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115 results for “The Qinghai-Tibet plateau”
CO₂ and related biogeochemical data from permafrost rivers on the Qinghai-Tibet Plateau (2016–2018, 2023)
This dataset includes four years of direct measurements from 50 permafrost rivers in the Qinghai-Tibet Plateau’s major Asian headwaters (Yellow, Yangtze, Lancang, Nu, Derung, Yarlung Tsangpo, Marja Tsangpo, and Indus Rivers), collected during the ice-free seasons (April–October) of 2016–2018 and 2023. It includes CO₂ partial pressure (pCO₂) and emission rates, concentrations of DOC, DIC, and major dissolved ions, carbon (δ¹³C), sulfur (δ³⁴S), and oxygen (δ¹⁸O) isotopes, alongside site location and other physiochemical data. Given its scarcity and scientific significance, this dataset will greatly support updates to global river CO2 flux estimates.
Greenhouse gas data of permafrost-affected reservoirs on the Qinghai-Tibet Plateau, 2016-2018
This dataset is a collation of 3-year direct measurement values of CO2, CH4 and N2O concentrations and fluxes of permafrost-affected reservoirs on the Qinghai-Tibet Plateau, along with information on location and chemical conditions of the study sites. Given the rarity and high value of this data set, it will be very valuable for the next update to global reservoir GHG flux estimates.
Annual human footprint maps at 100-meter resolution from 1990 to 2020 for Qinghai-Tibet Plateau
<p>This Human footprint product is the first annual dataset with a resolution of 100 meters from 1990 to 2020 covering the Qinghai-Tibet Plateau (QTP). It integrates eight variables: population density, cropland, built environments, grazing activities, nighttime lights, roads, railways, and hydroelectric projects, each providing insights into various human impacts on the plateau. Additionally, the dataset's accuracy was assessed using 1,043 samples with a median resolution of 0.5 m, uniformly distributed across the QTP. These HF maps can serve as a critical tool for understanding the extensive human influence on the QTP, and can aid in conservation planning, resource management, and informing decision-making processes related to ecological restoration objectives. </p>
Multi-year dataset for groundwater level, temperature, and chemical and isotopic compositions of different water bodies in an alpine catchment on the northeastern Qinghai-Tibet Plateau, China
<p>Here we provide the multi-year dataset for groundwater level, temperature, and chemical and isotopic compositions of different water bodies in an alpine catchment on the northeastern Qinghai-Tibet Plateau, China. The first file contains monitoring data, including groundwater levels and ground temperatures. The second file includes the results of the sample analyses as well as the numbers and locations of the sampling sites.</p>
Fig 2 in Ptilagrostis contracta (Stipeae, Poaceae), a New Species Endemic to Qinghai-Tibet Plateau
Fig 2. Phylogram of Ptilagrostis obtained from MrBayes analysis of ITS dataset. Numbers above branches are support values of ML, numbers below the branches are support values of BI. doi:10.1371/journal.pone.0166603.g002
Fig 1. Ptilagrostis contracta. A-B in Ptilagrostis contracta (Stipeae, Poaceae), a New Species Endemic to Qinghai-Tibet Plateau
Fig 1. Ptilagrostis contracta. A-B. habitat; C. contracted panicle; D. base of panicle branch (pulvinae absent); E. spikelets; F. floret (lemma evenly pubescent); G. chromosomes; H. karyotype; I. Lemma epidermal pattern. Scale bar in D represents 2 mm; in E, F represent 5 mm; in G, H represent 5 μm. doi:10.1371/journal.pone.0166603.g001
Fig 3 in Ptilagrostis contracta (Stipeae, Poaceae), a New Species Endemic to Qinghai-Tibet Plateau
Fig 3. Illustration of Ptilagrostis contracta. A. habit; B. glume; C. floret; D. palea; E. callus F. outer surface of ligule; G. inner surface of ligule. Scale bar in B, C, F, G represent 5 mm; in D represent 2 mm; in E represent 0.5 mm (Drawn by Z. S. Zhang). doi:10.1371/journal.pone.0166603.g003
Two-step fusion method for generating 1 km seamless multi-layer soil moisture with high accuracy in the Qinghai-Tibet plateau
<p>Current remote sensing techniques fail to observe and generate large scale multi-layer soil moisture (SM) due to the inherent features of the satellite sensors. The lack of comprehensive understanding of multi-layer SM hinders the sustainable development of agriculture, hydrology, and food security. In order to overcome the depth barrier of traditional SM assimilation and downscaling methods, we developed a Two-step Multi-layer SM Downscaling (TMSMD) framework by fusing multi-source remotely sensed, reanalysis, and in-situ data through both machine learning and state-of-the-art deep learning models to generate multi-layer SM. The produced multi-layer SM was characterized by high resolution (1 km), high spatio-temporal continuity (cloud-free and daily), and high accuracy (i.e., 3H data). Firstly, the coarse resolution SMAP SM was downscaled to 1 km spatial resolution using LightGBM to weaken the effects of scale mismatch issue and provide high-resolution input for the subsequent calibration. Results indicated that the downscaled SMAP SM remained high consistency with the original SMAP SM product. With the high-resolution inputs, we calibrated the downscaled SMAP SM using multi-layer in-situ SM through state-of-the-art attention-based LSTM. Results demonstrated that the average PCC, RMSE, ubRMSE, and MAE were improved by 22.3%, 50.7%, 26.2%, and 56.7% compared to SMAP L4 SM while 38.5%, 52.1%, 29.5%, and 58.7% compared to downscaled SMAP SM. Further spatio-temporal and comparative analysis confirmed that the multi-layer SM produced by the TMSMD framework had excellent performance in capturing the spatial and temporal dynamics. In conclude, the proposed TMSMD framework successfully generated 3H multi-layer SM data and is promising for accurate assessment and monitoring in agriculture, water resources, and environmental domains.</p> <p> </p> <p>The remaining data will be uploaded soon.</p>
