As a powerful heat source, the Tibetan Plateau (TP) affects the onset, advance and retreat of the Asian monsoon, and the interaction between the westerly belt and the monsoon belt. In order to study the variation of TP thermal effect and its influence on the surrounding climate, the basic data related to TP heat source are needed. This data set is composed of monthly basic heat source data of the TP and its surrounding areas calculated from reanalysis data, and its horizontal range covers 40°E-180° and 20°S-80°N. The spatial resolution is 2.5 ° x2.5 °, and the datasets mainly included ERA5 and NCEP/NCAR reanalysis data.
LI Qingquan
The triple pole aerosol type data product is an aerosol type result obtained through a series of data pre-processing, quality control, statistical analysis and comparative analysis processes by comprehensively using MEERA 2 assimilation data and active satellite CALIPSO products. The key of the aerosol type fusion algorithm is to judge the aerosol type of CALIPSO. During the data fusion of aerosol type, the final aerosol type data (12 types in total) and quality control results in the three polar regions are obtained according to the types and quality control of CALIPSO aerosol types and referring to MERRA 2 aerosol types. The data product fully considers the vertical and spatial distribution of aerosols, and has a high spatial resolution (0.625 ° × 0.5 °) and time resolution (month).
ZHAO Chuanfeng
The Tibetan Plateau (TP) is the largest glacier enrichment area in the middle and low latitudes except the South Arctic and Greenland. The solid water body glaciers and liquid water bodies lakes and rivers together form the Asian Water Tower. The thermal and dynamic effects of the TP and their variability are one of the main driving forces for the TP to affect the Asian monsoon and global atmospheric circulation anomalies. To study the thermal properties of the TP itself and its feedback effect, it is necessary to use the results of climate model experiments to carry out the 100-year historical examination of the TP and its surrounding areas and the future 100-year prediction (temperature, precipitation, radiation, etc.). This dataset consists of grid point temperature, precipitation, radiation and other data of the TP and its surrounding areas. Its horizontal range covers 40 ° E-180 °, 20 ° S-80 ° N, and the time resolution includes annual and seasonal average. The data are based on the results of the BCC-CSM2-MR model test conducted by the National Climate Center of China in the Coupled Model Intercomparison Project Phase 6 (CMIP6), including historical, SSP126, SSP245, SSP370, and SSP585 experiments. According to the bilinear interpolation method, the data are uniformly interpolated to the resolution level of 1 ° x1 °. The data can provide basic information on regional climate and water cycle changes for the second TP investigation period, provide reference for the field investigation results, and study the possible change mechanism.
LI Qingquan
This dataset is about the historical yield data (yield per unit area and sown area) of the main crops (hull-less barley and wheat) on Tibetan Plateau between years 1988-2018, covering some prefectures and cities located in Tibetan Plateau. The data are obtained from Tibet Statistical Yearbook, Qinghai Statistical Yearbook, Sichuan Statistical Yearbook, Gansu Statistical Yearbook, Yunnan Statistical Yearbook and the aba Tibetan and Qiang Autonomous Prefecture and Ganzi Tibetan Autonomous Prefecture Agriculture and Animal Husbandry Bureau with the same accuracy. Hull-less barley and wheat are the main crops on the Tibetan Plateau. This data set is of great value for the study of food security and agricultural production on Tibetan Plateau.
PAN Zhifen
1) Soil environmental quality data of typical industrial parks in Huangshui basin of Qinghai Province provide basic support for soil pollution control caused by regional industrial activities; 2) The data source is the soil samples of typical areas in Huangshui River Basin. After collection, the samples are quickly stored in the refrigerator at - 4 ℃ and sent to the laboratory as soon as possible. After pretreatment, the relevant parameters are tested; 3) The process of sample collection and transportation meets the specifications, and the experimental detection process strictly follows the relevant standards. Due to the changes of various factors of soil environment, the results are only aimed at the investigation results; 4) The data can be used to analyze regional soil pollution and heavy metal risk assessment;
WANG Lingqing
This data is the plant diversity and distribution data of chnz016 grid on Qinghai Tibet Plateau, including the Chinese name, Latin name, latitude and longitude, altitude, collection number, number of molecular materials, number of specimens, administrative division, small place, collector, collection time and creator of plants in this grid. The data is obtained from e scientific research website（ http://ekk.kib.ac.cn/web/index/#/ ）And partially complete the identification. This data has covered the list and specific distribution information of all plants in this flora. This data can be used not only to study the floristic nature of this region, but also to explore the horizontal and vertical gradient pattern of plants in this region.
