OSOcean ScienceOSOcean Sci.1812-0792Copernicus PublicationsGöttingen, Germany10.5194/os-12-87-2016The Civitavecchia Coastal Environment Monitoring System (C-CEMS): a new tool
to analyze the conflicts between coastal pressures and
sensitivity areasBonamanoS.simo_bonamano@unitus.itPiermatteiV.MadoniaA.Paladini de MendozaF.PierattiniA.MartellucciR.https://orcid.org/0000-0001-7859-3967StefanìC.https://orcid.org/0000-0002-0316-0291ZappalàG.CarusoG.MarcelliM.Laboratory of Experimental Oceanology and Marine Ecology
(LOSEM), DEB – University of Tuscia, Molo Vespucci, Port of Civitavecchia,
Civitavecchia 00053, Rome, ItalyCNR – Istituto per l'Ambiente Marino Costiero, Messina, ItalyS. Bonamano (simo_bonamano@unitus.it)15January20161218710029June201528July201517December201521December2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://os.copernicus.org/articles/12/87/2016/os-12-87-2016.htmlThe full text article is available as a PDF file from https://os.copernicus.org/articles/12/87/2016/os-12-87-2016.pdf
The understanding of the coastal environment is fundamental for efficiently
and effectively facing the pollution phenomena as expected by the Marine
Strategy Framework Directive, and for limiting the conflicts between
anthropic activities and sensitivity areas, as stated by Maritime Spatial
Planning Directive. To address this, the Laboratory of Experimental
Oceanology and Marine Ecology developed a multi-platform observing network
that has been in operation since 2005 in the coastal marine area of
Civitavecchia (Latium, Italy) where multiple uses and high ecological values
closely coexist. The Civitavecchia Coastal Environment Monitoring System
(C-CEMS), implemented in the current configuration, includes various
components allowing one to analyze the coastal conflicts by an
ecosystem-based approach. The long-term observations acquired by the fixed
stations are integrated with in situ data collected for the analysis of the
physical, chemical and biological parameters of the water column, sea bottom
and pollution sources detected along the coast. The in situ data, integrated
with satellite observations (e.g., temperature, chlorophyll a and TSM), are
used to feed and validate the numerical models, which allow the analysis and
forecasting of the dynamics of pollutant dispersion under different
conditions. To test the potential capabilities of C-CEMS, two case studies
are reported here: (1) the analysis of fecal bacteria dispersion for bathing
water quality assessment, and (2) the evaluation of the effects of the
dredged activities on Posidonia meadows, which make up most of the
two sites of community importance located along the Civitavecchia coastal
zone. The simulation outputs are overlapped by the thematic maps showing
bathing areas and Posidonia oceanica distribution, thus giving a
first practical tool that could improve the resolution of the conflicts
between coastal uses (in terms of stress produced by anthropic activities)
and sensitivity areas.
Introduction
Coastal ecosystems are characterized by multiple human activities such as
aquaculture, energy production, maritime transport, tourism, and fishery that
coexist both spatially and temporally in these areas. The overlap of such
activities and their objectives leads to the generation of user–user and
user–environment conflicts (Douvere, 2008) that result in increasingly
undesirable effects such as loss and destruction of habitat, pollution,
climate change, over-fishing, and cumulative threats to the oceans and human
health as a whole.
The Integrated Marine Policy (IMP) has faced this issue by the adoption of
the Maritime Spatial Planning Directive (MSP, 2014/89/EU), whose main purpose
is to promote the sustainable management of uses and conflicts in coastal
areas through an ecosystem-based approach. The MSP strategy allows one to
minimize the impacts on sensitivity areas, also enabling the achievement of
the Good Environmental Status (GES) by 2020, requested by the Marine Strategy
Framework Directive (MSFD 2008/56/EC). In the last few years, a concerted”
effort has been made by the scientific community to provide new approaches
for the analysis of GES descriptors, like the study of eutrophication
(Descriptor 5) through satellite ocean color data (Cristina et al., 2015) and
the assessment of sea-floor integrity (Descriptor 6) by SAR imagery
(Pieralice et al., 2014). Important results have also been obtained by the
analysis of both commercial fishes and food web (descriptors 3 and 4), to
assess the environmental status of European seas (Jayasinghe et al., 2015)
and the levels of major contaminants (descriptors 8 and 9) and their
pollution effects on aquatic biota (Tornero and Ribera d'Alcalà, 2014).
In line with the holistic approach pursued by the MSFD, the achievement and
the maintenance of marine ecological standards need the support of monitoring
networks that use L-TER (long-term ecological research) observations and
integrate multi-disciplinary data sets, fundamental to forecasting specific
events (Schofield et al., 2002). So, it is necessary to develop observational
monitoring systems in the southern European coastal areas capable of
collecting both high-resolution and long-term data and building
multi-disciplinary data sets.
