Theor Appl Climatol (2011) 103:479–488
DOI 10.1007/s00704-010-0315-6
ORIGINAL PAPER
Reference evapotranspiration changes in China:
natural processes or human influences?
Qiang Zhang & Chong-Yu Xu & Xiaohong Chen
Received: 20 January 2010 / Accepted: 28 July 2010 / Published online: 9 August 2010
# Springer-Verlag 2010
Abstract In this study, we systematically analyze the
changing properties of reference evapotranspiration (ETref)
across China using Penman–Monteith (P-M) method,
exploring the major sensitive meteorological variables for
ETref, and investigating influences of human activities,
mainly urbanization in this study, on ETref changes in both
space and time. We obtain some important conclusions: (1)
decreasing annual and seasonal ETref is observed in the east,
south and northwest China. However, a long strip lying
between these regions is identified to be characterized by
increasing ETref; (2) in the regions east to 100°E, the net total
solar radiation is the main cause behind the decreasing ETref.
In northwest China, however, relative humidity is recognized
as the most sensitive variable for the ETref; (3) in the east and
south China, urbanization greatly influences the ETref by
directly decreasing net solar radiation. The increased air
pollution and aerosols in the highly urbanized regions are the
main driving factors causing decreasing net radiation; and (4)
this study reveals accelerating hydrological cycle from south
to north China. Besides, increasing ETref in the source
regions of large rivers in China may pose new challenges for
the basin-scale water resource management. The results of this
Q. Zhang (*) : X. Chen
Department of Water Resources and Environment,
Sun Yat-sen University,
Guangzhou 510275, China
e-mail: zhangq68@mail.sysu.edu.cn
Q. Zhang : X. Chen
Key Laboratory of Water Cycle and Water Security in Southern China
of Guangdong High Education Institute, Sun Yat-sen University,
Guangzhou 510275, China
C.-Y. Xu
Department of Geosciences, University of Oslo,
P O Box 1047, Blindern,
0316 Oslo, Norway
study highlight the integrated effects of climate changes and
human activities on ETref changes in different regions of
China, which will be of great scientific and practical merits in
in-depth understanding of hydrological cycle alterations under
the changing environment in China.
1 Introduction
Reference evapotranspiration (ETref) is one of the most
important hydrological components for scheduling
irrigation systems, preparing input data to hydrological
water-balance models, assessing hydrological impacts of
climate changes (Blaney and Criddle 1950; Xu and Li
2003; Xu and Singh 2005; Gong et al. 2006). Besides, what
is important is that estimation of actual evapotranspiration
rate for a specific crop requires first calculating potential or
reference evapotranspiration (ETp or ETref) and then
applying the proper crop coefficients (Kc) to estimate actual
crop evapotranspiration (Xu et al. 2006). ETref is a kind of
measure of the evaporative demand of the atmosphere
independent of crop type, crop development and management practices and is affected only by climatic factors.
Therefore, ETref is a climatic parameter and can be
computed directly from meteorological data (Allen et al.
1998). There are some methods available for estimation of
ETref (e.g., Xu and Singh 2002). Wherein, the Penman–
Monteith (P-M) approach was recommended by FAO (e.g.,
Allen et al. 1998) as a standard tool to calculate ETref. The
P-M approach is a physically based technique and can be
used globally without any need for additional adjustments
of parameters. Xu et al. (2006) studied the changing
properties of Penman–Monteith ETref in the Yangtze River
basin, showing that the ETref changes are in good line with
those of pan evaporation in both space and time. Gong et al.
480
(2006) analyzed sensitivity of the P-M ETref to key climatic
variables in the Yangtze River basin, indicating that relative
humidity was the most sensitive variable, followed by
shortwave radiation, air temperature, and wind speed.
Evapotranspiration is the bridge connecting energy balance
and water balance (Xu et al. 2005), being one of the most
active hydrological components and is heavily influenced by
land use changes and climate variations regionally and
globally. The currently well-evidenced global warming
characterized by increasing temperature has the potential to
alter the hydrological cycle and therefore causes uneven
distribution of water resources. Hydrologists and meteorologists suggested that an increase in surface temperature can
result in higher evaporation rates and enables the atmosphere
to transport higher amounts of water vapor, which, in turn,
leads to accelerated hydrological cycle (e.g., Menzel and
Bürger 2002). Warmer temperature increases the holding
capacity of water vapor of atmosphere and which can cause
higher probability of rainstorm or high-intensity precipitation
and triggers occurrence of flood and drought hazards.
