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: 534
The data set records the urbanization rate data of each state of kazakhstan from 2000 to 2018.The data is from kazakhstan's national statistics bureau. Urbanization is a concept with broad implications.In a narrow sense, it generally refers to the urbanization of population, which refers to the increase of the number of cities and the expansion of the urban scale, and the process of population aggregation to cities in a certain period.Urbanization rate refers to the proportion of permanent urban residents in a region in the total permanent resident population.The name of the original index is Russian, which has been translated and edited.The accuracy of the official data can provide basic data basis for the study of the socio-economic development of central Asian countries.
2019-09-15 0 0 View Details
The Tibetan Plateau Glacial Data Product-TPG1976 is a glacial attribute product of the Tibetan Plateau around 1976. It was generated by remote sensing visual interpretation method adopting Landsat MSS multispectral data. The temporal coverage of the data were from 1972 to 1979. 61% of the remote sensing data were from 1976 to 1977. The data covered the Tibetan Plateau with a spatial resolution of approximately 60 m. Considering the large error of automatic remote sensing extraction method caused by the impact of cloud, shadow and seasonal snow on glacier area, the remote sensing inversion method adopted manual visual interpretation. By comparing the results of automatic methods and visual interpretation of glacier boundaries based on experts’ experiences, we know that the manual interpretation based on remote sensing images is still the most accurate method to obtain the glacier vector boundary at present. When selecting remote sensing images, the minimum effects of cloud and seasonal snow were mainly considered. Images of summer and cold season were both selected (different from the principle applied in selecting remote sensing image data source for China's second glacier inventory). At the same time, considering the differences in discriminant standards between different interpreters, the comparison of multiple typical regions showed that the relative deviation of manual visual interpretation was less than 4%. Based on the Arc map software platform, the abovementioned remote sensing images were geometrically corrected, and the final glacier vector boundary data were obtained by visual interpretation. According to the format and requirements of the second glacier inventory in China, the glacier code and area statistics were collected, and the elevation attribute data of each glacier was obtained based on the SRTM DEM data, and finally the 1976 glacial data product of the Tibetan Plateau was obtained.
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This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dunhuang Station from January 1 to December 31, 2018. The site (93.708° E, 40.348° N) was located on a wetland in the Dunhuang west lake, Gansu Province. The elevation is 990 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8 m, towards north), wind speed and direction profile (windsonic; 4m and 8 m, towards north), air pressure (1 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.05 and -0.2 m), photosynthetically active radiation (4 m, towards south), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_2 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_0.05m, Ts_0.2m) (℃), soil moisture (Ms_0.05m, Ms_0.2m) (%, volumetric water content), soil conductivity (Ec_0.05m, Ec_0.2m)(μs/cm), sun time(h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The data were missing during Jan. 23 to Jan. 24 because of collector failure; the data during Mar. 17 and May 24 were wrong because of the tower body tilt; The air humidity data were rejected due to program error. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
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This dataset contains the flux measurements from the A’rou superstation eddy covariance system (EC) in the upperstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (100.372° E, 38.856° N) was located in the Daban Village, near Qilian County in Qinghai Province. The elevation is 3033 m. The EC was installed at a height of 3.5 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&Li7500A) 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.