Recent advances in communication and sensor technology have led to the
development of worldwide multi-platform networks that provide a significant
amount of data on different spatial and temporal scales for the study of
oceanographic processes and marine ecosystem monitoring (Glasgow et al.,
2004; Hart and Martinez, 2006; Kröger et al., 2009). These observational
systems are especially suited for the monitoring of coastal areas (i.e., the
Chesapeake Bay Observing System, CBOS; Li et al., 2005; the Long-term
Ecosystem Observatory, LEO-15; Schofield et al., 2002) where many of the
processes related to natural or anthropic events (pollution spilling, water
discharges, river plume, etc.) are often episodic and occasional;
consequently, they are scarcely identifiable using traditional methods
(Schofield et al., 2002). Only an integrated and multi-platform approach,
which combines data and forecast models, allows the characterization of the
different events and conflicts in coastal waters (Smith et al., 1987; Glenn
et al., 2000; Haidvogel et al., 2000). Improved modeling and real-time
sensing capabilities in terms of accuracy and spatial and temporal resolution
are required, also in order to respond to both science and societal needs
(Tintoré et al., 2013). In particular, linking observations and models
has been recognized as a critical step to achieving effective integrated
ecosystem assessment (Malone et al., 2014). The mathematical models play a
fundamental role in the global and regional ocean forecasting systems since
they assimilate the observational data in order to produce reanalysis and
forecast products of the most relevant ocean and physical variables (Tonani
et al., 2015). Most of the regional operational systems in the Mediterranean
Sea are included in the Mediterranean Forecasting System (MFS), such as the
Adriatic Forecasting System (Oddo et al., 2005), the Sicily Channel Regional
Model (Olita et al., 2012), the Tyrrhenian Sea Forecasting (Vetrano et al.,
2010), the Aegean-Levantine Forecast System (Korres and Lascaratos, 2003) or
the Western Mediterranean Operational Forecasting System (Juza et al., 2015).
Most of the MSF products are disseminated by the MyOcean project
(http://marine.copernicus.eu) that, together with satellite and in situ
observations, developed the pre-operational European Copernicus marine
service. However, several simulations in the Mediterranean Sea are based on
basin-scale features and metrics (Tonani et al., 2008; Oddo et al., 2009;
Vidal-Vijande et al., 2011), partially because of the lack of data at
sub-basin scale. A recent study by Crise et al. (2015) revealed gaps of data
in the Mediterranean region (southern European seas), highlighting the
scarcity, dispersion and heterogeneity of coastal water data sets.
Conversely, the advancement from global- to regional- and local-scale
modeling, which is necessary to analyze and forecast the pollution phenomena
in coastal areas, is applicable only in the region where a large amount of
observation data exist.
As a first step in this direction, the Laboratory of Experimental Oceanology
and Marine Ecology developed a multi-platform observing network that has been
operating since 2005 in the coastal marine area of Civitavecchia (Italy,
Tyrrhenian Sea, western Mediterranean Sea), critically affected by the
presence of many conflicts.
This paper presents the Civitavecchia Coastal Environment Monitoring System
(C-CEMS) as a tool to support the management of conflicts between anthropic
uses and sensitivity areas. It focuses on (1) the functioning of C-CEMS and
its components (Sect. 3), (2) its capabilities in estimating the dispersion
of fecal bacteria for bathing water quality assessment and of dredged fine
sediments to evaluate the effects on Posidonia oceanica meadows
present in the sites of community importance (SCI; Sect. 4), and (3) the
resulting analysis of “urban discharge – bathing area” and “dredging –
SCI” conflicts (Sect. 5).
Location of the study area along the northeastern Tyrrhenian coast
of Italy (western Mediterranean Sea, a). Zoom-in on the area of
C-CEMS application: the location of coastal uses, SCIs, and measurement
stations indicated (b) and the Civitavecchia bathing areas with
discharge points and bather density indicated (one umbrella corresponds to
five bathers, c). The fixed station pictures are reported in the
top-left corner of the figure. The coordinate system is expressed in UTM 32
(WGS84).
Study area
The study area is located along the northeastern Tyrrhenian coast (western
Mediterranean Sea; Fig. 1a). The circulation of the Tyrrhenian basin is
affected by mesoscale and seasonal variability (Hopkins, 1988; Pinardi and
Navarra, 1993; Vetrano et al., 2010). The presence of a cyclonic gyre with a
very pronounced barotropic component suggests that the wind plays a major
role as a forcing agent (Pierini and Simioli, 1998). Like most of the Italian
coast, the northeastern Tyrrhenian one counts many tourist and industrial
areas primarily used for maritime transport and energy production, involving
an intense exploitation of marine resources. Nevertheless, it houses several
biodiversity hotspots and marine protected areas for the conservation of
priority habitats and species.
