This database includes slope, aspect and digital elevation model (DEM) data of Qinghai Tibet Plateau. The data comes from the 30m * 30m resolution numerical elevation model data downloaded from the geospatial data cloud website. Using the surface analysis function of ArcGIS software, the slope and aspect information of the Qinghai Tibet Plateau are extracted. The data has been rechecked and reviewed by many people, and its data integrity, position accuracy and attribute accuracy meet the standards, with excellent and reliable quality. As one of the engineering geological conditions, this data is the basic data for the research on the development law of major engineering disturbance disasters and major natural disasters in the Qinghai Tibet Plateau and the analysis of susceptibility, risk and risk.

As the “water tower of Asia”, Tibetan Plateau (TP) are the resource of major rivers in Asia. Black carbon (BC) aerosol emitted from surrounding regions can be transported to the inner TP by atmospheric circulation and consequently deposited in snow, which can significantly influence precipitation and mass balance of glaciers. Five Aethalometers are used to mornitoring black carbon concentration at 5 stations on the Tibetan Plateau. It can provide basic dataset to study the effects of BC to the environment and climate over the Tibetan Plateau, as well as the pollutants transport.

The dataset includes lake ice phenology information of 132 lakes across the Tibetan Plateau (with area larger than 40 km2) from 1978 to 2016 (freeze-up start date, freeze-up end date, break-up start date, break-up end, completely ice-duration and ice duration). The data set uses the combination of model and remote sensing to obtain the phenological information. Firstly, Using the average lake surface temperature extracted by MOD11A2 as calibration data, daily scale long-time series lake surface temperature series was simulated based on an improved lake semi-physical model (air2water). Then the temperature threshold of lake ice phenology was determined by the mod10a1 snow cover product. Compared with the existing research results and data sets, the correlation (R-square) is higher than 0.75. Combined with the advantages of remote sensing and numerical model, this dataset provides support for the analysis of water-air interface exchange, water or heat balance, biochemical processes and their response to climate change of lakes on a large spatio-temporal scale across the Tibetan Plateau.

The data is the phytoplankton data of 70 points in 26 lakes in Tibet in 2020. The sampling time is from August to September. The sampling method is the conventional phytoplankton sampling method. 1.5 liters of samples are collected, fixed by Lugo's solution, siphoned and concentrated after static precipitation, and the results are examined by inverted microscope. The data includes the density data of different phytoplankton of 77 species / genus in 10 categories, including diatom, green algae, cyanobacteria, dinoflagellate, naked algae, cryptoalgae, brown algae, brown algae and CHAROPHYTA. This data is original and unprocessed. The unit is piece / L. The data can be used to characterize the composition and abundance of phytoplankton in the open water areas of these lakes, and can also be used to calculate the diversity of phytoplankton communities in these lakes.

Precipitation over the Tibetan Plateau (TP) known as Asia's water tower plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP that features complex terrain and the harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) data set was generated at a daily timescale and a spatial resolution of 0.1° across the TP for the 1998‒2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root mean squared error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow rivers in the TP. The MSP achieved the best Nash-Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004‒2014. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high quality precipitation data for hydrological research. The latitude and longitude of the left bottom corner across the TP, the number of rows and columns, and grid cells information are all included in each ASCII file.

The precipitation dataset of the Third Pole region mainly contains two EXCEL files: (1) Daily precipitation data in China in the Third Pole region, named as China_daily.xlsx. The precipitation data in China were obtained from the China Meteorological Administration-National Meteorological Information Center (http://data.cma.gov.cn/site/index.html). (2) Daily precipitation data in other countries in the Third Pole region, named as Foreign_daily.xlsx. The precipitation data in other countries were obtained from NCDC International Climatic Data Center - NOAA Satellite Information Service Center (http://www7.ncdc.noaa.gov/CDO/country), Pakistan Meteorological Administration, Nepal Meteorological Administration, etc. There are seven variables in these two EXCEL data files: precipitation, corrected precipitation, correction factor, wind-induced loss, evaporation loss, wet loss, and trace precipitation. The detail characteristics of TPE stations were described in an EXCEL file either, named as "TPE station and gauge type.xls". The raw data has been strictly quality controlled by the relevant meteorological departments and has been applied in relevant academic papers.

The continuous snow cover area in time and space is one of key elements to study of land surface energy and water exhange, mountain hydrology, land surface model, numerical weather forecast and climate change. However, the large number of clouds causes data gaps in the snow cover area from optical remote sensing. The MODIS observations of Terra and aqua, FY-2E and FY-2F VISSR are used to obtain fractional snow cover (subpixel snow cover) which is less affected by the cloud, and the snow cover of the remaining cloud pixels is supplemented according to the time series information. Finally the cloudless daily snow fraction is obtained. This data set includes the daily fractional snow cover at 5 km spatial resolution in the Tibetan Plateau and China.

Based on a recently developed inventory of permafrost presence or absence from 1475 in situ observations, we developed and trained a statistical model and used it to compile a high‐resolution (30 arc‐ seconds) permafrost zonation index (PZI) map. The PZI model captures the high spatial variability of permafrost distribution over the QTP because it considers multi- ple controlling variables, including near‐surface air temperature downscaled from re‐ analysis, snow cover days and vegetation cover derived from remote sensing. Our results showed the new PZI map achieved the best performance compared to avail- able existing PZI and traditional categorical maps. Based on more than 1000 in situ measurements, the Cohen's kappa coefficient and overall classification accuracy were 0.62 and 82.5%, respectively. Excluding glaciers and lakes, the area of permafrost regions over the QTP is approximately 1.54 (1.35–1.66) ×106 km2, or 60.7 (54.5– 65.2)% of the exposed land, while area underlain by permafrost is about 1.17 (0.95–1.35) ×106 km2, or 46 (37.3–53.0)%.

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.

The long-term evolution of lakes on the Tibetan Plateau (TP) could be observed from Landsat series of satellite data since the 1970s. However, the seasonal cycles of lakes on the TP have received little attention due to high cloud contamination of the commonly-used optical images. In this study, for the first time, the seasonal cycle of lakes on the TP were detected using Sentinel-1 Synthetic Aperture Radar (SAR) data with a high repeat cycle. A total of approximately 6000 Level-1 scenes were obtained that covered all large lakes (> 50 km2) in the study area. The images were extracted from stripmap (SM) and interferometric wide swath (IW) modes that had a pixel spacing of 40 m in the range and azimuth directions. The lake boundaries extracted from Sentinel-1 data using the algorithm developed in this study were in good agreement with in-situ measurements of lake shoreline, lake outlines delineated from the corresponding Landsat images in 2015 and lake levels for Qinghai Lake. Upon analysis, it was found that the seasonal cycles of lakes exhibited drastically different patterns across the TP. For example, large size lakes (> 100 km2) reached their peaks in August−September while lakes with areas of 50−100 km2 reached their peaks in early June−July. The peaks of seasonal cycles for endorheic lakes were more pronounced than those for exorheic lakes with flat peaks, and glacier-fed lakes with additional supplies of water exhibited delayed peaks in their seasonal cycles relative to those of non-glacier-fed lakes. Large-scale atmospheric circulation systems, such as the westerlies, Indian summer monsoon, transition in between, and East Asian summer monsoon, were also found to affect the seasonal cycles of lakes. The results of this study suggest that Sentinel-1 SAR data are a powerful tool that can be used to fill gaps in intra-annual lake observations.

The Tibet-Obs established in 2008 consists of three regional-scale soil moisture (SM) monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15-min in situ measurements collected at a depth of 5 cm by multiple SM monitoring sites of all the networks, and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks.