This dataset includes the concentration and distribution data of poly- and perfluoroalkyl substances (PFAS) in the Yarlung Tsangpo River and three major rivers in Hengduan Mountain region. The samples were collected in 2020 and 2021 from 83 locations in four major rivers, including the Yarlung Tsangpo, Nu, Lancang and Jinsha Rivers. The water samples were prepared by solid phase extraction, purification, concentration steps, and then determined by HPLC (ThermoFisher Scientific, USA) coupled to a TSQ Quantiva triple quadrupole mass spectrometer. The target compounds included 10 perfluorinated carboxylic acids (PFCAs) and 3 perfluorinated sulfonic acids (PFSAs). Specifically, perfluorobutanoic acid (PFBA), perfluoropentanoic acid (PFPeA), perfluorohexanoic acid (PFHxA), perfluoroheptanoic acid (PFHpA), perfluorooctanoic acid (PFOA),perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnDA), perfluorododecanoic acid (PFDoA) and perfluorotridecanoic acid (PFTrA), perfluorobutanesulfonic acid (PFBS), perfluorohexanesulfonic acid (PFHxS), and perfluorooctanesulfonic acid (PFOS). In the process of sample pretreatment, isotope labeled recovery standards were added, and the sample recovery was calculated to be between 53% and 96%. Conventional water quality test parameters include temperature, dissolved oxygen, pH, conductivity, salinity, and dissolved organic carbon. The accuracy of the parameters were 0.1℃, 0.01mg/L, 0.01, 0.1μS/cm, 0.01ppt and 0.01mg/L, respectively. Among them, the dissolved organic carbon was measured by TOC analyzer, and the other water quality parameters were measured by YSI ProPlus portable multi-parameter water quality instrument. This dataset can provide a scientific basis for mapping the spatial distribution of organic pollution over the Tibetan Plateau and assessing the water quality safety of water towers in Asia.
REN Jiao , WANG Xiaoping
Provide the spatial distribution of water intake in six departments of agricultural irrigation, municipal administration, industrial production, animal husbandry, primary energy exploitation and power generation in China from 1990 to 2015, with a spatial accuracy of 0.5 °, and a geographic coordinate system of WGS84. The data comes from the data set of jgcri papers. The historical uniform water intake data of China is obtained after linear interpolation of the original data, mask extraction in China and coordinate system conversion, and is saved in GeoTIFF file format. The methods and standards of data over the years are consistent, the coverage is complete, and the collection and processing process is traceable and reliable. This data realizes the homogenization of existing data products and provides a basis for analyzing the laws of human factors and the interaction mechanism between human factors and natural factors.
WANG Can , WANG Jiachen
A dataset of spatio-temporal change of physical and virtual water in Qilian Mountains: Using the single-region input-output method, and the 2012 input-output table of Qilian Mountains, we developed a physical water-virtual water conversion model and explored the virtual water among different departments in Qilian Mountains in 2012. The law of water flow provides a theoretical basis for the optimal allocation of water resources in the natural-society complex system for the research on the optimal allocation of "mountains, waters, forests, fields, lakes, grass and sand" in the Qilian Mountains. It has been verified that this dataset has achieved the balance between the physical water consumption and the total virtual water consumption of various departments in the Qilian Mountains in 2012, indicating that the data is reliable. This data can provide a basis for the optimal allocation of water resources in the Qilian Mountains.
LIU Junguo
The water resource supply resilience of countries along the “Belt and Road” reflects the level of water supply resilience of countries along the route. The higher the data value, the stronger the resilience of water supply in countries along the route. Preparation of data products for water supply resilience of countries along the “Belt and Road”, using the annual precipitation, surface runoff and underground net data produced by FLDAS (Famine Early Warning System Network Land Data Assimilation System) based on the Noah land surface model from 2000 to 2019 The flow simulation data set, on the basis of considering the year-to-year changes, based on sensitivity and adaptability analysis, and through comprehensive diagnosis, prepared and generated water resource supply resilience products. The data set of water supply resilience of countries along the “Belt and Road” has important reference significance for analyzing and comparing the current status of water resources supply resilience in various countries.
XU Xinliang
About 70% of the world's water withdrawl is used for agriculture, and irrigation water accounts for more than 90% of the total water consumption. Due to varied irrigation water sources, irrigation facilities, and crop planting types, there is large spatial heterogeneity in irrigation water use. Irrigation water can be consumed by evapotranspiration or stored as soil water in the root zone soil layer, while the portion exceeding the saturation zone will recharge groundwater. The complexity of irrigation processes above makes it extremely difficult and challenging to estimate irrigation water use. Based on the soil water balance under irrigation, formulas to estimate irrigation water use (IWU) were deduced by us, considering multiple processes of irrigation (evapotranspiration, root zone soil moisture, and deep percolation). Remotely sensed and modeled actual evapotranspiration, modeled root zone soil moisture were used in our approach to generate the monthly IWU across the continental United States during 2000-2020 at a high spatial resolution (1 km). The results show that our approach has the mechnism to characterize multiple irrigation processes and can obtain IWU data with excelent accuracy at high spatiotemporal resolution.
