1) These data main included the GPR-surveyed ice thickness of six typical various-sized glaciers in 2016-2018; the GlabTop2-modeled ice thickness of the entire UIB sub-basins, discharge data of the hydrological stations, and related raw & derived data. 2) Data sources and processing methods: We compared the plots and profiles of GPR-surveyed ice bed elevation with the GlabTop2-simulated results and selected the optimal parametric scheme, then simulated the ice thickness of the whole UIB basin and assessed its hydrological effect. These processed results were stored as tables and tif format， 3) Data quality description: The simulated ice thickness has a spatial resolution of 30 m, and has been verified by the GPR-surveyed ice thickness for the MD values were less than 10 m. The maximum error of the GPR-measured data was 230.2 ± 5.4 m, within the quoted glacier error at ± 5%. 4) Synthesizing knowledge of the ice thickness and ice reserves provides critical information for water resources management and regional glacial scientific research, it is also essential for several other fields of glaciology, including hydrological effect, regional climate modeling, and assessment of glacier hazards.
This dataset includes annual mosaics of Antarctic ice velocity derived from Landsat 8 images between December, 2013 and April, 2019, which was updated in 2020 in order to produce multi-year annual ice velocity mosaics and improve the quality of products including non-local means (NLM) filter, and absolute calibration using rock outcrops data. The resulting Version 2 of the mosaics offer reduced local errors, improved spatial resolution as described in the README file.
The data are collected from the automatic weather station (AWS, Campbell company) in the moraine area of garongla glacier by the comprehensive observation and research station of alpine environment in Southeast Tibet, Chinese Academy of Sciences. The geographic coordinates are 29.765 ° n, 95.712 ° E and 3950 m above sea level. The data include daily arithmetic mean data of air temperature (℃), relative humidity (%), wind speed (M / s), net radiation (w / m2) and air pressure (kPa). In the original data, an average value is recorded every 30 minutes before October 2018, and then an average value is recorded every 10 minutes. The temperature and humidity are measured by hmp155a temperature and humidity probe. The net radiation probe is nr01, the atmospheric pressure sensor probe is ptb210, and the wind speed sensor is 05103. These probes are 2 m above the ground. Due to the thick snow cover and low temperature on the ice surface in winter and spring, some parameter data periods are missing, which can be used by scientific researchers studying climate, glacier and hydrology.
The data set contains the stable oxygen isotope data of ice core from 1864 to 2006. The ice core was obtained from Noijinkansang glacier in the south of Southern Tibetan Plateau, with a length of 55.1 meters. Oxygen isotopes were measured using a MAT-253 mass spectrometer (with an analytical precision of 0.05 ‰) at the Key Laboratory of CAS for Tibetan Environment and Land Surface Processes, China. Data collection location: Noijinkansang glacier (90.2 ° e, 29.04 ° n, altitude: 5950 m)
The ages of glacial traces of the last glacial maximum, Holocene and little ice age in the Westerlies and monsoon areas were determined by Cosmogenic Nuclide (10Be and 26Al) exposure dating method to determine the absolute age sequence of glacial advance and retreat. The distribution of glacial remains is investigated in the field, the location of moraine ridge is determined, and the geomorphic characteristics of moraine ridge are measured. According to the geomorphic location and weathering degree of glacial remains, the relationship between the new and the old is determined, and the moraine ridge of the last glacial maximum is preliminarily determined. The exposed age samples of glacial boulders on each row of moraine ridges were collected from the ridge upstream. This data includes the range of glacier advance and retreat in Karakoram area during climate transition period based on 10Be exposure age method.
Among many indicators reflecting climate and environmental change, the stable isotope index of ice core is an indispensable parameter in the study of ice core record, and is one of the most reliable and effective ways to recover the past climate change. Ice core accumulation is a direct record of precipitation on glaciers, and high resolution ice core records ensure the continuity of precipitation records. Therefore, ice core records provide an effective means to recover precipitation changes. The isotope and accumulation of ice cores drilled from the Qinghai Tibet Plateau can be used to reconstruct the changes of temperature and precipitation, which is a good record of climate and environment. This data set provides stable isotope records of hushe ice core in Karakoram area and provides data support for the study of climate change in Qinghai Tibet Plateau.
