China's second glacier inventory uses the high-resolution Landsat TM/ETM+ remote sensing satellite data as the main glacier boundary data source and extracts the data source with the latest global digital elevation model, SRTM V4, as the glacier attribute, using the current international ratio threshold segmentation method to extract the glacier boundary in bare ice areas. The ice ridge extraction algorithm is developed to extract the glacier ice ridge, and it is used for the segmentation of a single glacier. At the same time, the international general algorithm is used to calculate the glacier attributes, so that the vector data and attribute data that contain the glacier information of the main glacier regions in west China are obtained. Compared with some field GPS field measurement data and higher resolution remote sensing images (such as from QuickBird and WorldView), the glacial vector data in the second glacier inventory data set of China have higher positioning accuracy and can meet the requirements for glacial data in national land, water conservancy, transportation, environment and other fields. Glacier inventory attributes: Glc_Name, Drng_Code, FCGI_ID, GLIMS_ID, Mtn_Name, Pref_Name, Glc_Long, Glc_Lati, Glc_Area, Abs_Accu, Rel_Accu, Deb_Area, Deb_A_Accu, Deb_R_Accu, Glc_Vol_A, Glc_Vol_B, Max_Elev, Min_Elev, Mean_Elev, MA_Elev, Mean_Slp, Mean_Asp, Prm_Image, Aux_Image, Rep_Date, Elev_Src, Elev_Date, Compiler, Verifier. For a detailed data description, please refer to the second glacier inventory data description.

Vegetation functional type (PFT) is a combination of large plant species according to the ecosystem function and resource utilization mode of plant species. Each planting functional type shares similar plant attributes, which simplifies the diversity of plant species into the diversity of plant function and structure.The concept of vegetation-functional has been advocated by ecologists especially ecosystem modelers.The basic assumption is that globally important ecosystem dynamics can be expressed and simulated through limited vegetative functional types.At present, vegetation-functional model has been widely used in biogeographic model, biogeochemical model, land surface process model and global dynamic vegetation model. For example, the land surface process model of the national center for atmospheric research (NCAR) in the United States has changed the original land cover information into the applied vegetation-functional map (Bonan et al., 2002).Functional vegetation has been used in the dynamic global vegetation model (DGVM) to predict the changes of ecosystem structure and function under the global change scenario. 1. Functional classification system of vegetation 1 Needleleaf evergreen tree, temperate 2 Needleleaf evergreen tree, boreal 3 Needleleaf deciduous tree 4 Broadleaf evergreen tree, tropical 5 Broadleaf evergreen tree, temperate 6 Broadleaf deciduous tree, tropical 7 Broadleaf deciduous tree, temperate 8 Broadleaf deciduous tree, boreal 9 Broadleaf evergreen shrub, temperate 10 Broadleaf deciduous shrub, temperate 11 Broadleaf deciduous shrub, boreal 12 C3 grass, arctic 13 C3 grass 14 C4 grass 15 Crop 16 Permanent wetlands 17 Urban and built-up lands 18 Snow and ice 19 Barren or sparsely vegetated lands 20 Bodies of water 2. Drawing method China's 1km vegetation function map is based on the climate rules of land cover and vegetation function conversion proposed by Bonan et al. (Bonan et al., 2002).Ran et al., 2012).MICLCover land cover map is a blend of 1:100000 data of land use in China in 2000, the Chinese atlas (1:10 00000) the type of vegetation, China 1:100000 glacier map, China 1:10 00000 marshes and MODIS land cover 2001 products (MOD12Q1) released the latest land cover data, using IGBP land cover classification system.The evaluation shows that it may be the most accurate land cover map on the scale of 1km in China.Climate data is China's atmospheric driven data with spatial resolution of 0.1 and temporal resolution of 3 hours from 1981 to 2008 developed by he jie et al. (2010).The data incorporates Princeton land-surface model driven data (Sheffield et al., 2006), gewex-srb radiation data (Pinker et al., 2003), TRMM 3B42 and APHRODITE precipitation data, and observations from 740 meteorological stations and stations under the China meteorological administration.According to the evaluation results of RanYouhua et al. (2010), GLC2000 has a relatively high accuracy in the current global land cover data set, and there is no mixed forest in its classification system. Therefore, the mixed forest in the MICLCover land cover diagram USES GLC2000 (Bartholome and Belward, 2005).The information in xu wenting et al., 2005) was replaced.The data can be used in land surface process model and other related researches.

