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。
0 2022-04-18
Based on gipl1.0 permafrost spatial distribution model, combined with the existing basic data, including climate change, soil types, and vegetation data, the permafrost and seasonal permafrost characteristics of Sichuan Tibet railway are simulated. The data result is 500m spatial resolution grid, including the maximum depth of permafrost and the maximum freezing depth of seasonal permafrost. The results are verified by drilling data. The data date is 2001-20192041-20602081-2100 (20-year average), in which the water body and glacier area are excluded from the calculation range through the mask (null value). The climate data is monthly mean, other data remain unchanged in the process of simulation, and the spatial resolution is 500m. Data sources and "woeldc" lim:https :// www.worldclim.org/ , DEM and vegetation soil: https://data.tpdc.ac.cn/zh-hans/ ”According to the characteristics of different data sources, the authenticity and consistency of the original data are checked and standardized; The permafrost model is used to simulate the permafrost and seasonal frozen soil. The output results are ground temperature and active layer (maximum frozen depth). The simulation results are verified with the borehole ground temperature. Finally, the spatial data set is mapped by ArcGIS. Make digital processing operation standard. In the process of processing, the operators are required to strictly abide by the operation specifications, and the special person is responsible for the quality review. The data integrity, logical consistency, position accuracy, attribute accuracy, edge connection accuracy and current situation are all in line with the requirements of relevant technical regulations and standards formulated by the State Bureau of Surveying and mapping. The data can provide necessary data support for the later research on the freezing (thawing) depth of the corridor of Sichuan Tibet project.
0 2022-04-18
1) The Qinghai Tibet plateau surface meteorological driving data set (2019-2020) includes four meteorological elements: land surface temperature, mean total precipitation rate, mean surface downward long wave radiation flux and mean surface downward short wave radiation flux. 2) The data set is based on era5 reanalysis data, supplemented by MODIS NDVI, MODIS DEM and fy3d mwri DEM data products. The era5 reanalysis data were downscaled by multiple linear regression method, and finally generated by resampling. 3) All data elements of the Qinghai Tibet plateau surface meteorological driving data set (2019-2020) are stored in TIFF format. The time resolution includes (daily, monthly and annual), and the spatial resolution is unified as 0.1 ° × 0.1°。 4) This data is convenient for researchers and students who will not use such assimilated data in. NC format. Based on the long-term observation data of field stations of the alpine network and overseas stations in the pan third pole region, a series of data sets of meteorological, hydrological and ecological elements in the pan third pole region are established; Complete the inversion of meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacier and frozen soil change and other data products through intensive observation in key areas and verification of sample plots and sample points; Based on the Internet of things technology, a multi station networked meteorological, hydrological and ecological data management platform is developed to realize real-time acquisition, remote control and sharing of networked data.
0 2022-04-18
The Pan-Third Polar region has strong seismic activity, which is driven by the subduction and collision of the Indian plate, the Arab plate and the Eurasian plate. 3809 earthquakes with Magnitude 6 or larger have occurred in Pan-Third Polar region (north latitude 0-56 degrees and east longitude 43-139 degrees) since 1960. Among them, 59 earthquakes with Magnitude 8 or larger, 689 earthquakes with Magnitude 7.0-7.9 and 3061 earthquakes with Magnitude 6.0-6.9 have occurred. Earthquakes occurred mainly in the foothills of the India-Myanmar Mountains, the Himalaya Mountains, the Sulaiman Mountains, where the India Plate collided with the Eurasian plate, and the Zagros Mountains where the Arab plate collided with the Eurasian plate.
0 2022-04-18
Data Set of Key Elements of Desertification in Typical Watershed of Central and Western Asia includes four parts: distribution and change of agricultural land of Amu River Basin, distribution and change of grassland of Amu River Basin, distribution and change of shrub land of Amu River Basin, distribution and change of forests of Amu River Basin. the spatial resolution of data is 30 m. All the data is based on Landsat TM/ETM image data in 1990, 2000 and 2010. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
0 2022-04-18
The data set is the distribution of the average roughness in Central Asia including three temperate deserts, the Karakum, Kyzylkum and Muyunkun Deserts, and one of the world's largest arid zones. This is the MODIS-NDVI data set calculated by using the median particle diameter and the vegetation coverage. The space and time resolutions are 500 m and 16 days, respectively. The time is from 01, January, 2017 to 18, December, 2017. The data set uses the the Geodetic coordinate system. It can be used for the investigation of the Desert oil and gas field, and oasis cities.
0 2022-04-18
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.
0 2022-04-18
Data description: This dataset includes the grid data of annual temperature and annual precipitation on the Tibetan Plateau from 1998 to 2017. It is the basic data for study of climate change and its impact on the ecological environment. Data source and processing: The meta data was aquired from the temperature and precipitation daily data of China's ground high-density stations (above 2,400 national meteorological stations) based on the latest compilation of the National Meteorological Information Center's basic data. After removing the missing stations, the software's thin plate spline method in ANUSPLIN was used to perform spatial interpolation, in order to generate grid data with spactial resolution of 1 km on the Tibetan Plateau . Data application: This data can be used to indentify the impact of climate change on the ecological environment.
0 2022-04-18
PML_V2 terrestrial evapotranspiration and total primary productivity dataset, including gross primary product (GPP), vegetation transpiration (Ec), soil evaporation (Es), vaporization of intercepted rainfall , Ei) and water body, ice and snow evaporation (ET_water), a total of 5 elements. The data format is tiff, the space-time resolution is 8 days, 0.05°, and the time span is 2002.07-2019.08. Based on the Penman-Monteith-Leuning (PML) model, PML_V2 is coupled to the GPP process based on stomatal conductance theory. GPP and ET mutually restrict and restrict each other, which makes PML_V2 in ET simulation accuracy, which is greatly improved compared with the previous model. The parameters of PML_V2 are divided into different vegetation types and are determined on 95 vorticity-related flux stations around the world. The parameters were then migrated globally according to the MODIS MCD12Q2.006 IGBP classification. PML_V2 uses GLDAS 2.1 meteorological drive and MODIS leaf area index (LAI), reflectivity (Albedo), emissivity (Emissivity) as inputs, and finally obtains PML_V2 terrestrial evapotranspiration and total primary productivity data sets.
0 2022-04-18
This dataset includes Fraction Vegetation Coverage (FVC) data for five countries in Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) during 2010, 2015 and 2020. The data is calculated from the MODIS-NDVI data set (product number MOD13A2.006) based on the empirical relationship between FVC in arid areas and NDVI. The product has a time resolution of 1 year and a spatial resolution of 1 km. The algorithm selects the best available pixel value based on low cloud, low detection angle and highest NDVI value from all the observation data of the year, and performs conversion.
0 2022-04-18
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