Qinghai-Tibet Plateau Region Research Articles

A Refined Atmospheric Weighted Average Temperature Model Considering Multiple Factors in the Qinghai–Tibet Plateau Region

The Qinghai–Tibet Plateau region has significant altitude fluctuations and complex climate changes. However, the current global weighted average temperature (Tm) model does not fully consider the impact of meteorological and elevation factors on it, resulting in existing models being unable to accurately predict the Tm in the region. Therefore, this study constructed a weighted average temperature refinement model (XTm) related to surface temperature, water vapor pressure, geopotential height, annual variation, and semi-annual variation based on measured data from 13 radiosonde stations in the Qinghai–Tibet Plateau region from 2008 to 2017. Using the Tm calculated via the numerical integration method of radiosonde observations in the Qinghai–Tibet Plateau region from 2018 to 2019 as a reference value, the quality of the XTm model was tested and compared with the Bevis model and GPT2w (global pressure and temperature 2 wet) model. The results show that for 13 modeling stations, the bias and root-mean-square (RMS) values of the XTm model were −0.02 K and 2.83 K, respectively; compared with the Bevis, GPT2-1, and GPT2w-5 models, the quality of XTm was increased by 47%, 38%, and 47%, respectively. For the four non-modeling stations, the average bias and RMS values of the XTm model were 0.58 K and 2.78 K, respectively; compared with the other three Tm models, the RMS values and the mean bias were both minimal. In addition, the XTm model was also used to calculate the global navigation satellite system (GNSS) precipitable water vapor (PWV), and its average values for the theoretical RMSPWV and RMSPWV/PWV generated by water vapor calculation were 0.11 mm and 1.03%, respectively. Therefore, in the Qinghai–Tibet Plateau region, the XTm model could predict more accurate Tm values, which, in turn, is important for water vapor monitoring.

Development of pedotransfer functions for predicting hydraulic parameters of van Genuchten model by incorporating environmental variables on the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) region lacks large database of soil hydraulic parameters (SHPs) due to difficulty in soil sampling, and time- and labor-consuming measurement, which prevents accurate modeling of hydrological and biogeochemical processes. Pedotransfer functions (PTFs) using easily measurable soil properties may provide an alternative way to indirectly estimate SHPs. In this study, soil properties (bulk density (BD), clay, silt, sand, soil organic carbon, porosity, fitting parameter related to inverse of air entry pressure (α) and soil pore distribution (n), residual and saturated soil water content (θr and θs), and environmental parameters (altitude, slope gradient, annual average precipitation, air temperature, and evaporation) were determined from a north-south transect (ca. 500 km) in Qinghai Province and an east-west transect (ca. 1800 km) in Tibet Autonomous Region for establishing PTFs for SHPs on the QTP. The coefficient of variation of α was the largest, followed by θr, θs, and n, suggesting that α had the greatest spatial variability at the regional scale. There were significant differences in BD among 0–10, 10–20, and 20–30 cm soil layers (p < 0.05), whereas there were no significant differences in α, n, θr, and θs among these soil layers (p > 0.05). The five most important variables from the random forest analysis were selected to establish the PTFs of the SHPs and BD for the 0–30 cm soil layer on the QTP. The established PTFs for predicting SHPs and BD using multiple linear regression analysis were jointly affected by relief, climate, and soil properties. The accuracy of our newly established PTFs with the consideration of gravel content (< 10% or 10–30%) and mattic epipedon (existence or non-existence) was higher than that of the existing PTFs. We developed the first PTFs of SHPs on the QTP, providing a foundation to construct SHPs prediction models for other alpine regions in the world.

