Theses and Dissertations at Montana State University (MSU)
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/732
Browse
10 results
Search Results
Item 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.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.Item Influence of groundwater on streambank soil moisture content, storm runoff production and sediment production in a semi-arid watershed, southwest Montana(Montana State University - Bozeman, College of Letters & Science, 1989) Aspie, Jon MatthewItem Selected multivariate statistical methods applied to runoff data from Montana watersheds(Montana State University - Bozeman, College of Engineering, 1968) Lewis, Gary LeeItem Design and installation of a study to determine the effect of multiple logging roads on the soil mantle hydrology of a spruce-fir forest(Montana State University - Bozeman, College of Engineering, 1967) Burroughs, Edward RobbinsItem Analysis of the Soil Conservation Service Project Formulation Program - Hydrology(Montana State University - Bozeman, College of Engineering, 1968) Ferris, Orrin AlbertItem The effect of unit weight and slope on the erosion of unprotected slopes(Montana State University - Bozeman, College of Engineering, 1967) Foster, Richard LeeItem Frequency of peak flows predicted from rainfall frequencies(Montana State University - Bozeman, College of Engineering, 1968) Robinson, LeeItem A conceptual precipitation-runoff modeling suite : model selection, calibration and predictive uncertainty assessment(Montana State University - Bozeman, College of Engineering, 2008) Smith, Tyler Jon; Chairperson, Graduate Committee: Joel Cahoon; Lucy Marshall (co-chair)In Montana and much of the Rocky Mountain West, the single most important parameter in forecasting the controls on regional water resources is snowpack. Despite the heightened importance of snowpack, few studies have considered the representation of uncertainty in coupled snowmelt/hydrologic conceptual models. Uncertainty estimation provides a direct interpretation of the risk associated with predictive modeling results. Bayesian inference, through the application of Markov chain Monte Carlo methods, provides a statistical means of approximating uncertainty associated with both the parameters and the model structure. This thesis addresses the complexities of predictive modeling in hydrology through the development, implementation and analysis of a suite of conceptual hydrologic models under a Bayesian inference framework. The research is presented in four main sections. First, a comparative assessment of three recently developed Markov chain Monte Carlo algorithms, based on their performance across two case studies, is performed. This study has revealed that the extreme complexity of the parameter space associated with simple, conceptual models is best explored by the Delayed Rejection Adaptive Metropolis algorithm. Second, a complete description of the models and study site incorporated in the study are presented, building on established theories of model development. Third, an investigation of the value of each model structure, considering predictive performance, uncertainty and physical realism is presented. This section builds on results of the first section, through the application of the Delayed Rejection Adaptive Metropolis algorithm for model calibration and uncertainty quantification under Bayesian principles. Finally, a discussion of the Simulation and Prediction Lab for Analysis of Snowmelt Hydrology, developed to incorporate the tasks of model selection, calibration and uncertainty analysis into a simple graphical user interface is explained. The application of a complete modeling framework from model selection to calibration and assessment presented in this thesis represents a holistic approach to the development of improved understanding of snow-dominated watersheds through prediction by coupled snowmelt/hydrologic modeling strategies.Item The role of stream network hydrologic turnover in modifying watershed runoff composition(Montana State University - Bozeman, College of Agriculture, 2012) Mallard, John McDevitt; Chairperson, Graduate Committee: Brian L. McGlynn.Stream networks can attenuate and modify hydrological, biogeochemical, and ecological signals generated in the terrestrial and in-stream portions of watersheds. Stream networks can modify watershed signals through spatially variable stream gains and losses to and from groundwater, described herein as hydrologic turnover. We measured hydrologic gain and loss at the reach scale using conservative tracer experiments throughout the Bull Trout Watershed in the Sawtooth Mountains of central Idaho. These experiments allowed us to track water moving into and out of groundwater from and to stream water. We extended these measured reach scale water balance components to the stream network using observed empirical relationships between 1) accumulated watershed area and stream discharge, and 2) stream discharge and percent discharge lost from the stream. We developed a watershed and stream network-scale model to simulate hydrologic turnover across stream networks to quantify its effects across watershed of varying structure and stream networks of varying geometry. These analyses elucidated the influence of watershed inputs to streams on downstream stream water composition. We determined that the magnitude of contributions to discharge from any upstream watershed input depended on the magnitude of the initial input, but also on the amount of hydrologic turnover downstream along the stream network. Downstream hydrologic turnover was a function of the intersection of watershed structure and stream network geometry. Our results suggest that a distributed representation of hydrologic turnover at the stream network scale is requisite for understanding how the stream network filters and modifies watershed inputs signals observed in streams or watershed outlets.