XU Baiqing WANG Mo
The coverage time of glacier runoff data set in the five major river source areas of the Qinghai Tibet Plateau is from 1971 to 2015, and the time resolution is year by year, covering the source areas of five major rivers (Yellow River source, Yangtze River source, Lancang River source, Nu River source, Yarlung Zangbo River source). The data is based on multi-source remote sensing and measured data. The glacier runoff data is simulated by using the daily scale meteorological data of five major river source areas and their surrounding meteorological stations, the global vegetation products of umd-1km, the igbp-dis soil database, the first and second glacier catalogue data, and the distributed hydrological model vic-cas coupled with the glacier module is used to simulate the glacier runoff data. The simulation results are verified by the site measured data to enhance the quality control. Data indicators include: Glacier runoff (rate of glacier runoff:%), total runoff (mm / a), snow runoff (rate of snow runoff:%), and rainfall runoff rate (rainfall runoff rate:%).
The data involved three periods of geodetic glacier mass storage change of three Rongbuk glaciers and its debris-covered ice in the Rongbuk Catchment from 1974-2016 (unit: m w.e. a-1). It is stored in the ESRI vector polygon format. The data sets are composed of three periods of glacier surface elevation difference between 1974-2000，2000-2016 and 1974-2006, i.e. DHPRISM2006-DEM1974（DH2006-1974）、DHSRTM2000-DEM1974（DH2000-1974）、DHASTER2016-SRTM2000（DH2016-2000）. DH2006-1974 was surface elevation change between ALOS/PRISMDEM(PRISM2006) and DEM1974, i.e. the DEM1974 was subtracted from PRISM2006, DH2006-1974 =PRISM2006 – DEM1974. The PRISM2006 was generated from stereo pairs of ALOS/PRISM on 4 Dec. 2006. The earlier historical DEM (DEM1974, spatial resolution 25m) was derived from 1:50,000 topographic maps in October 1974(DEM1974,spatial resolution 25m). The uncertainty in the ice free areas of DHPRISM2006-DEM1974 was ±0.24 m a-1. DHSRTM2000-DEM1974（DH2000-1974）was surface elevation change between SRTM DEM(SRTM2000) and DEM1974. The uncertainty in the ice free areas of DHSRTM2000-DEM1974 was ±0.13 m a-1. DHASTER2016-SRTM2000（DH2016-2000）was the surface elevation change between ASTER DEM2016 and SRTM DEM(SRTM2000). The uncertainty in the ice free areas of DHASTER2016-SRTM2000 was ±0.08 m a-1. Glacier-averaged annual mass balance change (m w.e.a-1) was averaged annually for each glacier, which was calculated by DH2006-1974/DH2000-1974/DH2016-2000, glacier coverage area and ice density of 850 ± 60 kg m−3. The attribute data includes Glacier area by Shape_Area (m2), EC2000-1974/EC2016-2000/EC2006-1974, i.e. Glacier-averaged surface elevation change in each period(m a-1), MB2000-1974/ MB2016-2000/MB2006-1974, i.e. Glacier-averaged annual mass balance in each period (m w.e.a-1), and MC2000-1974/ MC2016-2000/MC2006-1974,Glacier-averaged annual mass change in each period(m3 w.e.a-1), Uncerty_EC is the maximum uncertainty of glacier surface elevation change（m a-1）、Uncerty_MB, is the maximum uncertainty of glacier mass balance（m w.e. a-1），Uncerty_MC, is the maximum uncertainty of glacier mass change（m3w.e. a-1）。 MinUnty_EC，is the minimum uncertainty of glacier surface elevation change，MinUnty_MB，is the minimum uncertainty of glacier mass balance（m w.e. a-1），MinUnty_MC is the minimum uncertainty of glacier mass change（m3 w.e. a-1.The data sets could be used for glacier change, hydrological and climate change studies in the Himalayas and High Mountain Asia.
