Journal of Hydrology 324 (2006) 255–265
www.elsevier.com/locate/jhydrol
Observed trends of annual maximum water level and streamflow
during past 130 years in the Yangtze River basin, China
Qiang Zhang a,*, Chunling Liu c, Chong-yu Xu a,b, Youpeng Xu c, Tong Jiang a
a
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China
b
Department of Geosciences, University of Oslo, Norway
c
Department of Urban and Resources Sciences, Nanjing University, Nanjing 210093, China
Received 12 July 2004; revised 13 June 2005; accepted 19 September 2005
Abstract
Annual maximum streamflow and annual maximum water level and their variations exert most serious influences on human
society. In this paper, temporal trends and frequency changes at three major stations of Yangtze River, i.e. Yichang, Hankou and
Datong representing upper, middle and lower reaches, respectively, were detected with the help of parametric t-test, Mann–
Kendall (MK) analysis and wavelet transform methods. The results show that: (1) there is a significant upward trend in
streamflow at middle Yangtze River, indicating that flood hazard in the middle reach of the river, the flood rich region, will be
more serious; (2) there is a consistent increase of water level from upper to lower reaches of the river which does not always
coincide with the maximum streamflow variations; and (3) the periods of water level changes are decreasing over time,
indicating the increasing occurrence frequency of annual maximum water level over time. This phenomenon is more obvious
from upper Yangtze River to the lower Yangtze River. Human activities like destruction of vegetation, land reclamation and
construction of levees reduced lake sizes and filled up the river bed, reducing the flood storage capacity of lakes and fluvial
channel. These factors led to higher water level even some times the streamflow is small. Human should adjust his activity to
enhance his adaptive capacity to flood hazard in the future.
q 2005 Elsevier B.V. All rights reserved.
Keywords: Wavelet analysis; Mann–Kendall analysis; The Yangtze catchments; Water level changes; Runoff changes
1. Introduction
Global warming as the result of human-induced
‘greenhouse effect’ will lead to the changes in spatial
and temporal distributions of regional water resources
and the global hydrological cycles (Qader, 2002;
* Corresponding author. Tel.: C86 25 86882125, fax: C86 25
86882125.
E-mail address: zhangq@niglas.ac.cn (Q. Zhang).
0022-1694/$ - see front matter q 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.jhydrol.2005.09.023
Labat et al., 2004). More and more researchers drew
their concerns on the trends of streamflow of
watersheds in the world and explored their relations
with the global climate variability and changes and
human activities (e.g. Lettenmaier et al., 1994; Lins
and Slack, 1999; Zhang et al., 2001; Burn and Elnur,
2002; Kahya and Kalayci, 2004). Different conclusions have been drawn which reflect the great
diversity of the regional and global climates and
hydrological regimes. Zhang et al. (2001) analyzed
256
Q. Zhang et al. / Journal of Hydrology 324 (2006) 255–265
the monthly mean streamflow in Canada and stated
that there was almost no basins exhibiting an upward
trend, while Lettenmaier et al. (1994) showed that an
increasing trend of streamflow exists for most part of
the USA, except for a small number of catchments in
the Northwest, Florida and coastal Georgia regions
where a downward trend has been detected. More
research results concerning the trends in the streamflow and other water balance components related to
global climate warming can be found in the recent
IPCC report (McCarthy et al., 2001). The main
viewpoint is that there is an increasing risk of floods
and droughts at local or regional scales and an
increasing or decreasing water availability at the
continental scale (Milly et al., 2002; Vörösmarty
et al., 2000; Labat et al., 2004).
