HYDROLOGICAL PROCESSES
Hydrol. Process. 29, 1406–1417 (2015)
Published online 15 July 2014 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/hyp.10278
Flood frequency under the influence of trends in the Pearl River
basin, China: changing patterns, causes and implications
Qiang Zhang,1,2,3* Xihui Gu,1,3 Vijay P. Singh,4 Mingzhong Xiao1,3 and Chong-Yu Xu5
1
Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China
2
School of Earth Sciences and Engineering, Suzhou University, Anhui 234000, China
3
Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou
510275, China
4
Department of Biological and Agricultural Engineering and Zachry Department of Civil Engineering, Texas A&M University, College Station, TX, USA
5
Department of Geosciences, University of Oslo, Oslo, Norway
Abstract:
Using a nonstationary flood frequency model, this study investigates the impact of trends on the estimation of flood frequencies and
flood magnification factors. Analysis of annual peak streamflow data from 28 hydrological stations across the Pearl River basin, China,
shows that: (1) northeast parts of the West and the North River basins are dominated by increasing annual peak streamflow, whereas
decreasing trends of annual peak streamflow are prevailing in other regions of the Pearl River basin; (2) trends significantly impact the
estimation of flood frequencies. The changing frequency of the same flood magnitude is related to the changing magnitude or
significance/insignificance of trends, larger increasing frequency can be detected for stations with significant increasing trends of
annual peak streamflow and vice versa, and smaller increasing magnitude for stations with not significant increasing annual peak
streamflow, pointing to the critical impact of trends on estimation of flood frequencies; (3) larger-than-1 flood magnification factors are
observed mainly in the northeast parts of the West River basin and in the North River basin, implying magnifying flood processes in
these regions and a higher flood risk in comparison with design flood-control standards; and (4) changes in hydrological extremes result
from the integrated influence of human activities and climate change. Generally, magnifying flood regimes in the northeast Pearl River
basin and in the North River basin are mainly the result of intensifying precipitation regime; smaller-than-1 flood magnification factors
along the mainstream of the West River basin and also in the East River basin are the result of hydrological regulations of water
reservoirs. Copyright © 2014 John Wiley & Sons, Ltd.
KEY WORDS
two-parameter log-normal distribution; exponential trend model; nonstationarity; trends; flood risk; Pearl River basin
Received 26 March 2014; Accepted 21 June 2014
INTRODUCTION
In the wake of global warming and climate change and the
consequent alteration of the hydrological cycle, there is a
growing concern pertaining to spatiotemporal patterns of
precipitation extremes and the subsequent influence on
variations of floods and droughts in both space and time
(IPCC, 2007; Hsu and Li, 2010). It is now well-accepted
that the projected global climate change has the potential
to accelerate the global hydrological cycle (Alan et al.,
2003; Allan and Soden, 2008; Mishra and Singh, 2010)
and the acceleration of the hydrological cycle is reflected
mainly by the changes in hydrological processes, such as
the intensity and frequency of precipitation, river flow,
evapotranspiration, and soil moisture (e.g. Huntington, 2006),
altering the spatiotemporal patterns of precipitation and
*Correspondence to: Qiang Zhang, Department of Water Resources and
Environment, Sun Yat-sen University, Guangzhou 510275, China.
E-mail: zhangq68@mail.sysu.edu.cn
Copyright © 2014 John Wiley & Sons, Ltd.
subsequent increased occurrences of extremes (Easterling
et al., 2000; Mirza, 2002; Wang and Zhou, 2005; Zhang et al.,
2011a) and in turn increased probabilities of flood
and drought occurrences over many regions of the world
(e.g. Easterling et al., 2000; Mirza, 2002).
It is well established that precipitation regimes are
changing in both space and time (Zolina et al., 2010;
Zhang et al., 2011a) and precipitation processes are
intensifying with increased precipitation intensity (Zhang
et al., 2011b). Besides, intensifying human activities,
such as urbanization (Villarini et al., 2009), construction
of water reservoirs (Zhang et al., 2009), etc., are also
occurring. All these factors combine to alter hydrological
processes and introduce nonstationarity in hydrological
extremes, such as annual peak flood regimes (e.g. Milly
et al., 2008; Hattermann et al., 2013). Actually, the
magnification of floods has been identified in the rivers of
the world. Vogel et al. (2011) obtained flood magnification factors in excess of 2–5 for many regions of the
United States, particularly those regions with higher
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FLOOD FREQUENCY UNDER INFLUENCES OF TRENDS
population densities. They also argued that what is now
considered a 100-year flood may become much more common
in many watersheds via computation of recurrence reduction
factors. Furthermore, they also considered trends in annual
peak floods. Pinter et al. (2006) documented statistically
significant increases in both flood magnitudes and frequencies
in the German Rhine, showing that flood magnification had
been driven by upstream factors, including an increase in floodproducing precipitation of roughly 25% during the past
100 years and increase in runoff yield. Thus, magnification of
floods could be the cause behind flood intensification which
may be varying from one river basin to another.