The data involved geodetic glacier mass change of 71pieces of glaciers during 2000-2014 in the east of the Yigongzangbu, Southeast Tibetan Plateau. It is stored in the ESRI vector polygon format.Glacier-averaged mass balance (m w.e.a-1) was calculated by the surface elevation difference between 2000-2014 ( Dh2000-2014)、glacier coveraged vector data (CGI2/TPG1976/RGI6.0) and ice density of 850 ± 60 kg m−3. Dh2000-2014 is obtained from surface elevation change by D-InSAR technique from a pair of TSX / TDx SAR images on February 7, 2014 and SRTM DEM. CGI2/TPG1976/RGI6.0 were used to extract glacier boundary and GLIMS-ID. SRTM DEM is the reference DEM and datum DEM with spatial resolution 30m. The attribute data includes GLIMS-ID, Area,EC_m_a-1,,MB_m w.e.a-1, MC_m3 w.e.a-1, MC_Gt.a-1, Uncerty_EC, Uncerty_MB, UT_MCm3w.e. a-1. Respectively, EC_m_a-1,,is the glacier-averaged annual elevation change during 2000-2014(m a-1),MB_m w.e.a-1, is glacier-averaged annual mass balance during 2000-2014(m w.e.a-1), MC_m3 w.e.a-1, is glacier-averaged annual mass change during 2000-2014 (m3 w.e.a-1), MC_Gt.a-1,is glacier-averaged annual mass change during 2000-2014 (Gt.a-1)Uncerty_EC is the uncertainty of glacier surface elevation change（±m a-1）、Uncerty_MB ,is the uncertainty of glacier mass balance（±m w.e. a-1），UT_MCm3w.e. a-1, is the uncertainty of glacier mass change（±m3w.e. a-1）。The data sets could be used for glacier change, hydrological and climate change studies in the southeast of Tibetan Plateau.
The data set involved geodetic annual glacier-averaged mass balance and mass change data atMt.Xixiabangma areasin the Himalayas from 1974 to 2017. It is stored in the ESRI vector polygon format and is composed of two periods, which includes surface elevation difference between 1974-2000 (DH1974-2000, from KH-9 DEM1974 and SRTM DEM2000), surface elevation difference between 2000-2017(DH2000-2017, by DinSAR techniquesfrom SRTM DEM2000 and TSX/TDX data in 2017). KH-9 DEM is a DEM of the study area in 1974, which was generated from three scenes of optical stereo pairs from KH-9. Geodetic glacier mass change was calculated by DH above, glacier cover vector data from TPG1976/CGI2/RGI6.0 with ice density of 850 ± 60 kg m−3. The attribute data included: GLIMSId means the glacier code from GLIMS data base, Area（km2）is the glacier area by km2, area_m2 is glacier area by (m2）, the glacier name, EC74_2000, the surface elevation change rate from 1974 to 2000(m a-1), EC00_2017, the surface elevation change rate from 2000 to 2017 (m a-1), MB74_2000, the geodetic glacier mass balance between 1974 and 2000（m w.e. a-1），MB00_2017, the geodetic glacier mass balance between 2000 and 2017（m w.e. a-1）.MC74_2000, the geodetic glacier mass change from 1974 to 2000 (m3w.e. a-1), MC00_2017, the geodetic glacier mass change from 2000 to 2017(m3 w.e. a-1). Ut_EC74_00 is the uncertainty of glacier surface elevation change（m a-1） in 1974-2000、Ut_MB74_00, is the uncertainty of glacier mass balance for each glacier（m w.e. a-1）in 1974-2000，Ut_MC74_00, is the uncertainty of glacier mass change for each glacier（m3w.e. a-1）in 1974-2000. Ut_EC00_17，is the uncertainty of glacier surface elevation change in 2000-2017（m a-1），Ut_MB00_17，is the uncertainty of glacier mass balance for each glacier in 2000-2017（m w.e. a-1），Ut_MC00_17 is the uncertainty of glacier mass change for each glacier in 2000-2017（m3 w.e. a-1）.This data set is used for the study glaciers melting and its hydrological effects in the Central Himalayas.It also could be used in studies of climatic change and disasters research in the Himalayas.
