Search

The majority of drowning deaths on Australian beaches occur significant distances away from lifeguard services. This study uses results of 459 surveys of beachgoers at five beaches unpatrolled by lifeguards in New South Wales, Australia to improve understanding of who visits these beaches and why, and to identify risk factors associated with their beach safety knowledge and behaviour. Many unpatrolled beach users were infrequent beachgoers and weak swimmers, with poor rip current hazard identification skills, who did not observe safety signage that was present, and yet intended to enter the water to swim despite being aware that no lifeguards were present. The survey found that the main reasons beachgoers visited unpatrolled beaches were because they were conveniently close to their holiday accommodation, or they represented a quieter location away from crowds. Future beach safety interventions in Australia need to extend beyond the standard ‘swim between the flags’ message in recognition that people will always frequent unpatrolled beaches. Future beach safety interventions for unpatrolled beaches should be tailored towards the varied types of demographic beach users, such as domestic tourist families, males, and day visitors attracted by social media.

The Australian Forum for Operational Oceanography (FOO), started in 2015, established a working group to focus on wind-waves. One of the aims of this working group was to identify the key priorities of wind-waves research. This undertaking has been driven by Australian needs, but Australia is just one part of the larger international waves community; results of this process are also relevant to other marine-focused nations. The process to identify research priorities engaged both researchers and stakeholders in a democratic, collaborative, and iterative process. The main steps were 1) soliciting possible research questions via an online survey, 2) reviewing and editing the questions at a face-to-face workshop, and 3) ranking the research questions. A total of 360 survey invitations were emailed to possible participants, with 69 respondents. Half of these were from research organizations, and the remainder from private industry, service providers, or government. The survey gathered a list of ideas that were consolidated to 155 suggestions, which were further reviewed by participants at a wind-waves research symposium and then ranked via a voting process. A second round of online voting was then undertaken that specifically targeted the industry and stakeholder community. The top five priorities were identified, and are referred to here as “tier 1” priorities. A further 10 priorities were identified, and are referred to here as “tier 2,” providing a total of 15 top-ranked priorities.

Citizen science is growing rapidly in Australia and globally, and presents valuable opportunities to engage with the community and amplify scientific research. The recent growth in citizen science is largely attributed to technology and has resulted in citizen science now recognised as having the potential to augment and enhance traditional scientific research and monitoring. Citizen science can deliver a level of spatial granularity often not possible with conventional research. This, coupled with its potential to engage the public meaningfully in science, uniquely positions citizen science to monitor and thereby effect genuine scientific outcomes. However, the rapid growth in citizen science has also resulted in some data and information challenges that need to be overcome. Here we present a general overview of citizen science and some of the opportunities and challenges associated with its rapid growth, with a focus on Australia. We use case studies of successful citizen science projects in New South Wales to demonstrate its potential across areas such as cost efficiency and scalability. Overall, these examples show how citizen science has the potential to provide a monumental shift in our ability to monitor the environment while simultaneously increasing understanding and trust in science within the broader community.

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.

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.

Following the rapid and destructive impacts of storm erosion, beach recovery is a key natural process of restoration, returning eroded sediment to the subaerial beach and rebuilding coastal morphology. While the effects of storm erosion have commonly been investigated, detailed studies into post‐storm recovery are currently lacking. This study investigates wave‐driven recovery processes of the berm and beachface on a microtidal, swash‐aligned sandy beach. Following complete removal of the berm by a significant storm event, the entire 76‐day rebuilding of a swash berm is analysed at the timescale of every semi‐diurnal tidal cycle, utilising high resolution (5 Hz) swash and subaerial beach profile measurements from a continuously scanning fixed lidar. Tide‐by‐tide rates of subaerial volume change during berm recovery were most frequently observed between 1‐2 m3/m/day, including losses and gains on the order of several m3/m/day, substantially larger in magnitude than the more gradual rate of net gain (0.7 m3/m/day) observed for the entire recovery period. Patterns of berm crest formation and vertical growth were found to be primarily governed by the neap‐spring tide variations in total water levels. Tide‐by‐tide beachface and berm volume changes were used to classify four principal behavioural modes of subaerial profile variability during recovery. Using decision tree classification, modes were differentiated according to nearshore dimensionless fall velocity, swash exceedance of the berm crest and ocean water levels. The findings provide novel behavioural and parametric insight into the tide‐by‐tide rebuilding of the beachface and berm by swash throughout a complete post‐storm recovery period.