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The data set contains two tables detailing the total cultivated land area and the cultivated land area in every county at the end of each year. The data are time series data of cultivated land, dry land, paddy field and effective irrigated area in Tibet from 1959 to 2016 and were derived from the Tibet Society and Economics Statistical Yearbook and Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbooks. Table 1: The table of cultivated land area contains 7 fields. Field 1: Year of the data Field 2: Year-end actual cultivated land area, unit: 1000 hectares Field 3: Dry land area, unit: 1000 hectares Field 4: Paddy field area, unit: 1000 hectares Field 5: Reduced area in the current year, unit: 1000 hectares Field 6: Land occupation of national infrastructure, unit: 1000 hectares Field 7: Increased area in the current year, unit: 1000 hectares Table 2: The table of year-end cultivated land area in each county contains 5 fields. Field 1: Year of the data Field 2: The districts and counties included in the data Field 3: Actual cultivated land area, unit: hectare Field 4: Dry land area, unit: hectare Field 5: Effective irrigated area, unit: hectare
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This dataset is the Digital Elevation Model （DEM）in the Qilian Mountain， spatial resolution 30m. This dataset is based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model （ASTER-GDEM）. The data set has a vertical accuracy of 20 m and a horizontal accuracy of 30 m. Through the data download, preprocessing and splicing, the 30m×30m DEM data of Qilian Mountain is generated. This data set can extract a large amount of surface morphology information, which is an important basic data for terrain analysis and feature recognition in Qilian Mountain. The data will serve the ecological environment monitoring, ecological environmental protection and treatment project implementation, hydrology and water resources analysis and evaluation in Qilian Mountain area.
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The data set describes the hypocentre parameters of intermediate- and deep-focus earthquakes in the Pamir-Hindu Kush region from 1964 to 2011. The earthquake relocation results clarified the complex deformation characteristics of underground structures in the deep subduction area in the Pamir-Xindu Kush region. The seismic waveform data are from the IRIS website (http://ds.iris.edu/wilber3/find_event), and the arrival time data are from the ISC website (http://www.isc.ac.uk/) and the CEDC website (http:// Data.earthquake.cn/data/index.jsp?id=11number=9). Seismic location was determined using the teleseismic waveform fitting and the multi-scale double-difference (Multi-DD) method developed in this study. The errors in latitude and longitude data are approximately ±7 km and ±7 km, respectively. Origin Time: yyyy (year), mm (month), dd (day), hh (hour), mm (minute), ss.ss (second) Earthquake Magnitude: Magnitude (from the ISC seismic catalogue) Earthquake Location: Latitude, Longitude, Depth Hypocentre determination method: Hypocentres marked with an "F" were determined by the waveform fitting method
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The data set is extracted from the NDVI data of long time series acquired by VEGETATION sensor on SPOT satellite. The time range of the data set is from May 1998 to 2013. In order to remove the noise in NDVI data, the maximum synthesis is carried out. A NDVI image is synthesized every 10 days. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is geotiff, spatial resolution is 1 km, temporal resolution is 10 days, time range: May 1998 to December 2013.
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This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of Yulei station on Qinghai lake from January 1 to October 12, 2018. The site (100° 29' 59.726'' E, 36° 35' 27.337'' N) was located on the Yulei Platform in Erlangjian scenic area, Qinghai Province. The elevation is 3209m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 12 and 12.5 m above the water surface, towards north), wind speed and direction profile (windsonic; 14 m above the water surface, towards north) , rain gauge (TE525M; 10m above the water surface in the eastern part of the Yulei platform ), four-component radiometer (NR01; 10 m above the water surface, towards south), one infrared temperature sensors (SI-111; 10 m above the water surface, towards south, vertically downward), photosynthetically active radiation (LI190SB; 10 m above the water surface, towards south), water temperature profile (109, -0.2, -0.5, -1.0, -2.0, and -3.0 m). The observations included the following: air temperature and humidity (Ta_12 m, Ta_12.5 m; RH_12 m, RH_12.5 m) (℃ and %, respectively), wind speed (Ws_14 m) (m/s), wind direction (WD_14 m) (°) , precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), water temperature (Tw_20cm、Tw_50cm、Tw_100cm、Tw_200cm、Tw_300cm) (℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The other data in addition to the four-component radiation data during January 1 to October 12 were missing because the malfunction of datalogger. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-1-1 10:30. Moreover, suspicious data were marked in red.
