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Browsing by Author "Lee, Jun-Whan"

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    Probabilistic tropical cyclone surge hazard under future sea-level rise scenarios: A case study in the Chesapeake Bay region, USA
    Kim, Kyutae; Lee, Jun-Whan; Irish, Jennifer L. (2023-05-21)
    Storm surge flooding caused by tropical cyclones is a devastating threat to coastal regions, and this threat is growing due to sea-level rise (SLR). Therefore, accurate and rapid projection of the storm surge hazard is critical for coastal communities. This study focuses on developing a new framework that can rapidly predict storm surges under SLR scenarios for any random synthetic storm of interest and assign a probability to its likelihood. The framework leverages the Joint Probability Method with Response Surfaces (JPM-RS) for probabilistic hazard characterization, a storm surge machine learning model, and a SLR model. The JPM probabilities are based on historical tropical cyclone track observations. The storm surge machine learning model was trained based on high-fidelity storm surge simulations provided by the U.S. Army Corps of Engineers (USACE). The SLR was considered by adding the product of the normalized nonlinearity, arising from surge-SLR interaction, and the sea-level change from 1992 to the target year, where nonlinearities are based on high-fidelity storm surge simulations and subsequent analysis by USACE. In this study, this framework was applied to the Chesapeake Bay region of the U.S. and used to estimate the SLR-adjusted probabilistic tropical cyclone flood hazard in two areas: One is an urban Virginia site, and the other is a rural Maryland site. This new framework has the potential to aid in reducing future coastal storm risks in coastal communities by providing robust and rapid hazard assessment that accounts for future sea-level rise.
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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.
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    Real-Time Prediction of Alongshore Near-Field Tsunami Runup Distribution From Heterogeneous Earthquake Slip Distribution
    Lee, Jun-Whan; Irish, Jennifer L.; Weiss, Robert (American Geophysical Union, 2023-01-05)
    Real-time tsunami prediction is necessary for tsunami forecasting. Although tsunami forecasting based on a precomputed tsunami simulation database is fast, it is difficult to respond to earthquakes that are not in the database. As the computation speed increases, various alternatives based on physics-based models have been proposed. However, physics-based models still require several minutes to simulate tsunamis and can have numerical stability issues that potentially make them unreliable for use in forecasting—particularly in the case of near-field tsunamis. This paper presents a data-driven model called the tsunami runup response function for finite faults (TRRF-FF) model that can predict alongshore near-field tsunami runup distribution from heterogeneous earthquake slip distribution in less than a second. Once the TRRF-FF model is trained and calibrated based on a discrete set of tsunami simulations, the TRRF-FF model can predict alongshore tsunami runup distribution from any combination of finite fault parameters. The TRRF-FF model treats the leading-order contribution and the residual part of the alongshore tsunami runup distribution separately. The interaction between finite faults is modeled based on the leading-order alongshore tsunami runup distribution. We validated the TRRF-FF modeling approach with more than 200 synthetic tsunami scenarios in eastern Japan. We further explored the performance of the TRRF-FF model by applying it to the 2011 Tohoku (Japan) tsunami event. The results show that the TRRF-FF model is more flexible, occupies much less storage space than a precomputed tsunami simulation database, and is more rapid and reliable than real-time physics-based numerical simulation.