The dataset is the vector map of the administrative boundary of the Tarim River Basin, with a scale of 250,000 and projection: latitude and longitude. The data includes spatial data and attribute data, mainly the name and administrative code of the county boundary of the Tarim River Basin.

The data is the boundary distribution map of the Tarim River Basin with a scale of 250,000. Projection: latitude and longitude. This data include spatial data and attribute data of the Tarim River Basin sub-watershed. The attribute data fields are: Area (area), Perimeter (perimeter), WRRNM (watershed name), WRRCD ( watershed coding)

The source data for this dataset is derived from world soil maps and multiple regional and national soil databases, including soil attributes and soil maps. We have adopted a unified data structure and data processing process to fuse diverse data. We then used the soil type connection method and the soil variable line connection method to obtain the spatial distribution of soil properties. To aggregate these data, we currently use the area weighting method. The raw data has a resolution of 30 seconds, and aggregated data with a 5-minute resolution (about 10km) is provided here. There are eight vertical layers with a maximum depth of 2.3 meters (ie 0- 0.045, 0.045- 0.091, 0.091- 0.166, 0.166- 0.289, 0.289- 0.493, 0.493- 0.829, 0.829- 1.383 and 1.383- 2.296 m). 1. Data characteristics: Projection: WGS_1984 Coverage: Global Resolution: 0.083333 degrees (about 10 kilometers) Data format: netCDF 2. The data set contains 11 items of general soil information and 34 properties of soil. (1) The general information of the soil is as follows, the file general.zip: No. Description Units 1 additional property 2 available water capacity 3 drainage class 4 impermeable layer 5 nonsoil class 6 phase1 7 phase2 8 reference soil depth cm 9 obstacle to roots 10 soil water regime 11 topsoil texture (2) The 34 soil properties are as follows, files 1-9.zip, 10-18.zip, 19-26.zip, 27-34.zip Soil organic carbon density: SOCD5min.zip: No. Attrubute units Scale factor 1 total carbon% of weight 0.01 2 organic carbon% of weight 0.01 3 total N% of weight 0.01 4 total S% of weight 0.01 5 CaCO3% of weight 0.01 6 gypsum% of weight 0.01 7 pH (H2O) 0.1 8 pH (KCl) 0.1 9 pH (CaCl2) 0.1 10 Electrical conductivity ds / m 0.01 11 Exchangeable calcium cmol / kg 0.01 12 Exchangeable magnesium cmol / kg 0.01 13 Exchangeable sodium cmol / kg 0.01 14 Exchangeable potassium cmol / kg 0.01 15 Exchangeable aluminum cmol / kg 0.01 16 Exchangeable acidity cmol / kg 0.01 17 Cation exchange capacity cmol / kg 0.01 18 Base saturation% 19 Sand content% of weight 20 Silt content% of weight 21 Clay content% of weight 22 Gravel content% of volume 23 Bulk density g / cm3 0.01 24 Volumetric water content at -10 kPa% of volume 25 Volumetric water content at -33 kPa% of volume 26 Volumetric water content at -1500 kPa% of volume 27 The amount of phosphorous using the Bray1 method ppm of weight 0.01 28 The amount of phosphorous by Olsen method ppm of weight 0.01 29 Phosphorous retention by New Zealand method% of weight 0.01 30 The amount of water soluble phosphorous ppm of weight 0.0001 31 The amount of phosphorous by Mehlich method ppm of weight 0.01 32 exchangeable sodium percentage% of weight 0.01 33 Total phosphorus% of weight 0.0001 34 Total potassium% of weight 0.01

The frozen soil type map of Kazakhstan (1:10,000,000) includes three .shp vector layers: 1, Polyline ranges.shp, indicating the extent of frozen soil; 2, Polygon kaz_perm.shp, frozen soil; 3, An attribute description Word file. The kaz_perm attribute table includes four fields: ID, REGION, SUBREGION, M_RANGE. Comparison of the main attributes: First, Area I. Altai-TienShan Second, Region: High mountains I.1. Altai, I.2. Saur-Tarbagatai, I.3.Dzhungarskyi, I.4. Northern Tien Shan, I.5. Western Tien Shan Intermountain depressions I.6. Zaysanskyi, I.7. Alakulskyi, I.8. Iliyskyi II. Western Siberian Second, Region: Planes II.1. Northern Kazakhstanskyi V. Western Kazakhstanskaya III. Kazakh small hills area IV. Turanskaya: IV.1. Turgayskyi IV.2. Near Aaralskyi IV.3. Chuysko-Syrdaryinskyi IV.4. South-Balkhashskyi V. Western Kazakhstanskaya: V.1. Mugodzhar-Uralskyi V.2. Near Caspian V.3. manghyshlak-Ustyrtskyi Third, Sub-region: I.1.1. Western Altai I.1.2. South Altai I.1.3. Kalbinskyi I.2.1. Tarbagatayskyi I.2.2. Saurskyi I.3.1. Nortern Dzhungarskyi I.3.2. Western Dzhungarskyi I.3.3. Southern Dzhungarskyi I.4.1. Kirgizskyi Alatau I.4.2. Zailiyskyi-Kungeyskyi I.4.3. Ketmenskyi I.4.4. Bayankolskyi I.5.1. Karatauskyi I.5.2. Talaso-Ugamskyi The layer projection information is as follows: GEOGCS["GCS_WGS_1984", DATUM["WGS_1984", SPHEROID["WGS_1984", 6378137.0, 298.257223563]], PRIMEM["Greenwich", 0.0], UNIT["Degree",0.0174532925199433]] Different regions feature different frozen soil attributes, and the specific attribute information can be found in the Word file.