China experiences more frequent natural disasters,
such as floods, droughts and typhoons. Of which,
flooding is most serious which inflicts considerable
economic and human and animal life losses (Zhang
et al., 2002). The Yangtze River (Changjiang), being
the longest river in China and the third longest river in
the world, plays a vital role in the economic
development of China. The river originates in the
Qinghai–Tibet Plateau and flows about 6300 km
eastwards to the East China Sea. In a recent study,
Zhang et al. (2005) evaluated the relations between
the temperature, the precipitation and the streamflow
during 1951–2002 of the Yangtze River suggesting
that the present global warming will intensify the
flood hazards in the Catchment. Historical flood
records (CWRC, 2000a) showed that, during the
past 200 years, about eight floods occurred in the 3rd
cold period of the Little Ice Age, about 19 floods,
however, occurred in the warm 20th century. 1990s is
the warmest period of past 1000 years (IPCC, 2001)
and seven floods occurred in that period.
Therefore, Chinese scholars paid an increasing
attention on human mitigation and control of the
Yangtze floods (Ge, 1999; Li et al., 1999). Many
researches were carried out on water level and
streamflow of the Yangtze River. Yin (2002) analyzed
the water level data from the main hydrological
stations in the middle Yangtze and showed that the
water level in the middle Yangtze River is on
increase, which attributes to the intensified human
activities, such as construction of levees, filling up of
the riverbed (Peng, 1996). Li (2002) evaluated the
lowest water record at the Yichang station during
1877–2000 and suggested that the lowest water level
in the Yichang river reach has a decreasing trend
because of sand mining and downcut of the riverbed.
Zhang and Chen (2003) analyzed the streamflow
between Datong and the Changjiang estuary during
the dry season and indicated that the streamflow to the
East China Sea is decreasing. However, study on the
trends of both maximum streamflow and maximum
water level at different reaches of the Yangtze River
has not been carried out. Previous researches as
mentioned above have studied either the water level
or streamflow at a particular reach of the Yangtze
River (cf. Jiang et al., 2003) and focused on the dry
season or the lowest water level. However, floods,
especially the flood hazards overwhelm the floodcontrolling facilities and inflict great losses on
economy of human society (Adamowski, 2000).
Therefore, this study analyzes the annual maximum
water level and streamflow at the upper, the middle
and the lower reaches of the Yangtze River.
The main objectives of this paper have been to: (1)
detect the trends of annual maximum water level and
annual maximum streamflow of the Yangtze River
during the past 130 years; and (2) discuss how the
changes in maximum annual water level and streamflow are related to the human activities. This study
will provide a better understanding on how the human
activities impact on the changes of maximum water
level and streamflow in a long-term perspective.
2. Data and methods
2.1. Data
Annual maximum water level and annual maximum streamflow data analyzed in this paper are taken
from the three main gauge stations of the Yangtze
River: Yichang station (controlling 10,05,501 km2),
Hankou station (controlling 14,488,036 km2) and
Datong station (controlling 17,05,383 km2), which
represent the upper, the middle and the lower reaches
of the river, respectively (CWRC, 2000a,b) (Fig. 1).
The streamflow from the upstream station Yichang
and the large tributary of the middle Yangtze River—
Hanjiang River are passing through the Hankou
station. Hankou station is the important reference
Q. Zhang et al. / Journal of Hydrology 324 (2006) 255–265
257
Fig. 1. Location of three study hydrological stations in the Yangtze River Catchment.
station for the flood mitigation and flood controlling
activities in Jingjiang and Wuhan river reaches (These
two river reaches are the major flood-affected regions
in the Yangtze Catchments). Datong station is the
monitoring station for the lower Yangtze Catchment,
receiving the streamflow from the mainstream station
Hankou and a tributary Poyang water system. Therefore, the changes of water level and streamflow of
these three gauging stations represent the fundamental
principles of the whole Yangtze River Catchment.
The data have been provided by the Changjiang Water
Resources Commission (CWRC), China. The homogeneity and reliability of the data have been checked
by CWRC before the data was released. More detailed
information concerning hydrological records of these
three gauging stations is shown in Table 1.
