RIVER RESEARCH AND APPLICATIONS
River. Res. Applic. 25: 1153–1168 (2009)
Published online 23 November 2008 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/rra.1212
CHANGE-POINT ALTERATIONS OF EXTREME WATER LEVELS AND
UNDERLYING CAUSES IN THE PEARL RIVER DELTA, CHINA
YONGQIN DAVID CHEN,a QIANG ZHANG,a,b* CHONG-YU XU,c TAO YANG,a
XIAOHONG CHEN d and TAO JIANG d
a
b
Department of Geography and Resource Management, Institute of Space and Earth Information Science,
The Chinese University of Hong Kong, Shatin, Hong Kong, China
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,
Nanjing 210008, China
c
Department of Geosciences, University of Oslo, Oslo, Norway
d
Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China
ABSTRACT
In this paper, the Bayesian model and Lepage test were used to detect change point and to analyse associated statistical properties
of high/low water levels in summer (June, July and August (JJA)) and winter (December, January and February (DJF)) months
across the PRD (Pearl River Delta). The results indicate that: (1) two time intervals, that is 1979–1981 and 1988–1995, witness
abrupt changes of SmH/SmL (summer mean high water level/summer mean low water level). The lower PRD is dominated by
increased mean and coefficient of variation (Cv) of SmH. Increased mean but decreased Cv of SmL can be observed in the
Mainstem Pearl River; (2) WmL (winter mean low water level) and WmH (winter mean high water level) of about 74% of
the total stations have two change points occurred roughly during 1969–1971 and 1993–1995. First change points of WmH are
mainly characterized by increased mean and Cv, but decreased mean and increased Cv of WmL can be observed across major
parts of the PRD. The driving factors causing abrupt changes of water levels are various. Intensive human activities cannot be
ignored, for example in-channel dredging and reallocation of the streamflow within the river channels due to human-induced
topographical changes of river channel. Different responses of high/low water levels to externally influencing factors and
interactions between influencing factors make the alterations of the water levels across the PRD more complicated. The findings
of this paper will be helpful for the management of the PRD and human mitigation to natural hazards under the changing
environment. Copyright # 2008 John Wiley & Sons, Ltd.
key words: Bayesian model; Lepage test; change-point detection; hydrologic alterations; Pearl River Delta
Received 23 November 2007; Revised 24 July 2008; Accepted 18 September 2008
INTRODUCTION
Located in South China, the Pearl River (Zhujiang in Chinese) basin is mainly consisted of three tributaries, that is
West River (Xijiang), North River (Beijiang) and East River (Dongjiang). The Pearl River Delta (PRD) involves
one of the most complicated deltaic drainage systems in the world (Chen and Chen, 2002). Flat terrain at low-lying
altitude and downstream location, together with rapid economic development and population growth over the past
three decades have made the PRD region more and more vulnerable to natural hazards such as flood, salinity
intrusion and storm surge. In recent years, engineering facilities and other modifications of the Pearl River network
have been designed to strengthen flood protection and to cater for huge requirements of building materials. Since
the mid-1980s, intensive channel dredging and levee construction have significantly affected flood stages and
caused serious hydrologic alterations in the study region (Liu et al., 2003).
Growing requirement of the building materials resulted in extensive and intensive in-channel dredging and sand
mining. During 1984–1999, the total amount of the sand removed from the river channel was about 17.6 times more
than the annual sediment transport and about 120 times more than the total suspended sediment transport of the
*Correspondence to: Dr Qiang Zhang, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
E-mail: zhangqnj@gmail.com
Copyright # 2008 John Wiley & Sons, Ltd.
1154
Y. D. CHEN ET AL.
Pearl River (Luo et al., 2000), which greatly altered channel shapes and the associated hydrologic processes within
the river channels (Luo et al., 2007). In recent years, hydrologic alterations and the underlying causes have drawn
increasing concerns from policy-makers and researchers and many relevant studies have been carried out (e.g.
Huang et al., 2000; Luo et al., 2000; Yang et al., 2002; Liu et al., 2003; Chen et al., 2004). Based on tidal records of
54 tide gauges distributed across the delta plain, Huang et al. (2004) examined the increased potential risk of tidal
inundation driven by sea level rise in the PRD. Xu (1998) found that the rising sea level in the estuary had led to
obvious backwater effect which in turn further forced the flood stage upward. Several studies suggested a rather
clear change in the deltaic hydrologic system in terms of both stage and discharge patterns in the early 1990s when
human interferences reached a massive level (e.g. Chen and Chen, 2002).
Given the increasing evidence of rising sea level and intensification of human activities, study on hydrological
alternations will be greatly important for sustainable development and mitigation of natural hazards. Although the
previous studies have analysed the changes of water levels and possible causes, several important scientific
questions remain unanswered: (1) When did abrupt changes of water levels, if any, occur across the PRD region?
(2) What are the statistical properties of water level before and after change point? and (3) What are the implications
of abrupt changes of water level for water management in this rapidly changing environment? To address these
questions, we have carried out this study to achieve the following three objectives: (1) identifying change points, if
any, of the water level series across the PRD region; (2) analysing the statistical properties (mean and coefficient of
variation (Cv)) of water levels before and after the change points and (3) examining the spatial patterns of water
level statistics over the study region and elucidating the possible underlying causes. This study carries not only
important scientific merits for understanding hydrologic alternations under massive human interferences in a very
complicated delta, but also highly valuable practical significance for environmental management in the PRD
region.
