Front. Earth Sci. China 2009, 3(2): 154–163
DOI 10.1007/s11707-009-0025-5
RESEARCH ARTICLE
Extreme value analysis of annual maximum water levels in
the Pearl River Delta, China
Qiang ZHANG (✉)1,2, Chong-Yu XU3, Yongqin David CHEN4, Chun-ling LIU4
1 Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
2 Department of Water Resources and Environment, Sun Yat-Sen University, GuangZhou 510275, China
3 Department of Geosciences, University of Oslo, Norway
4 Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
© Higher Education Press and Springer-Verlag 2009
Abstract We analyzed the statistical properties of water
level extremes in the Pearl River Delta using five
probability distribution functions. Estimation of parameters was performed using the L-moment technique.
Goodness-of-fit was done based on KolmogorovSmirnov’s statistic D (K-S D). The research results indicate
that Wakeby distribution is the best statistical model for
description of statistical behaviors of water level extremes
in the study region. Statistical analysis indicates that water
levels corresponding to different return periods and
associated variability tend to be larger in the landward
side of the Pearl River Delta and vice versa. A ridge
characterized by higher water level can be identified
expanding along the West River and the Modaomen
channel, showing the impacts of the hydrologic process of
the West River basin. Trough and higher grades of water
level changes can be detected in the region drained by
Xi’nanyong channel, Dongping channel, and mainstream
of Pearl River. The Pearl River Delta region is characterized by low-lying topography and a highly-advanced
socio-economy, and is heavily populated, being prone to
flood hazards and flood inundation due to rising sea level
and typhoons. Therefore, sound and effective countermeasures should be made for human mitigation to natural
hazards such as floods and typhoons.
Keywords extreme values, probability distribution functions, annual maximum water level, extreme value analysis,
Pearl River estuary
1
Introduction
The modeling of water level extremes along coastal
regions is essential in the design of flood-related
Received September 22, 2008; accepted January 29, 2009
E-mail: zhangqnj@gmail.com
infrastructures. Moreover, a rational assessment of marine
climate at any site would involve extreme value analysis of
water levels. Investigations of coastal flooding and the
associated design and operation of marine facilities also
require sound estimation of the water level extremes
(Sobey, 2005). It should be noted that tremendous impacts
of extreme events on human society are more likely to
accrue through changes of extreme events than through
slow changes in mean conditions (Wigley, 1985). Therefore, public awareness of extreme events has risen sharply
in recent years partly because of the catastrophic nature of
floods, typhoons, and the sea-level rising (Beniston and
Stephenson, 2004; Zhang et al., 2006a, 2006b). In
particular, the currently well-evidenced global warming
has the potential to increase the probability of extreme
events. Thus, it is necessary to explore the changing
properties of water level extremes in the coastal regions,
which are usually dominated by a highly developed socioeconomy and are prone to natural hazards such as floods,
typhoons, etc.
The Pearl River Delta is heavily populated and
dominated by a highly developed socio-economy. Furthermore, it is characterized by low-lying topography. The area
of the Pearl River Delta is about 6932.5 km2. Most parts of
it are lower than 1 m a.s.l. and about 13% is below sea
level, making the Pearl River Delta prone to floods and
rising sea level (Li et al., 1993). Considerable attention has
been paid to water level changes and particularly to water
level alterations due to human activities across the river
networks of the Pearl River Delta. Lu et al. (2007)
indicated that the river channel in the upper Pearl River
Delta was greatly altered due to in-channel dredging and
levee construction after about the 1980s, resulting in
decreasing water level. However, Xu (1998) and Huang et
al. (2000) suggested that the rising sea level in the estuary
leads to an obvious backwater effect, which in turn further
forces the flood stage upward. Mao et al. (2004)
Qiang ZHANG et al. Extreme value analysis of annual maximum water levels in the Pearl River Delta, China
investigated tidal level variations, tidal flows, and water
circulation in the Pearl River estuary during the dry and
wet seasons in 1998, showing that the average tidal range
was small offshore and increased towards the estuary.
Many other researches about the water levels and possible
causes can be found in Chinese literatures (Zeng et al.,
1992; Liu et al., 2003; Chen et al., 2004).