In the Pacific Basin, El Niño/Southern Oscillation (ENSO) is the dominant mode of interannual climate variability and drives substantial changes in oceanographic forcing, likely having a significant impact on Pacific coastlines. Yet, how sandy coasts respond to these basin-scale changes has to date been limited to a few long-term beach monitoring sites, predominantly on developed coasts. Here we use 35 years of Landsat imagery to map shoreline variability around the Pacific Rim (72,000 beach transects) and identify coherent patterns of beach erosion and accretion controlled by ENSO. We find that approximately one third of all beaches experience significant erosion during El Niño phases, with the Eastern Pacific particularly vulnerable to widespread erosion (most notably during the large 1997/1998 event). In contrast, La Niña events coincide with significant accretion for approximately one quarter of all beaches, although conversely drives substantial erosion in south-east Australia and other localized regions. The significant regional variability in coastal response to ENSO should be considered in light of future projected intensification and shifts in ENSO amplitudes and flavors.

The global COVID-19 pandemic has seen extended lockdowns, isolation periods and travel restrictions across many countries around the world since early 2020. Some countries, such as Australia and New Zealand, closed their international borders in early 2020 preventing researchers travelling to other parts of the world. To facilitate the exposure of our students' work, and for them to meet international researchers, as well as foster a sense of coastal community, we started a zoominar series (seminars via Zoom) in April 2020. The Coast2Coast zoominar series had therefore humble origins but we soon discovered that there was an appetite for more widely sharing science across the coastal research disciplines. The Coast2Coast zoominar grew rapidly, attracting researchers from many countries around the world who presented and attended fortnightly online seminars. In just one year and a half we had 38 presentations with roughly 1900 attendees, creating a sense of community and belonging for the researchers involved. In early 2021, two of the co-authors, Giovanni (GC) and Ana (AVC) decided to expand and take this sense of community further creating the Coast2Cast podcast series, where researchers are asked research and non-research questions. In only 7 months, the podcasts have attracted more than 3700 listeners. Importantly, while the main prerequisite was high-quality and impactful research, diversity and inclusion were also a priority in selecting and inviting speakers for the zoominars and guests for the podcast. Importantly, our survey results suggest that there is a place for online events similar to Coast2Coast and Coast2Cast in a pandemic-free future, and that the coastal community involved has greatly benefited from such initiatives.

Shorelines respond to a number of "drivers" operating on a variety of time-scales. For some time-scales (e.g., seasonal), the driver-shoreline relationship is often evident; however, at longer timescales (e.g., multiannual), the shoreline changes may be superimposed on changes at shorter time-scales and thus are diffcult to identify. Here, we predict shoreline evolution from storm events to decadal timescales, using a novel approach based on the Complete Ensemble Empirical Mode Decomposition. This approach identifies and links the primary time-scales in the model drivers (large-scale sea level pressure [SLP] and/or waves) with the same time-scales in the shoreline position. The multiscale approach reproduced shoreline changes at two beaches more skillfully than a common shoreline model when SLP and wave information were used in combination. In addition, the analysis can be applied to climate indices, providing the opportunity to link longer time-scales with climate patterns (e.g., El Niño Southern Oscillation).

A novel approach to improve seasonal to interannual sandy shoreline predictions is presented, whereby model free parameters can vary in time, adjusting to potential non-stationarity in the underlyi...

