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.