Spatio-Temporal Variation of Reference Evapotranspiration and Its Climatic Drivers over the Tibetan Plateau during 1970–2018

: Reference evapotranspiration ( ET 0 ) is a key component of hydrologic cycle and it is important for water resources management. Analysis of ET 0 changes is particularly critical for understanding the impacts of climatic change on hydrology in ecologically fragile regions. In this study, using the Penman–Monteith method and the Mann–Kendall test, the variation characteristics of ET 0 on the Tibetan Plateau (TP) from 1970 to 2018 was analyzed, and the dominant climatic factors controlling the change of ET 0 was also explored. The result shows that in TP region: (1) there was an abrupt change in the trend of ET 0 around 1997, and the ET 0 declined at a rate of − 25.9 mm/decade during 1970–1996 but increased by 31.1 mm/decade during 1997–2018; (2) ET 0 is most sensitive to solar radiation, then relative humidity, wind speed and mean temperature; (3) the decrease of ET 0 before 1997 was mainly due to the decline of wind speed and the increase of relative humidity, while the increase of ET 0 after 1997 was mainly due to the decrease of relative humidity. The results of this study can provide data reference for the research of water balance on the TP. visualization, S.H. R.G.; writing—review and S.H., T.Z., P.B.


Introduction
As a link between water balance and surface energy balance [1], evapotranspiration is an important index reflecting climate variation and water circulation [2,3]. Reference evapotranspiration (ET 0 ) represents the maximum amount of evapotranspiration from a hypothetical reference surface under specific climatic conditions, given an adequate supply of water [4]. ET 0 plays a critical role in irrigation management [5] and is also an important parameter for agriculture water demand estimation and water balance analysis [6]. The Penman-Monteith (P-M) method is a fairly reliable method to estimate ET 0 , which considers radiative and aerodynamic terms [7]. It focuses on the meteorological factors controlling evaporation process and is widely used in numerous research [8,9]. Understanding the spatial-temporal changes in ET 0 and the main causes can provide considerable understanding into the impact of climatic variation on water resources and hydrological cycles [10].
Extensive research has explored the spatial variation characteristics of ET 0 and the impact of climate factors on ET 0 in the late years [11]. It reported that pan-evaporation and ET 0 both have shown a steady downward trend in many regions of the world over the past several decades, including China, India, New Zealand and Italy [12][13][14][15], which is called 'evaporation paradox' [16]. The reasons for decreasing ET 0 have been widely discussed, global dimming and wind stilling were usually employed to explain the changes. For example, Han et al. [17] analyzed the entire trend in ET 0 in Jing-Jin-Ji region, finding that the decrease of wind speed and sunshine hours offset the influence of increased air temperature and led to a reduction in ET 0 before 1992. Xu et al. [18] found downward The TP is the largest geomorphologic unit on the Eurasian continent, ranging from 25 • -40 • N and 75 • -105 • E, covering most of the Tibet Autonomous Region and Qinghai Province in western China, with a total area of more than 2.5 million km 2 ( Figure 1). The TP is the highest and most extensive plateau in the world, known as the 'roof of the world', with about 56% of the region over 4000 m above sea level [28]. Due to its high altitude and large volume, it forms a unique 'plateau climate'. The annual average temperature of the TP is reduced from 20 • C in the southeast to below −6 • C in the northwest. The annual precipitation is also reduced from 2000 mm to below 50 mm. The main vegetation cover in this area is grassland and forest. The grassland is mainly divided into alpine meadow, alpine grassland and alpine desert [29].
in this area is grassland and forest. The grassland is mainly divided into alpine meadow, alpine grassland and alpine desert [29].

Data Sources
Daily meteorological records from 73 meteorological stations in and around the TP during the period 1970-2018 were used in this study and the altitude of these stations ranges from 1231.2 to 4645.1 m. The meteorological data includes daily average air temperature, maximum air temperature, minimum air temperature, relative humidity, wind speed, air pressure, sunshine duration and the 20 cm pan evaporation. These datasets were provided by the China Meteorological Data Service Center (CMDC) (http://data.cma.cn/, accessed on 6 May 2019) and were processed with quality control. The 90 m DEM data set of the TP is provided by Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn, accessed on 12 August 2019).

