Coupling of Wave Data and Underwater Acoustic Measurements in a Maritime High-Traffic Coastal Area: A Case Study in the Strait of Sicily
1. Introduction
The monitoring of sea sounds and sea wave heights in coastal areas is more challenging than in the open sea, because of correlations occurring between these factors on a smaller spatial scale. Indeed, in coastal environments it is extremely difficult to measure sea sound levels and sea waves with the accuracy and spatial resolution required by numerical models for weather forecasting, which are useful also for safety at sea and coastal defense. Natural abiotic underwater sounds in coastal areas are determined mainly by waves, including breaking surface waves (Haxel et al. 2013), rainfall (Prosperetti and Oguz 1993) and waves beating against cliffs. Surface waves cause mostly infrasonic noise at frequencies from 10 to 100 Hz (Haxel et al. 2013). Their sources are interrelated although surf noise, which is defined as wave noise localized near the land–sea surface, is prominent. Rainfall produces energy peaks from 15 to 20 kHz (Nystuen 1986), while thunder and lightning can produce sounds at lower frequencies that contribute to background noise even when the locality of the storm is at considerable distances (Cotter 2009).
Anthropogenic noise in coastal areas could represent an important component of underwater sound level (Buscaino et al. 2016) and could negatively impact many marine organisms (Filiciotto et al. 2014; Celi et al. 2016; Buscaino et al. 2010; Sarà et al. 2007). Anthropogenic noise is mainly due to vessel traffic, particularly at low frequencies (<1 kHz) (Hildebrand 2009). Despite its high level of biodiversity (Myers et al. 2000), the Mediterranean Sea is affected by heavy traffic, and especially at lower frequencies, the Strait of Sicily, the principal corridor between the eastern and western basins, is characterized by heavy acoustic pollution (Buscaino et al. 2016).
Characterizing and evaluating the contributions of natural and anthropogenic sources is crucial for assessing the impact of human-made disturbances on marine habitats (Haxel et al. 2013; Hildebrand 2009). The European Union Marine Strategy Framework Directive (European Parliament, Council of the European Union 2008) promotes the achievement of good environmental status of European waters by 2020. In particular, Descriptor 11.2 regarding “continuous low frequency sound” aims to monitor trends in the ambient noise level within one-third octave bands (Maccarrone et al. 2015). In the assessment of good-quality environmental status, it has become fundamental to distinguish the contribution to ambient noise level of abiotic natural sound from that of anthropogenic sound. In fact, the simple monitoring of acoustic trends within one-third octave bands does not distinguish noise from vessel traffic (anthropogenic acoustic pollution) from noise caused by waves (natural noise); that is, an area could be very “naturally” noisy because of the effect of exposition to high waves although it is very far from vessel traffic and vice versa. Therefore, in the development of a noise-monitoring plan in marine shallow waters, a comparative study coupling wave data and underwater acoustic measurements contributes to distinguishing the main natural abiotic underwater noise from anthropogenic noise (Buscaino et al. 2016).
X-band marine radars are useful active microwave remote sensing systems for sea-state monitoring either offshore or close to the coastline. Sea surface analyses by marine radar are based on the acquisition of consecutive radar images. The sea surface in radar images is affected by a number of distortions, known as modulation effects (i.e., shadowing, tilt modulation, hydrodynamic modulation), that depend on both sea state (wavelength, wave height, and wave direction) and radar parameters (e.g., the space–time resolution of radar data), as well as on acquisition geometry (e.g., the radar height) (Plant and Keller 1990; Lee et al. 1995; Wenzel 1990; Carratelli et al. 2007; Pugliese Carratelli et al. 2006). To retrieve hydrodynamic parameters such as period, wavelength, direction of the dominant waves, and significant wave height, as well as surface current and bathymetry from a sequence of radar images, an inversion procedure is necessary (Nieto Borge et al. 2004; Serafino et al. 2010). A key element of the inversion procedure is the estimation of the surface current and bathymetry from the radar data, since the knowledge of these quantities makes it possible to build a bandpass (BP) filter to separate out background noise with respect to the spectral energy associated with wave motion.
