Search Results (109)

The spatiotemporal changes and driving mechanisms of rain-on-snow (ROS) events and their intensities are crucial for responding to disasters triggered by such events. However, there is currently a lack of detailed assessment of the seasonal variations and driving mechanisms of ROS events and their intensities in China. Therefore, this study utilized daily meteorological data and daily snow depth data from 513 stations in China during 1978–2020 to investigate spatiotemporal variations of ROS events and their intensities. Also, based on the detrend and partial correlation analysis model, the driving factors of ROS events and their intensity were explored. The results showed that ROS events primarily occurred in northern Xinjiang, the Qinghai–Tibet Plateau, Northeast China, and central and eastern China. ROS events frequently occurred in the middle and lower Yangtze River Plain in winter but were easily overlooked. The number and intensity of ROS events increased significantly (p < 0.05) in the Changbai Mountains in spring and the Altay Mountains and the southeast part of the Qinghai–Tibet Plateau in winter, leading to heightened ROS flood risks. However, the number and intensity of ROS events decreased significantly (p < 0.05) in the middle and lower Yangtze River Plain in winter. The driving mechanisms of the changes for ROS events and their intensities were different. Changes in the number of ROS events and their intensities in snow-rich regions were driven by rainfall days and quantity of rainfall, respectively. In regions with more rainfall, these changes were driven by snow cover days and snow water equivalent, respectively. Air temperature had no direct impact on ROS events and their intensities. These findings provide reliable evidence for responding to disasters and changes triggered by ROS events. Full article

On 18 December 2023, a M_w 6.2 earthquake occurred in close proximity to Jishishan County, located on the northeastern edge of the Qinghai–Tibet Plateau. The event struck the structural intersection of the Haiyuan fault, Lajishan fault, and West Qinling fault, providing empirical evidence for investigating the crustal compression mechanisms associated with the northeastward expansion of the Qinghai–Tibet Plateau. In this study, we successfully acquired a high-resolution coseismic deformation field of the earthquake by employing interferometric synthetic aperture radar (InSAR) technology. This was accomplished through the analysis of image data obtained from both the ascending and descending orbits of the Sentinel-1A satellite, as well as from the ascending orbit of the ALOS-2 satellite. Our findings indicate that the coseismic deformation is predominantly localized around the Lajishan fault zone, without leading to the development of a surface rupture zone. The maximum deformations recorded from the Sentinel-1A ascending and descending datasets are 7.5 cm and 7.7 cm, respectively, while the maximum deformation observed from the ALOS-2 ascending data reaches 10 cm. Geodetic inversion confirms that the seismogenic structure is a northeast-dipping thrust fault. The geometric parameters indicate a strike of 313° and a dip angle of 50°. The slip distribution model reveals that the rupture depth predominantly ranges between 5.7 and 15 km, with a maximum displacement of 0.47 m occurring at a depth of 9.6 km. By integrating the coseismic slip distribution and aftershock relocation, this study comprehensively elucidates the stress coupling mechanism between the mainshock and its subsequent aftershock sequence. Quantitative analysis indicates that aftershocks are primarily located within the stress enhancement zone, with an increase in stress ranging from 0.12 to 0.30 bar. It is crucial to highlight that the structural units, including the western segment of the northern margin fault of West Qinling, the eastern segment of the Daotanghe fault, the eastern segment of the Linxia fault, and both the northern and southern segment of Lajishan fault, exhibit characteristics indicative of continuous stress loading. This observation suggests a potential risk for fractures in these areas. Full article

The Southwest Alpine Canyon Area (SACA) is a typical ecologically sensitive location in China; therefore, constructing and optimizing an ecological network for this area is essential to ensure the regional ecological security of its fragile ecosystems. This study employed the InVEST model to quantitatively assess the habitat quality of the SACA for the years 2000, 2010, and 2020. The ecological sources were determined based on the results of a habitat quality assessment and a Morphological Spatial Pattern Analysis (MSPA). Finally, ecological corridors, ecological pinch points, and ecological barrier points were identified using circuit theory. The results indicated that the SACA’s habitat quality was relatively good, but experienced slight degradation from 0.87 in 2000 to 0.84 in 2020. Anthropogenic activities have been identified as the primary contributor to habitat quality decline in the region. Geographically, the habitat quality is significantly poorer in the southeast and northwest of the SACA. A total of 319 ecological sources were identified, predominantly located in the southwest and northeast of the SACA, comprising 43.27% of the total area. Furthermore, 94 ecological corridors were delineated, covering an area of 74,015.61 km² and extending over 182.80 km in length in total. A total of 38 ecological pinch points and 39 ecological barrier points were distinguished, with a noticeable concentration in regions undergoing ecological degradation. Overall, while the ecological network structure in the SACA is complex and highly interconnected, it faces challenges relating to material cycling and ecological network circulation. Future ecological restoration and protection efforts should focus on areas along the border between the ecological maintenance area in southeastern Tibet (Region I) and the water conservation area in eastern Tibet–western Sichuan (Region II). Additionally, the establishment of ecological protection belts around potential ecological corridors is proposed to enhance ecosystem connectivity. These findings could provide a robust scientific foundation for territorial spatial planning, ecological preservation, and restoration in the SACA. Full article

