Available via license: CC BY 4.0
Content may be subject to copyright.
Developing a framework for the assessment of current and future
flood risk in Venice, Italy
Julius Schlumberger1, Christian Ferrarin2, Sebastiaan N. Jonkman1, Andres Diaz Loaiza1,
Alessandro Antonini1, and Sandra Fatori´
c3
1Department of Hydraulic Engineering, Faculty of Civil Engineering & Geosciences, Delft University of Technology, Delft,
the Netherlands
2CNR - National Research Council of Italy, ISMAR - Marine Sciences Institute, Castello 2737/F, 30122, Venezia, Italy
3Faculty of Architecture and the Built Environment, Delft University of Technology, Delft, the Netherlands
Correspondence: J. Schlumberger (j.schlumberger@posteo.de)
Abstract. Flooding has been a serious struggle to the old-town of Venice, its residents and cultural heritage and continues to be a
challenge in the future. Despite this existence-defining condition, limited scientific knowledge of flood hazard and flood damage
modelling of the old-town of Venice is available to support decisions to mitigate existing and future flood risk. Therefore, this
study proposes a risk assessment framework to provide a methodical and flexible instrument for decision-making for flood risk
management in Venice. It uses a state-of-the-art hydrodynamic urban model to identify the hazard characteristics inside the5
city of Venice. Exposure, vulnerability, and corresponding damages are modelled by a multi-parametric, micro-scale damage
model which is adapted to the specific context of Venice with its dense urban structure and high risk awareness. A set of
individual protection scenarios is implemented to account for possible variability of flood preparedness of the residents. The
developed risk assessment framework was tested for the flood event of 12 November 2019. It was able to reproduce flood
characteristics and resulting damages well. A scenario analysis based on a meteorological event like 12 November 2019 was10
conducted to derive flood damage estimates for the year 2060 for a set of sea level rise scenarios in combination with a
(partially) functioning storm surge barrier MOSE. The analysis suggests that a functioning MOSE barrier could prevent flood
damages for the considered storm event and sea level scenarios almost entirely. It could reduce the damages by up to 34% for
optimistic sea level rise prognoses. However, damages could be 10% to 600% times higher in 2060 compared to 2019 for a
partial closure of the storm surge barrier, depending on different levels of individual protection.15
1 Introduction
Flood events are among the most disastrous natural catastrophes, causing significant damages and fatalities all around the
world. In Europe, coastal flood events are estimated to affect more than 100,000 citizens, causing losses of about EUR 1.4 bil-
lion annually (Vousdoukas et al., 2020). Under consideration of climate change scenarios, future flood damages are expected
to increase due rising sea level (Hinkel et al., 2014).20
1
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
In this context, hazard and flood risk assessment has been broadly implemented according to the 60/2007/EC directive in
the EU (European Commission, 2007). According to the IPCC, flood risk is defined as the combination of a specific hazardous
flood event, the exposure of human systems, and their vulnerability, meaning predisposition to be adversely affected (Field
et al., 2012). It can therefore include adverse effects on human health, environment, cultural heritage and economic activi-25
ties. As such, outcomes of a flood risk assessment framework can support systemic and individual decisions to mitigate flood
damages or adapt accordingly, increase preparedness, and strengthen coping capacities (Arrighi et al., 2018b; Molinari and
Scorzini, 2017; Scorzini and Frank, 2017; Amadio et al., 2016; Thieken et al.; Merz and Thieken, 2009).
A flood risk assessment framework typically follows four steps: 1) hazard modelling, 2) assessment of vulnerability of ex-30
posed assets, 3) damage estimation and 4) flood risk estimation (Arrighi et al., 2018a). The application of 2D hydrodynamic
models is currently the state of the art method for deriving information about coastal and urban flood events (Yin et al., 2020;
Sai et al., 2020; Xing et al., 2019; Teng et al., 2017; Gallien et al., 2014). Damage modelling traditionally focuses on direct,
tangible damages in terms of replacement costs related to structures, interior, and public infrastructure since the cost-benefit
analysis of flood mitigation measures is straight forward and indisputable (Molinaroli et al., 2018; Scorzini and Frank, 2017;35
Dottori et al., 2016; Merz and Thieken, 2009). The vulnerability of exposed assets is determined not only by the type of exposed
structure, its construction material (quality), age, and level of maintenance (Huijbregts et al., 2014; Drdácký, 2010; Merz and
Thieken, 2009), but also by the level of present awareness. Risk awareness influences the level of preparedness by means of
physical measures (e.g. permanent or mobile water barriers, emergency works like sand bags) or behavioral adjustments (e.g.
adapting the vertical distribution of goods and values). Vulnerability therefore varies highly spatially and temporally (Hudson40
et al., 2016; Kreibich et al., 2011; López-Marrero, 2010).
This study focuses on the assessment of flood damage in Venice. The low-lying historic city has a long-lasting record of flood
events (Battistin and Canestrelli, 2006) which is likely to extend in future mainly due to relative sea level rise and continuing
subsidence (Lionello et al., 2021; Me ¯
dugorac et al., 2020; Morucci et al., 2020; Tiggeloven et al., 2020; Jordà et al., 2012).45
Since 1987, the city of Venice is part of the UNESCO World Cultural and Natural Heritage site that spans the Venetian lagoon
(Molinaroli et al., 2018). Consequently, not only economic and individual risk prevails, but also risk of damage or loss of
highly valued cultural sites which can be expected to contribute significantly to the tangible damages due to special restoration
and reconstruction requirements (Arrighi et al., 2018a). Additionally, intangible damages to cultural heritage sites and their
meaning for the cultural identity of the region and nation can be expected (Wang, 2015).50
Thus, dealing with flooding and mitigating adverse effects is an existence-defining task in Venice now and in the future.
Over the past decades, flood protection mainly relied on individual preparedness, which was supported by forecasting systems
for storm surges incorporated into a multi-stage warning system (Umgiesser et al., 2021; Comune di Venezia., 2016). As part
of an extensive flood protection plan, the Modulo Sperimentale Elettromeccanico (MOSE) barrier has been designed in the55
follow-up of the record flood in 1966. It is expected to be functional by the end of 2021. The barrier consists of a series of
2
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
submersed gates located in the three inlets of the Venetian Lagoon. MOSE is designed to protect Venice against high water
exceeding 1.1 m ZMPS1, up to a water level of 3.0 m ZMPS (Cavallaro et al., 2017; Umgiesser and Matticchio, 2006).
Despite much attention to flooding in the city of Venice, no detailed and methodical risk assessment framework is publicly60
available. Lack of such a framework makes it more difficult to compare and evaluate various measures (such as MOSE barrier)
and justify distribution of resources for flood risk mitigation measures (Arrighi et al., 2018a). Moreover, only a few of studies
on damage or loss modelling cover the old-town of Venice. Some studies investigated potential flood damages based on basic
depth-damage relations to analyse the benefit of a functioning barrier (Fontini et al., 2008; Nunes et al., 2005), others looked
into remaining flood risk for floods up to a level of 1.10 m ZMPS (Caporin and Fontini, 2014). These studies mainly focus on65
different closure scenarios of the MOSE barrier consider flood risk implicitly by using a maximum safeguard water level at the
city of Venice (Umgiesser, 2020; Cavallaro et al., 2017; Umgiesser and Matticchio, 2006).
To develop a better understanding of the existing and future risk due to damages to structures and cultural heritage in Venice,
a risk assessment framework is developed in this study as shown in Fig. 1. High resolution flood hazard characteristics are70
computed by means of a 2D-hydrodynamic model. They feed into a micro-scale damage model to estimate expected absolute
direct damages of the exposed buildings (Dottori et al., 2016). The flood model is calibrated and partly validated using data
from the storm surge of 12 November 2019. Additionally, a damage claim data-set for the the same event is used for perfor-
mance analysis of the damage model. Finally, the framework is applied to a set of scenarios of varying sea level change and
MOSE closure to analyse potential development of flood damage in mid-term future.75
The paper proposes a methodical and flexible assessment framework for Venice that is useful to analyse existing and future
flood damages for different meteorological storm events. It is methodical, as it uses a hydrodynamic model along with a dam-
age model that can resolve physical damage modelling of separate building components. The framework is flexible because
both models can be refined to consider additional elements of influence or additional elements at risk. This could be of partic-80
ular interest for accounting more specific conditions of cultural heritage as well as incorporating additional knowledge about
(changing) flood protection measures in Venice.
2 Methods
2.1 Study area and storm event of 12 November 201985
The old-town of Venice covers an area of about 6 km2and is pervaded by more than 100 canals of depths between 1 and 5
meters (Madricardo et al., 2017). The old-town is located in the Venetian lagoon, the largest in the Mediterranean with an area
1If not highlighted otherwise, all levels refer to the local chart datum in Venice, given as Zero Mareographic of Punta della Salute (ZMPS), corresponding
to the mean sea level of the 1885-1909 period. Present mean sea level (2019 annual mean sea level) is today 0.34 m ZMPS.
3
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
Figure 1. Risk assessment framework
of about 550 km2. The lagoon is connected to the Northern Adriatic Sea via three inlets at Lido, Malamocco and Chioggia,
see Fig. 2.
