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Article

Comparison of the Distribution of Evapotranspiration on Shady and Sunny Slopes in Southwest China

by
Yixi Kan
1,
Huaiyong Shao
1,*,
Chang Du
2,
Yimeng Guo
1 and
Xianglong Dai
2
1
College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
2
College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(22), 4310; https://doi.org/10.3390/rs16224310
Submission received: 5 October 2024 / Revised: 11 November 2024 / Accepted: 15 November 2024 / Published: 19 November 2024
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Abstract

:
Evapotranspiration (ET) plays a significant role in the surface water cycle, particularly within the unique geographical context of Southwest China. The region’s different topography, driven by mountain uplift and variations in slope direction, alters regional hydrothermal conditions, thereby affecting local ecoclimatic patterns. ET characteristics, shaped by slope orientation, can also serve as important indicators of climate variability in the study area. While most existing ET research on shady and sunny slopes has been conducted at the point scale, this study employed Global Land Surface Satellite (GLASS) ET products to estimate the average ET for shady and sunny slopes across five provinces in Southwest China between 2003 and 2018. The driving factors behind the variation in ET across different regions were also explored. Key results include the following: (1) Annual ET in Southwest China ranges between 200 mm and 800 mm, with Tibet exhibiting the lowest values and Yunnan the highest. (2) ET decreases gradually with increasing altitude in the altitude range of 0 m to 5000 m. The ET is higher on the sunny slopes than on the shady slopes. Notably, when the altitude is higher than 5000 m, ET on shady slopes in Tibet is greater than that on sunny slopes as the altitude increases. (3) ET initially increases with slope inclination before decreasing. Notably, in areas with slopes exceeding 35° in Yunnan, the ET value is found to be significantly higher on shady slopes compared to sunny slopes. (4) The effects of soil moisture, the Normalized Difference Vegetation Index, relative humidity, and land surface temperature on ET are more substantial on shady slopes than sunny slopes, whereas air temperature has a stronger impact on ET on sunny slopes. These results provide valuable data for research on regional climate change and contribute to strategies for ecological and environmental protection.

1. Introduction

Evapotranspiration (ET) constitutes a fundamental physical process occurring at the Earth’s surface. This process involves the transition of water from a liquid to a gaseous state and plays a crucial role in the surface water cycle [1,2]. ET is a significant variable in the surface energy balance, as it entails energy transformations [3]. Furthermore, ET has a considerable impact on global ecosystems and climate change, serving as a vital meteorological factor that responds to global natural variations [4]. Southwest China is characterized by relatively high elevations, with numerous river systems originating from this area [5]. Variations in ET within this region significantly impact the climate and hydrological patterns of downstream river systems, as well as the broader national context [6,7]. A comprehensive understanding of ET in the southwestern region is essential for effective regional irrigation planning, the adaptation of agricultural planting structures, and the preservation of the ecological environment.
It has been established that mountain uplift can lead to modifications in regional hydrothermal conditions, which may subsequently affect the prevailing ecological and climatic patterns within the region [8,9]. The natural environment and geographical features in mountainous regions undergo changes with increasing altitude [10]. As altitude increases, both temperature and vegetation density tend to decrease. This reduction in altitude is accompanied by diminished transpiration from vegetation and evaporation from soil moisture (SM), resulting in a corresponding decline in ET [11,12]. In addition to elevation, slope orientation also exerts a significant impact on the distribution of ET. The direction of the slope affects the amount of incoming solar radiation, which subsequently alters the energy flux and water exchange between the atmosphere and the surface. This dynamic creates different local microclimates with varying near-surface characteristics, potentially causing ET trends to diverge from the overall regional pattern [13,14]. The interrelationship between vegetation and ET is substantial. The moisture content of soil across different slope orientations varies due to fluctuations in temperature and air relative humidity (RH). This variation results in differing conditions for vegetation growth on both negative and positive slopes, which in turn impacts ET [15,16]. Overall, ET characteristics associated with different slope orientations can provide valuable insights into the climate change occurring within the study area, thereby offering support for the implementation of regional conservation measures.
Numerous studies have examined the differences in ET and its associated meteorological factors between shady and sunny slopes [13,17]. Liu [18] investigated the physical, chemical, and hydrological properties of soil across varying slope orientations in semi-arid regions. The findings revealed that the surface soil on shady slopes exhibited a significant water retention capacity and demonstrated minimal susceptibility to soil erosion and water stress. Chen [19] conducted an analysis of the microclimatic ecological environment of shady and sunny slopes in the afforested areas of the western mountains of Beijing, indicating that daily evaporation on sunny slopes generally exceeds that on shady slopes. The difference in ET is not significant when the disparity in net radiation is minimal. However, most of the existing research on shady and sunny slopes has focused on point-scale analyses, which are limited to representing regions within a 1 m radius. Given that Southwest China is a vast area characterized by strong spatial heterogeneity, point-scale factor analysis is insufficient to support large-scale regional climate research.
Remote sensing (RS) offers the capability to provide spatially continuous and temporally consistent measurements of terrestrial variables, which is particularly beneficial for regional climate studies. The application of RS has become a widely adopted methodology in the assessment of regional ET, primarily due to its advantageous features, including an extensive detection range and a rapid information update frequency [20,21,22]. The process-driven evapotranspiration estimation method has a good physical basis and strong interpretability [23]. The surface energy balance method has a strong physical mechanism and requires only a few driving data to achieve good results. However, the impedance parameterization is relatively complex and does not work well under non-clear-sky conditions [24]. The Penman-type models can combine meteorological data and remote sensing inversion data at different time scales to solve for evapotranspiration, avoiding the error of extrapolating from instantaneous scales to other time scales. It is worth noting that Penman-type models rely on crop coefficients that are related to crop type, fertility stage, and other factors that are difficult to obtain directly using remote sensing techniques [25]. Different models estimate different ETs and there are limitations in relying on a single algorithm to generate regional ETs. The fusion of multiple algorithms can combine the strengths of each model and improve the accuracy of evapotranspiration estimates. Data fusion methods use advanced fusion techniques to combine ET products with different spatial and temporal resolutions to improve spatial and temporal resolution and accuracy [26].
Yao et al. [27] fused two Penman–Monteith-based process models, two Priestley–Taylor-based process models, and a data-driven semi-empirical model using a Bayesian averaging method for different surface cover types, which significantly improved the accuracy of the GLASS evapotranspiration products after the fusion of the different land classes compared to the fused five evapotranspiration products. The accuracy of the fused GLASS evapotranspiration products for different land classes is significantly higher than the five fused algorithmic products and is closer to the real value of ground observation. Ge et al. [28] used GLEAM, MOD16A2, GLDAS_Noah, ERA5, and ET datasets produced by TC fusion to obtain more accurate potential evapotranspiration in the Yarlung Tsangpo River Basin and reveal its changing pattern. Cai et al. [29] used GLASS products in combination with the eco-geographic zoning scheme of the Tibetan Plateau to analyze the spatial and temporal characteristics of evapotranspiration over the Tibetan Plateau over a number of years and its relationship with temperature, precipitation, and vegetation. However, the impacts of slope orientation, specifically the impacts of shady and sunny slopes on regional ET, remain largely unexamined [30].
This study aims to clarify the spatial distribution of ET across shady and sunny slopes in Southwest China. To achieve this, the research focused on three key objectives: (1) employing GLASS ET products to estimate ET on both shady and sunny slopes, (2) analyzing regional variations in ET across different slope aspects, and (3) conducting a driving factor analysis to identify the key determinants affecting ET on shady and sunny slopes.

