Addressing coastal risks related to sea storms requires an integrative approach
which combines monitoring stations, forecasting models, early warning systems,
and coastal management and planning. Such great effort is sometimes possible
only through transnational cooperation, which becomes thus vital to face,
effectively and promptly, the marine events which are responsible for
damage impacting the environment and citizens' life. Here we present a
shared and interoperable system to allow a better exchange of and elaboration on
information related to sea storms among countries. The proposed integrated web
system (IWS) is a combination of a common data system for sharing ocean
observations and forecasts, a multi-model ensemble system, a geoportal, and
interactive geo-visualisation tools to make results available to the general
public. The multi-model ensemble mean and spread for sea level height and wave
characteristics are used to describe three different sea condition scenarios.
The IWS is designed to provide sea state information required for issuing coastal
risk alerts over the analysed region as well as for being easily integrated
into existing local early warning systems. This study describes the
application of the developed system to the exceptional storm event of
29 October 2018 that caused severe flooding and damage
to coastal infrastructure in the Adriatic Sea. The forecasted
ensemble products were successfully compared with in situ observations. The
hazards estimated by integrating IWS results in existing early warning systems
were confirmed by documented storm impacts along the coast of Slovenia,
Emilia-Romagna and the city of Venice. For the investigated event, the most
severe simulated scenario results provide a realistic and conservative
estimation of the peak storm conditions to be used in coastal risk management.
Introduction
Sea storms represent the main threat in coastal areas. In fact, they can
cause a range of potential hazards, such as coastal erosion and inundation,
as well as damage to infrastructure and to the important cultural heritage
exposed to these phenomena . Along the coast, extreme storms can also significantly
affect businesses activities, such as aquaculture, fisheries, tourism and
beach facilities. The potential future effects of global climate
change emphasise the need for strategies based on an anticipatory approach
particularly in coastal areas at immediate and high risk . This is particularly true for coastal wetlands if
enough additional accommodation space will not be created through careful
nature-based adaptation solutions to coastal management .
Coastal flooding is induced by extreme sea levels, determined by the increase
in sea level caused by strong winds and low atmospheric pressure (storm surge),
often in combination with high tides . Under such extreme
meteorological conditions, the coast could be also vulnerable to stormy waves
with potential damage to infrastructure and erosion. Moreover, when waves
reach the coast they interact with the bathymetry and drive an additional
increase in water levels through wave set-up , and they
travel up and down the beach before being reflected seaward (swash processes).
The maximum vertical excursion of wave uprush on a beach or structure above
the still water level is called the wave run-up .
The water levels along the coast can be estimated by numerical models and
combined with a digital elevation model (DEM) for forecasting inundation
intensity and extent. Several methodologies have been developed and applied
at the basin and local scales for estimating hazard maps for coastal flooding
.
It must be taken into account that meteorological and ocean models provide
just an approximation of reality, despite their continuous development
and improvements. Moreover, the interactions between atmospheric, oceanic and
coastal processes are not fully understood, resulting in large uncertainties in
the predictions of coastal flooding, in particular under extreme conditions
. This is mainly due to the chaotic nature of
the atmosphere and the complexity of the air–sea interactions across scales
over several orders of magnitude . Small errors
in the initial conditions of a numerical weather prediction model grow rapidly
and affect predictability; forecasted atmospheric conditions are then affected
by errors . However, as stated by ,
atmospheric forcing is not the only source of uncertainty in storm
surge forecasting. Many other sources of uncertainty, like the model numerics,
resolution, parameterisation, boundary conditions and initial sea state,
contribute non-linearly to the final forecast uncertainty.
Coastal flooding of urban areas, beach erosion, and damage to infrastructure
and productive activities can worsen if combined with the absence of an
adequate sea storm management strategy with significant related economic
costs . The difficulty of reacting promptly
to extreme events is also connected to the lack of shared data and know-how.
Recognising the importance of information sharing for disaster risk reduction and
human safety and well-being, the World Meteorological Organization (WMO;
https://public.wmo.int/, last access: 20 October 2019) has
promoted the standardisation and exchange of observations since 1873.
Similarly, the Permanent Service for Mean Sea Level (PSMSL;
http://www.psmsl.org/, last access: 20 October 2019)
and the Global Sea Level Observing System (GLOSS;
http://www.ioc-sealevelmonitoring.org/, last access: 20 October 2019)
are responsible for the collection, publication, analysis and interpretation of
sea level data from the global network of tide gauges. In the same way,
at the European level, the Copernicus Marine Environment Monitoring Service
(CMEMS; http://marine.copernicus.eu/, last access: 20 October 2019),
the European Marine Observation and Data Network (EMODnet; http://www.emodnet.eu/, last access: 20 October 2019), and the European Global Ocean Observing System
(http://eurogoos.eu/, last access: 20 October 2019) aim at sharing information
from both satellite and in situ observations as well as state-of-the-art analyses
and daily forecasts, which offer an unprecedented capability to observe,
understand and anticipate marine environment events.
Despite such international effort on sharing data, as weather, climate and
ocean know no national boundaries, the insufficient level of cooperation among
neighbouring countries is often a cause of ineffective actions at the local level and
missed opportunities to collaborate with other actors to increase overall
preparedness for sea storms .
The problem of sea storms is particularly relevant for the Adriatic Sea, where
extreme sea levels are higher than in other parts of the Mediterranean Basin
and where several coastal cultural World Heritage sites
are located (http://whc.unesco.org/, last access: 20 October 2019)
at risk of coastal flooding and erosion .
This study presents the management approach for sea storm hazards initiated as
part of the I-STORMS (Integrated Sea sTORm Management Strategies) project for
the coastline of the Adriatic–Ionian macro-region
(https://istorms.adrioninterreg.eu/, last access: 20 October 2019).
This paper describes a joint strategy for safeguarding the coast from sea
storm emergencies by sharing knowledge, data and forecasts among involved
countries and improving their capacities in terms of early warning and
management procedures. This study focuses on the recent exceptional storm event
of 29 October 2018, which is taken here as a pilot study
for applying and testing the developed approach.
Study area
The Adriatic and Ionian seas are part of the Mediterranean Sea positioned
between the eastern coastline of Italy, countries of the Balkan Peninsula (from
Slovenia to the south through Croatia, Bosnia and Herzegovina, Montenegro and to Albania)
and Greece. The Adriatic Sea is an 800 km long, 150 km wide elongated
semi-enclosed basin interacting with the Ionian Sea through the Otranto Strait in
the southern part (Fig. ). The shallow northern Adriatic Sea
is the Mediterranean sub-basin where storm surges reach higher values
, mainly triggered by a strong south-easterly moist and
warm wind, called sirocco. For this reason, in this area storm surges and
waves have been deeply investigated in the past . Tidal dynamics are particularly
evident in the northern Adriatic Sea, where the most energetic tidal constituents,
the semi-diurnal M2 and the diurnal K1, reach
amplitudes of 27 and 18 cm, respectively .
Bathymetry of the Adriatic Sea, with monitoring stations for sea
surface height (yellow dots) and waves (red stars). The 50-year extreme sea
levels (ESLs) from are also reported. Background:
EMODnet bathymetry .
The weather in the Adriatic area is strongly influenced by local orography and
small-scale processes . The use of high-resolution
meteorological models is essential to capture the temporal and spatial
inhomogeneity of north-easterly bora winds, characterised by topographically
controlled high-speed wind jets along the eastern shore . The same holds for sirocco: global and regional numerical
models have been shown to consistently underestimate its speed due to the fact
that orography, and hence the channelling of the air flow, is not well
represented at typical model resolution . Long-term analyses
of general wind conditions over the Adriatic basin further indicate a trend
of reduction of the intensity of wind events – mostly due to bora – and a general increase in terms of frequency, mostly associated
with the increasing storminess of sirocco .
The eastern and western coasts of the Adriatic Sea greatly differ in appearance
and are therefore differently impacted by sea storms. The western coast is
largely sedimentary, with mild sloping and sandy beaches, while the eastern
coast is composed of many islands and headlands and is generally high and
rocky. Due to its alluvial origin, natural subsidence occurs in the
north-western Adriatic Sea because of compaction of fine-grained deposits
, which is worsened by the human exploitation of
underground water and gas in some areas. Several shallow coastal transitional
water bodies are present along the Italian coastline; the main ones are the
lagoon of Marano–Grado, the lagoon of Venice, the system of lagoons of the Po delta,
the lagoon of Lesina and the lagoon of Varano .
Extreme sea levels cause the flooding of several coastal cities on both sides
of the Adriatic Sea , especially
when the storm is associated with spring tides . Part of the
western coast is below sea level, and therefore it is also very vulnerable
to such hazards . These coastal zones are also strongly
impacted by north-easterly storms with severe morphological impacts on natural
sectors and damage to structures along urbanised zones . Conversely, recurrent meteotsunami events occur on the
eastern side of the Adriatic Sea, and particularly on the Croatian coast and
islands, causing flooding and damage in some harbours .
According to and , the northern Adriatic
coastline, due to its low elevation, will be one of the regions in the
Mediterranean area most exposed – in terms of coastal risk for flooding and
erosion – to future climate change.
Material and methods
In order to address the territorial challenges related to sea storms' effect on
the coastal areas, we developed a shared and interoperable system (integrated web system – IWS) to allow a better exchange of information at a basin scale.
Therefore, available resources can be accessed simultaneously in an aggregated
and standard way. IWS was designed to specifically store, visualise and
share the following category of geospatial and informative contents:
historical and real-time (or near-real-time) time series of observations
from fixed-point sensor networks;
outputs from existing operational forecast models;
localisation and description of coastal sea storm events that have
damaged the environment, sociocultural and economic assets;
bi-dimensional geospatial layers to provide georeferenced
representations of the study area, with such layers being organised into thematic
categories (e.g. terrestrial and maritime boundaries, ports, shorelines,
morphology and bathymetry, cultural heritage, and coastal defence work);
datasets, model outputs and time-series metadata to improve
discoverability and proper reuse of the shared resources.
