Glacial Lake Observatory (GLO): Annual dataset of glacial lakes in Nepal and transboundary catchments (2017–2024)

Abstract. Global glacier mass loss is accelerating the formation and expansion of glacial lakes. These lakes store meltwater, contribute to enhanced glacier mass loss through positive feedback mechanisms, and in some cases can pose a risk to downstream populations, infrastructure, and ecosystems through glacial lake outburst floods (GLOFs). Although satellite-derived inventories of glacial lakes exist at both global and regional scales, they vary in spatial and temporal resolution. Critically, fully automated and systematic monitoring of lake area changes is lacking, yet such monitoring is essential for detecting anomalous changes, estimating water storage, and understanding lake-glacier feedbacks. Here, we present a foundational dataset to support lake monitoring for the Glacial Lake Observatory (GLO), with an initial focus on lakes in Nepal and transboundary catchments. We trained a deep learning model to extract water bodies from Sentinel-1 and Sentinel-2 image mosaics from 2017 to 2024, subsequently classifying them as glacier-fed or non-glacier-fed based on their hydrological connectivity. In total, 18,389 and 22,419 individual lake outlines (≥ 0.001 km²) were mapped respectively from Sentinel-1 and Sentinel-2 imagery (2017–2024), resulting in 2,966 and 4,150 uniquely identified lakes (respectively). The number and total area of lakes increased over the eight-year period, driven largely by sustained expansion in the Koshi basin, which hosts about 61% of all mapped lakes and nine out of ten of the fastest expanding. On average, glacial lakes covered an average annual area of 169 km², with growth concentrated in high-elevation, glacier-fed systems. Validation against existing inventories and manually digitised outlines demonstrated good accuracy of our deep learning datasets (F1 scores = 0.80–0.92), with Sentinel-2 most reliably capturing smaller lakes. Datasets, as well as deep learning models, are openly available (https://doi.org/10.5281/zenodo.17802334).