The Pearl River is the third largest river in drainage area
and the second largest river in discharge in China and has
abundant water resources (Zhang et al., 2010a). However,
uneven spatial and temporal distribution of water resources,
with 80% of the total discharge occurring in the flooding
season, i.e. April–September, negatively affects the effective
use of its water resources. Uneven seasonal and spatial
distribution of precipitation triggers occurrences of floods and
droughts in the basin. This may explain why the study of
precipitation extremes has been drawing tremendous concern
from scholars and policy makers alike (e.g. Zhang et al.,
2011c). The East River, a tributary of the Pearl River, bears
the responsibility of water supply for Shenzhen and Hong
Kong with about 80% of Hong Kong’s annual water demand
coming from the East River basin (Chen et al., 2010).
Besides, considerable influences of water reservoirs on
hydrological processes have been evidenced, and that may
have significant impacts on the occurrences of floods or
droughts (Zhou et al., 2014). However, evaluation of flood
frequencies in a changing environment due to climate change
and human activities has not been investigated. Results of this
study will be of scientific and practical value of the sustainable
exploitation and management of water resources in the Pearl
River basin, one of the highly economically developed river
basins in China. This constitutes the motivation of this study.
The objectives of this study therefore are to: (1) analyse
trends of annual peak streamflow and their impact on
flood frequency; (2) investigate the spatial patterns of
trends and flood magnification factors across the Pearl
River basin and related causes; and (3) investigate
temporal variations of floods with consideration of trends
in hydrological extremes. The results of this study will be
useful for the understanding spatiotemporal variations of
floods under the influence of human activities and climate
change, design of hydraulic infrastructure, and enhancement of flood hazard mitigation measures in the backdrop
of rising temperatures (Zhang et al., 2013a).
DATA
Annual flood peak records from 28 hydrological stations
were obtained for analysis. Locations of these stations are
shown in Figure 1. Information on the data, such as the
length of annual flood peak series and drainage areas of
tributaries, is given in Table I. There are no missing data
in the datasets considered. The data were obtained from
the Hydraulic Bureau of Guangdong Province and the
quality of the data is firmly controlled before their release.
METHODOLOGIES
Linear trends and multi-step trend test
Trends in annual peak streamflow series were detected
using methods based on linear trends, Spearman’s correlation
Figure 1. Locations of study river basin and hydrological stations. The tributaries: I, the East River basin; II, the North River basin; III, the West River
basin; and IV, the Pearl River Delta
Copyright © 2014 John Wiley & Sons, Ltd.
Hydrol. Process. 29, 1406–1417 (2015)
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Q. ZANG ET AL.
Table I. Information on hydrological data
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
River
basin
Station
West River
Qianjiang
Dahuangjiangkou
Wuzhou
Gaoyao
Jiangbian
Panjiangqiao
Zhexiang
Chongwei
Sancha
Liuzhou
Pingle
Baise
Xinhe
Nanning
Guigang
Jinji
North River
Changba
Pingshi
Lishi
Hengshi
Gaodao
Shijiao
East River
Longchuan
Heyuan
Lingxia
Boluo
Moyang River Shuangjie
Qin River
Changle
Drainage Length of
area (km2) time series
128 938
288 544
327 006
351 535
25 116
14 492
82 480
13 045
16 280
45 413
12 159
21 720
5791
72 656
86 333
9103
6794
3567
7097
34 013
9007
38 363
7699
15 750
20 557
25 325
4345
6645
1951–2010
1951–2010
1951–2010
1951–2010
1951–2010
1951–2010
1951–2009
1951–2010
1951–2010
1951–2010
1951–2010
1951–2010
1951–2010
1951–2010
1951–2010
1951–2010
1951–2010
1964–2008
1955–2009
1956–1998
1951–2010
1951–2010
1954–2009
1951–2010
1956–2009
1951–2010
1951–2010
1951–2010
coefficient (Gauthier, 2001), and the Mann–Kendall trend
test. The effect of autocorrelation on trend tests was also
considered (Hamed and Rao, 1998). The integrated trend test
was used to overcome uncertainty and bias in the results by
statistical methods (Xie et al., 2009). In the integrated trend
test technique, 1 was assigned to significant trend and 1 to
insignificant trend, and then these 1 and/or 1 values were
summed, which was considered as the integrated trend. The
summed value being larger than 1 points to significant trends,
and the summed value being smaller than 1 points to
insignificant trends. The multi-step trend test was done for
slices of time series which were defined by parts of the time
series by changing starting and ending times with a time step
of 5 years.
