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
Taking 2005 as the base year, the future population scenario prediction adopted the Logistic model of population; not only is it better able to describe the change pattern of population and biomass, but it is also widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then according to the economic development level of each county (or city) in each period (in terms of real 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 changing industrial structure in China and the research area lagged behind the growth of GDP and was therefore adjusted according to the need of the future industrial structure scenarios of the research area.
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It includes sunshine hours data of Xining, Haidong, Menyuan, Huangnan, Hainan, Guoluo, Yushu, Haixi and other major areas of Qinghai from 1988 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 recorded the sunshine hours of every month and year in eight regions of Qinghai. Unit: hour This data set is mainly used in geography and socioeconomic research.
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The data set contains data on the natural resources in Tibet from 1988 to 1994. 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 37 fields. Field 1: Year Field 2: Total surface area of the whole region, unit: 10,000 square kilometres. Field 3: Cultivated land area, unit: 10,000 mu (1 mu=0.0667 hectares) Field 4: Paddy field area, unit: 10,000 mu (1 mu=0.0667 hectares) Field 5: Forest area, unit: 10,000 mu (1 mu=0.0667 hectares) Field 6: Forest coverage proportion, unit: % Field 7: Forest stocks, unit: 100 million cubic metres Field 8: Grassland area, unit: 100 million mu (1 mu=0.0667 hectares) Field 9: Grassland available area, unit: 100 million mu (1 mu=0.0667 hectares) Field 10: Total annual runoff of rivers, unit: 100 million cubic metres. Field 11: Hydraulic resource reserves, unit: 10,000 kilowatt Field 12: Hydraulic potential exploitation amount, unit: 10,000 kilowatt Field 13: Length of the national boundary, unit: kilometres Field 14: Iron mine reserve amount, unit: 100 million tons Field 15: Chromite reserve amount, unit: 10,000 tons Field 16: Copper (ore), unit: 100 million tons Field 17: Borate ore reserve amount, unit: 10,000 tons Field 18: Salt reserve amount, unit: 100 million tons Field 19: Graphite reserve amount, unit: 10,000 tons Field 20: Gypsum reserve amount, unit: 100 million tons Field 21: Coal reserve amount, unit: 10,000 tons Field 22: Peat reserve amount, unit: 10,000 tons Field 23: Geothermal reserve amount, unit: 10,000 cubic metres / day and night Field 24: Species number of national key protected animals Field 25: Species number of class 1 national key protected animals Field 26: Species number of class 2 national key protected animals Field 27: Species number of national key protected plants Field 28: Species number of class 1 national key protected plants Field 29: Species number of class 2 national key protected plants Field 30: Species number of class 3 national key protected plants Field 31: Number of nature reserves Field 32: Number of national nature reserves Field 33: Number of local nature reserves Field 34: Total area of nature reserves, unit: 10,000 mu Field 35: Proportion of nature reserves to the total area of the region Field 36: Annual average precipitation, unit: mm Field 37: Annual sunshine duration, unit: hour
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The datasets are topics for russian-soviet pipelines -- crude oil (oil) pipelines -- natural gas pipelines -- product pipelines; the data are presented at www.theodora.com. The main elements include: （1）The following table lists pipelines in Russia and the other countries of the former Soviet Union, including cross-border, international pipelines which originate or end in these countries, as shown on the map. （2）The pipeline routes on the map are labeled with the codes that are explained in the table. （3） Pipeline label codes are colored green for oil, red for gas and blue for products, such as gasoline and ethylene. （4）The diameter, length and capacity of the pipeline, if known, are shown on the table.
