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: 661

  • The Tarim River Basin boundary (2019)

    The Tarim River Basin boundary (2019)

    Extract by DEM and Hydrological Station

    2021-02-25 2002 74 View Details

  • Glacier temperature dataset of Xiaodong Kemadi (2012-2015)

    Glacier temperature dataset of Xiaodong Kemadi (2012-2015)

    Xiaodongkemadi glacier, located in Tanggula Mountain, is a continental glacier. The glacier is a compound valley glacier formed by the confluence of a southward main glacier (also known as dadongkemadi glacier) and a Southwest Branch glacier (also known as xiaodongkemadi glacier). The daily temperature and humidity observation data of 6 points in xiaodongkemadi, 4 points in Yangbajing and 4 points in hariqin from 2012 to 2015.

    2021-02-25 1942 10 View Details

  • Data content: precipitation data of the Aral Sea basin from 2015 to 2018. Data sources and processing methods: from the new generation of global precipitation measurement (GPM) of NASA (version 06, global precipitation observation program), the daily rainfall can be obtained by adding the three-hour rainfall data, and then the eight day rainfall can be obtained. Data quality: the spatial resolution is 0.1 ° x 0.1 ° and the temporal resolution is 8 days. The value of each pixel is the sum of rainfall in 8 days. Data application results: under the background of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics.

    2021-02-23 1334 66 View Details

  • Data on soil temperature, humidity and carbon Flux obtained from a station in southeast Tibet (2007-2019)

    Data on soil temperature, humidity and carbon Flux obtained from a station in southeast Tibet (2007-2019)

    This data set includes daily average data on soil temperature, humidity and carbon flux obtained from a station in Southeast Tibet from 2007 to December 2019. The data collection site is the atmospheric environment observation site of the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet, which is run by the Chinese Academy of Sciences. The site is located at longitude 94°44'18", latitude 29°45'56" and is at an elevation of 3326 m. The observation instrument models are as follows: Soil temperature: Campbell Co 107; Soil humidity: Campbell Co CS616; Carbon flux: Collector model: C3000, Measurement interval: 10 seconds; The observations and data collection were performed in strict accordance with the instrument operating specifications, and the data have been published in relevant academic journals. Data with obvious errors were removed, and missing data were replaced with null values. Observation of the soil thermal flux was stopped in 2013. In 2015, due to damage to the station probe, soil temperature and humidity data were recorded only for the first two months, the probe was repaired in April 2016.

    2021-01-26 1602 61 View Details

  • Glacier coverage data  on the Tibetan Plateau  in 1970s (TPG1976, Version 1.0)

    Glacier coverage data on the Tibetan Plateau in 1970s (TPG1976, Version 1.0)

    The Tibetan Plateau Glacial Data -TPG1976 is a glacial coverage data on the Tibetan Plateau in the 1970s. It was generated by manual interpretation from Landsat MSS multispectral image data. The temporal coverage was mainly from 1972 to 1979 by 60 m spatial resolution. It involved 205 scenes of Landsat MSS/TM. There were 189 scenes(92% coverage on TP)in 1972-79,including 116 scenes in 1976/77 (61% of all the collected satellite data).As high quality of MSS data is not accessible due to cloud and snow effects in the South-east Tibetan Plateau, earlier Landsat TM data was collected for usage, including 14 scenes of 1980s(1981,1986-89,which covers 6.5% of TP) and 2 scenes in 1994(by 1.5% coverage on TP).Among all satellite data,77% was collected in winter with the minimum effects of cloud and seasonal snow. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 1976. Glacier outlines were digitized on-screen manually from the 1976 image mosaic, relying on false-colour image composites (MSS: red, green and blue (RGB) represented by bands 321; TM: RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG1976. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG1976 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 6.4% due to the 60 m spatial resolution images.

