Grain-size analysis of hurricane-induced event beds in a New England salt marsh, Massachusetts, USA

Author Posting. © Coastal Education and Research Foundation, 2021. This article is posted here by permission of Coastal Education and Research Foundation for personal use, not for redistribution. The definitive version was published in Journal of Coastal Research 37(2), (2021): 326-33, https://doi.org/10.2112/JCOASTRES-D-19-00159.1.Tropical cyclones pose a growing threat to coastal infrastructure and livelihood. Because instrumental and historic records are too short to help us understand interactions between tropical cyclones and climate on a longer scale, proxy records are the only means for reconstructing millennia of tropical cyclone impacts. This study determines grain-size trends in storm-induced overwash deposits along a transect of sediment cores from a salt marsh in Mattapoisett, Massachusetts, to characterize sorting trends and compare deposits associated with individual storms. The overwash deposits preserved within the high-marsh peat provide a record spanning the last two millennia. Building on a 2010 study, a different approach was used to accurately determine the grain-size distribution of overwash deposits from cores in a transect running perpendicular to the adjacent sandy/gravely barrier. Although maximum grain-size values are expected to decrease as distance from the barrier increases, not all event deposits that were studied follow this trend within uncertainty. Analysis of the storm event beds reveal a significant difference in settling trends between historic and prehistoric deposits, with historic deposits largely displaying landward-fining trends and prehistoric deposits largely displaying landward-coarsening trends. This suggests changes in the hydrodynamic or that geomorphic regime may have altered the way in which storm beds were deposited at this site. This new in-depth, transect-based approach has utility for improving the accuracy of future storm reconstructions, particularly for events for which no historic record exists

Carbon isotope (δ13Ccarb) heterogeneity in deep-water Cambro-Ordovician carbonates, western Newfoundland

© The Author(s), 2015. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Palaeogeography, Palaeoclimatology, Palaeoecology 458 (2016): 52-62, doi:10.1016/j.palaeo.2015.10.004.Carbonates of western Newfoundland span the Cambro-Ordovician interval and preserve a record of slope-basinal deposition in the Cow Head Group near Cow Head. This unit consists of conglomerates and ribbon and laminated limestone interbedded with shale that is well exposed in sea cliffs at Cow Head Peninsula. These conglomerates, although prevalent throughout the section, vary in thickness and abundance stratigraphically and record both local disruption and large-scale episodic sedimentation events. Microfacies drilled for carbon isotope (δ13Ccarb) analysis of conglomerates reveal isotopic heterogeneity within individual samples, in some cases more than 1‰. While this might be an expected outcome of drilling multiple areas of a heterogeneous conglomerate hand sample, permil-level variability was observed both between individual clasts in a sample, between different parts of the same matrix, and between a clast and its surrounding matrix. No associated variation in δ18Ocarb or trace element distributions exists to suggest that this δ13Ccarb variability is the result of later-stage meteoric diagenesis. The δ13Ccarb variability suggests multiple sources of dissolved inorganic carbon (DIC) associated with carbonate precipitation for phases within these individual samples. These data indicate that processes such as local organic matter remineralization and early authigenic carbonate precipitation during lithification at the sediment-water interface (SWI) are either contributing to or controlling δ13Ccarb values in Cambrian carbonates, perhaps more so than at other intervals in Earth history.SBP would like to acknowledge Smith College for funding.2017-10-0

How much marsh restoration is enough to deliver wave attenuation coastal protection benefits?

