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Spatial climate data sets that extend back in time over many decades are an important resource for climate monitoring. The long‐term consistency of such data sets is, however, compromised by changes in the measurement systems over time. In this paper, we introduce a data set of monthly precipitation on a 5‐km grid over the European Alps that extends back to the late 19th century. In deriving the "long‐term Alpine precipitation reconstruction" (LAPrec), special care is taken of variations in the station network, in order for the data set to satisfy high standards in long‐term consistency. LAPrec builds on a reconstruction method that integrates the available information in two portions: The first is a set of high‐quality homogenized station series, taken from the HISTALP data archive, covering the entire period almost continuously. The second is a high‐resolution gridded precipitation analysis, taken from the "Alpine Precipitation Grid Data Set," constructed from thousands of rain‐gauges but covering a few decades only. We demonstrate how the reconstruction approach successfully introduces mesoscale structures that are not resolved by the available long‐term station series, more plausibly so than a predecessor data set using conventional interpolation. We also illustrate that LAPrec reveals long‐term precipitation trends that are spatially more consistent and more detailed than the trends in popular climate monitoring data sets. Over the period 1871–2017 a statistically significant increase is found in winter over the northern parts of the Alps (1%–2% per 10 years). LAPrec is available in two versions (back until 1871 and 1901 respectively) from the Copernicus climate data store. Plain Language Summary: Data sets extending back in time over many decades are an important resource for climate monitoring. In this paper, we introduce a data set on a regular grid of monthly precipitation over the European Alps that extends back to 1871. In deriving the "long‐term Alpine precipitation reconstruction" (LAPrec), special care is taken to keep the station network constant and make use of high‐quality homogenized time series, in order for the data set to be as consistent as possible over time. LAPrec builds on two sources of information: The first is a set of high‐quality station series from the HISTALP data archive, covering the entire period almost continuously. The second is a high‐resolution gridded precipitation analysis, the "Alpine Precipitation Grid Data Set," constructed from thousands of rain‐gauges but covering a few decades only. We demonstrate how this approach successfully introduces details that are not resolved by the available long‐term station series. We also illustrate that LAPrec reveals long‐term precipitation trends that are spatially more consistent and detailed than the trends in popular climate monitoring data sets. Over the period 1871–2017 a statistically significant increase in winter precipitation is found over the northern parts of the Alps. LAPrec is available from the Copernicus climate data store. Key Points: Long‐term spatial analyses of precipitation in the European Alps since 1871For applications such as climate monitoring and trend analysis, the data set meets high standards in long‐term consistency and homogeneityLong‐term Alpine precipitation reconstruction detects a significant trend in winter precipitation over the northern part of the Alps in the period starting from 1871 [ABSTRACT FROM AUTHOR]

Copyright of Water Conservancy Science & Techonlogy & Economy is the property of Water Conservancy Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ecosystem in the Qinghai-Tibetan Plateau region is fragile. And the excessive altitude and lower temperatures can lead to regional variations in the community composition and metabolic processes of microorganisms in wastewater treatment systems. To identify these variations, this paper explores the microbial community structure and metabolic processes of activated sludge in A2O wastewater treatment systems in alpine regions based on 16S rRNA sequencing technology and the PICRUSt2 database. Also, the effect of gradual changes in DO concentration on the biological treatment system was examined and analyzed. The results show that the composition of the microbial community is distinct compared to that of the plain areas. norank_f__AKYH767 and IMCC26207 were found as enriched bacterial genera in the aerobic and anoxic tanks in the alpine region, respectively. Microbial communities in alpine areas are more perceptive and competent in processing environmental signals. The degradation of carbon and nitrogen pollutants is not strongly affected by high altitude, provided that the DO concentration is not lower than 1.0 mg/L. [ABSTRACT FROM AUTHOR]