Study on the variation characteristics of ionospheric F2 layer in Qinghai-Tibet plateau and surrounding areas

The Qinghai-Tibet Plateau (QTP) region is the largest plateau in China and the highest altitude in the world, with a unique geographical environment. Based on the observation data of the 24th solar cycle from six sites on the QTP and its surrounding areas, the multi-station joint detection method is adopted to analyze the temporal variation and spatial distribution characteristics of the critical frequency of the F2 layer(foF2) and the bottom height of the F2 layer (h'F2). The results indicate that the critical frequency of the ionospheric F layer in the QTP is controlled by solar illumination conditions, and conforms to the ionospheric variation law of middle and low latitudes. The height of the F layer has significant regional characteristics, and the F layer height variation in Kunming and Chongqing is particularly drastic, and the average height of the F layer in Chongqing is significantly higher than that in Kunming, which is at a lower latitude, and Lhasa, which is at the same latitude as Chongqing. This phenomenon may be attributed to the unique geographical environment of the QTP, which has an influential effect on the motion of the lower atmosphere, and through the coupling mechanism between the lower and upper atmosphere, has a substantial impact on the F2 layer. The research findings of this article are of great interest to the study of the coupling mechanism between the upper and lower atmosphere.

Application and evaluation of CNN-LSTM classification regression based multi-source precipitation data fusion model in water resources research in Qinghai Province

Qinghai Province is located in the Qinghai-Tibet Plateau region, with complex and diverse topography and sparse precipitation stations, which makes it difficult to obtain reliable precipitation data. This study proposes a classification and regression model based on a deep learning algorithm, which combines a convolutional neural network (CNN) and a long short-term memory neural network (LSTM), with the CNN extracting the spatial features of multi-source data, the LSTM capturing their temporal dependencies. The regression results are used to determine whether rainfall is occurring and to further calibrate the non-rainfall component of the precipitation forecast results. ERA5, IMERG, CHIRPS and DEM were selected as feature data and rain gauge data as label data. The findings indicate that the proposed CNN-LSTM classification regression model (CLCR) is superior to other models (CNN, CNN-LSTM, LSTM). The Kling-Gupta efficiency (KGE) of the data fused using CLCR was 0.66, which was significantly better than that of the raw rainfall data (0.53, -0.36, 0.34) and other models (0.58, 0.65, 0.63). CLCR also showed more performance in daily precipitation detection than other models and raw precipitation data, with Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR) 0.61, 0.25 and 0.76 respectively. This study generated a high-precision daily rainfall dataset with a precision of 0.01° resolution for 2013-2017 in Qinghai Province, which provides reliable data support for hydrological studies in Qinghai Province.

Molecular phylogenetics and biogeography reveal the origin of cherries (Prunus subg. Cerasus, Rosaceae)

Abstract Cherries (subg. Cerasus) belong to Prunus s.l. (Rosaceae) and are widely distributed in temperate and subtropical regions of the Northern Hemisphere. Given its ornamental and edible functions, it is a hugely preferred germplasm resource for human societies. However, the interspecific phylogenetic relationships, divergence times, and routes of this subgenus remain unclear. To reveal its enigmatic and intricate evolutionary history, we newly sequenced 12 subg. Cerasus species, reconstructed the plastome phylogeny with 24 previously published subg. Cerasus species. This study supports that subg. Cerasus split into five clades, among which P. mahaleb is the basal group of subg. Cerasus; P. campanulata is genetically distantly related to P. cerasoides and is not recommended for treatment as a variety of the latter; P. subhirtella is involved in the speciation of P. yedoensis. Molecular dating and ancestral area reconstructions infer that subg. Cerasus originated in the Eastern Mediterranean–Central Asia before 11.57 Mya (95% HPD = 5.34–19.1 Mya) and subsequently spread into the Qinghai-Tibetan Plateau (QTP) and adjacent regions. The first dispersal event of modern subg. Cerasus was facilitated by ‘out-of-QTP’, colonizing North America across the Bering Land Bridge and re-dispersing into Europe from the Late Miocene to Pliocene and forming the initial distribution pattern of extant subg. Cerasus in the Pliocene; The end of the Quaternary glacial period witnessed a second dispersal event, and core subg. Cerasus population diversity flourished radially in its separate refugia during the Late Pleistocene.

