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Qinghai Province is situated deep in inland China, on the Qinghai-Tibet plateau, and it has unique climate change characteristics. Therefore, understanding the temporal and spatial distributions of water vapor in this region can be of great significance. The present study applied global navigation satellite system (GNSS) technology to retrieve precipitable water vapor (PWV) in Qinghai and analyzed its relationship with rainfall and drought. Firstly, radiosonde (RS) data is used to verify the precision of the surface pressure (P) and temperature (T) from the fifth-generation atmosphere reanalysis data set (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as the zenith troposphere delay (ZTD), calculated based on the data from continuously operating reference stations (CORS) in Qinghai. Secondly, a regional atmospheric weighted mean temperature (Tm) (QH-Tm) model was developed for Qinghai based on P, T, and relative humidity, as well as the consideration of the influence of seasonal changes in Tm. Finally, the PWV of each CORS in Qinghai was calculated using the GNSS-derived ZTD and ERA5-derived meteorological data, and its relationship with rainfall and drought was evaluated. The results show that the ERA5-derived P and T have high precision, and their average root mean square (RMS), mean absolute error (MAE) and bias were 1.06/0.85/0.01 hPa and 2.98/2.42/0.03 K, respectively. The RMS, MAE and bias of GNSS-derived ZTD were 13.2 mm, 10.3 mm and −1.8 mm, respectively. The theoretical error for PWV was 1.98 mm; compared with that of RS- and ERA5-derived PWV, the actual error was 2.69 mm and 2.16 mm, respectively. In addition, the changing trend of GNSS-derived PWV was consistent with that of rainfall events, and it closely and negatively correlated with the standardized precipitation evapotranspiration index. Therefore, the PWV retrieved from GNSS data in this study offers high precision and good feasibility for practical applications; thus, it can serve as a crucial tool for investigating water vapor distribution and climate change in Qinghai. [ABSTRACT FROM AUTHOR]

Due to the increasing air temperatures, retrogressive thaw slumps (RTSs) have become more common on the Qinghai–Tibet Plateau (QTP), but their inventory, frequency, and initiation have not been fully investigated. In satellite imagery for the period 2018–2020, 2669 active RTSs were identified in permafrost regions of the QTP. Most of the RTSs occur in regions with elevations ranging from 4,600 to 5,100 m along north‐facing slopes varying from 3° to 8°. The number of RTSs and their affected areas increased by 4 and 6 times, respectively, in three densely distributed RTS regions from 2008/2013 to 2021. This increase in the RTS occurrence mainly occurred during the warm season in 2010 and 2016 and is likely due to high air temperatures during the thawing season, which induced the massive occurrence of active‐layer detachment slides. Another intensification of RTS occurrences may appear when higher anomaly air temperatures are observed. Plain Language Summary: Climate warming has induced the thawing of permafrost (frozen ground), which increases the probability of retrogressive thaw slump (RTS) occurrence in permafrost regions of the Qinghai–Tibet Plateau (QTP). A total of 2669 active RTSs were identified on the entire QTP based on satellite imagery interpretation. These RTSs are mainly distributed in areas with higher elevations and gentle and shady slopes. To determine how these RTSs change over time, we compared satellite imagery from 2008/2013 to 2021 in three densely distributed RTS regions. We observed that the number and areas of RTSs significantly increased during the last 12 years. We also determined that such increases mainly occurred in 2010 and 2016 when the air temperature was extremely high. Anomalously high air temperatures during the thawing season might be the main reason for the recent RTS acceleration on the QTP. Key Points: Two thousand six hundred and sixty nine active retrogressive thaw slumps (RTSs) were identified in the permafrost regions of the Qinghai–Tibet PlateauRTSs significantly increased in number and area, mainly in 2010 and 2016 when summer temperatures were extremely highIntensification of RTSs could occur in the future in association with higher air temperatures appear in thawing season [ABSTRACT FROM AUTHOR]

Wheat stripe rust-damaged leaves present challenges to automatic disease index calculation, including high similarity between spores and spots, and difficulty in distinguishing edge contours. In actual field applications, investigators rely on the naked eye to judge the disease extent, which is subjective, of low accuracy, and essentially qualitative. To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, screening, filtering, and manual annotation. There were 33,238 images in our dataset with a size of 512 × 512 pixels. A new segmentation paradigm was defined. Dividing indistinguishable spores and spots into different classes, the task of accurate segmentation of the background, leaf (containing spots), and spores was investigated. To assign different weights to high- and low-frequency features, we used the Octave-UNet model that replaces the original convolutional operation with the octave convolution in the U-Net model. The Octave-UNet model obtained the best benchmark results among four models (PSPNet, DeepLabv3, U-Net, Octave-UNet), the mean intersection over a union of the Octave-UNet model was 83.44%, the mean pixel accuracy was 94.58%, and the accuracy was 96.06%, respectively. The results showed that the state-of-art Octave-UNet model can better represent and discern the semantic information over a small region and improve the segmentation accuracy of spores, leaves, and backgrounds in our constructed dataset. [ABSTRACT FROM AUTHOR]

The white yak, a type of unique and valuable farm animals on the Qinghai-Tibet Plateau, are mainly distributed in Tianzhu (County of Gansu Province), Menyuan, Huzhu and Ledu (three Counties of Qinghai Province) in China. In the present study, the Y-chromosomal genetic diversity, differentiation and phylogeny of three Chinese white yak breeds/populations (Tianzhu, Huzhu and Menyuan) were comprehensively explored using five Y-SNPs (SRY4, USP9Y, UTY19, AMELY3 and OFD1Y10) and one Y-STR (INRA189) markers. The results showed that six Y-haplotypes (H1Y1, H9Y1, H10Y1, H11Y2, H12Y2 and H13Y2) were identified in 97 male yak from three white yak breeds/populations. Among these haplotypes, H1Y1, H10Y1 and H11Y2 were shared by all of breeds/populations and H12Y2 was shared by Tianzhu and Huzhu populations. However, H9Y1 and H13Y2 haplotypes were only detected in Menyuan and Tianzhu white yak populations, respectively. The Y-haplotype diversity was maximum in Huzhu white yak (0.7500 ± 0.0349), the medium in Tianzhu white yak (0.6881 ± 0.0614) and the lowest in Menyuan white yak (0.5720 ± 0.0657). The total Y-haplotype diversity of three white yak breeds/populations was 0.7567 ± 0.0233, indicating rich paternal genetic diversity in white yak. The FST values showed a moderate differentiation between Tianzhu and Menyuan (FST = 0.0763, P < 0.05) populations, but a weak differentiation between Huzhu and Tianzhu white yak breeds/populations (FST = 0.0186, P > 0.05) and Huzhu and Menyuan (FST = − 0.005, P > 0.05) populations. The clustering analysis revealed a close genetic relationship between Huzhu and Menyuan white yak, both were far from Tianzhu white yak breed. The phylogenetic analyses showed that white yak had two Y-haplogroups/lineages (Y1 and Y2) with two potential paternal origins. The findings of present study provide new insight into the basic information for the formulation of molecular breeding programs of white yak. Moreover, it also contributes to the conservation and utilization of this special animal genetic resource. [ABSTRACT FROM AUTHOR]