US Geological
Survey Earth Resources Observation and Science
Center. Land-use maps were generated using supervised
classification based on the maximum likelihood algorithm in the ENVI Version 4.4 image processing software. Overall accuracy and kappa statistic (κ) were used
to assess classification accuracy based on 256 ground
control points selected from the referenced land-use map


Impact of climate and land-use changes on hydrological processes

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Table 1 Spatial model input data for the Be River catchment.
Data type

Description

Resolution Source

Topographic map
Land-use map
Soil map
Weather

Digital elevation map (DEM)
Land-use classification
Soil types
Daily precipitation, minimum and maximum temperature

90 m
1 km
10 km
9 stations

for 2001, which was obtained from the Southern
Institute for Water Resources Planning (SIWRP 2002).
Land-use types were classified into the following categories: forest, rangeland, agricultural land, urban area
and water.
Daily river flow data measured at Phuoc Long
(1981–1993) and Phuoc Hoa (1981–2000) gauging stations (Fig. 1) were used for the model calibration and
validation of flow simulation. Monthly sediment load
data measured at Phuoc Hoa station (07/1999–2004)
were used for the calibration and validation of sediment
simulation. Streamflow and sediment load data were
provided by the Hydro-Meteorological Data Center of
Vietnam.
The model set-up consists of five steps: (a) data
preparation, (b) sub-basin discretization, (c) HRU definition, (d) parameter sensitivity analysis, and (e) calibration and validation. Sensitivity analysis was carried
out to identify the most sensitive parameters for the
model calibration using Latin hypercube and one-factor-at-a-time (LH-OAT), an automatic sensitivity analysis tool implemented in SWAT (Van Griensven et al.
2006). Those sensitive parameters were calibrated
using the auto-calibration tool that is currently available
in the SWAT interface (Van Liew et al. 2005).
3.4 Performance evaluation of the SWAT model
The model performance was evaluated using statistical analysis to compare the quality and reliability of
the simulated discharge with the observed data. In
this study, the model evaluation methods used
included: the Nash-Sutcliffe (1970) efficiency criterion, NSE; per cent bias, PBIAS; and the ratio of the

root mean square error (RMSE) to the standard
deviation (STDEV) of measured data, RSR. The
RSR is calculated as (Moriasi et al. 2007):
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
N
P
ðOi À Pi Þ2

RSR ¼

i¼1
RMSE
¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
STDEVobs
N
P
 Þ2
ð Oi À O
i¼1

(10)

SRTM
Landsat TM, ETM+ (USGS/GLOVIS)
FAO
Hydro-Meteorological Data Center (HMDC)

where Oi is the observed value Pi is the simulated
 is the mean of the observed data, and N is
value, O

the total number of observations.
According to Moriasi et al. (2007) and Rossi
et al. (2008), model simulation can be judged as
satisfactory if NSE > 0.5, RSR ≤ 0.70 and
PBIAS = ±25% for streamflow simulation, and
NSE > 0.5, RSR ≤ 0.70 and PBIAS = ±55% for
sediment simulation.
4 RESULTS AND DISCUSSION
4.1 Land-use changes
Based on the Landsat images, land-use maps were
generated for 1990 and 2001, as illustrated in Fig. 2.
An accuracy assessment of land-cover classification,
obtained by computing the confusion matrix in ENVI
4.4 software, showed an overall accuracy value of
98.2% for 1990 and 98.1% for 2001. The κ coefficients for 1990 and 2001 were 0.96 and 0.97, respectively. The dominant land-use types in the Be River
catchment were agricultural land and forest (Table 2),
which accounted, respectively, for 40.28% and
50.69% in 1990 and 55.18% and 36.62% in 2001.
Range land, urban and water covered about 8.89%,
0.03% and 0.11%, respectively, of the total catchment
area for 1990, and 6.85%, 0.13% and 1.22%, respectively, of the total area for 2001. In general, there
were two main trends of land-use change: a decrease
in the forest (deforestation) and an increase in agricultural land (agricultural expansion). Compared with
1990, the forest decreased by 14.07% and cropland
increased by 14.89% of the catchment area. Aside
from this, there were slight changes in the range land
(–2.03%), water (1.11%), and urban area (0.11%).
These changes were likely caused by a population
increase, which led to the expansion of settlements
and agricultural land, and ineffective forest management that led to excessive forest exploitation (SIWRP

2002, 2008). The population of the Be River catchment was about 680 000 in 2000 compared to
400 000 in 1990 (SIWRP 2002). This represents a
population increase of about 170%.