The GAME/Tibet project conducted a short-term pre-intensive observing period (PIOP) at the Amdo station in the summer of 1997. From May to September 1998, five consecutive IOPs were scheduled, with approximately one month per IOP. More than 80 scientific workers from China, Japan and South Korea went to the Tibetan Plateau in batches and carried out arduous and fruitful work. The observation tests and plans were successfully completed. After the completion of the IOP in September, 1998, five automatic weather stations (AWS), one Portable Atmospheric Mosonet (PAM), one boundary layer tower and integrated radiation observatory (Amdo) and nine soil temperature and moisture observation stations have been continuously observed to date and have obtained extremely valuable information for 8 years and 6 months consecutively (starting from June 1997). The experimental area is located in Nagqu, in northern Tibet, and has an area of 150 km × 200 km (Fig. 1), and observation points are also established in D66, Tuotuohe and the Tanggula Mountain Pass (D105) along the Qinghai-Tibet Highway. The following observation stations (sites) are set up on different underlying surfaces including plateau meadows, plateau lakes, and desert steppe. (1) Two multidisciplinary (atmosphere and soil) observation stations, Amdo and NaquFx, have multicomponent radiation observation systems, gradient observation towers, turbulent flux direct measurement systems, soil temperature and moisture gradient observations, radiosonde, ground soil moisture observation networks and multiangle spectrometer observations used as ground truth values for satellite data, etc. (2) There are six automatic weather stations (D66, Tuotuohe, D105, D110, Nagqu and MS3608), each of which has observations of wind, temperature, humidity, pressure, radiation, surface temperature, soil temperature and moisture, precipitation, etc. (3) PAM stations (Portable Automated Meso - net) located approximately 80 km north and south of Nagqu (MS3478 and MS3637) have major projects similar to the two integrated observation stations (Amdo and NaquFx) above and to the wind, temperature and humidity turbulence observations. (4) There are nine soil temperature and moisture observation sites (D66, Tuotuohe, D110, WADD, NODA, Amdo, MS3478, MS3478 and MS3637), each of which has soil temperature measurements of 6 layers and soil moisture measurement of 9 layers. (5) A 3D Doppler Radar Station is located in the south of Nagqu, and there are seven encrypted precipitation gauges in the adjacent (within approximately 100 km) area. The radiation observation system mainly studies the plateau cloud and precipitation system and serves as a ground true value station for the TRMM satellite. The GAME-Tibet project seeks to gain insight into the land-atmosphere interaction on the Tibetan Plateau and its impact on the Asian monsoon system through enhanced observational experiments and long-term monitoring at different spatial scales. After the end of 2000, the GAME/Tibet project joined the “Coordinated Enhanced Observing Period (CEOP)” jointly organized by two international plans, GEWEX (Global Energy and Water Cycle Experiment) and CL IVAR (Climate Change and Forecast). The Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau of the Global Coordinated Enhanced Observation Program (CEOP) has been started. The data set contains POP data for 1997 and IOP data for 1998. Ⅰ. The POP data of 1997 contain the following. 1. Precipitation Gauge Network (PGN) 2. Radiosonde Observation at Naqu 3. Analysis of Stable Isotope for Water Cycle Studies 4. Doppler radar observation 5. Large-Scale Hydrological Cycle in Tibet (Link to Numaguchi's home page) 6. Portable Automated Mesonet (PAM) [Japanese] 7. Ground Truth Data Collection (GTDC) for Satellite Remote Sensing 8. Tanggula AWS (D105 station in Tibet) 9. Syamboche AWS (GEN/GAME AWS in Nepal) Ⅱ. The IOP data of 1998 contain the following. 1. Anduo （1） PBL Tower, 2） Radiation, 3） Turbulence SMTMS 2. D66 （1） AWS （2） SMTMS （3） GTDC （4) Precipitation 3. Toutouhe （1） AWS （2） SMTMS （3 ）GTDC 4. D110 （1） AWS （2） SMTMS (3) GTDC (4) SMTMS 5. MS3608 （1） AWS （2） SMTMS （3） Precipitation 6. D105 （1） Precipitation (2) GTDC 7. MS3478(NPAM) （1） PAM （2） Precipitation 8. MS3637 （1） PAM （2） SMTMS （3） Precipitation 9. NODAA （1） SMTMS (2) Precipitation 10. WADD （1） SMTMS （2） Precipitation （3） Barometricmd 11. AQB （1） Precipitation 12. Dienpa (RS2) （1） Precipitation 13. Zuri （1） Precipitation （2） Barometricmd 14. Juze （1） Precipitation 15. Naqu hydrological station （1） Precipitation 16. MSofNaqu （1） Barometricmd 16. Naquradarsite （1）Radar system （2） Precipitation 17. Syangboche [Nepal] （1） AWS 18. Shiqu-anhe （1） AWS （2） GTDC 19. Seqin-Xiang （1） Barometricmd 20. NODA （1）Barometricmd （2） Precipitation （3) SMTMS 21. NaquHY （1） Barometricmd （2） Precipitation 22. NaquFx(BJ) （1） GTDC（2) PBLmd (3) Precipitation 23. MS3543 （1） Precipitation 24. MNofAmdo （1） Barometricmd 25. Mardi （1） Runoff 26. Gaize （1） AWS （2） GTDC （3） Sonde A CD of the data GAME-Tibet POP/IOP dataset cd （vol. 1) GAME-Tibet POP/IOP dataset cd （vol. 2)

