Coastal vulnerability is
evaluated against inundation risk triggered by wave run-up through the
evaluation of vulnerability levels (referred to as VLs) introduced by
Coastal zones are often characterized by a fragile equilibrium, being
subjected to hydro-geomorphic processes that change their shape over time and
space and are also under stress due to the presence of conflicting human
activities
The present paper focuses on extreme natural storm events and on their impact
on coastal vulnerability within such a complex framework. As clearly specified
by the Integrated Protocol on Coastal Zone Management (ICZM), the effect
of storms should be embedded into coastal zone territorial plans and
policies, yielding coastal vulnerability assessment
This usually requires taking into account the long-term wave statistics and the
geomorphology of the beaches to evaluate the level of risk
they are exposed to. The estimate of the environmental risk, coupled
with the evaluation of the existing anthropic pressure (economic and
industrial activities), leads to vulnerability maps. Different
approaches to compute coastal vulnerability have been so far proposed, which differently combine relevant
environmental and socio-economic variables
The main goal of the present paper is to quantify differences in assessing
coastal vulnerability to inundation when using a regional rather than a local
(nearshore) characterization of the wave climate. The study refers to the
Bay of Lalzit, immediately north of the city of Durrës (Albania; see Fig.
VL assessment was performed referring to both offshore and nearshore wave data to evaluate variations in shoreline vulnerability depending on the employed spatial (regional or local) and temporal scales (extreme events, seasonal, directional).
The paper is organized as follows: in Sect. 2 we present the index computation procedure, along with the investigation area and the data used; in Sect. 3 we show results of coastal vulnerability using a wave dataset at regional and nearshore scales; in Sect. 4, results are presented and possible future developments and improvements are discussed.
Map showing the area under investigation.
The vulnerability assessment is part of a wider research project, aimed at evaluating and quantifying the ongoing coastal erosion affecting the Bay of Lalzit area. In order to collect all the required data, a 2-week field campaign was performed during the month of July 2015.
The Bay of Lalzit is included between two capes and can therefore be considered an independent physiographic unit; it is possible to focus on the
processes affecting this coastline independently from those characterizing
the nearby physiographic units. A physiographic unit is indeed defined as a
portion of shoreline with coherent characteristics in terms of natural
coastal processes and of land use, which can thus be studied independently
from neighbouring shores
Field activities were aimed at collecting the minimum required
data to investigate the relevant processes affecting the local coastal
dynamics. The geomorphology of the beaches along the bay was characterized
through 16 sections crossing the shoreline, spaced nearly every
kilometre along almost 20 km of the bay length (from section
Typical cross-shore
profile along the Bay of Lalzit (example of section 2;
see Fig.
Results of the grain size surveys are summarized in Fig.
Run-up VLs are meant to quantify the vulnerability of a
coast toward extreme inundation events. VL assessment follows the approach
proposed by
It can be noticed that the minimum and the maximum values of IV have a clear
physical meaning: actually, IV
This interval is then scaled to a range from 0 to 1, grouped in five classes
of equally spaced VLs (very low, low, medium,
high, very high) as reported in Table
The assessment of VL first requires us to compute the long-term run-up
statistics. Regardless of the reference model, run-up computation always implies combining information about both characteristic wave climate and morphology
of a shore
Run-up is therefore computed according to S2006 as follows:
Vulnerability level assessment due to the IV variable.
When dealing with run-up estimation, if the data linked to the shore characteristics can be well defined, more uncertainties arise when trying to empirically parametrize exceptional phenomena (extreme events), of which run-up can be considered an instance. For this reason we tested two different approaches for the estimation of extreme run-up values.
First, in the frame of a regional analysis, we considered the deep-water data
as defined in Point_002550, selecting the annual maximum sea storms from the
wave dataset and evaluating the annual maximum run-ups through Eq. (
Return period curves for the run-up parameter; results are presented for just some of the cross-sections for the sake of clarity.
