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The development and implementation of Early Warning Systems (EWSs) are decisive as they allow for timely measures before the arrival of the flooding waters. EWS enhances the prevention and preparedness activities that mitigate the effects of disasters on lives, property, and the environment. Forecasting outcomes supply decision-makers at local, regional, or national levels with relevant information and comprise an essential part of monitoring and warning procedures. These decisions must be taken quickly to allow mitigation measures, so computer time acceleration is decisive. The proposed innovation for implementing an Early Warning System (EWS) is the parallelization model. The proposed parallelization model is based on different parallel sub-schemes. Each parallelization sub-schema can be combinable with each other, providing a hierarchical parallelization scheme. The problem size (the number of transects along the Campania coast) is divided into lots and distributed to several executors. Each executor is an instance of a computer program (process) in charge of computing the partition of the problem in its duty. Due to CPUs being composed of more computing cores, each process can decompose its part of the problem to each computing core running concurrently (threads). While the threads of the same process share the same memory, processes communicate by exchanging data messages. As demonstrated later in the paper, the problem decomposition makes the overall computing performance remarkable as the problem size increase.The EWS has been designed and developed, leveraging a high-performance computing system. This approach is motivated by the goal of managing and running scientific workflows. Therefore, the performance evaluation has been performed considering a production workflow executing diverse and different numerical and A.I. models): the community Weather Research and Forecasting (WRF), the Wavewatch III (WW3) numerical models, and, finally, 3) the novel Shoreline Alert Model (SAM).SAM implements the empirical approach to evaluate the alert level as a function of the shoreline characteristics. The workflow starts with the WRF numerical model to forecast the atmospheric forcing needed to fuel the WW3 model for estimating the offshore waves, which drives the initial and boundary conditions for modeling waves in shallow water. Then, according to the wave decay submodel, these conditions assess the run-up height and overtopping discharge. The results are associated with an alert system triggered by the duration and intensity of storm events forecasted by the models. Finally, it is obtained considering the geomorphology of the area of interest and the presence/absence of protection structures.The case study under examination covers a coastal stretch located in the municipality of Torre del Greco, in the Gulf of Naples, consisting of a beach varying in width from 10 to 20 meters, which is protected by an artificial reef up of natural blocks. In recent years, the succession of extreme weather events has created coastal flooding, like during the extreme storm of October 2018, which caused considerable damage in this area.

Procida Island, located in the Gulf of Naples (southern Italy), is characterized by steep cliffed coasts, articulated in a succession of headlands and small embayments with narrow pocket beaches, such as Ciraccio and Chiaia, often characterized by instability. In this study, a methodology for coastal cliff susceptibility assessment has been conceived based on hydraulic and geomorphological characteristics, which supported the construction of a Cliff Stability Index (CSI). The geomorphological characteristics are related to the whole cliff face, the cliff material resistance, and the cliff failure mechanisms. The hydraulic actions on the cliff are related to the wave impact which is exerted by the breaking waves once the wave run-up distance exceeds the beach width. The index takes into account the slope of the cliff, the rock strength, the wave energy at the cliff base produced by the broken wave and the presence of defence structures at the cliff base. The resulting index classification, obtained by addition of the partial sub-indices, has been compared with the observed coastal cliff evolution from 1954 to 2021.

The cliff vulnerability monitoring and analysis are scientific challenge that can be achieved by means of different complementary methods. In this paper the analysis of experimental data, taken from UAV (Unmanned Aerial Vehicle) observations allowed us to detect the presence of cliff discontinuities on the beach of Ciraccio, in the Procida Island (Gulf of Naples, Italy). The dataset consisted of a photographic survey taken from the beach completed with UAV observations. The observed discontinuities taken from the survey were compared with the vulnerability analysis obtained from a previous work.

Workflow engines are commonly used to orchestrate large-scale scientific computations such as, but not limited to weather, climate, natural disasters, food safety, and territorial management. However, to implement, manage, and execute real-world scientific applications in the form of workflows on multiple infrastructures (servers, clusters, cloud) remains a challenge. In this paper, we present DagOnStar ( D irected A cyclic G raph On Anything ), a lightweight Python library implementing a workflow paradigm based on parallel patterns that can be executed on any combination of local machines, on-premise high performance computing clusters, containers, and cloud-based virtual infrastructures. DagOnStar is designed to minimize data movement to reduce the application storage footprint. A case study based on a real-world application is explored to illustrate the use of this novel workflow engine: a containerized weather data collection application deployed on multiple infrastructures. An experimental comparison with other state-of-the-art workflow engines shows that DagOnStar can run workflows on multiple types of infrastructure with an improvement of 50.19% in run time when using a parallel pattern with eight task-level workers.

