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  • Characterizing Snow Surface Properties Using Airborne Hyperspectral Imagery for Autonomous Winter Mobility

    Abstract: With changing conditions in northern climates it is crucial for the United States to have assured mobility in these high-latitude regions. Winter terrain conditions adversely affect vehicle mobility and, as such, they must be accurately characterized to ensure mission success. Previous studies have attempted to remotely characterize snow properties using varied sensors. However, these studies have primarily used satellite-based products that provide coarse spatial and temporal resolution, which is unsuitable for autonomous mobility. Our work employs the use of an Unmanned Aerial Vehicle (UAV) mounted hyperspectral camera in tandem with machine learning frameworks to predict snow surface properties at finer scales. Several machine learning models were trained using hyperspectral imagery in tandem with in-situ snow measurements. The results indicate that random forest and k-nearest neighbors models had the lowest Mean Absolute Error for all surface snow properties. A Pearson correlation matrix showed that density, grain size, and moisture content all had a significant positive correlation to one another. Mechanically, density and grain size had a slightly positive correlation to compressive strength, while moisture had a much weaker negative correlation. This work provides preliminary insight into the efficacy of using hyperspectral imagery for characterizing snow properties for autonomous vehicle mobility.
  • Changes in Climate and Its Effect on Timing of Snowmelt and Intensity-Duration-Frequency Curves

    Abstract: Snow is a critical water resource for much of the U.S. and failure to ac-count for changes in climate could deleteriously impact military assets. In this study, we produced historical and future snow trends through modeling at three military sites (in Washington, Colorado, and North Dakota) and the Western U.S. For selected rivers, we performed seasonal trend analysis of discharge extremes. We calculated flood frequency curves and estimated the probability of occurrence of future annual maximum daily rainfall depths. Additionally, we generated intensity-duration-frequency curves (IDF) to find rainfall intensities at several return levels. Generally, our results showed a decreasing trend in historical and future snow duration, rain-on-snow events, and snowmelt runoff. This decreasing trend in snowpack could reduce water resources. A statistically significant increase in maximum streamflow for most rivers at the Washington and North Dakota sites occurred for several months of the year. In Colorado, only a few months indicated such an increase. Future IDF curves for Colorado and North Dakota indicated a slight increase in rainfall intensity whereas the Washington site had about a twofold increase. This increase in rainfall in-tensity could result in major flood events, demonstrating the importance of accounting for climate changes in infrastructure planning.
  • Machine Learning Analyses of Remote Sensing Measurements Establish Strong Relationships Between Vegetation and Snow Depth in the Boreal Forest of Interior Alaska

    Abstract: The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.
  • A Pulse of Mercury and Major Ions in Snowmelt Runoff from a Small Arctic Alaska Watershed

    Abstract: Atmospheric mercury (Hg) is deposited to Polar Regions during springtime atmospheric mercury depletion events (AMDEs) that require halogens and snow or ice surfaces. The fate of this Hg during and following snowmelt is largely unknown. We measured Hg, major ions, and stable water isotopes from the snowpack through the entire spring melt runoff period for two years. Our small (2.5 ha) watershed is near Barrow (now Utqiaġvik), Alaska. We measured discharge, made 10 000 snow depths, and collected over 100 samples of snow and meltwater for chemical analysis in 2008 and 2009 from the watershed snowpack and ephemeral stream channel. Our results suggest AMDE Hg complexed with Cl− or Br− may be less likely to be photochemically reduced and re-emitted to the atmosphere prior to snowmelt, and we estimate that roughly 25% of the Hg in snowmelt is attributable to AMDEs. Projected Arctic warming, with more open sea ice leads providing halogen sources that promote AMDEs, may provide enhanced Hg deposition, reduced Hg emission and, ultimately, an increase in snowpack and snowmelt runoff Hg concentrations.
  • Mercury Isotopes Reveal Atmospheric Gaseous Mercury Deposition Directly to the Arctic Coastal Snowpack

    Abstract: Springtime atmospheric mercury depletion events (AMDEs) lead to snow with elevated mercury concentrations (>200 ng Hg/L) in the Arctic and Antarctic. During AMDEs gaseous elemental mercury (GEM) is photochemically oxidized by halogens to reactive gaseous mercury which is deposited to the snowpack. This reactive mercury is either photochemically reduced back to GEM and re-emitted to the atmosphere or remains in the snowpack until spring snowmelt. GEM is also deposited to the snowpack and tundra vegetation by reactive surface uptake (dry deposition) from the atmosphere. There is little consensus on the proportion of AMDE-sourced Hg versus Hg from dry deposition that is released in spring runoff. We used mercury stable isotope measurements of GEM, snowfall, snowpack, snowmelt, surface water, vegetation, and peat from a northern Alaska coastal watershed to quantify Hg sources. Although high Hg concentrations are deposited to the snowpack during AMDEs, we estimate that ∼76 to 91% is released back to the atmosphere prior to snowmelt. Mercury deposited to the snowpack as GEM comprises the majority of snowmelt Hg and has a Hg stable isotope composition similar to Hg deposited by reactive surface uptake of GEM into the leaves of trees in temperate forests. This GEM-sourced Hg is the dominant Hg we measured in the spring snowpack and in tundra peat permafrost deposits.
  • Wintertime Snow and Precipitation Conditions in the Willow Creek Watershed above Ririe Dam, Idaho