All information on coastal disaster due to sea storm events (historical and
more recent) is organised and mapped into geospatial layers which constitute
the Sea Storms Atlas. This series can be used to draw the map of risk
characterisation of the coast with the aim of identifying the most vulnerable
areas and supporting the planning of coastal area use and development
.
The IWS architecture design follows a resource-centred and service-oriented
approach as described in and .
Following the so-called service-oriented geoportal architecture, the IWS
includes three main layers:
The resource layer corresponds to the physical storage of the structured
information in databases or files.
The access layer includes all code and software designed to provide
access to the resources in the appropriate format.
The graphical user interface (GUI) is the client-side component of the geoportal architecture; the role of GUIs is not limited to the rendering of a given set of resources but also includes the aggregation of relevant resources through lightweight and loosely coupled JavaScript code. In other words, the GUI is not only a presentation layer but also creates a mash-up of relevant resources.
The IWS overall architecture is described in Fig. .
Furthermore, the schema highlights the user typologies served by the IWS and
the interactions and connections with the data sources and with external
portals and early warning systems. The IWS is structured into the following six main components.
The data importer for data ingestion, harmonisation and preparation, and deposit of the datasets in the storage facilities of the resource layer. For this purpose, we implemented the use of data servers (e.g. THREDDS and
Hyrax), with the advantage that such web systems are open source and
already implement services like DAP (Data Access Protocol), WCS (Web Coverage
Service), WMS (Web Map Service) and SOS (Sensor Observation Service).
The resource layer for storing the datasets, metadata, model forecasts, resources and all the necessary information. It consists of a
combination of different storage solutions in order to support the several
heterogeneous data models and formats shared and all the information
needed to achieve a fully operational infrastructure (e.g. metadata, catalogue
information, and user accounts and profiles).
The transnational multi-model ensemble system (TMES) for combining the results from existing operational forecasting systems (described in Sect. ). TMES outputs are also stored in the
resource layer.
The task manager middleware for orchestrating the communication with IWS components (e.g. data importer and TMES) in order to launch the process (e.g. download the data from the partners' node), monitor the execution status and concatenate multiple tasks in a single processing pipeline. The task
manager middleware supports a time-based job scheduler, a
synchronous–asynchronous task queue system and a message broker system.
The common data sharing system (CDSS; access layer) for publishing the API and the web services to interact (e.g. search, visualise, download and manage) with the informative resources through standardised interfaces (e.g OGC-Web service and web API).
The Geoportal (graphical user interface) for implementing the end-user interfaces and tools to search, visualise, explore and analyse informative resources. The map viewer and composer is an interactive and dedicated GUI for creating, managing and sharing multi-layered maps and for navigating and querying them.
Schematic representation of the IWS architecture.
IWS implementation follows a
full-fledged free and open-source software (FOSS) approach in order to foster
transparency, transferability and durability of the system and to be in accord
with the open-source software strategy of the European Commission .
IWS provides spatial data infrastructure functionalities for accessing
geospatial layers and forecast model outputs through OGC (Open Geospatial
Consortium; http://www.opengeospatial.org/, last access: 20 October 2019)
interoperable services. Such an approach is widely accepted and implemented at
the European (INSPIRE directive – ;
EuroGEOSS initiative – )
and global level (GEOSS – Global Earth Observation System
of Systems) to facilitate intergovernmental and inter-agency data exchange and
harmonisation . Incorporating the THREDDS data server,
IWS provides access to stored resources also through OPeNDAP and netCDF
standard services and formats. These standards are all products of the
scientific communities in oceanography, meteorology and climate sciences and
are designed to specifically meet their needs , providing
coherent access to a large collection of real-time and archived datasets from a
variety of environmental data sources at a number of distributed server sites
.
The monitoring networks
A joint asset which could be exploited through fruitful cooperation is the
presence in the whole Adriatic–Ionian coastal territories of large networks of
sensors and stations. In the Adriatic region, we mapped 35 tide gauges (9
inside the lagoon of Venice) and nine wave stations, with the highest concentration
in the northern Adriatic Sea. The location of all reported monitoring stations
is illustrated in Fig. , and their general characteristics
are summarised in Tables and
for sea level and wave, respectively. The stations' lists are not exhaustive,
since there are other monitoring stations active in the area, the data of which
were not available at the time of writing this document.
In several cases, the stations are also equipped with sensors for monitoring
meteorological (wind speed and direction, sea surface pressure, air
temperature, relative humidity, and precipitation) or oceanographic parameters
(seawater temperature, salinity, and current speed and direction).
The forecasting systems
A multi-model ensemble was developed to combine the outcomes of existing
ocean and wave forecasting systems, helping with improving the forecast accuracy
and reliability on one hand and by adding indications on the forecast
uncertainty on the other hand. The error of multi-model ensemble products
should be the lowest on average compared to those of the ensemble members
. According to , operational forecasts
benefit from the combination of different ocean models by considering
different physical parameterisations, numerical schemes, model resolutions and
forcings.
Several operational ocean forecasting models are currently available for the
Adriatic–Ionian region. Here we combined 17 forecasting systems, with
10 predicting sea level height (either storm surge or total water level) and 9
predicting wave characteristics. The general characteristics of the forecasting
systems are summarised in Tables and ,
respectively, for sea level and wave. MED-Currents and MED-Waves forecasts were
retrieved from CMEMS (http://marine.copernicus.eu/). We would like to point out that there
are other operational systems active in the area e.g. the pan-European
Storm Surge Forecasting System; , the results of which were
not available at the time of writing this document.
Details of the sea level forecasting systems used in the TMES.
Key references are reported at the bottom of the table.
Managing authority – countrySystem nameDomainForecastHorizontal res.Core engineTideBaroclinicMeteo. forcingrangeCity of Venice – ITSHYMED1Mediterranean Sea10 dVar. up to 200 mSHYFEMNoNoECMWF 1/8∘National Research Council – ITKassandra2Mediterranean Sea4 dVar. up to 100 mSHYFEMYesNoBOLAM 8.3 km,MOLOCH 1.25 kmNational Research Council – ITISSOSMediterranean Sea3 dVar. up to 200 mSHYFEMNoNoBOLAM 8.3 kmNational Research Council – ITTiresias3Adriatic Sea3 dVar. up to 10 mSHYFEMYesYesMOLOCH 1.25 kmAg. for Env. Protection and Energy ER – ITAdriaROMS4Adriatic Sea3 d2 kmROMSYesYesCOSMO-5M 5 kmAg. for Env. Protection and Energy ER – ITAdriac5Adriatic Sea3 d1 kmROMSYesYesCOSMO-2I 2.2 km,COSMO-5M 5 kmInst. for Env. Protection and Research – ITSIMMb6Mediterranean Sea3.5 dVar. up to 1 kmSHYFEMNoNoBOLAM 11 kmInst. for Env. Protection and Research – ITSIMMe6Mediterranean Sea4 dVar. up to 1 kmSHYFEMNoNoECMWF 55 kmSlovenian Environment Agency – SLSMMO7Adriatic Sea3 d1/216∘NEMOYesYesALADIN 4.4 kmCMCC – ITMED-Currents8,9Mediterranean Sea10 d1/24∘NEMONoYesECMWF 1/8∘
Details of the wave forecasting systems used in the TMES.
Key references are reported at the bottom of the table.
Managing authority – countrySystem nameDomainForecastHorizontal res.Core engineMeteo. forcingrangeNational Research Council – ITKassandra1Mediterranean Sea4 dVar. up to 100 mWWMIIIBOLAM 8.3 km,MOLOCH 1.25 kmCNMCA/National Research Council – ITNettuno2Mediterranean Sea3 d4.5 kmWAMCOSMO-ME 5kmNational Research Council – ITHenetus3Adriatic Sea5 d1/12∘WAMECMWF 1/8∘Ag. for Env. Protection and Energy ER – ITSWAN4Med., Adriatic Sea3 d25, 8 kmSWANCOSMO-5M 5 kmAg. for Env. Protection and Energy ER – ITAdriacAdriatic Sea3 d1 kmSWANCOSMO-2I 2.2 km,COSMO-5M 5 kmInst. for Env. Protection and Research – ITSIMM5Med., Adriatic Sea3.5 d1/30, 1/240∘WAM, SWANBOLAM 11 kmSlovenian Environment Agency – SLSMMOCentral Med. Sea3 d1/60∘WAMALADIN 4.4 kmMet. and Hydrol. Serv. – HRWWM6Adriatic Sea3 dVar. up to 10 mWWMALADIN 8 kmHCMR – GRMED-Waves7,8Mediterranean Sea10 d1/24∘WAMECMWF 1/8∘
The different operational models are forced at the surface boundary by several
meteorological models (ECMWF, BOLAM, MOLOCH, COSMO, WFR and ALADIN) with a horizontal
resolution ranging from 55 to 1.25 km. The length of the ocean forecast is mostly
related to the length of the meteorological forecast and varies from 3 to 10 d.
There is large variability in the model's set-up in terms of spatial resolution,
temporal frequency, spatial domain (Mediterranean Sea, Adriatic Sea and northern
Adriatic Sea), grid arrangement (e.g. structured or unstructured) and data
format (netCDF and GRIB). Three of the considered systems (Kassandra, MED-Currents
and Adriac) account for the current–wave coupling, and two forecasting systems
perform data assimilation of tide gauge observations in the operational chain
(SIMMb and SIMMe).
TMES is implemented as an internal processing engine which interacts directly
with the resource layer to access the datasets (e.g. time series and forecasts)
and to deposit the processing results (e.g. ensemble model result, report and
statistics). Such outputs are available to the end users and external portal
through the common data sharing system and the geoportal web interfaces.
All numerical model results are interpolated, through a distance-weighted
average remapping of the nearest neighbours, on a common regular latitude–longitude grid
covering the Adriatic–Ionian macro-region with a resolution of 0.02∘. For the purpose of
the coastal flooding hazard, the total sea level height must be forecasted.