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1100

Dao Nguyen Khoi and Tadashi Suetsugi

Fig. 2 Land-use maps of the Be River catchment.
Table 2 Statistics for land-use changes in the Be River catchment for the period of 1978–2007.
Land-use types

Agricultural land
Range land
Forest
Urban
Water
Total

1990

2001

2

2

(km )

(%)

(km )

(%)

(km2)

(%)

3015
665
3794
2
8
7484

40.28
8.89
50.69
0.03
0.11
100

5129
513
2741
10

91
7484

55.18
6.85
36.62
0.13
1.22
100

1114
–152
–1053
8
83

14.89
–2.03
–14.07
0.11
1.11

4.2 Change detection for hydro-meteorological
data
Annual temperature, precipitation and streamflow
were tested using the Mann-Kendall and Pettitt methods, as reported in Table 3 and illustrated in Fig. 3.
The results showed rises in annual temperature, precipitation and streamflow (by 0.035°C year-1,

Table 3 Summary of Mann-Kendall trend test and Pettitt
test statistics for annual rainfall, temperature and streamflow in the Be River catchment.

Mann-Kendall test

Precipitation
Temperature
Streamflow

Pettitt test for change point

Zc

β

p

KT

t

p

2.27
3.16
1.74

20.613
0.035
3.142

*
*

*

88
102
78

1989
1986
1989

*
*
*

*indicates significant at p < 0.05.

Change

20.613 mm year-1 and 3.142 m3 s-1 year-1, respectively) at the 5% significance level. In other words,
the null hypothesis H0 was not accepted for the
annual temperature, rainfall and streamflow time series. Change points in the annual rainfall and streamflow were detected as occurring around 1989, with a
significance level of 5%, while the change point in
annual temperature was statistically significant
in 1986.
The Mann-Kendall test was also applied to the
data series for monthly precipitation and temperature,
as summarized in Table 4. There are no significant
trends in most of the monthly precipitation time
series, except for October and December. The precipitation in October and December showed significant increasing trends of 2.24 and 1.73 mm year-1,
respectively. In the case of the monthly temperature,

significant increasing trends were detected for most
of the monthly temperature time series, except for
February, March, April and May.


1101

Impact of climate and land-use changes on hydrological processes

Table 4 Summary of Mann-Kendall trend test statistics for
monthly rainfall and temperature in the Be River
catchment.
Month

Precipitation
Zc

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January
February
March
April
May
June
July
August
September
October
November

December

0.66
1.58
0.63
0.58
1.85
–0.48
1.40
–1.11
–0.16
2.27
0.53
2.09

β
0.208
0.589
0.762
0.741
3.313
–2.740
2.783
–3.676
–0.233
2.244
1.325
1.733

Temperature

P

*
*

Zc
2.82
1.50
0.37
–0.62
0.06
2.51
2.37
3.10
3.21
2.51
2.51
3.59

β
0.108
0.041
0.010
–0.021
0.001
0.051
0.024
0.043
0.042
0.048

0.050
0.085

p
*

*
*
*
*
*
*
*

*indicates significant at p < 0.05.

catchment scale. The meteorological data were
divided into two periods, 1978–1989 and 1990–
2000, based on the change point analysis, and each
period included one land-use map. The land-use map
for 1990 was used to represent the 1978–1989 period, and that for 2001 was used to represent the
1990–2000 period. The following four scenarios
were investigated:
–
–
–
–

Fig. 3 Variations of mean values in (a) annual precipitation, (b) annual temperature and (c) annual discharge in the
Be River catchment (1978–2000).

