Analysis of the coastal Mississippi storm surge hazard

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

doi:10.1016/j.oceaneng.2009.08.019

Cited by 87 publications

(66 citation statements)

References 9 publications

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“…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%

“…To minimize computational simulations in the central Gulf Coast region, tidal effects were added as a randomized linear superposition onto the modeled storm surges [63]. 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].…”

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

confidence: 99%

“…This means that areas more susceptible to significant wind surge generation, namely those areas that are low-lying and are exposed to intense storms, will exhibit a relatively larger increase in surge hazard. A temperature rise of 1 to 5°C over the next century (e.g., [74]) translates to an approximate increase in storm surge hazard of 8 to 40 %, for example, a 2.0-m surge (on the order of the 100-year event in New York City; [53]) increases by 0.2 to 0.8 m while a 5.0-m surge (on the order of the 100-year event in greater New Orleans, Louisiana; [63]) increases by 0.4 to 2.0 m.…”

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: 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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“…In the aftermath of Hurricane Katrina, extensive efforts were funded and major progress was made in this area Irish et al 2009;Niedoroda et al 2010;Toro et al 2010;Irish and Resio 2010). These new methods represented significant advances over the previous approaches which were based only on hindcasts of historical or hypothetical ''design storms'' to estimate storm surges in an area.…”

Section: Introductionmentioning

confidence: 99%

The effects of natural structure on estimated tropical cyclone surge extremes

2017

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The past 12 years have seen significant steps forward in the science and practice of coastal flood analysis. This paper aims to recount and critically assess these advances, while helping identify next steps for the field. This paper then focuses on a key problem, connecting the probabilistic characterization of flood hazards to their physical mechanisms. Our investigation into the effects of natural structure on the probabilities of storm surges shows that several different types of spatial-, temporal-, and process-related organizations affect key assumptions made in many of the methods used to estimate these probabilities. Following a brief introduction to general historical methods, we analyze the two joint probability methods used in most tropical cyclone hazard and risk studies today: the surface response function and Bayesian quadrature. A major difference between these two methods is that the response function creates continuous surfaces, which can be interpolated or extrapolated on a fine scale if necessary, and the Bayesian quadrature optimizes a set of probability masses, which cannot be directly interpolated or extrapolated. Several examples are given here showing significant impacts related to natural structure that should not be neglected in hazard and risk assessment for tropical cyclones including: (1) differences between omnidirectional sampling and directional-dependent sampling of storms in near coastal areas; (2) the impact of surge probability discontinuities on the treatment of epistemic uncertainty; (3) the ability to reduce aleatory uncertainty when sampling over larger spatial domains; and (4) the need to quantify trade-offs between aleatory and epistemic uncertainties in long-term stochastic sampling.

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“…ADCIRC (Advance CIRCulation) numerical model was applied along the GOM, Caribbean Sea and BoB (Blain et al 1994;Rao et al 2012). SLOSH and ADCIRC models along with parametric wind field models were extensively used for storm surge studies by Dietsche et al 2007;Cardone and Cox 2009;Melton et al 2009;Bunya et al 2010;Dietrich et al 2010 andNiedoroda et al 2010 in different oceanic basins. POM was implemented by Xia et al (2008) to simulate the storm induced surge, inundation, and coastal circulation at the Cape Fear River estuary and adjacent Long Bay.…”

Section: Introductionmentioning

confidence: 99%

Development of tropical cyclone wind field for simulation of storm surge/sea surface height using numerical ocean model

Das

Mohanty

Indu

2015

Model. Earth Syst. Environ.

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Coupled ocean-atmospheric phenomenon such as tropical cyclones (TC's) is governed by geophysical fluid dynamics. TC associated strong wind stress transfer momentum energy to the ocean surface that acts as the prime mechanism in modulating the sea surface and in generating the storm surge. A primitive equation, Princeton ocean model (POM) with free surface, sigma (terrain-following) coordinates and realistic bottom topography is configured in Bay of Bengal (BoB) to simulate the storm surge/sea surface height (SSH) and surface currents during a super cyclone TC05B 1999. TC wind fields are developed by adopting a suitable formulation based on partial conservation of angular momentum. Modeled TC wind fields are superimposed with QuikSCAT Satellite/National Centre for Environmental Prediction (QSCAT/NCEP) blended ocean surface winds to drive the three-dimensional ocean model. Model simulated storm surge and SSH are compared with limited available surge estimates/observations and multi-satellite observed AVISO (Archive, Validation and Interpolation of Satellite Oceanography) SSH, respectively to evaluate its performance.

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Analysis of the coastal Mississippi storm surge hazard

Cited by 87 publications

References 9 publications

Tropical Cyclone Storm Surge Risk

Tropical Cyclone Storm Surge Risk

The effects of natural structure on estimated tropical cyclone surge extremes

Development of tropical cyclone wind field for simulation of storm surge/sea surface height using numerical ocean model

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