Scholarship & Research
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Item When and where does irrigation water originate? Leveraging stable water isotopes and synthetic aperture radar to assess the complex hydrology of a snow-dominated catchment in southwestern Montana(Montana State University - Bozeman, College of Letters & Science, 2023) Rickenbaugh, Eliza Apple; Chairperson, Graduate Committee: Eric A. Sproles; This is a manuscript style paper that includes co-authored chapters.Many agricultural regions around the world rely on water stored in mountainous snowpacks for irrigation supply. Consequently, our current and future ability to produce food is threatened by more frequent, severe, and extended snow droughts. As these snow droughts intensify, water resource managers will need more efficient and accurate methods to characterize the snowmelt cycle and forecast water availability. Focusing on a montane headwater catchment in Southwestern Montana (423 km 2 in area, between 1465 m to 3270 m in elevation), we integrate in-situ and remotely sensed data to assess the relative contributions of groundwater and the current season's snowmelt to irrigation supply for water year (WY, Oct 1 - Sep 30) 2023. To understand the period over which snow contributes to stream water in this catchment, we analyze backscatter data from Sentinel-1 Synthetic Aperture Radar (SAR). This provides approximate dates of snowmelt runoff onset at 10 m resolution every twelve days. We find that the median date of snowmelt runoff onset in WY 2023 in this catchment was April 20, six days later than the 7-year median date of snowmelt runoff onset. To assess relative contributions to streamflow we compare stable water isotope ratios (deltaH2 and deltaO18) from biweekly samples of stream water at low elevations against monthly samples of snow and groundwater. Samples range in elevation from 1,475 m to 2,555 m. We find that stream water below the highest diversion point is predominantly composed of groundwater. Results demonstrate alignment between two disparate approaches for estimating temporal trends in snowpack contribution to stream flow. While our work focuses on a catchment in Montana, the efforts and approaches used are potentially applicable globally for agricultural regions that rely on snowmelt for irrigation.Item A framework for the quantitative assessment of new data streams in avalanche forecasting(Montana State University - Bozeman, College of Letters & Science, 2023) Haddad, Alexander Sean; Co-chairs, Graduate Committee: Eric A. Sproles and Jordy HendrikxData used by avalanche forecasters are typically collected using weather stations, manual field-based observations (e.g., avalanche events, snow profiles, stability tests, personal observations, public observations, etc.) and weather forecasts ("traditional observations"). Today, snow cover observations can be delivered via remote sensing (e.g., satellite data, UAV, TLS, time-lapse camera etc.). Forecasting operations can also use statistical forecasting, weather models, and physical modeling to support decisions. This paper presents a framework and methodology to quantify the impact these new, complex data streams have on the formulation of, and associated uncertainty of, avalanche forecasting. We use data from a case study in Norway. Avalanche forecasters in Norway assessed size (D), likelihood, avalanche problem, and hazard level for a highway corridor in Grasdalen, Stryn Norway. The control groups were given access to traditional observations. The experimental groups were given access to the same traditional data, but also near-real-time snow surface LiDAR data ("RS+"). In case study one the RS+ (n=10) consensus findings were a hazard level two steps lower than the control group (n=10). In case study two the traditional (n=10) and RS+ groups' (n=7) consensus findings assessed the northeastern avalanche path at the same hazard level. Assessing the southwestern slide path, the traditional group (n=10) and RS+ group (n=9) had the same consensus finding for hazard level. In 2 of 3 case studies, the RS+ groups had fewer selections for size, likelihood, and avalanche problem which indicates reduced uncertainty in their forecasts. Throughout the 2022-2023 winter season Norwegian Public Roads Administration avalanche forecasters performed a real-time experiment throughout the season - with and without additional RS+ data when forecasting. They agreed on hazard level in 6 of 10 forecasts. In the other 4 forecasts, RS+ forecasters assessed the hazard level higher than traditional data forecasts. When RS+ data reveals aspects of conditions that traditional observations did not detail, RS+ forecasters adjust their selections in the hazard matrix, resulting in greater clustering of their predictions, indicating reduced uncertainty. Due to uncertainty associated with avalanche forecasting, this framework for assessment should be used