4.3 Hydrological and sediment responses to
land-use and climate changes
To investigate the impacts of climate change and
land-use change on hydrological processes and sediment yield, the approach of one factor at a time was
used (Li et al. 2009). The change point of precipitation is selected as the change point in climate data,
because rainfall plays a key role in hydrology and is
the most fundamental meteorological variable on the

Scenario 1 (Baseline): Land-use in 1990 and climate data for the 1978–1989 period.
Scenario 2 (Climate change): Land-use in 1990
and climate data for the 1990–2000 period.
Scenario 3 (Land-use change): Land-use in 2001
and climate data for the 1978–1989 period.
Scenario 4 (Climate and land-use changes): Landuse in 2001 and climate data for the 1990–2000
period.

4.4 Model calibration and validation
The LH-OAT parameter sensitivity analysis procedure
showed that the most sensitive parameters for flow
simulation were curve number (CN2), soil evaporation
compensation factor (ESCO), threshold water depth in
the shallow aquifer for flow (GQWMN), baseflow alpha
factor (ALPHA_BF), soil depth (SOL_Z), available
water capacity (SOL_AWC), channel effective hydraulic conductivity (CH_K2), groundwater ‘revap’ coefficient (GW_REVAP), Manning’s value for the main
channel (CH_N2), and saturated hydraulic conductivity
(SOL_K). The most sensitive parameters for sediment
simulation were the linear re-entrainment parameter for
channel sediment routing (SPCON), the exponent of re-

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Dao Nguyen Khoi and Tadashi Suetsugi

Table 5 SWAT sensitivity parameters and calibrated values.
Simulation Parameter

Flow

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Sediment

CN2
ESCO
GQWMN
ALPHA_BF
SOL_Z
SOL_AWC
CH_K2
GW_REVAP
CH_N2
SOL_K
SPCON
SPEXP
USLE_P

Description of parameter

Range

Initial SCS CN II value***
Soil evaporation compensation factor*
Threshold water depth in the shallow aquifer for flow**
Baseflow alpha factor*
Soil depth***
Available water capacity***
Channel effective hydraulic conductivity**
Groundwater ‘revap’ coefficient**
Manning’s value for main channel*
Saturated hydraulic conductivity***
Linear re-entrainment parameter for channel sediment
routing*
Exponent of re-entrainment parameter for channel sediment
routing*
USLE support practice factor*

±0.5
0
0
0
±0.5
±0.5
–0.01
0.02
–0.01
±0.5
0.0001

Calibrated value

–1
– 5000
–1
– 500
– 0.2
– 0.3
– 0.01

Phuoc Long

Phuoc Hoa

–0.29
0.95
456
0.11
0.03
0.23
184
0.17
0.04
–0.06
0.001

–0.36
0.42
2356
0.61

0.28
0.40
184
0.17
0.04
0.07

1 – 1.5

1.01

0–1

0.42

*Parameter value is replaced by given value.
**Parameter value is added by given value.
***Parameter value is multiplied by (1 + a given value).

entrainment parameter for channel sediment routing
(SPEXP), and the USLE support practice factor
(USLE_P). These sensitive parameters were optimized
using the auto-calibration extension of ArcSWAT 2009
to calibrate the model. The daily streamflow for 1981–
1989 at the Phuoc Long and Phuoc Hoa stations and the
land-use map for 1990 were used for model calibration.
The daily streamflow for 1990–1993 for the Phuoc Long
station and 1990–2000 for the Phuoc Hoa station and
land-use map for 2001 were used for the model validation of flow simulation. This approach was used in the
study undertaken by Li et al. (2009). Because of the lack

