HYDROLOGICAL PROCESSES
Hydrol. Process. 28, 6151–6162 (2014)
Published online 2 December 2013 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/hyp.10117
Variation of reference evapotranspiration and its contributing
climatic factors in the Poyang Lake catchment, China
Xuchun Ye,1,2 Xianghu Li,2 Jian Liu,3 Chong-Yu Xu4,5 and Qi Zhang2*
2
1
School of Geographical Sciences, Southwest University, Chongqing 400715, China
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China
3
Water Research Institute of Shandong Province, Jinan 250013, China
4
Department of Geosciences, University of Oslo, Oslo, Norway
5
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Abstract:
By using linear regression (parametric), Mann–Kendall (nonparametric) and attribution analysis methods, this study
systematically analysed the changing properties of reference evapotranspiration (ETr) calculated using the Penman–Monteith
method over the Poyang Lake catchment during 1960–2008 and investigated the contribution of major climatic variables to ETr
changes and their temporal evolution. Generally, a significant decreasing trend of annual ETr is found in the catchment. The
decrease of annual ETr in the Poyang Lake basin is mostly affected by the decline of summer ETr. Over the study period,
climatic variables, i.e. sunshine duration (SD), relative humidity (RH), wind speed (WS) and vapour pressure all showed
decreasing trends, whereas mean daily temperature (DT) increased significantly. Multivariate regression analysis indicated that
SD is the most sensitive climatic variable to the variability of ETr on annual basis, followed by RH, WS and DT, whereas the
effect of vapour pressure is obscure. Although recent warming trend and decrease of relative humidity over the catchment could
have increased ETr, the combined effect of shortened SD and reduced WS negated the effect and caused significant decrease of
ETr. Our investigation reveals that the relative contributions of climatic variables to ETr are temporally unstable and vary
considerably with large fluctuation. In consideration of the changes of climatic variables over time, further analysis indicated that
changes of mean annual ETr in 1970–2008 were primarily affected by SD followed by WS, RH and DT with reference to 1960s.
However, WS became the predominant factor during the period 2000–2008 compared with reference period 1960s, and followed
by SD. Copyright © 2013 John Wiley & Sons, Ltd.
KEY WORDS
climate change; reference evapotranspiration; Penman–Monteith method; multiple regression analysis; the Poyang
Lake basin
Received 14 August 2013; Accepted 11 November 2013
INTRODUCTION
Global warming characterized by the increasing temperature has become a worldwide indisputable fact since the
late 19th century. Rising global surface temperatures are
likely to increase the water holding capacity and water
vapour transport in the atmosphere, which in turn cause
the changes in atmospheric circulation (Menzel and
Bürger 2002; Bates et al., 2008). Evidence of the changes
of precipitation, runoff and soil moisture suggests that the
hydrological cycle has been accelerated in many parts of
the world during the past century (e.g. Groisman et al.,
1999; Alan et al., 2003; Gao et al., 2006, 2012;
Huntington, 2006; Zhang et al., 2009a). Extreme climate
*Correspondence to: Zhang Qi, State Key Laboratory of Lake Science and
Environment, Nanjing Institute of Geography and Limnology, CAS,
Nanjing 210008, China.
E-mail: qzhang@niglas.ac.cn
Copyright © 2013 John Wiley & Sons, Ltd.
events, such as the frequency and severity of heat waves
and very heavy precipitation, are expected to increase in
recent decades and cause more and more severe floods
and droughts (e.g. Bates et al., 2008; Déry et al., 2009;
Thompson, 2012; Xiong et al., 2013).
Actual evapotranspiration (ET), the sum of evaporation
and plant transpiration, is one of the most active and
complicated hydrological components that links water
balance and land surface energy balance in an ecosystem
(Xu and Singh, 2005, Xu et al., 2005). The process of ET
governs the moisture transfer between soil and the
atmosphere in a catchment and is heavily influenced by
surface land-use changes and climate variations (Fisher
et al., 2011). Reference evapotranspiration (ETr) is often
used to estimate actual ET in water balance studies (e.g.
Xu and Chen 2005). In a warming climate, ETr is
expected to increase. However, contrary to the expectation, many studies have shown that observed pan
evaporation and calculated ETr were declining in many
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X. YE ET AL.
places of the world (Peterson et al., 1995; Lawrimore and
Peterson, 2000; Thomas, 2000; Roderick and Farquhar,
2002, Chen et al., 2005; Xu et al., 2006; Roderick et al.,
2009; McVicar et al., 2012). This is known as
‘evaporation paradox’, a famous scientific problem that
has been widely discussed. For the reasons of ‘evaporation paradox’, most studies concluded that the decreasing
trend of solar radiation and wind speeds (WSs) would be
the major causes, whereas temperature actually plays a
lesser role (Thomas 2000; Gong et al., 2006; Xu et al.,
2006; Fan and Thomas, 2013). Because of the complicated process of ET, our knowledge to the mechanism of
decreasing ET is still limited. Studies from Roderick and
Farquhar (2002) indicated that the decrease in pan
evaporation was consistent with the observed decrease
in sunshine hours, which resulted from the increasing
cloudiness or aerosols concentration (global dimming).