2. Study Area and Data

2.1. Research Area

Southwest China, recognized as one of the seven physical geographical divisions of the country, is located between 97°21′ and 110°11′ east longitude and 21°08′ and 33°41′ north latitude (Figure 1). This region comprises Sichuan, Chongqing, Guizhou, Yunnan, and Tibet [31,32,33,34,35]. It is positioned within the primary and secondary echelons of China. The topographical features of this area include the Sichuan Basin, the Yunnan–Guizhou Plateau, and the South Qinghai–Tibet Plateau. As elevation increases from the southeast to the northwest, there is a corresponding gradual decrease in temperature and precipitation. Additionally, the climate transitions from a subtropical monsoon climate to a plateau monsoon humid climate [36].

2.2. Data Source

For this paper, the Global Land Surface Satellite (GLASS) ET products [37] with low uncertainty in the Chinese region were selected. This dataset integrates multiple algorithms, which reduces the uncertainty of a single algorithm and ensures the accuracy and quality of the products. GLASS ET has a temporal resolution of 8 days and a spatial resolution of 1 km. To facilitate the study, we transformed the 8-day ET into a monthly-scale ET. The GLASS dataset is derived from the National Earth System Science Data Center (https://www.geodata.cn/oldindex.html, accessed on 14 November 2024). Normalized Difference Vegetation Index (NDVI) data utilized in this study was sourced from the MYD13A2 dataset available on the National Aeronautics and Space Administration (NASA) platform (https://search.earthdata.nasa.gov, accessed on 14 November 2024). Additionally, SM data, mean air temperature (Ta), RH data, land surface temperature (LST), and surface solar radiation (Rs) were acquired from the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/, accessed on 14 November 2024) on a monthly temporal scale. Flux observation sites were used to validate the accuracy of the ET estimates and details of these sites are shown in Table 1.
The Digital Elevation Model (DEM) utilized in this study was derived from the Shuttle Radar Topography Mission (SRTM) dataset (https://asterweb.jpl.nasa.gov/gdem.asp, accessed on 14 November 2024), which has a resolution of 90 m. This DEM was employed to generate slope aspect data within the study area. Given that the study area was located in the northern hemisphere, slopes with orientations ranging from 135° to 225° were classified as sunny slopes, while those with orientations from 0° to 45° and 315° to 360° were classified as shady slopes. The remaining slope orientations were not considered in the analysis presented in this paper. The aforementioned data were resampled to a monthly scale of 1 km to facilitate subsequent data processing (Figure 2). Specific data on the statistics of the area of shady and sunny slopes in each province are listed in Table 2. It can be seen that the areas in both directions are roughly the same, which rules out the possibility of large differences in evapotranspiration that might be caused by excessive differences in area. Meanwhile, all details of the used datasets are shown in Table 3. It is worth noting that the original LST dataset contained both daytime and nighttime data. In this paper, we averaged all the data within a day to obtain the daily scale LST.

3. Materials and Methods

3.1. Accuracy Evaluation Indicators

To evaluate the performance of the two methods, we selected 3 statistical measures to assess the difference between the ET estimates of the SEBS model and the flux tower observations. R2 and RMSE were used to evaluate the goodness of fit of the model and measure the closeness between the predicted value and the observed value, respectively. In order to quantify the difference between the average value of the predicted value and the observed value, Bias is a great indicator. The mathematical equations of the indicators are as follows:
R 2 = [ ∑ i = 1 n ( X i − X ¯ ) ( Y i − Y ¯ ) ∑ i = 1 n ( X i − X ¯ ) 2 ( Y i − Y ¯ ) 2 ] 2
R M S E = 1 n ∑ i = 1 n ( X i − Y i ) 2
B i a s = ∑ i = 1 n ( X i − Y i ) n
where X i is the observed value, Y i is the estimated value, X ¯   and Y ¯ are the average values of X i and Y i , respectively, and n is the total number of samples.

3.2. Correlation Analysis

Correlation analysis is a method to analyze the degree of correlation between different variables [59]. In this paper, Pearson correlation coefficient method was used to investigate the correlation between the ET of shady and sunny slopes and meteorological factors based on pixel scale. The calculation formula is as follows:
R X Y = ∑ k = 1 n ( X k − X ¯ ) ( Y k − Y ¯ ) ∑ k = 1 n ( X k − X ¯ ) 2 ∑ k = 1 n ( Y k − Y ¯ ) 2
where R X Y represents the correlation coefficient, and X k and Y k represent the values of variables X and Y in the K t h year, respectively. X ¯ and Y ¯ represent the average of the variables X and Y . The value interval of the correlation coefficient is [−1, 1]. The greater the absolute value of the correlation coefficient, the higher the correlation between variables; otherwise, the lower the correlation.

4. Results

4.1. Validation of Evapotranspiration Estimation

To appraise the performance of the GLASS ET dataset, we compared the monthly estimated results with tower-based data observed by the EC of the flux station (Figure 3). The validation results show that the estimated ET had similar performance on all sites, with a high R2 value of 0.69 and a low RMSE value of 0.94 mm and Bias value of −0.57 mm.

4.2. Spatiotemporal Variation of ET in Southwest China

The ET estimates for shady and sunny slopes for each year from 2003–2018 are shown in Table 4. During the study period, the average ET in Southwest China exhibited a slight increasing trend (Figure 4). Overall, the annual ET in this region ranges from 200 mm to 800 mm. The variation in ET on the shady slope aligns with that on the sunny slope. However, a notable difference in ET values is evident. Among the five provinces analyzed, Yunnan demonstrates the highest ET values, with the maximum ET on the sunny slope reaching 764.76 mm and that on the shady slope reaching 747.53 mm. This is followed by Guizhou, Chongqing, and Sichuan. In Tibet, the highest ET values recorded were 285.46 mm on the sunny slope and 279.76 mm on the shady slope.
In all provinces, the ET values on sunny slopes surpass those of negative slopes. However, the numerical disparity between shady and sunny slopes varies across regions. The multi-year average ET difference between shady and sunny slopes (Figure 5) indicates that Yunnan exhibits the most significant ET difference, measuring 18.67 mm. Conversely, Tibet demonstrates the smallest ET difference, which is less than 9 mm. Additionally, Sichuan (13.5 mm), Guizhou (10.67 mm), and Chongqing (10.1 mm) also present ET differences between shady and sunny slopes that exceed 10 mm.
The annual distribution trend of ET in Southwest China and its provinces exhibits consistency (Figure 6). ET values initially increase and subsequently decrease, reaching their peak in July and August, while the lowest values are observed in January and February. In the regions of Yunnan, Guizhou, and Chongqing, the ET differential between shady and sunny slopes demonstrates a bimodal pattern, with elevated values occurring in spring and autumn, and reduced values in summer and winter. On the contrary, in the other regions, the evapotranspiration difference showed an inverted ‘U’ shape, which was consistent with the trend of the monthly mean evapotranspiration (Figure 7). The minimum ET values are recorded in winter, while the maximum values are noted in summer. In all months, evapotranspiration values were higher on sunny slopes than on shady slopes.
In summary, the spatial distribution of ET on the negative slopes in Southwest China exhibits a notable similarity to that on the positive slopes (Figure 8). The spatiotemporal distribution of annual mean ET demonstrates a decreasing trend from the southeast to the northwest. The highest ET values are observed in Southwest Yunnan, Northeast Sichuan, and Southwest Guizhou, regions characterized by a subtropical humid climate at lower elevations. Conversely, the lowest ET values are predominantly found in Northwest Tibet, as well as in the Kunlun Mountains and Nali areas adjacent to Xinjiang, which exhibit reduced levels of ET.