STUDY REGION AND DATA
Study region
As mentioned earlier, three rivers (Xijiang, Beijiang and Dongjiang, literally West, North and East River) join
together and form the PRD covering an area of 9750 km2 (Figure 1). The crisscross river network (density: 0.68–
1.07 km km2) in the PRD is one of the most complex deltaic drainage systems in the world. There are 424 cities
and towns with a population of over 10 000 each in the region, including major cities such as Hong Kong, Macau
and Guangzhou. The average distance between towns and cities is <10 km. With the rapid socio-economic
development and urbanization, significant changes of the hydrological characteristics have taken place in the river
network of the PRD over the past two decades. These changes should be mainly attributed to the combined effects
caused by following human activities: sand dredging in the river, reclamation of former flood-afflicted areas,
connection of dykes, construction of numerous bridges, docks and sluices along the river and irrational regulation
of water locks. Human activities, along with strong riverbed scouring and sea level rise, respectively, give rise to
riverbed degradation and stage reduction in the upper river reaches of the Delta but sedimentation and backwater
resistance in the river mouths.
Data
Monthly mean high and low water level data from 1958 to 2005 were collected from 19 gauging stations in the
PRD. Location of the gauging stations and detailed information of the data can be referred to Figure 1 and Table I,
respectively. Table I presents the data series lengths vary and some stations have missing data within certain time
periods. The missing data were filled up based on data of neighbouring stations using regression method (R2 > 0.8
and even R2 > 0.95). High water level usually occurs in high flow periods in summer (June, July and August (JJA))
and low water level usually occurs in low flow periods in winter (December, January and February (DJF)). High
water level may cause flood hazards and low water level is usually closely related to salinity intrusion. As a result of
channel and drainage system modification caused by human perturbation, water level and other hydrologic regimes
may change abruptly and the statistical properties of time series segments divided by change points, if any, will
Copyright # 2008 John Wiley & Sons, Ltd.
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
ALTERATIONS AND ABRUPT CHANGES OF WATER LEVEL
1155
Figure 1. Location of the Pearl River Delta in China and gauging stations. The river channels denoted with numbers are where the gauging
stations are located. The names of the river channels are listed as follows. Region I, region II and region III divided by dashed lines are the upper,
middle and lower PRD. 1, N. mainstem of Dongjiang; 2, Modaomen channel; 3, Hengmen channel; 4, Yamen channel; 5, Jitimen channel; 6,
mainstem of Zhujiang; 7, Xijiang channel; 8, Xi’nanyong channel; 9, Ronggui channel; 10, Jiaomen channel; 11, Shunde channel; 12, Shawan
channel; 13, Beijiang channel; 14, Tanjiang channel; 15, S. mainstem of Dongjiang; 16, Hongqili channel; 17, Xiaolan channel; 18, Hutiaomen
channel; 19, Dongping channel; I, Upper Pearl River Delta; II, Middle Pearl River Delta; III, lower Pearl River Delta
differ significantly from each other. Therefore, this study mainly focused on the detection and characterization of
abrupt changes of mean high/low water levels in summer and winter, respectively.
METHODS
Numerous techniques have been developed and applied to detect and analyse abrupt changes in hydrologic and
meteorological time series (e.g. Richter et al., 1996; Matsuyama et al., 2002; Mohammad-Djafari and Olivier,
2007). While this study did not aim to make comparison of these techniques, our first task was to identify and select
appropriate and robust change-point analysis methods. Two techniques, that is Bayesian model and Lepage test,
have been widely used by hydrologists and the literature has shown their pros and cons for evaluating hydrologic
time series. Bayesian model is highly robust in identifying one single change point, but becomes less certain when
multiple change points may exist (Chernoff and Zacks, 1963; Berger, 1985; Kotz and Wu, 2000; Xiong and Guo,
2004). On the other hand, Lepage test is theoretically capable of detecting multiple change points. However, the
change points detected may vary when time windows of different lengths are adopted (Lepage, 1971). With
Copyright # 2008 John Wiley & Sons, Ltd.
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
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Y. D. CHEN ET AL.
Table I. Dataset of the water levels in the Pearl River Delta and the river channels where the gauging stations are located along
Station name Longitude Latitude Series length
Upper PRD
Sanshui
Makou
Middle PRD
Jiangmen
Laoyagang
Nanhua
Rongqi
Sanduo
Tianhe
Xiaolan
Lower PRD
Dasheng
Denglongshan
Hengmen
Huangchong
Huangpu
Nansha
Sanshakou
Sishengwei
Xipaotai
Zhuyin
1128500
1128480
238100
238070
1958–2005
1958–2006
1138070
1138120
1138050
1138160
1128590
1138040
1138140
1138320
1138240
1138310
1138040
1138280
1138340
1138300
1138360
1138070
1138170
228360
238140
228480
228470
228590
228440
228410
238030
228140
228350
228180
238060
228450
228540
228550
228130
228220
1958–2005
1958–2005
1958–2005
1958–2005
1958–2005
1958–1988
1975–2005
1958–2005
1959–2005
1959–2005
1961–2005
1958–2005
1963–2005
1958–2005
1958–2005
1958–2005
1959–2005
Missing data
River channels
Sep.-Dec. 1959; 1960
Sep.-Dec. 1959; 1966; 1968;
Oct.-Dec. 1969
2000
Dec. 1959
Beijiang channel
Xijiang channel
Jun.-Dec. 1963
Jan.-Sep. 1958
2000–2005
1959
1964
1968–73
Xijiang channel
Xi’nanyong channel
Ronggui channel
Ronggui channel
Shunde channel
Xijiang channel
Xiaolan channel
North mainstem East River
Modaomen channel
Hengmen channel
Yamen channel
Mainstem Zhujiang River
Jiaomen channel
Shawan channel
South mainstem East River
Hutiaomen channel
Modaomen channel
reference to past studies, we consider a combination of these two techniques which complement to each other
would help us best to achieve our research objectives. Finally, it should be pointed out that the Bayesian model
requires normality of water level time series and otherwise the Box–Cox transformation (Box and Jenkins, 1976)
must be implemented to transform the original data series into a new series with a normal distribution (Xiong and
Guo, 2004).
