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Article

Temporal Evolution and Extremes of Urban Thermal and Humidity Environments in a Tibetan Plateau City

by
Jinzhao Wang
1,2,
Suonam Kealdrup Tysa
1,3,*,
Guoxin Chen
3 and
Qiong Li
3
1
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
2
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
3
State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
*
Author to whom correspondence should be addressed.
Land 2026, 15(1), 64; https://doi.org/10.3390/land15010064 (registering DOI)
Submission received: 26 November 2025 / Revised: 24 December 2025 / Accepted: 26 December 2025 / Published: 29 December 2025
(This article belongs to the Special Issue Influence of Urbanization-Related Radical Land Modification on Urban Extreme Climate: 2nd Edition)
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Abstract

To elucidate features of the recent urban thermal–humidity climate, this study interrogates high-density observational data (2018–2023) from Xining, a key urban area on the Tibetan Plateau (TP), focusing on recent changes and extremes. Results show that the summer urban heat island intensity (UHII) has intensified in recent years, marked by a surging frequency of extreme heat island days and increased variability, as exemplified by the maximum hourly UHII increasing from 3.95 °C to 6.60 °C during 2018–2023. Conversely, the summer urban dry island intensity (UDII) exhibited a clear weakening, yet this is accompanied by a dramatic increase in transient extreme events, characterized by a sharp rise in weak dry island occurrences and the emergence of urban moist islands. Furthermore, the hourly UHII is dominantly modulated by atmospheric humidity and temperature conditions, and these influences displayed a pronounced diurnal asymmetry, being strongest at night while weak or even reversed during the pre-noon hours. These findings underscore the escalating thermal risks and complex humidity dynamics in this highland city, providing critical insights for urban planning and climate adaptation strategies in similar environments.

1. Introduction

Rapid urban development has altered the properties and structure of underlying surfaces, thereby affecting the urban moisture and energy balance [1,2,3]. Concurrently, urbanization has intensified socio-economic activities, leading to increased energy consumption and anthropogenic heat release. Among the urban climate effects induced by urbanization, the most extensively studied and widely recognized phenomenon is the urban heat island (UHI) effect [4,5,6,7,8].
To investigate the impact of urbanization on the local climate of the Tibetan Plateau (TP), Zhong [9] classified urban and rural stations across the plateau and identified six typical urban sites. Their results showed that the trend of surface air temperature (SAT) increase at urban stations (0.44 °C decade−1) was significantly higher than at rural stations (0.31 °C decade−1) during 1975–2021, indicating that urbanization contributed approximately 30% to the total regional warming. In Lhasa, the city with the most pronounced warming on the plateau, urban expansion contributed 0.21 °C decade−1 to the warming from 2000 to 2020, accounting for about 40% of the near-surface temperature rise, a proportion higher than the average for typical plateau cities. Numerical experiments further quantified that urban expansion in Lhasa during the same period led to a 1.44 °C increase in surface temperature.
Using sensitivity experiments based on numerical modeling, Zhu et al. [10] examined the influence of land use and land cover change (LULCC) on the UHI effect in another representative city on the plateau—Xining. Their findings revealed that both the surface and canopy UHI intensities continued to strengthen with urban expansion, increasing nearly twofold from 1994 to 2018, demonstrating that LULCC is a primary driver of UHI intensification. Furthermore, influenced by the valley topography, the enhanced UHI in Xining modulates local mountain–valley wind circulation through UHI-induced flows, which accelerates near-surface wind speed and suppresses cloud formation, allowing for more solar radiation to heat the surface and creating a positive feedback mechanism that amplifies UHI intensity (UHII). However, as noted by Liang [11], most plateau cities are situated in river valleys or mountainous basins, where confined topography inhibits heat dissipation, resulting in more pronounced UHI effects and greater diurnal temperature ranges in these regions.
Many studies indicate that the canopy UHI and heatwaves can act synergistically [12,13,14,15], resulting in stronger heat stress in urban areas, though divergent findings exist regarding the amplification or reduction of the urban UHI during heatwaves [16]. It is found that urban areas in mainland China exhibit a substantial UHII enhancement of up to 0.94 °C during heatwaves, nearly double the intensity observed in normal periods [17]. The vapor pressure deficit is a key factor regulating the interaction between heatwaves and UHIs [18]. In the United States, the UHI effect intensifies during heatwaves in the eastern and western regions but weakens in the central region. This regional disparity stems from the differential inhibition of evapotranspiration by vapor pressure deficit between urban and rural areas. Specifically, in the central region, the higher vapor pressure deficit during heatwaves more significantly suppresses evapotranspiration in rural areas, diminishing their cooling effect and thereby reducing the urban–rural temperature difference and mitigating the UHI effect.
A similar local urban phenomenon is the urban dry island (UDI) effect, which refers to the lower atmospheric moisture and humidity in urban areas compared to their rural surroundings [1,19]. As early as 1967, research had revealed the influence of urban environments on near-surface and canopy-level atmospheric humidity [20], finding that relative humidity (RH) at urban observation stations is generally lower than in surrounding suburban areas [21,22,23]. Existing studies attribute the formation of UDIs to two main factors: Unique urban surface characteristics, including materials, land cover, and urban structure, that may influence urban humidity through radiative, thermal, and dynamic processes [1]. Increased saturation vapor pressure resulting from higher urban SAT, which also leads to reduced RH in the urban canopy and boundary layer [24]. Studies have pointed out that urban agglomerations across different climate zones in China have exhibited a drying trend over the past four decades, with the emergence of UDIs after 2000, and dry island intensity is stronger in humid and semi-humid regions [25]. Among various factors, the surface evaporation resistance term related to vegetation canopy is the dominant control factor influencing atmospheric humidity differences over different underlying surfaces and the formation of UDIs in humid/semi-humid areas.
The UHII change with background humidity has also been examined, identifying a negative correlation between UHII and RH [26,27,28]. Hoffmann [29] argued that an increase in RH tends to enhance nocturnal condensation heating, particularly in rural areas, thereby reducing UHII. Schatz and Kucharik [26] proposed that higher RH leads to an elevation in air thermal admittance, which in turn narrows the urban–rural differences in air heating/cooling rates.
Based on multi-source observational data, studies have indicated that the TP region has exhibited an overall dipole-type precipitation change pattern characterized by wetness in the north and dryness in the south from the late 1970s to the 2010s [30,31,32]. Specifically, precipitation in the northern part of the TP has increased significantly, while the southern part, particularly the eastern section of the Himalayas, has shown a persistent aridification trend. Meanwhile, against the backdrop of a slowdown in the rate of global warming since the late 20th century [33,34], the TP region has displayed a distinct characteristic of accelerated warming [35,36], reflecting its unique regional sensitivity in response to climate change.
Thus, despite growing recognition of urbanization-induced local effects, systematic research focusing on the plateau remains relatively limited, with significant knowledge gaps persisting in understanding their spatiotemporal characteristics, underlying mechanisms, and influencing factors. Due to sparse station coverage and limited observational data across the plateau, comprehensive and systematic analysis of the physical processes driving these localized effects has been challenging. Existing studies predominantly rely on numerical modeling and have focused on the impacts of urban-related land use changes over recent decades on both urban heat and dry islands. In particular, there has been insufficient exploration of fine-scale phenomena at hourly resolutions, extreme event characteristics, and their interactions with regional background temperature and humidity in the TP region.
Xining, the largest city on the TP with a population exceeding one million, has undergone rapid urbanization [37,38,39,40]. It thus serves as a case study for understanding UHI dynamics in high-altitude cities. Based on summer observational data from 2018 to 2023, this study aims to investigate the following three key gaps: the intensity and temporal distribution characteristics of the summer UHI and UDI on the plateau; the variability and extremes of the UHI and UDI; and the interaction between the UHI and background meteorological conditions. This study seeks to systematically elucidate the spatiotemporal patterns of the summer thermal and moisture environment in the plateau city of Xining, the new characteristics of its extreme variations, and its feedback mechanisms with the background climate, thereby providing a scientific basis for urban climate adaptation planning and heat island mitigation.

