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Reg Environ Change (2013) 13:1195–1210
DOI 10.1007/s10113-013-0432-8
ORIGINAL ARTICLE
Hydrologic response to climate change and human activities
in a subtropical coastal watershed of southeast China
Jinliang Huang • Zhenyu Zhang • Yuan Feng
Huasheng Hong
•
Received: 4 August 2012 / Accepted: 23 February 2013 / Published online: 7 March 2013
Ó Springer-Verlag Berlin Heidelberg 2013
Abstract It is essential to investigate hydrologic
responses to climate change and human activities across
different physiographic regions so as to formulate sound
strategies for water resource management. Mann–Kendall,
wavelet and geospatial analyses were coupled in this study,
associated with ENSO indicators, flashiness index and
baseflow index, in order to explore the hydrologic sensitivity to climate change and human activities in the Jiulong
River Basin (JRB), a subtropical coastal watershed of
southeast China. The results showed that the average
annual precipitation presented an increasing trend
(Z = 2.263, p = 0.024) and that this tendency has become
weaker from estuary to inland in the JRB over the past
50 years. The annual frequency of rainstorm events
increased from 3.4 to 5.2 days in the estuary and from 5.1
to 5.6 days in the West River, whereas it decreased from
6.0 to 5.5 days in the North River from 1954 to 2010. The
10-year average streamflow during 2001–2010 in the North
River and West River decreased by 9.2 and 6.7 %,
respectively, compared to the average annual streamflow
during 1967–2000. Annual fluctuations were the most
representative signals in streamflow variability for the
North River and West River over the period 1967–2010.
Human activities including dam construction, land change
and socioeconomic development posed increasing influences on hydrologic conditions in the JRB. Seasonal
J. Huang (&) Z. Zhang H. Hong
Coastal and Ocean Management Institute, Xiamen University,
Xiamen 361005, China
e-mail: [email protected]
J. Huang Z. Zhang Y. Feng H. Hong
Fujian Provincial Key Laboratory of Coastal Ecology
and Environmental Studies, Xiamen University,
Xiamen 361005, China
variability of streamflow and sediment discharge changed
significantly between the two periods divided by the
jumping point (1992), identified when dams were constructed extensively in the North River and West River.
This research provided important insights into the hydrologic consequences of climate change and human activities
in a subtropical coastal watershed of southeast China.
Keywords Climate variability Precipitation Streamflow Human activities Jiulong River Basin
Introduction
Change in streamflow is the result of complex interactions
between climate change and human activities (e.g.,
occurrence of extreme weather events, dam construction)
(Tu 2009; Liu and Cui 2011; Costigan and Daniels 2012).
Climate change combined with human activities leads to
remarkable changes in the hydrological recycling pattern,
which in turn leads to changes in the hydrological processes of watersheds, and these have caused a series of
water resource problems all over the world (Zalewski
2000; Zhang et al. 2001; Li et al. 2007; McVicar et al.
2007). Therefore, it is essential to investigate the sensitivity
of hydrologic response to climate change and human
activities across different physiographic regions so as to
formulate sound strategies for water resource management.
Many attempts have been made to understand hydrologic sensitivity to climate change and human activities
from the local to regional and global scales (Magilligan and
Nislow 2005; IPCC 2007; Hao et al. 2008; Jiang 2009;
Rossi et al. 2009; Li et al. 2012; Zhang et al. 2012).
Streamflow conditions are strongly controlled by climate
change (Kling et al. 2012). As a result of climate change,
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the frequency and intensity of extreme precipitation events
and drought related to ENSO events increased in the past
few decades (Cruz et al. 2007; Vasiliades et al. 2009; Das
et al. 2011). On the other hand, intensive human activities
such as dam construction and urbanization play critical
roles in hydrologic alteration (Xu et al. 2007; Liu et al.
2008; Bergerot et al. 2011). The construction of large
modern dams produces a dramatic change in the magnitude
of hydrologic, geomorphologic and ecological impacts on
large rivers, which is well documented (Magilligan and
Nislow 2005; Shieh et al. 2007; Yang et al. 2008; Rossi
et al. 2009; Chen et al. 2010; Costigan and Daniels 2012;
Zhao et al. 2012). In addition, land use and land cover
changes which reflect underlying human activities also
cause streamflow regime change. Land use changes, such
as conversion of forest to cropland and conversion of
cropland and woodland to suburban and urban land use,
also alter streamflow regimes (Baker et al. 2004).
With its high population pressure and long-term human
disturbances, China is a key vulnerable region of climate
change in the world (Ni 2011). In recent years, the effect of
climate variability and human activities on runoff has been
widely explored in large river basins such as the Yellow
River, Yangtze River, Haihe River and Pearl River (Zhang
et al. 2008; Xu et al. 2010; Cao et al. 2011; Liu and Cui
2011; Li et al. 2012; Bao et al. 2012; Chen et al. 2010);
inland basins such as the TarimRiver, Shiyang River and
Lancang River (Xu et al. 2004; Fan et al. 2011; Yang et al.
