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Journal of Hydrology 494 (2013) 83–95
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Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol
Distinguishing the relative impacts of climate change and human
activities on variation of streamflow in the Poyang Lake catchment,
China
Xuchun Ye a,b, Qi Zhang b,⇑, Jian Liu c, Xianghu Li b, Chong-yu Xu d,e
a
School of Geographical Sciences, Southwest University, Chongqing 400715, China
Nanjing Institute of Geography and Limnology, State Key Laboratory of Lake Science and Environment, Nanjing 210008, China
Key Laboratory of Water Resources and Environment, Water Research Institute of Shandong Province, Jinan 250013, China
d
Department of Geosciences, University of Oslo, Norway
e
Department of Earth Sciences, Uppsala University, Sweden
b
c
a r t i c l e
i n f o
Article history:
Received 18 January 2013
Received in revised form 8 April 2013
Accepted 20 April 2013
Available online 3 May 2013
This manuscript was handled by
Konstantine P. Georgakakos, Editor-in-Chief,
with the assistance of Ashish Sharma,
Associate Editor
Keywords:
Climate change
Human activities
Hydrological response
MK test
Poyang Lake catchment
s u m m a r y
Under the background of global climate change and local anthropogenic stresses, many regions of the
world have suffered from frequent droughts and floods in recent decades. Assessing the relative effect
of climate change and human activities is essential not only for understanding the mechanism of hydrological response in the catchment, but also for local water resources management as well as floods and
droughts protection. The Poyang Lake catchment in the middle reaches of the Yangtze River has experienced significant changes in hydro-climatic variables and human activities during the past decades and
therefore provides an excellent site for studying the hydrological impact of climate change and human
activities. In this study, the characteristics of hydro-climatic changes of the Poyang Lake catchment were
analyzed based on the observed data for the period 1960–2007. The relative effect of climate change and
human activities was first empirically distinguished by a coupled water and energy budgets analysis, and
then the result was further confirmed by a quantitative assessment. A major finding of this study is that
the relative effects of climate change and human activities varied among sub-catchments as well as the
whole catchment under different decades. For the whole Poyang Lake catchment, the variations of mean
annual streamflow in 1970–2007 were primarily affected by climate change with reference to 1960s,
while human activities played a complementary role. However, due to the intensified water utilization,
the decrease of streamflow in the Fuhe River sub-catchment in 2000s was primarily affected by human
activities, rather than climate change. For the catchment average water balance, quantitative assessment
revealed that climate change resulted in an increased annual runoff of 75.3–261.7 mm in 1970s–2000s
for the Poyang Lake catchment, accounting for 105.0–212.1% of runoff changes relative to 1960s. However, human activities should be responsible for the decreased annual runoff of 5.4–56.3 mm in the other
decades, accounting for 5.0% to 112.1% of runoff changes. It is noted that the effects of human activities including soil conservation, water conservancy projects and changes in land cover might accumulate
or counteract each other simultaneously, and attempts were not made in this paper to further distinguish
them.
Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction
Climate change and human activities are the two factors that affect the change of catchment hydrology. According to the IPCC
(2007), the average global surface temperature increased by
0.74 °C over the last 100 years. One of the most significant potential consequences of climate change may be alterations in regional
hydrological cycles (e.g., Huntington, 2006). General consensus
⇑ Corresponding author. Tel.: +86 25 86882102; fax: +86 25 57714759.
E-mail address: [email protected] (Q. Zhang).
0022-1694/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.jhydrol.2013.04.036
have revealed that global warming and related changes to the
hydrological cycle are likely to enhance the frequency and severity
of extreme climate events, causing more severe floods and
droughts (e.g., Milliman et al., 2008; Bates et al., 2008; Déry
et al., 2009; Jung et al., 2012; Thompson, 2012; Li et al., in press;
Xiong et al., in press). In addition to global climate change, increases in human activities such as cultivation, irrigation, afforestation, deforestation and urban construction have also introduced
changes to flow regime, especially large scale changes of land cover
or its management (e.g., Yang et al., 2004; Brown et al., 2005; Jiang
et al., 2012; Yang et al., 2012a,b). Depending on the study region,
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X. Ye et al. / Journal of Hydrology 494 (2013) 83–95
impacts of human activities on streamflow may be different.
Reduction in streamflow has been shown in several arid and
semi-arid catchments in north China due to implementation of
conservation practices and increased water utilization (e.g., Li
et al., 2007; Wang and Meng, 2008; Liu et al., 2009a). However,
studies in Iowa’s rivers noted that increasing agricultural intensity
may increase stream discharge but reduce its variance due to the
decreasing surface runoff and increasing baseflow (e.g., Tomer
et al., 2005; Schilling, 2004). Moreover, water utilization for agricultural and industrial development can also lead to significant
change in the water cycle and affect the variation of surface or
sub-surface runoff (e.g. Du et al., 2012).
Within the last decades, water quantity and quality have become increasingly serious issues for water resources management
at catchment and/or regional scale (e.g., Kizza et al., in press; Li
et al., in press; Ren et al., 2002; IPCC, 2007; Tomer and Schilliing,
2009; Lakshmi et al., 2012). Therefore, understanding the influence
and relative importance of climate change and human-induced
change on hydrology and water resources has recently drawn considerable concerns (e.g. Siriwardena et al., 2006; Ma et al., 2008; St.
