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At least 73 records · Page 4Linked to original sources
USGS2018

Wave attenuation across a tidal marsh in San Francisco Bay

Wave attenuation is a central process in the mechanics of a healthy salt marsh. Understanding how wave attenuation varies with vegetation and hydrodynamic conditions informs models of other marsh processes that are a function of wave energy (e.g. sediment transport) and allows for the incorporation of marshes into coastal protection plans. Here, we examine the evolution of wave height across a tidal salt marsh in San Francisco Bay. Instruments were deployed along a cross-shore transect, starting on the mudflat and crossing through zones dominated by Spartina foliosa and Salicornia pacifica . This dataset is the first to quantify wave attenuation for these vegetation species, which are abundant in the intertidal zone of California estuaries. Measurements were collected in the summer and winter to assess seasonal variation in wave attenuation. Calculated drag coefficients of S. foliosa and S. pacifica were similar, indicating equal amounts of vegetation would lead to similar energy dissipation; however, S. pacifica has much greater biomass close to the bed (<20 cm) and retains biomass throughout the year, and therefore, it causes more total attenuation. S. foliosa dies back in the winter, and waves often grow across this section of the marsh. For both vegetation types, attenuation was greatest for low water depths, when the vegetation was emergent. For both seasons, attenuation rates across S. pacifica were the highest and were greater than published attenuation rates across similar ( Spartina alterniflora ) salt marshes for the comparable depths. These results can inform designs for marsh restorations and management plans in San Francisco Bay and other estuaries containing these species.

California↗
USGS2018

Identification of storm events and contiguous coastal sections for deterministic modeling of extreme coastal flood events in response to climate change

Deterministic dynamical modeling of future climate conditions and associated hazards, such as flooding, can be computationally-expensive if century-long time-series of waves, sea level variations, and overland flow patterns are simulated. To alleviate some of the computational costs, local impacts of individual coastal storms can be explored by first identifying particular events or scenarios of interest and dynamically modeling those events in detail. In this study, an efficient approach to selecting storm events for subsequent deterministic detailed modeling of coastal flooding is presented. The approach identifies locally relevant scenarios derived from regional datasets spanning long time-periods and covering large geographic areas. This is done by identifying storm events from global climate models using a robust, yet computationally simple approach for calculating total water level proxies at the shore, assuming a linear superposition of the important processes contributing to the overall total water level. Clustering of the total water level time-series is used to define coherent coastal cells where similar return period water level extrema occur in response to region-wide storms. Results show that the more severe but rare coastal flood events (e.g., the 100-year (yr) event) typically occur from the same storm across the region, but that a number of different storms are responsible for the less severe but more frequent local extreme water levels (e.g., the 1-yr event). This new ‘storm selection’ approach is applied to the Southern California Bight, a region of varying shoreline orientations that is subject to wave refraction across complex bathymetry, and shadowing, focusing, diffraction, and dissipation of wave energy by islands. Results indicate that wave runup dominates total water level extremes at this study site, highlighting the importance of downscaling global-scale models to nearshore waves when seeking accurate projections of local coastal hazards in response to climate change.

California↗
USGS2019

The influence of shelf bathymetry and beach topography on extreme total water levels: Linking large-scale changes of the wave climate to local coastal hazards

Total water levels (TWLs) at the coast are driven by a combination of deterministic (e.g., tides) and stochastic (e.g., waves, storm surge, and sea level anomalies) processes. The contribution of each process to TWLs varies depending on regional differences in climate and framework geology, as well as local-scale variations in beach morphology, coastal orientation, and shelf bathymetry. Large-scale changes to the climate altering the frequency, direction, and intensity of storms, may therefore propagate to the nearshore differently, amplifying or suppressing local coastal hazards and changing the exposure of coastal communities to extreme TWLs. This study investigates the hydrodynamic and geomorphologic factors controlling local TWLs along high-energy United States coastlines where wave-influences dominate TWLs. Three study sites in the states of Washington, Oregon, and California are chosen to explore how regional and local differences in beach topography and wave transformation over shelf bathymetry drives variations in the magnitude and impacts of extreme TWLs. Results indicate that TWLs are most influenced by wave transformation processes in locations with steep beach slopes (which drive larger relative contributions of wave runup) and complex offshore bathymetry, while beach topography influences the severity of coastal impacts. Once the relative morphologic controls on TWLs are better understood, hypothetical future climate scenarios are explored to assess how changes to the average deepwater wave climate (height, period, and direction) may alter local TWLs when compared to estimates of likely sea level rise and future coastal management strategies. Changes to the wave climate are found to be as detrimental to the coastline as sea level rise in some locations, where small variations of the TWL drive large, nonlinear changes in hours of impact to the backshore beach. Overall, this study develops an approach for quantifying the range of hydrodynamic and morphologic controls on the magnitude of TWLs which will ultimately better prepare coastal communities for uncertain changes to the global climate.

