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Spatial distribution of measured salinity of lakes on TP

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.


Data set of soil physicochemical in Qinghai-Tibet Plateau (2019-2021)

The data set contains soil physicochemical properties of ten scientific expedition routes in Qinghai-Tibet Plateu during 2019-2021, including sample colletor, sampling time, sampling location, longitude and latitude, altitude, vegetation type, sampling depth, soil water content, pH, organic matter content, total carbon content, total nitrogen content, total phosphorus content, inorganic nitrogen content, heavy metal elements content, and etc. The physicochemical properties were measured in the laboratory with quality control, including measuring blanks, replicates and standard samples.The data set can be used for evaluating soil quality and function under the influence of climate change and human activities.


Hydrological data set of surface process and environment observation network in alpine region of China (2019)

Based on the long-term observation data of the field stations in the alpine network and the overseas stations in the pan third polar region, a series of data sets of meteorological, hydrological and ecological elements in the pan third polar region are established; through the intensive observation and sample plot and sample point verification in key areas, the inversion of meteorological elements, lake water and water quality, aboveground vegetation biomass, glacier and frozen soil change and other data products are completed; based on the Internet of things, the data products are retrieved Network technology, research and establish meteorological, hydrological, ecological data management platform of multi station networking, to achieve real-time data acquisition and remote control and sharing. The hydrological data set of the surface process and environment observation network in China's alpine regions in 2019 mainly collects the measured hydrological (runoff, water level, water temperature, etc.) data at six stations, including Southeast Tibet station, Zhufeng station, Yulong Snow Mountain station, Namco station, Ali station and Tianshan station. Southeast Tibet station: flow data, including 4 times of using M9 to measure flow in 2019, including average velocity, flow and maximum water depth; relative water level data is measured by hobo pressure water level meter, including daily average relative water level and water temperature data in 2019. Namco station: discharge data, including the data measured by domestic ls-1206b hand-held current meter for 4 times in 2019, including river width and flow data. The water level data is measured by hobo pressure water level meter, including the water pressure, water temperature and electricity of the original 1 hour in 2019. The relative water level can be calculated by water pressure; Everest station: rongbuhe river discharge, including river width and discharge data measured by domestic ls-1206b hand-held current meter 13 times from June to September 2019; Ali station: flow data: including 22 times of irregular measurement data by river anchor M9 in 2019, and relative water level data measured by hobo pressure water level meter, including hourly water level and water temperature data of the whole year in 2019; Tianshan station: water level data: including daily average water level of 3 points in 2019 Yulong Xueshan station: including mujiaqiao flow data from January to October in 2019


Hydrological data of Kafinigan hydrological station in Amu Darya River Basin,Central Asia (2020)

This data is from the hydrological station of kafinigan River, a tributary of the upper Amu Darya River. The station is jointly built by Urumqi Institute of desert meteorology of China Meteorological Administration, Institute of water energy and ecology of Tajik National Academy of Sciences and Tajik hydrometeorological Bureau. The data can be used for scientific research such as water resources assessment and water conservancy projects in Central Asia. Data period: November 3, 2019 to December 3, 2020. Data elements: Hourly velocity (M / s), hourly water level (m) and hourly rainfall (m). Site location: 37 ° 36 ′ 01 ″ n, 68 ° 08 ′ 01 ″ e, 420m 1、 300w-qx River velocity and water level observation instrument (1) Flow rate parameters: 1 power supply voltage 12 (9 ~ 27) V (DC) The working current is 120 (110 ~ 135) MA 3 working temperature (- 40 ~ 85) ℃ 4 measurement range (0.15 ~ 20) m / S The measurement accuracy is ± 0.02m/s The resolution is less than 1 mm The detection range is less than 0.1 ~ 50 m 8 installation height 0.15 ~ 25 m 9 sampling frequency < 20sps (2) Water level parameters: 1 measuring range: 0.5 ~ 20 m The measurement accuracy is ± 3 mm The resolution is less than 1 mm The repeatability was ± 1 mm 2、 SL3-1 tipping bucket rain sensor 1. Water bearing diameter Φ 200mm 2. The measured precipitation intensity is less than 4mm / min 3. Minimum precipitation of 0.1 mm 4. The maximum allowable error is ± 4% mm 3、 Flow velocity, frequency of data acquisition of the observation instrument: the sensor measures the flow velocity and water level data every 5S 4、 Calculation of hourly average velocity: the hourly average velocity and water level data are obtained from the average of all the velocity and water level data measured every 5S within one hour 5、 Description of a large number of values of 0 in water level data: the value of 0 in water level data is caused by power failure and restart of sensor due to insufficient power supply. The first data of initial start-up is 0, resulting in the hourly average value of 0. After the power supply transformation on July 26, 2020, the data returned to normal. At the end of September 2020, the power supply began to be insufficient. After the secondary power supply transformation on December 25, 2020, the data returned to normal 6、 Description of water level monitoring (such as line 7358, 2020 / 11 / 3, 16:00, maximum water level 6.7m, minimum water level 0m, how to explain? In addition, the maximum value of the highest water level is 6.7m, which appears many times in the data. It seems that 6.7m is the limit value of the monitoring data. Is this the case? ): 6.7m is the height from the initial sensor to the bottom of the river bed. The appearance of 6.7m is the abnormal data when the sensor is just started. The sensor is restarted due to the power failure caused by the insufficient power supply of the equipment. This abnormal value appears in the initial start-up. After the power supply transformation on December 25, 2020, the data returns to normal


