Journal of Hydrology (2007) 333, 265– 274
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/jhydrol
Possible influence of ENSO on annual maximum
streamflow of the Yangtze River, China
Qiang Zhang
a,b,*
, Chong-yu Xu
a,c
, Tong Jiang a, Yijin Wu
d
a
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road,
Nanjing 210008, PR China
b
Geographical Institute, Giessen University, Giessen 35390, Germany
c
Department of Geosciences, University of Oslo, Norway
d
School of Urban and Environmental Sciences, Huazhong Normal University, Wuhan 430079, China
Received 12 January 2006; received in revised form 17 July 2006; accepted 30 August 2006
KEYWORDS
Annual maximum
streamflow;
El Niño/Southern
Oscillation (ENSO);
Wavelet approach;
Yangtze River basin
Summary Variability and possible teleconnections between annual maximum streamflow from
the lower, the middle and the upper Yangtze River basin and El Niño/Southern Oscillation
(ENSO) are detected by continuous wavelet transform (CWT), cross-wavelet and wavelet coherence methods. The results show that: (1) different phase relations are found between annual
maximum streamflow of the Yangtze River and El Niño/Southern Oscillation (ENSO) in the
lower, the middle and the upper Yangtze River basin. In-phase relations are detected between
annual maximum streamflow of the lower Yangtze River and anti-phase relations are found in
the upper Yangtze River. But ambiguous phase relations occur in the middle Yangtze River,
showing that the middle Yangtze River basin is a transition zone. Different climatic systems
control the upper and the lower Yangtze River. The upper Yangtze River is mainly influenced
by the Indian summer monsoon and the lower Yangtze is mainly influenced by the East Asian
summer monsoon; (2) as for the individual stations, different phase relations are found in
the longer and the shorter periods, respectively. In the longer periods, the annual maximum
streamflow is more influenced by climatic variabilities, while in the shorter periods, it is influenced by other factors, e.g. human activities. The results of the study provide valuable information for improving the long-term forecasting of the streamflow using its relationship with
ENSO and the Indian Monsoon.
ª 2006 Elsevier B.V. All rights reserved.
* Corresponding author. Address: Nanjing Institute of Geography
and Limnology, Chinese Academy of Sciences, 73 East Beijing Road,
Nanjing 210008, PR China. Tel./fax: +86 25 86882125.
E-mail address: zhangq@niglas.ac.cn (Q. Zhang).
Introduction
Flood hazards cause enormous economical, social and environmental damages and loss of lives. Floods usually include
0022-1694/$ - see front matter ª 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.jhydrol.2006.08.010
266
three factors: peak flood discharge, water level and flood
duration; extreme flood discharge usually plays the key
role in the occurrence of flood hazards and is likely to have
a greater potential to impact water resources in many regions than the mean annual discharge does. More frequent
or larger floods could lead to increased expenditures for
flood management. It is why more and more researchers
draw concerns on the study of extreme flood events (Jain
and Lall, 2001; Camilloni and Barros, 2003). The intensifying human activities (e.g. urbanization, forestation/deforestation, construction of water reservoir) will exert
tremendous influences on flood frequency, and temporal
and spatial distributions of water resources. Furthermore,
climatic variability combined with human-induced emission
of green-house gases result in an increase in mean global
temperature (IPCC, 2001), which in turn, leads to higher
evaporation rates and makes the atmosphere transport larger amounts of water vapor. The global hydrological cycle
is accelerated (Menzel and Bürger, 2002). Influence of the
slowly changing climate on flood frequency has attracted
interest (e.g. Robson et al., 1998; Jain and Lall, 2000,
2001; Olsen et al., 1999; Zhang et al., 2005). The El
Niño/Southern Oscillation (ENSO) represents the dominant
coupled ocean–atmosphere mode of the tropical Pacific
(Cane, 1992). On inter-annual timescales the significant
part of the global climatic changes can be linked to ENSO
(Trenberth et al., 1998). The ENSO extreme phases are
usually in linkage with major episodes of floods and
droughts (e.g. Barlow et al., 2001) in many locations
worldwide (Jain and Lall, 2001; Aceituno, 1988; Amarasekera et al., 1997).
