Brief Introduction: Pan-third-polar environmental change and green silk road construction

Number of Datasets: 490

  • Gross national income of countries along One Belt One Road (1960-2017)

    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

  • Natural environment and social economic emergy accounting database in typical countries along One Belt One Road (2008-2014)

    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 0 View Details

  • Aerosol optical property dataset of the Tibetan Plateau by ground-based observation (2009-2016)

    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 2 View Details

  • Railway, highway and civil aviation mileage Statistics of Qinghai (1952-2016)

    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

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  • Population, urbanization, GDP and industrial structure forecast scenario data of the Yerqiang River Basin (Version 1.0) (2010-2050)

    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.

    2019-09-15 0 1 View Details

  • The dataset of sunshine hours of main areas in Qinghai Province (1988-2016)

    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.

    2019-09-15 0 4 View Details

  • Basic data on natural resources in the Tibetan Autonomous Region (1988-1994)

    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

    2019-09-15 0 1 View Details

  • Energy Pipelines map of Russia (Former Soviet Union) - central Asia - China

    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.

    2019-09-15 0 0 View Details

  • Passive microwave SSM/I brightness temperature dataset for China (1987-2007)

    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.

    2019-09-15 0 1 View Details

  • The Tarim River Basin boundary

    Extract by DEM and Hydrological Station

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