Wavelet-based characterization of water level behaviors
Stoch Environ Res Risk Assess (2010) 24:81–92
DOI 10.1007/s00477-008-0302-y
O R I G I N A L P A P E R
Wavelet-based characterization of water level behaviors in the Pearl River estuary, China
Qiang Zhang Æ Chong-Yu Xu Æ Yongqin David Chen
Published online: 13 January 2009
Springer-Verlag 2008
Abstract In this paper, we analyzed the high/low water levels of eight stations along the Pearl River estuary and the high/low tidal levels of Sanzao station, and streamflow series of Sanshui and Makou stations using wavelet transform technique and correlation analysis method. The behaviors of high/low water levels of the Pearl River estuary, possible impacts of hydrological processes of the upper Pearl River Delta and astronomical tidal fluctuations were investigated. The results indicate that: (1) the streamflow variability of Sanshui and Makou stations is characterized by 1-year period; 1-, 0.5- and 0.25-year periods can be detected in the high tidal level series of
Sanzao station, which reflect the fluctuations of astronomical tidal levels. The low tidal level series of Sanzao station has two periodicity elements, i.e. 0.5- and 0.25-year periods; (2) different periodicity properties have been revealed: the periods of high water levels of the Pearl River estuary are characterized by 1-, 0.5- and 0.25-year periods; and 1-year period is the major period in the low water levels of the Pearl River estuary; (3) periodicity properties indicate that behaviors of low water levels are mainly influenced by hydrological processes of the upper Pearl
River Delta. High water levels of the Pearl River estuary seem to be affected by both hydrological processes and fluctuations of astronomical tidal levels represented by tidal level changes of Sanzao station. Correlation analysis results further corroborate this conclusion; (4) slight differences can be observed in wavelet transform patterns and properties of relationships between high/low water levels and streamflow changes. This can be formulated by altered hydrodynamic and morphodynamic processes due to intensifying human activities such as construction of engineering infrastructures and land reclamation.
Keywords Wavelet transform Correlation analysis
Water level behaviors Pearl River estuary
Q. Zhang (
&
)
State Key Laboratory of Lake Science and Environment,
Nanjing Institute of Geography and Limnology, Chinese
Academy of Sciences, 73 East Beijing Road,
210008 Nanjing, China e-mail: zhangqnj@gmail.com
Q. Zhang
Institute of Space and Earth Information Science,
The Chinese University of Hong Kong,
Shatin, Hong Kong, China
C.-Y. Xu
Department of Geosciences, University of Oslo, Oslo, Norway
Y. D. Chen
Department of Geography and Resource Management,
The Chinese University of Hong Kong,
Shatin, Hong Kong, China
1 Introduction
Short- and long-term sea level fluctuations strongly influence how the ocean affects both human activities and coastal ecosystems within the coastal zone (Percival and
Mofjeld
). Tides are the periodic rise and fall of the sea level as a result of attractive forces of the sun, the moon, and the earth. Tides and tidal currents are major sources of energy for turbulence and mixing in estuaries and they play important roles in the movement of dissolved and particulate material (Mao et al.
also dominated by intensifying human activities such as engineering structure built to protect buildings and agricultural land (DEFRA
; Byun et al.
greatly altered the hydrodynamic and morphodynamic
123
82 Stoch Environ Res Risk Assess (2010) 24:81–92 processes in the estuary (Brown
Delta (PRD) region is highly developed in socio-economy.
Booming economy and heavy human settlement overwhelmingly affect the hydrological processes within the river channels of the river network, one of the most complicated river networks of the world. During recent decades, such human activities as levee construction, sand dredging, land reclamation have accelerated the seaward growth of the PRD, which have influenced the function of harbors and navigational channels (Huang and Zhang
).
After about 1990s, intense sand dredging aiming to satisfy increasing requirement of building materials has caused distinct riverbed down-cutting in the mainstream of
North River, being one of the major factors responsible for decreasing water level of Sanshui station (Hou et al.
;
Chen and Chen
2002 ). Decreasing magnitude of water
level of Sanshui station is much more than that of Makou station, resulting in significant decreasing Makou/Sanshui streamflow ratio (Hou et al.
; Chen and Chen
Changes of Makou/Sanshui streamflow ratio has further altered the filling and scouring process within the river channels in the PRD region (Huang and Zhang
Generally, the Pearl River estuary is dominated by depositional process, and this process is different along the Pearl
River estuary due to changing streamflow diffluence ratio
(Liu et al.