    2021-01-20 3297 62 View Details

  • Glacier coverage data on the Tibetan Plateau in 2013 (TPG2013, Version1.0)

    Glacier coverage data on the Tibetan Plateau in 2013 (TPG2013, Version1.0)

    The Tibetan Plateau Glacier Data –TPG2013 is a glacial coverage data on the Tibetan Plateau around 2013. 128 Landsat 8 Operational Land Imager (OLI) images were selected with 30-m spatial resolution, for comparability with previous and current glacier inventories. Besides, about 20 images acquired in 2014 were used to complete the full coverage of the TP. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 2013. Glacier outlines were digitized on-screen manually from the 2013 image mosaic, relying on false-colour image composites (RGB by bands 654), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. [To minimize the effects of snow or cloud cover on glacierized areas, high-resolution (30 m spatial resolution and 4-day repetition cycle) images were also used for reference in glacier delineation from the Chinese satellites HJ-1A and HJ-1B, which were launched on Sep.6th 2008. Both carried as payload two 4-band CCD cameras with swath width 700 km (360 km per camera). All HJ-1A/1B data in 2012, 2013 and 2014 (65 scenes, Fig.S1, Table S1) were from China Centre for Resources Satellite Data and Application (CRESDA; http://www.cresda.com/n16/n92006/n92066/n98627/index.html). Each scene was orthorectified with respect to the 30m-resolution digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) and Landsat images.] The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. Topographic maps from the 1970s and all available satellite images (including Google EarthTM imagery and HJ-1A/1B satellite data) were used as base reference data. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2013. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2013 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.9%.

    2021-01-20 2797 80 View Details

  • Glacier coverage data on the Tibetan Plateau in 2001 (TPG2001, Version1.0)

    Glacier coverage data on the Tibetan Plateau in 2001 (TPG2001, Version1.0)

    The Tibetan Plateau Glacial Data –TPG2001 is a glacial coverage data on the Tibetan Plateau in around 2000 from 150 scenes of Landsat7 TM/ETM+ images by 30 m spatial resolution. The selected Landsat7 TM/ETM+ images were within the period between 1999 and 2002, including 61 scenes (41%) in 2001 and 47 scenes (31%) in 2000. Among all the images, 71% was taken in winter. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 2001. Glacier outlines were digitized on-screen manually from the 2001 image mosaic, relying on false-colour image composites (RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. Topographic maps from the 1970s and all available satellite images (including Google EarthTM imagery) were used as base reference data. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2001. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2001 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.8%.

    2021-01-20 2730 71 View Details

  • Dataset of the multi-year average of  relative humidity for the Green Silk Road (Version 1.0)

    Dataset of the multi-year average of relative humidity for the Green Silk Road (Version 1.0)

    Temperature-humidity index (THI) was adopted to evalulate the climate suitability for the Green Silk Road. The relative humidity isone of the basic parameters to calculate THI. Refering to theTHI model of Tanget al. (2008), the multi-year average of relative humidity is calculted based on the observation data (1981-2017) of weather stations provided by National Meteorological Information Center. The multi-year average values were interpolated into the raster dataset at the resolution of 11km×1km by Kriging method based on GIS software. The climate suitability evaluation results calculated based on this dataset could highlight regional differences.

    2021-01-15 1022 19 View Details

  • Vulnerability forecast scenarios dataset of the water resources, agriculture, and ecosystem of the Manasi River Basin (Version 1.0) (2010-2050)

    Vulnerability forecast scenarios dataset of the water resources, agriculture, and ecosystem of the Manasi River Basin (Version 1.0) (2010-2050)

    By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, on which basis the vulnerability of the water resources system of the basin was evaluated. The IPAT equation was used to set a future water resource demand scenario, setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption to establish the scenario. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were predicted. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations for the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.

    2021-01-08 1797 9 View Details

  • Vulnerability forecast scenarios dataset of water resources, agriculture, and ecosystem of the Urmuqi River Basin (Version 1.0) ( 2010-2050)

    Vulnerability forecast scenarios dataset of water resources, agriculture, and ecosystem of the Urmuqi River Basin (Version 1.0) ( 2010-2050)

    By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, based on which the vulnerability of the water resources system of the basin was evaluated. The IPAT equation was used to set a future water resource demand scenario, which was to establish the scenario by setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were predicted. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations of the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.