of observed sediment load, these data were only available from 07/1999 to 2004 at monthly levels. They were
divided into two periods for calibration (07/1999–2001)
and validation (2002–2004) using the land-use map for
2001. The flow calibration and validation was conducted
first, and then the sediment calibration and validation.
As a result of the calibration, the most sensitive flowrelated parameter of CN2 was adjusted to have values of
–0.29 for Phuoc Long and –0.36 for Phuoc Hoa, and the
most sensitive sediment-related parameter SPCON was
adjusted to have a value of 0.001. This value of SPCON
found here was similar to that in the study conducted by
Phan et al. (2011) in the Cau River watershed in northern
Vietnam. The details of the calibrated parameters are
presented in Table 5.
The SWAT flow simulations were calibrated
against the daily flow from 1981 to 1989 and validated
from 1990 to 1993 at the Phuoc Long gauging station,
as shown in Fig. 4. The simulated daily flow fit the

Fig. 4 Observed and simulated daily flow hydrograph at
the Phuoc Long station: (a) calibration and (b) validation.

observed data for the calibrated period well, with NSE,
PBIAS and RSR values of 0.77, 1.60% and 0.47,
respectively. For the validation period, the values of
NSE = 0.79, PBIAS = 3.30% and RSR = 0.45 suggest
that there was good agreement between the simulated
and observed streamflow during this period, based on


1103

Impact of climate and land-use changes on hydrological processes

Table 6 Model performance for the simulation of runoff.
Period

Calibration (1981–1989)

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Validation (1990–1993)

Phuoc Long station

Phuoc Hoa station

Time step

NSE

PBIAS

RSR

Period

Time step

NSE

PBIAS

RSR

Daily
Monthly
Daily
Monthly

0.77
0.87
0.79
0.91

1.60%
1.60%
3.30%
3.30%

0.48
0.36
0.45
0.30

Calibration (1981–1989)

Daily
Monthly
Daily
Monthly

0.86
0.94
0.71
0.79

–1.90%
–1.90%
–6.20%
–6.20%

0.37
0.25
0.54
0.46

the performance criteria given by Moriasi et al. (2007).
The aggregated monthly average flow values from the
daily flow values improved the fit between the model
predictions and observed flows. More detail can be seen
in Table 6. Figure 5 shows a hydrograph of the simulated and observed daily flow for the calibration and
validation periods at the Phuoc Hoa station. The statistical evaluations shown in Table 6 also suggest that
there was good agreement between the daily measured
and simulated streamflow during these periods, according to Moriasi et al. (2007). This agreement is shown by
values of NSE = 0.86, RSR = 0.37 and
PBIAS = –1.90% for the calibration period and
NSE = 0.71, RSR = 0.54 and PBIAS = –6.20% for
the validation period. In the case of the aggregated
monthly average flow, the match between the simulated
flow values and the observed values was improved.

Validation (1990–2000)

This match is shown in Table 6. Although the simulated
and observed streamflow followed the same trend, the
peak flow was overestimated for Phuoc Long station
and underestimated for Phuoc Hoa station. This may
have resulted from the uneven spatial distribution of the
rain gauges. In the study area, eight rain gauges are
located in the lower area of the catchment; however,
only one rain gauge located in the upper area of the
catchment has long-term records (Fig. 1). A further
reason can be attributed to the CN2, which is used to
simulate the surface runoff. The CN2 method assumes a
unique relationship between cumulative rainfall and
cumulative runoff for the same antecedent moisture
conditions (Betrie et al. 2011). Generally speaking,
these results reveal that the hydrological processes in
SWAT are modelled realistically for the Be River catchment, which is important for the simulation of sediment.
The simulated sediment load values were calibrated against monthly observed data from 07/1999 to
2001 and validated from 2002 to 2004 at the Phuoc Hoa
station, as presented in Fig. 6. The fit between the
simulated and observed sediment loads was acceptable,
according to Moriasi et al. (2007). The fit was indicated
by the values of NSE = 0.74, RSR = 0.51 and
PBIAS = –1.10% for the calibration period and
NSE = 0.55, RSR = 0.66 and PBIAS = 33.77% for
the validation period (Table 7). Although an underestimation of the monthly sediment yield by the model for
the validation period was within the satisfactory level of
acceptance, it can generally be said that the simulated

result was relatively satisfactory.
From the results of the calibration and validation, it is reasonable to conclude that the SWAT
model could simulate the hydrology and sediment
yield in this catchment well. The calibrated parameters were accepted for the scenario simulations.
4.5 Response to climate change

Fig. 5 Observed and simulated daily flow hydrograph at
the Phuoc Hoa station: (a) calibration and (b) validation.