Jacques et al., 2010; Jin et al., 2012; Carless and Whitehead, in
press; Zhan et al., in press). Lahmer et al. (2001) indicated that climate change is the dominant factor that affects the change of
streamflow in wet regions, while human activities such as some
extreme land-use change only resulted in comparatively small impacts on regional water balance. Similar result was also revealed by
Legesse et al. (2003) for tropical Africa. However, the study by Raymond et al. (2008) suggested that land use change and management were more important than climate change for explaining
the increasing water export from the Mississippi River. In northern
China, increasing water shortage is very common in recent years
due to significant regional precipitation variation as well as rapid
development of local economy (e.g., Piao et al., 2010). A quantitative assessment revealed that local human activities since the
1970s led to a decrease of the water diverted into the main stream
of the Tarim River catchment, which has been aggravated in the
2000s (Tao et al., 2011). In Haihe River catchment, an important
economic center of China, human activities were estimated to be
responsible for the decline in annual water discharge, which accounts for over 50% of runoff reduction, while the contribution of
climate change is relatively small (Wang et al., 2012). In a recent
study conducted by Zhang et al. (2011a), the trends of the annual
streamflow and precipitation and the relationship between them
were analyzed in nine large river basins of China during 1956–
2005. The results indicated that annual runoff has the same changing trend as precipitation in humid regions revealing a stationary
rainfall–runoff relationship is still held. However, in arid and
semi-arid regions of north China the decline in streamflow is faster
than the decreases of precipitation since 1970s, indicating that the
relationship between the annual precipitation and streamflow presents a non-stationary state. This non-stationary relationship is
strongly influenced by human activities, especially by the increase
of irrigation water use.
Poyang Lake, the largest fresh water lake in China, is located in
the middle reaches of the Yangtze River with a catchment area of
162,225 km2. The Lake and its surrounding catchments have suffered from frequent droughts and floods in recent decades, especially in 1990s and 2000s (e.g., Wang et al., 2008; Min et al.,
2011). These severe drought and flood events have raised concerns
for the lake ecology and local water resources management. Studies on hydrological response suggested that the changes of annual
streamflow in the catchment were primarily caused by climate
anomalies in the Yangtze River catchment, while human activities
such as land-use change and modifications to river systems including the Yangtze River also exerted some impacts (e.g., Min, 2002;
Guo et al., 2008, 2011; Zhang et al., 2012). Several studies showed
that the variations of streamflow is much more strongly related to
regional climate especially precipitation, but this is insufficient to
explain all the changes (e.g., Guo et al., 2007; Zhao et al., 2009;
Ye et al., 2009). For example, the increased vulnerability of the lake
to floods is further elevated by deforestation and change of landscape in the basin. In addition, the construction of large-scale
water conservancy facilities (reservoir and irrigation system) in
the catchment is another important factor altering the annual hydrograph and increasing the water utilization (e.g., Zhang et al.,
2011b; Liu et al., 2009b). Large amount of water demand severely
decreased the catchment discharges to Poyang Lake and elevated
the drought in the lake area, especially in dry years.
Effects of climate change and human activities on runoff variation are significantly sensitive, especially in arid and semi-arid regions, and these effects have resulted in severe environmental
degradation and water crises. However, previous studies revealed
that the relative importance of the influence of climate variability/change and human activities varies from region to region. To
our knowledge, the relative contribution of climate change and human activities to runoff change in the Poyang Lake catchment has
not been well investigated. Further studies are needed in order to
provide a generalized and conclusive interpretation of the changes
observed. The answer to this is essential not only for an improved
understanding of the mechanism of hydrological response in the
catchment, but also for local water resources management as well
as floods and droughts protection and mitigation in the Poyang
Lake catchment and the lower reaches of the Yangtze River. The
purposes of this study are: (1) to investigate the variability of
long-term historical records of climate and hydrological data in
the Poyang Lake catchment; and (2) to evaluate the relative impacts of climate change and human activities on catchment-scale
streamflow response under different spatial and temporal scales.
1.1. Overview of the Poyang Lake catchment
The Poyang Lake, connected to the Yangtze River, lies on the
northern border of the Jiangxi Province, China. The lake receives
water flows mainly from five rivers: Ganjiang, Fuhe, Xinjiang,
Raohe and Xiushui, and discharges into the Yangtze River from a
narrow outlet in the north (see Fig. 1). Among the five major rivers,
the Ganjiang is the largest river in the region extending 750 km
and contributes almost 55% of the total discharge into the Poyang
Lake (Shankman et al., 2006). The topography of the Poyang Lake
catchment varies from highly mountainous regions (maximum
elevation of about 2200 m above sea-level) to alluvial plains in
the lower reaches of the primary watercourses. Headwater of these
rivers are located in boundaries of the east, south and west of the
Jiangxi Province that surrounded by high mountains. Stream gradient decreases as these rivers flow onto the relatively flat region
surrounding the Poyang Lake. The wide alluvial plains surrounding
Poyang Lake and the broad alluvial valleys of the tributary streams
are important rice growing regions in Jiangxi Province as well as in
China; most notably the lower reaches of Ganjiang and Fuhe subcatchments have large irrigation areas over 10,000 ha (see in
Fig. 1).
The Poyang Lake catchment belongs to a subtropical wet climate zone with an annual mean precipitation of 1680 mm and annual mean temperature of 17.5 °C. Annual precipitation in the
catchment shows a wet and a dry season and a short transition
period in between (see Fig. 2). Water inputs from the five subcatchments are particularly important during the wet season from
April through June when heavy rainfall produces large surface
flows from the sub-catchments to the lake (Shankman et al.,
2006). Rainfall decreases sharply from July to September, while
evapotranspiration is still very strong in these months (Fig. 2).
After September, the dry season sets in and lasts through
X. Ye et al. / Journal of Hydrology 494 (2013) 83–95
85
Fig. 1. Topography and river networks of the Poyang Lake catchment, with stream gauging stations (‘‘Hydrostation’’) and meteorological stations (‘‘Meterostation’’) are
marked.
Fig. 2. Mean monthly precipitation and evapotranspiration of the Poyang Lake
catchment for 1960–2007.
December, and surface flow of the catchment is very low during
this period. In response to annual cycle of precipitation, about
59.1% of the annual discharge arrives from March to June, but only
13.7% arrives from October to next January. In normal years, the
Poyang Lake can expand to a large water surface of 4000 km2 with
a volume of 320 108 m3 in the wet season, but shrinks to little
more than a river during the dry season (Xu et al., 2001).
