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496 results for “Tibetan Plateau”
Model data for "Surface heating over the Tibetan Plateau associated with the Antarctic Oscillation"
<p>In HYSPLIT.rar, the ????06.backjectory.10day.sh.p.pnum.nc data are the hysplit results.</p> <p>In CESM.rar, the pres_f.inc6hr.????.cam.h0.????-05_06.nc and pres_f.ins6hr.????.cam.h0.????-05_06.nc are the CTL and EXP experiment results of AGCM.</p> <p>resp_Amundv7.t42l20.nc is the response of the LBM model.</p> <p>Detailed description is shown in the paper "Surface heating over the Tibetan Plateau associated with the Antarctic Oscillation". </p>
CH4 and CO2 dataset for the Qinghai-Tibetan Plateau rivers
<p>This dataset is a collection of direct field measurement values of CH4 and CO2 concentrations and fluxes from the Qinghai-Tibetan Plateau rivers.</p>
Glaciers elevation change over Tibetan Plateau's endorheic basin during 1975-2000
<p>Glacier elevation changes over the endorheic basin of the Tibetan plateau with KH-9 in 1975-2000.</p>
Glaicer elvation change in Tibetan Plateau's endorheic basin during 1975-2000
<p>Glacier elevation change results over Tibetan Plateau's endorheic basin during 1975-2000.</p>
Combined U-series and in situ U-Pb dating of fault-related carbonates for reconstructing a long-term history of fault activity in response to SE Tibetan Plateau brittle deformation
<p>Table S1. Analytical conditions for LA-ICP-MS U-Pb dating</p> <p>Table S2. Analytical conditions for LA-ICP-MS elemental mapping</p> <p>Table S3.<em> In situ </em>calcite LA-ICAPMS U-Pb dating data</p>
GHGs data from thermokarst lakes on Tibetan Plateau
<p>Data tables containing methane and carbon dioxide concentrations and fluxes from 10 thermokarst lakes and ponds on the Tibetan Plateau, including methane and carbon dioxide concentrations in thermokarst lakes and ponds with different water depths, and methane and carbon dioxide diffusive fluxes during ice thaw and ice free periods. </p>
Fig. 5 in Description of a gynander of Colletes hedini (Hymenoptera: Colletidae) from the Qinghai-Tibetan Plateau, China: the first record of gynandromorphism for the genus after 30 years
Fig. 5. Neighbor-joining tree based on DNA barcode sequence data showing the placement
Fig. 4 in Description of a gynander of Colletes hedini (Hymenoptera: Colletidae) from the Qinghai-Tibetan Plateau, China: the first record of gynandromorphism for the genus after 30 years
Fig. 4. Male of Colletes fulvicornis. A – habitus, lateral view; B – head, frontal view; C –
Fig. 3 in Description of a gynander of Colletes hedini (Hymenoptera: Colletidae) from the Qinghai-Tibetan Plateau, China: the first record of gynandromorphism for the genus after 30 years
Fig. 3. Metasomal sterna and male terminalia of the gynander of Colletes hedini. A –
Fig. 1 in Description of a gynander of Colletes hedini (Hymenoptera: Colletidae) from the Qinghai-Tibetan Plateau, China: the first record of gynandromorphism for the genus after 30 years
Fig. 1. Habitus of the gynander of Colletes hedini. A – right side of the body, lateral
Plant species richness on the Tibetan Plateau: Patterns and determinants
<p><span><span>Whether current hypotheses for geographic patterns of species richness (SR) have a strong explanatory power for the Tibetan Plateau (TP) with extreme climatic conditions remains unclear. </span><span>In comparison with the classic "water–energy dynamics hypothesis", the unique climate factors (e.g., extreme low temperature and low oxygen partial pressure) on the TP likely significantly affect the spatial variation of SR. Here, </span></span><span>we investigate</span><span> geographic patterns and determinants of SR on the TP </span><span>through a systematic field investigation. We systematically analyzed a total of 2,013 plant communities covering 11 different vegetation types on the TP. The SR per 400 m<sup>2</sup> in the forests and shrubs and that per 1 </span><span>m<sup>2</sup></span><span> in alpine grasslands and deserts was 62.76 (±1.80 SE), 44.53 (±7.57 SE), 16.84 (±0.39 SE), and 3.62 (±0.55 SE), respectively. Unique climate factors, such as </span><span>extremely low temperature, mean diurnal temperature, and oxygen partial pressure,</span><span> act synergistically with water–energy dynamics and influence the spatial pattern of SR on the TP. </span><span>Our findings provide novel insights into the mechanisms underlying the spatial variation in plant diversity, especially on plateaus and in high-latitude regions. </span><span>Our findings and the SR map with 1 km resolution provide important benchmarks for biodiversity conservation and may help to improve predictions of the effect of climate change on biodiversity.</span></p>
