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 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
To understand the potential impact of projected climate changes on the vulnerable agriculture in Central Asia (CA) in the future, six agroclimatic indicators are calculated based on the 9km-resolution dynamical downscaled results of three different global climate models and a high-resolution projection dataset of agroclimatic indicators over CA is produced. These indicators are growing season length (GSL, days), biologically effective degree days (BEDD, ℃), frost days (FD, days), summer days (SU, days), warm spell duration index (WSDI, days), and tropical nights (TR, days). The periods are 1986-2005 and 2031-2050. The spatial resolution is 0.1°. As all the indicators except WSDI are defined with absolute temperature thresholds and particularly sensitive to the systematics biases in the model data, the quantile mapping (QM) method is applied to correct the simulated temperature. Results show the QM method largely reduces the biases in all the indicators. GSL, SU, WSDI, and TR will significantly increase over CA and FD will decrease. However, changes in BEDD are spatially heterogeneous, with the increases in northern CA and the mountainous areas and decreases in the southern and middle part of the plain areas. This dataset can be applied for assessing the future risks in the local agriculture for climate changes and will be beneficial to adaption and mitigation actions for food security in this region.
QIU Yuan
Based on the historical daily maximum temperature data and reanalysis data set of stations, a daily maximum temperature statistical downscaling model based on first-order autoregressive and multiple linear regression models is developed. Driven by the IPCC cmip6 scenario data of the global climate model (cnrm-cm6-1), the statistical downscaling model predicts the number of five heat wave indexes (heat wave events) of 65 stations in Central Asia from 2015 to 2100 (HWM), heat wave frequency (HWF), heat wave intensity (HWM), maximum duration of heat wave (HWD), heat wave amplitude (HWA)). Finally, the heat wave change scenario data sets of 65 stations in Central Asia under four emission scenarios (ssp126, ssp245, ssp370, ssp585) from 2015 to 2100 were obtained.
FAN Lijun
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