On the prediction of runup, setup and swash on beaches

Silva, Paula Gomes da; Coco, Giovanni; Garnier, Roland; Klein, Antonio H. F.

doi:10.1016/j.earscirev.2020.103148

Cited by 63 publications

(26 citation statements)

References 62 publications

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“…Significant wave heights and spectra are considered rather than using these to compute wave runup for two main reasons: firstly, run-up formula predominantly use deep water wave heights and hence calculating from the measured intertidal values is not achievable; secondly, there is a large variation in the results of these formulae and hence uncertainty in which formula is appropriate [85,86]. The beach profiles between the PT location and the dune area (Figure 6) further enhance the effect that differences in significant wave heights would have on dune erosion: the profile gradients for PT1 and PT2 (where significant wave heights are lower) are shallower and hence would have lesser predicted run-up values for many formulas [87] even if wave heights were similar.…”

Section: Discussionmentioning

confidence: 99%

Spatial Variation in Coastal Dune Evolution in a High Tidal Range Environment

et al. 2020

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Coastal dunes have global importance as ecological habitats, recreational areas, and vital natural coastal protection. Dunes evolve due to variations in the supply and removal of sediment via both wind and waves, and on stabilization through vegetation colonization and growth. One aspect of dune evolution that is poorly understood is the longshore variation in dune response to morphodynamic forcing, which can occur over small spatial scales. In this paper, a fixed wing unmanned aerial vehicle (UAV), is used to measure the longshore variation in evolution of a dune system in a megatidal environment. Dune sections to the east and west of the study site are prograding whereas the central portion is static or eroding. The measured variation in dune response is compared to mesoscale intertidal bar migration and short-term measurements of longshore variation in wave characteristics during two storms. Intertidal sand bar migration is measured using satellite imagery: crescentic intertidal bars are present in front of the accreting portion of the beach to the west and migrate onshore at a rate of 0.1–0.2 m/day; episodically the eastern end of the bar detaches from the main bar and migrates eastward to attach near the eastern end of the study area; bypassing the central eroding section. Statistically significant longshore variation in intertidal wave heights were measured using beachface mounted pressure transducers: the largest significant wave heights are found in front of the dune section suffering erosion. Spectral differences were noted with more narrow-banded spectra in this area but differences are not statistically significant. These observations demonstrate the importance of three-dimensionality in intertidal beach morphology on longshore variation in dune evolution; both through longshore variation in onshore sediment supply and through causing longshore variation in near-dune significant wave heights.

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Section: Discussionmentioning

confidence: 99%

Spatial Variation in Coastal Dune Evolution in a High Tidal Range Environment

et al. 2020

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“…It shows that the best reconstruction performance can be obtained when the coastal video is used for fine-tuning even in the same model architecture of Raindrop-aware GAN. By creating timestack image and visually assessment it, we can confirm that the performance of the proposed method is the best and it also has high applicability in studying nearshore wave dynamics with video remote sensing, in particular data preparation step, such as breaking wave height estimation from coastal video [31,32], video sensing of nearshore bathymetry evolution [33,34], nearshore wave transform with video imagery [35], shoreline response and resilience through video monitoring [36][37][38][39], wave run-up prediction [40,41], and nearshore wave tracking through coastal video [42,43].…”

Section: Discussionmentioning

confidence: 76%

Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement

et al. 2020

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We propose an unsupervised network with adversarial learning, the Raindrop-aware GAN, which enhances the quality of coastal video images contaminated by raindrops. Raindrop removal from coastal videos faces two main difficulties: converting the degraded image into a clean one by visually removing the raindrops, and restoring the background coastal wave information in the raindrop regions. The components of the proposed network—a generator and a discriminator for adversarial learning—are trained on unpaired images degraded by raindrops and clean images free from raindrops. By creating raindrop masks and background-restored images, the generator restores the background information in the raindrop regions alone, preserving the input as much as possible. The proposed network was trained and tested on an open-access dataset and directly collected dataset from the coastal area. It was then evaluated by three metrics: the peak signal-to-noise ratio, structural similarity, and a naturalness-quality evaluator. The indices of metrics are 8.2% (+2.012), 0.2% (+0.002), and 1.6% (−0.196) better than the state-of-the-art method, respectively. In the visual assessment of the enhanced video image quality, our method better restored the image patterns of steep wave crests and breaking than the other methods. In both quantitative and qualitative experiments, the proposed method more effectively removed the raindrops in coastal video and recovered the damaged background wave information than state-of-the-art methods.

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“…Similarly, Several global models (e.g. NOAA Wavewatch III, Deltares Global Flood Forecasting and Information System, Aviso Global Tide FES model) can provide deepwater wave, tide and surge information along coastlines where these are not measured locally, however the estimation of wave setup and runup is still likely to be dependent on calculations of sitespecific wave transformation in the nearshore (da Silva et al, 2020). Probabilistic methods such as ensembles (Beuzen et al, 2019a), Monte Carlo (Davidson et al, 2017) and Bayesian networks (Beuzen et al, 2018) are practical approaches that enable uncertainty in local morphology and storm hydrodynamics to be appropriately considered.…”

Section: Synthesis and Future Directionsmentioning

confidence: 99%

A Storm Hazard Matrix combining coastal flooding and beach erosion

Leaman¹,

Harley²,

Splinter³

et al. 2022

Preprint

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Coastal storms cause widespread damage to property, infrastructure, economic activity and the environment. Along open sandy coastlines, two of the primary coastal storm hazards are coastal flooding by elevated ocean water levels and beach erosion as the result of storm wave action. At continental margins characterized by a shallow, wide continental shelf, coastal storms are more commonly associated with amplified storm surge and the damaging impacts caused by flooding of low-lying land. In contrast, along margins where the continental shelf is narrow and deep, coastal storm impacts are more often characterized by extensive beach erosion, due to the typically lower magnitude of storm surge but a higher proportion of deepwater wave energy reaching the shoreline. A new Storm Hazard Matrix is presented that integrates these two distinct but inherently linked open coast hazards. The approach is based on the combination of two hazard scales. The first is a ‘coastal flooding hazard scale’ that follows an established framework in which hazards are predominately driven by the vertical increase in ocean water levels during storms. The second is a storm wave ‘beach erosion hazard scale’ where hazards are predominately driven by the horizontal recession of the sandy beach and dune. The resulting framework comprises a total of nine unique combinations of flooding/erosion storm hazard regimes, from which six unified, qualitative indicators of the total storm hazard level ranging from ‘Low’ to ‘Extreme’ are defined. Real-world application of the Storm Hazard Matrix is explored at contrasting coastlines for two major storm events, encompassing an extratropical cyclone that impacted the coastline of southeast Australia in June 2016, and Hurricane Ivan that impacted the Gulf Coast of the United States in 2004. The new approach is shown to identify and distinguish between the severity of localized coastal flooding and/or coastal erosion, as well as provide enhanced insight to the nature, magnitude and alongshore variation of coastal storm hazards along the impacted coastline. Within the context of disaster risk reduction, preparedness and operational early warning, implementation of the Storm Hazard Matrix has the potential to deliver robust evaluations of storm hazards spanning a wider variety of both wave-dominated and surge-dominated coasts.

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On the prediction of runup, setup and swash on beaches

Cited by 63 publications

References 62 publications

Spatial Variation in Coastal Dune Evolution in a High Tidal Range Environment

Spatial Variation in Coastal Dune Evolution in a High Tidal Range Environment

Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement

A Storm Hazard Matrix combining coastal flooding and beach erosion

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