Brief Introduction: 泛第三极是从第三极向西、向北扩展，涵盖青藏高原、帕米尔、兴都库什、伊朗高原、高加索、喀尔巴阡等山脉的欧亚高地及其环境影响区，面积2000多万平方公里，和30多亿人的生存环境有关。泛第三极地区与“一带一路”核心区高度重合。深入研究泛第三极地区环境变化规律、机制与未来变化趋势，解决重点地区、重点国家和重点工程的资源环境问题，将为环境变化和人类活动最强烈的丝绸之路经济带可持续发展提供科学依据，为打造绿色、健康、智力、和平的“一带一路”提供决策支持。 中国科学院A类战略性先导科技专项“泛第三极环境变化与绿色丝绸之路建设”（以下简称“丝路环境专项”）于9月30日在北京。本专项将遵循习近平总书记对第二次青藏高原综合科学考察研究的重要指示精神和新时代青藏高原生态文明建设理念的系列重要讲话指示精神，与第二次青藏高原综合科学考察研究和三极环境与气候变化国际大科学计划有机结合，聚焦水、生态、人类活动，着力解决环境变化机理、资源环境承载力、灾害风险、绿色发展途径等方面的问题。围绕专项的两大统领科学问题，在科学贡献层面，预期在泛第三极环境变化与西风-季风相互作用和水资源变化及广域联动、泛第三极环境变化对关键物种和典型生态系统影响的预警体系与适应模式、人类文明发展与泛第三极环境相互作用及其适应策略等方面产出重大成果，推动从高极到三极的全球环境研究新前沿和三极环境与气候变化国际大科学计划的实施；在国家需求层面，预期在绿色丝绸之路建设的路线图、绿色丝绸之路建设的技术示范、优化青藏高原生态安全屏障体系的科学方案等方面产出重大成果，推动青藏高原可持续发展、推进国家生态文明建设、促进全球生态环境保护。
Number of Datasets: 536
This is the flow discharge observation data observed in the estuary of Boquzangbu, which is Selincuo Lake’s inflow river. It is measured by Flow Tracker portable hydrological flow rate meter. It can be used in Hydrologic Process in Cold Regions and other disciplinary areas. The data is observed on August 16, 2017. The observation includes time, location, depth of water, water flow rate, and water flow discharge. The data is stored as an excel file.
2019-09-15 0 0 View Details
The dataset records 1960-2017 years of urban population statistics in 65 along the Belt and Road.Data sources: (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme. The data set contains 3 tables: (1))Urban population;(2)Urban population (% of total population;(3)Urban population growth (annual %).
2019-09-15 0 4 View Details
This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2017. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “AMSR-E and AMSR2 TB-based SMAP Time-Expanded Daily 0.25°×0.25° Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou and Lat/Lon information.
2019-09-15 0 8 View Details
This dataset is the data of human activities in the key areas of Qilian Mountain in 2018, spatial resolution 2m. This dataset focuses on mine mining, urban expansion, cultivated land development, hydropower construction, and tourism development in the key areas of Qilian Mountain.Through high-resolution remote sensing images, compare the changes before and after the statistics. For the maps of the landforms in the Qilian Mountains, check and verify them one by one; re-interpret the plots that are suspicious of the map; collect the relevant data in the field that cannot be reflected by the images, check and correct the location. At the same time, unified input and editing of map attribute information. Generating a data set of human activities in the key areas of the Qilian Mountains in 2018.