; Chen et al.
). Based on Thematic
Mapper (TM) images (Liu et al.
), sediment deposition and transportation are mainly observed in the western Pearl
River estuary, especially in the Modaomen channel. Sediment deposition has shrunk river channels and fostered sand bars which force the flood stage upward during flood season (Chen
). Decreasing riverbed slope and river channel storage due to depositional process of estuary will prevent seaward discharge of floodwater and be further beneficial for sediment deposition (Chen
level will further deteriorate this situation and has the potential to cause higher probability of flood hazards and salinity intrusion in the hinterland of the PRD (Li et al.
), which will threaten the sustainable development of local socio-economy. Therefore, it is of great scientific/ practical merits to understand changing characteristics of high/low water level extremes along the Pearl River estuary.
Tidal fluctuation is a complex but stationary astronomical phenomenon, which renders reasonable the harmonic analysis method. Internal tides, however, because of their manner of generation and propagation, are inherently irregular (Jay and Kukulka
). River tides, where the tidal wave is damped and advected by river discharge, have been studied for more than 20 years (e.g. Godin
). The non-stationary character of these tidal processes provides an opportunity to obtain insights into tidal
123 dynamics and the interaction of tidal and non-tidal processes (Jay and Kukulka
2003 ; Jay and Flinchem 1997 ).
The modulation and generation of tidal frequency motion by non-periodic processes produce non-stationary tides. It is vital to apply a consistent means to evaluate the timevarying variance of all processes and to decide all frequency bands. Wavelet transform has been advocated in river tidal analysis (Jay and Flinchem
; Flinchem and
Jay
2000 ) because of tremendous interest in analyzing,
transmitting and compressing diverse non-stationary signals (e.g. Farge
1992 ). In this paper, we use continuous
wavelet transform technique to investigate behaviors of extreme high/low water levels along the Pearl River estuary. We do not modify our time series by eliminating longterm trends. All the statistical properties of the time series will be well preserved, taking the original series into account as a combination of long-term trends, quasi-periodic oscillations and noise. The objectives of this paper are: (1) to characterize periodicity of high/low water levels of the eight stations along the Pearl River estuary; and (2) to explore impacts of tidal fluctuations and streamflow changes on water level variations of the eight stations in the
Pearl River estuary.
2 Data and methodology
2.1 Data
The monthly data of extreme high/low water levels covering 1958–2005 were collected from 8 gauging stations located along the Pearl River estuary. Detailed information of the data can be referred to Table
data before 1989 were extracted from the Hydrological
Year Book (published by the Hydrological Bureau of the
Ministry of Water Resources of China) and those after
1989 were provided by the Hydrological Bureau of
Guangdong Province. The location of the gauging stations can be referred to Fig.
. The missing data are filled based on the data of neighboring stations using regression method with determination coefficient of R
2
[ 0.8 and even
R
2
[ 0.95. To demonstrate hydrological alterations of the
Pearl River delta, we collected daily streamflow data for
1958–2005 from Makou and Sanshui stations (Fig.
) which represent hydrological conditions of the upper Pearl
River delta. We also collected monthly data of extreme tidal levels (during 1964–1988) of Sanzao station showing typical astronomical tidal fluctuations.
2.2 Methodology
Wavelet transform (WT) is a powerful tool for characterizing the frequency, the intensity, the time position, and the
Stoch Environ Res Risk Assess (2010) 24:81–92
Table 1 Dataset of the water levels along the Pearl River estuary
Station name
Sishengwei
Sanshakou
Nansha
Hengmen
Denglongshan
Huangjin
Xipaotai
Huangchong
Sanzao
Longitude
113 36
0
113 30 0
113 34 0
113 31 0
113 24
0
113 17
0
113 07
0
113 04
0
113 24 0
Latitude
22 55
0
22 54 0
22 45 0
22 35 0
22 14
0
22 08
0
22 13
0
22 18
0
20 00 0
Periods with missing data
1964
1959
83
Time interval
1958–2005
1958–2005
1963–2005
1959–2005
1959–2005
1965–2005
1958–2005
1961–2005
1965–1988
January–September 1958
1968–1973
2000–2005
Fig. 1 Location of the study region. The names of the numbered river channels are: 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 duration of variations in hydro-meteorological series
(Zhang et al.
