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This dataset provides a comprehensive, field-validated Synthetic Aperture Radar (SAR) dataset for Arctic lake ice classification, with a particular emphasis on under-ice water salinity. It includes in situ measurements from 104 lakes (132 measurement sites) across northern Alaska collected in May 2024, capturing data on lake ice thickness, snow depth, lake depth, and specific conductance of unfrozen water beneath the ice. These field observations are integrated with multi-season Sentinel-1 SAR imagery from early winter (January) to late winter (May), along with additional geospatial datasets such as Interferometric Synthetic Aperture Radar (IfSAR)-derived elevation models and summer ice-off timing. The dataset enables improved differentiation of bedfast and floating ice lakes, particularly identifying lakes with brackish to saline water that were previously misclassified as bedfast ice lakes using traditional SAR-based remote sensing approaches. This resource supports research in permafrost stability, Arctic hydrology, climate change impacts, and winter water resource availability. This work was supported by grants from the U.S. National Science Foundation (OPP-2336164 and OPP-2336165) and the European Research Council project No. 951288 (Q-Arctic). Additional support was provided under a Broad Agency Announcement award from ERDC-CRREL, PE 0603119A.
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Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Abstract. Lakes in the Arctic are important reservoirs of heat withmuch lower albedo in summer and greater absorption of solar radiation thansurrounding tundra vegetation. In the winter, lakes that do not freeze totheir bed have a mean annual bed temperature >0 ∘C inan otherwise frozen landscape. Under climate warming scenarios, we expectArctic lakes to accelerate thawing of underlying permafrost due to warmingwater temperatures in the summer and winter. Previous studies of Arcticlakes have focused on ice cover and thickness, the ice decay process,catchment hydrology, lake water balance, and eddy covariance measurements,but little work has been done in the Arctic to model lake heat balance. Weapplied the LAKE 2.0 model to simulate water temperatures in three Arcticlakes in northern Alaska over several years and tested the sensitivity ofthe model to several perturbations of input meteorological variables(precipitation, shortwave radiation, and air temperature) and several modelparameters (water vertical resolution, sediment vertical resolution, depthof soil column, and temporal resolution). The LAKE 2.0 model is aone-dimensional model that explicitly solves vertical profiles of waterstate variables on a grid. We used a combination of meteorological data fromlocal and remote weather stations, as well as data derived from remotesensing, to drive the model. We validated modeled water temperatures withdata of observed lake water temperatures at several depths over severalyears for each lake. Our validation of the LAKE 2.0 model is a necessarystep toward modeling changes in Arctic lake ice regimes, lake heat balance,and thermal interactions with permafrost. The sensitivity analysis shows usthat lake water temperature is not highly sensitive to small changes in airtemperature or precipitation, while changes in shortwave radiation and largechanges in precipitation produced larger effects. Snow depth and lake icestrongly affect water temperatures during the frozen season, which dominatesthe annual thermal regime of Arctic lakes. These findings suggest thatreductions in lake ice thickness and duration could lead to more heatstorage by lakes and enhanced permafrost degradation.
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- PAR ID:
- 10377114
- Date Published:
- Journal Name:
- Geoscientific Model Development
- Volume:
- 15
- Issue:
- 19
- ISSN:
- 1991-9603
- Page Range / eLocation ID:
- 7421 to 7448
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.5194/gmd-15-7421-2022
