Pan-third-polar environmental change and green silk road construction

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

Number of Datasets: 627

  • Basic dataset of Tibetan Plateau in Chinese cryospheric resources and environment information system

    Basic dataset of Tibetan Plateau in Chinese cryospheric resources and environment information system

    Chinese cryospheric resources and environment information system is a comprehensive information system for the management and analysis of Chinese cryospheric resources and environment data. The establishment of Chinese cryospheric resources and environment information system is to meet the needs of earth system science, to provide parameters and validation data for the development of response and feedback model of frozen soil, glacier and snow cover to global change under GIS framework; on the other hand, it is to systemically sort out and rescue valuable cryospheric data, to provide a scientific, efficient and safe management and division for it Analysis tools. The basic database of the Qinghai Tibet Plateau mainly takes the Tibetan Plateau as the research area, ranging from longitude 70 -- 105 ° east and latitude 20 -- 40 ° north, containing the following types of data: 1. Cryosphere data. Includes: Permafrost type (Frozengd), (Fromap); Snow depth distribution (Snowdpt) Quatgla (Quatgla) 2. Natural environment and resources. Includes: Terrain: elevation, elevation zoning, slope, slope direction (DEM); Hydrology: surface water (Stram_line), (Lake); Basic geology: Quatgeo, Hydrogeo; Surface properties: Vegetat; 4. Climate data: temperature, surface temperature, and precipitation. 3. Socio-economic resources (Stations) : distribution of meteorological Stations on the Tibetan Plateau and it surrounding areas. 4. Response model of plateau permafrost to global change (named "Fgmodel"): permafrost distribution data in 2009, 2049 and 2099 were projected. Please refer to the following documents (in Chinese): "Design of Chinese Cryospheric Resources and Environmental Information System.doc", "Datasheet of Chinese Cryospheric Resources and Environmental Information System.DOC", "Database of Tibetan Plateau.DOC" and "Database of Tibetan Plateau 2.DOC".

    2020-03-28 20302 14 View Details

  • Land cover of Tibetan Plateau (2008)

    Land cover of Tibetan Plateau (2008)

    The dataset is the land cover of Tibetan Plateau in 2008. The data format is a TIFF file, spatial resolution is 300 meters, including crop land, grassland, forest land, urban land, and so on. The dataset offers a geographic fundation for studying the interaction between urbanization and ecological reservation of Qing-Tibet Plateau. This land cover data is a product of CCI-LC project conducted by European Space Agency. The coordinate reference system of the dataset is a geographic coordinate system based on the World Geodetic System 84 reference ellipsoid. There are 22 major classes of land covers. The data were generated using multiple satellite data sources, including MERIS FR/RR, AVHRR, SPOT-VGT, PROBA-V. Validation analysis shows the overall accuracy of the dataset is more than 70%, but it varies with locations and land cover types.

    2020-03-27 299 10 View Details

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of A’rou superstation, 2018)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of A’rou superstation, 2018)

    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.

    2020-03-24 719 33 View Details

  • Long-term series of daily snow depth dataset in China (1979-2019)

    Long-term series of daily snow depth dataset in China (1979-2019)

    This data set is an upgraded version of the “Long-term series of daily snow depth dataset in China". This dataset provides daily data of snow depth distribution in China from January 1, 1979, to December 31, 2019, with a spatial resolution of 0.25 degrees. The original data used to derive the snow depth dataset are the daily passive microwave brightness temperature data (EASE-Grid) from SMMR (1979-1987), SSM/I (1987-2007) and SSMI/S (2008-2019) which were archived in the National Snow and Ice Data Center (NSIDC). Because the brightness temperatures come from different sensors, there is a certain system inconsistency among them. Therefore, before the derivation of snow depth, the inter-sensor calibration were performed to improve the temporal consistency of the brightness temperature data. Based on the calibrated brightness temperatures, the modified Chang algorithm developed by Dr. Tao Che, was used to retrieve daily snow depth. The algorithm details were introduced in the data specification document- “Long-term Sequence Data Set of China Snow Depth (1979-2019) Introduction. doc". The projection of the data set is latitude and longitude. The data of each day was stored in a file, and the naming convention of which is year + day; for example, 1990001 represents the first day of 1990, and 1990207 represents the 207th day of 1990. For a detailed data description, please refer to the data specification document.