A 1 km daily soil moisture dataset over the Qinghai-Tibet Plateau (2001-2010)
<p>Soil moisture is the key variable of water and energy cycle, but the long-term, high-resolution soil moisture data with high accuracy is still relatively lacking on the Qinghai-Tibet Plateau. Therefore, we provide the 1 km seamless daily soil moisture data over the Qinghai-Tibet Plateau during 2001-2010 (named as BTCH). Firstly, several predictors including the vegetation index (NDVI and EVI), land surface temperature (LST), evapotranspiration, precipitation, topography (DEM, aspect, slope, TWI), soil properties and three soil moisture related indices (SWCI SIWSI VSDI) were utilized. Five machine/deep learning methods including the artificial neural network (ANN), convolutional neural network (CNN), residual neural networks (ResNet), the long short-term memory network (LSTM) and XGBoost were trained for each year taking the ESA CCI soil moisture data as target. Then the Bayesian three-cornered hat method was adopted for model integration and the final dataset was generated. Evaluaion against four in-situ measurement networks shows that the BTCH dataset has relativey high accuracy both as station and network scales with mean unbiased RMSE values of 0.048 m3/m3 and 0.034 m3/m3. The dataset can be used for various regional hydrological, meteorological, ecological analysis and modeling.</p>
The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 1 (2001-2010)
<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>
A dataset for methane concentrations and fluxes for alpine permafrost streams and rivers on the East Qinghai-Tibet Plateau
This dataset is a collation of 3-year direct measurement values of CH4 concentrations and fluxes for 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 C flux estimates.
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅱ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For Tmin in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmin(Tmin>2606)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅰ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For Tmax in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmax(Tmax>2606)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
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
<p>Encompassing some of the major hotspots of biodiversity on Earth, large mountain systems have long held the attention of evolutionary biologists. The region of the Qinghai-Tibet Plateau (QTP) is considered a biogeographic source for multiple colonization events into adjacent areas including the northern Palearctic. The faunal exchange between the QTP and adjacent regions could thus represent a one-way street ('out of' the QTP). However, immigration into the QTP region has so far received only little attention, despite its potential to shape faunal and floral communities of the QTP. In this study, we investigated centers of origin and dispersal routes between the QTP, its forested margins and adjacent regions for five clades of alpine and montane birds of the passerine superfamily Passeroidea (Johansson et al., 2008; Selvatti et al., 2015). We performed an ancestral area reconstruction using BioGeoBEARS and inferred a time-calibrated backbone phylogeny for 279 taxa of Passeroidea. The oldest endemic species of the QTP was dated to the early Miocene (ca. 18 Ma). Several additional QTP endemics evolved in the mid to late Miocene (12–7 Ma). The inferred centers of origin and diversification for some of our target clades matched the 'out of Tibet hypothesis' or the 'out of Himalayas hypothesis' for others they matched the 'into Tibet hypothesis'. Three radiations included multiple independent Pleistocene colonization events to regions as distant as the Western Palearctic and the Nearctic. We conclude that faunal exchange between the QTP and adjacent regions was bidirectional through time, and the QTP region has thus harbored both centers of diversification and centers of immigration.</p>
Faunal remains data from Paleolithic to early Iron Age archaeological sites in the Qinghai-Tibet Plateau in China
<p>According to published archaeological sources, zooarchaeological data collection on the Qinghai-Tibet Plateau and its marginal and transitional areas is inadequate, and relevant datasets have not been published. For this reason, we collected and collated relevant information. Our database provides the geographical location, elevation, cultural type and faunal assemblage of each site on the Qinghai-Tibet Plateau and its periphery for which zooarchaeological data have been published from the Paleolithic to the Early Iron Age. The patterns of human faunal resource use, habitat patterns, and animal abundance and spatial distribution on the Qinghai-Tibet Plateau and its surrounding areas during the Prehistoric-Early Iron Age are represented in this dataset. The data provide a reference for further understanding prehistoric-early Iron Age human behavior, subsistence patterns and material and cultural exchanges between East and West on the Qinghai-Tibet Plateau and its environs.</p>
Physicochemical Characterization of Religious Burning Aerosols in Lhasa on the Qinghai-Tibet Plateau
<p>Data from Lhasa in the Tibetan Plateau, including chemical composition, Infrared spectrum (IR), and light absorption data.</p>
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>
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>
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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.