2. Data and Methods

2.1. Data

This study employs station-based observational hourly 2 m SAT and RH data during the summers (June–August) of 2018 to 2023, from which the actual vapor pressure (e) is calculated. The study area encompasses Xining City and its surrounding complex terrain (36.2–36.5 °N, 101.2–102.2 °E) (Figure 1a). The dataset comprises a high-density automatic weather station network of 89 stations in total. This spatial resolution provides a highly detailed and granular view of the metropolitan climate, capturing fine-scale thermal and humidity gradients between urban and rural environments. The high-frequency hourly data allow for a robust analysis of diurnal variation, which is crucial for understanding local thermal and humidity dynamics.
To ensure the highest data quality and reliability, a rigorous, multi-step quality control procedure was implemented. This involved removing obvious physical outliers (e.g., SAT values outside a plausible range for the region and season) and ensuring temporal continuity by flagging and excluding stations with excessive missing data, thereby guaranteeing a complete and consistent temporal series for robust statistical analysis.
To support station classification and contextualize the findings within the region’s rapid urbanization, we integrated multi-source geospatial data: The spatial extent of the urban core was quantified using the Global Artificial Impervious Areas (GAIA) dataset for the baseline year of 2018 [39], which provides a high-resolution delineation of human settlements. High-accuracy digital elevation model data with a spatial resolution of 90 m and land use/land cover data at 1 km resolution were obtained from the Resources and Environmental Science Data Platform (https://www.resdc.cn, accessed on 30 September 2025). The integration of this high-resolution, quality-controlled station data with complementary geospatial datasets forms a robust foundation for investigating the spatial–temporal patterns and extremes of local urban climate effects in this study.

2.2. Urban and Rural Station Classification

The classification of urban and rural stations follows the methodology established in Chen et al [41]. Urban stations are defined as those located within the impervious surface boundary of Xining City. All candidate rural stations are located outside the urban impervious surface boundary. Then, from these, stations are further screened based on elevation control and spatial uniformity. That is, stations are situated within ±50 m of the mean elevation of the urban stations to mitigate orographic thermal effects, and they are evenly distributed across the four cardinal directions around the urban periphery to ensure representative spatial coverage. This systematic procedure resulted in the selection of 29 urban and 6 rural stations (Figure 1b; Table 1), ensuring that the observed urban climate signals primarily reflect anthropogenic influences.

2.3. Calculation of UHII and UDII

UHIIs are calculated as the difference in air temperature between the urban and rural averages, and UDIIs are defined as the negative difference in e between urban and rural areas, as follows:
U H I I = S A T u r b a n − S A T r u r a l
U D I I = − ( e u r b a n − e r u r a l )
A positive UDII indicates drier urban conditions (dry island), while a negative UDII indicates a moist urban environment relative to the rural area.

2.4. Definition of Extreme UHII and UDII Days

To identify extreme urban climate days, we defined strong and weak UHII/UDII events based on the six-summer mean and standard deviation. Days are classified as experiencing a strong urban heat (or dry) island effect if the daily UHII (or UDII) value exceeded the six-summer average by more than 1.5 times its standard deviation. In contrast, days are classified as experiencing a weak urban heat (or dry) island effect if the daily UHII (or UDII) value fell below the six-summer average by more than 1.5 times its standard deviation. These thresholds help characterize the frequency and intensity of extreme urban thermal and moisture events.

2.5. Other Statistical Methods

We applied linear regression to examine the relationship between hourly UHII and background meteorological conditions (SAT and e). Statistical significance is assessed at p < 0.01. A 5-point moving average is applied to diurnal variation to highlight underlying patterns.