analytical formulation of peak flood and maximum annual
peak flood; Iacobellis et al. (2011) presented a regional
probabilistic model for the estimation of medium–high
return period flood quantiles. However, as suggested by
Vogel et al. (2011), the two-parameter log-normal
probability distribution function has been used in the
study. Assuming that the annual maximum flood series xt
is observed in each of the following years t = t1, t2,…, tn
and x t is followed as the log-normal probability
distribution function, then the quantile function xp defined
as the annual maximum flood with an exceedance
probability p is given by:
(1)
xp ¼ exp μy þ zp σy
where μy and σy are the mean and standard deviation of the
natural logarithms of x and zp is the value of a standard
normal random variable with exceedance probability p and
y = ln(x).
After that, an exponential trend model was used to
approximate the relationship between annual peak streamflow
series and time (Vogel et al., 2011). The exponential trend
model can be expressed as:
xt ¼ expðα þ βt þ εt Þ
where t is the time (year) when the annual peak flood flow
occurs, α and β are the model parameters, and εt is the
model error. The following equation can be attained after
taking logarithms:
yt ¼ lnðxt Þ ¼ α þ βt þ εt
Copyright © 2014 John Wiley & Sons, Ltd.
(3)
The regression model provides an estimate of the
conditional mean of the natural logarithms of x, that is μy
defined in Equation (1). As stated by Vogel et al. (2011),
the model residuals in Equations (2) and (3) are only
needed to explain the variations of the observations about
the regression line, but the regression line itself provides
an estimate of μy as a function of time, termed as μy(t).
And then with the fact that an ordinary least squares
^ the
estimate of the intercept term is given by α ¼ y βt,
trend model can be written as:
μy ðtÞ ¼ y þ β^ ðt tÞ
Exponential trend model
To explore trends in annual maximum flood series and
their consequence on flood frequency analyses, it is
necessary to assume a probabilistic model which
describes the relationship between the magnitude and
frequency of annual. There are many studies concerning
the flood frequency analysis (Eagleson, 1972; De Michele
and Salvadori, 2002; Franchini et al., 2005; Iacobellis
et al., 2011; Vogel et al., 2011; Gioia et al., 2012), such
as that De Michele and Salvadori (2002) derived an
(2)
(4)
n
and t ¼ t1 þt
2 .
Nonstationary flood frequency model
Nonstationarity of flood series is well acknowledged
(Milly et al., 2008). Vogel et al. (2011) developed a
nonstationary flood frequency model that was used in this
study. Substitution of nonstationary trend model for μy (t) in
Equation (4) for the fixed value of μy in Equation (1), the
nonstationarity model is defined as (Vogel et al., 2011):
Hydrol. Process. 29, 1406–1417 (2015)
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FLOOD FREQUENCY UNDER INFLUENCES OF TRENDS
h
i
⌢
t1 þ tn þ zp sy
xp ðt Þ ¼ exp y þ β t 2
(5)
where sy is the estimate of standard deviation of natural
logarithms of the annual peak streamflow values. The index
of time is assumed to be t = t1, t2, t3, … tn; zp is the standard
normal random variable with exceedance probability p; t1
and tn are, respectively, the first year and the last year of the
flood record; and xp(t) is the designed flood flow with a
return probability of p at time t.
Flood magnification factor
The flood magnification factor is defined so that the
current design flood quantile would have to be multiplied
by the flood magnification factor to obtain the magnitude
of the flood in some future year. The flood magnification
factor is defined by Vogel et al. (2011) as:
M¼
⌢ xp ðt þ ΔtÞ
¼ exp β Δt
xp ðtÞ
(6)
If the flood magnification factor is larger than 1, then it
points to floods with larger magnitudes in the future, and
hence the designed flood-mitigation infrastructure may
satisfy the flood-mitigation objective, and vice versa. In
Equation (5), M is the flood magnification factor, and Δt is
the time interval. The other terms in Equation (5) retain the
same meaning as those in the above-mentioned equations.
year are To and Tf, respectively, then their respective
exceedance probabilities are given by po = 1⁄To and pf = 1⁄
Tf, respectively. The Tf can be computed as:
Tf ¼
h
1
⌢
1 Φ zp0 βsΔt
y
i
(7)
Φ() is the cumulative probability distribution function
of a standardized normal variable and represents the
probability that a standardized normal variable is less than
the value inside the parentheses (Vogel et al., 2011).