California, Oregon, Washington↗
USGS2019

Optical wave gauging using deep neural networks

We develop a remote wave gauging technique to estimate wave height and period from imagery of waves in the surf zone. In this proof-of-concept study, we apply the same framework to three datasets: the first, a set of close-range monochrome infrared (IR) images of individual nearshore waves at Duck, NC, USA; the second, a set of visible (i.e. RGB) band orthomosaics of a larger nearshore area near Santa Cruz, CA, USA; and the third, a set of oblique (unrectified) images from the same site. The network is trained using coincident images and in situ wave measurements. The optical wave gauge (OWG) consists of a deep convolutional neural network (CNN) to extract features from imagery — called a ‘base model’, with additional layers to distill the feature information into lower dimensional spaces, and a final layer of dense neurons to predict continuously varying quantities. Four base models are compared. The OWG is trained for both individual wave height and period, and statistical quantities like significant wave height and peak wave period. The best performing OWG on the IR dataset achieved RMS errors of 0.14 m and 0.41 s for height and period, respectively, capturing up to 98% of the variance in these quantities. The best performing OWG on the visible band rectified dataset achieved RMS errors of 0.08 m and 0.79 s, respectively, for height and period. The same values for the oblique RGB imagery were 0.11 m and 0.81 s for height and period, respectively. Overall, wave height and period accuracy is sensitive to choice of base model; OWGs built upon MobilenetV2 tend to perform worst and those built on Inception-ResnetV2 have the smallest RMS error. The presence or otherwise of residual layers in the model makes little systematic difference to the final OWG accuracy. Smaller batch sizes used in model training tend to result in more accurate OWGs. An out-of-calibration validation, using images associated with wave heights or periods outside the range of values represented in the training data, showed that the ability for OWGs to predict the bottom 5% of low wave heights and the top 5% of high wave heights was reasonably good, but the same was not generally true of wave period. The same framework, not optimized for either dataset, predicts both quantities with high accuracy when trained on imagery, despite the differences in electromagnetic band, perspective, and scale. The OWG estimates wave properties from an image in less than 100 ms on a modestly sized CPU, allowing for the possibility of continuous real-time wave estimates.

California, North Carolina↗
USGS2019

Morphodynamic modelling of the wilderness breach, Fire Island, New York. Part I: Model set-up and validation

On October 29, 2012, storm surge and large waves produced by Hurricane 13 Sandy resulted in the formation of a breach in eastern Fire Island, NY. The goals of this study 14 are to gain a better understanding of the physical processes that govern breach behavior and 15 to assess whether process-based models can be used to forecast the evolution of future 16 breaches. The Wilderness Breach grew rapidly in size during the first winter following 17 formation. Growth of the breach was accompanied by the formation of a complex of flood 18 shoals inside Great South Bay, a primary channel that flowed through the eastern part of the 19 flood shoals, and an ebb shoal on the ocean side of the breach. From the summer of 2013 20 through late 2015, the breach continued to change and evolve, albeit at a much slower pace 21 than in the first year after formation. A hybrid combination of Delft3D and XBeach models is 22 used to hindcast the morphodynamic evolution of the Wilderness Breach over the first three 23 years after formation. The formation of the breach during Hurricane Sandy is not part of the 24 simulations. Model simulations are initiated with a post-storm topography in which the 25 breach is already present. The models are capable of hindcasting the main morphodynamic 26 changes of the Wilderness Breach. The spatial patterns, as well as the bulk statistics, such as 27 2 breach geometry and sediment volume changes, are reasonably 28 well reproduced by the model. 29 The model sheds light on previously unknown processes of breach evolution, especially 30 regarding sediment transport and flow regimes within the breach complex.