The slope of the beach face is a critical parameter for coastal scientists and engineers studying sandy coastlines. However, despite its importance for coastal applications (engineering formulations, coastal flood modelling, swimming safety), it remains extremely difficult to obtain reliable estimates of the beachface slope over large spatial scales (hundreds to thousands of km of coastline). This presentation describes a new method to estimate the beach-face slope exclusively from space-borne observations: shoreline positions derived from publicly available optical imaging satellites and tide heights from satellite altimetry. This new technique is first validated against field measurements and then applied across hundreds of beaches in eastern Australia and California, USA (data available at http://coastsat.wrl.unsw.edu.au/).Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/U9zMbFX4gPk

The steepness of the beach face is a fundamental parameter for coastal morphodynamic research. Despite its importance, it remains extremely difficult to obtain reliable estimates of the beach-face ...

International audience; Sandy coast changes on timescales from days to years and sometimes decades, primarily result from the erosion-recovery (im)balance that is controlled by the respective contributions of storms and recovery conditions. Over the last decade, our understanding and predictive ability of storm-driven erosion and subsequent multi-day to multi-annual recovery has greatly improved, notably thanks to long-term and rapidresponse coastal monitoring programs. This chapter gives a broad overview of the definitions and processes that control storm-driven beach and dune erosion and subsequent (partial, complete or excess) recovery. Key conceptual concepts are illustrated using two well-documented case studies: response and (partial) multi-annual recovery from (1) over a severe winter period along the Atlantic coast of Europe characterised by unusually strong storm clustering episode; and (2) from a single severe storm with an anomalous wave direction along the southeast coastline of Australia. Finally, future perspectives and knowledge gaps in relation to impacts and recovery from extreme events are discussed.

International audience; 10 Shoreline variability along the 3.6-km long Narrabeen Beach embayment in SE Australia is 11 investigated over a 5-year period. We apply the one-line shoreline change model LX-Shore, 12 which couples longshore and cross-shore processes and can handle complex shoreline 13 planforms, non-erodible emerged headlands and submerged rocky features. The model 14 skilfully reproduces the three dominant modes of shoreline variability, which are by 15 decreasing order of variance: cross-shore migration, rotation, and a third mode possibly 16 related to breathing. Model results confirm previous observations that longshore processes 17 primarily contribute to the rotation and third modes on the timescales of months to seasons, 18 while cross-shore processes control the shoreline migration on shorter timescales from hours 19 (storms) to months. Additional simulations simplifying progressively the bathymetry show 20 how the inherent geology strongly modulates the spatial modes of shoreline variability. The 21 offshore central rocky outcrop is found to limit the rotation. In contrast, the submerged rocky 22 platforms that extend from the headlands enhance the shoreline rotation mode and increase 23 alongshore variability of the cross-shore migration mode, owing to increased alongshore 24 variability in wave exposure. Offshore wave transformation across large-scale submerged 25 rocky features and headland shape are therefore critical to contemporary shoreline dynamics. 26 2

The Australian marine research, industry, and stakeholder community has recently undertaken an extensive collaborative process to identify the highest national priorities for wind-waves research. This was undertaken under the auspices of the Forum for Operational Oceanography Surface Waves Working Group. The main steps in the process were first, soliciting possible research questions from the community via an online survey; second, reviewing the questions at a face-to-face workshop; and third, online ranking of the research questions by individuals. This process resulted in 15 identified priorities, covering research activities and the development of infrastructure. The top five priorities are 1) enhanced and updated nearshore and coastal bathymetry; 2) improved understanding of extreme sea states; 3) maintain and enhance the in situ buoy network; 4) improved data access and sharing; and 5) ensemble and probabilistic wave modeling and forecasting. In this paper, each of the 15 priorities is discussed in detail, providing insight into why each priority is important, and the current state of the art, both nationally and internationally, where relevant. While this process has been driven by Australian needs, it is likely that the results will be relevant to other marine-focused nations.