Before flood frequency analysis using the nonstationary
model, the annual peak streamflow series should be tested
for normality, and the normality test was done for the
residuals from the exponential trend model. In this study,
Kolmogorov–Smirnov’s statistic D (K–S D) method was
used to test the normality. The empirical (or experimental)
frequency was computed using the Blom equation
recommended by Heo et al. (2008), i.e.
pi ¼
i 0:375
n þ 0:25
(8)
where pi is the experimental frequency, i is the order, and n
is the sample size of the data under consideration.
RESULTS
Test of normality
Return reduction
Return reduction is defined as the average time between
floods in some future year tf associated with the flood with
an average recurrence interval of To in some reference
year to (Vogel et al., 2011). If the average recurrence
intervals related with a flood today and in some future
Figure 2 illustrates the K–S D values for the normality
test of log-transformed annual peak streamflow and
residuals from the exponential trend model. It can be
observed from Figures 2a and 2b that the K–S D values
were all statistically significant at the 95% confidence
level and the K–S D values of most of the stations were
Figure 2. K–S D-based test of normality of residuals from log-transformed annual peak flow records and exponential trend model for annual peak flow.
(a): log-transformed annual peak flow; (b): exponential trend model
Copyright © 2014 John Wiley & Sons, Ltd.
Hydrol. Process. 29, 1406–1417 (2015)
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Q. ZANG ET AL.
smaller than 0.1. The K–S D values of 23 and 25 out of 28
stations were smaller than 0.1, respectively, for Figures 2a
and 2b, implying that the log-transformed annual peak
streamflow and the residuals of the exponential trend
model followed the normal distribution. For further
corroboration, the goodness of fit of the two-parameter
log-normal distribution model was evaluated (Figure 3). It
can be observed that the log-normal distribution fitted the
annual peak streamflow series at the Nanning station with
the smallest K–S D value, at the Jinji and Shijiao stations
with moderate K–S D values, and at the Pingle station
with the largest K–S D value. These results suggest
subsequent analysis with consideration of nonstationarity.
Analysis of trends
Trends in annual peak streamflow series were detected
using three methods and integrated trend test results are
given in Table II. It can be seen from Table II that
different trends were exhibited by different methods. For
example, the trend in annual peak streamflow at the
Wuzhou station was statistically significant using a linear
regression and the Spearman–Kendall trend test but was
not significant using Mann–Kendall trend test. Stations
with significant integrated trends are bold and underlined
in Table II. For intuitive scrutiny, case studies of stations
with significant/insignificant decreasing/increasing trends
are illustrated in Figure 4. Figure 4a shows significant
annual peak streamflow changes at the Dahuangjiangkou
station, Figure 4b does not show a significant increasing
tendency at the Lishi station, Figure 4c shows a
significant decreasing trend at the Heyuan station, and
Figure 4d does not show a significant increasing tendency
at the Shuangjie station. Figure 5 demonstrates spatial
distribution of trends in annual peak streamflow. It can be
observed from Figure 5 that increasing annual peak
streamflow is identified mainly in the north parts of the
Pearl River basin, specifically in the northeast parts of the
West River basin and in the North River basin. Figure 5
also indicates that there are 16 stations characterized by
increasing tendency and 7 out of 16 stations are featured
by significant increasing trends; there are 12 stations that
are dominated by decreasing tendency and 4 out of 12
stations are characterized by significant decreasing trends.
In this case, the Pearl River basin is dominated by
increasing flood risk and higher flood risk is detected
mainly in the north and central parts of the Pearl River
basin. Trends were analysed for time intervals divided by
a time step of 5 years. The time when an increasing trend
in annual peak streamflow started to occur (Figure 6a) and
that when a decreasing trend in annual peak streamflow
started occur (Figure 6b) were discerned in an intuitive way. It
can be seen from Figure 6a that significant increasing annual
peak streamflow can be detected mainly during 1981–2010.
The percentage of significant increasing trends with respect to
other trend components, such as not significant increasing
trends, was ranging between 20 and 25%; significant
decreasing trends were found mainly during 1966–1990,
and the percentage of significant decreasing trends to other
trend components such as not significant decreasing trends
was ranging between 25 and 30%. These results point to
increasing flood risk during 1981–2010 and decreasing flood
risk during 1966–1990.