Many scholars try to detect possible connections between ENSO and precipitation and streamflow. Lan et al.
(2002) suggested that ENSO contributed to the runoff in
the upper reaches of the Yellow River in China; the occurrence of El Niño is usually accompanied by high probability
of low flow, while flood events in the Yellow River usually
accompanied by the occurrence of La Niña event. Cardoso
and Silva Dias (2006) also investigated the relationship between the Paraná River (27.36S, 55.90W) flow and the
ENSO mode, and statistical forecasts of river flow are
made using the relationship. An evaluation of the relationship between the Pacific sea surface temperature and the
Paraná River flow indicates an ENSO pattern over the
equatorial Pacific. Gong and Wang (1999), however, studied the teleconnection between ENSO and precipitation in
China with the help of statistical analysis (v2 test), suggesting that the decreasing precipitation in China usually
matches the El Niño events and there exists a significant
relationship between winter and autumn rainfall and the
ENSO in eastern China. Some other scientists have also detected strong correlations between flood events and ENSO
events (e.g. Chang and King, 1999; Dilley and Heyman,
1999).
Many researches were performed on streamflow changes
of the Yangtze River. Zhang et al. (2006) analyzed the
changes of trends and periodicity of the annual maximum
streamflow and water level at different stations along the
Yangtze River during the past 130 years, indicating that
the annual maximum streamflow in the upper Yangtze River
is in a decreasing trend while the opposite is true in the middle and the lower Yangtze River. Annual maximum stream-
Q. Zhang et al.
flow in the middle Yangtze River has a significant upward
trend, which shows that the flood hazard in the middle
Yangtze River is of a serious concern. Jiang et al. (2006)
analyzed the teleconnections between flood/drought
events in the Yangtze River basin and ENSO events during
1868–2003 with the help of v2 test and spectral analysis,
suggesting that ENSO events and flood/drought variations
are significantly correlated at a 5.04-year period and a
10- to 12-year period. These researches are greatly helpful
for understanding and controlling the floods and droughts
problems in the Yangtze River basin. However, what are
the possible connections between ENSO and annual maximum streamflow of the Yangtze River, especially in terms
of periods? To what degree does the ENSO impact the annual
maximum streamflow? These questions are remaining
unanswered and are seldom studied, especially with the
help of the powerful cross and coherence wavelet analysis
methods.
The main objectives of the present study are: (1) to explore the changes of variance and in-phase linkages between Niño3 (sea surface temperature) and annual
maximum streamflow of the three major monitoring hydrologic stations along the main Yangtze River, i.e. Datong station, Hankou station and Yichang station; and (2) to
evaluate the possible impacts of ENSO on flood hazards in
the Yangtze River Basin. This study uses the wavelet transform (WT) approach, the cross-wavelet power and coherence wavelet analyses methods to detect the relations
between Niño3 SST and annual maximum streamflow of
the Yangtze River.
Yangtze River: climate and hydrology
The Yangtze River (Changjiang), being the longest river in
China and the third longest river in the world, lies between
91E and 122E and 25N and 35N. It has a drainage area of
1,808,500 km2 with the mean annual discharge of
23,400 m3 s1 measured at Hankou Station. The river originates in the Qinghai-Tibet Plateau and flows about
6300 km eastwards to the East China Sea (Zhang et al.,
2006). The Yangtze River Basin is located in the monsoon region of East Asia subtropical zone, and has a mean annual
precipitation of about 1090 mm (Zhang et al., 2005; Jiang
et al., 2006). Climatically, the southern part of the basin
is close to the tropical zone and the northern part is close
to the temperate zone, making it an ideal place for studying
the influence of climate changes on hydrological conditions.
