Search

Wind wave observations in shallow coastal waters are essential for calibrating, validating, and improving numerical wave models to predict sediment transport, shoreline change, and coastal hazards such as beach erosion and oceanic inundation. Although ocean buoys and satellites provide near-global coverage of deep-water wave conditions, shallow-water wave observations remain sparse and often inaccessible. Nearshore wave conditions may vary considerably alongshore due to coastline orientation and shape, bathymetry and islands. We present a growing dataset of in-situ wave buoy observations from shallow waters (<35 m) in southeast Australia that comprises over 7,000 days of measurements at 20 locations. The moored buoys measured wave conditions continuously for several months to multiple years, capturing ambient and storm conditions in diverse settings, including coastal hazard risk sites. The dataset includes tabulated time series of spectral and time-domain parameters describing wave height, period and direction at half-hourly temporal resolution. Buoy displacement and wave spectra data are also available for advanced applications. Summary plots and tables describing wave conditions measured at each location are provided. [ABSTRACT FROM AUTHOR]

Emergency managers have an increasing need for tools to enhance preparedness to extreme coastal storms and support disaster risk reduction measures. With the emergence of Early Warning Systems (EWSs) for coastal storm hazards, a fundamental challenge is the accurate prediction of sandy beach erosion at lead times of days to weeks corresponding to an approaching storm event. This work presents a data-driven modelling approach to predict storm-driven beach erosion (shoreline change) using a large dataset of 276 individual storm events at Narrabeen-Collaroy Beach, SE Australia. Correlation analysis between individual storm characteristics and shoreline response at three locations along the embayment with varying exposure to the prevailing waves indicates that cumulative storm wave energy is the dominant driver of storm erosion at this site. This is followed by the pre-storm beach width, storm wave direction and to a minimal extent, storm wave period and water levels. A multi-linear regression model of storm erosion is developed and found to accurately predict shoreline change due to individual storm events (RMSE = 3.7 m–6.4 m). This work highlights the value of high-frequency shoreline data for storm erosion forecasting and provides a framework for real-time forecasting applications. • Large dataset of shoreline change associated with 276 individual storm events at Narrabeen-Collaroy Beach (SE Australia) is used to develop a multi-linear regression model of storm erosion • Cumulative storm wave energy found to be the dominant driver of storm erosion, followed by the pre-storm beach width and storm wave direction • Water levels during storms only a minor contributor to storm erosion at this wave-dominated site • Data-driven erosion model can be used for real-time applications based on global wave forecast systems. [ABSTRACT FROM AUTHOR]

Many estuaries contain sandy beaches that provide habitats and offer protective buffers for wetlands and infrastructure, alongside cultural and recreational resources. Research underpinning coastal management tends to focus on tide‐ and swell‐dominated sandy beaches, but little attention is given to beaches in estuaries and bays (BEBs) that exist along a continuum of wind/swell wave, tide and riverine influence. BEBs are subject to less wave energy than open coast locations because of the generally narrow window of directions for which ocean waves can propagate through the entrance. However, when storm wave direction coincides with the orientation of the estuary or bay entrance, waves can penetrate several kilometres inside. Here we focus on eight BEBs in two major bays/estuaries in Sydney, Australia and present observations from before and after a major extratropical storm with waves from an atypical direction in June 2016. We quantify magnitudes of beach erosion and recovery rates for 3 years post‐storm. We show that when high‐energy storm waves penetrate bays and estuaries, BEBs can undergo up to 100% of subaerial beach erosion. Three years after the storm, only 5 of the 29 (17%) eroded subaerial beach profiles had recovered to their pre‐storm volume. This is likely due to the lack of low‐frequency, beach‐building waves at BEBs under modal weather conditions in between storms, in contrast to open coast beaches. We also show that the recovery of BEBs may be limited by the absence of adjacent sediment reservoirs due to the dominance of tidal processes mid‐channel. Our study highlights the unique behaviour of BEBs relative to beaches on the open coast, and that shifting wave direction needs to be considered in long‐term beach resilience under climate change. © 2020 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A novel approach to improve seasonal to interannual sandy shoreline predictions is presented, whereby model‐free parameters can vary in time, adjusting to potential nonstationarity in the underlying model forcing. This is achieved by adopting a suitable data assimilation technique (dual state‐parameter ensemble Kalman filter) within the established shoreline evolution model ShoreFor. The method is first tested and evaluated using synthetic scenarios, specifically designed to emulate a broad range of natural sandy shoreline behavior. This approach is then applied to a real‐world shoreline data set, revealing that time‐varying model‐free parameters are linked through physical processes to changing characteristics of the wave forcing. Greater accuracy of shoreline predictions is achieved, compared to existing stationary modeling approaches. It is anticipated that the wider application of this method can improve our understanding and prediction of future beach erosion patterns and trends in a changing wave climate. Plain Language Summary: Understanding and predicting future changes along sandy coastlines worldwide is highly relevant for coastal management in the context of climate change. In the future, the changing occurrence of storms—and over longer time scales, rising sea levels—are expected to result in new patterns of shoreline erosion. It is very common for shoreline change models to use past records of measured shorelines and waves to match mathematical equations to these existing observations. However, the validity of these types of shoreline models to predict the future is questionable, when waves and storm patterns around the world in coming decades are expected to be different to those observed in the past. A new methodology is presented to address this issue by exploring how a mathematical shoreline model can self‐adjust to wave climates that vary through time. The proposed methodology is shown to be successful at improving shoreline predictions. Key Points: A data‐assimilation dual state‐parameter ensemble Kalman filter (EnKF) methodology is integrated within an established shoreline modelNonstationary model parameters are obtained, with the accuracy and sampling frequency of shoreline data critical to overall EnKF skillTime‐varying model parametrizations are physically linked to nonstationary wave forcing, resulting in more accurate shoreline predictions [ABSTRACT FROM AUTHOR]