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This data set includes the microwave brightness temperatures obtained by the spaceborne microwave radiometer SSM/I carried by the US Defense Meteorological Satellite Program (DMSP) satellite. It contains the twice daily (ascending and descending) brightness temperatures of seven channels, which are 19H, 19V, 22V, 37H, 37V, 85H, and 85V. The Specialized Microwave Imager (SSM/I) was developed by the Hughes Corporation of the United States. In 1987, it was first carried into the space on the Block 5D-/F8 satellite of the US Defense Meteorological Satellite Program (DMSP) to perform a detection mission. In the 10 years from when the DMSP soared to orbit in 1987 to when the TRMM soared to orbit in 1997, the SSM/I was the world's most advanced spaceborne passive microwave remote sensing detection instrument, having the highest spatial resolution in the world. The DMSP satellite is in a near-polar circular solar synchronous orbit; the elevation is approximately 833 km, the inclination is 98.8 degrees, and the orbital period is 102.2 minutes. It passes through the equator at approximately 6:00 local time and covers the whole world once every 24 hours. The SSM/I consists of seven channels set at four frequencies, and the center frequencies are 19.35, 22.24, 37.05, and 85.50 GHz. The instrument actually comprises seven independent, total-power, balanced-mixing, superheterodyne passive microwave radiometer systems, and it can simultaneously measure microwave radiation from Earth and the atmospheric systems. Except for the 22.24 GHz frequency, all the frequencies have both horizontal and vertical polarization states. Some Eigenvalues of SSM/I Channel Frequency (GHz) Polarization Mode (V/H) Spatial Resolution (km * km) Footprint Size (km) 19V 19.35 V 25×25 56 19H 19.35 H 25×25 56 22V 22.24 V 25×25 45 37V 37.05 V 25×25 33 37H 37.05 H 25×25 33 85V 85.50 V 12.5×12.5 14 85H 85.50 H 12.5×12.5 14 1. File Format and Naming: Each group of data consists of remote sensing data files, .JPG image files and .met auxiliary information files as well as .TIM time information files and the corresponding .met time information auxiliary files. The data file names and naming rules for each group in the SSMI_Grid_China directory are as follows: China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V (remote sensing data); China-EASE-Fnn -ML/HaaaabbbA/D.ccH/V.jpg (image file); China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V.met (auxiliary information document); China-EASE-Fnn-ML/HaaaabbbA/D.TIM (time information file); and China-EASE- Fnn -ML/HaaaabbbA/D.TIM.met (time information auxiliary file). Among them, EASE stands for EASE-Grid projection mode; Fnn represents carrier satellite number (F08, F11, and F13); ML/H represents multichannel low resolution and multichannel high resolution; A/D stands for ascending (A) and descending (D); aaaa represents the year; bbb represents the Julian day of the year; cc represents the channel number (19H, 19V, 22V, 37H, 37V, 85H, and 85V); and H/V represents horizontal polarization (H) and vertical polarization (V). 2. Coordinate System and Projection: The projection method is an equal-area secant cylindrical projection, and the double standard latitude is 30 degrees north and south. For more information on EASE-GRID, please refer to http://www.ncgia.ucsb.edu/globalgrids-book/ease_grid/. If you need to convert the EASE-Grid projection method into a geographic projection method, please refer to the ease2geo.prj file, which reads as follows. Input Projection cylindrical Units meters Parameters 6371228 6371228 1 /* Enter projection type (1, 2, or 3) 0 00 00 /* Longitude of central meridian 30 00 00 /* Latitude of standard parallel Output Projection GEOGRAPHIC Spheroid KRASovsky Units dd Parameters End 3. Data Format: Stored as binary integers, each datum occupies 2 bytes. The data that are actually stored in this data set are the brightness temperatures *10, and after reading the data, they need to be divided by 10 to obtain true brightness temperature. 4. Data Resolution: Spatial resolution: 25 km, 12.5 km (SSM/I 85 GHz); Time resolution: day by day, from 1978 to 2007. 5. The Spatial Coverage: Longitude: 60°-140° east longitude; Latitude: 15°-55° north latitude. 6. Data Reading: Each group of data includes remote sensing image data files, .JPG image files and .met auxiliary information files. The JPG files can be opened with Windows image and fax viewers. The .met auxiliary information files can be opened with notepad, and the remote sensing image data files can be opened in ENVI and ERDAS software.
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This data set is the land use data of the key areas of Qilian mountain in 2018, spatial resolution 2m. This data set is based on the data of climate, altitude, topography, and land cover type of the Qilian mountain. Through the high-resolution remote sensing images to interprets the surface cover types. For the land types that cannot be reflected by the images, collect relevant data in the field, check and correct the land use types. At the same time, the maps and attribute information are uniformly entered and edited to form land use data in the Qilian Mountain area in 2018.
