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Temporal variations of reference évapotranspiration and its sensitivity to meteorological factors in Heihe River Basin, China
Jie Zhao, Zong-xue Xu, De-peng Zuo, Xu-ming Wang
PII: S1674-2370(15)00005-8
DOI: 10.1016/j.wse.2015.01.004
Reference: WSE 4
To appear in: Water Science and Engineering
Received Date: 12 July 2014 Accepted Date: 22 December 2014
Please cite this article as: Zhao, J., Xu, Z.-x., Zuo, D.-p., Wang, X.-m., Temporal variations of reference evapotranspiration and its sensitivity to meteorological factors in Heihe River Basin, China, Water Science and Engineering (2015), doi: 10.1016/j.wse.2015.01.004.
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ACCEPTED MANUSCRIPT
Temporal variations of reference évapotranspiration and its sensitivity to meteorological factors in Heihe River Basin, China
Jie ZHAO, Zong-xue XU*, De-peng ZUO, Xu-ming WANG
College of Water Sciences, Beijing Normal University, Beijing 100875, P. R. China
Abstract: On the basis of daily meteorological data from 15 meteorological stations in the Heihe River Basin (HRB) during the period from 1959 to 2012, long-term trends of reference évapotranspiration ( ET0 ) and key meteorological factors that affect ET0 were analyzed using the Mann-Kendall test. The evaporation paradox was also investigated at 15 meteorological stations. In order to explore the contribution of key meteorological factors to the temporal variation of ET0, a sensitivity coefficient method was employed in this study. The results show that: (1) mean annual air temperature significantly increased at all 15 meteorological stations, while the mean annual ET0 decreased at most of sites; (2) the evaporation paradox did exist in the HRB, while the evaporation paradox was not continuous in space and time; and (3) relative humidity was the most sensitive meteorological factor with regard to the temporal variation of ET0 in the HRB, followed by wind speed, air temperature, and solar radiation. Air temperature and solar radiation contributed most to the temporal variation of ET0 in the upper reaches; solar radiation and wind speed were the determining factors for the temporal variation of ET0 in the middle-lower reaches. Key words: reference évapotranspiration; evaporation paradox; meteorological factor; Heihe River Basin
1 Introduction
Evapotranspiration plays an important role in the hydrological cycle as well as the global energy budget. It contributes 2/3 of annual precipitation and has an essential influence on the Earth's climate system (Jayawardena 1989; Chahine 1992; Zhan et al. 2011; Zuo et al. 2012; Dulian et al. 2013). In addition, évapotranspiration is a key input to hydrological models (Liang et al. 1994; Gerten et al. 2004; Zhao et al. 2013). Therefore, a comprehensive understanding of temporal trends and spatial distribution of évapotranspiration is highly significant to water resource management, especially in places where the water availability is limited.
Global wanning has been one of the most concerning issues for governments. As reported in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), the surface temperature of the Earth has increased by about 0.13°C per decade over the past 50 years (IPCC 2007). This has significant impacts on environmental systems, by causing glaciers to melt, the sea level to rise, etc. Global wanning also breaks the balance of eco-systems and threatens food supplies. Some studies on climate change have predicted that one of the phenomena that global wanning will bring about is an increase in the rate of evaporation from tenestrial open water bodies, which will enhance the scarcity of water resources in arid regions (Jackson 2001; Scheffer et al. 2001; Yang et al. 2009; Jayantha et al. 2011; Sjoegersten 2013).
Observed pan evaporation data have revealed the fact that evaporation from open water bodies has been decreasing over the past several decades in different regions around the world (Brutsaert et al. 1998; Gao et al. 2012; Yesihnnak 2013), including Australia (Roderick et al. 2004), Canada (Aziz et al. 2006), Eurasia (Velichko et al. 2008), China (Cong et al. 2009), and India (Rao et al. 2011). The contrast between the increase in air temperature and the decrease in observed pan evaporation rate is refened to as the evaporation paradox (Roderick et al. 2002). Furthennore, a similar decreasing trend of reference évapotranspiration ( /•."/', ) was also found by Thomas (2000) and Roderick (2004).
