Mountain glaciers are important freshwater resources in Western China and its surrounding areas. It is at the drainage basin scale that mountain glaciers provide meltwater that humans exploit and utilize. Therefore, the determination of glacierized river basins is the basis for the research on glacier meltwater provisioning functions and their services. Based on the Randolph glacier inventory 6.0, Chinese Glacier Inventories, China's river basin classifications (collected from the Data Centre for Resources and Environmental Sciences, Chinese Academy of Sciences), and global-scale HydroBASINS (www.hydrosheds.org), the following dataset was generated by the intersection between river basins and glacier inventory: (1) Chinese glacierized macroscale and microscale river basins; (2) International glacierized macroscale river basin fed by China’s glaciers; (3) Glacierized macroscale river basin data across High Mountain Asia. This data takes the common river basin boundaries in China and the globe into account, which is poised to provide basic data for the study of historical and future glacier water resources in China and its surrounding areas.

Among many indicators reflecting changes in climate and environment, the stable isotope index of ice core is an indispensable parameter in ice core record research, and it is one of the most reliable means and the most effective way to restore past climate change. Meanwhile, ice core accumulation is a direct record of precipitation on the glacier, and high-resolution ice core records ensure continuity of precipitation records. Therefore, ice core records provide an effective means of restoring changes in precipitation. Stable isotopes from ice cores drilled throughout the TP have been used to reconstruct climate histories extending back several thousands of years. This dataset provides data support for studying climate change on the Tibetan Plateau.

Different forms of precipitation (snow, sleet, and rain) have divergent effects on the Earth’s surface water and energy fluxes. Therefore, discriminating between these forms is of significant importance, especially under a changing climate. We applied a state-of-the-art parameterization scheme with wet-bulb temperature, relative humidity, surface air pressure, and elevation as inputs, as well as observational gridded datasets with a maximum spatial resolution of 0.25◦, to generate a gridded dataset of different forms of daily precipitation (snow, sleet, and rain) and their temperature threshold across mainland China from 1961-2016. The annual snow, sleet, and rain amount were further calculated. The dataset may benefit various research communities, such as cryosphere science, hydrology, ecology, and climate change.

In recent years, branched chain glycerol dialkyl glycerol tetraethers (brGDGTs) derived from microbial cell membrane lipids are sensitive to environmental parameters (temperature and pH, etc.) and are widely used in the quantitative reconstruction of paleoenvironment. Based on the surface sediments of lakes in the Qinghai Tibet Plateau and brGDGTs in the surface sediments of other lakes published in China, we developed a new brGDGT-air temperature calibration. By collecting the annual suspended particulate matter of Dagze Co and analyzing brGDGTs, the distribution of brGDGTs in different water column layers were reconstructed. Combined with the modern observation results and the new calibration, using the results of brGDGT in the sediments of Xiada Co, the atmospheric temperature changes in the western Qinghai Tibet Plateau in the past 2000 years are reconstructed. This result provides an important theoretical reference for the reconstruction of temperature by brGDGTs in the future.

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

The sand drift potential (DP, in vector units (VU)) is calculated by DPi=∑U^2*[U-Ut]*fu where i represents 16 directions: N, NNE, NE, NEE, E, EES, ES, ESS, S, SSW, WS, WWS, W, WWN, NW and NNW; U is the sand-moving wind speed at the standard height of 10 m; Ut is the threshold wind velocity, which is the minimum wind velocity at the 10 m height to cause sediment particles in saltation; and fu is the fraction of time when the wind speed is higher than Ut. The 2 m s-1 bin is adopted in each sand-moving wind direction, corresponding to the mean wind speeds of 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33 and 34 m s-1, and the sum of these bins is the final DP in the wind direction. Note that these mean wind speeds have been expressed in knots by the approximate conversion (1 knot = 0.5144 m s-1) to ensure the valid classification of wind energy (low energy, <200 VU; intermediate energy, ≥200 VU and <400 VU; high energy, ≥400 VU) developed by Fryberger. The divisor used in calculating the frequency of effective sand-moving winds from different directions is the total hour number of Julian years (8760 hours for common years or 8784 hours for leap years). The wind speed and wind direction data from 1950 to 2021 were hourly estimates of 10 m u-component of wind and 10 m v-component wind with a horizontal resolution of 0.1°×0.1° generated with the ERA5-Land dataset.

