Wavelet-based characterization of water level behaviors in...

ORIGINAL PAPER

Wavelet-based characterization of water level behaviorsin 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 astro-

nomical 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 dif-

ferences 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

1 Introduction

Short- and long-term sea level fluctuations strongly influ-

ence how the ocean affects both human activities and

coastal ecosystems within the coastal zone (Percival and

Mofjeld 1997). 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. 2004). The estuary is

also dominated by intensifying human activities such as

engineering structure built to protect buildings and agri-

cultural land (DEFRA 2001; Byun et al. 2004) which have

greatly altered the hydrodynamic and morphodynamic

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: [email protected]

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

123

Stoch Environ Res Risk Assess (2010) 24:81–92

DOI 10.1007/s00477-008-0302-y

processes in the estuary (Brown 2006). The Pearl River

Delta (PRD) region is highly developed in socio-economy.

Booming economy and heavy human settlement over-

whelmingly affect the hydrological processes within the

river channels of the river network, one of the most com-

plicated 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

2005).

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. 2004;

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. 2004; Chen and Chen 2002).

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 2005).

Generally, the Pearl River estuary is dominated by depo-

sitional process, and this process is different along the Pearl

River estuary due to changing streamflow diffluence ratio

(Liu et al. 1998; Chen et al. 2008). Based on Thematic

Mapper (TM) images (Liu et al. 1998), sediment deposition

and transportation are mainly observed in the western Pearl

River estuary, especially in the Modaomen channel. Sedi-

ment deposition has shrunk river channels and fostered

sand bars which force the flood stage upward during flood

season (Chen 2000). 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 2000). Rising sea

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.

1993), 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 astronom-

ical 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 2003). River tides, where the

tidal wave is damped and advected by river discharge, have

been studied for more than 20 years (e.g. Godin 1983,

1999). The non-stationary character of these tidal processes

provides an opportunity to obtain insights into tidal

dynamics and the interaction of tidal and non-tidal pro-

cesses (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 time-

varying variance of all processes and to decide all fre-

quency bands. Wavelet transform has been advocated in

river tidal analysis (Jay and Flinchem 1997; Flinchem and

Jay 2000) because of tremendous interest in analyzing,

transmitting and compressing diverse non-stationary sig-

nals (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 estu-

ary. We do not modify our time series by eliminating long-

term 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-peri-

odic 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 cov-

ering 1958–2005 were collected from 8 gauging stations

located along the Pearl River estuary. Detailed information

of the data can be referred to Table 1. The hydrological

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. 1. The missing data are filled based

on the data of neighboring stations using regression method

with determination coefficient of R2 [ 0.8 and even

R2 [ 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. 1)

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 character-

izing the frequency, the intensity, the time position, and the

82 Stoch Environ Res Risk Assess (2010) 24:81–92

123

duration of variations in hydro-meteorological series

(Zhang et al. 2006). 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 1998). 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 (1998).

xn is assumed to be a time series with equal time spacing dt

and n = 0,…,N - 1. wo(g) is a wavelet function depending

on the dimensionless ‘time’ g with zero mean and localized

in both frequency and time (Farge 1992; Torrence and

Compo 1998). We applied the Morlet wavelet due to its

good balance between time and frequency. The Morlet

wavelet is defined as:

woðgÞ ¼ p�1=4eixoge�g2=2 ð1Þ

where x0 is the nondimensional frequency and is 6 to

satisfy the admissibility condition (Farge 1992; Torrence

and Compo 1998). The continuous wavelet transform of xn

with a scaled wo(g):

WnðsÞ ¼XN�1

n0¼0

xn0w �ðn0 � nÞdt

s

� �ð2Þ

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.

Table 1 Dataset of the water

levels along the Pearl River

estuary

Station name Longitude Latitude Time interval Periods with missing data

Sishengwei 113�360 22�550 1958–2005 1964

Sanshakou 113�300 22�540 1958–2005 1959

Nansha 113�340 22�450 1963–2005

Hengmen 113�310 22�350 1959–2005

Denglongshan 113�240 22�140 1959–2005 January–September 1958

Huangjin 113�170 22�080 1965–2005

Xipaotai 113�070 22�130 1958–2005 1968–1973

Huangchong 113�040 22�180 1961–2005 2000–2005

Sanzao 113�240 20�000 1965–1988

Fig. 1 Location of the study

region. The names of the

numbered river channels are: 1North mainstream East River; 2Modaomen channel; 3Hengmen channel; 4 Yamen

channel; 5 Jitimen channel; 6Mainstream Pearl River; 7 West

River channel; 8 Xi’nanyong

channel; 9 Ronggui channel; 10Jiaomen channel; 11 Shunde

channel; 12 Shawan channel; 13North River Channel; 14Tanjiang channel; 15 South

mainstream East River; 16Hongqili channel; 17 Xiaolan

channel; 18 Hutiaomen channel;

19 Dongping channel

Stoch Environ Res Risk Assess (2010) 24:81–92 83

123

2004; Torrence and Compo 1998). The significance of

wavelet power was evaluated under the assumption that the

signal is a stationary process with the background power

spectrum (Pk). 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. 2004):

Pk ¼1� a2

j1� ae�2ipkj2ð3Þ

where k is the Fourier frequency index. Torrence and

Compo (1998), with the Monte Carlo method, indicated

that the probability the wavelet power of a process with a

given power spectrum (Pk) is greater than p is

DjWX

n ðsÞj2

r2X

\p

!¼ 1

2pkv

2vðpÞ ð4Þ

where v equals to 1 for real and 2 for complex wavelets. In

this study, we used continuous wavelet transform to char-

acterize periodicity properties of water level serious

because of its good performance in study of geophysical

series (Jay and Flinchem 1997; Unal et al. 2004).