90
On 12 November 2019, the second highest storm surge ever recorded flooded the old-town of Venice and other parts of the
Venetian lagoon. The maximum measured water level inside the old town was 1.89 m ZMPS, measured by the tidal gauge
station Punta della Salute at 22:50 on 12 November 2019. It was comprised of a tidal contribution of 0.36 m, 0.47 m of storm
surge induced by a strong Sirocco wind over the Adriatic Sea, 0.35 m of long-term preconditioning, and 0.34 m mean sea
level with regards to the local datum (Ferrarin et al., 2021). At the same time, a secondary, local cyclone passed over the95
Northern Adriatic Sea resulting in additional set-up by causing an inverse barotropic effect and very high wind speeds from
south-westerly directions of about 70 up to 110 km/h. It is noteworthy that the secondary low pressure field was not forecasted
properly which lead to an underestimation of the flood by about 0.40 m (Ferrarin et al., 2021). Unlike a storm event that oc-
curred in 2018 where an even higher tidal peak (1.56 m ZMPS) coincided with low astronomical tides (-0.10 m ZMPS), the
extreme sea level of 12 November 2019 was the product of less extreme, thus more likely conditions (Morucci et al., 2020;100
Cavaleri et al., 2019).
As a response to the unexpected extreme meteorological event of 12 November 2019, financial support to the affected parties
was provided in two rounds: 1) limited amounts for immediate response (up to EUR 5,000 for residents and EUR 20,000 for
non-residential entities (companies, NGOs,...)) and 2) support for more extensive flood damages. Residents and entities could105
4
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
Figure 2. Study area consisting of part of the Adriatic shelf, the Venetian lagoon and the old-town of Venice. Green line indicates applied
boundary condition for the water-level time-series.
apply for compensation for either one or both rounds. In total, 7,644 eligible claims were issued inside the study area with a
total cost of EUR 56.2 million2.
For residents and entities which submitted only immediate response claims (3,728 claims covering EUR 26.99 million of
damages), the physical addresses of the claimants are publicly available. It was possible to allocate 95% of the reported im-110
mediate response claims (EUR 25.73 million) to 2,778 structures inside Venice using a set of 33,096 addresses3. For claimants
that submitted claims in both rounds or just for more extensive flood damages (EUR 29.21 million), the available information
provided was aggregated by city-district for data protection reasons4.
2Data made available by the Office of the Delegated Commissioner for the management of exceptional meteorological events from 12 November 2019 in
the territory of the Municipality of Venice.
3accessed at: https://portale.comune.venezia.it/node/117/12181978
4More information on and analysis of the available damage claim data can be found in the supplementary material of this study.
5
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
2.2 SLR and MOSE scenarios115
The developed framework is applied to a set of seven different scenarios to derive indications of potential development of
flood damage and flood risk in future. The scenarios differ in mean sea level and closure behaviour of the MOSE barrier as
summarized in Tab. 1. For all scenarios, the meteorological forcing of a storm equivalent to the extreme event of 12 November
2019 is used. SLR0 considers a mean sea level as present in 2019. ’SLR0-allopen’ represents the real flood event of 2019
without an operational MOSE barrier. Scenarios of 0.15 m and and 0.45 m sea level rise with respect to 2019 are selected in120
line with latest research on sea level rise prognosis in Venice. They correspond to the lower and upper confidence bounds of
the projected sea level change in the Northern Adriatic Sea under RCP2.6 and RCP8.5 scenarios for the year 2060 respectively
(Zanchettin et al., 2021). Regarding the MOSE barrier, two closure states are considered: a fully functioning MOSE barrier
(’allclosed’) and a set-up where all inlets but the Lido inlet close (’lidoopen’). The second closure state is chosen in line with
previous studies indicating the prominent importance of the Lido inlet to manage water levels in Venice (Zampato et al., 2016;125
Umgiesser and Matticchio, 2006).
Table 1. Applied scenarios to assess future flood damages
scenario MSL [m ZMPS]
present
conditions
SLR0-allopen 0.34
SLR0-allclosed 0.34
SLR0-lidoopen 0.34
RCP 2.6
scenario
SLR1-allclosed 0.49
SLR1-lidoopen 0.49
RCP 8.5
scenario
SLR2-allclosed 0.79
SLR2-lidoopen 0.79
2.3 The modelling framework
As visualized in Fig. 1, the modelling framework consists of a combination of a hydrodynamic and a damage model which is
presented in this section.130
2.3.1 Hydrodynamic model
In the study area, hydrodynamic models have been used frequently but they do not account for the urban area of Venice
(Umgiesser et al., 2021; Ferrarin et al., 2015; D’Alpaos and Defina, 2007; Umgiesser et al., 2004; Roland et al., 2009). Studies
looking into the distribution of flood depths in Venice have used a static model, also called bathtub model (Cellerino et al.,
6
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
1998)5. It uses the water level at the tidal gauge of Punta della Salute and compares it with the surface elevation of the old-town135
of Venice to identify the flood extent and depth.
Figure 3. Nested model domains with observation points from parent model used as boundary forcing
For this study, a 2D hydrodynamic model based on Delft3D Flexible Mesh Suite 2021.04 was used (Deltares, 2021). The
software provides a flexible unstructured grid framework which facilitates the grid generation in the complex coastal and urban
setting (Martyr-Koller et al., 2017). Furthermore, it provides additional modules that can be used for a better physical repre-140
sentation of the system. Only 2D flow was considered in this study, but the model allows to account for additional processes
like wave action or 1D flow of the sewage system6.
An offline grid nesting framework was chosen, consisting of a parent model covering the study area and seven sub-models
of higher resolution covering the area of the old-town of Venice. The parent model used 2.73 million elements covering the145
study area with an average grid size between 2.6 m in the old-town and 200 m at the Adriatic shelf. In the seven nested models,
grid size was increased to an average of 1.3 m to reproduce the narrow street system in Venice. Water level time-series from
the parent model simulation were extracted at 168 locations inside and around the old-town of Venice and used as inputs for
the nested sub-models, as shown in Fig. 3. For every nested model, the maximum water level at each grid point was extracted.
All grid points inside a 4m buffer around each structure were used to derive an average water level.150
Most recent information on the depth of the lagoon flood plains, channels, and the elevation of the islands of the old-town
were accessed from various sources. Table 2 presents an overview of all the elevation data used. All altimetry data were cor-
rected to refer to ZMPS, the local chart datum in Venice.
155
5also mentioned here: http://www.comune.venezia.it/maree
6A more detailed reasoning along with additional information on the model set up are described in the supplementary material.
7
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
Table 2. List of used altimetry data
altimetry data datum resolution year source
Venetian lagoon IGM42 ∗10m 2002 Sarretta et al. (2010)
Tidal channels ZMPS 0.50m 2013 Madricardo et al. (2017)
Adriatic shelf LAT ∗∗ 550m 2018 EMODnet (2018)
old-town surface IGM42 1m 2011 ArcGis (2020)∗∗∗
Canals in old-town IGM42 varying 2000 City of Venice (2000)
∗0 m IGM42 corresponds to + 0.23 m ZMPS
∗∗ When analyzing the water level time series of the Aqua Alta platform for different months of 2019, the
LAT was chosen to correspond aproximately to −0.40mZM P S.
∗∗∗ The original altimetry data were collected by the RAMSES project (www.ramses.it which was
conducted in the year 2011 as a topographic survey characterized by high precision (altimetric of 1 cm and
planimetric of 2 cm). The used files have been made available by ArcGIS. Used data were accessed here:
https://learn.arcgis.com/en/projects/map-venice- in-2d- and-3d/ (accessed: 08/04/2021)
Constant standard values were used for the viscosity, diffusivity, and density as the flow in the Venetian lagoon is relatively
well mixed without stratification (Ferrarin et al., 2010). Roughness was added as Manning-type n. A standard roughness value
of 0.023 was applied to the entire study area and eventually altered in different areas of the model domain based on the pre-
dominant characteristics, as outlined in Tab. 3. Roughness was used as a calibration factor and checked that the values lie in
the range of commonly applied roughness values for the different land types (Ahn et al., 2019; Xing et al., 2019; Ferrarin and160
Umgiesser, 2005).
Table 3. Applied roughness values
area n
tidal channels 0.025
tidal plains 0.040
northern lagoon 0.020
vegetation Venice 0.035
streets Venice 0.019
canals Venice 0.023
inlets 0.030
Similarly, the wind-induced shear stress, by means of drag coefficient, was used as a calibration parameter. It was imple-
mented based on a linearly increasing relation between wind speed and wind drag developed by Smith and Banke (1975).
However, their relation was derived for wind speeds between 6 and 21 m/s, but extreme wind speeds for the 12 November 2019165
reached up to 27 m/s. Therefore a higher drag coefficient of 0.00876 (for 100 m/s wind speed) was used. A comprehensive
analysis of commonly used wind drag formulations confirmed that the chosen drag coefficient is within the range of available
8
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
estimates (Bryant and Akbar, 2016). In addition, it was confirmed that the chosen values are in line with other Delft3D-FM
studies of the Venetian lagoon7.
170
The barrier system was modelled by means of a set of three simple weirs with a crest height defined by a time-series. It
is assumed that the barrier crest height increases at constant speed from the bottom of the respective inlet up to a height of
3.00 m ZMPS and closes within 30 minutes (Umgiesser et al., 2021). For the considered meteorological storm conditions, the
MOSE barrier starts closing when the tidal gauge station of Punta della Salute reaches a water level of 0.65 m ZMPS (Zampato
et al., 2016). This threshold is assumed to be constant for all analysed scenarios. The starting time of closure was determined by175
modelled tidal gauge information from Punta della Salute for the different scenarios without a closing MOSE barrier, see Tab. 4.
