Abstract. This paper outlines a new approach devoted to the analysis of extreme waves in presence of several wave regimes. It entails discriminating the different wave regimes from offshore wave data using classification algorithms, before conducting the extreme wave analysis for each regime separately. The concept is applied to the pilot site of Reunion Island which is affected by three main wave regimes: southern waves, trade-wind waves and cyclonic waves. Several extreme wave scenarios are determined for each regime, based on real historical cases (for cyclonic waves) and extreme value analysis (for non-cyclonic waves). For each scenario, the nearshore wave characteristics are modelled all around Reunion Island and the linear theory equations are used to back calculate the equivalent deep-water wave characteristics for each portion of the coast. The relative exposure of the coastline to the extreme waves of each regime is determined by comparing the equivalent deep-water wave characteristics.This method provides a practical framework to perform an analysis of extremes within a complex environment presenting several sources of extreme waves. First, at a particular coastal location, it allows for inter-comparison between various kinds of extreme waves that are generated by different processes and that may occur at different periods of the year. Then, it enables us to analyse the alongshore variability in wave exposition, which is a good indicator of potential runup extreme values. For the case of Reunion Island, cyclonic waves are dominant offshore around the island, with equivalent deep-water wave heights up to 18 m for the northern part. Nevertheless, due to nearshore wave refraction, southern waves may become as energetic as cyclonic waves on the western part of the island and induce similar impacts in terms of runup and submersion. This method can be easily transposed to other case studies and can be adapted, depending on the data availability.

Abstract. Sea-level rise due to anthropogenic climate change is projected not only to exacerbate extreme events such as cyclones and storms but also to cause more frequent chronic flooding occurring at high tides under calm weather conditions. Chronic flooding occasionally takes place today in the low-lying areas of the Petit Cul-de-sac marin (Guadeloupe, West Indies, French Antilles). This area includes critical industrial and harbor and major economic infrastructures for the islands. As sea level rises, concerns are growing regarding the possibility of repeated chronic flooding events, which would alter the operations at these critical coastal infrastructures without appropriate adaptation. Here, we use information on past and future sea levels, vertical ground motion, and tides to assess times of emergence of chronic flooding in the Petit Cul-de-sac marin. For RCP8.5 (Representative Concentration Pathway 8.5; i.e., continued growth of greenhouse gas emissions), the number of flood days is projected to increase rapidly after the emergence of the process so that coastal sites will be flooded 180 d a year within 2 decades of the onset of chronic flooding. For coastal locations with the lowest altitude, we show that the reconstructed number of floods is consistent with observations known from a previous survey. Vertical ground motions are a key source of uncertainty in our projections. Yet, our satellite interferometric synthetic-aperture radar results show that the local variability in this subsidence is smaller than the uncertainties in the technique, which we estimate to be between 1 (standard deviation of measurements) and 5 mm/yr (upper theoretical bound). Despite these uncertainties, our results imply that adaptation pathways considering a rapid increase in recurrent chronic flooding are required for the critical port and industrial and commercial center of Guadeloupe. Similar processes are expected to take place in many low-elevation coastal zones worldwide, including on other tropical islands. The method used in this study can be applied to other locations, provided tide gauge records and local knowledge of vertical ground motions are available. We argue that identifying times of emergence of chronic flooding events is urgently needed in most low-lying coastal areas, because adaptation requires decades to be implemented, whereas chronic flooding hazards can worsen drastically within years of the first event being observed.

Climate change is projected to challenge adaptation capacity in small islands worldwide due to rising temperatures, sea-level rise, extreme events and changing rainfall patterns. However, adaptation planning and implementation may be delayed where people perceive a lack of urgency and put forward competing priorities such as economic development. Here, we assess perceptions of climate change and adaptation in Saint-Pierre-and-Miquelon, a subarctic archipelago located south of Newfoundland, Canada. We performed and analysed a social survey reaching 289 individuals out of a population of 6260, through a questionnaire conducted both faceto-face and online. We show that inhabitants of Saint-Pierre-and-Miquelon generally have a clear understanding of climate change and perceive adaptation as urgent for a number of vulnerable coastal sites. Despite some disagreements on adaptation options and the timing for implementation, it is noteworthy that even relocation action is mentioned and sometimes requested. We show that perceptions of climate change and of adaptation within Saint-Pierre-and-Miquelon are heavily influenced by place attachment, personal experience of coastal hazards and environmental awareness. From a methodological point of view, our results highlight the relevance of using online surveys in well-connected but geographically isolated communities. From an adaptation perspective, our results suggest that people's perceptions and beliefs are not only a barrier, but rather offer in some cases opportunities for adaptation planning and implementation. Such favourable attitudes toward adaptation do not exist across all small islands, so our results may be useful in determining the conditions under which people's perceptions are conducive to adaptation.

Coastal sandy environments are extremely dynamic and require regular monitoring that can easily be achieved by using an unmanned aerial system (UAS) including a drone and a photo camera. The acquired images have low contrast and homogeneous texture. Using these images and with very few, if any, ground control points (GCPs), it is difficult to obtain a digital surface model (DSM) by classical correlation and automatic interest points determination approach. A possible response to this problem is to work with enhanced, contrast filtered images. To achieve this, we use and tune the free open-source software MicMac.

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