2.2. Methods
Three methods, namely, Simple linear regression,
Mann–Kendall and Wavelet transform, are used in the
study to detect trend from the annual maximum
streamflow and water level time series. Each method
has its own strength and weakness, the results of the
three methods complement each other as will be
shown in the next section. The simple linear
regression method is a parametric t-test method,
which consists of two steps, fitting a linear simple
regression equation with the time t as independent
variable and the hydrological variable (in this case
annual maximum discharge or water level), Y as
dependent variable, and testing the statistical significance of the slope of the regression equation. The
Table 1
Detailed information on the extreme hydrological records of Yichang, Hankou and Datong gauging stations
Station
name
Yichang
Hankou
Datong
Warning
water-level
m.a.s.l.
52
27.3
14.5
m.a.s.l., meters above sea level.
Safe waterlevel m.a.s.l.
55.73
29.73
16.64
Max. water
level m.a.s.l.
55.92
29.73
16.64
Occurrence
time of max.
water level
1896.09.04
1954.08.18
1954.08.01
Max. runoff
(m3/s)
71,100
76,100
92,600
Occurrence
time of max.
runoff
1896.09.04
1954.08.14
1954.08.01
Time series
of data
Water level
Runoff
1877–2000
1865–2000
1922–2000
1877–2000
1865–2000
1951–2000
258
Q. Zhang et al. / Journal of Hydrology 324 (2006) 255–265
parametric t-test requires the data to be tested is
normally distributed. The normality of the data series
is first tested in the study by applying the Kolmogorov–Smirnov test. 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.
The rank-based Mann–Kendall method (MK)
(Mann, 1945; Kendall, 1975) is a nonparametric
and commonly used method to assess the significance of monotonic trends in hydro-meteorological
time series (e.g. Helsel and Hirsch, 1992; Burn and
Elnur, 2002; Yue et al., 2003). In a recent study,
Yue and Pilon (2004) applied the Monte Carlo
simulation method to compare the power of
statistical tests like non-parametric Mann–Kendall
(MK) and bootstrap-based slop, and indicated that
the MK and BS-based MK tests have the same
power. Thus, this test has the advantage of not
assuming any distribution form for the data and has
the similar power as its parametric competitors
(Serrano et al., 1999). Therefore, it is highly
recommended for general use by the World
Meteorological Organization (Mitchell et al., 1966).
MK test considers only the relative values of all
terms in the series x1,x2,.,xn to be analyzed. For each
term pi was computed as the number of later terms in
the series whose values exceed xi. Then the MK rank
statistic dk was given by:
dk Z
n
X
pi
ð2% k% nÞ
(1)
iZ1
Under the null hypothesis of no trend, the statistic
dk is distributed as a normal distribution with the
expected value of E(dk) and the variance var(dk) as
follows:
E½dk Z
kðkK1Þ
4
Var½dk Z
kðkK1Þð2k C 5Þ
72
(2)
2% k% n
(3)
Under the above assumption, the definition of the
statistic index Zk is calculated as:
d KE½d Zk Z pk ffiffiffiffiffiffiffiffiffiffiffiffiffiffik
var½dk k Z 1; 2; 3; .; n
(4)
Zk follows the standard normal distribution (here,
we call it Z1, and later we will get another Z2). In a
two-sided test for trend, the null hypothesis is
rejected at the significance level of a if jZjO
Z(1Ka/2), where Z(1Ka/2) is the critical value of the
standard normal distribution with a probability
exceeding a/2. A positive Z value denotes a positive
trend and a negative Z value denotes a negative
trend. In this paper, the significant level of aZ5%
is used. After this, Zk will be computed again based
on the adverse course, which means that the
original time series will be xn, xnK1,.,x1 and dk,
E(dk), var(dk) and Zk will be computed again
following the procedure showed in Eqs. (1)–(4),
and then Z2 is obtained. The two lines, Z1 and Z2
(kZ1,2,.,n) will make an intersection point during
a certain time interval. If the intersection point is
significant at 95% level, we say that the critical
point occurred in the analyzed time series at that
time.