). Using WT, we can decompose the time series into time–frequency space, determining both the dominant modes of variability and how these modes vary in time (Torrence and Compo
). In this paper, the continuous wavelet transform (CWT, Morlet wavelet) was used because it is well localized in both time–frequency space. This method was briefly introduced here and more information can be referred to Torrence and Compo (
x n is assumed to be a time series with equal time spacing d and n = 0, … , N 1.
w o
( g ) is a wavelet function depending t on the dimensionless ‘time’ g with zero mean and localized in both frequency and time (Farge
; Torrence and
Compo
1998 ). We applied the Morlet wavelet due to its
good balance between time and frequency. The Morlet wavelet is defined as: w o
ð g Þ ¼ p 1 = 4 e i x o g e g 2
= 2 ð 1 Þ where x
0 is the nondimensional frequency and is 6 to satisfy the admissibility condition (Farge
and Compo
). The continuous wavelet transform of x n with a scaled w o
( g ):
W n
ð s Þ ¼ n 0 ¼ 0 x n 0 w
ð n
0 s n Þ d t
ð 2 Þ where (*) indicates the complex conjugate. The Cone of
Influence (COI) was introduced to ignore the edge effects.
The COI is the region where edge effects become important and is defined as the e -folding time. This e -folding time is decided with aim to drop the wavelet power for a discontinuity at the edge by e
2
(Grinsted et al.
123
84 Stoch Environ Res Risk Assess (2010) 24:81–92
; Torrence and Compo
). The significance of wavelet power was evaluated under the assumption that the signal is a stationary process with the background power spectrum ( P k
). The time series is assumed to own a mean power spectrum given by (3); it can be assumed to be a true feature with a certain confidence if the wavelet power spectrum peak is significantly above this background spectrum. The Fourier power spectrum of an AR(1) process with lag-1 autocorrelation a is given as (Grinsted et al.
):
P k
1
¼ j 1 a e a 2
2 i p k j
2
ð 3 Þ where k is the Fourier frequency index. Torrence and
), with the Monte Carlo method, indicated that the probability the wavelet power of a process with a given power spectrum ( P
D j W
X n r
ð
2
X s Þj
2
\ p
!
¼
1
2 p k k v
) is greater than p is
2 v
ð p Þ ð 4 Þ where v equals to 1 for real and 2 for complex wavelets. In this study, we used continuous wavelet transform to characterize periodicity properties of water level serious because of its good performance in study of geophysical series (Jay and Flinchem
3 Results
3.1 WT of streamflow and tidal levels of Sanzao station levels of Sanzao station representing the astronomical tidal fluctuations are used as independent variables affecting the water levels of other eight stations in the study region
(Fig.
). Figure
illustrates the wavelet transform of monthly streamflow of Makou station (Fig.
a) and
Sanshui station (Fig.
b). The monthly streamflow series of
Sanshui and Makou stations show significant power in the wavelet power spectrum at 1-year period. From a detailed inspection of the spectrum, it is confirmed that the 1-year band of the monthly streamflow of Sanshui station is not consistent throughout the entire time series. The 1-year band disappears twice: one is during 1963–1965 and another is during 1982–1992. Correspondingly, the 1-year band of monthly streamflow of Makou station is also relatively weaker in these two time intervals. After * 1992, the 1-year band of the streamflow of Sanshui station is stronger than that of Makou station, which can be well elucidated by human-induced streamflow diffluence between Makou station and Sanshui station. After about
1990s, distinct riverbed downcutting in the mainstream of
North River as a result of intense dredging caused obviously decreasing water level of Sanshui station (Hou et al.
; Chen and Chen
2002 ). This is the major driving
factor being responsible for increasing Sanshui/Makou streamflow diffluence which leads to more streamflow in
Sanshui, especially in flood season. Similar changing patterns of wavelet power spectrum can be observed in the
0.25- and 0.5-year band. Wang et al. ( 2006
) indicated that upper West River basin is dominated by decreasing precipitation, especially in summer and autumn. Increasing precipitation however is identified in the North River basin and the East River basin. The summer precipitation in the
North River basin is decreasing. Discharge of the West
River (Wuzhou station and Gaoyao station) is decreasing
In this study, the streamflow data of Makou and Sanshui stations representing the upstream flow variations and tidal
Fig. 2 Wavelet transform of monthly streamflow of a Makou station and b Sanshui station.
