In 2017, 27 surface sediments were collected in Qinghai Lake by gravity sampler, and the top 1cm was taken as the surface layer, which was freeze-dried and ground into powder after being taken back to the laboratory. Before testing the content of organic carbon and nitrogen, 1mol / L hydrochloric acid should be used to stir the reaction for more than 10 hours, so that the carbonate is completely removed, then dried and ground, and the organic carbon and nitrogen are tested on the element analyzer. The total inorganic carbon content is the carbonate content of the whole rock powder sample measured by infrared spectrum, which is then calculated as the total inorganic carbon content. The contents of organic carbon and inorganic carbon constitute the total carbon content of the lake, and they are close to each other, indicating that the inorganic carbon burial flux and organic carbon burial flux of Qinghai Lake are similar.
Paleo-shorelines are widely developed in the lakes of the Tibetan Plateau (TP), which record the history of paleo-lake level changes. The development age of the mega-lake represented by the highest paleo-shoreline is controversial. The age of the shoreline or the mega-lake can be obtained by measuring the burial age of the shoreline sand in the sedimentary strata of the paleo-shoreline by using the optical stimulated luminescence (OSL) dating technology. This data includes the OSL ages of the highest paleo-shorelines of three lakes in the northwestern TP. The dating is based on the K-feldspar pIRIR method developed in recent years, which effectively solves the problem that the quartz OSL signal is not suitable for dating in the study area. This data can provide key information for the evolution history of the mega-lakes on the TP.
This dataset is a daily lake surface water temperature (LSWT) products of 164 lakes in Tibetan Plateau from 1978 to 2017. Firstly, we calculate the mean values of lake surface pixels based on MOD11A1 products to obtained the daily lake surface temperature series from 2000 to 2017. Secondly, we modified the air2water model to simulate the lake surface temperature continuously. The daily air temperature from meteorological stations were used as forcing data, and the lake surface temperature monitored by MOD11A1 as the model calibration and validation data. Finally, the daily lake surface temperature across Tibet Plateau from 1978 to 2017 were reconstructed. Compared with the results from remote sensing monitoring, the Nash-Sutcliffe efficiency coefficients of all lakes are higher than 0.6 with bias ranging at ±0.55℃. The dataset is suitable to analyze the long-term changes of lake surface temperature over the past decades, which is of great significance for assessing the impacts of climate warming on the water and heat balance, water quality and lake ecosystem changes on the Tibetan Plateau.
This data provides the annual lake area of 582 lakes with an area greater than 1 km2 in the enorheic basin of the Qinghai-Tibet Plateau from 1986 to 2019. First, based on JRC and SRTM DEM data, 582 lakes are identified in the area that are larger than 1 km2. All Landsat 5/7/8 remote sensing images covering a lake are used to make annual composite images. NDWI index and Ostu algorithm were used to dynamically segment lakes, and the size of each lake from 1986 to 2019 is then calculated. This study is based on the Landsat satellite remote sensing images, and using Google Earth Engine allowed us to process all Landsat images available to create the most complete annual lake area data set of more than 1 km2 in the Qinghai-Tibet Plateau area; A set of lake area automatic extraction algorithms were developed to calculate of the area of a lake for many years; This data is of great significance for the analysis of lake area dynamics and water balance in the Qinghai-Tibet Plateau region, as well as the study of the climate change of the Qinghai-Tibet Plateau lake.
1) data content (including elements and significance): transparency data of 152 lakes greater than 50 km2 in the Qinghai Tibet Plateau in 2000-2019 (Saybolt disk value) 2) Data source and processing method: the data inversion is based on the high-precision transparency inversion model and modis-modocga product data. The remote sensing data is converted into the remote sensing reflectivity R ﹐ RS inversion transparency value, and the annual mean value is calculated. The average value of 3 × 3 pixels in the geometric center of the lake represents the lake. For the case where the geometric center is located outside the lake, the open water area of the lake is taken for calculation. 3) Data quality description: annual average value of lakes. 4) Results and prospects of data application: climate change may change Lake transparency, while the change of Lake transparency will play a feedback role in regional climate change. In this study, the inversion of Lake transparency in the Qinghai Tibet Plateau provides basic data for the energy exchange of the lake air interface.
1) area data of 317 lakes larger than 10 km2 in 1976, 1990, 2000, 2005 and 2013 were obtained based on multi temporal Landsat images; 2) Combining SRTM DEM and Landsat images, the data of lake water volume change in 1976-1990, 1990-2000, 2000-2005 and 2005-2013 were obtained; 3) The accuracy of Lake area is controlled in one pixel, and the accuracy of water volume change is about 5%; 4) This data has been applied to the study of recent changes in lake water volume in the Qinghai Tibet Plateau, and the results have been published in remote sensing of environment. In other future studies, this data can also be used as basic data, as well as in the analysis of changes in ecological environment, climate change, Lake water quality, etc
The dataset is the distribution map of lakes in Qinghai Lake Basin. The projection is latitude and longitude. The data includes the spatial distribution data and attribute data of the lake. The attribute fields of the lake are: NAME (lake name), CODE (lake code).
This data includes the daily average water temperature data at different depths of Nam Co Lake in Tibet which is obtained through field monitoring. The data is continuously recorded by deploying the water quality multi-parameter sonde and temperature thermistors in the water with the resolution of 10 minutes and 2 hours, respectively, and the daily average water temperature is calculated based on the original observed data. The instruments and methods used are very mature and data processing is strictly controlled to ensure the authenticity and reliability of the data; the data has been used in the basic research of physical limnology such as the study of water thermal stratification, the study of lake-air heat balance, etc., and to validate the lake water temperature data derived from remote sensing and different lake models studies. The data can be used in physical limnology, hydrology, lake-air interaction, remote sensing data assimilation verification and lake model research.
The data set includes the vertical profile of water quality and the multi-parameter data of surface water quality of Selincho Lake during the investigation of the sources of rivers and lakes from June to July of 2017. The main water quality parameters measured are dissolved oxygen, conductivity, pH, water temperature, etc. YSI EXO2 water quality multi-parameter measuring instrument is calibrated according to lake surface elevation and local pressure before each measurement. The time interval of measurement is set at 0.25s, and the speed of putting in is slow, so he high continuity of data acquisition is guaranteed. The original data obtained include the measured data exposed to air above the water surface, which are eliminated in the later processing.
This data set includes the water depth measurement data during the Jianghuyuan expedition from June to July 2017 over the Kering Tso Lake. The measurement time is on July 2, 2017. The data was measured by Lowrance HDS-5 sonar sounder. The original data was generated by surfer 13 software and Kriging difference method. The original data contained more invalid depth data, which had been screened out in the later stage of collation. The survey line is reasonable to ensure that the data cover all depth gradients.