    2021-01-08 2692 9 View Details

  • Dataset of high-resolution (3 hour, 10 km) global surface solar radiation (1983-2018)

    Dataset of high-resolution (3 hour, 10 km) global surface solar radiation (1983-2018)

    The dataset is a nearly 36-year (1983.7-2018.12) high-resolution (3 h, 10 km) global SSR (surface solar radiation) dataset, which can be used for hydrological modeling, land surface modeling and engineering application. The dataset was produced based on ISCCP-HXG cloud products, ERA5 reanalysis data, and MODIS aerosol and albedo products with an improved physical parameterization scheme. Validation and comparisons with other global satellite radiation products indicate that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). This SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The unit is W/㎡, instantaneous value.

    2020-12-30 18694 942 View Details

  • Distribution dataset of Chinese major deserts (version 1.0)

    Distribution dataset of Chinese major deserts (version 1.0)

    The major deserts in China include the Taklamakan Desert, Gurban Tunggut Desert, Qaidam Desert, Kumtag Desert, Badain Jaran Desert, Tengger Desert, Ulan Buh Desert, Hobq Desert, MU US Desert, Hunshandake Desert, Hulunbuir Sands, and Horqin Sands. All the desert boundaries were derived from Google Earth Pro® via manual interpretation. We delineated the desert boundaries using the Digital Global Feature Imagery and SpotImage (2011, 10 m resolution) collections of Google Earth Pro®, whose spatial resolution is finer than 30 m. The acquisition time of most images was in 2011.

    2020-12-30 2022 32 View Details

  • 10-meter resolution (Sentinel) land use dataset of  key nodes (2017)
  • Global Cryosat-2 GDR dataset (version 1.0) (2010-2016)

    Global Cryosat-2 GDR dataset (version 1.0) (2010-2016)

    The global Cryosat-2 GDR dataset is generated by the European Space Agency (ESA); it has a temporal coverage from 2010 to 2016 and covers the globe. On April 8, 2010, the ESA launched the Cryosat-2 high-tilt polar orbit satellite. The satellite is equipped with an SAR Interferometer Radar Altimeter (SIRAL), which is mainly used to monitor polar ice thickness and sea ice thickness changes, and, furthermore, to study the effects of melting polar ice on global sea level rise and that of global climate change on Antarctic ice thickness. The altimeter operates in the Ku-band and at a frequency of 13.575 GHz, it includes three measurement modes. One is a low-resolution altimeter measurement mode (LRM) that points to the subsatellite point to obtain all surface observations for land, sea, and ice sheets; its processing is similar to ENVISAT/RA-2, with an orbital resolution of 5 to 7 km. The second is the Synthetic Aperture Radar (SAR) measurement mode, which is mainly used to improve the accuracy and resolution of sea ice observations; it can make the resolution along the orbit reach approximately 250 m. The third is the Interferometric Synthetic Aperture Radar (InSAR), which is mainly used to improve the accuracy of areas with complex terrain such as the edges of ice sheets or ice shelves. The CryoSat -2/SIRAL data products mainly include 0-level data, 1b-level data, 2-level data and high-level data. The Cryosat-2/SIRAL products consist of two files: an XML head file (.HDR) and a data product file (.DBL). The HDR file is an auxiliary ASCII file for fast identification and retrieval of the data files. 1b-level products are stored separately according to the measurement modes, and the data recording formats of different modes are also different. Each waveform in LRM mode and SAR mode has 128 sampling points, while that in SARIn mode has 512 sampling points. 2-level GDR products are available for most scientific applications, including measurement time, geographic location, altitude, and more. In addition, the altitude information in GDR products has been obtained through instrumental calibration, transmission delay corrections, geometric corrections, and geophysical corrections (such as atmospheric corrections and tidal corrections). The GDR products are single global full-track data, that is, the measurement results of the three modes. After different processing, they are combined in chronological order; thereby, the data recording formats are unified. The data in the three modes use different waveform retracking algorithms to obtain altitude values. In the latest updated Baseline C data, the LRM mode data use three algorithms: Refined CFI, UCL and Refined OCOG.