In particular, this study is focused on the coastal zone between Marina di
Tarquinia and Macchia Tonda in the northern Latium region of Italy (Fig. 1b)
including Civitavecchia, where all the above-mentioned uses could produce
potential conflicts. The Civitavecchia harbor is one of the largest in Europe
in terms of cruise and ferry traffic; it represents a fundamental point of
commercial exchange in Europe. Thanks to the new Port Regulating Plan, the
port of Civitavecchia has increased its commercial traffic and cruise
passenger flow. The Interministerial Committee for Economic Planning (CIPE)
approved the final project for the “strengthening of Civitavecchia harbor
hub – first parcel functional interventions: Cristoforo Colombo embankment
extension, ferries and services docks realization” (Decision 140/2007, 2008). All of these operations
involve the handling of significant quantities of sediments; the impacts of
dredging on the adjacent natural ecosystems can be varied and difficult to
predict (Windom, 1976; Cheung and Wong, 1993; Lohrer and Wetz, 2003;
Zimmerman et al., 2003; Nayar et al., 2007). Many studies have recently
focused on the importance of management of dredged sediments in harbor areas
(Cappucci et al., 2011; Cutroneo et al., 2014; Bigongiari et al., 2015). In
conflict with the port activities, the study area hosts four SCIs. They are
characterized by the presence of habitats (Posidonia oceanica
meadows and reefs of rocky substrates and biogenic concretions) and species
(Pinna nobilis and Corallium rubrum) enclosed in
attachments 1 and 2 of European Union (EU) directive 92/43/EEC.
Moreover, the promotion of underwater natural beauty, touristic exploitation
connected to the increased cruise traffic and the realization of new bathing
facilities has led to a drastic increase in the population density in
Civitavecchia during the summer. Many services are now available for
recreation thanks to the several beach licenses granted for food, bathing,
mooring of private vessels, and sport activities. An updated list of the
Latium Regional Office contains 72 beach licences released in 2014 to the
municipal districts of Santa Marinella and Civitavecchia. However, this urban
development was not associated with an improvement in the wastewater
treatment plant, which often caused the discharge of untreated water into the
bathing areas. Along the coast, between Civitavecchia harbor and the Punta
del Pecoraro bathing areas, four discharge points have been identified as
shown in Fig. 1c, in conflict with the recreational use of the coastal zone.
These discharge points present high concentrations of pathogenic bacteria
deriving from fecal contamination episodes.
Components of the C-CEMS
C-CEMS is a multi-platform observing system implemented in 2005 to face the
coastal conflicts by an ecosystem-based approach. According to the Copernicus
program, C-CEMS provides a monitoring service for the marine environment
through multi-source data including in situ and remote sensing observations.
In addition, C-CEMS integrates this information within mathematical models
that allow one to simulate specific events and forecast potential impacts
with a high spatial and temporal resolution, necessary for analyzing the
conflicts in coastal areas (Bonamano et al., 2015b).
The role of C-CEMS in the analysis of the conflicts between coastal
pressures, in terms of pollutant dispersion, and sensitivity areas,
represented by thematic maps. This observing system includes different
components such as fixed stations, in situ surveys, satellite observations
and numerical models. The components interact between them to transfer data
(by input I and validation V) from the in situ and satellite observations to
numerical models in order to reach enough temporal and spatial resolution to
analyze the pollutant dispersion in coastal waters. Only if conflicts between
anthropic activity and sensitivity areas occur are the potential impacts on
environment and socio-economical resources analyzed (Impacts) and suitable
mitigation measures applied (Response) in order to achieve Good Environmental
Status (GES) and implement Marine Spatial Planning Directive (MSPD).
The workflow reported in Fig. 2 shows the interaction between the C-CEMS
components and its functioning within the
Driver–Pressure–State–Impact–Response (DPSIR) scheme. C-CEMS allows one
to assess the coastal pressures (Pressure) through the analysis of the
dispersion of pollutants connected to the anthropic activities of the
Civitavecchia area. It also enables one to obtain thematic maps giving
information about the sensitivity areas (State) represented mainly by marine
protected areas and zones designated for recreational uses (bathing, diving,
watersports, fishing, etc.). The overlap between them makes a fundamental
contribution to GES achievement and MSP implementation, also playing a
crucial role in the detection of the ongoing conflicts. If a conflict occurs,
C-CEMS helps in the analysis of its potential impacts (Impact) on environment
and socio-economical resources, supporting the choice of the best mitigation
practices to be applied (Response).
The workflow also indicates all of the components of the C-CEMS that are
described in detail in the following paragraphs.
