Wind speed data is widely used in many sciences, management, and policy fields to assess renewable energy potential, address wind hazards, investigate biological phenomena, and explore climate change/variability, among other applications. The challenge is obtaining complete and accurate wind datasets, as observations are limited in distribution. Global-scale weather stations suffer from spatial and temporal discontinuities that limit their utility. While reanalysis products and climate model simulations achieve data continuity, they often fail to reproduce significant wind speed trends because few of them assimilate in-situ wind observations on land. Data interpolation helps fill gaps, but the high variability of wind speed data, combined with a low distribution of observations worldwide, prevents standard statistical interpolation methods such as kriging or principal component analysis from being accurate for areas with sparse data. As a result, wind speed data has been the bottleneck in related studies. Here, based on the partial convolutional neural network, we reconstructed the global near-surface wind speed data during 1973-2021 by assimilating simulation outputs from 34 climate models and the HadISD dataset, which the Met Office Hadley Center creates. Our dataset has a spatial resolution of 1.25°×2.5° and containers observed wind speed trends.
ZHOU Lihong , ZENG Zhenzhong , JIANG Xin
The dataset is from the transient experiment TRN40ka in Zhang et al (2021, Nature Geoscience), spanning 40ka-32ka BP with changing orbital parameters. For detailed description of experimental design, please refer to the original paper. Model details: COSMOS (ECHAM5-JSBACH-MPI-OM), a comprehensive fully coupled atmosphere–ocean general circulation model (AOGCM), is used to generate the dataset. The atmospheric model ECHAM5, complemented by the land surface component JSBACH, is used at T31 resolution (∼3.75°), with 19 vertical layers. The ocean model MPI-OM, including sea-ice dynamics that is formulated using viscous-plastic rheology, has a resolution of GR30 (3°×1.8°) in the horizontal, with 40 uneven vertical layers.
ZHANG Xu
Surface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia-Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016-2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy and high resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher but the product accuracy is also higher than that of those products. In particular, hourly and daily root mean square errors of hourly and daily of the new SWDR are 104.9 and 31.5 Wm-2, respectively, which are much smaller than those of CERES (at 121.6 and 38.6 Wm-2, respectively), ERA5 (at 176.6 and 39.5 Wm-2, respectively) and GLASS (daily of 36.5 Wm-2). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 Wm-2, respectively, which are comparable to that of CERES and ERA5, and even better over high altitude regions.
HUSI Letu, WANG Tianxing, DU Yihan
The basic principle of ancient recipe analysis based on carbon and nitrogen stable isotope analysis method is you are what you eat, that is, the chemical composition of animal tissues and organs is closely related to their diet. Through the detection of isotope ratio of relevant elements, the food structure of ancient people and animals can be directly revealed Then it discusses the research means of people's livelihood and livestock domestication. The collagen of human and animal bones from shilinggang site in Nujiang, Yunnan Province in the southwest of Qinghai Tibet Plateau was analyzed by carbon and nitrogen stable isotopes.
DONG Guanghui , REN Lele
The data sources of this dataset mainly include domestic satellite images such as HJ-1A/B, GF-1/2, ZY-3, and Landsat TM/ETM+/OLI series satellite image data. Using the domestic satellite images supplemented by Google Earth images to generate the component training sample and validation sample data of different geographical divisions. Using Google Earth Engine (GEE) to test and correct the model algorithm parameters. The normalized settlement density index (NSDI) is obtained based on random forest algorithm, Landsat TM/ETM+/OLI series satellite images and auxiliary data. The vector boundary of urban built-up area is obtained by density segmentation method after manual interactive interpretation and correction. The NSDI, vegetation coverage index and vector boundary of the Tibetan Plateau are used to produce the original data of urban impervious surface and urban green space fractions in the Tibetan Plateau. After correction and accuracy evaluation, the datasets of urban impervious surface area and green space fractions in the Tibetan Plateau from 2000 to 2020 are generated. The resolution of the data product is 30 m, and the coordinate system and storage format of the data files are unified. The geographic coordinate system is WGS84, the projected coordinate system is Albers, and the data storage format is GeoTIFF, the data unit is percentage (the value range is 0~10000), and the scale factor is 0.01. In order to quantify the change of urban land cover more accurately, samples from several typical cities are selected to verify the dataset. The specific verification methods and accuracy are shown in the published results. The data can be used to analyze and reveal the impact of land cover change and future scenario simulation on the Tibetan Plateau, to provide a scientific basis for building environmentally livable cities and improving the quality of human settlements on the Tibetan Plateau.