Cryptic diversity and rampant hybridization in annual gentians on the Qinghai-Tibet Plateau revealed by population genomic analysis

Understanding the evolutionary and ecological processes involved in population differentiation and speciation provides critical insights into biodiversity formation. In this study, we employed 29,865 single nucleotide polymorphisms (SNPs) and complete plastomes to examine genomic divergence and hybridization in Gentiana aristata, which is endemic to the Qinghai-Tibet Plateau (QTP) region. Genetic clustering revealed that G. aristata is characterized by geographic genetic structures with five clusters (West, East, Central, South and North). The West cluster has a specific morphological character (i.e., blue corolla) and higher values of FST compared to the remaining clusters, likely the result of the geological barrier formed by the Yangtze River. The West cluster diverged from the other clusters in the Early Pliocene; these remaining clusters diverged from one another in the Early Quaternary. Phylogenetic reconstructions based on SNPs and plastid data revealed substantial cyto-nuclear conflicts. Genetic clustering and D-statistics demonstrated rampant hybridization between the Central and North clusters, along the Bayankala Mountains, which form the geological barrier between the Central and North clusters. Species distribution modeling demonstrated the range of G. aristata expanded since the Last Interglacial period. Our findings provide genetic and morphological evidence of cryptic diversity in G. aristata, and identified rampant hybridization between genetic clusters along a geological barrier. These findings suggest that geological barriers and climatic fluctuations have an important role in triggering diversification as well as hybridization, indicating that cryptic diversity and hybridization are essential factors in biodiversity formation within the QTP region.

Widespread decrease in chromophoric dissolved organic matter in Chinese lakes derived from satellite observations

Chromophoric dissolved organic matter (CDOM) can significantly influence underwater light fields, nutrient availability, and microbial activity; thus, it plays an essential role in physical, chemical, and biological processes in aquatic ecosystems. China has undergone significant changes in climate, water quality improvement, and human processes over the past few decades. However, the impact of these processes on CDOM in Chinese lakes is unclear. Therefore, in this study, we aimed to reconstruct long-term and high-resolution CDOM data from Landsat satellite images using a simple model to characterise changes in CDOM in 2462 Chinese lakes (surface area ≥ 1 km2) from 1986 to 2021. Validation results demonstrated that the model performed adequately in deriving the CDOM absorption coefficient at 350 nm, with a root mean square deviation, median deviation, median absolute percentage difference, and median ratio of 0.68 m−1, 0.02 m−1, 19.76%, and 1.01, respectively. We observed a distinct spatial pattern of CDOM, with higher CDOM in eastern China with intensive human activities but lower CDOM in western China with strong ultraviolet radiation. Temporally, mean aCDOM(350) of Chinese lakes from 1986 to 2021 decreased at a rate of - 0.25 m−1/10 years. We also revealed that increased regional vegetation coverage and improved water quality mainly drive CDOM decline in lakes on the Qinghai Tibet Plateau Region and Eastern Plain Region, respectively. The widespread decrease in CDOM, in turn, has contributed to changes in the optical properties and biogeochemical cycles of Chinese lakes. Our results provide valuable insights for lake environmental protection and water quality management in China.

Water use efficiency of China's karst ecosystems: The effect of different ecohydrological and climatic factors

Water use efficiency (WUE) is an important indicator for understanding the coupled ecosystem carbon and water cycles. However, the effect and contributions of factors on WUE variations in China's karst ecosystems for different climatic conditions have not been extensively studied. Our studies on WUE variations of China's karst ecosystems from 2001 to 2021 based on evapotranspiration and net primary productivity (NPP) from Moderate-resolution imaging spectroradiometer revealed the contributions of soil moisture (SM), leaf area index (LAI), precipitation (P), temperature (T), vapor pressure deficit (VPD), and CO2 concentration (CO2). Results showed that the trend of WUE was similar to that of NPP in terms of the latitude, longitude, and elevation, and WUE started abruptly decreasing after an elevation >3000 m until it reached 0 at 4500 m. WUE was primarily “slightly increased” in the humid region (H) and “slightly decreased” in the semi-humid region (SH), arid and semi-arid regions (ASA), and Qinghai–Tibet plateau region (QTP). CO2 (0.34), LAI (0.60), P (0.58), and LAI (0.55) exhibited the strongest positive direct effects on WUE in H, SH, ASA, and QTP, while VPD exhibited the strongest negative direct effect. VPD (0.26), VPD (0.28), SM (0.47), and P (0.39) had the strongest positive indirect effect, while T (−0.24), T (−0.18), VPD (−0.35), and P (−0.03) had the strongest negative indirect effect on WUE. The positive contributions of WUE variations in H, SH, ASA, and QTP were dominated by T (47.96 %), CO2 (26.36 %), P (8.81 %), and CO2 (52.97 %), whereas the negative contributions were dominated by P (−7.95 %), LAI (−26.57 %), CO2 (−35.98 %), and VPD (−9.59 %), respectively. This study quantifies the spatial and temporal distribution patterns of WUE in China's karst ecosystems and the regional differences between the multiple ecohydrological factors, thereby facilitating in-depth understanding and effective regulation for the carbon and water cycles in karst ecosystems.

