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

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

Recommendations for Implementing Hepatitis C Virus Care in Homeless Shelters: The Stakeholder Perspective.

Compared with the general population, homeless individuals are at higher risk of hepatitis C infection (HCV) and may face unique barriers in receipt of HCV care. This study sought the perspectives of key stakeholders toward establishing a universal HCV screening, testing, and treatment protocol for individuals accessing homeless shelters. Four focus groups were conducted with homeless shelter staff, practice providers, and social service outreach workers (n = 27) in San Francisco, California, and Minneapolis, Minnesota. Focus groups evaluated key societal, system, and individual-level facilitators and barriers to HCV testing and management. Interviews were transcribed and analyzed thematically. The societal-level barriers identified were lack of insurance, high-out-of-pocket expenses, restriction of access to HCV treatment due to active drug and/or alcohol use, and excessive paperwork required for HCV treatment authorization from payers. System-level barriers included workforce constraints and limited health care infrastructure, HCV stigma, low knowledge of HCV treatment, and existing shelter policies. At the individual level, client barriers included competing priorities, behavioral health concerns, and health attitudes. Facilitators at the system level for HCV care service integration in the shelter setting included high acceptability and buy in, and linkage with social service providers. Conclusion: Despite societal, system, and individual-level barriers identified with respect to the scale-up of HCV services in homeless shelters, there was broad support from key stakeholders for increasing capacity for the provision of HCV services in shelter settings. Recommendations for the scale-up of HCV services in homeless shelter settings are discussed

Little genomic support for Cyclophilin A-matrix metalloproteinase-9 pathway as a therapeutic target for cognitive impairment in APOE4 carriers

Therapeutic targets for halting the progression of Alzheimer’s disease pathology are lacking. Recent evidence suggests that APOE4, but not APOE3, activates the Cyclophilin-A matrix metalloproteinase-9 (CypA-MMP9) pathway, leading to an accelerated breakdown of the blood–brain barrier (BBB) and thereby causing neuronal and synaptic dysfunction. Furthermore, blockade of the CypA-MMP9 pathway in APOE4 knock-in mice restores BBB integrity and subsequently normalizes neuronal and synaptic function. Thus, CypA has been suggested as a potential target for treating APOE4 mediated neurovascular injury and the resulting neuronal dysfunction and degeneration. The odds of drug targets passing through clinical trials are greatly increased if they are supported by genomic evidence. We found little evidence to suggest that CypA or MMP9 affects the risk of Alzheimer’s disease or cognitive impairment using two-sample Mendelian randomization and polygenic risk score analysis in humans. This casts doubt on whether they are likely to represent effective drug targets for cognitive impairment in human APOE4 carriers

CoastalImageLib: An open- source Python package for creating common coastal image products

CoastalImageLib is a Python library that produces common coastal image products intended for quantitative analysis of coastal environments. This library contains functions to georectify and merge multiple oblique camera views, produce statistical image products for a given set of images, and create subsampled pixel instruments for use in bathymetric inversion, surface current estimation, run-up calculations, and other quantitative analyses. This package intends to be an open-source broadly generalizable front end to future coastal imaging applications, ultimately expanding user accessibility to optical remote sensing of coastal environments. This package was developed and tested on data collected from the Argus Tower, a 43 m tall observation structure in Duck, North Carolina at the US Army Engineer Research and Development Center’s Field Research Facility that holds six stationary cameras which collect twice-hourly coastal image products. Thus, CoastalImageLib also contains functions designed to interface with the file storage and collection system implemented at the Argus Tower