Bayesian model
Change-point detection for time series is an important research area in statistics and has been widely used in
hydrologic analysis (Mohammad-Djafari and Olivier, 2007). The hydrologic series (x1,. . .,xn) is divided into two
segments by a change point denoted as k (1 k < n). The two segments with mean values denoted as ma and mb are
assumed to be normally distributed as
xi Nðma; s 2 ÞI ¼ 1; 2; . . . ; k
and
xj Nðmb ; s 2 Þ; j ¼ k þ 1; 2; . . . ; n
(1)
If we focus on the shift in the mean value, we just take the mean value of the time series as a random variable
following a certain distribution. Typically, the prior distributions of both ma and mb are assumed to be the same
normal distribution given by
ma Nðm0 ; s 20 Þ;
mb Nðm0 ; s 20 Þ
(2)
when s 20 is large enough, the above normal distribution will approach the non-informative prior distribution (Xiong
and Guo, 2004).
Copyright # 2008 John Wiley & Sons, Ltd.
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
ALTERATIONS AND ABRUPT CHANGES OF WATER LEVEL
1157
The variance s2 can be regarded as a constant in the two segments and it can be estimated from the water level
series. The distribution of the means ma and mb can be determined as
(3)
ma jXk ma
P
n m0 þ ki¼1 xi
;
¼
n þ k
Nðma ; s 2
a Þ
s 2
a ¼
s2
s2
;
n
¼
n þ k
s 20
(5)
mb jX kþ1 Nðmb ; s 2
b Þ
mb ¼
P
n m0 þ ni¼kþ1 xi
;
n þ ðn kÞ
s 2
b ¼
n
(4)
s2
s2
; n ¼ 2
þ ðn kÞ
s0
(6)
The likelihood function based on Equation (1) can be formulated as
"
#
"
#
n
1
ðxi ma Þ2 Y
1
ðxi mb Þ2
pffiffiffiffiffiffiffiffiffi exp pffiffiffiffiffiffiffiffiffi exp pðXjk; ma ; mb Þ ¼
2s 2
2s 2
2ps
i¼1
i¼kþ1 2ps
k
Y
(7)
Based on the Bayesian theorem, the posterior distribution of the change point k is derived as
pðXjk; ma ; mb ÞpðkÞ
pðkjX; ma ; mb Þ ¼ Pn1
j¼1 pðXj j; ma ; mb ÞpðjÞ
(8)
where p( j) represents the prior distribution of the change point k, and is often assumed to be a uniform distribution.
The full conditional distribution of k can be estimated by Markov Chain Monte Carlo methods (e.g. Smith and
Roberts, 1993). Detailed information of the computation procedure can be referred to Xiong and Guo (2004).
Lepage test
The Lepage test is a non-parametric, two-sample test for location and dispersion (Lepage, 1971). It has been
widely used to detect changes such as long-term trends, cyclic variations and step-like changes for rainfall
(Yonetani, 1993; Matsuyama et al., 2002; Benjamin and Roger, 2005). Following the x2 distribution with two
degrees of freedom, the Lepage statistic (HK) is a sum of the squares of the standardized Wilcoxon’s and Ansari–
Bradley’s statistics, that is
HK ¼
½W EðWÞ2 ½A EðAÞ2
þ
VðWÞ
VðAÞ
(9)
If HK exceeds 5.99 and 9.21, the difference between two sample means is judged as significant at 95% and 99%
confidence level, respectively. HK is calculated as follows: let x ¼ (x1, x2,. . .,xn1) and y ¼ (y1, y2,. . .,yn2) be two
independent samples of size n1 and n2. Assume that ui ¼ 1, if the ith smallest observation in a combined sample of
the size (n1 + n2) belongs to x and ui ¼ 0 if it belongs to y.The terms in Equation (8) can be derived based on the
Copyright # 2008 John Wiley & Sons, Ltd.