New York↗
USGS2020

The importance of explicitly modelling sea-swell waves for runup on reef-lined coasts

The importance of explicitly modelling sea-swell waves for runup was examined using a 2D XBeach short wave-averaged (surfbeat, “XB-SB”) and a wave-resolving (non-hydrostatic, “XB-NH”) model of Roi-Namur Island on Kwajalein Atoll in the Republic of Marshall Islands. Field observations on water levels, wave heights, and wave runup were used to drive and evaluate both models, which were subsequently used to determine the effect of sea-level rise and extreme wave conditions on wave runup and its components. Results show that specifically modelling the sea-swell component (using XB-NH) provides a better approximation of the observed runup than XB-SB (which only models the time-variation of the sea-swell wave height), despite good model performance of both models on reef flat water levels and wave heights. XB-SB has a bias of −0.108 – 0.057 m and scatter index of 0.083–0.639, whereas XB-NH has bias of −0.132 – 0.055 m and 0.122–0.490, respectively. However, both models under-predict runup peaks. The difference between XB-SB and XB-NH increases for more extreme wave events and higher sea levels, as XB-NH resolves individual waves and therefore captures SS-wave motions in runup. However, for even larger forcing conditions with offshore wave heights of 6 m, the island is flooded in both XB-SB and XB-NH computations, regardless the sea-swell wave energy contribution. In such cases XB-SB would be adequate to model flooding depths and extents on the island while requiring 4–5 times less computational effort.

Roi-Namur Island↗
USGS2020

Simulating wave runup on an intermediate–reflective beach using a wave-resolving and a wave-averaged version of XBeach

The prediction of wave runup, as well as its components, time-averaged setup and the time-varying swash, is a key element of coastal storm hazard assessments, as wave runup controls the transitions between morphodynamic response types such as dune erosion and overwash, and the potential for flooding by wave overtopping. While theoretically able to simulate the dominant low-frequency swash, previous studies using the infragravity-wave–resolving model XBeach (XBSB) have shown an underestimation of the observed swash variance and wave runup, which was in part related to the absence of incident-band swash motions in the model. Here, we use an incident-band wave-resolving, non-hydrostatic version of the XBeach model (XBNH) to simulate wave runup observed during the SandyDuck '97 experiment on an intermediate–reflective sandy beach. The results show that the XBNH model describes wave runup and the individual setup and swash components well. We subsequently examine differences in wave runup prediction between the XBSB and XBNH models and find that the XBNH model is a better predictor of wave runup than XBSB for this beach, which is due to better predictions of both the incident-band and infragravity-band swash. For a range of beach states from reflective to dissipative it is shown that incident-band swash is underestimated by XBSB relative to XBNH, in particular for reflective conditions. Infragravity-band swash is shown to be lower in XBSB than XBNH for most conditions, including dissipative conditions for which the mean difference is 16% of the deep water wave height. The difference in infragravity-band swash in XBNH relative to XBSB is shown to mainly be the result of processes occurring outside the swash zone, but approximately 15% of the difference is caused by explicitly resolving incident-band wave motions within the swash zone, such as swash-swash interactions, which inherently cannot be simulated by wave-averaged models.

Coastal Engineering↗
USGS2021

Drivers of extreme water levels in a large, urban, high-energy coastal estuary – A case study of the San Francisco Bay

Reliable and long-term hindcast data of water levels are essential in quantifying return period and values of extreme water levels. In order to inform design decisions on a local flood control district level, process-based numerical modeling has proven an essential tool to provide the needed temporal and spatial coverage for different extreme value analysis methods. To determine the importance of different physical processes to the extreme water levels we developed a process-based numerical model (Delft3D Flexible Mesh) and applied it to simulate a large, urban, high-energy coastal estuary (the San Francisco Bay). The unstructured grid with 1D/2DH model elements, allows for efficient model simulations and therefore it was possible to simulate over 70 years between 1950 and 2019. Results show significant skill in reproducing observations for the entire modeled time period with an average root-mean-square error of 8.0 cm. A process-based modeling approach allows for the explicit in- and exclusion of different physical processes to quantify their importance to the extremes. For the 100-year still water level (SWL), tide (70%) and non-tidal residual (NTR) (25%) explain the majority of the simulated high water levels in the Bay relative to Mean Higher High Water (MHHW). However, closer to the Delta, local fluvial inflow increases in importance. For longer return periods, the importance of tide decreases and the importance of remote NTRs and fluvial inflow increases.