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This dataset contains the flux measurements from the Yakou station eddy covariance system (EC) in the upper stream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (100.2421° E, 38.0142° N) was located in the Qilian County in Qinghai Province. The elevation is 4148 m. The EC was installed at a height of 3.2 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 3% 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. The power loss occurs occasionally at this site. Data during May 24 to June 21, 2018 were missing due to the insufficient pow supply. 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 (z/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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This data set includes daily values of temperature, pressure, relative humidity, wind speed, wind direction, precipitation, radiation, water vapour pressure and other elements obtained from the Integrated Observation and Research Station of the Westerly Environment in Muztagh Ata from 18 May 2003 to 31 December 2016. The data are obtained by an automatic meteorological station (Vaisala) that recorded one measurement every 30 minutes. The data set was processed as a continuous time series after the original data were quality controlled. This data set satisfies the accuracy requirements of the meteorological observations of the National Weather Service and the World Meteorological Organization (WMO), and the systematic errors caused by the tracking data and sensor failure have been eliminated. The data set has mainly been applied in the fields of glaciology, climatology, environmental change research, cold zone hydrological process research and frozen soil science. Furthermore, this data set is mainly used by professionals engaged in scientific research and training in atmospheric physics, atmospheric environment, climate, glaciers, frozen soil and other disciplines.
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The data set records the Chinese name, English name and the affiliation between the districts and counties 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. This data set contains two tables: Table 1: The table of administrative divisions in Tibet contains 5 fields. Field 1: Regions Interpretation: Chinese names of the regions Field 2: English names of the regions Interpretation: English names of the regions Field 3: Districts and counties Interpretation: Chinese names of the districts and counties Field 4: English names of the districts and counties Interpretation: English names of the districts and counties Field 5: Land area Unit: square kilometers Table 2: The table of division changes of each county contains 5 fields. Field 1: Districts and counties Field 2: Year Field 3: Area Unit: square kilometers Field 4: Number of townships Field 5: Number of Village Committees
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The data set includes data of land and natural resources in Qinghai from 1984 to 2012. The data were derived from the Qinghai Society and Economics Statistical Yearbook and the Qinghai Statistical Yearbook. P.S: The land use data have not been updated in the yearbook since 2008. The 2008 data have been cited; therefore. The accuracy of the data is consistent with that of the statistical yearbook. There are two tables, one for natural resources data of every year, and the other is for land use data in different regions. “The land and natural resources in Qinghai” table contains the following information: Year, land, total land area, mountain, basin, river valley, Gobi desert, hilly land; cultivated land area, irrigated land; total grassland area, usable grassland, winter and spring grassland, summer and autumn grassland; forest area, forest coverage ratio, sparse forestland, shrub land, wood stocks; annual total surface runoff, Yellow River Basin, Yangtze River Basin, hydraulic theoretical reserves, installed capacity, annual power generation; coal reserves, iron ore reserves, asbestos reserves, pool salt, magnesium salt, potassium salt, boron, gold ore, lead ore, zinc ore, antimony ore, and limestone for cement. The “Land use in different regions” table includes the following information for each prefecture from 2003 to 2012: Year, region name, total area, cultivated land, garden land, forestland, grassplot, residential land use and industrial and mining land use, land for transportation, land for water conservancy facilities, and unused land.
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The data set includes the estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on GIMMS3g version 1.0, the latest version of the GIMMS NDVI data set. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 1982 to 2015, and the spatial resolution is 8 km.
