Search Results (50)

Vajrayāna teaching places the guru outside space and time, while simultaneously manifest in the teacher’s physical body. Those who regard Danzanravjaa primarily as a Buddhist teacher even today have his poems as a potent source of his teaching and consequently as a catalyst for their own spiritual development. But what can we hear across two centuries, and how can we actively listen to his religious teaching through his singular, aphoristic, and complex poetics? And to what extent can we understand today his nomadic perspective on Buddhist teaching in order better to understand the particular nature of Mongolian Buddhism? This paper will examine Danzanravjaa’s poetry in both Mongolian and Tibetan through the intertwining outer, inner, and secret levels of Tibeto-Mongolian Vajrayāna Buddhism, listening to how his poetic language and down-to-earth themes might have spoken to his contemporaries, as well as how they might speak to us today. In doing so, it presents Danzanravjaa’s poetry in a different light—not in terms of nineteenth century literature but as actionable spiritual wisdom from a teacher who, like any other, presents his own direct apprehension of Buddha nature in a challenging, personal style. Full article

We incorporated a comprehensive dataset encompassing recent measurements from satellites such as the Macau Science Satgellite-1 (MSS-1), Swarm, and CHAMP, as well as aero and ocean magnetic measurements, alongside ground-based data from 1936 to 2000. This amalgamation is the basis for constructing a lithospheric magnetic field model of the Chinese mainland, employing the three-dimensional Surface Spline (3DSS) model. Additionally, we used the World Digital Magnetic Anomaly Map (WDMAM)-2.1 and CHAOS-7.13 models to address data gaps horizontally and vertically. To evaluate the efficacy of the new model, we compared it not only with established models such as SHA1050, NGDC720, and LCS-1 but also with the new model excluding the MSS-1 data. The results show a high agreement between the 3DSS model and other global models at a spatial resolution of 0.05°. Furthermore, we inspected the rapid variations in the magnetic field with increasing altitude, demonstrating a smooth transition across the altitudes covered by the three satellites. Error analyses reflected the importance of MSS-1 data, which contributed notably to modelling by capturing finer-scale magnetic structures. The increased data availability correlated positively with the model’s accuracy, as evidenced by the Root Mean Square Error (RMSE), registering an optimal value of 0.02 nT. The new model reveals additional geological details in southern Tibet, northeastern Inner Mongolia, and the adjacent areas of Liaoning and Jilin provinces, which are not discernible in other global models. The relationship between these anomalies and heat flow in northeastern China appears less evident, suggesting a complex interplay of orogenic processes and surface mineralogy in shaping these magnetic signatures. Full article

To address the conceptual ambiguity surrounding climate carrying capacity and the lack of a unified quantitative assessment method, here we integrate existing concepts and propose a comprehensive evaluation framework for climate carrying capacity, emphasizing the threshold of the climate system’s capacity to support human activities. Additionally, the evaluation framework incorporates carbon carrying capacity, climate resource production potential, wind resource potential, and solar resource potential as its four key components, and employs a vector modeling approach to quantitatively evaluate the temporal and spatial variations in climate carrying capacity across China’s provinces, municipalities, and autonomous regions from 1986 to 2020. The results reveal that most regions exhibited low coefficients of variation in the climate carrying capacity index during the study period, indicating an overall stable climate carrying capacity and good potential for further development. However, Beijing, Shanghai, and Tianjin demonstrated exceptionally high coefficients of variation, at 62.8%, 74.3%, and 74.4%, respectively, warranting further attention. Tibet, Xinjiang, and Inner Mongolia exhibited significantly higher climate carrying capacity indices than other areas, whereas Ningxia, Hainan, and Chongqing generally displayed lower indices. This study enhances the theoretical framework of climate carrying capacity and offers a scientific foundation for formulating sustainable development policies, promoting coordinated regional development, protecting the environment, and addressing climate change. Full article