The ability to repeatedly observe and quantify the changing position of the shoreline is key to present-day coastal management and future coastal planning. This study evaluates the capability of satellite remote sensing to resolve at differing temporal scales the variability and trends in shoreline position along sandy coastlines. Shorelines are extracted from 30 + years of publicly available satellite imagery and compared to long-term in-situ measurements at 5 diverse test sites in Europe, Australia, the USA and New Zealand. These sites span a range of different beach characteristics including wave energy and tide range as well as timescales of observed shoreline variability, from strongly seasonal (e.g., Truc Vert, France), to storm-dominated (e.g., Narrabeen-Collaroy, Australia), to only minor annual to multi-annual signals (e.g., Duck, USA). For the 5 sites, the observed typical horizontal errors varied between a root-mean-squared error (RMSE) of 7.3 m and 12.7 m. An analysis of the typical magnitudes of shoreline variability at temporal scales ranging from a single month up to 10 years indicates that, by the implementation of targeted image pre-processing then the application of a robust sub-pixel shoreline extraction technique, the resulting satellite-derived shorelines are generally able to resolve (signal-to-noise ratio > 1) the observed shoreline variance at timescales of 6 months and longer. The only exception to this is along coastlines where minimal annual to multi-annual shoreline variability occurs (e.g. Duck, USA); at these sites decadal-scale variations are successfully captured. The results of this analysis demonstrate that satellite-derived shorelines spanning the past 30 years as well as into the future can be used to explore and quantify intra- and inter-annual shoreline behaviour at a wide range of beaches around the world. Moreover, it is demonstrated that present-day satellite observations are also capable of capturing event-scale shoreline changes (e.g. individual storms) that occur at timescales shorter than 6 months, where this rapid response exceeds the typical magnitude of shoreline variability. Finally, several practical coastal engineering applications are presented, demonstrating the use of freely-available satellite imagery to monitor inter-annual embayed beach rotation, rapid storm-induced shoreline retreat and a major sand nourishment. • A refined sub-pixel resolution shoreline detection technique capable of mapping the sand/water interface is presented. • Satellite-derived shoreline accuracy is assessed at diverse sandy beach sites in Europe, Australia, the USA and New Zealand. • The observed RMSE in shoreline positions for the sites tested ranged between 7.3 m and 12.7 m. • Rapid (e.g., storm erosion, beach nourishment), seasonal and inter-annual shoreline changes are able to be captured. [ABSTRACT FROM AUTHOR]

Shoreline change information is critical for effective management of the coastal zone. This study presents a low-cost method for mapping shoreline change that harnesses smartphone images collected by the community and uploaded to social media platforms. A smartphone camera cradle installed overlooking a coastal region is used to constrain the crowd-sourced camera extrinsic parameters and accompanying signage instructs participants on how to share their image to social media using a site-specific hashtag identifier. Surveyed ground control points solve for the focal length of the smartphone lens and more-accurately resolve the camera extrinsic parameters. Shoreline position is subsequently mapped on georectified images using an edge detection technique based on the red and blue colour channels. A validation of the method was conducted at two sandy beaches in SE Australia and resulted in strong community participation (400 images submitted over 7 months by 198 individual contributors). Concurrent shoreline surveys using RTK-GNSS indicated that shoreline accuracy using this crowd-sourced approach is comparable to that of established coastal imaging systems, with cross-shore shoreline accuracy best for these two elevated validation sites (camera elevation = 17.3 m–27.1 m above MSL) in the camera nearfield (RMSD ≈ 1.4 m) and RMSD ranging between 2.6 and 3.9 m over coastal stretches spanning up to 1 km. Minimal differences in shoreline accuracy were observed between low resolution images characteristic of those uploaded to social media and higher resolution images sourced from the smartphone. The successful application of this low-cost approach, combined with the proliferation of smartphones and social media usage, open up new possibilities for crowd-sourced shoreline change mapping at suitable coastal locations worldwide. • An innovative shoreline mapping approach using community-sourced smartphone images uploaded to social media is presented. • Shoreline accuracy is found to be comparable to established coastal imaging systems such as Argus. • Accurate shorelines are obtained despite unknown lens characteristics, use of snapshot images and low image resolution. [ABSTRACT FROM AUTHOR]