Development of the Nebraska Department of Transportation Winter Severity Index

Adverse weather conditions are responsible for millions of vehicular crashes, thousands of vehicular deaths and billions of dollars in economic and congestion costs. Many transportation agencies utilize a performance or mobility metric to assess how well they are maintaining road access. This research focuses on the development of a winter severity index for the State of Nebraska (NEWINS). NEWINS is an event-driven index that was derived for the Nebraska Department of Transportation (NDOT) and its districts across the state. The NEWINS framework includes a categorical storm classification framework and climatological aspect to capture atmospheric conditions more accurately across the diverse spatial regions of Nebraska. A ten-year (2006-2016) winter season database of meteorological variables for Nebraska was obtained from the National Centers for Environmental Information. Meteorological parameters were grouped into categories that subsequently provided a storm classification database. The NEWINS is based on a weighted linear combination to the collected database to measure severity statewide and across NDOT individual districts. The NEWINS results were compared to other meteorological variables, many used in other agencies’ winter severity indices. This comparison verified the NEWINS robustness for the observed events for the ten year period. For example, an assessment of the difference between days with observed snowfall versus days with accumulated snowfall revealed a 39% average reduction in days. Furthermore, the NEWINS results highlight the greater number of events during the 2009-2010 winter season, and the lack of events during the 2011-2012 drought year. The NEWINS also shows strong differences monthly and among NDOT districts across the state with a general decrease in events from the western to eastern NDOT districts. In addition, NEWINS storm classifications were compared to NDOT winter maintenance operations performance data for a sample winter season. Last, the 2016-17 winter season was computed to provide a testbed for the NEWINS procedure. It is expected that the NEWINS could help transportation personnel to efficiently allocate resources during adverse weather events, while balancing safety, mobility, and available budget. Further, the theoretical and practical contributions provided by the NEWINS can be used by other agencies to assess their weather sensitivity

Development of the Nebraska Department of Transportation Winter Severity Index

Adverse weather conditions are responsible for millions of vehicular crashes, thousands of vehicular deaths and billions of dollars in economic and congestion costs. Many transportation agencies utilize a performance or mobility metric to assess how well they are maintaining road access. This research focuses on the development of a winter severity index for the State of Nebraska (NEWINS). NEWINS is an event-driven index that was derived for the Nebraska Department of Transportation (NDOT) and its districts across the state. The NEWINS framework includes a categorical storm classification framework and climatological aspect to capture atmospheric conditions more accurately across the diverse spatial regions of Nebraska. A ten-year (2006-2016) winter season database of meteorological variables for Nebraska was obtained from the National Centers for Environmental Information. Meteorological parameters were grouped into categories that subsequently provided a storm classification database. The NEWINS is based on a weighted linear combination to the collected database to measure severity statewide and across NDOT individual districts. The NEWINS results were compared to other meteorological variables, many used in other agencies’ winter severity indices. This comparison verified the NEWINS robustness for the observed events for the ten year period. For example, an assessment of the difference between days with observed snowfall versus days with accumulated snowfall revealed a 39% average reduction in days. Furthermore, the NEWINS results highlight the greater number of events during the 2009-2010 winter season, and the lack of events during the 2011-2012 drought year. The NEWINS also shows strong differences monthly and among NDOT districts across the state with a general decrease in events from the western to eastern NDOT districts. In addition, NEWINS storm classifications were compared to NDOT winter maintenance operations performance data for a sample winter season. Last, the 2016-17 winter season was computed to provide a testbed for the NEWINS procedure. It is expected that the NEWINS could help transportation personnel to efficiently allocate resources during adverse weather events, while balancing safety, mobility, and available budget. Further, the theoretical and practical contributions provided by the NEWINS can be used by other agencies to assess their weather sensitivity

Satellite detection of dinoflagellate blooms off California by UV reflectance ratios

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kahru, M., Anderson, C., Barton, A. D., Carter, M. L., Catlett, D., Send, U., Sosik, H. M., Weiss, E. L., & Mitchell, B. G. Satellite detection of dinoflagellate blooms off California by UV reflectance ratios. Elementa: Science of the Anthropocene, 9(1), (2021): 00157, https://doi.org/10.1525/elementa.2020.00157.As harmful algae blooms are increasing in frequency and magnitude, one goal of a new generation of higher spectral resolution satellite missions is to improve the potential of satellite optical data to monitor these events. A satellite-based algorithm proposed over two decades ago was used for the first time to monitor the extent and temporal evolution of a massive bloom of the dinoflagellate Lingulodinium polyedra off Southern California during April and May 2020. The algorithm uses ultraviolet (UV) data that have only recently become available from the single ocean color sensor on the Japanese GCOM-C satellite. Dinoflagellates contain high concentrations of mycosporine-like amino acids and release colored dissolved organic matter, both of which absorb strongly in the UV part of the spectrum. Ratios 1, consistent with historical observations showing a sharp transition from dinoflagellate- to diatom-dominated waters in these areas. UV bands are thus potentially useful in the remote sensing of phytoplankton blooms but are currently available only from a single ocean color sensor. As several new satellites such as the NASA Plankton, Aerosol, Cloud, and marine Ecosystem mission will include UV bands, new algorithms using these bands are needed to enable better monitoring of blooms, especially potentially harmful algal blooms, across large spatiotemporal scales.Part of this work was funded by National Science Foundation (NSF) grants to the CCE-LTER Program, most recently OCE-1637632. Processing of Second-Generation Global Imager satellite data was funded by Japan Aerospace Exploration Agency. Data shown in Figure 1 were collected by BGM and MK with support from the NASA SIMBIOS project. DC was supported by the NASA Biodiversity and Ecological Forecasting Program (Grant NNX14AR62A), the Bureau of Ocean and Energy Management Ecosystem Studies Program (BOEM award MC15AC00006), and the NOAA through the Santa Barbara Channel Marine Biodiversity Observation Network. HMS was supported by NSF (Grant OCE-1810927) and the Simons Foundation (Grant 561126). ELW was supported by NSF GRFP (Grant DGE-1650112). Funding for Scripps and Santa Monica Piers sampling was through the Southern California Coastal Ocean Observing Harmful Algal Bloom Monitoring Program by NOAA NA16NOS0120022