The U-shape line shows cone of influence. The thick solid lines denote 95% confidence level using red noise model
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Stoch Environ Res Risk Assess (2010) 24:81–92
Fig. 3 Wavelet transform of high ( a ) and low tidal level series ( b ) of Sanzao station. The
U-shape line shows cone of influence. The thick solid lines denote 95% confidence level using red noise model
85 and that of the North River (Shijiao station) is increasing
(Zhang et al.
). All these findings indicate that the streamflow of Sanshui Station and Makou station is impacted by similar climate system such as precipitation changes (Wang et al.
). Global wavelet spectrum indicates that the monthly streamflow series of Makou and
Sanshui stations are dominated by significant 1-year period, and the other period components are not significant at
[ 95% confidence level.
The wavelet power spectrum of high/low tidal levels of
Sanzao station (Fig.
) presents different patterns as compared with those of monthly streamflow of Makou and
Sanshui stations (Fig.
). It can be observed from Fig.
that, whether for high or low tidal levels, the power is broadly distributed with peaks in the 0.25
* 1-year band.
The 95% confidence regions demonstrate that 1967–1968 and 1978–1980 include intervals of higher variance of high tidal level, while low variance can be identified during
1968–1978 and 1980–1985. Global wavelet spectrum confirms 1-year, 0.5-year and 0.25-year periods of high tidal level of Sanzao station (Fig.
a), and these periods are both significant at [ 95% confidence level. These periodicity components are clearly the results of movement of sun and moon. Continuous wavelet power spectrum for the normalized time series of the low tidal level series of
Sanzao station (Fig.
3 b) shows high wavelet power in the
0.5-year band around 1966–1975, 1976–1980, * 1982–
1984 and 1986–1988. It can be identified from Fig.
b that the low tidal level of Sanzao station displays different properties of wavelet power spectrum as compared with those of high tidal levels. Significant year bands can be identified in the 0.5- and 0.25-year periods, wherein
0.5-year period is dominant. The 1-year period is not significant at [ 95% confidence level, while the 1-year period is dominant for the high tidal variability of Sanzao station. Tidal level changes of Sanzao station can be representative of sea level changes (Huang et al.
the wavelet transform of high/low tidal levels of Sanzao station can represent the sea level fluctuations in the ocean area near the Pearl River estuary, which shows different periodicity patterns as compared with wavelet transformation of monthly stream of Makou and Sanshui stations.
3.2 WT of high water levels for the eight stations
Figure
displays the wavelet transform of high water levels of Sishengwei station (A), Sanshakou station (B),
Nansha station (C), and Hengmen station (D). Similar patterns of wavelet power spectrum can be identified in
Fig.
in distribution of 0.5- and 0.25-year bands. Wavelet power distributes broadly in the 1-year, 0.5-year, and
0.25-year band. The 95% confidence regions are more consecutive in the 1-year band for Nansha station and
Hengmen station as compared with Sishengwei station and
Sanshakou station. Furthermore, similar changing properties of wavelet power spectrum in 1-year band can be observed for Nansha station and Hengmen station; and similar characteristics of wavelet power spectrum can be found for Sishengwei station and Sanshakou station. In addition, global wavelet spectrum indicates that the high water level series have the significant periods of 1 year,
0.5 years and 0.25 years.
Figure
shows the wavelet transform of high water levels of Denglongshan station (A), Huangjin station (B),
Xipaotai station (C), and Huangchong station (D). Just as what Fig.
shows, the wavelet power of the high water level series of Denglongshan station (Fig.
a) distributed broadly with peaks in the 1-year, 0.5-year and 0.25-year
123
86
Fig. 4 Wavelet transform of high water level series of a Sishengwei station; b Sanshakou station; c Nansha station; and d Hengmen station.
The U-shape line shows cone of influence. The thick solid lines denote 95% confidence level using red noise model
Stoch Environ Res Risk Assess (2010) 24:81–92 band. As for the 1-year band, the time intervals of 1963–
1970, 1975–1980, 1990–2000, have the 95% confidence regions dominated by higher variance. All these three stations, except Huangjin station, have the similar changing properties of wavelet power spectrum in different year bands. The power of high water level series of Huangjin station appears sporadically in the 0.25-, 0.5- and 1-year bands with peaks during * 1975 and 1985–1995 in the
1-year and 0.5-year bands. Different properties of wavelet power spectrum in various year bands imply different driving factors influencing the hydrodynamic and morphodynamic processes in the estuary, and details of which are discussed in Sect.