The sensitivity of a one-line longshore shoreline change model to the incident wave direction is evaluated at Narrabeen Beach (Australia). As previously observed, the application of the one-line model using wave conditions generated along the 10-m depth contour produces a long-term reorientation of the coastline, with an initial transition period and then a new stable equilibrium during the 10-year period from 2005 to 2015. However, this coastline change in shoreline planform shape is not observed in the shoreline position measurements. The source of this error is investigated by assuming that it is caused by biases in the incident wave direction and by using a Monte Carlo approach to search for the optimal set of wave angle bias corrections to apply at each cross-shore transect. The obtained optimal values enable the one-line model to reproduce accurately the shoreline planform, and they are coherent with estimates of the wave breaking angle obtained independently using a nearshore wave propagation model. Then, using the corrected wave angle time series as a reference, a second Monte Carlo analysis is completed to investigate the sensitivity of the model to errors in the mean wave direction drawn from Gaussian distributions with varying standard deviations. The analysis shows the range of expected errors in shoreline position for an estimated range of errors in the mean wave direction at Narrabeen beach. This work highlights the importance of considering the sensitivity of one-line longshore model simulations to errors in the incident wave angle, which can be relatively large given the uncertainties in spectral wave model estimates of wave direction, in wave buoy observations, and in wave propagation methods or input bathymetry used to estimate wave breaking conditions.

Bayesian networks (BNs) are increasingly being used to model complex coastal processes due to their ability to integrate non-linear systems, their transparent probabilistic framework, and low computational cost. A BN may be suited to descriptive or predictive application. Descriptive BNs are highly calibrated models that are useful for better understanding the physics and causal relationships driving a system. Predictive BNs are generalisations of a system that have skill at predicting outside of the training domain. The predictive and descriptive usefulness of a BN depends on its complexity and the amount of data available to train it, but there is often a trade-off; higher descriptive skill comes at the cost of reduced predictive skill. To demonstrate the differences between predictive and descriptive BNs in a coastal engineering context, a BN to predict shoreline recession caused by coastal storm events is developed and tested using an extensive 10-year dataset incorporating 137 individual storms events monitored at Narrabeen-Collaroy Beach, Australia. A parsimonious approach to BN development is used to separately determine the optimum predictive and descriptive BNs for this dataset. Results show that for this dataset two quite different BNs can be developed: one that is optimized to achieve the highest predictive skill, and a second network that is optimized to maximize descriptive skill. The optimum predictive BN is found to comprise 3 nodes (variables) and can predict the shoreline recession caused by unseen storm events with a skill of 65%. The optimum descriptive BN is composed of 5 nodes and can reproduce 88% of the training dataset, but with more limited predictive capabilities. The uses and limitations of these two different approaches to BN formulation are illustrated with example applications to coastal process modelling. It is anticipated that the insights provided in this paper will help to clarify the further development of Bayesian Networks applied to coastal modelling.

This paper presents a new storm impact database for European coastlines that facilitates the upload, browsing and download of a broad range of physical and impact information related to historical and recent marine storm events. The database is transparent in terms of open access to raw data and metadata, makes use of version control systems through the OpenEarth repository and promotes the use of international standards. A total of 298 storm events are currently stored in the database from the ten RISC-KIT case study sites, including historical events dating back to the sixteenth century. To demonstrate the application of the tool, examples of typical event data contained within the database as well as the ability of the database to identify impacts of events across regions are presented. It is envisaged that this database will expand beyond the ten case study sites, with the aim of promoting and greatly improving the collection and reporting of extreme hydro-meteorological events across Europe into the future.

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.