Supergene Hydrous Sulfates in the Tuolugou Co-Au Deposit, Northern Qinghai–Tibet Plateau: Implications for Genetic Mechanism and Exploration

Supergene hydrous sulfate minerals form through the oxygenation and weathering of primary sulfides. In the Qinghai–Tibet Plateau region, with an alpine and dry environment, hydrous sulfate minerals oxidized from pyrite-bearing ore bodies provide important clues regarding the mineralization and environment. The Tuolugou sedimentary-exhalative (SEDEX) Co-Au deposit is located in the East Kunlun metallogenic belt of the northern Qinghai–Tibet Plateau in China. In the mining district, pyrite is the prevalent Co-hosting sulfide mineral, and is partially exposed on the surface to weathering and oxidation. Herein, we document the mineral assemblages in the supergene oxidation zone in the Tuolugou deposit, probe the genesis of supergene assemblage, and explore the implications for exploration. Three zones can be recognized in the oxidation zone of the Tuolugou deposit, including the outer zone (natrojarosite), intermediate zone (rozenite and aplowite), and inner zone (roemerite and melanterite). The mechanism of oxidation under aerobic and anaerobic conditions, as well as zoning with different oxidation degrees, are described in detail. Hydrous sulfates such as natrojarosite can be used as possible indicators of the exploration of albitite-related SEDEX deposit in this region.

Revitalizing the grassland on the Qinghai–Tibetan Plateau

AbstractGrassland is the largest ecosystem on the Qinghai–Tibetan Plateau (QTP) and provides multiple ecosystem functions and services. Understanding the endowment of the QTP grassland and how to revitalize it have profound implications for the sustainable use and efficient conservation of these unique and globally valuable ecosystems. In this paper, we highlight the importance of the QTP grassland in regional and global settings, stress the values of the QTP grassland in ecological and socioeconomic dimensions, and emphasize the actions needed to restore degraded grassland in the QTP region. The QTP is the largest single area of alpine grassland in the world and an important gene pool of alpine biological resources. The QTP grassland covers two critical ecoregions for conserving the best and most representative habitats for alpine biodiversity on the planet. The QTP grassland is also regarded as one of the best carriers and objects of socio‐ecological systems in the world. To promote the resilience and sustainability of the QTP grassland through adaptation, different parties need to work together to find feasible options to resist shock, stresses, and disturbance and to maintain the fundamental functions and basic structures of the QTP grassland.

Assessment of atmospheric heavy metal pollution in Qinghai-Tibet Plateau: Using mosses as biomonitor

Atmospheric heavy metal (HM) pollution may pose a significant threat to the fragile ecosystem of Qinghai-Tibet Plateau (QTP). To investigate potential atmospheric HM pollution within the QTP region of China, mosses, along with other higher plants and soil, were collected from 33 sites for heavy metal measurement. The concentration ranges of Zn, Pb, Cd, and Cu in mosses were 6.07–69.9, 5.36–23.9, 0.60–1.05, and 14.4–50.5 mg·kg−1 (dry weight), respectively, significantly higher than those in other higher plants, except for Zn. The spatial distribution of relative concentrations (RCs; moss to top soil) of HMs varied considerably, indicating distinct differences in atmospheric Zn and Cu pollution levels between the northern and southern QTP. This study first reported that moderate regional atmospheric Cu pollution, primarily due to large-scale mining in recent years, had occurred, particularly in southern QTP. Pb also presented slight pollution due to anthropogenic activities. However, Cd showed almost no atmospheric pollution, while Zn concentrations were relatively high in southern QTP. Although less severe than atmospheric pollution levels in Chinese inland or coastal cities, the atmospheric pollution of Pb and Cu in QTP indicated by mosses were far more severe than global background areas, or even worse than most European cities.

