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Institute of Tibetan Plateau Research, CAS

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MODIS Daily Cloud-free Snow Cover Product over the Tibetan Plateau (2002-2015)
  • 2019-07-23
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Snow duration on the Tibetan Plateau changes relatively quickly, and the mountainous areas around the plateau are characterized by abundant snow and ice resources and active atmospheric convection. Optical remote sensing is often affected by clouds. Snow cover monitoring needs to consider the cloud-removal problem on a daily time scale. Taking full account of the terrain of the Tibetan Plateau and the characteristics of snow on the mountains, this data set adopted a combination of various cloud-removing processes and steps to gradually remove the daily snow cover by maintaining the cloud-classify accuracy of the snow cover. In addition, a step-by-step comprehensive classification algorithm was formed, and the “MODIS daily cloud-free snow cover product over the Tibetan Plateau (2002-2015)” was completed. Two snow seasons from October 1, 2009, to April 30, 2011, were selected as test data for algorithm research and accuracy verification, and the snow depth data provided by 145 ground stations in the study area were used as a ground reference. The results showed that in the plateau region, when the snow depth exceeds 3 cm, the total classification accuracy of the cloud-free snow cover products is 96.6%, and the snow cover classification accuracy is 89.0%. The whole algorithm procedure, based on WGS84 projected MODIS snow products (MOD10A1 and MYD10A1) with medium resolution, results in a small loss of cloud-removal accuracy, which made the data highly reliable.

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Data on Glacial Lakes in the Third Pole Region (V1.0) ( (1990, 2000, 2010)
  • 2019-07-19
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There are three types of glacial lakes: supraglacial lakes, lakes attached to the end of the glacier and lakes not attached to the end of the glacier. Based on this classification, the following properties are studied: the variation in the number and area of glacial lakes in different basins in the Third Pole region, the changes in extent in terms of size and area, distance from glaciers, the differences in area changes between lakes with and without the supply of glacial melt water runoff, the characteristics of changes in the glacial lake area with respect to elevation, etc. Data source: Landsat TM/ETM+ 1990, 2000, 2010. The data were visually interpreted, which included checking and editing by comparing the original image with Google Earth images when the area was greater than 0.003 square kilometres. The data were applied to glacial lake changes and glacial lake outburst flood assessments in the Third Pole region. Data type: Vector data. Projected Coordinate System: Albers Conical Equal Area.

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The Second Glacier Inventory Data Set of China (Version 1.0) (2006-2011)
  • 2019-07-18
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China's second glacier inventory uses the high-resolution Landsat TM/ETM+ remote sensing satellite data as the main glacier boundary data source and extracts the data source with the latest global digital elevation model, SRTM V4, as the glacier attribute, using the current international ratio threshold segmentation method to extract the glacier boundary in bare ice areas. The ice ridge extraction algorithm is developed to extract the glacier ice ridge, and it is used for the segmentation of a single glacier. At the same time, the international general algorithm is used to calculate the glacier attributes, so that the vector data and attribute data that contain the glacier information of the main glacier regions in west China are obtained. Compared with some field GPS field measurement data and higher resolution remote sensing images (such as from QuickBird and WorldView), the glacial vector data in the second glacier inventory data set of China have higher positioning accuracy and can meet the requirements for glacial data in national land, water conservancy, transportation, environment and other fields. Glacier inventory attributes: Glc_Name, Drng_Code, FCGI_ID, GLIMS_ID, Mtn_Name, Pref_Name, Glc_Long, Glc_Lati, Glc_Area, Abs_Accu, Rel_Accu, Deb_Area, Deb_A_Accu, Deb_R_Accu, Glc_Vol_A, Glc_Vol_B, Max_Elev, Min_Elev, Mean_Elev, MA_Elev, Mean_Slp, Mean_Asp, Prm_Image, Aux_Image, Rep_Date, Elev_Src, Elev_Date, Compiler, Verifier. For a detailed data description, please refer to the second glacier inventory data description.

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  • 2019-07-08
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Sentine-1 SAR data were used to monitor the permafrost of Biuniugou in Heihe River Basin of Qinghai-Tibet Plateau. Based on the Sentine-1 SAR image of Bison Valley from 2014 to 2018, the active layer thickness in the study area was estimated by using the small baseline set time series InSAR (DSs-SBAS) frozen soil deformation monitoring method based on distributed radar target, combined with SAR backscattering coefficient, MODIS surface temperature and Stefan model. The results show that the thickness of active layer is between 0.8 m and 6.6 m, with an average of about 3.3 M. It is of great significance to carry out large-scale and high-resolution monitoring.

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A new map of permafrost distribution on the Tibetan Plateau(2017)
  • 2019-07-04
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The Tibetan Plateau (TP) has the largest areas of permafrost terrain in the mid- and low-latitude regions of the world. Some permafrost distribution maps have been compiled but, due to limited data sources, ambiguous criteria, inadequate validation, and deficiency of high-quality spatial data sets, there is high uncertainty in the mapping of the permafrost distribution on the TP. We generated a new permafrost map based on freezing and thawing indices from modified Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperatures (LSTs)、The temperature at the top of permafrost (TTOP) model was applied to simulate the permafrost distribution , validated this map using various ground-based data sets. The properties of frozen soil include: Seasonally frozen ground、Permafrost、Unfrozen ground. The results provide more detailed information on the permafrost distribution and basic data for use in future research on the Tibetan Plateau permafrost.