Table 4. Closure times for scenarios
Scenario Closure time
SLR0 12/11/19 18:40
SLR1 12/11/19 18:10
SLR2 11/11/19 18:10
2.3.2 Damage Modelling
While general damage drivers are broadly acknowledged (Patt and Jüpner, 2013; Kelman and Spence, 2004), the exact effect
of hazard characteristics on an exposed structure is still poorly understood as it also heavily depends on the material and its180
quality (Huijbregts et al., 2014; Merz and Thieken, 2009). This is particularly relevant for cultural heritage sites built by ma-
terials which have deteriorated by centuries of existence (Drdácký, 2010). Consequently, the chosen model was selected with
special care to allow for an inclusion of differing exposure and vulnerability characteristics.
Various approaches and post-flood data analysis have been conducted to develop relations between the flood hazard char-185
acteristics and corresponding tangible, direct damages. Several comparative studies have looked into the characterization and
performance analysis of some frequently used damage models (Molinari et al., 2020; Gerl et al., 2016)8. In general, loss esti-
mates reflect high uncertainties and disparities because of the inaccuracy of the models and the lack of knowledge about the
system in which they have been applied (Scorzini and Frank, 2017; Gerl et al., 2016).
190
In this study, a flood model based on INSYDE (In-depth Synthetic Model for Flood Damage Estimation) was applied. It is a
synthetic damage model developed based on ’what if’ - scenario analysis to provide a methodical and generalized perspective
on the flood-damage process for different building components individually (Dottori et al., 2016). It has been validated based
7Personal communication G.Lemos, 24.05.2021
8An overview of commonly applied damage models in Italy can be found here: http://www.fdm.polimi.it/models (accessed 27/04/2021)
9
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
on flood data from a river flood in Caldogno, Veneto, 2010. INSYDE is a multi-parametric model adopting 23 parameters
to describe hazard, exposure and vulnerability characteristics of buildings9. As the model explicitly considers many damage195
mediating factors, it allows for direct adjustments or extensions of the model based on the available knowledge or considered
research purposes (Molinari et al., 2020; Scorzini and Frank, 2017; Dottori et al., 2016). As such, it is ideal to be extended
to include new building types, e.g. cultural heritage sites like churches etc., with specific hazard-structure responses. The
INSYDE model also makes use of categorization into building types to account for differences in the exposure or vulnerability
characteristics between typical buildings in a study area. As a result, the absolute damage, D, per structure is calculated as the200
sum of a set of damage components summarized in Tab. 5:
D=
n
X
i=1
m
X
j=1
Ci,j =
n
X
i=1
m
X
j=1
upi,j ∗exti,j ∗E[R](1)
where jrepresents the damage component and idescribes the considered activity, e.g. cleaning, removal, and replacing. upi,j is
the unit price per damage component for for a given activity, exti,j is the extent of exposed component and E[R]the (expected)
damage ratio. E[R]∼[0,1] is derived from fragility functions for different hazard characteristics with gradual influence on the205
damage. They have been developed based on expert knowledge but are transparently reported as part of the supplementary
material of Dottori et al. (2016).
These fragility functions follow truncated normal distributions and relate a probability of damage of a specific component
to one flood hazard characteristic: flood depth, flood velocity, or flood duration. In the present study, flood depth is the only210
damage mediating factor since flow velocity and flood duration were found to be too low to add an additional source of damage
(Dottori et al., 2016; Penning-Rowsell et al., 2005)10. The fragility functions allow not only for a deterministic multi-parametric
consideration of the flood-structure interaction, but also to account for uncertainties in the flood-structure interaction in a prob-
abilistic framework. An example is shown in Fig. 4: damage to partition walls occurs if the partition walls absorb too much
water to be dried up, i.a. if water depth exceeds a certain threshold (Dottori et al., 2016). The fragility function can be used to215
determine an expected damage ratio or expected share of damaged partition wall for a given flood depth. However, damage to
partition walls due to a certain water depth could range from ’no damage’ to ’full damage’, depending on factors such as the
quality of wall (material). In the probabilistic framework, a large set of realizations for each component is drawn to derive the
5- and 95-percentiles expressing an optimistic and pessimistic estimate of the absolute damages. Even though the probabilistic
framework was not used in this study, it may be useful in case of extending the framework to explicitly cover cultural heritage220
sites in Venice, which may be more sensitive to varying flood characteristics.
Information on the individual building area and extent were derived from cadastral data of the city of Venice11. A total
of 14,460 structures were considered. Information on the structural properties, the year of construction and the maintenance
9More details regarding the background and set up of the INSYDE model is provided in the supplementary material of this study.
10Results of the hydrodynamic model suggest that flood velocities are generally lower than 0.3 m/s and the flood duration is between 2 and 4 hours.
11Accessible here: http://geoportale.comune.venezia.it(accessed 05/07/2021)
10
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
Table 5. Damage components considered in INSYDE. Red: not taken into account in this study.
sub-component sub-component
Clean-up
C1 – Pumping
Structural
S1 – Soil consolidation
C2 – Waste disposal S2 – Local repair
C3 – Cleaning S3 – Pillar repair
C4 – Dehumidification
Removal
R1 – Screed
Finishing
F1 – External plaster replacement
R2 – Pavement F2 – Internal plaster replacement
R3 – Skirting F3 – External painting
R4 – Partition walls F4 – Internal painting
R5 – Plasterboard F5 – Pavement replacement
R6 – External plaster F6 – Skirting replacement
R7 – Internal plaster
Windows
& Doors
W1 – Door replacements
R8 – Doors W2 – Window replacements
R9 – Windows
R10 – Boiler
Non-
structural
N1 – Partition replacements
Building
systems
P1 – Boiler replacement
N2 – Screed replacement P2 – Radiator painting
N3 – Plasterboard replacement P3 – Underfl. heating replacement
P4 – Electrical system replacement
P5 – Plumbing system replacement
Figure 4. Fragility function for partition walls relative to water depth
11
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
level were accessed from census data from year 2011 by the Italian National Institute of Statistics (ISTAT, 2020). The census225
data is not building-specific but aggregated in census blocks covering multiple buildings. As a consequence, the most frequent
characteristic was applied to all buildings within a census block12.
GoogleMaps StreetView was used to gather visual information about typical house fronts, size and number of windows
along with information about possible elevations of the entrance at ten random locations in different districts of the old-town.230
Moreover, advertisements by real estate agencies were used to characterise the interior of housings on the ground-floor in the
old-town of Venice. They were used to estimate the average minimum height of electrical sockets, type of floor cover, presence
of water-proof skirting boards and other protection measures. In addition, graphic documentation of the 12 November 2019
storm surge by the Aqua Grande project13 was used to search for installed flood protection measures.
235
It was found that typical characteristics of residential buildings do not differ significantly from the implemented character-
istics in INSYDE. One major difference related to the external wall perimeter exposed to floods was detected and incorporated
as a new parameter EPef f : most buildings in Venice are attached to other buildings reducing the exposed perimeter. Addition-
ally, a new building type ’buildings with economic activities on the ground floor’ (BEA) was added to account for observed
differences in the exposure and vulnerability characteristics from typical residential buildings: the windows are generally larger240
(increased from 1.4m x 1.4m to 2m x 2m), the window sills are lower (new sill height of 0.5m instead of 1.2m), and many
shops are on ground level without any steps of elevation. Additionally, the internal perimeter (reduced from 2.5 to 1.5 time the
external perimeter) and number of doors is smaller (reduced to 3 per 100 m2).
It was detected that many buildings had installed mobile protection systems, mainly bulkhead protections, at doors and245
windows to protect the interior from flooding during the 12 November 2019 storm event. Other protection measures were not
commonly installed and therefore not incorporated in the damage model. A new parameter BuHe, representing the bulkhead
protection height, was implemented to mediate the water level inside the buildings. Due to lack of data on the spatial distribution
and protection height of mobile protection systems, three conceptual individual protection scenarios (IPS) were characterized
and applied: expected IPS, risk averse IPS and risk-taking IPS. For the risk taking IPS, it was assumed that no bulkhead250
protection was installed at all. For the expected IPS, it was assumed that residents would install bulkheads protecting their
building against the forecasted maximum water level (FC ) at Punta della Salute incremented by a safety margin of 10 cm.
For a risk averse IPS, the protection height also refers to the forecasted maximum water level at Punta della Salute but is
12More detailed information on the census block data can be found in the supplementary material of this study.
13accessed from: https://www.aquagrandainvenice.it/en/welcome
12
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
incremented by a safety margin of 50 cm. The water level hinside the buildings is consequently calculated as
h=he−GL −BuH e and BuHe =
0, if risk-taking IPS
F C + 0.1, if expected IPS
F C + 0.5, if risk averse IPS
(2)255
where heis the water level outside the buildings, GL is the ground floor level of the considered structure and BuH e is the
bulkhead protection height as visualized in Fig. 5. F C was set to 1.50 m ZMPS for ’SLR0-allopen’ and to 1.10 m ZMPS in all
other scenarios given that a functional MOSE barrier is expected to keep the water level below a threshold of 1.10 m ZMPS.
Figure 5. Visualization of bulkhead protection height
As a third parameter, information on the cultural heritage status of buildings14 inside Venice was used to account for higher260
reconstruction costs. In line with a previous study assuming cost increase of reconstruction for historic buildings by 7 to 11%
(Fontini et al., 2008), total damage costs were incremented by 10% in case of cultural heritage status. This is also in line with
commonly mentioned ranges of reconstruction costs in Venice15 . Unit prices for cleaning, removal, and replacement were used
from the INSYDE model assuming that those values do not significantly vary across Italy. INSYDE provides prices at 2015
price level. They were corrected for inflation and referenced to the year 2019.265
14Provided by the cultural heritage office of the city of Venice.
