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Modeling snow water equivalent in complex mountainous terrain
(Montana State University - Bozeman, College of Letters & Science, 2023) Beck, Madeline Makenzie; Chairperson, Graduate Committee: Eric A. Sproles; This is a manuscript style paper that includes co-authored chapters.
The water stored in seasonal mountain snowpacks is a vital resource that approximately 20% of the world's population relies on for freshwater availability. However, accurately quantifying the amount of water stored in a snowpack, known as snow water equivalent (SWE), is difficult. The longest employed technique to quantify SWE is manual measurements. However, manual measurements of SWE are time intensive. As a result, researchers can collect relatively few point-based measurements across spatially extensive and complex regions. Automated weather stations may provide additional measurements of SWE and meteorological conditions but are expensive and difficult to maintain. Thus, reliable measurements of snow characteristics like SWE are scarce across time and space. A lack of extensive measurements causes data from few points to be extrapolated across spatially heterogeneous environments which increases uncertainty in estimates of water availability. Recent advances in satellite remote sensing allow researchers to observe snowpack dynamics across spatially continuous scales instead of relying solely on point-based measurements. However, current satellite technologies are incapable of collecting high- resolution snow data at the hillslope scale. Previous work has shown the importance of high elevation, hillslope-scale water storage reservoirs. Uncrewed aerial vehicles (UAVs) address the limitations of satellite remote sensing on the hillslope scale and are used to create high accuracy (<5 cm) models of snow depth. However, these models of snow depth provide no information on the amount of water stored without a value for snow bulk density. Thus, to capture hillslope dynamics of SWE, researchers must pair high-resolution models of snow depth with either directly measured or modeled bulk density of snow. This master's thesis integrates UAV-derived measurements of snow depth with modeled snow bulk density values to create continuous representations of hillslope-scale SWE across 9 flight dates. We found that each density modeling approach consistently underestimated SWE for the field site for each flight date except one. Further, each method of modeling snow bulk density was statistically indiscernible from each other. These findings highlight the heterogeneity of snow in mountainous terrain. In future work, bulk density models can be further parameterized to better represent site-specific values of SWE.
Advancing airborne and spaceborne synthetic aperture radar measurements of ice and snow in the northern Great Plains
(Montana State University - Bozeman, College of Letters & Science, 2023) Palomaki, Ross Theodore; Chairperson, Graduate Committee: Eric A. Sproles; This is a manuscript style paper that includes co-authored chapters.
The cryosphere is responding to climate change in ways that have negatively impacted socio-environmental systems. Accurate and timely observations of the cryosphere are critical to adapting our infrastructure to these rapid changes. This dissertation contributes novel approaches to validating synthetic aperture radar (SAR) measurements over river ice and seasonal prairie snow. Previous C-band SAR-based river ice studies typically validate regional ice cover maps using aerial photos of frozen rivers. This qualitative approach relies on the principle that visually rougher ice should result in stronger SAR backscatter. In Chapter 2 of this dissertation I present the first systematic, quantitative investigation of the effect of river ice surface roughness on C-band Sentinel-1 backscatter. I employ Random Forest algorithms first to replicate qualitative classification results from previous studies, and then as regression models to explore relationships between Sentinel-1 backscatter and novel, quantitative surface roughness metrics derived from drone-based Structure-from-Motion datasets. Classification accuracies are similar to those reported in previous studies, but poor regression performance indicates a weak relationship between river ice roughness and Sentinel-1 backscatter. In Chapter 3, I extend these drone-based surface measurements of river ice with GPR-based subsurface measurements. Results from this smaller, richer dataset demonstrate that Sentinel-1 VV backscatter is correlated with ice thickness and VH backscatter with structural properties, but results are site-specific and more work is necessary to create generalized river ice models from Sentinel-1 measurements. Interferometric SAR techniques have been used to estimate snow water equivalent (SWE) using L-band measurements from the UAVSAR platform. These methods have been developed in mountainous areas and have not been investigated over prairie snowpacks, which typically feature exposed agricultural vegetation and greater spatial variability than found in mountain snowpacks. In Chapter 4 I develop a rigorous statistical framework to demonstrate that UAVSAR measurements over prairie snowpacks are sensitive to small changes in SWE, and are relatively unaffected by exposed agricultural vegetation. However, sub-pixel snow depth variability decreases the accuracy of SWE estimates derived from UAVSAR measurements. The upcoming NISAR satellite mission provides an opportunity to extend this work with repeated L-band measurements over a wider range of prairie snow conditions.
