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Discover and access geospatial datasets from the Hindu Kush Himalaya region

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Decadal glacier changes from 1990 to 2020 in the Hindu Kush Himalaya

Glaciers are an indispensable component of the Hindu Kush Himalayan (HKH) region, acting as a vast “water tower” that stores large volumes of freshwater and sustains Asia’s ten major river systems, supporting ecosystems, communities, and livelihoods across the continent. This study presents a decadal glacier change dataset for the HKH region developed using a semi-automatic mapping approach to ensure high accuracy and enable reliable comparison of glacier changes over time. The dataset was generated using consistent data sources and methods, with Landsat imagery from 1990, 2000, 2010, and 2020, maintaining a narrow temporal window (±1 year from the base year) and a uniform spatial resolution. Glacier outlines were mapped at a scale of 1:50,000 with a minimum area threshold of 0.02 km². Glacier area and associated topographic parameters were derived using the Albers Equal Area projection and the global void-filled SRTM DEM v4. This dataset represents a first-of-its-kind, region-wide glacier inventory created using a consistent methodology and data source, providing a comprehensive assessment of glacier distribution and decadal change across the entire HKH region.

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GLOF database of High Mountain Asia

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.

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Forest Types of Nepal

The dataset on forest types of Nepal for 2022 is jointly developed by the Forest Research and Training Centre (FRTC) and International Centre for Integrated Mountain Development (ICIMOD) with financial support from the World Bank under the "Sub-National Biophysical Forest Indicators for Sustainable Forest Management and Revenue Sharing in Nepal" project. The dataset has been prepared using freely available remote-sensing data (Sentinel 2) and a cloud-based machine learning algorithm in the Google Earth Engine (GEE) platform, field sampling methods, and extensive stakeholder consultations.

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Land cover of Nepal

The annual land cover data of Nepal (2000-2022) have been created through the National Land Cover Monitoring System (NLCMS) for Nepal. The system uses freely available remote-sensing data (Landsat) and a cloud-based machine learning architecture in the Google Earth Engine (GEE) platform to generate land cover maps on an annual basis using a harmonized and consistent classification system. The NLCMS is developed by the Forest Research and Training Centre (FRTC), Ministry of Forests and Environment, Government of Nepal with support from the International Centre for Integrated Mountain Development (ICIMOD) through SERVIR Hindu Kush Himalaya (SERVIR-HKH), a joint initiative in partnership with the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). Collaborators include SERVIR–Mekong at the Asian Disaster Preparedness Center (ADPC), SilvaCarbon, Global Land Analysis and Discovery (GLAD) group at the University of Maryland, and the US Forest Service. The annual land cover data of Nepal for 2000-2019 was first published in 2022 while the data for 2020-2022 was released in 2024.

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Land cover of HKH region

Land cover change is a significant contributor to environmental change. The degradation of forests and conversion of natural areas, forests, and farmlands to other land use impact ecosystem services and biodiversity significantly. Using multiple methodologies and input data sources, national agencies in different countries of the Hindu Kush Himalayan region have conducted land cover mapping at various times. Due to the differences in classification schema, methodologies, and input data sources used, currently available land cover data is not suitable for analysis of land cover changes over time. ICIMOD collaborated with SERVIR-Mekong at Asian Disaster Preparedness Center (ADPC), Afghanistan’s Ministry of Agriculture, Irrigation and Livestock, Bangladesh’s Forest Department, Nepal’s Forest Research and Training Centre, Myanmar’s Forest Department, SilvaCarbon, the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland, and the United States Forest Services to develop the Regional Land Cover Monitoring System (RLCMS) for the HKH region. The system uses state-of-the-art remote sensing science and technology on the Google Earth Engine, and a standard set of input data sources to regularly generate high-quality land cover data at the regional level for the HKH, and at national levels for Afghanistan, Bangladesh, Myanmar, and Nepal. In developing the RLCMS, ICIMOD focused on collaboration and co-development with partner organizations to define different land cover typologies/classes, collect reference data samples, and validate results. Land cover maps for the HKH region spanning 2000–2022 have been produced under its SERVIR–HKH Initiative.

