Meteo and Hydrodynamic Measurements to Detect Physical Processes in Confined Shallow Seas

• Main Authors: Francesca De Serio, Michele Mossa
• Format: Article
• Language: English
• Published: MDPI AG 2018-01-01
• Series: Sensors
• Subjects: hydrodynamic patterns; field measurements; monitoring stations; semi-enclosed basin
• Online Access: http://www.mdpi.com/1424-8220/18/1/280

Coastal sites with typical lagoon features are extremely vulnerable, often suffering from scarce circulation. Especially in the case of shallow basins subjected to strong anthropization and urban discharges, it is fundamental to monitor their hydrodynamics and water quality. The proper detection of events by high performance sensors and appropriate analysis of sensor signals has proved to be a necessary tool for local authorities and stakeholders, leading to early warning and preventive measures against environmental degradation and related hazards. At the same time, assessed datasets are not only essential to deepen the knowledge of the physical processes in the target basin, but are also necessary to calibrate and validate modelling systems providing forecasts. The present paper aims to show how long-term and continuous recordings of meteorological and hydrodynamic data, collected in a semi-enclosed sea, can be managed to rapidly provide fundamental insights on its hydrodynamic structure. The acquired signals have been analyzed in time domain, processed and finally, correlated. The adopted method is simple, feasible and easily replicable. Even if the results are site-dependent, the procedure is generic, and depends on having good quality available data. To show how this might be employed, a case study is examined. In fact, it has been applied to a coastal system, located in Southern Italy, where two monitoring stations are placed in two interconnected basins. The inferred results show that the system is not wind dominated, and that the annual trends in the wind regime, wave spreading and current circulation are not independent, but rather reiterate. These deductions are of great interest as a predictive perspective and for numerical modelling.