Advanced numerical models used to predict coastal change at a variety of time and spatial scales often contain many free parameters that require calibration to the available field data. At present, little guidance (beyond the adoption of the default values provided) is available in the field of coastal engineering to inform the selection of best-fit parameter values. Common calibration techniques can often lack a rigorous quantification of model sensitivity to parameters and parameter-induced model uncertainty. Here we employ the Generalised Likelihood Uncertainty Estimation (GLUE) method to address these issues. The GLUE method uses Monte Carlo sampling to assess the skill of many different combinations of model parameters when compared to observational data. As a rigorous modelling framework, the GLUE method provides a series of standard tools that assist the modeller to analyse model sensitivity, undertake parameter optimisation and quantify parameter-induced uncertainty. In addition, new tools are presented here to identify where unique calibrated parameter sets are required for different observational data (e.g., should the calibrated parameter set differ between alongshore locations at a site) and investigate the convergence of GLUE estimated optimum parameter values over increasing numbers of Monte Carlo samples. As the methodology and philosophy of GLUE is well established in other fields, this paper presents a practical case study to explore the strengths and weaknesses of the method when applied to a relatively complex coastal numerical model (XBeach). The results obtained are compared to a previously reported and more ‘standard’ model calibration undertaken within the context of a coastal storm early warning system. While the GLUE method requires orders of magnitude more computational power, it is shown that its use in place of the more common one-at-a-time ‘trial-and-error’ approach to model calibration, provides: a significant improvement in predictive skill; a more rigorous evaluation of the model sensitivity to parameters; the ability to identify distinct differences in the XBeach model performance dependent on dune impact processes; and additional analysis including the quantification of parameter-induced uncertainty.

This study quantifies and characterises the temporal variability of shoreline recovery on a high-energy sandy coastline using a 10-year dataset of daily shoreline and sandbar positions from a Coastal Imaging station at Narrabeen-Collaroy Beach, Australia. Following a total of 82 individual storm events, rates of the cross-shore return of the shoreline to its pre-storm position were analysed. Observed rates during shoreline recovery were characterised by an overall mean of ~ 0.2 m/day. Temporal variability in rates was most evident at shorter timescales of 1–2 weeks and included rates most frequently between 0 and 0.3 m/day, less frequent more rapid rates of up to 2 m/day and also minor landward movements. This temporal variability was significantly correlated with nearshore forcing parameters describing the ratio of wave height to wave period, the cross-shore proximity (and attachment) of the sandbar to the shoreline and the rate of cross-shore sandbar migration. These findings are summarised in a new conceptual model that characterises temporal phases and rates of shoreline recovery corresponding to stages of onshore sandbar migration following a storm, from fully detached storm-deposited sandbar morphology through to complete sandbar welding with the shoreline. More gradual shoreline recovery rates are observed with fully detached and semi-attached sandbar conditions. In contrast, more rapid rates of shoreline recovery occur when sandbars are closer and attached to the shoreline, observed to be on average 3–4 times greater than for detached sandbars. In conditions with attached and semi-attached sandbars, shoreline recovery rates are negatively correlated to the forcing of nearshore wave steepness and dimensionless fall velocity, and coupled with concurrent rates of onshore sandbar migration. The findings provide insight into key parameters influencing shoreline recovery following storms.

This Short Communication provides a Coastal Engineering perspective on present and emerging capabilities of satellite optical imagery, including real-world applications that can now be realistically implemented from the desktop. Significantly, at the vast majority of locations worldwide, satellite remote sensing is currently the only source of information to complement much more limited in-situ instrumentation for land and sea mapping, monitoring and measurement. Less well recognised is that publicly available, routinely sampled and now easily accessible optical imagery covering virtually every position along the world's coastlines already spans multiple decades. In the past five years the common obstacles of (1) limited access to high-performance computing and (2) specialist remote sensing technical expertise, have been largely removed. The emergence of several internet-accessible application programming interfaces (APIs) now enable applied users to access petabytes of satellite imagery along with the necessary tools and processing power to extract, manipulate and analyse information of practical interest. Following a brief overview and timeline of civilian Earth observations from space, satellite-derived shorelines (SDS) and satellite-derived bathymetry (SDB) are used to introduce and demonstrate some of the present real-world capabilities of satellite optical imagery most relevant to coastal professionals and researchers. These practical examples illustrate the use of satellite imagery to monitor and quantify both engineered and storm-induced coastline changes, as well as the emerging potential to obtain seamless topo/bathy surveys along coastal regions. Significantly, timescales of satellite-derived changes at the coast can range from decades to days, with spatial scales of interest extending from individual project sites up to unprecedented regional and global studies. While we foresee the uptake and routine use of satellite-derived information becoming quickly ubiquitous within the Coastal Engineering profession, on-ground observations are – and in our view will remain - fundamentally important. Compared to precision in-situ instrumentation, present intrinsic limitations of satellites are their relatively low rates of revisit and decimetre spatial accuracy. New satellite advances including ‘video from space’ and the potential to combine Earth observation with numerical and data-driven coastal models through assimilation and artificial intelligence are advances that we foresee will have future major impact in Coastal Engineering.