Forests play a crucial role in the global carbon cycle, climate regulation, and biodiversity conservation, making them essential for understanding ecosystem responses to environmental change. However, the spatiotemporal dynamics of forest vegetation and their responses to climate change have yet to be fully explored. This study assessed the spatiotemporal dynamics and adaptation of forest vegetation from Northern China by extracting changes in forest vegetation and phenological characteristics from 2001 to 2023 with the time-series MODIS Normalized Difference Vegetation Index (NDVI) data and analyzing the impact of climate variables on these changes. The linear regression analysis method and the four-parameter double logistic model were employed to assess forest vegetation changes and identify forest vegetation phenological phases, respectively. Partial correlation analysis was used to assess the relationship between forest vegetation and climate variables. The results of this study indicate that over the past two decades, the annual mean NDVI of forest vegetation has exhibited a slow increasing trend of approximately 0.002 yr⁻¹, with a spatial distribution pattern that gradually decreases from south to north, showing a significant correlation with latitude. The magnitude of annual mean NDVI changes varies considerably among different forest vegetation types. However, except for evergreen broadleaf forests, the NDVI of all other forest types has shown a significant increasing trend. Additionally, central North China and southeastern Tibet exhibit higher NDVI values in both spring (>0.55) and autumn (>0.65) than other areas, while the NDVI values in Northeast China and North China are higher in summer (>0.8) compared to other areas. The study reveals substantial spatial heterogeneity in the average phenological phases and NDVI values of forest vegetation across different regions, influenced by latitude, altitude, and regional climatic conditions. The spatial distribution patterns of NDVI during the green-up and senescence phases remain relatively consistent, yet significant regional differences exist within the same phenological phase. Partial correlation analysis indicates that forest vegetation in different regions responds distinctly to meteorological factors. These findings contribute to a deeper understanding of the spatiotemporal dynamics of vegetation change and its complex interactions with climate change, offering valuable insights for forest ecosystem management and climate adaptation of forest vegetation. Full article

Soil organic carbon (SOC) and soil inorganic carbon (SIC) are key components of soil carbon pools in arid ecosystems, playing a crucial role in regional carbon cycling and climate change mitigation. However, the interactions between these two forms of carbon in arid alpine ecosystems remain underexplored. This study was conducted in the Heihe River Basin (HRB) in the northeastern Qinghai–Tibet Plateau, focusing on the distribution and dynamics of SOC and SIC in deep soil layers. Using data from 329 samples collected from 49 soil profiles extending to the bedrock, combined with path analysis, we explored the inter-relationships between SOC and SIC and quantified the influence of environmental factors. The results showed that (1) SOC exhibited a unimodal distribution with elevation, peaking at 3300–3600 m, while SIC continuously decreased with elevation, with reduction rates ranging from −0.39% to −31.18%; (2) SOC and SIC were significantly positively correlated (r = 0.55, p < 0.01), with SOC decreasing with depth and SIC showing an inflection point at 50 cm depth; (3) SOC was primarily driven by nutrient factors, such as total nitrogen (TN), with a path coefficient of 0.988, while SIC was influenced by abiotic factors, including potential evapotranspiration (PET), with a coefficient of −1.987; (4) SOC density accounted for 81.62% of the total soil carbon pool, playing a dominant role in carbon storage, whereas SIC density exhibited dynamic changes, particularly at depths of 110–150 cm. These findings advance our understanding of deep soil carbon dynamics in arid alpine ecosystems and provide critical data for improving carbon management strategies in similar regions. Full article