Fixed stations. Time series data collection is fundamental to improving the
ability to control and forecast spatial and temporal variations in a marine
environment. Fixed stations were installed along the Civitavecchia coast to
acquire physical, chemical, and biological data, as shown in Fig. 1. In
particular, a weather station (WS) acquires every 10 min wind speed, wind
direction, air temperature, air pressure, humidity and solar radiation. The
wind speed and direction represent the main forcing of the hydrodynamic
model, while the solar radiation data are used as input in the water quality
model. Two buoys (WB1 offshore, WB2 nearshore) measure every 30 min wave
statistical parameters (significant height, peak period, and mean direction).
The wave model is fed with WB1 data and then validated with the wave height
data collected by WB2. An acoustic Doppler profiler, ADP (WCS), deployed on a
Barnacle seafloor platform, acquires both current (with an acquisition rate
of 20 min) and wave height and direction (at intervals of 3 h). The current
velocity components are employed for the validation of the hydrodynamic
model. Three water quality fixed stations, one buoy (Water Quality Buoy, WQB)
outside the Civitavecchia harbor, and two coastal stations (WQS1 and WQS2)
make it possible to acquire every 20 min sub-superficial sea temperature,
conductivity (salinity, density), pH, dissolved oxygen, fluorescence of
chlorophyll a, and turbidity. In order to validate the satellite ocean
color data, chlorophyll a (Chl a) and total suspended matter (TSM) data
acquired by WQB were calibrated with the concentrations obtained by the water
sample analyses. The physical and biological parameters of the WQS1 and WQS2,
as well as those acquired by satellite observations, are used as initial
conditions of the water quality model.
WQB and WQS data are processed following the SeaDataNet parameter quality
control procedures: daily validated data sets are produced in order to
monitor in near real time the water quality; Edios xml files are provided for
monthly time series and stored following ISO 19139 and ISO 19115 formats
provided for metadata.
In situ surveys. A spatial extension of the observatory system is provided
by in situ collected data. The sampling strategy was conceived within the
scope and context of the project objectives in order to select the most
appropriate and efficient sampling approach. The field surveys typically
include periodic and ad hoc activities. The first concern the measurement of
the physical, chemical and biological variables of the water column using
multiparametric probes and seawater samples. Data acquired during periodic
surveys are used to validate and integrate the satellite observations in
order to give the spatial distributions of the seawater parameters as the
initial conditions of the water quality model. The ad hoc samplings are
carried out in order to define the nature and composition of the sea bottom
and to analyze the indicators of pollution near the human activity outputs.
These data feed the water quality model for the estimate of the bottom shear
stress, as well as the dispersion and/or the decay of pollutants in the
nearshore coastal waters.
Satellite observations. Remote sensing data are essential to provide
synoptic and extensive maps of biological and physical properties of the
oceans (Schofield et al., 2002). Recently, Earth observation (EO) data have
also been used to investigate the dynamic processes at high spatial
resolution along the Italian coasts (Filipponi et al., 2015; Manzo et al.,
2015). A few studies, among them Cristina et al. (2015), demonstrated the
usefulness of remote sensing for supporting the MSFD, using MEdium Resolution
Imaging Spectrometer (MERIS) sensor products. Similarly, we exploited both
ocean color from the Moderate Resolution Imaging Spectroradiometer (MODIS)
sensor and thermal infrared color from the Advanced Very High Resolution
Radiometer (AVHRR) to obtain daily Chl a, TSM and sea surface temperature
(SST) data. Such sensor data were chosen for their availability both in the
region of interest and in the period of C-CEMS data acquisition.
To estimate Chl a concentration, the MedOC3 bio-optical algorithm was
applied (Qin et al., 2007; Santoleri et al., 2008), while TSM was estimated
from the 645 normalized water-leaving radiance (645 nLw) by applying the
MUMM NIR atmospheric correction (Ruddick et al., 2006; Ondrusek et al.,
2012).
Chl a and TSM data collected by WQB and periodic in situ surveys were used
to validate the algorithms used for remote sensing data. A work is in
progress to implement a local algorithm specifically developed in the area of
interest (CASE II waters) in order to reach a better quantification of
Chl a and TSM concentrations along the study area (Cui et al., 2014). In
accordance with the Copernicus vision, the future development of this module
considers integrating EO data coming from the Optical High-Resolution
Sentinel sensors (Drusch et al., 2012), in order to increase the spatial
resolution for a more accurate analysis of coastal dynamic processes.