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
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Y. D. CHEN ET AL.
following equations:
W¼
nX
1 þn2
iui
(10)
i¼1
n1 ðn1 þ n2 þ 1Þ
2
(11)
n2 n1 ðn1 þ n2 þ 1Þ
2
(12)
EðWÞ ¼
VðWÞ ¼
A¼
n1
X
i¼1
nX
1 þn2
iui þ
ðn1 þ n2 i þ 1Þui
(13)
i¼n1 þ1
If n1 + n2 is even, E(A) and V(A) will be estimated as
n1 ðn1 þ n2 þ 2Þ
4
(14)
n1 n2 ðn1 þ n2 2Þðn1 þ n2 þ 2Þ
48ðn1 þ n2 1Þ
(15)
EðAÞ ¼
VðAÞ ¼
If n1 + n2 is odd, E(A) and V(A) will be estimated as
EðAÞ ¼
VðAÞ ¼
n1 ðn1 þ n2 þ 1Þ2
4ðn1 þ n2 Þ
n1 n2 ðn1 þ n2 þ 1Þ½ðn1 þ n2 Þ2 þ 3
48ðn1 þ n2 Þ2
(16)
(17)
The statistical properties of time series segments divided by change points can be characterized by mean and Cv.
The mean, mx, of a random variable, X, is its expected value. Thus,
mx ¼ EðXÞ ¼ m01
(18)
where m01 is the first moment about the origin. A sample estimate of the population mean is the arithmetic
average,X, calculated from
X¼
n
X
xi
i¼1
n
(19)
A dimensionless measure of dispersion is the Cv, defined as the standard deviation divided by the mean. The Cv
is estimated from
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Pk
2
sx
i¼1 ðxi xÞ
Cv ¼
wheresx ¼
(20)
x
n1
Spatial interpolation
Besides the temporal analysis for detecting abrupt changes of water level series, the spatial patterns of water level
characteristics before and after change points must be analysed for understanding the spatio-temporal dynamics of
hydrologic alternations across the entire study region. To generate water level surface from point-based values,
Kriging, a commonly used geostatistical technique, was employed for spatial interpolation which was implemented
Copyright # 2008 John Wiley & Sons, Ltd.
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
ALTERATIONS AND ABRUPT CHANGES OF WATER LEVEL
1159
Figure 2. Power of Lepage test and Bayes model in change-point detection. A: The normal plot of summer mean high water level of Sanduo
station; B: the change point detected based on the Bayes model; C: the change point detected using Lepage test; D: three segments divided by the
timing of the change point detected using Lepage test and Bayes model and related summer mean high level of each segments. The timing of the
change point, after assessment of the results of the Lepage test and the Bayes model, is 1979 and 1992. The change point is significant at >95%
confidence level
using Surfer software (e.g. Goovaerts, 1999; Hartkamp et al., 1999; Sauquet, 2006). In fact, the Kriging
interpolation method has already been applied in the study of water level changes within the PRD region (e.g. Chen
et al., 2004; Zhang and Chen, 2004).
RESULTS
Lepage test and Bayesian model for change-point detection
For illustrative purpose, a comparison of Lepage test and Bayesian model in change-point detection of summer
mean high (SmH) water level data at Sanduo station is shown in Figure 2. An example of normality test for SmH is
presented in Figure 2A, indicating that SmH of Sanduo station fits the normal distribution well (a straight line is
fitted for the accumulated frequency on a normal distribution paper) and thus it is not necessary to perform Box–
Cox transformation on the series. Figure 2B demonstrates the performance of Bayesian model in change-point
analysis. The timing of 1979 has the highest probability and so it can be accepted as a change point. The next
change point could be 1992, but no further information could be extracted from Figure 2B. Given the single change
point detected by the Bayesian model, we used Lepage test to search for other possible change points. As shown in
Figure 2C, at least three change points emerged after a series of Lepage tests with time windows of different
Copyright # 2008 John Wiley & Sons, Ltd.
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
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Y. D. CHEN ET AL.
Table II. Change points and related statistical features of the summer Mean high water level (SmH)
Region
Upper PRD
Middle PRD
Lower PRD
Station
Sanshui
Makou
Jiangmen
Laoyagang
Nanhua
Rongqi
Sanduo
Tianhe
Xiaolan
Sanshakou
Nansha
Dasheng
Hengmen
Sishengwei
Denglongshan
Zhuyin
Xipaotai
Huangchong
Huangpu
Cp 1
—
—
1981
1981
1979
1979
1979
1979
1979
1970
—
—
—
1967
1981
1981
—
—
—
Cp 2
1994
1993
—
1992
—
1992
1992
—
1992
1988
1993
1993
1991
1993
1991
—
—
1993
1993
Segment 1
Segment 2
Segment 3
Mean
Cv
Mean
Cv
Mean
Cv
2
2.07
1.67
0.94
2.01
1.25
1.94
1.99
1.62
0.74
0.7
0.8
0.68
0.62
0.51
0.79
—
0.58
0.81
0.15
0.15
0.25
0.16
0.21
2.12
0.27
0.27
0.24
2.41
0.06
0.08
0.02
0.26
0.11
0.19
—
0.08
0.09
1.88
1.87
1.32
0.8
1.66
1.04
1.45
1.62
1.3
0.94
0.75
0.86
0.8
0.79
0.45
0.7
—
0.62
0.86
0.21
0.23
0.06
0.07
0.26
1.21
1.57
0.31
0.16
2.89
0.08
0.07
0.11
0.06
0.11
0.2
—
0.09
0.09
—
—
—
0.92
—
1.26
1.82
—
1.49
0.78
—
—
—
0.84
0.52
—
—
—
—
—
—
—
0.13
—
0.26
0.31
—
0.28
2.55
—
—
—
0.07
0.17
—
—
—
—
Note: Cp: change point
lengths. The year 1979 was obviously a change point because of its highest confidence level (nearly 99%) and this
finding was also supported by the Bayesian model. A comparison of Figure 2B and C clearly exhibits that 1992 was
the second change point and the time series did not change abruptly in 1982. We used the two change points of 1979
and 1992 to divide the entire time series into three segments and found that their mean values differ significantly
(see Figure 2D), offering further evidence to verify the results of change-point detection. Overall, Figure 2
demonstrates that the Bayesian model performs well in detecting one change point and the Lepage test can decide
to what degree a possible change point is statistically significant. These two methods together can assure the
validity of change-point detection. The above-mentioned procedures were used in change-point analysis in this
study for all the stations and the results are shown in the following sections.
