Current Browsing: cryosphere


Ice velocity of the Amery ice shelf in the Antarctic (Version 1.0) (2003-2013)

Using the Modis1B data of 11 scenes from 2003 to 2013 (the ice shelf Modis1B data published on the NSIDC website), the surface velocity of the Antarctic Amery Ice Shelf was extracted by the subpixel cross-correlation method, the ice velocity was extracted by the COSI-Corr software, and then the time sequence of annual average velocities for nearly ten years was obtained. Due to the lack of field observations in the study area, the accuracy of the ice flow results was estimated by using the offset value of the stable region, and the ice flow error was approximately ±50 m/year. The ice velocity data date from 2003 to 2013, the temporal resolution is one year, and the data cover the Amery area with a spatial resolution of 500 m. A GeoTIFF file of velocity data is stored every year. For details regarding the data, please refer to the Amery Ice Flow Field - Data Description.

2020-04-29

Bacteria distribution in the Arctic and Antarctic (version 1.0) (2005-2006)

The Antarctic and Arctic bacterial distribution data set provides distribution characteristics of bacteria in the Arctic and Antarctic. The collection period of the samples was from December 13,2005, to December 8,2006; 52 samples were obtained from 3 Arctic regions (Spitsbergen Slijeringa, Spitsbergen Vestpynten, and Alexandra Fjord_Highlands), and 171 samples were obtained from 5 Antarctic regions (the Mitchell Peninsula, Casey station main Power house, Robinsons Ridge, Herring Island, and Browning Peninsula). The soil surface samples were stored in liquid nitrogen after collection, shipped to a Sydney laboratory, and extracted using the FastPrep DNA kit. The extracted DNA samples were processed by 27F (5'-GAGTTTGATCNTGGCTCA-3' and 519R (5'-GTNTTACNGCGGCKGCTG-3') to amplify the 16S rRNA gene fragments. The amplified fragments were sequenced by the 454 method, and the raw data were analyzed by Mothur software. First, the sequences with poor sequencing quality were removed, the sequences were then sorted, and the chimera sequences were removed. The similarities between the sequences were calculated, the sequences with similarities above 97% were clustered into one OTU, and the OTU representative sequence was defined. By comparison with the Silva database, the OTU sequences with reliabilities greater than 80% were identified as level one. This data system compared the diversity of microorganisms in the eastern Antarctic with that in the Arctic and is of great significance for the study of the distributions of microorganisms in the Antarctic and Arctic.

2020-04-29

Snow water equivalent dataset for the High Asia Region (2002-2011)

Snow water equivalent (the product of snow depth and density) is an important factor reflecting the change in snow cover on the ground surface, and it is also an important parameter in surface hydrological models and climatic models. As the “Headwaters of Asia”, the Tibetan Plateau is the source of several major rivers, which are fed with glacier and snow meltwater. Based on the sensitivity of passive microwave radiation to snow, these monitoring data enable long-term inversion of snow water equivalents in the High Asia region. The data set includes daily snow water equivalent, monthly snow water equivalent and five-day snow water equivalent, and these data can be applied in analyses of local hydrology, animal husbandry production and other fields.

2020-04-29

The surface temperature data of the Tibet engineering corridor (2000-2010)

As the main parameter in the land surface energy balance, surface temperature indicates the degree of land-atmosphere energy and water transfer and is widely used in research on climatology, hydrology and ecology. In the study of frozen soil, climate is one of the decisive factors for the existence and development of frozen soil. The surface temperature is the main climatic factor affecting the distribution of frozen soil and affects the occurrence, development and distribution of frozen soil. It is the upper boundary condition for modelling frozen soil and is significant to the study of hydrological processes in cold regions. The data set was based on the DEM and observation station data of the Tibetan Plateau Engineering Corridor and analysed the changing trend of surface temperature on the Tibetan Plateau from 2000 to 2014. Using the surface temperature data products MOD11A1/A2 and MYD11A1/A2 of MODIS aboard Terra and Aqua, the surface temperature information under cloud cover was reconstructed based on the spatio-temporal information of the images. The reconstruction information and surface temperature representativeness problems were analysed using information obtained from 8 sites, including the Kunlun Mountains (wetland, grassland), Beiluhe (grassland, meadow), Kaixinling (meadow, grassland), and Tanggula Mountain (meadow, wetland). According to the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE) and mean deviation (MBE), the following results were obtained: (1) the reconstruction accuracy of MODIS surface temperature under cloud cover is higher when it is based on spatio-temporal information; (2) the weighted average representation is the best when generalizing four observations of Terra and Aqua. By analysing the reconstruction of MODIS surface temperature information and representativeness problems, the average annual MODIS surface temperature data of the Tibetan Plateau and the engineering corridor from 2000 to 2010 were obtained. According to the data set, the surface temperature from 2000 to 2010 also experienced volatile rising trends from 2000 to 2010, which is basically consistent with the changing trend of the climate change in the permafrost regions of the Tibetan Plateau and the Qinghai-Tibet Engineering Corridor.