Calculation of ET0
Penman-Monteith (P-M) formula was used to estimate daily ET0 in this article which was proposed by the FAO (Food and Agriculture Organization). The P-M method estimates ET0 from hypothetical reference grass with an assumed height of 0.12 m, a fixed surface resistance of 70 m·s −1 , and an albedo of 0.23 [30]. The formula is: where ET0 is the daily reference evapotranspiration (mm/d), Δ is the slope of the vapor pressure curve and γ is the psychometric constant (kPa/°C), Rn is the net radiation and G is the soil heat flux density (MJ·m −2 ·day −1 ), Tmean is the mean air temperature (°C), U2 is the wind speed at 2 m height (m/s), es is the saturation vapor pressure and ea is the actual vapor pressure (kPa).
Reference evapotranspiration and pan evaporation both represent evaporative capacity of a basin under certain climatic conditions and their good relationship had been manifested by previous research [31]. The pan coefficient and correlation between them

Data Sources
Daily meteorological records from 73 meteorological stations in and around the TP during the period 1970-2018 were used in this study and the altitude of these stations ranges from 1231.2 to 4645.1 m. The meteorological data includes daily average air temperature, maximum air temperature, minimum air temperature, relative humidity, wind speed, air pressure, sunshine duration and the 20 cm pan evaporation. These datasets were provided by the China Meteorological Data Service Center (CMDC) (http://data.cma.cn/, accessed on 6 May 2019) and were processed with quality control. The 90 m DEM data set of the TP is provided by Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn, accessed on 12 August 2019).

Calculation of ET 0
Penman-Monteith (P-M) formula was used to estimate daily ET 0 in this article which was proposed by the FAO (Food and Agriculture Organization). The P-M method estimates ET 0 from hypothetical reference grass with an assumed height of 0.12 m, a fixed surface resistance of 70 m·s −1 , and an albedo of 0.23 [30]. The formula is: where ET 0 is the daily reference evapotranspiration (mm/d), ∆ is the slope of the vapor pressure curve and γ is the psychometric constant (kPa/ • C), R n is the net radiation and G is the soil heat flux density (MJ·m −2 ·day −1 ), T mean is the mean air temperature ( • C), U 2 is the wind speed at 2 m height (m/s), e s is the saturation vapor pressure and e a is the actual vapor pressure (kPa). Reference evapotranspiration and pan evaporation both represent evaporative capacity of a basin under certain climatic conditions and their good relationship had been manifested by previous research [31]. The pan coefficient and correlation between them were calculated to verify the applicability of the P-M model ( Figure 2). The correlation coefficient between pan evaporation and reference evapotranspiration was 0.71 with a pan coefficient of 1.75, which indicates that the P-M model is suitable for the analysis of reference evapotranspiration on the TP.
were calculated to verify the applicability of the P-M model ( Figure 2). The correlation coefficient between pan evaporation and reference evapotranspiration was 0.71 with a pan coefficient of 1.75, which indicates that the P-M model is suitable for the analysis of reference evapotranspiration on the TP.

Cramer Test for Abrupt Change Point
The Cramer test is used to detect the mutation of ET0 series. Cramer's test has displayed fine performance in checking the stabilization of records by comparing the whole average value of the record and the means of certain parts of the record [32]. The Cramer's test statistic tk is calculated as follows: where and S are the mean and standard deviation, for the entire period of N years, and is the mean of the sub period of the n years to be compared with .

Mann-Kendall Test for Trend Analysis
The Mann-Kendall (MK) test is used to analyze ET0 and climate factors trends. This method does not need to consider the distribution form of the detection sequence, and can exclude the influence of individual extreme values on the sequence test, which is suitable for testing the trend change of time series of meteorological, hydrological and other types of variables [32,33]. It is highly recommended for general use by the World Meteorological Organization [34]. Its principle is as follows: the original assumption is that H0 is n (x1, x2, ..., xn) independent time series samples with the same distribution; alternative hypothesis H1 is a bilateral test, the statistical value S is calculated as follows [17]:

Cramer Test for Abrupt Change Point
The Cramer test is used to detect the mutation of ET 0 series. Cramer's test has displayed fine performance in checking the stabilization of records by comparing the whole average value of the record and the means of certain parts of the record [32]. The Cramer's test statistic t k is calculated as follows: where x and S are the mean and standard deviation, for the entire period of N years, and x k is the mean of the sub period of the n years to be compared with x.

