Meteorological elements of the dataset include the near-surface land-air exchange parameters, such as downward/upward longwave/shortwave radiation flux, momentum flux, sensible heat flux, latent heat flux, etc. In addition, the vertical distributions of 3-dimensional wind, temperature, humidity, and pressure from the surface to the tropopause are also included. Independent evaluations were conducted for the dataset by comparison between the observational data and the most recent ERA5 reanalysis data. The results demonstrate the accuracy and superiority of this dataset against reanalysis data, which provides great potential for future climate change research.
LI Fei, Ma Shupo, ZHU Jinhuan, ZOU Han , LI Peng , ZHOU Libo
The basic data of hydrometeorology, land use and DEM were collected through the National Meteorological Information Center, the Hydrological Yearbook, the China Statistical Yearbook and the Institute of Geographic Sciences and Resources of the Chinese Academy of Sciences. The distributed time-varying gain hydrological model with independent intellectual property rights is used for modeling, and the Qinghai Tibet Plateau is divided into 10937 sub basins with a threshold of 100 square kilometers. In Heihe River, Yarlung Zangbo River, the source of Yangtze River, the source of Yellow River, Yalong River, Minjiang River and Lancang River basins, 14 flow stations were selected to observe the daily flow data to develop and verify the model. The daily scale Naxi efficiency coefficient is above 0.7, and the correlation coefficient is above 0.8. The precipitation and temperature data output from 13 models and 4 scenarios provided by CMIP6 are used to post process the future precipitation and temperature data. The post processed precipitation and temperature driven hydrological model simulates the water cycle process from 2046 to 2065, and gives the possible future spatial and temporal distribution of 0.1 degree daily scale runoff across the Qinghai Tibet Plateau.
YE Aizhong
Based on the data of GF-1 and GF-2 in China, the freeze-thaw disaster distribution data of Qinghai Tibet project corridor is produced by using the deep learning classification method and manual visual interpretation and correction. The geographical range of the data is 40km along the Xidatan Anduo section of Qinghai Tibet highway. The data include the distribution data of thermokast lakes and the distribution data of thermal melting landslides. The dataset can provide data basis for the research of freeze-thaw disaster and engineering disaster prevention and reduction in Qinghai Tibet engineering corridor. The spatial distribution of freezing and thawing disasters within 40km along the Xidatan-Anduo section of Qinghai Tibet highway is self-made based on the domestic GF-2 image data. Firstly, the deep learning method is used to extract the mud flow terrace block from GF-2 data; Then, ArcGIS is used for manual editing.
NIU Fujun, LUO Jing LUO Jing
This data is generated based on meteorological observation data, hydrological station data, combined with various assimilation data and remote sensing data, through the preparation of the Qinghai Tibet Plateau multi-level hydrological model system WEB-DHM (distributed hydrological model based on water and energy balance) coupling snow, glacier and frozen soil physical processes. The time resolution is monthly, the spatial resolution is 5km, and the original data format is ASCII text format, Data types include grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation in the month). If the asc cannot be opened normally in arcmap, please top the first 5 lines of the asc file.
WANG Lei, CHAI Chenhao
This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation), simulated and output through the WEB-DHM distributed hydrological model of the Indus River basin, with temperature, precipitation, barometric pressure, etc. as input data.
WANG Lei, LIU Hu
This phenological data is based on the MOD13A2 data of the Qinghai Tibet Plateau from 2000 to 2015 (with a temporal resolution of 16 days and a spatial resolution of 1km). The NDVI curve is fitted using the segmented Gaussian function in the TIMESAT software. The spring phenology, autumn phenology and the length of the growth season are extracted using the dynamic threshold method. The thresholds of spring phenology and autumn phenology are set to 0.2 and 0.7 respectively. The phenological data were masked. Among them, the mask rules are: 1) The maximum value of NDVI must be met between June and September; 2) The average value of NDVI from June to September shall not be less than 0.2; 3) The average NDVI in winter shall not exceed 0.3.