Numerical models. Mathematical models play a key role in the C-CEMS,
enabling one to analyze coastal processes at high spatial and temporal
resolution. In this context, the entire data sets collected by fixed
stations, satellite observations, and in situ samplings were employed as
input conditions and as a validation of the numerical simulations. The
mathematical models used in C-CEMS included the DELFT3D package, specifically
DELFT3D-FLOW (Lesser et al., 2004), to calculate marine current velocity,
SWAN (Booij et al., 1999) to simulate the wave propagation toward the coast,
and DELFT3D-WAQ (Van Gils et al., 1993; Los et al., 2004) to reproduce the
dispersion of conservative and non-conservative substances. The governing
equations of these models are described in detail in Lesser et al. (2004) and
Bonamano et al. (2015a).
The DELFT3D-FLOW model domain is rectangular and covers 70 km of the coastal
area with the Civitavecchia port located at the center. Neumann boundary
conditions were applied on the cross-shore boundaries in combination with a
water-level boundary on the seaward side, which is necessary to ensure that
the solution of the mathematical boundary value problem is well posed. Since
small errors may occur near the boundaries, the study area was positioned
away from the side of the model domain. The hydrodynamic equations were
solved on a finite difference curvilinear grid with approximately 39 000
elements. In order to limit computational requirements, a different
resolution was applied in the model domain extending from 15 × 15 m
in the Civitavecchia harbor area to 300 × 300 m near the seaward
boundary. The water column was subdivided into 10 sigma layers with a uniform
thickness to ensure sufficient resolution in the near-coastal zone.
Since dynamical processes occurring in coastal areas are modulated by wind
and wave conditions (tidal forcing was neglected because it does not exceed
0.40 m over the simulation periods), the hydrodynamic field was obtained by
coupling the DELFT3D-FLOW with SWAN that uses the same computational grid.
Wind data collected by WS were used to feed DELFT3D-FLOW, and the wave
parameters acquired by WB1 (offshore wave buoy) were employed to generate
the JONSWAP wave spectra (Hasselmann et al., 1980) as boundary conditions of
the SWAN model.
To resolve the turbulent scale of motion, the values of horizontal background
eddy viscosity and diffusivity were both set equal to 1 m2s-1
(Briere et al., 2011), and the k-ε turbulence closure model was
taken into account (Launder and Spalding, 1974). To assign the spatial
patterns of physical and biological parameters as initial conditions of
DELFT3D-WAQ, the satellite observations in the offshore zone and the WQS
measures in the nearshore one were used, respectively. These data were
integrated into the water quality model by applying the DINEOF technique
(Beckers et al., 2006; Volpe et al., 2012) that reconstructs the missing data
along the coast and in the areas affected by clouds.
Since the pollutant dispersion represents the C-CEMS results, the capability
of the observation system in reproducing the output of coastal pressures was
evaluated by comparing the model results with sea currents (WQB) and wave
(WB2) data.
The performance of the hydrodynamic models (DELFT3D-FLOW and SWAN) was
evaluated using the relative mean absolute error (RMAE) and the associated
qualitative ranking (excellent, good, reasonable, and poor; Van Rijn et al.,
2003).
Validation of current-speed (a), cross-shore (b),
and along-shore (c) components. The solid and dotted lines represent
the measured and computed time series, respectively. Statistics (RMAE) for
current-speed, cross-shore, and along-shore components are reported
in (d).
The marine currents resulting from the coupling between DELFT3D-FLOW and SWAN
were compared with in situ measurements collected by WCS from 13 to 18
January 2015. The velocity magnitude was reproduced with a “good” accuracy
since the RMAE value was less than 0.2. The long-shore and cross-shore
components of the marine currents exhibited a higher RMAE: 0.28 and 0.3,
respectively. The validation of current speed, cross-shore, and along-shore
components is shown in Fig. 3.
The performance of the SWAN model was evaluated using data acquired by the
WB2. We calculated the RMAE both for the entire data set and for three wave
direction intervals: 139–198∘ N (first interval),
198–257∘ N (second interval), and 257–316∘ N (third
interval). Considering the entire data set, the wave height was accurately
simulated (RMAE < 0.1), but the model error changed significantly
on the basis of the wave direction: the RMAE was higher between 139 and
198∘ N (0.26; reasonable agreement) and lower in the second and
third intervals (< 0.01; excellent agreement), as reported in
Fig. 4.
Validation of the SWAN model using RMAE values calculated both for
the entire data set and for three wave direction intervals.
C-CEMS applications
To test the capabilities of C-CEMS in defining the areas mainly affected by
pollutant dispersion, we considered two case studies that concerned the
potential effects produced by untreated wastewater discharge and dredging
activities (coastal pressures) on bathing areas and SCIs (sensitivity areas),
respectively. For both cases, two scenarios with different weather conditions
were considered: one reproduced a low wind intensity and low wave height (low
condition, LC), and the other simulated a strong high wind speed and high
wave height (high condition, HC).