    ABSTRACT:  The Ririe Dam and Reservoir project is located on Willow Creek near Idaho Falls, Idaho, and is important for flood risk reduction and water supply. The current operating criteria is based on fully storing a large winter runoff event. These winter runoff events are generally from large storm events, termed atmospheric rivers, which produce substantial precipitation. In addition to the precipitation, enhanced runoff is produced due to frozen soil and snowmelt. However, the need for additional water supply by local stakeholders has prompted the U.S. Army Corps of Engineers to seek to better understand the current level of flood risk reduction provided by Ririe Dam and Reservoir.  Flood risk analysis using hydrologic modeling software requires quantification of the probability for all of the hydrometeorologic inputs. Our study develops the precipitation, SWE, and frozen ground probabilities that are required for the hydrologic modeling necessary to quantify the current winter flood risk.
  • Microscale Dynamics between Dust and Microorganisms in Alpine Snowpack

    ABSTRACT:  Dust particles carry microbial and chemical signatures from source regions to deposition regions. Dust and its occupying microorganisms are incorporated into, and can alter, snowpack physical properties including snow structure and resultant radiative and mechanical properties that in turn affect larger-scale properties, including surrounding hydrology and maneuverability. Microorganisms attached to deposited dust maintain genetic evidence of source substrates and can be potentially used as bio-sensors. The objective of this study was to investigate the impact of dust-associated microbial deposition on snowpack and microstructure. As part of this effort, we characterized the microbial communities deposited through dust transport, examined dust provenance, and identified the microscale location and fate of dust within a changing snow matrix. We found dust characteristics varied with deposition event and that dust particles were generally embedded in the snow grains, with a small fraction of the dust particles residing on the exterior of the snow matrix. Dust deposition appears to retard expected late season snow grain growth. Both bacteria and fungi were identified in the collected snow samples.
  • Snow-Covered Obstacles’ Effect on Vehicle Mobility

    ABSTRACT:  The Mobility in Complex Environments project used unmanned aerial systems (UAS) to identify obstacles and to provide path planning in forward operational locations. The UAS were equipped with remote-sensing devices, such as photogrammetry and lidar, to identify obstacles. The path-planning algorithms incorporated the detected obstacles to then identify the fastest and safest vehicle routes. Future algorithms should incorporate vehicle characteristics as each type of vehicle will perform differently over a given obstacle, resulting in distinctive optimal paths. This study explored the effect of snow-covered obstacles on dynamic vehicle response. Vehicle tests used an instrumented HMMWV (high mobility multipurpose wheeled vehicle) driven over obstacles with and without snow cover. Tests showed a 45% reduction in normal force variation and a 43% reduction in body acceleration associated with a 14.5 cm snow cover. To predict vehicle body acceleration and normal force response, we developed two quarter-car models: rigid terrain and deformable snow terrain quarter-car models. The simple quarter models provided reasonable agreement with the vehicle test data. We also used the models to analyze the effects of vehicle parameters, such as ground pressure, to understand the effect of snow cover on vehicle response.
  • PUBLICATION NOTICE: Spatial Analysis of Precipitation and Snow Water Equivalent Extremes for the Columbia River Basin

    Abstract: Recent advances in the spatial statistics of extremes and model calibration were applied to develop and deliver areal-exceedance estimates for precipitation (PREC), by season and duration, and snow water equivalent (SWE), by cool season month and for the water year, for 758 delineated sub-basins of the Columbia River Basin (CRB), which correspond to a new CRB hydrology model watershed delineation. Understanding that future US Army Corps of Engineers, Northwestern Division, mission requirements may change, project execution also included the development and delivery of an application guidance document to credibly compute areal-exceedance estimates, including uncertainty, for PREC or SWE for any arbitrary area within the CRB. R, a free software environment for statistical computing and graphics (https://www.r-project.org/), and QGIS, a free and open source geographic information system (https://qgis.org/en/site/index.html), were the primary tools used for product development and delivery. The following R software packages were primarily used during project execution: evd, Glmnet, maps, raster, rgdal, SDMTools, sp, and SpatialExtremes.
  • PUBLICATION NOTICE: A Generalized Approach for Modeling Creep of Snow Foundations

    ABSTRACT:  When an external load is applied, snow will continue to deform in time, or creep, until the load is removed. When using snow as a foundation material, one must consider the time-dependent nature of snow mechanics to understand its long-term structural performance. In this work, we develop a general approach for predicting the creep behavior of snow. This new approach spans the primary (nonlinear) to secondary (linear) creep regimes. Our method is based on a uniaxial rheological Burgers model and is extended to three dimensions. We parameterize the model with density- and temperature-dependent constants that we calculate from experimental snow creep data. A finite element implementation of the multiaxial snow creep model is derived, and its inclusion in an ABAQUS user material model is discussed. We verified the user material model against our analytical snow creep model and validated our model against additional experimental data sets. The results show that the model captures the creep behavior of snow over various time scales, temperatures, densities, and external loads. By furthering our ability to more accurately predict snow foundation movement, we can help prevent unexpected failures and extend the useful lifespan of structures that are constructed on snow.