3. Results and Discussion

3.1. Summer UHII and Their Extreme

During the period from 2018 to 2023, the overall mean summer daily UHII in Xining City is measured at 1.16 °C, indicating a discernible UHI effect, a finding that aligns closely with previous research conclusions [41]. The mean UHII values are consistently positive (ranging from 0.89 °C to 1.36 °C) (Figure 2). With the exception of a single negative value recorded in 2022, all daily values across the other summers are positive, confirming the UHI effect as a persistent phenomenon in Xining. Temporal analysis reveals a pattern of recent intensification in the mean daily UHII, rising from 0.89 °C in 2018 to a peak of 1.36 °C in 2021, followed by a slight decrease in 2022 and 2023. Despite this recent decline, the values remained above the six-year average, suggesting that the UHII has generally intensified. In addition, the variability of daily UHII increases significantly. While the standard deviation over the six years is 0.46 °C, the annual standard deviation varies from 0.30 °C (2018) to 0.63 °C (2022), indicating growing inter-summer instability and a more volatile urban thermal environment.
Regarding the extremity of summer daily UHII, the frequency of strong UHI days show a marked increase (Figure 2). The frequency was minimal from 2018 to 2020 (0–2 days), surged to 8 days in 2021, peaked at 23 days in 2022, and remained high in 2023 (12 days). This trend highlights that intense UHI events have become increasingly frequent and severe, representing the most prominent extreme characteristic. Conversely, weak UHI days are observed across all summers but exhibited no change, primarily fluctuating interannually. Notably, the extremity of daily UHI is most pronounced in 2022 (Figure 2e). This year not only registered the only negative daily UHII value (−0.05 °C) but also recorded the highest number of both strong (23 days) and weak (10 days) UHI days, coupled with the largest standard deviation. It signifies enhanced volatility in UHII extremes in recent years, characterized by more frequent alternation between intense and weak UHI days. Previous research found that a heatwave event occurred in the northern part of the plateau in July 2022 [41,42].
At the hourly scale, the UHI effect persisted but demonstrated significant diurnal variation, with a substantial daily amplitude of 1.14 °C (Figure 3). The diurnal pattern followed a classic single peak and valley curve; UHI consistently decreased from its nocturnal peak to a daytime trough before rising again, forming a complete cycle. The summer mean nocturnal UHII (1.50 °C) is 1.7 times greater than its diurnal counterpart (0.86 °C). In addition, the standard deviation of UHII is higher during the night (e.g., exceeding 0.89 °C between 00:00 and 05:00), indicating greater summer intra-day in nocturnal UHII compared to daytime.
During the study period, the basic diurnal pattern of hourly UHII remains stable. However, significant changes occurred in its intensity, variability, and extremes (Table 2). The standard deviation of hourly UHII increases markedly from 0.69 °C in 2018 to 1.17 °C in 2021, remaining near 1.0 °C in subsequent years. It suggests that intra-day fluctuations in UHI have become larger and more pronounced, signaling significantly increased instability in the urban thermal environment. Furthermore, the annual maximum hourly UHII rose steadily from 3.95 °C in 2018 to 6.60 °C in 2023, indicating a sharp increase in the risk of intense, short-duration UHI events.