Mann-Kendall Test for Trend Analysis
The Mann-Kendall (MK) test is used to analyze ET 0 and climate factors trends. This method does not need to consider the distribution form of the detection sequence, and can exclude the influence of individual extreme values on the sequence test, which is suitable for testing the trend change of time series of meteorological, hydrological and other types of variables [32,33]. It is highly recommended for general use by the World Meteorological Organization [34]. Its principle is as follows: the original assumption is that H 0 is n (x 1 , x 2 , ..., x n ) independent time series samples with the same distribution; alternative hypothesis H 1 is a bilateral test, the statistical value S is calculated as follows [17]: where: any i = j, i ≤ n, j ≤ n, when S approximately obeys normal distribution, and the trend test statistical value Z is obtained by means of S standardization processing: If |Z| > Z 1−α/2 , the null hypothesis is rejected with a given confidence level α; namely, there is a significant trend in the time series data. When α is equal to 0.05, Z 1−α is equal to 1.96, respectively. The Z value is used to assess the statistics trend; a positive Z value expresses an upward trend, a negative Z value expresses a downward trend.
The annual change rate of data is evaluated by the least square method. The principle is to look for the best function matching of data by minimizing the square sum of errors [35].

Spatial Interpolation Method
Spatial interpolation is very important for obtaining the spatial distribution characteristics of climate variables when meteorological stations are few and widely separated. In this paper, the gradient plus inverse distance squared (GIDS) method is used for interpolating climate variables and ET 0 [36]. It is an inverse distance weighting method that takes into account the changes of meteorological factors with latitude, longitude and altitude. Previous studies have shown that this method has good applicability in areas with large terrain undulations and uneven site distribution [37]. The interpolating climate variable is calculated as follows [36,37]: where Z is the estimated climatic variable, Z i is the value at climate station i, d i is the distance from the site to climate station i and N is the number of climate stations used for the interpolation. X, Y and E are the longitude, latitude, and altitude of the point to be estimated, respectively, X i , Y i and E i are the longitude, latitude, and altitude of climate station i, respectively, and C x , C y and C e are regression coefficients for X, Y and E, respectively. The daily ET 0 of 73 meteorological stations on the TP in 1970-2018 is calculated and transformed into ET 0 at annual scale. The annual average ET 0 on the TP is obtained by interpolating the annual ET 0 of 73 stations to the centroid of the area using the GIDS method.

Sensitivity Analysis and Contribution Rate Calculation
The sensitivity coefficient method is used to analyze the sensitivity of climate factors to ET 0 quantitatively [38]. The commonly used sensitivity analysis method is to assume that other variables are fixed, and to evaluate the impact of a variable change on the model output. The sensitivity coefficient of the ET 0 to a climate variable x is defined by [39,40]: A first-order Taylor series approximation was applied to calculate S x [41,42]: where S x is the sensitivity coefficient of the climate variable x, ∆x is the relative change of climate factor x, and ∆ET 0 is the relative change in ET 0 induced by ∆x.
In this study, ET 0 was recalculated by making a ±10% change to each meteorological factors (solar radiation (RS), relative humidity (RH), air temperature (T mean ) and wind speed (U 2 )) while keeping the other factors constant; sensitivity coefficient S x was calculated from Equation (9). A positive value of S x indicates that ET 0 increases as x increases, and vice versa. The greater the absolute value of the sensitivity coefficient, the greater the effect of the variable on ET 0 .
The sensitivity coefficient reflects the influence of meteorological variables on ET 0 , but it is not equal to the ET 0 change caused by a certain variable. Here, the contribution rate is introduced to analyze the change of ET 0 caused by a single meteorological factor. It is the product of the sensitivity coefficient of a climate factor and its multi-year change rate [11]. A positive contribution means that the variety of a meteorological factor has a positive effect on the variety of ET 0 and vice versa. The larger the absolute value is, the greater the impact on ET 0 will be. The contribution rate was calculated using the following formulas [43,44]: where CR x is the contribution rate climate variable x to ET 0 ; RC x is the multi-year change rate of x; n is the number of years; a x represents the trend rate of meteorological factor x during the corresponding study period; x is the multi-year absolute average value of x during the evaluation period. In this paper, we calculated the sensitivity of ET 0 to RS, RH, T mean and U 2 , and calculated the contribution rate of these four climatic factors to ET 0 . The evaluation period includes three periods: during the period from 1979 to 2018 and two periods before and after the mutation point.
where CR RS , CR RH , CR Tmean , CR U2 are the individual contributions to the long-term trend in ET 0 as a result of the change in RS, RH, T mean and U 2 , respectively. C_(ET 0 ) is the sum of the contribution of variation in RS, RH, T mean and U 2 to the change in ET 0 . ε is the error between C(ET 0 ) and ET 0 trend calculated by P-M model (LR_(ET 0 )). Figure 3a shows the tendency of annual ET 0 on the TP. The annual average ET 0 was 1037.17 mm, ranging from 983.53 to 1091.94 mm from 1979 to 2018. The annual ET 0 experienced a non-significant (α = 0.05) decrease by 2.1 mm/decade (Table 1). According to the Cramer's test, there are two abrupt change points in ET 0 series of TP, which are 1997 and 2000, respectively (Figure 3b). We found the decline rate of ET 0 was −25.9 mm/ decade before 1997 and the change is significant, but the annual ET 0 increased significantly (α = 0.05) at a rate of 31.1 mm/decade after 1997. Furthermore, the Cramer's test was also performed on all 73 stations to verify the representativeness of 1997, and the results showed that 41 of them could detect the mutation point in 1997. Therefore, it can represent the main mutation characteristics of ET 0 in the region. Since the variation of ET 0 is the result of the comprehensive effects of climate variables, it is necessary to analyze each climate variable during the two periods separately. Using the inflection point of ET 0 as the basis for dividing the period, all the time series of the 73 stations were divided in these two periods for the subsequent analysis.