Hawthorns (Crataegus L.) are widely distributed and well known for their medicinal properties and health benefits. Nevertheless, the phylogenetic relationships among Crataegus native to China remain unclear. Additionally, no consensus exists on the origin and evolution of Crataegus, and the relationship between Crataegus and Mespilus is is unclear. Here, we sequenced 20 chloroplast (cp) genomes (19 from Crataegus and 1 from Mespilus) and combined them with 2 existing cp genomes to investigate the phylogenetic relationships, divergence times and biogeographic history of Crataegus. Four hypervariable loci emerged from the newly sequenced genomes. The phylogenetic results indicated that the 14 Chinese Crataegus species analyzed clustered into two clades. One clade and the North American Crataegus species grouped together, while the other clade grouped with the European Crataegus species. Our results favor recognizing Mespilus and Crataegus as one genus. Molecular dating and biogeographic analyses showed that Crataegus originated in Southwest China during the early Oligocene, approximately 30.23 Ma ago. Transoceanic migration of East Asian Crataegus species across the Bering land bridge led to the development of North American species, whereas westward migration of the ancestors of C. songarica drove the formation of European species. C. cuneata may represent the earliest lineage of Chinese Crataegus. The uplift of the Qinghai–Tibet Plateau (QTP) and the Asian monsoon system may have led the ancestors of C. cuneata in south-western China to migrate toward the northeast, giving rise to other Chinese Crataegus species. This study offers crucial insights into the origins of Crataegus and proposes an evolutionary model for the genus. Full article

Financial resources play a crucial role in rural revitalization. Understanding the efficiency of financial support is essential for the scientific and rational allocation of these resources. Therefore, we conducted an assessment over the period 2011–2020 utilizing the three-stage DEA model and the Malmquist index model to measure the efficiency of financial support for rural revitalization across 30 Chinese provinces (excluding Hong Kong, Macao, Taiwan, and Tibet) from both static and dynamic perspectives. The results indicate the following: (1) Despite an overall downward trend, efficiency increased during specific intervals, namely 2012–2013, 2015–2016, and 2018–2019. (2) Regionally, the decline in the efficiency of financial support for rural revitalization is particularly notable in the northeast region. The eastern and central regions also experienced this trend to a lesser extent, whereas the western region experienced a more moderate decrease. However, a detailed analysis revealed that 10 provinces experienced efficiency gains. (3) Stochastic Frontier Analysis (SFA) regression results suggest that environmental variables have a measurable impact on the efficiency of financial support for rural revitalization. Full article

As one of the countries with the most severe extreme climate disasters in the world, it is of great significance for China to scientifically understand the characteristics of extreme precipitation. The artificial neural network near-real-time dynamic infrared rainfall rate satellite precipitation data (PDIR-Now) is a global, long-term resource with diverse spatial resolutions, rich temporal scales, and broad spatiotemporal coverage, providing an important data source for the study of extreme precipitation. But its applicability and accuracy still need to be evaluated in specific applications. Based on the observation data of 824 surface meteorological stations in China, the correlation coefficient (R), relative deviation (RB), root mean square error (RMSE), and relative root mean square error (RRMSE) of quantitative statistical indicators were used to evaluate the annual maximum daily precipitation of PDIR-Now from 2000 to 2016 in this study, in order to explore the ability of PDIR-Now satellite precipitation products to monitor extreme precipitation in Chinese mainland. The results show that from the perspective of long-term series, the annual maximum daily precipitation of PDIR-Now has a good ability to monitor extreme precipitation across the country, and the R exceeds 0.6 in 65% of the years. The RMSE of different years is generally distributed between 40 and 60 mm, and in terms of time characteristics, the error of each year is relatively stable and does not fluctuate greatly with dry precipitation or abundant years. From the perspective of spatial characteristics, the distribution of RMSE is very regional, with the RMSE in the Qinghai–Tibet Plateau and Northwest China basically in the range of 0~20 mm, the Yunnan–Guizhou Plateau, the Sichuan Basin, Northeast China, and the central part of the study area in the range of 20~50 mm, and the RMSE in a few stations in the southeast coast greater than 80 mm. The RRMSE distribution of most sites is between 0 and 0.6, and the RRMSE distribution of a few sites is between 0.6 and 1.5. Generally, higher RRMSE values and larger errors are observed in the northwest and southeast coastal regions. Overall, PDIR-Now captures the regional characteristics of extreme precipitation in the study area, but it is underestimated in the wet season in humid and semi-humid regions and overestimated in the dry season in arid and semi-arid regions. Full article

