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Frozen ground is an important consideration in cold regions hydrology because pore ice can impede the ability of water to infiltrate into and migrate within soils, thereby altering water flow paths and increasing surface runoff. High latitude regions are particularly susceptible to changes in climate, where increases in temperature and changing precipitation trends can alter soil freeze/thaw dynamics. However, there has been limited research on infiltration processes in subarctic alpine environments due to sparse historic data and difficulties with gathering direct measurements. In addition, few hydrological models consider the complexity of frozen soils in such environments. The objectives of this thesis are to assess the ability of the GeoStudio finite element modelling suite to simulate observed soil temperature and moisture data and to evaluate the sensitivity of the models to changing climate scenarios. GeoStudio’s Multiphysics model integrates several models that allow it to simulate concurrent water flow and temperature dynamics in variably saturated environments experiencing soil freezing and thawing. Field data for this study are obtained from Wolf Creek Research Basin (WCRB) in southern Yukon, Canada. Data for quantifying snowmelt, soil moisture, soil temperature, and soil composition were collected at three sites in WCRB from April 2015 to August 2016, adding to the available historical data. Results of the GeoStudio models illustrate the dominance of snow in controlling freeze/thaw dynamics and simulate the study environment to reasonable accuracy with some discrepancies in timing and variability. In addition, GeoStudio is particularly sensitive to surface conditions affecting both coupled heat and water flow processes compared to independent changes of air temperature and precipitation, suggesting future climatic scenarios may have a notable impact on frozen soils. This research helps elucidate the complex heat transfer and water movement processes that control infiltration in northern environments and provides a quantitative assessment of their sensitivity to future climate warming. / Thesis / Master of Science (MSc)

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The research was undertaken to provide basic information on Mn in Virginia soils and to evaluate rates and methods of Mn application to correct Mn deficiency in soybeans. Soil profile samples were analyzed from catenas representing the Appalachian, Piedmont, and Coastal Plain regions. The exchangeable, easily reducible, and total Mn contents were higher in the Appalachian soils than in the Piedmont or Coastal Plain soils. The total Mn content of the well-drained soil was higher than the poorly-drained soil in the Appalachian catena. No other differences were found between soils varying in drainage. Parent material and time appear to be the soil forming factors that exert the largest influence on total soil Mn. Further research is necessary to fully characterize soil Mn in these broad physiographic regions. / Ph. D.

For parameter inversion problems governed by systems of partial differential equations, such as those arising in Diffuse Optical Tomography (DOT), even the cost of repeated objective function evaluation can be overwhelming. Despite the linear (in the state variable) nature of the DOT problem, the nonlinear parameter inversion process is dominated by the computational burden of solving a large linear system for each source and frequency. To compute the Jacobian for use in Newton-type methods, an adjoint solve is required for each detector and frequency. When a three-dimensional tomography problem may have nearly 1,000 sources and detectors, the computational cost of an optimization routine is a large burden. While techniques from model order reduction can partially alleviate the computational cost, obtaining error bounds in parameter space is typically not feasible. In this work, we examine two different remedies based on stochastic estimates of the objective function. In the first manuscript, we focus on maximizing the efficiency of using stochastic estimates by replacing our objective function with a surrogate objective function computed from a reduced order model (ROM). We use as few as a single sample to detect a misfit between the full-order and surrogate objective functions. Once a sufficiently large difference is detected, it is necessary to update the ROM to reduce the error. We propose a new technique for improving the ROM with very few large linear solutions. Using this techniques, we observe a reduction of up to 98% in the number of large linear solutions for a three-dimensional tomography problem. In the second manuscript, we focus on establishing a robust algorithm. We propose a new trust region framework that replaces the objective function evaluations with stochastic estimates of the improvement factor and the misfit between the model and objective function gradients. If these estimates satisfy a fixed multiplicative error bound with a high, but fixed, probability, we show that this framework converges almost surely to a stationary point of the objective function. We derive suitable bounds for the DOT problem and present results illustrating the robust nature of these estimates with only 10 samples per iteration. / Doctor of Philosophy / For problems such as medical imaging, the process of reconstructing the state of a system from measurement data can be very expensive to compute. The ever increasing need for high accuracy requires very large models to be used. Reducing the computational burden by replacing the model with a specially constructed smaller model is an established and effective technique. However, it can be difficult to determine how well the smaller model matches the original model. In this thesis, we examine two techniques for estimating the quality of a smaller model based on randomized combinations of sources and detectors. The first technique focuses on reducing the computational cost as much as possible. With the equivalent of a single randomized source, we show that this estimate is an effective measure of the model quality. Coupled with a new technique for improving the smaller model, we demonstrate a highly efficient and robust method. The second technique prioritizes robustness in its algorithm. The algorithm uses these randomized combinations to estimate how the observations change for different system states. If these estimates are accurate with a high probability, we show that this leads to a method that always finds a minimum misfit between predicted values and the observed data.

