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167 results for “Tibet plateau”
Annual inventories of retrogressive thaw slumps across the Qinghai-Tibet Plateau from 2016 to 2022
<p>The dataset is retrogressive thaw slump inventories with annual intervals across the plateau from 2016 to 2022. It contains the boundaries of thaw slumps delineated yearly based on the PlanetScope Scenes with high resolution (3-5 m). The inventories were compiled semi-automatically by combining deep-learning detection and manual delineation. We assigned a unique ID to every RTS in 2022 and the corresponding polygons in previous years. For RTSs merged into one in 2022, we assigned the same ID to make them traceable. We also clustered the RTSs based on the locations. It is the first of this kind to provide annual large-scale thaw slump observations across the QTP and can potentially be a benchmark for monitoring permafrost and evaluating its impact. The vector file in shapefile format contains the boundary of each hot melt collapse. The name of the file is “QTP_RTS_YYYY”, with the ‘YYYY’ representing the year of the boundaries. Relevant attribute tables include unique numbers, area (units: m<sup>2</sup>), longitude and latitude, and clusters that RTSs are in, with the corresponding names of the table fields 'id', 'Area', 'Longitude', 'Latitude' and 'Cluster'.</p>
An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau
<p>Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 soil moisture monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a soil hydraulic properties and soil moisture dataset for a high and cold mountainous area, Northeastern Qinghai-Tibet Plateau. Specifically, the soil hydraulic properties include clay, silt, sand, soil organic carbon, soil saturated hydraulic conductivity, soil water retention curve parameters (Van Genuchten model) and soil dry bulk density.</p>
Thunderstorm activity over the Qinghai–Tibet Plateau indicated by the combined data of the FY-2E geostationary satellite and WWLLN
<p>This dataset is for the article titled "Thunderstorm activity over the Qinghai–Tibet Plateau indicated by the combined data of the FY-2E geostationary satellite and WWLLN" which is being submitted to the Remote Sensing for review. </p> <p><strong>Abstract: </strong>Thunderstorm activity over the Qinghai–Tibet Plateau (QTP) has important climatic effects and disaster impacts. Using the thunderstorm feature dataset (TFD) established based on the black body temperature (TBB) and cloud classification (CLC) products of the Fengyun-2E (FY-2E) geostationary satellite, as well as the lightning data of the World Wide Lightning Location Network (WWLLN), the temporal and spatial distributions and some cloud properties of the thunderstorms over the QTP were analyzed. Approximately 93.9% and 82.7% of thunderstorms over the QTP occur from May to September and from 12 to 21 o'clock local time, and the corresponding peaks are in August and at 14:00, respectively. There are three centers featuring frequent thunderstorms in the southeast, south-central, and southwest regions of the QTP. The average thunderstorm cloud area (the region with TBB ≤ −32℃) is 1.8 × 10<sup>4</sup> km<sup>2</sup>. Approximately 32.9% of thunderstorms have strong convective cells (SCCs) composed of areas with TBB ≤ −52℃.The average number and area ratio of SCCs are 3.6 and 25.4%, respectively, and their spatial distribution is given. The average cloud area and the number and area ratio of SCCs of extreme-lightning thunderstorms (thunderstorms with the top 10% of lightning numbers) are approximately 30.0, 3.9, and 1.5 times those of normal thunderstorms. The spatial distribution of the thunderstorm activity is quite different from that of lightning activity given by the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) over the northeastern and southwestern QTP, which may mean that the convection intensity, cloud structure, and charge structure of the thunderstorms over the QTP are different between different regions and seasons.</p>
Fig 4 in Ptilagrostis contracta (Stipeae, Poaceae), a New Species Endemic to Qinghai-Tibet Plateau
Fig 4. Distribution map of Ptilagrostis contracta. doi:10.1371/journal.pone.0166603.g004
Spatial distribution pattern of mustelids in the eastern edge of the Qinghai-Tibet plateau