DENG Tao
This data is the plant diversity and distribution data of chnac006 grid on the Qinghai Tibet Plateau, including the Chinese name, Latin name, latitude and longitude, altitude, collection number, number of molecular materials, number of specimens, administrative division, small place, collector, collection time and creator of plants in this grid. The data is obtained from e scientific research website（ http://ekk.kib.ac.cn/web/index/#/ ）And partially complete the identification. This data has covered the list and specific distribution information of more than 600 species of plants in more than 200 genera and 91 families in this flora. This data can be used not only to study the floristic nature of this region, but also to explore the horizontal and vertical gradient pattern of plants in this region.
DENG Tao
1. The total number is the unified number of the survey year, such as 17-001 (the first survey point in 2017), and the field number is the single field number. 2. Time: Beijing time at the time of measurement, such as: 13:25, August 1, 2017 (13:25, August 1, 2017). 3. Geographical location: the longitude and latitude of the measuring point, such as 29.6584101.0884 (29.6584 ° n, 101.0884 ° E), which is measured by Garmin 63sc GPS in the field. 4. Altitude: the absolute altitude of the measuring point, such as 4500m (4500m above sea level), is measured by Garmin 63sc GPS in the field with an accuracy of 1m. 5. Measured vegetation coverage (%): measured in the field with quadrat (1000 m * 1000 m). 6. Atmospheric pressure: measured by dph-103 intelligent digital temperature and humidity barometer in the field, such as 651.7kpa, accuracy: 0.1 kPa. 7. Air temperature: measured by dph-103 intelligent digital temperature, humidity and barometer in the field, such as 15.61 ℃, accuracy: 0.01 ℃. 8. Relative humidity: measured by dph-103 intelligent digital temperature, humidity and barometer in the field, such as 79.1%, accuracy: 0.1%. 9. Relative oxygen content: measured by td400-sh-o2 portable oxygen detector in the field, such as 20.16%, accuracy: 0.01%. Among them, the altitude of sampling points 17-001 to 17-065 is measured by Garmin Oregon 450 GPS with an accuracy of 1 m; The atmospheric pressure is measured by Casio prg-130gc barometer with an accuracy of 5 HPA; The relative oxygen content is measured by cy-12c digital oxygen meter, with a range of 0-50.0%, a resolution of 0.1% and an accuracy of ± 1%.
SHI Peijun
This data is the plant diversity and distribution data of chnyb013 grid on the Qinghai Tibet Plateau, including the Chinese name, Latin name, latitude and longitude, altitude, collection number, number of molecular materials, number of specimens, administrative division, small place, collector, collection time and creator of plants in this grid. The data is obtained from e scientific research website（ http://ekk.kib.ac.cn/web/index/#/ ）And partially complete the identification. This data has covered a large number of plant catalogues and specific distribution information in this flora. This data can be used not only to study the floristic nature of this region, but also to explore the horizontal and vertical gradient pattern of plants in this region.
DENG Tao
This data is the plant diversity and distribution data of the chnab005 grid on the Qinghai Tibet Plateau, including the Chinese name, Latin name, latitude and longitude, altitude, collection number, number of molecular materials, number of specimens, administrative division, small place, collector, collection time and creator of the plants in this grid. This data is obtained from e-Science website（ http://ekk.kib.ac.cn/web/index/#/ ）And partially complete the identification. This data has covered the list of plants in this flora and the specific distribution information. This data can be used not only to study the floristic nature of this region, but also to explore the horizontal and vertical gradient pattern of plants in this region. What is different from last year is that the grid with the most scientific research data this year has changed, which may be affected by the epidemic or the environment.