2012; Huo et al. 2008; Zhao et al. 2012); and lake basins in
southeast China such as the Poyang Lake Basin and Taihu
Lake Basin (Guo et al. 2008; Liu et al. 2012). Climate
variability and human activities have been commonly
recognized as two major driving factors interactively
influencing the hydrologic cycle in China’s watersheds.
Hydro-climate characteristics were significantly altered in
the past 50 years in China, but such change exhibited
somewhat regional differences. Good relationships
between the annual streamflow and precipitation exist in
most large river basins where the trend in streamflow
dynamics results to a large extent from changes in precipitation, but the change in streamflow is greater than that
of precipitation in the arid and semiarid regions of North
China and the inland areas of China (Zhang et al. 2011).
The increasing use of water from rivers for agricultural
irrigation is a main cause for the decreases in observed
runoff in the northern part of China (Ren et al. 2002), while
dam construction in the southern part of China has greatly
altered the natural flow regime conditions (Chen et al.
2010; Zhao et al. 2012). However, few studies on hydroclimate trends in the context of climate change and human
activities have been reported in the coastal medium-sized
watersheds of southeast China. Given the accelerating
process of land transformation in the eastern coastal areas
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of China and that the hydrologic response to climate
change and human activities in small- or medium-sized
watersheds should be more sensitive to environmental
changes involving stronger signals (e.g., streamflow, sediment discharge) (Huang et al. 2012; Tomer and Schilling
2009), it is imperative to understand the hydrologic
response to environmental changes in watersheds in such a
geographic region.
The Jiulong River Basin (JRB), covering 14,700 sq km,
is a typical medium-sized subtropical coastal watershed in
southeast China and suffers from a high frequency of
extreme weather events (e.g., typhoons) and drastic human
activities (e.g., extensive dam construction and accelerating land use changes). The objectives of this study were as
follows: (1) to characterize the variability in climate and
hydrology in terms of precipitation and streamflow over the
past 4–5 decades and (2) to explore the hydrologic
response in the JRB to climate change and human
activities.
Materials and methods
Site description
The JRB covers approximately 14,700 sq km in southeast
China (from 116°460 5500 E to 118°020 1700 E and from
24°230 5300 to 25°530 3800 N) and consists mainly of eight
counties or districts (Fig. 1). The watershed’s gross
domestic product (GDP) accounts for a quarter of Fujian
Province’s economic output. Nearly 10 million residents
from Xiamen, Zhangzhou and Longyan use the Jiulong
River as their source of water for residential, industrial and
agricultural use. The watershed includes two river reaches,
namely the North River and West River reaches, which
meet in Zhangzhou and produce an annual flow of 12 billion
cubic meter into the Jiulong River estuary and the XiamenKinmen coast. The JRB plays an extremely important role
in the region’s economic and ecological health (Huang et al.
2013). Since 1992, hydropower plants have been extensively established in the JRB, and now there are more than
120 large-scale dams in the river basin. According to the
typhoon yearbooks 1968–2006, the JRB was subject to
approximately three typhoon events every year.
Data sources
Hydrologic data
In this study, streamflow and sediment discharge data for
four hydrologic stations in two river reaches were used,
that is, two gauge stations in the upstream (Zhangping and
Longshan) and two gauge stations in the downstream
Hydrologic response to climate change and human activities
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Fig. 1 Study area
(Punan and Zhendian) (Fig. 1). Detailed information
regarding hydrologic data was available for: (1) Punan and
Zhendian: daily streamflow and monthly sediment discharge data during 1967–2010; (2) Zhangping: monthly
streamflow and monthly sediment discharge data during
1967–2010; and (3) Longshan: monthly streamflow and
monthly sediment discharge data during 1972–2010.
estuary, West River and North River, respectively, were
obtained from the China Meteorological Administration
(http://cdc.cma.gov.cn). Other meteorological data came
from the individual hydrologic stations in the region
(Fig. 1), including Chuangchang, Baisha, Maiyuan, Longshan, Punan, Zhendian, Zhangping and Longmen during
1985–2005.
Meteorological data
Socioeconomic data
Daily precipitation data during 1954–2010 for three meteorological stations, namely Xiamen, Zhangzhou and Longyan, which were used to delineate climate change in the
Socioeconomic data for eight counties or districts in the
JRB from 1986 to 2009 (Fig. 1), namely Xinluo, Zhangping, Huaan, Changtai, Zhangzhou, Longhai, Nanjing and
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Pinghe, were collected from statistical yearbooks. Nine
indices with regard to socioeconomic development included GDP (x1), values of primary industry output (x2),
values of secondary industry output (x3), total population
(x4), nonagricultural population (x5), amount of pigs (x6),
amount of livestock (x7), area of orchard plantation (x8)
and sown area of grain crop (x9).