Flood frequency analysis with consideration of nonstationarity
Figure 7 shows changes in flood frequencies for
different flood magnitudes during different years. Distinct
flood frequency changes were observed for stations with
significant trends when compared to those without
Figure 3. Case studies of the goodness of fit of lognormal distribution for annual peak flow at (a): Nanning station with a K–S D value of 0.052; (b): Jinji
station with a K–S D value of 0.078; (c): Shijiao station with a K–S D value of 0.103; and (d): Pingle station with a K–S D value of 0.129
Copyright © 2014 John Wiley & Sons, Ltd.
Hydrol. Process. 29, 1406–1417 (2015)
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FLOOD FREQUENCY UNDER INFLUENCES OF TRENDS
Table II. Trends in log-transformed annual maximum streamflow in the Pearl River basin
Trends from different methods
1 denotes significant and 1 not significant
Stations
Linear
Spearman
Kendall
Linear
Spearman
Kendall
Total
Qianjiang
Dahuangjiangkou
Wuzhou
Gaoyao
Jiangbian
Panjiangqiao
Zhexiang
Chongwei
Sancha
Liuzhou
Pingle
Baise
Xinhe
Nanning
Guigang
Jinji
Changba
Pingshi
Lishi
Hengshi
Gaodao
Shijiao
Longchuan
Heyuan
Lingxia
Boluo
Shuangjie
Changle
0.06
0.34
0.28
0.26
0.03
0.09
0.23
0.27
0.37
0.23
0.17
0.34
0.20
0.21
0.03
0.14
0.25
0.25
0.20
0.12
0.02
0.03
0.42
0.53
0.32
0.16
0.04
0.07
0.48
2.77
2.09
1.80
0.06
0.46
1.61
1.94
2.65
1.66
0.91
2.25
1.97
1.57
0.10
0.98
2.48
1.06
0.89
0.67
0.01
0.05
3.16
4.56
2.30
0.93
0.48
0.31
0.54
2.54
1.90
1.62
0.06
0.45
1.68
1.85
2.59
1.48
0.77
2.25
1.98
1.47
0.06
0.98
2.44
0.98
0.78
0.72
0.01
0.06
3.21
4.23
2.53
1.22
0.55
0.47
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
3
1
1
3
3
3
1
3
3
3
3
1
3
3
3
1
3
3
3
3
3
3
3
3
3
3
3
Number of 1 denotes significant trends and 1 not significant trends. The total value of the numbers showing significant or not significant trends shows
the general trends. The total value of ≥1 implies significant trends and vice versa. Underlined stations mean stations with significant trends.
Figure 4. Linear trends of log-transformed annual peak flow from four stations: (a), Dahuangjiangkou station with significant increasing trend; (b) Lishi
station with increasing tendency; (c), Heyuan station with significant decreasing trend; and (d) Shuangjie station with decreasing tendency
significant trends. Differences between frequency changes
for flood events with the same flood magnitude were
apparently distinct during different years at stations with
Copyright © 2014 John Wiley & Sons, Ltd.
significant trends in annual peak streamflow, and these
differences were slight for stations without significant
trends. Moreover, the frequency for the same flood
Hydrol. Process. 29, 1406–1417 (2015)
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Q. ZANG ET AL.
Figure 5. Spatial distribution of linear trends of log-transformed annual peak flow over the Pearl River basin. Red filled circles denote increasing
tendency, and red filled circles with black dots indicate significant increasing trends; the same denotations for the filled blue circles with respect to the
decreasing tendency. The pie diagram in the lower right corner shows percentages of the stations with different trend components to the total stations
considered. The number in the bracket shows the number of stations with significant trends
Figure 6. Percentage of stations with significant trends in annual peak flow with different series lengths. The shortest length of the annual peak flow time
series is 10 years. The difference in the lengths is 5 years
magnitude was increasing with the lapse of time at
stations with increasing annual peak streamflow, and
reverse changing direction was detected at stations with
decreasing annual peak streamflow. In this case, it can be
seen that the occurrence frequency was shifting if trends
in annual peak streamflow were detectable. However, the
changing occurrence frequency was different with respect
to magnitudes of trends. Thus, the impact of trends on
flood frequency was confirmed.