ZHANG Caijin , LONG Di
Landslide drainage and seepage prevention is a common technology for the treatment of landslide source area in Qinghai Tibet Plateau. The existing siphon drainage technology is inefficient when applied to high altitude areas. Through improvement, a variable pipe diameter and high head siphon drainage technology is proposed to solve the deep drainage problem of landslide in high altitude and low pressure areas. 12 groups of siphon drainage tests with variable pipe diameter were carried out to verify the correctness of the theoretical velocity calculation formula. The test results show that the theoretical calculation results of siphon velocity are in good agreement with the test results, and the relative error of theoretical calculation is within 5%; Different schemes of variable pipe diameter increase the siphon flow rate by 15% - 116%. It can be seen that variable pipe diameter can significantly enhance the drainage capacity of siphons, especially for high lift siphons.
ZHENG Jun
Quantitative evaluation and comprehensive measurement of resource and environment carrying capacity is the key technical link of resource and environment carrying capacity research from classification to synthesis. Based on the evaluation of the suitability of human settlements, the limitation of resource carrying capacity and socio-economic adaptability, and according to the research idea and technical route of "suitability zoning restrictive classification adaptability classification warning classification", a three-dimensional tetrahedral model for the comprehensive evaluation of resource and environmental carrying capacity with balanced significance is constructed. Based on the 10km grid, a comprehensive study on the resource and environment carrying capacity was carried out, and the resource and environment carrying capacity index of the areas along the silk road was quantitatively simulated. Taking 1 as the equilibrium significance, it provided support for the comprehensive evaluation of the resource and environment carrying capacity of the areas along the silk road.
YOU Zhen
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
This vegetation water content data set is derived from the ground synchronous observation in the Luanhe River Basin soil moisture remote sensing experiment, including 55 sampled plots.The vegetation types involved in these sampled plots include grass, corn, potatoes, naked oats and carrots. The data measurement time is from September 13, 2018 to September 26, 2018.
ZHENG Xingming, JIANG Tao
This data set is the summary of the survey results of rural small hydropower in Tibet in 2018. The main contents include the name, installed capacity, start-up time and completion time of small hydropower stations in different districts and counties of each prefecture and city in Tibet Autonomous Region, as well as the operation status of each hydropower station. The hydropower development in Tibet Autonomous Region has an early history. There are not many large and medium-sized hydropower stations, mainly in rural areas. With the development of social economy, most of the small hydropower stations in Tibet Autonomous Region have been shut down. At present, the development of large and medium-sized hydropower projects is the main one. In plateau areas where Hydropower Survey data are scarce, this data set reflects the history and current situation of small hydropower in Tibet Autonomous Region, and can provide a certain data basis for hydropower development survey and evaluation in Tibet Autonomous Region.
FU Bin
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
The data set records the information disclosure form of county-level centralized drinking water quality monitoring (2019-2020) in Haixi Prefecture. The data is collected from the data set of Qinghai Provincial Department of ecological environment, including nine data tables: information disclosure form of county-level centralized drinking water quality monitoring in the first quarter of 2019 in Haixi Prefecture, and information disclosure form of county-level centralized drinking water quality monitoring in the second quarter of 2019 in Haixi Prefecture Information disclosure form of quality monitoring, information disclosure form of centralized drinking water quality monitoring at county level in the third quarter of 2019, information disclosure form of centralized drinking water quality monitoring at county level in the fourth quarter of 2019, information disclosure form of centralized drinking water quality monitoring at county level in the first half of 2019, and information disclosure form of centralized drinking water quality monitoring at county level in the second half of 2019 In the first quarter of 2020, the county-level surface water centralized drinking water source water quality information disclosure form, the county-level surface water centralized drinking water source water quality information disclosure form in the second quarter of 2020, and the county-level groundwater centralized drinking water source water quality information disclosure form in the first half of 2020 The table structure is the same. There are 11 fields in each data table Field 1: serial number Field 2: name of water source Field 3: water level Field 4: water source type Field 5: water quality category requirements Field 6: monitoring unit Field 7: monitoring factors Field 8: monitoring frequency Field 9: is it up to standard Field 10: over standard factor Field 11: remarks
Department of Ecology and Environment of Qinghai Province