Present‑day crustal deformation across the Daliang Shan, southeastern Tibetan Plateau: constrained by a dense GPS network
<p><strong>1. Intensive observations</strong> </p> <p>In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas.In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas.</p> <p><strong>2. Data processing</strong></p> <p>We employed the GAMIT and GLOBK software (Herring et al., 2015a, 2015b) to process the raw GPS data and derived the GPS positioning time series with respect to the international terrestrial reference frame for 2014 (ITRF2014) (Altamimi et al., 2017). We utilized the GAMIT software to process the double-differenced carrier-phase observations and acquired regional daily loosely constrained solutions for the site coordinates and satellite orbits. The geophysical models used have been described by Hao et al. (2021). In addition, we employed the same strategy to process ~70 evenly distributed ITRF core GPS sites to acquire global daily loosely constrained solutions. Then, we employed the GLOBK software to combine the same regional and global daily solutions to obtain a GPS time series.</p> <p>Three large earthquakes occurred in the study area: the 2004 M 9.1 Sumatra earthquake, the 2008 M 8.0 Sichuan Wenchuan earthquake, and the 2013 M 7.0 Sichuan Lushan earthquake. For the GPS time series for the campaign sites, we utilized the coseismic slip model of the 2004 Sumatra earthquake (Chlieh et al., 2007). We interpolated the coseismic displacements of the 2008 Wenchuan earthquake (Shen et al., 2009) to correct the coseismic offsets. We only used the data observed before 2008 for those GPS sites contaminated by significant postseismic deformation related to the 2008 Wenchuan earthquake (Wang & Shen, 2020). For the GPS sites affected by the coseismic deformation caused by the 2013 Lushan earthquake (Jiang et al., 2014), we also used data observed before the mainshock to mitigate the coseismic and postseismic deformation. After removing the transient deformation caused by the earthquakes, we used the weighted least-squares adjustment method to estimate linear trends of the velocities. We used the linear trend, seasonal variations, coseismic offset, and color noise model for the continuous GPS sites to fit the time series. We utilized the maximum likelihood estimation (MLE) technique and the CATS software (Williams et al., 2004; Williams., 2008) to estimate the characteristics of the noise in the residuals of the GPS time series after removing the linear trend and seasonal variations (Hao et al., 2016). Then, we obtained the GPS velocities with respect to the ITRF2014 and applied Euler rotation to transfer it to the Eurasia-fixed frame (Altamimi et al., 2017).</p> <p>The reference frames of the GPS velocities reported in previous studies are different from ours. Therefore, to transfer the latter to our selected frame, we employed the Helmert transformation with four parameters through common sites for our velocities and the published velocities. We only chose spatially uniformly distributed common sites with post-fit residuals of less than 1.0 mm/yr in the north-ward and east-ward components. Finally, we derived the geodetically consistent GPS crustal movement in the Daliang Shan and its adjacent areas with respect to the stable Eurasian Plate. Additionally, in order to reduce the residual rigid motion caused by the far-field reference of the Eurasian Plate, we chose the stable South China block as the near-field reference frame. Subsequently, our derived GPS velocities were translated into the South China block reference frame using the published Euler rotation vectors (Hao et al., 2019).</p> <p> </p> <p><strong>References </strong></p> <p>Altamimi, Z., Métivier, L, Rebischung, P., Rouby, H., Collilieux, X., 2017. ITRF2014 plate motion model. Geophys. J. Int. 209:1906–1912</p> <p>Chlieh, M., Avouac, J. P. , Hjorleifsdottir, V. , Song, T. , Ji, C. , Sieh, K., Sladen, A., Hebert, H., Prawirodirdjo, L., Bock, Y., Galetzka, J., 2007. Coseismic slip and afterslip of the great <em>M</em>w 9.15 Sumatra-Andaman earthquake of 2004. Bulletin of the Seismological Society of America, 97(1A), 152–173.</p> <p>Hao, M., Freymueller, J. T., Wang, Q. L., Cui, D. X., Qin, S. L. 2016. Vertical crustal movement around the southeastern Tibetan Plateau constrained by GPS and GRACE data. Earth and Planetary Science Letters, 437, 1-8. http://dx.doi.org/10.1016/j.epsl.2015.12.038.