In order to investigate the impact of climate change
on hydrological processes and sediment yield, the
simulation was carried out using the land-use


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1104

Dao Nguyen Khoi and Tadashi Suetsugi

Fig. 7 Change in temperature and precipitation in the Be
River catchment between period 1 (1978–1989) and period
2 (1990–2000).

Fig. 6 Observed and simulated monthly sediment load
hydrograph at the Phuoc Hoa station: (a) calibration and
(b) validation.
Table 7 Model performance for the simulation of sediment at the Phuoc Hoa station.
Period

Time step NSE PBIAS

Calibration (07/1999–2001) Monthly
Validation (2002–2004)
Monthly

0.74
0.55

Fig. 8 Annual changes of hydrological components under
the impact of climate and land-use changes.

RSR

–1.10% 0.51
33.77% 0.66

conditions of the 1990s and the climate data of two
different periods: 1978–1989 and 1990–2000.
Figure 7 shows the absolute changes in the monthly
climate variables between the two periods. Compared
with the 1978–1989 period, the annual temperature
increased by 0.4°C and the annual precipitation
increased by 292.3 mm (12.8%). The increases in
temperature and precipitation were higher in the dry
season (0.5°C and 44.8%) than in the wet season
(0.4°C and 10.1%).
In the case of water balance components, climate
change caused increases in all water balance components, including a 7.9% increase in actual evapotranspiration, a 19.5% increase in groundwater discharge,
a 34.2% increase in surface runoff and a 56.1%

increase in soil water content (Fig. 8). These
increases could be a result of the increases in

temperature and precipitation in the 1990–2000 period compared with those in the 1978–1989 period.
Generally, the pattern of changes in water balance
components is mainly determined by the changes in
precipitation and temperature.
Under the impact of climate change, the annual
streamflow and sediment load increased by 26.3%
and 31.7%, respectively (Fig. 9). The increases in
flow and sediment load can be explained by increases
in precipitation and runoff in the 1990–2000 period
compared with the 1978–1989 period. Considering
the seasonal change, the streamflow and sediment
load increased significantly, by 26.8% and 31.1% in
the wet season and 21.8% and 56.4% in the dry
season, respectively. In general, the changes in the
streamflow and sediment load occur in the same
direction, which is similar to the findings of the
study on the impacts of climate change on the discharge and sediment load in the Cau River catchment, conducted by Phan et al. (2011). The Phan
et al. (2011) study indicated that an increase in the


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Impact of climate and land-use changes on hydrological processes

Fig. 9 Changes in annual and seasonal (a) streamflow and
(b) sediment load under the impact of climate and landuse changes.

streamflow will increase the sediment load, while a
decrease in the streamflow will decrease the sediment
load.
4.6 Response to land-use change
The impact of land-use change on the water balance
components is illustrated in Fig. 8. Under the impact
of land-use change, surface runoff, soil water content
and sediment yield increased considerably, by 18.4%,
10.4% and 12.8%, respectively, while actual evapotranspiration and water yield increased slightly, by
approximately 1.3% and 1.1%, respectively. Aside
from this, the other water balance components
decreased, including a 5.8% decrease in groundwater
discharge, a 4.6% decrease in lateral flow, and a 4%
decrease in the amount of water percolating out of the
root zone. Deforestation and agricultural expansion
could be the cause of these changes. This is because
forest vegetation intercepts more water than other
land-use types (Ma et al. 2009), and the infiltration
rate of forest land is large compared with the other