Thermal contact resistance at the snow-ice interface: dependence on grain size
(Montana State University - Bozeman, College of Engineering, 2022) Dvorsak, Michael Alan; Chairperson, Graduate Committee: Kevin Hammonds
Seasonal snow covers consist of many stratigraphic layers of varying density and, therefore, thermal conductivity. Weak layers can develop at the interface between these snow layers, reducing stability and increasing avalanche danger. While it is known that a bulk temperature gradient of -10?C m -1 across a snowpack enhances weak layer development via kinetic snow metamorphism, recent studies have identified an enhancement of this temperature gradient across snow interfaces. Previous work has determined that at a snow-ice interface, such as might exist around ice crusts in the snowpack, the driving factor for a temperature gradient enhancement could be a thermal contact resistance. This creates an interfacial phenomenon that induces a large temperature drop at the interface between two connected materials. The primary mechanism is a reduction of contact area for conduction to occur due to the porous nature of snow. Here, we further investigate the thermodynamics of a snow-ice interface by varying the grain size, which directly correlates to the total contact area. Within a controlled laboratory environment, a 4 mm ice lens was artificially made and placed between rounded grains that varied in size (1, 2, and 3 mm) between experiments. Temperature gradients of -10, -50, and -100 ?C m -1 were then applied across the sample. The temperature gradient was measured in-situ within 1 mm of the ice lens using micro-thermocouple measurements. The local temperature gradient at the snowice interface was found to be up to four times the imposed temperature gradient with 2-3 mm snow grains and near the bulk temperature gradient with the 1 mm grains. Following a thermal analysis, it was concluded that the enhancement in the temperature gradient was also due to a thermal contact resistance at the snow-ice interface. Utilizing timelapse x-ray computed microtomography, a microstructural characterization of the snow-ice interface was also performed, where it was observed that new ice crystal growth, kinetic snow metamorphism, and sublimation were all occurring simultaneously near the ice lens. These results indicate that the observed grain size near an ice lens or crust in a natural snowpack may be a pertinent parameter for better understanding kinetic snow metamorphism regimes that may exist at these interfaces.
Remote sensing of wet snow processes in a controlled laboratory environment
(Montana State University - Bozeman, College of Engineering, 2022) Donahue, Christopher Paul; Chairperson, Graduate Committee: Kevin Hammonds; This is a manuscript style paper that includes co-authored chapters.
Water flow through snow, due to snowmelt or rain-on-snow events, is a heterogeneous process that has implications for snowmelt timing and magnitude, snow metamorphism, albedo evolution, and avalanche hazard. Remote sensing technologies, ranging from ground-based to satellite-borne scales, offer a non-destructive method for monitoring seasonal snowpacks, although there is no single technique that is ideal for monitoring snow. Wet snow, specifically, presents a challenge to both optical and radar remote sensing retrievals. The primary aim of this dissertation was to develop wet snow remote sensing methods from within a controlled laboratory environment, allowing for precise characterization of snow properties. Experiments were conducted by preparing laboratory snow samples of prescribed structures and monitoring them during and after melt using hyperspectral imaging and polarimetric radar. Snow properties were characterized using X-ray computed microtomography, a dielectric liquid water content sensor, and serial-section reconstructions. In addition to laboratory experiments, hyperspectral imaging snow property retrieval methods were developed and tested in the field during wet snow conditions at the ground-based scale. The primary outcomes from this work were three new remote sensing applications for monitoring wet snow processes. First, a new hyperspectral imaging method to map effective snow grain size was developed and used to quantify grain growth due to wet snow metamorphism. Second, the optimal radiative transfer mixing model to simulate wet snow reflectance was determined and used to map liquid water content in snow in 2- and 3- dimensions. Lastly, snow melt progression was monitored using continuous upward-looking polarimetric radar and it was found, by comparison to 3-dimensional liquid water content retrievals from hyperspectral imaging, that the cross-polarized radar signal was sensitive to the presence of preferential flow paths. The work presented here highlights the utility of using a multi-sensor fusion approach to snow remote sensing. Although these laboratory remote sensing experiments were at a small scale, the remote sensing instrument response to specific snow conditions directly translates to larger scales, which is valuable to support algorithm development for ground, airborne, and spaceborne remote sensing missions.