Architecture is the stone book of history, and the evolution of architectural styles showcases a non-verbal history constructed through images. As an important part of China’s historical and cultural heritage, the architectural forms and styles of Tibetan Buddhist temples were initially modeled on Tang dynasty temple architecture and gradually evolved into the most significant architectural types in regions such as Tibet and Qinghai in China. Religious architecture has also played a significant role in shaping regional cultural landscapes. Existing research on Tibetan Buddhist temples is primarily focused on qualitative studies of individual temple buildings. This research takes the spatiotemporal evolution of architectural styles of Tibetan Buddhist temples as an entry point and, for the first time, employs ArcGIS technology to visualize the spatial and geographical distribution of Tibetan Buddhist temples from the 7th to the 18th century, establishing a comprehensive academic vision that encompasses both historical stratification and cross-regional spatial correlations. By analyzing the cultural symbolic features embodied in the construction styles of Tibetan Buddhist temples and the visual characteristics reflected in their decorative arts, we propose two spatiotemporal dimensions for the formation and transmission of Tibetan Buddhist temple architectural styles: “Westward Transmission” and “Eastward Diffusion”. Firstly, from the 7th to the 9th centuries, the architectural style and construction techniques of Tang dynasty Buddhist temples were transmitted westward along the Tang–Tibet ancient road, integrating with local Tibetan elements to form the Tubo architectural style, which was further refined into the “Sino–Tibetan Combined Style” with strong visual characteristics around the 13th century. Subsequently, along with the spread of Tibetan Buddhism, this temple architectural style underwent an eastward diffusion from the 13th to the 18th century, reaching regions, such as Sichuan, Qinghai, Gansu, Inner Mongolia, Hebei, and Beijing, presenting a spatial gradient from west to east in the geographical dimension. On this basis, in this research, we construct a historical evolution mapping of Tibetan Buddhist temple architectural styles based on bidirectional transmission, attempting to elucidate that the intrinsic driving forces are religious and the cultural identity that guided the bidirectional transmission mechanism of these architectural styles under the historical context of the formation and dissemination of Tibetan Buddhism from the 7th to the 18th century. Full article

Solar photovoltaics is a direct use of solar resources to generate electricity, which is one of the most important renewable energy application approaches. Regional PV output could be affected by the regional patterns of temperature and irradiance, which are impacted by climate change. This study examines the impact of climate change on the energy yields from solar PV across China in the future under the medium-emission scenario (SSP245) and high-emission scenario (SSP585) by calculating PV potential using the data of solar radiation on a tilted surface and temperature. Generally, under the SSP245 scenario, solar radiation increased by 0.8% and 2.15%, and PV energy yields increased by 0.28% and 1.21% in 2020–2060 and 2061–2099, respectively; under the SSP585 scenario, solar radiation increased by 0.73% and 1.35%, and PV energy yields increased by 0.04% and −1.21% in 2020–2060 and 2061–2099, respectively. Under both scenarios, PV energy potential showed an obvious increase in southeast and central China and a significant decrease in northwest China, Tibet, and Inner Mongolia. Therefore, it is suggested that under the medium-emission scenario, climate change could increase the PV energy potential, while under the high-emission scenario, it could inhibit the PV energy potential in China. Full article

Between 2020 and 2023, rust fungus specimens were collected from the primary forested regions of the Sanjiangyuan area in Qinghai Province, resulting in over 300 samples. A taxonomic and phylogenetic study of the rust fungi from these forests was conducted using morphological and molecular biological techniques. The investigation identified rust fungi from 7 families, 12 genera, 56 species and varieties, including 10 new host records, 1 new record for China, and 2 novel species. The host plants involved belonged to 26 families, 48 genera, and 78 species. Pucciniaceae and Coleosporiaceae were the dominant families, with the genera Puccinia, Melampsora, and Gymnosporangium being prevalent. The rust fungi in the Sanjiangyuan forests showed a biogeographical affinity with the North Temperate Zone. Floristic comparisons revealed a higher similarity with rust fungi from Inner Mongolia, Gansu, and Tibet and a lower similarity with those from Hainan. An analysis of the life forms of rust fungus host plants indicated that herbaceous plants were the most common, followed by shrubs and trees. In different regions of Sanjiangyuan, rust fungi were found as follows: Golog Prefecture with 6 families, 9 genera, and 28 species; Yushu Prefecture with 5 families, 8 genera, and 31 species; Huangnan Prefecture with 5 families, 9 genera, and 26 species; and Hainan Prefecture with 4 families, 5 genera, and 10 species. The families Pucciniaceae, Melampsoraceae, and Coleosporiaceae were common across all four regions. Moreover, the families Rosaceae, Asteraceae, Ranunculaceae, Salicaceae, and Caprifoliaceae were shared among the host plants in these regions. Full article