4.3. Variation Trend of ET at Different Elevations

The elevation range in Southwest China is significant (Figure 9). Generally, there is a decrease in elevation from the southeast to the northwest. The highest elevations are predominantly concentrated within the Qinghai–Tibet Plateau, while the lowest elevations are primarily found in the Sichuan Basin and in close proximity to rivers. The lowest recorded elevation is 147 m, located in the southwestern region of Guizhou Province, whereas the highest elevation is situated in Tibet, specifically on Mount Qomolangma. The variation in elevation notably impacts the patterns of vertical zonation within the study area.
The variation of ET at different altitudes in the study area (Figure 10) showed a decreasing trend in ET values with increasing altitude. Overall, ET decreased gradually with increasing altitude in the altitude range of 0 m to 5000 m. ET on sunny slopes consistently exceeded that on shady slopes. However, when the altitude was higher than 5000 m, ET on shady slopes was greater than that on sunny slopes as the altitude increased in Tibet. It is noteworthy that in Yunnan, ET did not reach its maximum at the lowest elevation, but rather at 750m to 1250m.
The data showed a unimodal pattern of ET differences between shady and sunny slopes, initially increasing with increasing altitude and then decreasing with decreasing altitude (Figure 11). In Tibet and Yunnan, the difference between shady and sunny slopes peaked between about 750 m and 1500 m. The difference between shady and sunny slopes is higher in Tibet than in Yunnan. In Sichuan, the maximum value of the difference in ET between shady and sunny slopes occurs at about 5750 m. The rest of the provinces do not show very significant fluctuations. It is noteworthy that the ET values of shady slopes exceed those of sunny slopes above 5000 m in Tibet.

4.4. Variation Trend of ET at Different Slopes

In this paper, the whole southwest region is divided into nine categories with five degrees as a slope interval, and detailed information on the classification standards is listed in Table 5. The degree of surface fluctuation is significant in Southwest China (Figure 12). The Northern Tibet Plateau and the Sichuan Basin exhibit relatively gentle gradients, ranging from 0° to 5°, whereas the Hengduan Mountains and the Himalayas present an average gradient exceeding 25°. Notably, the area characterized by the steepest slope is situated in the intermediate zone between the Sichuan Basin and the Tibetan Plateau, where there is an elevation drop of up to 3000 m. The slope initially increases in a southeasterly direction before subsequently decreasing in a northwesterly direction.
As the slope increases, the overall ET in the study area initially rises before subsequently declining (Figure 13). However, the patterns observed in each province were not uniform. In Chongqing, ET on the shady slopes exhibits a gradual decrease with increasing slope, whereas ET on the sunny slopes shows an increase in the same direction. This phenomenon may be attributed to the fact that the slopes in Chongqing do not exceed 40°, thereby not reaching a critical turning point. Conversely, ET on the shady and sunny slopes in Sichuan and Yunnan provinces demonstrates a trend that is contrary to that of the shady slopes overall in the study area. In Sichuan, ET on the shady slopes initially decreases before increasing, with the lowest values occurring within the 35~40° slope range. In Yunnan, ET values are notably low in areas with slopes of 30~35°. In regions with slopes exceeding 35°, the ET values on the shady slopes are greater than those on the sunny slopes.
As the slope increases, the difference in ET between shady and sunny slopes in Yunnan and Sichuan first becomes larger, reaches a peak, and then decreases, whereas the difference in ET in the other provinces becomes larger all the time (Figure 14). In Yunnan, the peak value occurs within the range of 20~25°, while in Sichuan, the peak values are observed at 40°. The greatest difference in ET between shady and sunny slopes is found in Tibet, followed by Sichuan, Chongqing, and Guizhou.

5. Discussion

5.1. Differentiation Effect of Slope Aspect on Related Elements

The classification of slopes as negative or positive is determined by the orientation of the sun in relation to the mountain’s direction. Given that China is situated north of the equator, the sun predominantly occupies a southerly position. Consequently, the southern aspect of a mountain receives a greater amount of solar radiation, thereby categorizing it as a sunny slope. In contrast, the northern aspect, which is often shielded by the mountain’s elevation, receives significantly less solar energy. This differential exposure to solar radiation creates a marked contrast between the shady and sunny slopes of east–west oriented mountain ranges. Furthermore, the variation in slope orientation leads to the differentiation of other physical geographical factors within mountainous regions (Figure 15, Figure 16, Figure 17, Figure 18, Figure 19 and Figure 20).
For surface energy balance, solar radiation constitutes the predominant source of energy received by the surface, while the slope aspect significantly impacts the reception and distribution of this radiation [60]. The amount of radiation received on sunny slopes exceeds that on shady slopes across all provinces in Southwest China. The variation in LST is dependent on the quantity of energy absorbed by the respective surface. Due to differences in heat transfer associated with radiation, the LST on sunny slopes is higher than that on shady slopes at equivalent altitudes, and there is also a notable difference in diurnal temperature variation [61,62].
At a local scale, soil water content is significantly impacted by topography [8,63]. The duration of snowmelt on sunny slopes occurs earlier than on shady slopes, resulting in reduced water infiltration into the soil on sunny slopes compared to shady slopes. Consequently, the RH and SM levels on sunny slopes are generally lower than those on shady slopes. This phenomenon is especially evident in regions characterized by water scarcity, particularly in arid and semi-arid areas [64]. The distribution patterns of RH and SM exhibit similarities.
In general, there is minimal variation in water conditions between shady and sunny slopes in humid regions, with the distribution of vegetation primarily affected by differences in light availability. However, in sub-humid, arid, and semi-arid areas, the contrast in radiation is not easily discernible, and the growth of vegetation is significantly limited by water scarcity [13]. On negatively sloped terrain, where SM and RH are higher, the degree of water deficiency affecting vegetation growth is less severe compared to positively sloped areas. Consequently, vegetation on negatively sloped terrain tends to be more lush than that on positively sloped terrain.