Understanding the spatial distribution of herbaceous vegetation is critical for assessing how biodiversity may respond to climate change, particularly in high-elevation ecosystems. The Qinghai-Tibet Plateau in China is a hotspot of biodiversity research in the world, and the relationship between plant species distribution in alpine communities and topography and soils is understudied in the Anyemaqen Mountains in the northeast of the Qinghai-Tibet Plateau. This study investigates the patterns of α and β diversity of herbaceous plants and their key environmental drivers in the subalpine ecosystem of the Anyemaqen Mountains on the Qinghai-Tibet Plateau. Data on vegetation and environmental variables were collected across a gradient of 10 elevations ranging from 3600 to 4600 m during the 2021 growing season. Statistical analyses, including one-way ANOVA, redundancy analysis (RDA), and Monte Carlo significance tests, revealed significant differences between sunny and shady slopes in species composition and diversity. Species richness decreased with increasing elevation on sunny slopes, while the reverse trend was observed on shady slopes. Elevation and gradient were the most influential factors in both slope aspects, while soil thickness was significant on shady slopes. These findings contribute to understanding the environmental mechanisms that regulate biodiversity in alpine ecosystems and provide valuable insights for formulating conservation strategies in response to climate change. Full article

Mountain apricot (Prunus sibirica) is an important fruit tree variety, and has a wide range of planting and application value in China and even the world. However, the current research on the suitable distribution area of P. sibirica is still inconclusive. In this study, we retrieved distribution data for P. sibirica in China from the Global Biodiversity Information Facility (GBIF), and identified six key environmental factors influencing its distribution through cluster analysis. Using these six selected climate factors and P. sibirica distribution points in China, we applied the maximum entropy model (MaxEnt) to evaluate 1160 candidate models for parameter optimization. The final results predict the potential distribution of P. sibirica under the current climate as well as two future climate scenarios (SSPs126 and SSPs585). This study shows that the model optimized with six key climate factors (AUC = 0.897, TSS = 0.658) outperforms the full model using nineteen climate factors (AUC = 0.894, TSS = 0.592). Under the high-emission scenario (SSPs585), the highly suitable habitat for P. sibirica is expected to gradually shrink towards the southeast and northwest, while expanding in the northeast and southwest. After the 2050s, highly suitable habitats are projected to completely disappear in Shandong, while new suitable areas may emerge in Tibet. Additionally, the total area of suitable habitat is projected to increase in the future, with a more significant expansion under the high-emission scenario (SSPs585) compared to the low-emission scenario (SSPs126) (7.33% vs. 0.16%). Seasonal changes in precipitation are identified as the most influential factor in driving the distribution of P. sibirica. Full article

The Qinghai–Tibet Plateau (QTP) wild boar is an excellent model for investigating high-altitude adaptation. In this study, we analyzed genome-wide data from 93 wild boars compiled from various studies worldwide, including the QTP, southern and northern regions of China, Europe, Northeast Asia, and Southeast Asia, to explore their phylogenetic patterns and high-altitude adaptation based on genome-wide selection signal analysis and run of homozygosity (ROH) estimation. The findings demonstrate the alignment between the phylogenetic associations among wild boars and their geographical location. An ADMIXTURE analysis indicated a relatively close genetic relationship between QTP and southern Chinese wild boars. Analyses of the fixation index and cross-population extended haplotype homozygosity between populations revealed 295 candidate genes (CDGs) associated with high-altitude adaptation, such as TSC2, TELO2, SLC5A1, and SLC5A4. These CDGs were significantly overrepresented in pathways such as the mammalian target of rapamycin signaling and Fanconi anemia pathways. In addition, 39 ROH islands and numerous selective CDGs (e.g., SLC5A1, SLC5A4, and VCP), which are implicated in glucose metabolism and mitochondrial function, were discovered in QTP wild boars. This study not only assessed the phylogenetic history of QTP wild boars but also advanced our comprehension of the genetic mechanisms underlying the adaptation of wild boars to high altitudes. Full article