Climatic changes because of global warming might result in
increase and intensification of extreme events (WMO 2003).
Groisman et al. (1999) demonstrated that the probability of
daily precipitation exceeding 50.8 mm in mid-latitude
countries (the USA, Mexico, China, and Australia) increased
by about 20% in the later twentieth century. Suppiah and
Hennessy (1998) pointed out that the heavy precipitation
events in most parts of Australia have increased. In China,
Zhai et al. (1999) indicated that the intensive precipitation
events have increased in the west China since 1950. Zhang
et al. (2008) found increasing precipitation intensity in the
middle and lower Yangtze River basin. Increasing precipitation concentration was also observed in the lower Pearl
River basin and which was attributed to increasing air
surface temperature (Zhang et al. 2009a). Therefore, we can
say that the increasing temperature has exerted tremendous
influences on hydrological cycle. In-depth study of
evapotranspiration changes in both time and space can shed
light on the way and degree to which the climate changes
impact the hydrological cycle.
Generally, it can be easily and readily accepted that
increasing temperature can cause increasing evaporation
(Robock et al. 2000; Taikan and Shinjiro 2005). However,
observations indicate decreasing pan evaporation and ETref
(Peterson et al. 1995; Chattopadhyay and Hulme 1997;
Brutsaert and Parlange 1998; Roderick and Farquhar 2002,
2004; Michael et al. 2004; Xu et al. 2006). This is usually
known as pan evaporation paradox (e.g., Brutsaert and
Parlange 1998). The evaporation paradox is a very
important scientific problem and was warmly discussed
by many scholars. Peterson et al. (1995) suggested that the
downward trend in pan evaporation over most of the USA
and former Soviet Union implies decreasing terrestrial
Q. Zhang et al.
evaporation component of the hydrological cycle. Roderick
and Farquhar (2002) advocated that the decrease in
evaporation is consistent with the observed widespread
decreases in sunlight resulting from increasing cloud
coverage and aerosol concentration. However, Brutsaert
and Parlange (1998) held different viewpoints, pointing out
that “in non-humid environments, measured pan evaporation is not a good measure of potential evaporation;
moreover, in many situations, decreasing pan evaporation
actually provides a strong indication of increasing terrestrial
evaporation.” The foregoing discussions imply that, due to
the complex nature of evaporation, our knowledge of
evaporation and related causes are still terribly limited.
Actually, the evaporation changes are the integrated consequences of more than one influencing factors. Biased
conclusions can be obtained if we focus on one or two
influencing factors only. Besides, the human-induced
impacts such as influences from urbanization, aerosol
changes on evaporation changes are seldom discussed.
Zhang et al. (2004) indicated that the decreasing net total
radiation is mainly due to increased air pollution, implying
evident human influences on net solar radiation. Xu et al.
(2006) pointed out that the most important predictor for the
decreasing trend in the ETref and pan evaporation in the
Yangtze River basin is the net total radiation followed by
wind speed, but they did not discuss the reasons that the
wind speed is significantly decreasing. Therefore, they
pointed out the direction for the further study. This is the
major motivation of this current study.
In this study, we analyze seasonal trends of ETref and
major meteorological variables, e.g. wind speed, temperature and so on. Comparison between the spatial distribution
of large cities with population density of >100 people/km2
and changes of meteorological variables is also performed
with aim to investigate influences of urbanization on
changes of meteorological variables and ETref. This study
will shed light on possible human-induced influences on
spatio-temporal variations of meteorological components
including ETref, clarifying how climate changes and human
activities work together to impact the ETref changes in both
time and space. This study also helps to understand ETref
variations under the changing environment and their
implications for hydrological cycle under the background
of global warming. In this case, the objectives of this study
are: (1) to explore annual and seasonal changes of ETref
over China; (2) to clarify the factors exerting greater
impacts on ETref changes in both time and space; (3) to
investigate role of human activities in ETref changes and
other major meteorological variables by comparing spatial
patterns of large cities in China and those of climate
variables; and (4) to highlight the possible implications of
ETref changes for water resource management under the
changing environment in China.