The data set involved geodetic annual glacier-averagedmass balance and mass change data at the Ponkar area in Nepal on the Southern slope of the Himalayas from 1974 to 2014. It is stored in the ESRI vector polygon format and is composed of two periods, which includes surface elevation difference between 1974-2000 (DH1974-2000, from KH-9 DEM1974 and SRTM DEM2000), surface elevation difference between 2000-2014 (DH2000-2014,by DinSAR techniques from SRTM DEM2000 and TSX/TDX data in 2014). KH-9 DEM is a DEM of the study area in 1974, which was generated from three scenes of optical stereo pairs from KH-9. Geodetic glacier mass change was calculated by DH above, glacier cover vector data from TPG1976/CGI2/RGI6.0 with ice density of 850 ± 60 kg m−3. The attribute data included: GLIMSId means the glacier code from GLIMS data base, the glacier_area（m2）、Area（km2）, the glacier name, EC74_2000, the surface elevation change rate from 1974 to 2000(m a-1), EC00_2014, the surface elevation change rate from 2000 to 2014 (m a-1), MB74_2000, the geodetic glacier mass balance between 1974 and 2000（m w.e. a-1），MB00_2014, the geodetic glacier mass balance between 2000 and 2014（m w.e. a-1）.MC74_2000, the geodetic glacier mass change from 1974 to 2000 (m3w.e. a-1), MC00_2014, the geodetic glacier mass change from 2000 to 2014(m3w.e. a-1). Ut_EC74_00 is the uncertainty of glacier surface elevation change（m a-1） in 1974-2000、Ut_MB74_00, is the uncertainty of glacier mass balance for each glacier（m w.e. a-1）in 1974-2000，Ut_MC74_00, is the uncertainty of glacier mass change for each glacier（m3w.e. a-1）in 1974-2000. Ut_EC00_14，is the uncertainty of glacier surface elevation change in 2000-2014（m a-1），Ut_MB00_14，is the uncertainty of glacier mass balance for each glacier in 2000-2014（m w.e. a-1），Ut_MC00_14 is the uncertainty of glacier mass change for each glacier in 2000-2014（m3 w.e. a-1）. This data set is used for the study glaciers melting and its hydrological effects in Ponkar area in Nepal in the Southern slope of the Himalayas. It also could be used in studies of climatic change and disasters research in the Himalayas.
The data involved two periods of geodetic glacier mass storage change of Naimona’Nyi glaciers in the western of Himalaya from 1974-2013 (unit: m w.e. a-1). It is stored in the ESRI vector polygon format. The data sets are composed of two periods of glacier surface elevation difference between 1974-2000 and 2000-2013, i.e. DHSRTM2000-DEM1974（DH2000-1974）、DHTanDEM2013-SRTM2000（DH2013-2000）. DH2000-1974 was surface elevation change between SRTM2000 and DEM1974, i.e. the earlier historical DEM (DEM1974, spatial resolution 25m) was derived from 1:50,000 topographic maps in October 1974(DEM1974,spatial resolution 25m). The uncertainty in the ice free areas of DH2000-1974 was ±0.13 m a-1. The surface elevation difference between 2000-2013 (DH2000-2013, by DinSAR techniques from SRTM DEM2000 and TSX/TDX data on Oct.17th in 2013) The uncertainty in the ice free areas of DH2013-2000 was ±0.04 m a-1. Glacier-averaged annual mass balance change (m w.e.a-1) was averaged annually for each glacier, which was calculated by DH2000-1974/DH2013-2000, glacier coverage area and ice density of 850 ± 60 kg m−3. The attribute data includes Glacier area by Shape_Area (m2), EC74_00, EC00_13, i.e. Glacier-averaged surface elevation change in 1974-2000 and 2000-2013(m a-1), MB74_00, MB00_13 i.e. Glacier-averaged annual mass balance in 1974-2000 and 2000-2013 (m w.e.a-1), and MC74_00, MC00_13, Glacier-averaged annual mass change in 1974-2000 and 2000-2013 (m3 w.e.a-1), Uncerty_MB, is the uncertainty of glacier-averaged annual mass balance（m w.e. a-1）， Uncerty_MC, is the Maximum uncertainty of glacier-averaged annual mass change（m3 w.e. a-1）. The data sets could be used for glacier change, hydrological and climate change studies in the Himalayas and High Mountain Asia.