The mean annual temperature in the southern and northern
parts of the middle and the lower Yangtze basin is 19 and
15 C, respectively. Summer is the main flooding season
for the Yangtze River basin due to the heavy monsoon rainfall. Temporal and spatial distributions of the rain zone are
closely related to monsoon activities and seasonal motion of
subtropical highs. Flood or drought events happed nearly
every year. The river reach between Yichang and Wuhan
(Fig. 1) is the most dangerous river section in the Yangtze
River basin concerning the flood events. In 1998, the entire
Yangtze River Basin suffered from tremendous flooding –
the largest flood since 1954, which led to the economic loss
of 166 billion Chinese Yuan (or 20 billion US$) (Yin and Li,
2001).
Possible influence of ENSO on annual maximum streamflow of the Yangtze River, China
Figure 1
267
Location of the study region and hydrological stations.
Data and method
Data
Annual maximum streamflow data from the three main
gauge stations of the Yangtze River are analyzed in this
study: Yichang station (controlling 1,005,501 km2), Hankou
station (controlling 14,488,036 km2) and Datong station
(controlling 1,705,383 km2) representing the upper, the
middle and the lower reaches of the river, respectively
(CWRC, 2000; Zhang et al., 2006) (Table 1). The streamflow
from the upper station Yichang and the large tributary of
the middle Yangtze River – Hanjiang River – is passing
through Hankou station, which is the key reference station
for flood mitigation and flood control in the basin. Datong
station is the monitoring station at the lower Yangtze River,
receiving the streamflow from Hankou and tributary Poyang
water system. It can be seen from Table 1 that there exist
long data series of annual maximum streamflow for all three
stations and the longest one has a 135-year record from
1865 to 2000.
Table 1 Detailed information on the extreme hydrological
records of Yichang, Hankou and Datong gauging stations
(revised after Zhang et al., 2006)
Station name
Max. runoff
(m3/s)
Occurrence
time of max.
runoff
Time series
of data
Yichang station
Hankou station
Datong station
71,100
76,100
92,600
1896.09.04
1954.08.14
1954.08.01
1877–2000
1865–2000
1922–2000
Niño3 sea surface temperature (SST) is used as a measure
of the amplitude of the El Niño3-Southern Oscillation
(ENSO). The Niño3 SST index is defined as the seasonal SST
averaged over the central Pacific (5S–5N, 90–150W).
The sea surface temperature fields are blended from ship,
buoy and bias-corrected satellite data (Reynolds and Smith,
1994). These data (1864–1950) are available from http://
ingrid.ldgo.columbia.edu/SOURCES/.Indices/.nino/.KAPLAN,
while the data for January 1951–December 2000 are obtained from the Climate Prediction Center (CPC).
Methods
The normality of the data series is first tested in the study by
applying the Kolmogorov–Smirnov test (Xu, 2001). The
method first compares the specified theoretical cumulative
distribution function (in our case normal distribution) with
the sample cumulative density function based on observations, then calculates the maximum deviation, D, of the
two. If, for the chosen significance level, the observed value
of D is greater than or equal to the critical tabulated value of
the Kolmogorov–Smirnov statistic, the hypothesis of normal
distribution is rejected. After this step, continuous wavelet
transform, wavelet coherence and cross-wavelet transform
were performed on annual maximum streamflow of Yichang,
Hankou and Datong stations and Nino3 SST series.