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In the mid-latitude region of Asia, the southeastern region is humid and affected by monsoon circulation (thus, it is referred to as the monsoon region), and the inland region is arid and controlled by the other circulation patterns (these areas include the cold and arid regions in the northern Tibetan Plateau, referred to as the westerly region). Based on the generalization of the climate change records published in recent years, the westerly region was humid in the mid-late Holocene, which was significantly different from the pattern of the Asian monsoon in the early-middle Holocene. In the past few millennia, the westerly region was arid during the Medieval Warm Period but relatively humid during the Little Ice Age. In contrast, the oxygen isotope records derived from a stalagmite in the Wanxiang Karst Cave showed that the monsoon precipitation was high in the Medieval Warm Period and low during the Little Ice Age. In the last century, especially in the last 50 years, the humidity of the arid regions in the northwest has increased, while the eastern areas of northwestern and northern China affected by the monsoon have become more arid. Moreover, in the northern and southern parts of the Tibetan Plateau, which are affected by the westerlies and the monsoon, respectively, the precipitation changes on the interdecadal and century scales have also shown an inverse phase. Based on these findings, we propose that the control zone of the westerly belt in central Asia has different humidity (precipitation) variation patterns than the monsoon region on every time scale (from millennial to interdecadal) in the modern interglacial period. The integrated research project on Holocene climate change in the arid and semi-arid regions of western China was a major research component of the project Environmental and Ecological Science for West China, which was funded by the National Natural Science Foundation of China. The leading executive of the project was Professor Fahu Chen from Lanzhou University. The project ran from January 2006 to December 2009. The data collected by the project include the following: 1. The integrate humidity data over the Holocene in the arid regions of Central-East Asia and 12 lakes (11000-0 cal yr BP): including Lake Van, Aral Sea, Issyk-Kul, Ulunguhai Lake, Bosten Lake, Barkol Lake, Bayan Nuur, Telmen Lake, Hovsgol Nuur, Juyan Lake, Gun Nuur and Hulun Nuur. 2. The integrated humidity data over the past millennium in the arid regions of Central-East Asia and at five research sites (1000-2000): including Aral Sea, Guliya, Bosten Lake, Sugan Lake, and the Badain Juran desert. Data format: excel table.
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The Tibetan Plateau has an average altitude of over 4000 m and is the region with the highest altitude and the largest snow cover in the middle and low latitudes of the Northern Hemisphere regions. Snow cover is the most important underlying surface of the seasonal changes on the Tibetan Plateau and an important composing element of ecological environment. Ice and snow melt water is an important water resource of the plateau and its downstream areas. At the same time, plateau snow, as an important land-surface forcing factor, is closely related to disastrous weather (such as droughts and floods) in East Asia, the South Asian monsoon and in the middle and lower reaches of the Yangtze River. It is an important indicator of short-term climate prediction and one of the most sensitive responses to global climate change. The snow depth refers to the vertical depth from the surface of the snow to the ground. It is an important parameter for snow characteristics and one of the conventional meteorological observation elements. It is the key parameter of snow water equivalent estimation, climate effect studies of snow cover, the basin water balance, the simulation and monitoring of snow-melt, and snow disaster evaluation and grading. In this data set, the Tibetan Plateau boundary was determined by adopting the natural topography as the leading factor and by comprehensive consideration of the principles of altitude, plateau and mountain integrity. The main part of the plateau is in the Tibetan Autonomous Region and Qinghai Province, with an area of 2.572 million square kilometers, accounting for 26.8% of the total land area of China. The snow depth observation data are the monthly maximum snow depth data after quality detection and quality control. There are 102 meteorological stations in the study area, most of which were built during the 1950s to 1970s. The data for some months or years for sites existing during this period were missing, and the complete observational records from 1961 to 2013 were adopted. The temporal resolution is daily, the spatial coverage is the Tibetan Plateau, and all the data were quality controlled. Accurate and detailed plateau snow depth data are of great significance for the diagnosis of climate change, the evolution of the Asian monsoon and the management of regional snow-melt water resources.