Reference evapotranspiration changes in China
2 Data
In this study, we analyze daily meteorological data at 590
stations. Locations of the meteorological stations are shown
in Fig. 1. The data include maximum and minimum
temperature and daily mean air temperature at 2 m height
above the ground, wind speed, relative humidity, sunshine
hours, vapor pressure covering 1960–2005. They have been
provided by the National Climate Center of the China
Meteorological Administration. The missing data are filled
up by building regression relations between neighboring
stations. The correlation coefficient, R2, is >0.85 and is
even as high as >0.98. Therefore, the filled series can
satisfy the quality requirements of this study.
3 Methods
3.1 The P-M method
The P-M method has been recommended as the sole standard
method for computation of ETref by FAO (Allen et al. 1998)
and was introduced with good details by Xu et al. (2006) and
Gong et al. (2006). For the sake of completeness of this
paper, we briefly introduce this method in this section. The
reason this method was widely used and was chosen in this
study is that this method is physically based and explicitly
incorporates both physiological and aerodynamic parameters.
The ETref can be computed as:
ETref ¼
0:408ΔðRn GÞ þ g Ta900
þ273 u2 ðes ea Þ
Δ þ gð1 þ 0:34u2 Þ
Fig. 1 Locations of the meteorological stations considered in this
study and the ten drainage basins. The solid dots denote the rain
gauging stations. The gray solid dots denote large cities with
population density of >100 people/km2. Numbers denote the ten
drainage basins: 1 SongHuajiang River, 2: Liaohe River, 3 Haihe
River, 4 Yellow River, 5 Huaihe River, 6 Yangtze River, 7 SE rivers
(rivers in the southeast China), 8 Pearl River, 9 SW rivers (rivers in the
southwest China);,10 NW rivers (rivers in the northwest China)
481
where ETref is the reference evapotranspiration (mmday−1),
Rn the net radiation at the crop surface (MJm−2 day−1), G is
the soil heat flux density (MJm−2 day−1), T the mean daily air
temperature at 2 m height (°C), u2 the wind speed at 2 m
height (ms−1), es the saturation vapor pressure (kPa), ea the
actual vapor pressure (kPa), es −ea the saturation vapor
pressure deficit (kPa), Δ the slope of the vapor pressure
(kPa °C−1), γ the psychrometric constant (kPa °C−1). The
unit conversion between MJ and watt is: 1,000 Wattsh=
3.6 MJ. The computation procedure follows that given in
Chapter 3 of the FAO paper 56 (Allen et al. 1998).
3.2 Trend test
Significance of the trends of the meteorological series is
evaluated by the Mann–Kendall trend test technique (MK
test). The rank-based nonparametric Mann–Kendall test
(Mann 1945; Kendall 1975) can test trends of a time series
without requiring normality or linearity (Wang et al. 2008),
and was therefore highly recommended for general use by
the World Meteorological Organization (Mitchell et al.
1966). It was widely used in detection of trends in
hydrological (e.g. Zhang et al. 2006) and meteorological
series (Zhang et al. 2009a). This paper also uses the Mann–
Kendall (MK) test method to analyze trends within the
meteorological series. The confidence level used in this
study is 95%.
4 Results and discussions
4.1 Trends of reference evapotranspiration
Figure 2 illustrates spatial patterns of temporal changes of
ETref. Discernable spatial patterns of annual ETref are
identified from Fig. 2a. Haihe River basin, Huaihe River
basin, the middle and lower Yangtze River basin, the SE
rivers and the Pearl River basin are dominated by significantly
decreasing ETref. The NW rivers are also characterized by
significantly decreasing ETref except eight stations showing
significantly increasing ETref. Generally, three regions could
be identified with different changing properties of ETref
(Fig. 2a): (1) the east China, the middle and the south China.
These regions are dominated by significantly decreasing ETref
except several stations located along the coastal regions of the
SE China which are characterized by not significant ETref
changes; (2) the northwest China. This place is again featured
by significantly decreasing ETref; and (3) a strip lying
between these two regions in the SW-NE direction. The
stations along this strip are characterized by not significant
ETref changes. Stations with significantly increasing ETref
mainly concentrate in the upper Yellow River basin, the upper
Yangtze River basin and in the SW rivers. The locations of
482
these rivers can be referred to Fig. 1. Besides, some stations
characterized by significantly increasing ETref are also found
in the SongHuajiang River and the northeast China.