Surface air temperature is a critical element in the surface–atmosphere interaction, energy exchange, and water cycle. Multi-source fusion reanalysis products (hereafter referred to as reanalysis) have spatiotemporal continuity and broad applicability that can provide key data support for various studies such as glacier melting, soil freeze-thaw and desertification, ecosystem, and climate change in the alpine region of the Qinghai–Tibet Plateau (QTP). Surface air temperature observations collected at 17 weather stations in the High-cold region Observation and Research Network for Land Surface Process and Environment of China (HORN) over the period of 2017–2018 are implemented to evaluate the advanced and widely used surface air temperature reanalysis datasets, which include the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5L), the U.S. Global Land Data Assimilation System (GLDAS), and China Meteorological Administration Land Data Assimilation System (CLDAS). Results are as follows: (1) Evaluation results of temporal changes and spatial distribution characteristics indicate that the three reanalysis datasets are consistent with in-situ observations in the alpine region of the QTP. CLDAS is more consistent with observations and can better describe details of temperature distribution and variation than ERA5L and GLDAS. (2) For the evaluation period, CLDAS is 0.53 °C higher than the in-situ observation, while ERA5L and GLDAS are lower than the in-situ observation by −3.45 °C and −1.40 °C, respectively. (3) The accuracy of CLDAS is better than ERA5L and GLDAS under different elevations and land covers. We resampled three reanalysis datasets with a spatial resolution of 0.25° and used the two most common interpolation methods to analyze the impact of spatial resolution and different interpolation methods on the evaluation results. We found that the impact is small. In summary, the three reanalysis datasets all have certain applicability in the alpine region of the QTP, and the accuracy of CLDAS is significantly higher than ERA5L and GLDAS. The results of the present paper have important implications for the selection of reanalysis data in the studies of climate, ecosystem, and sustainable development in the QTP. [ABSTRACT FROM AUTHOR]

[Objective] There are many environmental factors that affect vegetation ET0, and it is difficult to obtain some of them. Therefore, it is particularly important to find a simple and feasible method to simulate the response process of vegetation ET0 to environmental factors, and to quantitatively analyze the ecological water demand of typical vegetation in alpine region (taking Guide County as an example). [Method]Based on the meteorological data of Guide County from January to June, 2022, a BP network model for simulating vegetation ET0 under different algorithm optimization was established, 151 groups of data were selected to train the model, and the remaining 30 groups of data were tested and verified. [Result] The main factors affecting poplar ET0 are average temperature, 2 m wind speed, saturated vapor pressure difference, relative humidity, and the correlation from large to small is average temperature, saturated vapor pressure difference, 2 m wind speed and relative humidity. BP neural network model optimized by different algorithms is used to simulate poplar ET0, and the simulation effect is improved compared with the traditional BP network. [Conclusion] among which EWOA-BP optimization model has the best simulation result, which can be used as the first choice model to predict poplar ET0 in alpine region. [ABSTRACT FROM AUTHOR]

• Groundwater recharge changes with freeze–thaw processes. • The complete thawing period contributed the highest recharge amount at the fastest recharge speed. • Groundwater recharge during the freezing period is strongly influenced by temperature. Accurate estimation of groundwater recharge (GR) is crucial for sustainable groundwater use, particularly in alpine regions that are sensitive to climate change response. However, quantifying the amount of GR in these areas is challenging due to the absence of measurement data and an unclear understanding of the GR mechanism. In this paper, we proposed a new method (HS-IWTF) for estimating GR in the central Qinghai-Tibet Plateau. The method utilized Hydrus-1D to create an unsaturated zone soil water transport model, which provides a reference value for specific yield (Sy). Additionally, the method incorporated the impact of groundwater potential decline at various freeze–thaw stages to improve recharge estimation. The results showed that HS-IWTF method can be better applied for estimating GR in alpine regions. The simulated recharge of groundwater though vertical infiltration ranged from 191.7 to 217.8 mm/yr, primarily from precipitation infiltration. The total GR ranged from 312.1 to 405.0 mm/yr, with significant recharge contributed by snow and ice thaw and lateral runoff. The HS-IWTF method is more precise and reliable than simulated infiltration and traditional water table fluctuation (WTF) method in reflecting recharge changes at various monitoring sites during each freeze–thaw period. The thawing process period (0.95 mm/d, 11 %) and the complete thawing period (1.07 mm/d, 61 %) contributed more recharge at higher recharge rates due to improved recharge sources and conditions. The air temperature rise promoted the degradation of the cryosphere and raised the ratio of recharge precipitation (GR/PT), particularly in regions with high altitude and proximity to permafrost and glaciers where recharge was significantly high during the freezing period. This study provides valuable information on the measurement of GR in alpine regions and the crucial role of freeze–thaw processes in GR. [ABSTRACT FROM AUTHOR]

