The distribution of lakes in space and its change over time are closely related to agricultural, environmental and ecological issues, and are critical factors for human socio-economic development. In the past decades, satellite based remote sensing has been developed rapidly to provide essential data sources for monitoring temporal lakes dynamics with its advantage of rapidness, wide coverage, and lower cost. This dataset was produced from Landsat images using the automated water detection method (Feng et al, 2015). We collected 96,278 Landsat images (about 25 terabytes) that acquired since 2000 with less than 80% cloud contamination in the arid region of central Asia and Tibetan Plateau. Water is detected in each of the image and then aggregated to monthly temporal resolution by taking advantage of the high-performance processing capability and large data storage provided by Global Land Cover Facility (GLCF) at University of Maryland. The results are validated systematically and quantitatively using manually interpreted dataset, which consists of a set of locations collected by a stratified random sampling strategy to effectively represent different spatial-temporal distributions in the region. The validation suggests high accuracy of the results (overall accuracy: 99.45(±0.59); user accuracy: 85.37%±(3.74); produce accuracy: 98.17(±1.05)).

This data is originated from the 1:100,000 national basic geographic database, which was open freely for public by the National Basic Geographic Information Center in November 2017. The boundary of the Qinghai-Tibet Plateau was spliced and clipped as a whole, so as to facilitate the study on the Qinghai-Tibet plateau. This data set is the 1:100,000 administrative boundaries of the qinghai-tibet plateau, including National_Tibet_line、 Province_Tibet、City_Tibet、County_Tibet_poly and County_Tibet_line. Administrative boundary layer (County_Tibet_poly) property name and definition: Item Properties Describe Example PAC Administrative division code 513230 NAME The name of the County line name Administrative boundary layer (BOUL) attribute name and definition: Item Properties Describe Example GB classification code 630200 Administrative boundary layer (County_Tibet_line) attribute item meaning: Item Properties Describe Example GB 630200 Provincial boundary GB 640200 Prefectural, municipal and state administrative boundaries GB 650201 county administrative boundaries (determined)

Snow cover dataset is produced by snow and cloud identification method based on optical instrument observation data, covering the time from 1989 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground under clear sky or transparent thin cloud is covered by snow. The input data sources include AVHRR L1 data of NOAA and MetOp serials of satellites, and L1 data corresponding to AVHRR channels taken from TERRA/MODIS. Decision Tree algorithm (DT) with dynamic thresholds is employed independent of cloud mask and its cloud detection emphasizes on reserving snow, particularly under transparency cirrus. It considers a variety of methods for different situations, such as ice-cloud over the water-cloud, snow in forest and sand, thin snow or melting snow, etc. Besides those, setting dynamic threshold based on land-surface type, DEM and season variation, deleting false snow in low latitude forest covered by heavy aerosol or soot, referring to maximum monthly snowlines and minimum snow surface brightness temperature, and optimizing discrimination program, these techniques all contribute to DT. DT discriminates most snow and cloud under normal circumstances, but underestimates snow on the Qinghai-Tibet Plateau in October. Daily product achieves about 95% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.

This dataset contains five types of boundaries. 1. TPBoundary_ 2500m: Based on ETOPO5 Global Surface Relief, ENVI+IDL was used to extract data at an elevation of 2500m within the longitude (65~105E) and latitude (20~45N) range in the Tibetan Plateau. 2. TPBoundary_ 3000m: Based on ETOPO5 Global Surface Relief, ENVI+IDL was used to extract data at an elevation of 3000m within the longitude (65~105E) and latitude (20~45N) range in the Tibetan Plateau. 3. TPBoundary_ HF (high_frequency): This boundary is defined according to 2 previous studies. Bingyuan Li (1987) had a systematic discussion on the principles for determining the extent of the Tibetan Plateau and the specific boundaries. From the perspective of the formation and basic characteristics of the Tibetan Plateau, he proposed the basic principles for determining the extent of the Tibetan Plateau based on the geomorphological features, the plateau surface and its altitude, while considering the integrity of the mountain. Yili Zhang (2002) determined the extent and boundaries of the Tibetan Plateau based on the new results of research in related fields and years of field practice. He combined information technology methods to precisely locate and quantitatively analyze the extent and boundary location of the Tibetan Plateau, and concluded that the Tibetan Plateau in China extends from the Pamir Plateau in the west to the Hengduan Mountains in the east, from the southern edge of the Himalayas in the south to the northern side of the Kunlun-Qilian Mountains in the north. On April 14, 2017, the Ministry of Civil Affairs of the People's Republic of China issued the Announcement on Adding Geographical Names for Public Use in the Southern Tibetan Region (First Batch), adding six geographical names in the southern Tibetan region, including Wo’gyainling, Mila Ri, Qoidêngarbo Ri, Mainquka, Bümo La, and Namkapub Ri. ４. TPBoundary_ New (2021): Along with the in-depth research on the Tibetan Plateau, the improvement of multidisciplinary research and understanding inside and outside the plateau, and the progress of geographic big data and Earth observation science and technology, the development of the 2021 version of the Tibetan Plateau boundary data by Yili Zhang and et al. was completed based on the comprehensive analysis of ASTER GDEM and Google Earth remote sensing images. The range boundary starts from the northern foot of the West Kunlun Mountain-Qilian Mountain Range in the north and reaches the southern foot of the Himalayas and other mountain ranges in the south, with a maximum width of 1,560 km from north to south; from the western edge of the Hindu Kush Mountains and the Pamir Plateau in the west to the eastern edge of the Hengduan Mountains and other mountain ranges in the east, with a maximum length of about 3,360 km from east to west; the latitude and longitude range is 25°59′30″N~40°1′0″N, 67°40′37″E~104°40′57″E, with a total area of 3,083,400km2 and an average altitude of about 4,320m. Administratively, the Tibetan Plateau is distributed in nine countries, including China, India, Pakistan, Tajikistan, Afghanistan, Nepal, Bhutan, Myanmar, and Kyrgyzstan. ５. TPBoundary_ Rectangle: The rectangle was drawn according to the range of Lon (63~105E) and Lat (20~45N). The data are in latitude and longitude projection WGS84. As the basic data, the boundary of the Tibetan Plateau can be used as a reference basis for various geological data and scientific research on the Tibetan Plateau.