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  • 2019-06-28
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Snow depth Dataset of Eurasian (Version 1.0) (1980-2016)
  • 2019-06-18
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The Eurasia snow depth data set is produced by the passive microwave remote sensing inversion method. The data cover from 1980 to 2016 with a temporal resolution of one day, the spatial coverage of the data is Eurasia, and the spatial resolution is 0.25°. The remote sensing inversion method adopts a dynamic brightness temperature gradient algorithm. The algorithm considers the spatial and temporal variations of snow characteristics and establishes the spatial and seasonal dynamic relationships between the temperature difference at different frequencies and the measured snow depth. The long-term sequence of satellite-borne passive microwave brightness temperature data were derived from three sensors, SMMR, SSM/I and SSMI/S. For temporal consistency of the brightness temperature among different sensors, the brightness temperature of different sensors was intercalibrated before snow depth extraction. The verification of the measured site shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file includes a file header (projection mode) and a 720*332 snow depth matrix, and each snow depth represents a 0.25°*0.25° grid. For details of the data, please refer to the Eurasia Snow Depth Data Set - Data Description

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Long-term glacier melt fluctuations over the past 2500 yr in monsoonal High Asia revealed by radiocarbon-dated lacustrine pollen concentrates
  • 2019-06-11
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This data set is collected from the paper: Zhang, J. F. , Xu, B. , Turner, F. , Zhou, L. , Gao, P. , & Lü, Xinmiao, et al. (2017). Long-term glacier melt fluctuations over the past 2500 yr in monsoonal high asia revealed by radiocarbon-dated lacustrine pollen concentrates. Geology, 45(4), 359-362. The data is atracted from the supplement materials: GSA Data Repository 2017103, http://www.geosociety.org/pubs/ft2017.htm. In this paper, the researcher of Institute of Tibetan Plateau Research, Chinese Academy of Sciences and CAS Center for Excellence in Tibetan Plateau Earth Sciences, Baiqing Xu, with his doctoral student, Ji Feng Zhang, and collaborators from Peking University and other institutions, propose that the OPE (“old pollen effect”, the offset between the calibrated 14C ages of pollen in lake sediments and the sediment depositional age) as a new indicator of glacier melt intensity and fluctuations by measuring the radiocarbon ages of the sediments of a small proglacial lake of Qiangyong Glacier on the southern Tibetan Plateau with multi-methods (bulk organic matter, pollen concentrates and plant residues). According to the conceptual model in this research, young ice containing modern pollen is formed in the accumulation area and then flows slowly to the ablation area and becomes a reservoir of old pollen, intensified glacier melt releases more old pollen from the old ice enters proglacial lakes. A 2.5 k.y. record of glacier variability has been reconstructed, which agrees well with the Dasuopu ice core δ18O record and the old glacier fluctuations in the European and the Tibetan Plateau, suggesting that hemispheric-scale temperature variations and/or mid-latitude Westerlies may have controlled the late Holocene glacier variability in monsoonal High Asia. The research also shows that the 20th-century glacier melt intensity exceeded that of two historical warm epochs (the Medieval Warm Period, and the Iron/Roman Age Optimum) and is unprecedented at least for the past 2.5 k.y. This data set contains 1 table: Radiocarbon ages of bulk organic matter, pollen concentrates and PRC, the calculated ∆Age-pollen and the modelled sediment depositional ages of core QYL09-4. QYL09-4 is a 306 cm core retrieved with a piston corer in 2009 from the deepest part of Qiangyong Co at 30 m water depth. See attachments for data details: GSA Data Repository 2017103.pdf, Long-term glacier melt fluctuations over the past 2500 yr in monsoonal High Asia revealed by radiocarbon-dated lacustrine pollen concentrates.pdf.

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Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings (1970s-2000s)
  • 2019-06-05
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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.

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Bacteria Strain Resource Base of the Tibetan Plateau (version 1.0) (2010-2018)
  • 2019-06-01
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The glacial bacterial resource database of the Tibetan Plateau provides the bacterial 16S ribosomal RNA gene sequences of several glaciers, which are seven glaciers of the Tibetan Plateau separated by an experimental group led by Yongqin Liu during 2010 to 2018 (East Rongbuk Glacier of Mt. Qomolangma, Tianshan Glacier No.1, Guliya Glacier, Laohugou Glacier, Muztagh Ata Glacier, Qiyi Glacier and Yuzhufeng Glacier), the Malan Glacier separated by Shurong Xiang and the Puruogangri Glacier separated by Xinfang Zhang. After the glacier samples were collected, they were taken to the Ecological Laboratory of the Institute of Tibetan Plateau Research of the Chinese Academy of Sciences in Beijing and the National Cryosphere Laboratory in Lanzhou. After applying the spread plate method, the samples were cultured at different temperatures (4-25 °C) for 20 days to 90 days, and single colonies were picked out for purification. After the DNA was extracted from the isolated bacteria, the 16S ribosomal RNA gene fragment was amplified with 27F/1492R primer and sequenced using the Sanger method. The 16S ribosomal RNA gene sequence was compared with the RDP database using the "Classifier" software and identified as level one when the reliability exceeded 80%. These data contain the 16S ribosomal RNA gene fragment sequence and glacier sources of each sequence. Compared with sequences based on high-throughput sequencing, these data have a longer sequence and more accurate classification and can better serve in glacier microbiology research.

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