The diffuse presence of small copper ore deposits in the Alpine area, mostly exploited since Late Medieval times, led most scholars to assume that these deposits may actually be active much earlier and that many of the circulating prehistoric metal objects found in the area were produced with local copper sources. This assumption was recently validated for the Recent Bronze Age through the use of lead isotope tracers, and well supported by the archaeometallurgical evidences found in the South-Eastern Alps. However, the scarcity of available lead isotope data for pre-Bronze Age metals precluded to date the reconstruction of the metal flow through the Late Neolithic and Eneolithic (or Copper Age). Based on 49 new analyses of important archaeological objects from the Alpine region, the Po River Valley and Central Italy, mostly axes dated from the Late Neolithic to the Late Eneolithic, here we show that the diffusion of copper in Northern Italy (approximately 4500–2200 BC) includes three major periods of metal use and/or production, each related to specific ore sources. The South Alpine copper was massively used only starting from the middle of the 3rd millennium BC, in connection or slightly earlier than the Beaker event. [ABSTRACT FROM AUTHOR]

Insect pests pose a significant threat to alpine ecosystems, especially under rapid environmental change conditions. Therefore, it is necessary to explore the effects of environmental factors on insect pest risks and provide methods for pest management in alpine regions. Habitat heterogeneity and topographic variation are the indicators of insect pest risks. However, few studies have explored the effects of habitat heterogeneity and topographic variation on insect pest risks in alpine regions. We used species distribution modeling (i.e., maxent modeling) to project the distributions of insect pests in this alpine region based on occurrence records. Then, we delineated the high-risk areas for insect pests based on the species distributions under a conceptual risk framework using Zonation software for different ecoregional types. We determined the alpine conifer and mixed forests of the Nujiang Langcang Gorge, the conifer forests of the Qilian Mountains, and the shrublands and meadows of Southeast Tibet as the key areas requiring monitoring for insect pests in Qinghai province based on the scoring of insect pest risk rank with >0.7. Habitat heterogeneity and topographic variation could be developed as indicators of risk exposure to insect pests in alpine regions. Our study suggests that the prevention and control of insect pests should be conducted in areas with high habitat heterogeneity and topographic roughness in alpine regions. We provided new insights into the application of species distribution modeling based on habitat heterogeneity and topographic variation. The results of our study indicate that habitat heterogeneity and topographic variation should be considered for improving pest management effectiveness in alpine regions. [ABSTRACT FROM AUTHOR]

The Alpine region of Aosta Valley has an early warning system to issue hydrogeological alerts up to 36 h in advance based on the output of hydrological models and rainfall thresholds. However, those thresholds generally do not apply to the debris flows triggered by local summer thunderstorms, which typically are intense rainfalls of short duration, with cumulative precipitation lower than 20 mm. Therefore, it is necessary to formulate a specific predictive debris-flow model, which takes into account other possible triggering factors. In this study, we have developed a predictive model for debris flows with machine learning techniques, using a detailed dataset composed by a variety of geomorphological and hydro-meteorological variables. The variables of the dataset were collected from daily measured and modelled data for all of the 91 drainage basins in which at least one debris-flow event was generated during the time period considered in this study (2009–2019). The performance of the model, using different machine learning techniques, was evaluated, and the most suitable model was chosen to be experimentally implemented in the existing early warning system of the region. The output of the model provides a debris-flow probability (DFP) for individual basins computed from the geomorphological and hydro-meteorological input variables. [ABSTRACT FROM AUTHOR]