Change point and related statistical properties of summer mean high/low water level (SmH/SmL)
Table II shows the results of change-point analysis for SmH water level. It can be found from Table II that 7 out of
19 stations have two change points, among which four stations are located in the middle PRD (i.e. Laoyagang,
Rongqi, Sanduo and Xiaolan) and three stations in the lower PRD (Sanshakou, Sishengwei and Denglongshan).
Eleven out of 19 stations scattering across the whole study region have one change point, and only one station
(Xipaotai) has no change point. The first change point mostly occurring in 1979–1981 was identified mainly in the
middle PRD region. The statistical characteristics of SmH prior/posterior to the change point suggested decreasing
mean SmH for Sanduo, Laoyagang, Rongqi, Xiaolan and Denglongshan, and increasing mean SmH for Sanshakou
and Sishengwei. Decreasing Cv can be observed in Laoyagang, Sishengwei, Rongqi, Xiaolan and Denglongshan,
and increasing Cv in Sanduo and Sanshakou (Table II). Table II also indicates that almost all the second change
points detected (except Sanshakou in 1988) occurred from 1991 to 1994. Spatial distribution of the ratio of the
mean and Cv of SmH between posterior and prior to the second change point is shown in Figure 3.
Figure 3A indicates that mean SmH had increased in large parts of the PRD after the second change points in the
early 1990s except the middle Xijiang channel. Figure 3B shows that larger Cv after the second change point can be
Copyright # 2008 John Wiley & Sons, Ltd.
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
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ALTERATIONS AND ABRUPT CHANGES OF WATER LEVEL
Figure 3. Spatial patterns of the mean and the coefficient of variations (Cv) of the segments divided by the second change points. A: The ratio of
the average of the summer mean high (SmH) water level posterior/prior to the second change point. B: The ratio of Cv of SmH water level
posterior/prior to the second change point. The solid triangles in the figure and in the following figures are gauging stations
Table III. Change points and related statistical features of the summer Mean low water level (SmL)
Station
Upper PRD
Middle PRD
Lower PRD
Sanshui
Makou
Jiangmen
Laoyagang
Rongqi
Sanduo
Tianhe
Xiaolan
Nanhua
Nansha
Sanshakou
Dasheng
Hengmen
Sishengwei
Denglongshan
Zhuyin
Xipaotai
Huangchong
Huangpu
Cp 1
—
—
1979
1981
1979
1979
1979
1979
1979
1974
1970
1985
—
1967
—
1981
—
1967
1985
Copyright # 2008 John Wiley & Sons, Ltd.
Cp 2
1995
1994
—
—
1992
1992
—
1992
—
1990
1988
—
1992
—
—
—
—
—
—
Segment 1
Segment 2
Segment 3
Mean
Cv
Mean
Cv
Mean
Cv
1.88
1.9239
1.39
0.118
0.6
1.68
1.77
1.24
1.75
0.59
0.76
0.79
0.33
1.04
—
0.27
—
0.66
0.81
0.16
0.17
0.37
1.71
0.62
0.37
0.33
0.39
0.33
0.15
0.09
0.1
0.27
0.19
—
0.72
—
0.05
0.1
1.55
1.62
0.96
0.26
0.32
1.01
1.3131
0.81
1.31
0.66
0.54
0.71
0.15
0.78
—
0.14
—
0.62
0.77
0.32
0.31
0.46
0.5
0.59
0.23
0.42
0.32
0.38
0.1
0.2
0.1
1.1
0.1
—
1.47
—
0.1
0.1
—
—
—
—
0.65
1.43
—
1.06
—
0.39
0.68
—
—
—
—
—
—
—
—
—
—
—
—
0.71
0.47
—
0.48
—
0.29
0.11
—
—
—
—
—
—
—
—
River. Res. Applic. 25: 1153–1168 (2009)
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Y. D. CHEN ET AL.
Figure 4. Spatial patterns of the mean and the coefficient of variations (Cv) of the segments divided by the second change points. A: The ratio of
the average of the summer mean low (SmL) water level posterior/prior to the change point. B: The ratio of Cv of SmL water level posterior/prior
to the change point
Table IV. Change points and related statistical features of the winter Mean high water level (WmH)
Station
Upper PRD
Middle PRD
Lower PRD
Makou
Sanshui
Sanduo
Jiangmen
Laoyagang
Nanhua
Rongqi
Tianhe
Xiaolan
Sanshakou
Nansha
Dasheng
Hengmen
Sishengwei
Denglongshan
Zhuyin
Xipaotai
Huangchong
Huangpu
Cp 1
1975
1971
1981
1976
1986
1971
1978
1985
1971
1970
—
1969
1969
1969
1975
—
1969
1969
—
Copyright # 2008 John Wiley & Sons, Ltd.