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. 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 2018, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
LI Jia WANG Yingzheng LI Jianjiang LI Xin LIU Shaomin
The data set includes the mass balances of Hailuogou Glacier, Parlung No.94 Glacier, Qiyi glacier, Xiaodongkemadi Glacier, Muztagh No.15 Glacier, Meikuang Glacier and NM551 Glacier in the Qinghai Tibet Plateau from 1975 to 2013. Based on several mass balance observations collected from World Glacier Inventory (https://nsidc.org/data/g10002/versions/1) and The Third Pole Environment Database (http://en.tpedatabase.cn/, doi:10.11888/GlaciologyGeocryology.tpe.96.db) by Tandong Yao and the meteorological data obtained from Global Land Assimilation System (GLDAS) (meteorological variables, including precipitation, air temperature, net radiation, evaporation on snow surface, and snow depth, in the central grid of each glacier are extracted from GLDAS data set shown in meteo.xlsx), the mass balances of the above seven glaciers from 1975 to 2013 are reconstructed by using the glacier material balance calculation formula. This reconstruction data is based on the published glacier material balance data to calibrate the parameters in the glacier material balance formula, and to reconstruct the long-time series material balance by using the glacier material balance formula, in which the parameter calibration results and the reconstruction results of the long-time series data are compared with the relevant research results, demonstrating the rationality of the data results Please refer to the following papers. The data can be used to study the change of water resources in the glacial region, expand the data set of Glacier Mass Balance in the Qinghai Tibet Plateau, and provide reference for the future research of Glacier Mass Balance reconstruction.
Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).
Sher Muhammad Sher Muhammad
On the basis of RGI6.0, we use remote sensing and geographic information system technology to update the glacier inventory data in Alaska. The updated glacier inventory uses a data source for 2018 Landsat OLI spatial resolution 15m remote sensing image, and the method used is manual interpretation. The results show that the Alaska Glacier inventory includes 27043 glaciers with a total area of 81285km2. The uncertiany of this data is 4.3%. The data will provide important data support for the study of glacier change in Alaska and the regional and global impact of glacier change in the context of global change.
SHANGGUAN Donghui LI Yaojun
The data set integrated glacier inventory data and 426 Landsat TM/ETM+/OLI images, and adopted manual visual interpretation to extract glacial lake boundaries within a 10-km buffer from glacier terminals using ArcGIS and ENVI software, normalized difference water index maps, and Google Earth images. It was established that 26,089 and 28,953 glacial lakes in HMA, with sizes of 0.0054–5.83 km2, covered a combined area of 1692.74 ± 231.44 and 1955.94 ± 259.68 km2 in 1990 and 2018, respectively.The current glacial lake inventory provided fundamental data for water resource evaluation, assessment of glacial lake outburst floods, and glacier hydrology research in the mountain cryosphere region
WANG Xin GUO Xiaoyu YANG Chengde LIU Qionghuan WEI Junfeng ZHANG Yong LIU Shiyin ZHANG Yanlin JIANG Zongli TANG Zhiguang
The Tibetan Plateau Glacier Data –TPG2017 is a glacial coverage data on the Tibetan Plateau from selected 210 scenes of Landsat 8 Operational Land Imager (OLI) images with 30-m spatial resolution from 2013 to 2018, among of which 90% was in 2017 and 85% in winter. Therefore, 2017 was defined as the reference year for the mosaic image. Glacier outlines were digitized on-screen manually from the 2017 image mosaic, relying on false-colour image composites (RGB by bands 654), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2017. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2017 if they were identifiable on images in all other three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.9%.
1 High resolution gridded West Antarctic surface mass balance dataset, its project is Polar Stereographic Projection 2. The kriging like interpolation method is used to reconstruct the high‐spatial resolution surface mass balance (SMB) over the West Antarctic Ice Sheet (WAIS) from 1800 to 2010, based on ice core records, the outputs of the European Centre for Medium‐Range Weather Forecasts “Interim” reanalysis (ERA‐Interim) as well as the latest polar version of the Regional Atmospheric Climate Model (RACMO2.3p2). 3. Its accuracy is higher than reanalysis data. 4. Temporal resolution: 1800-2010; Temporal resolution: 1 year; Spatial coverage : the whole West Antarctic Ice Sheet, Spatial resolution: 25km х 25km
The recent glacial changes in the third polar region have become the focus of the governments of the surrounding countries because of their important significance to the downstream water supply. Based on SRTM acquired in 2000 and aster stereo image pairs before and after 2015, more than 40 Typical Glaciers in the third polar region were selected to estimate the glacial surface elevation in corresponding period. This product estimates the surface elevation changes of more than 14000 glaciers in the third polar region in 2000-2015s, and the investigated area accounts for about 25% of the total glaciers in the third polar region. The data covers the whole third pole area except Altai mountain, with a spatial resolution of 30m.