5.2. Analyses of ET and Its Influencing Factors on Shaded and Sunny Slopes

A correlation analysis between climatic variables and ET (Figure 21) indicates a statistically significant positive relationship between NDVI, SM, Ta, RH, and ET. Conversely, a significant negative relationship exists between LST and ET. The impact of SM, RH, and LST on ET, characterized by a shady slope, is more pronounced than that of the sunny slope, whereas the effect of Ta on ET, represented by a sunny slope, is greater than that of the shady slope.
ET is predominantly a consequence of the processes of vegetation transpiration and soil evaporation. As such, ET is inherently connected to the presence and distribution of vegetation, in addition to the overall RH levels of the surrounding environment [65,66]. The majority of the southwestern region exhibits a subtropical humid climate at elevations below 4500 m, which facilitates the growth of vegetation. In scenarios where water availability is not a limiting factor, the impact of energy on ET becomes more pronounced.
The mean elevation of the Qinghai–Tibet Plateau is approximately 4500 m, surpassing that of both the Sichuan Basin and the Yunnan–Guizhou Plateau. This elevation contributes to a pronounced mountain effect, which significantly impacts the distribution of temperature, the forest line, and the snow line within the region [67,68,69]. In areas situated below the snow line, both Ta and LST tend to be elevated, resulting in favorable conditions for vegetation growth. During this period, solar radiation on sunny slopes is marginally greater than that on shady slopes, leading to a more pronounced ET impact. The Qinghai–Tibet Plateau, characterized by permafrost at altitudes of 4500 m or higher, experiences an increase in effective radiation and sensible heat due to its elevated terrain [70]. SM emerges as a critical factor affecting ET, particularly under drought conditions [71,72]. Shady slopes typically exhibit higher moisture levels and cooler temperatures due to the protective effects of the mountains, whereas sunny slopes are generally drier and warmer [16,73,74]. Furthermore, the extended effect of freeze–thaw cycles has contributed to a decline in soil quality on the sunny slopes, which adversely impacts water retention [30,64]. The low water content in the soil is significantly impacted by water scarcity, which subsequently hinders the process of ET.

6. Conclusions

This study utilized a GLASS ET dataset to estimate the annual average ET of shady and sunny slopes across five provinces in Southwest China, while also analyzing the driving factors affecting ET in different regions. The key conclusions are outlined as follows:
(a) The annual ET in Southwest China ranges from 200 mm to 800 mm. Tibet recorded the lowest ET values, with 285.46 mm and 279.76 mm on the shady and sunny slopes, respectively, while Yunnan exhibited the highest values, reaching 764.76 mm on sunny slopes and 747.53 mm on shady slopes.
(b) ET decreased gradually with increasing altitude in the altitude range of 0 m to 5000 m. The ET is higher on the sunny slopes than on the shady slopes. Notably, when the altitude is higher than 5000 m in Tibet, ET on shady slopes is greater than that on sunny slopes as the altitude increases.
(c) With increasing slope inclination, the overall ET in the study area initially increases before decreasing. The difference in ET between the shady and sunny slopes increases until a peak is reached, after which it diminishes. Notably, in areas with slopes exceeding 35° in Yunnan, the ET value is found to be significantly higher on shady slopes compared to sunny slopes.
(d) A positive correlation was found between ET and NDVI, SM, RH, and Ta. Conversely, a negative correlation was observed between LST and ET. The effects of SM, NDVI, RH, and LST on ET are more substantial on shady slopes than sunny slopes, whereas Ta has a stronger impact on ET on sunny slopes.
These results provide critical insights into the spatial and temporal patterns of ET in complex terrains, contributing valuable data for regional climate studies and environmental management strategies in Southwest China.

Author Contributions

Conceptualization, H.S.; methodology, Y.K. and C.D.; software, Y.K.; formal analysis, Y.K. and Y.G.; writing—original draft preparation, Y.K. and X.D.; writing—review and editing, Y.K. and H.S.; visualization, Y.K.; supervision, H.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (42271405).