Run-up vulnerability levels for the Bay of Lalzit from the regional analysis, using deep
water data:
Afterwards, we switched from a regional to a locale scale: in this case, EVAs
were performed directly over the extreme sea storm wave parameters to
assess the 50- and the 500-year waves. We thus propagated the
target waves in front of each section, afterwards computing the long-term
run-up values. Here, as the wave climate shows different patterns with
respect to the average incident wave direction, we split the initial wave
dataset according to two meaningful directional fetches. This choice involved
an important consequence: when performing the directional analysis, reference
return periods for each of the identified sectors have to in fact be
carefully assigned
with
To completely characterize the target waves (to be downscaled at a later time
in the nearshore zone), we linked the peak periods to the computed long-term
significant wave heights following the empirical model proposed by
It is worth mentioning that the return period of a forcing variate is not
necessarily equal to the return period of the outcomes. As an instance, a
given return period wave may not lead to the corresponding return period
run-up
Once we computed the long-term run-ups, we evaluated the resulting VLs according to the morphology of the testing locations. Since results are punctual (e.g. one index for each of the 16 sampling locations), we linearly interpolated the VL values within hypothetical intermediate sections in order to obtain a more meaningful overview of the whole bay.
We initially referred to the regional scale; in this case, an omnidirectional analysis was performed, leading to two sets of results linked to the tested return periods. Secondly, we detailed our study to the local scale: in this case, we obtained two sets of results for every directional sector taken into account. We first present the VL obtained from the regional study.
At the regional scale the environmental inputs were the same for each
section, with the wave characteristics defined in deep water (Point_002550,
Fig.
Evaluation of coastal VLs has been carried out by also employing the propagated values of the wave climate at the local scale. It has to be remarked that, in this case, the mean cross-shore slope is not the only changing parameter between one section and another: as waves are propagated toward the shore in front of each of the investigated locations, they are modified due to the occurring transformation processes, resulting in different wave characteristics (heights, lengths and incident directions) depending on the position of a section along the bay.
The first step to compute VL at a local scale is to characterize the wave
climate. As shown in Fig.
Extreme events have been defined for each of the identified sectors,
computing the resulting 50- and 500-year-return-period wave
heights. The target wave incoming direction for each sector was defined
through a linear interpolation in order to minimize the root-mean-square
error with respect to the directions of the annual maxima
sea storms (see Fig.
where
We therefore characterized the design wave for each of the identified
directional sectors (W-NW and S-SW), defining its significant height, peak
period and angle of attack. These parameters were set at a time as inputs of
the wave propagation model, computing the shallow water waves. The starting
values are shown in Table
Directions of the extreme waves belonging to the two considered sectors.
For the sake of clarity, in order to compare the results obtained with the two different approaches mentioned before, we discuss just the results linked to the punctual investigated sections; analogous considerations can therefore be extended to the intermediate sections, whose VLs were assessed through a linear interpolation as previously explained.
Looking at the punctual results (Figs.
Design wave parameters for the directional sectors.
Referring to the regional-scale offshore analysis and 50-year return period, the
vulnerability towards inundation happens to be very high in section
0 and still high in sections 7 and 8; sections
Run-up vulnerability levels for the Bay of Lalzit, using
nearshore data for the 180–270
Run-up vulnerability levels for the Bay of Lalzit, using
nearshore data for the 270–360
The directional analysis indicates that results are less varying with respect
to the return period: if we refer to the first directional sector
(180–270
Comparison among the run-up vulnerability indexes for each sampling location; return period equal to 50 years.
It is interesting to evaluate how VL can change due to the starting wave features: the EVA performed using deep water data yields higher VLs than those obtained after propagating waves toward the shore. Referring to 50-year return period, the most exposed sections are still characterized by very high (0) and high (7, 8) levels of vulnerability, whereas through the directional analysis VLs never happen to be higher than medium, despite the considered return period; to increase from 50 to 500 years involves at most moving from low to one VL higher (section 7, first sector).
Actually, result divergence decreases for sections characterized by a very low VL in the northern part of the bay: in this case, the morphology of the surrounding beach seems to guarantee safe conditions, regardless of the magnitude of the forcing waves.
Comparison among the run-up vulnerability indexes for each sampling location; return period equal to 500 years.