    2020-03-19 17522 534 View Details

  • Dynamic downscaled daily 9 km precipitation and near-surface air temperature over the Pan Third Pole region (2000-2010)

    Dynamic downscaled daily 9 km precipitation and near-surface air temperature over the Pan Third Pole region (2000-2010)

    The data include daily precipitation (Precip) amount and daily mean near-surface air temperature (T2M) over the Pan Third Pole region. The data is downscaled by using the Weather Research and Forecasting (WRF) model (3.7.1). The boundary and initial condition come from the fifth-generation global reanalysis product by the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5. The seasonal cycle and summer mean of precipitation over Tibet is well reproduced in comparison to the in situ observations.

    2020-03-18 171 1 View Details

  • Archaeological site investigation and plant and animal resource utilization in the Tibet Plateau (Neolithic - Bronze Age)

    Archaeological site investigation and plant and animal resource utilization in the Tibet Plateau (Neolithic - Bronze Age)

    By archaeological investigation and excavation in Tibetan Plateau, we discovered 14 historic period sites, including Meinuo, Sariguo, Rongwaguo, Kaze, Jiha, Yarigei, Bami, Barongbadang, Qingtu, Labu ,Maisong Petroglyph, Gala, Yezere 1 and Yezere 4 . In this dataset, there are some basic informations about these sites, such as location, longitude, latitude, altitude, material culture and so on. On this Basis, we identified animal remains, plant macrofossil, selected some samples for radiocarbon dating and stable carbon and nitrogen isotopes. This dataset provide important basic data for understanding when and how prehistoric human lived in the Tibetan Plateau during the historic period.

    2020-03-17 26 0 View Details

  • Remote sensing products of thermal collapse in Heihe permafrost region of the Tibetan Plateau (2009-2018)

    Remote sensing products of thermal collapse in Heihe permafrost region of the Tibetan Plateau (2009-2018)

    Global warming and human activities have led to the degradation of permafrost and the collapse of permafrost, which have seriously affected the construction of permafrost projects and the ecological environment. Based on high-resolution satellite images, the permafrost of oboling in Heihe River Basin of Qinghai Tibet Plateau is taken as the research area, and the object-oriented classification technology of machine learning is used to extract the thermal collapse information in the research area. The results show that from 2009 to 2019, the number of thermal collapse increased from 12 to 16, and the total area increased from 14718.9 square meters to 28579.5 square meters, nearly twice. The combination of high spatial resolution remote sensing and object-oriented classification method has a broad application prospect in the monitoring of thermal thawing and collapse of frozen soil.

    2020-03-14 703 10 View Details

  • Dataset of plant distribution investigation in Three-River-Source National Park (2008-2017)

    Dataset of plant distribution investigation in Three-River-Source National Park (2008-2017)

    This data set is the plant collection and distribution site information of Three-River-Source National Park investigated by Northwest Plateau Biology Institute of Chinese Academy of Sciences. The data set covers the period from 2008 to 2017, and the survey covers theThree-River-Source National Park. The survey contents include information such as collection date, number, family, genus, species, survey date, collection place, collector, longitude, latitude, altitude, habitat, appraiser, etc. Three parks of the national park were investigated respectively. 88 species of vegetation belonging to 56 genera and 24 families were investigated in the Yangtze River Source Park, with 116 records in total. Vegetation of 110 species in 64 genera and 26 families was investigated in the Yellow River Source Park, with 159 records in total. The vegetation of 30 species in 22 genera and 12 families was investigated in Lancang River Source Park, with a total of 33 records.

