The Randolph Glacier Inventory (RGI) is a complete inventory of global glacier outlines published by GLIMS (Global Land Ice Measurements from Space). It is currently available in six versions: Version 1.0 was published in February 2012, version 2.0 was published in June 2012, version 3.0 was published in April 2013, version 4.0 was published in December 2014, version 5.0 was published in July 2015, and version 6.0 was published in July 2017. The data sets include four versions, which are 6.0, 5.0, 4.0 and 3.2 (revision, August 2013). The data are organized according to different regions. In each region, each glacier record includes a shape file (.shp file and its corresponding .dbf, .prj, and .shx files) and a .csv file of height measurement data. The data are from GLIMS: Global Land Ice Measurements from Space (http://www.glims.org/RGI/) Data quality checks include geometry, topology, and certain attributes, and the following checks were performed: 1) All polygons were checked by the ArcGIS Repair Geometry tool. 2) Glaciers with areas less than 0.01 square kilometres were removed. 3) The topology was checked with the Does Not Overlap rule. 4) The attribute sheet was checked by Fortran subroutines and Python scripts for data quality.
This data set contains the wide swath mode Level 1B SAR data acquired over Greenland in 2005 by the ASAR sensor of the ENVISAT-1 satellite. The width is 400 km, the spatial resolution is 75 m, and the absolute positioning accuracy is approximately 200 m. The SAR data are stored in a time-growth order, which causes the images of the descending track to be left-right mirror images and the images of the ascending track to be up-down images. The naming scheme for these data is as follows: ASA_IMS_1PPIPA 20050402_095556_000000162036_00065_16151_0388.N1 ASA: Product identification, ASAR Sensor IMS: Reception and processing information of the data (imaging modes, such as WS, WSS, IM, ...) 1PPIPA: Customized number 20050402: Acquisition time of the data (UTC time) 095556: Geographic location (start, end) 000000162036: Information on the satellite orbit 00065: Product trust data 16151: Size and structure information of the product 0388 => Check code
The data set of prokaryotic microorganism distribution in the snow and ice of the Arctic Antarctic and the Tibetan Plateau provides the bacterial 16S ribosomal RNA gene sequence collected by the experimental group led by Yongqin Liu from the NCBI database during 2010 to 2018. The keywords for NCBI database search are Antarctic, Arctic Tibetan, and Glacier. The collected sequences were calculated using the DOTOUR software to obtain the similarities between sequences, the sequences with similarities above 97% were clustered into one OTU, and the OTU representative sequence was defined. The OTU representative sequence was compared with the RDP database by the "Classifier" software and was identified as level one when the reliability exceeded 80%. After acquiring the sequence, the GPS coordinates of the sample were obtained by reading the sample information in the sequence file. These data contain the sequence of 16S ribosomal RNA gene fragments for each sequence, evolutionary classification, and sample GPS coordinates. Compared with sequences based on high-throughput sequencing, these data have a longer sequence and more accurate classification. It is significant for comparing the evolutionary information of three-pole microorganisms and understanding the evolution of psychrophilic microorganisms.
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
The Tibetan Plateau has an average altitude of over 4000 m and is the region with the highest altitude and the largest snow cover in the middle and low latitudes of the Northern Hemisphere regions. Snow cover is the most important underlying surface of the seasonal changes on the Tibetan Plateau and an important composing element of ecological environment. Ice and snow melt water is an important water resource of the plateau and its downstream areas. At the same time, plateau snow, as an important land-surface forcing factor, is closely related to disastrous weather (such as droughts and floods) in East Asia, the South Asian monsoon and in the middle and lower reaches of the Yangtze River. It is an important indicator of short-term climate prediction and one of the most sensitive responses to global climate change. The snow depth refers to the vertical depth from the surface of the snow to the ground. It is an important parameter for snow characteristics and one of the conventional meteorological observation elements. It is the key parameter of snow water equivalent estimation, climate effect studies of snow cover, the basin water balance, the simulation and monitoring of snow-melt, and snow disaster evaluation and grading. In this data set, the Tibetan Plateau boundary was determined by adopting the natural topography as the leading factor and by comprehensive consideration of the principles of altitude, plateau and mountain integrity. The main part of the plateau is in the Tibetan Autonomous Region and Qinghai Province, with an area of 2.572 million square kilometers, accounting for 26.8% of the total land area of China. The snow depth observation data are the monthly maximum snow depth data after quality detection and quality control. There are 102 meteorological stations in the study area, most of which were built during the 1950s to 1970s. The data for some months or years for sites existing during this period were missing, and the complete observational records from 1961 to 2013 were adopted. The temporal resolution is daily, the spatial coverage is the Tibetan Plateau, and all the data were quality controlled. Accurate and detailed plateau snow depth data are of great significance for the diagnosis of climate change, the evolution of the Asian monsoon and the management of regional snow-melt water resources.
The 2008 national remote sensing annual average surface temperature and freezing index is a 5 km instantaneous surface temperature data product based on MODIS Aqua/Terra four times a day by Ran Youhua et al. (2015). A new method for estimating the annual average surface temperature and freezing index has been developed. The method uses the average daily mean surface temperature observed by LST in morning and afternoon to obtain the daily mean surface temperature. The core of the method is how to recover the missing data of LST products. The method has two characteristics: (1) Spatial interpolation is carried out on the daily surface temperature variation observed by remote sensing, and the spatial continuous daily surface temperature variation obtained by interpolation is utilized, so that satellite observation data which is only once a day is applied; (2) A new time series filtering method for missing data is used, that is, the penalty least squares regression method based on discrete cosine transform. Verification shows that the accuracy of annual mean surface temperature and freezing index is only related to the accuracy of original MODIS LST, i.e. the accuracy of MODIS LST products is maintained. It can be used for frozen soil mapping and related resources and environment applications.
