The Tordera Delta, a hotspot to storm impacts in the coast northwards ofBarcelona (NW Mediterranean)

The Catalan coast, as most of the developed Mediterranean coastal zone, can be characterized as a high-risk area to the impact of storms due to the large concentrationof values together with the dominance of eroding shorelines. In consequence, any long-term coastal management scheme must include a risk analysis to permitdecision makers to better allocate resources. This can be done in a nested approach in which hotspots are first identified along the coast at a regional scale andsecondly, they are further analysed to produce dedicated risk reduction strategies. In this work, we apply the methodology developed within the RISC-KIT project foridentifying and analysing coastal hotspots in the Catalan coast as a test for applying it to Mediterranean conditions. Obtained results show that this methodology isvery efficient in identifying hotspots of storm-induced flooding and erosion at a regional scale. The adoption of the response approach resulted in the direct assessmentof the hazards' probability distributions, which allowed for the selection of the severity of the hotspots to be identified. When a given coastal stretch behaves as ahotspot for both hazards, it is identified as a very highly-sensitive area to storm impacts. In the study area, the Tordera Delta possesses this condition of very high“hotspotness.” This has been demonstrated by the large and frequent damages suffered by the site during the past decades. The paper analyses different aspects related to the risk management of this area, including stakeholder actions

Leveraging Bounding Box Annotations for Fish Segmentation in Underwater Images

The use of Deep learning techniques in the field of Marine Science has become popular in recent years. For instance, many works propose the application of instance segmentation neural networks (in particular, Mask R-CNN) for detection and classification of fish in underwater images. The performance of these learning-based approaches depends heavily on the volume of data used for training, which, in the case of instance segmentation models for fish detection, implies that human experts must label and mark the shapes of all the fish appearing in a vast amount of underwater images. This is an enormously time-consuming task that we seek to alleviate in this paper. We propose a training strategy that combines manual and semi-automatic annotations. The latter are obtained in a weakly-supervised manner: the bounding box that contains the fish is manually selected, but its shape is automatically obtained thanks to a pretrained encoder-decoder segmentation network. Several popular architectures for this encoder-decoder network are examined. This strategy permits to reduce drastically the annotation cost for instance segmentation, at the expense of a small drop in performance with respect to the use of fully manual annotations. We show that a balance can be achieved between the segmentation performance and the time used to collect the training data by using the proposed strategy

Shore and bar cross-shore migration, rotation, and breathing processes at an embayed beach

A principal component analysis (PCA) is used to decompose data on the coupled morphodynamics of the shoreline and nearshore sandbar of a typical single-barred embayed beach (Tairua Beach, New Zealand). Dynamic patterns are classified into simultaneous modes, where the bar and shoreline move at the same time, and nonsimultaneous modes, where the shore moves independently from the bar, and vice versa. Two simultaneous modes accounting for 65% of the variance of the shoreline and barline dominate the system. One mode describes inverse shoreline and sandbar cross-shore migrations (alongshore averaged), occurring with simultaneous rotations in the same direction. The other mode accounts for migration in the same direction accompanied by variations of the barline curvature (similar to 'breathing modes' previously described in embayed beach shoreline modeling studies). Two nonsimultaneous modes of lesser importance account separately for independent shoreline and barline rotations (10 to 15% of the variance explained). A PCA applied to the shore and sandbar behaviors modeled by four standard equilibrium models simulating shore and sandbar cross-shore migrations and rotations show that these are interrelated because of a correlation between wave energy and direction. Shore and bar rotations are coupled partially because the shape of the bay induces a correlation of their respective drivers, the wave angle of incidence and the alongshore gradient of wave energy. However, this correlation depends on the wave energy. This, in combination with different shore and sandbar response times (quantified using the models), also explains the independent rotations reflected by the nonsimultaneous modes