This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from very-high-resolution (VHR) commercial satellite imagery.

This data set contains 8-meter Digital Elevation Models (DEMs) of high mountain Asia glacier and snow regions generated from very-high-resolution commercial stereoscopic satellite imagery.

This High Mountain Asia data set contains 2 m resolution digital elevation models (DEMs), surface velocities, surface mass balance (SMB) rates, and SMB uncertainties for six debris-covered glaciers in Nepal.

SMB rate is estimated by applying a Lagrangian specification to DEMs derived from very-high-resolution optical stereo imagery acquired by Maxar Technologies satellites WorldView-1, WorldView-2, WorldView-3, and GeoEye-1.

This data set was granted permission for public release on 1 March 2024 under the National Reconnaissance Office (NRO) Electro-Optical Commercial Layer (EOCL) program.

This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from very-high-resolution (VHR) commercial satellite imagery.

This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from from very-high-resolution commercial stereo satellite imagery.

This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from from very-high-resolution commercial stereo satellite imagery.

This dataset is an inventory of surge-type glaciers across High Mountain Asia (HMA). The surge-type glaciers were identified through the glacier elevation change observations during 1970s to 2010s, and timeseries morphological changes from optical images. The source datasets include the KH-9 DEM, NASADEM, HMA8m DEM, Copernicus 30m DEM and GAMDAM2 glacier inventory. The elevation changes were estimated by differencing the DEMs of distinct time. This dataset documents 890 surging glaciers and 336 surge-like glaciers across HMA. The data is stored in the vector format and contains the glacier boundary vector, glacier ID, glacier geometric properties, and indexes of surge possibility. The dataset consists of 1 season. This dataset can be used for the regional hazard assessment of surge-type glaciers and the study of spatial-temporal glacier variation.

See manuscript for methodology and dataset description:

Shean DE, Bhushan S, Montesano P, Rounce DR, Arendt A and Osmanoglu B (2020) A Systematic, Regional Assessment of High-Mountain Asia Glacier Mass Balance. Front. Earth Sci. 7:363. DOI: 10.3389/feart.2019.00363

https://www.frontiersin.org/articles/10.3389/feart.2019.00363/full

GeoTiff header contains relevant metadata and georeferencing information (30 m pixel size, Albers Equal Area projection). Proj string is '+proj=aea +lat_1=25 +lat_2=47 +lat_0=36 +lon_0=85 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'

External overview file (.ovr) contains pyramidal overviews for improved visualization performance at different zoom levels.

Knowledge about the long-term response of High Mountain Asia (HMA) glaciers to climatic variations is paramount because of their important role in sustaining Asian river flow. Here. a satellite-based time series of glacier mass balance for seven climatically different regions across HMA since the 1960s were estimated by DEM differencing of multi-temporal optical data. The DEMs were corrected for planimetric and altimetric shifts using SRTM as a reference. Elevation dependent biases, present due to the tilt between two DEMs, were also estimated for each DEM using two-dimensional first order polynomial trend surfaces relative to the SRTM DEM. To remove outliers, we analyzed individual glacier elevation differences for each 100 m altitude bin. Considering the heterogeneity of the thickness change in glacierized terrain, outliers were removed by using an elevation dependent sigmoid function. Our study reveals a constant mass loss in all regions even in regions where glaciers were previously in balance with climate.

This data set contains Flood Geomorphic Potential (FGP) at 30 m resolution for the High Mountain Asia region and 8 m resolution over Nepal. FGP is a digital elevation model-derived index that provides high-resolution flood mapping based on bankfull elevations, defined in terms of river widths, and elevation differences between points under examination and the closest bankfull elevations in the river network.

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.

Here we present a dataset of Transient Snowline Altitude (TSLA) measurements for glaciers in High Mountain Asia (HMA) based on Landsat satellite imagery and digital elevation model data. The data were obtained using the MountAiN glacier Transient snowline Retrieval Algorithm (MANTRA), a Google Earth Engine tool to measure the average altitude of the snow-ice boundary. Each MANTRA result consists of reference data (e.g. Landsat scene, date, glacier ID), relevant topographic metrics (glacier area, minimum and maximum elevation of the glacier), results of the surface material classification (areas covered by ice, snow, debris and clouds), summary statistics of the TSLA measurement, and quality metrics (cloud cover close to snow-ice boundary, class coverage). For the dataset presented here, we applied MANTRA to all glaciers in HMA with an area larger than 0.5 km² (ca. 28,500 based on Randolph Glacier Inventory v6 glacier outlines). After filtering and postprocessing, the dataset comprises ca. 9.66 million TSLA measurements with an average of 341 ± 160 measurements per glacier, covering the time span 1985 to 2021. Time series of Transient Snowline Altitude (TSLA) metrics for glaciers in High Mountain Asia, 1986 to 2021.The file is in NetCDF format, with the date of the Landsat measurement (LS_DATE) as index.Individual glacier are identified through Randolph Glacier Inventory v6 IDs (RGI_ID).The recommended metric to use for analyses is the median elevation of the detected TSLA range (TSLrange_median_masl).

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.