Cp 2
1995
1983
—
1995
—
1985
1988
1995
1985
1984
1993
1993
1993
1993
1993
—
1993
1993
1993
Segment 1
Segment 2
Segment 3
Mean
Cv
Mean
Cv
Mean
Cv
0.59
0.57
0.46
0.41
0.49
0.44
0.49
0.4
0.44
0.53
0.48
0.56
0.43
0.5
0.34
—
0.36
0.37
0.59
0.13
0.1
0.11
0.1
0.1
0.1
0.07
0.3
0.07
0.06
0.09
0.05
0.07
0.1
0.09
—
0.1
0.1
0.06
0.54
0.66
0.54
0.36
0.55
0.49
0.44
0.36
0.49
0.75
0.51
0.6
0.46
0.63
0.31
—
0.39
0.41
0.63
0.13
0.26
0.13
0.21
0.08
0.23
0.27
0.12
0.2
0.05
0.08
0.06
0.08
0.05
0.09
—
0.11
0.1
0.07
0.47
0.57
—
0.3
—
0.46
0.48
0.27
0.46
0.58
—
0.64
0.53
0.68
0.34
—
0.41
0.43
—
0.19
0.16
—
0.38
—
0.11
0.09
0.54
0.1
0.17
—
0.06
0.09
0.06
0.14
—
0.1
0.09
—
River. Res. Applic. 25: 1153–1168 (2009)
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ALTERATIONS AND ABRUPT CHANGES OF WATER LEVEL
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Figure 5. Spatial patterns of the mean and the coefficient of variations (Cv) of the segments divided by the change points. A: The ratio of the
average of the winter mean high (WmH) water level posterior/prior to the first change point. B: The ratio of Cv of WmH water level posterior/
prior to the first change point. C: The ratio of the average of the winter mean high water level posterior/prior to the second change point. D: The
ratio of the coefficient of the variations of the winter mean high water level posterior/prior to the second change point
Copyright # 2008 John Wiley & Sons, Ltd.
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
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Y. D. CHEN ET AL.
observed in the upper and lower PRD while the majority of the stations in the middle PRD experienced some
reduction of Cv. On the contrary, Sanshui and Makou stations exhibit decreased mean and increased Cv of SmH. In
the lower Xijiang channel, the upper Modaomen channel and the Tianjiang channel, both mean and Cv of SmH
dropped after the second change points.
As for SmL water level, 5 out of 19 stations have two change points, the first one in 1970, 1974 and 1979 and the
second one in 1988, 1990 and 1992 (Table III). Three stations (Rongqi, Sanduo and Xiaolan) located along the
Ronggui channel in the middle PRD experienced the first change point in 1979 and two stations (Nansha and
Sanshakou) in the lower PRD had change points in 1974 and 1970, respectively. The decreased mean and increased
Cv of SmL are observed in Nanshan and Sanshakou. Eight out of 19 stations had change points in the early 1990s
(1990–1995 except Sanshakou in 1988). Only two stations have change points in 1967 (i.e. Sishengwei and
Huangchong) and two stations (i.e. Denglongshan and Xipaotai) have no change point. Figure 4A shows the spatial
patterns of the statistical characteristics posterior/prior to second change point across the PRD. Figure 4A illustrates
that the upper PRD, upper Modaomen channel, Tanjiang channel and lower PRD are dominated by the decreased
mean SmL. Other parts of the PRD are controlled by the increased mean SmL. The spatial patterns of the Cv of SmL
seem to display the adverse patterns, except for the Ronggui channel and the Shunde channel. Therefore, decreased
mean SmL after the second change is generally accompanied by increased Cv and vice versa.
Change point and related statistical properties of winter mean high/low water level (WmH/WmL)
Table IV shows the detected change points of winter mean high water level (WmH). First change points occur
during 1969–1986. The second change points in the middle PRD are detected during 1983–1995. No significant
abrupt changes can be detected in the WmH series of Zhuyin. Figure 5 illustrates the spatial patterns of the
statistical properties of water level abrupt changes across the PRD region. Figure 5A shows that increased mean
WmH characterized the first abrupt changes of the WmH. Meanwhile, decreased mean WmH can be identified in
the Tanhe channel, the lower Modaomen channel and the Xijiang channel. With respect to Cv changes, although
most parts of the PRD are characterized by increased Cv posterior to the first change point, decreased Cv can still be
Table V. Change points and related statistical features of winter mean low water level (WmL)
Region
Upper PRD
Middle PRD
Lower PRD
Station
Sanshui
Makou
Sanduo
Jiangmen
Laoyagang
Nanhua
Rongqi
Tianhe
Xiaolan
Sanshakou
Nansha
Dasheng
Hengmen
Sishengwei
Denglongshan
Zhuyin
Xipaotai
Huangchong
Huangpu
CP1
1983
1985
1975
1985
1968
—
1971
1971
1971
1970
1980
1969
1980
1967
1975
1985
1973
1980
—
Copyright # 2008 John Wiley & Sons, Ltd.