BACKGROUND: The roots are the main functional organs involved in the overwintering adaptability of alfalfa (Medicago sativa). However, it is still unclear how the roots are involved in the cold resistance in the high‐altitude area of the Qinghai–Tibet Plateau (QTP). In this study, three winter‐surviving 2‐year‐old alfalfa varieties (M. sativa Zhongmeng No.1, M. sativa Chiza No.1, and M. sativa Gongnong No.1) planted at two different altitudes (2812 m and 3109 m) in the northeast edge of the QTP were used to explore the cold‐resistance mechanism. RESULTS: At low altitudes (2812 m), the overwintering rate, taproot length, root area, root surface area, and root average diameter, plant height, fresh yield and hay yield of M. sativa Zhongmeng No.1 were significantly higher (P < 0.01) than for the other two varieties. At high altitude (3109 m), lateral root length, number of lateral roots, main root dry weight, and lateral root dry weight of M. sativa Chiza No.1 were higher (P < 0.01) than the other two varieties. At low and high altitudes, the activities of peroxidase and catalase were higher (P < 0.05) in M. sativa Chiza No.1 during post‐winter and pre‐winter respectively. At low altitude, higher soluble sugar (P < 0.05) and proline (P < 0.01) contents were recorded during the pre‑ and post‐winter periods. Membership function analysis showed that M. sativa Zhongmeng No.1 has the strongest cold resistance. The structural equation model showed that the overwintering rate of alfalfa was mainly affected by the morphological characteristics of roots and the physiological characteristics of roots, with contribution rates of 0.54 and 0.75 respectively, and the physiological characteristics of roots had the greatest effect on the overwintering rate. CONCLUSIONS: This study is of great significance to effectively solve the overwintering of alfalfa, the lack of high‐quality legume forage resources, and promote the development of animal husbandry in the alpine areas of the QTP. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The particle number size distribution (PNSD) and hygroscopicity on alpine sites can directly reflect the interaction between aerosol and cloud in the free troposphere, and narrow the errors caused by extrapolation from the ground observation. In this study, PNSD and cloud condensation nuclei (CCN) concentration (NCCN) were observed at the summit of Mt. Hua from December 16, 2020, to January 23, 2021. The result showed that the modes and hygroscopicity parameters (κ) of aerosols were significantly affected by the height and direction of the originated air masses. The particle in nucleation mode from new particle formation erupted frequently in 13:00–18:00 local time due to the intense photochemistry and had a potential contribution to CCN through the process of the growth with the ratio of 0.83 nm·h−1, which was affected by air masses, meteorological conditions, and gaseous pollutants. Subsequently, the hygroscopicity κ varied from 0.22 to 0.13 with the supersaturation from 0.2% to 1.0% as more components that were difficult to dissolve or had low hygroscopicity. The comprehensive analysis of PNSD and hygroscopicity showed that smaller k from two‐parameter power model corresponded to particles with higher hygroscopicity or larger diameter, while larger k matched to the hydrophobic or ultrafine particles. In addition, the interactions between aerosol and cloud were estimated more accurately and the uncertainty of indirect effect was shrunk. Overall, we provided insights into PNSD and hygroscopicity of alpine aerosol particles by direct observation on Mt. Hua, which may be important to other elevated regions as well. Plain Language Summary: The particle number size distribution (PNSD) and hygroscopicity on alpine sites are critical indicators of aerosol‐cloud interactions in the free troposphere. In this study, we investigated the particle number, size, and activation as cloud condensation nuclei (CCN) of alpine aerosol particles on Mt. Hua during winter 2020. Our results showed that the distribution modes and hygroscopicity parameters (κ) of aerosols were significantly affected by the height and direction of the originated air masses. The small‐diameter particle erupted from new particle formation and had a potential contribution to CCN through the process of the growth, which was affected by air masses, meteorological conditions, and gaseous pollutants. Furthermore, we calculated κ values ranging from 0.22 to 0.13 at supersaturations from 0.2% to 1.0%, which were attributed to components that were either difficult to dissolve or had low hygroscopicity. Comprehensive analysis of the PNSD and hygroscopicity data showed that a smaller k value corresponded to particles with higher hygroscopicity or larger diameters, while a larger k value was observed for ultrafine or hydrophobic particles. Key Points: New particle formation with a potential contribution to cloud condensation nuclei was originated from higher air masses due to the intense photochemistry in 13:00–18:00Smaller k matched to more hygroscopic or larger diameter particles, and larger k matched to hydrophobic or ultrafine diameter particlesThe hygroscopicity parameter decreased from 0.22 to 0.13 with the supersaturation elevated from 0.2% to 1.0% in the alpine region [ABSTRACT FROM AUTHOR]