California↗
USGS2023

Towards a unified drag coefficient formula for quantifying wave energy reduction by salt marshes

Coastal regions are susceptible to increasing flood risks amid climate change. Coastal wetlands play an important role in mitigating coastal hazards. Vegetation exerts a drag force to the flow and dampens storm surges and wind waves. The prediction of wave attenuation by vegetation typically relies on a pre-determined drag coefficient C D "> C D . Existing C D "> C D formulas are subject to vegetation biomechanical properties, especially the flexibility. Accounting for vegetation flexibility through the effective plant height (EPH), we propose and validate a species-independent relationship between C D "> C D and the Reynolds number R e "> Re based on three independent datasets that cover a wide range of hydrodynamic conditions and vegetation traits. The proposed C D &#x2212; R e "> C D − Re relationship, used together with EPH, allows for predicting wave attenuation in salt marshes with high accuracy. Furthermore, a total of 308,000 numerical experiments with diverse wave conditions are conducted using the proposed C D &#x2212; R e "> C D − Re relationship and EPH to quantify the wave attenuation capacity of two typical salt mash species: Elymus athericus (highly flexible) and Spartina alterniflora (relatively rigid). It is found that wave attenuation is controlled by wave height to water depth ratio and EPH to water depth ratio. When swaying in large waves in shallow to intermediate water depth, a 50-m-long Elymus athericus field may lose up to 30% capacity for wave attenuation. As wave height increases, highly flexible vegetation causes reduced wave attenuation, whereas relatively rigid vegetation induces increased wave attenuation. The leaf contribution to wave attenuation is highly dependent on the leaf rigidity. It is recommended that leaf properties, especially its Young’s modulus be collected in future field experiments.

Coastal Engineering↗
USGS2023

Monitoring and modeling dispersal of a submerged nearshore berm at the mouth of the Columbia River, USA

A submerged, low-relief nearshore berm was constructed in the Pacific Ocean near the mouth of the Columbia River, USA, using 216,000 m 3 of sediment dredged from the adjacent navigation channel. The material dredged from the navigation channel was placed on the northern flank of the ebb-tidal delta in water depths between 12 and 15 m and created a distinct feature that could be tracked over time. Field measurements and numerical modeling were used to evaluate the transport pathways, time scales, and physical processes responsible for dispersal of the berm and evaluate the suitability of the location for operational placement of dredged material to enhance the sediment supply to eroding beaches onshore of the placement site. Repeated multibeam bathymetric surveys characterized the initial berm morphology and dispersion of the berm between September 22, 2020, and March 10, 2021. During this time, the volume of sediment within the berm decreased by about 40%to 127,000 m 3 , the maximum height decreased by almost 60%, and the center of the deposit shifted onshore over 200 m. Observations of berm morphology were compared with predictions from a three-dimensional hydrodynamic and sediment transport model application to refine poorly constrained model input parameters including sediment transport coefficients, bed schematization, and grain size. The calibrated sediment transport model was used to predict the amount, timing, and direction of transport outside of the observed survey area. Model simulations predicted that tidal currents were weak in the vicinity of the berm and wave processes including enhanced bottom stresses and asymmetric bottom orbital velocities resulted in dominant onshore movement of sediment from the berm toward the coastline. Roughly 50% of the berm volume was predicted to disperse away from the initial placement site during the 169 day hindcast. Between 9 and 17% of the initial volume of the berm was predicted to accumulate along the shoreface of a shoreline reach experiencing chronic erosion directly onshore of the placement site. Scenarios exploring alternate placement locations suggested that the berm was relatively effective in enhancing the sediment supply along the eroding coastline north of the inlet. The transferable monitoring and modeling framework developed in this study can be used to inform implementation of strategic nearshore placements and regional sediment management in complex, high-energy coastal environments elsewhere.

Oregon, Washington↗
USGS2023

Remotely sensed short-crested breaking waves in a laboratory directional wave basin