Bacterial dispersion in bathing areas
The presence of pathogenic bacteria in seawater may cause several illnesses
including skin infections and dangerous gastrointestinal diseases (Cabelli
et al., 1982; Cheung et al., 1990; Calderon et al., 1991; McBride et al.,
1998; Haile et al., 1999; Colford et al., 2007).
The probability of human infection depends on the exposure time and the
concentration of the bacterial load in bathing areas. These parameters are
linked to the presence of untreated wastewater discharge in the study area
and the local hydrodynamical (currents and waves) and environmental
(salinity, temperature, and solar radiation) conditions. Among the bacteria
that can damage the health of bathers, Escherichia coli, a
Gram-negative enteric bacteria present in the feces of humans and
warm-blooded animals, is considered to be an indicator of water quality.
Although the pathogenic bacteria are neglected by MSFD, microbes are relevant
to several GES descriptors, notably Descriptor 1 (D1, Biological Diversity),
Descriptor 4 (D4, Foodwebs), Descriptor 5 (D5, Eutrophication), and
Descriptor 8 (Contaminants; Caruso, 2014; Caruso et al., 2015). However,
controlling water quality in bathing waters is required by national
(Legislative Decree 116/2008) and community environmental directives
(2006/7/EC).
Within the framework of C-CEMS to perform fecal pollution monitoring, in situ
water samplings were carried out weekly during the summer of 2012 at the
discharge points indicated in Fig. 1c to analyze the abundance of E. coli according to standard culture methods (APAT CNR, 2003).
LC (a) and HC (b) simulation results of the
bacterial dispersion in the Civitavecchia bathing areas. The distribution of
E. coli concentration refers to the end of the simulation period.
To define the zones mainly affected by the dispersion of pathogenic bacteria
in the Civitavecchia bathing area, we used the Microbiological Potential Risk
Area (MPRA), defined as the area over which the E. coli
concentration is greater than or equal to 1 % of the concentration
measured at a discharge point (Bonamano et al., 2015a). The dispersion of
E. coli was simulated by DELFT3D-WAQ using the mean bacterial
concentration measured during the summer at the discharge points. This model
shows a good performance in reproducing the bacterial load concentration near
the discharge points (Zappalà et al., 2015). The LC and HC simulations
that last 2 days were set to occur on August weekends when the beaches are
characterized by a larger number of bathers. The distribution of bacterial
concentration over the study area calculated by DELFT3D-WAQ depended on the
hydrodynamic field obtained from the coupling between DELFT3D-FLOW and SWAN
and on the decay rate proposed by Thoe (2010). It was calculated using the
salinity acquired by WQS1, WQS2 and WQB, and the surface solar radiation
measured by WS, TSM and SST obtained by the integration between satellite
observations and WQS station data.
The E. coli concentration calculated near the discharge points was high when low
marine currents (LC) were present, as reported in Fig. 5a. In particular,
the area around the PI18 point exhibited maximum values of pathogenic
bacteria because of the slow dilution of contaminated waters in that area.
During intense weather conditions (HC), the E. coli concentration near the
discharge points was lower than that calculated in the LC simulation.
However, the bacterial load was distributed over a more extended area, as
reported in Fig. 5b. In both simulations, the dispersion of E. coli did not affect
the bathing area located to the south of the study area.
Dredged sediment dispersion on Posidonia oceanica meadows
As previously reported, the port of Civitavecchia was subjected to extensive
dredging between 1 November 2012 and 31 January 2013. During the first phase
of the project, the dredging of the channel to access the port of
Civitavecchia was conducted by deepening the seabed to a depth of -17 m
above mean sea level over an area of approximately 31 000 m2. In the
ferry dock area, the seabed reached a depth of -10 m over an area of
approximately 123 650 m2 and -15 m over an area of approximately 51 900 m2. The total dredging volume was approximately 918 000 m3.
Studying sediment resuspension caused by these dredging activities is
critical because of its role in the dispersion of particulate matter in the
adjacent marine environment in both the sediment and water (Van den Berg et
al., 2001). Within MSFD, turbidity due to fine sediment dispersion is an
indicator reported in Descriptor 1 (D1, Biological biodiversity), Descriptor
5 (D5, Eutrophication) and Descriptor 7 (Hydrographical condition). In this
study, we considered two out of the four SCIs coded as IT60000005
(434.47 ha) and IT60000006 (745.86 ha) localized in the north and the south
of the Civitavecchia harbor, as shown in Fig. 1b. Since Posidonia oceanica makes up most of the SCIs, the study was focused on the effects of
dredging activities on the seagrass status. Dredging-induced suspended
sediment transport and deposition may have direct and indirect impacts on
this seagrass, such as reducing the underwater light penetration and
producing the burial of the shoot apical meristems, respectively. The plant
survival can be compromised if the light availability is less than 3–8 %
of SI (Erftemeijer and Lewis, 2006) or if low-light conditions persist for
more than 24 months (Gordon et al., 1994). The survival rates of
Posidonia oceanica can also be reduced if the sedimentation rate
exceeds 5 cm per year (Manzanera et al., 1995).
