Rapid Assessment of Wave and Surge Risk during Landfalling Hurricanes: Probabilistic Approach

Abstract

A probabilistic framework is presented for evaluation of hurricane wave and surge risk with particular emphasis on real-time automated estimation for hurricanes approaching landfall. This framework has two fundamental components. The first is the development of a surrogate model for the rapid evaluation of hurricane waves, water levels, and run-up based on a small number of parameters describing each hurricane: hurricane landfall location and heading, central pressure, forward speed, and radius of maximum winds. This surrogate model is developed using a response surface methodology fed by information from hundreds of precomputed, high-resolution Simulating Waves Nearshore (SWAN) 1 Advanced Circulation Model for Oceanic, Coastal and Estuarine Waters (ADCIRC) and One-Dimensional Boussinesq Model (BOUSS-1D) runs. For a specific set of hurricane parameters (i.e., a specific landfalling hurricane), the surrogate model is able to evaluate the maximum wave height, water level, and run-up during the storm at a cost that is more than seven orders of magnitude less than the high-fidelity models and thus meets time constraints imposed by emergency managers and decision makers. The second component of this framework is a description of the uncertainty in the parameters used to characterize the hurricane through appropriate probability models, which then leads to quantification of hurricane risk in terms of a probabilistic integral. This integral is then efficiently computed using the already established surrogate model by analyzing thousands of different scenarios (based on the aforementioned probabilistic description). This allows the rapid computation of, for example, the storm surge that might be exceeded 10% of the time based on hurricane parameters at 48 h from landfall. Finally, by leveraging the computational simplicity and efficiency of the surrogate model, a simple stand-alone PC-based risk-assessment tool is developed that allows nonexpert end users to take advantage of the full potential of the framework. The proposed framework ultimately facilitates the development of a rapid assessment tool for real-time implementation but requires a considerable upfront computational cost to produce high-fidelity model results. As an illustrative example, implementation of hurricane risk estimation for the Island of Oahu in Hawaii is presented; results demonstrate the versatility of the proposed approach for delivering accurate tools for real-time hurricane risk estimation that have the ability to cross over technology adoption barriers.

Simulating storm surge waves for structural vulnerability estimation and flood hazard mapping

“…The model has previously been used to simulate surge inundation from tropical cyclones in the Hawaiian Islands of Oahu and Kauai (Kennedy et al 2012). Due to its computational efficiency, it is likewise used by Taflanidis et al (2013) to pre-simulate inundation for a large parameter space of hurricane characteristics in order to develop surrogate surge models for rapid surge prediction during an event.…”

Natural Hazard Probabilistic Risk Assessment Through Surrogate Modeling

“…In other surrogate modeling approaches, this variance is typically treated as constant over the entire input domain (Taflanidis et al 2013a). …”

“…interest; (2) the properties of the system itself, for example, related to parameters influencing restoring forces or to damping characteristics for structural systems (Liel et al 2009); and (3) the parameters related to the performance of the system, for example, thresholds defining fragility (i.e., acceptable performance) of system components (Gardoni et al 2002). Characterizing these uncertainties through a probabilistic description leads then to a versatile quantification of natural hazard risk (Taflanidis and Beck 2009;Taflanidis et al 2013a).…”

“…(4.1), as will be discussed next, the framework illustrated in Fig. 4.1 for risk quantification imposes no restriction on the complexity of the adopted numerical or probability modes and ultimately facilitates a generalized, versatile description of natural hazard risk and has been implemented successfully for studies considering a variety of hazards (wind, surge, waves, earthquakes) and structural systems (Taflanidis et al , 2013a.…”

Assessing the Vulnerability of Structures and Residential Communities to Storm Surge: An Analysis of Flood Impact during Hurricane Sandy

“…This surf zone model is capable of modeling wave runup (i.e., uprush of water on a slope following wave breaking), wave overtopping (i.e., the rate of runup over the crest of a beach), and inland flooding conditions. Among recent applications of BOUSS1D, Taflanidis et al (2013) used the model's efficiency to presimulate flooding for a large parameter space of hurricane characteristics and develop surrogate models for rapid flood prediction during an event.…”