    2020-03-13 690 9 View Details

  • Long-term series of daily snow depth in Euroasia (1980-2016)

    Long-term series of daily snow depth in Euroasia (1980-2016)

    The “long-term series of daily snow depth in Eurasia” was produced using the passive microwave remote sensing data. The temporal range is 1980~2016, and the coverage is the Eurasia continent. The spatial resolutions is 0.25° and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file includes a file header (projection mode) and a 720*332 snow depth matrix, and each snow depth represents a 0.25°*0.25° grid. For details of the data, please refer to data specification “Snow depth dataset of Eurasian (Version 1.0) (1980-2016).doc”

    2020-03-13 1093 31 View Details

  • Hourly meteorological forcing & land surface state dataset of Tibet Plateau with 10 km spatial resolution (2000-2010)

    Hourly meteorological forcing & land surface state dataset of Tibet Plateau with 10 km spatial resolution (2000-2010)

    The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.

    2020-03-12 242 0 View Details

  • River networks dataset at 1:250 000 in Three Rivers Source Region (2015)

    River networks dataset at 1:250 000 in Three Rivers Source Region (2015)

    This data comes from the National Catalogue Service for Geographic Information, which was provided to the public free of charge by the National Basic Geographic Information Center in November 2017. We spliced ​​and trimmed Three Rivers Source Region as a whole to facilitate its use in the study of Three Rivers Source Region. The current status of the data is 2015. This dataset is the Three Rivers Source Region 1: 250,000 water system data, including three layers of water system surface (HYDA), water system line (HYDL) and water system point (HYDP). The water system surface (HYDA) includes lakes, reservoirs, double-line rivers, and ditches; the water system line (HYDL) includes single-line rivers, ditches, and river structure lines; and the water system points (HYDP) include springs and wells.         HYDA attribute item name and definition: Attribute item Description Sample GB National standard classification code 210101 HYDC Water system name code KJ2103 NAME Name Heihe WQL Water quality Fresh PERIOD Seasonal months 7-9 TYPE Type Pass          HYDL attribute item name and definition: Attribute item Description Sample GB National standard classification code 210101 HYDC Water system name code KJ2103 NAME Name Heihe PERIOD Seasonal months 7-9          HYDP attribute item name and definition: Attribute item Description Sample GB National standard classification code 210101 NAME Name Unfreezing spring TYPE Type Fresh ANGLE Angle 75           Water system GB code and its meaning:  Attribute item Code Description GB 210101 Ground river 210200 Seasonal river 210300 Dry up river 230101 Lake 230102 Pond 230200 Seasonal lake 230300 Dry lake 240101 Built reservoir 240102 Reservoir in building

    2020-03-12 729 37 View Details

  • Primary road network dataset at 1:250,000 of the Three Rivers Source Region (2015)

    Primary road network dataset at 1:250,000 of the Three Rivers Source Region (2015)

    This data comes from the National Catalogue Service for Geographic Information, which was provided to the public free of charge by the National Basic Geographic Information Center in November 2017. We spliced ​​and trimmed Three Rivers Source Region as a whole to facilitate its use in the study of Three Rivers Source Region. The current status of the data is 2015. This dataset is 1:25 million traffic data in the Three Rivers Source Region area, including two layers of highway (LRDL) and railway (LRRL). Highways (LRDL) include national, provincial, county, rural, and other highways; railways (LRRL) include standard-gauge, narrow-gauge, subway, and light rail.        Highway (LRDL) attribute item name and definition: Attribute item Description Sample GB National standard classification code 420301 RN Road number X828 NAME Road name Zhuoxiao fork-Baola Peak fork RTEG Road Level 4 TYPE Road type elevated         Meaning of highway attribute items: Attribute item Code Description GB 420101 National road 420102 National road in building 420201 Provincial road 420102 Provincial highway in building 420301 County road 420302 County road in building 420400 Country road 420800 Machine tillage 440100 Simple road 440200 Village road 440300 Trail         Railway (LRRL) attribute item name and definition: Attribute item Description Sample GB National standard classification code 410101 RN Railway number 0907 NAME Railway name Qinghai-Tibet Railway TYPE Rail type