The changing tendency of streamflow with return
periods of 50 and 100 years is shown in Figure 8. Larger
increasing tendency of streamflow with return periods of
100 years was identified at stations with significant
increasing trends in annual peak flow components, and
relatively smaller increasing tendency of streamflow with
return periods of 100 years was detected at stations with
Copyright © 2014 John Wiley & Sons, Ltd.
significant decreasing trends in annual peak flow components. However, in comparison with stations without
significant increasing trends of annual peak streamflow,
increasing tendencies of streamflow with return periods of
50 and 100 years were relatively larger at stations with
significant trends in annual peak streamflow. Similar
phenomena were observed for stations with decreasing
trends in annual peak streamflow but a decreasing tendency
of designed streamflow with different return periods was
observed. All these results point to significant impacts of
trends on design streamflow with different return periods.
Even for stations without significant trends in annual peak
streamflow, the cumulative effect of increasing tendency of
annual peak streamflow on changes in design peak
streamflow cannot be ignored, and this result is in agreement
with Porporato and Ridolfi (1998).
Hydrol. Process. 29, 1406–1417 (2015)
FLOOD FREQUENCY UNDER INFLUENCES OF TRENDS
1413
Figure 7. The exceedance probability of flood at different years and predicted flood frequency curves in 2020 at (a): Sanchan station with significant increasing
trend; (b) Pingle station with increasing tendency; (c) Heyuan station with significant decreasing trend; and (d) Changle station with decreasing tendency
Figure 8. Temporal changes of annual peak flow components with return periods of 50 and 100 years at (a): Sanchan station with significant increasing
trend; (b) Pingle station with increasing tendency; (c) Heyuan station with significant decreasing trend; and (d) Changle station with decreasing tendency
Flood magnification factor
Figure 9 shows variations of flood magnification factor
and T100 (return periods of 100 year) design floods with
the largest time interval of 30 years. A larger decrease of
T100 design flood was identified at stations with
significant increasing annual peak streamflow, e.g. the
Sancha station. Furthermore, the flood magnification was
also in evident increasing tendency and was larger than 1,
pointing to increased flood intensity and higher occurrence frequency. At stations without significant increasing
annual peak streamflow, increasing flood magnification
factor and decreasing T100 designed floods were detected
but with relatively smaller-magnitude of increasing or
decreasing tendency. However, the cumulative effect of
this increase/decrease after a relatively longer time
interval cannot be ignored in the case of not significant
increasing annual peak streamflow. Figure 9b shows that
Copyright © 2014 John Wiley & Sons, Ltd.
after 30 years the T100 design floods are less than 50 years,
which have serious implications for design of flood-control
infrastructure in the Pearl River basin. Similarly, it can be
seen from Figure 9d that, after 30 years, the T100 design
floods were nearly 150 years. Thus, it is logical to set design
standards for each flood-control structure.
For a closer look at the flood magnification for specific
river basins over the Pearl River basin, spatial distributions of flood magnification factors with time intervals of
10 and 20 years are shown in Figures 10a and 10b,
respectively. The flood magnification factor being larger
than 1 points to increased design flood streamflow with
the lapse of time and related necessary enhancement of
design standards, and vice versa for the flood magnification factor being less than 1. Figures 10a and 10b show
that, whether for the time intervals of 10 or 20 years,
larger-than-1 flood magnification factors were found
Hydrol. Process. 29, 1406–1417 (2015)
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Q. ZANG ET AL.
Figure 9. Temporal changes of flood magnification factor and annual peak flow with return periods of 100 years at (a): Sanchan station with significant increasing
trend; (b) Pingle station with increasing tendency; (c) Heyuan station with significant decreasing trend; and (d) Changle station with decreasing tendency
Figure 10. Spatial distribution of flood magnification factor at 28 stations across the Pearl River basin. (a): Δt = 10 years; (b): Δt = 20 years
mainly in the northeast parts of the West River basin and
the North River basin. The remaining parts of the Pearl
River basin were dominated by less-than-1 flood
magnification factors. The less-than-1 flood magnification
factors were identified mainly along the mainstem of the
West River basin and in the East River basin.
For further investigation of the influence of trends on
flood changes over the Pearl River basin, the number of
Copyright © 2014 John Wiley & Sons, Ltd.