KUANG Wenhui, GUO Changqing, DOU Yinyin
By archaeological investigation and excavation in the Tibet Plateau and neighbouring areas, we discovered Xichengyi site, Jinchankou site, Shannashuzha site, Jiangxifen site, Zongri site, Bangga site and so on. In this dataset, there are some basic informations about these sites, such as location, longitude, latitude, altitude, material culture and so on. On this Basis, we identified and analysed stone artifacts, animal remains, plant fossil, sedimentary sample, and obtained a batch of dating data of radiocarbon dating; pollen data; identification and isotopic composition and quality indicators of animal remains and plant fossil. At the same time, the relevant animal and plant remains and isotopes in the Tibet Plateau and neighbouring areas are sorted out. Based on natural geographical factors and sites in different periods, the method of realizing cumulative connection between nodes under the control of the lowest cost uses GIS(R language) tool to carry out spatial numerical calculation, and the result is used as the communication route in prehistoric times (Neolithic-bronze age). The shape of the route developed from the northeast-east-southeast-southwest edge of Neolithic Age in crescent shape to the trend of network development from the edge to the hinterland of Bronze Age, which is a manifestation of the gradual evolution from the exchange of plateau edge to the exchange of edge-hinterland, which is constantly strengthened. A total of 49 dung samples of grazing livestock (30 yak dung samples, 11 horse dung samples and 8 sheep dung samples) were collected in the alpine meadow area of the eastern Qinghai-Tibet Plateau, and the pollen analysis of dung samples was carried out on the basis of regional vegetation investigation. This dataset provide important basic data for understanding when and how human lived in the Tibet Plateau and neighbouring areas during the Neolithic Age and the Bronze age.
DONG Guanghui , HOU Guangliang, YANG Xiaoyan
A comprehensive understanding of the permafrost changes in the Qinghai Tibet Plateau, including the changes of annual mean ground temperature (Magt) and active layer thickness (ALT), is of great significance to the implementation of the permafrost change project caused by climate change. Based on the CMFD reanalysis data from 2000 to 2015, meteorological observation data of China Meteorological Administration, 1 km digital elevation model, geo spatial environment prediction factors, glacier and ice lake data, drilling data and so on, this paper uses statistics and machine learning (ML) method to simulate the current changes of permafrost flux and magnetic flux in Qinghai Tibet Plateau The range data of mean ground temperature (Magt) and active layer thickness (ALT) from 2000 to 2015 and 2061 to 2080 under rcp2.6, rcp4.5 and rcp8.5 concentration scenarios were obtained, with the resolution of 0.1 * 0.1 degree. The simulation results show that the combination of statistics and ML method needs less parameters and input variables to simulate the thermal state of frozen soil, which can effectively understand the response of frozen soil on the Qinghai Tibet Plateau to climate change.