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Status of Springs in Seven Municipalities of Kavre, Nepal

This dataset presents the findings of a springshed management initiative conducted across seven municipalities in the Kavrepalanchok district of Nepal, addressing critical water stress caused by the drying of springs and ponds. Spanning 743 sq. km, the dataset includes detailed mapping of 5,689 water sources, of which 27% have dried due to factors such as earthquakes, droughts, infrastructure development, and natural disasters, while 58% of the active springs show declining flow trends. The dataset provides information on water source locations, conditions, discharge rates, usage patterns, socio-economic data of dependent users, community-implemented adaptation measures, biophysical characteristics, and governance aspects. Data collection followed a six-step protocol for springshed management, with 150 trained community resource persons (CRPs) using the Survey123 app for efficient and accurate digital documentation. This initiative demonstrates the potential of a citizen science approach to large-scale spring documentation, offering a foundation for evidence-based water resource management and policy development. The emphasis on gender inclusivity, digital innovation, and local knowledge provides valuable insights for scaling springshed management across the Hindu Kush Himalaya and similar regions.

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Popular Datasets

2022-11-09

Land cover of HKH region

Land cover change is a significant contributor to environmental change. The degradation of forests and conversion of natural areas, forests, and farmlands to other land use impact ecosystem services and biodiversity significantly. Using multiple methodologies and input data sources, national agencies in different countries of the Hindu Kush Himalayan region have conducted land cover mapping at various times. Due to the differences in classification schema, methodologies, and input data sources used, currently available land cover data is not suitable for analysis of land cover changes over time. ICIMOD collaborated with SERVIR-Mekong at Asian Disaster Preparedness Center (ADPC), Afghanistan’s Ministry of Agriculture, Irrigation and Livestock, Bangladesh’s Forest Department, Nepal’s Forest Research and Training Centre, Myanmar’s Forest Department, SilvaCarbon, the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland, and the United States Forest Services to develop the Regional Land Cover Monitoring System (RLCMS) for the HKH region. The system uses state-of-the-art remote sensing science and technology on the Google Earth Engine, and a standard set of input data sources to regularly generate high-quality land cover data at the regional level for the HKH, and at national levels for Afghanistan, Bangladesh, Myanmar, and Nepal. In developing the RLCMS, ICIMOD focused on collaboration and co-development with partner organizations to define different land cover typologies/classes, collect reference data samples, and validate results. Land cover maps for the HKH region spanning 2000–2022 have been produced under its SERVIR–HKH Initiative.

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2021-03-30

Habitat suitability data for the Clouded Leopard

The Hindu Kush Himalayan region (HKH) is an important biodiversity hotspot with more than 488 protected areas covering 39% of the region's geographical coverage. A majority of the protected areas are small and isolated and are not large enough to address conservation challenges. Only about 20% of the protected areas are transboundary in nature. There is limited data available on habitat suitability for conservation planning and landscape management in the Far Eastern Himalayan Landscape. A study carried out under the Landscape Initiative for Far Eastern Himalayas (HI-LIFE) Initiative at ICIMOD, estimates habitat suitability for the Clouded Leopard in the Far Eastern Himalayas using remote sensing and geographic information system tools, and available secondary information.

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2021-03-30

Habitat suitability data for the Badger

The Hindu Kush Himalayan region (HKH) is an important biodiversity hotspot with more than 488 protected areas covering 39% of the region's geographical coverage. A majority of the protected areas are small and isolated and are not large enough to address conservation challenges. Only about 20% of the protected areas are transboundary in nature. There is limited data available on habitat suitability for conservation planning and landscape management in the Far Eastern Himalayan Landscape. A study carried out under the Landscape Initiative for Far Eastern Himalayas (HI-LIFE) Initiative at ICIMOD, estimates habitat suitability for the Badger in the Far Eastern Himalayas using remote sensing and geographic information system tools, and available secondary information.

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Data from the Region

Tibetan Plateau Data Center

TPDC is a regional data hub for the Hindu Kush Himalaya and wider Third Pole. It curates and shares environmental, cryosphere, climate, and related datasets to support research, monitoring, and decision-making in mountain regions.

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