Assessment of Beach Erosion Vulnerability in the Province of Valencia, Spain

Abstract

This research analyses beach vulnerability to erosion along the coast of Valencia province, Spain. The Coastal Vulnerability Index (CVI) is used to assess vulnerability, considering the following variables: beach width, beach erosion/accretion rate, dune width, wave height, relative coastal flood level, submerged vegetation, upper depth limit of submerged vegetation, and percentage of vegetated dune. The results show that vulnerability varies significantly along the coast. The vulnerability assessment revealed that 26.9% of the coastal sections were classified as having very low susceptibility to erosion, 34.5% as low, 22.3% as moderate, 12% as high, and 4.3% as very high. Urbanized areas with reduced dunes are more vulnerable than natural areas with wide beaches and well-developed dunes. The study highlights and discusses limitations of the CVI method and suggests using the mean instead of the square root to calculate the overall vulnerability index due to the influence of one single variable in this formula. It is concluded that natural areas characterized by the presence of dunes exhibit a diminished vulnerability to erosion when compared to highly urbanized regions devoid of dunes and marine vegetation.

1. Introduction

Coastlines are formed by the meeting of marine and terrestrial areas [1]. These geographical zones are the site of a vast array of socioeconomic and human activities, which often conflict with ecological and natural values. To develop sustainable coastal management, it is essential to understand the status and evolution of the coastal zone, particularly in beach areas, as sea levels rise and the increasing intensity and frequency of extreme events threaten both natural and human systems worldwide [2]. Moreover, coastal erosion is predicted to intensify as a result of the sea level rise [3].

Anthropogenic and exogenous forces driving morphological changes give rise to a range of risks, including economic losses, the destruction of natural and artificial defences, and an increased flood risk [4]. These are, therefore, highly dynamic environments that constitute geomorphologically complex systems with non-linear behaviours [5]. This is a large-scale problem throughout the Mediterranean, which has received considerable attention for years [6]. Furthermore, in 2011, 40.8% of the EU-27 population lived in coastal regions, which covered 40.0% of EU-27 territory [7].

One way to assess the current state and potential evolution of beaches, and to facilitate their better management, is through the evaluation of beach vulnerability and coastal erosion. Vulnerability is defined as the predisposition of an element to be damaged. This concept encompasses a range of factors, including sensitivity or susceptibility to damage and the capacity to cope with it. Thus, vulnerability can be understood as the degree to which a system is able to cope with adverse effects [2]. By analysing vulnerability and representing the potential damage that may occur, it is possible to identify needs and plan actions to reduce this vulnerability [8]. The various variables that can be taken into account allow for the consideration, in this complex analysis, of the local characteristics of different areas or sites, along with both anthropogenic and exogenous factors, such as those resulting from climate change and the sea level rise [9].

The Coastal Vulnerability Index (CVI) has been a cornerstone in coastal zone management, providing a method to assess the susceptibility of these areas to the impacts of climate change and other threats. Originally developed by Gornitz [10], the CVI has evolved over the years, incorporating new parameters and adapting to diverse geographical contexts. Although other methods exist for assessing coastal vulnerability, such as (i) index-based approaches, (ii) indicator-based approaches, (iii) GIS-based decision support systems, and (iv) dynamic computer models [11], the CVI remains widely used due to its simplicity and ability to provide a rapid, visual assessment [12]. Its application in numerous countries, including the US [13], Bangladesh [14], India [15,16], Spain [17,18], Australia [19], and Canada [20], has demonstrated its value in decision-making for sustainable coastal management.

The Coastal Vulnerability Index (CVI) serves as a dimensionless, unidimensional indicator of coastline susceptibility, based on a range of quantitative and qualitative variables [11]. Therefore, depending on the quality and availability of data, and given the potential of geographic information systems, it is possible to develop indices representative of the study area, providing a valuable analysis and the ability to consider and represent different scenarios in different zones. In this work, a Coastal Vulnerability Index is proposed that includes the most relevant variables related to coastal erosion, such as coastal geomorphology, shoreline evolution, etc.