ENSO indicators
The ENSO indicators used in this study were the Multivariate ENSO Index (MEI) and Southern Oscillation Index
(SOI). The MEI is an indicator that combines sea-level
pressure, zonal and meridional components of the surface
wind, sea surface temperature, surface air temperature and
total cloudiness fraction of the sky (Wolter 1987). The SOI
is defined as the standardized difference in the standardized
atmospheric pressures at Tahiti and Darwin, Australia
(Ropelewski and Jones 1987). Data for these two indicators
were obtained from the NOAA database over the period
1967–2010 (http://www.esrl.noaa.gov).
Land use and land cover data
Land use and land cover change is one of the important
factors influencing the streamflow regime (Baker et al. 2004;
Holko et al. 2011). The land use and land cover data in the
JRB were interpreted from Landsat TM/ETM? images in
1986, 1996, 2002, 2007 and 2010 using RS and GIS. The land
categories were generated using a combination of unsupervised classification and spatial reclassification based on
manual on-screen digitizing (Huang et al. 2012). There were
six categories, namely built, forest, cropland, water, orchard
and barren land. In this study, we used the proportion of builtup and forest area at five time points for further analysis.
Methods
The nonparametric Mann–Kendall method was employed
in this study to detect the long-term trends in the hydrologic and meteorological regimes. Jumping point analysis
was used to identify the potential jumping points for
alteration in precipitation or hydrology. We also used the
Mann–Kendall method to conduct jumping point analysis.
Two curves of Mann–Kendall U values for the forward and
backward statistical sequences were calculated and plotted
against time, and if the intersection occurred within the
confidence interval, then this indicated a jumping point.
The Mann–Whitney test was used to test the difference
between two precipitation time series. Wavelet analysis
was performed to identify the main modes of variability
and to characterize the possible instationarity of each time
series about ENSO indicators and streamflow. In addition,
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principal component analysis (PCA) was performed to
identify the potential impact of socioeconomic activities on
the hydrologic conditions.
Two indices, namely flashiness index (FI) and baseflow
index (BFI), were used to delineate the streamflow regime
over the period 1967–2010. The streamflow regime of a
stream reflects the operation of the hydrologic cycle within
its watershed (Baker et al. 2004), and the flashiness variability is attributed to the difference in dominant runoff
generation, surface runoff and macropore flow (Holko et al.
2011). Flashiness of streamflow was quantified using the
ratio of absolute day-to-day fluctuations of streamflow
relative to total flow in a year, which has been proved to be
a good index to evaluate the influence of human activities
on river basins especially dam construction and land use
and land cover change (Baker et al. 2004). The equation for
the flashiness index is as follows:
Pn
jqi qi1 j
FI ¼ i¼1Pn
i¼1 qi
where FI is the flashiness index and qi is the mean daily
flow at day i.
Streamflow can be separated into direct runoff and
baseflow based on the runoff response rate to precipitation.
Baseflow is generally defined as that part of the river
streamflow coming from groundwater storage and other
detention of water (Hall 1968). Baseflow separation was
made using a digital filter based on the Lyne–Hollick
method. Although the technique has no true physical basis,
it is objective and reproducible (Arnold and Allen 1999).
The equation is presented as follows:
qt ¼ bqt1 þ ð1 þ bÞ=2 ðQt Qt1 Þ
where qt is the filtered surface runoff at the time step
t (1 day); Qt is the original streamflow at the time step
t (1 day); and b is the filter parameter. The BFI was
calculated with the following equation:
BFI ¼ ðQt qt Þ=Qt :
Results
Climate variability
Interannual variability of precipitation
A long-term trend of annual precipitation was detected
from estuary to inland using the nonparametric Mann–
Kendall test. In general, an increasing tendency of precipitation with a 5 % level of significance was observed
(Z = 2.263, p = 0.024 \ 0.05). However, the increasing
tendency of precipitation became weaker with a decreasing
significance level from estuary to inland (Fig. 2). The
Hydrologic response to climate change and human activities
increasing tendency of the annual precipitation in the
estuary (i.e., Xiamen station) was significant at the 1 %
level (Z = 3.010, p = 0.003 \ 0.01) while the precipitation in the inland (i.e., Longyan station) seemed not to have
changed in the past 50 years.