Therefore, the astronomical tide level values obtained by a specific SHYFEM
application over the Mediterranean Sea are added to the
residual sea level simulated by the operational systems not accounting for the
tide (SHYMED, ISSOS, SIMMb, SIMMe and MFS). These obtained sea level heights
simulated by the different models are all referred to the geoid. The spread
among the operational simulations is expected to represent a measure of the
uncertainty of prediction and should be linked to the forecast error so that cases
with the largest spread are those with the highest uncertainty and where a large
error of the ensemble mean (and also of the deterministic forecast) is more
likely . TMES produces results in terms of the ensemble
mean and standard deviation, accounting for a measure of the forecast
uncertainty .
Storm impact assessment and early warning systems
The vulnerability to sea storms of a particular segment of coast depends on a
wide number of variables not only related to the magnitude of the storm but
also including the land characteristics and the social and economic activities that
distinguish that area. In order to draw a map showing the coastal areas affected
by stormy conditions along the Adriatic–Ionian region, the coast is
subdivided into segments of variable length in function of morphology, human
settlements and administrative boundaries. The coastal assessment units were
selected according to the Mediterranean coastal database (MCD) developed by
. The MCD segments have an average length of 4.5 km. For
each of these units, the database provides information on the characteristics
of the natural and socio-economic subsystems, such as vertical land movement,
coastal slope, coastal material, and the number of people exposed to sea level
rise and to extreme sea levels.
At each location, three sea condition scenarios are computed considering the mean
and standard deviation of predicted sea level and wave ensembles:
MIN: ensemble mean - ensemble SD;
MEAN: ensemble mean;
MAX: ensemble mean + ensemble SD.
Over the whole Adriatic–Ionian coastal region, the nearshore forecasts provided
by the TMES were combined with the coastal characteristics (coast material
and slope) provided by the MCD database for computing the total water level
(TWL). For the coastal segments characterised by sandy beaches, the TWL was
computed by combining the sea level height, wave set-up and wave run-up according
to Stockdon's formula (R2, the 2 % exceedance
level of run-up maxima; ). For gravel beaches and rocky
cliffs, other methodologies should be used for estimating wave run-up
, but they could not be applied in this study
due to the lack of the required detailed coastal information (sediment grain
size, type of rocks, and permeability of the structure).
It is well known that the estimation of the total water level is strongly
influenced by the local coast typology and morphology and that the MCD segments are
sometimes too coarse to represent complex morphologies, especially in confined
coastal systems (lagoons) and along the eastern rocky coast. Therefore, in
order to provide more reliable and resolute hazard assessment at a very fine
coastal scale, the IWS has been designed to provide multi-model forecast
products to existing early warning systems, developed in areas were a deep
knowledge of the coastal dynamics and high-resolution datasets (topography
and bathymetry) are available. In this study, we present three existing local
forecasting and early warning systems operative in the Adriatic Sea (Slovenia,
the Emilia-Romagna region and the city of Venice), to which IWS provides the
information required for issuing coastal risk alerts.
Slovenia
TMES forecasts can be used directly by regional authorities for assessing
the hazard of a particular segment of the coast to the storm event according to
predefined thresholds. As an example, we report here the IWS-based hazard
estimates for the Slovenian coast, which is predominantly rocky and steep
(flysch cliffs) and therefore well protected from flooding during storm surges.
Important exceptions are the salt pans (Sec̆ovlje and Strunjan) and
urban areas such as Piran, Koper and Izola, where lower parts get flooded
up to 17 times per year data for the 1963–2003 period;,
with consequent damage to private property and cultural heritage. The
Slovenian Environment Agency issues a warning when the predicted sea level
at Koper exceeds the yellow alert level, which is set at 300 cm (above local
datum). This is the value that marks the flooding of the lowest coastal
urban areas. Orange and red alert levels are set to 330 and 350 cm,
respectively.
Emilia-Romagna
In addition to the evaluation of thresholds for identifying critical storm
conditions at sea , since December 2012, the Emilia-Romagna
region (northern Italy) has provided daily 3 d forecasts of the coastal storm
hazard at eight key sites along the coast where several past sea storms
have induced significant morphological change and damage. The Emilia-Romagna
coastline is particularly vulnerable to sea storms due to its low-lying
nature and high coastal urbanisation . During major
storm events, the water levels often exceed those of the dune crest and
building foundations . The existing coastal early warning
system is based on the 1-D cross-shore implementation of
the XBeach morphodynamic model , a 2DH (depth-averaged)
cross-shore process-based model that solves intra-wave flow and surface
elevation variations for waves in intermediate and shallow water depths.
The XBeach model is used to forecast wave run-up and total water level during
storm events. For each key site, the IWS provides the sea level
and wave characteristics for the three above-mentioned sea condition scenarios to the XBeach model.
Hence, the developed methodology allows converting the forecast uncertainty on
nearshore sea conditions into a coastal flooding hazard range of predictions.
Coastal hazard is estimated here in terms of two storm impact indicators:
safe corridor width (SCW), a measure of the amount of dry beach
available between the dune foot and waterline for safe passage by beach
users,
building waterline distance (BWD), a measure of the amount of dry beach
available between the seaward edge of a building and the model-derived
waterline.
City of Venice
The city of Venice is located in the centre of a shallow lagoon and is composed
of more than a hundred islands linked by bridges. The elevation of these
islands is extremely low, subjecting them to flooding during storm tides
(resulting from the combination of storm surge and the astronomical tide), which
in turn threatens the unique cultural heritage of this city and affects its
everyday life, causing difficulties in transport, the use
of roads and internal channels, emergency procedure response, and commercial
activities. In the city of Venice, a bulletin of forecasted sea level up to 3 d is emitted three times per day (at 09:00, 13:00 and 17:00 UTC) by the Tide Forecast
and Early Warning Center (CPSM). The forecast is based on a combination of
statistical and deterministic models as well as an evaluation of the synoptic
meteorological conditions
(https://www.comune.venezia.it/it/content/centro-previsioni-e-segnalazioni-maree, last access: 20 October 2019).
Since Venice is protected from the sea by barrier islands (separated by three
inlets), storm waves do not affect significantly – through set-up and run-up –
the sea level height inside the lagoon . While propagating
from the sea to the lagoon through the inlets, the tidal signal is deformed, either
damped or amplified, according to a relationship between local flow resistance and
inertia and the characteristics of the incoming open-sea signal . For those reasons, sea level height forecasts are
used instead of TWL predictions in the operational system. To propagate
the sea level from the inlets to the inner lagoon, nearshore TMES values
of sea level height – for each of the above-mentioned three sea condition
scenarios – are referred to the local sea level reference datum (Punta della
Salute) and used as open-sea boundary conditions in the SHYFEM finite element
hydrodynamic model of the lagoon of Venice .
Such model adequately reproduces the complex geometry and
bathymetry of the lagoon of Venice using an unstructured numerical mesh composed
of triangular elements of variable form and size (down to a few metres in the
tidal channels). Flooding maps of the city floor are produced by imposing the
sea level height observed and predicted at Punta della Salute (at intervals of 10 cm) to a centimetre-accurate digital terrain model of the city
(http://www.ramses.it/, last access: 20 October 2019).
The municipality plan of procedure in case of high and low tide defines the actions several stakeholders (civil protection,
public security and rescue forces, transport companies, and public services) adopt
in case of a risk of flooding with respect to the specific forecasted sea
levels. Depending on the forecasted sea level, particular categories of
stakeholders are informed by CPSM and elevated wooden walkways are installed
in areas of the city that are prone to flooding. The communication channels for
the warning include the website, messages (SMS or social network), e-mails, phone
calls, acoustic signals, and direct information (door to door). Moreover, an
operating room with forecasters functions 24 h a day at CPSM during
the most severe storm tide event.
The 29 October 2018 eventStorm description
On 29 October 2018, an exceptional storm hit the central and northern part of
Italy with very strong south-easterly winds (called sirocco) over the Adriatic
Sea. The basic meteorological situation of the 2018 storm was similar to the
1966 and 1979 ones, although with a weaker pressure gradient over the Adriatic
area . The weather condition was governed by a semi-stationary
upper-level trough which was positioned over the western Mediterranean on
28 October and was only slowly moving north-eastward on
29 and 30 October (Fig. ).
The upper-level southerly flow on the eastern side of the trough was very intense
due to strong pressure gradients throughout the whole period of the event.
The occurrence of the upper-level trough resulted in a formation of a very
intense low-level low-pressure system over the Alps and central Mediterranean
which was the most prominent surface feature of the event.
ECMWF 10 m wind speed and mean sea level pressure fields over the
Mediterranean Sea on 29 October 2018 at 18:00 UTC.
The air mass over the Italian Peninsula and Adriatic was very unstable on
28 and 29 October due to the meridional flow
which was bringing moist and warm air from North Africa and central Mediterranean.
In this sense, it was a typical Autumn situation when the amount of precipitation
can be extreme, especially on the windward side of orographic barriers. The flow
at the surface was further intensified by extreme convection over the Apennines
and the Alps. The amount of precipitation in northern Italy and wind
storms over the Alps and northern Adriatic was rather extreme and not often
observed in such intensity.
It is worth mentioning that the sirocco wind started already on
26 October at the most of Adriatic and lasted for almost 4 d
without interruption, with the strongest wind in the northern Adriatic on
29 October afternoon, just before the passage of the cold front.
Most of this time, the sirocco was well developed over the entire Adriatic
basin and even further south in the Ionian Sea. This meant that the fetch was
exceptionally long for the locations in the northern Adriatic Sea.
Observed sea level height (a) and significant wave heights (b) measured at different locations (see Fig. for reference).
