Greenland Ice Sheet Wide Supraglacial Lake Evolution and Dynamics: Insights From the 2018 and 2019 Melt Seasons

dc.contributor.authorDunmire, Devon
dc.contributor.authorSubramanian, Aneesh C.
dc.contributor.authorHossain, Emam
dc.contributor.authorGani, Md Osman
dc.contributor.authorBanwell, Alison F.
dc.contributor.authorYounas, Hammad
dc.contributor.authorMyers, Brendan
dc.date.accessioned2025-03-11T14:42:51Z
dc.date.available2025-03-11T14:42:51Z
dc.date.issued2025-02-21
dc.description.abstractSupraglacial lakes on the Greenland Ice Sheet (GrIS) can impact both the ice sheet surface mass balance and ice dynamics. Thus, understanding the evolution and dynamics of supraglacial lakes is important to provide improved parameterizations for ice sheet models to enable better projections of future GrIS changes. In this study, we utilize the growing inventory of optical and microwave satellite imagery to automatically determine the fate of Greenland-wide supraglacial lakes during 2018 and 2019; low and high melt seasons respectively. We develop a novel time series classification method to categorize lakes into four classes: (a) Refreezing, (b) rapidly draining, (c) slowly draining, and (d) buried. Our findings reveal significant interannual variability between the two melt seasons, with a notable increase in the proportion of draining lakes, and a particular dominance of slowly draining lakes, in 2019. We also find that as mean lake depth increases, so does the percentage of lakes that drain, indicating that lake depth may influence hydrofracture potential. We further observe rapidly draining lakes at higher elevations than the previously hypothesized upper-elevation hydrofracture limit (1,600 m), and that non-draining lakes are generally deeper during the lower melt 2018 season. Our automatic classification approach and the resulting 2-year ice-sheet-wide data set provide new insights into GrIS supraglacial lake dynamics and evolution, offering a valuable resource for future research.
dc.description.sponsorshipDD, ACS, EH, MOG and AFB weresupported by the iHARP HDR Institute(NSF award #2118285). We acknowledgehigh?performance computing support fromCheyenne (https://doi.org/10.5065/D6RX99HX) provided by NCAR'sComputational and Information SystemsLaboratory, sponsored by the NSF. Thiswork also utilized the Summitsupercomputer, which is supported by theNSF (awards ACI?1532235 and ACI?1532236) and is a joint effort of the University of Colorado Boulder, and Colorado State University. Earth and Space Science 10.1029/2024EA003793DUNMIRE ET AL. 18 of 20
dc.description.urihttps://onlinelibrary.wiley.com/doi/abs/10.1029/2024EA003793
dc.format.extent20 pages
dc.genrejournal articles
dc.identifierdoi:10.13016/m2ulpe-jmva
dc.identifier.citationDunmire, Devon, Aneesh C. Subramanian, Emam Hossain, Md Osman Gani, Alison F. Banwell, Hammad Younas, and Brendan Myers. "Greenland Ice Sheet Wide Supraglacial Lake Evolution and Dynamics: Insights From the 2018 and 2019 Melt Seasons". Earth and Space Science 12, no. 2 (2025): e2024EA003793. https://doi.org/10.1029/2024EA003793.
dc.identifier.urihttps://doi.org/10.1029/2024EA003793
dc.identifier.urihttp://hdl.handle.net/11603/37780
dc.language.isoen_US
dc.publisherAGU
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Student Collection
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectremote sensing
dc.subjecthydrofracture
dc.subjectGreenland ice sheet
dc.subjectsupraglacial lakes
dc.subjectUMBC Causal Artificial Intelligence Lab (CAIL)
dc.subjectmachine learning
dc.subjecthydrology
dc.titleGreenland Ice Sheet Wide Supraglacial Lake Evolution and Dynamics: Insights From the 2018 and 2019 Melt Seasons
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-6422-1895
dcterms.creatorhttps://orcid.org/0000-0001-9962-358X

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