<p>Evolutionary theory predicts that the species of an evolutionarily successful taxon would not overlap in spatial distribution. To test the prediction, we document our research on the spatial associations of mustelids, an evolutionarily successful group of Order Carnivore, using infrared camera trap data on species distribution collected from the national nature reserves of Liancheng, Wolong, Tangjiahe, and Heizhugou in China in 2017-2021. Data showed seven mustelid species occurring in the study area, including <em>Arctonyx collaris</em>, <em>Mele leucurus</em>,<em> Martes foina</em>, <em>Martes flavigula</em>, <em>Mustela altaica</em>, <em>Mustela nivalis</em>, and <em>Mustela sibirica</em>. Following Ricklefs' definition of biological community, we identified five networks of species associations. The mustelids occurred in the networks. Species from the same genus, such as <em>M. foina</em> and <em>M. flavigula</em>, stayed in different networks to avoid competition due to similar feeding habits or habitat preferences. Species with different feeding habits or habitat preferences either occurred in different networks, such as <em>M. altaica</em> and <em>M. flavigula</em>, or coexist in the same networks but avoided direct spatial associations, such as <em>M. foina</em> and <em>A. collaris</em><em>.</em> Asymmetrical associations were found between different genera, such as <em>M. foina</em> and <em>M</em><em>.</em><em> altaica</em>; or between different subfamilies, such as <em>M. flavigula</em> and <em>A</em><em>.</em><em> collaris</em>. These associations may be attributed to interspecific killing or seed dispersal. However, these associations accounted for only a small proportion and would not impact the species diversity of Mustelidae. It is thus concluded that the prediction is supported by our research findings and that spatial avoidance may be the biogeographic strategy of maintaining the species diversity of the family. We also found that the well protection of the mustelids may benefit to the overall biodiversity conservation in Heizhugou, an NNR that has experienced severe deforestation.</p>
Altitude gradient pattern of grassland restoration of quarry on the Qinghai-Tibet Plateau
<p>It is very difficult to recover the alpine grassland on the Qinghai Tibet Plateau after being destroyed. However, the demand for economic construction forced the government to build highways on the Qinghai Tibet Plateau, leaving behind many quarries. However, it is uncertain whether these quarries can be restored and the extent of restoration. This study conducts a transect survey of 39 quarry-restored grasslands reseeded with Elymus nutans and adjacent natural grasslands on two recently constructed highways on the Qinghai–Tibet Plateau from 2800 to 5100 m above sea level. The first data set is the community survey data set of height, in which the rows describe the species names in the sample plots, the sample plots shown in the list and duplicates. The second data set is the community survey data set of coverage, in which the rows describe the species names in the sample plots, the sample plots shown in the list and duplicates. We believe that the height and coverage were not enough to evaluate the restoration, and the community species composition of the restored grassland was an important indicator to evaluate the restoration.</p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L2)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000.</p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L1)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000.</p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L3)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000.</p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L4)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000. </p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
Monthly surface water in the Tibet Plateau in 2020
<p>We produced a new high-resolution dataset (10 m spatial resolution) about the month-by-month surface water in the Tibet Plateau in 2020 by using the deep learning method and Sentinel-1 image.</p> <p>The related source code can be found at: https://github.com/xinluo2018/Tibet-Water-2020.git.</p> <p> </p>
The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 2 (2011-2020)
<p>Soil moisture (SM) is a vital variable in the water-energy cycle and characterizing its spatiotemporal dynamics is crucial for understanding the impacts of climate change. Although substantial efforts have been devoted to derive SM data at fine scale, there is still a research gap in obtaining the long-term, high-accuracy and high-resolution SM data over the Qinghai-Tibet Plateau (QTP) due to its complex topography. Therefore, this study generated the long-term, high-accuracy and seamless soil moisture (LHS-SM) dataset over the QTP during 2001-2020 using a two-step downscaling method. First the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1km utilizing five machine learning approaches. Then a dynamic data merging method that considers the spatiotemporal nonstationary error was applied to derive the final LHS-SM data. Results indicated that LHS-SM data exhibited satisfying accuracy (mean R = 0.55, ubRMSE = 0.049 m³/m³) and certain improvement to the ESA CCI SM data both at station and network scales. The dataset can be used for various regional hydrology, meteorology, ecological analysis and modeling.</p>