It can be observed from Fig. 2b that, in summer, similar
spatial patterns of ETref could be identified when compared
to annual changes (Fig. 2a). The difference is that fewer
(more) stations are characterized by significantly increasing
(decreasing) ETref in the strip between the southeast and
the northwest China in comparison with annual changes
(Fig. 2a). Only seven stations show significantly increasing
Q. Zhang et al.
ETref. This result suggests decreasing evaporation capacity
in summer. Figure 2c illustrates distinctly different spatial
patterns of ETref changes over China when compared to
annual changes of ETref and ETref variations in summer.
One remarkable difference is that the NW and the SE China
are characterized by not significant ETref changes. Besides,
some stations in these two regions are featured by
significantly increasing ETref. Therefore, comparatively,
the ETref in these two regions, i.e., the NW and the SE
China, increases in winter when compared to annual
changes and ETref changes in summer. As far as ETref
changes at stations in the strip are concerned, more stations
are found to be characterized by significantly increasing
ETref, and so do the ETref changes in the Songhuajiang
River. Thus, based on what aforementioned, the evaporation
capacity of winter increased over the entire China. ETref
changes are the results of many influencing factors, climate
changes or human influences. In the following sections, we
will discuss the effects of various influencing factors on ETref
variations in both space and time.
4.2 Sensitivity of ETref changes to meteorological variables
Xu et al. (2006) evaluated the sensitivity of ETref to major
meteorological variables by scenario analysis, i.e., they
generated seven scenarios for each meteorological variable
using the following equation:
X ðtÞ ¼ X ðtÞ þ ΔX ;
ΔX ¼ 0; 10%; 20%; 30% of X ðtÞ
Fig. 2 Spatio-temporal patterns of reference evapotranspiration changes
over China. a annual changes of reference evapotranspiration; b changes
of reference evapotranspiration in summer; and c changes of reference
evapotranspiration in winter. Thick solid contours indicate significant
increasing trend; thin solid contours show changes not significant at
>95% confidence level; and dashed contours show significant
decreasing trends
where X is the meteorological variable, and t is the time in
day.
The results of this method should be discussed based on
the actual variations of meteorological variables and ETref
changes. Xu et al. (2006) showed higher sensitivity of
ETref to relative humidity followed by net radiation, air
temperature and wind speed. However, the contribution of
relative humidity to the decreasing trend in the ETref in the
Yangtze River basin is much smaller than that of the net
radiation in that the trends of the relative humidity over the
whole Yangtze River basin are not significant (Xu et al.
2006). They suggested that the net total radiation be the
main cause behind the decreasing trend of the ETref
because it is not only one of the most sensitive variables
but also a variable with significantly decreasing trend. In
this study, we evaluate sensitivity of ETref to meteorological
variables by using the correlation coefficients between their
MK trends. The main idea behind this method is that the
higher the similarity between the changing trends of the
independent and dependent variables, the higher the sensitivity
of the dependent variable to the independent variable. Our
analysis results show larger correlation coefficient (r=0.55)
Reference evapotranspiration changes in China
483
between trends of ETref and net radiation on the annual basis
(Table 1 and the upper panel of Fig. 3) than the rest three
meteorological variables: −0.49 for the relative humidity (the
absolute value is 0.49); 0.43 for wind speed; and 0.33 for
temperature. Therefore, on annual basis, the net radiation
(r=0.55) is the most sensitive meteorological variable for the
ETref followed by relative humidity (r=−0.49). This
conclusion is in good line with that of Xu et al. (2006).
Therefore, the method used in this study has the similar
performance as that used by Xu et al. (2006). The difference
is that computation procedure of the method in this current
study is relatively simpler than that of Xu et al. (2006). As
for the meteorological variables the ETref is sensitive to at
the stations located along the strip lying between the SE and
the NW China, Fig. 4 shows that the wind speed could
be the meteorological variables exerting greater influences
on the ETref changes, and is followed by the net radiation
(Table 1). Table 1 shows that correlations between ETref,
wind speed and net radiation are statistically significant
at >99% confidence level.
4.3 Meteorological variables influencing ETref changes
and possible impacts from human activities
Figure 5 illustrates spatial distribution of wind speed
changes over China. Generally, China is dominated by
significantly decreasing wind speed. Tens of stations
showing significantly increasing wind speed distribute
sporadically between 100°E and 120°E. No fixed and
discernable spatial patterns can be identified. Relations
between ETref and wind speed in space are also ambiguous.