UAVs (Unmanned Aerial Vehicles or “drones”) for routine survey applications at the coast have come of age, and are no longer ‘the latest thing’ more suited to the specialist researcher or amateur enthusiast. Off-the-shelf, survey-grade UAV equipment, data processing and analysis tools are now readily available to practicing coastal engineers, managers and researchers. Within the regulatory constraints that determine their use in many countries, UAVs provide an efficient and cost-effective survey tool for topographic mapping and measurement in the coastal zone. At the practical level, the specialist training required to operate off-the-shelf UAV suited to coastal surveying is now comparable in time and degree of difficulty to learning how to use the equivalent survey capabilities of professional hand-held RTK-GPS equipment. While incremental improvements to both the flight technology and data processing will no doubt continue to occur, from the coastal practitioner's perspective, no more step changes in UAV technology or ease of useability are required. In particular, survey-grade UAVs that incorporate internal RTK-GPS for high accuracy positioning and requiring a single operator only to safely deploy in the field, remove the need for separate and time-consuming on-ground surveying of ground control points (GCPs), previously required during post-deployment data processing. A coastal engineering application of UAV is used here to exemplify the practical use and potential benefits of this now mature survey technology. Over the past 2 years, rapid post-storm deployment of UAV surveying has been successfully integrated into an established coastal monitoring program spanning 4 decades at Narrabeen Beach, Australia. This has extended the scope of this program to include detailed measurements of dune and beachface erosion spanning the full 3.5 km long embayment at a spatial scale and temporal resolution that were previously unfeasible. For both the researcher and practicing coastal engineer, UAVs now provide a practical option for routine coastal surveying.

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 f...

Coastal zones are often threatened by storms that elevate water levels and increase the wave energy impacting the shoreline. These storm conditions result in coastal flooding and erosion hazards for communities, threatening lives, properties and infrastructure. Coastal impact Early Warning Systems (EWSs) are currently used to alert authorities of potential impacts prior to advancing storms. Effective EWSs provide important windows of opportunity to undertake mitigating actions to minimize the damage caused by a storm.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/-U6uEHfLizA

Coastal zone planning requires tools to predict shoreline response to changes in waves, water levels and sediment supply at time scales ranging from daily to decades. Despite the complexity of the underlying processes driving coastal change, the emergence of a range of semi-empirical models is proving to be increasingly successful at predicting shoreline response at seasonal to interannual timescales (e.g. Davidson et al., 2013). Recent improvements include the addition of processes such as longshore sediment transport and shoreline recession by SLR (e.g. Vitousek et al., 2017). But notably, in all these model formulations to-date, free-parameters are assumed to be time-invariant, relying on calibration over relatively short periods to measured shorelines and wave climate. Adopting a time-invariant set of model free-parameters ignores the bias introduced by the training dataset and any likely future changes in beach state and forcing conditions. The alternative approach presented here allows for time-varying model parameters, with the potential to improve model predictability due to non-stationarity in the underlying forcing. This work makes significant advances on previous shoreline modelling efforts by considering model parameters as potential time-varying quantities, as is common in the field of hydrology (e.g. Pathiraja et al., 2016). This is achieved by adopting a suitable data assimilation technique (Dual State-Parameter Ensemble Kalman Filter, EnKF) within the established shoreline evolution model, ShoreFor (Splinter et al., 2014). The method is first tested and evaluated using synthetic scenarios, specifically designed to emulate a broad range of natural sandy shoreline behavior.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/doAvC89vs4s