Comparison between run-up values for each section obtained through
offshore (regional scale) and nearshore (local scale) conditions:
As a general trend, assessing coastal vulnerability to inundation using the
wave climate computed at the local scale leads to lower VLs compared to those
obtained through the regional analysis. If the VLs are similarly distributed
along the bay (depending on the single section profiles), the long-term
run-up estimates are clearly dependent on the reference spatial scale: the
geometry of the bay indeed strongly affects the waves' propagation toward the
coast. Moving onshore, wave heights likely decrease due to refraction and
diffraction, which can be expected to be the dominant processes as suggested
by the concave enclosed shape of the coast. Consequently, run-up estimates
come to be lower when dealing with the local-scale analysis, and resulting
VLs behave accordingly. It is worth mentioning that, as a common practice,
this kind of computation is performed the other way around. Literally, when
shallow water wave data are available, it is possible to propagate them
backward through simple formulations in order to obtain the equivalent deep
water data with which to feed the run-up model
Higher run-up estimates due to offshore analysis suggest another
consideration about the different variability in the results between regional
(offshore) and local (onshore) analysis: as previously demonstrated, the
directional data result in a more homogeneous VL along the
coastline. This can be simply justified looking at the VL
computation: the same IV index may belong to different vulnerability classes,
depending on the value that the IV
Finally, if we enlarge our analysis to the coastline as a whole, we
can better appreciate how vulnerability is distributed. Despite the
differences due to the reference wave data, the most vulnerable areas
happen to be those near the Erzeni outflow and, in the north, towards
the Cape of Rodon (see Fig.
The vulnerability assessment of a coastline can be a helpful device to plan its land use, for instance, not holding high-value activities when there is a high risk of the beaches being submerged or eroded. In this framework, VL estimates provide an easy and reliable tool in order to obtain an overall overview about a shore vulnerability distribution toward inundation and/or erosion events.
In this paper, we evaluated the coastal inundation vulnerability for the Bay of Lalzit (Durrës, Albania), following the model proposed by
We showed that, even if the vulnerability distribution does not change along the shore (e.g. the most exposed sections are placed in the same areas), the results linked to the local scale yield considerably lower VLs. This is mainly due to the run-up estimates, which are very sensitive to the input wave characteristics, which may be defined in shallow or deep waters. In the case of Lalzit, when wave propagation processes (such as refraction and breaking) become influential, run-up estimates can considerably change depending on the level of detail of wave characterization, as VLs accordingly do.
Since S2006 returns a high statistic for the run-up variable, it appears more
plausible to refer to the modified model as proposed by
The feasibility of VL assessment can represent a crucial ingredient for rapidly developing and transforming coastal regions such as the Bay of Lalzit in Albania, which present more options to drive virtuous future coastal development compared to industrialized countries, where coastal vulnerability assessment may mostly represent a tool for ICZM applied to manage conflicts among relevant stakeholders.
The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.
FDL and GB designed the vulnerability study. FDL, GB, GZ and MB designed the fieldwork and collected field data. FDL analysed data and prepared the first paper draft. FDL, GB, GZ and MB reviewed and refined the paper. FDL and GB finalized the revised paper.
The authors declare that they have no conflict of interest.
This study is part of a project shared between the University of Trento and
the University of Genoa (Italy), along with the Polytechnic University of Tirana
(Albania). The authors would like to thank everyone who joined the field data
collection: Alessandro Chesini, Alessandro Dotto, Alessio Maier, Daniele
Spada, Dario Guirreri, Erasmo Vella, Federica Pedon, Giorgio Gallerani, Laura
Dalla Valle, Martina Costi, Navarro Ferronato, Stefano Gobbi, Tommaso Tosi
(University of Trento), Ardit Omeri, Arsela Caka, Bardhe Gjini, Bestar
Cekrezi, Erida Beqiri, Ferdinand Fufaj, Idlir Lami, Marie Shyti, Mikel
Zhidro, Nelisa Haxhi, Xhon Kraja and Tania Floqi (Tirana Polytechnic). The
collected data were then analysed by the Italian partners in the framework
of the UNESCO Chair in Engineering for Human and Sustainable Development
(