Surface evapotranspiration (ET) is an important link of water cycle and energy transmission in the earth system. The accurate acquisition of ET is helpful to the study of global climate change, crop yield estimation, drought monitoring, and has important guiding significance for regional and even global water resources planning and management. With the development of remote sensing technology, remote sensing estimation of surface evapotranspiration has become an effective way to obtain regional and global evapotranspiration. At present, a variety of low and medium resolution surface evapotranspiration products have been produced and released in business. However, there are still many uncertainties in the model mechanism, input data, parameterization scheme of remote sensing estimation of surface evapotranspiration model. Therefore, it is necessary to use the real method. The accuracy of remote sensing estimation of evapotranspiration products was quantitatively evaluated by sex test. However, in the process of authenticity test, there is a problem of spatial scale mismatch between the remote sensing estimation value of surface evapotranspiration and the site observation value, so the key is to obtain the relative truth value of satellite pixel scale surface evapotranspiration. Based on the flux observation matrix of "multi-scale observation experiment of non-uniform underlying surface evaporation" in the middle reaches of Heihe River Basin from June to September 2012, the stations 4 (Village), 5 (corn), 6 (corn), 7 (corn), 8 (corn), 11 (corn), 12 (corn), 13 (corn), 14 (corn), 15 (corn), 17 (orchard) and the lower reaches of January to December 2014 Oasis Populus euphratica forest station (Populus euphratica forest), mixed forest station (Tamarix / Populus euphratica), bare land station (bare land), farmland station (melon), sidaoqiao station (Tamarix) observation data (automatic meteorological station, eddy correlator, large aperture scintillation meter, etc.) are used as auxiliary data, and the high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) are used as auxiliary data. See Fig. 1 for the distribution map. Considering the land Through direct test and cross test, six scale expansion methods (area weight method, scale expansion method based on Priestley Taylor formula, unequal weight surface to surface regression Kriging method, artificial neural network, random forest, depth belief network) were compared and analyzed, and finally a comprehensive method (on the underlying surface) was optimized. The area weight method is used when the underlying surface is moderately inhomogeneous; the unequal weight surface to surface regression Kriging method is used when the underlying surface is moderately inhomogeneous; the random forest method is used when the underlying surface is highly inhomogeneous) to obtain the relative true value (spatial resolution of 1km) of the surface evapotranspiration pixel scale of MODIS satellite transit instantaneous / day in the middle and lower reaches of the flux observation matrix area respectively, and to observe through the scintillation with large aperture. The results show that the overall accuracy of the data set is good. The average absolute percentage error (MAPE) of the pixel scale relative truth instantaneous and day-to-day is 2.6% and 4.5% for the midstream satellite, and 9.7% and 12.7% for the downstream satellite, respectively. It can be used to verify other remote sensing products. The evapotranspiration data of the pixel can not only solve the problem of spatial mismatch between the remote sensing estimation value and the station observation value, but also represent the uncertainty of the verification process. For all site information and scale expansion methods, please refer to Li et al. (2018) and Liu et al. (2016), and for observation data processing, please refer to Liu et al. (2016).

The long-term sequence data set of lake areas on the Tibetan Plateau contains area data of 364 lakes with areas greater than 10 square kilometers from 1970s to 2013. Based on Landsat images, Landsat data in October are mainly used, and one data is taken every three years to reduce seasonal variation and make the available data reach the maximum. The data set is extracted by the NDWI Water Index, and each lake undergoes manual visual inspection and edition. The data set can be used to study lake change, lake water balance and climate change on the Tibetan Plateau. Data type: Vector data. Projection: WGS84.

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.

Rainfall erosivity is one of the important basic data to quantify soil erosion in the Tibet Plateau. High precision rainfall erosivity data is the key to understand the current situation of soil and water loss in theTibet Plateau and formulate soil and water conservation measures. Meanwhile, it can provide a powerful reference for the prevention and control of geological disasters in the Tibet Plateau. Based on the 1-min dense precipitation observations and the grid precipitation product, a new annual rainfall erosivity dataset in Tibet Plateau from 1950 to 2020 is constructed through the steps of correction, reconstruction and validation. This dataset is the rainfall erosivity data set with the highest accuracy and the longest time series in the Tibet Plateau.