The continuous wavelet transform (CWT) (Torrence and
Compo, 1998) is used in this study. We assume that xn is a
time series with equal time spacing dt and n = 0, . . . ,
N 1. wo (g) is a wavelet function which depends on a
dimensionless ‘time’ parameter g with zero mean and localized in both frequency and time (Farge, 1992; Torrence and
Compo, 1998). Because Morlet wavelet provides a good
268
Q. Zhang et al.
balance between time and frequency localizations, we applied the Morlet wavelet that is defined as
wo ðgÞ ¼ p1=4 eixo g eg
2 =2
ð1Þ
;
where xo is the nondimensional frequency, here taken to be
6 to satisfy the admissibility condition (Farge, 1992; Torrence and Compo, 1998). The continuous wavelet transform
of a discrete sequence xn is defined as the convolution of xn
with a scaled and translated version of wo(g):
0
N1
X
ðn nÞdt
;
ð2Þ
W n ðsÞ ¼
xn0 w
s
n0
where the asterisk indicates the complex conjugate. Because the wavelet is not completely localized in time, to
ignore the edge effects the cone of influence (COI) was
introduced. Here COI is the region of the wavelet spectrum
in which edge effects become important and is defined here
as the e-folding time for the autocorrelation of wavelet
power at each scale. This e-folding time is chosen so that
the wavelet power for a discontinuity at the edge drops
by a factor e2 and ensures that the edge effects are negligible beyond this point (Grinsted et al., 2004; Torrence and
Compo, 1998). The statistical significance of wavelet power
can be assessed under the null hypothesis that the signal is
generated by a stationary process given the background
power spectrum (Pk). It is assumed that the time series
has a mean power spectrum, given by (3); if a peak in the
wavelet power spectrum is significantly above this background spectrum, then it can be assumed to be a true feature with a certain confidence level. The ‘‘95% confidence
interval’’ refers to the range of confidence about a given value. To determine the 95% confidence level (significant at
5%), one multiplies the background spectrum (3) by the
95th percentile value for v2 (Torrence and Compo, 1998).
Many geophysical series have the red noise characteristics
which can be modeled by a first-order autoregressive
(AR(1)) process. The Fourier power spectrum of an AR(1)
process with lag-1 autocorrelation a (estimated from the
observed time series, e.g. Allen and Smith, 1996) is given
by (Grinsted et al., 2004)
Pk ¼
1 a2
j1 a e2ipk j2
ð3Þ
;
where k is the Fourier frequency index. Torrence and Compo
(1998) used the Monte Carlo method to show that the probability that the wavelet power of a process with a given
power spectrum (Pk) is greater than p is
!
jW Xn ðsÞj2
1
<
p
¼ pk v2v ðpÞ;
ð4Þ
P
2
r2X
where v is equal to 1 for real and 2 for complex wavelets.
We use the circular mean of the phase over regions with
>95% confidence level which is outside the COI to quantify
the phase relationship. The circular mean of a set of angles
(ai, i = 1, . . . , n) is defined as (Zar, 1999; Grinsted et al.,
2004):
am ¼ argðX; YÞ; where X ¼
n
X
i¼1
cosðai Þ and Y ¼
n
X
sinðai Þ
i¼1
ð5Þ
Cross-wavelet power reveals areas with a high common
power. As for the covariance of two time series, Torrence
and Compo (1998) defined the cross-wavelet spectrum of
two time series X and Y with wavelet transform WX and
WY as
W XY ðs; tÞ ¼ W X ðs; tÞW Y ðs; tÞ;
ð6Þ
where the asterisk denotes complex conjugation. The phase
angle of WXY describes the phase relationship between X and
Y in time-frequency space. Statistical significance is estimated against a red noise model (Torrence and Compo,
1998).
Another useful tool is the wavelet coherence. Coherence
is a measure of the intensity of the covariance of the two
series in time-frequency space, unlike the cross-wavelet
power which is a measure of the common power. Again,
beginning with the approach of Torrence and Webster
(1999), the coherence was defined as
R2n ðsÞ ¼
2
jSðs1 W XY
n ðsÞÞj
Sðs1 jW Xn ðsÞj2 Þ Sðs1 jW Yn ðsÞj2 Þ
;
ð7Þ
where S is a smoothing operator. The scales in time and frequency over which S is smoothing define the scales at which
the coherence measures the covariance. We write the
smoothing operator S as (Jevrejeva et al., 2003)
SðWÞ ¼ Sscale ðStime ðWðs; tÞÞÞ;
ð8Þ
where Sscale denotes smoothing along the wavelet scale axis
and Stime smoothing in time, which are given by (Torrence
and Webster, 1998):
t2
Stime ðWÞjs ¼ W n ðsÞ c12s2 ;
s
Y
ð9Þ
Stime ðWÞjs ¼ W n ðsÞ c2 ð0:6sÞ ;
n
where c1 and c2 are normalization constants, and is the
rectangle function. The factor of 0.6 is the empirically
determined scale decorrelation length for the Morlet wavelet (Torrence and Compo, 1998).
