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Tag: snow
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  • Summary of Ground-Based Snow Measurements for the Northeastern United States

    ABSTRACT: Snow is an important resource for both communities and ecosystems of the Northeastern United States. Both flood risk management and water supply forecasts for major municipalities, including New York City, depend on the collection of snowpack information. Therefore, the purpose of this study is to summarize all of the snowpack data from ground-based networks currently available in the Northeast. The collection of snow-depth and snow water equivalent information extends back several decades, and there are over 2,200 active sites across the region. Sites are distributed across the entire range of elevations in the region. The number of locations collecting snow information has increased substantially in the last 20 years, primarily from the expansion of the CoCoRaHS (Community Collaborative Rain, Hail, and Snow) network. Our summary of regional snow measurement locations provides a foundation for future studies and analysis, including a template for other regions of the United States.
  • The Blowing Snow Hazard Assessment and Risk Prediction Model: A Python Based Downscaling and Risk Prediction for Snow Surface Erodibility and Probability

    Abstract: Blowing snow is an extreme terrain hazard causing intermittent severe reductions in ground visibility and snow drifting. These hazards pose significant risk to operations in snow-covered regions. While many ingredients-based forecasting methods can be employed to predict where blowing snow is likely to occur, there are currently no physically based tools to predict blowing snow from a weather forecast. However, there are several different process models that simulate the transport of snow over short distances that can be adapted into a terrain forecasting tool. This report documents a downscaling and blowing-snow prediction tool that leverages existing frameworks for snow erodibility, lateral snow transport, and visibility, and applies these frameworks for terrain prediction. This tool is designed to work with standard numerical weather model output and user-specified geographic models to generate spatially variable forecasts of snow erodibility, blowing snow probability, and deterministic blowing-snow visibility near the ground. Critically, this tool aims to account for the history of the snow surface as it relates to erodibility, which further refines the blowing-snow risk output. Qualitative evaluations of this tool suggest that it can provide more precise forecasts of blowing snow. Critically, this tool can aid in mission planning by downscaling high-resolution gridded weather forecast data using even higher resolution terrain dataset, to make physically based predictions of blowing snow.
  • Use of a Portable Friction Tester on Snow and Ice Pavement

    Abstract: The objective of this project was to determine if portable friction testers could be used for friction measurements on compacted snow and ice surfaces. First, the effect of cold temperatures on the operation, consistency, and accuracy of commercially available portable pavement friction measuring tools was evaluated. Tests entailed a series of experiments in a controlled cold room environment. Two portable fixed slip continuous measurement devices and one deceleration spot measurement device were evaluated. The controlled temperature testing determined how ambient temperature and duration of exposure can affect results, but that with care, the devices could be operated in conditions as cold as ˗25°C. This was followed by using one of the devices on outdoor testing on snow, ice, and asphalt surfaces and compared the portable tester to the well-known SAAB vehicle runway friction tester. Results showed good agreement between the portable tester and the SAAB Friction tester, providing validation for the operational use of a portable tester on frozen surfaces.
  • Beyond snow: How snowpack benefits reservoirs once it melts

    Snow offers immediate reservoir-recreational activities for those who love winter, but another benefit of snow extends well beyond the cold season.
  • Assessing the Mechanisms Thought to Govern Ice and Snow Friction and Their Interplay with Substrate Brittle Behavior

    Abstract: Sliding friction on ice and snow is characteristically low at temperatures common on Earth’s surface. This slipperiness underlies efficient sleds, winter sports, and the need for specialized tires. Friction can also play micro-mechanical role affecting ice compressive and crushing strengths. Researchers have proposed several mechanisms thought to govern ice and snow friction, but directly validating the underlying mechanics has been difficult. This may be changing, as instruments capable of micro-scale measurements and imaging are now being brought to bear on friction studies. Nevertheless, given the broad regimes of practical interest (interaction length, temperature, speed, pressure, slider properties, etc.), it may be unrealistic to expect that a single mechanism accounts for why ice and snow are slippery. Because bulk ice, and the ice grains that constitute snow, are solids near their melting point at terrestrial temperatures, most research has focused on whether a lubricating water film forms at the interface with a slider. However, ice is extremely brittle, and dry-contact abrasion and wear at the front of sliders could prevent or delay a transition to lubricated contact. Also, water is a poor lubricant, and lubricating films thick enough to separate surface asperities may not form for many systems of interest. This article aims to assess our knowledge of the mechanics underlying ice and snow friction.
  • Imagery Classification for Autonomous Ground Vehicle Mobility in Cold Weather Environments

    Abstract: Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.
  • Methodology for the Analysis of Geospatial and Vehicle Datasets in the R Language

    Abstract: The challenge of autonomous off-road operations necessitates a robust understanding of the relationships between remotely sensed terrain data and vehicle performance. The implementation of statistical analyses on large geospatial datasets often requires the transition between multiple software packages that may not be open-source. The lack of a single, modular, and open-source analysis environment can reduce the speed and reliability of an analysis due to an increased number of processing steps. Here we present the capabilities of a workflow, developed in R, to perform a series of spatial and statistical analyses on vehicle and terrain datasets to quantify the relationship between sensor data and vehicle performance in winter conditions. We implemented the R-based workflow on datasets from a large, coordinated field campaign aimed at quantifying the response of military vehicles on snow-covered terrains. This script greatly reduces processing times of these datasets by combining the GIS, data-assimilation and statistical analyses steps into one efficient and modular interface.
  • 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.