2019-09-15 0 11 View Details
The data set includes altitude data and temperature characteristics from 1988 to 1994 in Tibet. The data were derived from the Tibet Society and Economics Statistical Yearbook and the Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. The table contains 10 fields. Field 1: Year Interpretation: Year of the data Field 2: Location Field 3: Altitude Unit: meter Field 4: Extreme maximum temperature date Field 5: Extreme maximum temperature Unit: °C Field 6: Extreme minimum temperature date Field 7: Extreme minimum temperature Unit: °C Field 8: Annual average temperature Unit: °C Field 9: Average temperature in January Unit: °C Field 10: Average temperature in July Unit: °C
2019-09-15 0 3 View Details
This data set includes biomass survey data observed from the carbon flux station in the Guoluo Army Ranch in Qinghai from 2005 to 2009. Carbon flux data observation method: vorticity-related observation instruments were used for automatic recording; biomass observation method: harvest method, weighing in a 60-degree oven for 48 hours. The carbon flux data were automatically recorded by the instruments and manually checked. Observations and data collection were carried out in strict accordance with the instrument operating specifications and were published in relevant academic journals. During the data observation process, the operation of the instrument and the selection of the observational objects were in strict accordance with professional requirements, and the data could be applied to plant leaf photosynthetic parameter simulation and production estimation. 1) Biological observational data of the Guoluo meadow ecosystem: Date, site number, vegetation type, plot number, aboveground biomass (g/m²), underground biomass (g/m²), total biomass (g/m²) 2) Carbon flux observational data of the Guoluo meadow ecosystem: Site number, date, vegetation type, soil type, water vapor flux (w/m²), carbon flux (mg/m²·S) The fixed point observation data are of high precision.
2019-09-15 0 14 View Details
The DEMs of the typical glaciers on the Tibetan Plateau were provided by the bistatic InSAR method. The data were collected on November 21, 2013. It covered Puruogangri and west Qilian Mountains with a spatial resolution of 10 meters, and an elevation accuracy of 0.8 m which met the requirements of national 1:10 000 topographic mapping. Considering the characteristics of the bistatic InSAR in terms of imaging geometry and phase unwrapping, based on the TanDEM-X bistatic InSAR data, and adopting the improved SAR interference processing method, the surface DEMs of the two typical glaciers above were generated with high resolution and precision. The data set was in GeoTIFF format, and each typical glacial DEM was stored in a folder. For details of the data, please refer to the Surface DEMs for typical glaciers on the Tibetan Plateau - Data Description.
2019-09-15 0 11 View Details
These are the vegetation quadrat survey data of the alpine grassland and alpine meadow in Maduo County in September 2016. The dimensions of the square quadrat are 50 cm x 50 cm. The main contents of the survey include coverage, species name, vegetation height, biomass (dry weight and fresh weight), the latitude and longitude coordinates of the quadrat, slope, aspect, slope position, soil type, vegetation type, surface features (litter, gravel, wind erosion, water erosion, saline-alkaline spots, etc.), use patterns, utilization intensity and others.
2019-09-15 0 12 View Details
The data set includes the average temperature data of main areas in Qinghai Province such as Xining, Haidong, Menyuan, Huangnan, Hainan, Guoluo, Yushu and Haixi from 1998 to 2016. The data were derived from the Qinghai Society and Economics Statistical Yearbook and the Qinghai Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. The data table records the monthly and annual average wind speed in eight regions of Qinghai. Unit: Celsius. The data set is mainly applied in geography and socioeconomic research.
2019-09-15 0 5 View Details
This dataset contains the flux measurements from the Jingyangling station eddy covariance system (EC) in the upperstream reaches of the Heihe integrated observatory network from August 28 to December 31 in 2018. The site (101.1160E, 37.8384N) was located in the Jingyangling, near Qilian County in Qinghai Province. The elevation is 3750 m. The EC was installed at a height of 4.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Data during insufficient power supply, data were missing occasionally. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
2019-09-15 0 9 View Details
The data set contains vector data of 32,840 lakes which can be recognized in the remote sensing image on the Tibetan plateau in 2000. The data was obstracted by visual interpretation from GeoCover Landsat Mosaic 2000 image data with a spatial resolution of 14.25 m. The data format is vector data, and the projection coordinate system is Albers Conical Equal Area. The data property fields are as follows: Area: lake Area (km); X: lake center longitude (°); Y: lake center latitude (°); Perimeter: the Perimeter of a lake (km).