    2020-03-12 926 27 View Details

  • Dataset of GF-2 satellite images (2017)

    Dataset of GF-2 satellite images (2017)

    Gf-2 satellite is the first civil optical remote sensing satellite independently developed by China with a spatial resolution better than 1 meter. It is equipped with two high-resolution 1-meter panchromatic and 4-meter multi-spectral cameras, and the spatial resolution of the sub-satellite can reach 0.8 meters. This data set is the remote sensing image data of 6 jing gaofen-2 satellite in 2017.The folder list is: GF2_PMS1_E100.5_N37.2_20171013_L1A0002678101 GF2_PMS1_E100.5_N37.4_20171013_L1A0002678097 GF2_PMS1_E100.6_N37.6_20171013_L1A0002678096 GF2_PMS2_E100.3_N37.4_20170810_L1A0002534662 File naming rules: satellite name _ sensor name _ center longitude _ center latitude _ imaging time _L****

    2020-03-09 1052 19 View Details

  • Dataset of GF-1 satellite images (2017-2018)

    Dataset of GF-1 satellite images (2017-2018)

    This data set is the remote sensing data of gaofan-1 satellite, including the data of two scenes of PMS1 camera on 2017-8-13 and 2017-10-5, one scene of PMS2 camera on 2017-5-27, and one scene of WFV2 and WFV3 camera on September 23, 2018.File list: GF1_PMS1_E99.1_N37.2_20170813_L1A0002539236 GF1_PMS1_E101.2_N36.4_20171005_L1A0002653985 GF1_PMS2_E100.3_N37.7_20170527_L1A0002384098 GF1_WFV2_E98.4_N37.6_20180927_L1A0003481737 GF1_WFV3_E100.4_N37.3_20180927_L1A0003481706

    2020-03-09 792 22 View Details

  • The dataset of community statistics of each county in Three-River-Source National Park (2017)

    The dataset of community statistics of each county in Three-River-Source National Park (2017)

    This data set contains statistical tables on the community situation of each county in Three-River-Source National Park. The specific contents include: Table 1 includes: number of administrative villages, number of natural villages, number of households, population, number of rural labor force, total value of primary and secondary industries, net income per capita, and number of livestock. Table 2 includes: the ethnic composition of the population (population of each ethnic group), education-related statistics (number of primary and secondary schools and number of students), health-related statistics (number of hospitals, health rooms and medical personnel), and statistics on the education level of the population (number of people with different education levels); Table 3 includes: the grassland (total grassland area, usable grassland area, moderately degraded area and grassland vegetation coverage), woodland (total area, arbor forest area, shrub forest area and sparse forest area), water area (total area, river area, lake area, glacier area, snowy mountain area and wetland area). A total of four counties were designed: Maduo, Qumalai, Zaduo and Zhiduo. This data comes from statistics of government departments.

    2020-03-08 774 15 View Details

  • China meteorological forcing dataset (1979-2018)

    China meteorological forcing dataset (1979-2018)

    The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.

    2020-02-25 9626 643 View Details

  • The soil moisture dataset of China based on microwave data assimilation (2002-2011)

    The soil moisture dataset of China based on microwave data assimilation (2002-2011)