In recent years, there have been significant advancements in the application and refinement of the Coastal Vulnerability Index. Recent studies have expanded the scope of the CVI to include socioeconomic and ecological variables, thus overcoming one of its original principal limitations. For example, modified versions of the index have been developed that incorporate parameters such as the affected population, at-risk infrastructure, and potential economic costs [21,22,23]. Meanwhile, Vieira et al. [24] included variables such as land cover and anthropogenic activities. Other studies considered more geomorphological variables such as coastal material, grain size, or the exposure of the intertidal zone [25,26]. These advancements are enabling a more comprehensive and accurate assessment of coastal vulnerability, which is crucial for risk management and climate change adaptation in coastal areas.

This paper focuses exclusively on the study of beach areas (just sandy and gravelly beaches) within the province of Valencia. The aim is to derive a vulnerability index using only variables related to coastal erosion. For this reason, some widely used variables such as coastal geomorphology will be excluded, as the analysis will be restricted to beach areas. Additionally, other variables that are generally less considered in such indices, such as relative inundation level and the presence of both dune vegetation and submarine vegetation, will be included.

2. Materials and Methods

2.1. Study Area

The coastline of Valencia province is an extensive littoral zone stretching over approximately 103 km, bathed by the warm waters of the Mediterranean Sea (Figure 1). The coastline is characterized by a relatively homogeneous landscape, dominated by extensive sandy beaches interspersed with occasional port facilities and estuaries. The only significant landscape diversity is found at the Cape of Cullera, with its 2 km of cliffs, and the ecologically important Albufera wetland, located approximately one kilometre inland. It boasts approximately 62 beaches, varying from the urban and lively beaches of Valencia city to the more tranquil and pristine ones found in other municipalities. All beaches included in the study, except for two gravel beaches (median sediment size: 19.2 mm), were predominantly sandy, with a median sediment size of 0.26 mm, ranging from 0.163 mm to 0.712 mm. Some of the most well-known beaches include Malvarrosa in Valencia, Gandía, Cullera, and Oliva.

Regarding wave conditions, Valencia province experiences moderate wave heights, typically ranging between 1 and 2 m. Wave intensity and direction vary throughout the year, with more energetic conditions occurring during autumn and winter when Atlantic depressions can generate higher and longer waves, especially on the northern coast of the province. In terms of tides, the region exhibits a microtidal semi-diurnal regime with an average tidal range of approximately 15 cm [27].

Finally, as illustrated in Figure 1, the marine vegetation of special interest in the study area is mainly located between the northern limit of the province and the port of Valencia (mainly Caulerpa prolifera) and in the south of the province (Posidonia oceanica and Cymodocea nodosa).

2.2. Coastal Vulnerability Index

The Coastal Vulnerability Index (CVI) is a quantitative tool employed in coastal management and morphodynamic studies. Its primary objective is to assess and quantify the degree of susceptibility of a coastal area to damage or alteration due to natural or anthropogenic processes. The CVI is constructed by combining various physical and environmental indicators, which are weighted according to their relevance to the study region. This article will consider the following variables: beach width (a), beach erosion/accretion rate (b), dune width (c), wave height (d), relative coastal flood level (e), submerged vegetation (f), upper depth limit of submerged vegetation (g), and percentage of vegetated dune (h).

The CVI was calculated as the square root of the product of the vulnerability values for each individual variable, divided by the total number of variables (Equation (1)) [10]:

$$\mathrm{C}\mathrm{V}\mathrm{I}=\sqrt{{\displaystyle \frac{\mathrm{A}·\mathrm{B}·\mathrm{C}·\mathrm{D}·\mathrm{E}·\mathrm{F}·\mathrm{G}·\mathrm{H}}{8}}}$$

(1)

The vulnerability values were classified into five classes (very low, low, moderate, high, and very high) using the natural breaks classification (Jenks) method [28].

QGIS 3.34.12 ‘Prizren’ was employed to manage and analyse geospatial data. A geodatabase in GeoPackage format was created to store all relevant geospatial information, including orthoimagery, bathymetry, Digital Terrain Models (DTMs) and thematic cartography. In addition, calculations of Coastal Vulnerability Index (CVI) variables, as well as their associated cartographic maps, were obtained using this GIS software.