Based on the analysis of brgdgts and hydrogen isotopes of leaf wax in lake sediments from Tengchong Qinghai (tcqh) in Yunnan Province, this study shows for the first time the high-resolution annual average temperature change history of low latitude land since the last glacial period (since the last 88000 years). According to the annual average temperature of South Asia established by tcqh core, there are two warm periods of 88000-71000 years and 45000-22000 years in this region, and the temperature range is about 2-3 ° C. Since the Holocene, the temperature has been increasing for about 1-2 years ° C。

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.

Mean annual ground temperature (MAGT) at a depth of zero annual amplitude and permafrost thermal stability type are fundamental importance for engineering planning and design, ecosystem management in permafrost region. This dataset is produced by integrating remotely sensed freezing degree-days and thawing degree-days, snow cover days, leaf area index, soil bulk density, high-accuracy soil moisture data, and in situ MAGT measurements from 237 boreholes for the 2010s (2005-2015) on the Tibetan Plateau (TP) by using an ensemble learning method that employs a support vector regression (SVR) model based on distance-blocked resampling training data with 200 repetitions. Validation of the new permafrost map indicates that it is probably the most accurate of all available maps at present. The RMSE of MAGT is approximately 0.75 °C and the bias is approximately 0.01 °C. This map shows that the total area of permafrost on the TP is approximately 115.02 (105.47-129.59) *104 km2. The areas corresponding to the very stable, stable, semi-stable, transitional, and unstable types are 0.86*104 km2, 9.62*104 km2, 38.45*104 km2, 42.29*104 km2, and 23.80*104 km2, respectively. This new dataset is available for evaluate the permafrost change in the future on the TP as a baseline. More details can be found in Ran et al., (2020) that published at Science China Earth Sciences.

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.

This dataset is the monthly precipitation data of China, with a spatial resolution of 0.0083333 ° (about 1km) and a time range of 1901.1-2021.12. The data format is NETCDF, i.e.. Nc format. This dataset is generated in China through the Delta spatial downscaling scheme based on the global 0.5 ° climate dataset released by CRU and the global high-resolution climate dataset released by WorldClim. In addition, 496 independent meteorological observation point data are used for verification, and the verification results are reliable. This data set covers the main land areas in China (including Hong Kong, Macao and Taiwan), excluding islands and reefs in the South China Sea. In order to facilitate storage, the data are all int16 type and stored in nc files, with precipitation units of 0.1mm. NC data can be mapped using ArcMAP software; Matlab software can also be used for extraction processing. Matlab has released the function to read and store nc files. The read function is ncread, and switch to the nc file storage folder. The statement is expressed as: ncread ('XXX.nc ',' var ', [i j t], [leni lenj lent]), where XXX.nc is the file name, and is the string required' '; Var is from XXX The variable name read in NC. If it is a string, '' is required; i. J and t are the starting row, column and time of the read data respectively, and leni, lenj and lent i are the length of the read data in the row, column and time dimensions respectively. In this way, this function can be used to read in any region and any time period in the study area. There are many commands about NC data in the help of Matlab, which can be viewed. WGS84 is recommended for data coordinate system.