As a typical agriculture catchment, about 13% of the land area is
being irrigated. Although water resources in the Poyang Lake
catchment are pretty abundant, the difficulty of water utilization
is affected by the strong variability of annual and seasonal precipitation. Furthermore, with the rapid economic development and
population explosion in the catchment, influence of human activities on water resources has become increasingly important. Direct
effects of human activities mainly include the soil conservation
and water conservancy projects. The statistical data indicated that
a total amount of 9530 reservoirs were built across the catchment
until 2007, and most of these projects were constructed before
1980 (Min et al., 2011). Among which, 13 big reservoirs have a vol-
ume larger than 1.0 108 m3, including Zhelin Reservoir
(50.17 108 m3) on the Xiushui River, Wan’an Reservoir
(11.16 108 m3) on the Ganjiang River, and Hongmen Reservoir
(5.24 108 m3) on the Fuhe River (see Fig. 1). Most water conservancy projects have special functions for flood protection, water
supply, agriculture irrigation, power generation and navigation,
which dramatically increase water utilization and change the temporal and spatial distribution of the streamflow, but they do not
transfer water out of the Poyang Lake catchment. The recent water
resources bulletins from Water Conservancy Bureau of Jiangxi
Province indicated that annual water consumption is about
94.38 108–126.95 108 m3 during 1997–2007, of which 73%
for agriculture irrigation and 21% for domestic and industrial utilization. During the past decades, land use/land cover of the catchment has changed dramatically, which may have affected the
change of catchment hydrology indirectly. There is an indication
that the forest coverage of the catchment was reduced from over
60% in 1950s to only 32.7% in 1970s. The Grain for Green (returning
farmland to forest) policy was launched by the government in earlier 1980s in order to restore the ecological environment, and as a
result, the forest coverage recovered to nearly 60% at the end of
1990s (Liu et al., 2009b). Human activities have collectively changed the natural condition of catchment hydrology, and introduced
additional challenges for local water resources management.
2. Data and method
2.1. Available data
Observed discharges (daily streamflow) from five gauging stations on the lower reaches of the five rivers were obtained from
Management Bureau of the Yangtze River (MBYR) catchment,
accounting for 75.4% of the lake’s inflow from the catchment area.
Among the five gauging stations, Waizhou, Lijiadu and Meigang are
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X. Ye et al. / Journal of Hydrology 494 (2013) 83–95
Table 1
List of hydrological stations and their features for 1960–2007.
Gauging
station
Location
Drainage
area (km2)
Mean annual discharge
(108 m3/year)
Waizhou
Gangjiang River (Lower)
(28°370 N, 115°490 E)
Fuhe River (28°120 N, 116°090 E)
Xinjiang River (28°250 N,
116°480 E)
Raohe River (Le’anjiang branch)
(28°540 N, 117°180 E)
Xiushui River (Liaohe branch)
(28°510 N, 115°380 E)
80,948
685
15,811
15,535
Lijiadu
Meigang
Hushan
Wanjiabu
Coefficient of
variation (Cv)
Ratio of annual extreme
streamflow (Qmax/Qmin)
846
0.28
4.85
123
178
780
1144
0.33
0.36
4.31
6.18
6474
74
1137
0.32
3.93
3548
35
990
0.35
4.69
located at the lower reaches of Ganjiang, Fuhe and Xinjiang rivers,
which contribute more than 90% of the total inflow of the five rivers. While Hushan and Wanjiabu are located at the branches of
Raohe and Xiushui rivers with relative small drainage areas, contributing less than 10% of the total inflow of the five rivers. The basic features of these gauging stations are listed in Table 1.
The meteorological data from 19 weather stations inside the
catchment (see Fig. 1) were obtained from National Climate Centre
of China Meteorological Administration (CMA). They provide daily
observations of precipitation, temperature, relative humidity, sunshine duration, actual vapour pressure, and wind speed, among
others. The period of record of most weather stations used in this
study is 1960–2007, except for Nanxiong, Lichuan, Linchuan and
Shangrao stations where the available data period is 1980–2007.
Climate variables of these four weather stations are further interpolated for the period 1960–1979 using the nearest weather
station. Based on the meteorological datasets, potential evapotranspiration (PET) of the weather stations was estimated by
applying the Penman–Monteith equation (Allen et al., 1998). The
daily records of streamflow and climate variables provided by
MBYR and CMA had gone through a standard quality control process before delivery, and with no missing data on the variables
used in this study.
Before applying the data, all the hydro-meteorological variables
were aggregated from daily to monthly and to yearly. In consideration of the large degree of variation in topography and the uneven
distribution of weather stations across the catchment, an areabased weighting method was used to calculate the average precipitation, potential evapotranspiration for the whole catchment as
well as the individual sub-catchments. The weight coefficient, expressed by the percentage of the area represented by each meteorological station, was calculated using the Thiessen Polygon
method. Similarly, annual streamflow for the whole catchment
was calculated from the five individual hydro-stations by using
the same method.
Specific discharge
(mm/year)
In which, unused
To detect the existence of any step
change points in the hydro-climatic data Xt = (x1, x2, x3 . . . xn),
sequential Mann–Kendall test was used, in which, the accumulative number ni of samples that xi > xj (1 6 j 6 i) should be first calculated. The normally distributed statistic dk can be calculated via
the following formula:
dk ¼
k
X
ni
ð2 6 k 6 nÞ
ð1Þ
i¼1
Mean and variance of the normally distributed statistic dk are
given by
Eðdk Þ ¼ kðk 1Þ=4
ð2Þ
varðdk Þ ¼ kðk 1Þð2k þ 5Þ=72
ð3Þ
The normalized variable statistic UF(dk) is estimated as follows:
½dk Eðdk Þ
UFðdk Þ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
varðdk Þ
ðk ¼ 1; 2; 3 . . . ; nÞ
ð4Þ
where UF(dk) is the forward sequence, and the backward sequence
UB(dk) is calculated using the same equation but with a reversed series of data. The null hypothesis (no step change point) is rejected if
any of the points in the forward sequence (UF(dk)) are outside the
confidence interval. The sequential Mann–Kendall test was often
used to determine the approximate time of occurrence of the change
point by locating the intersection of the forward and backward curves
of the test statistic. An intersection point of UF(dk) and UB(dk) located
within the confidence interval indicates the beginning of a step
change point (Moraes et al., 1998; Zhang et al., 2011c).