3.2. Summer UDII and Their Extreme

The six-year summer mean daily UDII is 0.75 hPa, confirming a persistent UDI effect during summers in Xining City. Mean values are consistently positive, and most daily values are positive except for those in 2023 (Figure 4), indicating that the UDI is the norm for Xining summers. Nevertheless, the summer mean UDII exhibits a fluctuating but a pattern of recent reduction, from 1.51 hPa in 2018 to 0.33 hPa in 2023. It signifies a pronounced weakening UDI over the study period, reflecting a reduction in the humidity condition difference between urban and rural areas. A notable signal in 2023 is the first occurrence of 29 days with negative daily UDII values (i.e., an “urban moist island” phenomenon), with a minimum value of −0.84 hPa, breaking the previous record of exclusively positive values and suggesting changes in local urban climatic conditions (Figure 4f).
The frequency of weak UDI days increased dramatically, from sporadic occurrences (0–2 days) during 2018–2022, and further to 14 days in 2023. It means that for nearly two weeks in the summer of 2023, the humidity difference between urban and rural areas was weak, representing the most prominent extreme feature consistent with the overall weakening of the UDI effect. Conversely, the frequency of strong UDI days shows a pattern of recent reduction markedly. The year 2018, which has the strongest dry island effect, recorded 39 such days. With the exception of 2021 (7 days), subsequent years maintained low levels (0–3 days). It indicates that exceptionally intense dry island events have become increasingly rare. Consequently, 2018 and 2023 present a stark contrast (Figure 4a,f); 2018 represented a period of a strong and stable UDI effect (highest summer mean, most strong UDI days, few weak UDI days), whereas 2023 represented a period of a weak UDI effect and 1/3 days of urban moist island.
The UDI effect persisted throughout the 24 h cycle, exhibiting a regular diurnal pattern of daytime strengthening and nocturnal weakening (Figure 5). The mean daytime UDII (approximately 1.0 hPa) is substantially stronger than the mean nocturnal UDII (approximately 0.5 hPa), with the peak intensity occurring around 17:00–18:00 (1.18 hPa). Furthermore, the standard deviation of hourly UDII is higher during the afternoon and evening (e.g., 1.16 hPa at 18:00) compared to the early morning hours, indicating greater inter-summer or inter-day variability in daytime UDII. This suggests that the daytime intensity of the dry island may be more susceptible to specific synoptic conditions than its nighttime counterpart.
The standard deviations of hourly UDII are around 1.00 hPa during 2018 to 2023 (Table 3). The maximum hourly UDII mostly fluctuated between 2.0 hPa and 3.2 hPa. In contrast, the minimum (i.e., weakest dry island) hourly UDII reached −8.63 hPa in 2021, while in other years it mostly fluctuated between 5.0 hPa and 6.0 hPa. This suggests that the extremes of hourly UDII show interannual fluctuations.
In the local climate effects induced by changes in urban surface cover, the expansion of large-scale impervious surfaces is a key physical driver for the formation of UHI and UDI phenomena. As shown in Figure 6, with the expansion of built-up areas around urban stations, the observed UHII and UDII increase accordingly. When the proportion of built-up areas reaches 100%, the hourly UHII and UDII can reach 1.5 °C and 0.47 hPa, respectively. The core mechanism lies in the fundamental alteration of energy and moisture exchange between the surface and the atmosphere by impervious surfaces, manifested as a disruption of the surface energy balance and the natural hydrological cycle.
Natural vegetation is characterized by a surface energy distribution dominated by latent heat flux. Under conditions of sufficient moisture supply, a significant portion of the received net radiation is utilized for water evaporation or plant transpiration, a process that effectively cools the surface and the adjacent atmosphere. When the surface is covered by impervious materials such as concrete or asphalt, this energy distribution pattern is altered. First, the physical connectivity of surface moisture is disrupted, leading to the depletion of available water sources for evaporation and a consequent sharp decline in latent heat flux. The significant enhancement of sensible heat flux means more energy is directly used to heat the near-surface air, resulting in elevated SAT. Simultaneously, impervious materials typically exhibit higher thermal capacity, promoting an increase in heat storage flux. This leads to the absorption and storage of substantial thermal energy during the day and its gradual release at night, thereby sustaining and intensifying nocturnal UHI strength. This redistribution of energy can be quantitatively characterized by a sharp increase in the Bowen ratio. The decrease in e is a result of the disruption of the hydrological cycle by impervious surfaces. On the moisture supply side, impervious surfaces severely weaken local-scale moisture sources by intercepting precipitation infiltration, removing vegetation transpiration, and accelerating surface runoff discharge. This directly leads to a persistently lower actual vapor pressure contributed by surface evaporation and plant transpiration to the atmosphere.
The main findings of this study indicate a recent intensification of the UHI and a weakening of the UDI in Xining. The water vapor emitted from anthropogenic sources can directly increase the actual vapor pressure in the near-surface air of urban areas, sufficient to offset the reduction in evaporation caused by the decrease in natural surfaces. For instance, intensive urban irrigation, persistently carried out during hot weather, provides a source of water vapor through evapotranspiration. Additionally, the influence of meteorological conditions cannot be ruled out. In recent years, enhanced precipitation on plateaus, as well as temporary water accumulation on impervious urban surfaces, can serve as short-term but intense sources of evaporation. Under such conditions, cities, with their greater heat storage and faster temperature rise, also experience vigorous evaporation, which can result in actual vapor pressure being higher than in suburban areas. Therefore, when factors such as anthropogenic water vapor emissions and irrigation management in cities are sufficiently strong, it is entirely possible to weaken or even reverse the dry island effect while the heat island effect intensifies, creating a combined stress of “high temperature–high humidity”. This combination has a more severe negative impact on human thermal comfort (creating a muggy sensation) compared to simply hot and dry conditions.
Furthermore, the response of summer hourly UHII to the background meteorological conditions is explored. Regarding temperature condition, the strongest hourly UHII (mean: 1.40 °C) is observed under high-temperature conditions (mean daily SAT is 22.50 °C), whereas the weakest UHII (mean: 0.85 °C) occurred under low-temperature conditions (mean daily SAT is 12.70 °C) (Figure 7a). The linear regression analysis confirmed a statistically significant positive correlation between hourly UHII and background temperature (p < 0.01) (Figure 7b). It implies that Xining City endures not only extreme absolute temperatures during heatwaves, but also an intensified relative warming effect due to a stronger UHI. Based on our previous research findings, the UHI in Xining City responses as regional daytime heatwaves amplify mean intensity by 0.35 °C [41].
According to Park et al. [43], their idealized ensemble simulations revealed that UHI intensity increases with background temperature but decreases with background humidity. These variations are primarily regulated by near-surface thermodynamic processes. In the evening, higher background temperatures enhance radiative cooling in rural areas and reduce urban turbulent mixing, leading to a stronger nighttime UHI. In contrast, increased humidity weakens rural radiative cooling while enhancing urban turbulent mixing, thereby suppressing UHII.
Concerning background humidity, the most pronounced summer hourly UHII (mean: 1.405 °C) is identified under dry conditions (mean daily e is 7.57 hPa). In contrast, under humid conditions (mean daily e is 16.73 hPa), the UHII is weakest (mean: 0.82 °C), with negative values (as low as −0.05 °C) occasionally occurring, indicating brief “urban cool island” phenomena (Figure 7c). It demonstrates that the UHI effect in Xining is most prominent when the ambient air is dry. The inhibitory effect of humidity condition on the UHI is further quantified and corroborated by linear regression (Figure 7d). However, UHII exhibited greater uncertainty under dry conditions. Notably, the humidity condition also demonstrates a comparable explanatory power for hourly UHII variations with the temperature condition. This underscores the necessity of adopting a multi-factorial perspective in both analyzing and mitigating the UHI effect, with particular attention to the critical role played by atmospheric humidity.
Furthermore, the influences of both background temperature and humidity on hourly UHI exhibited pronounced diurnal asymmetry (Figure 8). Specifically, the impact of temperature and humidity on nocturnal UHI is exceptionally strong, with UHII under high-temperature/dry conditions far exceeding that under low-temperature/humid conditions. Conversely, their influence on the pre-noon UHI is weak or even reversed, where the city occasionally became warmer under low-temperature or high-humidity environments.
In this study, the calculation of UHII and UDII relies on a limited number of rural reference stations (n = 6) selected via strict criteria. While this approach ensures high comparability with urban sites, the potential influence of the specific choice of rural stations on the magnitude of the calculated signals cannot be assessed via sensitivity analysis due to the lack of alternative qualified stations in the region. This is a constraint of observational studies in areas with sparse station networks. In addition, the selection of the ±1.5 standard deviation threshold, while conventional for identifying climatic extremes, is inherently arbitrary. To assess the sensitivity of our findings to this choice, a comparative analysis using alternative thresholds of ±1.0 and ±2.0 standard deviations are conducted. The results confirms that the key findings on the evolving characteristics of UHI and UDI extremes are robust, and not highly sensitive to the specific standard deviation multiplier employed in the definition.
Furthermore, urban climatic conditions constitute a vital component of the human living environment, closely linked to well-being and public health [44]. For a comprehensive assessment of human thermal exposure, mechanism-based models that simulate heat exchange between the human body and the environment are essential. Among these, the Universal Thermal Climate Index (UTCI) integrates a multi-node model of human thermophysiological responses with a clothing model, effectively reflecting the combined impact of key bio-meteorological factors on thermal comfort [45,46] and is widely regarded as a highly applicable index [47,48,49]. In practice, UTCI is often derived from air temperature, vapor pressure, wind speed, and mean radiant temperature. However, the primary objective of this study is to clarify the impact of urban expansion itself on near-surface atmospheric temperature and humidity conditions, with a focus on urban thermal climate formation. For city-scale analysis over time, we rely on high-resolution station observations, which provide stable, long-term records of air temperature and humidity. In contrast, obtaining reliable, long-term, high-resolution urban-scale data for key UTCI inputs, particularly mean radiant temperature, remains a major challenge. Therefore, while this study focuses specifically on temperature and vapor pressure, it establishes a necessary foundation by highlighting that the urban area of Xining exhibits a more intense thermal–humid environment compared to its suburbs, a condition that may adversely affect human thermal comfort. This conceptual comparison underscores that whereas UTCI offers a more complete human-relevant assessment, our choice of indicators is justified by the research focus on urban climate drivers and by current data constraints for longitudinal urban scale analysis. Ultimately, further investigation using advanced indices like UTCI remains essential for detailed assessment of urban thermal comfort and population-relevant heat stress. The limitations of this study lie in its provision of only a preliminary, data-driven investigation into the drivers behind the intensifying UHI and weakening UDI in Xining. A more definitive elucidation of the underlying physical mechanisms will necessitate further validation through numerical modeling [50,51].