15See for example here: http://costo-ristrutturazione-casa.it/costo- ristrutturazione-appartamento- venezia/ (accessed 09/04/2021)
13
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
3 Results
This study developed a methodical framework to assess present and future flood risk in the historic city of Venice. As such,
a hydrodynamic model was developed, calibrated and validated. In addition, a damage model was compared against available
damage claim data of the storm event of 12 November 2019. Ultimately, the framework was applied to analyse development270
of future flood damages under sea level rise scenarios in case of a (partially) closing MOSE barrier.
3.1 Calibration & validation of the hydrodynamic model
For calibration and validation of the hydrodynamic parent model, modelled water levels were compared against measurements
obtained at seven tidal gauge stations: Lido inlet, Malamocco inlet, Chioggia inlet and San Nicolo, Murano, San Giorgia in275
Alga and Punta della Salute which are located in close proximity to the old-town, as visualized in Fig. 2. Water level informa-
tion was provided by the meteo-tidal network of the Venice Lagoon16 . Three events were used for calibration and validation
purposes as shown in Tab. 6. For the tide calibration, a summer period was chosen where influence of wind on the water levels
inside the lagoon can be expected to be low. The full model was calibrated for the storm event of 12 November 2019 and finally
validated for another storm event from October 2018.280
Table 6. Considered conditions for calibration and validation
used for period
tide calibration 01/07/13 00:00 - 04/07/13 23:50
wind calibration 12/11/19 00:00 - 13/11/19 02:00
model validation 28/10/18 16:00 - 30/10/18 02:00
To evaluate the performance of the model, the Pearson R coefficient and the Root-Mean-Square-Error were used. Results
for the three runs are compiled in Tab. 7 and suggest that measured data can be reproduced well, including the storm surge
peaks for the wind calibration and validation run. Accuracy of the maximum flood peak lies within a margin of ±5cm. For San
Nicolo, Malamocco and Murano, the observed water level data were partly corrupted or not available.17.285
The nested models were used to derive the flood depth estimates inside the city. Analysis of the difference in water depth
estimates inside the old-town of Venice from the parent and nested model domains suggest that the grid resolution of the hy-
drodynamic model has significant impact on the flood characteristics inside the city. As Fig. 6b shows, a coarser grid tends to
provide lower flood depth estimates. A coarser grid may fail (more often) to resolve possible flow paths in the very narrow290
16accessed here: https://www.venezia.isprambiente.it/rete-meteo-mareografica
17Further analysis of the results can be found in the supplementary material of this study.
14
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
Table 7. Parent model performance
tide calibration wind calibration model validation
station R RMSE [m] R RMSE [m] R RMSE [m]
Murano 0.969 0.048 - - 0.992 0.078
PuntaSalute 0.977 0.043 0.987 0.078 0.990 0.068
SanGiorgio 0.970 0.049 0.989 0.070 0.989 0.097
SanNicolo 0.989 0.027 0.945 0.136 - -
Malamocco 0.971 0.054 0.984 0.081 - -
Chioggia 0.993 0.025 0.977 0.091 0.934 0.114
Lido 0.986 0.040 0.974 0.097 0.945 0.121
Figure 6. Flood depth estimates for old-town of Venice. a: Cross-model comparison of average inundation depths. b: Comparison of average
flood depths (except Castello) for a grid resolution of 2.6m (parent model) and 1.3m (nested models).
street-system in Venice limiting water flow into the old-town.
Calibration was not possible inside the old-town due to lack of available measured data. Instead, a cross-model comparison
of the nested model flood depth estimates with a simple bathtub model was used to analyse the average maximum flood depth
estimates for the 12 November 2019 storm event. The bathtub model tends to provide higher inundation estimates as Fig. 6a295
shows. Additionally, it is visible that the hydrodynamic model gives high flood depths for some buildings while the bathtub
models suggests that those structures are not affected by water levels at all (or to a much lesser degree). This unexpected result
was linked to grid instabilities of the nested models. In total, higher water levels were suggested by the hydrodynamic model at
383 buildings. Additionally, grid instabilities of the nested sub-model ’Castello’ (refer to Fig. 3) could not be resolved, result-
ing in missing flood depth data based on the hydrodynamic model for 2,098 buildings (14 % of the total number of buildings).300
For buildings affected by instabilities, flood depth estimates from the bathtub model were used for the damage modelling of
15
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
these buildings.
3.2 Damage model performance
To analyse the performance of the transferred model, the total modelled damages for the old-town were compared against the305
total sum of the eligible 7,644 damage claims. Additionally, a structure-wise analysis was conducted for the sub-set of 2,778
structures with 3,728 immediate response claims.
Table 8. Comparison of damage estimates and claims [EUR million]
INSYDE claims
sub-set of
structures
risk averse IPS 12.9
25.7expected IPS 42.0
risk taking IPS 63.1
all
structures
risk averse IPS 52.3
56.2expected IPS 166.3
risk taking IPS 253.6
As shown in Tab. 8, the damage model is able to reproduce the damage claims well: for both sets of considered structures,
reported damage claims fall inside the range of modelled damage estimates for the different IPS. While the total volume of310
reported immediate response claims corresponds to a individual protection scenario between ’risk averse’ and ’expected’, the
total volume of all reported damages is closer aligned with a risk averse IPS.
Additionally, a structure-wise comparison was conducted for 2,778 structures. As shown in Tab. 9, correlation and average
relative error, computed as the ratio of the reported damage and the estimated damage per building, suggest limited alignment315
of the modelled damages with the reported claims. Both indicators suggest that the damage claims might be slightly better
estimated based on a expected IPS or risk taking IPS for the majority of buildings. At the same time the RMSE, which gives
more weight to extreme variations due to its definition, is lower when assuming a risk averse IPS. Moreover, the Kernel density
plot gives insight in the relative frequency of damages as shown in Fig. 7. It can be seen that in a risk averse IPS, the number
of structures with rather low damages is overestimated, meanwhile larger damages are underestimated. The opposite applies to320
risk neutral and risk taking scenarios.
According to the INSYDE model, the most affected building components are external and internal plaster removal (R6, R7),
replacement (F1, F2) and painting (F3, F4), followed by costs for the replacement of electrical (P3) and plumbing systems (P4),
as shown in Fig. 8. It can be seen that the model suggests no damage for many damage components as hazard characteristics are325
below thresholds for which damage is reported to occur. It can be seen that the expected IPS leads to limited damage reduction
16
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
Figure 7. Kernel density plot: damage estimates and claims
Table 9. Performance indicators for structures with immediate response claims
risk averse
IPS
expected
IPS
risk taking
IPS
R [-] 0.22 0.26 0.26
RMSE [EUR] 19,382 22,158 29,332
RE [%] 308.9 87.8 55.5
regarding plaster, but a strong reduction for the building systems. In a risk averse IPS, no damage occurs inside the buildings.
It is worth mentioning that damage estimates based on flood depth information from the bathtub model generally give similar
damage estimates for both sets of considered structures; deviations for risk averse and risk taking IPS is between 1.5 and 6.3%.330
For the expected IPS, damages are about 13.1 to 16% higher when using bathtub model depth estimates. This is a reasonable
observation, given that the bathtub model generally provides higher flood depth estimates. As a result, the number of buildings
where the flood depth of the bathtub model exceeds the protection height but flood depth of the hydrodynamic model does
not exceed the protection height is higher for the expected IPS than for the risk taking or risk averse IPS. Consequently, more
additional damage occurs according to the bathtub model for the expected IPS, as significantly more buildings are damaged335
inside according to the bathtub model.
17
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
Figure 8. Damage components and damage estimation for all structures for SLR0-allopen
3.3 Flood damage for future scenarios
The developed flood risk assessment framework was applied to a set of sea level rise scenarios for the reference year of 2060.
Flood damage was computed and used as a proxy for how flood damages and risk could evolve in future conditions. The set of340
seven scenarios is compiled in Tab. 1. As shown in Fig. 9a, a fully closed MOSE barrier keeps the peak flood level significantly
below the safety threshold of 1.10 m ZMPS for the given meteorological event for all scenarios. A partially closed barrier
would lead to a reduction of the flood peak of about 0.3 m for SLR0 and SLR1. Still, an open Lido inlet leads to high water
levels at Punta della Salute. Results suggest that the dampening effect by a partially closed barrier diminishes for SLR2. For a
sea level rise of 0.45 m, the peak at the Piattaforma CNR would be at 2.25 m ZMPS, and the peak at Punta della Salute at 2.10345
m ZMPS, implying that the damping effect is reduced by half.
It is noteworthy that for the ’allclosed’ scenarios, SLR2 results in a slightly lower flood peak estimate than the other two
scenarios. A possible explanation is that for SLR2 the closure of the MOSE barrier occurs about 24 hours earlier relative to the
flood peak, while for SLR0 and SLR1 it is closed about 4 hours before the flood peak. As the barrier is closing during flood,350
the part of the tidal wave that propagated into the lagoon before the full closure has more time to evenly spread out across
the lagoon, resulting in a slightly lower average flood depth in the centre of the lagoon than for the other two scenarios. This
ultimately influences the wind effect and maximum water levels at Punta della Salute.