Spatio-Temporal Variation Characteristics of ET 0
for dividing the period, all the time series of the 73 stations were divided in these two periods for the subsequent analysis.      Table 1. Z value of reference evapotranspiration (ET 0 ), solar radiation (RS), relative humidity (RH), air temperature (T mean ) and wind speed (U 2 ) in different periods.        Figure 5 and Table 1 shows the trends of climate variables on the TP from 1970 to 2018. The T mean increased significantly (α = 0.05) before and after 1997 at the rate of 0.125 and 0.373 • C/decade, respectively. The upward trend was steeper during the latter period than during the former period. RS and RH increased by 0.019 (MJ·m −2 ·day −1 )/decade and 1.68%/decade from 1970 to 1996 and decreased significantly at the rate of −0.163 (MJ·m −2 ·day −1 )/decade and −2.816%/decade after 1997, respectively. The variation of U 2 was opposite to the variation of RS and RH. U 2 decreased dramatically by 0.294 m/s/decade during the former period but increased significantly by 0.158 m/s/decade during the latter period.

Spatio-Temporal Variation Characteristics of Climatic Factors
Appl. Sci. 2021, 11, x FOR PEER REVIEW 8 of 14 Figure 5 and Table 1 shows the trends of climate variables on the TP from 1970 to 2018. The Tmean increased significantly (α = 0.05) before and after 1997 at the rate of 0.125 and 0.373 °C/decade, respectively. The upward trend was steeper during the latter period than during the former period. RS and RH increased by 0.019 (MJ·m −2 ·day −1 )/decade and 1.68%/decade from 1970 to 1996 and decreased significantly at the rate of −0.163 (MJ·m −2 ·day −1 )/decade and −2.816%/decade after 1997, respectively. The variation of U2 was opposite to the variation of RS and RH. U2 decreased dramatically by 0.294 m/s/decade during the former period but increased significantly by 0.158 m/s/decade during the latter period.     Figure 7 shows the spatial distribution of susceptibility coefficients on the TP. The sensitivity coefficient of RS (SRS) ranged from 0.303 to 0.587 and decreased from southeast to northwest on the TP. The sensitivity coefficient of Tmean (STmean) ranged from 0.025 to 0.172, which was similar to SRS. Different from SRS and STmean, the sensitivity coefficient of U2 (SU2) increased progressively from southeast to northwest and varied from 0.080 to 0.259. The sensitivity coefficient of RH (SRH) ranged from −0.444 to −0.019 and the smallest absolute value of the SRH found in the southeast corner of the TP and increases to the north.  Figure 7 shows the spatial distribution of susceptibility coefficients on the TP. The sensitivity coefficient of RS (S RS ) ranged from 0.303 to 0.587 and decreased from southeast to northwest on the TP. The sensitivity coefficient of T mean (S Tmean ) ranged from 0.025 to 0.172, which was similar to S RS . Different from S RS and S Tmean , the sensitivity coefficient of U 2 (S U2 ) increased progressively from southeast to northwest and varied from 0.080 to 0.259. The sensitivity coefficient of RH (S RH ) ranged from −0.444 to −0.019 and the smallest absolute value of the S RH found in the southeast corner of the TP and increases to the north. According to above discussion, the sensitivity of these four factors to the ET 0 on the TP was in the order: S RS > S RH > S U2 > S Tmean .