The Qinghai Tibet Plateau is a sensitive region of global climate change. Land surface temperature (LST), as the main parameter of land surface energy balance, characterizes the degree of energy and water exchange between land and atmosphere, and is widely used in the research of meteorology, climate, hydrology, ecology and other fields. In order to study the land atmosphere interaction over the Qinghai Tibet Plateau, it is urgent to develop an all-weather land surface temperature data set with long time series and high spatial-temporal resolution. However, due to the frequent cloud coverage in this region, the use of existing satellite thermal infrared remote sensing land surface temperature data sets is greatly limited. Compared with the previous version released in 2019, Western China Daily 1km spatial resolution all-weather land surface temperature data set (2003-2018) V1, this data set (V2) adopts a new preparation method, namely satellite thermal infrared remote sensing reanalysis data integration method based on new land surface temperature time decomposition model. The main input data of the method are Aqua MODIS LST products and GLDAS data, and the auxiliary data include vegetation index and surface albedo provided by satellite remote sensing. This method makes full use of the high frequency and low frequency components of land surface temperature and the spatial correlation of land surface temperature provided by satellite thermal infrared remote sensing and reanalysis data. The evaluation results show that this data set has good image quality and accuracy, which is not only seamless in space, but also highly consistent with the amplitude and spatial distribution of 1 km daily Aqua MODIS LST products widely used in current academic circles. When MODIS LST was used as the reference value, the mean deviation (MBE) of the data set in daytime and nighttime was -0.28 K and -0.29 K respectively, and the standard deviation (STD) of the deviation was 1.25 K and 1.36 K respectively. The test results based on the measured data of six stations in the Qinghai Tibet Plateau and Heihe River Basin show that under clear sky conditions, the data set is highly consistent with the measured LST in daytime / night, and its MBE is -0.42-0.25 K / - 0.35-0.19 K; The root mean square error (RMSE) was 1.03 ~ 2.28 K / 1.05 ~ 2.05 K; Under the condition of non clear sky, the MBE of this data set in daytime / night is -0.55 ~ 1.42 K / - 0.46 ~ 1.27 K; The RMSE was 2.24-3.87 K / 2.03-3.62 K. Compared with the V1 version of the data, the two kinds of all-weather land surface temperature show the characteristics of seamless (i.e. no missing value) in the spatial dimension, and in most areas, the spatial distribution and amplitude of the two kinds of all-weather land surface temperature are highly consistent with MODIS land surface temperature. However, in the region where the brightness temperature of AMSR-E orbital gap is missing, the V1 version of land surface temperature has a significant systematic underestimation. The mass of trims land surface temperature is close to that of V1 version outside AMSR-E orbital gap, while the mass of trims is more reliable inside the orbital gap. Therefore, it is recommended that users use V2 version. The time span of this data set is from 2000 to 2021 and will be updated continuously; The time resolution is twice a day (corresponding to the two transit times of aqua MODIS in the daytime and at night); The spatial resolution is 1 km. In order to facilitate the majority of colleagues to carry out targeted research around the Qinghai Tibet Plateau and its adjacent areas, and reduce the workload of data download and processing, the coverage of this data set is limited to Western China and its surrounding areas (72 ° E-104 ° E，20 ° N-45 ° N）。 Therefore, this dataset is abbreviated as trims lst-tp (thermal and reality integrating modem resolution spatial seamless LST – Tibetan Plateau) for user's convenience.

Land surface temperature (LST) is one of the important parameters of the interface between the earth's surface and atmosphere. It is not only the direct reflection of the interaction between the surface and the atmosphere, but also has a complex feedback effect on the earth atmosphere process. Therefore, land surface temperature is not only a sensitive indicator of climate change and an important prerequisite for mastering the law of climate change, but also a direct input parameter of many models, which has been widely used in many fields, such as meteorology, climate, environmental ecology, hydrology and so on. With the deepening and refinement of Geosciences and related fields, there is an urgent need for all weather LST based on satellite remote sensing. The generation principle of this dataset is a satellite thermal infrared remote sensing reanalysis data integration method based on a new land surface temperature time decomposition model. The main input data of the method are Aqua MODIS LST products and GLDAS data, and the auxiliary data include vegetation index and surface albedo provided by satellite remote sensing. The method makes full use of the high-frequency and low-frequency components of land surface temperature and the spatial correlation of land surface temperature provided by satellite thermal infrared remote sensing and reanalysis data, and finally reconstructs a high-quality all-weather land surface temperature data set. The evaluation results show that this data set has good image quality and accuracy, which is not only seamless in space, but also highly consistent with the amplitude and spatial distribution of 1 km daily Aqua MODIS LST products widely used in current academic circles. When MODIS LST is used as reference, the mean deviation (MBE) of the data set is 0.08k to 0.16k, and the standard deviation of deviation (STD) is 1.12k to 1.46k. Compared with the daily 1km AATSR LST product released by ESA, the MBE and STD of the product are -0.21k to 0.25k and 1.27k to 1.36k during the day and night. Based on the measured data of 15 stations in Heihe River Basin, Northeast China, North China and South China, the test results show that the MBE is -0.06k to -1.17k, and the RMSE is 1.52k to 3.71k, and there is no significant difference between clear sky and non clear sky. The time resolution of this data set is twice a day, the spatial resolution is 1km, and the time span is from 2000 to 2021; The spatial scope includes the main areas of China's land (including Hong Kong, Macao and Taiwan, excluding the islands in the South China Sea) and the surrounding areas (72 ° E-135 ° E，19 ° N-55 ° N）。 This dataset is abbreviated as trims LST (thermal and reality integrating modem resolution spatial sealing LST) for users to use. It should be noted that the spatial subset of trims LST, trims lst-tp (1 km daily land surface temperature data set in Western China, trims lst-tp; 2000-2021) V2) has also been released in the national Qinghai Tibet Plateau scientific data center to reduce the workload of data download and processing for relevant users.