With respect to the statistical properties of water level
extremes based on probability distribution functions, Wang
(1986) fitted the extreme low water level series of the
Modaomen station in the Pearl River Delta using Pearson
type III distribution function and discussed parameter
estimation. Chen et al. (2001), taking Denglongshan
station as a case study, also advocated the application of
Pearson type III distribution in water level extreme value
analysis. However, case studies of extreme value analysis
indicated that generalized extreme value (GEV) distribution (D’Onofrio et al., 1999; Butler et al., 2007) and
Wakeby distribution (Griffiths, 1989) are usually regarded
as the first option in extreme value analysis. Thus, some
important scientific questions still remain unanswered
concerning extreme value analysis of water levels in the
region: 1) Is Pearson type III distribution the only choice in
describing behavior of extreme water levels in the Pearl
River Delta region? Are there alternatives to better
describe the statistical properties of water level extremes?
2) What could be the magnitudes of extreme water levels
corresponding to various return periods over the Pearl
River Delta region? Answers to these questions will be of
great importance in flood-related infrastructure and human
mitigation to changes of water level extremes under the
changing environment. In addition, vulnerability to flooding is a worldwide problem, which needs detailed local
studies and a warning system based on scientific
investigations (D’Onofrio et al., 1999). Therefore, the
objectives of this paper are: 1) to select the probability
distribution function that better describes behaviors of
water level extremes in the region; 2) to assess magnitudes
of water levels corresponding to different return periods;
and 3) to explore spatial distribution of different
magnitudes of water level extremes corresponding to
different return periods over the Pearl River Delta region.
This study will be of scientific and practical merits for the
human mitigation to the floods and sea level rising under
the changing environment in the Pearl River Delta.
2
Study region and data
2.1
Study region
The Pearl River Delta (112°26′E–114°24′E, 21°30′N–
23°42′N) involves one of the most complicated river
networks of the world with a density of 0.68–1.07 km/km2
(Chen and Chen, 2002). The Pearl River Delta region,
being the fastest developing region in China, is dominated
155
by a booming socio-economy and is heavily populated
with a highly dense agglomeration of over 100 towns and
cities. It is the engine of the socio-economy of China.
Salinity intrusion, frequent floods, and rising sea level are
the key factors affecting the sustainable development of the
Pearl River Delta. Therefore, it is a must to understand the
statistical behaviors of water level extremes (which refers
to annual maximum water levels in this paper) based on
probability distribution functions.
2.2
Data
Monthly maximum water level series is available for 21
gauging stations across the Pearl River Delta. Detailed
information about the dataset can be referred to in Table 1.
The basic descriptive statistics of water levels can be
referred to in Table 2. The hydrologic data before 1989 are
extracted from the Hydrological Year Book (published by
the Hydrological Bureau of the Ministry of Water
Resources of China), and those after 1989 are provided
by the Water Bureau of Guangdong Province. The location
of the gauging stations can be referred to in Fig. 1. The
missing data are filled based on the data of neighboring
stations using regression method (R2 > 0.8 and even
R2 > 0.95). For the sake of probability distribution
analysis, the annual maximum water level series for
individual stations is extracted from the dataset. The
annual maximum water levels have been checked for linear
trends using standard regression analysis. The trend
component, if any, was removed to ensure that they were
truly random (Graff, 1981; D’Onofrio et al., 1999). The
trend component will be added to the series after water
level extremes are assessed using probability distribution
functions to recover its true value (Chen et al., 2001).
Autocorrelation analysis also confirmed no persistence in
water level extreme series (figures not shown here).
The river channels denoted with numbers are the
locations of the gauging stations. The names of the river
channels are listed as following: 1: North mainstream East
River; 2: Modaomen channel; 3: Hengmen channel; 4:
Yamen channel; 5: Jitimen channel; 6: Mainstream Pearl
River; 7: West River channel; 8: Xi’nanyong channel; 9:
Ronggui channel; 10: Jiaomen channel; 11: Shunde
channel; 12: Shawan channel; 13: North River channel;
14: Tanjiang channel; 15: South mainstream East River;
16: Hongqili channel; 17: Xiaolan channel; 18: Hutiaomen
channel; 19: Dongping channel.