Monte Carlo method is used with a red noise to determine the 95% statistical confidence level of the coherence
(Torrence and Webster, 1999; Jevrejeva et al., 2003).
Results and discussions
The results of the Kolmogorov–Smirnov test and the serial
correlation analysis (not shown) reveal that the annual maximum streamflow at the three stations in the Yangtze River
are normally distributed and serial correlations are either
nonsignificant at 95% confidence level or relatively small.
This means that the use of cross-wavelet analysis and wavelet coherence is warranted.
Wavelet power spectra for Niño3 SST
The wavelet power spectra for the Niño3 SST (December–
February) are shown in Fig. 2, which reveal that the power
is broadly distributed with peaks in the 2- to 8-year ENSO
band. The 95% confidence regions demonstrate that 1875–
1920 and 1960–1990 include intervals of higher ENSO variance, while lower ENSO variance is found during 1920–
Possible influence of ENSO on annual maximum streamflow of the Yangtze River, China
269
Datong station runoff
Figure 2 Continuous wavelet power spectrum for the normalized time series of Niño3 SST (December–February). The
thick black contour designates the 95% confidence level against
red noise and the cone of influence (COI) where edge effects
might distort the picture is shown as a lighter shade. The
normalized Niño3 SST (December–February) has the AR(1)
coefficient of 0.023.
1960. Similar changing patterns were also discovered in east
Pacific SST and tropical zonal winds (Wang and Wang, 1996;
Gu and Philander, 1995; Torrence and Webster, 1998).
Therefore, only Niño3 SST is used in the study as a measure
of the amplitude of the El Niño3-Southern Oscillation
(ENSO).
Continuous wavelet power spectra for the runoff time series of annual maximum streamflow of Datong station show
a high wavelet power in the 3- to 8-year band around
1970–1985 (upper graph of Fig. 3). The wavelet power of
the annual maximum streamflow of Datong station is not
significant at >95% confidence level during 1970–1985.
The El Niño3 SST has a significant wavelet power in
1970–1985 (Fig. 2). The lower left graph of Fig. 3 demonstrates a significant common power in the 3- to 8-year
band from 1975 to 1988. It can also be seen from crosswavelet transform that the annual maximum streamflow
of Datong station and Niño3 SST are in the same phase in
the sectors with a significant common power (significant
at >95% confidence level). The wavelet coherence (the
lower right graph of Fig. 3) shows how coherent the
cross-wavelet transform is in the time-frequency space
(Torrence and Compo, 1998; Grinsted et al., 2004). The
squared wavelet coherence (WTC) is shown in the lower
right graph of Fig. 3. A relatively larger region (in the
3- to 7-year band during 1975–1985) is prominent and is
significant at >95% confidence level. This region shows
the in-phase relationship between annual maximum
streamflow of Datong station and the Niño3 SST. Part of
Figure 3 Continuous wavelet power spectrum for the normalized time series of annual maximum streamflow of Datong station
(upper graph). The thick black contour designates the 95% confidence level against red noise and the cone of influence (COI) where
edge effects might distort the picture is shown as a lighter shade. The normalized annual maximum streamflow of Datong station has
the AR(1) coefficient of 0.029. The lower left graph is the cross-wavelet transform and the lower right graph is squared wavelet
coherence result, showing the relations between annual maximum streamflow of Datong station and Niño3 SST (December–
February). The relative phase relationship is shown as arrows (with in-phase pointing right, anti-phase pointing left).