The health status of Posidonia oceanica meadows located in the two
SCIs was evaluated by a shoot density descriptor. This parameter was acquired
by scuba divers in the late spring of 2013 in correspondence with 14 stations
(3 in IT6000005 and 11 in IT6000006) following the method reported in Buia et
al. (2003). The thematic map was obtained by spatially interpolating the data
collected in the two areas.
LC (a) and HC (b) simulation results of the
dispersion of dredged materials in the study area. The distribution of fine
sediment concentration refers to the end of the simulation period.
The potential impact due to dredging activities was evaluated by DELFT3D-WAQ
simulations assuming a continuous release of fine sediments
(< 0.063 mm) in the northern zone of Civitavecchia harbor. The
amount of material released during dredging was calculated using a formula
from Hayes and Wu (2001) with a resuspension factor of 0.77 %, typical of
hydraulic dredges (Anchor Environmental, 2003). The percentage of fine
sediment fraction was 8.87 %, and its density was 2650 kg m-3
according to sedimentological data collected in the area affected by the
dredging works. Considering also that the dredging operations lasted
approximately 3 months (from November 2012 until January 2013), a continuous
release of 0.314 kg s-1 was assumed. TSM distribution, obtained by the
integration between satellite observations and WQB data, was used as a proxy
of spatial variation of fine sediment concentration in the study area to
provide the initial conditions of DELFT3D-WAQ. The transport, deposition, and
resuspension processes associated with the fine particles were reproduced
taking into account a settling velocity of approximately 0.25 m day-1,
a critical shear for sedimentation of 0.005 N m-2, and a critical
shear for resuspension of 0.6 N m-2 (Alonso, 2010). The DELFT3D-WAQ
simulations were run over the periods 26 November 2012 through 3 December
2012 (HC simulation) and 3–10 January 2013 (LC simulation). These time
intervals included the dredging period.
Like the analysis of bacterial dispersion, the fate of dredged sediments
within the study area was evaluated over an area in which the suspended solid
concentration was greater than or equal to 1 % of the value estimated at
the source point. This area was referred to as the Dredging Potential Impact
Area (DPIA). The results of the LC simulation, reported in Fig. 6a, revealed
that the dredged suspended materials were transported into the southern zone
of the study area, achieving a maximum distance of approximately 2 km from
the dredging point. In the HC simulation reported in Fig. 6b, the dredged
sediment dispersion moved toward the north, with a higher concentration in
the nearshore zone. Although the sediment plume extended 20 km from the
source, higher values of suspended solid concentration only affected the
Posidonia oceanica meadow closer to the harbor (the southern part of
SCI IT 6000005; Bonamano et al., 2015b).
Overlap between anthropic pressures indicated by the potentially
polluting zoning indicators (MPRA and DPIA) and sensitivity areas represented
as thematic maps to analyze urban discharge bathing area (a) and
dredging SCI (b) conflicts.
Discussion
In the last 2 decades, the importance of integrated ocean observing systems,
providing observations, numerical models and software infrastructures, has
been widely recognized, not only for scientific purposes, but also for
supporting societal needs such as the management of marine resources and the
mitigation of anthropic pressures through specific planning (Siddorn et al.,
2007; Weisberg et al., 2009; Tintoré, 2013; Sayol et al., 2014).
Especially in coastal environments where unpredictable pollution phenomena
often occur, the setup of multi-platform observing systems represents an
important step towards the analysis and forecasting of the impacts on both
environmental and socio-economical resources, overcoming the difficulties of
the traditional approach (Schofield et al., 2002), which does not allow a
proper identification.
To this aim, C-CEMS was implemented in 2005 along the coast of Civitavecchia,
a highly populated area characterized by the coexistence of industrial and
human pressures with environmental resources and values. It integrates fixed
stations, in situ surveys and satellite observations that ensure the
availability of a large amount of data allowing one to detect pollution
phenomena. Moreover, C-CEMS provides an ecosystem-based monitoring tool for
the analysis and forecasting of the coastal conflicts thanks to the use of
mathematical models. Kourafalou et al. (2015) highlighted the need to support
the advancement of coastal forecasting systems integrating the observational
and modeling components in order to analyze the high spatial and temporal
variability of coastal processes. The results of the hydrodynamic model
validation with sea currents (WCS) and wave (WB2) data show how C-CEMS is
able to reproduce accurately the output of coastal pressures in terms of
pollutant dispersion. DELFT3D-FLOW reproduces with good accuracy the velocity
components of marine currents, while SWAN calculates the wave height in the
nearshore area with a higher skill when the interval direction is
198–316∘ N. On the contrary, when the wave direction ranges between
139 and 198∘ N, the capacity of the model is more affected by the
increase in diffraction processes due to the Civitavecchia harbor breakwater.
