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Rose V. Palermo, Ph.D.

Rose Palermo is a coastal geomorphologist who studies the coupled natural-human coastal system. Specifically, she focuses on 1) barrier island evolution under changes in sea-level and wave climate, 2) how these geomorphic changes influence coastal management decisions, such as beach nourishment or artificial dune building, and 3) how the management actions in turn influence barrier morphodynamics.

My research interests broadly include coastal evolution and morphodynamics, long-term island stability, and coastal community resilience. At the USGS, I develop and use reduced complexity models to study long-term barrier island evolution and the feedbacks between coastal morphodynamics, coastal management decisions, and external forcings.

Professional Experience

  • Research Geologist, U.S. Geological Survey St. Petersburg Coastal and Marine Science Center, 2022 - present

Education and Certifications

  • Ph.D. Marine Geology, Massachusetts Institute of Technology and Woods Hole Oceanographic Institution

  • B.S. Geology, B.A. Plan II Honors, University of Texas at Austin

Science and Products

The evolution of natural and developed barriers under accelerating sea levels

Communities residing on barrier islands depend upon the ability of barriers to withstand forcings such as waves, sea-level rise, and storms, particularly under stresses from climate change. Using a barrier island evolution model, we compare barrier response to linear versus accelerating sea-level rise. Results suggest that barriers are more likely to drown under accelerating rather than linear sea
Authors
Rose Elizabeth Palermo, Andrew D. Ashton, Di Jin, Porter Hoagland, Jorge Lorenzo-Trueba
By
St. Petersburg Coastal and Marine Science Center

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

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 proc
Authors
Evan B. Goldstein, Daniel D. Buscombe, Eli D. Lazarus, Somya Mohanty, Shah N. Rafique, K A Anarde, Andrew D Ashton, Tomas Beuzen, Katherine A. Castagno, Nicholas Cohn, Matthew P. Conlin, Ashley Ellenson, Megan Gillen, Paige A. Hovenga, Jin-Si R. Over, Rose V. Palermo, Katherine Ratlif, Ian R Reeves, Lily H. Sanborn, Jessamin A. Straub, Luke A. Taylor, Elizabeth J. Wallace, Jonathan Warrick, Phillipe Alan Wernette, Hannah E Williams
By
Ecosystems Mission Area, Natural Hazards Mission Area, Coastal and Marine Hazards and Resources Program, Pacific Coastal and Marine Science Center, Southwest Biological Science Center, Woods Hole Coastal and Marine Science Center, Hurricane Florence, Hurricane Isaias, Hurricane Michael, Hurricanes
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Sandy barrier island with dunes near a sandy beach on one side, low vegetation in the middle, and a bay of water on the other.

Modeling Barrier Island Evolution, Shoreface Morphology, and Overwash

Barrier island field observations provide information about past and current environmental conditions and changes over time; however, they can’t tell us about the future. Models can predict possible future behaviors but are only as good as their input data. By integrating both observations and models, we can extend observations and arrive at more realistic predictions of barrier island behavior...
By
Natural Hazards Mission Area, Coastal and Marine Hazards and Resources Program, St. Petersburg Coastal and Marine Science Center
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Modeling Barrier Island Evolution, Shoreface Morphology, and Overwash

Barrier island field observations provide information about past and current environmental conditions and changes over time; however, they can’t tell us about the future. Models can predict possible future behaviors but are only as good as their input data. By integrating both observations and models, we can extend observations and arrive at more realistic predictions of barrier island behavior...
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Photo of Fire Island showing back barrier, dunes, beach, and nearshore environments

Coastal Sediment Availability and Flux (CSAF)

Sediments are the foundation of coastal systems, including barrier islands. Their behavior is driven by not only sediment availability, but also sediment exchanges between barrier island environments. We collect geophysical, remote sensing, and sediment data to estimate these parameters, which are integrated with models to improve prediction of coastal response to extreme storms and sea-level rise...
By
Natural Hazards Mission Area, Coastal and Marine Hazards and Resources Program, St. Petersburg Coastal and Marine Science Center, Gulf of Mexico
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Coastal Sediment Availability and Flux (CSAF)

Sediments are the foundation of coastal systems, including barrier islands. Their behavior is driven by not only sediment availability, but also sediment exchanges between barrier island environments. We collect geophysical, remote sensing, and sediment data to estimate these parameters, which are integrated with models to improve prediction of coastal response to extreme storms and sea-level rise...
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USGS Mendenhall Postdoctoral Researcher Rose Palermo receives prestigious award from Woods Hole Oceanographic Institution

Dr. Rose Palermo was selected to receive the George “Gera” Pavlovich Panteleyev Award from the Woods Hole Oceanographic Institution (WHOI). This award...

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