Efficient joint-probability methods for hurricane surge frequency analysis

Toro, Gabriel R.; Resio, Donald T.; Divoky, David; Niedoroda, Alan W.; Reed, C.W.

doi:10.1016/j.oceaneng.2009.09.004

Cited by 89 publications

(56 citation statements)

References 7 publications

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“…Whereas, this may be adequate in that region, larger tidal ranges in other areas introduce sampling errors of the type described by Irish et al [34]. Other assumptions, such as (1) a single generalized distribution for storm sizes exists along the entire US East Coast [83] and (2) an omnidirectional basis, which assumes storm heading and storm intensity are independent, provides an optimal estimate for the geographic sample size [80], are also somewhat speculative. In the case of the first of these assumptions, extratropical transitions increase substantially north of 30-35°latitude.…”

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

confidence: 99%

“…Hurricane Katrina provided direct evidence that there could be large discrepancies between these two types of information. As a result, since 2005, the USACE and FEMA adopted the JPM with some modifications to reduce the number of storms in the sample required for surge simulation, while maintaining good accuracy for planning purposes-sometimes termed the JPM-OS approach for Boptimal sampling^ [33,67,68,80]. Irish et al [34] showed the estimation power of the JPM significantly exceeds that of the HSM, and that the use of historical surge data alone for planning could introduce substantial local errors into design and planning decision-making.…”

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

confidence: 99%

“…Two primary methods have been developed to reduce the number of storms (i.e., samples) needed in surge simulations, while maintaining adequate sampling resolution and integration domain for representing the full JPM integral accurately: Surface Response Functions (SRF) ( [33,67,68]; others) and Bayesian Quadrature [63,80]. Both methods produce good results when compared to results using a larger number of samples; however, some assumptions initially developed for application in the central Gulf of Mexico are in need of additional review.…”

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

confidence: 99%

“…However, long-term storm surge hazard is also potentially influenced by both climate variability and long-term climate change. Traditional extreme-value statistic approaches assume that storm surge is a stationary process, governed by a homogeneous compound distribution of a GEV-Poisson or GPD-Poisson type [54,80]; however, in these studies and in all applications of the JPM-OS to date, the Poisson frequency is assumed to be independent from the intensity distribution. It is well-known that tropical cyclone frequency and intensity vary at the decadal and multi-decadal scale in response to cyclical, global phenomena such as El Niño-Southern Oscillation (ENSO) (e.g., [12,21]).…”

Section: Nonstationary Considerations-decadal Variability and Long-tementioning

confidence: 99%

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

confidence: 99%

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

confidence: 99%

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

confidence: 99%

Section: Nonstationary Considerations-decadal Variability and Long-tementioning

confidence: 99%

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Tropical Cyclone Storm Surge Risk

Resio

Irish

2015

Curr Clim Change Rep

Self Cite

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“…The Joint Probability Method with Optimal Sampling (JPM-OS) is one approach for estimating the probability distribution function of flooding in a region [9][10][11][12][13]. Storms are characterized using a set of parameters such as their minimum central pressure deficit, forward velocity, and location at landfall.…”

Section: Introductionmentioning

confidence: 99%

Bias and Efficiency Tradeoffs in the Selection of Storm Suites Used to Estimate Flood Risk

Fischbach

Johnson

Kuhn

2016

JMSE

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Modern joint probability methods for estimating storm surge or flood statistics are based on statistical aggregation of many hydrodynamic simulations that can be computationally expensive. Flood risk assessments that consider changing future conditions due to sea level rise or other drivers often require each storm to be run under a range of uncertain scenarios. Evaluating different flood risk mitigation measures, such as levees and floodwalls, in these future scenarios can further increase the computational cost. This study uses the Coastal Louisiana Risk Assessment model (CLARA) to examine tradeoffs between the accuracy of estimated flood depth exceedances and the number and type of storms used to produce the estimates. Inclusion of lower-intensity, higher-frequency storms significantly reduces bias relative to storm suites with a similar number of storms but only containing high-intensity, lower-frequency storms, even when estimating exceedances at very low-frequency return periods.

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Heightened hurricane surge risk in northwest Florida revealed from climatological‐hydrodynamic modeling and paleorecord reconstruction

et al. 2014

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Historical tropical cyclone (TC) and storm surge records are often too limited to quantify the risk to local populations. Paleohurricane sediment records uncover long‐term TC activity, but interpreting these records can be difficult and can introduce significant uncertainties. Here we compare and combine climatological‐hydrodynamic modeling (including a method to account for storm size uncertainty), historical observations, and paleohurricane records to investigate local surge risk, using Apalachee Bay in northwest Florida as an example. The modeling reveals relatively high risk, with 100 year, 500 year, and “worst case” surges estimated to be about 6.3 m, 8.3 m, and 11.3 m, respectively, at Bald Point (a paleorecord site) and about 7.4 m, 9.7 m, and 13.3 m, respectively, at St. Marks (the head of the Bay), supporting the inference from paleorecords that Apalachee Bay has frequently suffered severe inundation for thousands of years. Both the synthetic database and paleorecords contain a much higher frequency of extreme events than the historical record; the mean return period of surges greater than 5 m is about 40 years based on synthetic modeling and paleoreconstruction, whereas it is about 400 years based on historical storm analysis. Apalachee Bay surge risk is determined by storms of broad characteristics, varies spatially over the area, and is affected by coastally trapped Kelvin waves, all of which are important features to consider when accessing the risk and interpreting paleohurricane records. In particular, neglecting size uncertainty may induce great underestimation in surge risk, as the size distribution is positively skewed. While the most extreme surges were generated by the uppermost storm intensities, medium intensity storms (categories 1–3) can produce large to extreme surges, due to their larger inner core sizes. For Apalachee Bay, the storms that induced localized barrier breaching and limited sediment transport (overwash regime; surge between 3 and 5 m) are most likely to be category 2 or 3 storms, and the storms that inundated the entire barrier and deposited significantly more coarse materials (inundation regime; surge > 5 m) are most likely to be category 3 or 4 storms.

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Efficient joint-probability methods for hurricane surge frequency analysis

Cited by 89 publications

References 7 publications

Tropical Cyclone Storm Surge Risk

Tropical Cyclone Storm Surge Risk

Bias and Efficiency Tradeoffs in the Selection of Storm Suites Used to Estimate Flood Risk

Heightened hurricane surge risk in northwest Florida revealed from climatological‐hydrodynamic modeling and paleorecord reconstruction

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