1. Introduction
The Tibetan Plateau (TP) is one of the largest reservoirs of ice and is referred to as the third pole of the world [
1]. Due to its vast area and exceptional height (>4000 m above mean sea level), it plays a major role in shaping the regional climate especially for the east and south Asian monsoon [
2,
3]. TP is a climate-sensitive region with different climate change behavior from the surrounding regions [
4,
5]. According to some studies, the warming rate of TP is 0.3 °C per decade, which is more than any other part of the world. This affects permafrost and snow melting in TP. Eventually, freshwater reservoirs deplete at an alarming rate. Moreover, any change in the uplifted land affects the Asian monsoon upon which millions of people and thousands of hectares of crops depend [
1,
2,
4,
6,
7,
8].
Incoming solar radiation flux is high over TP [
9], which has an important impact on the energy budget. The surface energy budget (SEB) derives the local climate and shapes the heat and water cycle [
10]. SEB comprises mainly three parameters; (i) net radiation (RN), (ii) latent heat flux (LE), and (iii) sensible heat flux (SH). RN, as suggested by its name, is the radiative energy comprised of incoming and outgoing solar and thermal radiations. Land cover changes such as deforestation or snow melting influence RN because of the changing behavior of surface reflectivity in the form of albedo and land surface temperature (LST) [
11,
12,
13,
14,
15]. The aerosol optical depth and atmospheric greenhouse gasses also influenced the RN through the atmospheric counter radiation [
16,
17]. LE is the non-radiative energy flux, and transfer energy in the form of change of state of matter (vaporization) [
18]. Since vegetation cover involves largely in evapotranspiration, the vegetation index such as Normalized Difference Vegetation Index (NDVI) is a useful tool to analyze LE and its variability [
19,
20]. SH contributes to the non-radiative part of the SEB along with LE and relates to the heat cycle [
2,
21]. Transfer of heat between the surface and aloft air depends upon their temperature difference. SH is an important indicator but may not be the only triggering force for atmospheric temperature as wind speed, wind direction, and relative humidity also have a strong impact on air temperature.
There are plenty of great achievements that have been made in understanding climate change over TP. Many dedicated research programs have been launched to study TP climate such as the first, second, and third Tibetan Plateau Meteorological Science Experiment (TIPEX); the Global Energy and Water Cycle Experiment (GEWEX) of the Asian Monsoon Experiment in Tibet (GAME/Tibet); and the Coordinated and Enhanced Observation Period (CEOP) of the Asian-Australian Monsoon Project in Tibet (CAMP/Tibet). These projects provide quality of ground-based measurements [
22,
23,
24,
25,
26]. Many recent studies observed atmospheric and surface warming in TP during the past few decades. Many researchers used ground station data and reanalysis data while few studies used satellite data to observe surface temperature [
8,
27,
28,
29]. Duan et al. [
5] discussed heterogeneity of TP warming rates, pointing out that the northern TP had a higher warming rate than the southern TP, suggesting the higher precipitation of the southern TP as a potential cause of this heterogenetic behavior. Duan et al. [
4] observed the weakening of the annual temperature cycle and associated this weakening with anthropogenic activities since the late nineteenth century. The snow melting and precipitation have a strong impact on regional climate eventually affecting the local population and crop production [
7,
30]. Few recent studies analyzed different trend analysis methods such as Mann–Kendall trend, linear regression and innovative trend analysis while others used different data sets to analyze annual and seasonal trends of downward solar radiation over TP [
31,
32].
Shen et al. [
20] observed evaporative cooling using multiple data sources including satellite remote sensing and meteorological station data and established a strong link between NDVI and evaporative cooling in TP. SH trends especially in summer seasons were widely studied using reanalysis data sets and ground-based measurements. A decreasing trend for SH over TP was observed from many recent studies [
2,
33,
34]. Many interesting recent studies observed SH and LE over TP using reanalysis data and ground measurements [
3,
35]. Xie et al. [
6] observed the different trends for SH and LE from three different reanalysis data sets including ERA-Interim, JRA-55, and MERRA. Xin et al. [
25] studied SEB ratio over TP from 10 ground-based measurements for July–October 2014 and analyzed the impact of atmospheric stability, friction velocity, turbulent temperature scale, and wind direction on SEB ratio.