Consequently, the sea level rose in the northern part of the Adriatic Sea – the
area most affected by flooding – reaching peak values around 13:00 UTC in Venice,
Koper and Rovinj (Fig. a). Exceptional sea levels were reached
also along the Emilia-Romagna region, with values higher than 1 m for about 5 h,
as measured at Porto Garibaldi. It has to be noted that these maximum values
were not registered during the storm peak (happening at around 19:00 UTC in this
location) due to being out of phase with the astronomical tide. A secondary maximum
was recoded in Koper and Rovinj just after the cold front passed and when the
wind and waves were decreasing but the tide was rising. Along the central
and southern Croatian coast, sea level resulted in being marginally affected
by storm surge even if weak meteotsunamis were observed during the frontal
passage late in the evening on 29 October.
The very long wind fetch allowed waves to develop over the whole
basin. Available wave monitoring stations recorded values of significant wave
height (the average height of the highest one-third of all waves measured; SWH)
up to 6 m at the Piattaforma Acqua Alta (PTF), 15 km off the shore of the Venetian
littoral and up to 4.7 m (8 m of maximum wave height) near the city of Rovinj
(Fig. b). Along the north-western Italian coastline, due
to its mild slope, wave breaking occurs and the measured SWH
reaches values of about 2 m during the storm peak (Nausicaa and
Senigallia monitoring stations). On the Gulf of Trieste, the highest waves
occurred 6 h later (Zarja wave buoy), probably due to the eastward shift of
the wind induced by the passage of the cold front. In the south-eastern Adriatic
Sea, high wind and wave values were recorded even before the cold front on
28 October. The highest waves recorded in Dubrovnik reached
values of about 5 and 9 m for significant and maximum wave height, respectively.
Rough sea conditions (SWH >2.5 m) lasted for 57 h, while the very rough sea
state (SWH >4 m) was recorded for 9.5 h. According to long-term time
series of observations, the 29 October 2018 event reached the records of the
second highest sea state ever measured all along the Adriatic coast.
Storm predictability
Here we present the results of the forecasting system at the hourly time step and
for the day of the event only. However, as described by ,
up to 5 d (6 d for the surge) earlier there were indications of a severe
event.
Figure shows the TMES results in terms of the ensemble mean
and standard deviation for both the sea level height (Fig. a and b) and the
significant wave height (Fig. c and d). The storm surge during the 29 October
event affected mostly the northern Adriatic Sea (Fig. a),
while severe sea conditions occurred over most of the Adriatic Sea, with the
higher waves impacting the Croatian coast (Fig. c). The
ensemble operational system also provides the estimation of the uncertainty
associated with the forecast of this event. Uncertainty is generally higher
where the sea level and the waves reach the highest values
(Fig. b and d). The ensemble standard deviation reached
maximum values of about 30 cm for the sea level and 1.5 m for the
significant wave height.
29 October 2018 results of TMES in terms of the ensemble mean (a, c) and ensemble standard deviation (b, d) for sea level height at 13:00 UTC (a, b) and significant wave height at 19:00 UTC (c, d), respectively.
TMES sea level height extracted at PTF (a) and Rovinj (b), and significant wave height extracted at PTF (c) and Dubrovnik (d).
Model forecasts could be extracted at any location in the domain to provide a
clear representation of sea storm evolution. As an example, we reported in
Fig. the values extracted at PTF, Rovinj and Dubrovnik (see
Fig. for their location). The comparison with the observations
highlights the good performance of the ensemble methodology in reproducing such
a strong event. The ensemble mean time-series are smoother than the
observations and slightly underestimate the maximum sea level in the northern
Adriatic Sea (Figs. 5a and 6b) as well as the peak wave height at 20:00 UTC (5 m of
forecasted significant wave height with respect to almost 6 m of observed at
PTF; Fig. 6c). However, the observed values are – almost always – within the
TMES spread (i.e. the standard deviation of the ensemble members). It is worth
noting that the forecast uncertainty increases with the forecast lead time and
with the severity of the storm, the maximum of which was reached in the
northern Adriatic Sea between 19:00 and 20:00 UTC. In the southern Adriatic Sea
(Fig. d), the ensemble mean reproduces the observed
significant wave height well, which remained between 3 and 5 m for the whole day.
For this specific location the spread of the ensemble assumed values between
0.7 and 1.1 m on 29 October.
Storm impact and hazard assessment on the coast
In order to provide the perception of the physical processes acting along
the Adriatic–Ionian coastal areas that are responsible for storm-related hazards,
the results of the multi-model ensemble – in terms of sea level and wave
conditions – were processed for each coastal assessment unit. Considering
the general underestimation of the ensemble means during the peak of the
storm, we will mostly focus our basin-wide storm analysis on the MAX sea
condition scenario.
Forecasted 2 % exceedance level of run-up maxima (a) and total water level (b) for the MAX sea condition scenario at 19:00 UTC of 29 October 2018. Background: EMODnet bathymetry .
The 2 % exceedance level of wave run-up maxima (R2) computed
according the Stockdon's formula and the total water
levels forecasted for the 29 October 2018 event (at 19:00 UTC) are reported in
Fig. for the MAX scenario. As for sea level height results
(Fig. a), the maximum values of TWL are found in the northern
Adriatic along the Veneto and Friuli Venezia Giulia coasts. Indeed, during the
29 October storm, several coastal lowlands in the northern Adriatic were
flooded. At these locations, characterised by gently sloping sandy beaches,
the estimated R2 reached maximum values of 0.8 m, accounting
therefore for about 50 % of the total water level.
The combination of the sea level height and the wave set-up and run-up generated high
values of the total water level (TWL >1 m) also at some locations on the
along the eastern Adriatic coast and the Marche region. Along the Croatian
coast, the extremely high waves and the high sea levels caused inundation and
widespread damage to the coastal infrastructure (Istria and Zadar). Moreover,
because of the rough sea conditions, there was a disruption of the maritime
traffic during 27–30 October, and the ferry service cancelled almost
all the scheduled sailings on 29 October, so most of the
Croatian islands were cut off from the mainland for more than a day.
TWL differences between the MEAN and MAX scenarios (not shown) reached
the maximum values of about 0.4 m there, which is higher than the standard deviation
of the multi-model ensemble for the sea level height.
As stated in Sect. , previous studies demonstrated that
the wave run-up estimation increases with the beach slope. Therefore, the high
wave run-up values found at some coastal segments (e.g. along the Marche and
Abruzzo regions) are due not only to the severe wave conditions but also to the fact
that they are characterised by a steep coast (slope >0.15). In such reflective
conditions, the use of an alongshore-averaged beach slope in practical
applications of the run-up parameterisation may result in large run-up error
.
In the following paragraphs, we describe the results of the application of
the multi-model ensemble to the existing early warning systems and investigate
the details of the storm impact and hazard at the three selected locations.
Observed and predicted (according to the three sea condition scenarios) sea level height at Koper (Slovenia). The yellow, orange and red lines indicate the adopted thresholds for flooding alerts.
Due to its northward orientation, the Slovenian coast is relatively well
protected from the waves generated by southerly winds, as in the case of the 29 October 2018 storm. Indeed, over there and for this event, the wave contribution
to the total water level is negligible. According to the 10 min observation
data presented in Fig. , the sea level in Koper reached
peak values well above the orange alert level (343 cm at 12:50 UTC and 341 cm
at 23:20 UTC) and lasted for almost 10 h above the yellow alert level.
As a consequence, the sea flooded several coastal locations, where the
firemen set up anti-flooding barrages. As shown in Fig. ,
the MEAN scenario predicted the exceedance of the yellow flooding alert level
but underestimated the observed peak values. A more realistic – even if
slightly overestimated – prediction of the flooding hazard for the
Slovenian coast is provided by the MAX scenario.
Throughout the Emilia-Romagna region, several coastal sites were affected by
flooding and erosion during the 29 October 2018 sea storm due to the
combination of the high sea level and energetic wave conditions. The documented
coastal impacts are reported in Fig. b and include the erosion
of the beach and of the winter dunes, coastal flooding, and damage to coastal
infrastructure and defence structures. Damage and impacts were reported
especially for the northern part of the region, while along the southern
coastal area between Cesena and Riccione, real impact data are not
available. The hazard index computed for this event using the XBeach model
forced with the three (MEAN, MIN and MAX) conditions reveals that the most
severe sea condition scenario (MAX scenario) provides an exceedance of the
predefined alert thresholds, indicating a high level of coastal risk. An example
of the safe corridor width (described in Sect. ) calculated for a
single cross-shore section, located at Lido di Classe, is reported in
Fig. . The predicted coastal hazard
(Fig. a) shows that the most critical scenario is in good
agreement with the documented coastal impacts, displayed in the right panel.
For this event, by comparing hazard forecasts and observations, the use of IWS
provides a good prediction (MAX scenario) of coastal impacts for the major part
of the Emilia-Romagna coastal area. Moreover, considering the distance between
the MIN and the MAX conditions, IWS provides useful information about the range
of the possible impacts.
Forecasted safe-corridor-width index for the beach profile of classe06
(Lido di Classe, Emilia-Romagna, Italy). The dashed orange and red lines
indicate, respectively, the medium and high thresholds for coastal alerts.
On 29 October 2018, the city of Venice was inundated by the exceptional sea
storm. At 13:40 UTC the sea level reached the peak value of 156 cm at Punta
della Salute (fourth historical level of flooding in Venice since 1872), which
put three-quarters of the pedestrian public area of the historic town under
water. Sea levels reaching 120 cm (above local datum), at which point flooding
covers 28 % of Venice, lasted for about 14 h. The SHYFEM application to
the lagoon of Venice, forced by the open-sea TMES results, forecasted sea level
peak values of 142 and 161 cm for the MEAN and MAX scenarios, respectively.
Figure shows the corresponding flooding map of the city of Venice according to the predicted peak values (rounded at 140 and 160 cm);
59 % and 77 % of the pedestrian surface is flooded for the two
scenarios, respectively. In the first case, the public navigation transport is
strongly limited, and only links to the islands are guaranteed; besides this, most of
the elevated walkways are unusable. In the second case, the public
navigation transport is no longer guaranteed as well as all of the elevated
walkways. Moreover, many internal channels are no longer navigable due to
insufficient vertical space under some bridges, and hence the emergency rescue
response may be strongly delayed. Since the observed peak was 156 cm, the MAX
scenario provided a realistic prediction of the flooding hazard for the city of
Venice.