to track avalanche forecast efficacy and build a qualitative and quantitative historical record.Item Quanitfying snow depth distributions and spatial variability in complex mountain terrain(Montana State University - Bozeman, College of Letters & Science, 2021) Miller, Zachary Stephen; Chairperson, Graduate Committee: Eric A. SprolesThe spatial variability of snow depth is a major source of uncertainty in avalanche and hydrologic forecasting. Identification of spatial and temporal patterns in snow depth is further complicated by the interactions of complex mountain topography and localized micro-meteorology. Recent studies have dramatically improved our understanding of snow depth spatial variability by utilizing increasingly accessible remote sensing technologies such as satellite imagery, terrestrial laser scanning, airborne laser scanning and uninhabited aerial systems (UAS) to map spatially continuous snow depths over a variety of spatiotemporal scales. However, much of this work focuses on relatively low-relief topographies or limited temporal frequencies. Our research presents a thorough evaluation of the evolution of snow depth spatial variability at the slope scale in steep complex mountain terrain (45.834 N, -110.935 E) using analysis from UAS imagery. We apply 13 spatially complete UAS-derived snow depth datasets collected throughout the course of the 2019/2020 winter to analyze spatial and temporal patterns of snow depth and snow depth change variability. Our results show greater spatial variability in steep complex mountain terrain than an adjacent mountain meadow both in the seasonal context and during individual meteorological periods. We analyze 2 cm horizontal resolution snow depth models by (i) comparing spatial patterns with coincident meteorological data, (ii) analysis of the temporal elevation specific patterns of snow depth, and (iii) a comprehensive multi-scalar evaluation of spatial variability. We quantify the unique spatial signature of four specific events: a major snow accumulation, a natural avalanche, a calm period, and a significant wind event. We find a non-linear relationship between elevation and snow depth, with upper elevations proving to be the most variable. We also verify that significant storm events result in the largest snow depth change variability throughout our study area, as compared to other meteorological events. The synthesis of these findings illustrate the dynamic spatial and temporal snow depth distribution patterns observed in complex mountain terrain during the course of a winter season. These findings are relevant to avalanche forecasters and researchers, snow hydrologists and local water resource managers, and downstream communities dependent on snow as a hydrologic reservoir.Item Snow avalanche identification using Sentinel-1: detection rates and controlling factors(Montana State University - Bozeman, College of Letters & Science, 2021) Keskinen, Zachary Marshall; Chairperson, Graduate Committee: Jordy Hendrikx; Jordy Hendrikx, Karl Birkeland and Markus Eckerstorfer were co-authors of the article, 'Snow avalanche identification using Sentinel-1 backscatter imagery: detection rates and controlling factors' submitted to the journal 'Natural hazards and Earth system sciences' which is contained within this thesis.Snow avalanches present a significant hazard that endangers lives and infrastructure. Consistent and accurate datasets of avalanche events is valuable for improving forecasting ability and furthering knowledge of avalanches' spatial and temporal patterns. Remote sensing-based techniques of identifying avalanche debris allow for continuous and spatially consistent datasets of avalanches to be acquired. This study utilizes expert manual interpretations of Sentinel-1 synthetic aperture radar (SAR) satellite backscatter images to identify avalanche debris and compares those detections against historical field records of avalanches in the transitional snow climates of Wyoming and Utah. This study explores the utility of Sentinel-1 (a SAR satellite) images to detect avalanche debris on primarily dry slab avalanches. The overall probability of detection (POD) rate for avalanches large enough to destroy trees or bury a car (i.e., D3 on the Destructive Size Scale) was 64.6%. There was a significant variance in the POD among the 13 individual SAR image pairs (15.4 - 87.0%). Additionally, this study investigated the connection between successful avalanche detections and SAR-specific, topographic, and avalanche type variables. The most correlated variables with higher detection rates were avalanche path lengths, destructive size of the avalanche, incidence angles for the incoming microwaves, slope angle, and elapsed time between the avalanche and a Sentinel-1 satellite passing over. This study provides an initial exploration of the controlling variables in the likelihood of detecting avalanches using Sentinel-1 