The research on the significant toxic weed Oxytropis glabra, which adversely affects the grazing industry and the ecological integrity of natural grasslands in the arid and semi-arid regions of northern China, aims to delineate its potential distribution amidst changing climate conditions. This analysis involves both current conditions (1970–2000) and future projections (2050s and 2070s) under four climate scenarios using an R-optimized MaxEnt model. The results indicate that the distribution of O. glabra was primarily influenced by the temperature of the coldest quarter (bio11, ranging from −12.04 to −0.07 °C), precipitation of the coldest quarter (bio19, 0 to 15.17 mm), and precipitation of the warmest quarter (bio18, 0 to 269.50 mm). Currently, the weed predominantly occupies parts of Xinjiang, Inner Mongolia, Gansu, Qinghai, Ningxia, and Tibet. Projections indicate that, across four future climate scenarios, the area of suitable habitats for O. glabra is expected to expand and shift toward higher latitudes and elevations. The research provides valuable information and a theoretical foundation for the management of O. glabra, alongside advancing grassland ecological research and grazing practices. Full article

The Pallas’s cat (Otocolobus manul) is one of the most understudied taxa in the Felidae family. The species is currently assessed as being of “Least Concern” in the IUCN Red List, but this assessment is based on incomplete data. Additional ecological and genetic information is necessary for the long-term in situ and ex situ conservation of this species. We identified 29 microsatellite loci with sufficient diversity to enable studies into the individual identification, population structure, and phylogeography of Pallas’s cats. These microsatellites were genotyped on six wild Pallas’s cats from the Tibet Autonomous Region and Mongolia and ten cats from a United States zoo-managed population that originated in Russia and Mongolia. Additionally, we examined diversity in a 91 bp segment of the mitochondrial 12S ribosomal RNA (MT-RNR1) locus and a hypoxia-related gene, endothelial PAS domain protein 1 (EPAS1). Based on the microsatellite and MT-RNR1 loci, we established that the Pallas’s cat displays moderate genetic diversity. Intriguingly, we found that the Pallas’s cats had one unique nonsynonymous substitution in EPAS1 not present in snow leopards (Panthera uncia) or domestic cats (Felis catus). The analysis of the zoo-managed population indicated reduced genetic diversity compared to wild individuals. The genetic information from this study is a valuable resource for future research into and the conservation of the Pallas’s cat. Full article

The objective of this study is to examine the impact of climate change on forestry efficiency (FRE) and total factor productivity change (TFPC) in 31 provinces of China for a study period of 2001–2020. Additionally, the study aims to evaluate the success level of governmental initiatives used to mitigate climate change. Using the DEA-SBM, this study estimates the forestry efficiency for 31 Chinese provinces and seven regions. Results indicate that the average forestry efficiency score obtained is 0.7155. After considering climatic factors, the efficiency level is 0.5412. East China demonstrates the highest average efficiency with a value of 0.9247, while the lowest score of 0.2473 is observed in Northwest China. Heilongjiang, Anhui, Yunnan, and Tibet exhibit the highest efficiency scores. Mongolia, Heilongjiang, Sichuan, Hebei, and Hunan are the five provinces most affected by climate change. This study’s findings indicate that the average total factor forestry productivity (TFPC) is 1.0480, representing an increase of 4.80%. The primary determinant for change is technology change (TC), which surpasses efficiency change (EC). Including climate variables reduces total factor productivity change (TFPC) to 1.0205, mainly driven by a decrease in TC. The region of South China exhibits the highest total factor productivity change (TFPC) with a value of 1.087, whereas both Northeast China and Central China observe falls below 1 in TFPC. The Mann–Whitney U test provides evidence of statistically significant disparities in forestry efficiency and TFPC scores when estimated with and without incorporating climate factors. Kruskal–Wallis found a statistically significant difference in FRE and TFPC among seven regions. Full article