Short-crested breaking waves that result from directionally spread wave conditions dissipate energy and generate turbulence within the surf zone, altering sediment transport processes, wave runup, and forces on structures. Additionally, vertical vorticity generated near crest ends during breaking, which depends on the gradient in wave height along a crest, may enhance nearshore dispersion of pollutants, nutrients, and larvae. Although directionally spread irregular wave fields are ubiquitous on ocean and large lake coastlines, the dependence of short-crested breaking wave characteristics (including the along-crest length and number of crest ends) on offshore wave conditions is not well established. To assess this relationship, laboratory experiments with alongshore-uniform barred bathymetry were performed in a large-scale directional wave basin. A three-dimensional scanning lidar, trinocular camera stereo processing methods, and in situ measurements were used to study short-crested wave field breaking characteristics in the laboratory, yielding a dataset with dense spatio-temporal coverage relative to prior laboratory or field measurements. Wave height estimates are similar for remotely sensed and in situ observations, except in the outer surf zone where plunging breaking occurred. Directional wave properties estimated with an array of in situ or remotely sensed sea-surface elevation estimates are similar and yield smaller directional spreads than single-point colocated pressure and velocity based in situ estimates when waves are less directionally spread. Using a breaking crest identification procedure combining visible imagery and stereo sea-surface elevation, we find that the average along-crest length of breaking waves decreases and the average number of crest ends increases with increasing directional spread. Relative to observations, a parameterized relationship between directional spread and crest characteristics based on theory for non-breaking, refracting waves generally over-estimates breaking crest lengths and is similar to or underestimates the total number of crest ends observed in the surf zone. The wave-field-dependent breaking-wave characteristics examined in the laboratory with remote sensing techniques can inform future investigations of depth-limited short-crested wave breaking and resulting surfzone eddy processes.

Coastal Engineering↗
USGS2023

Combining field observations and high-resolution numerical modeling to demonstrate the effect of coral reef roughness on turbulence and its implications for reef restoration design

Coral reefs are effective natural barriers that protect adjacent coastal communities from hazards such as erosion and storm-induced flooding. However, the degradation of coral reefs compromises their ability to protect against these hazards, making degraded reefs a target for restoration. There have been limited field and numerical modeling studies conducted to understand how an increase in coral reef roughness, as would occur due to restoration, can affect wave energy dissipation for a range of real-world wave and water level conditions. To address this knowledge gap, field measurements were collected over adjacent low-roughness and high-roughness reefs off Molokaʻi, Hawaiʻi, USA, subjected to the same oceanographic forcing. Those field data were then used to calibrate and validate OpenFOAM computational fluid dynamics models of the reef. These calibrated models were then used to explore energy dissipation for a range of wave conditions based on measurements from a suite of existing datasets and values from the literature. In general, wave dissipation scales with incident wave conditions, where greater dissipation occurred for shallow depths and shorter-period waves. This tendency for short-period waves to be more readily attenuated is supported by wave energy dissipation factors in the range of 0.1–5, which decline with increasing wave period. Near-bed turbulent kinetic energy dissipation also scales with incident wave conditions, where the greatest difference in dissipation between low and high relief cases occurs for short wave periods. Turbulence becomes less affected by bottom roughness as the wave period increases. Based on this study, wave attenuation and turbulent energy dissipation could be enhanced by 0.5–1 order of magnitude (45% per across-shore meter) if the seabed roughness at the field site were increased by 13%, an achievable goal in coral reef restoration.

Coastal Engineering↗
USGS2023

Evaluation of nearshore bathymetric inversion algorithms using camera observations and synthetic numerical input of surface waves during storms

Nearshore bathymetry is difficult to measure using survey methods when wave heights approach the breaking limit. Remote sensing using digital cameras offers a way to observe the surf zone and calculate water depths based on phase speed but comes with its challenges of potentially noisy data that can introduce error into estimates of frequency and wavenumber used in phase speed calculation. This study investigates the robustness of a new version of a bathymetric inversion algorithm (cBathy, version 2.0) in moderate to energetic wave conditions by comparing depth estimates from timeseries’ of pixel intensity with depth estimates from synthetic water level data. The synthetic data are generated by the phase-resolving numerical model, SWASH, and optical data were collected during a field experiment in 2015. Model results from SWASH computed with known bathymetry are used as input to cBathy, and depth estimates are compared to nearshore surveys. Argus camera observations are also used as input to cBathy for the same times as the SWASH simulations. The SWASH simulations resolve breaking waves and do not include (optical) changes to the relation between water surface slope and pixel intensity, termed modulation transfer function, that occur during wave breaking, enabling better estimates from bathymetric inversion algorithms near morphologic features like sand bars. The results indicate that improvements result from eliminating of disruptions to the modulation transfer function caused by wave breaking and residual foam. We show that the use of synthetic wave data is a valuable means of isolating errors in bathymetric inversion algorithms.

