Multi-model chain for climate change scenario analysis to support coastal erosion and water quality risk management for the Metropolitan city of Venice.

Under the influence of anthropogenic climate change, hazardous climate and weather events are increasing in frequency and severity, with wide-ranging impacts across ecosystems and landscapes, especially fragile and dynamic coastal zones. The presented multi-model chain approach combines ocean hydrodynamics, wave fields, and shoreline extraction models to build a Bayesian Network-based coastal risk assessment model for the future analysis of shoreline evolution and seawater quality (i.e., suspended particulate matter, diffuse attenuation of light). In particular, the model was designed around a baseline scenario exploiting historical shoreline and oceanographic data within the 2015-2017 timeframe. Shoreline erosion and water quality changes along the coastal area of the Metropolitan city of Venice were evaluated for 2021-2050, under the RCP8.5 future scenario. The results showed a destabilizing trend in both shoreline evolution and seawater quality under the selected climate change scenario. Specifically, after a stable period (2021-2030), the shoreline will be affected by periods of erosion (2031-2040) and then accretion (2041-2050), with a simultaneous decrease in seawater quality in terms of higher turbidity. The decadal analysis and sensitivity evaluation of the input variables demonstrates a strong influence of oceanographic variables on the assessed endpoints, highlighting how the factors are strongly connected. The integration of regional and global climate models with Machine Learning and satellite imagery within the proposed multi-model chain represents an innovative update on state-of-the-art techniques. The validated outputs represent a good promise for better understanding the varying impacts due to future climate change conditions (e.g., wind, wave, tide, and sea-level). Moreover, the flexibility of the approach allows for the quick integration of climate and multi-risk data as it becomes available, and would represent a useful tool for forward-looking coastal risk management for decision-makers.

Coastal accumulation of microplastic particles emitted from the Po River, Northern Italy: Comparing remote sensing and hydrodynamic modelling with in situ sample collections.

Microplastic research has mainly concentrated on open seas, while riverine plumes remain largely unexplored despite their hypothesized importance as a microplastic source to coastal waters. This work aimed to model coastal accumulation of microplastic particles (1-5 mm) emitted by the Po River over 1.5 years. We posit that river-induced microplastic accumulation on adjacent coasts can be predicted using (1) hydrodynamic-based and (2) remote sensing-based modelling. Model accumulation maps were validated against sampling at nine beaches, with sediment microplastic concentrations up to 78 particles/kg (dry weight). Hydrodynamic modelling revealed that discharged particle amount is only semi-coupled to beaching rates, which are strongly mouth dependent and occur within the first ten days. Remote sensing modelling was found to better capture river mouth relative strength, and accumulation patterns were found consistent with hydrodynamic modelling. This methodology lays groundwork for developing an operational monitoring system to assess microplastic pollution emitted by a major river.

Massive shelf dense water flow influences plankton community structure and particle transport over long distance.

Dense waters (DW) formation in shelf areas and their cascading off the shelf break play a major role in ventilating deep waters, thus potentially affecting ecosystem functioning and biogeochemical cycles. However, whether DW flow across shelves may affect the composition and structure of plankton communities down to the seafloor and the particles transport over long distances has not been fully investigated. Following the 2012 north Adriatic Sea cold outbreak, DW masses were intercepted at ca. 460 km south the area of origin and compared to resident ones in term of plankton biomass partitioning (pico to micro size) and phytoplankton species composition. Results indicated a relatively higher contribution of heterotrophs in DW than in deep resident water masses, probably as result of DW-mediated advection of fresh organic matter available to consumers. DWs showed unusual high abundances of Skeletonema sp., a diatom that bloomed in the north Adriatic during DW formation. The Lagrangian numerical model set up on this diatom confirmed that DW flow could be an important mechanism for plankton/particles export to deep waters. We conclude that the predicted climate-induced variability in DW formation events could have the potential to affect the ecosystem functioning of the deeper part of the Mediterranean basin, even at significant distance from generation sites.

On the shape and likelihood of oceanic rogue waves.

We consider the observation and analysis of oceanic rogue waves collected within spatio-temporal (ST) records of 3D wave fields. This class of records, allowing a sea surface region to be retrieved, is appropriate for the observation of rogue waves, which come up as a random phenomenon that can occur at any time and location of the sea surface. To verify this aspect, we used three stereo wave imaging systems to gather ST records of the sea surface elevation, which were collected in different sea conditions. The wave with the ST maximum elevation (happening to be larger than the rogue threshold 1.25H s) was then isolated within each record, along with its temporal profile. The rogue waves show similar profiles, in agreement with the theory of extreme wave groups. We analyze the rogue wave probability of occurrence, also in the context of ST extreme value distributions, and we conclude that rogue waves are more likely than previously reported; the key point is coming across them, in space as well as in time. The dependence of the rogue wave profile and likelihood on the sea state conditions is also investigated. Results may prove useful in predicting extreme wave occurrence probability and strength during oceanic storms.