The glacial lakes of Afghanistan were mapped using Landsat image that were selected based in a one-year buffer surrounding a representative year. For instance, the Landsat images from 2009 to 2011 were used to represent 2010 depending on the quality of images (least snow cover and cloud cover). The glacial lakes were extracted semi-automatically through an object-based image classification (OBIC) method and were then subjected to manual editing for quality control. The attributes of the data were extracted from the SRTM DEM. This dataset was produced in collaboration between the National Water Affairs Regulation Authority (NWARA) of the Government of Afghanistan and ICIMOD as part of the SERVIR-HKH Initiative.

The glacial lakes of Afghanistan were mapped using Landsat image that were selected based in a one-year buffer surrounding a representative year. For instance, the Landsat images from 1999 to 2001 were used to represent 2000 depending on the quality of images (least snow cover and cloud cover). The glacial lakes were extracted semi-automatically through an object-based image classification (OBIC) method and were then subjected to manual editing for quality control. The attributes of the data were extracted from the SRTM DEM. This dataset was produced in collaboration between the National Water Affairs Regulation Authority (NWARA) of the Government of Afghanistan and ICIMOD as part of the SERVIR-HKH Initiative.

The glacial lakes of Afghanistan were mapped using Landsat image that were selected based in a one-year buffer surrounding a representative year. For instance, the Landsat images from 2014 to 2016 were used to represent 2015 depending on the quality of images (least snow cover and cloud cover). The glacial lakes were extracted semi-automatically through an object-based image classification (OBIC) method and were then subjected to manual editing for quality control. The attributes of the data were extracted from the SRTM DEM. This dataset was produced in collaboration between the National Water Affairs Regulation Authority (NWARA) of the Government of Afghanistan and ICIMOD as part of the SERVIR-HKH Initiative.

The glacial lakes of Afghanistan were mapped using Landsat image that were selected based in a one-year buffer surrounding a representative year. For instance, the Landsat images from 1989 to 1991 were used to represent 1990 depending on the quality of images (least snow cover and cloud cover). The glacial lakes were extracted semi-automatically through an object-based image classification (OBIC) method and were then subjected to manual editing for quality control. The attributes of the data were extracted from the SRTM DEM. This dataset was produced in collaboration between the National Water Affairs Regulation Authority (NWARA) of the Government of Afghanistan and ICIMOD as part of the SERVIR-HKH Initiative.

Glacier data of Afghanistan were prepared on the basis of Landsat imageries from 2010. The glacier outlines were derived semi-automatically using object-based image classification (OBIC) separately for clean-ice and debris-covered glaciers and further manual editing for quality assurance. The attributes of glacier data were derived from SRTM DEM. This dataset was jointly prepared by the Ministry of Energy and Water (MEW), Government of Afghanistan, and ICIMOD under the SERVIR-HKH Initiative.

Glacier data of Afghanistan were prepared on the basis of Landsat imageries from 1990. The glacier outlines were derived semi-automatically using object-based image classification (OBIC) separately for clean-ice and debris-covered glaciers and further manual editing for quality assurance. The attributes of glacier data were derived from SRTM DEM. This dataset was jointly prepared by the Ministry of Energy and Water (MEW), Government of Afghanistan, and ICIMOD under the SERVIR-HKH Initiative.

Glacier data of Afghanistan were prepared on the basis of Landsat imageries from 2000. The glacier outlines were derived semi-automatically using object-based image classification (OBIC) separately for clean-ice and debris-covered glaciers and further manual editing for quality assurance. The attributes of glacier data were derived from SRTM DEM. This dataset was jointly prepared by the Ministry of Energy and Water (MEW), Government of Afghanistan, and ICIMOD under the SERVIR-HKH Initiative.

Glacier data of Afghanistan were prepared on the basis of Landsat imageries from 2015. The glacier outlines were derived semi-automatically using object-based image classification (OBIC) separately for clean-ice and debris-covered glaciers and further manual editing for quality assurance. The attributes of glacier data were derived from SRTM DEM. This dataset was jointly prepared by the Ministry of Energy and Water (MEW), Government of Afghanistan, and ICIMOD under the SERVIR-HKH Initiative.

This database was compiled under WP-2 of the SustainIndus project between August 2019 and August 2021 to consolidate publicly available information on hydropower projects in various stages of development in the upper Indus basin in national and global datasets. The database is developed with the objective of quantifying and characterizing the visualised hydropower potential which is defined as the total potential of existing and future hydropower plants that are have been envisioned in publicly available national documents. Future hydropower plants entail plants that may be under construction, planned plants in other stages of development or raw plants that have been shortlisted by government studies but may not have clarity on if and when they will materialize. The effort manually validated the quality of four parameters: geo-reference (latitude-longitude), development status, plant capacity and annual energy generation. Many other plant parameters found in the review have also been consolidated for future use here.

The hydropower potential in the upper Indus basin under future hydro-climatology was prepared using the Hydropower Potential Exploration (HyPE) as part of Work Package 2 in the SustainIndus project. Under future hydro-climatology, the HyPE model was run to explore theoretical potential followed by technical, financial and sustainable potential under policy assumptions for the mixed energy focus scenarios and risk-averse geo-hazard risk representation. In total, 72 future scenarios are considered combining CMIP6 model ensembles for 2 future time horizons (Mid: 2036-2065, Far: 2066-2095), 3 Representative Concentration Pathways (RCP 4.5, 7.0 and 8.5), 4 corner Global Climate Models for each RCP (Cold Dry, Cold Wet, Warm Dry, Warm Wet) and 3 Hydropower Potential classes (Technical, financial and sustainable).