    2020-12-17 2729 28 View Details

  • Surface energy balance based global land evapotranspiration (EB-ET 2000-2017)

    Surface energy balance based global land evapotranspiration (EB-ET 2000-2017)

    Daily and Monthly evapotranspiration (5km x 5km spatial resolution) for global land area was derived from satellite data and a surface energy balance method (EB). The global 5 km daily and monthly ET dataset is produced with the revised SEBS algorithm in Chen et al. 2019 JGR and Chen et al. 2013 (JAMC). MODIS LST, NDVI, Global forest height, GlobAlbedo, GLASS LAI have been used in this ET calculation. The ET dataset will be updated to near-present with the availability of input dataset. The global 5 km sensible heat flux, net radiation, latent heat flux will be open with the email contact with Dr. Xuelong Chen. Daily ET File name: 20001201-ET-V1.mat, 2000-year, 12-month,01-day, ET-Evapotranspiration, V1-version 1;unit: mm/day (unit8 need transfer to single or double and should be divided by 10);data type: unit8 was used to save the disk space, 255 is used for ocean and water body pixels. Monthly ET File name: ETm200012-ET-V1.mat, 2000-year, 12-month, ET-Evapotranspiration, V1-version 1;unit: mm/month (int16 need transfer to single or double and should be divided by 10);data type: int16 was used to save the disk space, 0 is used for ocean and water body pixels. The daily ET dataset is produced with a similar method and satellite data as in Chen, X., et al., 2014: Development of a 10 year (2001–2010) 0.1° dataset of land-surface energy balance for mainland China, Atmos. Chem. Phys., 14, 13097–13117, doi:10.5194/acp-14-13097-2014. The calculation of roughness length and kB_1 for global land were updated by the method in Chen, X., et al, 2019, A Column Canopy‐Air Turbulent Diffusion Method for Different Canopy Structures, Journal of Geophysical Research: Atmospheres, 2019.01.15, 124. Most of the satellite input data were from MODIS. Meteorological data was from ERA-Interim. Global canopy height information was derived from GLAS and MODIS NDVI. The daily ET has a mean bias (MB) of 0.04 mm/day, RMSE is 1.56 (±0.25) mm/day.

    2020-12-16 2897 137 View Details

  • NDVI 16-Day 250m dataset in Xinjiang provice (2000-2017)

    NDVI 16-Day 250m dataset in Xinjiang provice (2000-2017)

    1. Data source: MODIS/Terra Vegetation Indices 16-day L3 Global 250m SIN Grid V006 products (2000-2017) Download address https://search.earthdata.nasa.gov/ 2. Data name: (1) resize is automatically generated in the batch cropping process, which means that it has been extracted by mask and the data range after processing is xinjiang provice; (2) seven digits represent the time of data acquisition, the first four digits are years, and the last three digits are days of the year.For example, "2000049" means that the year of data acquisition is 2000 and the specific time is the 49th day of that year. (3) 250m represents the ground resolution, i.e. 250 meters; (4) 16_days represents the time resolution, that is, 16 days; (5) NDVI represents data type, namely normalized vegetation index; 3. Data time range: 2000049-2017353, data interval of 16 days; 4..Tif file and.hdr file . Tif file is the original NDVI data with the same name. HDR file is the mask data that supports normal use of. 5. To analyze the ecological effects of cryosphere

    2020-12-16 2574 51 View Details

  • Monthly dataset of ERA-Interim based on pressure levels from 1979 to 2018 released from ECMWF

    Monthly dataset of ERA-Interim based on pressure levels from 1979 to 2018 released from ECMWF

    This dataset is derived from the global atmospheric reanalysis dataset, ERA-Interim, based on the 4-dimensional variational analysis (4D-Var) released by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA-Interim represents a major undertaking by ECMWF (European Centre for Medium-Range Weather Forecasts) to produce a reanalysis with an improved atmospheric model and assimilation system which replaces those used in ERA-40, particularly for the data-rich 1990s and 2000s, and to be continued as an ECMWF Climate Data Assimilation System (ECDAS) until superseded by a new reanalysis. Through systematic increases in computing power, 4-dimensional variational assimilation (4D-Var) became feasible and part of ECMWF operations since 1997. Enhanced computing power also allowed horizontal resolution to be increased from T159 to T255, and the latest Integrated Forecasting System(IFS CY31r1 and CY31r2) to be used, taking advantage of improved model physics. ERA-interim retains the same 60 model levels used for ERA-40 with the highest level being 0.1 hPa. Besides, data assimilation of ERA-Interim also benefits from quality control that draws on experience from ERA-40 and JRA-25, variational bias correction of satellite radiance data, and more extensive use of radiances with an improved fast radiative transfer model. In addition, ERA-Interim uses the new ERS (European Remote Sensing Satellite) altimeter wave heights, EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) reprocessed winds and clear-sky radiances, GOME (Global Ozone Monitoring Experiment) ozone data from the Rutherford Appleton Laboratory, and CHAMP (CHAllenging Minisatellite Payload), GRACE (Gravity Recovery and Climate Experiment), and COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) GPS radio occultation measurements processed and archived by UCAR (University Corporation for Atmospheric Research).