Based on Mann–Kendall analysis, the jumping points for
Xiamen, Zhangzhou and Longyan were 1979, 1976 and
1986, respectively (Fig. 3). Compared with the values
between the two periods divided by the identified jumping
points, precipitation in the estuary increased by 18 % and in
the West River reach by 6 %, while it decreased by 1 % in
the North River reach. The result of the Mann–Whiney test
further showed that the level of significance between the
two time series became lower from estuary to inland. The
p value of the Mann–Whiney test in Xiamen, Zhangzhou
and Longyan was 0.003, 0.238 and 0.930, respectively.
Seasonal variation of precipitation
Seasonal variation of precipitation was distinct in the JRB,
characterized by a wet season in summer and a dry season
in winter, due to the influence of the subtropical maritime
monsoon climate. Comparing the values between the two
periods divided by the identified jumping points, seasonal
variation of precipitation has changed in the past 50 years.
That is, the precipitation amount increased during the
period from July to September, while there was almost no
change in the other months. This trend also became weaker
from estuary to inland (Fig. 4). Additionally, the monthly
1199
precipitation changed from a single peak to a double peak
in the wet season in the estuary and West River reach.
Extreme weather events
The increasing trend of annual frequency of rainstorm
events was similar with that for precipitation and also
became weaker from estuary to inland. Comparing the
values between the two periods divided by the jumping
points identified, the annual frequency of rainstorm events
(24-h rainfall C50 mm) increased from 3.4 to 5.2 day/year
in the estuary and from 5.1 to 5.6 day/year in the West
River reach, whereas it decreased from 6.0 to 5.5 day/year
in the North River reach from 1954 to 2010 (Fig. 5).
Correlation analysis further showed that annual frequency
of rainstorm events was strongly correlated to annual precipitation in the estuary, West River and North River, with
Pearson coefficients of 0.817, 0.780 and 0.710 (p \ 0.01),
respectively, indicating that the annual variation in precipitation was strongly related to the annual frequency of
rainstorm event variation over the past 50 years.
Streamflow variability
Long-term trend of annual streamflow change
Based on Mann–Kendall analysis, there was no significant
increasing trend of annual streamflow in the North River
reach (Z = 0.383, p = 0.701 [ 0.05) or West River reach
Fig. 2 Variability of annual
precipitation in the Jiulong
River Basin
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J. Huang et al.
Fig. 3 Jumping point analysis
of precipitation in the Jiulong
River Basin
Fig. 4 Seasonal variability of precipitation between the two periods divided by the jumping points identified
(Z = 0.871, p = 0.384 [ 0.05), nor were there any obvious jumping points for the three hydrologic gauge stations.
However, the annual streamflow tended to decrease over
the past 10 years (Fig. 6). The 10-year average streamflow
during 2001–2010 in the North River and West River
reaches decreased by 9.2 and 6.7 %, respectively, compared to the average annual streamflow during 1967–2000.
in the JRB over the past 40 years. Furthermore, the
increasing BFI value suggested that the ratio of runoff in the
streamflow and the contribution of precipitation to streamflow both became less, which may be largely due to the
influence of human activities. Meanwhile, the mean value
and coefficient of variation of the FI in the West River reach
were higher than that in the North River reach (Table 1).
Streamflow regime change
Hydrologic sensitivity to climate variability
The decreasing tendency of the FI was significant at the 1 %
level while the BFI was significantly increasing at the 1 %
level from 1967 to 2010, using the Mann–Kendall test
(Fig. 7), indicating that the streamflow regime had changed
Influence of precipitation on streamflow
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Streamflow was more sensitive to precipitation in the
upstream than in the downstream for both the North River
Hydrologic response to climate change and human activities
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Fig. 5 Dynamics of the annual
frequency of rainstorm events
during 1954–2010
Linkage between streamflow variability and ENSO
indicators
Fig. 6 Streamflow variability for the North River and West River
reaches during 1967–2010
and West River reaches. Pearson coefficient between
streamflow and precipitation in the downstream (e.g.,
Punan and Zhengdian) was lower than that in the upstream
(e.g., Zhangping and Longshang) of the North and West
River reaches (Table 2), suggesting that climate variability
played a more important role upstream, compared to the
downstream.
Two periodic oscillations (i.e., a 1-year scale and a 5- to
7-year scale) were observed in the monthly streamflow
variation in the North River and West River reaches.
Similar results were found in ENSO indicators (i.e., 4-year
scale and 13-year scale) (Fig. 8). As shown in Fig. 8, the
second periodic oscillation concerning monthly streamflow
(i.e., a 5- to 7-year scale) was close to the first periodic
oscillation for MEI and SOI (i.e., 4-year scale), indicating
close relationships between monthly streamflow and ENSO
indicators. Such a phenomenon was probably attributable
to the ENSO cycle, the regulated quasiperiodic oscillation
of ENSO, which occurs across the tropical Pacific Ocean
roughly every 5 years. The second 13-year signal of ENSO
indicators might also have been related to the occurrence of
ENSO events.