Spatial Distribution of the Sensitivity Coefficients
Appl. Sci. 2021, 11, x FOR PEER REVIEW 10 of 14 According to above discussion, the sensitivity of these four factors to the ET0 on the TP was in the order: SRS > SRH > SU2 > STmean.

Dominant Climate Factors of ET0 Change
In order to identify the dominant climate factors affecting ET0, the contribution rate of each climatic factor was calculated using Equation (11). Comparing the ET0 trends caused by the four climate factors (C_ET0) and the ET0 trends calculated by P-M model (LR_ET0), the relative errors were 12.79%, 4.70% and 7.93% during 1970-1996, 1997-2018 and 1970-2018, respectively (Table 2). Therefore, equation (11) was a reasonable method to estimate the contribution rates of the four climatic factors to ET0.  Table 2 shows the contribution rate of the four climate factors to ET0 in different periods. During 1970-1996, U2 was the dominant factor influencing ET0, followed by RH, Tmean and RS. The decrease of U2 and increase of RH caused a −4.41% and −2.43% decrease to ET0, respectively. However, the increases in Tmean and RS led to a 0.80% and 0.10% increase to ET0, respectively. The comprehensive effect of climate factors finally led to a −6.7% decline to ET0. During 1997-2018, the decrease of RH was the main reason causing the increase in ET0 by 3.33%. In addition, the rise of U2 and Tmean also aggravated the increase of ET0 to a certain degree, resulting in the increase of ET0 of 2.34% and 1.58%, respectively. The decrease of RS led to a variation in ET0 of −0.96%. The effects of the climatic condition eventually caused the increase of ET0 by 6.29%. In a word, the decrease of ET0

Dominant Climate Factors of ET 0 Change
In order to identify the dominant climate factors affecting ET 0 , the contribution rate of each climatic factor was calculated using Equation (11). Comparing the ET 0 trends caused by the four climate factors (C_ET 0 ) and the ET 0 trends calculated by P-M model (LR_ET 0 ), the relative errors were 12.79%, 4.70% and 7.93% during 1970-1996, 1997-2018 and 1970-2018, respectively (Table 2). Therefore, equation (11) was a reasonable method to estimate the contribution rates of the four climatic factors to ET 0 .  Table 2 shows the contribution rate of the four climate factors to ET 0 in different periods. During 1970-1996, U 2 was the dominant factor influencing ET 0 , followed by RH, T mean and RS. The decrease of U 2 and increase of RH caused a −4.41% and −2.43% decrease to ET 0 , respectively. However, the increases in T mean and RS led to a 0.80% and 0.10% increase to ET 0 , respectively. The comprehensive effect of climate factors finally led to a −6.7% decline to ET 0 . During 1997-2018, the decrease of RH was the main reason causing the increase in ET 0 by 3.33%. In addition, the rise of U 2 and T mean also aggravated the increase of ET 0 to a certain degree, resulting in the increase of ET 0 of 2.34% and 1.58%, respectively. The decrease of RS led to a variation in ET 0 of −0.96%. The effects of the climatic condition eventually caused the increase of ET 0 by 6.29%. In a word, the decrease of ET 0 during 1970 and 1996 was mainly due to the decrease of U 2 , while the increase of ET 0 during 1997 and 2018 was mainly due to the decrease of RH. Figure 8 gives the spatial distribution of contribution rates of four climatic factors to ET 0 during 1970-1996and 1997. From 1970to 1996, the contribution rate of RS (CR RS ) was positive in the southeast and western TP, and the negative CR RS was found in eastern and central parts of TP. From 1997 to 2018, CR RS was mainly positive and the negative contribution distributed in the northern and central sporadic areas. The contribution rate of RH (CR RH ) was negative in most parts of TP and the positive CR RH was only distributed partly in northwest and east before 1997; while, most regions on the TP exhibited a positive CR RH , except some areas of eastern TP after 1997. The distribution of contribution rate of U 2 (CR U2 ) was similar to that of RH. The contribution rate of T mean (CRT mean ) was positive in most parts of TP during both periods. during 1970 and 1996 was mainly due to the decrease of U2, while the increase of ET0 during 1997 and 2018 was mainly due to the decrease of RH. Figure 8 gives the spatial distribution of contribution rates of four climatic factors to ET0 during 1970-1996and 1997. From 1970to 1996, the contribution rate of RS (CRRS) was positive in the southeast and western TP, and the negative CRRS was found in eastern and central parts of TP. From 1997 to 2018, CRRS was mainly positive and the negative contribution distributed in the northern and central sporadic areas. The contribution rate of RH (CRRH) was negative in most parts of TP and the positive CRRH was only distributed partly in northwest and east before 1997; while, most regions on the TP exhibited a positive CRRH, except some areas of eastern TP after 1997. The distribution of contribution rate of U2 (CRU2) was similar to that of RH. The contribution rate of Tmean (CRTmean) was positive in most parts of TP during both periods.