The influence of serial correlation in the time
series on the results of MK test has been discussed
in the literature (e.g. Yue et al., 2002; Yue and
Wang, 2002). Prewhitening has been used to
eliminate the influence of serial correlation (if it
is significant) on the Mann–Kendall (MK) test in
trend-detection studies of hydrological time series.
However, the study conducted by Yue and Wang
(2002) demonstrates that when trend exists in a
time series, the effect of positive/negative serial
correlation on the MK test is dependent upon
sample size, magnitude of serial correlation, and
magnitude of trend. When sample size and
magnitude of trend are large enough, serial
correlation no longer significantly affects the MK
test statistics. In this study, before the MK test was
applied, the series of annual maximum streamflow
and annual maximum water level of the Yangtze
River were tested for persistence by the serial
correlation analysis method presented in Haan
(2002) using the follow equation
Q. Zhang et al. / Journal of Hydrology 324 (2006) 255–265
rm Z
CovðXt ; XtCm Þ
VarðXt Þ
1
nKm
Z
nK
Pm
259
temporal scales which are critical to identify the
anthropogenic components of the hydrological record
(Nakken, 1999).
ðXt KXÞðXtCm KXÞ
tZ1
1
nK1
n
P
(5)
ðXt KXÞ2
3. Results
tZ1
where Xt (tZ1,2,.) is the tested time series; XtCm
is the same time series with a time lag of m; X is
the mean of the time series. The equation shows
that K1!r!1, if mZ0 then rZ1. For a purely
random (stochastic) series, rmz0 for all ms0. If
the series of rm (for ms0) falls between the 95%
confidence
level calculated by ðu=lÞZ ðK1Gz1Ka=2
pffiffiffiffiffiffiffiffiffiffi
nK2Þ=ðnK1Þ (n is the length of the tested time
series, l and u are the lower and upper limits, a is
the significance level, 5% in this case, z is the
critical value of the standard normal distribution for
a given a), the tested series is an independent series
at 95% confidence level.
Wavelet transform is a powerful way to characterize the frequency, the intensity, the time position, and
the duration of variations in a climate data series
(Jiang et al., 1997), which reveals the localized time
and frequency information without requiring the time
series to be stationary as required by the Fourier
transform and other spectral methods. We use the
‘Mexican hat’ in this study to analyze the runoff and
the water level datasets. Details of the wavelet
transform formulae and the ‘Mexican hat’ functions
are described in Jiang et al. (1997). In the wavelet
transform, the scale parameter a represents the timescale of the function. A smaller a value refers to a
higher frequency. The location parameter b corresponds to the time points in a year–year sequence.
Usage of the wavelet transform in the study of
climatic changes and hydrological changes and other
fields is receiving an increasing attention. Nakken
(1999) applied the continuous wavelet transforms
(CWTs) to detect the temporal changing characteristics of the precipitation and the runoff processes, and
their correlations and separating roles of climatic
changes caused by human activities on stream flow
changes. Other scholars (e.g. Bradshaw and Mcintosh,
1994; Fraedrich et al., 1997) used CWTs for analyzing
stream discharge data and flood levels. In this paper,
CWTs was used to detect and isolate patterns across
The results of Kolmogorov–Smirnov test and the
serial correlation analysis (not shown) reveal that the
annual maximum streamflow and water level 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 the use of linear regression method and
the ordinary MK test is warranted. Therefore, the
results of the trend analysis using the three methods
are presented in the following sections for the three
stations.