ZU Jiaxing , ZHANG Yangjian
This data set is the global vegetation productivity data, including total primary productivity (GPP), net primary productivity (NPP) and net ecosystem productivity (NEP). It is simulated by BCC-ESM1 model in Phase 6 of the Coupling Model Comparison Plan (CMIP6) under the historical scenario. The data time range is 1850-2014, the time resolution is month, and the spatial resolution is about 2.8125 °. Analog Data Details Visible Link https://www.wdc-climate.de/ui/cmip6?input=CMIP6.CMIP.BCC.BCC -ESM1。
ZHENG Zhoutao
The feedback of the biosphere to the atmosphere is one of the core contents of global change research. When the atmospheric CO2 concentration rises, the behavior of the terrestrial ecosystem is the main uncertainty factor to predict this feedback effect. Elevated CO2 concentration (eCO2) can directly stimulate plant growth and ecosystem C absorption by increasing carboxylation and inhibiting photorespiration rate. Through the impact of CO2 fertilization effect (CFE) on photosynthesis and carbon sequestration, the terrestrial ecosystem can buffer the surge of atmospheric CO2 concentration, thereby slowing down climate change. In order to study the impact of CO2 enrichment on vegetation productivity, CO2 enrichment experiments were conducted at Naqu Grassland Station (31 ° 38 ′ 31 ″ N, 92 ° 00 ′ 54 ″ E, 4600m above sea level) in the north of the Qinghai Tibet Plateau. The test is designed in zones, with CO2 as the main treatment factor and N as the secondary treatment factor; A total of four experimental treatments span two CO2 concentration levels [ambient CO2 (aCO2), increased CO2 (eCO2):+100ppm]. Considering the low vegetation height and windy weather in the study area, octagonal open top chambers (OTCs) are used to control the carbon dioxide concentration, rather than the free FACE system. The design height of OTC is 2.5 meters, the length of each side is 1.5 meters, and each OTC occupies 7.7 square meters.
ZHANG Yangjian
This data set is the daily vorticity related flux observation data of Naqu flux station (31.64 ° N 92.01 ° E, 4598 m a.s.l.), including ecosystem net ecosystem productivity (NEP), total primary productivity (GPP) and ecosystem respiration (ER) data. The main steps of data pre-processing include wild point removal (± 3 σ）、 Coordinate axis rotation (3D wind rotation), Webb Pearman Leuning correction, outlier elimination, carbon flux interpolation and decomposition, etc. Missing data are interpolated through the nonlinear empirical formula between CO2 flux value (Fc) and environmental factors.
ZHANG Yangjian
Vegetation survey data is essential for the study of ecosystem structure and function. The Qinghai Tibet Plateau contains a vast grassland ecosystem, mainly including alpine meadow, alpine grassland, and alpine desertification grassland. Due to the unique geographical location and high altitude anoxic environmental conditions, the community survey data in the northern Tibetan Plateau is relatively scarce. This data set includes the aboveground biomass and coverage data of 47 sampling points on the northern Tibet transect in 2019, and the sampling time is from July to August. The sample size is 50cm × 50cm, dry weight of the plant is weighed after drying. This data set can be used for spatial analysis of productivity and calibration of models.
ZHANG Yangjian, ZHU Juntao
This dataset is the daily vorticity related flux observation data of Naqu flux station (31.64 ° N 92.01 ° E, 4598 m a.s.l.), including net ecosystem productivity (NEP), total primary productivity (GPP), ecosystem respiration (ER), evapotranspiration, latent heat, sensible heat, air temperature, relative humidity, wind speed, soil temperature, soil moisture and other data. The main steps of data pre-processing include wild point removal (± 3 σ）、 Coordinate axis rotation (3D wind rotation), Webb Pearman Leuning correction, outlier elimination, carbon flux interpolation and decomposition, etc. Missing data are interpolated through the nonlinear empirical formula between CO2 flux value (Fc) and environmental factors.
ZHANG Yangjian
This dataset is global respiration data, including autotrophic respiration (ra) and heterotrophic respiration (rh). It is simulated by TaiESM1 model in Phase 6 of the Coupling Model Comparison Plan (CMIP6) under historical scenarios. The data time range is 1850-2014, the time resolution is month, and the spatial resolution is about 0.9 ° x1.25 °. Analog Data Details Visible Link https://www.wdc-climate.de/ui/cmip6?input=CMIP6.CMIP.AS -RCEC.TaiESM1.historical。
Program for Climate Model Diagnosis and Intercomparison (PCMDI)
The dataset is the remote sensing image data ofGF-1 satellite in the Qinghai-Tibet engineering corridor obtained by China High Resolution Earth Observation Center. After the fusion processing of multispectral and panchromatic bands, the image data with a spatial resolution of 2 m is obtained. In the process of obtaining ground vegetation information, the classification technology of combining object-oriented computer automatic interpretation and manual interpretation is adopted, The object-oriented classification technology is to collect adjacent pixels as objects to identify the spectral elements of interest, make full use of high-resolution panchromatic and multispectral data space, texture and spectral information to segment and classify, and output high-precision classification results or vectors. In actual operation, the image is automatically extracted by eCognition software. The main processes are image segmentation, information extraction and accuracy evaluation. After verification with the field survey, the overall extraction accuracy is more than 90%.