Therefore, we do not intend to attribute ETref changes to wind
speed variations based on the foregoing analysis results. A
closer look at Fig. 5 indicates that the stations characterized
by significantly increasing wind speed are mostly outside of
regions covered by large cities denoted by large gray dots.
This result may imply possible influences of urbanization on
measured ground surface wind speed; at least it is true in the
regions south to 40°N and east to 100°E. Figure 6 illustrates
spatial patterns of net radiation. On the annual basis, regions
of China east to 100°E are dominated by significantly
decreasing net radiation. Significantly increasing net radiation can be found in the upper Yangtze River basin, the
upper Yellow River basin, the west parts of the NW rivers,
the west parts of the NW China and also in the north corner
of the NW China (Fig. 6a). Figure 2a demonstrates that the
places dominated by significantly increasing net radiation are
roughly also those characterized by significantly increasing
annual ETref. Regions in the east and the south China which
are dominated by significantly decreasing net radiation
(Fig. 6a) correspond well to those featured by significantly
decreasing annual ETref (Fig. 2a). Figure 6b shows that
significant decrease of net radiation prevails across major
parts of China, only a couple of stations in the upper Yangtze
River basin and the west corner of China show significantly
increasing net radiation. Similarly, ETref in summer shows
Table 1 Sensitivity of ETref to meteorological variables evaluated by correlation coefficients between MK trends of ETref and the four
meteorological variables, i.e. net solar radiation, relative humidity, wind speed and temperature
Annual changes of ETref
Net solar radiation
Relative humidity
Wind speed
Temperature
ETref in summer
Net solar radiation
Relative humidity
Wind speed
Temperature
ETref in winter
Net solar radiation
Relative humidity
Wind speed
Temperature
At stations in the SE China
At stations located along the strip
At stations in the NW China
Correlation coefficient
p values
Correlation coefficient
p values
Correlation coefficient
p values
0.55
−0.49
0.43
0.33
0.00
0.00
0.00
0.00
0.42
−0.30
0.59
0.11
0.00
0.00
0.00
0.22
0.25
−0.53
0.79
0.16
0.06
0.00
0.00
0.28
0.76
−0.45
0.43
0.40
0.00
0.00
0.00
0.00
0.43
−0.43
0.63
0.30
0.00
0.00
0.00
0.00
0.19
−0.53
0.86
0.27
0.19
0.00
0.00
0.06
0.27
−0.54
0.64
0.07
0.00
0.00
0.00
0.42
−0.32
−0.13
0.56
−0.06
0.00
0.13
0.00
0.52
−0.12
−0.21
0.60
−0.03
0.40
0.15
0.00
0.85
The p values show the significant level of the correlation coefficients. The p value of <0.05 shows significant correlation at 95% confidence level
and vice versa
484
Q. Zhang et al.
2
1
0.55
0
Correlation coefficient
−1
−2
2
1
0.76
0
−1
−2
2
1
0.64
0
−1
−2
NR
RH
WS
TP
Fig. 3 Correlations between reference evapotranspiration in southeast
China and four major meteorological variables, i.e., NR: net solar
radiation (MJm−2 day−1); RH relative humidity (%), WS wind speed
(m/s), TP temperature. The upper panel is annual, middle summer and
bottom winter
similar spatial patterns, confirming tremendous influences of
net radiation on ETref. Besides, Fig. 6b shows that the
regions east to 110°E are dominated by significantly
decreasing net radiation and are also mostly covered by
large cities. Figure 6c shows different changing properties of
2
1
0
0.59
Correlation coefficient
−1
−2
2
Fig. 5 Spatio-temporal patterns of wind speed variations over China.
a Annual changes of wind speed; b changes of wind speed in summer;
and c changes of wind speed in winter. Thick solid contours indicate
significant increasing trend; thin solid contours denote changes
not significant at >95% confidence level; and dashed contours show
significant decreasing trends
1
0
0.63
−1
−2
2
1
0
0.56
−1
−2
NR
RH
WS
TP
Climate variables
Fig. 4 Correlations between reference evapotranspiration and four
major meteorological variables, i.e., NR net solar radiation (MJm−2
day−1); RH relative humidity (%), WS wind speed (m/s), and TP
temperature in the belt lying between southeastern China and
northwestern China. The upper panel is annual, middle summer and
bottom winter
the net radiation in winter when compared to those in
summer (Fig. 6b) and annual changes of the net radiation
(Fig. 6c). The major difference is that a majority of stations
located along the strip between the SE and the NW China
are characterized by significantly increasing net radiation.