Although TP has been widely studied for the SEB, there is still a gap for the detailed study of the trends of all major SEB parameters over a broader spatial and temporal coverage. Many factors such as land cover change, anthropogenic activities, altitude, latitudinal association, and local climatic conditions influence the spatio-temporal pattern of SEB [
36,
37,
38]. This multidimensional phenomenon demands vast spatial and temporal coverage. Satellite remote sensing is an important tool to study long term SEB trends [
39]. An alternative appropriate choice is the global climatic reanalysis data sets which are available for a longer historical duration on the global scale. In the current study, the trends of SEB over TP from satellite remote sensing data are analyzed and compared with reanalysis data for the period of 2001 to 2019. Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and Earth’s Radiant Energy System (CERES) are used as satellite data sources, while ERA5 product released by European Centre for Medium-range Weather Forecasts (ECMWF) is used as the reanalysis data source. The main objectives of this study are to study (i) how the SEB varied in TP over the past two decades and (ii) the difference in SEB trends between satellite observations and reanalysis data. This approach verifies not only our obtained results but also validates the methods from both data sets.
2. Study Area
TP is located in East Asia, with almost the whole region belongs to China, yet some parts of TP are included in Pakistan, Nepal, India, and Tajikistan [
40]. Along with many populated cities like Lhasa, Xining, Qamdo, and Nagchu, TP is the home of millions of people. With an average elevation of more than 4000 m above mean sea level and a total area of 2.3 × 10
6 km
2, it is the highest and largest plateau in the world [
41]. TP is surrounded by many mountainous ranges, amongst, Himalayas is the most famous and important range. TP is the origin of Asia’s five major rivers: Indus, Ganges, Brahmaputra, Yagtzee, and Yellow river. The life and food security of more than 1.4 billion people depend upon these rivers [
7]. Precipitation over TP is small in amount and highly heterogeneous which decreases from southeast (700 mm) to northwest (50 mm) with a mean annual precipitation of approximately 37 to 296 mm [
9,
40,
42]. The major land cover of TP is grasslands which cover approximately two-thirds of the total TP followed by deserted barren lands which cover approximately 14% of TP [
40].
Being an enormous uplifted land parcel, TP has a huge impact on Asian monsoon and local climatic conditions. The area is highly affected by climate change and high solar radiation activity [
4,
9]. Thus, the SEB analysis is an important study to understand its regional climatic phenomena.
Figure 1 shows the elevation, land cover, and administrative map of TP.
4. Results
4.1. Validation of SEB Parameters
Before the analysis of SEB, first, the accuracy of data as well as method shall be verified. For this purpose, FluxNet tower observations were used.
Figure 2 describes the relationship between satellite, ERA5, and in situ ground observations. LR slope, Pearson’s correlation coefficient (r-value), MBE, and MAE were used to check the accuracy of satellite and ERA5 data.
Table 3 provides a summary of statistical analyses for the validation of SEB parameters. It is noteworthy that the significance of the correlation values (r-value and LR slope) was obtained at a 95% confidence level. Any value which unsatisfied with this condition is mentioned as insignificant.
RN observations from satellite and ERA5 data sets are 99% significant with r-values of 0.87 and 0.88, respectively. All four statistical indicators validate the accuracy of the RN from both data sets. LE values are 99% significant and show very less MBE −0.03 and 5.55 Wm−2 and MAE 18.98 and 11.59 Wm−2 for satellite and ERA5 data, respectively. The LR slope value of satellite LE is the only indicator that shows a weak relation with FluxNet observed LE. However, based on the other three statistical parameters, the accuracy of satellite LE is established. For SH, ERA5 observations are validated from 95% significance with 0.81 r-value and 13.19 and 18.92 Wm−2 MBE and MAE, respectively. Satellite observed SH is insignificant (less than 95% significance level). Although the slope value is very high (1.49), yet the low r-value 0.63, and very high MAE 62.85 Wm−2, make this parameter less accurate.
4.2. Spatio-Temporal Analysis
For SEB spatio-temporal trends over TP, the M-K trend test was performed on mean annual raster images of R
N, LE and SH obtained from satellite and ERA5 data. The results of M-K tau (τ) values are shown in
Figure 3. Positive values (in green and blue) represent increasing trends while negative values (in red and yellow) represent a decreasing trend. R
N shows a positive trend in east and southeast TP in both satellite and ERA5 data. Major land covers in these regions are forest and shrub lands as shown in
Figure 1. Few pixels in southeast TP show more increasing trends (trend values in between 0.5 to 1) in ERA5 data while such increasing trend is not observable in satellite data. The central regions of TP exhibit a decreasing trend in both data sets. For spatio-temporal trends in the east, southeast and central TP, satellite and ERA5 data are approximately in agreement, but the upper region, the northern part of TP, shows an opposite trend from both data sets. In ERA5 data, these regions show increasing R
N trend, with positive M-K τ values, but satellite R
N exhibits a decreasing trend (<0 trend values). The major land cover in northern TP is barren land. An overall net positive spatio-temporal trend is dominant in the entire TP.