Climate-driven marmot-plague dynamics in Mongolia and China

The incidence of plague has rebounded in the Americas, Asia, and Africa alongside rapid globalization and climate change. Previous studies have shown local climate to have significant nonlinear effects on plague dynamics among rodent communities. We analyzed an 18-year database of plague, spanning 1998 to 2015, in the foci of Mongolia and China to trace the associations between marmot plague and climate factors. Our results suggested a density-dependent effect of precipitation and a geographic location-dependent effect of temperature on marmot plague. That is, a significantly positive relationship was evident between risk of plague and precipitation only when the marmot density exceeded a certain threshold. The geographical heterogeneity of the temperature effect and the contrasting slopes of influence for the Qinghai-Tibet Plateau (QTP) and other regions in the study (nQTP) were primarily related to diversity of climate and landscape types.

Construction and Research of Ultra-Short Term Prediction Model of Solar Short Wave Irradiance Suitable for Qinghai–Tibet Plateau

The Qinghai–Tibet Plateau region has abundant solar energy, which presents enormous potential for the development of solar power generation. Accurate prediction of solar radiation is crucial for the safe and cost-effective operation of the power grid. Therefore, constructing a suitable ultra-short-term prediction model for the Tibetan Plateau region holds significant importance. This study was based on the autoregressive integrated moving average model (ARIMA), random forest model (RF), and long short-term memory model (LSTM) to construct a prediction model for forecasting the average irradiance for the next 10 min. By locally testing and optimizing the model parameter, the study explored the applicability of each model in different seasons and investigates the impact of factors such as training dataset and prediction time range on model accuracy. The results showed that: (1) the accuracy of the ARIMA model was lower than the persistence model used as a reference model, while both the RF model and LSTM model had higher accuracy than the persistence model; (2) the sample size and distribution of the training dataset significantly affected the accuracy of the models. When both the season (distribution) and sample size were the same, RF achieved the highest accuracy. The optimal sample sizes for ARIMA, RF, and LSTM models in each season were as follows: spring (3564, 1980, 4356), summer (2772, 4752, 2772), autumn (3564, 3564, 4752), and winter (3168, 3168, 4752). (3) The prediction forecast horizon had a significant impact on the model accuracy. As the forecast horizon increased, the errors of all models gradually increased, reaching a peak between 80 and 100 min before slightly decreasing and then continuing to rise. When both the season and forecast horizon were the same, RF had the highest accuracy, with an RMSE lower than ARIMA by 65.6–258.3 W/m2 and lower than LSTM by 3.7–83.3 W/m2. Therefore, machine learning can be used for ultra-short-term forecasting of solar irradiance in the Qinghai–Tibet Plateau region to meet the forecast requirements for solar power generation, providing a reference for similar studies.

Blood Service in a Region of China's Qinghai-Tibetan Plateau.

Objectives: The purpose of this paper is to describe blood services in the Aba Tibetan and Qiang Regions, (hereinafter referred to as Aba Prefecture), a region of China's Qinghai-Tibetan Plateau, the third largest area of Tibet and the main inhabited area of the Qiang people. Design: We present a comprehensive investigation into blood donations, donors, screening and supply in the 13 counties of Aba Prefecture based on data from 2013 to 2018. Geography and population were also used to analyze the differences in blood services among different regions. Participants: The number of blood donors totaled 19,047. Results: Over the past 6 years, blood donations have increased by 29 and clinical blood usage by 45%. The blood donation rate was 3.4‱ and per capita blood use was 1.04 mL, both of which were significantly lower than the national average, and blood donation decreased with altitude. It should be noted that the donation rate of the Tibetan and Qiang peoples is much lower than that of the Han population. Moreover, the rejection rate of blood in laboratory testing was found to be higher than the national average, especially in counties located at higher altitudes. Conclusions: Blood donations and usage increased every year in Aba Prefecture, but blood shortage is still an important issue. In addition, the prevalence of transfusion-transmitted diseases is relatively high, which may be linked to lower-education and unfavorable geographical and medical conditions.