However, changes of radiation in the SE China are not
significant. Besides, spatial distribution of significantly
decreasing net radiation in the east and the south China
match well that of large cities. Correspondingly, the regions
in the strip are dominated by significantly increasing net
radiation and ETref. Changes of net radiation and ETref in
the east and south China are not significant (Figs. 2c and 6c).
Reference evapotranspiration changes in China
485
2
1
0
0.79
Correlation coefficient
−1
−2
2
1
0
0.86
−1
−2
2
1
0
0.60
−1
−2
NR
RH
WS
TP
Time variables
Fig. 7 Correlations between reference evapotranspiration and four
major meteorological variables in the northwest China. NR net solar
radiation (MJm−2 day−1); RH relative humidity (%); WS wind speed
(m/s); and TP temperature. The upper panel is annual, middle summer
and bottom winter
Fig. 6 Spatio-temporal patterns of net radiation variations over China.
a Annual changes of net radiation; b changes of net radiation in
summer; and c changes of net radiation in winter. Thick solid contours
indicate significant increasing trend; thin solid contours denote
changes not significant at >95% confidence level; and dashed
contours show significant decreasing trends
As for underlying causes behind the ETref in the NW
China, Table 1 and Fig. 7 show two sensitive meteorological
variables for ETref, i.e., the wind speed and the relative
humidity. In winter, only the winter speed could be accepted
as the sensitive meteorological variables for ETref in the NW
China. We demonstrate in the aforementioned sections that
the wind speed in China is decreasing and no obvious
relations could be identified between ETref and the wind
speed. The results of this study also provided no evidence
for good relations between ETref and wind speed. Therefore,
we try to analyze spatio-temporal patterns of relative
humidity and ETref in NW China. Figure 8a shows annual
variations of relative humidity in the NW China. It can be
observed from Fig. 8a that majority of the NW China are
characterized by significantly increasing relative humidity.
Northeast parts of the NW China are featured by not
significant relativity humidity changes. Similar changes of
relative humidity are observed in the NW China. ETref in
the NW China is in significantly decreasing trends. Table 1
also indicates significant negative correlations between ETref
and the relative humidity in the NW China. In winter, not
significant changes of the relative humidity are detected in
the NW China except that several stations distribute
sporadically in the west parts of the NW China showing
significantly increasing relative humidity (Fig. 8c). Figure 2c
shows not significant ETref changes in winter. All these
results clearly indicate remarkable influences of relative
humidity changes on ETref in the NW China.
In the regions east to 110°E, ETref changes are heavily
influenced by the net radiation variations, and the decreasing
trend of the net total radiation is the main cause behind the
decreasing ETref. Urbanization, to a certain degree, plays an
important role in decrease of the net radiation due to the fact
that the spatial distribution of decreasing net radiation matches
well that of large cities with population density of
>100 persons/km2. Studies by Zhang et al. (2004) and Liu
et al. (2004) attributed the decreasing net total radiation to
increased air pollution or aerosol in highly urbanized regions
in the east China. Roderick and Farquhar (2002), Brutsaert
and Parlange (1998) also advocated that the decrease in
486
Q. Zhang et al.
evaporation trend is not determined by temperature alone.
Increasing temperature does not necessarily cause increasing
evaporation due to the changes in other dominant variables.
Increasing ETref in winter when compared to ETref in
summer in this study is good evidence. Furthermore, our
previous (Zhang et al. 2009b) studies indicate significantly
increasing temperature in the NW China, particularly in
winter. But ETref changes in the NW China are not
significant. Zhang et al. (2009c) studied the changing
properties of precipitation in both space and time over China
and found increasing annual and summer precipitation.
Therefore, increasing relative humidity in the NW China
could be due to the increasing precipitation. NW China is
characterized by arid climate. Increasing precipitation could
lead to increasing relative humidity. Thus, decreasing ETref
in the NW China could be accounted for based on the
aforementioned observations.
5 Conclusions
We analyze spatio-temporal changes of ETref across China
by using P-M approach. Underlying causes behind ETref
changes are also thoroughly investigated by using sensitivity
analysis of ETref to major meteorological variables, i.e.,
temperature, relative humidity, net radiation, and wind speed.
Besides, influences of human activities, mainly urbanization
in this study, on ETref changes are also discussed based on the
analysis results of this study and also based on the previous
studies. Interesting and important conclusions are obtained as
the follows.
