Theses and Dissertations at Montana State University (MSU)
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Item 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.Item 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.