Large runup controls on a gently sloping dissipative beach

García‐Medina, Gabriel; Özkan-Haller, H. Tuba; Holman, Robert A.; Ruggiero, Peter

doi:10.1002/2017jc012862

Cited by 25 publications

(30 citation statements)

References 39 publications

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“…Associated with the highest slopes (Figure 3), they resulted in the largest runup events measured in our data set. Using a numerical model base on the Reynold‐averaged Navier‐Stokes equations, Garcia‐Medina et al () showed that the largest runup events are associated with bore merging (bore‐bore capture) on gently sloping beaches even if they also showed that bore‐bore capture is found to be a necessary but not sufficient condition for large runup generation. In particular, they also showed that the phase difference between incident and infragravity waves is also a key factor, explaining up to 30% of the variability in the largest runup.…”

Section: Discussionmentioning

confidence: 99%

Field Observations of Alongshore Runup Variability Under Dissipative Conditions in the Presence of a Shoreline Sandwave

et al. 2018

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Video measurements of runup were collected at low tide along several profiles covering an alongshore distance of 500 m. The morphology displayed a complex shape with a shoreline sandwave in the lower beach face of about 250 m long mirrored in the inner sandbar. Wave conditions were stationary and moderate (offshore height of 2 m and peak period of nearly 13 s) but yet dissipative. Runup energy was dominated by infragravity frequencies. Alongshore variations in runup (by a factor up to 3) observed both in the incident and infragravity bands were much higher than reported previously (e.g., Guedes et al., 2012Guedes et al., , https://doi.org/10.1016Guedes et al., /j.csr.2012Ruggiero et al., 2004, https://doi.org/10.1029/2003JC002160) while the alongshore variations in other environmental parameters (e.g., foreshore beach slope) appear to be much lower. Our data suggest that the beach morphology in the inner surf zone plays a crucial role by inducing rapid and significant modification in the incident wave pattern and the alongshore coherence length scales were consistent with the typical alongshore length scale of the morphology.Plain Language Summary The topic of this work is to present new field data focusing an alongshore variation of the runup. The runup is the time-varying position of the water's edge on the foreshore of the beach. This parameter is crucial in better forecasting coastal hazards (e.g., coastal flooding and erosion) and coastal structure defense design.

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Section: Discussionmentioning

confidence: 99%

Field Observations of Alongshore Runup Variability Under Dissipative Conditions in the Presence of a Shoreline Sandwave

et al. 2018

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“…In the bottom panel, lidar data show two separate bores merging as they approach the shoreline (blue to red from x = 95 m to x = 70 m, Figure 10b). Bore-bore capture has been shown to play a role in controlling wave runup patterns and other surf-zone processes [49]. The long duration of the dataset provides an opportunity to analyze inner-surf wave shapes in a range of wave conditions and over a range of surf-zone and beach morphologies.…”

Section: Short (Wave-by-wave) Time Scalesmentioning

confidence: 99%

Continuous Coastal Monitoring with an Automated Terrestrial Lidar Scanner

O’Dea

Brodie

Hartzell

2019

JMSE

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This paper details the collection, geo-referencing, and data processing algorithms for a fully-automated, permanently deployed terrestrial lidar system for coastal monitoring. The lidar is fixed on a 4-m structure located on a shore-backing dune in Duck, North Carolina. Each hour, the lidar collects a three-dimensional framescan of the nearshore region along with a 30-min two-dimensional linescan time series oriented directly offshore, with a linescan repetition rate of approximately 7 Hz. The data are geo-referenced each hour using a rigorous co-registration process that fits 11 fixed planes to a baseline scan to account for small platform movements, and the residual errors from the fit are used to assess the accuracy of the rectification. This process decreased the mean error (defined as the magnitude of the offset in three planes) over a two-year period by 24.41 cm relative to using a fixed rectification matrix. The automated data processing algorithm then filters and grids the data to generate a dry-beach digital elevation model (DEM) from the framescan along with hourly wave runup, hydrodynamic, and morphologic statistics from the linescan time series. The lidar has collected data semi-continuously since January 2015 (with gaps occurring while the lidar was malfunctioning or being serviced), resulting in an hourly data set spanning four years as of January 2019. Examples of data products and potential applications spanning a range of spatial and temporal scales relevant to coastal processes are discussed.

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“…In the majority of runup formulae, these input conditions are easily obtainable parameters such as significant wave height, peak wave period, and beach slope (Atkinson et al, 2017;Holman, 1986;Hunt, 1959;Ruggiero et al, 2001;Stockdon et al, 2006). However, wave dispersion (Guza and Feddersen, 2012), wave spectrum (Van Oorschot and d'Angremond, 1969), nearshore morphology (Cohn and Ruggiero, 2016), bore-bore interaction (García-Medina et al, 2017), tidal stage (Guedes et al, 2013), and a range of other possible processes have been shown to influence swash zone processes. Since typical wave runup parameterizations do not account for these more complex processes, there is often significant scatter in runup predictions when compared to observations (e.g., Atkinson et al, 2017;Stockdon et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Ensemble models from machine learning: an example of wave runup and coastal dune erosion

Beuzen

Goldstein

Splinter

2019

Nat. Hazards Earth Syst. Sci.

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Abstract. After decades of study and significant data collection of time-varying swash on sandy beaches, there is no single deterministic prediction scheme for wave runup that eliminates prediction error – even bespoke, locally tuned predictors present scatter when compared to observations. Scatter in runup prediction is meaningful and can be used to create probabilistic predictions of runup for a given wave climate and beach slope. This contribution demonstrates this using a data-driven Gaussian process predictor; a probabilistic machine-learning technique. The runup predictor is developed using 1 year of hourly wave runup data (8328 observations) collected by a fixed lidar at Narrabeen Beach, Sydney, Australia. The Gaussian process predictor accurately predicts hourly wave runup elevation when tested on unseen data with a root-mean-squared error of 0.18 m and bias of 0.02 m. The uncertainty estimates output from the probabilistic GP predictor are then used practically in a deterministic numerical model of coastal dune erosion, which relies on a parameterization of wave runup, to generate ensemble predictions. When applied to a dataset of dune erosion caused by a storm event that impacted Narrabeen Beach in 2011, the ensemble approach reproduced ∼85 % of the observed variability in dune erosion along the 3.5 km beach and provided clear uncertainty estimates around these predictions. This work demonstrates how data-driven methods can be used with traditional deterministic models to develop ensemble predictions that provide more information and greater forecasting skill when compared to a single model using a deterministic parameterization – an idea that could be applied more generally to other numerical models of geomorphic systems.

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Large runup controls on a gently sloping dissipative beach

Cited by 25 publications

References 39 publications

Field Observations of Alongshore Runup Variability Under Dissipative Conditions in the Presence of a Shoreline Sandwave

Field Observations of Alongshore Runup Variability Under Dissipative Conditions in the Presence of a Shoreline Sandwave

Continuous Coastal Monitoring with an Automated Terrestrial Lidar Scanner

Ensemble models from machine learning: an example of wave runup and coastal dune erosion

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