2.2.1. Beach Width

To determine the beach width between 2018 and 2023, high-resolution orthophotos obtained from the Institut Cartogràfic Valencià were employed. These images, with a spatial resolution of 25 cm/pixel and in ECW format, were processed in a GIS to digitally delineate the shoreline. Following the methodology of Pagán et al. [29], the maximum high-water mark was visually identified in each image, thus establishing the shoreline. To ensure comparability of results, all images were acquired in the summer under calm sea conditions. A baseline was established based on fixed elements such as the first construction line or a seafront and transects were generated perpendicular to this line approximately every 200 m. By measuring the distance between the shoreline and the baseline at each transect and for each year, the evolution of beach width was calculated (Figure 2). Finally, the vulnerability of the beach width was established using the average width of the measurements for the last three years available, according to the criteria established in

Table 1. Ranges of the Coastal Vulnerability Index.

Variable		Very Low (1)	Low (2)	Moderate (3)	High (4)	Very High (5)
Geomorphology	A—Beach width (m)	>100	100–60	60–30	30–15	<15
	B—Beach erosion/accretion rate (m/yr)	>[+1.5]	[+1.5]–[+0.5]	[+0.5]–[−0.5]	[−0.5]–[−1.5]	<[−1.5]
	C—Dune width (m)	>75	75–100	50–25	25–10	<10
Hydrodynamics	D—Wave heigh (m)t	<0.5	0.5–1.0	1.0–1.5	1.5–2	>2
	E—Relative coastal flood level (m)	<[−1.5]	[−1.5]–[−0.5]	[−0.5]–[0.0]	[0.0]–[+0.5]	>[+0.5]
Vegetation	F—Submerged vegetation meadows	P. oceanica	P. oceanica in regression	Cymodocea nodosa	C. prolifera, Racemosa or Mixed	Absence
	G—Upper depth limit of submerged vegetation (m)	<4.0	4.0–6.0	6.0–9.0	9.0–12	>12
	H—Percentage of vegetated dune	100–80	80–60	60–40	40–20	<20

.

2.2.2. Beach Erosion/Accretion Rate

Shoreline displacement is unique in that it is not merely a measure of the positive or negative influence of external factors on erosion or flood vulnerability, but rather a direct indicator of the impact itself. This means that variations in the shoreline act as a gauge of the erosive damage incurred. A positive rate of change indicates accretion, or coastal expansion, while a negative rate reflects erosion, or coastal retreat.

In this study, the erosion rate was determined using the Linear Regression Rate (LRR), as described by Thieler et al. [30]. From the shorelines vectorized, an own model was created using QGIS geoprocesses to intersect each transect with the shorelines and thus, obtain the rate of change as the slope of the linear regression over the last 5 years (2018–2023) of shoreline position data. The criteria for determining the vulnerability ranges for this variable are shown in Table 1.

2.2.3. Dune Width

Dune width was determined through the analysis of aerial images or orthophotos, allowing for a precise assessment of their extent along different segments of the coast. Similar to the beach width, transects were vectorised perpendicular to the coastline and across the dunes, measuring from the beginning of stabilizing vegetation or the nearest building to the point where the dune meets the beach.

Dunes are dynamic structures that respond to a variety of environmental factors, including wind patterns, sediment availability, and stabilizing vegetation. Events such as storms and high tides can significantly erode dunes, reducing their width and, consequently, their protective capacity. On the other hand, under low-energy conditions and with adequate sediment supply, dunes can grow and expand, increasing their width and effectiveness as a natural barrier. Due to the influence that external factors such as storms can have on the dune, a single measurement was not considered, but the average value of the last three years was used. Therefore, the vulnerability criteria shown in Table 1 were established.

2.2.4. Wave Height

To conduct the wave analysis, historical data from the 10 nearest SIMAR nodes to the study area (Figure 1) were used, selecting the closest one in each section. The SIMAR dataset consists of a time series of wind and wave parameters generated through numerical modelling, which means that they are simulated data and not directly obtained from natural measurements. The SIMAR dataset, obtained from the Puertos del Estado website (https://www.puertos.es/es-es/oceanografia/paginas/portus.aspx, accessed on 19 June 2024), provides hourly information on wave height, period, and wave direction from 1958 to the present.