Two examples of C-CEMS capacity to provide information related to some of the
most pressing conflicts facing our coastal zone, such as “urban discharge –
bathing area” and “dredging – SCI”, have been reported in this study. The
application of C-CEMS to these case studies allowed one to define the output
of human activities by the use of potentially polluting zoning indicators
such as MPRA and DPIA, giving the potential impacts produced by pathogenic
bacteria and dredged fine sediment on sensitivity areas under different
weather conditions (HC and LC). The overlap of the model results with the
thematic maps of the sensitivity areas enabled the detection of the coastal
areas affected by conflicts. In the first case, the overlap of MPRAs
calculated in the LC and HC scenarios shows that most of the bathing areas
were affected by a high level of bacterial contamination (Fig. 7a). Maximum
values of E.coli abundance were found near the PI18 and PP24
discharges because the dilution of the contaminated waters was inhibited by
the presence of artificial barriers. These unfavorable conditions may cause
risks to human health related to the contamination from potentially
infectious microorganisms for bathers. As a result, the bathing facilities
located within this zone were at risk of suffering significant economic
losses. However, the southern bathing area, where more bathers are found, was
never affected by E. coli dispersion (Fig. 7a). In the second case
study, the simulation results differ among the LC and HC scenarios (Fig. 7b).
In the LC scenario, DPIA does not overlap the southern SCI (IT 6000006), even
though the seagrass meadows were characterized by poorer health than in the
northern SCI. In HC, DPIA includes a restricted zone of Posidonia oceanica meadow (98.84 ha) in the northern SCI, closer to Civitavecchia
harbor, characterized by high shoot density values (between 400 and 550
shoots m-2). A previous study (Bonamano et al., 2015b) showed that
after the dredging activities the shoot density values were slightly higher
than before, highlighting how this conflict did not produce a loss of
environmental resources.
These results show how C-CEMS works to give a rapid environmental assessment
enabling one to analyze the impacts and potential mitigation practices when
an user–environment conflict is detected. If there are no conflicts, the
system still provides integrated information for the sustainable management
of the coastal zone as requested by IMP for the EU.
To make C-CEMS more effective, a flexible X-Band Radar system to continuously
measure the sea state (surface currents and wave field) in the nearshore zone
(Serafino et al., 2012) has been recently integrated. Moreover, to improve
the resolution of multi-spectral imagery in the study area, C-CEMS will soon
be available to get data also from the Sentinel-2 mission.
Since coastal marine ecosystems have been acknowledged to provide the most
benefits among all terrestrial and marine ecosystems (Costanza et al., 1997),
the assignment of an economic value to these natural resources is essential
for correct planning of marine coastal areas. The last step toward in
adequate management and conservation of marine environmental resources
concerns the implementation of C-CEMS for the quantification of economic
impacts in terms of losses of ecosystem services and goods.
Compared to other regional operational monitoring systems currently available
and reported in the literature, the practical innovation offered by the
C-CEMS relies on the fact that this new system allows one to detect the
impacts arising from the potential conflicts between coastal pressures and
sensitivity areas; in this sense, C-CEMS can be considered an operational
tool to meet the needs of MSFD and MSP directives.
Conclusions
The activities and techniques employed are in line with those used in several
environmental monitoring experiences; what really is new is their integration
into an operational network, the first in the Tyrrhenian Sea, actually used
by a professional stakeholder such as the Port Authority of Civitavecchia.
Coastal observatories play a major role in providing the information needed
to face the new European environmental challenges mainly focused on the GES
achievement and MSP implementation. Thanks to the integration of different
observing platforms at different scales, and to the provision of data and
tools, these systems contribute to the monitoring of coastal pressures and
environmental states. C-CEMS has been conceived to include all the
above-mentioned features to support the coastal management about the
detection of the conflicts between anthropic pressure and sensitivity areas.
Such information overlapped with the characteristics of coastal marine
ecosystems intended for recreational uses can be considered to be the first
step in the establishment of the marine functional zoning scheme made by
different types of zones with varying levels of limited uses (Douvere, 2008).
Acknowledgements
The authors thank the Environmental Office of the Civitavecchia Port
Authority for funding the implementation of C-CEMS. The authors are also
thankful to two anonymous reviewers for providing useful comments that helped
in improving a former version of this paper. Finally, the authors acknowledge
the NOAA CoastWatch program and NASA's Goddard Space Flight Center,
OceanColor Web, for data availability. Edited
by: V. Brando
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