Retrogressive thaw slump (RTS) inventory in central Qinghai-Tibet Plateau (northwest of Beiluhe basin)
<p>A new retrogressive thaw slump (RTS) inventory in central Qinghai–Tibet Plateau (QTP) were generated based on visual interpretation of nine satellite images (WV-2, Google Earth image, Ziyuan-3, Gaofen-2, Gaofen-1) and field investigations. A total 459 RTSs were confirmed with an accumulative area of 1199.49 ha (in time slice of 2018-2020). To reduce the uncertainty in identifying the RTS boundaries, all of the images were spatially corrected based on a reference image, which was obtained on 29 December 2015. The RTS inventory published by Luo et al., (2022) and Xia et al., (2022) were refered when we conduct visual interpretation. Note: the RTSs were distingusihed into active RTSs (TYPE=Y) and non-active RTSs (TYPE=N). Those RTSs were neither active nor non-active RTSs when their area increase vary from 0 to 0.01 ha or less than 0 (TYPE=T). The field named "area_ha", "perime_km" are the area and perimeter of the RTSs in the attribute tables of shapefiles. The units of area and perimeters are hectare (ha) and kilometer (km), respectively. The field named "type" indicates the status of RTSs. The "Y" and "N" are means the RTS is belongs to active RTS and non-active, respectively.</p>
Improving gross primary production estimation accuracy on the Qinghai-Tibet Plateau considering the effect of atmospheric CO2 fertilization
<p>This GPP dataset was generated by the improved GPP estimation model which introduced atmospheric CO<sub>2</sub> fertilization effect and canopy-to-leaf CO<sub>2</sub> concentration gradients into the CASA model. The dataset was provided in TIF format at a month interval. The valid value ranges from 0 to 1000, and the background filled value is set to NoData. The scale factor of the data is 1. Each TIF file represents a month GPP at a daily cumulative value (unit: g C m<sup>-2</sup> month<sup>-1</sup>).</p>
Taxonomic and functional biogeographies of soil bacterial communities across the Tibet plateau are better explained by abiotic conditions than distance and plant community composition
<p><span>The processes governing soil bacteria biogeography are still not fully understood. It remains unknown how the importance of environmental filtering and dispersal differs between bacterial taxonomic and functional biogeography, and whether their importance is scale-dependent. We sampled soils across the Tibet plateau, with distances among plots ranging from 20 m to 1,550 km. Taxonomic composition of bacterial community was characterized by 16S amplicon sequencing and functional community composition by qPCR targeting 9 functional groups involved in N dynamics. Factors representing climate, soil, and plant community were measured to assess different facets of environmental dissimilarity. Both bacterial taxonomic and functional dissimilarities were more related to abiotic dissimilarity than biotic (vegetation) dissimilarity or distance. Taxonomic dissimilarity was mostly explained by differences in soil pH and mean annual temperature (MAT), while functional dissimilarity was linked to differences in soil N and P availabilities and N:P ratio. Soil pH and MAT remained the main determinants of taxonomic dissimilarity across spatial scales. In contrast, the explanatory variables of N-related functional dissimilarity varied across the scales, with soil moisture and organic matter having the highest role across short distances (<~330 km), and available P, N:P ratio and distance being important over long distances (>~660 km). Our results demonstrate how biodiversity dimension (taxonomic versus functional aspects) and spatial scale influence the factors driving soil bacterial biogeography.</span></p>
Land use and ecosystem service value spatiotemporal dynamics, topographic gradient effect and their driving factors in typical alpine ecosystems of the east Qinghai-Tibet Plateau: Implications for conservation and development
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Data from: Repeated evolution of storage root and invasions of alpine biome drove replicated radiations of the megadiverse Corydalis (Papaveraceae) in the Qinghai–Tibet Plateau
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Spatial distribution pattern of mustelids in the eastern edge of the Qinghai-Tibet plateau
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The historical connection of the Arctic and Qinghai-Tibet Plateau floras and their asynchronous diversification in response to Cenozoic climate cooling
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Taxonomic and functional biogeographies of soil bacterial communities across the Tibet plateau are better explained by abiotic conditions than distance and plant community composition
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