Over the last two decades, several approaches for current and bathymetry estimation have been developed, such as the least squares (LS) (Young et al. 1985), the iterative least squares (ILS) (Senet et al. 2001), the normalized scalar product (NSP) (Serafino et al. 2010), and polar current shell (PCS) (Shen et al. 2015). In particular, in Huang and Gill (2012) the performances of the LS, ILS, and NSP approaches were compared with an ADCP under low state; the comparison pointed out that the NSP method provided the best results in terms of agreement with the buoy.
In the inversion procedure, after building the BP filter, the following step consists of applying a modulation transfer function (MTF) to convert the filtered radar spectrum into the desired sea wave spectrum; finally, it is possible to derive the abovementioned hydrodynamic parameters (Nieto Borge et al. 2004; Ludeno et al. 2014a; Lund et al. 2014; Fucile et al. 2016). Therefore, some estimated hydrodynamic parameters—for example, significant wave height—need a calibration using an external reference, such as the wave buoy. The most commonly used methods to derive significant wave height from radar images are based on the analysis of the zero-order moment of the wave spectrum and on that of the signal-to-noise ratio (SNR) between the spectral wave energy and the background noise component (Nieto Borge 1997; Nieto Borge et al. 2004; Nieto Borge and Guedes Soares 2000). Another possible strategy that also makes use of the peak wavelength and the mean wave period is based on multilayer perceptions (Vicen-Bueno et al. 2012). However, the problem of estimating the significant wave height from radar data has still not been completely solved, since most of the methods require some data provided by external references to be used for calibration purposes. A way to overcome this issue is shown in Gangeskar (2014), Salcedo-Sanz et al. (2015), and Liu et al. (2016), where a method to get the significant wave height from the analysis of shadowing occurring in radar images without any reference measurement was proposed.
A number of wave models are actually used for forecasting systems at global and local scales. Most of these models are spectral models based on the representation of the sea state through the energy spectrum, discretized in terms of frequencies and propagation directions. The evolution of the spectrum in space and time is computed by means of the energy balance equation, where the forcing is represented by sources and sinks of the wave energy, which are wind input, dissipation, and nonlinear interactions between spectral components.
The first spectral third-generation wave model developed was the Wave Model (WAM) (WAMDI Group 1988), which is still used worldwide for the simulation of waves in open ocean. This generation of models includes the direct evaluation of nonlinear interactions without any a priori assumptions on spectral shapes. Some spectral models have been developed specifically for simulations in coastal areas and include formulations for shallow waters, among them Simulating Waves Nearshore (SWAN) (Booij et al. 1999) is one of the most used. While WAM uses an explicit numerical scheme subject to the Courant stability criterion, the SWAN formulation is based on an implicit numerical scheme that is unconditionally stable and more helpful in shallow waters. The model includes, in addition to all the formulations for deep waters present in WAM, formulations for shallow-water processes like dissipation as a result of bottom friction, triad wave–wave interactions, and depth-induced breaking. It is generally applied in nested grid simulations performed by coarser-resolution wave models.
This manuscript is devoted to the comparison of the levels of underwater noise at different frequencies with the wave heights assessed by the wave radar system and the SWAN model, in the coastal area of the Sicilian Channel (southern Italy). The innovative contribution given by the article regards the comparison between the significant wave height, measured using the X-band radar system and derived from the model, and the sound pressure level in order to discriminate natural sound sources (waves) from other sources of biological and anthropic origin, and thus have a better and quicker understanding of the effect of acoustic pollution on a marine environment. The paper is organized as follows. In section 2, the materials and methods used are introduced. Section 3 briefly presents the results achieved through the comparison of the wave height data obtained with independent information provided by the hydrophones used. Conclusions end the paper.
