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Projections of wave climate for the Gold Coast, Queensland suggest a reduction in wave height combined with an anticlockwise shift in wave direction which may lead to an overall local reduction in longshore sediment transport (LST). Understanding changes in LST variability into the future is crucial to better manage the coastline and prepare for future adaptation pathways. The aim of this paper is to analyse the variability of LST at the end of the 21st century and the implications it may have on coastal management. To do so, a benchmark model was established to assess the LST along the entire coast by using a Delft3D model, where boundary conditions are derived from a global wave hindcast forced with the Climate Forecast System Reanalysis (CFSR). The Delft3D model was calibrated using the pumping rates of an artificial sand bypassing system at the northern Gold Coast for three representative years encompassing: low (2004), average (2001) and high (2003) LST rates. Then a hybrid wave downscaling technique was applied to obtain the wave data at the breaking point at three locations along the coast where the Coastal Engineering Research Center (CERC) equation was calibrated based on the benchmark model. Finally, future wave climate scenarios (Contributions to the Coordinated Ocean Wave Climate Project, COWCliP) derived from CMIP5 (seven GCMs) and CMIP6 (two GCMs) were bias-corrected and used to generate ensembles to analyse future projected changes in the variability of LST along the Gold Coast by comparing historical and future projection model runs. The changes in interannual variability projections are inconclusive with the CMIP5 ensemble indicating a decrease in LST interannual variability while the CMIP6-derived models (ensemble with two members) indicate an increase. To allow for a fair comparison, more models to compose the CMIP6 ensemble are required. As for the seasonal variability, CMIP5-derived models suggest a decrease in LST between February and May and virtually no changes for the rest of the year, decreasing the seasonal variability in LST. Changes in LST and its variability may have flow-on effects for coastal management with changes to the creek dredging programs, potentially reduced input to the by- and back-passing systems on the coast and the dispersion of beach nourishment campaigns. [ABSTRACT FROM AUTHOR]

Understanding shoreline dynamics is a key step towards the successful management of coastal environments. This study investigates natural shoreline variability at Grassy Beach, focusing on the relationship to the Southern Annular Mode (SAM) climate driver. The present analysis contributes to ongoing investigations into the influence of a nearshore wave energy converter (WEC), installed in 2021, on the local shoreline dynamics. Grassy Beach is a sandy embayment on the southeast coast of King Island in Bass Strait, Australia, with a wave climate dominated by southerly refracted Southern Ocean swells. Shoreline position from 1987 to 2021 (pre-WEC deployment) was determined from 420 Sentinel and Landsat satellite images using the CoastSat Python toolkit. Empirical orthogonal function (EOF) analysis found a seasonal beach rotation signal to be a dominant mode of variability (20% of total variability). The corresponding temporal EOF alternated in sign between winter and summer. These results show shoreline retreat (erosion) occurring in winter at the exposed eastern end and during summer at the western end when waves from the east increase in frequency. The Bass Strait wave climate is influenced by SAM, with more powerful waves likely in Austral autumn and winter when the SAM index is positive. A positive phase of SAM during winter was found to increase shoreline retreat at the eastern end leading to a stronger beach rotation signal describing 24% of the total variability. These results provide new insights into the influence of SAM on shoreline dynamics and highlight the importance of considering relevant climate drivers as an indicator of beach erosion. For nearshore structures such as WECs, it is critical to first assess the site's natural variability and relevant driving factors through a multi-decade spatiotemporal analysis before analysing the coastal impact of such structures. [ABSTRACT FROM AUTHOR]