Analysis of the implications of the different scenarios on the average inundation depths concludes that a partially functioning355
MOSE barrier would significantly reduce the expected average flood depth for 90% of the buildings for sea level rise scenarios
of SLR0 and SLR1. In SLR2 the increased sea level dominates over the dampening effect of the partial closure as visualized in
Fig. 9b. This analysis also shows that for the storm surge of 12 November 2019, 50 % of all structures in Venice experienced a
flood depth of 0.55 m or higher. Only 10% of buildings experienced flood depths lower than 0.10 m and only 5% of buildings
18
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
were not exposed to floods at all.360
Figure 9. Flood depths for scenarios. a: Modelled flood peaks at Punta della Salute. b: Share of buildings exposed to certain average flood
depths
Table 10. Flood peak level at Punta della Salute [m ZMPS] and damage estimates [EUR million] for different scenarios
scenario peak
level
risk averse
IPS
expected
IPS
risk taking
IPS
SLR0-allopen 1.89 52.2 166.3 253.6
SLR0-lidoopen 1.56 37.1 95.0 132.0
SLR0-allclosed 0.82 0.004 0.004 0.015
SLR1-lidoopen 1.62 42.6 129.4 166.7
SLR1-allclosed 0.87 0.006 0.006 0.02
SLR2-lidoopen 2.10 179.7 289.6 309.4
SLR2-allclosed 0.81 0.004 0.004 0.015
Corresponding damage estimates for the different scenarios were computed using the calibrated INSYDE model. For the
scenarios accounting for an (assumed) protecting MOSE barrier, the forecasting water level relevant to determine the height
of mobile protections at doors and windows was set to the safety threshold of 1.10 m ZMPS. As a result, the damage cost
difference between expected IPS and risk averse IPS decreases with increasing flood depths. At the same time, the difference365
for the risk averse IPS is less apparent given that for SLR0-allopen, damages only occurred at the external walls, but for SLR0-
lidoopen also partly on the inside due to lower protection levels. Results are compiled in Tab. 10.
An interesting observation can be made when comparing the damage estimates of SLR0-allopen to those of SLR2-lidoopen.
Despite an approximately 0.21 m higher flood depth for SLR2-lidoopen, the effect on damage estimates for risk taking IPS and370
19
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
expected IPS are smaller than expected even though protection heights are on average also 0.40 m lower than in SLR0-allopen.
Analysis of the formulations for vulnerability and exposure implemented in INSYDE provide a possible explanation: not only
the part of external and internal plaster in direct contact with the water has to be replaced, but also an additional height of one
meter. Given that cost for plaster removal is independent of the required removal height, this implies that for a small flood
depth, higher replacement costs occur already which are only incremented linearly for higher flood depths. As extreme flood375
depths are frequently lower than one meter, the influence of the additional height weights heavier compared to the difference
for higher water level scenarios.
4 Discussion
Venice is a city with a long history of flooding that is likely to extend into future despite the presence of the MOSE barrier.380
Until now, limited methodological approaches exist which provide estimations of future flood risk to structures and particularly
to cultural heritage. This study developed a flood risk assessment framework that can be used for assessment of direct, tangible
damages to residential and economic buildings, and can be extended in future research to account for the special conditions of
cultural heritage as well. The framework performs well compared to available damage claim data and gives some indications
about possible future flood risk for extreme storm surges under a partially failing MOSE barrier system.385
The developed hydrodynamic model provides reliable estimates of hazard characteristics inside the old-town. First, the val-
idated hydrodynamic coastal model reproduces the flood peaks with an accuracy of ±5cm despite some simplifications of the
lagoon system, such as applying uniform meteorological conditions over the entire domain and neglecting freshwater inputs
and wave action. Second, the cross-model comparison suggests that the hydrodynamic model performs as expected and may390
provide optimistic flood depth estimates inside the city compared to the presently used static model (Liu et al., 2018). A final
confirmation of the flood depths inside the city by means of calibration and validation with flood depth records was not possible
but should be a key focus in future studies as flood-enhancing components, such as the sewage system, water coming from
the ground, or wave influence were neglected. In addition, following from the comparison of parent and nested model depth
estimates, a grid convergence analysis should be conducted to find the optimal grid resolution for the city of Venice. Despite a395
grid size of 1.3m near structures, which is already rather high compared with other hydrodynamic urban models (Xing et al.,
2019), the specific setting of Venice with its narrow street system may require increasing the resolution even further.
Some modelling challenges of the hydrodynamic model have to be highlighted. Due to the complex urban structures and
altimetry, some extreme local water levels occurred in the parent and nested models were likely caused by the complex grid400
structure and the algorithm describing the wetting and drying process inside the model (Deltares, 2021). This led not only to
incorrectly high flood depths at a few buildings but also prevented the consideration of one of the nested sub-models. Part of the
instabilities can be solved by grid refinement, bathymetry alteration, or adjusting the modelled time periods. In accordance to
20
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
previous studies (Scorzini and Frank, 2017; Arrighi et al., 2013), it was found acceptable to use bathtub flood depth estimates
for the remaining structures instead, given the limited influence of flood depth variation on the damage estimate. Additionally, a405
fully functioning hydrodynamic model may add additional benefits to the flood risk assessment framework as it can account for
(changing) physical characteristics explicitly, allow for a proper calibration, and incorporate additional flow path-components
such as a 1D sewage system.
The adjusted version of the INSYDE damage model is able to reproduce the total damage claim volume related to the storm410
event of 12 November 2019 as shown in Tab. 8. Analysis of the sub-set of immediate response damage claims also confirm
initial expectations of relatively high individual protections levels in Venice as frequent and intense experience of flooding have
been reported to contribute to higher levels of individual flood preparedness (Kreibich et al., 2015). Moreover, results imply
that the effect of protection measures has a strong influence on the estimated damages.
415
However, the poor structure-wise depth-damage correlation and the alignment of the two considered sets of reported damage
claims with different (combinations) of IPS reiterate commonly faced challenges of flood damage modelling (Ahn et al., 2019).
Limited knowledge of the system introduces uncertainty in the damage estimates. As an example, about half of all damage
claims ( 7,644) were linked to about 20% of the structures in Venice only. Meanwhile 90% of structures were found to be
exposed to an average flood depth of at least 0.1 m according to the hydrodynamic model. Thus, it is questionable whether420
exposure and vulnerability of the system are adequately represented given that modelled damages of external walls alone are
almost as high as the reported damages. In addition, preparedness was simplified as perfectly functioning mobile barrier sys-
tems installed at all buildings, like in this study. However, protection levels have been reported to be very diverse and could
also (partially) fail to provide the promised level of protection in reality. Additionally, more protection measures may be in
place to reduce the flood damages. Moreover, many exposure and vulnerability relations of the synthetic damage model were425
transferred unaltered, despite the possibility that they may not reproduce the present hazard-structure interaction processes in
Venice.
At the same time, limitations of the available damage claim data-sets have to be accounted for as well. It can generally
be questioned whether reported damages represent the full set of effective damages of a flood event. Potential claimants may430
have opted to undergo significant bureaucratic efforts for (sometimes) limited financial support (Molinari et al., 2020). Al-
ternatively, claimants may not have seen the need to replace (some) damaged elements, e.g. because of their experience with
frequent flooding. Marks of previous floods at house fronts throughout the old-town support this hypothesis. Additionally,
given that the available damage data are spatially and/or component-wise aggregated, limited conclusions can be drawn from
the damage data analysis to address the mentioned limitations of the framework. Information from a detailed investigation435
of the effective and reported damages for the 12 November 2019 flood event may provide required additional confidence in
the developed damage model. Also, a thorough analysis of the variety and spatial distribution of building types and installed
preparation and protection measures on structure and neighborhood level, as well as other exposure characteristics, in Venice
21
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
would be required for a better representation of the system.
440
When discussing the accuracy and reliability of the applied damage model, it is also worth considering that another study
analysing exceptionally extreme flood events suggests much higher flood damages (Caporin and Fontini, 2014); for flood events
exceeding 1.80 m ZMPS, damage estimates amount to EUR 196.33 million18 even though only the refurbishment (plastering)
of walls is considered. Given the varying approaches, many reasons could contribute to the diverging damage estimates. Two
striking reasons were identified: estimates of the buildings requiring special care due to their historical importance diverge for445
the two studies (present study: 25% of buildings declared as cultural heritage, in other study 50% of buildings) along with the
corresponding increase in refurbishment cost (present study: 10%, other study 50%). Additionally, the assumed basis recon-
struction costs may vary: in the present study, reconstruction cost values from another region were used under the assumption
of limited variation across Italy. It would be recommended to investigate possible differences and use reconstruction cost in-
formation for the Veneto region instead19 .450
Results on the effect of the MOSE barrier on the water level inside the lagoon align with previous studies, suggesting that
a partial closure will still cause flooding of the old-town of Venice (Umgiesser et al., 2021). The study adds to the existing
knowledge as it considers the second most extreme flood event experienced, while previous studies have mainly investigated
more frequent, less extreme flood events (Zampato et al., 2016; Vergano and Nunes, 2007). The present study adds new insights455
suggesting that the damping effect of a partially closed MOSE barrier on the flood wave will reduce as sea level rises and may
consequently amplify flood risk in future. To confirm this finding in future studies, some of the present’s study limitations
should be addressed: for the applied future scenarios, present conditions of the system were used. However, the sediment bud-
get of the lagoon is negative, meaning that the lagoon currently deepens and may look significantly different in 40 years from
now (Tambroni and Seminara, 2006). The same applies for local subsidence processes which have significantly contributed to460
flood risk in the past and may continue to do so in future as well (Zanchettin et al., 2021). Also, variation in tidal amplitude
due to changes in bathymetry and mean sea level as observed in the past, may continue in future as well (Ferrarin et al., 2015).
In addition, some inaccuracy regarding the flood levels is likely to be introduced as processes of seepage through the barrier
and freshwater input in the lagoon have been neglected in the present study. This is particularly relevant for SLR2, where the465
MOSE barrier would be closed for more than 36 hours. In previous studies it has been suggested that seepage through the
fully closed barrier could result in water level increase between 0.27 cm to 2.1 cm per hour (Umgiesser and Matticchio, 2006).
