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
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
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
MAO Kebiao
The UHSLC offers tide gauge data with two levels of quality-control (QC). Fast Delivery (FD) data are released within 1-2 months of data collection and receive only basic QC focused on large level shifts and obvious outliers. The GLOSS/CLIVAR (formerly known as the WOCE) "fast" sea level data is distributed as hourly, daily, and monthly values. This project is supported by the NOAA Climate and Global Change program, and is one of the activities of the University of Hawaii Sea Level Center. Each file is given a name "h###.dat" where "h" denotes hourly sea level data and "###" denotes the station number. A file exists for every station with hourly data. The UHSLC datasets are GLOSS data streams (read more here). There are many tide gauge records in the UHSLC database, but the backbone is the GLOSS Core Network (GCN) – a global set of ~300 tide gauge stations that serve as the foundation of the global in situ sea level network. The network is designed to provide evenly distributed sampling of global coastal sea level variation at a variety of time-scales.
DONG Wen, University of hawaii sealevel center （UHSLC）
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
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