North Carolina↗
USGS2023

The influence of vegetated marshes on wave transformation in sheltered estuaries

Assessing the influence of marshes on mitigating flooding along estuarine shorelines under the pressures of sea level rise requires understanding wave transformation across the marsh. A numerical model was applied to investigate how vegetated marshes influence wave transformation. XBeach non-hydrostatic (XB-NH) was calibrated and validated with high frequency pressure data from the marsh at China Camp State Park in San Pablo Bay, California (USA). The model was used to examine how marsh and hydrodynamic characteristics change the potential for marshes to mitigate wave driven flooding. Model results demonstrate that hydrodynamics, vegetation, and marsh width influence wave transformation most, while marsh morphology parameters such as elevation and slope had least effect. Results suggest that in the range of settings explored here (incident wave heights ranging from 0.5 to 3 m and water levels ranging from current mean higher high water to 3 m above current mean higher high water), in comparison to wave propagation over an unvegetated mudflat, marsh vegetation reduces runup by a median of 40 cm and wave height by a median of 35 cm. Results illustrate how marshes can be strategically utilized to provide flood reduction benefits.

Coastal Engineering↗
USGS2024

Skill assessment of a total water level and coastal change forecast during the landfall of a hurricane

The Total Water Level and Coastal Change Forecast (TWL&CC Forecast) provides coastal communities with 6-day notice of potential elevated water levels and coastal change (i.e., dune erosion, overwash, or inundation) on sandy beaches that threatens safety, infrastructure, or resources. This continuously operating model provides hourly information for select regions along U.S. Gulf of Mexico and Atlantic Ocean coastlines. The objective of this work is to assess the skill of forecasts during a period of elevated water levels along the coasts of North Carolina (NC) and South Carolina, USA caused by Hurricane Isaias in August 2020, using a combination of observations and model hindcasts. Water levels and waves were observed throughout the storm at three locations near Wrightsville Beach, NC, which provided information to assess forecast skill; a wave buoy offshore, a tide gage at a local pier, and a pressure sensor deployed at the pier. In addition to observations, the non-hydrostatic phase-resolving model SWASH (Simulating WAves till SHore) was forced with hourly wave energy spectra derived from a coupled Delft3D-SWAN simulation during the peak of Isaias, to complement observations by computing nearshore wave height and wave-induced setup and runup at the shoreline. During the storm peak, SWASH-simulated water levels at the sensor position were comparable to those at the maximum landward extent (bias = −0.05 m; gain = 0.26; r 2 = 0.99), suggesting that observations at the USGS sensor location were a useful proxy for total water level (TWL; sum of tide, surge and wave runup) at the shoreline that are predicted by the TWL&CC Forecast. The TWL forecast at Wrightsville Beach was consistent with observations from the USGS sensor (bias = −0.38 m and −0.74 m, scatter index = 0.22 and 0.28 for the two forecast model grids considered, respectively; weighted regression considering model uncertainty explained 95 percent of variability in observed TWL). Observed TWL was within the confidence interval of the TWL&CC Forecast for the 5 h at the storm peak. Forecast mean water levels (MWL; sum of tide, surge and wave setup) and tide gage observations were also consistent (bias = 0.07 m and 0.02 m for the forecast model grids; scatter index = 0.46; r 2 = 0.80). Forecast MWL at the storm peak was within 0.06 m of the observed MWL from the tide gage for both sites. In the region where Isaias made landfall, eight additional pressure sensors were compared to the peak TWL forecast (bias = 0.14 m; scatter index = 0.18). Forecast TWL explained 90 percent of observed variability in TWL when considering uncertainty of the forecast with a weighted regression. The results demonstrate that wave-driven water levels contributed a significant portion of the forecast TWL during Isaias (52 percent during the three peak hours of the storm), and that TWL were represented using the forecast model. Mean absolute error of the coastal change forecast and observed overwash is 0.4 and 0.14 for the two forecast model grids considered. The skill demonstrated by this computationally efficient method indicates that the forecasting system can provide fast and reliable predictions of TWL across hundreds of km of coastline at sub-km resolution, days to hours in advance of when storms threaten coastal regions.

