Biausque, M.; Senechal, N.; Blossier, B., and Bryan, K.R., 2016. Seasonal variations in recovery timescales of shoreline change on an embayed beach, Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 353–357. Coconut Creek (Florida), ISSN 0749-0208.Video images acquired during ten years (from January 1999 until June 2009) were analyzed to study cross-shore and alongshore variability of the shoreline on an embayed beach at Tairua Beach (New Zealand). Cross-shore landward migrations occur not only due to high energetic events (such as storms), but are a result of a mix of different parameters. In particular the shoreline of embayed beaches experiences rotation events during which opposite accretion and erosion patterns are observed at the extremities of the beach. When the beach is in an unrotated state, the erosion of the shore is accentuated. The notion of dynamic equilibrium between morphology and wave energy is common approach to understanding the drivers of such shoreline variations. Therefore, to quantify erosion and accretion rates, we used an empirical shoreline prediction model. In this application of the model, we showed that seasonality exists in these rates, between austral summer and winter. The difference in these rates could be because of the influence of the beach rotation on recovery periods. Indeed, the winter beach is generally in a more rotated state than the summer beach.
L'influence de la pente de plage et de la morphologie sur le run-up sont étudiées, au cours d'un cycle de marée dans des conditions dissipatives, à partir de données vidéo.Le run-up est dominé par la composante infragravitaire qui présente une variation d'un facteur deux au cours du cycle de marée qui ne peut s'expliquer uniquement par une variabilité du forçage au large.Une forte variabilité de la pente de plage est observée et, contrairement aux études précédentes, le run-up n'est pas relié linéairement à H 0. La forme du profil de plage ainsi que l'évolution des vagues dans la zone de surf sont significativement corrélées au run-up.Mots-clés :
Application of nonlinear forecasting and bispectral analysis to video observations of runup over cuspate topography shows that these alongshore patterns in the morphology are accompanied by changes to the fundamental behavior of the runup time series. Nonlinear forecasting indicates that at beach cusp horns, the behavior of swash flow is more predictable and global (meaning that characteristics of individual swash events are well represented by the behavior of the time series as a whole). Conversely, at beach cusp bays, the behavior of swash flow is less predictable and more local (meaning that the characteristics of individual swash events are best represented by the behavior of a small fraction of the time series). Bispectral analysis indicates that there is a nonlinear transfer of energy from the incident wave frequency f to the infragravity frequency ∼ f /2 which only occurs in the bay, suggesting that the local behavior is caused by interactions between successive swash cycles which are magnified by channeling caused by the beach cusp geometry. The local behavior and the bispectral signatures are not present in offshore measurements, and they are not present in runup time series collected when the beach was planar. These results provide evidence that interactions between successive runups are a fundamental characteristic of beach cusp bays. Ultimately, these interactions could lead to the growth of an infragravity wave with an alongshore wavelength forced by the presence of beach cusps.
This overview paper details the findings from the Diving Deep: Forecasting Sea Surface Temperatures and Anomalies Challenge at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2024. The challenge focused on the data-driven predictability of global sea surface temperatures (SSTs), a key factor in climate forecasting, ecosystem management, fisheries management, and climate change monitoring. The challenge involved forecasting SST anomalies (SSTAs) three months in advance using historical data and included a special task of predicting SSTAs nine months ahead for the Baltic Sea. Participants utilized various machine learning approaches to tackle the task, leveraging data from ERA5. This paper discusses the methodologies employed, the results obtained, and the lessons learned, offering insights into the future of climate-related predictive modeling.
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