3.1. Yichang station
The annual maximum streamflow and water level
of Yichang station is first plotted in Fig. 2. The MK
test shows an increasing trend during 1877–1975
(positive Z1 values), and a decreasing trend after 1975
for the maximum stream flow (not significant at O
95% confidence level). The result of simple linear
regression (with the streamflow as the dependent
variable and the time as the independent variable)
indicates a slightly downward trend (not significant at
O95% confidence level) for the whole streamflow
series (the slope of the simple linear regression line is
K6.53). The result of simple linear regression also
indicates a slightly downward trend (not significant at
O95% confidence level) for the whole water level
series (the slope of simple linear regression line is
K0.004). The intersection point of Z1 and Z2 curves of
streamflow and water level of Yichang station
occurred during 1979 and 1983, respectively.
The periodicity of streamflow of Yichang station is
plotted in Fig. 3(a), which demonstrates a 3–4 years
and a 7–8 years periods. A comparison between the
wavelet transform result and the MK result indicates
that a positive wavelet coefficient (negative wavelet
coefficient) corresponds to an upward trend (downward trend) of streamflow / water level changes.
Fig. 3(a) indicates that the maximum value of the
wavelet coefficient occurs in about 1919 with a period
260
Q. Zhang et al. / Journal of Hydrology 324 (2006) 255–265
4
4
95% confidence level
2
2
0
0
–2
–2
–4
56
y=64166.4-6.53x
p=0.16
Water level (m)
–4
70000
Streamflow (m3/s)
Z1
Z2
60000
50000
40000
Z1
Z2
95% confidence level
y=60.1-0.004x
p=0.53
54
52
50
30000
1880 1900 1920 1940 1960 1980 2000
Year
48
1880 1900 1920 1940 1960 1980 2000
Year
Fig. 2. Mann–Kendall and parametric t-test (simple regression analysis) trends of the annual maximum streamflow (left) and annual maximum
water level (right) of Yichang station.
Time scale (a) in year
A
43
38
33
28
23
18
13
8
3
1877 1887 1897 1907 1917 1927 1937 1947 1957 1967 1977 1987 1997
Year
Time scale (a) in year
B
43
38
of 13 years, demonstrating that the strongest
fluctuation in streamflow change of Yichang is in
about 1919. Furthermore, the isolines of the wavelet
coefficient present stage features. In a long-term
perspective, the inter-annual variation of streamflow
of Yichang station has a downward trend, while the
opposite is true for the short-period fluctuations. This
phenomenon can also be seen from Fig. 2, while the
MK trend is positive during shorter time interval, the
simple linear regression, however, shows a slightly
negative trend. It is seen from Fig. 3(b) that the
wavelet transform results of water level are pretty
much the same as for streamflow at this station
(Fig. 3(a)).
33
28
3.2. Hankou station
23
18
13
8
3
1877 1887 1897 1907 1917 1927 1937 1947 1957 1967 1977 1987 1997
Year
Fig. 3. Wavelet analysis of the annual maximum streamflow (A) and
annual maximum water level (B) of the Yichang station.
The MK trends of annual maximum streamflow of
Hankou station are shown in Fig. 4 (left part). It is
seen that during 1865 and 1925, the annual maximum
streamflow series shows a downward trend, while
after 1925 the streamflow series shows an upward
trend (this upward trend is significant at O95%
confidence level after 1965). The MK trends of annual
Q. Zhang et al. / Journal of Hydrology 324 (2006) 255–265
Z1
Z2
95% confidence level
6
4
4
2
2
0
0
–2
–2
70000
y=–130882+93.4x
p=0.00
30
Water level (m)
Streamflow (m3/s)
6
60000
50000
40000
Z1
Z2
261
95% confidence level
y=8.2+0.01x
p=0.15
28
26
24
22
30000
20
1880 1900 1920 1940 1960 1980 2000
Year
1880 1900 1920 1940 1960 1980 2000
Year
Fig. 4. Mann–Kendall and parametric t-test (simple regression analysis) trends of the annual maximum streamflow (left) and annual maximum
water level (right) of Hankou station.
