Improvements in Hurricane Surge Response Functions: Incorporating the Effects of Forward Speed, Approach Angle, and Sea Level Rise

Udoh, Ikpoto E.; Irish, Jennifer L.

doi:10.1061/41170(400)71

Cited by 7 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These require a high level of user sophistication and computational expense, e.g., a single ADCIRC run used to populate the RRA backside database took over CPU 1500 h. As such, these cannot be easily executed in real-time by non-experts to support decision making. Motivated by this, low-cost dimensional surge response functions were developed for specific locations in Texas, but were restricted to hurricane surge only Udoh and Irish, 2011). Later the Disaster Response Intelligent System (DRIS) was introduced to offer rapid spatial modeling and fused data analysis tools to support decision making.…”

Section: Current Standard Of Practicementioning

confidence: 99%

CyberEye: Development of integrated cyber-infrastructure to support rapid hurricane risk assessment

Kijewski-Correa

Smith

Taflanidis

et al. 2014

Journal of Wind Engineering and Industrial Aerodynamics

View full text Add to dashboard Cite

Section: Current Standard Of Practicementioning

confidence: 99%

CyberEye: Development of integrated cyber-infrastructure to support rapid hurricane risk assessment

Kijewski-Correa

Smith

Taflanidis

et al. 2014

Journal of Wind Engineering and Industrial Aerodynamics

View full text Add to dashboard Cite

“…There exist numerous parametric or non-parametric climatic models replicating the landing of a hypothetical hurricane and the formation of flooding zones after the landing. We can adopt an analytical model developed by Hsu (2014) and (Udoh, 2012), which is shown below: Next, a storm surge height map can be overlapped with the digital elevation image, resulting in flooded areas. As for traffic condition simulation, a traditional 4-step travel demand model may be applied.…”

Section: ) Model Selection and Criticality Assessmentmentioning

confidence: 99%

Measuring Criticality in Urban Systems: A Quantitative Approach for Assessing Urban Vulnerability to Natural Hazards

Gomaa

2020

International Journal of Advanced Scientific Research and Innov

View full text Add to dashboard Cite

This paper introduces criticality analysis as a tool to identify and rank the most critical elements of a city system when impacted by natural hazards. This research discusses the limitations of current vulnerability assessment tools in providing details on how and where to distribute hazard mitigation resources. It proposes criticality analysis as a quantitative approach that goes beyond vulnerability assessment. The analysis of this paper provides a detailed description of the criticality analysis procedure and highlights the multifarious considerations that determine how criticality is defined. This research explains the steps involved in conducting criticality analysis. The analysis involves developing research questions and defining the unit of analysis, identifying vulnerable entities and hazard sources, establishing analytical scenarios, and selecting models to measure criticality. The objective is to detect critical components under disaster scenarios and quantify their significance to the entire system. Researchers need to identify the unit of analysis by using quantitative indicators, and the measurement scale must also be identified. At least one disaster scenario needs to be constructed, and the disaster scenarios typically couple with those of the units of analysis.

show abstract

“…To alleviate this problem, a lowcost dimensional surge response function was proposed (Irish et al 2009), but it only addressed the variation with respect to hurricane storm size, intensity, and track and was restricted to hurricane surge only and limited to specific locations of interest on the Texas coast. Udoh and Irish (2011) recently presented preliminary discussions for extending these surge response functions to address additional hurricane model parameters, namely, the forward speed and heading.…”

Section: Introductionmentioning

confidence: 99%

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

Taflanidis

Kennedy

Westerink

et al. 2013

J. Waterway, Port, Coastal, Ocean Eng.

View full text Add to dashboard Cite

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.

show abstract

Improvements in Hurricane Surge Response Functions: Incorporating the Effects of Forward Speed, Approach Angle, and Sea Level Rise

Cited by 7 publications

References 5 publications

CyberEye: Development of integrated cyber-infrastructure to support rapid hurricane risk assessment

CyberEye: Development of integrated cyber-infrastructure to support rapid hurricane risk assessment

Measuring Criticality in Urban Systems: A Quantitative Approach for Assessing Urban Vulnerability to Natural Hazards

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

Contact Info

Product

Resources

About