In coastal areas, the wind vector at sea is one of the most important geophysical variable of interest. Wind field information is crucial for navigation, green energy production, fishing, etc. Nonetheless, to obtain distributed and accurate sea wind information in coastal areas is a challenging task that cannot be achieved by buoys, anemometers or satellite sensors as lidars and scatterometers. Hence, in this study, the behavior of sea wind field is analyzed over the selected test site, i. e., the Gulf of Naples (Italy, Mediterranean Sea) using level-2 ocean products obtained by the C-band Synthetic Aperture Radar (SAR) on board of the European Space Agency mission Sentinel-1. The latter are compared with space/time co-located wind vectors obtained by the Weather and Research Forecasting (WRF) model. Preliminary experimental results indicate that there is a fairly good agreement between wind vectors estimated from SAR and the ones predicted by the WRF model. They suggest that a moderate eastern wind blowed over the study area in the considered period.

The rise of the Internet of Things has generated high expectations about the improvement in people's lifestyles. In the last decade, we saw several examples of instrumented cities where different types of data were gathered, processed, and made available to inspire the next generation of scientists and engineers. In this framework, sensors and actuators became leading actors of technologically pervasive urban environments. However, in coastal areas, marine data crowdsourcing is difficult to apply due to the challenging operational conditions, extremely unstable network connectivity, and security issues in data movement. To fill this gap, we present a novel version of our DYNAMO transfer protocol (DTP), a platform-independent data mover framework where data collected on board of vessels are stored locally and then moved from the edge to the cloud when the operating conditions are favorable. We evaluate the performance of DTP in a controlled environment with a private cloud by measuring the time it takes for the clouds ide to process and store a fixed amount of data while varying the number of microservice instances. We show that the time decreases exponentially when the number of microservice instances goes from 1 to 16 and it remains constant above that number.

Coastal area constitutes a vulnerable environment and requires special attention to preserve ecosystems and human activities therein. To this aim, many studies have been devoted both in past and recent years to analyzing the main factors affecting coastal vulnerability and susceptibility. Among the most used approaches, the Coastal Vulnerability Index (CVI) accounts for all relevant variables that characterize the coastal environment dealing with: (i) forcing actions (waves, tidal range, sea-level rise, etc.), (ii) morphological characteristics (geomorphology, foreshore slope, dune features, etc.), (iii) socio-economic, ecological and cultural aspects (tourism activities, natural habitats, etc.). Each variable is evaluated at each portion of the investigated coast, and associated with a vulnerability level which usually ranges from 1 (very low vulnerability), to 5 (very high vulnerability). Following a susceptibility/vulnerability analysis of a coastal stretch, specific strategies must be chosen and implemented to favor coastal resilience and adaptation, spanning from hard solutions (e.g., groins, breakwaters, etc.) to soft solutions (e.g., beach and dune nourishment projects), to the relocation option and the establishment of accommodation strategies (e.g., emergency preparedness).

Destructive marine storm bring large waves and unusually high surges of water to coastal areas, resulting in significant damages and economic loss. In this study it is examined the characteristics of a destructive marine storm on the strongly inhabited coastal area of Naples Gulf, along the Italian coasts of the Tyrrhenian Sea, which is highly vulnerable to marine storms due to the accelerated relative sea level rise trend and the increased anthropogenic impact on the coastal area. The marine storm, occurred on the 28th December 2020, was analysed through an unstructured wind-wave coupled model that takes into account the main weather-marine components of the coastal setup. The model, validated with in-situ data allowed to establish threshold values for the most significant marine and atmospheric parameters (i.e., wind intensity and duration) beyond which an event can produce destructive effects. Finally, a first assessment of the return period of this event was evaluated using local press reports on damage on urban furniture and port infrastructures.

Sensors and actuators became first class citizens in technologically pervasive urban environments. However, the full potential of data crowdsourcing is still unexploited in marine coastal areas, due to the challenging operational conditions, extremely unstable network connectivity and security issues in data movement. In this paper, we present the latest specification of our DYNAMO Transfer Protocol (DTP), a platform-independent data mover framework specifically designed for the Internet of Floating Things applications, where data collected on board of professional or leisure vessels are stored locally and then moved from the edge to the cloud. We evaluate the performance achieved by the DTP in data movement in a controlled environment.

While the everything as a sensor is a typical data gathering pattern in the Internet of Things (IoT) applications in contexts such as smart cities, smart factories, and precision agriculture, among others, the use of the same technique in the coastal marine environment is still not explored at full potential. Nevertheless, when it comes to maritime scenarios, the application of IoT and networks of distributed sensors and actuators are still limited, even though the development of marine electronics and extreme network technologies are present for decades also in this area. In this paper, we first introduce the concept of the Internet of Floating Things (IoFT), which extends the IoT to the maritime scenario. Next, we present our latest implementation of the DYNAMO (Distributed leisure Yachts sensor Network for Atmosphere and Marine Observations) system, a framework for coastal data collection from sensors and devices deployed in marine equipment. To demonstrate the importance of IoFT data collection in the real-world environmental science context, we consider a scientific workflow for coastal water quality. The selected application focuses on predicting the spatial and temporal pattern of sea pollutants and their possible presence and time of persistence in the proximity of mussel farm areas in the Bay of Pozzuoli in Italy. The pollutants are simple Lagrangian particles, so the ocean dynamics play an important role in the simulation. Our results show that integrating crowdsourced bathymetry data in the workflow numerical model setup improves the accuracy of the final results, allowing for a more detailed spatial distribution pattern of the sea current driving the Lagrangian tracers.