In order to investigate the evaporation paradox, many studies have been carried out. Generally, these studies can be divided into two categories. One includes studies meant to detennine the key factors that impact pan evaporation and /•."/', and analyze variations of these key factors so as to explain the reason why pan evaporation and //, have decreased over the past several decades. The other includes studies that focus on detennining whether decreasing pan evaporation or / /,, definitely leads to the decrease in actual
This work was supported by the National Natural Science Foundation of China (Grant No. 91125015), and the Central Nonprofit
Research Institutes Fundamental Research of the Yellow River Institute of Hydraulic Research (Grant No. HYK-JBYW-2013-18). ■"Corresponding author (e-mail: zongxiiexii(a)vip.sina.com) Received Jul. 12, 2014; accepted Dec. 22,2014
evapotranspiration. Studies concerning spatial and temporal variations in pan evaporation and ET0 have been carried out by researchers worldwide. Gao (2006) studied spatial and temporal variations in ET0 at 580 stations in China during the period from 1956 to 2000, and, through a partial correlation analysis, the study determined that sunshine duration, wind speed, and relative humidity have a significant impact on ET0 . Wang (2014) analyzed the relationship between the variations of ET0 and each climatic variable at Linhe Station, a representative weather station in the Hetao Irrigation District of China, during the period from 1954 to 2012. The results showed that ET0 in the Hetao Irrigation District is most sensitive to mean daily air temperature, followed by wind speed. Changes in sunshine duration had only a minor effect on ET0 during the study period. Recent analysis from Wang et al. (2012) indicated that the aerodynamic component of ET0 accounted for 86% of the long-term changes in global ET0 from 1973 to 2008. However, Matsoukas (2011) showed the opposite conclusion: trends in ET0 more closely followed trends in energy availability than trends in atmospheric holding capability for vapor transfer.
These studies have come to quite different conclusions in different regions, indicating a need for new methods to identify the most important meteorological factors in explaining changes in ET0 at the regional level. Besides, most of these studies focused on the theoretical sensitivity of ET0, which is the expected variation of ET0 due to changes in variables under the assumption that only one variable changes while other variables remain the same. In fact, the theoretical sensitivity of ET0 does not consider the actual changes in meteorological variables. However, the explanation of meteorological factors controlling changes in ET0 must consider both the sensitivity of and long-term changes in the meteorological factors themselves.
In this study, overall analysis of the variation of ET0 in the arid region in northwestern China was carried out. The study mainly focused on both the temporal trends of annual and seasonal ET0 and quantitative analysis of the contributions of different meteorological variables to the variation of ET0. The objectives of this study included: (1) to detect the long-term trends in ET0 and air temperature using the Mann-Kendall test; (2) to investigate the evaporation paradox at 15 stations by comparing the changing trends in annual ET0 with the changing trends in air temperature, in order to compensate for the lack of pan evaporation data in the Heihe River Basin (HRB), because Zuo (2010) found a linear relationship between pan evaporation and ET0 in northwestern China and a coefficient of determination greater than 0.97, verifying the rationality of using the variation of ET0 to reflect the variation of pan evaporation in this study; and (3) to quantify the contribution of key meteorological factors (air temperature, solar radiation, relative humidity, and wind speed) to the variation of ET0 and explain the reason for the evaporation paradox using the sensitivity coefficient method.
2 Study area and data 2.1 Study area
The HRB, covering an area of approximately 134 000 km2, is the second largest inland river basin in northwestern China and spans Qinghai and Gansu provinces as well as the Inner Mongolia Autonomous Region from upper reaches to lower reaches. The HRB is located between latitude 37.50°N and 42.40°N, and longitude 98°E and 102°E (Fig. 1).
Fig. 1 Meteorological stations in and around HRB The HRB is situated in the interior of the Eurasian continent and dominated by arid hydrological characteristics with a mean annual precipitation of approximately 400 mm and a mean annual ET0 of approximately 1 600 mm. The precipitation, temperature, evaporation, and runoff in the HRB vary greatly at both spatial and temporal scales. The dominant land use types are desert land and grass land, occupying approximately 60% and 25% of the total area, respectively. Due to its important role in water resources management in northwestern China, the HRB has long been a focus of studies on inland rivers in arid regions.