Also, Liu et al. (2004) suggested that the aerosol caused
decrease in solar irradiance [sunshine duration (SD)] was
most likely the driving force for the reduced pan
evaporation in China. The observed decline of SD has
partly reversed in the last decade (Wild et al., 2005),
whereas terrestrial WS has been observed to decrease on a
global scale (McVicar et al., 2012). In addition, humaninduced impacts such as influences from rapid urbanization, aerosol concentration on changes of evaporation
have become more and more important. In China, several
studies have reported that the trends of ETr decreased in
most parts (Thomas 2000; Chen et al., 2006; Gao et al.,
2006; Zhang et al., 2011a). Zhang et al. (2004) indicated
that the decreasing net total radiation of major cities in the
Yangtze River Delta region is mainly attributed to the
increased air pollution, implying evident human influences on net solar radiation. Zhang et al. (2011a)
pointed out that in the east and south China, urbanization
greatly influences the ETr by directly decreasing net solar
radiation. However, there is no conclusive evidence for
the decreasing WS in the rapidly growing urban areas of
China (Guo et al., 2011).
The Yangtze River basin belongs to the subtropical and
temperate climate zone, which is mainly affected by the
East Asia Monsoon climate in summer. The advance and
retreat of the monsoons determine to a large degree the
timing of the rainy season and the amount of rainfall
throughout the basin (Xu et al., 2006). Previous studies
demonstrated that the trends of temperature, precipitation,
storm and ET have changed obviously in the basin under
the background of global warming (e.g. Jiang et al., 2005;
Su et al., 2005; Xu et al., 2006). The Poyang Lake
catchment, located on the south bank in the middle
reaches of the Yangtze River is one of the most sensitive
regions of climate change in the Yangtze River basin. In
recent decades, increasing frequency and severity of
droughts and floods has occurred in the catchment (Wang
Copyright © 2013 John Wiley & Sons, Ltd.
et al., 2008; Min et al., 2011). These extreme hydrological events have raised wide concerns for the lake ecology
and water resources management in the Yangtze River
basin. Studies on hydrological response have revealed
that the changes of annual streamflow in the catchment
were primarily caused by the changes of climate anomalies
in the Yangtze River catchment (e.g. Guo et al., 2008;
Zhao et al., 2010; Ye et al., 2013). Hu and Feng (2001)
pointed out that the increase of warm season rainfall in the
regions south of the Yangtze River and the Poyang Lake
basin in 1990s could be a consequence of the southward
shift of the major rain bands in eastern China and increase
of precipitation intensity. Therefore, there is no doubt that
the change of regional climate has exerted tremendous
influences on local hydrological cycle.
Variation of ETr means the changes of regional
climatic variables and therefore changes in atmospheric
water demand. In-depth investigation on spatio-temporal
variation of ETr changes is essential for the improved
understanding of the mechanism of climate change impacts
on hydrological cycle. By conducting the sensitivity
analysis, Xu et al. (2006) suggested that the most important
predictor for the decreasing trend in the ETr and pan
evaporation in the Yangtze River basin is net total radiation
followed by WS. However, they did not identify the
specific relationship between ETr and its driving climatic
variables. Some studies investigated the relative contribution of climatic variables to ETr (e.g. Zhang et al., 2009b,
2011a,2011b) but neglected any temporal changes. Recent
study from Fan and Thomas (2013) indicated that temporal
evolution of contribution of climatic variables was unstable
in Yunnan Province, southwest China, and they proposed
to further analyse the temporal evolution of climatic
contributions to ETr over a wide range of climates and
locations. Because of strong spatial heterogeneity in the
Yangtze River basin, influence of climatic variables on ETr
may vary in different regions. There is a need to better
understand and quantify the contribution of climatic
variables to the spatio-temporial changes of ETr in the
Poyang Lake catchment, which partly motivated this study.
This study was conducted in the context of an on-going
project to investigate the changing water balance across the
Poyang Lake catchment and the mechanism of droughts
and floods occurred in the lake area. The objectives of this
study are as follows: (1) to evaluate temporal trends of ETr
and major climatic variables in the Poyang Lake
catchment, (2) to explore the quantitative relationship
between ETr and the influencing climatic variables, and (3)
to identify the contributions of major climatic variables to
ETr and their temporal evolution. We believe that this
study will help to determine the importance of contributing
factors to the change of ETr and to achieve a better
understanding of the climate change impacts on hydrological cycle under global warming.
Hydrol. Process. 28, 6151–6162 (2014)
VARIATION OF ETR AND ITS CONTRIBUTING CLIMATIC FACTORS
STUDY REGION
The Poyang Lake catchment is located in the middle
reaches of Yangtze River, covering an area of 162 200 km2.