A 43
Time scale (a) in year
38
33
28
23
18
13
8
3
1865 1875 1885 1895 1905 1915 1925 1935 1945 1955 1965 1975 1985 1995
Year
B
43
38
Time scale (a) in year
maximum water level present a somewhat similar
pattern as compared with the annual maximum
streamflow series except the intersection point of Z1
and Z2 lines occurs later. Annual maximum water
level presents a downward trend during 1865 and
1932 and an upward trend after 1932 (this upward
trend is significant at O95% confidence level after
1995). The intersection point of Z1 and Z2 curves of
annual maximum streamflow and annual maximum
water level occurred between 1960 and 1965 and
between 1990 and 1995, respectively. Simple linear
regression analysis shows a stronger upward trend of
annual maximum streamflow and annual maximum
water level compared to those of Yichang station (the
slope of linear regression line is 93.4 and 0.01,
respectively).
Wavelet transform result of streamflow and water
level of Hankou station is shown in Fig. 5. For the
maximum stream flow, the maximum wavelet
coefficient occurs in about 1923 with a period of 13
years (Fig. 5(a)), indicating that the strongest
fluctuation occurred in about 1923. It can also be
seen from the Mann–Kendall analysis result that 1923
acts as the threshold time when the streamflow
33
28
23
18
13
8
3
1865 1875 1885 1895 1905 1915 1925 1935 1945 1955 1965 1975 1985 1995
Year
Fig. 5. Wavelet analysis of the annual maximum streamflow (a) and
annual maximum water level (b) of the Hankou station.
262
Q. Zhang et al. / Journal of Hydrology 324 (2006) 255–265
changes in Hankou station transferred from a downward trend to an upward trend. The isolines of the
wavelet coefficient are scarce comparing to Yichang
station, showing a more even transition between the
upward trend and the downward trend. Furthermore,
after 1980, the wavelet coefficient of streamflow of
Hankou station is positive from a standpoint of view
of longer period.
Fig. 5(b) shows that for the maximum water level
at Hankou station, the maximum wavelet coefficient
value also occurs in about 1923 with a period of 12
years, indicating that the strongest oscillation of water
level changes occurred in about 1923. After 1923, the
water level of Hankou station is in an obvious upward
trend with decreasing major periods, which also
means that the occurrence frequency of higher water
level is increasing.
3.3. Datong station
Fig. 6(left) displays the upward MK trend of
annual maximum streamflow except during 1958–
1966, and this upward trend becomes significant after
6
4
6
95% confidence level
Z2
4
2
2
0
0
–2
–2
y=–449949+258.1x
p=0.02
16
waterlevel (m)
90000
Streamflow (m3/s)
Z1
1995 at 95% confidence level. Comparing Fig. 6 with
Figs. 2 and 4 it reveals that the MK trend changes of
annual maximum streamflow of Datong station are
relatively moderate. During 1925–1950, the annual
maximum water level of Datong station is in
downward trend, and the trend becomes positive
after 1950; after 1970 this upward trend is significant
at O95% confidence level. Furthermore, the slope of
Z1 curve of annual maximum water level of Datong
station is larger than those of Yichang and Hankou
stations, displaying a stronger increasing trend. The
results of the simple linear regression analysis of
annual maximum streamflow and annual maximum
annual water level of Datong station also reveal this
point (Fig. 6).
Wavelet transform of annual maximum streamflow
of Datong station (Fig. 7(a)) shows different changing
features when compared to Yichang station and
Hankou station. The low-frequent oscillations are
relatively stable, but the high-frequent oscillations are
strong. Different changing structures occur between
low-frequent oscillations and high-frequent oscillations. It can be seen from Fig. 7(a) that the
80000
70000
60000
50000
95% confidence level
y=–26.67+0.02x
p=0.03
14
12
10
40000
1950
Z1
Z2
1960
1970
1980
Year
1990
2000
1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
Fig. 6. Mann–Kendall and parametric t-test (simple regression analysis) trends of the annual maximum streamflow (left) and annual maximum
water level (right) of Datong station.