LE shows an increasing trend in both data sets, especially satellite LE exhibits a highly increasing trend (M-K τ values approaching 1) in northeast TP. From satellite LE the only decreasing trend is observed in the central region of TP One limitation of the MODIS LE product is that the product does not produce any values for barren lands, thus the northern part of TP cannot be observed from satellite LE data, while ERA5 data covers the whole region. In ERA5 LE, few regions from the northeast and southwest exhibit decreasing trends. The main contradiction is observed in the northeast where satellite LE shows a prominent increasing trend (0.5 to 1 trend value) while ERA5 LE shows a decreasing trend (−0.5 to 0 trend values). Overall a significant increasing trend is observed for LE from both data sets.
The limitation of LE data from MODIS product prolongs to SH as SEB method (Equation (3)) was used to calculate SH. SH shows the only visibly negative trend amongst all SEB parameters. From satellite SH a minor increasing trend in east and central regions of TP is observed, while from ERA5, east, northwest and few central regions of TP show increasing trends. In satellite SH some regions from northeast and southwest show a significant decreasing trend (−1 to −0.5 trend values). In ERA5 SH, such low trend values are not observed.
4.3. Spatial Analysis
For spatial analysis; 19 years mean spatial images were used for R
N, LE, and SH from satellite and ERA5 data sets.
Figure 4 shows the spatial coverage of all SEB parameters. The upper panel shows the spatial distribution of ERA5 SEB parameters, while the lower panel shows the spatial distribution of satellite SEB parameters. The southeast part of TP exhibits the highest R
N values; >150 Wm
−2 in satellite data and 90–150 Wm
−2 in ERA5 data. In satellite R
N, these high values extended towards eastern regions, but in ERA5 R
N, the southern regions showed higher values (90–120 Wm
−2). Major land covers in the southeast and eastern regions of TP are forests, shrublands, and savannas, while in the southwest part majority of land cover is croplands and grass fields. Northern regions of TP where there are large barren lands as major land cover exhibit least values (from 30 to 60 Wm
−2) of R
N in satellite and ERA5 data. A contradiction between two data is observed in the west and central regions of TP where ERA5 data show R
N as less than 30 and 30–60 Wm
−2, respectively, but the same regions from satellite data show the R
N as 60 to 120 wm
−2.
The highest values of LE also belong to southeast regions of TP from both ERA5 (60–90 Wm
−2) and satellite (45–60 Wm
−2) data sets. This region is rich in forests. In satellite data, the least values which are less than 30 Wm
−2 belong to the central and southwest regions. For ERA5 LE, the least values which are less than 15 Wm
−2 belong to the northern regions of TP. As mentioned in the above sections, the MODIS LE product has a limitation that the product does not provide values for barren lands, which is abundant in the north TP. One interesting point is that these least values (less than 15 Wm
−2) of ERA5 LE are only in the regions which are at the lowest height in the entire TP (as shown in the elevation map in
Figure 1). However, there is no scholarly proof of the relationship between LE with altitude. LE has no significant contradiction in spatial patterns between satellite and ERA5 data.
The spatial pattern of SH shows many contradictions in both data sets. The most prominent fact is that satellite SH values are between 45 to 120 Wm−2 (only a few pixels show less than 30 Wm−2) while ERA5 SH values are between 0 to 60 Wm−2. The highest ERA5 SH values (45 to 60 Wm−2) are exhibited in southwest TP, and the highest satellite SH values (90 to 120 Wm-2) are exhibited in southeast TP. A thin strip in the extreme southeast shows the least satellite SH values (less than 30 Wm−2), these pixels in ERA5 SH show the same values. In the northwest, central and southern regions of TP, ERA5 SH exhibit the least values (less than 15 Wm−2), but this trend is not observed in satellite SH.
4.4. Temporal Analysis
For temporal analysis, mean annual aerial averages of R
N, LE and SH were computed over the whole TP. On these aerial averages, LR slope and Sen’s slope were computed on a decadal scale and for the whole study duration (2001–19). These slope values are calculated at a 95% confidence level.