4. Conclusions

This study utilizes dense summertime observations (2018–2023) from Xining, the largest city on the TP, to quantify the contrasting evolution of the UHI and UDI, and to decipher their differential responses to background meteorological drivers. Key findings include the following:
  • Despite interannual variability, the summer mean UHII has intensified recently. The variability in both daily and hourly UHII increased significantly, reflecting greater instability in the urban thermal environment both on a diurnal and inter-summer scale. This heightened volatility is exemplified by the steady rise in the summer maximum hourly UHII, which climbed from 3.95 °C in 2018 to 6.60 °C in 2023, signaling a growing risk of intense, short-duration heat island events. The frequency of strong UHI days surged markedly. Such days were rare during 2018–2020 (0–2 days per summer), peaked at 23 days in 2022, and remained elevated at 12 days in 2023, representing the most prominent extreme characteristic in recent years. The year 2022 stood out in particular, not only recording the only negative daily UHII value (–0.05 °C) but also exhibiting the highest number of both strong and weak UHI days, along with the largest standard deviation. This underscores a pronounced increase in the volatility of UHII extremes in the latter part of the study period.
  • Based on actual vapor pressure calculations, UDI effect remains a persistent summer feature in Xining, with a six-year mean daily UDII of 0.75 hPa. However, the summer mean UDII shows a fluctuating yet clear weakening, declining from 1.51 hPa in 2018 to 0.33 hPa in 2023. This indicates a recent reduction in the urban–rural humidity contrast over the study period. Corresponding changes are observed in the frequency of extreme dry island days, weak UDI days increased substantially, reaching 14 days in 2023, whereas strong UDI days decreased markedly from 39 days in 2018 to only 0–3 days in most subsequent years. Diurnally, the UDI follows a regular pattern of daytime intensification and nocturnal weakening. Mean daytime UDII is substantially stronger than nighttime UDII, peaking around 17:00–18:00.
  • Summer hourly UHII in Xining is significantly modulated by background meteorological conditions, exhibiting a strong positive correlation with air temperature and a more substantial negative correlation with humidity. Notably, atmospheric humidity demonstrates greater explanatory power for UHII variability than temperature, underscoring the necessity of a multi-factorial perspective in UHI analysis and mitigation. Furthermore, these influences display pronounced diurnal asymmetry, being strongest at night and weak or even reversed during the pre-noon hours.

Author Contributions

Conceptualization: J.W.; Methodology: J.W. and Q.L.; Investigation: G.C. and S.K.T.; Visualization: G.C. and J.W.; Supervision: S.K.T.; Writing—original draft: J.W. and S.K.T.; Writing—review and editing: S.K.T. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported by Open Research Fund Program of the State Key Laboratory of Hydroscience and Engineering (sklhse-2024-C-02).

Data Availability Statement

The Global Artificial Impervious Areas dataset is openly accessible via the Peng Cheng Laboratory repository at https://data-starcloud.pcl.ac.cn/iearthdata/ (accessed on 30 September 2025).

Conflicts of Interest

There are no relevant financial or non-financial competing interests to report.