Q. Zhang et al. / Journal of Hydrology 324 (2006) 255–265
A
phase features. All these results demonstrate the
complexity of the water level changes and the
multiple factors influencing the water level changes.
43
38
Time scale (a) in year
263
33
28
23
18
4. Discussions and conclusions
13
8
3
1951
1956
1961
1966
1971
1976
1981
1986
1991
1996
Year
B
43
Time scale (a) in year
38
33
28
23
18
13
8
3
1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997
Year
Fig. 7. Wavelet analysis of the annual maximum streamflow (a) and
annual maximum water level (b) of the Datong station.
streamflow changes of Datong station have the
periods of about 3 and 7 years. Fig. 7(a) also
demonstrates that the maximum wavelet coefficient
value occurred in about 1996 with a period of 38
years, showing the strongest oscillation. During
1956–1966 and 1976–1991, the streamflow of Datong
station is in downward trend; during 1966–1976,
however, the streamflow is in upward trend. After
1991, the wavelet coefficient values are positive in
long time period. These changing features are somewhat similar as those of Hankou station.
The wavelet transform fabric of water level
(Fig. 7(b)) is somewhat similar as that of streamflow
in Datong station. The maximum wavelet coefficient
value of water level occurs in about 1954 with a
period of 9 years. This means that strongest oscillation
occurred in about 1954. From the point of view of
inter-annual changes, during 1922–1937 the water
level is in downward trend and during 1937–1957 in
upward trend; during 1957–1990, the water level
changes are dominated by downward trends again. As
for the high-frequent oscillation, the changing
fluctuations are quick and intensive without obvious
The results of parametric t-test and MK test
indicate that annual maximum streamflow in the
upper Yangtze River is in decreasing trend while the
opposite is true in the middle and lower Yangtze
River. Annual maximum streamflow of the middle
Yangtze River, has the strongest and most significant
upward trend. This result indicates that the flood
hazard in the middle Yangtze River is of serious
concern. As for the annual maximum water level of
the Yangtze River, there is a slightly downward trend
in the upper stream, while the upward trend becomes
more obvious from the middle to lower stream of the
Yangtze River.
Wavelet transform analysis results show that the
changes of streamflow over time are not obvious when
compared to water level changes. Wavelet transform
results indicate that the periods of water level changes
are decreasing over time. In other words, the
occurrence frequency of annual maximum water
level becomes higher over time. This phenomenon
is more obvious from upper Yangtze River to the
lower Yangtze River. This means that water level
changes are not influenced by the single factor like
climatic change, but by multiple factors like human
activities.
Destruction of vegetation, land reclamation and
siltation resulting in reduced lake sizes and filling up
of the river bed reduced the flood storage capacity of
lakes in the Yangtze Catchment and fluvial channel
(Yin and Li, 2001). Construction of levees also
reduced the room for floodwater, which deteriorates
the flood hazards situations. These factors led to a
consistent increase of water level from upper to lower
reaches of the river which does not always coincide
with the maximum streamflow variations. The
intensity of human activities becomes more serious
from upper Yangtze River to lower Yangtze River,
which leads to a more significant upward trend of
water level for the Yangtze River. For example, the
1998 flood has the ‘smaller streamflow but more
264
Q. Zhang et al. / Journal of Hydrology 324 (2006) 255–265
serious hazard’ (Yin and Li, 2001), indicating the
human impacts on water level changes (Yin, 2002).
Acknowledgements
The work presented in this paper was partly
supported by the Key project of the Knowledge
Innovation Project of the Chinese Academy Sciences
(KZCX3-SW-331), the Outstanding Overseas Chinese
Scholars Fund from CAS (The Chinese Academy of
Sciences), and the Alexander von Humboldt Foundation
(Germany). The referees’ comments are gratefully
acknowledged.
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