Applicability Assessment of Multi-Source DEM-Assisted InSAR Deformation Monitoring Considering Two Topographical Features

The high-precision digital elevation model (DEM) is of great significance for improving the accuracy of InSAR deformation monitoring. In today’s free opening of multi-source DEM, there is no consensus on how to select suitable DEMs to assist InSAR in deformation monitoring for different landforms. This article introduces five types of DEMs: ALOS12.5, SRTM-1, ASTER V3, AW3D30, and Copernicus 30, and uses SBAS-InSAR technology to analyze the applicability of deformation monitoring in the Qinghai Tibet Plateau and Central China Plain regions. The coverage, average value, standard deviation, and unwrapping efficiency of the phase unwrapping results, the temporal deformation rate curves of six random deformation points in the key deformation area, as well as the consistency with the second-level data and the comparative analysis of RMSE of all deformation points, show that in the Qinghai Tibet Plateau region, Copernicus 30 is the best, followed by ASTER V3, AW3D30, and SRTM-1 having low accuracy, and ALOS12.5 is the worst. In the Central China Plain region, AW3D30 is the best, followed by Copernicus 30, SRTM-1, and ASTER V3 having low accuracy, and ALOS12.5 is still the worst. Although ALOS12.5 has the highest resolution, it is not recommended for deformation monitoring based on its worst performance in plateau and plain areas. It is recommended to use Copernicus 30 in plateau areas and AW3D30 for deformation monitoring in plain areas.

Co-management for sustainable development and conservation in Sanjiangyuan National Park and the surrounding Tibetan nomadic pastoralist areas

The Qinghai-Tibet Plateau plays an essential role in national to regional ecological security, biodiversity conservation, and sustaining livelihood. An array of natural resource management, environmental conservation, and ecological restoration projects have been trialed and implemented in recent years in the vast Sanjiangyuan region of the central Qinghai-Tibet Plateau, aiming especially to ensure socio-ecologically appropriate and sustainable development of animal husbandry in the alpine grasslands. Novel approaches in China have included the introduction of more collaborative approaches in protected area management and the development and formal establishment of a new multi-purpose national park system. Many milestones have been achieved. However, such developments are driven largely by national and global goals and very little has been heard to date directly from the people most affected: those residing within the protected landscapes, i.e. the community stakeholders themselves. This empirical, perceptions-based study aims to partially fill this gap, drawing on the results of focus group discussions with community representatives supplemented by key informant interviews and a targeted review of the literature, to provide synthesized feedback and priority recommendations for improving “community co-management” collaborations for the joint benefit of Tibetan herders and protected areas. The mixed-method approach employed in this study was based on a conceptual model derived from Elinor Ostrom’s social–ecological systems framework, calibrated to local residents’ self-assessments of their household well-being. Results highlight how the most recent configuration of China’s national park model (i.e., its form and the approaches it utilizes) is generally deemed successful by community stakeholders, albeit with some notable perceived limitations mainly relating to a sense of lack of fairness and inclusiveness in the “one household, one post” co-management mechanism. The paper closes with discussion and recommendations around fundamental issues of equity, empowerment, and gender, finally pointing to the significance and, ultimately, the need to move even beyond co-management per se and to adopt a model of inclusive governance for conservation wherein joint deliberations and decision-making amongst diverse stakeholders are prioritized over the simple implementation of externally developed programs and management plans.