2. Materials and methods
a. Study area
Cape Granitola and Cape San Marco are located in the southwestern part of Sicily (see Fig. 1), in the northern part of the Sicilian Channel, in southern Italy. The channel divides the eastern from the western Mediterranean basins, which are a marine biodiversity hot spot (Myers et al. 2000). The area is affected by a series of complex oceanographic processes (Bonanno et al. 2014) that affect the high productivity of its waters (Cuttitta et al. 2004; Patti et al. 2004), and it represents the principal corridor between the eastern and western basins with heavy and very noisy vessel traffic.
(a) Map of the investigated area in the Strait of Sicily, Mediterranean Sea. (b) Relative position of the buoy and the wave radar.
Citation: Journal of Atmospheric and Oceanic Technology 34, 12; 10.1175/JTECH-D-17-0046.1
At the Cape San Marco site, a wave radar system was used as a sensor for sea surface wave measurements and a SWAN model was applied. At the Cape Granitola site, a fixed buoy was located far from the coast and was used to house a digital recording system. The two periods selected for carrying out of the research activity presented are from 28 February to 16 March 2015 and from 23 April to 27 May 2015, because that was when the hydrophone near the Cape Granitola site and the wave radar installed at the Cape San Marco site worked simultaneously.
b. Acoustic data collection and analysis
Acoustic data were collected using a digital recording system mounted on a fixed buoy having coordinates 37.51782°N, 12.65288°E located 4.9 km from the coast in front of Cape Granitola and 33 km from the radar (see Fig. 1). The buoy was fixed to the sea bottom with a semirigid antitorsion iron cable to avoid vertical oscillations caused by wave surface. A preamplifier omnidirectional hydrophone (TC 4014, Teledyne Reson, Denmark) was fixed at a depth of 20 m from the surface on a bathymetry of 48 m. The hydrophone, connected to the buoy by means of a beam, did not change position with the wave surface. The hydrophone had a flat response from 30 to 100 kHz (±2dB) with a sensitivity of −180 dB ±3 dB re 1V (μPa)−1 (sensitivity of frequencies below 30 Hz is not given by the company). The hydrophone was connected to an analog–digital converter [UltraSoundGate (USG) 116H, Avisoft Bioacousics, Germany] that was managed by the software Avisoft-RECORDER (Avisoft Bioacoustics, Germany) running on an embedded PC (VIA EPIA-P720 Pico-ITX board). The data were acquired in continuous mode with a variable sampling rate of 50 000 samples per second at 16 bit. For each X-band radar sequence collected every 5 min, the power spectral density (PSD) [dB re 1 µPa2 (Hz)−1] with the Welch’s overlapped segment averaging estimator method (Welch 1967) was calculated. The calculation was made by dividing the acoustic data into Hanning windows of 215samples (
c. Wave radar data collection and analysis
A Sperry Marine X-band radar was installed at the Cape San Marco site on a cliff at a height of about 25 m MSL. The radar antenna was located at coordinates 37.495 903°N, 13.020 878°E.
The radar system radiates a maximum power of 25 kW, operates in short-pulse mode (i.e., a pulse duration of about 50 ns) and is equipped with an 8-ft (2.4 m)-long antenna, with horizontal-transmit horizontal-receive (HH) polarization and a point of view of 180°. These features make it possible to get a range resolution of about 7.5 m, an angular resolution of approximately 0.9°, radar coverage of 1.20 nm and, then, a sea area monitored by the wave radar of 2.26 nm2. The signal received by the antenna radar was converted through an analog–digital converter and interpolated onto the Cartesian grid so as to obtain 2D sea surface images. The image sequence acquired by the X-band radar was stored and processed in an elaboration unit. Each raw data sequence consists of 64 individual images, with a 2.0-s interval between successive images. The details of the Cartesian grid for the radar image are reported in Table 1.
Measurement parameters of the wave radar system.