2.2 Data
Six daily meteorological variables (observed daily mean, maximum and minimum air temperatures, relative humidity, wind speed at the height of 2 m, and sunshine duration) derived from the ground surface climatic data sets at 15 national meteorological stations from 1959 to 2012 (Fig. 1) in and around the HRB were obtained from the Environmental and Ecological Science Data Center for West China. The six variables were used as input data for the FA056 Penman-Monteith (FA056 P-M) method to estimate daily values of ET0. The autocorrelation method was employed in this study to analyze the persistence, which is the tendency for successive values of a meteorological data series to remember the antecedent values (Giles and Flocas 1984). The results reveal that, for all 15 stations in the HRB, autocorrelation coefficients of annual and seasonal air temperatures and ET0 series are quite low, which means a low persistence in the data series.
3 Method
3.1 FAO 56 P-M method
The FA056 P-M method, which is considered the most accurate method to estimate ET0 under different climatic conditions, was employed to estimate the daily values of ET0 in this study. Monthly and annual values of ET0 were obtained by adding up the daily values. The equation of the FA056 P-M method (Allen et al. 1998) is as follows:
0.408/1 -G) + y
T + 273
A + y(\ + 03Au2)
where ET0 is reference évapotranspiration (mm), is the slope of the saturated vapor pressure (kPa/°C), Rn is net radiation at the surface (MJ/(m2-d)), G is soil heat flux density (MJ/(m2-d)), y is the psychrometric constant (kPa/°C), T is the mean air temperature at the height of 2 m (°C), u2 is wind speed at the height of 2 m (m/s), es is saturation vapor pressure (kPa), and eà is actual vapor pressure (kPa). Each term in Eq. (1) was obtained using methods described by Allen et al. (1998).
3.2 Mann-Kendall test
The Mann-Kendall (M-K) test, which was developed by Mann and Kendall and is superior for detecting
linear or non-linear trends (Hisdal et al. 2001), was employed to analyze the long-term trends in ET0 and air temperature. This method has been widely used for detecting trends in hydro-meteorological variables such as streamflow, air temperature, ET0, and precipitation in different regions around the world (Zuo et al. 2012; Gong et al. 2011).
The related equations for calculating the M-K test statistic S and the standardized test statistic ZMK are as follows:
S = XI sgn ( Xj - X, )
/=1 j=i+1
sgn (Xj - X, ) =
X. -X, > 0
0 X. - X, = 0
-1 X, - X, < 0
Var ( S ) = — v ' 18
n(n -1)(2n + 5)-1tp (tp -1)(2tp + 5)
4Var ( S )
y¡Var ( S )
S = 0 S < 0
where X and Xj are the sequential data values of the time series in the years i and j, n is the length of the time series, tp is the number of ties for the pth value, and q is the number of tried values. Positive values of ZMK indicate increasing trends, while negative values of ZMK indicate decreasing trends in the time series. When |ZMK| > Zj_a2, the null hypothesis, which assumes that there is no significant trend in the time series, is
is the critical value of Z from the standard is 1.96.
1-a/ 2
rejected and a significant trend exists in the time series. Z, normal table, and for the 5% significance level the value of Z1-a/2
3.3 Sensitivity coefficient method
For multivariable models, such as the FAO56 P-M method, different variables have different dimensions and ranges of values, which makes it difficult to compare sensitivity with partial derivatives (Zuo et al. 2012). Therefore, the partial derivative is transformed into a non-dimensional form to interpret the sensitivity of the variables (McCuen 1974; Beven 1979):
SV = lim
V AV ®0
( AETJ ET^
Ep v^ dV ET
G =AV/S
(6) (7)
where SV is the sensitivity coefficient and Vi is the ith variable. SV represents the subtle change in ET0 resulting from the subtle change in Vt. GV indicates the contribution of the ith variable to the variation of ET0. The sensitivity coefficient has been widely used in studies on evapotranspiration (Estevez et al. 2009). The positive SV of one variable means that the changing trends in ET0 and the variable are the same, while the negative SV of one variable means that the changing trends in ET0 and the variable are opposite. It is the same for GV . Sensitivity coefficients are different for different variables at different times. The larger the absolute value of the sensitivity coefficient is, the greater the effect the variable exerts on ET0. Also, the larger the absolute value of GV is, the greater the contribution the variable makes to the variation of ET0. In this study, SV and GV for daily air temperature, solar radiation, relative humidity, and wind speed were estimated to quantify the contribution of each factor selected to the variation of ET0.