</p> <p>Hao, M., Li, Y., Zhuang, W., 2019. Crustal movement and strain distribution in east Asia revealed by GPS observations. Scientific Reports, https://doi.org/10.1038/s41598-019-53306-y, 16797.</p> <p>Hao, M., Wang, Q., Zhang, P., Li, Z., Li, Y., Zhuang, W., 2021. “Frame wobbling” causing crustal deformation around the Ordos block. Geophysical Research Letters 48, e2020GL091008. https://doi.org/10.1029/2020GL091008.</p> <p>Herring, T.A., King, R.W., McClusky, S.C., 2015a. GAMIT reference manual, GPS analysis at MIT, Release 10.6. Massachusetts Institute of Technology, Cambridge.</p> <p>Herring, T.A., King, R.W., McClusky, S.C., 2015b. GAMIT reference manual, global Kalman filter VLBI and GPS analysis program, Release 10.6. Massachusetts Institute of Technology, Cambridge.</p> <p>Jiang, Z., Wang, M., Wang, Y., Wu, Y., Che, S., Shen, Z.K., Bürgmann, R., Sun, J., Yang, Y., Liao, H., Li, Q., 2014. GPS constrained coseismic source and slip distribution of the 2013 Mw6.6 Lushan, China, earthquake and its tectonic implications. Geophysical Research Letters 41, 407–413, doi:10.1002/2013GL058812.</p> <p>Shen, Z.K., Sun, J., Zhang, P., Wan, Y., Wang, M., Bürgmann, R., Zeng, Y.H., Gan, W.J., Wang, Q.L., 2009. Slip maxima at fault junctions and rupturing of barriers during the 2008 Wenchuan earthquake. Nat Geosci 2:718–724.</p> <p>Wang, M., Shen, Z.K., 2020. Present-day crustal deformation of continental China derived from GPS and its tectonic implications. J. Geophys. Res. 125 (2) https://doi. org/10.1029/2019JB018774.</p> <p>Williams, S.D.P., 2008. CATS: GPS coordinate time series analysis software. GPS Solutions, 12, 147–153. <a href="http://dx.doi.org/10.1007/s10291-007-0086-4">http://dx.doi.org/10.1007/s10291-007-0086-4</a>.</p> <p>Williams, S.D.P., Bock, Y., Fang, P., Jamason, P., Nikolaidis, R.M., Prawirodirdjo, L., Miller, M., Johnson, D.J. 2004. Error analysis of continuous GPS position time series. J. Geophys. Res. 109 (B03412). http://dx.doi.org/10.1029/2003JB002741.</p> <p> </p> <p> </p>
FIGURE 1 in New fossil representative of the genus Helius (Diptera, Limoniidae) from the little known and newly discovered locality Caergen Village of northeastern Tibetan Plateau (China)
FIGURE 1. Map showing the location of the fossil site near Caergen Village.
Formation of meandering streams in a young floodplain within the Yarlung Tsangpo Grand Canyon in the Tibetan Plateau
<p>For submission to the Journal of Geophysical Research Letters, the accompanying dataset comprises seven Excel sheets that encapsulate comprehensive measurements and observations of two meandering streams within the Cuoka floodplain, located in the Yarlung Tsangpo Grand Canyon. The specific contents of each sheet are as follows:</p> <ol> <li> <p><strong>Width</strong>: This sheet documents the varying widths of the main and tributary streams throughout different segments of the Cuoka floodplain.</p> </li> <li> <p><strong>Meander Center Coordinate at 46R</strong>: Contains the GPS coordinates for the center points of meander bends at the designated 46R marker, providing spatial data essential for geomorphological analysis.</p> </li> <li> <p><strong>Geometric Data</strong>: Includes detailed geometric parameters such as meander wavelength, amplitude, and radius of curvature, which are critical for understanding the morphodynamic characteristics of the streams.</p> </li> <li> <p><strong>Migration of Main and Tributary Streams</strong>: Tracks the lateral migration rates and patterns of both streams, offering insights into the dynamic processes shaping the floodplain over time.</p> </li> <li> <p><strong>Elevation from the Water Source</strong>: Presents elevation data from the origin of the streams to their current endpoints, aiding in the analysis of gradient-driven sediment transport and deposition mechanisms.</p> </li> </ol> <p>This dataset serves as a fundamental resource for elucidating the formative and evolutionary processes of meandering streams in a geologically unique setting, contributing to broader discussions on fluvial dynamics and landscape development.</p>
Data from: No slowdown of growing season extension with warming in a permafrost-affected meadow on the Tibetan Plateau