2020-04-29

Long-term serial data of snow area on the Tibetan Plateau (2007-2015)

The variation in the duration of snow on the Tibetan Plateau is relatively great, and the high mountainous areas around the plateau are rich in snow and ice resources. Taking full account of the terrain of the Tibetan Plateau and the snow characteristics in the mountains, the data set adopted AVHRR data to gradually realize generating data products for daily, ten-day, and monthly snow cover areas while maintaining the snow classification accuracy. These data included the daily/10-day/monthly snow cover area data for the Tibetan Plateau from 2007 to 2015, the average accuracy of which is 0.92. It can provide reliable data for snow changes during the historical periods of the Tibetan Plateau.

2020-04-29

Long time series data of snow cover area on Qinghai-Tibet Plateau (2003-2014)

The long-time series data set of snow cover area on the qinghai-tibet plateau is derived from the fusion of MODIS 005 version and IMS data set, andThe cloud-free products of daily snow cover area were obtained by using interpolation de-cloud algorithm.The projection is latitude and longitude, the spatial resolution is 0.005 degrees (about 500m), and the time is a long time series from January 1, 2003 to December 31, 2014. Each file is the result of the proportion of snow cover area on that day, and the value is 0-100 (%). It is the ENVI standard file, The naming convention: ims_mts_yyyyddd.tif, where YYYY stands for year and DDD stands for Julian day (001-365/366).Files can be directly used ENVI or ARCMAP software open view. Document description: 200 snow, 100 lake ice, 25 land, 37 sea

2020-04-29

A permafrost thermal type map on the Tibetan Plateau (2000-2010)

The past frozen soil map of the Tibetan Plateau was based on a small number of temperature station observations and used a classification system based on continuity. This data set used the geographically weighted regression model (GWR) to synthesize MODIS surface temperature, leaf area index, snow cover ratio and multimodel soil moisture forecast products of the National Meteorological Information Center through spatiotemporal reconstruction. In addition, precipitation observations of more than 40 meteorological stations, the precipitation products of FY2 satellite observations and the multiyear average temperature observation data of 152 meteorological stations from 2000 to 2010 were integrated to simulate the average temperature data of the Tibetan Plateau, and the permafrost thermal condition classification system was used to classify permafrost into several types: Very cold, Cold, Cool, Warm, Very warm, and Likely thawing. The map shows that, after deducting lakes and glaciers, the total area of permafrost on the Tibetan Plateau is approximately 1,071,900 square kilometers. Verification shows that this map has higher accuracy. It can provide support for future planning and design of frozen soil projects and environmental management.

2020-04-29

Global 0.05° near-surface freeze-thaw states data set (2002-2018)

The near-surface freeze-thaw affects the water and energy exchanges mode and efficiency between the land and atmosphere. The transition of the freeze/thaw state affects the pattern of runoff concentration, which has an important impact on regional and global water cycle. Based on the remote sensing data of AMSR-E/2 passive microwave sensors and MODIS optical sensor, this data set uses the discriminant function algorithm and its downscaling method to produce a global mapping of near-surface freeze-thaw states with higher spatial resolution. This product covers the time period from 2002 to 2018 (daily), and spatial coverage is global scale (spatial resolution of 0.05°). It can be used to analyze the start/end time of global near-surface freeze/thaw states, the duration of freezing/thawing and their changing trends, and provide data support for studying the mechanism of water cycle and energy exchanges in the context of global change.

2020-04-28

A dataset of area change for the Karuola glacier (1972-2017)

The Karuola Glacier of Tibet is located at the junction of Langkazi County, the Shannan Area of the Tibetan Autonomous Region and Jiangzi County of the Shigatse Region. Latitude: 28°54'23.30′′~28°56'50.95′′N, Longitude 90°11′42.21′′~90°09′26.23′′E. It is a continental glacier with an average elevation of 5042 meters. It is the north-south spreading part of the Ningjingangsang peak. Based on the integration of the first glacier inventory data of China from the Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences, the 1:100,000 inventory data of the Yarlu Zangbu River Basin Glacier from the Sharing Platform for the Earth Systematic Science Data, and Google Earth remote sensing image and field survey data, the dataset was obtained with the help of ArcGIS, ENVI and other software by the following steps: first, the research and development of the data was achieved by band combination, research area clipping, manual visual interpretation and other techniques, and the accuracy of the obtained data was then verified. This dataset includes a total of 25 statistics of vector and area data of Tibet’s Karuola Glacier. It recorded the changes at the borders of Karuola Glacier in the past 45 years and could be used as reference data for the study of glacier and climate changes on the Tibetan Plateau.

2020-04-28