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Fralin Life Sciences Institute

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https://hdl.handle.net/10919/25796
Note: In 2019, the Biocomplexity Institute became part of the Fralin Life Sciences Institute.

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Browsing Fralin Life Sciences Institute by Subject "0405 Oceanography"

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    Anticipating and adapting to the future impacts of climate change on the health, security and welfare of low elevation coastal zone (LECZ) communities in Southeastern USA
    Allen, Thomas; Behr, Joshua; Bukvic, Anamaria; Calder, Ryan S. D.; Caruson, Kiki; Connor, Charles; D'Elia, Christopher; Dismukes, David; Ersing, Robin; Franklin, Rima; Goldstein, Jesse; Goodall, Jonathon; Hemmerling, Scott; Irish, Jennifer L.; Lazarus, Steven; Loftis, Derek; Luther, Mark; McCallister, Leigh; McGlathery, Karen; Mitchell, Molly; Moore, William B.; Nichols, C. Reid; Nunez, Karinna; Reidenbach, Matthew; Shortridge, Julie; Weisberg, Robert; Weiss, Robert; Donelson Wright, Lynn; Xia, Meng; Xu, Kehui; Young, Donald; Zarillo, Gary; Zinnert, Julie C. (MDPI, 2021-10-29)
    Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.
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    Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning
    Lee, Jun-Whan; Irish, Jennifer L.; Bensi, Michelle T.; Marcy, Douglas C. (Elsevier, 2021-12-01)
    Rapid and accurate prediction of peak storm surges across an extensive coastal region is necessary to inform assessments used to design the systems that protect coastal communities’ life and property. Significant advances in high-fidelity, physics-based numerical models have been made in recent years, but use of these models for probabilistic forecasting and probabilistic hazard assessment is computationally intensive. Several surrogate modeling approaches based on existing databases of high-fidelity synthetic storm surge simulations have been recently suggested to reduce computational burden without substantial loss of accuracy. In these previous studies, however, the surrogate modeling approaches relied on a tropical cyclone condition at one moment (usually at or near landfall), which is not always most correlated with the peak storm surge. In this study, a new one-dimensional convolutional neural network model combined with principal component analysis and a k-means clustering (C1PKNet) is presented that can rapidly predict peak storm surge across an extensive coastal region from time-series of tropical cyclone conditions, namely the storm track. The C1PKNet model was trained and cross-validated for the Chesapeake Bay area of the United States using existing database of 1031 high-fidelity storm surge simulations, including both landfalling and bypassing storms. Moreover, the performance of the C1PKNet model was evaluated based on observations from three historical hurricanes (Hurricane Isabel in 2003, Hurricane Irene in 2011, and Hurricane Sandy in 2012). The results indicate that the C1PKNet model is computationally efficient and can predict peak storm surges from realistic tropical cyclone track time-series. We believe that this new surrogate model can enhance coastal resilience by providing rapid storm surge predictions.