Immediate pre-storm bathymetry is a key input required for numerical models used in coastal hazard Early Warning Systems. However, the expense and challenging nature of hydrographic surveying means that the availability of high-quality data is extremely rare. This study evaluates the extent to which synthetic and representative bathymetry alternatives can be used to obtain reliable predictions of storm induced sub-aerial erosion using the XBeach coastal erosion numerical model. Multiple storm events at 2 contrasting sites are modelled using 6 bathymetry scenarios including pre-storm surveyed bathymetries, an average bathymetry, and Dean profiles. The output is analysed to evaluate the skill of XBeach erosion predictions as a function of the bathymetry used.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/bE3aXVXxZqQ

How storm events contribute to long-term shoreline change over decades to centuries remains an open question in coastal research. Sand and gravel coasts exhibit remarkable resilience to event-driven disturbances, and, in settings where sea level is rising, shorelines retain almost no detailed information about their own past positions. Here, we use a high-frequency, multi-decadal observational record of shoreline position to demonstrate quantitative indications of morphodynamic turbulence – “signal shredding” – in a sandy beach system. We find that, much as in other dynamic sedimentary systems, processes of sediment transport that affect shoreline position at relatively short timescales may obscure or erase evidence of external forcing. This suggests that the physical effects of annual (or intra-annual) forcing events, including major storms, may convey less about the dynamics of long-term shoreline change – and vice versa – than coastal researchers might wish.

Narrabeen-Collaroy Beach, located on the Northern Beaches of Sydney along the Pacific coast of southeast Australia, is one of the longest continuously monitored beaches in the world. This paper provides an overview of the evolution and international scientific impact of this long-term beach monitoring program, from its humble beginnings over 40 years ago using the rod and tape measure Emery field survey method; to today, where the application of remote sensing data collection including drones, satellites and crowd-sourced smartphone images, are now core aspects of this continuing and much expanded monitoring effort. Commenced in 1976, surveying at this beach for the first 30 years focused on in-situ methods, whereby the growing database of monthly beach profile surveys informed the coastal science community about fundamental processes such as beach state evolution and the role of cross-shore and alongshore sediment transport in embayment morphodynamics. In the mid-2000s, continuous (hourly) video-based monitoring was the first application of routine remote sensing at the site, providing much greater spatial and temporal resolution over the traditional monthly surveys. This implementation of video as the first of a now rapidly expanding range of remote sensing tools and techniques also facilitated much wider access by the international research community to the continuing data collection program at Narrabeen-Collaroy. In the past decade the video-based data streams have formed the basis of deeper understanding into storm to multi-year response of the shoreline to changing wave conditions and also contributed to progress in the understanding of estuary entrance dynamics. More recently, ‘opportunistic’ remote sensing platforms such as surf cameras and smartphones have also been used for image-based shoreline data collection. Commencing in 2011, a significant new focus for the Narrabeen-Collaroy monitoring program shifted to include airborne lidar (and later Unmanned Aerial Vehicles (UAVs)), in an enhanced effort to quantify the morphological impacts of individual storm events, understand key drivers of erosion, and the placing of these observations within their broader regional context. A fixed continuous scanning lidar installed in 2014 again improved the spatial and temporal resolution of the remote-sensed data collection, providing new insight into swash dynamics and the often-overlooked processes of post-storm beach recovery. The use of satellite data that is now readily available to all coastal researchers via Google Earth Engine continues to expand the routine data collection program and provide key insight into multi-decadal shoreline variability. As new and expanding remote sensing technologies continue to emerge, a key lesson from the long-term monitoring at Narrabeen-Collaroy is the importance of a regular re-evaluation of what data is most needed to progress the science.