Data Availability Statement

Normalized Difference Vegetation Index (NDVI) data utilized in this study were sourced from the MYD13A2 dataset available on the National Aeronautics and Space Administration (NASA) platform (https://search.earthdata.nasa.gov, accessed on 14 November 2024). Soil moisture data, mean air temperature, relative humidity, land surface temperature, and surface solar radiation were acquired from the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/, accessed on 14 November 2024). The Digital Elevation Model (DEM) utilized in this study was derived from the Shuttle Radar Topography Mission (SRTM) dataset (https://asterweb.jpl.nasa.gov/gdem.asp, accessed on 14 November 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Wang, K.; Dickinson, R.E. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys. 2012, 50, RG2005. [Google Scholar] [CrossRef]
  2. Wanniarachchi, S.; Sarukkalige, R. A review on evapotranspiration estimation in agricultural water management: Past, present, and future. Hydrology 2022, 9, 123. [Google Scholar] [CrossRef]
  3. Anderson, M.C.; Kustas, W.P.; Norman, J.M.; Hain, C.R.; Mecikalski, J.R.; Schultz, L.; Gonzalez-Dugo, M.P.; Cammalleri, C.; d’Urso, G.; Pimstein, A. Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrol. Earth Syst. Sci. 2011, 15, 223–239. [Google Scholar] [CrossRef]
  4. Liu, Y.; Lu, M.; Yang, H.; Duan, A.; He, B.; Yang, S.; Wu, G. Land–atmosphere–ocean coupling associated with the Tibetan Plateau and its climate impacts. Natl. Sci. Rev. 2020, 7, 534–552. [Google Scholar] [CrossRef] [PubMed]
  5. Li, X.; Walker, D. The plant geography of Yunnan Province, southwest China. J. Biogeogr. 1986, 13, 367–397. [Google Scholar]
  6. Lin, W.; Wen, C.; Wen, Z.; Gang, H. Drought in Southwest China: A review. Atmos. Ocean. Sci. Lett. 2015, 8, 339–344. [Google Scholar]
  7. Zhao, J.; Xu, T.; Xiao, J.; Liu, S.; Mao, K.; Song, L.; Yao, Y.; He, X.; Feng, H. Responses of water use efficiency to drought in southwest China. Remote Sens. 2020, 12, 199. [Google Scholar] [CrossRef]
  8. Zhao, W.; Huang, P.; Li, A. A Review of Evapotranspiration Estimation Using Remotely Sensed Data in Mountainous Region. Mt. Res. 2017, 35, 908–918. [Google Scholar] [CrossRef]
  9. Barnard, D.M.; Barnard, H.; Molotch, N. Topoclimate effects on growing season length and montane conifer growth in complex terrain. Environ. Res. Lett. 2017, 12, 064003. [Google Scholar] [CrossRef]
  10. Goulden, M.; Anderson, R.; Bales, R.; Kelly, A.; Meadows, M.; Winston, G. Evapotranspiration along an elevation gradient in California’s Sierra Nevada. J. Geophys. Res. Biogeosci. 2012, 117, G03028. [Google Scholar] [CrossRef]
  11. Liu, W.; Yang, L.; Zhu, M.; Adamowski, J.F.; Barzegar, R.; Wen, X.; Yin, Z. Effect of elevation on variation in reference evapotranspiration under climate change in Northwest China. Sustainability 2021, 13, 10151. [Google Scholar] [CrossRef]
  12. Hu, Z.; Wang, G.; Sun, X.; Zhu, M.; Song, C.; Huang, K.; Chen, X. Spatial-temporal patterns of evapotranspiration along an elevation gradient on Mount Gongga, Southwest China. Water Resour. Res. 2018, 54, 4180–4192. [Google Scholar] [CrossRef]
  13. Yin, G.; Xie, J.; Ma, D.; Xie, Q.; Verger, A.; Descals, A.; Filella, I.; Penuelas, J. Aspect Matters: Unraveling Microclimate Impacts on Mountain Greenness and Greening. Geophys. Res. Lett. 2023, 50, e2023GL105879. [Google Scholar] [CrossRef]
  14. Singh, S. Understanding the role of slope aspect in shaping the vegetation attributes and soil properties in Montane ecosystems. Trop. Ecol. 2018, 59, 417–430. [Google Scholar]
  15. Fekedulegn, D.; Hicks, R.R., Jr.; Colbert, J. Influence of topographic aspect, precipitation and drought on radial growth of four major tree species in an Appalachian watershed. For. Ecol. Manag. 2003, 177, 409–425. [Google Scholar] [CrossRef]
  16. Kumari, N.; Saco, P.M.; Rodriguez, J.F.; Johnstone, S.A.; Srivastava, A.; Chun, K.P.; Yetemen, O. The grass is not always greener on the other side: Seasonal reversal of vegetation greenness in aspect-driven semiarid ecosystems. Geophys. Res. Lett. 2020, 47, e2020GL088918. [Google Scholar] [CrossRef]
  17. Bennie, J.; Huntley, B.; Wiltshire, A.; Hill, M.O.; Baxter, R. Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol. Model. 2008, 216, 47–59. [Google Scholar] [CrossRef]
  18. Liu, Q.; Chen, Z.; Wang, S.; Liang, T.; Gao, Z.; Dong, Y. Changes in Soil Hydrological Retention Properties and Controlling Factors on Shaded and Sunny Slopes in Semi-Arid Alpine Woodlands. Forests 2024, 15, 1136. [Google Scholar] [CrossRef]
  19. Guan, Y.; Peng, J. Study on microclimate characteristics and energy benefit of afforestation preparation in West Mountain slope of Beijing. J. Beijing For. Univ. 1992, s5, 70–82. [Google Scholar]
  20. Chen, L.; Zhang, F.; Si, J.; Feng, Q.; Wei, Y. Spatially differentiated characteristics of vegetation carbon sequestration function in Helan Mountain area and its driving factors. Acta Ecol. Sin. 2023, 43, 10250–10262. [Google Scholar] [CrossRef]
  21. Mokhtar, A.; He, H.; Alsafadi, K.; Li, Y.; Zhao, H.; Keo, S.; Bai, C.; Abuarab, M.; Zhang, C.; Elbagoury, K. Evapotranspiration as a response to climate variability and ecosystem changes in southwest, China. Environ. Earth Sci. 2020, 79, 312. [Google Scholar] [CrossRef]
  22. Zeng, Z.; Wu, W.; Zhou, Y.; Li, Z.; Hou, M.; Huang, H. Changes in reference evapotranspiration over Southwest China during 1960–2018: Attributions and implications for drought. Atmosphere 2019, 10, 705. [Google Scholar] [CrossRef]
  23. Liu, M.; Tang, R.; Li, Z.-L.; Yao, Y.; Yan, G. Global land surface evapotranspiration estimation from meteorological and satellite data using the support vector machine and semiempirical algorithm. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2018, 11, 513–521. [Google Scholar] [CrossRef]
  24. Guo, X.; Shi, J. Global review of the research progress and trend of evapotranspiration. Geol. Rev. 2019, 65, 1473–1486. [Google Scholar] [CrossRef]
  25. Zhang, C.; Long, D.; Cui, Y.; Cui, Y.; Bai, L.; Dong, L. Applications of remote sensing retrieval of evapotranspiration in irrigation water management: A review. J. Hydraul. Eng. 2023, 54, 1087–1098. [Google Scholar] [CrossRef]
  26. Liu, M.; Tang, R.; Li, Z.; Gao, M.; Yao, Y. Progress of data-driven remotely sensed retrieval methods and products on land surface evapotranspiration. Natl. Remote Sens. Bull. 2021, 25, 1517–1537. [Google Scholar] [CrossRef]
  27. Yao, Y.; Liang, S.; Li, X.; Hong, Y.; Fisher, J.B.; Zhang, N.; Chen, J.; Cheng, J.; Zhao, S.; Zhang, X. Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations. J. Geophys. Res. Atmos. 2014, 119, 4521–4545. [Google Scholar] [CrossRef]
  28. Ge, S.; Guan, T.; Liu, Y.; Jin, J.; Wang, G.; Liu, C.; Ju, Q. Applicability evaluation and fusion on multi-source potential evapotranspiration products in the Yarlung Zangbo River basin. South-North Water Transf. Water Sci. Technol. 2024, 22, 491–501. [Google Scholar] [CrossRef]