An operational methodology for validating satellite-based snow albedo measurements using a UAV
(Montana State University - Bozeman, College of Letters & Science, 2021) Mullen, Andrew Louiselle; Chairperson, Graduate Committee: Eric A. Sproles; Eric A. Sproles, Jordy Hendrikx, Joseph A. Shaw and Charles K. Gatebe were co-authors of the article, 'An operational methodology for validating satellite-based snow albedo measurements using a UAV' submitted to the journal 'Frontiers in remote sensing' which is contained within this thesis.
The albedo, or reflectivity, of seasonal snowpack directly controls the timing and magnitude of snowmelt and runoff. Snow albedo is affected by a large number of snow physical and environmental properties that vary considerably at multiple spatiotemporal scales. This variability introduces a high degree of uncertainty into existing modeling techniques. Models for snowmelt that require snow albedo can be improved by incorporating satellite measurements to inform and update estimates of this snow property. However, satellite measurements are susceptible to a multitude of error sources, which requires them to be calibrated and validated by means of ground-based measurements. Ground-based measurements from automated weather stations are often located at sparsely-distributed monitoring sites in homogeneous meadow environments. These spatially restricted in-situ data provide biased validation and calibration data that are not representative of the heterogeneous landscapes that comprise many seasonally snow-covered watersheds. In order to provide comprehensive validation and calibration of satellite albedo products, multiple near-surface measurements should be taken across large areas to capture the high degree of spatial variability that snow albedo can exhibit. UAV albedo measurements can be used to bridge the scaling gap between satellite and point-based measurements. Since these platforms are in a novel stage, the requisite methodologies for topographic correction and comparison to gridded albedo products do not exist. Additionally, there lacks a general understanding of the spatial scaling of albedo measurements in heterogeneous terrain. This research aims to develop these methodologies and provide a comprehensive understanding of how to deploy these platforms and properly interpret their measurements. We first developed and validated a topographic correction using ground-based measurements of snow albedo in a sloping alpine meadow. Sensitivity analyses on both ground validation measurements and UAV-based albedo surveys in our alpine study area highlight the implications of using different user-defined parameters for the proposed topographic correction and satellite comparison methods. Improvements to the methodology can be made in the way it accounts for trees, shading, and cloud cover. This research develops the initial steps requisite to the operationalization of UAV albedo measurements and standardization of the techniques.
Atmospheric processes related to deep persistent slab avalanches in the western United States
(Montana State University - Bozeman, College of Letters & Science, 2019) Schauer, Andrew Robert; Chairperson, Graduate Committee: Jordy Hendrikx
Deep persistent slab avalanches are a natural hazard that are particularly difficult to predict. These avalanches are capable of destroying infrastructure in mountain settings, and are generally unsurvivable by humans. Deep persistent slab avalanches are characterized by a thick (> 1 m) slab of cohesive snow overlaying a weak layer in the snowpack, which can fail due to overburden stress of the slab itself or to external triggers such as falling cornices, explosives, or a human. While formation of such snowpack structure is controlled by persistent weather patterns early in the winter, a snowpack exhibiting characteristics capable of producing a deep persistent slab avalanche may exist for weeks or months before a specific weather event such as a heavy precipitation or rapid warming pushes the weak layer to its breaking point. Mountain weather patterns are highly variable down to the local scale (1-10 m), but they are largely driven by atmospheric processes on the continental scale (1000 km). This work relates atmospheric circulation to deep persistent slab events at Mammoth, CA; Bridger Bowl, MT; and Jackson, WY. We classify 5,899 daily 500 millibar geopotential height maps into 20 synoptic types using Self-Organizing Maps. At each location, we examine the frequency of occurrence of each of the 20 types during November through January during major deep persistent slab seasons and compare those frequencies to seasons without deep persistent slab avalanches. We also consider the 72-hour time period preceding deep persistent slab avalanches at each location and identify synoptic types occurring frequently, as well as those rarely occurring prior to onset of activity. At each location, we find specific synoptic types that tend to occur at a higher rate during major deep persistent slab years, while minor years are characterized by different circulation patterns. We also find a small number of synoptic types dominating the 72-hour period prior to onset of deep slab activity. With this improved understanding of the atmospheric processes preceding deep persistent slab avalanches, we provide avalanche practitioners with an additional tool to better anticipate a difficult to predict natural hazard.