The data set records the monitoring status of centralized drinking water quality in Haixi Prefecture of Qinghai Province from January 2019 to June 2020. The data were collected from the ecological environment bureau of Haixi Prefecture. The data set includes six data tables, which are: information disclosure data of centralized drinking water quality monitoring in Haixi Prefecture in the first quarter of 2019, information disclosure data of centralized drinking water quality monitoring in Haixi Prefecture in the second quarter of 2019, information disclosure data of centralized drinking water quality monitoring in Haixi Prefecture in the third quarter of 2019, and information disclosure data of centralized drinking water quality monitoring in Haixi Prefecture in the second quarter of 2019 The structure of information disclosure data and data table is the same for the fourth quarter of 2020, the first quarter of 2020 and the second quarter of 2020. Each data table has a total of 11 fields, such as the information disclosure table of prefecture level centralized drinking water quality monitoring in the second quarter of 2020 in Haixi prefecture (only 6 fields are listed) Field 1: serial number Field 2: name of water source Field 3: water level Field 4: water source type Field 5: water quality category requirements Field 6: testing unit Field 7: monitoring items Field 8: monitoring frequency Field 9: exceedance factor Field 10: is it up to standard Field 11: remarks
Ecological Environment Bureau of Haixi Prefecture Qinghai Province
The data set records the dynamic statistical data of groundwater level in the monitoring area of Golmud City, Qinghai Province from 2012 to 2018, and the statistics are classified according to the year and quantity. The data were collected from the official website of the Department of natural resources of Qinghai Province. The data set contains seven data tables, which are the dynamic statistics of groundwater level in Golmud monitoring area in 2012, 2013, 2014, 2015, 2016, 2017 and 2018, with the same structure. For example, the data table in 2012 has five fields: Field 1: year Field 2: Potassium view5 Field 3: View 4 Field 4: View 39 Field 5: Potassium view 1
ZHAO Hu
1. The data content is the monthly groundwater level data measured between the tail of chengdina River, Kuqa Weigan River and Kashgar river of Tarim River, which is required to be the water level data of 30 wells, but the number of wells in this data reaches 44; 2. The data is translated into CSV through hobo interpretation, and the single bit time-lapse value is found through MATLAB, and then extracted and calculated through Excel screening, that is, through the interpretation of original data, through the communication Out of date and daily data, calculated monthly data; 3. Data is measured data, 2 decimal places are reserved, unit is meter, data is accurate; 4. Data can be applied to scientific research and develop groundwater level data for local health.
CHEN Yaning, HAO Xingming
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 data set integrated glacier inventory data and 426 Landsat TM/ETM+/OLI images, and adopted manual visual interpretation to extract glacial lake boundaries within a 10-km buffer from glacier terminals using ArcGIS and ENVI software, normalized difference water index maps, and Google Earth images. It was established that 26,089 and 28,953 glacial lakes in HMA, with sizes of 0.0054–5.83 km2, covered a combined area of 1692.74 ± 231.44 and 1955.94 ± 259.68 km2 in 1990 and 2018, respectively.The current glacial lake inventory provided fundamental data for water resource evaluation, assessment of glacial lake outburst floods, and glacier hydrology research in the mountain cryosphere region
WANG Xin, GUO Xiaoyu, YANG Chengde, LIU Qionghuan, WEI Junfeng, ZHANG Yong, LIU Shiyin, ZHANG Yanlin, JIANG Zongli, TANG Zhiguang
Firstly, country-wise sectorial water withdrawal data are collected from FAO AQUASTAT database, Peter Gleick’s water use data, country statistics and literatures. In order to get consistent data, all data are unified to 2015 due to inconsistent times. For the data of year 2013-2017 close to 2015, the values of these years are directly used as water withdrawals of 2015. For the others, GDP, population, temperature, precipitation, irrigation area, carbon dioxide emission, nighttime light index, coal production, urban population corresponding to the water use data of different years in each country are collected, the panel data regression model of fixed effect and random effect between industrial water, agricultural water and domestic water and these factors are established, respectively. Sectorial water withdrawals in 2015 are estimated for every country.
The fraction snow cover (FSC) is the ratio of the snow cover area SCA to the pixel space. The data set covers the Arctic region (35 ° to 90 ° north latitude). Using Google Earth engine platform, the initial data is the global surface reflectance product with a resolution of 1000m with mod09ga, and the data preparation time is from February 24, 2000 to November 18, 2019. The methods are as follows: in the training sample area, the reference data set of FSC is prepared by using Landsat 8 surface reflectance data and snomap algorithm, and the data set is taken as the true value of FSC in the training sample area, so as to establish the linear regression model between FSC in the training sample area and NDSI based on MODIS surface reflectance products. Using this model, MODIS global surface reflectance product is used as input to prepare snow area ratio time series data in the Arctic region. The data set can provide quantitative information of snow distribution for regional climate simulation and hydrological model.
MA Yuan, LI Hongyi
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