stations with occurrences of floods of different return
periods was analysed with the assumption of stationarity/
nonstationarity of the annual peak streamflow series. It
can be seen from Figure 11 that more stations are
observed with higher occurrences of floods of return
periods of 10–20 years with the assumption of stationarity
of annual peak streamflow series when compared to those
with the assumption of nonstationarity of annual peak
Hydrol. Process. 29, 1406–1417 (2015)
FLOOD FREQUENCY UNDER INFLUENCES OF TRENDS
1415
Figure 11. Temporal variations of the number of stations with occurrences of flood events of different return periods by taking annual peak flood flow as
nonstationary and stationary series. Difference values show differences between the number of stations with flood occurrences with the assumption of
stationarity and that with the assumption of nonstationarity
streamflow series, and it is particularly the case during the
periods of 1951–1971 and the early 1990s–2010. In this
sense, the assumption of stationarity of the hydrological
extreme series when it is actually nonstationary tends to
overestimate the occurrence frequency of floods. This
finding supports the fact that flood events with return
periods of 10–20 years are observed to occur almost every
year. In fact, this observation goes against the concept of
return periods of flood events, and which can be attributed
to the assumption of stationarity of hydrological extreme
series. Similar findings were also attained for flood events
with return periods of 20–50 years or even >50 years.
However, the occurrence frequencies of floods with return
periods of >20 years were sometimes overestimated and
sometimes underestimated, due to different integrated
influences of human activities and climate change on
extreme hydrological processes in different parts of the Pearl
River basin. However, biased estimation of the occurrence
frequencies of flood events due to the inappropriate
assumption of stationarity/nonstationarity of extreme
hydrological series was confirmed.
DISCUSSION
Extreme hydrological processes are influenced by human
activities and climate change, and the assumption of
stationarity is inappropriate in the estimation of flood
frequencies, having critical implications for design of floodcontrol structures. Investigation of spatiotemporal patterns
of precipitation regimes (Zhang et al., 2012a) indicated a
decreasing occurrence and fractional contribution of wet
periods with longer durations in recent decades and wet
periods with shorter durations, e.g. 2–5 days are tending to
be predominant in recent decades with increasing total
precipitation amount. Besides, intensifying precipitation
regimes across the Pearl River basin are also reflected
mainly by higher occurrences of shorter wet periods with
Copyright © 2014 John Wiley & Sons, Ltd.
increased total precipitation amount (Zhang et al., 2012a).
Moreover, regions dominated by increased annual precipitation intensity and also increased annual total precipitation
amount match spatially well those featured by increasing
annual peak streamflow flow and larger-than-1 flood
magnification factor, implying significant effects of intensified precipitation regimes on magnified flood processes in the
northeast West River basin and also in the North River basin.
However, human activities in general and construction
of water reservoirs or other hydraulic structures in
particular have increasing impacts on extreme hydrological processes. Smaller-than-1 flood magnification factors
were observed mainly along the mainstem of the West
River basin and in the East River basin. So far, 36 largesized water reservoirs with a total storage capacity of
29 billion m3 (Dai et al., 2007) have been constructed in
the Pearl River basin. These reservoirs are mainly located
on the tributaries and in the upper Pearl River basin. The
reservoirs may have a limited influence on monthly or
even annual streamflow change; however, the reservoir
regulation can significantly reduce peak streamflow
(Zhang et al., 2012b). Some large-sized water reservoirs
have been constructed along the mainstem of the West
River basin, such as the Tianshengqiao water reservoir
with a storage capacity of 26 million m3 and the Yantan
water reservoir, with a storage capacity of 3.35 billion m3
(Zhang et al., 2012b). Locations of these water reservoirs
are shown in Figure 1 of the paper by Zhang et al.
(2012b) found that these large water reservoirs have a
considerable trapping effect on the sediment load and also
trigger a decrease in annual peak streamflow. In the East
River basin, up to the end of 2006, 896 hydraulic facilities
with a total storage capacity of about 19 billion m3 were
constructed. The Xinfengjiang and the Fengshuba water
reservoirs, with a total storage capacity of 15.83 billion m3
(Zhou et al., 2012), were constructed in 1961 and 1974,
respectively. And Zhang et al. (2014) found that the
Hydrol. Process. 29, 1406–1417 (2015)
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Q. ZANG ET AL.
annual multi-day maxima flow have reduced, while multiday minima flow have increased, due to hydrological
regulations of water reservoirs in the East River basin.
Regulations of water reservoirs in the West River basin
and the East River basin significantly have reduced annual
peak streamflow and have led to less-than-1 flood
magnification factors. And also the annual peak
streamflow has been regulated by the urbanization, such
as recently the construction land in the watershed has
been increased rapidly and these will increase the runoff
during flood (Zhang et al., 2013b).
Integrated influences from climate change, such as
intensified precipitation regimes, and human activities,
such as construction of water reservoirs, impact the spatial
patterns of flood magnification factor and changes in
annual peak streamflow. The assumption of stationarity of
annual peak streamflow series is found inappropriate and
nonstationarity should be included in the estimation of
flood risks across the Pearl River basin. Overestimation or
underestimation of the occurrence frequency and related
design flood will have negative implications for design
and construction of flood-control structures, water
reservoirs, and other hydraulic facilities.