DENG Tao
The Qinghai-Tibet Plateau is the source of many major rivers in Asia, providing essential water for hundreds of millions of people, and is known as the "Water Tower of Asia". The main source of water recharge for the Asian Water Tower is precipitation from the Tibetan Plateau, of which the Tibetan Plateau vortex (TPV) is one of the important precipitation-producing systems on the Tibetan Plateau. Due to the complex topography of the Tibetan Plateau and the lack of observational data, there are still many gaps in the understanding of the climatic and structural characteristics of the TPVs and their formation and change mechanisms. This dataset uses multiple sets of reanalysis data and objective identification methods to obtain a long time series TPVs dataset, including the location, radius, intensity, life history, and movement path and other characteristics. The reanalysis datasets used in the dataset are: NCEP1 (NCEP/NCAR), NCEP2 (NCEP/DOE), ERA-Interim, ERA-40, ERA-5, CFSR, MERRA2, JRA55, NCEP FNL, CRA40, etc. NCEP1 and NCEP2 have lower resolution and the obtained highland low vortices are not applicable as climate feature analysis.
LIN Zhiqiang , LIN Zhiqiang, GUO Weidong GUO Weidong
Based on the distribution locations of the Qinghai toad-headed lizard (Phrynocephalus vlangalii) collected by field investigation and literature investigation, combined with five climate factors from WorldClim database, the current (1960-1990) and future (2061-2080) climate data were input into the trained species distribution model to predict the current and future suitable habitats. The prediction results shows that the lizard will lose a lot of original habitats under the climate change, and the protection measures for the lizard species should focus on the eastern margin of Qinghai-Tibet Plateau, the northern and eastern parts of Qaidam Basin. The model also predicts that after the climate change, new suitable habitats will appear in areas that were not suitable for the Qinghai toad-headed lizard. However, due to the very limited diffusion ability of reptiles (the maximum annual diffusion distance recorded in the literature is less than 500m), the newly emerging suitable habitats may not be used by the Qinghai toad-headed lizard. Meanwhile, based on the physiological, life history, behavior and morphological data of three altitudinal populations of the Qinghai toad-headed lizard collected by field work, and combined with microclimate data, the physiological consequences of climate change on the Qinghai toad-headed lizard in the current suitable distribution area were predicted by using the mechanism niche model. The prediction results of the model show that, whether in the SSP245 or SSP585 climate change scenarios, the activity time of the lizard will increase in most areas (> 93%) of the current suitable distribution area, and the thermal safety threshold will decrease in all places of the current suitable distribution area. The increase of activity time of high-altitude populations is less than that of low-altitude populations, but the decrease of thermal safety threshold is greater than that of low-altitude populations. The results reveal that climate change may have a greater impact on lizard populations in high altitude areas.
ZENG Zhigao
1) Data content: the value data set of ecological assets of Qinghai Tibet plateau for five periods from 2000 to 2020, once every five years. The contents include Water Yield, Soil Retention, Carbon Fixation, Climate Regulation, and Biodiversity potential ecological asset flows. 2) Data source and processing method: Based on land use data products. See the description document for the processing method. 3) Data quality description: the data from 2000 to 2015 evaluate the land use data products released by ourselves, and the data in 2020 is the predicted value of land use data products. 4) Results and prospects of data application: provide spatial location guidance for the optimization of ecological security barrier and the management of natural resources and assets on the Qinghai Tibet Plateau.
LIU Yanxu
Rainfall erosivity is one of the important basic data to quantify soil erosion in the Tibet Plateau. High precision rainfall erosivity data is the key to understand the current situation of soil and water loss in theTibet Plateau and formulate soil and water conservation measures. Meanwhile, it can provide a powerful reference for the prevention and control of geological disasters in the Tibet Plateau. Based on the 1-min dense precipitation observations and the grid precipitation product, a new annual rainfall erosivity dataset in Tibet Plateau from 1950 to 2020 is constructed through the steps of correction, reconstruction and validation. This dataset is the rainfall erosivity data set with the highest accuracy and the longest time series in the Tibet Plateau.
CHEN Yueli
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