P TED MANUSCRIPT
4 Result analysis and discussion 4.1 Temporal trend of ET0
Fig. 2 shows inter-annual variation of seasonal ET0 in each season. It can be seen that seasonal ET0 in the middle reaches was similar to that across the whole basin in both values of seasonal ET0 and the inter-annual trends.
— Whole---Upper
Middle — - Lower
400 ? 350 4 300 w 250 200
600 500 400 300
1958 1967 1976 1985 1994 2003 2012 Year (a) Spring
1958 1967 1976 1985 1994 2003 2012 Year (b) Summer
250 200
' \/ w \
1958 1967 1976 1985 1994 2003 2012 Year (c) Autumn
1958 1967 1976 1985 1994 2003 2012 Year (d) Winter
Fig. 2 Temporal variations of seasonal ET0 of HRB from 1959 to 2012 The M-K test was carried out at the 15 stations to investigate the changing trends in annual ET0 series during the period from 1959 to 2012 in the HRB at the significance level of 5%. The results show that the annual ET0 series exhibited decreasing trends at most of meteorological stations (Fig. 3(a)). Fig. 3(a) also shows that seven stations in the basin showed a significant decreasing trend. In the legend of Fig. 3, Decrease Sig, Decrease Insig, Increase Sig, and Increase Insig mean decrease significant, decrease insignificant, increase significant, and increase insignificant, respectively.
▼ Decrease Sig
Decrease Insig
Banner
Increase Insig
a Increase Sig
, Guaizihu
Alxa Right
Banner w
andan > „Yongchang
Banner Guaizihu
Alxa Right Banner
andan TYongchang
▼ Gangcha (a) Annual
▼ Gangcha (b) Spring
0 110 220 km
Banner
.Guaizihu
Alxa Right Banner
andan Yongchang
Qilian^
▼ Gangcha (c) Summer
Ejiry Banner
Guaizihu
Alxa Right Banner ;ye v Landan
Yongchang
Banner
Alxa Right
Banner ;ye ▼ Landan
Qilian
▼Gangcha (e) Winter
Yongchang
a Gangcha (d) Autumn
Fig. 3 Spatial distributions of annual and seasonal ET0 trends in HRB from 1959 to 2012
Changing trends in seasonal ET0 at each station were also detected using the M-K test. Results reveal that the spring ET0 of all 15 stations exhibited an insignificant decreasing trend. A decreasing trend in the summer ET0 took place at most of the 15 stations, except at Yeniugou, Mazongshan, and Guaizihu stations. Of the three stations, Guaizihu Station, located in the lower reaches of the HRB, exhibited a significant increasing trend, while Yeniugou Station in the upper reaches and Mazongshan Station in the lower reaches exhibited an insignificant increasing trend. In autumn, eight stations showed an increasing trend in ET0, much more than in other seasons. For the eight stations, the autumn ET0 at five stations increased significantly. The other seven stations showed a significant decreasing trend, except for Jiuquan Station and Zhangye Station in the middle reaches. Four stations showed an increasing trend in the winter ET0, two of which are located in the upper reaches, while Shandan Station and Guaizihu Station are in the middle and lower reaches, respectively.
Generally, at the temporal scale, most of the stations showed a decreasing trend over the four seasons, especially in spring. An increasing trend in annual and seasonal ET0 mostly took place at stations in the upper and lower reaches.