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Castagno, K., Ganju, N., Beck, M., Bowden, A., & Scyphers, S. How much marsh restoration is enough to deliver wave attenuation coastal protection benefits? Frontiers in Marine Science, 8, (2022): 756670, https://doi.org/10.3389/fmars.2021.756670.As coastal communities grow more vulnerable to sea-level rise and increased storminess, communities have turned to nature-based solutions to bolster coastal resilience and protection. Marshes have significant wave attenuation properties and can play an important role in coastal protection for many communities. Many restoration projects seek to maximize this ecosystem service but how much marsh restoration is enough to deliver measurable coastal protection benefits is still unknown. This question is critical to guiding assessments of cost effectiveness and for funding, implementation, and optimizing of marsh restoration for risk reduction projects. This study uses SWAN model simulations to determine empirical relationships between wave attenuation and marsh vegetation. The model runs consider several different common marsh morphologies (including systems with channels, ponds, and fringing mudflats), vegetation placement, and simulated storm intensity. Up to a 95% reduction in wave energy is seen at as low as 50% vegetation cover. Although these empirical relationships between vegetative cover and wave attenuation provide essential insight for marsh restoration, it is also important to factor in lifespan estimates of restored marshes when making overall restoration decisions. The results of this study are important for coastal practitioners and managers seeking performance goals and metrics for marsh restoration, enhancement, and creation

Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goldstein, E. B., Buscombe, D., Lazarus, E. D., Mohanty, S. D., Rafique, S. N., Anarde, K. A., Ashton, A. D., Beuzen, T., Castagno, K. A., Cohn, N., Conlin, M. P., Ellenson, A., Gillen, M., Hovenga, P. A., Over, J.-S. R., Palermo, R., Ratliff, K. M., Reeves, I. R. B., Sanborn, L. H., Straub, J. A., Taylor, L. A., Wallace E. J., Warrick, J., Wernette, P., Williams, H. E. Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement. Earth and Space Science, 8(9), (2021): e2021EA001896, https://doi.org/10.1029/2021EA001896.Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.The authors gratefully acknowledge support from the U.S. Geological Survey (G20AC00403 to EBG and SDM), NSF (1953412 to EBG and SDM; 1939954 to EBG), Microsoft AI for Earth (to EBG and SDM), The Leverhulme Trust (RPG-2018-282 to EDL and EBG), and an Early Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (to EBG). U.S. Geological Survey researchers (DB, J-SRO, JW, and PW) were supported by the U.S. Geological Survey Coastal and Marine Hazards and Resources Program as part of the response and recovery efforts under congressional appropriations through the Additional Supplemental Appropriations for Disaster Relief Act, 2019 (Public Law 116-20; 133 Stat. 871)

Storm signatures in coastal ponds and marshes over the late Holocene

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Geology and Geophysics at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2019.Tropical cyclones pose a growing threat to coastal populations, especially as both populations and infrastructure are increasingly concentrated along the eastern coast of the United States. This thesis seeks to characterize the impacts of storms on coastal ponds and marshes along the eastern coast of the United States. Tropical cyclones and other storms have been shown to cause a spectrum of effects on these coastal systems, ranging from widespread erosion to deposition of thick sediment deposits. Sediments deposited and preserved in coastal ponds and marshes can provide a proxy for tropical cyclone landfall, the development and interpretation of which is imperative to understanding past climate trends and informing decisions for the future. This thesis uses a variety of methods to quantify the spatiotemporal signatures of tropical cyclone events in coastal, marsh, bay, and pond systems in Massachusetts, Connecticut, and Virginia. Trends in grain-size distribution and sediment coarse fraction are used to broaden our understanding of deposition and sediment sources during tropical cyclone events. The complexity of how storms interact with these systems requires a process-based, whole-site analysis to adequately develop a storm record. Given the many nuances to storm deposition in these systems (including reverse grading trends and apparent spatiotemporal variation in sediment source), the potential utility and caveats to inversely modeling storm intensity from deposit grain-size characteristics is discussed. Finally, the question of whether hurricanes can produce widespread erosion of marsh platforms is addressed through both field and modeling techniques. While storms typically deposit sediment, field evidence suggests that marshes have the potential to be eroded by a series of storms over time—a deviation from our traditional understanding of marsh evolution. Deposition and erosion of sediment during major storms remain complex, emphasizing the importance of contextualizing storm signatures within a broader view of the study area. This provides an opportunity to strengthen both paleo-reconstructions of storm activity and our ability to make informed decisions for coastal management in response to potential future changes in storminess

