Extreme Water Levels for Australian Beaches Using Empirical Equations for Shoreline Wave Setup

O’Grady, Julian; McInnes, Kathleen L.; Hemer, Mark; Hoeke, Ron; Stephenson, Alec; Colberg, Frank

doi:10.1029/2018jc014871

Cited by 36 publications

(47 citation statements)

References 45 publications

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“…Finally, the qn‐SSJPM does not consider the impact of wave processes on flood hazard and is therefore most suitable for wave‐sheltered harbors and embayments. During flood events, wave set‐up elevates the time‐averaged water level, and wave run‐up periodically further raises water level (O'Grady et al, 2019; Stockdon et al, 2006). These processes must be included for hazard analyses to be reliable at wave‐exposed coastlines; e.g., Lambert et al (2020) demonstrate that neglecting waves can lead to overestimating the time it will take for SLR to double the frequency of a given extreme water level.…”

Section: Resultsmentioning

confidence: 99%

Tidally Driven Interannual Variation in Extreme Sea Level Frequencies in the Gulf of Maine

et al. 2020

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Astronomical variations in tidal magnitude can strongly modulate the severity of coastal flooding on daily, monthly, and interannual timescales. Here we present a new quasi-nonstationary skew surge joint probability method (qn-SSJPM) that estimates interannual fluctuations in flood hazard caused by the 18.6-and quasi 4.4-year modulations of tides. We demonstrate that qn-SSJPM-derived storm tide frequency estimates are more precise and stable compared with the standard practice of fitting an extreme value distribution to measured storm tides, which is often biased by the largest few events within the observational period. Applying the qn-SSJPM in the Gulf of Maine, we find significant tidal forcing of winter storm season flood hazard by the 18.6-year nodal cycle, whereas 4.4-year modulations and a secular trend in tides are small compared to interannual variation and long-term trends in sea-level. The nodal cycle forces decadal oscillations in the 1% annual chance storm tide at an average rate of ±13.5 mm/year in Eastport, ME; ±4.0 mm/year in Portland, ME; and ±5.9 mm/year in Boston, MA. Currently (in 2020), nodal forcing is counteracting the sea-level rise-induced increase in flood hazard; however, in 2025, the nodal cycle will reach a minimum and then begin to accelerate flood hazard increase as it moves toward its maximum phase over the subsequent decade. Along the world's meso-to-macrotidal coastlines, it is therefore critical to consider both sea-level rise and tidal nonstationarity in planning for the transition to chronic flooding that will be driven by sea-level rise in many regions over the next century. Plain Language Summary Coastal management practices around flood risk often rely on estimates of the percent chance of a particular flood height occurring within a year. For example, U.S. flood insurance requires designating areas with a 100-year flood recurrence interval (the "100-year flood zone"). When storms hit regions with large tides, the height and timing of high tide often determine flood severity. Thus, the relationship between flood height and annual frequency can be altered by natural, daily-to-decadal cyclical variation in tide heights. Here we present a new method for calculating annually varying flood height-frequency relationships based on known tidal cycles. Applying the new method in the Gulf of Maine, we find an 18.6-year-long tidal cycle (the nodal cycle) has forced decadal variation in the 1% annual chance flood at a faster rate than the historical average rate of sea-level rise over the past century. Currently, nodal cycle forcing is counteracting the sea-level rise-induced increase in flood hazard; however, in 2025, the nodal cycle will reach a minimum in the Gulf and then begin to accelerate flood hazard as it moves toward its maximum over the subsequent decade. It is therefore critical to consider sea-level rise and tidal variation in medium-term flood hazard planning.

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Section: Resultsmentioning

confidence: 99%

Tidally Driven Interannual Variation in Extreme Sea Level Frequencies in the Gulf of Maine

et al. 2020

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“…A variety of empirical formulae exist to estimate wave setup and runup. Recent studies have extensively tested the most commonly used ones (e.g., Atkinson et al, 2017; Cohn & Ruggiero, 2016; Di Luccio et al, 2018; Díaz‐Sánchez et al, 2014; Ji et al, 2018; O'Grady et al, 2019; Passarella et al, 2018; Power et al, 2019; Pullen et al, 2007; Sénéchal et al, 2011; Stockdon et al, 2014; Vousdoukas et al, 2012). These studies have shown the significant skills of these formulae in different study cases and their ability to outperform process‐based models for R 2% (Stockdon et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…An exception is the study of Gainza et al (2018) in which an interannual time series of wave runup was produced using a metamodeling approach. A complicating factor in studying low‐frequency changes in wave setup and runup is also their sensitivity to poorly known time‐varying local morphology (e.g., O'Grady et al, 2019). Yet, several studies have shown interannual variability of deep water wave energy flux, nearshore profile, shoreline position, and beach width and volume (e.g., Harley et al, 2017; Karunarathna et al, 2016; Kuriyama et al, 2012; Norcross et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Contribution of Wave Setup to Projected Coastal Sea Level Changes