backscatter change detection techniques. This study also supports the generalizability of SAR backscatter difference analysis by applying the methodology in different regions with distinct snow climates from previous studies.Item Soil storage on steep forested and non-forested mountain hillslopes in the Bitterroot Mountains, Montana(Montana State University - Bozeman, College of Letters & Science, 2018) Quinn, Colin Aidan; Chairperson, Graduate Committee: Jean DixonMountain hillslopes are dynamic settings with discontinuous soils affected by a suite of variables including climate, lithology, hydrology, and vegetation. Our study seeks to understand how forest cover influences soil and rock distribution at decadal to century timescales. We focus on a series of post-glacial hillslopes in Lost Horse Creek of the Bitterroot Mountains, Montana. In this system, avalanche paths maintain parallel, topographically similar swaths of forested and non-forested slopes with uniform aspect, lithology, and climate. We combine field observations, fallout radionuclide analysis (210 Pbex & 137 Cs), and remote sensing data to understand both landscape- and fine-scale patterns in soil and rock distribution. Local soil and rock measurements indicate more extensive soil cover (forest = 94.4 + or = 2.6%; non-forest = 88.3 + or = 1.9%) and thicker soils (6cm greater median) in the forested system. We compare landcover-classified rock to topographic metrics from LiDAR data and find a doubling of rock cover (from 40% to 80%) as hillslope angles transition across slopes of ~24-42 ?. Topographic roughness, calculated as the standard deviation of slope, is predictive of only ~60% of total landscape rock cover, but can identify large boulders and coarse-scale outcrops with higher accuracy (79%). These calibrated remote sensing metrics indicate higher rock cover in non-forested regions (34%, compared to 20% in forested areas), though with high uncertainty. Additionally, we measure fallout-radionuclide inventories in soils to explore variations in decadal transport processes and soil residence times. We find distinct 210 Pb and 137 Cs behaviors in forested and non-forested systems, controlled both by unique partitioning of each nuclide within organic and mineral soil horizons, but also due to depth-driven differences in their physical mobility. Average 210 Pb ex inventories in non-forested soils are 33% lower, and half as variable as soils in the forested region (10.45 + or = 0.97 and 15.49 + or = 1.91 kBq/m 2 respectively), while 137 Cs inventories are indistinguishable (4.04 + or = 0.34 and 3.73 + or = 0.42 kBq/m 2). Together, our spatial, field, and isotope analyses suggest forested systems have greater soil storage and longer residence times than non-forested soils, mediated by differences in surface erosion processes within a larger fire disturbance landscape.Item Infrared temperature sensing of snow covered terrain(Montana State University - Bozeman, College of Letters & Science, 1971) Shafer, Bernard AllenItem The use of multispectral digital imagery to map hydrogeomorphic stream units in Soda Butte and Cache Creeks, Montana and Wyoming(Montana State University - Bozeman, College of Letters & Science, 1998) Wright, AndreaItem Using terrain attributes to assist remote sensing vegetation mapping of northern Great Plains grasslands(Montana State University - Bozeman, College of Letters & Science, 1996) Wheatley, Jonathan MarkItem Sediment routing system response to tectonic activity in the Argentine Precordillera : Sierra Las Penas-Las Higueras(Montana State University - Bozeman, College of Letters & Science, 2010) Abrahamson, Ingrid Syverine; Chairperson, Graduate Committee: James G. SchmittAlluvial fan deposition in the Argentine Central Precordillera is part of a sediment routing system that changes along strike of an active thrust front. This study area is partitioned into erosional and depositional sectors for analysis. The erosional sector drainage basins are analyzed using topographic data from a digital elevation model, to see how morphology changes with fault displacement. Drainage basins become shorter with more displacement. The depositional sector alluvial fans are classified using spectral characteristics from satellite imagery. The fans are classified based on thermal, near infrared, and elevation parameters. Fans close to the thrust front are interpreted to be old sheetflood deposits, with younger fans more distal from the front in the foreland. In this setting, progressive fault displacement causes shortening of the erosional sector, increasing the efficiency of sediment evacuation from the range, and causing a progradation of sheetflood fans into the foreland basin. Remote sensing analysis techniques are useful for characterizing the sediment routing system of alluvial systems where field-based information (geodetic, seismic, structural and lithologic data) is not available.