1. The data content includes: year, month, day, hour, longitude, latitude, altitude, meridional (UQ) and latitudinal (VQ) components of water vapor flux; 2. Data source and processing method: GPS meteorological sounding data of voyages in the eastern Indian Ocean, and calculate water vapor flux through relative humidity, wind field, air pressure and altitude; 3. Data quality description: vertical continuous observation with 1 second vertical resolution; 4. Data application achievements and prospects: Study on the changes of water vapor transport in the tropical Indian Ocean;
LIU Zhaofei, YAO Zhijun
Lake salinity is an important parameter of lake water environment, an important embodiment of water resources, and an important part of climate change research. This data is based on the measured salinity data of lakes in the Qinghai Tibet Plateau. The salinity is characterized by the practical salinity unit (PSU), which is converted from the specific conductivity (SPC) measured by the conductivity sensor. ArcGIS software was used to convert the measured data into space vector format. SHP format, and the measured salinity spatial distribution data file was obtained. The data can be used as the basic data of lake environment, hydrology, water ecology, water resources and other related research reference.
ZHU Liping
This dataset provides the in-situ lake water parameters of 124 closed lakes with a total lake area of 24,570 km2, occupying 53% of the total lake area of the TP.These in-situ water quality parameters include water temperature, salinity, pH,chlorophyll-a concentration, blue-green algae (BGA) concentration, turbidity, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), and water clarity of Secchi Depth (SD).
ZHU Liping
This data includes the benthos data of 21 lakes around Selinco and Namco in 2019. The sampling mainly uses bottom trawl in the littoral zone and Ekman collector in the deep-water area. After integrating the materials obtained by the two ways, the relative abundance of benthos data of each lake is given. The main benthos species are Gammarus, water beetles, and chironomid larvae. However, the frequency of Gastropoda and Ostracoda is low, which may be related to the sampling sites. The data further divided different types of benthos into 21 taxa, which improved the recognition accuracy and cognitive range and would provide a reference for the assessment of aquatic animal diversity and fishery resources in plateau lakes.
TANG Hongqu
The matching data of water and soil resources in the Qinghai Tibet Plateau, the potential evapotranspiration data calculated by Penman formula from the site meteorological data (2008-2016, national meteorological data sharing network), the evapotranspiration under the existing land use according to the influence coefficient of underlying surface, and the rainfall data obtained by interpolation from the site rainfall data in the meteorological data, are used to calculate the evapotranspiration under the existing land use according to the different land types of land use According to the difference, the matching coefficient of water and soil resources is obtained. The difference between the actual rainfall and the water demand under the existing land use conditions reflects the matching of water and soil resources. The larger the value is, the better the matching is. The spatial distribution of the matching of soil and water resources can pave the way for further understanding of the agricultural and animal husbandry resources in the Qinghai Tibet Plateau.
DONG Lingxiao
The dataset includes vector map of the lakes larger than 1k㎡ on Tibetan Plateau in 1970s, 1990, 2000, 2010. The lake boundry data was extracted from remote sensing image like Landsat MSS, TM, ETM+, by means of visual interpretation. The data type is vector data, and it's attribute class includes Area (km²). The Projected Coordinate System is Albers Conical Equal Area. It is mainly used in the study of changes in lakes, hydrological and meteorological on the Tibetan Plateau.
ZHANG Guoqing
Microbial diversity data of lakes on the Tibetan Plateau. One hundred and thirty-eight samples were collected from July 1st to July 15th, 2015, from 28 lakes (Bamco, Baima Lake, Bange Salt Lake, Bangong Lake, Bengco, Bieruozeco, Cuoeco, Cuoe (Pingcuo North), Dawaco, Dangqiongco, Dangreyongco, Dongco, Eyacuoqiong, Gongzhuco, Guogenco, Jiarebuco, Mapangyongco, Namco, Nieerco (Salt Lake), Normaco, Pengyanco, Pengco, Qiangyong, Selinco, Wuruco, Wumaco, Zharinanmuco, and Zhaxico). The salinity gradients range from 0.07-118 ppm. The DNA extraction method: The DNA was extracted using an MO BIO PowerSoil DNA kit after the lake water was filtered onto a 0.45 membrane. The 16S rRNA gene fragment amplification primers were 515F (5'-GTGCCAGCMGCCGCGGTAA-3') and 909r (5'-GGACTACHVGGGTWTCTAAT-3'). The sequencing method was Illumina MiSeq PE250, and the raw data were analyzed by Mothur software, including quality filtering and chimera removal. The sequence classification was based on the Silva109 database, and archaea, eukaryotic and unknown source sequences have been removed. OTUs were classified by 97% similarity, and sequences that appear once in the database were then removed. Finally, each sample was resampled to 7,230 sequences/sample. GPS coordinates, evolutionary information, and environmental factors are listed in the data.
JI Mukan
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