The catchment is surrounded by a series of low mountains
and hills in east, south and west. The largest fresh water
lake in China – Poyang Lake in the catchment is fed by the
five primary rivers: Ganjiang River, Xiushui River, Fuhe
River, Raohe River and Xinjiang River, and discharges
into the Yangtze River from a narrow outlet in the north
(Figure 1). Topography of the Poyang Lake catchment
varies from mountainous regions (maximum elevation of
about 2200 m a.s.l.) to alluvial plains in the lower reaches
of the primary watercourses.
The catchment belongs to a subtropical weather
predominated by East Asia Monsoon. Monthly precipitation in the catchment shows a wet and a dry season in a
year with short transition period in between (Figure 1).
Forty-five percent of annual precipitation is concentrated
in the wet season from April to June, but rainfall
decreases sharply from July to September. After
September, the dry season sets in and lasts through
December. Seasonal pattern of ETr is different from that
of precipitation with a relative symmetric distribution
around July. ETr increases slowly from January to July
and decreases faster after that. Obviously, ETr is larger
than precipitation during July to October, usually
causing severe agricultural droughts in these months,
especially in September and October.
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DATA AND METHODOLOGY
Meteorological data
In this study, meteorological data from 15 weather
stations inside the catchment were obtained from National
Climate Centre of China Meteorological Administration, all
of which are standard national weather stations. Altitudes
for these stations are mainly below 300 m a.s.l. except for
Lushan in the north, a station located at the famous scenic
mountain has an altitude of 1164 m a.s.l. Specific location
and their landscape are shown in Figure 1. These stations
provide daily observations of maximum and minimum
temperature and daily mean air temperature at 2 m height
above the ground, relative humidity (RH), SD, WS and
vapour pressure (VP) covering the period of 1960–2008.
Daily records of the entire climate variables had been quality
controlled by China Meteorological Administration before
delivery, and there is no missing data on the variables.
Penman–Monteith method
The Penman–Monteith method has been regarded as a
global standard method for computation of ETr by Food
and Agriculture Organization of the United Nations (FAO)
(Allen et al. 1998). The method was applied in this study
because it is physically based and explicitly incorporates
both physiological and aerodynamic parameters. In this
method, the calculation of ETr is given as
ETr ¼
0:409ΔðRn GÞ þ γ T a900
þ273 u2 ðes ea Þ
Δ þ γð1 þ 0:34u2 Þ
(1)
where ETr is reference evapotranspiration (mm day1), Rn
is net radiation at the crop surface (MJm2 day1), G is the
soil heat flux density (MJm2 day1), Ta is mean daily air
temperature at 2 m height (°C), u2 is WS at 2 m height
(ms1), es is saturation VP (kPa), ea is actual VP (kPa),
es ea the saturation VP deficit (kPa), Δ is slope of the VP
(kPa °C1) and γ is psychrometric constant (kPa °C1).
The Penman–Monteith method has been widely
introduced with good details in previous studies in
different regions (e.g. Gong et al., 2006; Xu et al.,
2006; Hosseinzadeh Talaee et al., 2013; Ngongondo
et al., 2013). The computation procedure can be found in
Chapter 3 of the FAO paper 56 (Allen et al. 1998).
Trend analysis
Figure 1. Sketch map of the Poyang Lake catchment with distribution of
15 meteorological stations
Copyright © 2013 John Wiley & Sons, Ltd.
An ordinary linear regression model in the form of
ŷ = αt + β is used to estimate the rate of change α, with t as
the time (year), ŷ being the annual or seasonal ETr and
other climatic variables.
Significance of the trends of the climatic series is
evaluated by the Mann–Kendall trend test technique (MK
test). The MK test is a rank-based nonparametric method,
Hydrol. Process. 28, 6151–6162 (2014)
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X. YE ET AL.
which has been widely applied for trend detecting in
hydro-climatic time series because of 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 et al. 2009a,2009c; Li
et al. 2013a,2013b). The adoption of MK trend test is
started from the calculation of the statistic:
S¼
n1 X
n
X
sgn xj xi
(2)
importance of different independent variables in explaining
the dependent variable (e.g. Clow, 2010; Myoung et al.,
2011; Tang et al., 2011; Zhang et al., 2011b).
In this study, the multiple regression analysis was used to
evaluate the effects of climatic variables on ETr. Before
application of this method, normalization of original data of
ETr and climatic variables is performed using Equation (7):
X is ¼
i¼1 j¼iþ1
where
8
þ1 xj > xi
>
<
xj ¼ xi
sgn xj xi ¼ 0
>
:
1 xj < xi
(3)
where xi and xj are the sequential data values and n is the
length of the data set. The statistics S is approximately
normally distributed when n ≥ 8, with the mean and the
variance as follows:
E ðSÞ ¼ 0
"
V ðSÞ ¼ nðn 1Þð2n þ 5Þ n
X
(4)
#
ti iði 1Þð2i þ 5Þ =18
i¼1
(5)
where t is the extent of any given time.