Figure 9 shows the intensity of the wavelet energy with
regard to monthly streamflow and ENSO indicators (MEI
and SOI). The intensity at each x–y point represents the
magnitude of the wavelet energy. The higher the values of
wavelet energy, the higher the values of the monthly
streamflow and ENSO indicators. As shown in Fig. 9,
annual fluctuations (i.e., 1-year scale) were the most representative signal in monthly streamflow variability over
the period 1967–2010 for monthly streamflow in the North
River and West River reaches. Additionally, the wavelet
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Fig. 7 Variability of
streamflow flashiness index and
baseflow index of the North
River and West River during
1967–2010
Table 1 Descriptive statistics of flashiness index in the North River
and West River reaches during 1967–2010
N
Mean
SD
Coefficient of
variation
North River
44
0.28
0.033
0.118
West River
44
0.30
0.058
0.193
energy of MEI corresponded well to the wavelet energy of
monthly streamflow. For the low frequency (i.e., 4-year
scale in Fig. 9), the wavelet energy of monthly streamflow
was low, and the wavelet energy of MEI was also low.
However, the high frequency (i.e., 13-year scale in Fig. 9)
shows the opposite tendency, namely the wavelet energy of
the monthly streamflow was high; the wavelet energy of
the MEI was low.
In order to remove the interference of the seasonal
variability of precipitation, the linkage between monthly
streamflow and monthly MEI was analyzed in this study
using Pearson correlation analysis. The result showed that
MEI was strongly positively correlated to the monthly
streamflow change during the period from February to
April. In contrast, there was no significant relationship
between MEI and monthly streamflow in other months,
except September (Table 3). Similar results were obtained
when analyzing the relationship between SOI and monthly
streamflow variability. It can be concluded that the impact
of ENSO events on streamflow was greater in the wet
seasons than that in the other seasons.
Influence of human activities
Effect of dam construction
Dam construction causes changes to the streamflow
regime. The hydropower stations distributed extensively
along the Jiulong River (as shown in Fig. 1) caused the
natural streamflow regime to change; thus, the FI decreased
significantly (Fig. 10). Based on Mann–Kendall analysis,
the jumping point, for the North River and West River in
which the FI dropped off, was identified as 1992, which
was consistent with the fact that hydropower plants had
been developed extensively since 1992. With the development of hydropower plants, the hydrologic regime
Table 2 Pearson coefficient between monthly streamflow and monthly precipitation during 1985–2005 (n = 252)
Rc
Chuangchang
Longshang
Zhengdian
Baisha
Longmen
Maiyuan
Zhangping
Punan
0.810**
0.815**
0.755**
0.790**
0.810**
0.772**
0.782**
0.744**
** p \ 0.01
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Hydrologic response to climate change and human activities
1203
Fig. 8 Wavelet variances of the
monthly streamflow,
Multivariate ENSO Index and
Southern Oscillation Index in
the North River and West River
during 1967–2010
Fig. 9 Wavelet analysis of the monthly streamflow, Multivariate ENSO Index (MEI) and Southern Oscillation Index (SOI) during 1967–2010
changed accordingly. The seasonal variability of monthly
streamflow changed over the past 20 years of dam construction, as did the seasonal variability of monthly sediment discharge (Fig. 11).
The monthly streamflow extended from a single peak to
a double peak in the North River reach either upstream or
downstream. In contrast, the peak value of monthly
streamflow increased and was delayed in the West River
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Table 3 Pearson coefficient between ENSO indicators and monthly streamflow during 1967–2010 (n = 43)
Jan
MEIa
Feb
Mar
0.301*
0.538**
0.645**
-0.368*
-0.376**
-0.528**
MEIb
0.319*
0.488**
0.628**
SOIb
-0.379*
a
SOI
-0.367*
-0.536**
Apr
0.403**
-0.064
0.459**
-0.153
May
Jun
Jul
-0.073
-0.065
0.155
0.289
0.143
0.038
0.163
Aug
Sep
Oct
Nov
Dec
0.157
-0.343*
-0.336*
-0.219
-0.034
-0.122
-0.153
0.299*
0.379*
0.242
-0.051
0.129
0.189
-0.347*
-0.399**
-0.328*
0.145
-0.124
-0.258
0.263
0.374*
0.208
0.153
-0.120
0.177
* p \ 0.05
** p \ 0.01
a
b
North River reach
West River reach
reach as a result of precipitation variability and dam construction (Figs. 5, 11).
Monthly sediment discharge was altered obviously due
to dam construction over the past 20 years (Fig. 11). The
peak of monthly sediment discharge increased significantly
in the North River reach, compared with the values
between the two periods divided by the jumping point.