270
the regions covered by COI shows an anti-phase relation.
However, the in-phase relation is the dominant one.
Hankou station runoff
The continuous wavelet power spectra for the time series of
annual maximum streamflow of Hankou station (upper graph
in Fig. 4) show a high wavelet power in the 2- to 8-year band
around 1920–1960 and 1970–1980. The peak power value
occurred in the 2- to 8-year band, which is in good agreement with the continuous wavelet power analysis of Niño3
SST and COI (Torrence and Webster, 1999). The wavelet
power spectra for annual maximum streamflow indicate a
significant (at 95% confidence level) nonstationarity of variance in the 2- to 8-year band, especially during 1920–1930
and 1950–1960, indicating the strongest fluctuation occurred in about 1923 and 1947. In the 9- to 16-year band,
there also exist regions with a higher wavelet power, but
not significant at >95% confidence level. The cross-power
spectra results (lower left graph of Fig. 4) show a significant
common power in the 2- to 8-year band during 1875–1882
and 1960–1980. However, the phase changes in these regions with the significant wavelet power show ambiguous
changing patterns as compared with that of Datong station.
But the anti-phase in these sectors is still relatively obvious.
The squared wavelet coherence (WTC) (lower right graph of
Fig. 4) demonstrates that more regions (in the 2- to 16-year
Q. Zhang et al.
band during 1880–1920 and 1960–1985) are prominent and
are significant at >95% confidence level. The phase relations
within these regions between annual maximum streamflow
of Hankou station and Niño3 SST are not stable. The phase
relations in the 2- to 4-year band and the 5- to 8-year band
during 1880–1885 have in-phase and anti-phase simultaneously, showing the changing and ambiguous phase relationships between annual maximum streamflow of Hankou
station and Niño3 SST.
Yichang station runoff
Fig. 5 (upper graph) shows the continuous wavelet power
spectra for the time series of annual maximum streamflow
of Yichang station. The significant wavelet power spectra
are in the 2- to 7-year band during 1885–1905 and 1940–
1950, and in the 8- to 16-year band during 1910–1950 and
1970–1980. There are common features in the wavelet
power of the two time series (annual maximum streamflow
of Yichang station and Niño3 SST) such as the significant
peak in the 4- to 8-year band around 1935–1945; they also
have a high power in the 2- to 4-year band around 1900
and the 4- to 8-year band in 1980 (Fig. 2 and upper graph
of Fig. 5).
Cross-wavelet power spectra (lower left graph of Fig. 5)
show common features with significant wavelet power spectra at >95% confidence level, and these significant common
Figure 4 Continuous wavelet power spectrum for the normalized time series of annual maximum streamflow of Hankou station
(upper graph). The thick black contour designates the 95% confidence level against red noise and the cone of influence (COI) where
edge effects might distort the picture is shown as a lighter shade. The normalized annual maximum streamflow of Hankou station has
the AR(1) coefficient of 0.054. The lower left graph is the cross-wavelet transform and the lower right graph is squared wavelet
coherence result, showing the relations between annual maximum streamflow of Hankou station and Niño3 SST (December–
February). The relative phase relationship is shown as arrows (with in-phase pointing right, anti-phase pointing left).
Possible influence of ENSO on annual maximum streamflow of the Yangtze River, China
271
Figure 5 Continuous wavelet power spectrum for the normalized time series of annual maximum streamflow of Yichang station
(upper graph). The thick black contour designates the 95% confidence level against red noise and the cone of influence (COI) where
edge effects might distort the picture is shown as a lighter shade. The normalized annual maximum streamflow of Yichang station
has the AR(1) coefficient of 0.151. The lower left graph is the cross-wavelet transform and the lower right graph is squared wavelet
coherence result, showing the relations between annual maximum streamflow of Yichang station and Niño3 SST (December–
February). The relative phase relationship is shown as arrows (with in-phase pointing right, anti-phase pointing left).