Geochemical analysis of the Cambro-Ordovician Cow Head Group, western Newfoundland, Canada

The causal mechanisms for the largely depauperate interval following the Cambrian Explosion and the GOBE are still poorly understood. The carbonate and shale of the Cambro- Ordovician Cow Head Group in Western Newfoundland, Canada, span this important interval in a deep-water slope setting. Similarities in the δ13Ccarb profile to other Cambro-Ordovician sections suggest that the Cambro-Ordovician Cow Head Peninsula may be accurately correlated with other profiles worldwide. Though the positive excursion of the later Cambrian (SPICE Event) was not recorded in this deep-water slope environment, the enriched δ13Ccarb values in beds correlated to the SPICE Event on the shelf suggest possible correlation between the strata. No trends between δ13Ccarb and δ18O values exist over time in a bed-by-bed analysis, suggesting that the δ13Ccarb profile was not significantly affected by meteoric diagenesis. To determine the influence of conglomerates on the overall profile, multiple matrix and clast values from the same sample were compared. The conglomerate study was consistent with the larger δ13Ccarb profile, and the vast majority of samples do not display a statistically significant difference between clast and matrix. The conglomerate study suggests that even when only clasts can be sampled, they may still be representative of the general trends in the δ13Ccarb profile. This, coupled with the correlation of the δ13Ccarb profile to three other Cambro-Ordovician profiles, indicates that the deep-water slope Cambro-Ordovician Cow Head Peninsula is an accurate record of the changes in seawater carbon isotope composition over this critical time in Earth history

Intense Storms Increase the Stability of Tidal Bays

Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of [publisher] for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 45 (2018): 5491-5500, doi:10.1029/2018GL078208.Coastal bays and, specifically, back‐barrier tidal basins host productive ecosystems, coastal communities, and critical infrastructure. As sea level continues to rise and tropical cyclones increase in intensity, these coastal systems are increasingly at risk. Developing a sediment budget is imperative to understanding how storm events affect the system's resilience, where net import of sediment indicates growth and resilience against sea level rise, and net export of sediment indicates deterioration. Using high‐resolution numerical simulations, we show that intense storms import sediment into a system of bays in Virginia, USA. Duration and magnitude of storm surge are among the most important factors in sediment import, suggesting that intense storms increase the stability of tidal bays by providing the sediment necessary to counteract sea level rise. Since climate models project that tropical cyclones will increase in intensity in coming decades, our results have significant implications for the resilience of tidal bays and the future of coastal communities worldwide.National Science Foundation Grant Numbers: NSF 1237733, NSF 1637630, NSF 1636302018-11-2

Intense Storms Increase the Stability of Tidal Bays

Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of [publisher] for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 45 (2018): 5491-5500, doi:10.1029/2018GL078208.Coastal bays and, specifically, back‐barrier tidal basins host productive ecosystems, coastal communities, and critical infrastructure. As sea level continues to rise and tropical cyclones increase in intensity, these coastal systems are increasingly at risk. Developing a sediment budget is imperative to understanding how storm events affect the system's resilience, where net import of sediment indicates growth and resilience against sea level rise, and net export of sediment indicates deterioration. Using high‐resolution numerical simulations, we show that intense storms import sediment into a system of bays in Virginia, USA. Duration and magnitude of storm surge are among the most important factors in sediment import, suggesting that intense storms increase the stability of tidal bays by providing the sediment necessary to counteract sea level rise. Since climate models project that tropical cyclones will increase in intensity in coming decades, our results have significant implications for the resilience of tidal bays and the future of coastal communities worldwide.National Science Foundation Grant Numbers: NSF 1237733, NSF 1637630, NSF 1636302018-11-2