Consequently, peak water level could be expected to be about 8.1 to 63 cm higher for SLR2-allclosed, while the effect of
seepage could add between 1 and 8.4 cm in a SLR0-allclosed scenario where MOSE closure happens about 4 hours before the
flood peak. Seepage and freshwater input may also increase water levels for scenarios with an open inlet at Lido.470
18Price level of 2013, not adjusted for inflation.
19accessible here: https://www.regione.veneto.it/web/lavori-pubblici/prezzario-regionale
22
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
The results of the scenario analysis highlight the importance of a fully functioning MOSE barrier and the damage mediating
influence of the individual protection scenarios. In line with previous studies investigating the remaining flood risk under cli-
mate change with a fully functioning barrier (Nunes et al., 2005), the present study suggests that a fully closed MOSE barrier
limits the effect of flooding for the considered meteorological flood event to very few buildings inside the old-town with very475
small damages for all considered sea level rise scenarios as shown in Tab. 10.
Even though the applied methodology to represent preparedness and individual flood risk protection by means of different
IPS and their effectiveness has mainly a conceptual value, some insights can be derived nevertheless: the warning level and
how residents will respond to this in terms of individual protection in light of a (expected) functioning MOSE barrier appear480
to have significant influence on the expected damages as shown in Tab. 11. Table 11 gives the change of estimated damage
for the different scenarios relative to the modelled damages for the flood event of 12 November 2019 represented by SLR0-
allopen. It shows that a partially functioning MOSE barrier could reduce damages of a storm surge event like 12 November
2019 by 17% to 48% for SLR0 or SLR1 under the assumption of unaltered levels of individual protection in future. The re-
duction is strongest for SLR0-lidoopen, assuming a (constant) risk-taking IPS, where damage would be reduced to 52% of485
the estimation for SLR0-allopen. As discussed, the damping effect of a partially closed barrier diminishes for SLR2-lidoopen.
As a result, damages could increase by a factor 1.08 to 3.44 if sea level rise follows the pessimistic prognosis of climate change.
At the same time, individual protection levels may change in future depending on the performance and reliability of the
MOSE barrier. In the worst case, meaning that protection levels change from a risk averse IPS to a risk taking IPS, damages490
could be up to 5.92 times higher compared to flood damages of SLR0-allopen as shown in Tab. 11. Compared with a scenario
where the individual protection level remains constant, damages would be about 72% higher in this case. At the same time, in
the case that individual protection levels increase from an expected IPS to a risk averse IPS, damages could be reduced to 26%
for SLR1-lidoopen or just slightly increase by 8% in case of SLR2-lidoopen.
495
Table 11. Ratio of future flood damages and SLR0-allopen under varying IPS (developments in future). I: risk averse IPS, II: expected IPS,
III: risk taking IPS.
SLR0_lidoopen SLR1_lidoopen SLR2_lidoopen
I II III I II III I II III
SLR0
allopen
I 0.71 1.82 2.53 0.82 2.47 3.19 3.44 5.54 5.92
II 0.22 0.57 0.79 0.26 0.78 1.00 1.08 1.74 1.86
III 0.15 0.37 0.52 0.17 0.51 0.66 0.71 1.14 1.22
As present knowledge of influencing drivers of future flood risk is very limited, this study is only a starting point for a
more concise analysis of the implications of the MOSE barrier on the old-town of Venice and the individual protection levels
in particular. At this point, it is unknown what effect the operational MOSE barrier will have on the early-warning system in
23
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
Venice and the level (and types) of installed protection measures by residents. Additionally, the provided estimates are all based
on present monetary values and present exposure and preparedness conditions. They are expected to change in future, again500
depending on both possible socio-economic and political developments and the reliability of the MOSE barrier to protect the
old-town and its residents in the future.
5 Conclusions
In this study, a flood risk assessment framework has been developed. It was able reproduce the flood event of 12 November505
2019 with an accuracy of ±5cm in the proximity of the old-town and provides damage estimates in accordance with available
damage claim data. The implemented damage model can reproduce damage claim data but faces commonly acknowledged
uncertainties due to limited knowledge about the system and damage processes.
Developing a methodical risk assessment framework for the cultural heritage city has provided some valuable insights into510
expected flood exposure and damages in the old-town of Venice. While this study confirms the general appropriateness of the
MOSE barrier to protect the city of Venice for extreme storm events for additional rising sea level up to 45 cm, it was also
found that the damages in case of a partially closed MOSE barrier may still increase significantly for most considered scenar-
ios. While an improved individual protection level in future could lead to a damage reduction of up to 78% for present sea level
and 74% for an optimistic sea level rise prognosis, damages could be up to 1.08 to 5.92 times higher in 2060 in case of un-515
changed or decreased level of individual protection. Based on the findings of relative importance of individual flood protection
in light of a potentially failing MOSE barrier, this study provides indication that a better understanding of presently applied
flood protection is needed to identify realistic individual protection scenarios for future conditions. This would be helpful to
identify possible areas of action to maintain (or advance) existing structure-wise flood protections and individual preparedness.
In addition, the influence of the MOSE barrier on the reported warning levels and the effectively installed protections was520
identified as an important question to address in order to reduce flood risk in Venice until 2060. As such, the proposed flood
risk assessment framework provides a methodical approach useful to support future decisions on flood risk management.
Additional studies should be done to improve the presented framework. Addressing some of the limitations, particularly the
simplification of the system by excluding the sewage system, grid instabilities and lack of calibration data, may add additional525
confidence to the exposure modelling. Moreover, incorporating information on future return levels of storm events as well as
failure probabilities of the MOSE barrier should be addressed and incorporated in the present framework to allow for a proper
flood risk assessment to support the efficient and effective allocation of (additional) resources to flood protection in Venice.
Also, a better understanding of the spatial distribution of protection measures and other exposure mediating characteristics
within the districts of the old-town, ideally for each structure, is required for a better representation of the system. Additionally,530
new building types in the damage model can be implemented to account for some characteristic cultural heritage buildings as
24
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
proposed in the supplementary material. This would contribute to a better and multidimensional understanding of the present
and future flood risk.
Code and data availability. Files and data used for the hydrodynamic and damage modelling are made available on the following repository535
along with an explanatory overview document: https://1drv.ms/u/s!AujDMT3F11JwgpEoTj2zfvrJqDcOdA?e=dY2c6O
Author contributions. The paper is product of the M.Sc. thesis work of JS. JS was responsible for the progression of research, the model
runs and the post-processing analysis and writing the paper. CF provided data and information regarding the 12 November 2019 flood event
and contributed to the analysis of the hydrodynamic and flood modelling results. BJ was chair of the M.Sc thesis, reviewed the paper and
contributed to defining the general scope and approach of the study. ADL provided support on the hydrodynamic modelling and writing540
process. AA supported the communication with Italian official entities. SF provided data and information on cultural heritage evaluation. CF,
BJ, ADL, AA and SF also contributed with discussion and revision.
Competing interests. The authors declare that they have no conflict of interest.
Financial support. Christian Ferrarin has been supported in this work by the STREAM project (strategic development of flood management,
project ID 10249186) funded by the European Union under the V-A Interreg Italy-Croatia CBC program.545
Acknowledgements. We would like to issue special thanks to Dr. ir. A. R. Scorzini for her immediate support and for sharing her insights and
experience in the complex field of damage modelling in the context of Italy. Furthermore we would like to thank the office of the delegated
Commissioner Delegate for the Emergency resulting from the exceptional tide of 12 November 2019 in Venice for their willingness and
cooperation in providing statistical data related to the declared damages and in particular M. Calligaro for his valuable and extensive effort
to provide all possible damage claim information. Finally, we want to thank G. M. Lemos for sharing insights and data from her experience550
in D3DFM modelling of the Venetian lagoon.
25
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
References
Ahn, J., Na, Y., and Park, S. W.: Development of Two-Dimensional Inundation Modelling Process using MIKE21 Model, KSCE Journal of
Civil Engineering, 23, 3968–3977, https://doi.org/10.1007/s12205-019-1586-9, 2019.
Amadio, M., Mysiak, J., Carrera, L., and Koks, E.: Improving flood damage assessment models in Italy, Natural Hazards, 82, 2075–2088,555
https://doi.org/10.1007/s11069-016-2286-0, 2016.
Arrighi, C., Brugioni, M., Castelli, F., Franceschini, S., and Mazzanti, B.: Urban micro-scale flood risk estimation with parsimonious hy-
draulic modelling and census data, Natural Hazards and Earth System Sciences, 13, 1375–1391, https://doi.org/10.5194/nhess-13-1375-
2013, 2013.
Arrighi, C., Brugioni, M., Castelli, F., Franceschini, S., and Mazzanti, B.: Flood risk assessment in art cities: the exemplary case of Florence560
(Italy), Journal of Flood Risk Management, 11, S616–S631, https://doi.org/10.1111/jfr3.12226, 2018a.
Arrighi, C., Rossi, L., Trasforini, E., Rudari, R., Ferraris, L., Brugioni, M., Franceschini, S., and Castelli, F.: Quantification of flood risk
mitigation benefits: A building-scale damage assessment through the RASOR platform, Journal of environmental management, 207, 92–
104, https://doi.org/10.1016/j.jenvman.2017.11.017, 2018b.
Battistin, D. and Canestrelli, P.: 1872-2004: la serie storica delle maree a Venezia, Instituzione Centro Previsioni e Segnalazioni Maree, 2006.565
Bryant, K. and Akbar, M.: An Exploration of Wind Stress Calculation Techniques in Hurricane Storm Surge Modeling, Journal of Marine
Science and Engineering, 4, 58, https://doi.org/10.3390/jmse4030058, 2016.