  29. Cai, J.; Zhao, W.; Yang, M.; Zhan, Q.; Fu, H.; He, K. Spatiotemporal Changes of Evapotranspiration on the Qinghai-Tibet Plateau from 2001 to 2018 based on GLASS Data. Remote Sens. Technol. Appl. 2022, 37, 888–896. [Google Scholar]
  30. Lan, X. Influence of Mass Elevation Effect Factors on Latent and Sensible Heat Fluxes on the Tibetan Plateau. Master’s Thesis, Henan University, Kaifeng, China, 2023. [Google Scholar]
  31. Huang, X.; Wu, W.; Xu, J.; Li, S.; Jiang, Y.; Du, B.; Wang, L. The Applicability Performance of the ERA5-Land Precipitation Datasets in Southwest China. Plateau Meteorol. 2023, 42, 1562–1575. [Google Scholar]
  32. Zhang, M.; He, J.; Wang, B.; Wang, S.; Li, S.; Liu, W.; Ma, X. Extreme drought changes in Southwest China from 1960 to 2009. J. Geogr. Sci. 2013, 23, 3–16. [Google Scholar] [CrossRef]
  33. Dodson, J.; Shi, G.; Lu, F.; Yan, H. A 40,000 year record of vegetation, environment and climate change from Chongqing, Central China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 2021, 573, 110441. [Google Scholar] [CrossRef]
  34. Yu, Z.; Yang, K.; Luo, Y.; Shang, C.; Zhu, Y. Lake surface water temperature prediction and changing characteristics analysis-A case study of 11 natural lakes in Yunnan-Guizhou Plateau. J. Clean. Prod. 2020, 276, 122689. [Google Scholar] [CrossRef]
  35. Feng, L.; Maqunzong, G.; Guanglong, W.; Langzhuoga, S.; Budunzhu, P.; Weixiong, L. Thoughts on high-quality development of Xizang nature protected area system with national parks as main body. For. Constr. 2023, 41, 14–17. [Google Scholar]
  36. Wang, W.; Wang, W.; Li, J.; Wu, H.; Xu, C.; Liu, T. The impact of sustained drought on vegetation ecosystem in Southwest China based on remote sensing. Procedia Environ. Sci. 2010, 2, 1679–1691. [Google Scholar] [CrossRef]
  37. Guo, L.A.; Wu, Y.H.; Zheng, H.X.; Zhang, B.; Fan, L.X.; Chi, H.J.; Yan, B.K.; Wang, X.Q. Consistency and uncertainty of gridded terrestrial evapotranspiration estimations over China. J. Hydrol. 2022, 612, 128245. [Google Scholar] [CrossRef]
  38. Ma, Y.; Hu, Z.; Xie, Z.; Ma, W.; Wang, B.; Chen, X.; Li, M.; Zhong, L.; Sun, F.; Gu, L. A long-term (2005–2016) dataset of hourly integrated land–atmosphere interaction observations on the Tibetan Plateau. Earth Syst. Sci. Data 2020, 12, 2937–2957. [Google Scholar] [CrossRef]
  39. Pastorello, G.; Trotta, C.; Canfora, E.; Chu, H.; Christianson, D.; Cheah, Y.-W.; Poindexter, C.; Chen, J.; Elbashandy, A.; Humphrey, M. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 2020, 7, 225. [Google Scholar] [CrossRef]
  40. Wang, Y.; Luo, W.; Chen, J.; Ca, X.; Zhang, L.; Cheng, A.; Wang, S. A dataset of carbon and water fluxes in the sample plots of natural regeneration at Puding Station of Guizhou Province 2015–2019. China Sci. Data 2023, 8, 78–89. [Google Scholar] [CrossRef]
  41. Qi, D.; Yang, D.; Song, Q.; Liu, Y.; Zhang, Y.; Fei, X.; Sha, L.; Zhou, W.; Chen, A.; Wen, G. An observation dataset of carbon and water fluxes at Yuanjiang Dry-hot Valley Ecological Station (2013–2015). China Sci. Data 2021, 6, 110–122. [Google Scholar] [CrossRef]
  42. Qi, D.; Fei, X.; Song, Q.; Zhang, Y.; Sha, L.; Liu, Y.; Zhou, W.; Lu, Z.; Fan, Z. A dataset of carbon and water fluxes observation in subtropical evergreen broad-leaved forest in Ailao Shan from 2009 to 2013. China Sci. Data 2021, 6, 87–97. [Google Scholar] [CrossRef]
  43. Peng, P.; Zhu, L. Observations of land surface processes of the Tibetan Plateau based on the field stations network. Sci. Technol. Rev. 2017, 35, 97–102. [Google Scholar]
  44. Yao, Y.; Liang, S.; Cheng, J.; Liu, S.; Fisher, J.B.; Zhang, X.; Jia, K.; Zhao, X.; Qin, Q.; Zhao, B. MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley–Taylor algorithm. Agric. For. Meteorol. 2013, 171, 187–202. [Google Scholar] [CrossRef]
  45. Zheng, C.; Jia, L.; Zhao, T. A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution. Sci. Data 2023, 10, 139. [Google Scholar] [CrossRef]
  46. Peng, S.; Gang, C.; Cao, Y.; Chen, Y. Assessment of climate change trends over the Loess Plateau in China from 1901 to 2100. Int. J. Climatol. 2018, 38, 2250–2264. [Google Scholar] [CrossRef]
  47. Peng, S.; Ding, Y.; Wen, Z.; Chen, Y.; Cao, Y.; Ren, J. Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011–2100. Agric. For. Meteorol. 2017, 233, 183–194. [Google Scholar] [CrossRef]
  48. Ding, Y.; Peng, S. Spatiotemporal trends and attribution of drought across China from 1901–2100. Sustainability 2020, 12, 477. [Google Scholar] [CrossRef]
  49. Peng, S.; Ding, Y.; Liu, W.; Li, Z. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst. Sci. Data 2019, 11, 1931–1946. [Google Scholar] [CrossRef]
  50. Zhang, H.; Luo, M.; Zhan, W.; Zhao, Y.; Yang, Y.; Ge, E.; Ning, G.; Cong, J. HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003–2020. Sci. Data 2024, 11, 425. [Google Scholar] [CrossRef]
  51. Tang, W.; Zhou, J.; Ma, J.; Wang, Z.; Ding, L.; Zhang, X.; Zhang, X. TRIMS LST: A daily 1 km all-weather land surface temperature dataset for China’s landmass and surrounding areas (2000–2022). Earth Syst. Sci. Data 2024, 16, 387–419. [Google Scholar] [CrossRef]
  52. Zhang, X.; Zhou, J.; Liang, S.; Wang, D. A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature. Remote Sens. Environ. 2021, 260, 112437. [Google Scholar] [CrossRef]
  53. Zhang, X.; Zhou, J.; Göttsche, F.-M.; Zhan, W.; Liu, S.; Cao, R. A method based on temporal component decomposition for estimating 1-km all-weather land surface temperature by merging satellite thermal infrared and passive microwave observations. IEEE Trans. Geosci. Remote Sens. 2019, 57, 4670–4691. [Google Scholar] [CrossRef]
  54. Zhou, J.; Zhang, X.; Zhan, W.; Göttsche, F.-M.; Liu, S.; Olesen, F.-S.; Hu, W.; Dai, F. A thermal sampling depth correction method for land surface temperature estimation from satellite passive microwave observation over barren land. IEEE Trans. Geosci. Remote Sens. 2017, 55, 4743–4756. [Google Scholar] [CrossRef]
  55. Feng, F.; Wang, K. Merging satellite retrievals and reanalyses to produce global long-term and consistent surface incident solar radiation datasets. Remote Sens. 2018, 10, 115. [Google Scholar] [CrossRef]
  56. Feng, F.; Wang, K. Merging ground-based sunshine duration observations with satellite cloud and aerosol retrievals to produce high-resolution long-term surface solar radiation over China. Earth Syst. Sci. Data 2021, 13, 907–922. [Google Scholar] [CrossRef]
  57. Mao, Y.; Wang, K.; Liu, X.; Liu, C. Water storage in reservoirs built from 1997 to 2014 significantly altered the calculated evapotranspiration trends over China. J. Geophys. Res. Atmos. 2016, 121, 10097–10112. [Google Scholar] [CrossRef]
  58. Mao, Y.; Wang, K. Comparison of evapotranspiration estimates based on the surface water balance, modified Penman-Monteith model, and reanalysis data sets for continental China. J. Geophys. Res. Atmos. 2017, 122, 3228–3244. [Google Scholar] [CrossRef]
  59. Gogtay, N.; Thatte, U. Principles of correlation analysis. J. Assoc. Physicians India 2017, 65, 78–81. [Google Scholar]