References

  1. Oke, T.R.; Mills, G.; Christen, A.; Voogt, J.A. Urban Climates; Cambridge University Press: Cambridge, UK, 2017. [Google Scholar]
  2. Carlson, T.N.; Boland, F.E. Analysis of urban-rural canopy using a surface heat flux/temperature model. J. Appl. Meteorol. Climatol. 1978, 17, 998–1013. [Google Scholar] [CrossRef]
  3. IPCC. The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013; p. 1535. [Google Scholar]
  4. He, J.F.; Liu, J.Y.; Zhuang, D.F.; Zhang, W.; Liu, M.L. Assessing the effect of land use/land cover change on the change of urban heat island intensity. Theor. Appl. Climatol. 2007, 90, 217–226. [Google Scholar] [CrossRef]
  5. Zhou, L.M.; Dickinson, R.E.; Tian, Y.; Fang, J.; Li, Q.; Kaufmann, R.K.; Tucker, C.J.; Myneni, R.B. Evidence for a significant urbanization effect on climate in China. Proc. Natl. Acad. Sci. USA 2004, 101, 9540–9544. [Google Scholar] [CrossRef] [PubMed]
  6. Tysa, S.K.; Ren, G.; Qin, Y.; Zhang, P.; Ren, Y.; Jia, W.; Wen, K. Urbanization Effect in Regional Temperature Series Based on a Remote Sensing Classification Scheme of Stations. J. Geophys. Res. Atmos. 2019, 124, 10646–10661. [Google Scholar] [CrossRef]
  7. Ren, G.Y.; Zhou, Y.; Chu, Z.; Zhou, J.; Zhang, A.; Guo, J.; Liu, X. Urbanization effects on observed surface air temperature trends in north China. J. Clim. 2008, 21, 1333–1348. [Google Scholar] [CrossRef]
  8. Yang, Y.J.; Wu, B.-W.; Shi, C.-E.; Zhang, J.-H.; Li, Y.-B.; Tang, W.-A.; Wen, H.-Y.; Zhang, H.-Q.; Shi, T. Impacts of urbanization and station-relocation on surface air temperature series in Anhui province, China. Pure Appl. Geophys. 2013, 170, 1969–1983. [Google Scholar] [CrossRef]
  9. Zhong, Y.; Chen, S.; Mo, H.; Wang, W.; Yu, P.; Wang, X.; Chuduo, N.; Ba, B. Contribution of urban expansion to surface warming in high-altitude cities of the Tibetan Plateau. Clim. Change 2022, 175, 6. [Google Scholar] [CrossRef]
  10. Zhu, L.; Meng, C.; Xie, Q. Characterizing surface and canopy urban heat islands and impacts of land use and land cover change in a third-pole city. Urban Clim. 2025, 63, 102590. [Google Scholar] [CrossRef]
  11. Liang, L.; Yu, L.; Wang, Z. Identifying the dominant impact factors and their contributions to heatwave events over mainland China. Sci. Total Environ. 2022, 848, 157527. [Google Scholar] [CrossRef]
  12. Ao, X.; Tan, J.; Zhi, X.; Gyi, J.; Lu, Y.; Liu, D. Synergistic interaction between urban heat island and heat waves and its impact factors in Shanghai. Acta Geogr. Sin. 2019, 74, 1789–1802. (In Chinese) [Google Scholar]
  13. Founda, D.; Santamouris, M. Synergies between Urban Heat Island and Heat Waves in Athens (Greece), during an extremely hot summer (2012). Sci. Rep. 2017, 7, 10973. [Google Scholar] [CrossRef] [PubMed]
  14. Jiang, S.; Lee, X.; Wang, J.; Wang, K. Amplified Urban Heat Islands during Heat Wave Periods. J. Geophys. Res. Atmos. 2019, 124, 7797–7812. [Google Scholar] [CrossRef]
  15. Xue, J.; Zong, L.; Yang, Y.; Bi, X.; Zhang, Y.; Zhao, M. Diurnal and interannual variations of canopy urban heat island (CUHI) effects over a mountain–valley city with a semi-arid climate. Urban Clim. 2023, 48, 101425. [Google Scholar] [CrossRef]
  16. Chew, L.W.; Liu, X.; Li, X.-X.; Norford, L.K. Interaction between heat wave and urban heat island: A case study in a tropical coastal city, Singapore. Atmos. Res. 2021, 247, 105134. [Google Scholar] [CrossRef]
  17. Shi, Z.; Jia, G. Changes in urban heat island intensity during heatwaves in China. Environ. Res. Lett. 2024, 19, 074061. [Google Scholar] [CrossRef]
  18. Wang, L.; Li, D.; Yuan, X. The Role of Vapor Pressure Deficit in the CLM Simulated Interaction Between Urban Heat Islands and Heat Waves Over CONUS. Geophys. Res. Lett. 2025, 52, e2024GL113257. [Google Scholar] [CrossRef]
  19. Chow, S.D.; Chang, C. Shanghai urban influences on humidity and precipitation distribution. GeoJournal 1984, 8, 201–204. [Google Scholar] [CrossRef]
  20. Chandler, T.J. Absolute and relative humidities in towns. Bull. Am. Meteorol. Soc. 1967, 48, 394–399. [Google Scholar] [CrossRef]
  21. Ackerman, B. Climatology of Chicago area urban-rural differences in humidity. J. Clim. Appl. Meteorol. 1987, 26, 427–430. [Google Scholar] [CrossRef]
  22. Lee, D.O. Urban—Rural humidity differences in London. Int. J. Climatol. 1991, 11, 577–582. [Google Scholar] [CrossRef]
  23. Omoto, Y.; Hamotani, K.; Um, H.H. Recent changes in trends of humidity of Japanese cities. J. Jpn. Soc. Hydrol. Water Resour. 1994, 7, 106–113. [Google Scholar] [CrossRef]
  24. Yang, P.; Ren, G.Y.; Hou, W. Temporal–spatial patterns of relative humidity and the urban dryness island effect in Beijing City. J. Appl. Meteorol. Climatol. 2017, 56, 2221–2237. [Google Scholar] [CrossRef]
  25. Huang, X.; Hao, L.; Sun, G.; Yang, Z.; Li, W.; Chen, D. Urbanization Aggravates Effects of Global Warming on Local Atmospheric Drying. Geophys. Res. Lett. 2022, 49, e2021GL095709. [Google Scholar] [CrossRef]
  26. Schatz, J.; Kucharik, C.J. Seasonality of the Urban Heat Island Effect in Madison, Wisconsin. J. Appl. Meteorol. Climatol. 2014, 53, 2371–2386. [Google Scholar] [CrossRef]
  27. Arnds, D.; Böhner, J.; Bechtel, B. Spatio-temporal variance and meteorological drivers of the urban heat island in a European city. Theor. Appl. Climatol. 2017, 128, 43–61. [Google Scholar] [CrossRef]
  28. Liu, Y.; Li, Q.; Yang, L.; Mu, K.; Zhang, M.; Liu, J. Urban heat island effects of various urban morphologies under regional climate conditions. Sci. Total Environ. 2020, 743, 140589. [Google Scholar] [CrossRef] [PubMed]
  29. Hoffmann, P.; Krueger, O.; Schlünzen, K.H. A statistical model for the urban heat island and its application to a climate change scenario. Int. J. Climatol. 2012, 32, 1238–1248. [Google Scholar] [CrossRef]
  30. Yao, T.; Bolch, T.; Chen, D.; Gao, J.; Immerzeel, W.; Piao, S.; Su, F.; Thompson, L.; Wada, Y.; Wang, L.; et al. The imbalance of the Asian water tower. Nat. Rev. Earth Environ. 2022, 3, 618–632. [Google Scholar] [CrossRef]
  31. Kuang, X.; Jiao, J.J. Review on climate change on the Tibetan Plateau during the last half century. J. Geophys. Res. Atmos. 2016, 121, 3979–4007. [Google Scholar] [CrossRef]