<p>The Tibetan Plateau holds the world's largest alpine permafrost and is undergoing an acceleration of warming. Phenological shifts over alpine permafrost in a warmer world have been little studied and are greatly underrepresented in current syntheses. Here, we conducted seasonal and gradient temperature-controlled experiments in a permafrost-affected meadow to evaluate how warming drives shifts in spring and autumn phenology, and associated growing-season length at both community and species levels. Our results showed that there is no sign of slowdown in spring advance with warming under a higher year-around warming treatment, aligning with a future medium warming scenario. This finding can be attributed to the possibility that winter warming is insufficient to reduce chilling accumulation, which would not delay spring phenology and then lead to a non-slowdown in spring phenological advancement. Although spring advance led to an advance in autumn senescence according to spring-only warming experiments, the advance could not offset the delay due to concurrent warming. As a result, year-around warming significantly delayed autumn senescence, although there was a deceleration in delay with warming under high temperature treatment than under the low one. Taken together, there is no slowdown in an extension of growing season length with warming under a higher year-around warming treatment, with an increase of length by 9 and 21 days at the end of this century under a CO<sub>2</sub> stabilization and medium warming scenarios, respectively. Our results suggest that a continued growing season extension at least under the medium warming scenario would help permafrost-affected meadow ecosystems to mitigate permafrost carbon release on the Tibetan Plateau.</p>
High-resolution topography data and fault trace along the Southern Riyueshan fault, NE margin of Tibetan Plateau, China
<p>High-resolution digital elevation models (DEM) topography data extracted from the uncrewed aerial vehicle (UAV) of a DJI (Dajiang Innovations Science and Technology Co., Ltd.) Phantom 4 RTK, and GF-7 satellite stereo imagery. The trace of the Riyueshan fault is interpreted based on these high-resolution topography data.</p>
Data for Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options
<p>This dataset accompanies the submitted paper in the Earth and Space Science:Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options.</p>
Velocity and strain rate fields of the Southern Tibetan Plateau
<p>This data set contains velocity and strain rate fields over the southern Tibetan Plateau, which are derived from Sentinel-1A and -1B synthetic aperture radar satellite data (SAR) and NETCDF (.grd) formats.</p> <p>This repository contains:</p> <p>(1) asc.grd : the InSAR LOS velocity field in the ascending tracks in a resolution of ~1000 m.</p> <p>(2) desc.grd : the InSAR LOS velocity field in the descending tracks in a resolution of ~1000 m.</p> <p>(3) dilatation_strain_rate.grd : the dilatational strain rate calculated from the interpolated GNSS Vn and InSAR-derived Ve.</p> <p>(4) second_invariant_horizontal_strain_rate.grd: the second invariant horizontal strain rate calculated from the interpolated GNSS Vn and InSAR-derived Ve.</p>
Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS
<p>InSAR Line-of-Sight (LOS) velocities and their associated uncertainties in the southeastern Tibetan Plateau, along with the strain rate fields.</p> <p><br>Citations:</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS. Geophysical Research Letters.</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13731812</p>
Measurement report: Unexpected high volatile organic compounds emission from vehicles on the Tibetan Plateau Dataset
<p>This dataset includes various emission profiles and related data, specifically:</p> <ol> <li> <p><strong>Source Profile Data at Different Altitudes</strong>.</p> </li> <li> <p><strong>Emission Factor Data</strong>.</p> </li> <li> <p><strong>Emission Ratio Data</strong>.</p> </li> <li><strong>Source Profile Data from PMF Source Apportionment</strong>: Data obtained through Positive Matrix Factorization (PMF), revealing the composition of emission sources.</li> <li> <p><strong>Average Profiles of Gasoline Vapors</strong>: Derived from sealed housing evaporative determination (SHED) tests, with references 1-7.</p> </li> <li> <p><strong>Average Profiles of Gasoline Vehicle Exhaust</strong>: Based on dynamometer tests, with references 2, 8-13.</p> </li> <li> <p><strong>Average Profiles of Vehicular Emissions</strong>: Collected from low-altitude tunnel measurements, reflecting emissions in real-world driving scenarios, with references 14-24.</p> </li> </ol>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.