This glacier inventory is supported by the International Centre for Integrated Mountain Development (ICIMOD) and the United Nations Environment Programme/Regional Resource Centre, Asia and The Pacific (UNEP/RRC-AP). 1.The glacier inventory incorporates topographic map data, and reflects the status of glaciers in the region in 2000. 2.The spatial coverage of the glacier inventory includes the following: Pa Chu Sub-basin,Mo Chu Sub-basin,Thim Chu Sub-basin,Pho Chu Sub-basin,Mangde Chu Sub-basin, Chamkhar Chu Sub-basin,Kuri Chu Sub-basin,Dangme Chu Sub-basin,Northern Basin, etc. 3.The glacier inventory includes the following data fields: glacier location, glacier code, glacier name, glacier area, glacier length, glacier thickness, glacier stocks, glacier type, glacier orientation, etc. 4.Data projection: Projection: Polyconic Ellipsoid: Everest (India 1956) Datum: Indian (India, Nepal) False easting: 2,743,196.4 False northing: 914,398.80 Central meridian: 90°0'00'' E Central parallel: 26°0'00' N Scale factor: 0.998786 For a detailed description of the data, please refer to the data file and report.

A map of the frozen soil distribution in the Republic of Mongolia is digitized from the National Atlas of the Republic of Mongolia (Sodnom and Yanshin, 1990). This data set describes the distribution and general properties of permafrost, seasonally frozen soil, and low-temperature phenomena in the Republic of Mongolia. Two plates were specifically digitized. The first plate, with a scale of 1:12,000,000, describes four general frozen soil regions: (1) continuous and discontinuous permafrost; (2) island-like and sparse island-like permafrost; (3) sporadic permafrost; and (4) seasonally frozen soil. The second plate, with a scale of 1:4,500,000, describes 14 different terrain types. The terrain types are divided based on elevation, annual average temperature, permafrost thickness, melting depth, and freezing depth of seasonally frozen soil. The locations of the six types of low-temperature phenomena in Mongolia are also included: pingos, ice cones, hot karst, detachment failures, solifluction, and cryoplatation processes. The data are provided in the ESRI shape file format and can be downloaded from the US Ice and Snow Data Center.

This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.

This glacial lake inventory receives joint support from the International Centre for Integrated Mountain Development (ICIMOD) and United Nations Environment Programme/Regional Resource Centre, Asia and the Pacific (UNEP/RRC-AP). 5. This glacial lake inventory referred to Landsat 4/5 (MSS and TM), SPOT(XS), IRS-1C/1D(LISS-III) and other remote sensing data. It reflects the current situation of glacial lakes with areas larger than 0.01 km2 in 2004. 6. Glacial Lake Inventory Coverage: Yamuna basin, Ravi basin, Chenab basin, Satluj River Basin and others. 7. The Glacial Lake Inventory includes glacial lake inventory, glacial lake type, glacial lake width, glacial lake orientation, glacial lake length from the glacier and other attributes. 8. Projection parameter: Projection: Albers Equal Area Conic Ellipsoid: WGS 84 Datum: WGS 1984 False easting: 0.0000000 False northing: 0.0000000 Central meridian: 82° 30’E Central parallel: 0° 0’ N Latitude of first parallel: 20° N Latitude of second parallel: 35° N For a detailed data description, please refer to the data file and report.

This dataset includes: remote sensing data _ETM around 2000 in Western China; Data attributes: Pixel Size: 15-meter panchromatic: Band 8                 30-meter: Bands 1-5 and Band 7                 60-meter: Bands 6H and 6L Resampling Method: Cubic Convolution (CC) Map Projection: UTM – WGS 84 Polar Stereographic for the continent of Antarctica. Image Orientation: Map (North Up) The data was downloaded from USGS: http://glovis.usgs.gov/ImgViewer/Java2ImgViewer.html?lat=38.3&lon=78.9&mission=LANDSAT&sensor=ETM. Part of the remote sensing images collected from various research projects. The folder contains ETM 8 band images (* .tif) and header files (* .met). The naming format of image files is row and column number _ETM image logo (7k, 7x, 7t), image acquisition time _ image 6 degree band number _ band number. The data also includes an image index map in shp format.