NIU Fujun
This data includes bacterial 16S ribosomal RNA gene sequence data from 25 lakes in the middle of the Qinghai Tibet Plateau. The sample was collected from July to August 2015, and the surface water was sampled three times with a 2.5 liter sampler. The samples were immediately taken back to the Ecological Laboratory of the Beijing Qinghai Tibet Plateau Research Institute, and the salinity gradient of the salt lake was 0.14~118.07 g/L. This data is the result of amplification sequencing. Concentrate the lake water to 0.22 at 0.6 atm filtration pressure μ The 16S rRNA gene fragment amplification primers were 515F (5 '- GTGCCAAGCCGCGGTAA-3') and 909r (5 '- GGACTACHVGGGTWTCTAAT-3'). The Illumina MiSeq PE250 sequencer was used for end-to-end sequencing. The original data was analyzed by Mothur software. The sequence was compared with the Silva128 database and divided into operation classification units (OTUs) with 97% homology. This data can be used to analyze the microbial diversity of lakes in the Qinghai Tibet Plateau.
KONG Weidong
This data includes the distribution data of soil bacteria in Namco region of the Qinghai Tibet Plateau, which can be used to explore the seasonal impact of fencing and grazing on soil microorganisms in Namco region. The sample was collected from May to September 2015, and the soil samples were stored in ice bags and transported back to the Ecological Laboratory of Beijing Institute of Qinghai Tibet Plateau Research; This data is the result of amplification sequencing, using MoBio Powersoil ™ Soil DNA was extracted with DNA isolation kit, and the primers were 515F (5 '- GTGCCAAGCGCCGGTAA-3') and 806R (5'GGACTACNVGGGTWTCTAAT-3 '). The amplified fragments were sequenced by Illumina Miseq PE250. The original data is analyzed by Qiime software, and then the similarity between sequences is calculated, and the sequences with a similarity of more than 97% are clustered into an OTU. The Greengenes reference library is used for sequence alignment to remove the sequence that only appears once in the database. The soil moisture content and soil temperature were measured by a soil hygrometer, and the soil pH was measured by a pH meter (Sartorius PB-10, Germany). The soil nitrate nitrogen (NO3 −) and ammonium nitrogen (NH4+) concentrations were extracted with 2 M KCl (soil/solution, 1:5), and analyzed with a Smartchem200 discrete automatic analyzer. This data set is of great significance to the study of soil microbial diversity in arid and semi-arid grasslands.
KONG Weidong
Data on soil bacterial diversity of grassland in Qinghai Tibet Plateau. The samples were collected from July to August 2017, including 120 samples of alpine meadow, typical grassland and desert grassland. The soil surface samples were collected and stored in ice bags, and then transported back to the ecological laboratory of the Beijing Qinghai Tibet Plateau Research Institute. The soil DNA was extracted by MO BIO PowerSoil DNA kit. The 16S rRNA gene fragment amplification primers were 515F (5 '- GTGCCAAGCCGGTAA-3') and 806R (5 ´ GGACTACNVGGGTWTCTAAT-3 ´). The amplified fragments were sequenced by Illumina Miseq PE250. The original data is analyzed by Qiime software, and the sequence classification is based on the Silva128 database. Sequences with a similarity of more than 97% are clustered into an operation classification unit (OTU). This data systematically compares the bacterial diversity of soil microorganisms in the Qinghai Tibet Plateau transect, which is of great significance to the study of the distribution of microorganisms in the Qinghai Tibet Plateau.