Ni Jie, Wu Tonghua
Relationship between modern pollen and climate, and its representative to vegetation are the important references in explaining and reconstructing past climate and vegetation qualitatively or quantitatively. To extrct past climate and vegetation signals from fossil pollen spectrum of a lacustrine sediment, a corresponding modern pollen dataset collected from lake-sediment surface is necessary. At present, there are a few modern pollen datasets extracted from lake sediment-surface established on the Tibetan Plateau, however, the geographic gaps (e.g. the central and east Tibetan Plateau) of available sampled lakes influence the correct understanding. To ensure the even distribution of the representative lakes, we collected lake sediment-surface samples (n=117) covering the alpine meadow evenly on the east and central Tibetan Plateau, in July and August 2018. For pollen extraction, approximately 10 g (wet original sediment) per sample were sub-sampled. Pollen sample was processed by the standard acid-alkali-acid procedures followed by 7-μm-mesh sieving. More than 500 terrestrial pollen grains were counted for each sample. Pollen assemblages of the dataset from alpine meadow are dominated by Cyperaceae (mean is 68.4%, maximum is 95.9%), with other herbaceous pollen taxa as commen taxa including Poaceae (mean is 10.3%, maximum is 87.7%), Ranunculaceae (mean is 4.8%, maximum is 33.6%), Artemisia (mean is 3.7%, maximum is 24.5%), Asteraceae (mean is 2.1%, maximum is 33.6%), etc. Salix (mean is 0.4%, maximum is 5.3%) is the major shrub taxon in these pollen assemblages, while arboreal taxa occur with low percentages generally (mean of total arboreal percentages is 0.9% (maximum is 5.8%), including mainly Pinus (mean is 0.3%, maximum is 1.8%), Betula (mean is 0.1%, maximum is 0.9%) and Alnus (mean is 0.1%, maximum is 0.7%). These pollen assemblages represent the plant components well in the alpine meadow communities, although they are influenced slightly by long-distance pollen grain transported by wind or river (such as these arboreal pollen taxa). Together with pollen counts and percentages, we also provided the modern climatic data for the sampled lakes. The China Meteorological Forcing Dataset (CMFD; gridded near-surface meteorological dataset) with a temporal resolution of three hours and a spatial resolution of 0.1° was employed, and the climatic data of the nearest pixel of one sampled lake was defined to represent climatic conditions of the lake. Finally, the mean annual precipitation (Pann), mean annual temperature (Tann) and mean temperature of the coldest month (Mtco) and warmest month (Mtwa) are calculated for each sampled lake.
CAO Xianyong, TIAN Fang, LI Kai, NI Jian
Plant functional types (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 plant-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 plant 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 plant-functional map (Bonan et al., 2002).Functional plant 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 Plant 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 plant function map is based on the climate rules of land cover and plant 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.
RAN Youhua, LI Xin
This dataset includes the monthly maximum temperature data with 0.0083333 arc degree (~1km) for China from Jan 1901 to Dec 2021. The data form belongs to NETCDF, namely .nc file. The unit of the data is 0.1 ℃. The dataset was spatially downscaled from CRU TS v4.02 with WorldClim datasets based on Delta downscaling method. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China. The dataset covers the main land area of China, including Hong Kong, Macao and Taiwan regions, and excluding islands and reefs in South China Sea. WGS84 is recommended for data coordinate system.
PENG Shouzhang
This data is the lowest monthly temperature data of China, with a spatial resolution of 0.0083333 ° (about 1km) and a time of 1901.1-2021.12. The data format is NETCDF, i.e.. Nc format. The unit of data is 0.1 ℃. 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. WGS84 is recommended for data coordinate system.
PENG Shouzhang
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.
PENG Shouzhang
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.
ZHANG Guoqing
These datasets include mean annual ground temperature (MAGT) at the depth of zero annual amplitude (approximately 3 m to 25 m), active layer thickness (ALT), the probability of the permafrost occurrence, and the new permafrost zonation based on hydrothermal condition for the period of 2000-2016 in the Northern Hemisphere with an 1-km resolution by integrate unprecedentedly large amounts of field data (1,002 boreholes for MAGT and 452 sites for ALT) and multisource geospatial data, especially remote sensing data, using statistical learning modelling with an ensemble strategy, and thus more accurate than previous circumpolar maps.
RAN Youhua, LI Xin, CHENG Guodong, CHE Jinxing, Juha Aalto, Olli Karjalainen, Jan Hjort, Miska Luoto, JIN Huijun, Jaroslav Obu, Masahiro Hori, YU Qihao, CHANG Xiaoli
Effective evaluation of future climate change, especially prediction of future precipitation, is an important basis for formulating adaptation strategies. This data is based on the RegCM4.6 model, which is compatible with multi-model and different carbon emission scenarios: CanEMS2 (RCP 45 and RCP85), GFDL-ESM2M (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), HadGEM2-ES (RCP2.6, RCP4.5 And RCP8.5), IPSL-CM5A-LR (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), MIROC5 (RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The future climate data (2007-2099) has 21 sets, with a spatial resolution at 0.25 degrees and the temporal resolution at 3 hours (or 6 hours), daily and yearly scales.