Once the data from each SIMAR point were obtained, the first step was to determine the wave sectors incident on each of the study sections. Subsequently, using only the incident waves in each section, the wave height that exceeded only 2% of the time was determined (in the study area, this translates to two to three storm events occurring annually), so that the vulnerability criterion was established according to Table 1.

2.2.5. Relative Coastal Flood Level

The relative coastal flood level represents the difference between the inundation level (run-up plus tidal range) and the maximum beach elevation (typically in the dune or, failing that, the beach berm). Beach profile data (Figure 3) were acquired during a GNSS survey campaign using a Leica Zeno FLX1000 antenna (Leica Geosystems, St. Gallen, Sweden) with RTK corrections., obtaining a horizontal accuracy of 2 cm and vertical of 3 cm. The coastal inundation level was obtained as the run-up plus the maximum tide produced in the area. The maximum tide in the area according to GIOC [27] is 0.69 m. The run-up was obtained according to the Stockdon et al. [31] equation (Equation (2)).

$${\mathrm{R}}_u=1.1·\left(0.35·\mathsf{\beta }·\sqrt{{\mathrm{H}}_{\mathrm{o}}·{\mathrm{L}}_{\mathrm{o}}}+0.5·\sqrt{{\mathrm{H}}_{\mathrm{o}}·{\mathrm{L}}_{\mathrm{o}}·\left(0.563·{\mathsf{\beta }}^2+0.004\right)}\right)$$

(2)

where β is the beach slope, H_o is the wave height that exceeded 2% of the time in deep water, and L_o is the wavelength in deep water associated with the previous wave height.

Table 1 presents the weighting criteria from 1 to 5 for the variable “relative sea level inundation” for beach areas.

2.2.6. Submerged Vegetation

Seagrass plays a fundamental role in protecting coasts from erosion. These plants, rooted to the seabed, form natural barriers that dampen the impact of waves, reducing wave energy and, consequently, the erosion of beaches and cliffs. In the study area, the following species can be found: Posidonia oceanica, Cymodocea nodosa, and Caulerpa prolifera. The advantages of each are described below, and based on them, vulnerability criteria were established (Table 1).

• Posidonia oceanica: Its dense and flexible leaves act as a natural barrier that dissipates wave energy and retains suspended sediment. Roots and rhizomes penetrate deep into the seabed, anchoring sediments [32]. Dead leaves, known as wrack, accumulate on beaches, forming a protective layer that cushions the impact of waves and prevents sand loss [33].
• Cymodocea nodosa: Although to a lesser extent than P. oceanica, Cymodocea meadows also contribute to dissipating wave energy. In addition, its roots and rhizomes penetrate the seabed, anchoring sediments and preventing them from being carried away by marine currents [34].
• Caulerpa prolifera: Although they are invasive species in the study area, these algae contribute to the formation of reefs and the stabilization of seabeds [35].

Cartographic data were provided by the Ecocartography of the coastline of the provinces of Alicante, Valencia and Castellón carried out by the Ministerio para la Transición Ecológica y el Reto Demográfico [36,37].

2.2.7. Upper Depth Limit of Submerged Vegetation

The depth of seagrass directly affects its influence on reducing wave energy. These data and bathymetry were obtained from the cartography of Ecolevante and Ecocastellon [36,37]. Intersecting both layers, the upper limit of the meadows is obtained for each transect. Therefore, the criterion in Table 1 is established for the upper depth of submerged vegetation.

2.2.8. Percentage of Vegetated Dune

The percentage of vegetation behind the beach will be calculated using the Normalized Difference Vegetation Index (NDVI), an effective indicator for assessing the greenness, density, and health of vegetation in each pixel of an image.

Natural colour (RGB) and infrared false colour (IRG) orthophotos covering the Valencian community will be used (Figure 4a). These orthophotos were produced at a 25 cm resolution from a digital RGBI photogrammetric flight and are obtained from the Valencian Spatial Data Infrastructure (IDEV) for the years 2021, 2022, and 2023. The orthophotography is distributed in 1:5000 sheets in TIFF format (4-band RGBI). Colour depth is 8 bits per band.