KONG Weidong
Data content: this data set is the historical archived satellite data of the domestic high score series (GF1 / 2 / 3 / 4) in the key river and lake research areas of the Qinghai Tibet Plateau from 2015 to 2020, which can cover the typical river and lake areas for effective monitoring. The time range of the data is from 2015 to 2020. Data source and processing method: the data are level 1 products. After equalizing radiation correction, the changes affecting the sensors are corrected by the equalizing functions of different detectors. Some data are based on the Landsat 8 images in the same period as the base map, and control points are selected for geometric correction of the images. Then, orthophoto correction is carried out based on DEM data, and band fusion processing is carried out for the corresponding data. Data quality description: the Gaofen series satellites are processed by the China Resources Satellite Application Center. There are raw data received by the satellite ground receiving station of the Chinese Academy of Sciences and processed products at all levels. Among them, level 1a (pre-processing level radiometric correction image product): image data processed by data analysis, uniform radiometric correction, noise removal, MTFC, CCD splicing, band registration, etc; And provide RPC files for satellite direct attitude orbit data production. Refer to the data website of China Resources Satellite Application Center for details. Data application achievements and prospects: the data are domestic high-resolution data with high resolution, which can be used to monitor the changes of the Qinghai Tibet Plateau as a water tower in Asia and the generated images, and test the accuracy of other data in the region
QIU Yubao
The basic data of hydrometeorology, land use and DEM were collected through the National Meteorological Information Center, the hydrological Yearbook, the China Statistical Yearbook and the Institute of geographical science and resources of the Chinese Academy of Sciences. The distributed time-varying gain hydrological model (DTVGM) with independent intellectual property rights is adopted for modeling, and the Qinghai Tibet Plateau is divided into 10937 sub basins with a threshold of 100 square kilometers. The daily flow data of 14 flow stations in Heihe River, Yarlung Zangbo River, Yangtze River source, Yellow River source, Yalong River, Minjiang River and Lancang River Basin were selected to draft and verify the model. The daily scale Naxi efficiency coefficient is above 0.7 and the correlation coefficient is above 0.8. The actual evaporation simulation is basically consistent with the station observation published by the Meteorological Bureau. The model simulates the water cycle process from 1998 to 2017. After verification, the spatial and temporal distribution of the actual evaporation (including soil evaporation and plant transpiration) on the 0.01 degree daily scale in the whole Tibetan Plateau is given.
YE Aizhong
The basic data of hydrometeorology, land use and DEM were collected through the National Meteorological Information Center, the hydrological Yearbook, the China Statistical Yearbook and the Institute of geographical science and resources of the Chinese Academy of Sciences. The distributed time-varying gain hydrological model (DTVGM) with independent intellectual property rights is adopted for modeling, and the Qinghai Tibet Plateau is divided into 10937 sub basins with a threshold of 100 square kilometers. The daily flow data of 14 flow stations in Heihe River, Yarlung Zangbo River, Yangtze River source, Yellow River source, Yalong River, Minjiang River and Lancang River Basin were selected to draft and verify the model. The daily scale Naxi efficiency coefficient is above 0.7 and the correlation coefficient is above 0.8. The model simulates the water cycle process from 1998 to 2017, and gives the spatial and temporal distribution of 0.01 degree daily scale runoff in the whole Qinghai Tibet Plateau.
YE Aizhong
The data set of bacterial post-treatment products and conventional water quality parameters of some lakes in the third pole in 2015 collected the bacterial analysis results and conventional water quality parameters of some lakes in the Qinghai Tibet Plateau during 2015. Through sorting, summarizing and summarizing, the bacterial post-treatment products of some lakes in the third pole in 2015 are obtained. The data format is excel, which is convenient for users to view. The samples were collected by Mr. Ji mukan from July 1 to July 15, 2015, including 28 Lakes (bamuco, baimanamuco, bangoso (Salt Lake), Bangong Cuo, bengcuo, bieruozhao, cuo'e (Shenza), cuo'e (Naqu), dawaco, dangqiong Cuo, dangjayong Cuo, Dongcuo, eyaco, gongzhucuo, guogencuo, jiarehbu Cuo, mabongyong Cuo, Namuco, Nier CuO (Salt Lake), Norma Cuo, Peng yancuo (Salt Lake), Peng Cuo, gun Yong Cuo, Se lincuo, Wu rucuo, Wu Ma Cuo, Zha RI Nan Mu Cuo, Zha Xi CuO), a total of 138 samples. The extraction method of bacterial DNA in lake water is as follows: the lake water is filtered onto a 0.45 membrane, and then DNA is extracted by Mo bio powerOil DNA kit. The 16S rRNA gene fragment amplification primers were 515f (5'-gtgccagcmgcgcggtaa-3') and 909r (5'-ggactachvggtwtctaat-3'). The sequencing method was Illumina miseq PE250. The original data were analyzed by mothur software, including quality filtering and chimera removal. The sequence classification was based on the silva109 database. The archaeal, eukaryotic and unknown source sequences had been removed. OTU classifies with 97% similarity and then removes sequences that appear only once in the database. Conventional water quality detection parameters include dissolved oxygen, conductivity, total dissolved solids, salinity, redox potential, nonvolatile organic carbon, total nitrogen, etc. The dissolved oxygen is determined by electrode polarography; Conductivity meter is used for conductivity; Salinity is measured by a salinity meter; TDS tester is used for total dissolved solids; ORP online analyzer was used for redox potential; TOC analyzer is used for non-volatile organic carbon; The water quality parameters of total nitrogen were obtained by Spectrophotometry for reference.
YE Aizhong
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