Sub-monthly volumetric evolution of the supraglacial lakes in Northeastern Greenland based on the topographic depression method

Study region Supraglacial lakes on the northeastern Greenland Ice Sheet (GrIS) during the melt seasons (May–September) from 2018 to 2025. Study focus This study develops a multi-parameter inversion framework for supraglacial lakes by integrating multi-source remote sensing data and machine learning. Using Landsat-8 multispectral bands and indices combined with ArcticDEM, a machine learning model is constructed to map the supraglacial lake area. Lake volume is estimated via the terrain depression method. The framework enables high-temporal-resolution (sub-monthly) monitoring of supraglacial lake area and volume over a decadal scale. A key innovation is the quantitative analysis of altitudinal shifts in the spatial distribution of supraglacial lakes at high temporal resolution, along with region-specific attribution of climatic drivers. New hydrological insights The inversion reveals that the coverage of supraglacial lakes in high-elevation regions increased from less than 10–25% during the study period. Quantitative climate attribution shows significant elevation‑dependent heterogeneity: in mid‑elevation regions, temperature‑driven surface meltwater flux contributes 42.45% to changes in supraglacial lake volume, surpassing precipitation as the dominant factor; in high‑elevation regions, snowmelt recharge and surface runoff inputs are the primary drivers. This framework provides a scientifically robust technical solution for sub‑monthly dynamic monitoring of supraglacial lakes, filling the gap in high‑frequency, long‑term hydrological observation on the GrIS.