Shule River Basin is one of the three inland river basins in Hexi corridor. In recent years, with the obvious change of climate and the aggravation of human activities, the shortage of water resources and the problem of ecological environment in Shule River Basin have become increasingly prominent. It is of great significance to study the runoff change of Shule River Basin in the future climate situation for making rational water resources planning and ecological environment protection. The Shule River basin boundary is cut from "China's 1:100000 desert sand data set". Taking the 2000 TM image as the data source, it interprets, extracts, revises, and uses remote sensing and geographic information system technology to combine with the 1:100000 scale mapping requirements to carry out thematic mapping of desert, sand and gravel gobi. Data attribute table: Area (area), perimeter (perimeter), ash_ (sequence code), class (desert code), ash_id (desert code). The desert code is as follows: mobile sand 2341010, semi mobile sand 2341020, semi fixed sand 2341030, Gobi 2342000, salt alkali land 2343000. Collect and sort out the basic, meteorological, topographical and geomorphic data of Shule River Basin, and provide data support for the management of Shule River Basin.
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
DAI Yongjiu SHANGGUAN Wei
The data includes the runoff components of the main stream and four tributaries in the source area of the Yellow River. In 2014-2016, spring, summer and winter, based on the measurement of radon and tritium isotopic contents of river water samples from several permafrost regions in the source area of the Yellow River, and according to the mass conservation model and isotope balance model of river water flow, the runoff component analysis of river flow was carried out, and the proportion of groundwater supply and underground ice melt water in river runoff was preliminarily divided. The quality of the data calculated by the model is good, and the relative error is less than 20%. The data can provide help for the parameter calibration of future hydrological model and the simulation of hydrological runoff process.
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.
International Centre for Integrated Mountain Development (ICIMOD)
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.
International Centre for Integrated Mountain Development (ICIMOD)
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.
EROS DATA CENTER WU Lizong
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. +-).
WU Lizong TAN Ming ZHANG Hucai LI Tieying
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
WU Lizong Tucker, C.J., J.E.Pinzon, M.E.Brown
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.
Vegetation index (NDVI) can be used to detect vegetation growth state, vegetation coverage and eliminate some radiation errors. The data set is the NDVI product data synthesized by MODIS in 500 meters and 16 days in the black river basin from 2000 to 2010 after graphic processing, and the no-value zone is -32768.The coordinate system is the longitude and latitude projection, and the spatial range is 96.5E -- 102.5E, 37.5N -- 43N.The data format is GEOTIFF.
1. Data Overview: This data includes groundwater buried depth observation datal from 4 observation points in Ganzhou District of Zhangye Basin in the middle reaches of the Heihe River (The nursery garden of Xindun Town, Suijia temple of Xindun Town, the Wuzhi management house of Dangzhai Town, Shangqin Station of Shangqin Town). The data was obtained from July 12, 2012 to July 5,2014. 2. Data Content: The HOBO water level sensor is installed in the underground well, which is mainly used to monitor the dynamic change of groundwater level in Ganzhou District of Zhangye. The data contents are absolute air pressure (kPa), temperature (°C), and groundwater depth (m). The data was recorded hourly. 3. Time and Space Range: The geographical coordinates of the nursery garden well of Xindun Town (1559 m) : Longitude 100°20.8′E; Latitude: 38°54′N; The geographical coordinates of Suijia temple well of Xindun Town(1518 m) : Longitude: 100°23.9′E; Latitude: 38°54.1′N; The geographical coordinates of Wuzhi management house well of Dangzhai Town (1675 m): Longitude: 100°30.7′E; Latitude: 38°52.8′N; The geographical coordinates of Shangqin Station well of Shangqin Town(1480 m): Longitude: 100°31.7′E; Latitude: 38°54.5′N. Note: The number in brackets is elevation.
Water demand in the middle and lower reaches of Heihe River (mainly including water demand for living, livestock, industry, agriculture, tertiary industry, artificial forest and grass ecology in the middle reaches of Heihe River in current year, 2020 and 2030; water demand for living, industry, tertiary industry and ecology in Ejina Banner in the middle reaches of Heihe River in current year, 2020 and 2030)
1. The data set is the soil water content data set of the upper reaches of Heihe River Basin, and the data is the measured data of location points from 2013 to 2014. 2. The infiltration data is measured with ech2o. Including 5 layers of soil moisture content and soil temperature 3. Some instruments lack of data due to insufficient battery life, broken roads, stolen instruments and other reasons
1、 Data Description: the data includes the river flow data at the outlet of No.2 catchment of hulugou small watershed from May 4, 2016 to September 3, 2016. 2、 Sampling location: the coordinates of river flow monitoring section are located at the outlet of No. 2 catchment near the red wall, with the coordinates of 99 ° 52 ′ 58.40 ″ E and 38 ° 14 ′ 36.85 ″ n.
HU Yalu MA Rui
The North American Multi-Model Ensemble (NMME) Forecast is a multi-modal ensemble seasonal forecasting system jointly published by the US Model Center (including NOAA/NCEP, NOAA/GFDL, IRI, NCAR, and NASA) and the Canadian Meteorological Centre. The data include retrieval data from 1982 to 2010 and real-time weather forecast data from 2011 to the present. The forecasting system covers the whole world with a temporal resolution of one month and a horizontal spatial resolution of 1°. NMME has nine climate forecasting models, and each contains 6-28 ensemble members, with a forecasting period of 9-12 months. The name, source, ensemble members, and forecasting period of the climate models are as follows: 1) CMC1-CanCM3, Environment Canada, 10 models, 12 months 2) CMC2-CanCM4, Environment Canada, 10 models, 12 months 3) COLA-RSMAS-CCSM3, National Center for Atmospheric Research, 6 models, 12 months 4) COLA-RSMAS-CCSM34, National Center for Atmospheric Research, 10 models, 12 months 5) GFDL-CM2p1-aer04, NOAA Geophysical Fluid Dynamics Laboratory, 10 models, 12 months 6) GFDL-CM2p5-FLOR-A06, NOAA Geophysical Fluid Dynamics Laboratory, 12 models, 12 months 7) GFDL-CM2p5-FLOR-B01, NOAA Geophysical Fluid Dynamics Laboratory, 12 models, 12 months 8) NASA-GMAO-062012, NASA Global Modeling and Assimilation Office, 12 models, 9 months 9) NCEP-CFSv2, NOAA National Centers for Environmental Prediction, 24/28 models, 10 months With the exception of the CFSv2 model (which includes only precipitation and average temperature), the variables of other models include precipitation, average temperature, maximum temperature, and minimum temperature. Each model ensemble member stores one NC file every month for each variable. The meteorological elements, variable names, units, and physical meanings of each variable are as follows: 1) Average temperature, tref, K, monthly average near-surface (2-m) average air temperature 2) Maximum temperature, tmax, K, monthly average near-surface (2-m) maximum air temperature 3) Minimum temperature, tmin, K, monthly average near-surface (2-m) minimum air temperature 4) Precipitation, prec, mm/day, monthly average precipitation. The dataset has been widely applied in climate forecasting, hydrological forecasting, and quantitatively estimating model forecasting uncertainty.