Flooding map of the city of Venice according to the predicted
sea level height at Punta della Salute (black dot). The colours represent
the flooded pedestrian area for sea levels of 140 cm (blue; MEAN scenario)
and 160 cm (blue and red; MAX scenarios). Light blue indicates the canals.
Summary and concluding discussion
To improve knowledge on sea storms events in order to progress the prevention
and protection measures integrated into coastal defence plan and procedures, we
developed a transnational integrated web system (IWS) for sharing information,
observations and forecasts across the Adriatic and Ionian seas.
The IWS allows strengthening the forecasts with useful information of their
degree of uncertainty and hence analyse the propagation of uncertainty towards
the coastal forecasts. The awareness of the prediction uncertainties and errors
has led many operational and research flood forecasting systems around the
world to move toward numerical forecasts based on a probabilistic concept:
the ensemble technique . In this context, a probabilistic
forecasting system could be based on the perturbation of initial conditions,
forcing and parameters of a single model . Such an approach has already been applied to the Adriatic Sea
for improving storm surge forecast and providing a realistic estimate of the
prediction uncertainty .
In order to improve sea storm predictions, here we instead implemented, for the
Adriatic–Ionian macro-region, a multi-model ensemble system which
combines several existing oceanographic and wave forecasting systems.
The magnitude of the ensemble spread is a good indicator of how the forecast
accuracy varies between different forecasting situations, indicating a
decrease in reliability when the spread increases .
It is not straightforward which averaging weights should be used for the
multi-model ensemble forecast, and therefore we used equally weighted
members, despite the fact that forecasts which are more precise than others should have
more importance in the TMES .
Here we applied a simple average of the forecasts at every timestamp to compute
the ensemble mean, but more sophisticated methods based on weighting function
determined by comparison of the single model results with near-real-time
observations can be implemented in future .
Taking advantage of the near-real-time observations acquired by the
aggregated monitoring network, the root-mean-square error of the
individual forecast will be evaluated and stored for long-term statistics.
Nearshore TMES sea levels and wave characteristics can be directly used in an
early warning procedure on the basis of predefined thresholds for morphological
change and for coastal erosion and flooding e.g.. TMES
predictions are also used to compute the alongshore total water level
time series. The estimated run-up values need to be considered with care due to the
uncertainty associated with the geomorphological characteristics of the coastal
segment units (beach material and slope in particular). Indeed,
and found large variability in
hazard intensity and vulnerability along limited coast sectors (20 to
50 km long), even with homogeneous offshore wave conditions. Therefore,
the choice of the coastal segment database and its resolution has a
substantial effect on the accuracy of the hazard model. The MCD dataset has
some limits in reproducing detailed coastal morphologies (i.e. northern
Adriatic lagoons and Croatian islands) as well as storm defence structures as
breakwaters and seawalls. However, the developed IWS has been designed to be
flexible in integrating better defined coastal segment units. If detailed beach
geomorphological information is available, process-based hazard indicators
could be used for assessing the potential of a coastal
system to be harmed by the impact of a storm (inundation or erosion), comparing
the magnitude of the impact (total water level for inundation and beach–shoreline
retreat for erosion) with the morphological characteristics of the system
(dune or
berm height for inundation and beach width for erosion).
The developed system has been tested against observations acquired during a
very severe storm that affected the Adriatic Sea on 29 October 2018. TMES
forecast results were in agreement – even if slightly underestimated during
the storm peak – with the observed sea level height and significant wave
height. The predicted ensemble mean and standard deviation were combined for
creating three different sea condition scenarios all along the Adriatic and
Ionian coastline, allowing to evaluate a range of coastal hazard forecast.
Moreover, nearshore forecasts were successfully integrated into existing early
warning systems for estimating storm hazard at three locations (Slovenia,
the Emilia-Romagna region and the city of Venice). Through this system coupling, the
predicted sea conditions were translated into local storm impact indicators and
very detailed flooding maps. The underestimation of predicted sea levels and
waves during the 29 October storm peak is probably a consequence of a general
underestimation of the wind forecasts produced by the operational
meteorological models. provided clear evidence of
the wind speed problem over the Adriatic Sea. In particular, for the sea storm
of 29 October 2018, compared the ECMWF model wind
with scatterometer data and estimated a 1.11 average enhancement factor.
For the reasons stated above and considering the results presented in this
study, the most severe sea condition scenario (MAX = ensemble mean + ensemble
standard deviation) can be considered for the investigated area to be a
conservative estimation of the peak storm conditions to be used for coastal
risk management. Another possible application of TMES outputs could be to use
all possible combinations of ensemble mean and standard deviation for the sea
level and wave characteristics, providing a large number of sea state
combinations. In that way, it would be possible to calculate and estimate the
frequency of exceeding predefined thresholds for coastal hazards. This approach
is closer to the idea of the probability of threshold exceedance and will be
explored in future.
The aggregating approach for collecting and sharing observations is crucial for
providing real-time information about the sea state – and its evolution – to be
used by several countries for prompt emergency response and to increase the
overall preparedness to sea storms. Moreover, by merging data from several
stations and sensors, the IWS is an important storage server for any data
assimilation system. According to and
, the assimilation of tide-gauge data in the Adriatic Sea
has a strong positive impact on the forecast performance, lasting several days,
despite the underestimation in the atmospheric forcing. The forecast
improvement is particularly relevant in the case of consecutive sea storms when
storm surge levels are influenced by pre-existing oscillations of the basin
(seiches) induced by previous events. It is worth mentioning that in the case
of the Adriatic Sea – but there could be many other similar situations –
transnational cooperation is crucial for sharing observations acquired along
the whole basin in order to provide real-time information on the sea state to
be used in a data assimilation system.
Real-time observations and numerical model forecasts required to address
environmental management problems such as sea storms are commonly excessively
intricate for civil protection and stakeholder use .
The IWS is equipped with geoportal functionalities and interactive
geo-visualisation tools for simplifying searching and accessing geospatial data
(including forecast model outputs) and monitoring networks' time series. Such
tools help and assist people who want to use IWS concepts, databases and
results in their work and support their activities. Moreover, a dedicated
website (https://iws.seastorms.eu/, last access: 20 October 2019),
designed to foster the data dissemination according to the community-based
paradigm and to the open data principles (https://opendatacharter.net/, last access: 20 October 2019),
will allow the public data, the forecast results and related statistics to be
explored by non-experts over the Internet through the use of shared maps,
dashboards, graphics, tables and other interactive geo-visualisation tools.
In conclusion, to improve the capacity to react to sea storms, all relevant actors
of the coastal area (public authorities, regional and national authorities in
charge of civil protection, meteorological and forecast services, universities
and research institutes, cruise ship enterprises, maritime business
enterprises, marinas, aquaculture small- and medium-sized enterprises (SMEs), and stakeholders from the touristic sector)
should be involved – through the web and social media – in a transnational
cooperation strategy to foster the following:
knowledge and data exchange for providing real-time information about
the basin-wide sea state through the use of standardised formats, protocols and
services;
coordination of all available ocean forecasting systems in a multi-model
ensemble for enhancing the predictability of extreme events and for evaluating
the uncertainty of the operational ocean products;
integration of observations and numerical models through data
assimilation techniques for improving the forecast accuracy;
downscaling of open-sea ensemble forecasts to be integrated into site-specific early warning systems managed by local authorities;
data and forecasts dissemination to all relevant coastal actors and
citizens over the Internet.
Details of the sea level monitoring stations (yellow dots in
Fig. ).
Managing authority – countryStation nameLongitude (∘ E)Latitude (∘ N)City of Venice – ITDiga Sud Lido12.4345.42City of Venice – ITDiga Nord Malamocco12.3445.33City of Venice – ITDiga Sud Chioggia12.3145.23City of Venice – ITPunta della Salute CG12.3445.43City of Venice – ITLaguna Nord Saline12.4745.50City of Venice – ITMisericordia12.3445.45City of Venice – ITBurano12.4245.48City of Venice – ITMalamocco Porto12.2945.34City of Venice – ITChioggia Porto12.2845.23City of Venice – ITChioggia Vigo12.2845.22City of Venice – ITFusina12.2645.41City of Venice – ITPunta Salute Giudecca12.3445.43National Research Council – ITPTF Acqua Alta12.5145.31National Research Council – ITMeda Abate12.7845.25National Research Council – ITSenigallia13.2043.75Ag. for Env. Protection and Energy ER – ITPorto Garibaldi12.2544.68Ag. for Env. Protection and Energy ER – ITVolano12.2744.80Ag. for Env. Protection and Energy ER – ITFaro12.4044.79Inst. for Env. Protection and Research – ITTrieste13.7645.65Inst. for Env. Protection and Research – ITAncona13.5143.62Inst. for Env. Protection and Research – ITSan Benedetto del T.13.8942.96Inst. for Env. Protection and Research – ITVieste16.1841.89Inst. for Env. Protection and Research – ITOtranto18.5040.15Inst. for Env. Protection and Research – ITCrotone17.1439.08Slovenian Environment Agency – SLKoper13.7245.55Hydrographic Institute – HRRovinj13.6345.08Hydrographic Institute – HRDubrovnik18.0742.67Institute of Oceanography and Fisheries – HRKaštelanski zaljev16.3943.51Institute of Oceanography and Fisheries – HRVela Luka*16.7142.96Institute of Oceanography and Fisheries – HRStari Grad*16.6043.18Institute of Oceanography and Fisheries – HRSobra*17.6042.74Institute of GeoSciences – ALVlorë Triport19.3940.51Institute of GeoSciences – ALDurrës19.4541.30Institute of GeoSciences – ALVlorë19.4840.45Institute of GeoSciences – ALSarandë20.0039.87Institute of GeoSciences – ALShëngjin19.5941.81
∗ Available through http://www.ioc-sealevelmonitoring.org/.