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Land use data of Dushanbe, with a resolution of 30 meters, was in the form of TIF and the time was 1990.03.03 and 2018.03.16, respectively.Data source GLC, the raw data of its global land cover data comes from Envisat satellite and is captured by MERIS (Medium Resolution Imaging Spectrometer) sensor.There are currently two issues, GlobCover (Global Land Cover Map) and GlobCover (Global Land Cover Product).
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Taking 2005 as the base year, the future population scenario was predicted by adopting the logistic model of population. This model not only effectively describes the pattern of changes in population and biomass but is also widely applied in the field of economics. The urbanization rate was predicted using the urbanization logistic model. Based on the observed horizontal pattern of urbanization, a predictive model was established by determining the parameters in the parametric equation by applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The data represent the non-agricultural population. The logistic model was used to predict the future gross domestic product of each county (or city), and then the economic development level of each county (or city) in each period (in terms of GDP per capita). The corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changes in China and the research area lagged behind the growth in GDP, so the changes were adjusted according to the need for future industrial structure scenarios in the research area.
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The data set describes the social welfare and medical care statistics in Tibet over time. The data include the number of beds in hospitals, the number of social welfare institutions, and the number of beds in social welfare institutions. The data were derived from the Tibet Society and Economics Statistical Yearbook and Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbooks. The table contains 6 fields. Field 1: Districts and counties Field 2: Year Field 3: Number of beds in hospitals per 10,000 people Field 4: Number of beds in hospitals Field 5: Number of social welfare institutions Field 6: Number of beds in social welfare institutions
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The data set contains data on the annual sunshine hours and total solar radiation in Tibet from 1988 to 1994. 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 5 fields. Field 1: Year Interpretation: Year of the data Field 2: Location Field 3: Annual sunshine hours Unit：hour Field 4: Annual sunshine percentage Unit: % Field 5: Total solar radiation Unit: Kcal/cm2 • Year
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This data set collected zircon U-Pb isotope age data of the granites in the southern Qiangtang terrane of the Tibetan Plateau from articles published before October 2014. The data were analyzed by Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICPMS), Sensitive High-Resolution Ion Microprobe (SHRIMP), and Isotope Dilution Thermal Ionization Mass Spectrometry (ID TIMS). The data were obtained according to laboratory standards, and the data quality met laboratory requirements. The data contents are as follows: Region Locality Lithology Sample No. Dating method Age (Ma) References
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Due to the short snow duration and thin snow layer on the Tibetan Plateau, dynamic monitoring data for daily fractional snow cover are urgently needed in order to better understand water cycling and other processes. This data set is based on MODIS Snow Cover Daily L3 Global 500 m Grid data and includes the Normalized Difference Snow Index (NDSI) data product generated from MODIS/Terra data (MOD10A1) and MODIS/Aqua data (MYD10A1). The data are in the .hdf format. The projection method is sinusoidal map projection. Combining the advantages of 90 m SRTM terrain data and fractional snow cover estimation algorithms under multiple cloud coverage types, the fractional snow cover under different cloud coverage conditions can be re-estimated to meet the production requirements of the daily less cloud (< 10%) data products in High Asia. On the basis of this method, the MODIS daily fractional snow cover data set over High Asia (2002-2016) was constructed. By taking the binary snow product under cloudless conditions as a reference, the spatial and temporal comparisons between snow distribution and snow coverage show that the spatio-temporal characteristics of the product and the binary products are highly consistent. Taking the winter of 2013 as an example, when the fractional snow cover is greater than 50%, the correlation can reach 0.8628. This data set provides daily fractional snow cover data for use in studying snow dynamics, the climate and environment, hydrology, energy balance, and disaster assessment in High Asia.