    The soil moisture data set of China based on microwave data assimilation contains three layers of soil moisture data (0-5 cm, 5-20 cm, and 20-100 cm) from 2002 to 2011. The data adopted the automatically calibrating parameters land data assimilation system (ITPLDAS) developed by Yang et al. (2007), the land surface process model SiB2 was then driven by the high spatiotemporal resolution ground meteorological element data set (ITP-forcing data set) in China, the brightness temperature observed by the AMSR-E satellite was assimilated, and, finally, three layers of soil moisture data were obtained. Soil moisture content accuracy: ± 5% VWC (evaluation accuracy of Naqu and Maqu on the Tibetan Plateau). Data file name: Soil-Moisture_from_ITPLDAS_daily_0.25deg_v2.1.nc Description of data content variables: The file mainly consists of five variables: lon, lat, lev, time and www; www (time, lev, lat, and lon) is the soil moisture content (missing value: -999.0), where lon, lat, lev, and time are the four dimensions of longitude, latitude, depth and time, respectively; Variable unit description: Soil water volume content (www): m3/m3 p.s: The ncdump –h command can be used to directly view the head file information.

    2020-02-21 876 30 View Details

  • Long-term serial data of snow area on the Tibetan Plateau (2007-2015)

    Long-term serial data of snow area on the Tibetan Plateau (2007-2015)

    The variation in the duration of snow on the Tibetan Plateau is relatively great, and the high mountainous areas around the plateau are rich in snow and ice resources. Taking full account of the terrain of the Tibetan Plateau and the snow characteristics in the mountains, the data set adopted AVHRR data to gradually realize generating data products for daily, ten-day, and monthly snow cover areas while maintaining the snow classification accuracy. These data included the daily/10-day/monthly snow cover area data for the Tibetan Plateau from 2007 to 2015, the average accuracy of which is 0.92. It can provide reliable data for snow changes during the historical periods of the Tibetan Plateau.

    2020-01-18 781 19 View Details

  • Ice elevation changes for typical glaciers on the QTP V1.0 (2000-2013)

    Ice elevation changes for typical glaciers on the QTP V1.0 (2000-2013)

    The continuous advancement of SAR interferometry technology makes it possible to obtain multitemporal DEMs with high precision in the glacial area. In particular, in 2000, the Shuttle Radar Topography Mission (SRTM) led by NASA provided DEM data covering the area from 56ºS to 60ºN; the TanDEM-X bistatic SAR interferometry system of DLR could provide the global DEM data with high resolution and precision. These high-quality, large-coverage SAR interferometry data, as well as published DEM data products, provided valuable information for using the multitemporal DEMs to detect changes in ice thickness. The temporal coverage of the ice thickness variation data of typical glaciers on the Tibetan Plateau was from 2000 to 2013, covering Puruogangri and the west Qilian Mountains with a spatial resolution of 30 meters. Using TanDEM-X bistatic InSAR data and a C-band SRTM DEM, the differential radar interferometry method was first used to generate a TanDEM-X DEM with high precision. Then, based on the precise registration of DEM, the DEM data obtained in different periods were compared. Lastly, the ice thickness changes were estimated. The format of the data set was GeoTIFF, and each typical glacier ice thickness change was stored in a folder. For details of the data, please refer to the Ice elevation changes for typical glaciers on the Tibetan Plateau - Data Description.

    2020-01-18 692 22 View Details

  • Historical dataset of chemical properties of soil profile in the Balkhash lake area (2010)

    Historical dataset of chemical properties of soil profile in the Balkhash lake area (2010)

    Content of data: the chamical property database of seven sites of soil profiles in Balkhash Lake Basin. Source of data: 2010 national science and technology cooperation project: the impact of climate change on land productivity in central Asia and soil investigation in balkhash lake region. Data quality: The soil profile was stratified and sampled according to soil genetics. The analysis and determination items included: organic matter content, soil total nitrogen, carbon-nitrogen ratio, carbonate content, calcium carbonate content, and cation exchange capacity (Ca \ Mg \ Na \ K \ total), pH value, soil available nutrients (available phosphorus, available potassium and alkaline nitrogen) and so on. Data application prospects: With precise coordinates, historical data can be compared. Note: Sample point 1 and sample point 2 are 20 m away; sample point 3 and sample point 4 are 50 m away.

    2020-01-16 110 0 View Details