Conversely, the peak of monthly sediment discharge
decreased and was delayed in the West River reach.
As a result of the dam construction, the relationship
between monthly precipitation and monthly streamflow
became weaker (Table 4). The impact of monthly precipitation on monthly streamflow dropped off slightly after
1992, and the Pearson coefficient and Mann–Kendall
coefficient were reduced accordingly over the past
20 years.
Association between land use change and flashiness index
regime
Land use change can cause changes in the FI in a stream.
Interestingly, the FI rose since 2002, which may be related
to the fact that the proportion of forest land decreased and
the proportion of built-up land increased. As shown in
Fig. 12, the proportion of forest increased during
1986–2002, whereas it decreased after 2002. Meanwhile,
the portion of built-up land increased over the past
40 years, from 1.54 % in 1996 to 5.04 % in 2010.
Factor analysis of human activities
PCA was performed in this study to explore the impact of
socioeconomic development on water resources in the JRB.
The nine indices mentioned above were normalized to
eliminate the influence of difference dimensions. The
Kaiser–Meyer–Olkin values and the Barlett test values
using PCA (1986–2009) were 0.838 and 0.000, respectively, indicating that the results of the analysis were
credible and suitable for PCA. As shown in Fig. 13, the
first two components explained 92.4 % of the total variance. Component 1 had a high positive relationship with
the independent variables x1, x2, x3, x4, x5, x7 and x8,
accounting for approximately 74 %, while component 2
had a good positive relationship with the independent
variables x6 and x9, accounting for over 18 % variance.
The most important driving force for streamflow regime
change in the JRB was the economic factor, which was
closely related to population growth and economic development. Agricultural activities including pig breeding and
crop practices were identified as the next most important
factors influencing the streamflow regime.
Discussion
Precipitation variability and climate change
Fig. 10 Jumping point analysis of flashiness index in the North River
and West River reaches over the period 1967–2010
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Average annual precipitation in the JRB presented an
increasing trend (Z = 2.263, p = 0.024 \ 0.05) over the
period 1954–2010, and this trend became weaker from
estuary to inland. Such an increasing trend in precipitation
was similar to the other observations in the southern part of
China. Geographically, decreasing trends of interannual
Hydrologic response to climate change and human activities
1205
Fig. 11 Seasonal variability of streamflow and sediment discharge between the two periods divided by the jumping points identified
Table 4 Pearson coefficient and Mann–Kendall sa/sb between
monthly precipitation and monthly streamflow between the two
periods divided by the jumping points identified during 1967–2010
Two periods
North River reach
West River reach
Pearson
Mann–
Kendall
Pearson
Mann–
Kendall
1967–1991
(n = 300)
0.856**
0.641**
0.816**
0.612**
1992–2010
(n = 228)
0.832**
0.591**
0.796**
0.533**
** p \ 0.01
precipitation are concentrated in the northern part of China
including the Songliao River, Huai River and Yellow River
Basin, while the increasing trends appear primarily in the
southern part of China including the Yangtze River,
Zhujiang River Basin and Southeastern Region (Xu et al.
2004). Based on the records from 160 meteorological stations in China, precipitation from 1951 to 2000 increased
by 2 % in the south but decreased by 4–11 % in the north
(Xu et al. 2010).
The precipitation variability in the JRB may be attributed to global climate change. The jumping points in the
estuary, West River and North River reaches were identified as 1979, 1976 and 1986, respectively. Similar changes
are found in the Tarim River Basin (Xu et al. 2004; Zhou
et al. 2012), North Xijiang (Yang et al. 2012), Yellow
River Basin (Zhao et al. 2008), Japan (Xu et al. 2003),
Europe (Franks 2002) and Argentina (Boulanger et al.
2005; Doyle et al. 2012). The significance of the Mann–
Fig. 12 Dynamics of land use
and flashiness index change in
the JRB during 1986–2010
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Whitney test in the estuary, West River and North River
reaches between the two periods divided by the jumping
points were 0.005, 0.078 and 0.863, respectively. It seemed
that the influence of global climate change was becoming
weaker from estuary to inland within the JRB.
The variability of annual precipitation in the JRB was
greatly related to the seasonally increasing intense rainstorm events during the period from July to September.
Muttiah and Wurbs (2002) conclude that average precipitation in the USA increased by approximately 5–10 %
throughout the twentieth century, mostly due to the
increases in intense rainstorms. Because of precipitation
variation, the streamflow decreased significantly around
September 1990, and this caused the annual average
streamflow to change significantly, so that annual variation
moved from double peaks to a single peak in the Yellow
River (Li et al. 2012). Xu et al. (2010) note that rainfall in
summer over the period from June to August accounts for
30 % of the annual precipitation in South China (Guangzhou) with peak rainfall shifting from May to August.
and precipitation in the upstream have been decreasing
(Fig. 7). Precipitation in the upstream was identified as the
major factor influencing streamflow downstream in the JRB.