  60. Luo, X.; Yu, Q.; Ma, Q.; Guo, L. Study on the heat and deformation characteristics of an expressway embankment with shady and sunny slopes in warm and ice-rich permafrost regions. Transp. Geotech. 2020, 24, 100390. [Google Scholar] [CrossRef]
  61. Jin, H.; Sun, L.; Wang, S.; He, R.; Lv, L.; Yu, S. Dual Influences of Local Environmental Variables on Ground Temperatures on the Interior-Eastern Qinghai-Tibet Plateau(I): Vegetation and Snow Cover. J. Glaciol. Geocryol. 2008, 4, 535–545. [Google Scholar]
  62. Zhao, W.; Li, A.; Zhang, Z.; Bian, J.; Jin, H.; Yin, G.; Nan, X.; Lei, G. A Study on Land Surface Temperature Terrain Effect over Mountainous Area based on Landsat 8 Thermal Infrared Data. Remote Sens. Technol. Appl. 2016, 31, 63–73. [Google Scholar]
  63. Famiglietti, J.S.; Rudnicki, J.W.; Rodell, M. Variability in surface moisture content along a hillslope transect: Rattlesnake Hill, Texas. J. Hydrol. 1998, 210, 259–281. [Google Scholar] [CrossRef]
  64. Lan, A.; Lin, Z.; Fan, X.; Yao, M. Differences of Surface Energy and Shallow Soil Temperature and Humidity at Sunny and Shady Slopes in Permafrost Region, Beiluhe Basin, Qinghai-Tibet Plateau. Adv. Earth Sci. 2021, 36, 962–979. [Google Scholar]
  65. Wang, Y.; Jing, J.; Liu, H. Spatio-temporal Variation of Evapotranspiration and Its Driving Factors in Southwest China from 2000 to 2020. Resour. Environ. Yangtze Basin 2023, 32, 2568–2580. [Google Scholar]
  66. Cai, Y.; Chen, L.; Lyv, S.; Zhang, Y.; Wu, Q.; Wang, F.; Song, J.; Cheng, L.; Liu, Q.; Zhang, J. Impact of vegetation change on evapotranspiration components in the Loess Plateau based on Google Earth Engine. Sci. Geogr. Sin. 2023, 43, 1649–1658. [Google Scholar] [CrossRef]
  67. Zhao, X. The Tibetan Plateau and vegetation in China: Characteristics of the geographical distribution of vegetation in China in relation to the effect of the plateau on atmospheric circulation. J. Xinjiang Agric. Univ. 1979, 1, 1–10. [Google Scholar]
  68. Wu, X.; Zhu, Y. Elevation calculation and variation of climatic snowline in China. Chin. Sci. Bull. 1990, 6, 451–453. [Google Scholar]
  69. Jiang, F.; Wu, X.; Wang, S.; Fu, J.; Wang, Y.; Zhao, Z. Features of space distribution of the forest line and relations between the forest line and climatic limit of permafrost and climatic snowline in China. J. Geomech. 2004, 4, 289–299. [Google Scholar]
  70. Liu, J. Study on the Influence of Mass Elevation Effect on Solar Radiation and Altitudinal Belt of Vegetation in the Oinling-Daba Mountains. Master’s Thesis, Henan University, Kaifeng, China, 2020. [Google Scholar]
  71. Che, Y.; Zhang, M.; Li, Z.; Wei, Y.; Nan, Z.; Li, H.; Wang, S.; Su, B. Energy balance model of mass balance and its sensitivity to meteorological variability on Urumqi River Glacier No. 1 in the Chinese Tien Shan. Sci. Rep. 2019, 9, 13958. [Google Scholar] [CrossRef]
  72. Yao, Y.; Zhang, Y.; Liu, Q.; Liu, S.; Jia, K.; Zhang, X.; Xu, Z.; Xu, T.; Chen, J.; Fisher, J.B. Evaluation of a satellite-derived model parameterized by three soil moisture constraints to estimate terrestrial latent heat flux in the Heihe River basin of Northwest China. Sci. Total Environ. 2019, 695, 133787. [Google Scholar] [CrossRef]
  73. Dobrowski, S.Z. A climatic basis for microrefugia: The influence of terrain on climate. Glob. Chang. Biol. 2011, 17, 1022–1035. [Google Scholar] [CrossRef]
  74. Murphy, P.C.; Knowles, J.F.; Moore, D.J.; Anchukaitis, K.; Potts, D.L.; Barron-Gafford, G.A. Topography influences species-specific patterns of seasonal primary productivity in a semiarid montane forest. Tree Physiol. 2020, 40, 1343–1354. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Overview of study area.
Figure 1. Overview of study area.
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Figure 2. Overview of shady and sunny slopes.
Figure 2. Overview of shady and sunny slopes.
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Figure 3. Comparison of estimated and measured values.
Figure 3. Comparison of estimated and measured values.
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Figure 4. Temporal trends in ET across Southwest China (2003–2018).
Figure 4. Temporal trends in ET across Southwest China (2003–2018).
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Figure 5. Temporal variation of ET in Southwest China.
Figure 5. Temporal variation of ET in Southwest China.
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Figure 6. Monthly average distribution of ET over 2003–2018.
Figure 6. Monthly average distribution of ET over 2003–2018.
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Figure 7. Distribution of monthly average ET differences between sunny slope and shady slopes.
Figure 7. Distribution of monthly average ET differences between sunny slope and shady slopes.
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Figure 8. Spatial distribution of ET in entire region: (a) shady slopes; (b) sunny slopes.
Figure 8. Spatial distribution of ET in entire region: (a) shady slopes; (b) sunny slopes.
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Figure 9. Elevation distribution in study area.
Figure 9. Elevation distribution in study area.
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Figure 10. ET on shady and sunny slopes at different elevations.
Figure 10. ET on shady and sunny slopes at different elevations.
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Figure 11. Vertical distribution of ET (shady and sunny slope differences) (overall and by province).
Figure 11. Vertical distribution of ET (shady and sunny slope differences) (overall and by province).
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Figure 12. Slope gradient distribution.
Figure 12. Slope gradient distribution.
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Figure 13. Distribution of ET according to slope changes.
Figure 13. Distribution of ET according to slope changes.
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Figure 14. Distribution of ET differences according to slope changes.
Figure 14. Distribution of ET differences according to slope changes.
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Figure 15. Distribution of SM: (a) spatial distribution; (b) numerical statistics of SM for shady and sunny slopes.
Figure 15. Distribution of SM: (a) spatial distribution; (b) numerical statistics of SM for shady and sunny slopes.
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Figure 16. Distribution of Ta: (a) spatial distribution; (b) numerical statistics of Ta for shady and sunny slopes.
Figure 16. Distribution of Ta: (a) spatial distribution; (b) numerical statistics of Ta for shady and sunny slopes.
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Figure 17. Distribution of RH: (a) spatial distribution; (b) numerical statistics of RH for shady and sunny slopes.
Figure 17. Distribution of RH: (a) spatial distribution; (b) numerical statistics of RH for shady and sunny slopes.