Discussion
According to the analysis of this study, there are significant differences in climate factors between 1970-1996 and 1997-2018 (Figures 5 and 6; Table 1). The spatiotemporal dynamic of the four climate factors in this study is basically consistent with a lot of research on climatic change. Before 1997, the wind speed decreased significantly, but then increased significantly. The weakening of the Asian monsoon system may explicate the decrease of U 2 on the TP, while the strengthening of zonal wind may have a significant contribution to the increase of U 2 after 1997 [44]. An insignificantly decreasing trend of RS was found during 1970-1996, but a significantly decreasing trend was found during 1997-2018. The solar dimming over the TP is mainly caused by the increase in water vapor and deep cloud cover [3]. RH also decreased significantly after 1997. Most areas of TP belong to the arid region and the amount of water is limited. The rise of air temperature makes the saturated vapor pressure rise, which may be the reason for the decrease in RH [45]. The climatic factors and ET 0 have complex mutual feed mechanism and give rise to an abrupt change in ET 0 in the meantime.
ET 0 decreased significantly before 1997 while increased significantly after 1997 on the TP in this study (Figures 3 and 4; Table 1). The change of ET 0 is influenced by climate factors, and the weight of each climate factor is different. The dominant factor causing the decrease of ET 0 on the TP was U 2 before 1997( Figure 8; Table 2), and this is similar to many studies on the TP. For example, Chen et al. [46] found that wind speed is the dominant meteorological factor affecting the ET 0 of the TP. Zhang et al. [26] considered that wind speed caused changes in the ET 0 of the TP, especially in the northern part of the TP. Yin et al. [47] have shown that the decrease of wind speed can explain the variation of the potential annual evapotranspiration in the temperate zone of the north of China and the TP. In this study, the dominant factor that caused the increase of ET 0 on the TP after 1997 is RH, which is consistent with Wang's [27] study that relative humidity may be an important factor affecting ET 0 . However, this is not consistent with the view of Xie Hong [48] that relative humidity is considered to have little effect. This is due to the difference in the research periods and methods.
Some uncertainties remain for the estimation of ET 0 in this study. Some of the parameters in the P-M equation are empirical and local. Therefore, regional optimization of parameters is an important means to improve the accuracy of ET 0 calculation. Additionally, the number of meteorological stations used in this study is limited and unevenly distributed. The TP has large area and complex natural environment. It is difficult to fully describe the spatiotemporal dynamic of the climatic factors and ET 0 . More data from different sources and interpolation methods should be introduced in the future.

Conclusions
In this study, we analyzed the temporal-spatial trends, transition characteristics of ET 0 and identified the main reasons for the change of ET 0 in different stages over the TP based on data from 73 weather stations during 1970-2018.The main conclusions are:

1.
There is an abrupt point in the TP's annual ET 0 series around 1997. The ET 0 decreased remarkably at a rate of −25.9 mm/decade before 1997, while increasing significantly at a speed of 31.1 mm/decade after 1997; 2.
Second item: before 1997, T mean and RH increased significantly while U 2 decreased significantly and RS decreased insignificantly. After 1997, T mean and U 2 increased insignificantly, RH and RS decreased significantly; 3.
The sensitivity analysis of each climate factor to ET 0 indicated the ET 0 on the TP is most sensitive to RS, followed by RH, U 2 and T mean ; 4.
Third item: from 1970 to 1996, U 2 was the most important meteorological factor contributing to the decline of ET 0 , followed by RH, T mean , and RS. During 1997-2018, the decreasing of RH was the dominant factor causing an increase in ET 0 . The increase of U 2 and T mean also intensified the increase of ET 0 to a certain extent.