The standardized statistics (Z) for one-tailed test is
formulated as
pffiffiffiffiffiffiffiffiffiffiffiffiffiffi
8
>
< ðS 1Þ= varðSÞ ðS > 0Þ
Z¼ 0
(6)
ðS ¼ 0Þ
>
pffiffiffiffiffiffiffiffiffiffiffiffiffiffi
:
ðS þ 1Þ= varðSÞ ðS < 0Þ
The null hypothesis of no trend is rejected if |Z| > 1.96
at the 0.05 significance level and rejected if |Z| > 2.32 at
the 0.01 significance level. A positive value of Z denotes
an increasing trend, and a negative value corresponds to a
decreasing trend. Because the presence of serial and cross
correlations can influence the identification of trends (Yue
et al., 2003; Khaliq et al., 2009), autocorrelation (serial
correlation) was examined through the autocorrelation
and partial autocorrelation function for all the meteorological data before trend analysis.
xi x min
x max x min
(7)
where Xis is normalized variable, xi is the value of the
sequential data and xmin and xmax are the minimum and
maximum values of the sequential data.
The relationship between normalized ETr and normalized climatic variables was then analysed by stepwise
multiple regression, with normalized ETr as dependent
variable and normalized climatic variables as the predictors
(independent variables). The general formula of multiple
regression is shown as follows:
Y s ¼ aX 1s þ bX 2s þ cX 3s þ ⋯⋯
(8)
where Ys is normalized dependent variable of ETr; X1s, X2s,
X3s,…… are normalized predictors of climatic variables;
and a, b and c are regression coefficients.
In this study, the candidate variables were selected into
the model based on 0.05 significance level, and the
significance level for a predictor to be removed from the
model was 0.1. F-test is used to test the overall significance
of a multiple regression model as reflected by the ratio of
R-square values (coefficient of determination) (Haan, 1977).
On the basis of the regression coefficients, relative
contribution rate of each predictor Xis in explaining Ys can
be estimated as
η1 ¼
jaj
jaj þ jbj þ jcj þ ⋯⋯
(9)
Actual contribution rate is given as
η2 ¼
a ΔX 1s
ΔY s
(10)
where ΔX1s is the change of X1s and ΔYs is the change
of Ys.
RESULTS
Attribution analysis
Trends of reference evapotranspiration
The general purpose of multiple regression is to learn
more about the relationship between several independent or
predictor variables and a dependent or criterion variable
(Haan, 1977), and it can be used to determine the relative
Mean annual ETr of the Poyang Lake catchment during
the period 1960–2008 is 1053 mm with a range from 940
to 1209 mm. As shown in Figure 2a, variation of annual
ETr shows a long-term decreasing trend with change rate
Copyright © 2013 John Wiley & Sons, Ltd.
Hydrol. Process. 28, 6151–6162 (2014)
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VARIATION OF ETR AND ITS CONTRIBUTING CLIMATIC FACTORS
Figure 2. Linear trends and area-averaged curve of annual and seasonal reference evapotranspiration over the Poyang Lake catchment during 1960–2008
of 14.08 mm per decade. The decreasing trend of ETr was
almost monotonic before 2002 but increased obviously
after that. Seasonal ETr of the Poyang Lake catchment
declined except in the spring season (Figures 2b–e). ETr
in spring shows a long-term increasing trend with change
rate of 1.52 mm per decade. The annual variation of ETr
in spring shows relative larger fluctuation before 1975
than after that. Seasonal ETr in summer, autumn and
winter all decreased during the study period, with the
largest change rate in summer (9.64 mm per decade)
and the least in winter (1.71 mm per decade). Among the
three seasons, only the decreasing trend in summer was
statistically significant (p < 0.01) (Table I). Particularly
obvious was a distinct minimum of ETr in summer around
1997, whereas the decreasing trends of ETr in autumn and
winter were almost monotonic during the study period. In
consideration of change rate, it can be concluded that the
decrease of annual ETr in the Poyang Lake catchment is
mostly affected by the decline of ETr in summer.
Spatially, Ganzhou station has the highest annual ETr
rate of ~1144 mm followed by Nanchang station. Lushan
in the north, the famous scenic mountain in China shows
the lowest annual ETr of ~890 mm in the catchment.
Generally, the discrepancy of ETr across the catchment is
not big except for Lushan station; the difference of trends
of annual ETr at all the metero-stations is also not evident.
As shown in Figure 3, the annual ETr at all metero-stations
Copyright © 2013 John Wiley & Sons, Ltd.