. Furthermore, the high water level wavelet transform patterns in the eight stations are more similar to those of Sanzao water level (Fig.
a) than to those of upstream flow at Makou and Sanshui stations
(Fig.
).
3.3 WT of low water levels for the eight stations
Figures
and
display the wavelet transform of low water level series of the eight stations along the Pearl River estuary. Figure
shows patterns of wavelet power spectrum of low water level series of Sishengwei station (A),
Sanshakou station (B), Nansha station (C), and Hengmen station (D). The low water levels of these 4 stations have similar time intervals with higher wavelet power in the
1-year band. However, slight shift in time intervals can be
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Stoch Environ Res Risk Assess (2010) 24:81–92
Fig. 5 Wavelet transform of high water level series of a Denglongshan station; b Huangjin station; c Xipaotai station; and d Huangchong station. The U-shape line shows cone of influence. The thick solid lines denote 95% confidence level using red noise model
87 identified as for individual stations. The higher wavelet power of low water level series of Sishengwei station
(Fig.
6 a) in the 1-year band can be observed during
1965–1980 and around 1995. The low water level series of
Sanshakou station has the higher wavelet power during
1965–1983 and 1992–1998 (Fig.
power of low water levels of Nansha station can be found during 1966–1980 and 1992–2005 (Fig.
Hengmen station, the 95% confidence region distributes consistently in the 1-year band throughout the whole studied time interval (Fig.
6 d). In addition, 95% significant
regions can be detected and distribute sporadically in the
0.25-year band, but no 95% significant regions can be observed in 0.5-year band. It can be seen from the global wavelet spectrum that 1-year period is dominant. Periods of
0.5 and 0.25 years are not significant at [ 95% confidence level.
Figure
presents the wavelet power spectrum of low water series of Denglongshan station (A), Huangjin station
(B), Xipaotai station (C), and Huangchong station (D).
Figure
indicates a significant (at 95% confidence level) wavelet variance in the 1- and 0.25-year bands, especially during 1960–1980 and 1992–2000, with strong fluctuations occurring in these time intervals. In the 0.25-year band, there also exist regions with higher wavelet power, but the regions distribute sporadically. This is particularly the case for Denglongshan station (Fig.
(Fig.
b). It can be observed from Fig.
b that 95% confidence regions in the 1-year band disappear during
1980–1992 and also appear sporadically after 1998.
123
88
Fig. 6 Wavelet transform of low water level series of a Sishengwei station; b Sanshakou station; c Nansha station; and d Hengmen station.
The U-shape line shows cone of influence. The thick solid lines denote 95% confidence level using red noise model
Stoch Environ Res Risk Assess (2010) 24:81–92
Comparatively, the wavelet power spectrum of the low water level series of Denglongshan station indicates that the 95% confidence region distributed consecutively in the
1-year band. Global wavelet spectrum suggests that low water level series of these stations mentioned above are dominated by 1-year period. 0.25-year period can be detected in the low water level series of Xipaotai station
(Fig.
7 c) and Huangchong station (Fig.
d), but can not be identified in the low water series of Denglongshan station
(Fig.
7 a) and Huangjin station (Fig.
b). No 0.5-year periods can be detected within low water level series of these four stations. In general, Figs.
and
indicate the low water level wavelet transform patterns in the eight stations are more similar to those of upstream flow at
Makou and Sanshui stations (Fig.
Sanzao water level (Fig.
b), which is opposite to high water levels discussed above.
3.4 Correlation analysis
To further understand behaviors of water levels along the
Pearl River estuary and their association with tidal level changes of Sanzao station and streamflow variability of
Sanshui station and Makou station. We study correlations between the water levels at the 8 stations with streamflow of Makou and Sanshui stations, and correlation with the water levels at Sanzao station. For illustrative purpose, correlations between high/low water level changes of
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Stoch Environ Res Risk Assess (2010) 24:81–92
Fig. 7 Wavelet transform of low water level series of a Denglongshan station; b Huangjin station; c Xipaotai station; and d Huangchong station. The U-shape line shows cone of influence. The thick solid lines denote 95% confidence level using red noise model
89
Denglongshan station, high/low tidal variations of Sanzao station and streamflow variability of Sanshui and Makou stations are shown in Fig.