Construction of flood-control structures was mostly
completed before the 1980s and 30 years have since elapsed.
Evident changes in design flood, flood magnification factor,
and return periods are observed (Figures 8–10). In the
northeast parts of the West River basin and in the North
River basin, designed hydraulic facilities cannot satisfy the
altered hydrological extremes, and the populated areas
downstream of the hydraulic facilities are encountering
increasing flood risks. Special concern therefore has to be
attached to the re-design of these flood-control facilities
(Figure 10). It should be noted here that the Pearl River Delta
region is highly and densely populated with highly
developed socio-economy. Hydrodynamic conditions of
the river network are heavily influenced by streamflow
processes in the upper Pearl River Delta region (Zhang et al.,
2010b). Synchronous occurrences of floods from the
northeast West River basin and the North River basin will
cause higher inundation risks within the central parts of the
Pearl River Delta where most of the megacities of the Pearl
River Delta are located. Hence, right estimation of flood
risks is of great value for the sustainable development of
socio-economy within the Pearl River Delta region.
CONCLUSIONS
A nonstationary flood frequency model is used to evaluate
the impact of trends on flood frequencies, based on annual
peak streamflow data from 28 hydrological stations across
the Pearl River basin. Spatial patterns of flood magnification factor and return periods are analysed. Important
conclusions of this study are as follows:
Copyright © 2014 John Wiley & Sons, Ltd.
The northeast parts of the West River basin and the North
River basin are dominated by increasing annual peak
streamflow, and decreasing trends in annual peak
streamflow prevail in other regions of the Pearl River basin.
An increasing tendency of annual peak streamflow is
dominant during 1980–2010, and a decreasing tendency
of annual peak streamflow is prevalent during 1966–1990.
Significant impacts of trends flood frequencies are
observed. The frequency for the same flood magnitude is
increasing at stations with increasing annual peak
streamflow, and a reverse direction of change is observed
for stations with decreasing annual peak streamflow.
Larger increasing frequency is found for stations with
significant increasing trends in annual peak streamflow,
and smaller increasing magnitude for stations with not
significant increasing annual peak streamflow, pointing to
serious impacts of trends for flood frequency estimation.
Trends also have an effect on the flood magnification
factor and return period. Magnitudes of trends are negatively
proportional to the changes in flood magnification factor and
return periods, and it is particularly the case for return
periods. Even weak trends in annual peak streamflow can
have a significant impact on changes in return periods.
Larger-than-1 flood magnification factors are observed
mainly in the northeast parts of the West River basin and
in the North River basin, implying magnifying flood
processes in these regions. These results point to increasing
intensity and higher frequency or magnitude of flood
regimes in the northeast parts of the West River basin and
also in the North River basin. These point to higher flood
risk for designed flood-control structures.
Changes in hydrological extremes are the result of
integrated influences of human activities, such as construction of water reservoirs, and climate change, such as
intensifying precipitation regime. Magnifying flood regimes
in the northeast Pearl River basin and in the North River
basin are mainly the result of intensifying precipitation
regimes and higher occurrence of heavy precipitation.
Smaller-than-1 flood magnification factors along the
mainstem of the West River basin and in the East River
basin are the result of reservoir regulation. However, these
two influencing factors cannot be separated clearly.
The assumption of stationarity/nonstationarity can produce
distinctly different results. The assumption of stationarity of
annual peak streamflow can overestimate frequencies
of floods and particularly floods with return periods of
10–20 years. This explains why floods with return periods of
10–20 years have been observed more often in recent
decades. However, for flood events with return periods
longer than 20 years, the assumption of stationarity of annual
peak streamflow can over- or/and under-estimate the return
periods of floods. Thus, under the apparent influence
of climate change and human activities, the assumption of
nonstationarity of annual peak streamflow is right.
Hydrol. Process. 29, 1406–1417 (2015)
FLOOD FREQUENCY UNDER INFLUENCES OF TRENDS
ACKNOWLEDGEMENTS
Special Project for the leading scientist in the Anhui
Province, China, the Program for New Century Excellent
Talents in University (NCET), and was fully supported by
a grant from the Research Grants Council of the Hong
Kong Special Administrative Region, China (Project No.
CUHK441313). Cordial gratitude should be extended to
the editor, Prof. Dr. Amilcare Porporato, and also the
reviewers for their professional and pertinent comments
and revision suggestions which are greatly helpful for
further improvement of the quality of this manuscript.
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