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This report demonstrates the snow water equivalent (SWE) modeling capabilities present within the Gridded Surface Sub-surface Hydrologic Analysis (GSSHA) model (Downer and Ogden 2004, 2006) by comparing simulated and observed snow accumulation data. GSSHA is used to simulate runoff, streamflow, and sediment and constituent transport in military and civil works projects. An energy balance method was originally used for calculating snow accumulation and melt, but now GSSHA has an optional temperature-index method and hybrid energy method. The full snowpack modeling capabilities of the Snowmelt Numerical-Analytical Package (SNAP) model (Albert and Krajeski 1998) have been included to simulate snowpack depth and density regardless of which snow accumulation model was used. With proficient SWE modeling capabilities, the GSSHA model can be utilized to solve complex watershed-related issues in regions where snow accumulation and melt are often the most critical source of water.

During April 2007, a coordinated series of snow measurements were made across the Northwest Territories and Nunavut, Canada, during a 4200-km snowmobile traverse from Fairbanks, Alaska, to Baker Lake, Nunavut. While detailed, local snow measurements have been made as part of ongoing studies at tundra field sites in this region (Daring Lake and Trail Valley Creek in the Northwest Territories), systematic measurements at the regional scale have not been previously collected across this region. Consistent with observations of tundra snow in Alaska and northern Manitoba, the snow cover consisted of depth hoar and wind slab with small and ephemeral fractions of new, recent, and icy snow. The snow was shallow (less than 40 cm deep), usually with less than six layers. Where deposited on lake and river ice, the snow was shallower, denser, and more metamorphosed than where deposited on tundra. The snow characteristics were highly variable at a local scale, but no longitudinal gradients in snow distribution, magnitude, or structure were detected. Lakes and lake ice confounded passive microwave remote sensing of the snow cover in this area because the lake signal overwhelmed the snow signal. Consequently, challenges remain in developing methods to monitor this snow cover by satellite. Appendixes present near-infrared images, snow depths, soot measurements, mercury measurements, ion measurements, and isotope measurements., Prepared in cooperation with the Climate Research Division, Environment Canada, Toronto, Ontario; the Cooperative Institute for Research and Engineering in the Atmosphere, Colorado State University, Fort Collins, CO; the University of Alaska Fairbanks, Fairbanks, AK; and Huntington Consulting, Anchorage, AK. The original document contains color images.

Vertical soundings of the snowpack are essential diagnostic tools for snow hydrologists and avalanche forecasters. Hydrologists require quantitative profiles of snow density from which they can infer the total amount of snow coverage in a region. Because variations in altitude and terrain can result in widely different coverage, they must dig a relatively large number of pits or extract and weigh many core samples to estimate the snow water equivalent from the corresponding average density. Avalanche forecasters are concerned with recent precipitation and relatively rapid metamorphosis of buried snow layers. While they too require a large number of pits to assess the avalanche potential of a basin, they often perform a more qualitative inspection aimed at identifying the presence and depth of weak layers in a relatively immature snowpack. To address these needs, we have developed a penetration field-portable capacitance probe capable of recording profiles of complex dielectric permittivity and temperature through the snowpack. Density profiles were acquired without the need for multiple excavations. The probe consists of a lance with a wedged capacitance tip allowing penetration to variable depths. The tip is connected to a portable signal conditioner for data processing and storage., Prepared in cooperation with Cornell Univ., Sibley School of Mechanical and Aerospace Engineering, Ithaca, NY.

We describe a penetration field-portable capacitance probe capable of recording profiles of dielectric permittivity through the snow pack. The probe was developed under the auspices of the Army Research Office as a Phase I SBIR project. It consists of a wedged capacitance tip, which is mounted at the end of a pole allowing its penetration through depths of at least 2 m. The capacitance instrument is integrated in the tip. By appropriate placement of its ground, guard and sensor conductive surfaces, the probe sheds horizontal electric field lines permitting it to resolve horizontal snow layers of 2.5 mm., Prepared in cooperation with Cornell University, Upson Hall, Ithaca, NY 14853.

