Meteo and hydrodynamic data in the Mar Grande and Mar Piccolo by the LIC Survey, winter and summer 2015

The Coastal Engineering Laboratory (LIC) of the DICATECh of the Polytechnic University of Bari (Italy) maintains a place-based research program in the Mar Grande and Mar Piccolo of Taranto (a coastal system in southern Italy), providing 15 records of hydrodynamic and water-quality measurements. This site is one of the most complex marine ecosystem models in terms of ecological, social, and economic activities. It is considered highly vulnerable for the presence of the naval base, of the biggest refinery of Europe and of the oil refinery. Two fixed stations have been installed, one in the Mar Grande (MG station) and another in Mar Piccolo (MP station). In the MG station constituents include wind speed and direction, air temperature and humidity, barometric pressure, net solar radiation, water salinity, water temperature, water pressure, dissolved 20 oxygen, fluorescence, turbidity, CDOM, crude oil and refined fuels, sea currents and waves. In the MP station constituents include water temperature, sea currents and waves. We provide a summary of how these data have been collected by the research group and how they can be used to deepen understanding of the hydrodynamic structures and characteristics of the basin. These data are available at http://doi.org/10.5281/zenodo.4044121 (Mossa et al., 2020) 25 Design type(s) time series design Measurement Type(s) MG station: wind speed and direction, air temperature and humidity, barometric pressure, net solar radiation, water salinity, water temperature, water pressure, dissolved oxygen, fluorescence, turbidity, CDOM, crude oil and refined fuels, sea currents and wave. MP station: water temperature, sea currents and waves Technology Type(s) data acquisition system Factor Type(s) spatiotemporal_interval https://doi.org/10.5194/essd-2020-229 O pe n A cc es s Earth System Science Data D icu ssio n s Preprint. Discussion started: 1 October 2020 c © Author(s) 2020. CC BY 4.0 License.


Sample Characteristic(s)
Mar Grande and Mar Piccolo of Taranto, coastal waters

Background & Summary
Coastal sites with typical lagoon features are extremely vulnerable, often suffering scarce circulation (de Swart and Zimmerman, 2009;De Pascalis et al., 2016;De Serio and Mossa, 2016a;De Serio and Mossa, 2016b;Armenio et al., 2017). 30 The two bays of the Mar Piccolo have been considered as two different ecosystems influencing each other. The Mar Piccolo with its typical lagoon features is extremely vulnerable and is characterized by continue diffusion of contaminants with a strong ecological risk towards the marine ecosystem and human health.
This monitoring action has proved to be a necessary tool for local authorities and stakeholders, allowing to deepen the knowledge of the physical processes recurring in the target basin and to check its real-time status. Moreover, it allows to control sediment transport and effluent discharges, which are all phenomena strictly linked to current magnitudes and 40 directions (De Serio and Mossa, 2013;Green and Coco, 2014;Ben Meftah et al., 2014;Ben Meftah et al., 2015;Mossa et al., 2017, Ben Meftah et al., 2018De Serio and Mossa, 2016c). Therefore, coastal management plans and in situ decision-making should include such monitoring actions to guarantee a thorough knowledge of hydrodynamic and tracers diffusion processes. Generally numerical models are preferred to this scope, because they allow to reproduce and predict marine physical phenomena in relatively short time, with accuracy and with 45 moderate costs (De Serio et al., 2007;Monti and Leuzzi, 2010;Samaras et al., 2016;Di Bernardino et al., 2016;De Serio et al., 2020). Predictive operational oceanography commonly uses models covering regional, sub-regional and shelf-coastal scales. To study local scales, with resolution of few hundred meters, multiscale modelling systems based on a multiple-nesting approach have been implemented lately (Lane et al., 2009;Federico et al., 2017). Therefore, a large dataset is essential to calibrate and validate modelling systems providing forecasts (Lesser et al., 2004;Korotenko et al., 2010;Sánchez-Arcilla et 50 al., 2014).
At the same time, a large dataset allows to deduce information on the evolutionary state of the analyzed basin. The present note aims to show how long-term and continuous recordings of meteorological, hydrodynamic and water quality data collected in a semi-enclosed sea can be managed to rapidly provide fundamental insights on its hydrodynamic structure and environmental health. The acquired signals have been analyzed in both time and frequency domain, filtered and grouped in 55 classes with homogeneous features, then correlated. This simple and repeatable procedure has been applied with good results De Serio and Mossa, 2016a;Armenio et al., 2017), interesting in a predictive perspective and for https://doi.org/10.5194/essd-2020-229  (Kjerfve and Magill, 1989;Babu et al. 2005;Ferrarin et al., 2008;De Serio and Mossa, 2016c;Benetazzo et al., 2012). Although the typical trends in the water circulation and exchanges have been studied by numerous models developed for the seas of Taranto, more observations, monitoring actions and numerical modelling are still necessary to better 60 understand the most significant hydrodynamic-biological variability of this coastal basin. The results of these study can be applied for similar zones.