2019-09-15 0 16 View Details
DEM is the English abbreviation of Digital Elevation Model, which is the important original data of watershed topography and feature recognition.DEM is based on the principle that the watershed is divided into cells of m rows and n columns, the average elevation of each quadrilateral is calculated, and then the elevation is stored in a two-dimensional matrix.Since DEM data can reflect local topographic features with a certain resolution, a large amount of surface morphology information can be extracted through DEM, which includes slope, slope direction and relationship between cells of watershed grid cells, etc..At the same time, the surface flow path, river network and watershed boundary can be determined according to certain algorithm.Therefore, to extract watershed features from DEM, a good watershed structure pattern is the premise and key of the design algorithm. Elevation data map 1km data formed according to 1:250,000 contour lines and elevation points in China, including DEM, hillshade, Slope and Aspect maps. Data set projection: Two projection methods: Equal Area projection Albers Conical Equal Area (105, 25, 47) Geodetic coordinates WGS84 coordinate system
2019-09-15 0 21 View Details
This dataset contains SMAP time-expanded monthly 0.25°×0.25° land surface soil moisture products in Qilian Mountain Area of 1980, 1985, 1990, 1995 and 2000. The dataset was produced based on Random Forest method by utilizing SMMR, SSM/I and SSMIS brightness temperature from 19 GHz V/H and 37 GHz V channels along with some auxiliary datasets. The auxiliary datasets participating in the RF training include IGBP land cover type, GTOPO30 DEM and Lat/Lon information.
2019-09-15 0 8 View Details
This data comes from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2015. This data set includes 1:250,000 residential place names (AANP) in Sanjiangyuan area, including administrative place names at all levels and urban and rural residential place names. Names and Definitions of Attribute Items of Residential Place Name Data (AANP): Attribute Item Description Fill in Example NAME Name Quanqu Village PINYIN Chinese Pinyin Quanqucun CLASS Geographical Name Classification Code AK GNID Place Name Code 632524000000 XZNAME Township Name Ziketan Township
2019-09-15 0 7 View Details
This data set contains data on the concentrations of persistent organic pollutants (POPs) and total suspended particulate (TSP) in the atmosphere at a station in southeastern Tibet (Lulang). The samples were collected using an atmospheric active sampler equipped with a tandem fibreglass membrane-polyurethane foam sampling head. The gaseous POPs and TSPs were collected. The sampling period for each sample was 2 weeks. The types of observed POPs include organochlorine pesticides (OCPs), polychorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). Only gaseous concentrations were detected for OCPs and PCBs, while both gaseous concentrations and particulate concentrations were detected for PAHs. All of the data contained in the data set are measurement data. The samples were collected in the field at the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The sampler was an atmospheric flow active sampler equipped with a tandem fibreglass membrane-polyurethane foam sampling head, in which the fibreglass membrane was used to collect TSPs and the polyurethane foam was used to adsorb gaseous pollutants in the atmosphere. During the sampling period, the sampler was run every other day for approximately 24 hours each time, and each sample was collected for 2 weeks. The atmospheric volume collected for each sample was 500-700 cubic metres. Both gaseous and particulate POP samples were prepared and analysed in the Key Laboratory of Tibetan Environment Changes and Land Surface Processes, CAS. The sample preparation steps included Soxhlet extraction, silica-alumina column purification, removal of macromolecular impurities by a GPC column, concentration to a defined volume, etc. The analytical test instrument was a gas chromatography/ion trap mass spectrometer (Finnigan-TRACE GC/PolarisQ) produced by Thermo Fisher Scientific. The column used to separate OCPs and PCBs was a CP-Sil 8CB capillary column (50 m × 0.25 mm × 0.25 μm), and the column used to separate PAHs was a DB-5MS capillary column (60 m x 0.25 mm x 0.25 μm). The total suspended particulate concentration in the atmosphere was determined by the gravimetric method, and the accuracy of the weighing balance was 1/100,000 g. The field samples were subjected to strict quality control with laboratory blanks and field blanks. The detection limit of a given compound was 3 times the standard deviation of the concentration of the corresponding compound in the field blank; if the compound was not detected in the field blank, the detection limit of the method was determined by the lowest concentration of the working curve. For a sample, concentrations above the detection limit of the method are corrected by subtracting the detection limit; concentrations below the detection limit of the method but higher than 1/2 times the detection limit are corrected by subtracting half the method detection limit; and concentrations below 1/2 times the detection limit are considered undetected. The recovery rate of PAH laboratory samples was between 65-120%, and that of OCPs was between 70-130%; the sample concentrations were not corrected by the recovery rate. In the table, undetected data are marked as BDL; data marked in black italics are data corrected by subtracting 1/2 the method detection limit.