4.2 Temporal trend of air temperature
In order to investigate the evaporation paradox in the HRB, it is necessary to study the changing trend in air temperature. Fig. 4 shows the results of the M-K test performed on the annual mean air temperature as well as the seasonal mean air temperature. The HRB is dominated by an increasing trend in air temperature at the annual and seasonal scales, with all 15 stations experiencing warmer conditions. The seasonal mean air temperature in autumn and winter increases significantly at most stations, while the increase of the seasonal mean air temperature in spring and summer is insignificant. This means that the increase of air temperature in autumn and winter contributes more to the increase of annual mean air temperature.
¿Yongchang ▲ Gangcha (a) Annual
; ¿Yongchang
0 110 220 km
* Gangcha (b) Spring
Guaizihu
Alxa Right Banner .andan
Yongchang
Guaizihu
AAlxa Right Banner mdan
Qiliari^
a Gangcha (d) Autumn
Yongchang
. Guaizihu
Alxa Right A Banner landan
Qilian^
± Gangcha (e) Winter
^Yongchang
Fig. 4 Spatial distributions of mean annual and seasonal air temperature trends in HRB from 1959 to 2012
4.3 Evaporation paradox
As described above, there was a warming trend in the HRB during the period from 1959 to 2012, and annual ET0 exhibited a decreasing trend in the middle and lower reaches of the HRB. Accordingly, it can be concluded that the evaporation paradox did exist in the HRB, except in the upper reaches and at two stations in the lower reaches, where the annual ET0 showed an increasing trend or an insignificant decreasing trend (Fig. 5). In other words, the evaporation paradox mainly existed in the middle-lower reaches of the HRB.
Guaizihu N
Alxa Right p Banner Jiandan
-v ^yuv /// ¡Yongchang N Without evaporation paradox ^
P With evaporation paradox
N Gangcha
Fig. 5 Evaporation paradox in HRB from 1959 to 2012
4.4 Sensitivity of key meteorological factors for ET0
In order to quantify the contribution of key meteorological factors to the spatial and temporal variations of
ET0 and determine the reason why the evaporation paradox exists in the HRB, the sensitivity coefficients of main meteorological variables of ET0, i.e., air temperature (STA), solar radiation (SRS), relative humidity ( aS^ ), and wind speed (Sws), in different regions of the HRB were calculated and are plotted in Fig. 6.
-—Upper —Middle —Lower —Whole 0.27
0.22 0.17 0.12 0.07 0.02
a <u ^ Ph
(a)^RH
d d M ^ <
(b) Sta
O £ Q
0.25 0.20 0.15 0.10 0.05 0
0.8 0.6 If 0.4
£ 5 3
o £ Q
M ft ^ ^ °
3 « ie O <L>
c ^ O ^ Û
(d) ^ws
Fig. 6 Sensitivity coefficients in different regions of HRB
From Fig. 6(a), it can be seen that the sensitivities of relative humidity are all negative in different regions of the HRB, which means that ET0 will decrease when relative humidity increases. SRH reaches its peak in summer around July and attains its minimum value in December and January. Generally, the curves exhibit a single-peak shape, though they retain fluctuation over short temporal periods. SRH in the lower reaches is obviously smaller than in other regions. In other words, relative humidity has a greater negative effect on the variation of ET0 in the lower reaches than in the upper-middle reaches. Similarly, the sensitivity coefficient curves shown in Fig. 6(b) for the air temperature present a single-peak shape and reach their peak in May and June. STA in the lower reaches is higher than in the upper-middle reaches throughout the year. Curves of SRS (Fig. 6(c)) are similar to those of SRH and STA, reaching their maximum and minimum values in summer and winter, respectively. In the middle-lower reaches, the effect of solar radiation on ET0 in early summer (May and June) is the greatest, while in the upper reaches the peak comes around a little later in August. Fig. 6(d) shows that, in the middle-lower reaches, ET0 is more sensitive to wind speed in summer, while SWS in the upper reaches, which is much smaller than that in the middle-lower reaches, remains almost unchanged throughout the year, and does not show a significant peak.
A comparison of the four subgraphs of Fig. 6 shows that for the four meteorological factors considered in this study, relative humidity was the most sensitive factor for ET0 at the daily scale with absolute values of sensitivity coefficients reaching 6.0 in summer, several times higher than that of other meteorological factors. Wind speed was the second greatest sensitive factor to ET0, especially in the middle-lower reaches. Air temperature and solar radiation were the two least sensitive meteorological factors to ET0.