Compared to the variability of precipitation and
streamflow between the two periods divided by the jumping points identified in the West River and North River
reaches, the annual precipitation for the North River in the
later period decreased by 2 %, while the annual streamflow
only decreased by 1.5 %. Changes in precipitation were not
obviously amplified in the streamflow of the JRB, which
may be attributable to the combined impacts of global
climate and anthropogenic activities such as dam construction. On the other hand, Chiew et al. (1995) point out
that changes in precipitation are amplified in streamflow in
southeast Australian rivers. Chen et al. (2007) also find that
annual runoff coefficients are in direct proportion to annual
precipitation in the Tarim River Basin, and as such, it is
easier to detect variability or climate change in streamflow
rather than in precipitation or other climate variables.
In our study, we found that the occurrence of ENSO
events might have brought about a lower frequency of
streamflow variability. The streamflow was positively
correlated with the MEI and negatively correlated with the
SOI from February to April, and the situation was opposite
in September, which was greatly influenced by ENSO
events. It is well known that an ENSO event is a most
important phenomenon in tropical air–sea interactions and
one which can influence climate change in China, especially the summer climate change (Huang and Wu 1987).
The ENSO events bring about summer climate anomalies
in China (Fu and Ye 1988). ENSO has been widely recognized as an important factor influencing regional hydroclimatology in the United States, China and South
America (Chavasse and Seoane 2009; Rossi et al. 2009; Lv
et al. 2011). However, annual variations in precipitation
exert a greater impact on the frequency of streamflow
variability in the JRB, compared to the influence of ENSO
events.
Hydrologic response to climate change
Impact of human activities on streamflow regime
The annual precipitation has increased while the average
annual streamflow has not significantly increased in the
JRB in the past 40 years. The runoff generation process can
be considered as the conversion of precipitation to runoff
(Chen et al. 2007). Lu (2004) notes that the relationships
between precipitation and runoff in the 1950s and 1970s
seem to be strongly correlated (R2 = 0.897) for both
southern and northern large Chinese rivers. In our study,
precipitation was positively correlated to streamflow from
1967 to 2010, especially precipitation in the upstream
(Table 2). In the past 10 years, both the annual streamflow
Human activities including dam construction can alter
streamflow regimes. In our study, the FI decreased significantly while the BFI increased significantly, indicating
that the streamflow regime in the JRB has been greatly
influenced by human activities over the past 40 years.
Seasonal variability of streamflow changed significantly
between the two periods divided by the jumping point
(1992) identified when dams were constructed extensively
in the North River and West River reaches. Costigan and
Daniels (2012) note a significant increase in the 1- through
90-day minimum discharge and a significant decrease in
Fig. 13 Bitplot of PCA in the Jiulong River Basin
123
Hydrologic response to climate change and human activities
the 1- through 90-day maximum discharge for dam construction in the Great Plain river regimes. The FI decreased
significantly after the construction of a hydropower station
in 1989 in the Zborov-Bystrica watershed (Holko et al.
2011). Zhao et al. (2012) note that the degree of hydrologic
alteration changed obviously in the midstream area of the
Lancang River, and the situation worsened when the river
was simultaneously influenced by dam construction and
other human activities. Chen et al. (2010) also find six
indicators of hydrologic alteration remarkably induced by
dam construction in the East River Basin. Above all, the
most common changes in the hydrologic regime of a
stream are the changes in the baseflow and flashiness of
streamflow (Hirsch et al. 1990; Stanford et al. 1996; Poff
et al. 1997; Graf 2001).
FI and BFI are vital indices for characterizing the
influence of human activities on the hydrologic condition.
The FI was negatively correlated to the BFI in the JRB, and
a similar result is found in Estonia and Norway by Deelstra
and Iital (2008). The characteristics of seasonal variability
in streamflow have changed since the 1992 dam construction. The monthly streamflow increased in the period
from July to September for dam construction in 1992,
especially in the North River reach, where there is no
obvious change in precipitation but where streamflow
increased from July to September. After analyzing
streamflow and suspended sediment load with wavelet
analysis, Rossi et al. (2009) find a loss of energy in the
suspended sediment load signal which can be attributed to
anthropogenic changes, principally the construction of a
large reservoir on the Missouri River in the 1950s. The FI
in the West River reach was higher than that in the North
River reach, and this was related to physical characteristics: The area of the North River reach is larger than that of
the West River reach, which made the streamflow in the
North River reach more stable. A similar result is found in
other studies in the USA (Fonger et al. 2007; Baker et al.