Fig. 8 Spatio-temporal patterns of relative humidity variations over
China. a Annual changes of relative humidity; b changes of relative
humidity in summer; and c changes of relative humidity in winter.
Thick solid contours indicate significant increasing trend; thin solid
contours denote changes not significant at >95% confidence level; and
dashed contours show significant decreasing trends
evaporation could be attributed to the observed large and
widespread decreases in sunlight resulting from increasing
cloud coverage and aerosol concentration. The observations
in this study tend to further corroborate the aforementioned
perspectives. Besides, our analysis also points out no
significant correlations between temperature and ETref.
Table 1 indicates that the influences of temperature changes
on ETref could almost be ignored and it was particularly true
for winter and for annual ETref changes. Therefore, the
results of this study seem to be in agreement with those by
Ohmura and Wild (2002) that the changing direction of the
1. Significantly decreasing trends of annual and summer
ETref are found mainly in the Haihe River, Huaihe
River, the middle and the lower Yangtze River, the SE
rivers, the Pearl River, and the NW rivers. Significantly
increasing ETref could be observed mainly at the
stations located along a long strip lying between the
NW China and the SE China. Besides, increasing ETref
is detected in winter. Decreasing summer ETref is
observed along a long strip lying between the NW
China and the SE China.
2. Different meteorological variables to which the ETref is
sensitive are identified in different parts of China. In
the regions east to 100°E, changes of net radiation have
the remarkable contribution to the ETref variations.
However, in the NW China, relative humidity seems to
have greater impacts on ETref changes than other
meteorological variables. As for human influences,
stations characterized by significantly increasing wind
speed seem to be away from large cities, which might
imply influences of urbanization on wind speed on
regional scale. Besides, parts of China east to 100°E
Reference evapotranspiration changes in China
covered by large cities are also characterized by
significantly decreasing net radiation and vice versa,
showing remarkable impacts of urbanization on net
radiation. Study results (e.g. Zhang et al. 2004; Liu et
al. 2004) attributed decreasing net radiation in highly
urbanized regions to air pollution and aerosols. The
strip lying between the east China and the NW China is
characterized by increasing net radiation and ETref
which further ascertains the influences of both the net
radiation and urbanization-induced decreasing radiation
on ETref. In NW China, relative humidity is the major
meteorological variable having large contribution to
ETref changes. Our previous studies (Zhang et al.
2009b, c) demonstrated increasing temperature and
precipitation in the NW China, and which should be
regarded as the main cause for increasing relative
humidity and decreasing ETref. Considering increasing
ETref along the strip between the east China and the
NW China, we can say that the hydrological cycle
comes to be accelerated from the south to north China.
3. This study confirms the remarkable influences of human
activities, mainly the urbanization, on ETref by directly
influencing changes of net radiation. Increasing ETref is
mainly observed in the upper Yangtze River basin, the
Yellow River basin and the southwest parts of China.
These regions act as the major streamflow source of large
rivers in China, though precipitation also plays important
role in streamflow production in the middle and lower
reaches of these rivers. Therefore, altered hydrological
cycle as an integrated result of climate changes and
human activities should be taken into account with great
cautions when policy for river basin-scale water resource
management is making. This study clarifies different
sensitive meteorological variables for ETref and also
ascertains the way and degree to which human activities
impact ETref. Besides, ETref changes in both time and
space in China are thoroughly investigated. All these
important conclusions and study results are expected to
shed light on hydrological cycle changes under the
changing environment in China and also help to pave
the way for the similar studies in other places of the world.
Acknowledgments This work was financially supported by the ‘985
Project’ (Grant No.: 37000-3171315), the Program for Outstanding
Young Teachers of the Sun Yat-sen University (Grant No.: 200937000-1132381), Xinjiang Technology Innovative Program (Grant
No.: 200931105), the State Key Laboratory of Hydrology—Water
Resources and Hydraulic Engineering (Grant No.: 2009491511), the
Postdoctoral Foundation of the Guangdong Province (Grant No.:
2009-37000-4203384), and by the 111 Project under Grant B08048,
Ministry of Education and State Administration of Foreign Experts
Affairs, P. R. China. Cordial gratitude should be extended to the
editor, Prof. Dr. Hartmut Grassl, and anonymous reviewers for their
487
pertinent and professional comments and suggestions which greatly
improved the quality of this manuscript.
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