The sensitivity coefficient (SV ) indicates the sensitivity of ET0 to the meteorological factor (Vi), under the condition that changes in all meteorological factors are the same. However, at the 15 selected stations in the HRB, the changing percentage varies greatly for each meteorological factor. Thus, GV was employed in this study to indicate the relative change in ET0 resulting from each meteorological factor. Table 1 lists the annual GV value for each meteorological factor estimated by Eq. (7). The total estimated contribution was obtained by summing up the GV of each factor. From the table we can see that, in the upper reaches, GV values of air
temperature and solar radiation are much larger than those of other two factors, which means that air temperature and solar radiation contribute the most to the variation of ET0, while relative humidity and wind speed hardly make contributions to the variation of ET0 , due to the quite low relative change of humidity and wind speed in the upper reaches. In the middle reaches, the large decrease in solar radiation and wind speed lead to the decrease in ET0 at all stations except Shandan Station, which is consistent with results of the changing trend of ET0 (Fig. 3(a)). Though relative humidity is the most sensitive factor for ET0, its relative change is little during the study period, and contributed least to the variation of ET0 . In the lower reaches, solar radiation and wind speed are the two determining factors for the variation of ET0 because of their large impact on ET0 and significant variations during the study period.
Table 1 Contribution of meteorological fators to variation of ET0
Station Region Air temperature (%) Solar radiation (%) Relative humidity (%) Wind speed /%) Total contribution (%)
Tuole 3.00 7.73 0.07 1.65 12.45
Yeniugou 1.58 17.54 0.79 -0.46 19.54
Qilian Upper reaches 5.81 -1.78 0.21 -0.48 3.76
Yongchang 1.30 5.08 1.50 -3.90 3.98
Gangcha 19.49 -6.02 -0.28 -0.44 12.75
Alxa Right Banner 1.92 -12.06 0.25 -19.21 -29.10
Dingxin 1.34 5.78 -0.54 -7.72 -1.14
Jiuquan 1.13 -0.79 -0.17 -7.05 -6.88
Middle reaches
Gaotai 0.64 -12.83 0.38 -17.10 -28.91
Zhangye 1.03 2.50 0.05 -8.92 -5.34
Shandan 2.40 19.87 -0.55 -2.42 19.30
Ejin Banner 2.43 17.26 -0.55 -21.72 -2.58
Mazhongshan 3.71 7.76 0.04 -0.47 11.04
Guaizihu Lower reaches
3.05 23.17 -0.15 6.10 32.17
Yumen 1.31 -11.72 -0.53 -14.17 -25.11
5 Conclusions
In this study, temporal variation of ET0 was estimated using the FAO56 P-M method and key meteorological factors were analyzed at 15 meteorological stations in the HRB during the period from 1959 to 2012. Conclusions can be summed up as follows:
(1) Both annual and seasonal ET0 for most of the HRB displayed a decreasing trend throughout year, especially in spring. As for air temperature, all 15 stations showed increasing trends, which means that there was a warming trend in the HRB during the period from 1959 to 2012.
(2) From the fact that mean annual ET0 and air temperature exhibited contrasting trends, it can be concluded that the evaporation paradox did exist in the HRB, mainly in the middle-lower reaches.
(3) The results of sensitivity analysis show that relative humidity was the most sensitive factor for ET0 at the daily scale in the HRB, followed by wind speed, air temperature, and solar radiation. In the upper reaches, air temperature and solar radiation contributed most to the temporal variation of ET0 , and in the middle-lower reaches, solar radiation and wind speed were the determining factors for the temporal variation of ET0 .
References
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. 1998. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements FAO Irrigation and Drainage Paper 56. Rome: Food and Agriculture Organization of the United Nations (FAO).