Data and methods

On 3 October 2023, the collapse of a frozen lateral moraine into South Lhonak Lake triggered a multi-hazard cascade in the Sikkim Himalaya, India (Sattar et al, 2025). This repository contains two DEMs derived from Pléiades satellite images that were acquired before and after the event.

A post-event DEM computed from a pair of PHR1A stereoscopic images acquired on 2023-10-29 (20231029.tif)

A pre-event DEM computed from a triplet of PHR1A stereoscopic images acquired on 2022-10-18 (20221018.tif)

The DEMs (height above the WGS84 ellipsoid) were posted at 1.0 m with UTM 45N projection (EPSG:32645) and coregistered to the GLO30 Copernicus DEM. They were computed with the MicMac software in the DSM-OPT service using the following configuration: correlation window size: 3x3, regularization factor: 0.05, vertical uncertainty: 0.1, potential accuracy threshold: 0.4 (Rupnik et al. 2017).

These DEMs allow the analysis of topographic changes in the lake area. We included examples of such analysis with the addition of the High Mountain Asia 8 m DEM (Shean 2017).

Acknowledgements

Pléiades images were acquired and processed thanks to the CIEST2 service developed and performed with the French Space Agency (CNES) by FormaTerre, Solid Earth component of the Data Terra Research Infrastructure.

References

Sattar et al. (2025) The Sikkim flood of October 2023: Drivers, causes and impacts of a multihazard cascade. Science, eads2659, https://doi.org/10.1126/science.ads2659 Rupnik, E., Daakir, M., & Pierrot Deseilligny, M. (2017). MicMac–a free, open-source solution for photogrammetry. Open geospatial data, software and standards, 2, 1-9. https://doi.org/10.1186/s40965-017-0027-2

Shean, D. (2017). High Mountain Asia 8-meter DEM Mosaics Derived from Optical Imagery, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/KXOVQ9L172S2. Date Accessed 07-29-2024.

We have developed an updated and comprehensive inventory of glacial lakes across High Mountain Asia (HMA) for the year 2022, identifying a total of 31,698 glacial lakes. This inventory was created by integrating multi-source remote sensing datasets, including Landsat-8, Sentinel-1, Sentinel-2, and the Copernicus DEM. We achieved a high detection accuracy of 96% for smaller glacial lakes (20,000–100,000 m²) and successfully detected all larger glacial lakes with high delineation accuracy. Additionally, we generated median lake boundaries for two epochs (2016 and 2022) by using median composites of all available imagery for each period to minimize the influence of seasonal variations in lake extents. This database can serve as a valuable resource for monitoring GLOFs, downstream risk assessment, and water resource management in the region.

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.

1) Data Content: This dataset is derived from NASADEM (with a spatial resolution of 30 meters) and represents boundary data for high mountain areas in Asia at various altitudes (1500, 2000, 2500, 3000, and 3500 meters). 2) Data Source and Preparation Methodology: 1. The NASADEM data was resampled to a 1000-meter spatial resolution using an aggregation upscaling technique, with the primary aim of enhancing computational efficiency. 2. The resampled DEM data was subsequently reclassified according to distinct altitude bands. 3. The reclassified raster data was converted to vector format, followed by editing and modification, which involved removing internal voids within the polygons and excluding small patches with areas smaller than 1 km². 4. Bilateral filtering techniques were applied to the vector polygons to smooth the data. 3) Data Quality Description: The dataset is characterized by its accuracy, completeness, and ease of interpretation. 4) Applications and Future Prospects: This dataset is intended for use in cartographic mapping, regional analysis, and the study of changes in critical factors such as the cryosphere, ecological environment, climate, and carbon cycles within the high mountain regions of Asia.

This set of data is a dataset of annual changes in glacier mass balance in the Himalayan region from 2010 to 2020, stored in ESRI vector polygon format. It is calculated from the DEM elevation difference between 2010 and 2020, RGI the Himalayas glacier coverage thematic vector data and ice density of 850 ± 60 kg m − 3. The data items included in the attribute table include: RGIId and GLIMSID representing glacier number, Area representing glacier area, Name representing glacier name, meanElevCh representing average ice surface elevation change (m yr-1) for each glacier, meanElev_ 1 represents error (m yr-1), volChj represents volume change of each glacier (m3 yr-1), volChjSig represents error (m3 yr-1), geoMassBal represents glacier mass balance of each glacier (w.e.yr-1), geoMassB_ 1 represents error (w.e.yr-1), Massch_ M3 represents the change in melting amount of each glacier (m3 w.e.yr-1), Geo_ Melt_ 1 represents the variation error of melting amount for each glacier (m3 w.e.yr-1). The DEM elevation difference between the 2010 and 2020 time periods was calculated by Hugonnet et al. based on the ASTER data generated from these two time periods, using a multi period DEM with a spatial resolution of 100m. Using WGS84 ellipsoid. The maximum error of ice elevation change data is 0.47 m yr-1, and the maximum error of glacier mass balance change data is 0.28 w.e.yr-1. This data can be used for studying glacier changes in the Himalayan region, as well as for studying the hydrological and water resource effects of glacier melting in high-altitude Asia and its climatic reasons.