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Figure 18. Distribution of NDVI: (a) spatial distribution; (b) numerical statistics of NDVI for shady and sunny slopes.
Figure 18. Distribution of NDVI: (a) spatial distribution; (b) numerical statistics of NDVI for shady and sunny slopes.
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Figure 19. Distribution of LST: (a) spatial distribution; (b) numerical statistics of LST for shady and sunny slopes.
Figure 19. Distribution of LST: (a) spatial distribution; (b) numerical statistics of LST for shady and sunny slopes.
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Figure 20. Distribution of Rs: (a) spatial distribution; (b) numerical statistics of Rs for shady and sunny slope.
Figure 20. Distribution of Rs: (a) spatial distribution; (b) numerical statistics of Rs for shady and sunny slope.
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Figure 21. Correlation coefficients between climatic factors and ET: (a) shady slopes; (b) sunny slopes.
Figure 21. Correlation coefficients between climatic factors and ET: (a) shady slopes; (b) sunny slopes.
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Table 1. Information for the 10 EC flux tower sites in Southwest China, including the ID, site name, longitude E (Lon E), latitude N (Lat N), duration of data collection, and data source.
Table 1. Information for the 10 EC flux tower sites in Southwest China, including the ID, site name, longitude E (Lon E), latitude N (Lat N), duration of data collection, and data source.
IDSite NameLon ELat NDurationReferencesData Source
1BJ91.931.372010–2016[38]A Big Earth Data Platform for Three Poles, DOI: 10.11888/Meteoro.tpdc.270910. CSTR: 18406.11.Meteoro.tpdc.270910, accessed on 14 November 2024
2QOMS86.9528.362010–2016
3SETORS94.7329.772010–2016
4NADORS79.733.392010–2016
5NAMORS90.9830.772010–2016
6HGU102.5932.852015–2017[39]https://fluxnet.org/, accessed on 14 November 2024 https://doi.org/10.1038/s41597-020-0534-3
7Puding105.7226.252015–2019[40]https://nesdc.org.cn/, accessed on 14 November 2024
8Yuanjiang102.1823.472013–2015[41]https://nesdc.org.cn/, accessed on 14 November 2024
9Ailaoshan101.0324.542009–2013[42]https://nesdc.org.cn/, accessed on 14 November 2024
10Gongga101.9929.582004–2006[43]National Tibetan Plateau/Third Pole Environment Data Center. https://doi.org/10.11888/Meteoro.tpdc.270423, accessed on 14 November 2024
Table 2. Area of shady and sunny slopes in each province.
Table 2. Area of shady and sunny slopes in each province.
ProvinceShady Slope Area (km2)Sunny Slope Area (km2)
Guizhou3.753.88
Yunan7.597.95
Tibet31.6532.30
Sichuan11.2311.26
Chongqing1.871.86
Table 3. Information for used datasets, including the ID, dataset name, references, and source.
Table 3. Information for used datasets, including the ID, dataset name, references, and source.
Dataset NameTemporal ResolutionSpatial ResolutionReferencesSource
GLASS ET1 km8 day[27,44]National Earth System Science Data Center, National Science & Technology Infrastructure of China (http://www.geodata.cn, accessed on 14 November 2024)
NDVI MYD13A21 km16 day National Aeronautics and Space Administration (NASA) platform (https://search.earthdata.nasa.gov, accessed on 14 November 2024)
SM1 km1 month[45]National Tibetan Plateau/Third Pole Environment Data Center. https://doi.org/10.11888/RemoteSen.tpdc.272760
Ta1 km1 month[46,47,48,49]National Tibetan Plateau/Third Pole Environment Data Center. https://doi.org/10.11888/Meteoro.tpdc.270961
RH1 km1 month[50]National Tibetan Plateau/Third Pole Environment Data Center. https://doi.org/10.5281/zenodo.8070140
LST1 km1 day[50,51,52,53,54]National Tibetan Plateau/Third Pole Environment Data Center. https://doi.org/10.11888/Meteoro.tpdc.271252
Rs10 km1 month[55,56,57,58]National Tibetan Plateau/Third Pole Environment Data Center. https://doi.org/10.11888/Atmos.tpdc.272817
Table 4. Regional average ET per year by province.
Table 4. Regional average ET per year by province.
TimeDirectionYunnanGuizhouSichuanChongqingTibetSouthwest
2003shady slope746.31 741.74 501.15 674.19 254.06 415.56
sunny slope760.67 748.25 515.27 679.88 262.31 427.54
2004shady slope698.20 714.19 490.03 651.65 248.66 401.09
sunny slope714.51 721.83 504.93 658.21 257.49 413.93
2005shady slope719.56 735.53 509.54 683.76 259.14 416.27
sunny slope735.42 742.91 523.03 687.45 269.19 429.34
2006shady slope738.47 730.90 510.40 692.75 259.09 419.00
sunny slope753.92 738.06 524.19 695.98 269.66 432.33
2007shady slope730.46 743.15 497.42 693.18 252.94 412.79
sunny slope743.94 750.08 510.98 698.30 263.00 425.49
2008shady slope732.43 727.20 502.40 677.09 258.56 415.52
sunny slope747.36 734.97 517.40 684.23 268.34 428.81
2009shady slope747.53 742.19 505.38 685.48 249.50 414.35
sunny slope764.76 750.33 518.81 691.25 260.49 428.27
2010shady slope679.78 667.75 488.76 636.29 265.47 404.29
sunny slope701.84 680.61 501.29 647.50 272.43 416.69
2011shady slope713.33 701.83 500.40 644.22 264.72 413.24
sunny slope732.71 712.18 513.14 651.60 271.91 425.28
2012shady slope690.29 667.73 487.51 633.82 261.42 403.11
sunny slope711.90 683.55 500.19 644.67 267.01 414.89
2013shady slope707.52 736.94 518.75 686.45 264.94 419.83
sunny slope727.34 748.16 533.40 696.05 270.63 431.66
2014shady slope710.94 694.38 496.98 638.45 263.89 411.09
sunny slope733.18 709.23 509.24 650.92 269.27 422.85
2015shady slope727.03 723.43 510.33 674.48 256.32 414.67
sunny slope748.33 736.17 523.43 685.30 261.59 426.34
2016shady slope723.81 738.36 513.82 683.19 270.29 424.18
sunny slope744.37 751.42 526.57 692.55 275.88 435.78
2017shady slope724.21 711.75 509.94 671.12 279.76 426.73
sunny slope746.56 723.89 522.81 681.50 285.46 438.51
2018shady slope722.44 707.22 504.79 666.13 269.42 419.11
sunny slope744.27 723.37 518.92 676.51 275.30 431.52
Table 5. Grade classification information.
Table 5. Grade classification information.
Slope Range (°)CategorySlope Range (°)Category
0–5I25–30VI
5–10II30–35VII
10–15III35–40VIII
15–20IV>40IX
20–25V
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Kan, Y.; Shao, H.; Du, C.; Guo, Y.; Dai, X. Comparison of the Distribution of Evapotranspiration on Shady and Sunny Slopes in Southwest China. Remote Sens. 2024, 16, 4310. https://doi.org/10.3390/rs16224310

AMA Style

Kan Y, Shao H, Du C, Guo Y, Dai X. Comparison of the Distribution of Evapotranspiration on Shady and Sunny Slopes in Southwest China. Remote Sensing. 2024; 16(22):4310. https://doi.org/10.3390/rs16224310

Chicago/Turabian Style

Kan, Yixi, Huaiyong Shao, Chang Du, Yimeng Guo, and Xianglong Dai. 2024. "Comparison of the Distribution of Evapotranspiration on Shady and Sunny Slopes in Southwest China" Remote Sensing 16, no. 22: 4310. https://doi.org/10.3390/rs16224310

APA Style

Kan, Y., Shao, H., Du, C., Guo, Y., & Dai, X. (2024). Comparison of the Distribution of Evapotranspiration on Shady and Sunny Slopes in Southwest China. Remote Sensing, 16(22), 4310. https://doi.org/10.3390/rs16224310

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