Table I. The Z-value of Mann–Kendall trend test for the five
climatic variables and calculated reference evapotranspiration on
seasonal and annual basis 1960–2008
Annual
Spring
Summer
Autumn
Winter
DT
WS
SD
RH
VP
ETr
3.55**
2.65**
0.96
1.80
2.42**
7.97**
7.70**
5.08**
6.78**
6.88**
4.44**
0.25
4.37**
1.52
2.72**
0.74
2.51**
0.15
1.46
0.03
1.88
2.56**
0.35
1.46
0.09
2.34**
1.03
2.84**
1.42
1.28
, delineates negative trends based on the MK test; DT, daily temperature
1
(°C); WS, wind speed (m s ); SD, sunshine duration (h); RH, relative
humidity (%); VP, vapour pressure (kPa); ETr, reference evapotranspira1
tion (mm y ).
*Delineate significance at 0.05.
**Delineate significance at 0.01.
shows decreasing trends, among which nine stations with
significant decreasing trends (p < 0.05, i.e. Z < 1.96 in
the Figure 3) are mainly located in the central, southwest
and northeast parts of the Poyang Lake basin, and the other
six stations with decreasing but not significant trends are
located in northwest of the basin and some mountains
regions in the east and southwest.
Trends of climatic variables
Area-averaged time series 1960–2008 of the five climatic
variables were analysed to investigate the variation of
Hydrol. Process. 28, 6151–6162 (2014)
6156
X. YE ET AL.
Figure 3. Spatial distribution of annual trends of reference evapotranspiration during 1960–2008
regional climate in the Poyang Lake catchment, which
include mean daily temperature (DT), WS, SD, RH and VP.
Linear trends and area-averaged curve of these climatic
variables during the study period are shown in Figure 4.
Over the past 49 years (1960–2008), the variation of DT
experienced an obvious decrease before 1984 and rapid
increase after then, especially from the end of 1990s
(Figure 4a). This may be related to the intensified global
warming and rapid development of local industry. The
increasing rate of DT is about 0.16 °C per decade. The
decrease of WS was almost monotonic during the study
period with a change rate of 0.19 m s1 per decade
(Figure 4b). The decline rate of WS is particularly obvious
in 1990s. SD shows a stable decrease of 0.2 h per decade
(Figure 4c). Generally, the decrease of RH is about 0.31%
per decade, but the annual variation of RH keeps stable until
2002 and then a step change occurred in recent years
(Figure 4d). A slight decrease of 0.01 kp per decade for
VP is found during the study period (Figure 4e).
In order to evaluate the significance of variation of the
climatic series, annual and seasonal trends were further
Figure 4. Linear trends and area-averaged curve of meteorological variables during 1960–2008
Copyright © 2013 John Wiley & Sons, Ltd.
Hydrol. Process. 28, 6151–6162 (2014)
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VARIATION OF ETR AND ITS CONTRIBUTING CLIMATIC FACTORS
analysed by the MK test. Results of the trend test for the
five variables are displayed in Table I. It is seen that on
annual basis, with the exception of DT, which shows an
increasing trend, averaged WS, SD, RH and VP all show
negative trends. Among which, DT, WS and SD have
undergone significant trends at 0.01 significance level. On
seasonal basis, statistically significant (0.01) positive trends
of DT were detected for spring and winter; however, the
positive trends in summer and autumn are not significant.
Among the five climatic variables, WS is the only one which
decreased significantly in all seasons. In summer and winter,
SD decreased significantly during 1960–2008, however, the
negative trends in spring and autumn are not significant.
Both RH and VP decreased significantly in spring, but the
trends in other seasons are not significant.
Impact of climatic variables on reference
evapotranspiration
Reference evapotranspiration is a kind of measure of
the evaporative demand of the atmosphere and is affected
mainly by climatic factors (Zhang et al., 2011a;
Ngongondo et al., 2013). This section presents the results
of stepwise multiple regression analysis with ETr as
dependent variable and other climatic variables as
independent variables. Annual and seasonal regression
coefficients of the five climatic variables are shown in
Table II. It can be easily seen from the table that among
the five climatic variables, VP is the only variable that
was not entered into the regression model. This result
indicates that the four variables DT, WS, SD and RH are
the general factors influencing the variation of ETr at
annual and seasonal scale in the catchment, whereas VP is
less important. Very high R2 value indicates good
performance of these regression models.
Among the four climatic variables that entered into the
models, with the exception of RH, regression coefficients
of the other three variables are greater than 0, which
means, as expected, that RH is the only variable that
shows negative correlation with ETr. In consideration of
Table II. Standardized stepwise regression coefficients for
climatic variables at catchment scale
Coefficient
Catchment
average
DT
WS
SD
RH
VP
Intercept
R2
Annual
Spring
Summer
Autumn
Winter
0.26
0.29
0.19
0.37
0.54
0.28
0.14
0.12
0.34
0.27
0.48
0.45
0.58
0.46
0.38
0.41
0.43
0.22
0.47
0.63
0.00
0.00
0.00
0.00
0.00
0.21
0.30
0.18
0.13
0.20
0.94
0.96
0.99
0.96
0.98
DT, daily temperature; WS, wind speed; SD, sunshine duration; RH,
relative humidity; VP, vapour pressure.