The Emilia-Romagna early-warning system (ER-EWS) is a state-of-the-art coastal forecasting system that comprises a series of numerical models (COSMO, ROMS, SWAN and XBeach) to obtain a daily 3-day forecast of coastal storm hazard at eight key sites along the Emilia-Romagna coastline (northern Italy). On the night of 31 October 2012, a major storm event occurred that resulted in elevated water levels (equivalent to a 1-in-20- to 1-in-50-year event) and widespread erosion and flooding. Since this storm happened just 1 month prior to the roll-out of the ER-EWS, the forecast performance related to this event is unknown. The aim of this study was to therefore reanalyse the ER-EWS as if it had been operating a day before the event and determine to what extent the forecasts may have helped reduce storm impacts. Three different reanalysis modes were undertaken: (1) a default forecast (DF) mode based on 3-day wave and water-level forecasts and default XBeach parameters; (2) a measured offshore (MO) forecast mode using wave and water-level measurements and default XBeach parameters; and (3) a calibrated XBeach (CX) mode using measured boundary conditions and an optimized parameter set obtained through an extensive calibration process. The results indicate that, while a "code-red" alert would have been issued for the DF mode, an underprediction of the extreme water levels of this event limited high-hazard forecasts to only two of the eight ER-EWS sites. Forecasts based on measured offshore conditions (the MO mode) more-accurately indicate high-hazard conditions for all eight sites. Further considerable improvements are observed using an optimized XBeach parameter set (the CX mode) compared to default parameters. A series of what-if scenarios at one of the sites show that artificial dunes, which are a common management strategy along this coastline, could have hypothetically been constructed as an emergency procedure to potentially reduce storm impacts.

To predict future coastal hazards, it is important to quantify any links between climate drivers and spatial patterns of coastal change. However, most studies of future coastal vulnerability do not account for the dynamic components of coastal water levels during storms, notably wave-driven processes, storm surges and seasonal water level anomalies, although these components can add metres to water levels during extreme events. Here we synthesize multi-decadal, co-located data assimilated between 1979 and 2012 that describe wave climate, local water levels and coastal change for 48 beaches throughout the Pacific Ocean basin. We find that observed coastal erosion across the Pacific varies most closely with El Nino/Southern Oscillation, with a smaller influence from the Southern Annular Mode and the Pacific North American pattern. In the northern and southern Pacific Ocean, regional wave and water level anomalies are significantly correlated to a suite of climate indices, particularly during boreal winter; conditions in the northeast Pacific Ocean are often opposite to those in the western and southern Pacific. We conclude that, if projections for an increasing frequency of extreme El Nino and La Nina events over the twenty-first century are confirmed, then populated regions on opposite sides of the Pacific Ocean basin could be alternately exposed to extreme coastal erosion and flooding, independent of sea-level rise. The dynamic components of coastal water level can add metres to water levels during extreme events. A data synthesis reveals that Pacific regional wave and water level fluctuations are closely related to the El Nino/Southern Oscillation.

Although embayed beach rotation has been viewed and modeled as an alongshore sediment transport process acting on a uniform beach profile, recent research suggests a more complex response whereby alongshore variability in cross-shore sediment fluxes may be more significant. This study utilizes 5 years of fully three-dimensional beach surveys at Narrabeen-Collaroy Beach (SE Australia) to quantify the control of alongshore nonuniform wave exposure and cross-shore processes on embayed beach rotation. Empirical orthogonal function analysis of the alongshore variability in subaerial beach volume/width and berm slope confirms that the dominant mode of subaerial beach variability is an onshore/offshore sediment exchange that is strongly controlled (R > 0.8) by the alongshore gradient in breaker wave height and coincides with a uniform flattening/steepening of the berm slope. A secondary rotation-like signal is observed in both the subaerial beach volume/width data and, significantly, the berm slope. This inverse flattening/steepening of the berm slope between beach extremities is most likely a proxy for differing cross-shore processes within the surf zone between the exposed and sheltered ends of the embayment, particularly with regards to dissipation of storm wave energy by offshore sandbars and beach recovery following storms. Analysis of the corresponding wave data reveals two distinct time scales of wave forcing characteristic of short-term erosion and longer-term recovery processes. A new conceptual model is presented of three differing modes of embayed beach rotation, with the newly identified beach rotation mode controlled by offshore sandbars considered of particular importance at embayments where headland sheltering of oblique waves is pronounced.