Overviewing the various frozen soil maps in China, there are great differences in the classification systems, data sources, and mapping methods. These maps represent the stage of understanding of the permafrost distribution of China in the past half century. To reflect the distribution and area of frozen soil in our country more reasonably, we have made a new frozen soil distribution map based on the analysis of the existing frozen soil maps. The map combines several existing maps of permafrost and the simulation results of a permafrost distribution model on the Tibetan Plateau. It unifies the acquisition time of data from various parts of the country and reflects the distribution of permafrost in our country around 2000. In the new frozen soil map, the distributions of various types of frozen soil are determined according to the following principles. 1. The base map uses the Geocryological Regionalization and Classification Map of the Frozen Soil in China (1:10 000 000) (Guoqing Qiu et al., 2000). The distribution of permafrost and instantaneous frozen soil in the high mountains outside the Tibetan Plateau follows the original map; the boundaries of seasonal frozen soil and instantaneous frozen soil, instantaneous frozen soil and nonfrozen soil remain unchanged, too. The distribution of permafrost on the Tibetan Plateau and in the high latitudes of the Northeast is updated with the following results. 2. The distribution of high-altitude permafrost and alpine permafrost in the Tibetan Plateau region is updated using the simulation results of Zhuotong Nan et al. (2002). This model uses the measured average annual ground temperature data of 76 boreholes along the Qinghai-Tibet Highway to perform regression statistical analysis and obtains the relationship between annual mean geothermal data with latitude and elevation. Based on this relationship, combined with the GTOPO30 elevation data (global 1-km digital elevation model data developed under the leadership of the US Geological Survey's Earth Resources Observation and Technology Center), the average annual ground temperature distribution over the entire Tibetan Plateau is simulated, the average annual ground temperature is 0.5 C, and it is used as the boundary between permafrost and seasonal frozen soil. 3. The distribution of permafrost at high latitudes in the Northeast is based on the latest results from Jin et al. (2007). Jin et al. (2007) analyze the average annual precipitation and soil moisture in Northeast China over the past few decades and conclude that the relationship between the southern boundary of permafrost in Northeast China and the annual average temperature has not changed substantially in the past few decades. 4. Alpine permafrost distribution in other regions is updated with the Map of the Glaciers, Frozen Ground and Deserts in China (1:4 million) (Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, 2006). In terms of classification systems, the current existing frozen soil maps use continuous standards for the division of permafrost, but the specific definition of continuity is very different. Many studies have shown that the continuity criterion is a concept closely related to scale, it is not suitable for the classification of permafrost at high altitude (Guodong Cheng, 1984; Cheng et al., 1992), and it cannot be applied to the permafrost distribution model that uses grid as the basic simulation unit. In this paper, we abandon the continuity criteria and take the existence of frozen soil in the mapping unit (grid or region). The new frozen soil map divides China's frozen soil into several categories: (1) High latitude permafrost; (2) High altitude permafrost; (3) Plateau permafrost; (4) Alpine permafrost; (5) Medium-season seasonal frozen soil: the maximum seasonal freezing depth that can be reached is >1 m; (6) Shallow seasonal frozen soil: the maximum seasonal freezing depth that can be achieved is <1 m; (7) Instant frozen soil: less than one month of storage time; and (8) Nonfrozen soil. For a specific description of the data, please refer to the explanatory documents and citations.
Qinghai Tibet Plateau is the largest permafrost area in the world. At present, some permafrost distribution maps have been compiled. However, due to the limited data sources, unclear standards, insufficient verification and lack of high-quality spatial data sets, there is great uncertainty in drawing Permafrost Distribution Maps on TP. Based on the improved medium resolution imaging spectrometer (MODIS) surface temperature (LSTS) model of 1 km clear sky mod11a2 (Terra MODIS) and myd11a2 (Aqua MODIS) product (reprocessing version 5) in 2003-2012, the data set simulates the distribution of permafrost and generates the permafrost map of Qinghai Tibet Plateau. The map was verified by field observation, soil moisture content and bulk density. Permafrost attributes mainly include: seasonally frozen ground, permafrost and unfrozen ground. The data set provides more detailed data of Permafrost Distribution and basic data for the study of permafrost in the Qinghai Tibet Plateau.
The borehole is about 7km away from Jiagedaqi City （50.47°N, 124.23°E), located in a wetland with about 80cm-thick peaty soil. There are three boreholes, and one is 2m away from the pipe center and 20m deep, the second is 16.6m away and 20m deep, and the third is 50m away from the second pipeline and 60 m deep. Based on the temperature borehole with a diameter of 40 mm and depths of 20 to 60 m, the ground temperature along the China-Russia Crude Oil Pipeline was measured using the thermistor sensor, which was assembled by State Key Laboratory of Frozen Soil Engineering, and calibrated with an accuracy of ±0.05℃. Therefore, the critical characteristic parameters such as ground stratigraphy, temperature of permafrost, surface temperature and active layer thickness were obtained. During the period from October 2014 to October 2017, ground temperatures in the T1 and T2 boreholes were collected manually. The ground temperatures in T3 was collected automatically and continuously since 12 June of 2018. Then the continuous and complete record of ground temperature data uploaded to the specified server (fixed IP address) by the wireless transmission module utilizing cellular networks. From these measured data along the China-Russia Crude Oil Pipeline route, the development characteristics and historical evolution of permafrost, and its response to the climate change can be analyzed.
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.
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LinksNational Tibetan Plateau Data Center