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After three years of hard work, the cold region surface hydrology process study team of the Institute of Tibetan Plateau Research completed the investigation, sampling and identification of vegetation in the Selincuo Lake Region. More than 200 plant type specimens were collected, and more than 2,000 high-definition pictures of plants were taken. At present, researchers have identified 45 species of plants belonging to 23 families and 35 genera. Plant classification experts from the Institute of Botany of the Chinese Academy of Sciences were invited to undertake secondary verification and correction of all plant species names that have been identified. The plant resources of the Selincuo Lake region have been matched with corresponding diagrams and annotations. It has been professionally typeset, edited and published as a reference to relevant academic research of the region. The Selincuo Lake area is a semiarid grassland with trees and shrubs rarely distributed, and the herbaceous plants species are also relatively rare in the area. However, vegetation plays an indispensable role in maintaining the integrity of the local natural ecology, wildlife habitat and production and life of herdsmen. The investigation results showed that the plants in the lake area were abundant in families, but with few species and genera of the same family. Among them, Compositae has the most species, subordinate to seven genera and eight species, namely: one species of Leontopodium, two species of Artemisia, one species of Ajania, one species of Saussurea, one species of Heteropappus, one species of Taraxacum and one species of Anaphalis; followed by Boraginaceae with a total of 4 species, all of which are of the Microula; Ranunculaceae has three genera and three species, one species of Halerpestes, one species of Delphinium, and one species of Thalictrum; Scrophulariaceae has two genera and three species, one species of Lagotis, and two species of Pedicularis; Crassulaceae has three species, all of which are Sedum; Labiatae has two species, all of which are Dracocephalum; Cyperaceae has two species, one species of Kobresia, and one species of Carex; Polygonaceae has two species, all of which are Polgohum; Caryophyllaceae has two species, one species of Arenaria, and one species of Silene; Gramineae has two species, one species of Poa, and one species of Elymus L.; Liliaceae has two species, all of which are Allium; in addition, there are 12 species of plants with a single family and single genus, namely: Stellera of Thymelaeaceae, Urtica of Urticaceae, Gentiana of Gentianaceae, Morina of Dipsacaceae, Torularia of Cruciferae, Potentilla of Rosaceae, Myricaria of Tamaricaceae, Androsace of Primulaceae, Astragalus of Leguminosae, Oxytropis of Fabaceae, Chenopodium of Chenopodiaceae, and Incarvillea of Bignoniaceae.
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The contents include five Central Asian countries, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. The basic socio-economic indicators from 2012 to 2017 are divided into 12 categories: GDP, price, industry, agriculture, animal husbandry, construction, capital investment, transportation, foreign trade, labor market, wages, living standards and the exchange rate of the US dollar. Developments and changes. The data comes from ww. cisstat. com. The original index name is Russian, which is translated and edited. The accurate official data can provide basic data basis for the study of social and economic development in Central Asian countries.
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This is the water quality multi-parameter data set of Renqingxiubucuo Lake. It can be used to acquire basic physical and chemical indices of lakes. And it can prepare for the following modern observation studies of lakes. The data is observed on September 1 2017. It was measured by the YSI EXO2 multi-parameter water quality instrument. Instrument calibration is made before each measurement based on the altitude of the lake and the local pressure. The measuring interval is 0.25 s. To ensure the data is continuously acquired, the instrument is slowly released. The original data includes data measured above the water surface, which is exposed to the air, and it has all been eliminated in the post processing. The data is stored as an excel file.
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This is the slopes-runoff observation data set in typical underlying surface runoff fields of Selincuo Basin. It can be used in Hydrologic Process in Cold Regions, Geocryology and other disciplinary areas. The underlying surface of the observation point is the typical alpine steppe. The data is observed on July 2, August 10, August 17, August 26, August 30, September 1, September 2, September 3, and September 4, 2017. The observation includes the rainfall time, rainfall duration, rainfall, average rainfall, runoff, and the runoff coefficient. The precipitation duration is accurate to minute, the precipitation observation is accurate to 0.1mm, and the runoff observation is converted to mm, which is accurate to 0.01mm.The original data forms a continuous time series after quality control, and the daily mean index data is obtained through calculation. The data is stored as an excel file.
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