  32. Gao, Y.; Leung, L.R.; Zhang, Y.; Cuo, L. Changes in Moisture Flux over the Tibetan Plateau during 1979–2011: Insights from a High-Resolution Simulation. J. Clim. 2015, 28, 4185–4197. [Google Scholar] [CrossRef]
  33. Easterling, D.R.; Wehner, M.F. Is the climate warming or cooling? Geophys. Res. Lett. 2009, 36, L08706. [Google Scholar] [CrossRef]
  34. Santer, B.D.; Bonfils, C.; Painter, J.F.; Zelinka, M.D.; Mears, C.; Solomon, S.; Schmidt, G.A.; Fyfe, J.C.; Cole, J.N.S.; Nazarenko, L.; et al. Volcanic contribution to decadal changes in tropospheric temperature. Nat. Geosci. 2014, 7, 185–189. [Google Scholar] [CrossRef]
  35. You, Q.; Min, J.; Kang, S. Rapid warming in the Tibetan Plateau from observations and CMIP5 models in recent decades. Int. J. Climatol. 2016, 36, 2660–2670. [Google Scholar] [CrossRef]
  36. Li, Q.; Yang, S.; Xu, W.; Wang, X.L.; Jones, P.; Parker, D.; Zhou, L.; Feng, Y.; Gao, Y. China experiencing the recent warming hiatus. Geophys. Res. Lett. 2015, 42, 889–898. [Google Scholar] [CrossRef]
  37. Pan, X.; Wang, Y.; Liu, Z.; He, C.; Liu, H.; Chen, Z. Understanding Urban Expansion on the Tibetan Plateau over the Past Half Century Based on Remote Sensing: The Case of Xining City, China. Remote Sens. 2021, 13, 46. [Google Scholar] [CrossRef]
  38. Zhi, Z.; Liu, F.; Chen, Q.; Zhou, Q.; Ma, W. Study on the Urban Expansion of Typical Tibetan Plateau Valley Cities and Changes in Their Ecological Service Value: A Case Study of Xining, China. Sustainability 2024, 16, 4537. [Google Scholar] [CrossRef]
  39. Liu, D.; Cao, C.; Dubovyk, O.; Tian, R.; Chen, W.; Zhuang, Q.; Zhao, Y.; Menz, G. Using fuzzy analytic hierarchy process for spatio-temporal analysis of eco-environmental vulnerability change during 1990–2010 in Sanjiangyuan region, China. Ecol. Indic. 2017, 73, 612–625. [Google Scholar] [CrossRef]
  40. Zuo, Y.; Zhang, C.N.; Lin, Z.B.; Mao, X.F.; Liu, S.H.; Liu, Z.F. A Multi-scale evaluation study on the shading effects of street trees at noon in summer in Xining City. Acta Ecol. Sin. 2025, 45, 4728–4742. [Google Scholar]
  41. Chen, G.; Lu, X.; Li, Q.; Zhang, S.; Tysa, S.K. Divergent Urban Canopy Heat Island Responses to Heatwave Type over the Tibetan Plateau: A Case Study of Xining. Land 2025, 14, 2033. [Google Scholar] [CrossRef]
  42. Gui, K.; Zhou, T.; Zhang, W.; Zhang, X. Land-atmosphere coupling amplified the record-breaking heatwave at altitudes above 5000 meters on the Tibetan Plateau in July 2022. Weather Clim. Extrem. 2024, 45, 100717. [Google Scholar] [CrossRef]
  43. Park, K.; Baik, J.-J.; Jin, H.-G.; Tabassum, A. Changes in urban heat island intensity with background temperature and humidity and their associations with near-surface thermodynamic processes. Urban Clim. 2024, 58, 102191. [Google Scholar] [CrossRef]
  44. Shindell, D.; Kuylenstierna, J.C.I.; Vignati, E.; van Dingenen, R.; Amann, M.; Klimont, Z.; Anenberg, S.C.; Muller, N.; Janssens-Maenhout, G.; Raes, F.; et al. Simultaneously Mitigating Near-Term Climate Change and Improving Human Health and Food Security. Science 2012, 335, 183–189. [Google Scholar] [CrossRef] [PubMed]
  45. McGregor, G.R. Special issue: Universal Thermal Comfort Index (UTCI). Int. J. Biometeorol. 2012, 56, 419. [Google Scholar] [CrossRef] [PubMed]
  46. Bröde, P.; Fiala, D.; Błażejczyk, K.; Holmér, I.; Jendritzky, G.; Kampmann, B.; Tinz, B.; Havenith, G. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 2012, 56, 481–494. [Google Scholar] [CrossRef]
  47. Potchter, O.; Cohen, P.; Lin, T.-P.; Matzarakis, A. Outdoor human thermal perception in various climates: A comprehensive review of approaches, methods and quantification. Sci. Total Environ. 2018, 631–632, 390–406. [Google Scholar] [CrossRef]
  48. Blazejczyk, K.; Epstein, Y.; Jendritzky, G.; Staiger, H.; Tinz, B. Comparison of UTCI to selected thermal indices. Int. J. Biometeorol. 2012, 56, 515–535. [Google Scholar] [CrossRef]
  49. Jendritzky, G.; de Dear, R.; Havenith, G. UTCI—Why Another Therm. Index? Int. J. Biometeorol. 2012, 56, 421–428. [Google Scholar] [CrossRef]
  50. Jin, L.; Schubert, S.; Fenner, D.; Salim, M.H.; Schneider, C. Estimation of mean radiant temperature in cities using an urban parameterization and building energy model within a mesoscale atmospheric model. Meteorol. Z 2022, 31, 31–52. [Google Scholar] [CrossRef]
  51. Jin, L.; Schubert, S.; Salim, M.H.; Schneider, C. Impact of Air Conditioning Systems on the Outdoor Thermal Environment during Summer in Berlin, Germany. Int. J. Environ. Res. Public Health 2020, 17, 4645. [Google Scholar] [CrossRef]
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Figure 1. (a) The boundary line of the TP. (b) Observational stations around Xining City (marks) and elevation (shading). Urban stations: red dots; rural stations: blue star marks; other stations: black dots. The red line delineates the 2018 artificial impervious area; referenced from [41].
Figure 1. (a) The boundary line of the TP. (b) Observational stations around Xining City (marks) and elevation (shading). Urban stations: red dots; rural stations: blue star marks; other stations: black dots. The red line delineates the 2018 artificial impervious area; referenced from [41].
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Figure 2. The summer daily UHII from 2018 to 2023 (a–f). The red and blue dashed lines represent the mean daily UHII plus 1.5 times the standard deviation and the mean minus 1.5 times the standard deviation, respectively. Units: °C.
Figure 2. The summer daily UHII from 2018 to 2023 (a–f). The red and blue dashed lines represent the mean daily UHII plus 1.5 times the standard deviation and the mean minus 1.5 times the standard deviation, respectively. Units: °C.
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Figure 3. The summer mean diurnal variation of UHII from 2018 to 2023, applying a 5-point moving average. The shaded area represents the range of mean ± 1 standard deviation. Units: °C.
Figure 3. The summer mean diurnal variation of UHII from 2018 to 2023, applying a 5-point moving average. The shaded area represents the range of mean ± 1 standard deviation. Units: °C.