Caporin, M. and Fontini, F.: The Value of Protecting Venice from the Acqua Alta Phenomenon under Different Local Sea Level Rises,
https://mpra.ub.uni-muenchen.de/53779/, 2014.
Cavaleri, L., Bajo, M., Barbariol, F., Bastianini, M., Benetazzo, A., Bertotti, L., Chiggiato, J., Davolio, S., Ferrarin, C., Magnusson, L., Papa,570
A., Pezzutto, P., Pomaro, A., and Umgiesser, G.: The October 29, 2018 storm in Northern Italy – An exceptional event and its modeling,
Progress in Oceanography, 178, 102178, https://doi.org/10.1016/j.pocean.2019.102178, 2019.
Cavallaro, L., Iuppa, C., and Foti, E.: Effect of Partial Use of Venice Flood Barriers, Journal of Marine Science and Engineering, 5, 58,
https://doi.org/10.3390/jmse5040058, 2017.
Cellerino, R., Giancola, L., and Anghinelli, S.: Venezia atlantide: L’impatto economico delle acque alte, vol. 80 of Economia. Sezione 5,575
Ricerche di economia applicata, Angeli, Milano, 1998.
City of Venice: Città di Venezia | Comune di Venezia - Portale dei servizi, https://portale.comune.venezia.it/, 2000.
Comune di Venezia.: Rischio Idraulico, https://www.comune.venezia.it/it/content/rischio-idraulico, 2016.
D’Alpaos, L. and Defina, A.: Mathematical modeling of tidal hydrodynamics in shallow lagoons: A review of open issues and applications
to the Venice lagoon, Computers & Geosciences, 33, 476–496, https://doi.org/10.1016/j.cageo.2006.07.009, 2007.580
Deltares: D-Flow Flexible Mesh User Manual, 2021.
Dottori, F., Figueiredo, R., Martina, M. L. V., Molinari, D., and Scorzini, A. R.: INSYDE: a synthetic, probabilistic flood damage model
based on explicit cost analysis, Natural Hazards and Earth System Sciences, 16, 2577–2591, https://doi.org/10.5194/nhess-16-2577-2016,
2016.
Drdácký, M. F.: Flood Damage to Historic Buildings and Structures, Journal of Performance of Constructed Facilities, 24, 439–445,585
https://doi.org/10.1061/(ASCE)CF.1943-5509.0000065, 2010.
EMODnet: EMODnet Digital Bathymetry (DTM) - European Union Open Data Portal, https://data.europa.eu/euodp/en/data/dataset/
EMODnet_bathymetry, 2018.
26
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
European Commission: EUR-Lex - 32007L0060 - EN - EUR-Lex, https://eur-lex.europa.eu/eli/dir/2007/60/oj, 2007.
Ferrarin, C. and Umgiesser, G.: Hydrodynamic modeling of a coastal lagoon: The Cabras lagoon in Sardinia, Italy, Ecological Modelling,590
188, 340–357, https://doi.org/10.1016/j.ecolmodel.2005.01.061, 2005.
Ferrarin, C., Cucco, A., Umgiesser, G., Bellafiore, D., and Amos, C. L.: Modelling fluxes of water and sediment between Venice Lagoon and
the sea, Continental Shelf Research, 30, 904–914, https://doi.org/10.1016/j.csr.2009.08.014, 2010.
Ferrarin, C., Tomasin, A., Bajo, M., Petrizzo, A., and Umgiesser, G.: Tidal changes in a heavily modified coastal wetland, Continental Shelf
Research, 101, 22–33, https://doi.org/10.1016/j.csr.2015.04.002, 2015.595
Ferrarin, C., Bajo, M., Benetazzo, A., Cavaleri, L., Chiggiato, J., Davison, S., Davolio, S., Lionello, P., Orli´
c, M., and Umgiesser,
G.: Local and large-scale controls of the exceptional Venice floods of November 2019, Progress in Oceanography, p. 102628,
https://doi.org/10.1016/j.pocean.2021.102628, 2021.
Field, C. B., Barros, V., Stocker, T. F., and Dahe, Q., eds.: Managing the Risks of Extreme Events and Disasters to Advance Cli-
mate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge,600
https://doi.org/10.1017/CBO9781139177245, 2012.
Fontini, F., Umgiesser, G., and Vergano, L.: The Role of Ambiguity in the Evaluation of the Net Benefits of the MOSE System in the Venice
Lagoon, "Marco Fanno" Working Papers, 2008.
Gallien, T. W., Sanders, B. F., and Flick, R. E.: Urban coastal flood prediction: Integrating wave overtopping, flood defenses and drainage,
Coastal Engineering, 91, 18–28, https://doi.org/10.1016/j.coastaleng.2014.04.007, 2014.605
Gerl, T., Kreibich, H., Franco, G., Marechal, D., and Schröter, K.: A Review of Flood Loss Models as Basis for Harmonization and Bench-
marking, PLOS ONE, 11, e0159 791, https://doi.org/10.1371/journal.pone.0159791, 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, Proceedings of the National Academy of Sciences, 111,
3292–3297, https://doi.org/10.1073/pnas.1222469111, 2014.610
Hudson, P., Botzen, W. W., Feyen, L., and Aerts, J. C.: Incentivising flood risk adaptation through risk based insurance premiums: Trade-offs
between affordability and risk reduction, Ecological Economics, 125, 1–13, https://doi.org/10.1016/j.ecolecon.2016.01.015, 2016.
Huijbregts, Z., van Schijndel, J. W. M., Schellen, H. L., and Blades, N.: Hygrothermal modelling of flooding events within historic buildings,
Journal of Building Physics, 38, 170–187, https://doi.org/10.1177/1744259114532613, 2014.
ISTAT: Basi territoriali e variabili censuarie, https://www.istat.it/it/archivio/104317, 2020.615
Jordà, G., Gomis, D., and Marcos, M.: Comment on “Storm surge frequency reduction in Venice under climate change” by Troccoli et al,
Climatic Change, 113, 1081–1087, https://doi.org/10.1007/s10584-011-0349-5, 2012.
Kelman, I. and Spence, R.: An overview of flood actions on buildings, Engineering Geology, 73, 297–309,
https://doi.org/10.1016/j.enggeo.2004.01.010, 2004.
Kreibich, H., Seifert, I., Thieken, A. H., Lindquist, E., Wagner, K., and Merz, B.: Recent changes in flood preparedness of private households620
and businesses in Germany, Regional Environmental Change, 11, 59–71, https://doi.org/10.1007/s10113-010-0119-3, 2011.
Kreibich, H., Bubeck, P., van Vliet, M., and de Moel, H.: A review of damage-reducing measures to manage fluvial flood risks in a changing
climate, Mitigation and Adaptation Strategies for Global Change, 20, 967–989, https://doi.org/10.1007/s11027-014-9629-5, 2015.
Lionello, P., Barriopedro, D., Ferrarin, C., Nicholls, R. J., Orli´
c, M., Raicich, F., Reale, M., Umgiesser, G., Vousdoukas, M., and Zanchettin,
D.: Extreme floods of Venice: characteristics, dynamics, past and future evolution (review article), Natural Hazards and Earth System625
Sciences, 21, 2705–2731, https://doi.org/10.5194/nhess-21-2705-2021, 2021.
27
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
Liu, X. J., Zhong, D. H., Tong, D. W., Zhou, Z. Y., Ao, X. F., and Li, W. Q.: Dynamic visualisation of storm surge flood routing based on
three-dimensional numerical simulation, Journal of Flood Risk Management, 11, S729–S749, https://doi.org/10.1111/jfr3.12252, 2018.
López-Marrero, T.: An integrative approach to study and promote natural hazards adaptive capacity: a case study of two flood-prone com-
munities in Puerto Rico, Geographical Journal, 176, 150–163, https://doi.org/10.1111/j.1475-4959.2010.00353.x, 2010.630
Madricardo, F., Foglini, F., Kruss, A., Ferrarin, C., Pizzeghello, N. M., Murri, C., Rossi, M., Bajo, M., Bellafiore, D., Campiani, E., Fogarin,
S., Grande, V., Janowski, L., Keppel, E., Leidi, E., Lorenzetti, G., Maicu, F., Maselli, V., Mercorella, A., Montereale Gavazzi, G., Minuzzo,
T., Pellegrini, C., Petrizzo, A., Prampolini, M., Remia, A., Rizzetto, F., Rovere, M., Sarretta, A., Sigovini, M., Sinapi, L., Umgiesser, G.,
and Trincardi, F.: High resolution multibeam and hydrodynamic datasets of tidal channels and inlets of the Venice Lagoon, Scientific Data,
4, 170 121, https://doi.org/10.1038/sdata.2017.121, 2017.635
Martyr-Koller, R. C., Kernkamp, H., van Dam, A., van der Wegen, M., Lucas, L. V., Knowles, N., Jaffe, B., and Fregoso, T. A.: Application
of an unstructured 3D finite volume numerical model to flows and salinity dynamics in the San Francisco Bay-Delta, Estuarine, Coastal
and Shelf Science, 192, 86–107, https://doi.org/10.1016/j.ecss.2017.04.024, 2017.
Merz, B. and Thieken, A. H.: Flood risk curves and uncertainty bounds, Natural Hazards, 51, 437–458, https://doi.org/10.1007/s11069-009-
9452-6, 2009.640
Me ¯
dugorac, I., Pasari´
c, M., and Güttler, I.: Will the wind associated with the Adriatic storm surges change in future climate?, Theoretical
and Applied Climatology, pp. 1–18, https://doi.org/10.1007/s00704-020-03379-x, 2020.