Snow cover data in Arctic regions like Svalbard are scarce while interest in distribution, properties and development of Arctic snow is evident (in context of climate change, too). Contributing to that issue this work is based on comprehensive observations of physical snow parameters recorded in snow pits and using special instruments, respectively. The data were collected in spring 2011 and at several locations in a height transect along Kongsvegen glacier. Analysis mainly concerns description of gross profile characteristics, the identification and physical understanding of major structures and their spatial and temporal characteristics. Three of them are considered in detail in which context snow hardness data are particularly important. Analysis was supported by data from automatic weather stations operated at the sites of snow investigations. These data also served to drive a numerical snow model. Model output was compared to observations and effectively served development of a process-based interpretation and reconstruction of the history of the investigated key layers. Different depth scaling method were developed to make the snow profiles comparable because snow depth strongly depends on altitude. The layer-wise depth scaling method yielded significant improvements in correlation between neighbouring snow hardness profiles along the glacier and at local scale. Three layers characterized by clear maxima in snow hardness, high density and mostly rounded grain types could thus be traced along the major part of the glacier. Attributions were comparatively uncertain if not impossible in the lower section of the glacier due to the higher temperatures and associated melt influences. Snow profiles were significantly correlated across decameter distances. The evolution history of the selected, layers was investigated using relevant model output data. The latter were overall successfully validated by comparison with measured key parameters like snowheight, surface temperature or end-winter vertical profiles. Some deficits in simulation skill were reflected in e.g. biases towards too low snow temperature and failure to reproduce fine structures in the vertical density profiles. This points to a need to improve related aspects of the model setup and parameterizations. There are indications that some problems may lead back to inappropriate parameterization of the density of fresh snow. Concerning the investigated examplary layers, however, the simulations largely reproduce the observed characteristics. Uncerainties are generally larger at lower sites again, and a general tendency towards too rapidly evolving snow metamorphosis may be noted. Backtracing individual layers proved feasible and allows estimating the approximate date of deposition and associated meteorological conditions. Follow up work may concern even more detailed analysis of the observed profiles, including parameters and sites which have not yet been considered in this work. Advancing methods for more sophisticated depth attribution of profile data is desirable as well as developing enhanced understanding of processes governing the development of observed physical properties (grain types in particular). Related simulations may mainly be improved concerning their setup and parameterizations (of fresh snow density for example). On the other hand, existing output already hints on interesting new research topics related to e.g. the development of a firn aquifer in the upper regions of the glacier and its dependencies on near- surface snow developments, by Maik Brakemeier, Masterarbeit University of Innsbruck 2017

Snow cover data in Arctic regions like Svalbard are scarce while interest in distribution, properties and development of Arctic snow is evident (in context of climate change, too). Contributing to that issue this work is based on comprehensive observations of physical snow parameters recorded in snow pits and using special instruments, respectively. The data were collected in spring 2011 and at several locations in a height transect along Kongsvegen glacier. Analysis mainly concerns description of gross profile characteristics, the identification and physical understanding of major structures and their spatial and temporal characteristics. Three of them are considered in detail in which context snow hardness data are particularly important. Analysis was supported by data from automatic weather stations operated at the sites of snow investigations. These data also served to drive a numerical snow model. Model output was compared to observations and effectively served development of a process-based interpretation and reconstruction of the history of the investigated key layers. Different depth scaling method were developed to make the snow profiles comparable because snow depth strongly depends on altitude. The layer-wise depth scaling method yielded significant improvements in correlation between neighbouring snow hardness profiles along the glacier and at local scale. Three layers characterized by clear maxima in snow hardness, high density and mostly rounded grain types could thus be traced along the major part of the glacier. Attributions were comparatively uncertain if not impossible in the lower section of the glacier due to the higher temperatures and associated melt influences. Snow profiles were significantly correlated across decameter distances. The evolution history of the selected, layers was investigated using relevant model output data. The latter were overall successfully validated by comparison with measured key parameters like snowheight, surface temperature or end-winter vertical profiles. Some deficits in simulation skill were reflected in e.g. biases towards too low snow temperature and failure to reproduce fine structures in the vertical density profiles. This points to a need to improve related aspects of the model setup and parameterizations. There are indications that some problems may lead back to inappropriate parameterization of the density of fresh snow. Concerning the investigated examplary layers, however, the simulations largely reproduce the observed characteristics. Uncerainties are generally larger at lower sites again, and a general tendency towards too rapidly evolving snow metamorphosis may be noted. Backtracing individual layers proved feasible and allows estimating the approximate date of deposition and associated meteorological conditions. Follow up work may concern even more detailed analysis of the observed profiles, including parameters and sites which have not yet been considered in this work. Advancing methods for more sophisticated depth attribution of profile data is desirable as well as developing enhanced understanding of processes governing the development of observed physical properties (grain types in particular). Related simulations may mainly be improved concerning their setup and parameterizations (of fresh snow density for example). On the other hand, existing output already hints on interesting new research topics related to e.g. the development of a firn aquifer in the upper regions of the glacier and its dependencies on near- surface snow developments, by Maik Brakemeier, Masterarbeit University of Innsbruck 2017