2004). After investigating 279 watersheds and 515 watersheds, respectively, both groups find that the FI decreases
while the area of watershed increases. Furthermore, Holko
et al. (2011) note that the value and variation of FI increase
when the watershed becomes smaller. Our study showed a
similar result in the North River and West River, as shown
in Table 1.
The FI in the JRB decreased from 1986 to 2002, partly
because the proportion of forest increased in this period.
During 2002–2007 the FI decreased, probably because the
urbanization process accelerated and the area of forest
decreased over this period. During the period from 2007 to
2010, the FI decreased to a low level which might have
been related to an apparent slowdown in the urbanization
process and a potential increase in forest. Wissmar et al.
(2004) note that the discharge rates for all watersheds were
1207
higher in 1991 and 1998 than historically and suggest that
the chances for floods increase because of changing land
cover in the Cedar River, USA. Dow (2007) analyzes the
streamflow regime of nine New Jersey streams and finds
both decreasing and increasing trends of flashiness for four
rivers and suggests that this is related to an apparent
slowdown in urbanization and to potential changes in
wetland agricultural practices. More permeable soils in
forest areas as compared to open land (Jewitt 2005) lead to
a larger portion of subsurface flow and therefore reduce
flashiness. Additionally, Holko and Kostka (2008) note
good correlations between physiographic catchment attributes and the FI for selected small mountain catchments in
Slovakia, and the FI is well correlated with catchment
slope and percentage of agricultural land and forest.
Human activities have been posing increasing influences
on hydrologic conditions in the JRB over the past 40 years.
The driving forces of human activities were mainly
attributed to socioeconomic factors that are related to
urbanization and urban population growth, which is consistent with the result of land use and land change analysis,
indicating that the urbanization process and urban planning
in the JRB should be given more attention. Urbanization
has even been identified as one of the important factors
contributing to China’s water scarcity (Jiang 2009). Other
studies also demonstrate that human activities can alter
surface runoff significantly in watersheds. For example, Xu
et al. (2004) find that climate change increases the surface
runoff in the headstream of the Tarim River, while human
activities decrease the surface runoff in the mainstream.
Hao et al. (2008) also note that two principal components
are responsible for the influence of human activities on
surface runoff, one being economic factor which is highly
related to agricultural exploitation, the other being the
population factor that resulted in an increase in the area of
plantation and changed the crop structure in the Tarim
River Basin.
Conclusions
Our results showed the hydrologic sensitivity to climate
change and human activities in a medium-sized subtropical
coastal watershed in southeast China. Precipitation in
the JRB presented an increasing trend (Z = 2.263,
p = 0.024 \ 0.05) over the period 1954–2010 while the
trend became weaker from estuary to inland. The variability of annual precipitation was closely related to the
seasonally increasing intense rainstorm events and precipitation during the period from July to September. Changes
in precipitation were not obviously amplified in streamflow
in the JRB. Compared to the variability of precipitation and
streamflow between the two periods divided by the
123
1208
jumping points identified in the West River and North
River reaches, the annual precipitation for the North River
in the later period decreased by 2 %, while the annual
streamflow only decreased by 1.5 %. Annual fluctuations
were the most representative signals in streamflow variability over the period 1967–2010 for the North River and
West River reaches. The second signal of 5- to 7-year
fluctuation in streamflow variability corresponded to the
ENSO cycle. The hydrological signal of 13-year fluctuation
in MEI and SOI variation corresponded to the frequency of
ENSO events. The occurrence of ENSO events might have
brought about the lower frequency of streamflow variability, but annual variation in precipitation exerted a
greater influence on the frequency in streamflow variability
in the JRB, compared to the influence of ENSO events.
Human activities including dam construction, land change
and socioeconomic development are posing increasing
influences on hydrologic condition in the JRB. Seasonal
variability of streamflow changed significantly between the
two periods divided by the jumping point (1992) identified
when dams were extensively constructed in the North
River and West River reaches. Economic development and
land use changes were identified as the important factors
contributing to streamflow regime variations in the JRB
over the past 40 years. Our study provided important
insight into the hydrologic consequences of climate change
and human activities in a medium-sized subtropical coastal
watershed. Hopefully, these findings will be meaningful for
watershed-scale water resource management for the many
coastal watersheds in China which face similar situations in
terms of climate change and human activities.
Acknowledgments A preliminary form of this paper was presented
at the East Asian Sea Congress 2012 (EASC2012) held on July 8–13,
2012, in Changwon, Republic of Korea. This study is supported by
the Natural National Science Foundation of China (Grant No.
40810069004, Grant No. 40901100). Professor John Hodgkiss is
thanked for his assistance with the English. Anonymous reviewers
supplied constructive feedback that helped to improve this paper.
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