Geology topics

Ellyn Enderlin

Publications and source records attributed to Ellyn Enderlin.

4 recordsLinked to original sources

USGS2025

Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning

Tracking the extent of seasonal snow on glaciers over time is critical for assessing glacier vulnerability and the response of glacierized watersheds to climate change. Existing snow cover products do not reliably distinguish seasonal snow from glacier ice and firn, preventing their use for glacier snow cover detection. Despite previous efforts to classify glacier surface facies using machine learning on local scales, currently there is no published comparison of machine learning models for classifying glacier snow cover across different satellite image products. We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data from the U.S. Geological Survey Benchmark Glacier Project glaciers. The workflow produces daily to twice monthly time series of several glacier mass balance and snowmelt indicators (snow-covered area, accumulation area ratio, and seasonal snow line) from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snow lines to manual interpretations at each glacier site. The image classifiers exhibit overall accuracies of 92%–98%, K scores of 84%–96%, and F scores of 93%–98% for all image products. The median difference between automatically and manually delineated median snow line altitudes is 31m (IQR of 73to0m)across all image products. The Sentinel-2 classifier (support vector machine) produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and f irn the most reliably. Although they are less accurate, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage of observations. The transient accumulation area ratio produces the least noisy time series, making it the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal that the timing of minimum snow cover conditions varies by up to a month between Arctic (63°N) and midlatitude (48°N) sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, land ice representations within Earth system models, water resources, and the impacts of climate change on snow cover across broad spatial scales.

The Cryosphere↗

USGS2022

Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions

Seasonal snow melt dominates the hydrologic budget across a large portion of the globe. Snow accumulation and melt vary over a broad range of spatial scales, preventing accurate extrapolation of sparse in situ observations to watershed scales. The lidar onboard the Ice, Cloud, and land Elevation, Satellite (ICESat-2) was designed for precise mapping of ice sheets and sea ice, and here we assess the feasibility of snow depth-mapping using ICESat-2 data in more complex and rugged mountain landscapes. We explore the utility of ATL08 Land and Vegetation Height and ATL06 Land Ice Height differencing from reference elevation datasets in two end member study sites. We analyze ∼3 years of data for Reynolds Creek Experimental Watershed in Idaho's Owyhee Mountains and Wolverine Glacier in southcentral Alaska's Kenai Mountains. Our analysis reveals decimeter-scale uncertainties in derived snow depth and glacier mass balance at the watershed scale. Both accuracy and precision decrease as slope increases: the magnitudes of the median and median of the absolute deviation of elevation errors (MAD) vary from ∼0.2 m for slopes <5° to >1 m for slopes >20°. For glacierized regions, failure to account for intra- and inter-annual evolution of glacier surface elevations can strongly bias ATL06 elevations, resulting in under-estimation of the mass balance gradient with elevation. Based on these results, we conclude that ATL08 and ATL06 observations are best suited for characterization of watershed-scale snow depth and mass balance gradients over relatively shallow slopes with thick snowpacks . In these regions, ICESat-2 elevation residual-derived snow depth and mass balance transects can provide valuable watershed scale constraints on terrain parameter- and model-derived estimates of snow accumulation and melt.

Alaska, Idaho↗

USGS2018

Evolving environmental and geometric controls on Columbia Glacier’s continued retreat

Geometry strongly controls the dynamic behavior of marine‐terminating (tidewater) glaciers, significantly influencing advance and retreat cycles independent of climate. Yet the recent, nearly ubiquitous retreat of tidewater glaciers suggests that changes in atmospheric and oceanic forcing may also drive dynamic change. To isolate the influence of geometry on tidewater glacier dynamics, we analyzed detailed observational time series from 2012 to 2016 for two tidewater glaciers with shared dynamic histories and environmental forcing: Columbia Glacier and its former tributary (Post Glacier) in southcentral Alaska. We find that although terminus retreat has driven decadal‐scale changes in dynamics of the Columbia‐Post system, environmental factors contribute to short‐term (i.e., seasonal) dynamic variability. In particular, analysis of force balance time series indicates that observed variations in speed result from seasonal changes to the subglacial hydrologic system and associated changes in basal drag. Variations in terminus position only drive noticeable speed change when the terminus retreats from regions of relatively high basal drag. In agreement with long‐term analyses of Columbia Glacier, we find that terminus geometry can perturb the timing of seasonal ice flow patterns. Specifically, our data support the idea that retreat of a glacier terminus into deeper water is accompanied by a shift in the primary control on frontal ablation. Although our analysis focuses on two Alaskan glaciers, our data suggest that changes in the relative importance of surface meltwater and buoyancy effects on submarine melting and/or calving may manifest as a shift in terminus change seasonality and offer a mechanism to identify frontal ablation controls.

Alaska↗

USGS2016

An empirical approach for estimating stress-coupling lengths for marine-terminating glaciers

Variability in the dynamic behavior of marine-terminating glaciers is poorly understood, despite an increase in the abundance and resolution of observations. When paired with ice thicknesses, surface velocities can be used to quantify the dynamic redistribution of stresses in response to environmental perturbations through computation of the glacier force balance. However, because the force balance is not purely local, force balance calculations must be performed at the spatial scale over which stresses are transferred within glacier ice, or the stress-coupling length (SCL). Here we present a new empirical method to estimate the SCL for marine-terminating glaciers using high-resolution observations. We use the empirically-determined periodicity in resistive stress oscillations as a proxy for the SCL. Application of our empirical method to two well-studied tidewater glaciers (Helheim Glacier, SE Greenland, and Columbia Glacier, Alaska, USA) demonstrates that SCL estimates obtained using this approach are consistent with theory (i.e., can be parameterized as a function of the ice thickness) and with prior, independent SCL estimates. In order to accurately resolve stress variations, we suggest that similar empirical stress-coupling parameterizations be employed in future analyses of glacier dynamics.

Frontiers in Earth Science↗