1105

land-use types (Bruijnzeel 1990). Therefore, it is
likely that deforestation in the Be River catchment
caused an increase in runoff and decreases in groundwater discharge and lateral flow.
Under the impact of land-use change, deforestation and the increase in agricultural land resulted in
an increase in annual streamflow (1.2%) and sediment load (11.3%). Considering the seasonal change,
the streamflow decreased by 4.6% in the dry season
and increased by 1.8% in the wet season. In the case
of sediment load, it increased significantly in both the

dry and wet seasons by approximately 25.4% and
11.0%, respectively. The effect of land-use change
on the hydrology and sediment yield in the different
regions of Vietnam has been investigated by several
authors. For instance, Phan et al. (2010) investigated
the impact of land-use change on discharge and sediment yield in the Cau River catchment, and reported
that the conversion of 11.07% of forest land to agricultural land had caused increases in streamflow and
sediment load of 3.93% and 8.94%, respectively.
Ranzi et al. (2012) conducted a study of the landuse change effect on the sediment load in the Lo
River, and indicated that a 35% decrease in forest
area results in a 28% increase in sediment load. In
general, the changes in streamflow and sediment
yield under the land-use change in the Be River
catchment are fairly similar to the findings of the
studies by Phan et al. (2010) and Ranzi et al. (2012).
4.7 Response to combined climate and land-use
changes
To investigate the combined impact of climate and
land-use changes, the simulated streamflow, sediment
load and water balance components under Scenario 4
(land-use in 2001 and climate data in the 1990–2000
period) were compared to those during the baseline
period (land-use in 1990 and climate data in the
1978–1989 period). The results are displayed in
Figs 8 and 9.
The combined impact of land-use and climate
changes caused increases in streamflow and sediment
load, as well as in all water balance components.
When the changes caused by climate change alone
and land-use change alone occur in the same direction, the change is intensified, as climate change and

land-use change occur simultaneously. In contrast,
when the directions of the changes affected by climate change alone and land-use change alone are
opposite, the change is reduced when climate change
and land-use change occur concurrently.


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Dao Nguyen Khoi and Tadashi Suetsugi

Table 8 Simulated streamflow at the Phuoc Hoa station under the impacts of climate and land-use changes.
Scenario

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1
2
3
4

Land-use

1990
1990
2001
2001

Climate

1978–1989

1990–2000
1978–1989
1990–2000

Streamflow (m3 s-1)

Changes in observed flow

Sim.

(m s )

(%)

(m3 s-1)

(%)

208
–
–
254

213
267
216
270

–
–

–
46

–
–
–
22.1

–
54
3
57

–
25.4
1.4
26.8

4.8 Limitations and recommendations
The SWAT hydrological model was successfully
applied in this study to the Be River catchment to
assess the impact of climate and land-use changes on
hydrology and sediment yield. However, there are
limitations in both the data and the model, which
are described as follows.
One of the limitations in this study comes from
the unavailability of data. Because of the lack of
sediment load data, sediment simulation is calibrated
and validated in monthly time steps for only a short
period of time, whereas hydrological modelling is

calibrated and validated in daily time steps for a
long period of time. Aside from this, the calibration
and validation of streamflow and sediment simulations are not for the same period of time, because the
observed sediment data do not allow for the same
period to be applied, so it is required to extrapolate

-1

Changes in simulated flow

Obs.

Table 8 shows the details of the annual streamflow
simulated by the SWAT model under the different climate and land-use changes. Compared with Scenario 1,
the simulated streamflow in Scenario 4 increased by
57m3 s-1 (26.8%), which represents the combined
impacts of climate and land-use changes, while the
increase in observed streamflow caused by both climate
and land-use changes was 46 m3 s-1 (22.1%). The slight
difference of the increases in simulated and observed
streamflow could be explained by the overestimation in
simulation results of the SWAT model, but the simulation results were within the performance criteria given
by Moriasi et al. (2007). Aside from this, the simulation
results showed that both land-use change and climate
change increased the streamflow, with the percentage
contribution of 1.4% for land-use change and 25.4% for
climate change.
In general, the simulation results showed that the
hydrological processes have stronger responses to
climate change compared to land-use change (Figs

8 and 9, Table 8).