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This data set is the concentrations of atmospheric and water POPs in Nam Co, including time series of gas phase OCP, PCBs, PAHs and particulate PAHs in the atmosphere; dissolve and particulate POPs in water. An active air sampler (AAS) was deployed on the roof of Nam Co Monitoring and Research Station for Multisphere Interactions (NCMORS) and the air monitoring was conducted for two consecutive years from September 2012 to September 2014. The flow rate of AAS was 60 L min-1 and the air samples were collected every 2 weeks with a volume of approximately 600 m3 for each sample. The air stream passes first through glass fiber filters (GFFs 0.7 μm, Whatman) to collect the total suspended particulates (TSP) and then through polyurethane foam (PUF, 7.5×6 cm diameter) to retain the POPs in gas phase. Fifteen sites around the Nam Co Lake (surface lake water, 0–1 m depth) were selected to obtain the spatial distribution of POPs in lake water. The water samples (200 L) were filtered with GFFs to obtain the total suspended particulate matter (SPM), and then pumped through an XAD-2 resin column to collect the dissolved phase compounds. All the samples were analyzed at Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Chinese Academy of Sciences. The samples were Soxhlet-extracted, purified on an aluminium/silica column (i.d. 8 mm), a gel permeation chromatography (GPC) column subsequently, and were detected on a gas chromatograph with an ion-trap mass spectrometer (GC-MS, Finnigan Trace GC/PolarisQ) operating under MS–MS mode. A CP-Sil 8CB capillary column (50 m ×0.25 mm, film thickness 0.25 μm) was used for OCPs and PCBs and a DB-5MS column (60 m ×0.25mm, film thickness 0.25 μm) was used for PAHs. Field blanks and procedural blanks were prepared. The recoveries ranged from 64% to 112% for OCPs, and 65% to 92% forPAHs. The reported concentrations were not corrected for recoveries.
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This is the data set of typical glacier changes on the Tibetan Plateau and its surrounding areas, which includes the Qiangyong Glacier near Yamdrog Yumtso, the Palong Glacier in the Palongzangbu River Basin, the Xiaodongkemadi Glacier on Tanggula Mountain in the central Tibetan Plateau, the No. 2 Anglong Glacier in the Ngari Prefecture in the western Tibetan Plateau, the Aerqieteke Glacier in the Muztagata region, the No. 15 Glacier, the Qiaodumake Glacier, and the Qiyi Glacier in the Qilian Mountains on the northeastern Tibetan Plateau. It can be used to study the response of typical glaciers in typical areas of the plateau to climate change. On the ice surface of a typical glacier in a typical area, a steam drill is used to set a length rod. The height of the rod is measured at a fixed time every year and combined with snow pit observations to observe the glacier mass balance. Marks are set on the ground near the terminus of the glacier, and the distance between the marker and the terminus of the glacier is measured to observe changes in the position of the terminus of the glacier. Among the glaciers, there are terminus change data for the Qiaodumake Glacier and No. 94 Palong Glacier. In the data set processing method, a continuous sequence of time and space is formed after the quality control of the original data. It conforms to the accuracy of conventional glacier monitoring and research in China and the world, and it meets the requirements of the comparative study of glacier changes and related climate change records.
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The data set mainly recorded the series data of GDP, gross industrial output, and gross agricultural output in major cities and counties of the Tibetan Plateau from 1970 to 2006. It is used to study social and economic changes on the Tibetan Plateau. The table has ten fields. Field 1: Year Interpretation: Year of the data Field 2: Province Interpretation: The province from which the data were obtained Field 3: City/Prefecture Interpretation: The city or prefecture from which the data were obtained Field 4: County Interpretation: The name of the county Field 5: GDP (10,000 yuan) Interpretation: Gross domestic product Field 6: Gross Industrial and Agricultural Output (10,000 Yuan) Interpretation: Gross Industrial and Agricultural Output Field 7: Gross Agricultural Output (10,000 Yuan) Interpretation: Gross Agricultural Output Field 8: Gross Industrial Output (10,000 Yuan) Interpretation: Gross Industrial Output Field 9: Data Sources Interpretation: Source of Data Extraction Field 10: Remarks Interpretation: Calculation method of gross out value and description of constant prices The data comes from the statistical yearbook and county annals. Some are listed as follows.  