Harley, M.D.; Turner, I.L.; Splinter, K.D.; Phillips, M.S., and Simmons, J.A., 2016. Beach response to Australian East Coast Lows: a comparison between the 2007 and 2015 events, Narrabeen-Collaroy Beach. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 388-392. Coconut Creek (Florida), ISSN 0749-0208. East Coast Lows (ECLs) are intense extratropical cyclones that form off the east Australian coastline and are known to cause significant beach erosion. This study presents analysis of the beach response to two severe ECLs that occurred in the Sydney region in June 2007 and April 2015 based on a three-dimensional coastal monitoring program undertaken at Narrabeen-Collaroy Beach. The results indicate considerable reductions in the subaerial sand volume for both storms, with an average (maximum) reduction of 78 m3 (124 m3) per alongshore meter for the 2007 event and 58 m3 (104 m3) per alongshore meter for the 2015 event. The overwhelming majority (93%) of subaerial beach erosion for both storms was observed to be within the berm section of the beach profile. Further analysis into the alongshore variability of cross-shore beach response reveals that the enhanced erosion during the 2007 event was mainly concentrated in the mid to northern sections of the embayment. It was found that this enhanced erosion was predominantly a result of a greater berm volume in this section of the embayment prior to storm arrival and that berm response to these two events was very similar (R2 = 0.81) when considering erosion as a percentage of the pre-storm berm volume. It is concluded that the berm reduction as a percentage of the pre-storm berm volume can potentially provide a suitable predictor for the beach response to severe ECLs within littoral cells along this coastline. [ABSTRACT FROM AUTHOR]

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 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 and any significant morphological changes caused by the storm are inferred. The second is a storm wave 'beach erosion hazard scale' where hazards are predominately determined by the horizontal recession of the sandy beach and dune without necessarily large increases in water levels. The resulting framework comprises a total of sixteen unique combinations of flooding/erosion storm hazard regimes, each potentially requiring different disaster risk reduction approaches. 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 a large hurricane (Hurricane Ivan) that impacted the Gulf Coast of the United States in 2004. The new approach identifies and distinguishes between the severity of localized coastal flooding and/or coastal erosion, and also provides 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. • A new Storm Hazard Matrix integrates coastal flooding and beach erosion along open sandy coasts. • A vertical hazard scale for coastal flooding is adapted from Sallenger (2000) Storm Impact Scale. • A new horizontal hazard scale for beach erosion is based on the recession of key beach and dune features during a storm. • Applied at two storm events, the Storm Hazard Matrix identifies the nature and severity of flooding and erosion hazards. [ABSTRACT FROM AUTHOR]

National weather forecasting agencies routinely issue a range of hazard warnings. But to our knowledge, along sandy coastlines where storm waves and storm surge can result in widespread but location-specific beach erosion and beachfront flooding, no national-scale early warning service for these hazards is presently operational. This paper outlines the scientific basis and implementation of a new framework for large area coastal storm hazards forecasting, currently being tested along the southwest (Indian Ocean) and southeast (Pacific Ocean) coasts of Australia. The system provides 7-day rolling predictions of localized beach erosion and/or coastal flooding linked to forecasted extreme weather events. Coastal setting influences the nature and occurrence of these hazards, with sandy beaches along wave-dominated coasts more prone to erosion and at surge-dominated coasts to flooding. An existing nearshore water-level forecasting system and a new inshore wave modeling capability are used to forecast beach erosion and coastal flooding at every 100 m along the shore. At the regional scale O(100–1 000 km of coastline), a threshold-based decision tree model categorises the predicted extent, location, and severity of erosion and flooding. At a more local scale O(100–1 000 m), physics-based modeling using XBeach focuses on vulnerable or high-value locations, providing specific storm hazard indicators tailored to local needs. This two-tier approach is feasible for national implementation due to the reduced computational effort, limiting intensive modeling to pre-identified critical locations. Delft-FEWS manages the data and modeling workflow, ensuring scalability and compatibility with existing forecast infrastructure. Initial evaluations of the system are promising, with a detailed 2-year evaluation in progress. Future enhancements could include the use of satellite imagery for real-time beach width and dune topography assimilation and exploring alternative modeling approaches to further improve forecast accuracy. • A framework for coastal storm hazards early warning over very large areas is developed. • The severity of hazards is predicted every 100 m alongshore by parametric modeling. • Site-specific storm hazard indicators at vulnerable sites are predicted by XBeach. • 7-day rolling forecasts of storm hazards are communicated via a web portal. [ABSTRACT FROM AUTHOR]

