Palermo
Rose V.
Palermo
Rose V.
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ThesisCoastal evolution on Earth and Titan(Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2022-09) Palermo, Rose V. ; Ashton, Andrew D. ; Perron, J. TaylorThe morphology of a shoreline can provide insight into the processes that have modified the coast. This thesis investigates how coastal processes can leave fingerprints on the morphology of a coast in sandy environments (barrier islands) and detachment-limited environments (rocky coasts of Earth and possibly Titan). Barrier islands are dynamic and ephemeral, facing an uncertain future from climate change and anthropogenic redistribution of sediment. To evaluate barrier resilience to sea-level rise, I propose a novel dimensionless metric called the Washover Ratio which compares cross-shore (overwash) and alongshore transport. Using this ratio, I find that decreases in overwash flux within the narrow middle section—possibly representing the effects of development—lead to a diminished response to sea-level rise across the entire barrier, and therefore a more vulnerable barrier overall. Further investigation of the balance between overwash and alongshore sediment transport allows for an evaluation of barrier island stability to overwash-induced breaching, which is applied to barriers in the Gulf of Mexico. Beyond Earth, Titan, Saturn’s largest moon, is home to the only other active coastlines in our solar system. However, data is sparse for this icy moon. I investigate the signatures of coastal processes found in the planform shape of its coasts using a combination of landscape evolution models and measurements of shoreline shape. Results show that the coastlines of Titan’s seas are consistent with those of both modelled and Earth lakes with flooded river valleys that have been subsequently eroded by waves, particularly when waves saturate (no longer grow in height) at scales up to 10s of km.
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ArticleThe effects of storms and a transient sandy veneer on the interannual planform evolution of a low-relief coastal cliff and shore platform at Sargent Beach, Texas, USA(European Geosciences Union, 2021-09-08) Palermo, Rose V. ; Piliouras, Anastasia ; Swanson, Travis E. ; Ashton, Andrew D. ; Mohrig, DavidCoastal cliff erosion is alongshore-variable and episodic, with retreat rates that depend upon sediment as either tools of abrasion or protective cover. However, the feedbacks between coastal cliff planform morphology, retreat rate, and sediment cover are poorly quantified. This study investigates Sargent Beach, Texas, USA, at the annual to interannual scale to explore (1) the relationship between temporal and spatial variability in cliff retreat rate, roughness, and sinuosity and (2) the response of retreat rate and roughness to changes in sand and shell hash cover of the underlying mud substrate as well as the impact of major storms using field measurements of sediment cover, erosion, and aerial images to measure shore platform morphology and retreat. A storm event in 2009 increased the planform roughness and sinuosity of the coastal cliff at Sargent Beach. Following the storm, aerial-image-derived shorelines with annual resolution show a decrease in average alongshore erosion rates from 12 to 4 m yr−1, coincident with a decrease in shoreline roughness and sinuosity (smoothing). Like the previous storm, a storm event in 2017 increased the planform roughness and sinuosity of the cliff. Over shorter timescales, monthly retreat of the sea cliff occurred only when the platform was sparsely covered with sediment cover on the shore platform, indicating that the tools and cover effects can significantly affect short-term erosion rates. The timescale to return to a smooth shoreline following a storm or roughening event, given a steady-state erosion rate, is approximately 24 years, with the long-term rate suggesting a maximum of ∼107 years until Sargent Beach breaches, compromising the Gulf Intracoastal Waterway (GIWW) under current conditions and assuming no future storms or intervention. The observed retreat rate varies, both spatially and temporally, with cliff face morphology, demonstrating the importance of multi-scale measurements and analysis for interpretation of coastal processes and patterns of cliff retreat.
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ArticleLabeling poststorm coastal imagery for machine learning: measurement of interrater agreement(American Geophysical Union, 2021-09-03) Goldstein, Evan B. ; Buscombe, Daniel ; Lazarus, Eli ; Mohanty, Somya D. ; Rafique, Shah Nafis ; Anarde, Katherine A. ; Ashton, Andrew D. ; Beuzen, Tomas ; Castagno, Katherine ; Cohn, Nicholas ; Conlin, Matthew P. ; Ellenson, Ashley ; Gillen, Megan N. ; Hovenga, Paige A. ; Over, Jin-Si ; Palermo, Rose V. ; Ratliff, Katherine M. ; Reeves, Ian R. B. ; Sanborn, Lily H. ; Straub, Jessamin A. ; Taylor, Luke A. ; Wallace, Elizabeth J. ; Warrick, Jonathan ; Wernette, Phillipe ; Williams, Hannah E.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.
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ArticleReconstructing river flows remotely on Earth, Titan, and Mars(National Academy of Sciences, 2023-07-10) Birch, Samuel P. D. ; Parker, Gary ; Corlies, Paul ; Soderblom, Jason M. ; Miller, Julia W. ; Palermo, Rose V. ; Lora, Juan M. ; Ashton, Andrew D. ; Hayes, Alexander G. ; Perron, J. TaylorAlluvial rivers are conveyor belts of fluid and sediment that provide a record of upstream climate and erosion on Earth, Titan, and Mars. However, many of Earth’s rivers remain unsurveyed, Titan’s rivers are not well resolved by current spacecraft data, and Mars’ rivers are no longer active, hindering reconstructions of planetary surface conditions. To overcome these problems, we use dimensionless hydraulic geometry relations—scaling laws that relate river channel dimensions to flow and sediment transport rates—to calculate in-channel conditions using only remote sensing measurements of channel width and slope. On Earth, this offers a way to predict flow and sediment flux in rivers that lack field measurements and shows that the distinct dynamics of bedload-dominated, suspended load-dominated, and bedrock rivers give rise to distinct channel characteristics. On Mars, this approach not only predicts grain sizes at Gale Crater and Jezero Crater that overlap with those measured by the Curiosity and Perseverance rovers, it enables reconstructions of past flow conditions that are consistent with proposed long-lived hydrologic activity at both craters. On Titan, our predicted sediment fluxes to the coast of Ontario Lacus could build the lake’s river delta in as little as ~1,000 y, and our scaling relationships suggest that Titan’s rivers may be wider, slope more gently, and transport sediment at lower flows than rivers on Earth or Mars. Our approach provides a template for predicting channel properties remotely for alluvial rivers across Earth, along with interpreting spacecraft observations of rivers on Titan and Mars.