2.3. Estimating the relative impact of climate change and human
activities on streamflow
For a natural catchment, annual water balance can be quantified
as:
2.2. Statistical analysis
In order to analyze the temporal variation of the streamflow
data, the Partial Mann–Kendall test (hereafter we call it MK test),
which is known as Kendall’s statistic (Kahya and Partal, 2007),
was applied in this study. The MK test is a rank-based non-parametric method that has been widely applied for trend detecting
in hydro-climatic time series due to its robustness against the
influence of abnormal data and especially its reliability for biased
variables (e.g., Burn and Hag Elnur, 2002; Chen et al., 2007; Zhang
and Lu, 2009). Before the trend analysis was performed, autocorrelation (serialcorrelation) was examined through the autocorrelation
and partial autocorrelation function for all the hydrological data,
and results revealed (not shown here) that no significant autocorrelation existed in the data.
DS=Dt ¼ P ET Q
ð5Þ
where P is the precipitation, ET is the actual evapotranspiration, Q is
the streamflow, DS is the change in water storage and Dt is the time
step. Over a long period of time (i.e., 10 years or more), DS can be
reasonably assumed as zero, and then ET can be estimated as the
difference between P and Q.
Among the water balance components, ET is mainly controlled
by available water (P) and available energy (or evaporative demand – PET) of a catchment, and of course by the human activities
such as the landuse change and farmland irrigation. Based on the P,
PET and ET values in a catchment, a coupled water and energy budgets analysis is available to be used to evaluate the efficiency of
water and energy use by an ecosystem (Milne et al., 2002; Tomer
and Schilliing, 2009). In which, unused water (P–ET) and unused
X. Ye et al. / Journal of Hydrology 494 (2013) 83–95
energy (PET–ET) available in the environment can be used to calculate the proportions of available water and energy, i.e. Pex and Eex,
that are unused (i.e., in excess) as:
Pex ¼ ðP ETÞ=P
ð6Þ
Eex ¼ ðPET ETÞ=PET
ð7Þ
where Pex and Eex take values from 0 to 1. Based on the analysis of
Pex and Eex, Tomer and Schilliing (2009) developed a conceptual
model to simply distinguish land-use and climate change effects
on watershed hydrology (see Fig. 2 in Tomer and Schilliing, 2009).
In which, climate change influences P and PET, and causes the increase of excess water (Pex) and decrease of excess energy (Eex), or
vice versa. However, changes in vegetation or its management will
directly affect ET, but not P or PET, which result in a regime shift in
excess water and energy with either increase or decrease, depending on the effect of the change on ET.
The plots of excess water (Pex) versus excess energy (Eex) provide an effective way to empirically distinguish the relative effects
of climate change and human activities on watershed hydrology
for any two different periods. It should be noted that the applicability of the conceptual model is based on the assumptions that human activities are independent of climate change, and the landuse
change affects only ET. However, the effects of human activities
and climate are commonly interrelated with each other indirectly
at broad scales. For example, in large catchments, climate change
effects on hydrology have coincided with land cover changes
(Zheng et al., 2009; Tomer and Schilliing, 2009), and construction
of river regulation system or big reservoirs will affect regional precipitation and temperature and consequently results in changes in
the hydrological regime (Wu et al., 2006; Li et al., 2002). Despite
that human activities and climate system may interact with each
other, it is commonly not considered in most quantitative assessment studies (e.g. Li et al., 2007; Fan et al., 2010; Jiang et al.,
2011; Wang et al., 2012).
In the Poyang Lake catchment, human activities are complex
which include a wide range of landuse change, river regulation
and rapid development of local society and economy. Due to the
fact that specific human activities (e.g. land use change, farmland
irrigation and river regulation) may exist simultaneously in a
catchment and their effects may accumulate or counteract each
other, we extended this assumption that all anthropogenic stresses
in the Poyang Lake catchment will result in a change of ET. Then,
changes associated with local climate and human impacts are
likely to result in a shift in excess water and energy, and the rela-
87
tionship can be illustrated in Fig. 3. The directions of these hydrological shifts may indicate the relative impacts of climate change
and human activities on catchment hydrology. Here we applied
this empirical method aimed to analyze the hydrological shifts in
the Lake catchment associated with climate change and human
activities as an auxiliary analysis for demonstration of the relative
importance of climate change and human activities, and the result
is afterwards inspected from a quantitative assessment.
Under the assumption that water balance is controlled by
water availability and atmospheric demand, Zhang et al. (2001)
noted that long-term mean annual evapotranspiration has the
following relationship with local precipitation and potential
evapotranspiration:
ET
1 þ wðPET=PÞ
¼
P
1 þ wðPET=PÞ þ ðPET=PÞ1
ð8Þ
where w is a model parameter of available water coefficient related
to vegetation type (Zhang et al., 2001), and can be calibrated using
annual hydro-climatic data.
Hydrologic sensitivity can be described as the percentage
change in mean annual runoff in response to changes in mean annual precipitation and potential evapotranspiration (Jones et al.,
2006; Li et al., 2007). Variation in both precipitation and potential
evapotranspiration can lead to changes in water balance. It can be
assumed that a change in mean annual runoff due to climate
change can be approximated as follows (Koster and Suarez,
1999; Milly and Dunne, 2002):
DQ c lim ¼ a DP þ b DPET
ð9Þ
where DQclim, DP, DPET are the changes in streamflow, precipitation
and potential evapotranspiration respectively; a and b are the sensitivity parameters and can be further expressed as (Li et al., 2007):
a¼
b¼
1 þ 2x þ 3wx
ð1 þ x þ wx2 Þ2
1 þ 2wx
ð1 þ x þ wx2 Þ2
ð10Þ
ð11Þ
where x is the dryness index (equal to PET/P), w is same as in Eq. (8).