Numerical coastal erosion models are used by engineers to predict the magnitude of storm erosion at the coastline. In particular, the 'dry' region of the beach, extending landwards from the waterline to include the beach face, berm and dunes, is a particular focus of interest to managers, planners and other coastal practitioners. However, the choice of the most appropriate numerical model to predict subaerial beach erosion requires careful consideration of the strengths and limitations of each model, and the quality and quantity of field data available for both model calibration and prediction. While no model can perfectly replicate observed upper beach erosion, this study specifically assesses the quantity of field calibration data required to achieve optimum model performance for coastal storm erosion modelling applications. Two of the most commonly used, but differently formulated, coastal erosion profile models are compared: the process-based and more complex model XBeach, and the semi-empirical and significantly simpler model SBEACH. A rigorous calibration technique (the Generalised Likelihood Uncertainty Estimation) is applied to both models, using a comprehensive dataset of pre- and post-storm topographic measurements collected over four differing storm events at Narrabeen-Collaroy Beach in southeast Australia, as well as at two adjacent embayed beaches. When applying the two numerical models using their default parameters (i.e., with no model calibration), SBEACH was found to be the more skilful model and XBeach default parameters were found to have no predictive skill along this stretch of coastline. Once calibrated with detailed field observations obtained before and after a single storm event, XBeach validation skill rose considerably and provided predictions of subaerial beach profile change with greater skill (87% better Brier Skill Score on average for the same calibration data) than SBEACH. Overall XBeach model performance was found to marginally improve when field observations obtained from additional storm events were included in the calibration process, whereas SBEACH showed negligible improvement. A comparison was made of the options available for transferring previously-calibrated parameters to adjacent locations, finding spatial proximity to be the most sensitive indicator of model performance for a transferred parameter set. Due to the alongshore variability in pre-storm topography as well as wave exposure at the sites tested, the spatial coverage of data was more important for the calibration process than the magnitude of the individual storm(s) used for a specific calibration. The results of this study underscore the need for careful consideration of the available calibration field data when choosing and optimising coastal erosion models. • Default XBeach parameters were found to be non-skilful at coastal sites in southeast Australia. • The XBeach model outperformed the SBEACH model when one or more calibration storm event field datasets were available. • Collection of calibration pre- and post-storm field data at specific locations of interest was important. • Spatial proximity was found to be a good performance indicator of calibrated parameter values transferred to new locations. [ABSTRACT FROM AUTHOR]

Waves interact with headlands on embayed beaches through refraction, diffraction and attenuation and can create alongshore gradients in wave energy and beach response. This energy gradient shifts alongshore with changes in wave direction, especially during storms when waves may come from a direction different to average conditions. To better predict embayed beach storm responses, improved identification of exposed and headland shadowed zones is needed. Here we present a generalised geometric approach to quantify the alongshore distance shadowed by headlands, called the shadow edge (Y sh), differentiating exposed and headland shadowed zones. Our approach uses headland geometry collected from readily available imagery, combined with measured or modelled storm wave direction, reducing the reliance on complex nearshore wave models derived from relatively scarce bathymetric data. We use monthly topographic beach surveys (2015–2019) at nine embayed beaches in SE Australia to investigate the impacts of headland and embayment geometry relative to storm wave direction (i.e., exposure). Further, beach responses were compared to erosion and recovery rates across different embayment geometries with varying levels of geological control on beach morphodynamics. We found that storm frequency, headland shadowing and embayment geometry control the alongshore magnitude of beach erosion and recovery rates. Mean subaerial beach volume losses to six high-energy storms were on average 3.5 times higher in exposed zones (42.7 ± 40.7 m3/m) than headland shadowed zones (12.1 ± 23.6 m3/m). Our geometric approach provides a simple alternative to predict headland influence on beach morphodynamics. The approach can be applied at a regional scale and could be integrated into early warning systems to predict coastal erosion. [Display omitted] • Generalised geometric approach defines headland shadow zones on beaches using open-access data. • Storm erosion is 3.5 times higher in exposed zones than headland shadow zones. • Results could help predict beach response to climate change-induced shifts in storm wave direction. • This approach could easily complement complex nearshore wave models. [ABSTRACT FROM AUTHOR]