Figure 5a,c,e shows the ERA5 R
N, LE, and SH trends, while
Figure 5b,d,f shows the satellite R
N, LE, and SH temporal trends. LR slope lines for decadal trends (2001–10 and 2011–19) are shown in blue while temporal trends from 2001–19 are shown in the red line.
Table 4 summarizes the temporal trend values of the LR slope and Sen’s slope. Decadal trends of ERA5 R
N show negative trends in both LR and Sen’s slope for both decades, but when the temporal trend is computed for complete study duration (2001–19) LR slope and Sen’s slope show increasing trends with the value of 0.01 and 0.02, respectively. For satellite R
N, temporal trends for each decade as well as for total study duration show an increasing trend. The overall temporal trend for satellite R
N from LR slope and Sen’s slope is 0.01 and 0.03, respectively. Temporal trends of R
N from satellite and ERA5 data are in agreement for total study duration and show a nominal increasing trend.
For LE, all trends, including the decadal scale and for the total study duration from both data sets show increasing trends in both statistical slopes. However, ERA5 LE shows an overall minor increasing trend with the LR slope and Sen’s slope value 0.03, but for the same duration, satellite LE exhibits 0.25 LR slope and Sen’s slope values which exhibit the prominent increasing trends. For the decadal scale, satellite LE shows the more increasing temporal trend for the second decade (2011–19). This effect is more pronounced in ERA5 LE in which the second decade shows a prominently increasing temporal trend (LR slope and Sen’s slope value 0.15) with respect to the first decade (LR slope and Sen’s slope value 0.02).
SH shows the mixed decadal and overall temporal trends from both data sets. In the LR slope, ERA5 SH shows a continuously decreasing trend for all the temporal durations. Overall, ERA5 LR slope value is −0.016, which exhibits a weak decreasing trend. In the Sen’s slope, the first decade of ERA5 SH shows a decreasing trend while the second decade shows a very weak increasing trend, and for the total duration, Sen’s slope value for ERA5 SH is 0.005, exhibiting a very weak increasing trend. For satellite SH, LR slope values for the first and second decade are 0.04 and 0.02, respectively, and for Sen’s slope, these values are 0.1 and 0.14, respectively. All these four values exhibit a weak but increasing trend. But for the total study duration, both LR slope and Sen’s slope values are −0.18, which exhibit a weak negative trend. Increasing decadal temporal trends but decreasing trends over the longer duration signify the importance of SEB analysis over a longer duration.
4.5. Seasonal Aanalysis
Seasonal averages of R
N, LE, and SH from satellite and ERA5 data were computed using aerial averages of TP.
Figure 6 shows the seasonal averages of R
N, LE, and SH along with the LR slope, and
Table 5 summarized the numerical values of computed LR slopes of each trend line marked in
Figure 6. For R
N, the winter season shows a decreasing trend while the autumn season shows an increasing trend from both satellite and ERA5 observations concluding that R
N is reducing in winters and increasing in summers. For the spring and summer (mid-seasons of the year) both data sets exhibit different trends, i.e., in spring, the trend from satellite observations is decreasing while it is increasing in ERA5 observations and vice versa for summer.
For LE, all the seasons from both data sets show prominent increasing trends, except for the winter season from ERA5 data which shows a week decreasing trend, specifically, LE is prominently increasing in the spring and summer months.
Reciprocal to LE, SH exhibit decreasing trends in all four seasons from both data sets except for the spring seasons observed from ERA5 data in which an increasing trend is observed. From the above discussion, it is concluded that both data sets are in agreement for most of the seasonal trends with very few exceptions. Seasonal trends also described that LE is equally increasing while SH is equally decreasing throughout the year from 2001 to 19 over TP. The RN is balanced as it is increasing in half of the year (two seasons) while decreasing for the remaining half year. There is no prominent trend for RN in the overall annual cycle.
4.6. NDVI and LE
The most prominent trend is observed in LE for the temporal, spatial, and spatio-temporal domain. The LE and evapotranspiration are directly related to the vegetation cover of the surface. In this section, the trends of NDVI are analyzed, and the impact of NDVI on LE is observed.
Figure 7a describes the spatio-temporal trends of NDVI using M-K τ values.
Figure 7b gives the temporal variation of NDVI from mean annual aerial averages over TP. LR slope and Sen’s slope is used to find the temporal trend. The LR and Sen’s slope positive trend values of 0.008 exhibit an increasing temporal trend for NDVI in TP. From the spatio-temporal analysis, the north and northeast regions of TP show the most increasing trends (trend values 0.5 to 1). Other regions exhibit a moderate positive trend (trend values 0 to 0.5) of NDVI. Only a few central regions (with trend values −0.5 to 0) show a negative trend. Here, it is noteworthy that the same central regions of TP exhibit negative trends in LE, as discussed in
Section 4.2.