A change in mean annual streamflow can be calculated as
follows:
DQ obs ¼ Q obs2 Q obs1
ð12Þ
where DQobs indicates the observed change
in mean annual stream
flow between two different periods, Q obs1 is
the average annual
streamflow during the reference period, and Q obs2 is the average annual streamflow during the other period.
The change of catchment hydrology is mainly affected by the
two driving factors of climate change and human activities. As a
first-order approximation, the change in mean annual runoff can
be estimated as follows:
DQ obs ¼ DQ c lim þ DQ hum
ð13Þ
where DQclim and DQhum are the changes in the mean annual
streamflow due to climate change and human activities, respectively. The relative contributions of climate change and human
activities on streamflow can be further expressed as:
Fig. 3. Conceptual model of hydrological shifts associated with climate change and
human activities (modified from Tomer and Schilliing, 2009).
gc lim ¼
DQ c lim
100%
jDQ obs j
ð14Þ
ghum ¼
DQ hum
100%
jDQ obs j
ð15Þ
where = clim and = hum are the percentages of the impact of climate
change and human activities on streamflow, respectively.
88
X. Ye et al. / Journal of Hydrology 494 (2013) 83–95
Table 2
Results of MK test for seven variables on seasonal and annual basis 1960–2007.
Parameter
Annual mean
Cv (%)
T
P
RH
VP
W
SD
PET
17.7
1656.6
78.5
9.9
2.1
4.8
1050.6
3
15
2
0
13
10
5
Trends
Spring (MAM)
Summer (JJA)
Autumn (SON)
Winter (DJF)
Annual
2.30*
0.44
2.21*
2.21*
7.54**
0.63
0.67
0.84
1.31
0.12
0.12
4.92**
4.24**
2.82**
1.47
1.02
1.31
1.15
6.66**
1.48
2.04*
2.78*
1.68
0.42
0.40
6.82**
2.85**
1.52
3.40**
1.11
0.56
1.59
7.83**
4.47**
2.59**
Note: Delineates negative trends based on the MK test. Cv: coefficient of variation (%); P: precipitation (mm); T: temperature (°C); VP: vapour pressure (kPa); RH: relative
humidity (%); SD: sunshine duration (h); W: wind speed (m/s); PET: potential evapotranspiration (mm/y).
*
Delineate significance at 0.05 significance level.
**
Delineate significance at 0.01 significance level.
3. Results
3.1. Changes of catchment climate
In order to investigate the variation of regional climate in the
Poyang Lake catchment, basin-scale averaged time-series 1960–
2007 of six variables, i.e., precipitation, temperature, relative
humidity, sunshine duration, vapour pressure and wind speed
were examined by the MK test. The potential evapotranspiration
(PET) estimated by Penmam–Monteith method was also analyzed.
Results of the trend test for the seven variables are displayed in Table 2. It is seen that on annual basis, temperature and precipitation
show positive trends; relative humidity, vapour pressure, wind
speed, sunshine duration and PET show negative trends. Among
which, four variables, i.e., temperature, wind speed, sunshine duration and PET have undergone significant trends at 0.01 significance
level. Precipitation and wind speed show the highest coefficient of
variation with 15% and 13%, respectively, showing the highest
variability.
On seasonal basis, statistically significant (0.01) positive trends
of temperature were detected for spring and winter; however, the
positive trends in summer and autumn are not significant.
Although precipitation shows an upward trend for summer and
winter, and downward trend for spring and autumn, no significant
trend was detected through four seasons. Both relative humidity
and vapour pressure decreased significantly in spring, but trends
in other seasons are not significant. Among the seven variables,
wind speed is the only one which decreased significantly in all seasons. In summer and winter, sunshine duration decreased significantly during 1960–2007, but the negative trends in spring and
autumn are not significant. PET decreased through all seasons except spring, and the negative trends in summer and autumn are
statistically significant at 0.01 significance level, respectively.
It can be summarized that most climate variables have experienced significant trends during the past decades. For illustrative
purposes, the variation of annual precipitation and calculated potential evapotranspiration (PET) series and their corresponding
MK sequential test in 1960–2007 are shown in Fig. 4a, c and b, d,
respectively. As depicted in Fig. 4a and c, catchment annual precipitation and PET show a long-term increase and decrease linear
trend, respectively. Fig. 4b and d shows that an abrupt change of
the two variables occurred in 1969 at 0.05 significance level as
the intersection point of the two curves located within the confidence interval. Catchment annual precipitation showed a decreasing trend from 1960 to 1969, while opposite trend was found for
the other periods. There is an obvious increasing tendency since
1990, which is significant during the period 1999–2002 as the values of UF are above the critical limit. On the contrary, the values of
Fig. 4. Time series of catchment annual precipitation (a) and PET (c) and corresponding MK trend test (b and d). The long dashed line in the left figures means linear trend for
this period, and the horizontal dashed lines in the right figures represent the critical value of 0.05 significance level.
X. Ye et al. / Journal of Hydrology 494 (2013) 83–95
UF for catchment annual PET are below zero for almost the whole
period especially after 1969, and the long term decrease trend becomes significant after 1982.
3.2. Variation of annual and monthly streamflows
Fig. 5 shows the MK test of the annual and monthly streamflows
of each station in the five sub-catchments. As shown in Fig. 5a, the
UF curve of Waizhou station indicates a decreasing trend of
streamflow from 1960 to 1972, while an increasing trend is found
89
for the other periods. The variation in annual streamflow is small
before 1990, and after that streamflow increased obviously. For
Meigang station, the UF curve exceeds zero for almost the whole
period and it intersects with the critical value lines for a short period, which confirmed an increasing trend especially in the end of
1990s. No obvious trend was detected for the streamflow at Lijiadu
station with UF fluctuates between the two critical value lines.