Method and sampling
The hydrodynamics and water-quality studies in the Mar Grande and Mar Piccolo of Taranto ( In detail, the weather system by Met Pack was installed on the seamark where the monitoring MG station is present. It records 75 speed and wind direction by means of an ultrasonic sensor. Hourly-averaged values of wind speed and direction are provided with an accuracy of ±2% of the velocity value and ±3° of the direction. The ADCP (by Teledyne RDI) measured the 3D velocity of currents along the vertical. It uses a Janus configuration consisting of four acoustic beams, paired in orthogonal planes, where each beam is inclined at a fixed angle of 20° to the vertical. The ADCP is bottom mounted, upward facing and has a pressure sensor for measuring mean water depth. The transducer head is 80 at 0.50m above the seafloor. Velocities are sampled along the water column with 0.50m vertical bin resolution and a 1.60m blanking distance. Therefore, the water column is investigated from a distance from the sea bottom z=2.1m up to the most superficial bin not biased by waves. The surface layer, with a thickness on average equal to 2.0m, is excluded from the analysis, to filter out the possible noise in the measurements as well as the wave contribution to currents. Mean current velocity profiles are collected continuously at 1-hour intervals, using an average of 60 measurements acquired every 10s. In this way, hourly-85 averaged velocity components along the water column are available (De Serio and Mossa, 2016a;De Serio and Mossa, 2016b;Armenio et al., 2017). Figure 3 shows an example of polar plots of the measured bottom and surface currents in January 2015.
In May 2014, funded by the Flagship Project RITMARE, also the station MP was placed in the target area. Namely it was installed in the Navigable Channel, at the geographical coordinates 40.473° N and 17.235° E (Fig. 1). It is equipped with a bottom mounted ADCP and a wave array (by Teledyne RDI). The local depth in this station is on average equal to 13.7m. Also   They are for both months: current datafile; wave datafile; temperature datafile.
The format of the abovementioned datafile is the following. 120 Current datafile format: Progressive data number; Date (year/month/day/hour/minute) Cell of measurement with indication of its depth from surface (z=0); for each cell the column with current intensity (in m/s) and current direction of propagation (in degree, referenced to North) is shown. 125 Namely, the most superficial assessed cell is located at -2m from surface in MG station, while it is located at 1.5m from surface in MP station, thus to avoid the effects of wave disturbances in the signal.

Technical Validation
Results from each sampling data were examined carefully by the research team of the LIC to ensure that all values fell within expected ranges, to verify that calibration regressions were an acceptable basis for computing quantities from sensor measurements and to ensure completeness of each monthly acquisition. 175 When data were bad acquisitions or were lacking, they were eliminated from the file record.
Each data set was validated with: (1) tests to ensure that the measured values fell within ranges that are plausible and consistent with knowledge of the Mar Piccolo and Mar Grande systems; (2) pattern tests of time series of all measurements to ensure they followed plausible and understandable patterns of variability over time (Babu et al., 2005;Ferrarin et al., 2008). As also shown in previous works (De Serio and Mossa, 2016a;De Serio and Mossa, 2016b;Armenio et al., 2017), examples of plots deduced by the dataset are displayed below, for MG Station. On the contrary, the significant waves enter from the same opening and spread throughout, thus not influenced by prevailing winds (Figure 4). Therefore, this SW opening represents a dominant key factor in the hydrodynamic of the basin. Figure 5 displays the water temperature trend in July month, which increases as expected, and the salinity trend, which seems consistent with increasing evaporation rates and reduced riverine inputs. Finally, in Figure 6 the timeseries of dissolved oxygen 190 and chlorophyll are plotted, with peaks due to local effect, and average values consistent with the algal bloom of the period.