Figure 8a shows the correlation (r-value) between LE and NDVI.
Figure 8b shows the correlation (r-value) between the M-K trend of LE with the M-K trend of NDVI. Random points were observed from both trend images to establish a correlation between LE and NDVI. Only satellite LE is used to find the correlation to avoid sensor calibration factors. A total of 99% of significant correlations are observed at a 95% confidence level. From a significant r-value (0.754) between NDVI and LE, the spatio-temporal and temporal analysis, the argument becomes stronger that NDVI analysis is a useful mechanism to analyze LE.
4.7. LST, Air Temperature and SH
SH is defined as the heat which can change the temperature without changing the state of matter. Thus, the SH must have a strong relationship with surface and atmospheric temperature. In this section, a correlation is computed between satellite SH with satellite (MODIS) LST and ERA5 SH with the ERA5 air temperature at 2 m height. To avoid any sensor calibration and get a more synchronized correlation, parameters from the same sensors/data sets are selected.
Figure 9a shows the correlation r-value between satellite SH and MODIS LST, and
Figure 9b shows the r-value between ERA5 SH and ERA5 air temperature. Both correlations are 99% significant at a 95% confidence level. The relation between SH and LST observed from satellite data sources showed a strong positive correlation of 0.763. The correlation between SH and air temperature shows a weak r-value of 0.376. This weak correlation indicates the significance of other factors (such as wind direction, wind speed, and relative humidity) on air temperature.
Figure 10 shows the temporal variation of air temperature obtained from two reanalysis data sets, ERA5 and NCEP. It is observed that air temperature from ERA5 and NCEP shows a positive (increasing) trend with the LR slope value 0.018 and 0.073, respectively, and Sen’s slope value 0.022 and 0.069, respectively. The importance of the factors other than SH can be observed by comparing temporal trends of SH from
Figure 5 with the temporal trends of air temperature presented in
Figure 10. SH shows a decreasing trend over TP; still, the temporal trend of air temperature is increasing in the same region.
5. Discussions
In this study, two different data sources were used to analyze spatio-temporal trends of R
N, LE and SH over TP. the first data source is the satellite data obtained from two different sensors, CERES and MODIS, and the second data source is a reanalysis data product ERA5. In this way, a comparative spatio-temporal analysis was performed on SEB parameters. The first step was to validate the data and method; for this purpose, both data sets were compared with in situ ground measurements from FluxNet tower observations. Four statistical parameters (Pearson’s r, LR slope, MBE, and MAE) were used to validate satellite and ERA5 data. From the results, it is established satellite and ERA5 R
N is both valid and reliable with r-values of 0.87 and 0.88, respectively. ERA5 LE resulted in a more accurate LR slope than satellite LE. The LR slope of satellite LE (0.35) is significantly less than the LR slope of ERA5 LE (0.75), but smaller MBE (−0.36 Wm
−2) of satellite LE as compared to MBE of ERA5 LE (5.55 Wm
−2) shows that over the annual cycle, satellite LE is closer to the ground observations. Satellite SH is the only SEB parameter that shows an insignificant relation with ground observations. High MAE (62.85 Wm
−2) and low r-value (0.63) make satellite SH less accurate than ERA5 SH, which has low MAE (18.92 Wm
−2) and 95% significant high r-value (0.81). Although satellite SH obtained from CERES and MODIS using Equation (3) is evident from recent literature [
15,
38,
69], in this study, its accuracy is low.
For the cross validation, MBE and MAE were calculated on aerial averages of mean monthly satellite and ERA5 observations.
Table 6 presents the summary of statistical error values; here, bias was calculated using satellite—ERA5 observations. Positive MBE and MAE mean that satellite observations are higher than ERA5 observations. LE represents the least MBE and MAE, but MAE for SH is very high (53.91 Wm
-2) concluding that satellite SH prominently overestimates ERA5 SH.