However, streamflow trend for Lijiadu station shows a slight decrease during 1987–1996 with UF < 0, which is obviously different
from that of the other stations. Annual streamflow of Wanjiabu and
Fig. 5. (a) Trends variation and change point test for annual streamflow; the horizontal dashed lines represent the critical value of 0.05 significance level; (b) trends of
monthly streamflow, the horizontal solid lines and dashed lines represent the critical values of 0.01 and 0.05 significance level, respectively.
90
X. Ye et al. / Journal of Hydrology 494 (2013) 83–95
Hushan stations has similar changing trends which increased for
almost the whole period. Furthermore, streamflow of these two
stations increased significantly in 1970s and 1990s with UF curves
intersect with the critical value lines during these two periods.
Change point analysis for annual streamflow indicates that except
for the Wanjiabu station where the intersection of UF and UB
curves occurred in 1969 (at 0.05 significance level), no clear step
change can be identified for the other stations.
Estimated results for the long-term trends of annual streamflow
indicate that most gauging stations show increasing trends except
for Lijiadu station, but monthly variations are obviously different.
As shown in Fig. 5b, most stations present long-term decrease
but not significant trends during the wet season from April to June.
However, increasing trends are detected for the other months. Except for Hushan station, streamflow of most stations increases
obviously at 0.05 or 0.01 significance level for August and September. Also, streamflow of Wanjiabu and Hushan stations increases
significantly in January at 0.01 significance level. The monthly variability of streamflow may indicate the change of seasonal climate
regime and catchment management practices.
3.3. Hydrological response associated with changes in climate and
human activities
The spatial and temporal variation of streamflow in Fig. 5 reflects the combined effects of climate change and human activities,
which provides valuable information in water resources planning
and management in the catchment. Within the study period, we
cannot distinguish a natural period during which the effect of human activities on streamflow was less recognized like in most previous studies (e.g. Li et al., 2007; Wang et al., 2012). In this study,
attempts, therefore, are made to investigate the influence of
anthropogenic activities and climate change on the streamflow of
the sub-catchments as well as the whole catchment under different decades. The changes of precipitation, PET, ET and streamflow
relative to baseline period 1960s for the whole Poyang Lake catchment and the three largest sub-catchments are analysed in details
and the results are plotted in Fig. 6. Compared to 1960s, precipitation increased during all the other decades, especially in 1990s an
increase of 168.7–316.1 mm was detected for the three sub-catchments. The range of decrease in PET is 63.9–86.9 mm for Ganjiang,
42.3–130.8 mm for Xinjiang and 48.0–83.4 mm for Fuhe. Change of
streamflow is consistent with precipitation except for a decrease of
67.1 mm in Fuhe sub-catchment during 2000s. Calculated ET increases obviously in Fuhe especially in 1990s and 2000s with
69.1 mm and 83.2 mm, respectively, but decreases in Xinjiang in
recent decades.
Based on the water-energy budget analysis, excess water (Pex)
and excess energy (Eex) of the three sub-catchments as well as
the whole catchment were calculated under different decades.
Using the background of regional climate condition and anthropogenic stresses of 1960s as the reference baseline period (benchmark), the shifts of Pex and Eex relative to 1960s are illustrated in
Fig. 7. In the figure, an increasing trend for Pex and a decreasing
trend for Eex are detected in the other decades for Ganjiang and
Xinjiang sub-catchments, as well as the whole catchment. These
results indicate that compared to the baseline period 1960s,
changes of streamflow in 1970s, 1980s, 1990s and 2000s were primarily affected by climate change, while human activities played a
complementary role. However, there is some difference in the Fuhe
River sub-catchment with decreasing Pex and Eex in 2000s, implying
that relative changes of streamflow in 2000s were primarily affected by human activities rather than climate change.
3.4. Quantitative assessment of relative impacts of climate change and
human activities on streamflow trends
The effect of climate change on streamflow can be estimated
using the hydrologic sensitivity analysis method. In this method,
w is the main model parameter that needs to be calibrated. For
each river catchment, the calibration was conducted by comparing
the observed with simulated annual streamflow by using Eqs. (5)
and (8) for the baseline period 1960–1969. Generally, optimized
w values are 0.40, 0.20, 1.20 and 0.45 for Ganjiang, Xinjiang, Fuhe
and the whole Poyang Lake catchment, respectively. As w represents available water stress coefficient that related to vegetation
type (Zhang et al., 2001), optimized w values for each sub-catchment also indicate the inhomogeneous distribution of vegetation
in the Poyang Lake catchment. Fig. 8 shows the correlation between observed and simulated annual streamflow, in which, scatter points are concentrated around the 1:1 line. All the R2 values
are greater than 0.82 with small mean absolute error (MAE),
indicating that the simulated results are acceptable. With the optimized w values of Ganjiang, Xinjiang, Fuhe and the whole Poyang
Fig. 6. Change of precipitation, PET, ET and streamflow compared to the baseline period 1960s.
X. Ye et al. / Journal of Hydrology 494 (2013) 83–95
91
Fig. 7. Variation of excess water (Pex) and excess evaporative demand (Eex) relative to 1960s.
Fig. 8. Correlation between observed and simulated annual streamflow for the baseline period 1960s.
Lake catchment, the calculated values for the sensitivity coefficient
a were 0.86, 0.88, 0.96, 0.90 and b were 0.63, 0.75, 0.47, 0.64,
respectively. In addition, the absolute values of sensitivity coefficient also reveal that the change in streamflow was more sensitive
to precipitation (P) than to potential evapotranspiration (PET) in
this region.