Modeling mass balance at Robertson Glacier, Alberta, Canada 1912-2012
(Montana State University - Bozeman, College of Letters & Science, 2017) Scanlon, Ryan Scott; Chairperson, Graduate Committee: Mark L. Skidmore; Jordy Hendrikx (co-chair)
Glacier mass balance is important to study due to the role of glaciers in the hydrological cycle. Glacier mass balance is typically difficult to measure without numerous in situ measurements and monitoring over the course of many years. Physically based melt models are a good tool for estimating melt using temperature, solar radiation, and albedo and are used extensively in this thesis. A Degree Day (DD) model and an Enhanced Temperature Index (ETI) model are used to model mass balance for Robertson Glacier, Alberta, Canada during the period 1912-2012. The DD model only incorporates temperature, while the ETI model incorporates temperature, incoming solar radiation, and albedo. Incoming solar radiation was modeled for the period 2007-2012 and parameterized for the period 1912-2006 while temperature was measured at the regional scale and synthesized for Robertson Glacier and the snowpack thickness was modeled using PRISM. The DD and ETI models both assume a static ice mass, i.e. no flow or change in ice elevation due to mass loss over the century time period. Both models estimate a high value of annual and accumulated mean mass loss for the period 1912-2012. Sensitivity analyses of model inputs indicate that snowpack is an important factor, and it appears PRISM estimates may underrepresent beginning of the year snowpack by 220% based on a comparison of modelled to measured values on the adjacent Haig Glacier. Avalanching is not a key component of accumulation on the Haig Glacier but is a key process at Robertson Glacier, and could result in locally doubling the snowpack accumulation in avalanche zones. These factors including the resultant albedo changes with a thicker snowpack are all part of a compounding negative feedback cycle on glacier mass loss. In summary, the thesis has highlighted several potential limitations to the ETI and DD models for assessing mass loss for a small mountain glacier in the southern Canadian Rockies and provides suggestions for future modelling work in this region.
Snowpack driven changes in decadal soil evolution: insights from a 48-year snow manipulation experiment
(Montana State University - Bozeman, College of Letters & Science, 2017) Feldhaus, Aaron Michael; Chairperson, Graduate Committee: Jean Dixon
Soil mantled landscapes are a critical interface that support biological life, weather geologic materials, and develop in response to changes in climate. Climate has long been considered a dynamic control on the evolution of Earth's landscapes. However, we have limited understanding regarding how soils respond to short-term perturbations of key climate variables like precipitation and moisture availability. Furthermore, the timescales over which diverse weathering processes feedback and measurably change soil character are still relatively uncertain, as well as how they respond to swift changes in climate. Here, we explore the role of precipitation in decadal soil evolution by utilizing a 48-year snowpack experiment located in the Greater Yellowstone Ecosystem (GYE) of SW Montana. In this unique field site, we compare soil development across experimental plots with enhanced snowpack, where snow has been doubled (2x) and quadrupled (4x) above ambient conditions for almost five decades. We find that decadal snowpack addition provides multiple pathways for enhancing soil weathering, both physically and chemically. Soils under enhanced snowpack generally contain higher amounts of fine-grained material (clay and silt) and are more acidic (lower soil pH) in nature. Significant (>85%) surface depletions of the fallout radionuclide 210 Pb and reduced surface horizon carbon and nitrogen content, along with reduced above and below ground vegetation biomass provide evidence of increased wind erosion of soils that experience enhanced winter snowpack. Modeling of diffusion-like mixing from 210 Pb profiles also indicates there is increased bioturbation intensity (soil mixing) under enhanced snowpack. We find that snowpack addition, through associated changes in plant communities and vegetation biomass, along with its effects on physical and chemical weathering processes, produces rapid and measurable changes in the weathered state of soils. Our results indicate that short-term, decadal perturbations in snowpack significantly alter weathering mechanisms in this landscape, which measurably overprint thousands of years of soil development. These findings provide novel insight into the fundamental role of climate on short-term soil evolution and have significant implications for how mountainous or snowpack-dominated systems may respond to perturbations in climate.
