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The number and cumulative area of ice-marginal lakes have expanded globally in recent decades, with many lakes residing in glacier-bed overdeepenings, which are subglacial basins formed through preferential glacial erosion. However, current lake expansion rates, key drivers of expansion, and maximum future lake extents are poorly quantified. This is notable because glacial lakes pose hazards, alter hydrologic and ecological systems, and, in some cases, accelerate glacier flow and retreat. Here, we quantify recent ice-marginal lake growth and use existing ice thickness and topographic data to map glacier-bed overdeepenings in Alaska as a predictor of recent and future locations of lake growth. Ice-marginal lakes in the region grew by +156 km²(26 km²y⁻¹) between 2018 and 2024, representing a 50% increase relative to the 2009–2018 rate. Eighty percent of lake growth since 2018 occurred in mapped glacier-bed overdeepenings. Approximately 4,250 km²(2,966 to 5,503 km²accounting for ± ice thickness uncertainty) of the overdeepened area is connected to an ice-marginal lake, indicating the potential for more than fourfold lake growth of existing lakes as glaciers retreat. An additional 14,500 km²(12,469 to 17,134 km²) of remaining glacier area resides on glacier-bed overdeepenings not connected to existing lakes, highlighting the potential for substantial new lake development. Velocities from lake-terminating glaciers show clear passive and dynamic endmembers on a continuum of glacier–lake coupling. Glaciers with ice-marginal lakes thinned 23 to 54% more than glaciers of similar area without lakes, underscoring the critical importance of dynamic glacier–lake coupling on the evolution of glaciers in Alaska. 

Abstract Globally, glaciers are changing in response to climate warming, with those that terminate in water often undergoing the most rapid change. In Alaska and northwest Canada, proglacial lakes have grown in number and size but their influence on glacier mass loss is unclear. We characterized the rates of retreat and mass loss through frontal ablation of 55 lake-terminating glaciers (>14 000 km²⁾in the region using annual Landsat imagery from 1984 to 2021. We find a median retreat rate of 60 m a⁻¹(interquartile range = 35–89 m a⁻¹) over 1984–2018 and a median loss of 0.04 Gt a⁻¹(0.01–0.15 Gt a⁻¹) mass through frontal ablation over 2009–18. Summed over 2009–18, our study glaciers lost 6.1 Gt a⁻¹to frontal ablation. Analysis of bed profiles suggest that glaciers terminating in larger lakes and deeper water lose more mass to frontal ablation, and that the glaciers will remain lake-terminating for an average of 74 years (38–177 a). This work suggests that as more proglacial lakes form and as lakes become larger, enhanced frontal ablation could cause higher mass losses, which should be considered when projecting the future of lake-terminating glaciers. 

Lakes in direct contact with glaciers (ice-marginal lakes) are found across alpine and polar landscapes. Many studies characterize ice-marginal lake behavior over multi-decadal timescales using either episodic ~annual images or multi-year mosaics. However, ice-marginal lakes are dynamic features that experience short-term (i.e., day to year) variations in area and volume superimposed on longer-term trends. Through aliasing, this short-term variability could result in erroneous long-term estimates of lake change. We develop and implement an automated workflow in Google Earth Engine to quantify monthly behavior of ice-marginal lakes between 2013 and 2019 across south-central Alaska using Landsat 8 imagery. We employ a supervised Mahalanobis minimum-distance land cover classifier incorporating three datasets found to maximize classifier performance: shortwave infrared imagery, the normalized difference vegetation index (NDVI), and spatially filtered panchromatic reflectance. We observe physically-meaningful ice-marginal lake area variance on sub-annual timescales, with the median area fluctuation of an ice-marginal lake found to be 10.8% of its average area. The median signal (slow lake growth) to noise (physically-meaningful short-term area variability) ratio is 1.5:1, indicating that short-term variability is responsible for ~33% of observed area change in the median ice-marginal lake. The magnitude of short-term area variability is similar for ice-marginal and nonglacial lakes, suggesting that the cause of observed variations is not of glacial origin. These data provide a new context for interpreting behaviors observed in multi-decadal studies and encourage attention to sub-annual behavior of ice-marginal lakes even in long-term studies. 

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L.