et al. 2020

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Along open coasts, wind waves are a key driver of coastal changes and can be major contributors to coastal hazards. Wind wave characteristics are projected to change in response to climate change, notably due to changes in atmospheric circulation patterns and the associated surface winds. Here, a first‐order estimate of projected 20‐yr mean wave setup changes, excluding extreme events and subannual variability, is provided for sandy beaches along most of the world's coastline over the middle and end of the 21st century. Calculations are based on an ensemble of wave model projections under the representative concentration pathways (RCP) 8.5 and on empirical formulations for wave setup. Projected wave setup changes are compared to other contributors currently accounted for in regional sea level projections to extend existing projections of 21st century coastal sea level changes. Projected wave setup changes exhibit a clear spatial heterogeneity and mostly average out at global scale. However, at regional or local scale, wave setup changes are a small yet nonnegligible contributor to total coastal sea level 20‐yr mean changes (which include global mean sea level rise, GMSLR) over the middle and end of the 21st century. Wave setup can be a substantial contributor to local departures of coastal sea level changes from GMSLR. Wave setup changes should therefore be included in projections of regional patterns of coastal sea level changes. The reported long‐term changes in wave setup also advocate for the inclusion of nonstationary wave contributions to projected regional patterns of coastal sea level changes, including for studies on extreme events.

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“…However, very few have quanti ed the physical drivers in a way that facilitates national or regional warning and monitoring for these hazards. This is likely due to signi cant gaps in driver and impact data (Greenslade et al 2020) and the complexity in calculating important local scale characteristics, such as nearshore wave transformations (O'Grady et al 2019a). This is further made complex by changes in the total water level, including GMSL and ESL, not occurring homogeneously around the world's coastlines due to variability in the natural system (Kirezci et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we have decided to use sea level observations and residual data for Portland for both study locations as it is likely more representative of the broader astronomical forcing and less likely to be heavily in uenced by local factors.The daily maximum residuals (the non-tidal component of the observed sea level) are very highly correlated between Portland, Stony Point and Lorne, the three ABSLMP sites in Victoria. Residuals are commonly-used metrics to identify storm surges, the physical phenomenon typically associated with high still water levels that lead to coastal impacts inVictoria (McInnes et al 2016;O'Grady et al 2019a). Pair-wise correlations (using Pearson's R) of 0.86 (Portland-Stony Point), 0.93 (Portland-Lorne) and 0.96 (Lorne-Stony Point) were calculated using data over the 2013-2019 (inclusive) period.…”

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confidence: 99%

Identifying oceanographic conditions conducive to coastal impacts on temperate open coastal beaches and the importance of empirical impact data

Leach

Hague

Kennedy

et al. 2021

Preprint

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Warnings issued by meteorological or oceanographic agencies are a common means of allowing people to prepare for likely impactful events. Quantifying the relationships between ocean conditions and coastal impacts is crucial for developing operational coastal hazard warnings. Existing studies have largely omitted empirical data, relying on modelling to estimate total water levels and impact potentials. It is well documented that site-specific conditions influence local morphodynamics and as such, detailed data related to the physical environment is a necessary component of these existing approaches. The capacity to collect this data is not always available and there is a need for the inclusion of physical impact events to properly quantify the effects of natural hazards and identify the conditions that they are likely to occur. We propose an alternative empirically-based framework for isolating oceanic conditions that are conducive to impact along open coasts, using two case studies from Victoria, southeast Australia: Port Fairy and Inverloch. Oceanic conditions were defined using data obtained from a WAVEWATCH III (WW3) model hindcast, assessed against newly-installed wave buoys, which evidenced variation in mean conditions between the two sites. We coupled impact-based data derived from citizen-science and social media to modelled and observational data, to identify the oceanic conditions that led to these impacts. We found heterogeneity in the response of the case study locations to deviations from the local mean wave characteristics and still water levels. This paper demonstrates a framework through which impact-based thresholds for erosion could be developed for management applications and early-warning systems.

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Extreme Water Levels for Australian Beaches Using Empirical Equations for Shoreline Wave Setup

Cited by 36 publications

References 45 publications

Tidally Driven Interannual Variation in Extreme Sea Level Frequencies in the Gulf of Maine

Tidally Driven Interannual Variation in Extreme Sea Level Frequencies in the Gulf of Maine

Contribution of Wave Setup to Projected Coastal Sea Level Changes

Identifying oceanographic conditions conducive to coastal impacts on temperate open coastal beaches and the importance of empirical impact data

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