Based on the method of hydrological sensitivity analysis, all the
calculated parameters were then used to estimate the impacts of
climate change and human activities on variation of streamflow
reference to 1960s. As presented in Table 3, impacts of climate
change across the catchment were generally positive for the
streamflow compared to the reference period of 1960s, i.e. climate
change resulted in an increased streamflow. Take the whole catchment as an example, changes in regional climate (precipitation and
potential evapotranspiration) in 1970–2007 were the main factors
that increased runoff with a contribution of 150.7% relative to
1960s, while the reduction percentages due to human activities
were only 50.7%. Specific impacts of climate change on the increase
of annual streamflow were estimated to be 105.0 mm, 118.6 mm,
261.7 mm and 75.3 mm for 1970s, 1980s, 1990s and 2000s, respectively (Table 3). On the contrary, the impacts of human activities
were always negative on the water volume, leading to a decrease
of annual streamflow of 5.4 mm, 56.3 mm, 47.6 mm and 39.8 mm
during these periods. The proportional change in annual
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X. Ye et al. / Journal of Hydrology 494 (2013) 83–95
Table 3
Contribution of climate change and human activities on the change of annual streamflow compared to the baseline period 1960–1969.
Catchments
Periods
Q (mm)
DQ (mm)
Contribution of climate change
Contribution of human activities
DQclim (mm)
gclim (%)
Ganjiang
1960–1969
1970–1979
1980–1989
1990–1999
2000–2007
760.7
876.3
813.0
954.8
822.9
–
115.7
52.3
180.3
62.2
–
119.3
108.2
231.4
116.1
–
103.2
206.6
128.4
186.5
–
3.6
55.8
51.2
53.9
–
3.2
106.6
28.4
86.5
Xinjiang
1960–1969
1970–1979
1980–1989
1990–1999
2000–2007
1017.3
1158.8
1100.4
1372.2
1046.9
–
141.4
83.1
354.8
29.5
–
154.1
120.7
378.4
64.8
–
109.0
145.3
106.6
219.6
–
12.7
37.6
23.6
35.3
9.0
45.3
6.6
119.6
Fuhe
1960–1969
1970–1979
1980–1989
1990–1999
2000–2007
749.4
803.4
796.3
849.1
682.3
–
54.0
46.9
99.7
67.1
–
69.8
102.6
201.8
38.2
–
129.2
218.8
202.5
56.9
–
15.8
55.7
102.2
105.3
–
29.2
118.8
102.5
156.9
The whole Poyang Lake Catchment
1960–1969
1970–1979
1980–1989
1990–1999
2000–2007
1970–2007
801.9
911.4
864.2
1029.6
837.4
914.5
–
109.5
62.3
214.1
35.5
112.6
–
115.0
118.6
261.7
75.3
169.7
105.0
190.3
122.3
212.1
150.7
–
5.4
56.3
47.6
39.8
57.1
–
5.0
90.3
22.3
112.1
50.7
DQhum (mm)
ghum (%)
Fig. 9. (a) The sensitivity of the potential evapotranspiration to the four major meteorological variables; (b) trends analysis by MK test. The horizontal dashed lines in the
right figure represent the critical value of 0.01 significance level.
streamflow due to climate change (gclim) ranges from 105.0–
212.1%, while the proportional change due to human activities
(ghum) ranges from 5.0% to 112.1%. Relative effect of climate
change is greater than that of human activities, which is consistent
with the results from the coupled water-energy budgets analysis.
Due to the discrepancy in regional climate and the intensive human activities in the catchment, the contribution of individual impacts varied under different spatial and temporal scales. According
to Table 3, increased streamflows in Ganjiang, Xinjiang and Fuhe
sub-catchments due to climate change (DQclim) were estimated to
108.2–231.4 mm, 64.8–378.4 mm and 38.2–201.8 mm, while the
specific reduction impacts of human activities (DQhum) were 3.6–
55.8 mm, 12.7–37.6 mm and 15.8–105.3 mm, respectively. Relative to 1960s, climate change has the largest impact on the increase
in streamflow in 1990s, while the impact is the least in 2000s,
which is consistent with the increased frequency and severity of
floods and droughts during these time periods (Min et al., 2011;
Guo et al., 2007). However, percentage of the impact is bigger in
1980s in Ganjiang River sub-catchment and in 2000s in Xinjiang
River sub-catchment. The Fuhe River sub-catchment is the only
catchment where the reduction in streamflow during 2000s should
be mainly attributed to the intensive human activities. As shown in
Table 3, the contribution of climate change accounted for 56.9% of
the changes in streamflow, while human activities were responsible for 156.9% of the change.
4. Discussions
The empirical estimation and hydrological sensitivity analysis
performed in this study are mainly for studying the effect of mean
annual climate change on the variations in annual streamflow.
Although the long-term trends of annual streamflow of the five
gauging stations are not significant, monthly variations were obvious (as shown in Fig. 5b). Previous studies indicate that the increase
of precipitation, especially the increased frequency of summer
storm is the main reason for the increase of streamflow and for the
cause of floods in 1990s (e.g., Guo et al., 2007). Additionally, in both
methods, effect of climate change on streamflow is mainly through
the changes in precipitation and potential evapotranspiration. Because of the calculated potential evapotranspiration represents the
integrated effect of climate variables, further analysis indicated that
in the Poyang Lake catchment, the most important predictor for the
decreasing trend in potential evapotranspiration is the net radiation
and wind speed, since they are not only sensitive variables in determining PET, but also decrease significantly in the catchment (see
Fig. 9). Comparable result was given in Xu et al. (2006), who concluded that the decreasing trend of reference evapotranspiration
in the Yangtze River catchment is most attributed to the changes
in net radiation and wind speed.
