This data set includes 2002/04-2019/12 Greenland ice sheet mass changes derived from satellite gravimetry measurements. The satellite gravimetry data come from the joint NASA/DLR Gravity Recovery And Climate Experiment mission twin satellites (GRACE, 2002/04 to 2017/06) and its successor, GRACE Follow-On (GRACE-FO, 2018/06 to present). In order to fill the data gap between GRACE and GRACE-FO, we further utilize gravity field solutions derived from high-low GNSS tracking data of ESA's Swarm 3-satellite constellation whose primary scientific objective is geomagnetic surveying. The data set is provided in Matlab data format, the ice sheet mass changes are transformed to equivalent water height in meters, expressed on 0.25°x0.25° grid with monthly temporal resolution. This data set can be used to study the characteristics of Greenland ice sheet mass changes in recent two decades and their relation with the global climate change.
Continued global warming and degradation of the cryosphere are raising concerns about adaptation to environmental instability in mountain areas. In recent decades, glacier-related slope failures, such as ice avalanches and rock avalanches on glaciers, have been frequently documented. In this study, we create a global inventory of glacier-related landslides to examine their distribution, trends, fractures, and relationship to climate change. During the period 1901-2019, 737 glacier-related landslides were recorded, including 156 ice avalanches, 89 ice-rock avalanches, 26 glacier slides, and 466 supraglacial rock avalanches. The Pacific Northwest had the most recorded cases (N = 440, 60%), with supraglacial rockfalls being the most dominant. In addition, the currently published list of glacial lake outburst floods of regional or global nature is integrated and refined, and moraine lake outburst flood events are separated separately. 380 moraine lake outburst flood events were counted between 1901 and 2020, making it the most complete list available on a global scale.
In recent years, with the acceleration of the melting of the Antarctic ice sheet, a large amount of ice melt has formed on the surface of the ice sheet from 2000 to 2019. It is of great significance to study the material balance of the Antarctic ice sheet to deeply understand the spatial-temporal distribution and dynamic changes of the melt water on the Antarctic ice sheet. This data set is based on Landsat7 and landsat8 images with 30 m spatial resolution from 2000 to 2019. By using normalized water body index, Gabor filtering and morphological path opening operations, the ice melt grid data set is generated, and the grid water body mask is converted into vector data in ArcGIS. This data set is based on the 250m ice surface melt water data set of the Antarctic ice sheet melting area (Alexander Island, Antarctic Peninsula) from 2000 to 2019 extracted from Landsat images. The time is concentrated from December to February (Southern Hemisphere summer)
We propose an algorithm for ice crack identification and detection using u-net network, which can realize the automatic detection of Antarctic ice cracks. Based on the data of sentinel-1 EW from January to February every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking five typical ice shelves（Amery、Fimbul、Nickerson、Shackleton、Thwaiters) in Antarctica as an example, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
We propose an algorithm for ice fissure identification and detection using u-net network, which can realize the automatic detection of ice fissures of Typical Glaciers in Greenland ice sheet. Based on the data of sentinel-1 IW from July and August every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then the representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking two typical glaciers in Greenland (Jakobshavn and Kangerdlussuaq) as examples, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
1) The data include annual scale oxygen isotope and accumulation records of ice cores from 1900 to 2011, which respectively reflect the temperature and precipitation changes in the study area; 2) For analysis, the ice core samples were first measured using Picaro δ 18O and as per δ The seasonal variation characteristics of 18O determine the age of ice core; The accumulation of ice cores is calculated according to the density of ice cores, the length of ice cores per year and the glacier flow model; 3) Professional laboratory personnel and front-line scientific research personnel operate and maintain the instrument to ensure the reliability of analysis data; 4) The data can be used to analyze the climate and environmental changes in the typical westerly region of the Qinghai Tibet Plateau over the past 100 years, and can be used to explore the evolution of glaciers in this period, providing scientific reference for predicting the future evolution of glaciers, changes in hydrology and water resources and their impact on human activities.
Global solar radiation at Qomolangma station (The Tibetan Plateau) is measured by radiation sensor (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground is measured by HMP45C-GM (Vaisala Inc., Vantaa, Finland). This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Ma, Y.; Wang, B.; Zhao, C.; Yang, Y.; Guang, J.; Cong, Z.; Li, K.; Song, T. 2022. Long-Term Variations in Global Solar Radiation and Its Interaction with Atmospheric Substances at Qomolangma. Int. J. Environ. Res. Public Health, 19, 8906. https://doi.org/10.3390/ijerph19158906). The observed global solar radiation and meteorological variables are available at https://data.tpdc.ac.cn/zh-hans/data/b9ab35b2-81fb-4330-925f-4d9860ac47c3/. The data set can be used to study solar radiation and its attenuation at Qomolangma region.
This dataset contains the glacier outlines in Qilian Mountain Area in 2019. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2019 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2019, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
1) The data included the thickness, coordinates and elevation of Xiaodongkemadi glacier and was measured from July 26 to 28, 2021; 2) The data was measured by the ground penetrating radar with working frequency of 100MHz developed by China Institute of Water Resources and Hydropower Research. The thickness of the glacier was obtained through the processing and analysis of the radar echo image. The dielectric constant of the ice was 3.2, and the coordinates and elevation of the measuring points were measured by the RTK system; 3) The data can be used to study the changes of glacier thickness, mass balance , runoff and so on.
The dataset includes three high-resolution DSM data as well as Orthophoto Maps of Kuqionggangri Glacier, which were measured in September 2020, June 2021 and September 2021. The dataset is generated using the image data taken by Dajiang Phantom 4 RTK UAV, and the products are generated through tilt photogrammetry technology. The spatial resolution of the data reaches 0.15 m. This dataset is a supplement to the current low-resolution open-source topographic data, and can reflect the surface morphological changes of Kuoqionggangri Glacier from 2020 to 2021. The dataset helps to accurately study the melting process of Kuoqionggangri Glacier under climate change.