Digital polygon dataset of Snow cover area of Central Karakoram National Park (CKNP) area, Pakistan. This dataset is created using topographic sheet maps at scale of 20000 and verified using satellite image.

Digital polygon data of Glaciers of Central Karakoram National Park(CKNP) area, Pakistan. This dataset is created using topographic sheet maps at scale of 20000.

The data contains improved daily MODIS Terra/Aqua combined snow-cover merged with Randolph Glacier Inventory (RGI6.0) product. This product is generated using MODIS Terra and Aqua daily snow cover products MOD10A1 and MYD10A1 collection 6 (C6), respectively. The data covers High Mountain Asia (HMA) covering latitude 24.32− 49.19 N and Longitude 58.22 - 122.48 E with temporal coverage between 2002 and 2019. The data has daily temporal resolution and 500 m spatial resolution. The product is named as M*D10A1GL06 derived from MODIS Terra (MOD) MODIS Aqua (MYD), original product number (10A1) and Glacier (GL), Version 6 (06). The product is described in ordinal date available in GeoTIFF file format as described in the associated Dataset README. For more details about the data, please read the paper associated with this data titled "An improved Terra-Aqua MODIS daily cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019" in Earth System Science Data Journal.

Digital polygon dataset of Glacier exposed rocks of Central Karakoram National Park (CKNP) area, Pakistan. This dataset is created using topographic sheet maps at scale of 20000.

The dataset shows a layer of rock glaciers mapped comprehensively by a first person, using the polygon tool in Google Earth. Therefore, 4000 randomly distributed sample polygons have been created over the Hindu Kush Himalayan region, and each sample polygon has been examined for rock glaciers by two different individuals to increase confidence in the mapping.

Digital polygon data of Glaciers of Bhutan in 1980, 1990, 2000 and 2010. This dataset is created using Landsat MSS, TM and ETM+ imageries of respective years. The glacier outlines was derived semi-automatically using object-based image classification (OBIC ) method separately for clean ice and debris cover and further editing and validation was done carefully by draping over the high resolution images from Google Earth.

Glaciers are one of the essential climate variables (ECV) to better understand the climate system. The regular monitoring of the glacier mass balance gives information about the water stored and released in form of ice melt in a certain period of time. In Nepal, there are more than 3800 glaciers (Bajracharya et al., 2014). Among them, ICIMOD with its partners systematically monitors Yala and Rikha Samba Glacier since 2011, and submits the data to the World Glacier Monitoring Service (WGMS). In the Himalaya is the largest ice repository outside the polar regions, which contribute to 10 major rivers of Asia. Climate change severely impacts glacier melt and its associated geohazards, downstream water availability, which is of increasing concern in the HKH region. The region is sensitive to climate change and witnesses rapid glacier melt, increased frequency and magnitude of extreme weather events, and a shift in monsoon patterns. It is crucial for the countries in the region to improve their knowledge and understanding of the changes taking place in the cryosphere and determine what will be their implications to the region’s water supplies. The International Centre for Integrated Mountain Development (ICIMOD) in collaboration with partners from Nepal and with the support of the government of Norway and Switzerland is implementing the project ‘Monitoring and assessment of changes in Glaciers, Snow and Glacio-hydrology in the Hindu Kush - Himalayas with a special focus on strengthening the capacity of Nepalese organizations (the HKH Cryosphere Monitoring Project)’ since 2011.

Glacier lake outburst floods (GLOFs) have been intensely investigated in High Mountain Asia (HMA) in recent years and are the most well-known hazard associated with the cryosphere. As glaciers recede and surrounding slopes become increasingly unstable, such events are expected to increase, although current evidence for an increase in events is ambiguous. Many studies have investigated individual events and while several regional inventories exist, they either do not cover all types of GLOF or are geographically constrained. Further, downstream impacts are rarely discussed. Previous inventories have relied on academic sources and have not been combined with existing inventories of glaciers and lakes. In this study, we present the first comprehensive inventory of GLOFs in HMA, including details on the time of their occurrence, processes of lake formation and drainage involved as well as downstream impacts. We document 766 individual GLOFs that occurred between 1533 and 2025. Of these, 23% were recurring events from just three ephemeral ice-dammed lakes. In combination, the documented events resulted in 8996 fatalities of which 906 fatalities were from 24 events, which is three times higher than a previous assessment for the region. The integration of previous inventories of glaciers and lakes within this database will inform future assessments of potential drivers of GLOFs, allowing more robust projections to be developed. The database and future, updated versions, are traceable, version controlled and can be directly incorporated into further analysis. This dataset has been updated in December 2025.

The dataset shows areas identified as rock glaciers by two different persons (rock glacier mapping layer M1 and M2). It is obtained by intersecting M1 and M2 layer of rock glacier mapping.

The dataset shows a layer of rock glaciers mapped comprehensively by a second person, using the polygon tool in Google Earth. Therefore, 4000 randomly distributed sample polygons have been created over the Hindu Kush Himalayan region, and each sample polygon has been examined for rock glaciers by two different individuals to increase confidence in the mapping.