Spatio-temporal trends were calculated from M-K trend analysis. For R
N, east and southeast regions of TP show the same increasing trends in satellite and ERA5 data sets, but the northern TP shows opposite spatio-temporal trends in both data sets. In the spatial analysis, almost the same pattern was observed that both data sets are in agreement in southeast and eastern TP but the northern TP shows the opposite trends. From
Figure 1, it is clear that the majority of land covers in eastern regions of TP are forests, shrubs, and savannas. Moreover, these regions are relatively at low altitudes. Over these land covers, satellite and ERA5 data show similar results. The major land cover in northern TP is barren land; here, both data sets exhibit opposite results. Satellite (MODIS) LE product is the only satellite product that observes evapotranspiration and LE over 109.03 million km
2 global vegetated land [
57], but the limitation of the product is that it does not observe LE over barren lands. Hence in north TP where the majority of land cover is barren lands, satellite LE is not applicable. In other regions of TP, satellite LE and ERA5 LE are in agreement and exhibit a net increasing trend except for northeast TP, where ERA5 TP shows decreasing spatio-temporal trends. Over northern barren lands, ERA5 LE has the least spatial value between 0 to 15Wm
−2. NDVI is an important factor to analyze LE. In
Figure 7a, spatio-temporal analyses of NDVI emphasize the spatio-temporal pattern of LE, as the regions with increasing spatio-temporal NDVI trends are those which exhibit spatio-temporal increasing LE trends. For SH, spatio-temporal trends from satellite and ERA5 data show minor increasing trends for east and central TP, but the majority area in TP exhibits decreasing trends. Spatial analysis of SH pronounced the difference in satellite and ERA5 SH as discussed above. Satellite SH observes 60 to 120 Wm
−2 in the majority of the regions in TP while ERA5 SH observes 0 to 45 Wm
−2 for the same regions. MAE between satellite and ERA5 SH presented in
Table 6 points towards this difference. By comparing the MAE of SH from
Table 6 with those present in
Table 3, it is concluded that satellite SH is overestimated compared to real SH. Our observed spatial pattern of ERA5 LE and SH are identical with Xie et al. [
6] observed LE and SH from the ERA-Interim data set. One notable fact is that the east and southeast regions of TP exhibit increasing spatio-temporal trends and higher spatial values for almost all energy fluxes. A possible reason is the land cover change in the area, as it is evident from the literature that in these regions afforestation took place [
40,
70]. Lands converted to forest from any other land cover exhibit more positive R
N and LE [
15,
50].
For the temporal trends, two different statistical methods, LR slope and Sen’s slope were used in this study. Although LR slope is widely used for time series data, it has a limitation that it is more accurate for the data which best fits on a straight line; moreover, it is more sensitive to the outliers [
32]. Sen’s slope is a non-parametric trend method and is not limited to the aforementioned limitations. Thus, by choosing two different trend methods, we eliminate the chances of any bias which may appear because of the statistical method. From these two slopes, temporal trends for each decade (2001–10, 2011–19) and the total study duration (2001–19) were calculated. ERA5 R
N shows a minor decreasing decadal trend, but overall study duration exhibits a net increasing trend, while satellite R
N consistently shows an increasing trend in each decade and over the whole duration.
LE observed from ERA5 and satellite shows the significant increasing trend in each decade and over the total study duration. Increasing vegetation cover and afforestation is a potential cause for this increase. The argument is strengthening in
Figure 7b in which an increasing temporal trend for NDVI is observed. Shen et al. [
20] show the increasing vegetation growth in TP using multiple satellite NDVI products and relate this with the evaporative cooling. Our results are in agreement with this study. In
Figure 8a, we also established a significant correlation (r-value 0.754) between LE and NDVI in TP.
For ERA5 SH, the LR slope and Sen’s slope exhibit different behavior in the second decade (2011–19) and for the total duration, where LR slope shows a decreasing trend but Sen’s slope shows the increasing trend. The satellite SH shows even more contradictory results; both slopes show increasing trends in the first and second decade, but for the total duration, the trend is decreasing. The different longer trends than decadal trends lead to the conclusion that for the climatic phenomena longer studies are more important and may lead to different results. There is a huge body of literature that observed the SH in TP, some studies use reanalysis data especially for the spring and summer seasons. Almost all studies show the decreasing SH in TP [
2,
6,
30,
33,
34,
35]. Despite a decrease in SH, atmospheric temperature over TP is continuously increasing, resulting in climate warming [
1,
8,
27,
29,
33]. A weak correlation (r-value 0.376) between SH and air temperature is observed in
Figure 9b. Climate warming over TP is observed in
Figure 10, in which we show from two reanalysis data sets, ERA5 and NCEP, that mean annual air temperature over TP is temporally increasing from 2001 to 2019.