3

the streamflow beyond the time period of observed
sediment data using the calibrated SWAT model for
streamflow simulation in order to calibrate and validate the sediment simulation. These decrease the
accuracy of the model performance in sediment simulation. Therefore, to improve the simulation results,
collecting additional data on sediment load should be
considered to improve the model performance in
streamflow and sediment yield simulations.
Another limitation comes from the SWAT model.
The SWAT model uses a number of empirical and quasiphysical equations that were developed based on the
climate conditions in the United States, and those equations may not be appropriate for the tropical climate in
Vietnam. For example, the CN2 equation was a product
of more than 20 years of studies involving rainfall–runoff relationships in small rural watersheds across the
United States (Neitsch et al. 2011). In addition, the
MUSLE was also developed based on the hydrological
conditions throughout the United States. In the tropical
area, the heavy rainfall that may accompany a storm has
the potential to erode as much surface soil in the catchment as the subsequent runoff, but the MUSLE does not
account for such factors (Phomcha et al. 2011). It is
suggested that some parameters in the empirical equation should be modified to suit the tropical climate area
in order to improve the simulation results. The use of
oversimplified sediment routing algorithms to simulate
both landscape and in-stream erosion is a further limitation of the SWAT model. Aside from this, the SWAT
model allows all the soil eroded by runoff to reach the
channel directly, without considering sediment deposition remaining on surface catchment areas (Oeurng et al.
2011). Even though the SWAT model has some limitations, the simulation results were within the performance
criteria provided by Moriasi et al. (2007).

5 CONCLUSION
The SWAT model was applied to the Be River catchment to model the impacts of environmental changes,
including climate and land-use changes, on the


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Impact of climate and land-use changes on hydrological processes

hydrological processes and sediment yield. The
results of model calibration and validation showed
that the SWAT model could be a useful tool for
evaluating the impact of climate and land-use
changes on hydrological processes and sediment
yield in the Be River catchment.
Climate change in this study led to significantly
increased streamflow, sediment load and water balance
components in the 1990–2000 period compared to the
1978–1989 period. Increases in temperature and precipitation caused these increases in the course of the
period. The land-use change in the study area caused
increases in streamflow, sediment load, actual evapotranspiration, surface runoff and soil water, and
decreases in groundwater discharge, lateral flow and
amount of water percolating out of the root zone.
These changes are attributed to deforestation and agricultural expansion that happened in the 1990–2001 period (SIWRP 2002, 2008). Under the impact of coupled
land-use and climate changes, the streamflow, sediment
yield and water balance components increased in the
1990–2000 period compared with those in the 1978–
1989 period. These changes would raise concern regarding the increase of soil erosion in the Be River
catchment.
In general, climate variability influenced hydrological processes and sediment yield more strongly

than the land-use change in the catchment during the
1978–2000 period. Therefore, when planning and
managing for water resources, the importance of
increasing adaptation to climate change is emphasized. However, with the considerable changes in
the surface runoff and sediment load under the
impact of land-use change, the effect of land-use
change should be accounted for in water resource
management in the Be River catchment.
Investigating not only the separate but also the
combined impacts of climate and land-use changes
helps to enhance our understanding of these impacts
on hydrological processes and soil erosion in the
catchment. The results obtained from this study
could be of value to managers/decision-makers in
integrated river basin management, as well as to the
development of adaptation and mitigation strategies
regarding climate and land-use changes.

Acknowledgement The authors thank their colleagues in Vietnam for assisting with the data. They
are grateful for the comments of two anonymous
reviewers, which greatly enhanced the quality of the
manuscript.

1107

Funding The authors acknowledge the Global Center
of Excellence (GCOE) program of the University of
Yamanashi, which funded this study.

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