Due to the characteristics of complex climate, large temperature difference between day and night, and long winter in alpine region, concrete is prone to temperature cracks during the pouring process of intake tower. Therefore, it is crucial to adopt appropriate temperature control measures for concrete crack prevention of intake tower concrete in view of the local special climate. In this paper, a join intake tower in alpine region were taken as the project case. Additionally, the three-dimensional finite element simulation software is used to analyze the sensitivity of cooling water pipe spacing, cooling water supply time, cooling water temperature, insulation layer thickness, and thickness of pouring layer in foundation restraint zone, and the most favorable temperature control measure is selected based on the simulation results. In the optimum scheme, cooling pipe spacing is 1.5 m × 1.5 m, cooling water supply time is 15 d, cooling water temperature is 12Co, the insulation material is 10 cm foam sheet, and the thickness of pouring layer in foundation restraint zone is 1.5 m. When the optimum scheme is verified, the temperature and thermal stress of the intake tower meet the control standards. The research results can provide references for the design and construction of the joint intake tower project in Qinghai.

Copyright of Botanica Serbica is the property of University of Belgrade, Institute of Botany & Botanical Garden Jevremovac and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Quantitative plant species α-diversity of grasslands at multiple spatial and temporal scales is important for investigating the responses of biodiversity to global change and protecting biodiversity under global change. Potential plant species α-diversity (i.e., SRp, Shannonp, Simpsonp and Pieloup: potential species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by climate data (i.e., annual temperature, precipitation and radiation) and actual plant species α-diversity (i.e., SRa, Shannona, Simpsona and Pieloua: actual species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by normalized difference vegetation index and climate data. Six methods (i.e., random forest, generalized boosted regression, artificial neural network, multiple linear regression, support vector machine and recursive regression trees) were used in this study. Overall, the constructed random forest models performed the best among the six algorithms. The simulated plant species α-diversity based on the constructed random forest models can explain no less than 96% variation of the observed plant species α-diversity. The RMSE and relative biases between simulated α-diversity based on the constructed random forest models and observed α-diversity were ≤1.58 and within ±4.49%, respectively. Accordingly, plant species α-diversity can be quantified from the normalized difference vegetation index and climate data using random forest models. The random forest models of plant α-diversity build by this study had enough predicting accuracies, at least for alpine grassland ecosystems, Tibet. The proposed random forest models of plant α-diversity by this current study can help researchers to save time by abandoning plant community field surveys, and facilitate researchers to conduct studies on plant α-diversity over a long-term temporal scale and larger spatial scale under global change. [ABSTRACT FROM AUTHOR]

This study discusses the impact of two different ecological restoration approaches on the distribution of soil particle size and organic carbon, expecting to provide references for research on the effects of ecological restoration on the soil carbon pool in alpine regions. By replacing the method of time sampling with spatial sampling, grasslands enclosed only in the growing season and woodlands enclosed all year round were respectively selected as the research objects. Through centrifugation, the soil samples were classified by grain size into sand (50–2000 μm), silt (2–50 μm), and clay (<2 μm) to analyze the distribution of organic carbon in soil particles of different sizes. The major findings were as follows. First, sand accounted for the largest proportion of all the soil components in the grasslands and woodlands that had been restored for different years, followed by silt and clay. Second, most of the organic carbon in the grasslands and woodlands was from sand and silt. As the restoration years increased, the proportion of organic carbon in clay grew in fluctuation. In short, both ecological restoration approaches have improved the soil structure and raised the content of soil organic carbon (SOC). Specifically, the restoration scheme of the woodlands exerted a more significant influence on the soil components and the distribution of organic carbon than that of the grasslands. [ABSTRACT FROM AUTHOR]