Aziz, O., and Burn, D. H. 2006. Trends and variability in the hydrological regime of the Mackenzie River Basin. Journal of
Hydrology, 319(1-4), 282-294. [doi:10.1016/j.jhydrol.2005.06.039] Beven, K. 1979. A sensitivity analysis of the Penman-Monteith actual evapotranspiration estimates. Journal of Hydrology, 44(3-4), 169-190. [doi: 10.1016/0022-1694(79)90130-6]
Brutsaert, W., and Parlange, M. B. 1998. Hydrologic cycle explains the evaporation paradox. Nature, 396(6706), 30-30. [doi: 10.1038/23845]
Chahine, M. T. 1992. The hydrological cycle and its influence on climate. Nature, 359(6394), 373-380. [doi:10.1038/359373a0] Cong, Z. T., Yang, D. W., and Ni, G. H. 2009. Does evaporation paradox exist in China? Hydrology and Earth System Sciences,
13(3), 357-366. [doi:10.5194/hess-13-357-2009] Duhan, D., Pandey, A., and Pandey R. P. 2013. Analysing trends in reference evapotranspiration and weather variables in the Tons River Basin in Central India. Stochastic Environmental Research and Risk Assessment, 27(6), 1407-1421. [doi :10.1007/s00477-012-0677-7] Estevez, J., Gavilan, P., and Berengena, J. 2009. Sensitivity analysis of a Penman-Monteith type equation to estimate reference
evapotranspiration in southern Spain. Hydrological Processes, 23(23), 3342-3353. [doi:10.1002/hyp.7439] Gao, G., Chen, D., Ren, G., Chen, Y., and Liao, Y. 2006. Spatial and temporal variations and controlling factors of PET in China:
1956-2000. Journal of Geographical Sciences, 16(1), 3-12. [doi:10.1007/s11442-006-0101-7] Gao, G., Xu, C. Y., Chen, D. L., and Singh, V. P. 2012. Spatial and temporal characteristics of actual evapotranspiration over Haihe River basin in China. Stochastic Environmental Research and Risk Assessment, 26(5), 655-669. [doi :10.1007/s00477-011-0525-1] Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W., and Sitch, S. 2004. Terrestrial vegetation and water balance - hydrological evaluation of a dynamic global vegetation model. Journal of Hydrology, 286(1-4), 249-270. [doi: 10.1016/j .j hydrol .2003.09.029] Giles, B. D., and Flocas, A. A. 1984. Air-temperature variations in Greece, 1.: Persistence, trend, and fluctuations. Journal of
Climatology, 4(5), 531-539. [doi:10.1002/joc.3370040509] Gong, L., Halldin, S., and Xu, C. 2011. Large-scale runoff generation - parsimonious parameterisation using high-resolution
topography. Hydrology and Earth System Sciences, 15(8), 2481-2494. [doi:10.5194/hess-15-2481-2011] Hisdal, H., Stahl, K., Tallaksen, L. M., and Demuth, S. 2001. Have streamflow droughts in Europe become more severe or
frequent? International Journal of Climatology, 21(3), 317-325. [doi: 10.1002/joc.619] IPCC. 2007. Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment. New York: Cambridge University Press .
Jackson, J. 2001. Historical overfishing and the recent collapse of coastal ecosystems. Science, 293(5530), 629-638. [doi :10.1126/science. 1059199]
Jayantha, O., Michelle I., Joseph, P., Jenifer, B., and Tibebe, D. 2011. Climate change and its implications for water resources management in south Florida. Stochastic Environmental Research and Risk Assessment, 25(4), 495-516. [doi :10.1007/s00477-010-0418-8] Jayawardena, A. W. 1989. Calibration of some empirical equations for evaporation and evapotranspiration in Hong Kong.
Agricultural and Forest Meteorology, 47(1), 75-81. [doi:10.1016/0168-1923(89)90086-5] Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J. 1994. A simple hydrologically based model of land-surface water and energy fluxes for general-circulation models. Journal of Geophysical Research - Atmospheres, 99(D7), 14415-14428. [doi :10.1029/94JD00483]
Matsoukas, C., Benas, N., Hatzianastassiou, N., Pavlakis, K. G., Kanakidou, M., and Vardavas, I. 2011. Potential evaporation trends over land between 1983-2008 driven by radiative fluxes or vapour-pressure deficit? Atmospheric Chemistry and Physics, 11(15), 7601-7616. [doi:10.5194/acp-11-7601-2011] McCuen, R. H. 1974. A sensitivity and error analysis of procedures used for estimating evaporation. Water Resources Bulletin, 10(3), 486-497.
