Graduate Thesis Or Dissertation

 

The Art of Reducing Coastal Hazard Complexity: Coupling Physics and Probability to Understand Extremes, Variability, and Climate-dependence Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/vq27zv064

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  • Coastal hazards are the result of numerous physical processes cumulatively causing water levels to flood and erode the land. The waves, storm surges, tides, and run-off contributing to elevated water levels are each the product of chaotic and random weather patterns. These stochastic weather patterns dissipate energy in Earth's climate system. The climate is currently changing due to anthropogenic activities and the future with respect to weather patterns, storm strengths, and sea levels is highly uncertain. The chapters of this thesis consider multiple problems preventing accurate forecasting of coastal hazards, including difficulties linking climate to local impact, identifying oscillating trends within hazards, simulating hazards in efficient yet dynamics-based approaches, and the missing physics currently preventing nearshore erosion models from operating on management timescales. Chapter 2 develops an approach for considering stochastic weather downscaled to the multiple processes contributing to elevated coastal water levels. Relatively short records of the full multi-dimensional space contributing to total water level (TWL) coastal flooding events (astronomic tides, sea level anomalies, storm surges, wave runup, etc.) results in historical observations of only a small fraction of the possible range of conditions that could produce severe flooding. The developed framework is capable of producing new iterations of the sea-state parameters associated with the present-day climate to simulate many synthetic extreme compound events. The emulator utilizes weather typing of fundamental climate drivers (sea surface temperatures, sea level pressures, etc.) to reduce complexity and produces new daily synoptic weather chronologies with an auto-regressive logistic model accounting for conditional dependencies on the El Niño Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), seasonality, and the prior two days of weather progression. Joint probabilities of sea-state parameters unique to simulated weather patterns are used to simulate new time series of the hypothetical components contributing to synthetic TWLs. This work reveals the importance of considering the multi-variate nature of extreme coastal flooding, while progressing the ability to incorporate large-scale climate variability into site specific studies assessing hazards within the context of predicted climate change in the 21st century. Chapter 3 couples the climate model of Chapter 2 with a surrogate model to efficient extrapolate spatially varying flood hazards. Numerical models for tides, surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However, these models are often too computationally expensive to dynamically simulate the full parameter space of oceanographic, atmospheric, and hydrologic conditions that constructively compound in the nearshore. The hybrid statistical-dynamical modeling framework developed in Chapter 3 predicts spatially varying nearshore water levels contingent on any combination of forcing conditions. The hybrid framework can assess present day future coastal flood risk, including inundation depths, flooding frequencies, and wave-induced dune overtopping. The framework is demonstrated at Naval Base Coronado (NABC) in San Diego, CA, utilizing the regional flood model Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as a dynamic simulator and Gaussian process regression as a surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore deployment of a cross-shore array of pressure sensors in the open beach surf zone. The complete approach can readily explore sensitivities to a wide range of resilience metrics, while capturing the uncertainty that exists in those metrics as a result of stochastic climate processes. Chapter 4 uses a subset of the techniques from Chapter 1 to investigate interannual climate variability driving shoreline erosion. A recent 35-year endpoint shoreline change analysis revealed significant counterclockwise rotations occurring in north-central Oregon, USA, littoral cells that extend 10s of kilometers in length. While the potential for severe El Ninos to contribute to littoral cell rotations at seasonal to interannual scale was previously recognized, the dynamics resulting in persistent (multidecadal) rotation were unknown, largely due to a lack of historical wave conditions extending back multiple decades and the difficulty of separating the timescales of shoreline variability in a high energy region. This chapter addresses this question by (1) developing a statistical downscaling framework to characterize wave conditions relevant for longshore sediment transport during data-poor decades and (2) applying a one-line shoreline change model to quantitatively assess the potential for such large embayed beaches to rotate. A climate INdex was optimized to capture variability in longshore wave power as a proxy for potential LOngshore Sediment Transport (LOST_IN), and a procedure was developed to simulate many realizations of potential wave conditions from the index. Waves were transformed dynamically with Simulating Waves Nearshore to the nearshore as inputs to a one-line model that revealed shoreline rotations of embayed beaches at multiple time and spatial scales not previously discernible from infrequent observations. Model results indicate that littoral cells respond to both interannual and multidecadal oscillations, producing comparable shoreline excursions to extreme El Nino winters. The technique quantitatively relates morphodynamic forcing to specific climate patterns and has the potential to better identify and quantify coastal variability on timescales relevant to a changing climate. Chapter 5 investigates the physical processes causing onshore sand bar migrations. Sand bars are morphological features that affect surf zone processes and ultimately the TWLs causing hazards, but their behavior currently can not be predicted on management time scales. Horizontal and vertical pressure gradients may be important physical mechanisms contributing to the onshore sediment transport beneath steep, near-breaking waves in the surf zone. A barred beach was constructed in a large-scale laboratory wave flume with a fixed profile containing a mobile sediment layer on the crest of the sandbar. Horizontal and vertical pore pressure gradients were obtained by finite differences of measurements from an array of pressure transducers buried within the upper several centimeters of the bed. Colocated observations of erosion depth were made during asymmetric wave trials with wave heights between 0.10 and 0.98 m, consistently resulting in onshore sheet flow sediment transport. The pore pressure gradient vector within the bed exhibited temporal rotations during each wave cycle, directed predominantly upward under the trough and then rapidly rotating onshore and downward as the wavefront passed. The magnitude of the pore pressure gradient during each phase of rotation was correlated with local wave steepness and relative depth. Momentary bed failures as deep as 20 grain diameters were coincident with sharp increases in the onshore-directed pore pressure gradients, but occurred at horizontal pressure gradients less than theoretical critical values for initiation of the motion for compact beds. An expression combining the effects of both horizontal and vertical pore pressure gradients with bed shear stress and soil stability is used to determine that failure of the bed is initiated at nonnegligible values of both forces. Overall, this collection of manuscripts contributes to the goal of better predicting coastal hazards in the 21st century. Quantitatively linking climate phenomena to local coastal conditions and developing frameworks to efficiently make dynamical predictions of the resulting hazard is essential for understanding how climate change will impact vulnerable coastlines on policy and management time scales.
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  • This work was supported by the Oregon State University Provost Office, the National Science Foundation Graduate Research Fellowship Program, and the Strategic Environmental Research Development Program grant DOD/SERDP RC-2644.
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  • Ongoing Research
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  • 2019-06-13 to 2020-07-14

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