Understanding land-use sustainability with a systematical framework: An evaluation case of China

Appraisals of land-use sustainability (LUS) are indispensable for the attainment of sustainable land-use planning and management. At present, China is experiencing a pivotal phase of comprehensive reform in land-use policy, engendering a practical necessity for system-oriented land evaluations. Despite this, studies focused on national-scale systematic LUS evaluation remain scarce. Within this context, we developed a hierarchical structural framework for land-use systems based on the assumption that sustainable land-use systems adapted to development needs have mutually adaptable internal subsystems and fewer conflicting system elements. Employing this framework, we assessed the LUS across Chinese counties from 2000 to 2015 by using a newly introduced static equilibrium equation；we also analyzed the natural and socioeconomic influences and driving effect of land-use change to LUS. Findings reveal a significant spatial clustering and urban-rural differentiation within the LUS spatial pattern. Five LUS hot and cold spots and eight metropolitan areas with poor LUS were identified. Furthermore, conflicting land capability elements and the non-adaptability of land function subsystems with capability subsystems are the primary constraints on LUS; gross domestic product (GDP), population growth, and the GDP growth per unit land area had the most significant impacts on LUS; The transformation of construction and cropland into ecological land and the re-purposing of grassland into building land are primary drivers of LUS. Our findings highlight the prospective utility of the proposed LUS framework in integrated natural resource management and territorial spatial planning. Meanwhile, Insights from this evaluation case could be harnessed to optimize land-use in critical regions (including northern arid and semiarid regions, the Qinghai–Tibet Plateau region, the poverty belt around the capital, Yangtze River Delta and Pearl River Delta metropolitan areas, as well as Fuzhou and Xiamen metropolitan areas), thereby enhancing land management decision-making processes.

First description of Blastocystis sp. and Entamoeba sp. infecting zoo animals in the Qinghai-Tibetan plateau area, China.

Protozoan parasites are a well-known threat to human health, particularly for people working at or visiting zoos, and potentially cause zoonotic diseases in humans. Captive wildlife may be potential reservoirs for human infection with protozoan parasites. Therefore, focusing on zoonotic protozoan infections in zoo animals is critical. However, there is no report on this topic in the Qinghai-Tibetan Plateau region. In this study, a total of 167 and 103 fecal samples were collected from 12 animal species from Qinghai-Tibet Plateau Wildlife Park in winter and summer, respectively, to detection the prevalence of infections and subtype distribution with Entamoeba sp., Cryptosporidium sp., Giardia duodenalis, Enteromicrosporidia bieneusi sp., Blastocystis sp. by PCR assay. The results showed that a total of 21 fecal samples collected in winter, including from 2 white-lipped deer, 8 Sika deer, 6 blue sheep, 2 wolves and 3 bears, were positive for Entamoeba, with a 12.6% (21/167) positive rate. However, 4.9% (5/103) of animals in summer were positive for Entamoeba, including 1 snow leopard, 1 tiger, 1 Tibetan argali and 2 mouflon. Moreover, 1 white-lipped deer and 1 bear were found to be positive for Blastocystis sp., one zoonotic STs (ST10) was identified and found in white-lipped deer. We found no effect on season on Blastocystis sp. and Entamoeba sp. colonization. To the best of our knowledge, this study is the first description of Blastocystis sp. and Entamoeba sp. infecting zoo animals in the plateau area. The findings provide the latest data on Entamoeba sp. and Blastocystis sp. in zoo animals in China.

Time–Frequency Characteristics and SARIMA Forecasting of Atmospheric Water Vapor in East Asia

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.

Experimental study on the mix proportion of self-heating grouting materials in alpine regions

The frost heave disease of cement-based grouting materials occurred in the pre-stressed pipeline in alpine regions. This paper proposes a self-heating grouting material and verifies its feasibility by a series of mechanical properties and working properties experiments. Firstly, the water-cement ratio and the dosage of admixture are determined by an orthogonal test at room temperature at 5 °C, and relevant experiments determine the dosage of silica fume to improve its fluidity. Secondly, experiments carried out at −5 °C are used to analyze the influence of calcium oxide and nano-silica on the temperature of the grouting material system. Thirdly, through adiabatic temperature rise test, the exothermic contribution of calcium oxide and nano-silica heating materials in cement-based grouting material is tested, respectively, and the working performance and mechanical properties of grouting material are tested under different negative curing temperatures. Finally, the feasibility of the self-heating grouting material proposed is verified by the confirmatory test of wrapped concrete. The test results show that the self-heating grouting material under the hostile temperature can be stabilized above five degrees, and its expansion stress does not exceed the specification requirements, which can meet the construction requirements in the hostile temperature environment in the Qinghai Tibet Plateau and other Alpine regions.