    2020-12-16 1254 5 View Details

  • Revised dataset of temperature and precipitation in the Greater Naren River Basin (1951-2016)

    Revised dataset of temperature and precipitation in the Greater Naren River Basin (1951-2016)

    Precipitation and temperature are essential input variables for hydrological models. There are few meteorological stations in the big Naryn Basin of the Syr Darya, which cannot meet the needs of hydrological simulation. Precipitation data in the Syr Darya were collected through online resources and field research. The precipitation gradient in the study area is obtained. Based on the precipitation gradient, the precipitation and temperature grid products (PGMFD) (http://hydrology.princeton.edu/data.pgf.php)were then corrected to get this set of data sets. The year covered by this data is 1951-2016, the spatial precision is 10km, and the time resolution is daily. The more detail information about the correction method can be found in (Generation of High Mountain Precipitation and Temperature Data for a Quantitative Assessment of Flow Regime in the Upper Yarkant Basin in the Karakoram, Kan et al., 2018)

    2020-12-16 1609 11 View Details

  • Dataset of carbon flux, climate and surface albedo od alpine meadow and grassland ( 2003 - 2016)

    Dataset of carbon flux, climate and surface albedo od alpine meadow and grassland ( 2003 - 2016)

    1、Based on field eddy correlation (EC) measurement data, using the standard data processing method for EC data, including despiking, coordinate rotation, air density corrections, outlier rejection, and friction velocity threshold (u*) corrections, gap filled, and NEE partition. The dataset collects carbon flux data and microclimate measurement data from 2003 to 2016 in three typical alpine grassland ecosystems on the Qinghai-Tibet Plateau, including Damxung alpine meadow, Haibei alpine meadow ,Naqu alpine meadow,Zoige alpine grassland,Qilian mountion grassland . The time resolution of data is high (30 min), and the interpolation of data is complete throughout the year. This dataset can be applied to carbon flux assessment, comparison and prediction in these alpine meadows, attribution of climate factors affecting carbon flux, validation of model simulation results, etc. 2、Based on the MCDGF43 dataset, we produce the visible and near-infared albedo of Tibetan Plateau, using the standard data processing of hdf to tif , including the moasic, resample and masked by Tibetan Plateau's boundary. The time resolution of dataset is 8 days and the spatial resolution is 500 meters, which span the period of 2003-2016.

    2020-12-16 1261 43 View Details

  • Dataset of Soil  Erosion (water) Intensity with 300m resoluton in Tibetan Plateau (1992, 2005, 2015)

    Dataset of Soil Erosion (water) Intensity with 300m resoluton in Tibetan Plateau (1992, 2005, 2015)

    1)The data content includes three stages of soil erosion intensity in Qinghai-Tibet Plateau in 1992, 2005 and 2015, and the grid resolution is 300m. 2) China soil erosion prediction model (CSLE) was used to calculate the soil erosion amount of more than 4,000 investigation units on the Qinghai-Tibet Plateau. Soil erosion was interpolated according to land use on Qinghai-Tibet Plateau. According to the soil erosion classification standard, the soil erosion intensity map of Qinghai-Tibet Plateau was obtained. 3) By comparing the differences of three-stage soil erosion intensity data, it conforms to the actual change law and the data quality is good. 4) The data of soil erosion intensity are of great significance to the study of soil erosion in the Qinghai-Tibet Plateau and the sustainable development of local ecosystems. In the attribute table, "Value" represents the erosion intensity level, from 1 to 6, the value represents slight, mild, moderate, intense, extremely intense and severe. "BL" represents the percentage of echa erosion intensity in the total area.

    2020-12-16 2761 74 View Details