Rao, A. V., and Wani, S. P. 2011. Evapotranspiration paradox at a semi-arid location in India. Journal of Agro-meteorology, 13(1), 3-8.
Roderick, M. L., and Farquhar, G. D. 2002. The cause of the decreased pan evaporation over the past 50 years. Science, 298(5597),
1410-1411. [doi :10.1126/science.1075390] Roderick, M. L., and Farquhar, G. D. 2004. Changes in Australian pan evaporation from 1970 to 2002. International Journal of
Climatology, 24(9), 1077-1090. [doi:10.1002/joc.1061] Scheffer, M., Carpenter, S., Foley, J. A., Folke, C., and Walker, B. 2001. Catastrophic shifts in ecosystems. Nature, 413(6856),
591-596. [doi:10.1038/35098000] Sjoegersten, S., Atkin, C., Clarke, M. L., Mooney, S. J., Wu, B., and West, H. M. 2013. Responses to climate change and farming policies by rural communities in northern China: A report on field observation and farmers' perception in dryland north Shaanxi and Ningxia. Land Use Policy, 32, 125-133. [doi:10.1016/j.landusepol.2012.09.014] Thomas, A. 2000. Spatial and temporal characteristics of PET trends over China. International Journal of Climatology, 20(4),
381-396. [doi: 10.1002/(SICI) 1097-0088(20000330)20:4<381: :AID-JOC477>3.0 .CO;2-K] Velichko, A. A., Borisova, O. K., and Zelikson, E. M. 2008. Paradoxes of the Last Interglacial climate: Reconstruction of the
northern Eurasia climate based on palaeofloristic data. Boreas, 37(1), 1-19. [doi:10.1111/j.1502-3885.2007.00001.x] Wang, K., Dickinson, R. E., and Liang, S. L., 2012. Global atmospheric evaporative demand over land from 1973 to 2008. Journal
of Climate, 25(23), 8353-8361. [doi: http://dx.doi.org/10.1175/JCLI-D-11-00492.!] Wang, X., Liu H., Zhang, L., and Zhang, R. 2014. Climate change trend and its effects on reference evapotranspiration at Linhe
Station, Hetao Irrigation District. Water Science and Engineering, 7(3), 250-266. [doi:10.3882/j.issn.1674-2370.2014.03.002] Yang, Y. H., Zhang, Z. Y., and Xiang, X. Y. 2009. Spatial variation of reference crop evapotranspiration on Tibetan Plateau.
Water Science and Engineering, 2(1), 112-120. [doi:10.3882/j.issn.1674-2370.2009.01.011] Yesihrmak E. 2013. Temporal changes of warm-season pan evaporation in a semi-arid basin in Western Turkey. Stochastic
Environmental Research and Risk Assessment, 27(2), 311-321. [doi:10.1007/s00477-012-0605-x] Zhan, C. S., Zhao, J., Wang, H. X., and Yin, J. 2011. Quantitative estimation of land surface evapotranspiration in Taiwan based
on MODIS data. Water Science and Engineering, 4(3), 237-245. [doi: 10.3882/j.issn.1674-2370.2011.03.001] Zhao, L. 2013. Evapotranspiration estimation methods in hydrological models. Journal of Geographical Sciences, 23(2), 359-369.
[doi:10.1007/s11442-013-1015-9] (in Chinese) Zuo, D. P., Xu, Z. X., Cheng, L., and Zhao, F. F. 2010. Assessment on radiation-based methods to estimate PET. Arid Land
Geography, 34(4), 565-574. (in Chinese) Zuo, D. P., Xu, Z. X., Yang, H., and Liu, X. C. 2012. Spatiotemporal variations and abrupt changes of potential evapotranspiration and its sensitivity to key meteorological variables in the Wei River basin, China. Hydrological Processes, 26(8), 1149-1160.