Molinari, D. and Scorzini, A. R.: On the Influence of Input Data Quality to Flood Damage Estimation: The Performance of the INSYDE
Model, Water, 9, 688, https://doi.org/10.3390/w9090688, 2017.
Molinari, D., Scorzini, A. R., Arrighi, C., Carisi, F., Castelli, F., Domeneghetti, A., Gallazzi, A., Galliani, M., Grelot, F., Kellermann, P.,645
Kreibich, H., Mohor, G. S., Mosimann, M., Natho, S., Richert, C., Schroeter, K., Thieken, A. H., Zischg, A. P., and Ballio, F.: Are flood
damage models converging to “reality”? Lessons learnt from a blind test, Natural Hazards and Earth System Sciences, 20, 2997–3017,
https://doi.org/10.5194/nhess-20-2997-2020, 2020.
Molinaroli, E., Guerzoni, S., and Suman, D.: Adaptations to Sea Level Rise: A Tale of Two Cities – Venice and Miami,
https://doi.org/10.31230/osf.io/73a25, 2018.650
Morucci, S., Coraci, E., Crosato, F., and Ferla, M.: Extreme events in Venice and in the North Adriatic Sea: 28–29 October 2018, Rendiconti
Lincei. Scienze Fisiche e Naturali, 31, 113–122, https://doi.org/10.1007/s12210-020-00882-1, 2020.
Nunes, P. A. L. D., Breil, M., and Gambarelli, G.: Economic Valuation of on Site Material Damages of High Water on Economic Activities
based in the City of Venice: Results from a Dose-Response-Expert-Based Valuation Approach, https://doi.org/10.2139/ssrn.702965, 2005.
Patt, H. and Jüpner, R., eds.: Hochwasser-Handbuch: Auswirkungen und Schutz, Springer Berlin Heidelberg, Berlin, Heidelberg, 2. aufl.655
2013. neu bearb edn., https://doi.org/10.1007/978-3-642-28191-4, 2013.
Penning-Rowsell, E., Johnson, C., Tunstall, S., Tapsell, S., Morris, J., Chatterton, J., and Green, C.: The Benefits of Flood and Coastal
Risk Management: A Handbook of Assessment Techniques, ISBN 1904750516, https://repository.tudelft.nl/islandora/object/uuid%
3A33f2d216-c9bf-419c-b3b1- 415a6f6fd881, 2005.
Roland, A., Cucco, A., Ferrarin, C., Hsu, T.-W., Liau, J.-M., Ou, S.-H., Umgiesser, G., and Zanke, U.: On the development660
and verification of a 2-D coupled wave-current model on unstructured meshes, Journal of Marine Systems, 78, S244–S254,
https://doi.org/10.1016/j.jmarsys.2009.01.026, 2009.
Sai, H. A., Tabata, T., Hiramatsu, K., Harada, M., and Luong, N. C.: An optimal scenario for the emergency solution to protect Hanoi Capital
from the Red River floodwater using Van Coc Lake, Journal of Flood Risk Management, https://doi.org/10.1111/jfr3.12661, 2020.
28
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
Sarretta, A., Pillon, S., Molinaroli, E., Guerzoni, S., and Fontolan, G.: Sediment budget in the Lagoon of Venice, Italy: Continental Shelf665
Research, 30(8), 934-949, Continental Shelf Research, 30, 934–949, https://doi.org/10.1016/J.CSR.2009.07.002, 2010.
Scorzini, A. R. and Frank, E.: Flood damage curves: new insights from the 2010 flood in Veneto, Italy, Journal of Flood Risk Management,
10, 381–392, https://doi.org/10.1111/jfr3.12163, 2017.
Smith, S. D. and Banke, E. G.: Variation of the sea surface drag coefficient with wind speed, Quarterly Journal of the Royal Meteorological
Society, 101, 665–673, https://doi.org/10.1002/qj.49710142920, 1975.670
Tambroni, N. and Seminara, G.: Are inlets responsible for the morphological degradation of Venice Lagoon?, Journal of Geophysical Re-
search: Earth Surface, 111, n/a–n/a, https://doi.org/10.1029/2005JF000334, 2006.
Teng, J., Jakeman, A. J., Vaze, J., Croke, B., Dutta, D., and Kim, S.: Flood inundation modelling: A review of methods, recent advances and
uncertainty analysis, Environmental Modelling & Software, 90, 201–216, https://doi.org/10.1016/j.envsoft.2017.01.006, 2017.
Thieken, A., Piroth, K., Schwarz, J., Schwarze, R., and Müller, M.: Methods for the evaluation of direct and indirect flood losses, in:675
4th International Symposium on Flood Defence:4th International Symposium on Flood Defence:Managing Flood Risk, Reliability and
Vulnerability, https://www.researchgate.net/publication/259273112_Methods_for_the_evaluation_of_direct_and_indirect_flood_losses.
Tiggeloven, T., de Moel, H., Winsemius, H. C., Eilander, D., Erkens, G., Gebremedhin, E., Diaz Loaiza, A., Kuzma, S., Luo, T., Iceland, C.,
Bouwman, A., van Huijstee, J., Ligtvoet, W., and Ward, P. J.: Global-scale benefit–cost analysis of coastal flood adaptation to different
flood risk drivers using structural measures, Natural Hazards and Earth System Sciences, 20, 1025–1044, https://doi.org/10.5194/nhess-680
20-1025-2020, 2020.
Umgiesser, G.: The impact of operating the mobile barriers in Venice (MOSE) under climate change, Journal for Nature Conservation, 54,
125 783, https://doi.org/10.1016/j.jnc.2019.125783, 2020.
Umgiesser, G. and Matticchio, B.: Simulating the mobile barrier (MOSE) operation in the Venice Lagoon, Italy: global sea level rise and its
implication for navigation, Ocean Dynamics, 56, 320–332, https://doi.org/10.1007/s10236-006-0071-4, 2006.685
Umgiesser, G., Canu, D. M., Cucco, A., and Solidoro, C.: A finite element model for the Venice Lagoon. Development, set up, calibration
and validation, Journal of Marine Systems, 51, 123–145, https://doi.org/10.1016/j.jmarsys.2004.05.009, 2004.
Umgiesser, G., Bajo, M., Ferrarin, C., Cucco, A., Lionello, P., Zanchettin, D., Papa, A., Tosoni, A., Ferla, M., Coraci, E., Morucci, S., Crosato,
F., Bonometto, A., Valentini, A., Orli´
c, M., Haigh, I. D., Nielsen, J. W., Bertin, X., Fortunato, A. B., Pérez Gómez, B., Alvarez Fanjul, E.,
Paradis, D., Jourdan, D., Pasquet, A., Mourre, B., Tintoré, J., and Nicholls, R. J.: The prediction of floods in Venice: methods, models and690
uncertainty (review article), Natural Hazards and Earth System Sciences, 21, 2679–2704, https://doi.org/10.5194/nhess-21-2679-2021,
2021.
Vergano, L. and Nunes, P. A. L. D.: Analysis and evaluation of ecosystem resilience: an economic perspective with an application to the
Venice lagoon, Biodiversity and Conservation, 16, 3385–3408, https://doi.org/10.1007/s10531-006-9085-y, 2007.
Vousdoukas, M. I., Mentaschi, L., Hinkel, J., Ward, P. J., Mongelli, I., Ciscar, J.-C., and Feyen, L.: Economic motivation for raising coastal695
flood defenses in Europe, Nature Communications, 11, 2119, https://doi.org/10.1038/s41467-020-15665-3, 2020.
Wang, J.-J.: Flood risk maps to cultural heritage: Measures and process, Journal of Cultural Heritage, 16, 210–220,
https://doi.org/10.1016/j.culher.2014.03.002, 2015.
Xing, Y., Liang, Q., Wang, G., Ming, X., and Xia, X.: City-scale hydrodynamic modelling of urban flash floods: the issues of scale and
resolution, Natural Hazards, 96, 473–496, https://doi.org/10.1007/s11069-018-3553-z, 2019.700
Yin, J., Jonkman, S., Lin, N., Yu, D., Aerts, J., Wilby, R., Pan, M., Wood, E., Bricker, J., Ke, Q., Zeng, Z., Zhao, Q., Ge, J., and Wang, J.:
Flood Risks in Sinking Delta Cities: Time for a Reevaluation?, Earth’s Future, 8, https://doi.org/10.1029/2020EF001614, 2020.
29
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
Zampato, L., Bajo, M., Canestrelli, P., and Umgiesser, G.: Storm surge modelling in Venice: two years of operational results, Journal of
Operational Oceanography, 9, s46–s57, https://doi.org/10.1080/1755876X.2015.1118804, 2016.
Zanchettin, D., Bruni, S., Raicich, F., Lionello, P., Adloff, F., Androsov, A., Antonioli, F., Artale, V., Carminati, E., Ferrarin, C., Fofonova,705
V., Nicholls, R. J., Rubinetti, S., Rubino, A., Sannino, G., Spada, G., Thiéblemont, R., Tsimplis, M., Umgiesser, G., Vignudelli, S.,
Wöppelmann, G., and Zerbini, S.: Sea-level rise in Venice: historic and future trends (review article), Natural Hazards and Earth System
Sciences, 21, 2643–2678, https://doi.org/10.5194/nhess-21-2643-2021, 2021.
30
https://doi.org/10.5194/nhess-2021-272
Preprint. Discussion started: 18 October 2021
c
Author(s) 2021. CC BY 4.0 License.