CP 2
1995
1995
1995
1995
1990
1995
1981
1992
1985
1988
1991
1983
—
1983
1985
1995
—
—
—
Segment 1
Segment 2
Segment 3
Mean
Cv
Mean
Cv
Mean
Cv
0.27
0.27
0.15
0.13
0.6
0.104
0.47
0.12
0.27
0.92
0.85
1
0.65
1.15
0.57
0.39
0.78
0.815
—
0.34
0.42
0.3
0.54
0.06
0.63
0.07
0.37
0.15
0.03
0.07
0.03
0.07
0.14
0.05
0.15
0.04
0.04
—
0.14
0.17
0.23
0.2
0.53
0.22
0.51
0.02
0.21
0.68
0.79
0.95
0.55
0.96
0.55
0.43
0.745
0.75
—
0.64
0.33
0.3
0.2
0.1
0.3
0.1
7.9
0.4
0.1
0
0.1
0.1
0.1
0.2
0
0.1
0
—
0.12
0.08
0.29
0.35
0.58
—
0.43
0.32
0.27
0.86
0.68
0.87
—
0.84
0.56
0.48
—
—
—
1.05
1.48
0.26
0.25
0.06
—
0.14
0.36
0.18
0.05
0.07
0.05
—
0.07
0.06
0.1
—
—
—
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
ALTERATIONS AND ABRUPT CHANGES OF WATER LEVEL
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Figure 6. Spatial patterns of the mean and the coefficient of variations (Cv) of the segments divided by the change points. A: The ratio of the
average of the winter mean low water level posterior/prior to the first change point. B: The ratio of the coefficient of the variations of the winter
mean low water level posterior/prior to the first change point. C: The ratio of the average of the winter mean low water level posterior/prior to the
second change point. D: The ratio of the coefficient of the variations of the winter mean low water level posterior/prior to the second change point
observed in the Tanjiang channel, the lower Modaomen channel and the south mainstem East River. Figure 5A and
B shows that the south mainstem East River is dominated by increased mean and decreased Cv of WmH. However,
decreased mean and increased Cv of WmH can be observed in the Xijiang channel and WmH of Tanjiang channel is
characterized by decreased mean and Cv (Figures 5A and B and 1).
Copyright # 2008 John Wiley & Sons, Ltd.
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
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Y. D. CHEN ET AL.
The second change point of WmH in the Xijiang channel, the Tanjiang channel and Shawan channel is
characterized by decreasing mean WmH and that in the rest parts of the PRD is dominated by increasing mean
WmH. Larger increase of mean WmH after second change point can be observed in south/north mainstem East
River channel (Figures 5C and 1). Changes of Cv seem to manifest adverse spatial patterns (Figure 5D) when
compared to those demonstrated by Figure 5C. In Xijiang channel, Tanjiang channel and in the lower PRD
(Figures 5D and 1), the WmH Cv after second change point tends to be larger than that before second change point.
The Cv of WmH in Ronggui channel and lower Shunde channel tends to be decreasing after the second change
point. Increasing/decreasing mean WmH but decreasing/increasing Cv of WmH across the PRD characterized
second abrupt changes of the WmH (Figure 5C and D).
The stations having two change points account for 74% of the total stations (Table V). No abrupt changes can be
detected in the WmL series of Huangpu station. 89.5% stations have first change point during 1967–1985. The
second abrupt changes occurred mainly during 1990–1995 and 1980–1983 (Table V). Figure 6 demonstrates spatial
patterns of statistical characteristics for the first (Figure 6A and B) and second abrupt changes (Figure 6C and D).
Figure 6A shows that the first abrupt changes in the Modaomen, Yameng, Jitimen, Tanjiang, Shunde and Ronggui
channel are characterized by increased mean WmL. Larger increase of mean WmL can be identified in the upper
Modaomen channel. Most parts of PRD are dominated by increased WmL Cv, decreased WmL Cv, however, can be
observed in the upper Modaomen, south mainstem East River and Humen. The upper Modaomen channel is
dominated by increased mean but decreased Cv of WmL (Figure 6A and B). Decreased mean but increased Cv of
WmL can be identified in the lower Xijiang channel. Increased mean WmL characterized second abrupt changes
over large parts of the PRD (Figure 6C). Figure 6D demonstrates decreased Cv of the WmL in the lower Shunde
channel, Mainstem Pearl River, Shawan channel, lower Modaomen channel and larger increase of Cv in upper
Xijiang channel.
DISCUSSIONS AND CONCLUSION
In this study, we employed Bayesian model and Lepage test to detect one or two change points in the summer/
winter high/low water level (SmH/SmL and WmH/WmL) time series of over four decades and then analysed the
mean and Cv of water level before and after change points. Meanwhile, Kriging interpolation method was used to
evaluate the spatial patterns of statistical properties of abrupt changes. The following discussion of our findings will
help us to draw some interesting conclusions.
1. With respect to SmH and SmL water level changes, the lower PRD is characterized by increasing mean SmH and
SmL water level with increasing variability. Opposite changing properties of SmH and SmL can be identified in
the upper PRD. Increasing SmH and SmL were observed in the inner PRD with decreasing variability.
Specifically, abrupt changes of SmH and SmL largely occurred in two time periods of 1979–1981 and 1988–
1995, and only less than 1/3 stations have two change points. The lower PRD is dominated by increased mean
and Cv, but decreased mean and Cv of the SmH can be identified in Tanjiang and lower Xijiang channel.
Decreased mean but increased Cv characterized SmH in the upper Xijiang channel. The PRD region covered by
lower Shunde channel, Shawan channel and mainstem Pearl River is dominated by increased mean but
decreased Cv of SmH. SmL of the mainstem Pearl River is characterized by increased mean but decreased Cv.