Two main classes of inversion procedure can be used to elaborate the sequence of radar images. The first class is defined as the “global method” (Nieto Borge et al. 2004; Serafino et al. 2010) and relies on the assumption of the spatial homogeneity of sea-state parameters within the investigated area. This kind of approach can be typically applied in open sea conditions, where waves do not interact with the sea bottom and surface current circulation is not affected by the coast, so wave motion properties can be considered to be spatially homogeneous. The second class is based on the “local method” and can be applied to data acquired in nearshore areas, where the space-varying behavior of underwater topography and the presence of coastlines or coastal structures typically turns into a spatial inhomogeneity of the wave motion (Serafino et al. 2012; Ludeno et al. 2014a,b; Bell 2008; Senet et al. 2008).
Hereinafter, the local method proposed in Ludeno et al. (2014b, 2015) and Brandini et al. (2017) was adopted to deal with the reconstruction of inhomogeneous surface current fields from X-band radar data. Figure 2 illustrates the block diagram of the inversion procedure and can be summarized as follows. Each radar image belonging to the temporal sequence considered is partitioned into Ns spatially overlapping subareas, giving rise to Ns temporal subsequences. After data partitioning, Ns radar spectra, each relevant to a given subarea, are computed via the FFT algorithm (Ludeno et al. 2014a,b, 2015).
Block diagram of the inversion procedure.
Citation: Journal of Atmospheric and Oceanic Technology 34, 12; 10.1175/JTECH-D-17-0046.1
d. SWAN model
An operational wave forecast system for the entire Mediterranean basin has been running at ENEA since June 2013. The system provides daily 5-day forecasts, starting at time 0000 UTC. The simulations are performed with a parallel version of WAM cycle 4.5.3 (Günther and Behrens 2011) at the resolution of 1/32° in each direction, corresponding to a linear mesh size of about 3.5 km. Hourly wave spectra are used to force laterally higher-resolution simulations for an area in the northwestern edge of Sicily stretching from longitude 12.625° to 13.375°E and from latitude 37.16875° to 37.66875°N. For these simulations a SWAN model (Booij et al. 1999) at a horizontal resolution of 1/124° was applied. Model bathymetry was calculated from the General Bathymetric Chart of the Oceans (GEBCO) 30-arc-s gridded dataset (http://www.gebco.net/data_and_products/gridded_bathymetry_data). The initial conditions for each wave simulation were obtained as a restart from the simulation of the previous day.
The directional wave energy density spectrum for both the WAM and SWAN models was discretized using 36 directional bins, corresponding to an angular resolution of 10°, and 32 frequency bins starting from 0.06 Hz with relative size increments of 0.1 between one frequency bin and the next.
The entire forecast system composed of the WAM and SWAN models is forced with hourly wind fields obtained from the meteorological operational system SKIRON, developed by the Atmospheric Modeling and Weather Forecasting Group of the University of Athens (Kallos et al. 1997). The atmospheric model is run daily over the Mediterranean basin at the horizontal resolution of 0.05° × 0.05°.
The main wave-integrated variables, including significant wave height, mean, and peak wave period and wave direction, for each grid point of the computational domain of both the models were stored hourly. A validation of the significant wave heights produced by the operational system was achieved against data derived from satellite measurements over the period June 2013–November 2014 (Carillo et al. 2015).
In the study presented, the significant wave heights at buoy position are extracted from the first day of the forecast performed using the SWAN model. To evaluate the comparison with the hydrophone’s measurements, the time series values for the Hs higher than 0.5 m estimated by the SWAN model and measured from the wave radar were adequately selected. Because of the relative distance (about 19 nm) between the position of the buoy and the middle of the sea area of 2.26 nm2 monitored by the wave radar, the trend of the Hs values by the SWAN model in both positions was calculated. The two series of data are found to be well linearly correlated with a goodness-of-fit R2 value equal to 0.97 (not shown); thus, for convenience, the model values calculated at the position related to the radar was used for statistical analysis and graphs.