Based on the existing natural hole data of 15 active layer depth monitoring sites in the Qinghai-Tibet Engineering Corridor, the active layer depth distribution map of the Qinghai-Tibet Engineering Corridor was simulated using the GIPL2.0 frozen soil model. The model required synthesis of a temperature data set of time series. The temperature data were divided into two phases according to the time spans, which were 1980-2009 and 2010-2015. The data of the first phase were from the Chinese meteorological driving data set (http://dam. Itpcas.ac.cn/rs/?q=data#CMFD_0.1), and the data of the second phase was the application of MODIS surface temperature products (MOD11A1/A2 and MYD11A1/A2) with a spatial resolution of 1 km. In addition, the soil type data required by the model came from the China Soil Database (V1.1) and have a resolution of 1 km. At the same time, the topography was also considered. The research area was classified into 88 types based on the measured soil thermophysical parameters and land cover types, and then the simulation was performed. The simulation results were compared with the field measured data. The results showed that they were highly consistent, and the correlation coefficient reached 0.75. In alpine areas, the average depth of the active layer is below 2.0 m. However, in the river valleys, the average depth of the active layer is above 4.0 m. In the high plain area, the depth of the active layer is usually between 3.0 m and 4.0 m.

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.

This data set is collected from the supplementary information part of the paper: Yao, T. , Thompson, L. , & Yang, W. . (2012). Different glacier status with atmospheric circulations in tibetan plateau and surroundings. Nature Climate Change, 1580, 1-5. This paper report on the glacier status over the past 30 years by investigating the glacial retreat of 82 glaciers, area reductionof 7,090 glaciers and mass-balance change of 15 glaciers. This data set contains 8 tables, the names and content are as follows: Data list: The data name list of the rest tables; t1: Distribution of Glaciers in the TP and surroundings; t2: Data and method for analyzing glacial area reduction in each basin; t3: Glacial area reduction during the past three decades from remote sensing images in the TP and surroundings; t4: Glacial length fluctuationin the TP and surroundings in the past three decades; t5: Detailed information on the glaciers for recent mass balance measurement in the TP and surroundings; t6: Recent annual mass balances in different regions in the TP; t7: Mass balance of Long-time series for the Qiyi, Xiaodongkemadi and Kangwure Glaciers in the TP. See attachments for data details: Supplementary information.pdf, Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings.pdf.