3
Methodology
In this paper, analysis procedure was based on the
following steps: 1) Five probability distribution functions
(PDF) were chosen for candidates, namely, Log normal
distribution (LN3; three parameters), general extreme
value (GEV) distribution (three parameters), Pearson
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Table 1
Front. Earth Sci. China 2009, 3(2): 154–163
Dataset of the water levels in the Pearl Delta
station name
longitude
latitude
time interval
periods with missing data
Dasheng
113°32′
23°03′
1958–2005
Jun.—Dec. 1963
Denglongshan
113°24′
22°14′
1959–2005
Jan.—Sep. 1958
Hengmen
113°31′
22°35′
1959–2005
Huangchong
113°04′
22°18′
1961–2005
Huangjin
113°17′
22°08′
1965–2005
Huangpu
113°28′
23°06′
1958–2005
Jiangmen
113°07′
22°36′
1958–2005
Laoyagang
113°12′
23°14′
1958–2005
Dec. 1959
Makou
112°48′
23°07′
1958–2006
Sep.—Dec. 1959; 1966; 1968; Oct.—Dec. 1969
Nanhua
113°05′
22°48′
1958–2005
Nansha
113°34′
22°45′
1963–2005
Rongqi
113°16′
22°47′
1958–2005
2000–2005
2000
Sanduo
112°59′
22°59′
1958–2005
Sanshakou
113°30′
22°54′
1958–2005
1959
Sanshui
112°50′
23°10′
1958–2005
Sep.—Dec. 1959; 1960
Shizui
112°54′
22°28′
1959–2005
Nov.—Dec. 1968; 2000
Sishengwei
113°36′
22°55′
1958–2005
1964
Tianhe
113°04′
22°44′
1958–1988
Xiaolan
113°14′
22°41′
1975–2005
Sep.—Dec. 1981
Xipaotai
113°07′
22°13′
1958–2005
1968–73
Zhuyin
113°17′
22°22′
1959–2005
Table 2
Sample size (N), mean, minimum, median, interquantile range (IQR), sample L-skewness, and maximum of annual maximum water levels
(unit, m) for individual station
station
N
mean
min
median
IQR
L-skew
max
Dasheng
48
1.94
1.58
1.90
0.28
0.58
2.44
Denglongshan
48
1.66
1.29
1.57
0.32
1.46
2.65
Hengmen
48
1.86
1.52
1.78
0.32
1.24
2.62
Huangchong
48
1.79
1.45
1.70
0.24
1.32
2.51
Huangpu
48
1.97
1.60
1.93
0.32
0.40
2.48
Jiangmen
48
3.47
2.00
3.38
1.27
0.18
5.09
Laoyagang
48
2.12
1.52
2.10
0.34
0.37
2.85
Makou
48
7.20
3.04
7.22
2.36
– 0.41
10.00
Nanhua
48
4.23
2.31
4.23
1.51
– 0.02
6.05
Rongqi
48
2.71
1.99
2.61
0.74
0.68
3.99
Sanduo
48
4.86
2.37
4.79
2.03
– 0.02
7.10
Sanshakou
48
1.81
1.44
1.79
0.35
0.40
2.34
Sanshui
48
7.23
2.98
7.18
2.40
– 0.31
10.30
Shizui
48
1.84
1.53
1.84
0.30
0.67
2.48
Sishengwei
48
1.86
1.21
1.83
0.27
0.11
2.55
Tianhe
48
4.34
2.33
4.35
1.48
0.02
6.32
Xiaolan
48
3.36
1.99
3.27
1.06
0.34
5.05
Xipaotai
48
1.78
1.49
1.71
0.25
1.29
2.46
Zhuyin
48
1.90
1.51
1.87
0.34
0.54
2.50
Huangjin
41
1.63
1.22
1.55
0.31
0.91
2.38
Nansha
43
1.91
1.64
1.82
0.26
1.46
2.68
Qiang ZHANG et al. Extreme value analysis of annual maximum water levels in the Pearl River Delta, China
157
Fig. 1 Location of the study region and gauging stations
type III distribution (three parameters), Wakeby distribution (WAD) (five parameters), and General Pareto
distribution (GP; three parameters). These five PDFs are
commonly used in extreme value analysis. 2) For each
individual station, the annual maximum water level
series was fitted with these five PDFs, respectively.
