Snow cover is an important component of the cryosphere and an indispensable variable in the scientific research of global change and Earth system. The distribution range and phenological information of snow cover are important indicators to measure the variation characteristics of snow cover, and also important parameters for snow melting runoff simulation in the hydrological model of cold regions. The High Mountain Asia is the source of many international rivers, and also the hot spot of global climate change research; The ecological and environmental problems caused by the change of ice and snow in the region, such as the reduction of water resources, the increase of extreme weather events, and the frequent occurrence of disasters, have attracted extensive attention from all countries. Therefore, it is very important for climate change research, water resources management, disaster early warning and prevention to accurately obtain long-term snow distribution and snow phenology data in High Mountain Asia . The daily cloudless MODIS normalized snow cover index (NDSI) product (2000-2021500 m) in the High Mountain Asia is based on the MODIS daily snow cover product (including Terra Morning Star data product MOD10A1 and Aqua Afternoon Star data product MYD10A1, C6 versions), and is processed by the same day afternoon star data fusion and cubic spline interpolation cloud removal algorithm; Among them, when there was only Morningstar data product MOD10A1 from 2000 to 2002, the cubic spline interpolation algorithm was directly used for cloud removal. The snow cover phenological data set for hydrological years 2002-2020 is prepared based on cloudless MODIS NDSI products in hydrological years, including three parameters: snow onset date (SOD), snow end date (SED) and snow duration days (SDD). This data set has reliable accuracy.
TANG Zhiguang , DENG Gang
This dataset includes the glacier elevation change data in the High Mountain Asia (HMA) region from 2018 to 2020 derived from Ice, Cloud and land Elevation Satellite (ICESat-2) data. The glacial elevation changes in the High Mountain Asia region were calculated using ICESat-2 data (2018-2020) and SRTM DEM data in 2000, taking into account the inhomogeneity of glacier changes and area distribution at different elevations and slopes (weighted average of glacier area of elevation and slope bins in 1°×1° grid ). The dataset can provide the annual change information of glacier elevation in the High Mountain Asia region from 2018 to 2020 relative to 2000. These data can be used for studies of climate change in the High Mountain Asia.
SHEN Cong , JIA Li
This dataset includes the glacier elevation change data in the High Mountain Asia (HMA) region from 2003 to 2008 derived from Ice, Cloud and land Elevation Satellite (ICESat-1) data. The glacial elevation changes in the High Mountain Asia region were calculated using ICESat-1 data (2003-2008) and SRTM DEM data in 2000, taking into account the inhomogeneity of glacier changes and area distribution at different elevations and slopes (weighted average of glacier area of elevation and slope bins in 1°×1° grid ). The dataset can provide the annual change information of glacier elevation in the High Mountain Asia region from 2003 to 2008 relative to 2000. These data can be used for studies of climate change in the High Mountain Asia.
SHEN Cong , JIA Li
This data is the simulated data of glacier distribution in the alpine region of Asia since the last glacial maximum, It includes the annual resolution glacier area change sequence of typical regions (High mountain Asia, Tianshan Mountains, Himalayas and Pamir Plateau) and typical periods (LGM (20000 ~ 19000ka), HS1 (17000 ~ 16000ka), BA (~ 14900 ~ 14350ka), yd (12900 ~ 12000ka), eh (9500 ~ 8500ka), MH (6500 ~ 5500ka), LH (3500 ~ 2500ka) and modern (1951 ~ 1990)) 1 km resolution glacier distribution in High Mountain Asia. This data are created by taking the trace full forcing simulation based on ccsm3 climate model as the external forcing field to drive the 1 km resolution PISM ice sheet model. This data can be used to study the changes of glacier distribution in the alpine region of Asia since the last glacial maximum and its impact on environmental and climatic factors such as lake water level, runoff and landform.
This biophysical permafrost zonation map was produced using a rule-based GIS model that integrated a new permafrost extent, climate conditions, vegetation structure, soil and topographic conditions, as well as a yedoma map. Different from the previous maps, permafrost in this map is classified into five types: climate-driven, climate-driven/ecosystem-modified, climate-driven/ecosystem protected, ecosystem-driven, and ecosystem-protected. Excluding glaciers and lakes, the areas of these five types in the Northern Hemisphere are 3.66×106 km2, 8.06×106 km2, 0.62×106 km2, 5.79×106 km2, and 1.63×106 km2, respectively. 81% of the permafrost regions in the Northern Hemisphere are modified, driven, or protected by ecosystems, indicating the dominant role of ecosystems in permafrost stability in the Northern Hemisphere. Permafrost driven solely by climate occupies 19% of permafrost regions, mainly in High Arctic and high mountains areas, such as the Qinghai-Tibet Plateau.
RAN Youhua, M. Torre Jorgenson, LI Xin, JIN Huijun, Wu Tonghua, Li Ren, CHENG Guodong
Radar penetration correction is essential for accurately estimating glacier mass balance when using the geodetic methods based on the radar-derived Digital Elevation Model (DEM). Due to heterogeneous snow distribution and snowpack properties, the radar penetration depth varies by region and is basically dependent on the altitudes. Therefore, this data set gives the result of the penetration depth difference of SRTM C/X-band radar on 1°×1° grid of High Mountain Asia Glaciers. The data set contains 214 1°×1° grids SRTM X-band and C-band penetration depth difference in HMA, and a linear fitting expression for each grid. According to the geodetic method, the 30 m SRTM X-band and C-band DEM are used to obtain the results of the penetration depth difference between the SRTM X-band and C-band of the 1°×1° high grid in HMA, and obtain the relationship between the SRTM X-C-band penetration depth difference and the elevation in the glacier area (for specific methods, please refer to references). The data is stored in excel files. Observational data can provide important basic data for studying the glacier mass balance in HMA, and can be used by scientific researchers studying climate, hydrology and glaciers.
JIANG Liming JIANG Liming JIANG Liming
High Mountain Asia is the third largest cryosphere on earth other than the Antarctic and Arctic regions. The large amounts of glaciers and snow over the High Mountain Asia play an important role not only on global water cycle but also on water resources and ecology of the arid regions of central Asia. The snowline, as the lower boundary of the snow covered area at the end of melting season, its altitude changes can directly reflect the changes in snow and glaciers. The snowline altitude provides a possibility to rapidly obtain a proxy for their equilibrium line altitude (ELA) which in turn is an indicator for the glacier mass balance. In this dataset, the daily MODIS snow cover products from 2001 to 2019 are used as the main data source. The cloud removal of the daily MODIS snow cover products was firstly carried out based on the developed cubic spline interpolation cloud-removel method, and snow covered days (SCD) are extracted using the cloud-removed MODIS snow cover products. In addition, the MODIS SCD threshold for estimating perennial snow cover is calibrated using the observed data of glacier annual mass balance and Landsat data at the end of melting season. The altitude value of the snowline at the end of melting season is determined by combining the perennial snow cover area and the hypsometric (area-elevation) curve. Finally, the 30km gridded dataset of snowline altitude in the High Mountain Asia during 2001-2019 is generated. This dataset can provide data support for the study of cryosphere and climate change over the High Mountain Asia.
TANG Zhiguang, DENG Gang, WANG Xiaoru