The relative contribution of waves, tides, and nontidal residuals to extreme total water levels on U.S. West Coast sandy beaches

Serafin, Katherine A.; Ruggiero, Peter; Stockdon, Hilary F.

doi:10.1002/2016gl071020

Cited by 131 publications

(104 citation statements)

References 51 publications

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“…The direct simulation of extreme compound TWL events also allows for a greater investigation of the respective constitutive components. Similar to the findings of Serafin et al (), Figure demonstrates how annual maxima can occur across a broad range of values within each TWL component. Identifying the 1% recurrence events (red dots in Figure , defined as the fifth largest event in each simulation) reveals the degree to which this variability is retained in extreme events.…”

Section: Climate Emulator Twl Outputsupporting

confidence: 86%

“…While most of the 1% recurrence events occur when wave heights are greater than 5 m and wave periods longer than 15 s, there is an infinite combination of univariate drivers that can combine to generate compound extreme events—even for a simple TWL proxy such as equation . Such different TWL components can lead to different impacts on the open beach (Cohn et al, ; Serafin et al, ; Serafin et al, ) and different flood extents in the backshore (Bilskie & Hagen, ). The presented framework can be a tool used to make informed decisions for the input conditions to dynamical numerical modeling studies assessing flood extents and hazard zones contingent on future extremes (Barnard et al, ; Erikson et al, ).…”

Section: Climate Emulator Twl Outputmentioning

confidence: 99%

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Time‐Varying Emulator for Short and Long‐Term Analysis of Coastal Flood Hazard Potential

et al. 2019

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Rising seas coupled with ever increasing coastal populations present the potential for significant social and economic loss in the 21st century. Relatively short records of the full multidimensional space contributing to total water level coastal flooding events (astronomic tides, sea level anomalies, storm surges, wave run-up, etc.) result in historical observations of only a small fraction of the possible range of conditions that could produce severe flooding. The Time-varying Emulator for Short-and Long-Term analysis of coastal flood hazard potential is presented here as a methodology capable of producing new iterations of the sea-state parameters associated with the present-day Pacific Ocean climate to simulate many synthetic extreme compound events. The emulator utilizes weather typing of fundamental climate drivers (sea surface temperatures, sea level pressures, etc.) to reduce complexity and produces new daily synoptic weather chronologies with an auto-regressive logistic model accounting for conditional dependencies on the El Niño Southern Oscillation, the Madden-Julian Oscillation, seasonality, and the prior two days of weather progression. Joint probabilities of sea-state parameters unique to simulated weather patterns are used to create new time series of the hypothetical components contributing to synthetic total water levels (swells from multiple directions coupled with water levels due to wind setup, temperature anomalies, and tides). The Time-varying Emulator for Short-and Long-Term analysis of coastal flood hazard potential reveals the importance of considering the multivariate nature of extreme coastal flooding, while progressing the ability to incorporate large-scale climate variability into site specific studies assessing hazards within the context of predicted climate change in the 21st century.Plain Language Summary Predicting extreme coastal flooding is a present-day societal need and will only become more relevant as mean water levels increase due to sea level rise. However, the number of processes contributing to such events is too high for relatively short observational records to have measured all of the constructive combinations of waves, surge, wind, and sea level anomalies. We present a framework designed to create hypothetical combinations of relevant flood hazard potential processes by simulating the climate and weather patterns that drive coastal flooding. Including large-scale oceanic and atmospheric patterns as the drivers of coastal hazards reveals the climate a coastal community is most vulnerable to, which will be increasingly more important to understand as the climate changes during the 21st century.

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Section: Climate Emulator Twl Outputsupporting

confidence: 86%

Section: Climate Emulator Twl Outputmentioning

confidence: 99%

Time‐Varying Emulator for Short and Long‐Term Analysis of Coastal Flood Hazard Potential

et al. 2019

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“…Wave run-up (and therefore swash excursion) is a key component used to evaluate inundation hazards and vulnerability to storm impacts (e.g. Bosom and Jiménez, 2011;Stockdon et al, 2007;Serafin et al, 2017). Stockdon et al (2007) found that the wave action counted for about 48 % of the maximum total water level during two hurricanes along the USA coast.…”

Section: Introductionmentioning

confidence: 99%

The use of genetic programming to develop a predictor of swash excursion on sandy beaches

Passarella

Goldstein

Muro

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. We use genetic programming (GP), a type of machine learning (ML) approach, to predict the total and infragravity swash excursion using previously published data sets that have been used extensively in swash prediction studies. Three previously published works with a range of new conditions are added to this data set to extend the range of measured swash conditions. Using this newly compiled data set we demonstrate that a ML approach can reduce the prediction errors compared to well-established parameterizations and therefore it may improve coastal hazards assessment (e.g. coastal inundation). Predictors obtained using GP can also be physically sound and replicate the functionality and dependencies of previous published formulas. Overall, we show that ML techniques are capable of both improving predictability (compared to classical regression approaches) and providing physical insight into coastal processes.

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“…For example, we expect uncertainties in the water surface elevation imposed at the downstream boundary of the hydraulic model domain to strongly influence extreme water levels in the downstream reach. Wave contributions, such as wave setup and wave propagation, are also not directly represented by the BN but can strongly influence water levels [88][89][90]. While this study can be improved by using complementary data at these stations, this typically requires dedicated and extensive studies to properly capture complex coastal and hydrological processes [64,91,92] and is left for future studies.…”

Section: Discussionmentioning

confidence: 99%

A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale: An Application to the Houston Ship Channel, Texas

Couasnon¹,

Sebastian

Morales-Nápoles

2018

Preprint

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Abstract:Traditional flood hazard analyses often rely on univariate probability distributions; however, in many coastal catchments, flooding is the result of complex hydrodynamic interactions between multiple drivers. For example, synoptic meteorological conditions can produce considerable rainfall-runoff, while also generating wind-driven elevated sea-levels. When these drivers interact in space and time, they can exacerbate flood impacts, a phenomenon known as compound flooding. In this paper, we build a Bayesian Network based on Gaussian copulas to generate the equivalent of 500 years of daily stochastic boundary conditions for a coastal watershed in Southeast Texas. In doing so, we overcome many of the limitations of conventional univariate approaches and are able to probabilistically represent compound floods caused by riverine and coastal interactions. We model the resulting water levels using a one-dimensional (1D) steady-state hydraulic model and find that flood stages in the catchment are strongly affected by backwater effects from tributary inflows and downstream water levels. By comparing our results against a bathtub modeling approach, we show that simplifying the multivariate dependence between flood drivers can lead to an underestimation of flood impacts, highlighting that accounting for multivariate dependence is critical for the accurate representation of flood risk in coastal catchments prone to compound events.

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The relative contribution of waves, tides, and nontidal residuals to extreme total water levels on U.S. West Coast sandy beaches

Cited by 131 publications

References 51 publications

Time‐Varying Emulator for Short and Long‐Term Analysis of Coastal Flood Hazard Potential

Time‐Varying Emulator for Short and Long‐Term Analysis of Coastal Flood Hazard Potential

The use of genetic programming to develop a predictor of swash excursion on sandy beaches

A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale: An Application to the Houston Ship Channel, Texas

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