PAN Xiaoduo, ZHANG Lei
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.
YAO Tandong
Gridded climatic datasets with fine spatial resolution can potentially be used to depict the climatic characteristics across the complex topography of China. In this study we collected records of monthly temperature at 1153 stations and precipitation at 1202 stations in China and neighboring countries to construct a monthly climate dataset in China with a 0.025° resolution (~2.5 km). The dataset, named LZU0025, was designed by Lanzhou University and used a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of LZU0025 was evaluated based on three aspects: (1) Diagnostic statistics from the surface fitting model during 1951–2011. The results indicate a low mean square root of generalized cross validation (RTGCV) for the monthly air temperature surface (1.06 °C) and monthly precipitation surface (1.97 mm1/2). (2) Error statistics of comparisons between interpolated monthly LZU0025 with the withholding of climatic data from 265 stations during 1951–2011. The results show that the predicted values closely tracked the real true values with values of mean absolute error (MAE) of 0.59 °C and 70.5 mm, and standard deviation of the mean error (STD) of 1.27 °C and 122.6 mm. In addition, the monthly STDs exhibited a consistent pattern of variation with RTGCV. (3) Comparison with other datasets. This was done in two ways. The first was via comparison of standard deviation, mean and time trend derived from all datasets to a reference dataset released by the China Meteorological Administration (CMA), using Taylor diagrams. The second was to compare LZU0025 with the station dataset in the Tibetan Plateau. Taylor diagrams show that the standard deviation, mean and time trend derived from LZU had a higher correlation with that produced by the CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. LZU0025 had high correlation with the Coordinated Energy and Water Cycle Observation Project (CEOP) - Asian Monsoon Project, (CAMP) Tibet surface meteorology station dataset for air temperature, despite a non-significant correlation for precipitation at a few stations. Based on this comprehensive analysis, we conclude that LZU0025 is a reliable dataset. LZU0025, which has a fine resolution, can be used to identify a greater number of climate types, such as tundra and subpolar continental, along the Himalayan Mountain. We anticipate that LZU0025 can be used for the monitoring of regional climate change and precision agriculture modulation under global climate change.
HUANG Wei, ZHAO Hong
The field observation platform of the Tibetan Plateau is the forefront of scientific observation and research on the Tibetan Plateau. The land surface processes and environmental changes based comprehensive observation of the land-boundary layer in the Tibetan Plateau provides valuable data for the study of the mechanism of the land-atmosphere interaction on the Tibetan Plateau and its effects. This dataset integrates the 2005-2016 hourly atmospheric, soil hydrothermal and turbulent fluxes observations of Qomolangma Atmospheric and Environmental Observation and Research Station, Chinese Academy of Sciences (QOMS/CAS), Southeast Tibet Observation and Research Station for the Alpine Environment, CAS (SETORS), the BJ site of Nagqu Station of Plateau Climate and Environment, CAS (NPCE-BJ), Nam Co Monitoring and Research Station for Multisphere Interactions, CAS (NAMORS), Ngari Desert Observation and Research Station, CAS (NADORS), Muztagh Ata Westerly Observation and Research Station, CAS (MAWORS). It contains gradient observation data composed of multi-layer wind speed and direction, temperature, humidity, air pressure and precipitation data, four-component radiation data, multi-layer soil temperature and humidity and soil heat flux data, and turbulence data composed of sensible heat flux, latent heat flux and carbon dioxide flux. These data can be widely used in the analysis of the characteristics of meteorological elements on the Tibetan Plaetau, the evaluation of remote sensing products and development of the remote sensing retrieval algorithms, and the evaluation and development of numerical models.
MA Yaoming
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。
ZHAO Cheng
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.
ZHANG Yili
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