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483 results for “Qinghai”
Dataset of hydrogen isotope record of 1F core from Lake Qinghai
<p>The datasets include hydrogen isotope data (δDwax <em>n</em>-alkane C<sub>31</sub>) of 0 -9.8 m depth of 1F core from Lake Qinghai, representing the changes of the paleo-precipitation isotope over the northeastern Tibetan Plateau since the last glacial maximum.</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>
Exome sequencing of a hybrid pine species complex on the Qinghai-Tibetan Plateau
<p>This study investigates the evolutionary history of <em>Pinus</em> <em>densata</em> on the Qinghai-Tibetan Plateau (QTP) and genomic heterogeneity across a zone of species transition to understand contemporary dynamics of selection and evolution of species barriers. We analyzed the genetic diversity in a range-wide collection of <em>P. densata</em> and representative populations of its progenitors <em>P. tabuliformis</em> and <em>P. yunnanensis</em> using 40,000 exome probe capture sequencing.</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>
Lake-Watershed coupling model in Qinghai Lake Basin, with SHUD model
<p>Files structure</p> <p> </p> <p> </p> <ul> <li> <p>Calibration: The souce code and the part of results of the calibration.</p> <ul> <li> <p>CalibFiles: The configuration of the CMAES methods.</p> </li> <li> <p>cames_out: Part of the output of CMAES method, as the entire file is huge.</p> </li> <li> <p>input: the SHUD model input for the calibration method.</p> </li> <li> <p>qhh.nse.dai.R: the kernel R code to run the CMAES method.</p> </li> <li> <p>sub.sh: the script to submit job to LSF managment on High Performance Computer.</p> </li> </ul> </li> <li> <p>Code_Analysis: The R code for analysis the result and plot figures.</p> </li> <li> <p>Code_AutoSHUD: The R code to build the QHH model. To run the script, please call <strong>Step1_RawDataProcessng.R</strong>, Step2_DataSubset.R, <strong>Step3_BuidModel.R</strong> sequentially.</p> <ul> <li> <p>autoSHUD_qhh.txt: The configration of the autoshud project for the QHH project.</p> </li> <li> <p>GerReady.R: The initial files to build the environment, which is called by other script automatically.</p> </li> <li> <p><strong>Step1_RawDataProcessng.R</strong>: Raw data processing.</p> </li> <li> <p><strong>Step2_DataSubset.R</strong>: Data processing for soil, landuse and forcing data .</p> </li> <li> <p><strong>Step3_BuidModel.R</strong> : build the SHUD model.</p> </li> </ul> </li> <li> <p>Code_SHUD: the source code of SHUD used in this project.</p> </li> <li> <p>Data: The rawdata download from data centers.</p> </li> <li> <p>Modeling: The output folder for AutoSHUD code. These is the foundation of the simulation.</p> <p> </p> </li> </ul> <p> </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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Autumn migration of black-necked cranes on the Qinghai-Tibetan and Yunnan-Guizhou Plateaus
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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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Data from: Phylogenomics disentangles the evolutionary history of spruces (Picea) in the Qinghai-Tibetan Plateau: implications for the design of population genetic studies and species delimitation of conifers
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Spatial distribution pattern of mustelids in the eastern edge of the Qinghai-Tibet plateau
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Data from: Extensive sympatry and frequent hybridization of ecologically divergent aquatic plants on the Qinghai-Tibetan 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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Accumulation of sodium and manganese during litter decomposition of Qinghai spruce (Picea crassifolia) forest in China
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Data from: Examining differences in phylogenetic composition enhances understanding of the phylogenetic structure of the shrub community in the northeastern Qinghai-Tibetan Plateau
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