A new instrument for determining strength and temperature profiles in snowpack
(Montana State University - Bozeman, College of Engineering, 1984) Dowd, Timothy Francis
The purpose of this thesis project was the development of a new field instrument for determining strength and temperature profiles in snowpack. The standard tool now used for strength determination is the ram penetrometer, which is slow, cumbersome, inaccurate, and does not provide immediate results. Temperatures are generally taken with a dial stem thermometer in a snowpit wall, which is difficult to do accurately at specific intervals. The Digital Thermo-Resistograph was designed and built in an attempt to improve field snowpack data collection. The Digital Thermo-Resistograph is a portable microprocessor-based data acquisition system for quick and accurate field collection of snowpack compressive strength and temperature data. This was accomplished by building a probe with a load cell and thermistor, a small snow platform for probe position information, and a Z-80 microprocessor-based data acquisition system. The system provides information in digital form for every sampled point. A 64 x 240 dot matrix LCD graphic display unit is used to show complete strength and temperature profiles in the field. Provision is made to transfer these profiles to paper via an ordinary X-Y recorder for a permanent record of field data. Sufficient memory for the storage of 25 profiles is provided. The results of winter 1984 field tests are presented. The thermistor could not be made- to work accurately, and so was not integrated into the system. The Digital Thermo-Resistograph proved to be fast and reliable in collecting compressive snow strength information, which is measured from 0.0 to 2.55 kg/sq cm at five mm increments through the snowpack. Comparisons with the ram penetrometer are shown. Ideas for future developments are discussed.
Snowmobile exhaust emissions in the snowpack of Yellowstone National Park
(Montana State University - Bozeman, College of Engineering, 2000) Young, Jason Douglas
The use of snowmobiles within national park boundaries is a source of intense controversy. Exhaust emission levels of two-stroke engines used in snowmobiles are high in comparison to four-stroke engines, with as much as 30% of the fresh air-fuel mixture exiting the cylinder with the exhaust stream. In May of 1997, The Fund for Animals, Biodiversity Legal Foundation, Predator Project, Ecology Center, and five individuals filed suit against the National Park Service for failure to comply with the National Environmental Policy Act (NEPA), the Endangered Species Act (ESA), and other federal laws and regulations in connection with winter use in the three national parks. As a result, many investigations were initiated addressing issues such as air quality, exhaust emission levels in the snowpack due to snowmobiles, and emission reduction methods for snowmobiles. The purpose of this Masters thesis is to determine the levels of snowmobile exhaust products in the West Yellowstone snowpack. This research in combination with other completed and on-going investigations will determine the environmental impact of snowmobiles upon the West Yellowstone ecosystem. Analysis of snow samples was performed with a modified version of EPA method 525.2 originally developed for the determination of organic compounds in drinking water by liquid-solid extraction and GC/MS. N-Alkanes C15-22 were detected at concentrations of 0.2-3.7ppm in the snow. Organic Acids C-12,14,16, and 18 were also detected in the snow samples. Three PAH compounds were detected in the snow samples. fluorene and phenanthrene were identified at concentrations of 10-200ppt, and naphthalene was detected at concentrations of 5-25ppb. Data indicates that contamination is localized to the road with snowmobiles being the primary contributor to hydrocarbon contamination of the snowpack. The concentration of all analytes was observed to decrease rapidly with distance from the road. The concentration of N-Alkanes C15-22 decreased an average of 89% and Organic Acids C-12,14,16, and 18 decreased an average of 90%within 50 feet of the road. PAH concentrations were also noted to decrease with distance from the road.

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