, and similar results are obtained for the rest seven stations. It is seen that strong correlation is identified between high water level of
Denglongshan station and that of Sanzao station, while the same correlations with streamflow at Makou and Sanshui stations are low. On the contrary, correlation coefficients between low water level changes of Denglongshan station and streamflow variations of Sanshui/Makou station are higher than between low water level changes of Denglongshan station and Sanzao station. Table
summaries correlation coefficients between water levels at eight stations and streamflow of Sanshui station and Makou station, as well as correlation coefficients between water levels at 8 stations and water levels at Sanzao station. It is seen that, at high water levels, R values for correlation between streamflow of Sanshui station and Makou station and water levels of the eight stations are between 0.25 and
0.57. This relation can be categorized as low to moderate correlation. These relationships are different among stations along the Pearl River estuary. Low correlation is identified between water level changes of Huangjin and
Denglongshan stations and streamflow variations of
Sanshui and Makou stations. Moderate correlation is detected between streamflow changes of Sanshui and
123
90
Fig. 8 Correlation between monthly streamflow of Sanshui and Makou station, high/low water level of Denglongshan station and high/low tidal level of Sanzao station
(A)
Stoch Environ Res Risk Assess (2010) 24:81–92
(B)
(C) (D)
(E) (F)
Table 2 Correlation ( R value) between streamflow of Sanshui and Makou, high/low tidal levels of Sanzao station, high/low water levels of eight stations along the Pearl estuary
Sishengwei Sanshakou Nansha Hengmen Denglongshan Huangjin Xipaotai Huangchong
High water level
Makou
Sanshui
Sanzao_high
Sanzao_low
Low water level
Makou
Sanshui
Sanzao_high
Sanzao_low
0.46
0.46
0.77
–
0.55
0.52
–
0.45
0.55
0.54
0.76
–
0.69
0.67
–
0.35
0.45
0.41
0.35
–
0.53
0.46
–
0.12
0.57
0.54
0.79
–
0.83
0.78
–
0.19
0.33
0.25
0.86
–
0.82
0.76
–
0.23
0.29
0.3
0.3
–
0.43
0.38
–
0.3
0.5
0.46
0.78
–
0.7
0.64
–
0.28
0.47
0.44
0.79
–
0.66
0.61
–
0.29
High denotes high tidal level; low denotes low tidal level. The correlation coefficients are significant at [ 95% confidence level. Here we define
R [ [0 0.2] as very weak to negligible correlation; R [ (0.2 0.4] as weak, low correlation; R [ (0.4 0.7] as moderate correlation; R [ (0.7 0.9] as strong, high correlation; and R [ (0.9 1] as very strong correlation
Makou stations and water level changes of the rest gauging stations along the Pearl River estuary. Strong correlation is detected between high tidal levels of the Sanzao station and those stations along the Pearl River estuary except Nansha and Huangjin stations (low correlation for these two stations). This is probably because Huangjin and Nansha stations are farer away from the offshore ocean and are less influenced by astronomic tide as compared with rest stations.
Table
also indicates that moderate to high correlation is identified between streamflow changes of Sanshui and
Makou stations and low water level changes of Pearl River estuary. Huangjin station is an exception, which is located in a smaller river than others. However, very weak to weak correlation can be observed between low tidal level changes of Sanzao station and low water level changes of eight stations in the Pearl River estuary. Above results confirm the findings of wavelet transform analysis that at high water levels the eight stations are better correlated with
Sanzao water level than with upstream flow changes. On the contrary, at low water levels, correlations between the eight stations and upstream flow are higher than that with
123
Stoch Environ Res Risk Assess (2010) 24:81–92 91
Sanzao water level. It should be noted that the connection between water levels at the Pearl River estuary and upstream flow conditions as well as the tidal level changes of Sanzao station is more complex than the correlation coefficient can convey. The correlation coefficients are not as high as close to 1 meaning that the influencing factors for the eight stations are more than one. To judge which factor is dominating depends on the season and other factors.
4 Summary and discussions
The Pearl River estuary is dominated by intensifying human activities such as engineering structure built to protect buildings and agricultural land. All these factors have greatly altered the hydrodynamic and morphodynamic processes of the estuary. Increasing summer high water level along the Pearl River estuary has intensified the flood hazards in the hinterland of the Pearl River
Delta region in summer (Chen and Chen
; Chen et al.
2008 ). However sand dredging has deepened the
river channel which is beneficial to upstream propagation of the tidal current and has intensified the salinity intrusion (Luo et al.