Kashin–Beck disease (KBD) is a multifactorial endemic disease that only occurs in specific Asian areas. Mycotoxin contamination, especially from the Fusarium spp., has been considered as one of the environmental risk factors that could provoke chondrocyte and cartilage damage. This study aimed to investigate whether new mycotoxins could be identified in KBD-endemic regions as a potential KBD risk factor. This was investigated on 292 barley samples collected in Tibet during 2009–2016 and 19 wheat samples collected in Inner Mongolia in 2006, as control, from KBD-endemic and non-endemic areas. The LC-HRMS(/MS) data, obtained by a general mycotoxin extraction technic, were interpreted by both untargeted metabolomics and molecular networks, allowing us to identify a discriminating compound, enniatin B, a mycotoxin produced by some Fusarium spp. The presence of Fusarium spp. DNA was detected in KBD-endemic area barley samples. Further studies are required to investigate the role of this mycotoxin in KBD development in vivo. Full article

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments. Full article

In this study, the spatiotemporal variations in CH₄ concentrations in China from 2003 to 2021 are investigated, and their trends are forecasted over the next decade. Based on the seventh edition standard product retrieved by the atmospheric infrared detector (AIRS) at an altitude of 500 hPa, we verified monthly CH₄ products using observational data provided by the World Data Center for Greenhouse Gases (WDCGG) from six ground stations in and around China. The correlation coefficients (R values) between the two data sets ranged from 0.68 to 0.92, signifying the ability of AIRS inversion data to represent temporal and spatial changes in surface CH₄ concentrations. Additionally, China was classified into three regions (steps) based on terrain, and the changes in CH₄ concentrations were assessed from three perspectives: spatial distribution, interannual variation, and seasonal variation. The results revealed that the CH₄ concentration decreased with elevation along a topographic gradient, with high-value areas located in the first and second steps, corresponding to the eastern Qinghai–Tibet Plateau, northern Xinjiang Uygur Autonomous Region, and Inner Mongolia Autonomous Region. Over 19 years, the average increase in CH₄ concentration has ranged from 65 to 175 ppb. In addition, the CH₄ concentrations were higher during summer and autumn and lower during spring and winter. Finally, a SARIMA model was used to predict the near-surface CH₄ concentration trend in China over the next ten years, which indicated a continued seasonal increase. Full article

Given the increasing impact of extreme rainfall and flooding on human life, studying and predicting changes in atmospheric water vapor (AWV) becomes particularly important. This paper analyzes the moderate-resolution imaging spectroradiometer (MODIS) data of the East Asian region from January 2003 to February 2023. The AWV data are examined in the time and frequency domain using methods such as empirical orthogonal function (EOF), Mann–Kendall (MK) analysis, and others. Additionally, four prediction models are applied to forecast the monthly average AWV data for the next year. The accuracy of these models is evaluated using metrics such as mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The findings reveal several key insights: (1) The East Asian region exhibits highly variable seasonal variability in AWV, with identified mutation points after the MK test. (2) Spatial analysis shows high AWV data in the southern coastal areas of China, Thailand, Myanmar, Nansha Islands, and other regions during winter, while the Qinghai-Tibet Plateau region experiences low AWV during summer. (3) The first mode obtained through EOF decomposition contributes over 60% of the variance. Analysis of this mode reveals an increasing trend in AWV data for regions such as the Indian peninsula, Mongolia, and central and northeastern China over the past nine years. Conversely, the Bay of Bengal, Spratly Islands, eastern coast, and certain areas display a decreasing trend. (4) Employing the ensemble empirical mode decomposition (EEMD), the study identifies AWV data as a non-stationary series with an overall decreasing trend from 2003 to 2022. The filtered AWV series undergoes fast Fourier transform (FFT), uncovering periodicities of 2.6 years, 5 years, and 19 years. (5) Among the four forecasting models compared, the seasonal autoregressive integrated moving average model (SARIMA) demonstrates superior performance with the smallest MSE of 0.00782, MAE of 0.06977, RMSE of 0.08843, and the largest R² value of 0.98454. These results clearly indicate that the SARIMA model provides the best fit. Therefore, the SARIMA forecasting model can be effectively utilized for forecasting AWV data, offering valuable insights for studying weather variability. Full article