The Lijiang–Jinpingshan fault (LJF) is an important secondary boundary fault that obliquely cuts the Sichuan–Yunnan rhombic block. It is of great significance for understanding the tectonic evolution of the Sichuan–Yunnan rhombic block and even the southeastern margin of the Tibet Plateau. Based on a digital elevation model (DEM), this work combines ArcGIS with MATLAB script programs to extract geomorphic indices including slope, the relief degree of the land surface (RDLS), hypsometric integral (HI), and channel steepness index (k_sn) of 593 sub–watersheds and strip terrain profiles around the LJF. By analyzing the spatial distribution characteristics of the geomorphic indices and combining the regional lithology and precipitation conditions, the spatial distribution of the geomorphic indices around the study area was analyzed to reveal the implications of the LJF’s activity. The results of this work indicate that (1) the distribution of geomorphic indices around the LJF may not be controlled by climate and lithological conditions, and the LJF is the dominant factor controlling the geomorphic evolution of the region. (2) The spatial distribution patterns of geomorphic indices and strip terrain profiles reveal that the vertical movement of the LJF resulted in a pronounced uplift on its northwest side, with tectonic activity gradually diminishing from northeast to southwest. Furthermore, based on the spatial distribution characteristics of these geomorphic indices, the activity intensity of the LJF can be categorized into four distinct segments: Jianchuan–Lijiang, Lijiang–Ninglang, Ninglang–Muli, and Muli–Shimian. (3) The activity of the LJF obtained from tectonic geomorphology is consistent with the conclusions obtained in previous geological and geodesic studies. This work provides evidence of the activity and segmentation of the LJF in tectonic geomorphology. The results provide insight for the discussion of tectonic deformation and earthquake disaster mechanisms in the southeastern margin of the Tibet Plateau. Full article

As climatic and geographical conditions change in the future, species’ habitats will also change. Phoebe zhennan is a national second-level key protected wild plant in China with extremely high economic value and landscape value. In order to better protect the resources of P. zhennan and achieve the goal of the sustainable development of P. zhennan resources, we predict potential suitable areas for P. zhennan under different scenarios in the future. We collect the current distribution data of P. zhennan, and in combination with nine climate factors and three geographical factors, use the MaxEnt ecological niche model and ArcGIS geographic information system software to predict and analyze potential suitable areas for P. zhennan in different climate scenarios in the future. The result shows that more accurate prediction results can be obtained by using China’s climatic and geographical data before clipping as environmental variables. The precipitation of the warmest quarter and the slope are the main influencing factors in the prediction of potential suitable areas for P. zhennan. The future potential suitable areas for P. zhennan are mainly distributed in the central–southern and southern regions of China, with a tendency to expand towards the Tibet Autonomous Region and the northeast. The suitable habitat area will increase significantly, and the highly suitable habitat area will be more concentrated. These research results can provide valuable references for the effective protection of existing P. zhennan populations, the cultivation of P. zhennan within suitable habitats in the future, the establishment of a P. zhennan reserve, and the promotion of the sustainable utilization of P. zhennan resources. Full article

Alluvial parent material soil is an important soil type found on the Qinghai–Tibet Plateau (QTP) in China. However, due to the limited age data for alluvial soils, the relationship between alluvial geomorphological processes and soil pedogenic processes remains unclear. In this study, three representative alluvial parent material profiles on the Buha River alluvial plain in the Qinghai Lake Basin, northeast QTP, were analyzed using the optical luminescence (OSL) dating method. Combined with physical and chemical analyses of the soil, we further analyzed the pedogenic process of alluvial soil. The alluvial parent material of the Buha alluvial plain predominately yielded ages between 11.9 and 9.1 ka, indicating that the alluvial soil began to form during the early Holocene. The development of the alluvial soil on the first-order terrace presents characteristics of entisol with multiple burial episodes, mainly between 8.5 and 4.0 ka, responding to the warm and humid middle Holocene and high lake levels. Full article

Although atmospheric CO₂ concentrations collected by satellites play a crucial role in understanding global greenhouse gases, the sparse geographic distribution greatly affects their widespread application. In this paper, a hybrid CNN and spatiotemporal Kriging (CNN-STK) model is proposed to generate a monthly spatiotemporal continuous XCO2 dataset over China at 0.25° grid-scale from 2015 to 2020, utilizing OCO-2 XCO2 and geographic covariates. The validations against observation samples, CAMS XCO2 and TCCON measurements indicate the CNN-STK model is effective, robust, and reliable with high accuracy (validation set metrics: R² = 0.936, RMSE = 1.3 ppm, MAE = 0.946 ppm; compared with TCCON: R² = 0.954, RMSE = 0.898 ppm and MAE = 0.741 ppm). The accuracy of CNN-STK XCO2 exhibits spatial inhomogeneity, with higher accuracy in northern China during spring, autumn, and winter and lower accuracy in northeast China during summer. XCO2 in low-value-clustering areas is notably influenced by biological activities. Moreover, relatively high uncertainties are observed in the Qinghai-Tibet Plateau and Sichuan Basin. This study innovatively integrates deep learning with the geostatistical method, providing a stable and cost-effective approach for other countries and regions to obtain regional scales of atmospheric CO₂ concentrations, thereby supporting policy formulation and actions to address climate change. Full article