power occurred in the 2- to 4-year band during 1890–1900
and the 8- to 16-year band during 1920–1930. These phase
relations in the regions that are significant at >95% confidence level show clear anti-phase relations between annual
maximum streamflow and Niño3 SST. The changes of phase
relations are relatively complex. The squared wavelet
coherence (WTC) (lower right graph of Fig. 5) shows that
in 1920–1950 there are two regions with high coherency
peaks at the 8- to 16-year band and the 20- to 32-year band,
respectively. These regions correspond to the significant
period of Niño3 SST and annual maximum streamflow of Yichang station during 1920–1950. A visual comparison of annual maximum streamflow of Yichang station and Niño3 SST
suggests that higher Niño3 SST usually corresponds to a
smaller annual maximum streamflow at Yichang station.
The phase changes in the regions that are significant at
95% confidence level are dominated by anti-phase relations.
It should be noted that the phase is changed from 180 to
90 in the regions in the 8- to 16-year band during 1900–
1940. The phase changes in the regions in the 20- to 32-year
band during 1920–1950 are also changed. These phase
changes are related to the time lag between Niño3 SST
and annual maximum streamflow of Yichang station. Therefore, it can be said that the annual maximum streamflow of
the upper Yangtze River basin is not only influenced by one
factor of Niño3 SST, but also by East summer monsoon in the
upper Yangtze Basin. Research results (Torrence and Web-
ster, 1999) indicated that during El Nino the Indian monsoon
tends to be weaker, yet the weak monsoon actually occurs
approximately 4 months before the peak Niño3 SST (Webster et al., 1998). The strong monsoon is usually associated
with cold Niño3 SST (Torrence and Webster, 1999). This may
be the reason that the phase relations between annual maximum streamflow and Niño3 SST are changing in anti-phase
relation.
Summary and discussion
Cross-wavelet analysis and wavelet coherence are powerful
methods for testing a proposed linkage between two time
series (Grinsted et al., 2004). Relationships between annual
maximum streamflow of three monitoring stations of the
Yangtze River and ENSO are detected by cross-wavelet
and wavelet coherence. The following points can be concluded from the study:
1. The relationship between annual maximum streamflow
and ENSO is changing from the lower Yangtze River Basin
to the upper Yangtze River Basin. In the lower Yangtze
Basin, the in-phase relationship occurred between annual
maximum streamflow and ENSO. In the upper Yangtze
Basin, however, the anti-phase relationship is dominant.
The phase relation of annual maximum streamflow and
272
ENSO is ambiguous in the middle Yangtze, demonstrating
that the middle Yangtze River is the transition zone. It
can be seen from Fig. 5 that from the 8-year band to
the 16-year band the phase angle is changing, showing
that more than one factor influences the annual maximum streamflow from the upper Yangtze Basin.
2. Continuous wavelet transform analysis results indicate
that the annual maximum streamflow of the middle
and the lower Yangtze River is dominated by the 2- to
8-year periods. However, the annual maximum streamflow of the upper Yangtze River is dominated by both
the 2- to 4-year and the 8- to 16-year periods. Wavelet
power relations and phase relations between annual
maximum streamflow of the Yangtze River (at Datong,
Hankou and Yichang stations) and ENSO are relatively
stable in the longer periods (>2- to 16-year band), and
are relatively unstable in the shorter periods (mainly in
<2-year band), demonstrating that from the viewpoint
of longer periods, the annual maximum streamflow of
the Yangtze River is controlled by the slowly changing
climate, e.g. the large-scale ocean–atmosphere circulation of moisture. During shorter periods, however, the
annual maximum streamflow of the Yangtze River is
not only impacted by ENSO, but also by other factors,
like urbanization (Jain and Lall, 2001).