Like many regions in China, Poyang Lake catchment has
undergone intensive human activities since 1950s, including
X. Ye et al. / Journal of Hydrology 494 (2013) 83–95
afforestation and deforestation, land reclamation, river regulation,
agriculture intensification and extensive infrastructure construction, which may have exerted considerable impacts on catchment
hydrology. A modeling study in Xinjiang River sub-catchment indicated that when agriculture land changed into forest, which accounts for up to 23.3% of the catchment area, it would result in a
decrease of annual discharge by up to 3.2% (Guo et al., 2008). The
decreases of streamflow caused by increased forest cover are particularly strong in the wet season through the increase of evapotranspiration, but streamflow increase in the dry season has been
primarily resulted from the increased groundwater contribution
(Guo et al., 2007; Ye et al., 2011). Fig. 10 shows the changes of forest coverage and the corresponding area of soil erosion in the Poyang Lake catchment. The increase of forest coverage since 1980s
may play an important role in reducing streamflow during flood
season. In addition, the variation of annual streamflow has been
further elevated by the extensive water utilization according to
the local social and economic development. Due to the lack of
information of long-term water consumption, we here present
the changes of reservoir construction and the storage volume in
the catchment, which may well indicate the variation of water utilization. As shown in Fig. 11, although there is litter change in the
total amount of reservoirs in the catchment since 1980s, the volume of water storage increased steadily for the same period, especially after 1990s. The reasons for this are the rapid changes in
society and social life, population, economic forces and technological development, which increased the demand and the ability of
water supply for industrial and domestic consumptions. The different changing patterns in annual streamflow of Lijiadu station in
Fuhe River sub-catchment since 1990s (Fig. 5a and Table 3) as
compared with other sub-catchments are the results of high
water-use efficiency. The biggest irrigation farmland as well as
the irrigation systems of Jiangxi Province are located in the middle
and lower reach of Fuhe, which will notably increase the water utilization and directly decrease streamflow, especially in those dry
years. The results of this study indicated that the impact of human
activities on the change of catchment streamflow was negative
compared to the baseline period of 1960s. However, the impact
of human activities on water resources at local scale is complex
and different human activities may accumulate or counteract each
other; attempts have not been made in this study to distinguish
them from each other.
It is worth of noting that the concept of the model by Tomer and
Schilliing (2009) is based on long-term observations of landuse
change in the catchment. Although, the application of empirical
method in the Poyang Lake catchment gives a comparative result
in distinguishing the effects of human activities and climate
change on catchment hydrology, the applicability and implications
need more documentation, as suggested by Tomer and Schilliing
(2009), and this study contributes to this call.
Fig. 10. Changes of forest coverage and soil erosion in the Poyang Lake catchment.
93
Fig. 11. Changes of total reservoir amount and storage in the Poyang Lake
catchment.
In this study, the variability of climate indices is consistent with
that of the data time series. The 1960s were used as the baseline
period to analyze the relative impacts of climate change and human activities in other decades, which is because the intensity of
human activities in 1960s is relatively small. In addition, the coupled water-energy budget analysis is an empirical method which
heavily depends on the quality of P, PET and ET measurements or
estimates, especially the latter two being the most difficult components to estimate. The possible influence of data error on the results is yet to be investigated in the future study. Meteorological
data from 19 weather stations in the study area might not be sufficient coverage for such a large-scale catchment. Influence of reservoir storage on the estimation of mean annual ET is not
considered. Additionally, some uncertainties also exist in the
hydrologic sensitivity analysis method which separates the effects
of climate change and human activities on streamflow. The performance of the hydrologic sensitivity analysis depends on the
streamflow data of the long-term baseline period, with no effect
of human activities, for model calibration. In reality, there was a
lack of detailed long-term observation data in the Poyang Lake
catchment to distinguish a natural period, and even during the
baseline period of 1960s, there were some human disturbances
such as farmland irrigation, river regulation and land cover
changes. Although calibrated available water coefficient (w) in this
study well reflects the average vegetation condition of the catchment during 1960s, this could still affect the estimation results
to some extent.
5. Conclusions
Under the background of global warming, increased regional
climate change and human activities were identified as the main
factors causing the floods and droughts in the Poyang Lake catchment, especially since 1990s. In this study, we performed an
assessment of the relative effects of climate change and human
activities on the changes of streamflow in the catchment during
the past five decades. MK test indicated a long term increase trend
of precipitation and a decrease trend of potential evapotranspiration. An increasing trend of annual streamflow of the catchment
except for Fuhe sub-catchment was detected. The findings suggest
that relative effects of climate change and human activities on the
change of streamflow varied among sub-catchments as well as the
whole catchment under different decades, due to the discrepancies
in regional climate and anthropogenic stresses across the Poyang
Lake catchment. The increase of annual streamflow (52.35–
180.28 mm) was primarily affected by climate change for the
whole catchment compared to the reference period of 1960s, while
human activities played a complementary role. However, due to
the intensified water utilization for industrial and agricultural
94
X. Ye et al. / Journal of Hydrology 494 (2013) 83–95
development, especially in those dry years, the decrease of streamflow (67.13 mm) in the Fuhe sub-catchment in 2000s was primarily affected by human activities.
Quantitative assessment revealed that climate change resulted
in an increase in runoff of 75.3–261.7 mm in 1970s–2000s for
the whole catchment, accounting for 105.0–212.1% of runoff
changes relative to 1960s. However, human activities may be
responsible for the decrease in runoff of 5.4–56.3 mm in 1970s–
2000s, which accounts for 5.0% to 112.1% of runoff changes.
Moreover, human activities such as farmland irrigation, river regulation and deforestation were the main anthropogenic stresses in
altering hydrological processes before 1990s. Afterwards, afforestation and rapid local socio-economic development became
increasingly apparent. However, the effects of specific human
activities may accumulate or counteract each other, and attempts
were not made in this study to distinguish them from each other.
As a sub-tropical humid catchment with strong seasonal variations of river discharge, current and future water resource management and planning in the Poyang Lake catchment may take a wide
range of afforestation, soil conservation, and construction of water
conservancy engineering system into account. Implementation of a
possible integrated water regulation system incorporating the Yangtze River is needed in order to reduce flood and drought disasters
in the lake region and to maintain a healthy ecosystem of the lake.
Acknowledgements
This work is supported by the National Basic Research Program
of China (2012CB417003 and 2012CB956103-5), National Natural
Science Foundation of China (41201026), State Key Laboratory of
Lake Science and Environment (2010SKL014), and Science Foundation of Nanjing Institute of Geography and Limnology (NIGLAS2012135001 and NIGLAS2010XK02). The authors extend
their thanks to two anonymous reviewers for their constructive
comments, which greatly improved the quality of this paper.
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