2019-09-15 0 5 View Details
The data set records the gross national income of 1960-2017 countries along 65 countries along the belt and road. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data sources: World Bank national accounts data, and OECD National Accounts data files. The data set contains 5 tables：GNI (constant 2010 US$),GNI (constant LCU),GNI (current LCU),GNI (current US$),GNI growth (annual %).
2019-09-15 0 0 View Details
This database is based on the theory of emergy analysis for 17 typical countries along the “Belt and Road” during 2008-2014. These countries are include Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, Turkmenistan, Mongolia, Russia, Pakistan, Bangladesh, Afghanistan, Nepal, Thailand, Myanmar, Ukraine, Moldova, Belarus and Azerbaijan. The basic data sources of this database mainly include detailed information, goods and services on environmental resource flows, natural capital stocks and human production activities. The data in database is calculated and evaluated based on the solar emergy. The database consists of three tables, which are the emergy analysis table of main resource flow, comprehensive emergy analysis table of the main resource category and the system emergy indicators analysis table. The emergy transformities used in this database is updated and calculated according to the emergy baseline (12.0E+24seJ/y) given by Pro. Brown in 2016. Based on the basic data in the database, it can effectively calculate the emergy-based sustainability index system, and give the reasons for the analysis results, the solution and future planning direction for the study country. It is of great significance to the development of the national ecological economic system and provide a scientific basis for the government to improve the sustainable development status of the national ecological economic system.
2019-09-15 0 3 View Details
The measurement data of the sun spectrophotometer can be directly used to perform inversion on the optical thickness of the non-water vapor channel, Rayleigh scattering, aerosol optical thickness, and moisture content of the atmospheric air column (using the measurement data at 936 nm of the water vapor channel). The aerosol optical property data set of the Tibetan Plateau by ground-based observations was obtained by adopting the Cimel 318 sun photometer, and both the Mt. Qomolangma and Namco stations were involved. The temporal coverage of the data is from 2009 to 2016, and the temporal resolution is one day. The sun photometer has eight observation channels from visible light to near infrared. The center wavelengths are 340, 380, 440, 500, 670, 870, 940 and 1120 nm. The field angle of the instrument is 1.2°, and the sun tracking accuracy is 0.1°. According to the direct solar radiation, the aerosol optical thickness of 6 bands can be obtained, and the estimated accuracy is 0.01 to 0.02. Finally, the AERONET unified inversion algorithm was used to obtain aerosol optical thickness, Angstrom index, particle size spectrum, single scattering albedo, phase function, birefringence index, asymmetry factor, etc.
2019-09-15 0 5 View Details
The data set records the mileage of railways, highways and civil aviation in Qinghai from 1952 to 2015. The data were derived from the Qinghai Society and Economics Statistical Yearbook and the Qinghai Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook The table contains 12 fields. Field 1: Year Interpretation: Year of the data Field 2: Mileage of railway in use Unit: kilometers Field 3: Railway routes density Unit: kilometers / 10,000 square kilometers Field 4: Highway traffic mileage Unit: kilometers Field 5: Highway routes density Unit: kilometers Field 6: Mileage of Highway with pavements Unit: kilometers Field 7: High graded highway, Sub-high graded highway Unit: kilometers Field 8: Total highway grades Unit: kilometers Field 9: Highway High Speed Unit: kilometers Field 10: Grade one and two highway Unit: kilometers Field 11: Roads without grades Unit: kilometers Field 12: Civil Aviation Mileage Unit: kilometers
2019-09-15 0 3 View Details