Multi-purpose submerged coastal protection structures are increasingly being considered on sandy wave dominated coasts for their relatively low impact on visual and beach amenity compared to traditional emerged structural works such as breakwaters. However, knowledge of submerged coastal protection structure attributes, with respect to their impact on morphological responses, is scarce, mainly due to the difficulty in predicting outcomes in dynamic environments and a reliance on expensive laboratory or numerical modelling experiments combined with a general lack of in-situ monitoring data. The design and performance of multi-purpose submerged coastal protection structures for coastal protection and surf amenity is assessed here using a process-based model. The model is supported by field measurements used to calibrate models of two existing Gold Coast artificial reefs and nearshore beach nourishments and their morphological evolution. Twenty-five scenarios with varying crest width, shape, depth and distance offshore were tested to identify key physical parameters for the effective and efficient design of submerged nearshore protection structures. The open coast beach of the northern Gold Coast was used as a case study site however this research methodology can be used to inform coastal protection strategies for other wave-dominated, sandy beach coastlines with similar geology, geomorphology, tide and wave climate. The impacts of the submerged structures on nearshore waves, hydrodynamics and morphology were determined in several key areas such as the lee of the structure, updrift and downdrift active profile regions. The results demonstrate that validated morphological models can be applied with increasing confidence to develop evidence-based guidelines for defining the key planning, design, and structural parameters of nearshore coastal protection structures to achieve effective coastal protection and enhanced recreational amenity outcomes. [ABSTRACT FROM AUTHOR]

With the increased pressure on the coastlines including population growth, development of new infrastructure, wave pattern changes and sea level rise due to climate change, coastal erosion is likely to continue to be one of the primary concerns for coastal communities and coastal managers during the next years and decades. The Gold Coast City Council (City) manages over 50 km of coastline while receiving over 5 million visitors per year and is one of the fastest growing cities in Australia. While often the magnitude of coastal erosion is difficult to predict and manage, the City is developing a framework to monitor beach erosion and assess erosion risk based on a comprehensive coastal monitoring program including hydrographic surveying, shoreline monitoring using video cameras, asset mapping, numerical modelling, consultancy studies and scientific research. To do so, the methodology was divided into six steps: 1) the latest survey is used to define the current beach condition; 2) a series of pre-computed XBeach simulations are used to define expected storm demands for different wave Average Return Intervals (ARI); 3) ARGUS-derived beach widths are used to incorporate the long-term trends; 4) sand volumes stored on the bar are also accounted for, based on the surveys; 5) locations of specific built assets are incorporated; 6) climate change shoreline retreat is included. A score is provided for each section based on their ability to withstand a 5-year ARI storm and a 50-year ARI storm. The outputs of this framework will be used to inform maintenance operations as well as management and strategic decisions. [ABSTRACT FROM AUTHOR]

Surfing is an increasingly popular sport and recreation that holds significant socio-cultural and economic value for coastal communities worldwide. Recently, coastal cities such as the Gold Coast, Australia have acknowledged the importance of surfing in their local and regional coastal management. As such, the collection of surfer usage data is becoming increasingly important for informed policy decision making. For the first time surfer usage data is collected via a commercial coastal camera network and machine learning object detection techniques. The study site, Currumbin Alley, Gold Coast is a highly valued surf break and services both an advanced point break and inner learner wave. SurfzoneAI, a proprietary machine learning model was applied to 2593 hours (245 days) of video footage from a coastal camera at the site to collect surfer usage data. SurfzoneAI detection outputs for detection class 'surfers surfing' were combined with the open source Simple Online and Realtime Tracking (SORT) tool, to provide surfing ride count and duration data. The artificial intelligence (AI) model detections performed well (> 80% accuracy) for surfer count and surfing ride count data. Approximately 99,000 individual surfing visits were captured along with 617,000 surfing rides. This equated to an estimate of around six to seven surfing waves per visitor. Surfer usage varied throughout the day with visitation increasing through the morning (4-10am) to peak around 10-11am, drop slightly through lunch (12-3pm), a slight uptick in the late afternoon (4-5pm) before dropping again into the evening (5-7pm). As would be expected, there was a significantly higher number of average surfer visitors per hour on the weekends (46) as opposed to weekdays (25). There were no clear linear relationships between environmental data and surfer usage. Average ride durations at Currumbin Alley were of the order of 9-10 seconds which is higher than nearby reef and beach breaks. Future work will focus on improving the machine learning outputs, georectification of surfing rides and further exploration of environmental variables on surfer usage through multivariate statistics. Overall the study showed the usefulness of leveraging off existing commercial coastal camera networks to collect surfer usage data and the power of the AI dataset for informed policy decision making. [ABSTRACT FROM AUTHOR]