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115 results for “The Qinghai-Tibet plateau”
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>
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>
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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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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Data from: ‘In and out of’ the Qinghai-Tibet Plateau and the Himalayas: centers of origin and diversification compared across five clades of Eurasian montane and alpine passerine birds
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Nitrous oxide dataset for East Qinghai-Tibet Plateau waterways
This dataset is a collation of 3-year direct measurement values of N2O concentrations and fluxes for East Qinghai-Tibet Plateau (EQTP) streams and rivers, along with information on location, hydrological, physical, and chemical conditions of the study sites. Given the rarity and high value of this EQTP data set, it will be very valuable for the next update to global riverine N2O flux estimates.
Data used in "Evaluation of topography and vegetation coverage impacts on watershed-scale active layer freeze-thaw processes with a simple algorithm in permafrost region on the Qinghai-Tibet Plateau"
<p>This is the data used in the manuscript "Evaluation of topography and vegetation coverage impacts on watershed-scale active layer freeze-thaw processes with a simple algorithm in permafrost region on the Qinghai-Tibet Plateau" (JGR earth surface 2020JF005564 ).</p>
Data from: Comparative phylogeography of the plateau zokor (Eospalax baileyi) and its host-associated flea (Neopsylla paranoma) in the Qinghai-Tibet Plateau
Background: Specific host-parasite systems often embody a particular co-distribution phenomenon, in which the parasite's phylogeographic pattern is dependent on its host. In practice, however, both congruent and incongruent phylogeographic patterns between the host and the parasite have been reported. Here, we compared the population genetics of the plateau zokor (Eospalax baileyi), a subterranean rodent, and its host-associated flea species, Neopsylla paranoma, with an aim to determine whether the two animals share a similar phylogeographic pattern. Results: We sampled 130 host-parasite pairs from 17 localities in the Qinghai-Tibet Plateau (QTP), China, and sequenced a mitochondrial DNA (mtDNA) segment (~2,500 bp), including the complete COI and COII genes. We also detected 55 zokor and 75 flea haplotypes. AMOVA showed that the percentage of variation among the populations of zokors constituted 97.10%, while the within population variation was only 2.90%; for fleas, the values were 85.68% and 14.32%, respectively. Moreover, the flea Fst (fixation index) values were significantly smaller than in zokor. Although the Fst values between zokors and fleas were significantly and positively correlated (N =105, R =0.439, p =0.000), only a small amount (R2= 0.19) of the flea Fst variations could be explained by the zokor Fst variations. The two animals showed very distinct haplotype network structures from each other while co-phylogenetic analyses were unable to reject the hypothesis of an independence of speciation events. Conclusions: Zokors and fleas have very distinct population genetic patterns from each other, likely due to the influence of other sympatrically-distributed vertebrates on the transmission of fleas.
Data from: Pliocene intraspecific divergence and Plio-Pleistocene range expansions within Picea likiangensis (Lijiang spruce), a dominant forest tree of the Qinghai-Tibet Plateau
A knowledge of intraspecific divergence and range dynamics of dominant forest trees in response to past geological and climate change is of major importance to an understanding of their recent evolution and demography. Such knowledge is informative of how forests were affected by environmental factors in the past and may provide pointers to their response to future environmental change. However, genetic signatures of such historical events are often weak at individual loci due to large effective population sizes and long generation times of forest trees. This problem can be overcome by analysing genetic variation across multiple loci. We used this approach to examine intraspecific divergence and past range dynamics in the conifer Picea likiangensis, a dominant tree of forests occurring in eastern and southern areas of the Qinghai-Tibet Plateau (QTP). We sequenced 13 nuclear loci, two mitochondrial DNA regions and three plastid (chloroplast) DNA regions in 177 individuals sampled from 22 natural populations of this species, and tested the hypothesis that its evolutionary history was markedly affected by Pliocene QTP uplifts and Quaternary climatic oscillations. Consistent with the taxonomic delimitation of the three morphologically divergent varieties examined, all individuals clustered into three genetic groups with inter-variety admixture detected in regions of geographical overlap. Divergence between varieties was estimated to have occurred within the Pliocene and ecological niche modeling based on 20 ecological variables suggested that niche differentiation was high. Furthermore, modeling of population genetic data indicated that two of the varieties (var. rubescens and var. linzhiensis) expanded their population sizes after the largest Quaternary glaciation in the QTP, while expansion of the third variety (var. likiangensis) began prior to this, probably following the Pliocene QTP uplift. These findings point to the importance of geological and climatic changes during the Pliocene and Pleistocene as causes of intraspecific diversification and range shifts of dominant tree species in the QTP biodiversity hotspot region.
FIGURE 14 in Hydroporus sejilashan sp. n., a new diving beetle of the acutangulus - complex from Xizang, China (Qinghai-Tibet Plateau), and notes on other taxa of the genus (Coleoptera, Dytiscidae, Hydroporinae)
FIGURE 14. Distribution of (enlarged map): Hydroporus tuvaensis (triangle), Hydroporus tibetanus (stars), and Hydroporus sejilashan sp. n. (encircled star). Red squares indicate capitals of countries/provinces or other important and well-known cities.
FIGURES 6–10 in Hydroporus sejilashan sp. n., a new diving beetle of the acutangulus - complex from Xizang, China (Qinghai-Tibet Plateau), and notes on other taxa of the genus (Coleoptera, Dytiscidae, Hydroporinae)
FIGURES 6–10. (6) Median lobe of Hydroporus sejilashan sp. n. in ventral (a) and in lateral view (b); (7) left paramere of Hydroporus sejilashan sp. n.; (8) median lobe of Hydroporus tibetanus in ventral view: (a) lectotype, (b) specimen from Ganzi; same in lateral view: (c) specimen from Ganzi; left paramere of Hydroporus tibetanus; (9) specimen from Ganzi, (10) lectotype.
FIGURES 1–5 in Hydroporus sejilashan sp. n., a new diving beetle of the acutangulus - complex from Xizang, China (Qinghai-Tibet Plateau), and notes on other taxa of the genus (Coleoptera, Dytiscidae, Hydroporinae)
FIGURES 1–5. Habitus of: (1) Hydroporus acutangulus, (2) Hydroporus polaris, (3) Hydroporus tuvaensis, (4) Hydroporus tibetanus (lectotype), (5) Hydroporus sejilashan sp. n.
FIGURE 13 in Hydroporus sejilashan sp. n., a new diving beetle of the acutangulus - complex from Xizang, China (Qinghai-Tibet Plateau), and notes on other taxa of the genus (Coleoptera, Dytiscidae, Hydroporinae)
FIGURE 13. Distribution of: Hydroporus acutangulus (circles), Hydroporus polaris (squares), Hydroporus tuvaensis (triangle), Hydroporus tibetanus (stars), and Hydroporus sejilashan sp. n. (encircled star); the circle with question mark refers to the type locality of Hydroporus sumakovi (see text). Red squares indicate capitals of countries/provinces or other important and well-known cities.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.