Yes / The launch of the Solar Dynamics Observatory (SDO) in early 2010 has provided the solar physics community with the most detailed view of the Sun to date. However, this presents new challenges for the analysis of solar data. Currently, SDO sends over 1 terabyte of data per day back to Earth and methods for fast and reliable analysis are more important than ever. This article details four algorithms developed separately at the Universities of Bradford and Glasgow, the Royal Observatory of Belgium and Trinity College Dublin for the purposes of automated detection of solar active regions (ARs) and sunspots at different levels of the solar atmosphere.

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Northern Africa has experienced major shifts towards aridity and extensive C4 vegetation over the late Cenozoic, but due to a scarcity of spatially and temporally extensive paleoenvironmental records, the timing, patterns, and causes of these shifts are still under debate. Both long-term aridification and large amplitude orbital-scale climate variability have been recognized, with little understanding of how these two patterns relate to each other over time. African’s climate and environmental history of the last 7 Myr is of particular interest because hydrological and vegetation variability is considered the driving selection mechanism for human evolution. In addition, the age of the initiation of desert conditions in the modern Sahara desert, Earth’s largest warm desert and the largest source of dust to the modern atmosphere, is unknown. The stable isotope ratios of carbon and hydrogen in sedimentary plant leaf wax biomarker compounds have recently been shown to quantitatively track source vegetation photosynthetic pathways and the hydrogen isotope composition of plant source water, which is dominantly controlled by the amount of precipitation in Africa. These proxies have been applied to reconstruct long-term vegetation changes in East Africa and SW Africa over the last 14 Ma, as well as orbital-scale variability from various locations around the African continent, but they have not been extended further back in time or combined in tandem to robustly assess both long-term and orbital-scale climate and vegetation variability and how they relate to each other. In this thesis, I have utilized quantitative plant leaf wax stable isotope proxies to examine both orbital-scale and long-term changes in North African aridity and vegetation from a variety of regions over the last 25 Ma, with particular emphasis on the last 4.5 Ma. In Chapter 2, I investigated the evolution of hydrological and vegetation gradients from the equator to the sub-Sahara in NW Africa over the last 25 Myr using leaf wax stable isotopes at two marine sediment core locations, producing the longest existing leaf wax stable isotope record in Africa to my knowledge, and one of the longest such records globally. In this study I found that NW African environments were remarkably similar at both latitudes from 25 – 10 Ma, but at 10 Ma C4 vegetation abruptly expanded in the north, indicating sudden aridification in the Sahara region at that time. The hydrogen isotope record was stable long-term, with variability similar to that of known orbital-scale cyclicity in the Pliocene and Pleistocene, possibly suggesting that orbital-scale cyclicity or other factors obscured or were larger than any long-term changes in the hydrogen isotope ratio of precipitation. Saharan aridification at 10 Ma is consistent with climate model predictions of aridity due to the closure of the Tethys Seaway connection between the Indian Ocean and Mediterranean Sea near that time. The 10 Ma expansion in C4 vegetation is earlier than most other regions globally. To examine long-term changes in orbital-scale variability in the Eastern Sahara and Mediterranean Sea, I constructed a record of eastern Mediterranean sedimentary leaf wax carbon and hydrogen isotopes, leaf wax abundance, lignin biomarkers, and oxygen isotope ratios of planktonic foraminifera G. ruber during two 100-kyr periods of equal eccentricity near 3.0 and 1.7 Ma (Chapter 3). I found that precession-scale variability dominates the record during both periods, and Eastern Saharan precipitation and the vegetation assemblage, which was C4-dominated, do not change on average between the two periods. Chapter 4 extended the eastern Mediterranean record of Chapter 3 by sampling leaf wax stable isotopes in sapropel sediments (deposited during North African humid periods) at ~0.25 Myr resolution back to 4.5 Ma, placing the orbital-scale Chapter 3 results in long term context. I found that Eastern Saharan environments were persistently C4-dominated (>68%) throughout the entire interval, and that long-term hydrogen and carbon variability were similar in magnitude to orbital-scale cycles back to 4.5 Ma, strongly indicating that orbital-scale variability has been the dominant environmental control in NE Africa since the early Pliocene. This record contrasts sharply with observations of a transition from C3-C4 mixed vegetation to abundant C4 grasslands in East Africa over the same period of time. The results may suggest that long-term precipitation shifts did not occur in NE Africa since the Pliocene, or that the resolution of this approach is not sufficient to detect long-term shifts. It is likely that NW Africa also experienced similarly large hydrological variability over the same period of time, which may explain the unclear long-term hydrological signal in Chapter 2. The results emphasize that East Africa has not been representative of northern Africa as a whole since the Pliocene.