Great Lakes region of Central Asia basic data set hydrology (2015)

Runoff is formed by atmospheric precipitation and flows into rivers, lakes or oceans through different paths in the basin. It is also used to refer to the amount of water passing through a certain section of the river in a certain period of time, i.e. runoff. Runoff data plays an important role in the study of hydrology and water resources, which affects the social and economic development of Adam land. This data is the flow of five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan), which comes from the hydrometeorological bureaus of Central Asian countries. The time scale is the average annual data of 2015. This data provides basic data for the project, which is convenient to analyze the situation of eco hydrological water resources in Central Asia, and provides data support for project data analysis.


Basic dataset of soil over the Great Lakes in Central Asia - Soil (2015)

Soil is mineral particles of different sizes formed by weathering of rocks. Soil not only provides nutrients and water for crops, but also has a transforming effect on various nutrients. In addition, the soil also has a self-cleaning function, which can improve organic matter content, soil temperature and humidity, pH value, anion and cation. The soil pollution causes several environmental problems: industrial sewage, acid rain, exhaust emissions, accumulations, agricultural pollution. After the land is polluted, the contaminated tops with high concentration of heavy metals are easily entered under the action of wind and water. Other secondary ecological and environmental problems such as air pollution, surface water pollution, groundwater pollution and ecosystem degradation in the atmosphere and water.he data set comes from the World Soil Database (Harmonized World Soil Database version 1.1) (HWSD) UN Food and Agriculture (FAO) and the Vienna International Institute for Applied Systems Research Institute (IIASA) constructed, which provides data model input parameters for the modeler, At the same time, it provides a basis for research on ecological agriculture, food security and climate change.


Phytoplankton data of lakes in Qinghai Tibet Plateau in 2020

The data is the phytoplankton data of 70 points in 26 lakes in Tibet in 2020. The sampling time is from August to September. The sampling method is the conventional phytoplankton sampling method. 1.5 liters of samples are collected, fixed by Lugo's solution, siphoned and concentrated after static precipitation, and the results are examined by inverted microscope. The data includes the density data of different phytoplankton of 77 species / genus in 10 categories, including diatom, green algae, cyanobacteria, dinoflagellate, naked algae, cryptoalgae, brown algae, brown algae and CHAROPHYTA. This data is original and unprocessed. The unit is piece / L. The data can be used to characterize the composition and abundance of phytoplankton in the open water areas of these lakes, and can also be used to calculate the diversity of phytoplankton communities in these lakes.


Tree age data sampled from different glacier moraines in the central Himalayas

Data set contains tree age of trees growing at different glacier moraines in the central Himalayas. The data were obtained using tree ring samples. Cores samples were collected (almost near to the ground level to estimate the minimum age of the related moraine) using an increment borer. Samples were processed by using standard dendrochronological techniques.


Vegetation optical depth (VOD) dataset in Tibetan Plateau (1993-2012)

The data set is based on a series of microwave remote sensing data, including Special Sensor Microwave Imager (SSM/I), Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E), etc., which can be used as a reference for primary productivity. The data is from Liu et al. (2015), and the specific calculation method is shown in the article. The source data range is global, and Tibetan Plateau region is selected in this data set. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.


Antarctic ice sheet surface elevation data (2003-2009)

The Antarctic ice sheet elevation data were generated from radar altimeter data (Envisat RA-2) and lidar data (ICESat/GLAS). To improve the accuracy of the ICESat/GLAS data, five different quality control indicators were used to process the GLAS data, filtering out 8.36% unqualified data. These five quality control indicators were used to eliminate satellite location error, atmospheric forward scattering, saturation and cloud effects. At the same time, dry and wet tropospheric, correction, solid tide and extreme tide corrections were performed on the Envisat RA-2 data. For the two different elevation data, an elevation relative correction method based on the geometric intersection of Envisat RA-2 and GLAS data spot footprints was proposed, which was used to analyze the point pairs of GLAS footprints and Envisat RA-2 data center points, establish the correlation between the height difference of these intersection points (GLAS-RA-2) and the roughness of the terrain relief, and perform the relative correction of the Envisat RA-2 data to the point pairs with stable correlation. By analyzing the altimetry density in different areas of the Antarctic ice sheet, the final DEM resolution was determined to be 1000 meters. Considering the differences between the Prydz Bay and the inland regions of the Antarctic, the Antarctic ice sheet was divided into 16 sections. The best interpolation model and parameters were determined by semivariogram analysis, and the Antarctic ice sheet elevation data with a resolution of 1000 meters were generated by the Kriging interpolation method. The new Antarctic DEM was verified by two kinds of airborne lidar data and GPS data measured by multiple Antarctic expeditions of China. The results showed that the differences between the new DEM and the measured data ranged from 3.21 to 27.84 meters, and the error distribution was closely related to the slope.