This data set is based on the evaluation of existing land cover data and the evidence theory，including a 1:100,000 land use map for the year 20 2000、a 1:1,000,000 vegetation map、a 1:1,000,000 swamp-wetland map, a glacier map and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001) were merged，Finally, the decision is made based on the principle of maximum trust, and a new 1KM land cover data of China in 2000 with IGBP classification system is produced. The new land cover data not only maintain the overall accuracy of China's land use data, but also supplement the information of vegetation types and vegetation seasons in China's vegetation map, update China's wetland map, add the latest information of China's glacier map, and make the classification system more general.

DEM is the English abbreviation of Digital Elevation Model, which is the important original data of watershed topography and feature recognition.DEM is based on the principle that the watershed is divided into cells of m rows and n columns, the average elevation of each quadrilateral is calculated, and then the elevation is stored in a two-dimensional matrix.Since DEM data can reflect local topographic features with a certain resolution, a large amount of surface morphology information can be extracted through DEM, which includes slope, slope direction and relationship between cells of watershed grid cells, etc..At the same time, the surface flow path, river network and watershed boundary can be determined according to certain algorithm.Therefore, to extract watershed features from DEM, a good watershed structure pattern is the premise and key of the design algorithm. Elevation data map 1km data formed according to 1:250,000 contour lines and elevation points in China, including DEM, hillshade, Slope and Aspect maps. Data set projection: Two projection methods: Equal Area projection Albers Conical Equal Area (105, 25, 47) Geodetic coordinates WGS84 coordinate system

This biophysical permafrost zonation map was produced using a rule-based GIS model that integrated a new permafrost extent, climate conditions, vegetation structure, soil and topographic conditions, as well as a yedoma map. Different from the previous maps, permafrost in this map is classified into five types: climate-driven, climate-driven/ecosystem-modified, climate-driven/ecosystem protected, ecosystem-driven, and ecosystem-protected. Excluding glaciers and lakes, the areas of these five types in the Northern Hemisphere are 3.66×106 km2, 8.06×106 km2, 0.62×106 km2, 5.79×106 km2, and 1.63×106 km2, respectively. 81% of the permafrost regions in the Northern Hemisphere are modified, driven, or protected by ecosystems, indicating the dominant role of ecosystems in permafrost stability in the Northern Hemisphere. Permafrost driven solely by climate occupies 19% of permafrost regions, mainly in High Arctic and high mountains areas, such as the Qinghai-Tibet Plateau.

This data set is an upgraded version of "China snow depth long time series data set (1978-2012)". The long time series data set of snow depth in China (1979-2021) adopts longitude and latitude projection, and the data is floating-point. Data sets are stored by year. Each year is a compressed package, and each compressed package contains daily snow depth files. The daily snow depth is stored in a TXT file named "yyyyddd.txt", where yyyy stands for year, DDD stands for Julian date, and the unit of snow depth is cm. For example, 2005001 Txt represents this ASCII file to describe the snow cover in China on the first day of 2005. The ASCII code file of the data set is composed of a header file and the main content. The header file consists of 6 lines of description information, such as the number of rows, the number of columns, the coordinates of the x-axis center point, the y-axis center point, the grid size, and the label value of the no data area. The main content is a two-dimensional group composed of the number of rows and columns. The unit of snow depth is cm. Because the space described by all ASCII files in the data set is nationwide in China, the header files of these files are unchanged. Now the header files are excerpted as follows (where xllcenter, yllcenter and cellsize are in degrees): Ncols 321 Nrows 161 Xllcenter 60 Yllcenter 15 Cellsize 0.25 NoData_ Value -1