Copyright © 2013 John Wiley & Sons, Ltd.
trend test of the climatic variables (Table II), the
significant declines of WS and SD are responsible for
the decrease of annual ETr, but the increase of DT and
decrease of RH play a complementary role of enhancing
ETr. Only in spring season, the increase of ETr is mainly
attributed to the effect of increase of DT and decrease of
RH. Values of the regression coefficients further indicate
that SD is the most sensitive climatic variable in
influencing ETr on annual basis, followed by RH, WS
and DT. Little changes of the relative importance of these
climatic variables were found on seasonal basis.
In order to investigate the spatial difference of the
impacts of climatic variables on ETr, the same method of
stepwise multiple regression analysis for each climatic
station across the catchment was used. As shown in
Table III, SD and RH at all the stations were entered into
the regression models, but WS at Lushan, DT and WS at
Nancheng were not included at 0.01 significant level. VP
did not enter into the regression model at all the stations,
and the result is not shown in the table. This result
indicates that among the four climatic variables, only the
changes of DT and WS are not important to ETr at certain
areas of the catchment.
Contribution of climatic variables to reference
evapotranspiration
According to the standardized stepwise regression
coefficients for climatic variables, relative contribution
of these climatic variables on the change of ETr can be
calculated by using Equation (9). Statistical result indicates
that the relative contributions of DT, WS, SD and RH on
the change of annual ETr are 18.3%, 19.6%, 33.4% and
Table III. Standardized stepwise regression coefficient for
climatic variables of each station across the catchment
Coefficient
Station
DT
WS
SD
RH
Intercept
R2
Boyang
Ganzhou
Guangchang
Guixi
Ji’an
Jingdezhen
Lushan
Nanchang
Nancheng
Suichuan
Xiushui
Xunwu
Yichuan
Yushan
0.26
0.17
0.21
0.31
0.28
0.37
0.21
0.23
0.00
0.24
0.29
0.15
0.21
0.34
0.28
0.37
0.23
0.47
0.37
0.40
0.00
0.39
0.00
0.40
0.26
0.23
0.17
0.25
0.50
0.42
0.53
0.58
0.43
0.75
0.72
0.38
0.46
0.47
0.69
0.65
0.54
0.55
0.42
0.40
0.28
0.30
0.29
0.31
0.48
0.47
0.50
0.41
0.34
0.26
0.25
0.63
0.22
0.28
0.15
0.04
0.02
0.00
0.26
0.32
0.51
0.21
0.07
0.20
0.10
0.34
0.94
0.96
0.95
0.95
0.96
0.92
0.89
0.95
0.90
0.94
0.93
0.95
0.93
0.95
DT, daily temperature; WS, wind speed; SD, sunshine duration; RH,
relative humidity.
Hydrol. Process. 28, 6151–6162 (2014)
6158
X. YE ET AL.
Figure 5. Annual and seasonal relative contribution of the climatic
variables to reference evapotranspiration
28.7%, respectively. As shown in Figure 5, when individual
seasons were analysed, it is clear that SD shows the highest
contribution during spring and summer seasons, especially
in summer season its contribution rate is over 52%.
Although RH becomes the highest contribution variable in
autumn and winter seasons, all the variables show a
relatively similar level during autumn season. The relative
contribution of DT increased greatly in the winter season.
WS always shows the least contribution in all seasons. A
visual inspection of the spatial distribution in Figure 6 shows
that the relative contribution of SD is particularly high in
northwest and southeast parts of the catchment. Beside this,
no conclusive patterns can be found in the figure.
In order to analyse the temporal evolution of relative
importance of climatic variables contributing to ETr, we
further performed multiple regression for the moving
windows of 10 years width (1960–1969, 1961–1970,
1962–1971, … , 1999–2008) by using the four climatic
variables as independent variables, which produced a time
Figure 6. Spatial patterns of relative contribution of climatic variables to
reference evapotranspiration
Copyright © 2013 John Wiley & Sons, Ltd.
series of relative contribution for the whole catchment
(Figure 7). It is obvious that the relative contribution of
the driving climatic variables to ETr changed over time.
Relative contributions of SD and RH show almost
opposite fluctuation at decadal scale during the study
period with large range of 1.5–84.7% and 2.9–79.6%,
respectively. Both the contribution of DT and WS are
very low before 1985; however, their contributions
increase substantially during 1990s.
Although evaluation of relative contribution of climatic
variables is important in revealing the sensitivity of
driving factors on the change of ETr, actual contributions
are sometimes expected according to the changes of
climatic variables. By using the regional climate condition of 1960s as the reference baseline period, actual
contributions of climatic variables in other decades were
analysed on the basis of the actual changes of climatic
variables and ETr, and the results are shown in Table IV.