Details of the wave monitoring stations (red stars in Fig. ).
Managing authority – countryStation nameLongitude (∘ E)Latitude (∘ N)City of Venice – ITMisericordia12.3445.45National Research Council – ITSenigallia13.2043.75National Research Council – ITPTF Acqua Alta12.5145.31Ag. for Env. Protection and Energy ER – ITNausicaa12.4844.22National Institute of Biology – SLVida13.5545.55Slovenian Environment Agency – SLZora13.6745.60Slovenian Environment Agency – SLZarja13.5445.60Hydrographic Institute – HRRovinj13.5245.07Hydrographic Institute – HRDubrovnik17.9642.65Code and data availability
The IWS model is available as an open-source code from
https://github.com/CNR-ISMAR/iws.
The SHYFEM hydrodynamic model is open source and freely available at
https://github.com/SHYFEM-model (last access: 10 January 2020; ).
The data and forecast results used in this study can be accessed, depending on the
specific provider's data policy, upon request to the corresponding author.
Monitoring networks'
time series and forecast model outputs are operationally available at https://iws.seastorms.eu/.
Author contributions
CF conceived the idea of the study, with the support of AV,
SM and MV. SM and AFa designed the IWS structures. SM, AFa, AV, LB, CF, MB, MV, ML
and GM prepared the model results and developed the multi-model ensemble. AFa,
CF and SM developed the scripts for computing TWL on the MCD coastal segments.
JJ and AFe described the meteorological situation of the 29 October 2018 storm.
SU and AV elaborated the TMES results for computing the ER-EWS storm impact
indicators for the Emilia-Romagna coast. GM, AP, CF and MB applied the TMES
results to the early warning system of the city of Venice. All authors
discussed, reviewed and edited the different versions of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the I-STORMS project (Integrated Sea sTORm
Management Strategies) funded by the European Union under the Interreg V-B
Adriatic–Ionian programme with agreement no. 69 of 9 March 2019. The authors wish
to thank Isabella Marangoni, Denise Florean, Anna Zarotti,
Alessia Porcu and Silvia Comiati from the city of Venice – European Policies
Division – for promoting and supporting the project activities; Fabio Raicich
from CNR-ISMAR for providing sea level data for the Trieste tide gauge;
Pierluigi Penna from CNR-IRBIM for providing data for the Senigallia monitoring
station; and Luigi Cavaleri and Luciana Bertotti from CNR-ISMAR for
sharing wave results of the Nettuno and Henetus systems. Wave data of the
Nettuno forecasting systems were kindly provided by Centro Nazionale di
Meteorologia e Climatologia Aeronautica (CNMCA). We thank Maurizio Ferla, Elisa Coraci and Roberto Inghilesi from ISPRA for providing
sea level and wave forecasts of the SIMM systems; Janez Polajnar
from the Slovenian Environment Agency for the data on storm impacts on the
Slovenian coast; Luisa Perini, Lorenzo Calabrese and Paolo Luciani from the Geological Seismic and Soil Survey of the Emilia-Romagna
region for providing the report of impacts occurring along the regional
coast during the event; Alessandro Coluccelli and Aniello Russo
from the Università Politecnica delle Marche for the support on the AdriaROMS
model; and the Croatian Hydrographic Institute in Split for the tide-gauge
records originating from Rovinj and Dubrovnik and for wave riders' records
originating from buoys near Rovinj and Dubrovnik – Sveti Andrija.
Financial support
This research has been supported by the European Union under the Interreg V-B Adriatic–Ionian programme (grant no. 69 of 9 March 2019).
Review statement
This paper was edited by Piero Lionello and reviewed by two anonymous referees.
ReferencesArmaroli, C. and Duo, E.: Validation of the coastal storm risk assessment
framework along the Emilia-Romagna coast, Coast. Eng., 134, 159–167,
10.1016/j.coastaleng.2017.08.014, 2018.Armaroli, C., Ciavola, P., Perini, L., Calabrese, L., Lorito, S., Valentini,
A., and Masina, M.: Critical storm thresholds for significant
morphological changes and damage along the Emilia-Romagna
coastline, Italy, Geomorphology, 143–144, 34–51,
10.1016/j.geomorph.2011.09.006, 2012.
Baart, F., van Gelder, P. H. A. J. M., and van Koningsveld, M.: Confidence in
real-time forecasting of morphological storm impacts, J. Coast. Res., SI64,
1835–1839, 2011.Bajo, M. and Umgiesser, G.: Storm surge forecast through a combination of
dynamic and neural network models, Ocean Model., 33, 1–9,
10.1016/j.ocemod.2009.12.007, 2010.Bajo, M., De Biasio, F., Umgiesser, G., Vignudelli, S., and Zecchetto, S.:
Impact of using scatterometer and altimeter data on storm surge
forecasting, Ocean Model., 113, 85–94,
10.1016/j.ocemod.2017.03.014, 2017.Bajo, M., Medugorac, I., Umgiesser, G., and Orlić, M.: Storm surge and
seiche modelling in the Adriatic Sea and the impact of data assimilation,
Q. J. Roy. Meteor. Soc., 145, 2070–2084, 10.1002/qj.3544, 2019.Bernier, N. B. and Thompson, K. R.: Deterministic and ensemble storm surge
prediction for Atlantic Canada with lead times of hours to ten days, Ocean
Model., 86, 114–127, 10.1016/j.ocemod.2014.12.002, 2015.Bertotti, L., Canestrelli, P., Cavaleri, L., Pastore, F., and Zampato, L.: The Henetus wave forecast system in the Adriatic Sea, Nat. Hazards Earth Syst. Sci., 11, 2965–2979, 10.5194/nhess-11-2965-2011, 2011.Bertotti, L., Cavaleri, L., Loffredo, L., and Torrisi, L.: Nettuno: Analysis of
a Wind and Wave Forecast System for the Mediterranean Sea, Mon. Weather Rev.,
141, 3130–3141, 10.1175/mwr-d-12-00361.1, 2013.Bosom, E. and Jiménez, J. A.: Probabilistic coastal vulnerability assessment to storms at regional scale – application to Catalan beaches (NW Mediterranean), Nat. Hazards Earth Syst. Sci., 11, 475–484, 10.5194/nhess-11-475-2011, 2011.Bressan, L., Valentini, A., Paccagnella, T., Montani, A., Marsigli, C., and Tesini, M. S.: Sensitivity of sea-level forecasting to the horizontal resolution and sea surface forcing for different configurations of an oceanographic model of the Adriatic Sea, Adv. Sci. Res., 14, 77–84, 10.5194/asr-14-77-2017, 2017.Carbognin, L. and Tosi, L.: Interaction between Climate Changes, Eustacy and
Land Subsidence in the North Adriatic Region, Italy, Mar. Ecol., 23, 38–50,
10.1111/j.1439-0485.2002.tb00006.x, 2002.Cavaleri, L. and Bertotti, L.: Accuracy of the modelled wind and wave fields in
enclosed seas, Tellus A, 56, 167–175, 10.3402/tellusa.v56i2.14398,
2004.Cavaleri, L., Bertotti, L., Buizza, R., Buzzi, A., Masato, V., Umgiesser, G.,
and Zampieri, M.: Predictability of extreme meteo-oceanographic events in the
Adriatic Sea, Q. J. Roy. Meteor. Soc., 136, 400–413, 10.1002/qj.567,
2010.Cavaleri, L., Bajo, M., Barbariol, F., Bastianini, M., Benetazzo, A., Bertotti,
L., Chiggiato, J., Davolio, S., Ferrarin, C., Magnusson, L., Papa, A.,
Pezzutto, P., Pomaro, A., and Umgiesser, G.: The October 29, 2018 storm in
Northern Italy – an exceptional event and its modeling, Prog. Oceanogr., 178,
102178, 10.1016/j.pocean.2019.102178, 2019.Chaumillon, E., Bertin, X., Fortunato, A., Bajo, M., Schneider, J.-L.,
Dezileau, L., Walsh, J. P., Michelot, A., Chauveau, E., Créach, A.,
Hénaff, A., Sauzeau, T., Waeles, B., Gervais, B., Jan, G., Baumann, J.,
Breilh, J.-F., and Pedreros, R.: Storm-induced marine flooding: lessons from
a multidisciplinary approach, Earth-Sci. Rev., 165, 151–184,
10.1016/j.earscirev.2016.12.005, 2017.
City of Venice: Attachment to Municipality Deliberation no. 129 22/07/2002:
Piano integrato degli interventi in caso di alta marea e bassa marea, Venice, Italy, 2002.Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review, J.