Understanding the climatically suitable habitat of species plays a vital role in the sustainable use and management of target species. Calligonum mongolicum Turcz., a native shrub species found in desert areas of Central Asia, is generally considered as one of the top four tree species for desertification control. However, previous works on suitable habitat simulation had focused mainly on either the national or specific geographical scales rather than entire biota scales, which have underestimated the climatic tolerance of the species. Furthermore, the uncertainty outcomes of climate change were largely ignored. With these questions, the arid regions of Central Asia were selected as our research background area. Occurrence data of C. mongolicum were obtained from various sources, such as the Global Biodiversity Information Facility, the Chinese Virtual Herbarium, and the iPlant website. The maximum entropy model (MaxEnt) was used to simulate the suitable habitat change dynamics under various climate change scenarios [5 general circulation models (GCMs) × 3 shared socioeconomic pathways (SSPs)]. The uncertainty of climate change induced by GCMs and SSPs were decomposed by the two-way ANOVA method. Our results show that hydrological-related variables are more important for the species’ habitat suitability than thermal-related variables. The climatic threshold for the core suitable habitat was 1–30 mm for precipitation of the coldest quarter, 14–401 mm for annual precipitation, −16.01–12.42 °C for mean temperature of the driest quarter, 9.48–32.63 °C for mean temperature of the wettest quarter, and −25.01–−9.77 °C for the minimum temperature of the coldest month. The size of suitable habitat was about 287.4 × 10⁴ km² under the current climate condition, located in China and Mongolia. Climate change has less impact on the total area size, but it has bigger impacts on the gain area and loss area sizes. The loss area is mainly located in the southeast boundaries, whereas the gain area is mainly located in Mongolia and the Qinghai-Tibet Plateau. The decomposition uncertainty of climate change indicates that GCMs could explain 14.5%, 66.4%, and 97.0% of total variation, respectively, and SSPs could explain 85.5%, 33.6%, and 3.0% of the total variation for gain, loss, and total habitat sizes, respectively. Our work clearly demonstrates that while C. mongolicum has great planting potential in Central Asia under various climate change scenarios, the sensitive areas possess large uncertainties requiring long-term climate monitoring for afforestation projects. Full article

Drought is one of the most destructive natural disasters in China and can cause serious environmental and socio-economic impacts. Based on monthly precipitation and temperature data from 571 meteorological stations, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated on a 12-month scale (SPEI-12) from 1985 to 2018 in mainland China, and it was compared with the records of the Bulletin of Flood and Drought Disasters in China to verify its drought monitoring accuracy. Then, run theory was used to explore in depth the spatiotemporal distribution of drought characteristics and trends in various climatic sub-regions. The results showed that: (1) the comparison with the Bulletin of Flood and Drought Disasters in China indicated that the results of SPEI-12 monitoring drought had high accuracy and could identify drought events in mainland China. (2) About 70% of the regions in mainland China experienced droughts more than 50 times, and about 43% of the regions had drought durations of between 100 and 110 months. Nearly 11% of the regions had drought severities of more than 130, which were mainly located in the Northwest Desert (NWC), the southern part of Northeast China (NEC), and the western part of North China (NC). (3) In the past 34 years, the droughts in the Northwest Desert (NWC), the western part of Inner Mongolia (IM), and the Qinghai-Tibet Plateau (TP) showed an aridity trend, while the droughts in South China (SC) and eastern Central and South China (CSC) presented a mitigation tendency. (4) Specifically, the droughts in South China (SC) were more influenced by precipitation, while those in the Northwest Desert (NWC) and Northeast China (NEC) were influenced by temperature and potential evapotranspiration, and the evolution of drought in North China (NC) and the Qinghai-Tibet Plateau (TP) was mainly influenced by soil moisture. The study could provide scientific guidance and a reference for drought response and sustainable development in China. Full article