Climate-based emulator of distant swell trains and local seas approaching a Pacific atoll

Coastal flooding caused by remotely generated swell waves and local seas is a key issue in the low-lying nations of the Pacific area. Directional wave spectra data can be used to achieve a better understanding of the swell generation areas and be better prepared for flooding events. Swell dissipation results in wave set-up and generates infragravity waves that increase the flooding impact on reef-lined coasts. An increase of the inundation events associated to wave-driven effects is expected to increase over time (Cheriton et al., 2016), projecting that by the mid-21st century it will be impossible to live on many atolls without continuous coastal damage (Storlazzi et al., 2018), what is promoting local studies of coastal flooding risk and adaptation. For many coastal applications, the full spectrum is aggregated into bulk parameters such as significant wave height (Hs), peak wave period (Tp), and mean wave direction (Dir) resulting in the loss of a significant amount of detail related to multi-modal wave conditions (Portilla et al., 2015). This approach is especially unsuitable for Pacific atolls, which are located in the middle of a large ocean basin where concurrent seas and swells approaching from every direction are common, and therefore the analysis of the full spectrum is essential. For this reason, we propose to analyse the spectral wave energy arriving towards Majuro atoll, capital of the Marshall Islands, aggregating spectral information from different points surrounding the atoll into a central "super-point". With the aggregated information we perform a swell partitioning using the watershed methodology proposed in Hanson & Phillips, (2001) in order to isolate each swell train and parameterize its duration and enhancement and decay shape in terms of Hs, Tp and Dir. Then, taking into account the link with large-scale climatic patterns (i.e., El Niño Southern Oscillation), we present a new multi-modal seas emulator capable of generating infinitely long time series of individual swell trains and seas. This new climate-based emulator of swells train and local seas allows a better understanding of swell behaviour in the Pacific, and the generation of multimodal wave conditions to populate the historical records as a key point to perform robust coastal flood risk assessments considering climate variability.