Gansu Yearbook Editorial Committee. Gansu Yearbook [J]. Beijing: China Statistics Press, 1984, 1988-2009  Statistical Bureau of Yunnan Province. Yunnan Statistical Yearbook [J]. Beijing: China Statistics Press, 1988-2009  Statistical Bureau of Sichuan Province, Sichuan Survey Team. Sichuan Statistical Yearbook [J]. Beijing: China Statistics Press, 1987-1991, 1996-2009  Statistical Bureau of Xinjiang Uighur Autonomous Region . Xinjiang Statistical Yearbook [J]. Beijing: China Statistics Press, 1989-1996, 1998-2009  Statistical Bureau of Tibetan Autonomous Region. Tibet Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-2009  Statistical Bureau of Qinghai Province. Qinghai Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-1994, 1996-2008.  County Annals Editorial Committee of Huzhu Tu Autonomous County. County Annals of Huzhu Tu Autonomous County [J]. Qinghai: Qinghai People's Publishing House, 1993  Haiyan County Annals Editorial Committee. Haiyan County Annals[J]. Gansu: Gansu Cultural Publishing House, 1994  Menyuan County Annals Editorial Committee. Menyuan County Annals[J]. Gansu: Gansu People's Publishing House, 1993  Guinan County Annals Editorial Committee. Guinan County Annals [J]. Shanxi: Shanxi People's Publishing House, 1996  Guide County Annals Editorial Committee. Guide County Annals[J]. Shanxi: Shanxi People's Publishing House, 1995  Jianzha County Annals Editorial Committee. Jianzha County Annals [J]. Gansu: Gansu People's Publishing House, 2003  Dari County Annals Editorial Committee. Dari County Annals [J]. Shanxi: Shanxi People's Publishing House, 1993  Golmud City Annals Editorial Committee. Golmud City Annals [J]. Beijing: Fangzhi Publishing House, 2005  Delingha City Annals Editorial Committee. Delingha City Annals [J]. Beijing: Fangzhi Publishing House, 2004  Tianjun County Annals Editorial Committee. Tianjun County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995  Naidong County Annals Editorial Committee. Naidong County Annals [J]. Beijing: China Tibetology Press, 2006  Gulang County Annals Editorial Committee. Gulang County Annals [J]. Gansu: Gansu People's Publishing House, 1996  County Annals Editorial Committee of Akesai Kazak Autonomous County. County Annals of Akesai Kazakh Autonomous County [J]. Gansu: Gansu People's Publishing House, 1993  Minxian County Annals Editorial Committee. Minxian County Annals [J]. Gansu: Gansu People's Publishing House, 1995  Dangchang County Annals Editorial Committee. Dangchang County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995  Dangchang County Annals Editorial Committee. Dangchang County Annals(Sequel) (1985-2005) [J]. Gansu: Gansu Cultural Publishing House, 2006  Wenxian County Annals Editorial Committee. Wenxian County Annals[J]. Gansu: Gansu Cultural Publishing House, 1997  Kangle County Annals Editorial Committee. Kangle County Annals [J]. 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The meteorological elements distribution map of the plateau, which is based on the data from the Tibetan Plateau National Weather Station, was generated by PRISM model interpolation. It includes temperature and precipitation. Monthly average temperature distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): t1960-90_1.e00，t1960-90_2.e00，t1960-90_3.e00，t1960-90_4.e00，t1960-90_5.e00， t1960-90_6.e00，t1960-90_7.e00，t1960-90_8.e00，t1960-90_9.e00，t1960-90_10.e00， t1960-90_11.e00，t1960-90_12.e00 Monthly average temperature distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): t1991-20_1.e00，t1991-20_2.e00，t1991-20_3.e00，t1991-20_4.e00，t1991-20_5.e00， t1991-20_6.e00，t1991-20_7.e00，t1991-20_8.e00，t1991-20_9.e00，t1991-20_10.e00， t1991-20_11.e00，t1991-20_12.e00， Precipitation distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): p1960-90_1.e00，p1960-90_2.e00，p1960-90_3.e00，p1960-90_4.e00，p1960-90_5.e00， p1960-90_6.e00，p1960-90_7.e00，p1960-90_8.e00，p1960-90_9.e00，p1960-90_10.e00， p1960-90_11.e00，p1960-90_12.e00 Precipitation distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): p1991-20_1.e00，p1991-20_2.e00，p1991-20_3.e00，p1991-20_4.e00，p1991-20_5.e00， p1991-20_6.e00，p1991-20_7.e00，p1991-20_8.e00，p1991-20_9.e00，p1991-20_10.e00， p1991-20_11.e00，p1991-20_12.e00， The temporal coverage of the data is from 1961 to 1990 and from 1991 to 2020. The spatial coverage of the data is 73°~104.95° east longitude, 26.5°~44.95° north latitude, and the spatial resolution is 0.05 degrees×0.05 degrees (longitude×latitude), and it uses the geodetic coordinate projection. Name interpretation: Monthly average temperature: The average value of daily average temperature in a month. Monthly precipitation: The total precipitation in a month. Dimensions: The file format of the data is E00, and the DN value is the average value of monthly average temperature (×0.01°C) and the average monthly precipitation (×0.01 mm) from January to December. Data type: integer Data accuracy: 0.05 degrees × 0.05 degrees (longitude × latitude). The original sources of these data are two data sets of 1) monthly mean temperature and monthly precipitation observation data from 128 stations on the Tibetan Plateau and the surrounding areas from the establishing times of the stations to 2000 and 2) HadRM3 regional climate scenario simulation data of 50×50 km grids on the Tibetan Plateau, that is, the monthly average temperature and monthly precipitation simulation values from 1991 to 2020. From 1961 to 1990, the PRISM (Parameter elevation Regressions on Independent Slopes Model) interpolation method was used to generate grid data, and the interpolation model was adjusted and verified based on the site data. From 1991 to 2020, the regional climate scenario simulation data were downscaled to generate grid data by the terrain trend surface interpolation method. Part of the source data came from the results of the GCM model simulation; the GCM model used the Hadley Centre climate model HadCM2-SUL. a) Mitchell JFB, Johns TC, Gregory JM, Tett SFB (1995) Climate response to increasing levels of greenhouse gases and sulphate aerosols. Nature, 376, 501-504. b) Johns TC, Carnell RE, Crossley JF et al. (1997) The second Hadley Centre coupled ocean-atmosphere GCM: model description, spinup and validation. Climate Dynamics, 13, 103-134. The spatial interpolation of meteorological data adopted the PRISM (Parameter-elevation Regressions on Independent Slopes Model) method: Daly, C., R.P. Neilson, and D.L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140~158. Due to the difficult observational conditions in the plateau area and the lack of basic research data, there were deletions of meteorological data in some areas. After adjustment and verification, the accuracy of the data was only good enough to be used as a reference for macroscale climate research. The average relative error rate of the monthly average temperature distribution of the Tibetan Plateau from 1961 to 1990 was 8.9%, and that from 1991 to 2020 was 9.7%. The average relative error rate of precipitation data on the Tibetan Plateau from 1961 to 1990 was 20.9%, and that from 1991 to 2020 was 22.7%. The area of missing data was interpolated, and the values of obvious errors were corrected.
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This dataset contains the monthly hydrological characteristic values of the Jiangzi hydrological station on the Nianchu River in the Yarlung Zangbo River for many years (e.g., coefficient of variation, deviation factor, and nonuniform coefficient). It can be used to study the hydrological characteristics of the Yarlung Zangbo River. The original data are the national hydrological station data, and the quality requirements are the same as the national standards. This data sheet has four fields. Field 1: Time, month Field 2: Coefficient of variation Field 3: Deviation factor Field 4: Nonuniform coefficient
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The data are Intensity of water resources utilization in the area along the Belt and Road in 2015. This data reflects the overall situation and water use of water resources in a region. Water is an important factor restricting economic and social development, especially in areas with water shortage. Water utilization is related to people's survival and development. The data comes from the food and agriculture organization of the United Nations. The data set describe the total water consumption, development utilization rate and water proportion of each part of the world. It directly reflects the water resources content and demand of each region, and indirectly reflects the regional economic development. The utilization degree of water resources can show the development focus of the country and region, and the utilization rate of development also reflects the degree of social development to a certain extent. "the Belt and Road" regions are closely linked today, the situation of water resources measures the economic development status, but also reflects the economic constraints.
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