Empirical Simulation Technique Based Storm Surge Frequency Analyses

Scheffner, Norman W.; Borgman, Leon E.; Mark, David J.

doi:10.1061/(asce)0733-950x(1996)122:2(93)

Cited by 47 publications

(16 citation statements)

References 2 publications

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“…If we assume that F ( ζ max ) is time invariant, this can be related to return period [ T R ( ζ max )] by where λ is the rate of hurricane landfall occurrence, specified as the Poisson frequency parameter, taken to be the average number of hurricanes per year making landfall within some prescribed distance along the coast. Surge frequency is then determined by fitting a parametric (e.g., Gumbel, Weibull, generalized extreme‐value (GEV)) or nonparametric (e.g., empirical simulation technique [ Borgman et al , 1992; Scheffner et al , 1996]) distribution to the historical surge data.…”

Section: Introductionmentioning

confidence: 99%

Statistical properties of hurricane surge along a coast

2011

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[1] The validity and accuracy of approaches used to determine hurricane surge hazard risk received much attention following the hurricane seasons in mid-to late-2000, which caused record surge-related damage along the Gulf of Mexico coastline. Following Hurricane Katrina in 2005, research showed that most extreme-value statistics approaches underestimated the risk associated with this surge event. In this paper, two of the most popular methods for determining hurricane surge extreme-value statistics are reviewed: the historical surge population approach and the joint probability method. Here, it is demonstrated that both limited historical record length and random along-coast variability in hurricane landfall location can introduce significant errors into surge estimates. For example, the historical surge population approach gives errors of 9% to 17% for return periods between 50 and 1000 years when a surge record of 100 years is considered. In contrast, it is shown that the joint probability method yields significantly more reliable surge estimates, with errors of 2% to 3% for return periods between 50 and 1000 years when a storm record of 100 years is considered. Finally, we show that both methods remain robust when decadal-scale climate variability in the storm rate of occurrence is considered, so long as the hurricane history is long enough to capture the full decadal cycle. When used in conjunction with continuous surge response information, it can be concluded that the joint probability method is a practical and reliable approach for determining extreme-value hurricane surge statistics.

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Section: Introductionmentioning

confidence: 99%

Statistical properties of hurricane surge along a coast

2011

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“…Historical initial condition are usually not sufficient to catch all possible realizations that can occur: here we applied the methodology employed by James and Mason (2005) and Scheffner et al (1996), using the historical database, to obtain a new set of synthetic initial condition representative of a sufficient large number of cyclone. Historical initial condition are usually not sufficient to catch all possible realizations that can occur: here we applied the methodology employed by James and Mason (2005) and Scheffner et al (1996), using the historical database, to obtain a new set of synthetic initial condition representative of a sufficient large number of cyclone.…”

Section: Synthetic Tropical Cyclone Databasementioning

confidence: 99%

Metocean Design Criteria in Tropical Cyclone Prone Areas: the Mozambique Channel

Terrile

Guida

2016

Day 2 Tue, May 03, 2016

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Tropical cyclones represent one of the most catastrophic examples of Natural Hazard. The effect of such events on the environment and human activities is a foregone conclusion.Metocean design criteria are usually defined using databases of historical tropical cyclone tracks characterized by wind and central pressure values. From these historical databases wind and wave maxima are subjected to extrapolation in logarithmic space. In order to correctly evaluate the required return period (up to 1 on 10,000 years) extrapolating the tail of the distribution from historical database provides highly uncertain estimates, therefore, a synthetic tropical cyclone database is needed.The present work describes step-by-step the setup of a synthetic tropical cyclone database valid for the Mozambique Channel area and finalized at the definition of metocean design criteria in area characterized by such events. The synthetic tropical cyclone database, statistically consistent, has been build-up using an autoregressive model for increments in track speed, direction and maxima cyclone winds. Moreover the used methodology involves application of a physically based wave model in order to catch a correct representation of the main processes: wave shoaling/refraction, trapped fetch effects, wave breaking, correct representation of offshore islands, etc.In order to reduce the computational time without reducing the benefit of the wave modeling phase, the proposed methodology considers a track pre-selection step allowing wave simulation only for tropical cyclone tracks characterized by the higher waves at point of interest. In such way the tail of the distribution, obtained from the synthetic database and characterized by higher return periods (i.e. 1,000, 5,000 and 10,000 years), is effectively simulated by the wave numerical model allowing a correct definition of the corresponding metocean design criteria. Furthermore, fitting modeling results with an extreme distribution function (e.g. Weibull, Pareto, etc.) allows definition of extreme values at lower return periods (i.e. 100, 200 and 500 years).

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“…For example, in the US, cost-benefit calculations by the US Army Corps of Engineers (USACE) for coastal programs and programs within the Federal Emergency Management Agency (FEMA) such as the National Flood Insurance Program (NFIP) consider an expected statistical behavior over a multi-year scale, as do most planning groups worldwide; so this will be the focus here. Prior to Hurricane Katrina in 2005, the flood hazard assessments transitioned from the Joint Probability Method (JPM) in the 1970s [60,25] to a Historical Storm Method (HSM) in the 1990s [69] based on a Points Over Threshold (POT) approach combined with Monte Carlo simulations to quantify potential uncertainty. The use of Monte Carlo to quantify uncertainty provides an unbiased estimate of errors, assuming the initial estimate provides an unbiased estimate of the parent population given all quantiles of the sample match the equivalent quantiles of the parent population.…”

Section: Estimating Tropical Cyclone Surge Hazard On a Multi-year Scalementioning

confidence: 99%

Tropical Cyclone Storm Surge Risk

Resio

Irish

2015

Curr Clim Change Rep

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Tropical cyclone storm surge represents a significant threat to communities around the world. These surge characteristics vary spatially and temporally over a range of scales; therefore, conceptual frameworks for understanding and mitigating them must be cast within a context of risk that covers the complete range of hazards, their consequences, and methods for mitigation. A review of primary overlapping time scales and associated spatial scales for tropical cyclone surge hazards covers two scales of particular interest: (1) synopticscale predictions used for warnings and evacuation decisions and (2) long-term estimation of hazards and related risks needed for coastal planning and decision-making. Factors that can affect these estimates, such as episodic variations in tropical cyclone characteristics and longer-term climate change and sea-level rise are then examined in the context of their potential impacts on hazards and risks related to tropical cyclone surges.

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Empirical Simulation Technique Based Storm Surge Frequency Analyses

Cited by 47 publications

References 2 publications

Statistical properties of hurricane surge along a coast

Statistical properties of hurricane surge along a coast

Metocean Design Criteria in Tropical Cyclone Prone Areas: the Mozambique Channel

Tropical Cyclone Storm Surge Risk

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