Hydrol., 375, 613–626, 10.1016/j.jhydrol.2009.06.005, 2009.Davolio, S., Stocchi, P., Benetazzo, A., Bohm, E., Riminucci, F., Ravaioli, M.,
Li, X.-M., and Carniel, S.: Exceptional Bora outbreak in winter 2012:
Validation and analysis of high-resolution atmospheric model simulations in
the northern Adriatic area, Dynam. Atmos. Ocean, 71, 1–20,
10.1016/j.dynatmoce.2015.05.002, 2015.De Leo, F., Besio, G., Zolezzi, G., and Bezzi, M.: Coastal vulnerability assessment: through regional to local downscaling of wave characteristics along the Bay of Lalzit (Albania), Nat. Hazards Earth Syst. Sci., 19, 287–298, 10.5194/nhess-19-287-2019, 2019.Depellegrin, D., Menegon, S., Farella, G., Ghezzo, M., Gissi, E., Sarretta, A.,
Venier, C., and Barbanti, A.: Multi-objective spatial tools to inform
maritime spatial planning in the Adriatic Sea, Sci. Total Environ., 609, 1627–1639, 10.1016/j.scitotenv.2017.07.264, 2017.Di Liberto, T., Colle, B. A., Georgas, N., Blumberg, A. F., and Taylor,
A. A.: Verification of a Multimodel Storm Surge Ensemble around New York City
and Long Island for the Cool Season, Weather Forecast., 26, 922–939,
10.1175/waf-d-10-05055.1, 2011.Dodet, G., Leckler, F., Sous, D., Ardhuin, F., Filipot, J., and Suanez, S.:
Wave Runup Over Steep Rocky Cliffs, J. Geophys. Res.-Oceans, 123, 7185–7205,
10.1029/2018jc013967, 2018.Dutour Sikirić, M., Ivanković, D., Roland, A., Ivatek-Šahdan,
S., and Tudor, M.: Operational Wave Modelling in the Adriatic Sea with the
Wind Wave Model, Pure Appl. Geophys., 175, 3801–3815,
10.1007/s00024-018-1954-2, 2018.EMODnet Bathymetry Consortium: EMODnet Digital Bathymetry (DTM),
10.12770/18ff0d48-b203-4a65-94a9-5fd8b0ec35f6, 2018.European Commission: Commission of the European Communities, Directive
2007/2/EC of the European Parliament and of the Council of 14 March 2007
Establishing an Infrastructure for Spatial Information in the European
Community (INSPIRE),
https://inspire.ec.europa.eu/inspire-directive/2 (last access: 20 October 2019), 2007.European Commission: Open Source Software Strategy 2014-2017,
https://ec.europa.eu/info/departments/informatics/open-source-software-strategy_en (last access: 20 October 2019),
2016.Fadini, A., Menegon, S., and Ferrarin, C.: I-STORMS Web System code (IWS), available at: https://github.com/CNR-ISMAR/iws, last access: 20 October 2019a.Fadini, A., Menegon, S., and Ferrarin, C.: I-STORMS Web System (IWS), available at: https://iws.seastorms.eu/, last access: 20 October 2019b.Fernández-Montblanc, T., Vousdoukas, M. I., Ciavola, P., Voukouvalas, E.,
Mentaschi, L., Breyiannis, G., Feyen, L., and Salamon, P.: Towards robust
pan-European storm surge forecasting, Ocean Model., 133, 129–144,
10.1016/j.ocemod.2018.12.001, 2019.Ferrarin, C., Roland, A., Bajo, M., Umgiesser, G., Cucco, A., Davolio, S.,
Buzzi, A., Malguzzi, P., and Drofa, O.: Tide-surge-wave modelling and
forecasting in the Mediterranean Sea with focus on the Italian coast, Ocean
Model., 61, 38–48, 10.1016/j.ocemod.2012.10.003, 2013.Ferrarin, C., Tomasin, A., Bajo, M., Petrizzo, A., and Umgiesser, G.: Tidal
changes in a heavily modified coastal wetland, Cont. Shelf Res., 101, 22–33, 10.1016/j.csr.2015.04.002, 2015.Ferrarin, C., Maicu, F., and Umgiesser, G.: The effect of lagoons on Adriatic
Sea tidal dynamics, Ocean Model., 119, 57–71,
10.1016/j.ocemod.2017.09.009, 2017.Ferrarin, C., Bellafiore, D., Sannino, G., Bajo, M., and Umgiesser, G.: Tidal
dynamics in the inter-connected Mediterranean, Marmara, Black and Azov seas,
Prog. Oceanogr., 161, 102–115, 10.1016/j.pocean.2018.02.006, 2018.Ferrarin, C., Davolio, S., Bellafiore, D., Ghezzo, M., Maicu, F., Drofa, O.,
Umgiesser, G., Bajo, M., De Pascalis, F., Marguzzi, P., Zaggia, L.,
Lorenzetti, G., Manfè, G., and Mc Kiver, W.: Cross-scale operational
oceanography in the Adriatic Sea, J. Oper. Oceanogr., 12, 86–103,
10.1080/1755876X.2019.1576275, 2019.Ferreira, O., Plomaritis, T. A., and Costas, S.: Process-based indicators to
assess storm induced coastal hazards, Earth-Sci. Rev., 173, 159–167,
10.1016/j.earscirev.2017.07.010, 2017.Flowerdew, J., Horsburgh, K., Wilson, C., and Mylne, K.: Development and
evaluation of an ensemble forecasting system for coastal storm surges, Q. J. Roy.
Meteor. Soc., 136, 1444–1456, 10.1002/qj.648, 2010.Golbeck, I., Li, X., Janssen, F., Brüning, T., Nielsen, J. W., Huess, V.,
Söderkvist, J., Büchmann, B., Siiriä, S.-M.,
Vähä-Piikkiö, O., Hackett, B., Kristensen, N. M., Engedahl, H.,
Blockley, E., Sellar, A., Lagemaa, P., Ozer, J., Legrand, S., Ljungemyr, P.,
and Axell, L.: Uncertainty estimation for operational ocean forecast products
– a multi-model ensemble for the North Sea and the Baltic Sea, Ocean Dynam.,
65, 1603–1631, 10.1007/s10236-015-0897-8, 2015.Hankin, S., Blower, J., Carval, T., Casey, K., Donlon, C., Lauret, O.,
Loubrieu, T., Srinivasan, A., Trinanes, J., Godøy, Ø., Mendelssohn, R.,
Signell, R., de La Beaujardiere, J., Cornillon, P., Blanc, F., Rew, R., and
Harlan, J.: NetCDFf-CF-OPeNDAP: Standards for ocean data interoperability and
object lessons for community data standards processes, in: Proceedings of
OceanObs'09: Sustained Ocean Observations and Information for Society,
edited by: Hall, J., Harrison, D., and Stammer, D., vol. 2, ESA Publication,
10.5270/OceanObs09.cwp.41, 2010.Harley, M. D., Valentini, A., Armaroli, C., Perini, L., Calabrese, L., and Ciavola, P.: Can an early-warning system help minimize the impacts of coastal storms? A case study of the 2012 Halloween storm, northern Italy, Nat. Hazards Earth Syst. Sci., 16, 209–222, 10.5194/nhess-16-209-2016, 2016.Hinkel, J., Lincke, D., Vafeidis, A. T., Perrette, M., Nicholls, R. J., Tol, R.
S. J., Marzeion, B., Fettweis, X., Ionescu, C., and Levermann, A.: Coastal
flood damage and adaptation costs under 21st century sea-level rise, P.
Natl. Acad. Sci. USA, 111, 3292–3297, 10.1073/pnas.1222469111, 2014.Kolega, N.: Slovenian coast sea floods risk, Acta Geogr. Slov., 46,
143–167, 10.3986/ags46201, 2006.Ličer, M., Smerkol, P., Fettich, A., Ravdas, M., Papapostolou, A., Mantziafou, A., Strajnar, B., Cedilnik, J., Jeromel, M., Jerman, J., Petan, S., Malačič, V., and Sofianos, S.: Modeling the ocean and atmosphere during an extreme bora event in northern Adriatic using one-way and two-way atmosphere–ocean coupling, Ocean Sci., 12, 71–86, 10.5194/os-12-71-2016, 2016.Lionello, P., Sanna, A., Elvini, E., and Mufato, R.: A data assimilation
procedure for operational prediction of storm surge in the northern Adriatic
Sea, Cont. Shelf Res., 26, 539–553, 10.1016/j.csr.2006.01.003, 2006.Lionello, P., Cavaleri, L., Nissen, K., Pino, C., Raicich, F., and Ulbrich, U.:
Severe marine storms in the Northern Adriatic: Characteristics and trends,
Phys. Chem. Earth., 40–41, 93–105,
10.1016/j.pce.2010.10.002, 2012.
Longuet-Higgins, M. S. and Steward, R. W.: A note on wave set-up, J. Mar.
Res., 21, 4–10, 1963.Longueville, B. D.: Community-based geoportals: The next generation? Concepts
and methods for the geospatial Web 2.0, Comput. Environ. Urban, 34,
299–308, 10.1016/j.compenvurbsys.2010.04.004, 2010.Magaña, P., Bergillos, R. J., Del-Rosal-Salido, J., Reyes-Merlo, M. A.,
Díaz-Carrasco, P., and Ortega-Sánchez, M.: Integrating complex
numerical approaches into a user-friendly application for the management of
coastal environments, Sci. Total Environ., 624, 979–990,
10.1016/j.scitotenv.2017.12.154, 2018.Maguire, D. and Longley, P.: The emergence of geoportals and their role in
spatial data infrastructures, Comput. Environ. Urban, 29, 3–14,
10.1016/s0198-9715(04)00045-6, 2005.Marcos, M., Tsimplis, M. N., and Shaw, A. G. P.: Sea level extremes in
southern Europe, J. Geophys. Res., 144, C01007,
10.1029/2008JC004912, 2009.Mariani, S., Casaioli, M., Coraci, E., and Malguzzi, P.: A new high-resolution BOLAM-MOLOCH suite for the SIMM forecasting system: assessment over two HyMeX intense observation periods, Nat. Hazards Earth Syst. Sci., 15, 1–24, 10.5194/nhess-15-1-2015, 2015.Medugorac, I., Pasarić, M., and Orlić, M.: Severe flooding along the
eastern Adriatic coast: the case of 1 December 2008, Ocean Dynam., 65,
817–830, 10.1007/s10236-015-0835-9, 2015.Mel, R. and Lionello, P.: Verification of an ensemble prediction system for
storm surge forecast in the Adriatic Sea, Ocean Dynam., 64, 1803–1814,
10.1007/s10236-014-0782-x, 2014a.Mel, R. and Lionello, P.: Storm surge ensemble prediction for the city of
Venice, Weather Forecast., 29, 1044–1057, 10.1175/WAF-D-13-00117.1,
2014b.Mel, R. and Lionello, P.: Probabilistic dressing of a storm surge prediction in the Adriatic Sea, Adv. Meteorol., 2016, 3764519, 10.1155/2016/3764519, 2016.Molteni, F., Buizza, R., Marsigli, C., Montani, A., Nerozzi, F., and
Paccagnella, T.: A strategy for high-resolution ensemble prediction. I: Definition of representative members and global-model experiments, Q. J.