3. The upper Yangtze River is mainly influenced by the
Indian summer monsoon, and the lower Yangtze River
is controlled by East Asian summer monsoon (Ding and
Chan, 2005). A causal relationship was already found by
studies based on observations (Hu et al., 2000) and by
a coupled model study (Wei, 2005). The modeling results
from the ECHAM4 general circulation model (GCM)
(Cheng et al., 2005) indicate that the increases in SST
will strengthen the convective precipitation in the lower
Yangtze River Basin. Floods in East China including the
lower Yangtze River is more likely caused by the
strengthened convective precipitation associated with
the increases in SST. The different phase relationships
between ENSO and annual maximum streamflow in the
lower and the upper Yangtze River show the different
influences of different variables of the atmospheric system. Hydrological teleconnections linking climate indicators and the associated atmospheric fluxes of moisture
with a considerable spatial and temporal structure are
already understood through statistical analysis and
numerical modeling results (Jain and Lall, 2001). The
precipitation of the Yangtze River Basin is also influenced
by snow conditions of the Tibet Plateau, which in turn,
impacts the annual maximum streamflow. This can be
one of the reasons for the more complex changes of
phase angles outside the regions exceeding 95% confidence level.
4. The Asian monsoon system can be divided into two subsystems, the South Asian (or Indian) and the East Asian
monsoon systems, which are independent of each other
and, at the same time, interact with each other (Zhu,
1934; Yeh et al., 1959; Tao and Chen, 1987; Ding and
Chan, 2005). Zhu et al. (1986) pointed out that the interaction between South Asian monsoon and East Asian monsoon might be accomplished by energy exchange, the
propagation of low-frequency oscillation, and moisture
transport. Nino3 is a numerical measure of sea surface
Q. Zhang et al.
temperatures in the tropical Pacific which may be used
to identify and categorize ENSO events (Trenberth,
1997). It should be mentioned that ENSO affects monsoon
circulation and vise versa (Roy, 1998). There are still
mechanisms that are not well understood concerning
relationships between ENSO and various climatic/hydrological hazards in China (Cheng et al., 1998). Many scientific uncertainties exist in the understanding and
forecasting of ENSO and its impacts due to the lack of
proper observation networks (UNU report, 2000). This
can also be seen from the research results of this paper.
The phase relation between ENSO and annual maximum
runoff of the Yangtze River basin is changing in shorter
periods. However, the discussion of mechanism and possible teleconnections between ENSO and East Asian summer monsoon or Indian monsoon is out of the scope of the
current research. The research results of this paper present apparent opportunities for improving forecasting of
streamflow especially annual maximum streamflow along
the mainstream of the Yangtze River basin, which, in
turn, will improve water resources management and
human mitigation to hydrological hazards. Therefore, it
will be greatly helpful for planning human adaptation to
extreme hydrological events in the Yangtze River basin
based on ENSO events. The runoff changes of the tributaries of the Yangtze River basin may be influenced by water
reservoirs. The runoff of the mainstream of the Yangtze
River is less impacted by the water reservoirs along the
mainstream of the Yangtze River basin. Further study will
be carried out on the influences of the Three Gorges Dam
on runoff changes in the mainstream after its completion
in 2009. Further investigation will also be performed on
possible teleconnections between monthly maximum
streamflow and ENSO events in the basin.
Acknowledgements
This research was financially supported by Alexander von
Humboldt Foundation, Germany and Outstanding Oversea
Chinese Scholars Fund from CAS (The Chinese Academy of
Sciences) and Foundation from Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (Grant No.
S260018). Great thanks should be extended to Changjiang
Water Resources Commission (Ministry of Water Resources,
China) for providing the hydrological data and to two anonymous reviewers and Dr. Andreja Pisnik for their invaluable
and constructive suggestions which greatly improved the
quality of this paper. Wavelet software was provided by C.
Torrence and G. Compo, and is available at: http://
paos.colorado.edu/research/wavelets/. Software by Grinsted, A., et al., is available at http://www.pol.ac.uk/
home/research/waveletcoherence/.
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