It is clear that ETr in all the other decades decreased
compared with 1960s, especially in 1990s. Changes of
mean annual ETr in 1970–2008 were primarily affected
by SD followed by WS, RH and DT with reference to
1960s. The actual contributions of climatic variables
varied during different decades. During the periods of
1970s and 1980s, actual contributions of the four climatic
variables are all positive, with SD being the largest. A
distinct decrease of ETr is found in 1990s, in addition to
the primary contribution of decreased SD, the effect of
WS became obvious. However, the actual contribution of
DT is negative during 1990s due to increased temperature
reference to 1960s. The actual contribution of WS became
the largest during 2000–2008, which is up to 99.0%, and
followed by SD (71.1%). The actual contributions of DT
and RH are negative compared with the decrease of ETr
during this period, that is because obvious increase of
DT and decrease of RH. Generally, our analysis of
actual contribution indicates that the most important
climatic variables on the change of ETr are SD and WS,
and then RH and DT, which is a little different from
their relative contribution.
Figure 7. Temporal evolution of relative contribution of climatic variables
to reference evapotranspiration
Hydrol. Process. 28, 6151–6162 (2014)
VARIATION OF ETR AND ITS CONTRIBUTING CLIMATIC FACTORS
6159
Table IV. Changes and actual contribution of climatic variables to reference evapotranspiration in reference to 1960–1969
Period
1960–1969
1970–1979
1980–1989
1990–1999
2000–2008
1970–2008
Change (Δ)/actual contribution [η2 (%)]
Δ ETr
(change)
DT
WS
SD
RH
—
0.21
0.27
0.34
0.21
0.26
—
0.16/19.9
0.14/13.4
0.08/6.2
0.33/41.1
0.02/2.1
—
0.12/16.0
0.19/19.6
0.48/39.3
0.73/99.0
0.36/38.9
—
0.17/38.8
0.30/53.6
0.38/53.5
0.30/71.1
0.29/53.5
—
0.13/25.3
0.09/13.6
0.11/13.5
0.14/27.9
0.05/7.6
DT, daily temperature; WS, wind speed; SD, sunshine duration; RH, relative humidity; ETr, reference evapotranspiration.
DISCUSSIONS
Like most parts of China (Thomas 2000; Chen et al.,
2006; Zhang et al., 2011a), Poyang Lake catchment
shows significant decreasing trend of ETr over the past
decades. Our analysis suggests that the reduced SD
(radiation) was the leading factor for ETr decrease at most
stations in the catchment. This is consistent with the findings
of general decrease in pan evaporation in Northern
Hemisphere associated with observed widespread decreases
in sunlight due to increasing cloud coverage and aerosol
concentration during the past 50 years (Roderick and
Farquhar, 2002; Yin et al., 2010). Du et al. (2007) indicated
that the decrease of annual and summer SD in Tibet during
1971–2005 was mainly related to the increase of atmospheric water VP and precipitation. However, the major
influencing factors to changes of ETr are different in those
Western Plateaus and northwest of China. Zhang et al.
(2009b) concluded that WS predominated the changes of
ETr in the north of the Qinghai-Tibetan Plateau, whereas
radiation was the leading factor in the southeast. A
comparative study conducted in the Aksu River basin in
Xinjiang Province, northwest China indicated that RH is the
primary driver of ETr in high altitude area, but WSs
predominated in other areas (Zhang et al., 2011b). Zhang
et al. (2011a) found that in the regions east of 100 °E net
total solar radiation is the main cause of decreasing ETr
rates, whereas RH is recognized as the most important
variable for ETr in northwest China. For the whole Poyang
Lake catchment, the decreasing trends of ETr are somewhat
different at the 15 stations. Major reasons for this lie in the
differences in regional climate due to different geographical
location and topography and the different intensities of
human activities, such as land-use/land-cover change and
urbanization in such a large scale catchment.
Changes of ETr are the integrated consequences of
more than one influencing factors. On annual basis, the
integrated negative effect from the decreasing SD and WS
is much larger than the positive effect from the increasing
DT and decreasing RH, which leads to the significant
decrease of ETr. However, contrary to other seasons,
Copyright © 2013 John Wiley & Sons, Ltd.
significant decrease of RH plays a more important role for
the increasing trend of ETr in spring. Although temperature
is often seen as the primary driver of evapotranspiration
changes (IPCC 2007), it is not a decisive factor for the ETr
change in the Poyang Lake catchment, even though DT has
increased significantly over the past 50 years. Generally,
relative contribution analysis indicates that SD among the
climatic variables is the most sensitive climatic variable in
influencing ETr on annual basis, followed by RH, WS and
DT. The so called evaporation paradox just considers the
effect of DT but neglects the sensitivity and changes of other
relevant climatic variables that accompanied with the
increasing temperature. Spatial discrepancy of relative
contribution of climatic variables to ETr across the
catchment is mainly caused by the differences in regional
climate and human activities.
