Evolution of subglacial water pressure along a glacier’s length

Observations from along the length of Bench Glacier, Alaska, USA, show that the subglacial water-pressure field undergoes a multiphase transition from a winter mode to a summer mode. Data were collected at the glacier surface, the outlet stream, and in a network of 47 boreholes spanning the length of the 7 km long glacier. The winter pressure field was near overburden, with low-magnitude (centimeter to meter scale) and long-period (days to weeks) variations. During a spring speed-up event, boreholes showed synchronous variations and a slight pressure drop from prior winter values. Diurnal pressure variations followed the speed-up, with their onset associated with a glacier-wide pressure drop and flood at the terminus stream. Diurnal variations with swings of up to 80% of overburden pressure were typical of mid-summer. Several characteristics of our observations contradict common conceptions about the seasonal development of the subglacial drainage system and the linkages between subglacial hydrology and basal sliding: (1) increased water pressure did not accompany high sliding rates; (2) the drainage system showed activity characteristic of the spring season long before abundant water was available on the glacier surface; (3) the onset of both spring activity and diurnal variations of the drainage system did not show a spatial progression along the length of the glacier

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\u27s Owyhee Mountains and Wolverine Glacier in southcentral Alaska\u27s 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 \u3c 5° to \u3e 1 m for slopes \u3e 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

Seasonality of Solute Flux and Water Source Chemistry in a Coastal Glacierized Watershed Undergoing Rapid Change: Wolverine Glacier Watershed, Alaska

As glaciers around the world rapidly lose mass, the tight coupling between glaciers and downstream ecosystems is resulting in widespread impacts on global hydrologic and biogeochemical cycling. However, a range of challenges make it difficult to conduct research in glacierized systems, and our knowledge of seasonally changing hydrologic processes and solute sources and signatures is limited. This in turn hampers our ability to make predictions on solute composition and flux. We conducted a broad water sampling campaign in order to understand the present-day partitioning of water sources and associated solutes in Alaska\u27s Wolverine Glacier watershed. We established a relationship between electrical conductivity and streamflow at the watershed outlet to divide the melt season into four hydroclimatic periods. Across hydroclimatic periods, we observed a shift in nonglacial source waters from snowmelt-dominated overland and shallow subsurface flow paths to deeper groundwater flow paths. We also observed the shift from a low- to high-efficiency subglacial drainage network and the associated flushing of water stored subglacially with higher solute loads. We used calcium, the dominant dissolved ion, from watershed outlet samples to estimate solute fluxes for each hydroclimatic period across two melt seasons. We found between 40% and 55% of Ca2+ export occurred during the late season rainy period. This partitioning of the melt season coupled with a characterization of the chemical makeup and magnitude of solute export provides new insight into a rapidly changing watershed and creates a framework to quantify and predict changes to solute fluxes across a melt season

Dataset for Automated Snow Cover Detection on Mountain Glaciers Using Space-Borne Imagery

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 on local scales, a unified approach for monitoring glacier snow cover on larger spatial scales remains elusive. We present an automated snow detection workflow for mountain glaciers using supervised machine learning-based image classifiers and Landsat 8/9, 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 Glaciers. The workflow produces daily to biweekly time series of several glacier mass balance and snowmelt indicators (snow-covered area, accumulation area ratio, and seasonal snowline) from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snowlines to manual interpretations at each glacier site. The image classifiers exhibit overall accuracies of 92–98%, Kappa scores of 84–96%, and F-scores of 93–98% for all image products. The median difference between automatically and manually delineated median snowline altitudes, along with the interquartile range, averages 27 +/- 79 m 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 firn the most reliably. Yet, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage and frequency of observations. Additionally, the transient accumulation area ratio produces the least noisy time series, providing 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 mid-latitude (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, water resources, and the impacts of climate change on snow cover across broad spatial scales