The parameters of these five PDFs were estimated using
L-moment estimation technique (Hosking, 1990). 3)
Goodness-of-fit was performed using KolmogorovSmirnov’s statistic D (K-S D). In this paper, 95%
confidence level was used to reject or accept a fit (if n =
48, the critical value of K-S D is 0.196; if n = 41, 43, the
critical values of K-S D are 0.21 and 0.207, respectively).
4) Based on K-S D, the best two probability distribution
functions were selected for each station, and they are
marked in bold in Table 3. The probability distribution
function that fitted well the annual maximum water level of
most of the stations will be decided as the best choice in
describing the statistical properties of water level extremes
in the Pearl River Delta region. 5) Spatial distribution of
water level extremes corresponding to various return
periods such as 10-year, 30-year, 50-year, 70-year, 90year, and 100-year return periods was analyzed using
Kriging interpolation technique (Goovaerts, 1999).
4
Results
4.1
Basic statistical properties
To further understand the statistical properties of annual
maximum water (AMW) levels across the Pearl River
Delta region, we computed the basic descriptive statistics
(Table 2). Here we depict spatial patterns of descriptive
statistics such as mean, minimum, interquantile range
(IQR), and maximum (Fig. 2). The IQR was computed
between the 75th and the 25th percentiles of the sample in
AMW series. The IQR is a robust estimate of the spread of
the data, since changes in the upper and lower 25% of the
data do not affect it. If there are outliers in the data, then the
IQR is more representative than the standard deviation. It
can be seen from Fig. 2 that similar spatial patterns can be
identified for mean, minimum, IQR, and maximum of
annual maximum water levels across the Pearl River Delta
region. The values of descriptive statistics are decreasing
from land to sea. A ridge characterized by higher water
level values extends along the West River and the
Modaomen channels. Troughs can be observed in the
Xi’nanyong channel and the Tanjiang Channel. The
location of the above-mentioned channels can be referred
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Front. Earth Sci. China 2009, 3(2): 154–163
Table 3
K-S’s statistic D computed from annual maximum water level series of individual gauging stations for five candidate probability functions
station
Log normal(3)
GEV (3)
Pearson (3)
Wakeby (5)
General Pareto (3)
Dasheng
0.089
Denglongshan
0.068
0.086
0.119
0.067
0.066
0.054
0.085
0.054
Hengmen
0.080
0.074
0.066
0.085
0.057
0.065
Huangchong
0.090
0.075
0.103
0.052
0.059
Huangpu
0.102
0.098
0.096
0.071
0.059
Jiangmen
0.074
0.064
0.072
0.053
0.063
Laoyagang
0.079
0.082
0.079
0.059
0.111
Makou
0.084
0.067
0.083
0.054
0.104
Nanhua
0.087
0.067
0.097
0.050
0.069
Rongqi
0.068
0.059
0.071
0.042
0.051
Sanduo
0.077
0.066
0.074
0.047
0.059
Sanshakou
0.069
0.068
0.067
0.059
0.066
Sanshui
0.048
0.060
0.061
0.046
0.080
Shizui
0.079
0.061
0.088
0.058
0.066
Sishengwei
0.123
0.106
0.114
0.071
0.141
Tianhe
0.091
0.073
0.092
0.056
0.069
Xiaolan
0.073
0.063
0.071
0.052
0.081
Xipaotai
0.064
0.064
0.080
0.051
0.064
Zhuyin
0.067
0.060
0.060
0.047
0.071
Huangjin
0.090
0.092
0.096
0.110
0.130
Nansha
total
0.068
0.089
0.077
0.060
0.060
4 (19%)
9 (42.9%)
2 (9.5%)
20 (95%)
11 (52.4%)
Notes:
$K-S D critical value for all stations except Huangjin and Nansha is 0.196 (n = 48, 1 – α = 95%).
$K-S D critical value for Huangjin is 0.21 (n = 41) and for Nansha is 0.207 (n = 43).
$The bold values denote two probability distribution functions which best fit the extreme annual maximum water levels for individual stations. The criterion is that the
smaller the K-S’s statistic D the better the probability distribution function fits the annual maximum water levels.
to Fig. 1. In addition, Figs. 2(a), 2(d) depict grads field of
mean and maximum AMW series, showing that larger
grads than that of mean AMW were observed in maximum
AMW. Moreover, a larger difference between maximum
and minimum AMW was identified in the upper Pearl
River Delta region and a smaller difference in the lower
Pearl River Delta region (Figs. 2(b), 2(d)). Spatial
distribution of IQR corresponds well to that of mean,
minimum, and maximum AMW, meaning larger IQR
corresponds to larger mean, minimum, and maximum
AMW and vice versa.