3 Results

3.1 WT of streamflow and tidal levels of Sanzao

station

In this study, the streamflow data of Makou and Sanshui

stations representing the upstream flow variations and tidal

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. 1). Figure 2 illustrates the wavelet transform of

monthly streamflow of Makou station (Fig. 2a) and

Sanshui station (Fig. 2b). 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 rel-

atively 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 obvi-

ously decreasing water level of Sanshui station (Hou et al.

2004; 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 pat-

terns 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 pre-

cipitation, 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

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 linesdenote 95% confidence level

using red noise model


123

and that of the North River (Shijiao station) is increasing

(Zhang et al. 2007). 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. 2006). Global wavelet spectrum

indicates that the monthly streamflow series of Makou and

Sanshui stations are dominated by significant 1-year per-

iod, 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. 3) presents different patterns as com-

pared with those of monthly streamflow of Makou and

Sanshui stations (Fig. 2). It can be observed from Fig. 3

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. 3a), and these periods are

both significant at [95% confidence level. These period-

icity 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. 3b) 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. 3b 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 rep-

resentative of sea level changes (Huang et al. 2001). Thus,

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 transfor-

mation of monthly stream of Makou and Sanshui stations.

3.2 WT of high water levels for the eight stations

Figure 4 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. 4 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 proper-

ties 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 5 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. 5 shows, the wavelet power of the high water

level series of Denglongshan station (Fig. 5a) distributed

broadly with peaks in the 1-year, 0.5-year and 0.25-year


high (a) and low tidal level

series (b) of Sanzao station. The

U-shape line shows cone of




123

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 mor-

phodynamic processes in the estuary, and details of which

are discussed in Sect. 4. Furthermore, the high water level

wavelet transform patterns in the eight stations are more

similar to those of Sanzao water level (Fig. 3a) than to

those of upstream flow at Makou and Sanshui stations

(Fig. 2).

3.3 WT of low water levels for the eight stations

Figures 6 and 7 display the wavelet transform of low water

level series of the eight stations along the Pearl River

estuary. Figure 6 shows patterns of wavelet power spec-

trum 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


high water level series of

a Sishengwei station;

b Sanshakou station; c Nansha

station; and d Hengmen station.





123

identified as for individual stations. The higher wavelet

power of low water level series of Sishengwei station

(Fig. 6a) 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. 6b). The higher wavelet

power of low water levels of Nansha station can be found

during 1966–1980 and 1992–2005 (Fig. 6c). As for

Hengmen station, the 95% confidence region distributes

consistently in the 1-year band throughout the whole

studied time interval (Fig. 6d). 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 7 presents the wavelet power spectrum of low

water series of Denglongshan station (A), Huangjin station

(B), Xipaotai station (C), and Huangchong station (D).

Figure 7 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. 7a) and Huangjin station

(Fig. 7b). It can be observed from Fig. 7b that 95%

confidence regions in the 1-year band disappear during

1980–1992 and also appear sporadically after 1998.


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 thicksolid lines denote 95%

confidence level using red noise

model


123

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. 7c) and Huangchong station (Fig. 7d), but can not be

identified in the low water series of Denglongshan station

(Fig. 7a) and Huangjin station (Fig. 7b). No 0.5-year

periods can be detected within low water level series of

these four stations. In general, Figs. 6 and 7 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. 2) than to those of

Sanzao water level (Fig. 3b), 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


low water level series of

a Sishengwei station;

b Sanshakou station; c Nansha

station; and d Hengmen station.





123

Denglongshan station, high/low tidal variations of Sanzao

station and streamflow variability of Sanshui and Makou

stations are shown in Fig. 8, 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 Deng-

longshan station and Sanzao station. Table 2 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 sta-

tions 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


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 thicksolid lines denote 95%

confidence level using red noise

model


123

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 sta-

tions). 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 2 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 chan-

ges 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

(A) (B)

(D)(C)

(E) (F)

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

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 0.46 0.55 0.45 0.57 0.33 0.29 0.5 0.47

Sanshui 0.46 0.54 0.41 0.54 0.25 0.3 0.46 0.44

Sanzao_high 0.77 0.76 0.35 0.79 0.86 0.3 0.78 0.79

Sanzao_low – – – – – – – –

Low water level

Makou 0.55 0.69 0.53 0.83 0.82 0.43 0.7 0.66

Sanshui 0.52 0.67 0.46 0.78 0.76 0.38 0.64 0.61

Sanzao_high – – – – – – – –

Sanzao_low 0.45 0.35 0.12 0.19 0.23 0.3 0.28 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


123

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 morphody-

namic 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 2002; 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. 2000). 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 distrib-

ute 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 astro-

nomical 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 compar-

ison 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-econ-

omy 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 intensi-

fies the salinity intrusion (Lu et al. 2007). Human-

induced 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. 2000). In addition, different

intensities of land reclamation, sediment deposition,

and sand dredging, etc., have led to different hydro-

dynamic and morphodynamic processes in the Pearl

River estuary (Huang and Zhang 2005), which in turn


123

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 Disci-

pline 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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http://paos.colorado.edu/research/wavelets/

http://dx.doi.org/10.1002/rra.1212

http://dx.doi.org/10.1002/rra.1212

http://dx.doi.org/10.1016/j.gloplacha.2007.04.003

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