The GBT Diffuse Ionized Gas Survey (GDIGS): Survey Overview and First Data Release

The Green Bank Telescope (GBT) Diffuse Ionized Gas Survey (GDIGS) traces ionized gas in the Galactic midplane by measuring $4-8$GHz radio recombination line (RRL) emission. The nominal survey zone is $32.3^{\circ}> l >-5^{\circ}$, $|b|<0.5^{\circ}$, but coverage extends above and below the plane in select fields, and additionally includes the areas around W47 ($l \simeq 37.5^{\circ}$) and W49 ($l \simeq 43^{\circ}$). GDIGS simultaneously observes 22 Hn$\alpha$ (15 usable), 25 Hn$\beta$ (18 usable), and 8 Hn$\gamma$ RRLs (all usable), as well as multiple molecular line transitions (including of H$_2^{13}$CO, H$_2$CO, and CH$_3$OH). Here, we describe the GDIGS survey parameters and characterize the RRL data, focusing primarily on the Hn$\alpha$ data. We produce sensitive data cubes by averaging the usable RRLs, after first smoothing to a common spectral resolution of 0.5km/s and a spatial resolution of 2.65' for Hn$\alpha$, 2.62' for Hn$\beta$, and 2.09' for Hn$\gamma$. The average spectral noise per spaxel in the \hna\ data cubes is $\sim\!10$mK ($\sim\!5$mJy/beam). This sensitivity allows GDIGS to detect RRLs from plasma throughout the inner Galaxy. The GDIGS Hn$\alpha$ data are sensitive to emission measures $EM \gtrsim 1100$cm$^{-6}$pc, which corresponds to a mean electron density $\langle n_e \rangle \gtrsim 30$cm$^{-3}$ for a 1pc path length or $\langle n_e \rangle \gtrsim 1$cm$^{-3}$ for a 1kpc path length.Comment: Accepted for publication by ApJS. Data may be downloaded here: http://astro.phys.wvu.edu/gdigs

A quantitative meta-analysis and qualitative meta-synthesis of aged care residents’ experiences of autonomy, being controlled, and optimal functioning

Background and Objectives The poor mental health of adults living in aged care needs addressing. Improvements to nutrition and exercise are important, but mental health requires a psychological approach. Self-determination theory finds that autonomy is essential to wellbeing while experiences of being controlled undermine it. A review of existing quantitative data could underscore the importance of autonomy in aged care, and a review of the qualitative literature could inform ways to promote autonomy and avoid control. Testing these possibilities was the objective of this research. Research Design and Methods We conducted a mixed-methods systematic review of studies investigating autonomy, control, and indices of optimal functioning in aged care settings. The search identified 30 eligible reports (19 quantitative, 11 qualitative), including 141 quantitative effect sizes, 84 qualitative data items, and N = 2,668. Quantitative effects were pooled using three-level meta-analytic structural equation models, and the qualitative data were meta-synthesized using a grounded theory approach. Results As predicted, the meta-analysis showed a positive effect of aged care residents’ autonomy and their wellness, r = 0.33 [95% CI: 0.27, 0.39], and a negative effect of control, r = −0.16 [95% CI: −0.27, −0.06]. The meta-synthesis revealed seven primary and three sub-themes describing the nuanced ways autonomy, control, and help seeking are manifest in residential aged care settings. Discussion and Implications The results suggest that autonomy should be supported, and unnecessary external control should be minimized in residential aged care, and we discuss ways the sector could strive for both aims