Natural changes and human impacts of typical karst environments in historical periods: stalagmite recording project is a major research program of "Environmental and Ecological Science in Western China" sponsored by the National Natural Science Foundation of China. The person in charge is Tan Ming, a researcher at the Institute of Geology and Geophysics, Chinese Academy of Sciences. The project runs from January 2002 to December 2009. The temperature data of Beijing hot months (May, June, July and August) in 2650 (665 B.C.-A.D. 1985) are the results of the project. The data are reconstructed according to the correlation between the annual thickness of stalagmites in Shihua Cave in Beijing and meteorological observation data. The temperature signals reflected by soil carbon dioxide and cave dripping are amplified by the soil-organic matter-carbon dioxide system and recorded by the annual sequence of stalagmites. Although the general trend of temperature has decreased in recent thousands of years, the reconstructed temperature reveals that the climate has experienced repeated rapid warming on a century scale. This result is related to other records in the northern hemisphere, indicating that there is a hemispheric influence on the periodic changes of temperature in the sub-millennium scale. The data contains a txt file with attribute fields such as yr.AD, layer number, original thickness (um), maximum error in um (+-), sedimentary trend, detrended thickness (um), reconstructed temperature, maximum error in degree C (+ -), temperature anomaly, temperature anomaly + error, temperature anomaly-error, maximum error in age (yr. +-).

The data used in this research was provided by the Pathfinder database of the EROS (Earth Resource Observation System) data center. The vegetation index NDVI was prepared by using the NOAA-AVHRR data source after radiation correction and geometric rough correction. Every day, each track image is processed with geometric fine correction, removal of bad lines, and removal of clouds, etc., and then NDVI calculation and synthesis. The daily NDVI calculation formula is: 1000 × (b2-b1) / (b2 + b1), where b1 and b2 are the first and second channels of AVHRR.          Parameter table of Pathfinder AVHRR Parameter / Variable Definition Unit Range NDVI Normalized Vegetation Index None (-1,1) CLAVR identification Cloudiness index from CLAVR algorithm None (0,30) QC identification Data quality identification None (0,16) Scanning angle Sensor angle Radian (-1.05, 1.05) Solar zenith angle Solar zenith angle per pixel Radian (0, 1.04) Relative zenith angle Relative zenith angle of the sensor Radian (-1.05, 1.05) Ch1 reflectance Reflectance of the first channel (0.58-0.68um) Percent (0,100) Ch2 reflectance Reflectivity of the second channel (0.72--1.10um) Percentage (0, 100) Ch3 brightness temperature Bright temperature value of the third channel (3.55-3.95um) Kelvin temperature scale (160, 340) Ch4 brightness temperature Brightness value of the fourth channel (10.3-11.3um) Kelvin temperature scale (160, 340) Ch5 brightness temperature Bright temperature value of the fifth channel (11.5-12.5um) Kelvin temperature scale (160, 340)     The data set includes data on NDVI in China's sub-regions from 1981 to June-September 2001, and data on tens of months in each of the years 1982, 1986, 1991, and 1996 (a total of 343 in 84 months, of which 1981 in June 1981). Data are missing in January and July 1st, and September 3rd 1994) Dataset attributes and format: This data set is stored in a year folder, which contains .HDR header files, .IMG files, and .JPG image files under the same file name. The data in the IMG is stored as integers. The naming rules are as follows: avhrrpf. *. Intfgl.yymmdd_geo where * represents ch1 or ch2 or ch4 or ch5 or ndvi, please refer to Table 1 for its specific meaning and range; yy represents the last two digits of the year; mm represents the month; dd represents the specific date. Data projection: Size is 963, 688 Coordinate System is: GEOGCS ["WGS 84",     DATUM ["WGS_1984",         SPHEROID ["WGS 84", 6378137,298.257223563,             AUTHORITY ["EPSG", "7030"]],         TOWGS84 [0,0,0,0,0,0,0],         AUTHORITY ["EPSG", "6326"]],     PRIMEM ["Greenwich", 0,         AUTHORITY ["EPSG", "8901"]],     UNIT ["degree", 0.0174532925199433,         AUTHORITY ["EPSG", "9108"]],     AUTHORITY ["EPSG", "4326"]] Origin = (70.035426000000001, 54.945585999999999) Pixel Size = (0.072727000000000, -0.072727000000000) Corner Coordinates: Upper Left (70.0354260, 54.9455860) (70d 2'7.53 "E, 54d56'44.11" N) Lower Left (70.0354260, 4.9094100) (70d 2'7.53 "E, 4d54'33.88" N) Upper Right (140.0715270, 54.9455860) (140d 4'17.50 "E, 54d56'44.11" N) Lower Right (140.0715270, 4.9094100) (140d 4'17.50 "E, 4d54'33.88" N) Center (105.0534765, 29.9274980) (105d 3'12.52 "E, 29d55'38.99" N) Band 1 Block = 963x1 Type = UInt16, ColorInterp = Undefined     Computed Min / Max = 1.000,55480.000

The social accounting matrix, also known as the national economy comprehensive matrix or the national economy circulation matrix, uses the matrix method to connect the various accounts of the national economy systematically, represents the statistical index system of the national economy accounting system, and reflects the circulation process of the national economy operation. It uses the matrix form to arrange the national accounts orderly according to the flow and stock, domestic and foreign. The data reflects the balanced value of social accounting matrix in Gaotai County.