(1) This is a literature-based eddy covariance carbon exchange dataset on the Tibetan Plateau, including air temperature, soil temperature, precipitation, ecosystem productivity and other parameters. (2) The data set is based on the field measured data of vorticity, and adopts the internationally recognized standard processing method of vorticity related data. The basic process includes: outlier elimination coordinate rotation WPL correction storage item calculation precipitation synchronization data elimination threshold elimination outlier elimination U * correction missing data interpolation flux decomposition and statistics. This data set also contains the model simulation data calibrated based on the vorticity correlation data set. (3) the data set has been under data quality control, and the data missing rate is 37.3%, and the missing data has been supplemented by interpolation. (4) The data set has scientific value for understanding carbon sink function of alpine wetland, and can also be used for correction and verification of mechanism model.
0 2022-09-13
Iceberg calving, one of the key process of Antarctic mass balance, has been regarded as an important variable in fine monitoring the changes of ice shelves. The authors used multi-source remote sensing data near early August of each year from 2005 to 2020, including ENVISAT ASAR (WSM) images from 2005 to 2011, Terra/Aqua MODIS 7-2-1 band composite images from 2012 to 2014, Landsat-8 OLI 4-3-2 band composite images from 2013 to 2020, and Sentinel-1 SAR (EW) images from 2015 to 2020, to generate annual circum-Antarctic image mosaics after pre-processing. Next, combining MEaSUREs ice velocity dataset, grounding line, ice thickness dataset (Bedmap 2 and Bedmachine), spatial calculation and map digitization techniques were applied to extract all annual calving events larger than 1 km² that occurred on the Antarctic ice shelves from August 2005 to August 2020. Also, their area, thickness, mass and calving recurrence cycle were calculated to derive the annual iceberg calving dataset of the Antarctic ice shelves (2005-2020). This dataset contains the distribution of 15-year annual calving events, along with the attributes of each individual calving event including calving year, length, area, average thickness, mass, and recurrence interval. This dataset can directly reflect the magnitude characteristics and distribution of Antarctic iceberg calving in different years, which fills the gap of fine monitoring dataset of iceberg calving and provides fundamental data for subsequent research on calving mechanism and mass balance of Antarctic ice shelf-ice sheet system.
0 2022-09-13
The rainfall data set of the southern Qinghai Tibet Plateau is fused by the satellite and the ground station. The data is in ASCII format, with a temporal resolution of 1 day and a horizontal spatial resolution of 0.1 °， The time coverage is from June 10 to October 31 in 2014-2019, which can provide driving data for rainfall verification and hydrological simulation in the southern Tibetan Plateau. The data set is based on the rainfall data of China Meteorological Administration and Hydrological Bureau of the Ministry of water resources after strict quality control , which is the highest density ground station network in the region so far. Dynamic Bayesian Model Average method is used to merge satellite precipitation products, i.e., GPM-IMERG, GSMaP, and CMORPH, based on the likelihood measurements of a high-density rainfall gauge network. The statistical accuracy evaluation and hydrological simulation verification of the merged data preforms better than the source satellite data, and also better than the popular reanalysis data CHIRPS and MSWEP.
0 2022-09-13
For the snow distribution area in China, we prepared a MODIS day-by-day cloud-free snow area dataset with a spatial resolution of 500m from 2000 to 2020 based on the MODIS reflectivity product MOD/MYD09GA, using a decision tree snow discriminant algorithm for different surface types and a vacancy filling algorithm such as a spatiotemporal interpolation algorithm for the hidden Markov random field model. The dataset is stored in HDF5 file format, and each HDF5 file contains 18 data elements, which include data values, data start date, latitude, and longitude. Meanwhile, for a quick preview of snow distribution, the day-by-day file contains snow area thumbnails stored in jpg format. This dataset will be continuously supplemented and improved based on real-time satellite remote sensing data and algorithm updates (currently through December 2020), and will be shared in a fully open sharing format.
0 2022-09-13
(1) Data content: this data set is the soil profile water, heat and carbon data set of Shenzha alpine grassland from 2019 to 2020, including the daily average values of soil temperature, water content and CO2 concentration at different depths (5 cm, 10 cm, 20 cm, 40 cm, 100 cm and 150 cm)（ 2) Data sources and processing methods: the data are from field in situ observation. Among them, the data of soil temperature comes from cs109 probe, the data of soil water content comes from CS616 probe, and the data of soil CO2 concentration comes from gmm222 probe（ 3) The data quality is high, but due to the power supply problem, there is a lack of data at the end of April（ 4) It is helpful to improve the understanding of the underground carbon processes in the Tibetan Plateau.