Relative contributions of climatic variables to ETr are
temporally unstable and vary considerably. Our investigation indicates that relative contributions of SD and RH
in the Poyang Lake catchment show large fluctuation but
almost opposite direction during the study period. The
reason for this is that rainfall-runoff characteristics in the
catchment present periodic variation features at decade
scale, which was reported in previous studies (e.g. Liu
et al., 2009; Ye et al., 2012). Because air humidity is
closely related to the amount of precipitation, while SD
will be shortened due to the increase of cloudiness when
more precipitation happened. The relative contribution of
DT and WS increases substantially during 1990s should
be attributed to the quick change of these variables in
the period.
In consideration of the changes of climatic variables,
actual contribution rates of these variables may vary in
different periods. In reference to 1960s, changes of mean
annual ETr of the catchment in 1970–2008 were primarily
affected by SD and followed by WS, RH and DT. Although
SD still ranked as the most important variable, actual
contribution of WS is bigger than RH and DT compared
with their relative contributions. Consistent with the global
decrease of terrestrial WS (McVicar et al., 2012),
Hydrol. Process. 28, 6151–6162 (2014)
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X. YE ET AL.
significant decrease of WS was found in the Poyang Lake
catchment during the study period. Wang et al. (2004)
attributed the significant decline of surface WS in China
during the past 50 years to a weakening of winter and
summer monsoon, and this attribution in the Poyang Lake
catchment needs to be further studied and confirmed,
whereas Xu et al. (2006) found that local land-cover
change was the primary cause for decreasing WSs in
China. From the perspective of this, the rapid increase of
forest coverage in the Poyang Lake catchment (Ye et al.,
2013), especially since 1990s would be responsible for the
significant decrease of WS after that; however, more
evidence needs to be identified.
As an important water resource and iconic ecosystem in
a region, changes of ETr in the Poyang Lake catchment
would have special impact on local actual ET processes,
which determine the water use efficiency (Tomer and
Schilling, 2009). Because the Poyang Lake catchment
belongs to a wet climate, annual precipitation is much
larger than annual ET. Therefore, variability of annual ET
is mainly controlled by the changes of ETr, a typical
phenomenon of energy limited region. Preliminary results
from previous studies (e.g. Liu et al., 2010; Wang et al.,
2010; Ye et al., 2013) revealed that annual actual ET in
the Poyang Lake catchment shows a long-term decreasing
trend during the past decades, which is consistent with the
changing trend of ETr. Issues on how the changes of
annual and seasonal ETr would relate to actual ET and
consequently affect the water balance of the catchment
were not made in this paper and left to future study.
CONCLUSIONS
In this study, the spatio-temporal changes of ETr
calculated using the Penman–Monteith method was
examined over the Poyang Lake catchment during
1960–2008 by using linear regression (parametric),
Mann–Kendall (nonparametric) and attribution methods.
Impact of climatic variables, i.e. DT, WS, SD, RH and VP
on the change of ETr was thoroughly investigated. Some
interesting conclusions are obtained as follows:
1. Significant decreasing trend of annual ETr was found in
the Poyang Lake catchment over the period 1960–2008.
The decrease of annual ETr in the Poyang Lake basin is
mostly affected by the decline of summer ETr.
2. Climatic variables DT, WS, SD and RH are the general
factors in influencing the variation of ETr at annual and
seasonal scale of the catchment, whereas the effect of
VP is obscure. Analysis on relative contribution of
climatic variables to the change of ETr suggested that
SD is the most sensitive factor, followed by RH, WS
and DT.
Copyright © 2013 John Wiley & Sons, Ltd.
3. The study revealed that relative contributions of
climatic variables to ETr are temporally unstable and
vary considerably, which can be linked to their
observed inter-annual variations. The quick change of
DT and WS during 1990s should be responsible to the
substantially increase of their relative contributions in
this period.
4. In consideration of the changes of climatic variables in
the catchment, actual contributions to the changes of
ETr show little difference from their relative effects.
This study indicates that changes of mean annual ETr
in 1970–2008 were primarily affected by SD followed
by WS, RH and DT with reference to 1960s. However,
WS became the predominant factor during the period
2000–2008 compared with reference period 1960s and
followed by SD.
5. The results confirmed the evidence from a large
number of studies that the current warming of
atmosphere will not automatically lead to increased
ETr as claimed by the IPCC (2007). In the Poyang
Lake catchment, the combined effect of shortened SD
and reduced WS negated the effect of warming
temperature and decreasing RH and consequently
caused significant decrease of ETr.
ACKNOWLEDGEMENTS
This work was financially supported by the National
Basic Research Program of China (2012CB417003 and
2012CB956103-5), National Natural Science Foundation
of China (41201026) and Science Foundation of Nanjing
Institute of Geography and Limnology, CAS
(NIGLAS2012135001).
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