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.

(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.

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).

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.

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.

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.

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.

Population age structure resilience reflects the level of population age structure resilience in the countries along the Belt and Road. The World Bank's statistical database was used to prepare the data on the resilience of the population age structure of the countries along the Belt and Road. Based on the sensitivity and adaptability analysis, a comprehensive diagnosis was made based on the year-on-year change of each indicator, and the product on the resilience of population age structure was prepared.

The data set includes the reconstructed long-term annual, ablation-season, and cold-season glacier-wide mass balance and its components for Guliya Ice Cap and the reconstructed daily meteorological data on the glacier from 1969 to 2019. The reconstructed meteorological data includes air temperature (℃), relative humidity (%), wind speed (m s-1), air pressure (hPa) and downward shortwave radiation (W m-2) at an elevation of 6004 m a.s.l. and precipitation (mm) at an elevation of 5491 m a.s.l. ERA5 data from the grid point (35°N, 78°75′E) around AWS2 were calibrated by the measured meteorological data. The exact method has been described in the reference. The long-term mass balance of Guliya Ice Cap during 1970-2019 was reconstructed using an energy and mass balance model and calibrated ERA5 data, which was calibrated and validated by in-situ measurements and geodetic mass balances. Please see the reference. The data is stored in an Excel file. It can be used by researchers for studying the changes in climate, hydrology, glaciers, etc.

Known as the "Asian water tower", the Qinghai Tibet Plateau is the source of many rivers in Southeast Asia. As an important and easily accessible water resource, the runoff provided by it supports the production and life of billions of people around it and the diversity of the ecosystem. The glacier runoff data set in the five river source areas of the Qinghai Tibet Plateau covers the period from 2005 to 2010, with a time resolution of every five years. It covers the source areas of the five major rivers in the Qinghai Tibet Plateau (the source of the Yellow River, the source of the Yangtze River, the source of the Lancang River, the source of the Nujiang River, and the source of the Yarlung Zangbo River). The spatial resolution is 1km. Based on multi-source remote sensing, simulation, statistics, and measured data, GIS methods and ecological economics methods are used, The value of water resources service in the cryosphere in the source area of the river and river is quantified, and all its data are subject to quality control.

The data is the result of the prediction of Arctic sea ice density and sea ice coverage by the climate system model FGOALS independently developed by the project members. The correct selection of assimilation technology is an important factor for Arctic sea ice prediction. In the sea ice data assimilation technology, the singular value evolutionary interpolation Kalman filter (seik) is a relatively early but still commonly used filtering algorithm. However, due to the calculation of error covariance between all grid points, there is a false teleconnection error. Therefore, it is considered to develop a local filtering method to assimilate sea ice density and sea ice thickness. In the climate system model FGOALS, the project will initialize and process the sea ice thickness data retrieved by the European Space Agency (ESA) cryosat-2 and soil moisture and ocean salinity (SMOs) satellite remote sensing.

The data is the result of the prediction of Arctic sea ice density and sea ice coverage by the climate system model FGOALS independently developed by the project members. The correct selection of assimilation technology is an important factor for Arctic sea ice prediction. In the sea ice data assimilation technology, the singular value evolutionary interpolation Kalman filter (seik) is a relatively early but still commonly used filtering algorithm. However, due to the calculation of error covariance between all grid points, there is a false teleconnection error. Therefore, it is considered to develop a local filtering method to assimilate sea ice density and sea ice thickness. In the climate system model FGOALS, the project will initialize and process the sea ice thickness data retrieved by the European Space Agency (ESA) cryosat-2 and soil moisture and ocean salinity (SMOs) satellite remote sensing.