Bavaria and South Tyrol belong to the so-called 'Alpine (Macro-)Region', a transnational area located in the heart of Europe, where geopolitical, cultural and socio-economic peculiarities have resulted in distinctive democratic dynamics. The key question that this article aims to answer is whether these two regions can still be regarded as exceptional cases of political stability. It is shown that, since 2008, their political systems have experienced significant change. While transformations have also occurred at the national level in Germany and Italy, they seem even more dramatic in Bavaria and South Tyrol, particularly after decades of political continuity. It is argued that this unprecedented shift is due to the combined effect of regional and state-wide challengers and is linked to the multi-level character of party competition at the regional level. Generally, these two cases add a territorial dimension to the study of political stability and change in Western Europe. [ABSTRACT FROM AUTHOR]

Surface air temperature is a critical element in the surface–atmosphere interaction, energy exchange, and water cycle. Multi-source fusion reanalysis products (hereafter referred to as reanalysis) have spatiotemporal continuity and broad applicability that can provide key data support for various studies such as glacier melting, soil freeze-thaw and desertification, ecosystem, and climate change in the alpine region of the Qinghai–Tibet Plateau (QTP). Surface air temperature observations collected at 17 weather stations in the High-cold region Observation and Research Network for Land Surface Process and Environment of China (HORN) over the period of 2017–2018 are implemented to evaluate the advanced and widely used surface air temperature reanalysis datasets, which include the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5L), the U.S. Global Land Data Assimilation System (GLDAS), and China Meteorological Administration Land Data Assimilation System (CLDAS). Results are as follows: (1) Evaluation results of temporal changes and spatial distribution characteristics indicate that the three reanalysis datasets are consistent with in-situ observations in the alpine region of the QTP. CLDAS is more consistent with observations and can better describe details of temperature distribution and variation than ERA5L and GLDAS. (2) For the evaluation period, CLDAS is 0.53 °C higher than the in-situ observation, while ERA5L and GLDAS are lower than the in-situ observation by −3.45 °C and −1.40 °C, respectively. (3) The accuracy of CLDAS is better than ERA5L and GLDAS under different elevations and land covers. We resampled three reanalysis datasets with a spatial resolution of 0.25° and used the two most common interpolation methods to analyze the impact of spatial resolution and different interpolation methods on the evaluation results. We found that the impact is small. In summary, the three reanalysis datasets all have certain applicability in the alpine region of the QTP, and the accuracy of CLDAS is significantly higher than ERA5L and GLDAS. The results of the present paper have important implications for the selection of reanalysis data in the studies of climate, ecosystem, and sustainable development in the QTP.

The Chinese Tianshan mountainous region (CTMR) is a typical alpine region with high topographic heterogeneity, characterized by a large altitude span, complex topography, and diverse landscapes. A significant increase in air temperature had occurred in the CTMR during the last five decades. However, the detailed, comprehensive, and systematical characteristics of climate warming, such as its temporal and spatial heterogeneity, remain unclear. In this study, the temporal and spatial heterogeneity of climate warming across the CTMR had been comprehensively analyzed based on the 10-day air temperature data gathered during 1961–2020 from 26 meteorological stations. The results revealed local cooling in the context of general warming in the CTMR. The amplitude of variation (AV) varied from −0.57 to 3.64 °C, with the average value of 1.19 °C during the last six decades. The lapse rates of the elevation-dependent warming that existed annually, and in spring, summer, and autumn are −0.5 °C/100 m, −0.5 °C/100 m, −0.7 °C/100 m, and −0.4 °C/100 m, respectively. The warming in the CTMR is characteristic of high temporal heterogeneity, as represented by the amplified warming at 10-d scale for more than half a year, and the values of AV were higher than 1.09 °C of the global warming during 2011–2020 (GWV2011–2020). Meanwhile, the amplitudes of warming differed greatly on a seasonal scale, with the rates in spring, autumn, and winter higher than that in summer. The large spatial heterogeneity of climate warming also occurred across the CTMR. The warming pole existed in the warm part, the Turpan-Hami basin (below 1000 m asl) where the air temperature itself was high. That is, the warm places were warmer across the CTMR. The cooling pole was also found in the Kuqa region (about 1000 m asl). This study could greatly improve the understanding of the spatio-temporal dynamics, patterns, and regional heterogeneity of climate warming across the CTMR and even northwest China. [ABSTRACT FROM AUTHOR]