Over the past three decades, marine resource management has shifted conceptually from top-down sectoral approaches towards the more systems-oriented multi-stakeholder frameworks of integrated coastal management and ecosystem-based conservation. However, the successful implementation of such frameworks is commonly hindered by a lack of cross-disciplinary knowledge transfer, especially between natural and social sciences. This review represents a holistic synthesis of three decades of change in the oceanography, biology and human dimension of False Bay, South Africa. The productivity of marine life in this bay and its close vicinity to the steadily growing metropolis of Cape Town have led to its socio-economic significance throughout history. Considerable research has highlighted shifts driven by climate change, human population growth, serial overfishing, and coastal development. Upwelling-inducing winds have increased in the region, leading to cooling and likely to nutrient enrichment of the bay. Subsequently the distributions of key components of the marine ecosystem have shifted eastward, including kelp, rock lobsters, seabirds, pelagic fish, and several alien invasive species. Increasing sea level and exposure to storm surges contribute to coastal erosion of the sandy shorelines in the bay, causing losses in coastal infrastructure and posing risk to coastal developments. Since the 1980s, the human population of Cape Town has doubled, and with it pollution has amplified. Overfishing has led to drastic declines in the catches of numerous commercially and recreationally targeted fish, and illegal fishing is widespread. The tourism value of the bay contributes substantially to the country’s economy, and whale watching, shark-cage diving and water sports have become important sources of revenue. Compliance with fisheries and environmental regulations would benefit from a systems-oriented approach whereby coastal systems are managed holistically, embracing both social and ecological goals. In this context, we synthesize knowledge and provide recommendations for multidisciplinary research and monitoring to achieve a better balance between developmental and environmental agendas.

A computationally inexpensive operational wave forecasting system taking wave‐current interactions over the fast‐flowing Agulhas Current into account is explored. Spectral wave model hindcasts at 1/16 of a geographical degree are employed using the Simulating Waves in the Nearshore (SWAN) wave model. Unidirectional wave‐current coupling, with currents affecting waves, is performed using both Operational Mercator 1/12 degree (ORCA12) and GLOBCURRENT 1/4 degree currents. Comparisons between the coupled and uncoupled simulations indicate that wave amplification or lowering is highly dependent on the current magnitude and wave incident angle. In opposing current scenarios, the significant wave height can increase by 20–40% and by 60% in extreme cases. Model validation is performed using Jason‐3 satellite altimetry measurements and in situ directional wave buoy measurements. The nearshore wave directional dispersion is found to be significantly influenced by the core of the Agulhas Current. This is especially prominent in current‐following cases with wave‐current direction differences of up to 20°. The findings are all corroborated by simplistic, idealized models.

Macroalgal morphological variation is determined to a large extent by a combination of environmental factors, with wave exposure and temperature perhaps the main influences, as they are key environmental properties to which a species becomes locally adapted. Macroalgae have shown to exhibit different responses to different magnitudes of exposure to waves, such as reduction in overall size and strength increasing traits. In terms of temperature, warmer environments have been shown to reduce the overall size of resident and transplanted species. However, none of the past studies have identified specific wave and temperature metrics responsible for the morphological adaptation macroalgae exhibit. Past research has often used simple or two-dimensional models of wave exposure, which do not take into account important aspects of the nearshore environment such as wave breaking, refraction and diffraction. Furthermore, past studies have often used satellite-derived datasets as sources for temperature data, however, such data have been shown to have large bias when applied to the nearshore environment. This study used in situ temperature data and wave power metrics calculated from a 3D-numerical model to identify specific temperature and wave metrics responsible for morphological adaptation of the kelp, Ecklonia maxima and Laminaria pallida. Between temperature and wave exposure, the results identify wave exposure as the main influencer of morphological adaptation while identifying specific wave metrics. Furthermore, the results show differences in wave metrics between species; and between deep and shallow populations.

Regional storm tidal levels of the South African coastline are investigated by means of a calibrated and validated numerical model. The model was developed utilizing the shallow water hydrodynamic model, Delft3D. This model was coupled (online) with a non-stationary spectral wave model (developed in the Simulating WAves in the Nearshore (SWAN) numerical code). A local, 4.4 km version of the Unified Model was applied as atmospheric forcing for the coupled system. The models presented in this study form part of the operational marine forecasts of the South African Weather Service, Wave and Storm Surge model. The operational protocol and model calibration and validation are presented via statistical correlations with measured water levels at six South African coastal locations. The main calibration parameters and thus physical drivers were winds, atmospheric pressure and waves. The validated numerical model is used to provide an experimental physical description of South African storm surge characteristics, per coastline. The dominant driver of South African storm surge is winds associated with mid-latitude cyclones. Further novelty in the present study is the quantification of the relative contribution of extreme storm wave set-up to the South African storm surge signal. This wave set-up contributes approximately 20% of the total surge signal in the southwest, with winds contributing approximately 55%. The importance of the continental shelves is also elucidated concerning the frictional shoaling effects of the long surge wave propagation.

A rip current is a strong, localized current of water which moves along and away from the shore. Recent studies have suggested that drownings due to rip currents are still a major threat to beach safety. Identification of rip currents is important for lifeguards when making decisions on where to designate patrolled areas. The public also require information while deciding where to swim when lifeguards are not on patrol. In the present study we present an artificial intelligence (AI) algorithm that both identifies whether a rip current exists in images/video, and also localizes where that rip current occurs. While there have been some significant advances in AI for rip current detection and localization, there is a lack of research ensuring that an AI algorithm can generalize well to a diverse range of coastal environments and marine conditions. The present study made use of an interpretable AI method, gradient-weighted class-activation maps (Grad-CAM), which is a novel approach for amorphous rip current detection. The training data/images were diverse and encompass rip currents in a wide variety of environmental settings, ensuring model generalization. An open-access aerial catalogue of rip currents were used for model training. Here, the aerial imagery was also augmented by applying a wide variety of randomized image transformations (e.g., perspective, rotational transforms, and additive noise), which dramatically improves model performance through generalization. To account for diverse environmental settings, a synthetically generated training set, containing fog, shadows, and rain, was also added to the rip current images, thus increased the training dataset approximately 10-fold. Interpretable AI has dramatically improved the accuracy of unbounded rip current detection, which can correctly classify and localize rip currents about 89% of the time when validated on independent videos from surf-cameras at oblique angles. The novelty also lies in the ability to capture some shape characteristics of the amorphous rip current structure without the need of a predefined bounding box, therefore enabling the use of remote technology like drones. A comparison with well-established coastal image processing techniques is also presented via a short discussion and easy reference table. The strengths and weaknesses of both methods are highlighted and discussed.