4.2
Selection of probability distribution function
Selection of probability distribution functions hinges on KS D. We fitted AMW series with the five PDFs and
obtained associated K-S D (Table 3), then we decided on
the best two functions for individual stations based on K-S
D value (marked in bold in Table 3). Table 3 shows that
these five PDFs have good fit for the AMW series
at > 95% confidence level. However, Wakeby distribution
should be ranked as the best one in describing the behavior
of AMW across the Pearl River Delta region. Out of the 21
stations, 20 have the AMW series well fitted by Wakeby
distribution, accounting for 95% of the total stations.
Pearson type III distribution should be ranked as the worst
one. For illustrative purposes, we plotted the fitted PDFs
(Fig. 3(a)) and the cumulative distribution function
(Fig. 3(b)) of the AMW series of Dasheng station (figures
for other stations not shown here). It may be seen from
Fig. 3 that Wakeby function shows reasonably better fit
than other distributions with smaller K-S D values, which
supports the conclusion of the previous studies that
Wakeby distribution is a more flexible distribution than
other candidate distributions and is widely used in extreme
value analysis practice (Park et al., 2001). Table 4 lists all
the parameters estimated using L-moment techniques and
associated K-S D values for each gauging station in the
Pearl River delta region. Table 4 also indicates that
Wakeby has good and relatively consistent performance in
fitting AMW series over the Pearl River delta region with
smaller range of K-S D values among gauging stations.
Based on what mentioned above, Wakeby distribution will
be used in the following analysis.
Qiang ZHANG et al. Extreme value analysis of annual maximum water levels in the Pearl River Delta, China
159
Fig. 2 Statistical properties of annual maximum water level (m) of the Pearl River Delta. (a) mean, (b) minimum, (c) interquantile range,
(d) maximum
4.3 Water level extremes corresponding to different return
periods
Table 5 depicts the magnitudes of water level extremes
corresponding to 10-, 30-, 50-, 70-, 90-, and 100-year
periods. It can be seen from Table 5 that the highest water
levels can be identified in the Sanshui and Makou stations,
and lowest water levels in the Denglongshan and Huangjin
stations. Fig. 4 illustrates the spatial patterns of water levels
corresponding to different return periods: 10-year period
(Fig. 4(a)), 30-year period (Fig. 4(b)), 50-year period
(Fig. 4(c)), and 70-year period (Fig. 4(d)). Similar spatial
patterns can be identified in water level changes corresponding to 10- to 70-year return periods when compared
with those demonstrated in Fig. 2. Higher water levels are
observed in the landward side of the Pearl River Delta and
vice-versa. A ridge characterized by higher water level
expands along the West River and the Modaomen channel.
Moreover, smaller magnitudes of water level changes are
observed along the West River and the Modaomen
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Front. Earth Sci. China 2009, 3(2): 154–163
Fig. 3 Cumulative and probability distribution functions (Log normal, Generalized extreme value distribution, Pearson type III
distribution, Wakeby distribution and Generalized Pareto distribution) of annual maximum water level series of Dasheng station
Parameter estimates (L-ME) of WAD and K-S’s statistic D