Coastal Engineering↗
USGS2024

Wave runup and total water level observations from time series imagery at several sites with varying nearshore morphologies

Coastal imaging systems have been developed to measure wave runup and total water level (TWL) at the shoreline, which is a key metric for assessing coastal flooding and erosion. However, extracting quantitative measurements from coastal images has typically been done through the laborious task of hand-digitization of wave runup timestacks. Timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. We utilize over 7000 hand-digitized timestacks from six diverse locations to train and validate machine learning models to automate the process of TWL extraction. Using these data, we evaluate two deep learning model architectures for the task of runup detection. One is based on a fully convolutional architecture trained from scratch, and the other is a transformer-based architecture trained using transfer learning. The deep learning models provide a probability of each pixel being either wet or dry. When contoured at the 50% level (equal chance of being wet or dry), the deep learning models more accurately identified TWL maxima than minima at all sites. This resulted in accurate predictions of 2% exceedance runup, but under predictions of significant swash and over predictions of wave setup. Improved agreement with the complete TWL time series was obtained through post-processing by utilizing the wet/dry probability of each pixel to weight the contouring toward lower dryness probabilities for runup minima (maxima agreed well with observations without tuning). Overall, a transformer-based model using transfer learning provided the best agreement with wave runup statistics, including a) the 2% exceedance runup, b) significant swash, and c) wave setup at the shoreline. For a random subset of images, the model was found to be within the uncertainty range of hand-digitization. The relative success of the transfer learning model suggests that fine-tuning a large model has advantages compared to training a smaller model from scratch. Models provide per-pixel probabilistic estimates in less than 10 s per timestack on a single computational unit, versus the more than 5 min required for hand-digitization. The model is therefore well-suited for near real-time applications, allowing for the development of early warning systems for difficult to forecast events. Real-time wave runup and total water level observations can also be incorporated into coastal hazards forecasts for data assimilation and continual model validation and improvement.

Coastal Engineering↗
USGS2024

Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations

The coastal zone is a dynamic region that can change rapidly and significantly with respect to the morphology of the beach and incoming wave conditions. Runup forecasts may be improved by adapting a dynamic approach that allows for different runup models to be implemented in response to changes in beach state. Accurately forecasting wave runup is critical to characterize exposure to coastal hazards and provide an early warning against potential erosion and inundation. Here, we developed a decision tree model to produce a weighted ensemble of existing runup models to predict 1.25 years of runup at Duck, North Carolina, USA. We then applied the calibrated decision tree model to reproduce observed runup during the DUNEX experiment in Pea Island, North Carolina, USA. We found that the decision tree approach yielded a prediction that was comparable or greater in accuracy (i.e. higher r2, lower RMSE) than the individual runup models. We also interrogated the decision tree predictions to determine how the individual models perform relative to each other and why certain models perform better than others under the same observed wave and beach conditions. We found that the decision tree approach drew on the processes represented in the individual models in the ensemble to produce a forecast that is accurate and explainable without relying on prior knowledge of the study site(s) or requiring manual adjustments beyond the initial model training.

North Carolina↗
USGS2025

Cross-shore hydrodynamics and morphodynamics modeling of an erosive event in the inner surf zone

The phase-averaged and depth-integrated coastal morphodynamic model, XBeach-Surfbeat, was investigated for its capability of predicting the cross-shore hydrodynamics and morphodynamics in the inner surf zone by simulating the storm-induced berm erosion, sediment transport, and subsequent sand bar formation. By utilizing a comprehensive hydrodynamic and morphodynamic dataset measured in a large wave flume and high-fidelity 3D large-eddy simulation (LES) data, a rigorous model validation was conducted to assess its capability in predicting inner-surf zone hydrodynamics and to explore how the improved hydrodynamic performance impacts the predicted morphodynamics. Using the default model parameters of the model, the undertow was overestimated with the peak magnitude being 30%–35% larger in the inner surf zone. Combining Monte Carlo simulation, the optimum hydrodynamic calibration for the simulated undertow was achieved when the roller energy dissipation parameter ( &#x3B2; ) "> β ) was maintained below 0.1, and the threshold water depth ( h min "> h min ) exceeded 0.25 m. The calibrated undertow improved the morphodynamic predictions by reducing the excessive berm erosion (Event I) and sand bar growth in the inner surf zone (Event II). Further improved morphodynamic predictions were achieved by calibrating sediment transport parameters, including the onshore sediment transport coefficient ( &#x3B3; u a "> γua ) and the bore interval coefficient ( T b f a c "> T bfac ) associated with turbulence-bed interaction. A consistent set of optimized model coefficients for the model is shown to be effective in simulating the entire erosive event (combined Events I and II). This study reveals that further improvement of the model's capability may require incorporating new parameterizations and physics, such as wave-breaking-induced turbulence and wave nonlinearity associated with sediment transport in the inner surf and swash zones.

Coastal Engineering↗