). Therefore, tidal behaviors and related causes are different from station to station along the Pearl River estuary. In addition, changes of water level of the Pearl River estuary are also influenced by the hydrological process of the upper Pearl River
Delta. In this paper, wavelet transform and correlation analysis are performed to demonstrate driving factors influencing the high/low water level changes of the Pearl
River estuary. Some interesting conclusions can be obtained as follows:
1.
Wavelet transform patterns of high tidal level of
Sanzao station are more complicated as compared with those of low tidal level changes of Sanzao station. The wavelet transform of high tidal level of Sanzao station demonstrates that the 95% confidence regions distribute broadly and evenly in the 1-, 0.5-, and 0.25-year bands. The low tidal level variability of Sanzao station, however, only has 95% confidence level in 0.5- and
0.25-year bands. The wavelet transform patterns of streamflow series of Sanshui and Makou stations are monotonous and simple. The 95% confidence regions are consistent in the 1-year band, interruption can be found in the 95% confidence region for the streamflow series of Sanshui station during 1980–1992. Global wavelet spectrum shows that periodicity of streamflow series of Sanshui and Makou stations is dominated by
1-year period. Periods of 0.5 years and 0.25 years are not significant at 95% confidence level.
2.
Investigation of time-varying variance in the high/low water series of the Pearl River estuary through wavelet transform technique reveals different patterns of wavelet power spectrum. The wavelet transform patterns of high water level series are dominated by wavelet power in 1-, 0.5- and 0.25-year bands which aresignificant at [ 95% confidence level. However, different wavelet transform patterns are observed for the low water level series. The fluctuations of tidal levels usually have periodicity properties of driving factors influencing the behavior of water level series.
The behaviors of low water levels of the Pearl River estuary are heavily impacted by the hydrological processes of the upper Pearl River Delta since that low tidal level series of Sanzao station have no 1-year period, however significant 1-year period can be identified in the low water level series of the Pearl
River estuary, which is consistent with periodicity of wavelet transform of upstream discharge. Behavior of high water levels of the Pearl River estuary is influenced by both hydrological processes and astronomical tidal level changes. Correlation analysis further solidifies this finding. Strong correlation is observed between low water level changes of Pearl
River estuary and streamflow changes. Changes of high water levels of Pearl River estuary and those of
Sanzao station are in stronger correlation in comparison with the low water level variation of Pearl River estuary and that of Sanzao station.
3.
It should be noted that individual station presents different properties and deviates much from the general results. For example, weak correlation is detected between high water level of Huangjin and
Nansha stations and that of Sanzao station, but strong correlation is available between high water level series of the rest stations along the Pearl River estuary and high tidal level series of Sanzao station. This is mainly because of human perturbation. Thriving socio-economy and intensifying human activities such as levee construction, sand dredging, land reclamation, etc., have caused the rapid channel incision in the lower
Pearl River. The sediment depletion results in sea water encroachment in the coastal region and intensifies the salinity intrusion (Lu et al.
). Humaninduced topographical changes of river channels have affected the allocation of streamflow and sediment load within the river network of the Pearl River Delta, and altering spatial and temporal distribution of fluvial processes (Luo et al.
). In addition, different intensities of land reclamation, sediment deposition, and sand dredging, etc., have led to different hydrodynamic and morphodynamic processes in the Pearl
River estuary (Huang and Zhang
123
92 Stoch Environ Res Risk Assess (2010) 24:81–92 have affected changing properties of water level changes along the Pearl River estuary (Zeng et al.
1992 ). All these factors have further complicated the
behaviors of water level changes of the Pearl River estuary under the influences of hydrological process and astronomical tidal variations. In this paper, we explored the roles of hydrological processes and astronomical tidal variations in the behaviors of water levels in the Pearl River estuary using wavelet transform and correlation analysis. It will be helpful for coastal management and human mitigation to flood hazards and salinity intrusion as a result of rising sea level and human perturbations. Further research is still necessary to assess quantitatively the impacts of various driving factors on the water level changes of the Pearl River estuary using DEM-based Distributed rainfall-runoff models.
Acknowledgments 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; CUHK405308), Programme of Introducing Talents of Discipline to Universities—the 111 Project of Hohai University and by the
National Natural Science Foundation of China (Grant no.: 40701015).
Wavelet software was provided by C. Torrence and G. Compo, and is available at: http://paos.colorado.edu/research/wavelets/ .
Cordial thanks should be extended to the editor-in-chief, Prof. Dr. George
Christakos, and the three anonymous reviewers for their invaluable comments which greatly improved the quality of this paper.
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