Increase mean and Cv of the SmL can be observed in Shunde and Shawan channel.
2. As for the changes of WmL and WmH, more than 2/3 stations have two change points: The first change points
are during 1969–1971 and the second during 1993–1995. These two periods when change points occurred
witness different statistical properties: (1) the first abrupt changes are characterized by increased mean and Cv of
WmH but decreased mean and increased Cv of WmL across major parts of the PRD. Larger Cv of WmH/WmL
can be identified in the Ronggui channel, lower Xijaing channel and Shawan channel; (2) after the second
change point, the lower PRD is dominated by increased mean and Cv of WmH, decreased mean but increased Cv
of WmL. The Xijiang and Tanjiang channel are dominated by decreased mean, increased Cv of WmH and by
increased mean and Cv of WmL.
3. Alterations of water levels across the PRD are the results of such factors as streamflow variations, human
interferences and sea level fluctuations. These factors interact on each other and give rise to alterations of water
Copyright # 2008 John Wiley & Sons, Ltd.
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
ALTERATIONS AND ABRUPT CHANGES OF WATER LEVEL
1167
level components as mentioned above via dynamical mechanisms still remain unknown to us. Generally,
streamflow alterations and human activities such as in-channel dredging can be regarded as major causes for
water level alterations. Annual mean streamflow of the Xijiang River (controlled by the Makou station) is about
2.38 1011 m3, accounting for 73% of the total streamflow of the Pearl River, and that of the Beijiang River
(controlled by the Sanshui station) accounts for about 12.1%. Therefore, the streamflow from the Xijiang and
Beijiang River accounts for more than 85% of the total streamflow entering the ocean, controlling the hydrologic
processes of the PRD. Therefore, the changes of the streamflow from Xijiang River and the Beijiang River and
the reallocation of the streamflow within the river channels are in close association with the water level
alterations across the PRD. Makou/(Makou + Sanshui) streamflow ratio is 84.9–89% during 1959–1992. During
1993–1995, however, the ratio decreased to 78.2% and Sanshui/(Sanshui + Makou) streamflow ratio increased
to 21.8% (Luo et al., 2002). Changes of the streamflow reallocation between Makou and Sanshui directly caused
abrupt changes of water level during 1990–1995. After 1990–1995, SmH/SmL and WmH/WmL are increasing
in Dongping, Shunde, Shawan channel and the mainstem Pearl River. Increased mean and decreased Cv of the
summer/winter mean high/low water levels may be attributed to changing Sanshui/(Makou + Sanshui)
streamflow ratio. Increased mean and Cv of SmH/SmL and WmH/WmL were identified along above-mentioned
river channels after the first change point during 1979–1981. In addition, down-cutting and widening of riverbed
due to in-channel dredging and sand mining contribute to decrease of water level. The adoption of the ‘open door
and reform’ policy in the late 1970s aroused increasing requirement of building materials that directly led to
intensive in-dredging and sand mining, especially in 1980s. The timing of abrupt change of 1969–1971 and
1979–1981 corresponds well to the time when intensive sand mining started. In the early 1990s, intensive and
extensive in-channel dredging occurred to the Xiangjiang river networks (Luo et al., 2002), which caused
significant decreased water levels along the Xijiang channel. However, the winter mean low water levels of the
Xijiang channel is increasing after about 1993–1995. This may be the result of decreased Makou/(Sanshui +
Makou) streamflow ratio and associated backwater effect due to stronger tidal currents from the estuary of the
PRD. Take water level changes along the Xijiang channel as an example, after about 1993–1995, decreased
mean and Cv of SmH were identified; however, decreased mean but increased Cv of WmH were detected.
Larger Cv of the water level in high flow season may cause more serious salinity intrusion in the hinterland of the
PRD. In-channel dredging and sand mining deepened the river channel along the mainstem Pearl River and
Humen, which may be the main reason for the decreased mean but increased Cv of the summer/winter mean
water levels.
4. What mentioned above just describes impacts of two factors of streamflow changes and in-channel dredging/
sand mining on alterations of water levels across the PRD. The timing and associated statistical features of the
abrupt changes can partly be elucidated by streamflow changes and dredging. More than above two factors affect
changes of the water level and dynamic processes could be expected. Large amounts of sediments deposited in
the estuary of the PRD, together with increasing sea level, will cause increasing water level in the lower PRD
(Huang et al., 2000), which will further intensify flood hazards and salinity intrusion in the hinterland of the
PRD. Therefore, sound and effective policies should be necessary for human mitigation to floods, salinity
intrusion and sea level rising under the changing environment.
ACKNOWLEDGEMENTS
The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong
Kong Special Administrative Region, China (Project No. CUHK4627/05H), fully supported by a Direct Grant from
the Faculty of Social Science, The Chinese University of Hong Kong (Project No. 4450183), National Natural
Science Foundation of China (40701015) and by the Outstanding Overseas Chinese Scholars Fund from CAS (The
Chinese Academy of Sciences). Cordial thanks should be due to editor, Prof. Dr. Martin Thoms, and two
anonymous reviewers for their valuable comments and suggestions, which greatly improved the quality of this
paper.
Copyright # 2008 John Wiley & Sons, Ltd.
River. Res. Applic. 25: 1153–1168 (2009)
DOI: 10.1002/rra
1168
Y. D. CHEN ET AL.
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DOI: 10.1002/rra