e. Statistical analysis
The statistical analysis was performed on three time series data: radar significant wave height, model significant wave height, and acoustic noise at different octave bands. Since in a lower sea-state condition [i.e., significant wave height smaller than 0.5 m (<level 3 on the Douglas scale)] radar images do not allow a reliable estimation (Ludeno et al. 2016; Raffa et al. 2017), in this study only radar data estimating wave heights higher than 0.5 m were considered. In particular, the Hs values ranged from 0.5 to 4 m. Since the data were not normally distributed, the median and 10% and 90% percentiles were calculated. A linear correlation was carried out to compare the radar significant wave height, the model significant wave height (used to validate the radar measurements), and acoustic noise at different octave bands. Hereinafter, R is the measure of the goodness of fit of the linear correlation and p values are the significance of the test for linear correlation. The analysis was performed in Statistica, version 8 (United States). Furthermore, in order to have a more detailed view, the correlation between each frequency from 1 to 16 Hz (PSD, dB re 1 μPa2 Hz−1) and the significant wave height was assessed. A mean PSD for each different sea state, as assessed by the significant wave heights intensity (Hs radar), was calculated.
3. Results
The radar measurements and the SWAN model estimates obtained are well correlated with an 
Linear correlation between Hs radar and SWAN model wave height. The black line in the scatterplot represents the regression line, whose equation is provided in the lower-right part of the panel.
Citation: Journal of Atmospheric and Oceanic Technology 34, 12; 10.1175/JTECH-D-17-0046.1
Median values (10th and 90th percentiles) and R values for the linear correlation analysis between significant wave height from radar and the model, and BPLs at different frequency bands (the p values of the correlation are always <0.001). The measure of the goodness of fit of the linear regression is denoted by R, and p values are the significance of the test for linear regression.
Linear correlation between 16-Hz BPL and Hs radar. The black line in the scatterplot represents the regression line, whose equation is provided in the lower-right part of the panel.
Citation: Journal of Atmospheric and Oceanic Technology 34, 12; 10.1175/JTECH-D-17-0046.1
The highest value of correlation between PSD from 1 to 16 Hz and radar wave height was revealed at 8 Hz (
Mean of power spectral density (dB re 1 µPa2 Hz−1) for different sea states obtained by Hs radar. The PSD for the frequencies below 30 Hz is not calibrated. Total number of files = 681. Number of files used to calculate the curve of the following: sea state 3 = 571; sea state 4 = 89; sea state 5 = 20; sea state 6 = 1.
Citation: Journal of Atmospheric and Oceanic Technology 34, 12; 10.1175/JTECH-D-17-0046.1
(top) The significant wave height Hs from wave radar and from the SWAN model at radar position, and BPL at 16 Hz. (bottom) Same Hs values as for (top), but PSD at 8 Hz.
Citation: Journal of Atmospheric and Oceanic Technology 34, 12; 10.1175/JTECH-D-17-0046.1
4. Discussion and conclusions
This research work is focused on investigating the correlation of underwater noise on the significant wave heights assessed by radar methodology in an area of the Sicilian Channel. In situ acoustic data from a fixed observing system were coupled with significant wave height estimates provided by the coastal wave radar system installed at Cape San Marco and with reliable estimates from a SWAN model.
The results of the analysis of the acoustic data collected by a digital recording system installed at a depth of about 20 m were presented. The estimates provided by the SWAN model were compared to the wave radar measurements available over the coastal location. The comparison showed good linear correlation with a correlation coefficient greater than 
In the light of the results discussed above, it could be very useful to assimilate an integrated system with radar/acoustic measurements into the early warning systems in the Mediterranean Sea coastal areas of Italy for the monitoring of noise levels. Furthermore, to discriminate the anthropogenic sound sources from other sources in coastal areas, an automatic identification system (AIS) and/or radar for vessel tracking should be used. This experiment is still ongoing and the latest data acquired will be used for further analysis on the parameterization of both wave and noise, and new tests will be carried out to improve the capability of wave radar to distinguish a more complete set of different sources of oceanic noise.
The authors express sincere appreciation to the CNR IAMC UOS team in Cape Granitola (P. Calandrino, I. Fontana, and G. Giacalone) for having supported the installation of the radar systems and the experimental activities. Most of the research work leading to this paper was carried out within the framework of the RITMARE Flagship Project, funded by the Italian Ministry of University and Research.
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