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Figure 4. The summer daily UDII from 2018 to 2023 (a–f). The red and blue dashed lines represent the mean daily UDII plus 1.5 times the standard deviation and the mean minus 1.5 times the standard deviation, respectively. Units: hPa.
Figure 4. The summer daily UDII from 2018 to 2023 (a–f). The red and blue dashed lines represent the mean daily UDII plus 1.5 times the standard deviation and the mean minus 1.5 times the standard deviation, respectively. Units: hPa.
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Figure 5. The summer mean diurnal variation of UDII from 2018 to 2023, applying a 5-point moving average. The shaded area represents the range of mean ± 1 standard deviation. Units: hPa.
Figure 5. The summer mean diurnal variation of UDII from 2018 to 2023, applying a 5-point moving average. The shaded area represents the range of mean ± 1 standard deviation. Units: hPa.
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Figure 6. The hourly variations of UHII and UDII at the urban measurement site are illustrated, with UHII represented as a bar chart on the left y-axis and UDII depicted by a line graph on the right y-axis under different urban land use ratios.
Figure 6. The hourly variations of UHII and UDII at the urban measurement site are illustrated, with UHII represented as a bar chart on the left y-axis and UDII depicted by a line graph on the right y-axis under different urban land use ratios.
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Figure 7. The hourly UHII under different daily SAT (a) and e (c) decile groups (divided into deciles P0–10 (≤10%) to P90–100 (>90%)), and the regression relationship of daily SAT (b) and e (d) between hourly UHII. Units: °C. Black diamonds in panels (a,c) denote outliers in the data.
Figure 7. The hourly UHII under different daily SAT (a) and e (c) decile groups (divided into deciles P0–10 (≤10%) to P90–100 (>90%)), and the regression relationship of daily SAT (b) and e (d) between hourly UHII. Units: °C. Black diamonds in panels (a,c) denote outliers in the data.
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Figure 8. The diurnal variation of UHII under higher and lower daily SAT (a) and e conditions (c), and the different diurnal variation in UHII between higher and lower daily SAT (b) and e conditions (d). Units: °C. Solid lines represent mean UHII values, with shading indicating ±1 standard deviation (SD) of the data. Rectangular bars show the differences in UHII between higher and lower conditions, where red shades denote positive differences and blue shades indicate negative differences.
Figure 8. The diurnal variation of UHII under higher and lower daily SAT (a) and e conditions (c), and the different diurnal variation in UHII between higher and lower daily SAT (b) and e conditions (d). Units: °C. Solid lines represent mean UHII values, with shading indicating ±1 standard deviation (SD) of the data. Rectangular bars show the differences in UHII between higher and lower conditions, where red shades denote positive differences and blue shades indicate negative differences.
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Table 1. The geographic coordinates and altitude for the urban and rural stations.
Table 1. The geographic coordinates and altitude for the urban and rural stations.
Station
Type		Station IDLatitude
(°N)		Longitude
(°E)		Altitude
(m)		Station IDLatitude
(°N)		Longitude
(°E)		Altitude
(m)
Urban
station		136.6356101.770322291636.5258101.67282533
236.5778101.846122131736.5825101.74722321
336.5628101.731423681836.5911101.73672345
436.7172101.766922831936.61101.76642359
536.6317101.808322692036.565101.89192176
636.5994101.778124052136.5683101.87472187
736.6953101.745823122236.6728101.74032330
836.6628101.680822882336.6681101.66812315
936.6381101.701122972436.5436101.72502410
1036.6419101.776122412536.6644101.69922282
1136.5478101.698124132636.5981101.75142317
1236.6272101.764722602736.6297101.70082314
1336.7283101.752223122836.6161101.79532238
1436.6431101.743622452936.5931101.81562232
1536.65101.72112259
Rural
station		136.8156101.75892315436.5456101.59032349
236.6519101.59032336536.6797101.61892353
336.8292101.76672346636.7089101.88442354
Table 2. Key parameters of the diurnal variation of UHII from 2018 to 2023.
Table 2. Key parameters of the diurnal variation of UHII from 2018 to 2023.
YearPeak Time
(UTC + 8) 		Trough Time
(UTC + 8)		Standard
Deviation (°C)		Maximum Hourly UHII (°C)Minimum Hourly UHII (°C)
20185:0010:000.6873.950−2.075
20191:009:000.7204.011−2.000
20200:0010:000.9635.973−1.255
20215:009:001.1655.421−2.673
20220:009:001.0825.882−3.105
202323:0010:000.9886.603−1.971
Table 3. Key parameters of the diurnal variation of UHII from 2018 to 2023.
Table 3. Key parameters of the diurnal variation of UHII from 2018 to 2023.
YearPeak Time
(UTC + 8)		Trough Time
(UTC + 8)		Standard
Deviation (hPa)		Maximum Hourly UDII (hPa)Minimum Hourly UDII (hPa)
20187:0018:000.962.05−6.19
20196:0017:000.893.18−5.34
20206:0016:000.873.20−4.76
20216:0017:001.043.11−8.63
20226:0017:000.802.36−4.66
20236:0015:000.982.47−4.58
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Wang, J.; Tysa, S.K.; Chen, G.; Li, Q. Temporal Evolution and Extremes of Urban Thermal and Humidity Environments in a Tibetan Plateau City. Land 2026, 15, 64. https://doi.org/10.3390/land15010064

AMA Style

Wang J, Tysa SK, Chen G, Li Q. Temporal Evolution and Extremes of Urban Thermal and Humidity Environments in a Tibetan Plateau City. Land. 2026; 15(1):64. https://doi.org/10.3390/land15010064

Chicago/Turabian Style

Wang, Jinzhao, Suonam Kealdrup Tysa, Guoxin Chen, and Qiong Li. 2026. "Temporal Evolution and Extremes of Urban Thermal and Humidity Environments in a Tibetan Plateau City" Land 15, no. 1: 64. https://doi.org/10.3390/land15010064

APA Style

Wang, J., Tysa, S. K., Chen, G., & Li, Q. (2026). Temporal Evolution and Extremes of Urban Thermal and Humidity Environments in a Tibetan Plateau City. Land, 15(1), 64. https://doi.org/10.3390/land15010064

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