Table 5 Design values (unit: m) corresponding to various return
computed from the annual maximum water level series of individual
periods (T = 10, 30, 50, 70, 90 and 100 years) computed from the annual
stations
maximum water level series of individual stations
Table 4
station
Dasheng
ξ
1.57
Denglongshan 0.00
Α
β
γ
Δ
K-S D
1.82
14.48
0.34
– 0.35 0.07
736.21
536.02
0.29
0.01 0.05
station
T = 10
T = 30
T = 50
T = 70
T = 90
T = 100
Dasheng
2.23
2.37
2.42
2.45
2.47
2.47
Denglongshan
2.04
2.37
2.52
2.62
2.70
2.73
Hengmen
0.00
757.62
475.00
0.29
– 0.10 0.06
Hengmen
2.20
2.44
2.54
2.61
2.65
2.67
Huangchong
0.00
1012.40
657.99
0.26
– 0.04 0.05
Huangchong
2.11
2.37
2.48
2.56
2.61
2.64
Huangpu
1.58
1.85
16.95
0.44
– 0.50 0.07
Huangpu
2.28
2.40
2.43
2.45
2.47
2.47
Jiangmen
2.12
1.43
3.94
1.74
– 0.63 0.05
Jiangmen
4.59
4.92
5.00
5.05
5.08
5.09
Laoyagang
1.46
3.46
8.19
0.32
– 0.14 0.06
Laoyagang
2.51
2.75
2.85
2.91
2.95
2.97
Makou
1.37
136.83
38.51
4.53
– 0.91 0.05
Makou
9.27
9.65
9.73
9.77
9.79
9.80
Nanhua
2.45
3.11
3.84
1.86
– 0.64 0.05
Nanhua
5.51
5.85
5.94
5.99
6.01
6.02
Rongqi
1.94
0.66
1.79
0.65
– 0.22 0.04
Rongqi
3.48
3.87
4.02
4.11
4.17
4.20
7.10
Sanduo
2.27
11.44
17.10
3.62
– 0.85 0.05
Sanduo
6.59
6.95
7.03
7.07
7.09
Sanshakou
1.36
5.06
28.58
0.43
– 0.50 0.06
Sanshakou
2.12
2.24
2.27
2.29
2.30
2.31
Sanshui
1.69
99.01
28.67
3.83
– 0.74 0.05
Sanshui
9.39
9.92
10.05
10.11
10.15
10.16
Shizui
1.50
0.55
1.09
0.05
0.29 0.06
Shizui
2.13
2.29
2.37
2.42
2.47
2.49
Sishengwei
0.41
61.57
49.95
0.30
– 0.19 0.07
Sishengwei
2.20
2.39
2.46
2.51
2.54
2.55
Tianhe
2.47
3.67
3.59
1.62
– 0.52 0.06
Tianhe
5.67
6.09
6.21
6.28
6.32
6.34
Xiaolan
2.09
2.29
2.99
0.88
– 0.27 0.05
Xiaolan
4.36
4.82
4.98
5.08
5.15
5.18
Xipaotai
0.00
1327.10
863.85
0.26
– 0.03 0.05
Xipaotai
2.11
2.37
2.49
2.56
2.62
2.64
Zhuyin
1.50
0.88
5.25
0.33
– 0.27 0.05
Zhuyin
2.24
2.41
2.47
2.51
2.53
2.54
Huangjin
0.53
63.47
78.60
0.35
– 0.13 0.11
Huangjin
2.03
2.29
2.40
2.47
2.52
2.54
Nansha
0.00
5.8294E + 5
3.5283E + 5
0.26
– 0.02 0.06
Nansha
2.24
2.52
2.64
2.73
2.79
2.81
channel. Larger magnitudes of water level changes can be
identified in the region covered by Xinanyong channel,
Dongping channel, and mainstem Pearl River. Figs. 4(e),
4(f) depict little difference between the spatial patterns of
water level extremes corresponding to the 90- and the 100year return periods. Fig. 4 indicates that areas covered by
contour lines with water level of 30-year return periods are
larger in comparison with those of 10-year return periods,
and this phenomenon can also be observed in the spatial
variations of water level extremes corresponding to 50- to
100-year periods. Larger magnitudes of water level
extremes in the region covered by Xinanyong channel,
Dongping channel, and mainstem Pearl River indicate
higher occurrence probability and magnitudes of water
Qiang ZHANG et al. Extreme value analysis of annual maximum water levels in the Pearl River Delta, China
Fig. 4 Spatial distribution of the estimated design values (unit: m) corresponding to various periods: (a)10-year period; (b)30-year
period; (c)50-year period; (d)70-year period; (e)90-year period; and (f)100-year period
161
162
Front. Earth Sci. China 2009, 3(2): 154–163
level extremes in this region. In the coastal regions, the
anomaly between water level extremes of 10-year return
periods and 100-year return periods is about 0.5 m.