0 2022-09-13
In recent years, the melting of the Antarctic ice sheet has accelerated, and a large amount of surface melt water has appeared on the surface of the Antarctic ice sheet. Understandings of the spatial distribution and dynamics of surface melt water on the Antarctic ice sheet is of great significance for the study of the mass balance of the Antarctic ice sheet. This dataset is 2000-2020 surface melt water dataset of Antarctica Ice Sheet typical melting area (Prydz bay) based on 10-30m Landsat-7, 8 and Sentinel-2 images. The projections are polar azimuthal projections in vector format (ESRI Shapefile) and raster format (GeoTIFF) and the time is Southern Hemisphere summer (December-to-February).
0 2022-09-13
This data set includes evapotranspiration data set of the Tibet Plateau at the monthly scale from 1979 to 2018. The data set is based on the ERA5 net radiation and China meteorological forcing dataset (CMFD). The evapotranspiration is derived by the sigmoid generalized complementary equation, which is calibrated and verified by the observation data of 12 eddy flux sites and water balance data of 5 river basins (the source region of Yangtze river, the source region of Yellow River, the Nu River, the Yarlung Zangbo River, and the Hei River) on the Tibetan Plateau, which shows a high accuracy. The data set can be used to study the hydrological cycle and climate change in the Tibetan Plateau.
0 2022-09-13
The interannual variation data set of Lake area and water quantity in different regions of the Qinghai Tibet Plateau contains the continuous series data of 20 lakes with an area of more than 100 square kilometers in different regions from 1976 to 2019 （no data available from 1978 to 1985）. According to the October December data of Landsat series images, the seasonal variation can be reduced while the available data can be maximized. The NDWI water body index was used to extract the lake area, and the SRTM DEM was used to fit the relationship between the lake area and the change of water quantity. The data are applied to the study of lake change, lake water balance and climate change in the Qinghai Tibet Plateau.
0 2022-09-13
The surface elevation of the ice sheet is very sensitive to climate change, so the elevation change of the ice sheet is considered as an important variable to evaluate climate change. The time series of long-term ice sheet surface elevation change has become a fundamental data for understanding climate change. The longest time series of ice sheet surface elevation can be established by combining the observation records of radar satellite altimetry missions. However, the previous methods for correcting the intermission bias still have error residue when cross-calibrating different missions. Therefore，we modify the commonly used plane-fitting least-squares regression model by restricting the correction of intermission bias and the ascending–descending bias at the same time to ensure the self-consistency and coherence of surface elevation time series across different missions. Based on this method, we use Envisat and CryoSat-2 data to construct the time series of Antarctic ice sheet elevation change from 2002 to 2019. The time series is the monthly grid data, and the spatial grid resolution is 5 km×5 km. Using airborne and satellite laser altimetry data to evaluate the results, it is found that compared with the traditional method, this method can improve the accuracy of intermission bias correction by 40%. Using the merged elevation time series, combining with firn densification-modeled volume changes due to surface processes, we find that ice dynamic processes make the ice sheet along the Amundsen Sea sector the largest volume loss of the Antarctic ice sheet. The surface processes dominate the volume changes in Totten Glacier sector, Dronning Maud Land, Princess Elizabeth Land, and the Bellingshausen Sea sector. Overall, accelerated volume loss in the West Antarctic continues to outpace the gains observed in the East Antarctic. The total volume change during 2002–2019 for the AIS was −68.7 ± 8.1 km3/y, with an acceleration of −5.5 ± 0.9 km3/y2.
0 2022-09-13
Population growth resilience reflects the level of resilience of population growth in the countries along the belt and road, and the higher the value, the stronger the resilience of population growth in the countries along the belt and road. The data on the resilience of population growth is prepared by referring to the World Bank's statistical database, using the year-on-year changes in the population of countries along the Belt and Road from 2000 to 2019, taking into account the year-on-year changes in each indicator, and through comprehensive diagnosis based on sensitivity and adaptability analysis. The resilience of population growth product.
0 2022-09-13
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