Roy. Meteor. Soc., 127, 2069–2094, 10.1002/qj.49712757612, 2001.Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. J. H., and Ward, P. J.:
A global reanalysis of storm surges and extreme sea levels, Nat. Commun.,
7, 11969, 10.1038/ncomms11969, 2016.
Orlić, M.: The first attempt at cataloguing tsunami-like waves of
meteorological origin in Croatian coastal waters, Acta Adriat., 56, 83–96, 2015.
Orlić, M., Gačić, M., and La Violett, P. E.: The currents and
circulation of the Adriatic Sea, Oceanol. Acta, 15, 109–124, 1992.Pasarić, Z., Belušić, D., and Chiggiato, J.: Orographic effects on
meteorological fields over the Adriatic from different models, J. Marine
Syst., 78, S90–S100, 10.1016/j.jmarsys.2009.01.019, 2009.Pirazzoli, P. A. and Tomasin, A.: Recent near-surface wind changes in the
central Mediterranean and Adriatic area, Int. J. Climatol., 23, 963–973,
10.1002/joc.925, 2003.Poate, T. G., McCall, R. T., and Masselink, G.: A new parameterisation for
runup on gravel beaches, Coast. Eng., 117, 176–190,
10.1016/j.coastaleng.2016.08.003, 2016.Pomaro, A., Cavaleri, L., and Lionello, P.: Climatology and trends of the
Adriatic Sea wind waves: analysis of a 37-year long instrumental data set,
Int. J. Climatol., 37, 4237–4250, 10.1002/joc.5066, 2017.Prahl, B. F., Boettle, M., Costa, L., Kropp, J. P., and Rybski, D.: Damage and
protection cost curves for coastal floods within the 600 largest European
cities, Sci. Data, 5, 180034, 10.1038/sdata.2018.34, 2018.Reimann, L., Vafeidis, A. T., Brown, S., Hinkel, J., and Tol, R. S. J.:
Mediterranean UNESCO World Heritage at risk from coastal flooding and
erosion due to sea-level rise, Nat. Commun., 9, 4161,
10.1038/s41467-018-06645-9, 2018.Rizzi, J., Torresan, S., Zabeo, A., Critto, A., Tosoni, A., Tomasin, A., and
Marcomini, A.: Assessing storm surge risk under future sea-level rise
scenarios: a case study in the North Adriatic coast, J. Coast. Conser., 21,
453–471, 10.1007/s11852-017-0517-5, 2017.Roelvink, D., Reniers, A., van Dongeren, A., van Thiel de Vries, J., McCall,
R., and Lescinski, J.: Modelling storm impacts on beaches, dunes and barrier
islands, Coast. Eng., 56, 1133–1152, 10.1016/j.coastaleng.2009.08.006,
2009.Roland, A., Cucco, A., Ferrarin, C., Hsu, T.-W., Liau, J.-M., Ou, S.-H.,
Umgiesser, G., and Zanke, U.: On the development and verification of a 2d
coupled wave-current model on unstructured meshes, J. Marine Syst., 78,
Supplement, S244–S254, 10.1016/j.jmarsys.2009.01.026, 2009.Russo, A., Coluccelli, A., Carniel, S., Benetazzo, A., Valentini, A.,
Paccagnella, T., Ravaioli, M., and Bortoluzzi, G.: Operational models
hierarchy for short term marine predictions: The Adriatic Sea example, in:
2013 MTS/IEEE OCEANS – Bergen, IEEE,
10.1109/oceans-bergen.2013.6608139, 2013.Salighehdar, A., Ye, Z., Liu, M., Florescu, I., and Blumberg, A. F.:
Ensemble-Based Storm Surge Forecasting Models, Weather Forecast., 32,
1921–1936, 10.1175/waf-d-17-0017.1, 2017.Satta, A., Puddu, M., Venturini, S., and Giupponi, C.: Assessment of coastal
risks to climate change related impacts at the regional scale: The case of
the Mediterranean region, Int. J. Disast. Risk Re., 24, 284–296,
10.1016/j.ijdrr.2017.06.018, 2017.Schevenhoven, F. J. and Selten, F. M.: An efficient training scheme for supermodels, Earth Syst. Dynam., 8, 429–438, 10.5194/esd-8-429-2017, 2017.Schuerch, M., Spencer, T., Temmerman, S., Kirwan, M. L., Wolff, C., Lincke, D.,
McOwen, C. J., Pickering, M. D., Reef, R., Vafeidis, A. T., Hinkel, J.,
Nicholls, R. J., and Brown, S.: Future response of global coastal wetlands to
sea-level rise, Nature, 561, 231–234, 10.1038/s41586-018-0476-5,
2018.Signell, R. P., Carniel, S., Cavaleri, L., Chiggiato, J., Doyle, J. D., Pullen, J., and Sclavo, M.: Assessment of wind quality for oceanographic modelling in
semi-enclosed basins, J. Marine Syst., 53, 217–233,
10.1016/j.jmarsys.2004.03.006, 2005.Sorensen, R. M.: Coastal Engineering, in: Basic Coastal Engineering, Springer
US, 10.1007/978-1-4757-2665-7_1, 1997.Stockdon, H. F., Holman, R. A., Howd, P. A., and Sallenger, A. H.: Empirical
parameterization of setup, swash, and runup, Coast. Eng., 53, 573–588,
10.1016/j.coastaleng.2005.12.005, 2006.Tonani, M., Pinardi, N., Fratianni, C., Pistoia, J., Dobricic, S., Pensieri, S., de Alfonso, M., and Nittis, K.: Mediterranean Forecasting System: forecast and analysis assessment through skill scores, Ocean Sci., 5, 649–660, 10.5194/os-5-649-2009, 2009.Umgiesser, G., Ferrarin, C., Cucco, A., De Pascalis, F., Bellafiore, D.,
Ghezzo, M., and Bajo, M.: Comparative hydrodynamics of 10 Mediterranean
lagoons by means of numerical modeling, J. Geophys. Res.-Oceans, 119,
2212–2226, 10.1002/2013JC009512, 2014.Umgiesser, G., Ferrarin, C., and Bajo, M.: SHYFEM, shallow water hydrodynamic finite element model (Version VERS_7_5_67), 10.5281/zenodo.1311751, 2018.Unidata: THREDDS Data Server (TDS), version 4.6.13 [software],
UCAR/Unidata, Boulder, CO, USA, 10.5065/D6N014KG, 2019.Vaccari, L., Craglia, M., Fugazza, C., Nativi, S., and Santoro, M.: Integrative
Research: The EuroGEOSS Experience, IEEE J. Sel. Top. Appl., 5, 1603–1611, 10.1109/jstars.2012.2190382, 2012.
Valentini, A., Delli Passeri, L., Paccagnella, T., Patruno, P., Marsigli, C., Deserti, M., Chiggiato, J., and Tibaldi, S.: The Sea State forecast system of ARPA-SIM, Boll. Geof. Teor. Appl., 48, 333–349, 2007.Vilibić, I., Šepić, J., Pasarić, M., and Orlić, M.: The
Adriatic Sea: A Long-Standing Laboratory for Sea Level Studies, Pure Appl.
Geophys., 174, 3765–3811, 10.1007/s00024-017-1625-8, 2017.Vousdoukas, M. I., Voukouvalas, E., Mentaschi, L., Dottori, F., Giardino, A., Bouziotas, D., Bianchi, A., Salamon, P., and Feyen, L.: Developments in large-scale coastal flood hazard mapping, Nat. Hazards Earth Syst. Sci., 16, 1841–1853, 10.5194/nhess-16-1841-2016, 2016.Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Verlaan, M., and Feyen, L.:
Extreme sea levels on the rise along Europe's coasts, Earth's Future, 5,
304–323, 10.1002/2016ef000505, 2017.Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Bianchi, A., Dottori, F.,
and Feyen, L.: Climatic and socioeconomic controls of future coastal flood
risk in Europe, Nat. Clim. Change, 8, 776–780,
10.1038/s41558-018-0260-4, 2018a.Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Verlaan, M., Jevrejeva, S.,
Jackson, L. P., and Feyen, L.: Global probabilistic projections of extreme
sea levels show intensification of coastal flood hazard, Nat. Commun., 9,
2360, 10.1038/s41467-018-04692-w, 2018b.Wolff, C., Vafeidis, A. T., Lincke, D., Marasmi, C., and Hinkel, J.: Effects of Scale and Input Data on Assessing the Future Impacts of Coastal Flooding: An Application of DIVA for the Emilia-Romagna Coast, Front. Mar. Sci., 3, 41, 10.3389/fmars.2016.00041,
2016.Wolff, C., Vafeidis, A. T., Muis, S., Lincke, D., Satta, A., Lionello, P.,
Jimenez, J. A., Conte, D., and Hinkel, J.: A Mediterranean coastal database
for assessing the impacts of sea-level rise and associated hazards, Sci.
Data, 5, 180044, 10.1038/sdata.2018.44, 2018.
World Meteorological Organization: Guidelines on Ensemble Prediction Systems and Forecasting, World Meteorological Organization: Weather, Climate, and Water, Geneva, Switzerland, 32 pp., 2012.Yang, P., Evans, J., Cole, M., Marley, S., Alameh, N., and Bambacus, M.: The
Emerging Concepts and Applications of the Spatial Web Portal, Photogramm.
Eng. Remote Sens., 73, 691–698, 10.14358/pers.73.6.691, 2007.Zacharioudaki, A., Korres, G., and Perivoliotis, L.: Wave climate of the
Hellenic Seas obtained from a wave hindcast for the period 1960–2001, Ocean
Dynam., 65, 795–816, 10.1007/s10236-015-0840-z, 2015.Zou, Q.-P., Chen, Y., Cluckie, I., Hewston, R., Pan, S., Peng, Z., and Reeve,
D.: Ensemble prediction of coastal flood risk arising from overtopping by
linking meteorological, ocean, coastal and surf zone models, Q. J. Roy.
Meteor. Soc., 139, 298–313, 10.1002/qj.2078, 2013.