5
Discussion and conclusions
We analyzed the statistical properties of annual maximum
water levels in the Pearl River Delta using probability
distribution functions. Based on K-S D, we accepted
Wakeby distribution (five parameters) as the best option in
the description of statistical behaviors of annual maximum
water levels. Some interesting discussions and associated
conclusions are as the following:
1) Wakeby distribution excels other probability distribution functions with smaller K-S D value in describing the
statistical behaviors of annual maximum water levels
across the Pearl River Delta region. The Wakeby
distribution function is defined by five parameters, more
than other probability distribution functions, which allows
a wider variety of shapes, and is expected to have a
reasonably good fit to extreme values (Park et al., 2001).
Therefore, Wakeby distribution is successfully applied in
the analysis of hydrologic extremes and other extreme
value analysis (Park et al., 2001; Wilks and McKay, 1996).
Similarly, the numerical results of this study also confirm
the good performance of Wakeby distribution in extreme
value analysis of AMW in the Pearl River Delta region. KS D values indicate that Wakeby distribution excels other
candidate functions in fitting water level extremes in the
study region. Pearson type III distribution is not a good
function for extreme value analysis of water level extremes
in the study region when compared with Wakeby
distribution function. This result, to a certain degree,
may argue with the common idea that Pearson type III
distribution is usually the preferred function in risk
assessment. As for specific region, arithmetic analysis is
necessary to decide which probability functions should be
used with the aim not to choose analysis technique blindly.
2) Descriptive statistics indicate higher water levels
along the West River and the Modaomen river channel.
This may be due to more streamflow from the West River
than that from the North River and the larger altitude of this
region. Spatial changes of water level extremes corresponding to various return periods indicate larger magnitudes in the region covered by Xi’nanyong channel,
Dongping channel, and mainstem Pearl River, implying
higher probability of higher extreme water levels in this
region. Larger density of contours of water levels related to
various return periods are also identified in these regions,
implying higher flood risk in these regions. It should be
noted here that these places are closer to the important
cities and economically developed regions such as
Dongguan, Guangzhou, etc. Furthermore, the places
covered by Xi’nanyong channel, Dongping channel, and
mainstem Pearl River are characterized by low-lying and
flat terrain. It is not beneficial for human mitigation to flood
hazards in these regions. Therefore, considerable attention
should be paid to enhancing flood-defense facilities in
these areas to guarantee sustainable development in terms
of economy.
3) The floods that occurred in 1994, 1997, and 1998
caused tremendous loss of property (Liu et al., 2003). The
Pearl River Delta region is dominated by low-lying
topography, characterized by a highly-advanced socioeconomy, and is heavily populated. All these factors
combine to make this region prone to flood hazards and
flood inundation due to rising sea level and typhoons. Chen
et al. (2008) addressed the roles of altered streamflow
allocation between West River and North River in
hydrologic alterations in terms of water level across the
Pearl River Delta. More streamflow transferred from West
River to North River further intensified flood mitigation
conditions in the hinterland of the Pearl River Delta.
Intensive human activities, particularly sand dredging with
aim to satisfy construction requirements, interfere with the
natural balance of the filling and scouring processes of the
river channel within the Pearl River Delta. Fast downcut
occurred to river channels in the upper Pearl River Delta,
and finer sediments were transported to the river channels
along the coastal regions, which further raised the water
level along the estuary of the Pearl River Delta, which,
together with rising sea level, is expected to deteriorate
flood mitigation situations in the hinterland of the Pearl
River Delta. It should be noted here that precipitation
intensity is increasing in the Pearl River basin (Zhang et
al., 2008). Altered precipitation intensity may further
impact the hydrologic processes within the Pearl River
Delta. Rising sea level of the Pearl estuary may further
complicate the water level variations. All these factors
should be put under consideration in considering effective
measures for human mitigation to flood hazards in the
study region. This problem will be addressed in the
ongoing work. The results of this study may be helpful for
a better understanding of water level variations with
respect to probability within the Pearl River Delta under
the changing environment.
Acknowledgements The work was supported by a grant from the Research
Grants Council of the Hong Kong Special Administrative Region, China
(Project No. CUHK405308), the National Natural Science Foundation of
China (Grant No. 40701015), and the Programme of Introducing Talents of
Discipline to Universities—the 111 Project of Hohai University. Cordial
thanks should be extended to two anonymous reviewers for their invaluable
comments and suggestions which greatly helped to improve the quality of this
paper. We were also grateful to Prof. Chen Xiaohong, Dr. Yang Tao, and Dr.
Jiang Tao for their kind help in the collection and pre-processing of the data.
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