RGBI imagery facilitated the computation of the Normalized Difference Vegetation Index (NDVI), a robust metric for quantifying the greenness, density, and health of vegetation at the pixel level (Figure 4b). Vegetation cover was extracted from an NDVI value higher than 0.2 (Figure 4c), and clipped with the dune polygon, obtaining both dune area and vegetated surface, and thus, the dune vegetation coverage as a percentage. To avoid the effects of seasonal or interannual changes, the result is the average of the available images (Figure 4d).

3. Results

Figure 5 presents the derived Coastal Vulnerability Index (CVI). Overall, urbanized areas exhibit higher vulnerability levels compared to natural zones, which generally show very low vulnerability. This pattern is evident, for instance, in the north (Figure 5b) and south of the Port of Sagunto (Figure 5c). However, south of the Port of Sagunto, from Massalfasar onwards, in close proximity to the city of Valencia, vulnerability increases to a high level, except for the southernmost section where significant sand accumulation reduces vulnerability. The southern area of the Port of Valencia (Figure 5d) once again displays very low vulnerability with a few moderate sections due to its natural setting near the Albufera of Valencia and effective protection measures. The most vulnerable stretches in this area are attributable to the absence of dunes and reduced beach width. Moving southward, between the Perellonet estuary and Cape Cullera, there is a marked alternation between predominantly moderate and high vulnerability levels, with the latter being more pronounced near the cape (Figure 5e). Between Cape Cullera and the Júcar river mouth (Figure 5f), vulnerability ranges from low to moderate, owing to the beach orientation and the cape’s protection against the most intense wave action in the study area, which originates from the northeast. South of the Júcar river mouth lies a highly vulnerable zone characterized by narrow and anthropogenically altered beaches. Vulnerability decreases from this point as urbanization lessens, extending to the Port of Gandia (Figure 5f,g). Between the Ports of Gandia and Oliva (Figure 5h), vulnerability is high or very high in urbanized areas, while natural sections exhibit low vulnerability. A similar pattern is observed south of the Port of Oliva, but this stretch is dominated by natural areas with extensive dunes, resulting in lower vulnerability.

An analysis of 391 coastal sections reveals that over 60% exhibit low or very low overall vulnerability, whereas only 16% display high or very high vulnerability (Figure 6). The highest vulnerability to erosion is associated with variables related to marine vegetation, with over 60% and 70% of sections demonstrating very high vulnerability. As Figure 1 illustrates, the Valencian coastline has a limited presence of notable plant species in its vicinity. Variables linked to morphology (beach width, erosion rate, and dune width) generally exhibit moderate vulnerability. Regarding beach width, 40% of sections display high vulnerability and only 2% display very high vulnerability, indicating widths less than 15 m. However, despite adequate beach widths in most sections, over 40% experience high or very high erosion rates (exceeding 1.5 m of annual retreat for very high vulnerability) and 26% experience high erosion (between 0.5 and 1.5 m of annual retreat). Concerning dune systems, it is noteworthy that over 43% of the studied sections lack dunes or have negligible dune formations. Among sections with dunes, over 54% have vegetation coverage exceeding 40%, while the remainder has a coverage of less than 20%.

Finally, regarding wave action, 75% of sections exhibit high vulnerability. This is attributable to the orientation of most beaches and the limited natural protection against marine forces. A majority of the Valencia province is exposed to northeastern storms, which are the most intense in the region, without any capes or features to dissipate wave energy. However, most of the studied sections (over 67%) demonstrate low or very low vulnerability to the relative coastal flood level. This is due to the wide beach widths, which reduce beach slope and thus run-up, as well as to the high elevations of the backshore, providing a substantial margin of safety against coastal inundation.

4. Discussion

The results of this study reveal significant spatial variability in coastal vulnerability within the province of Valencia, highlighting the importance of factors such as coastal morphology, marine vegetation, and wave exposure. One variable that has been observed to have a particularly significant influence is the presence of dune systems. Less anthropogenically altered beaches with wide berms and dunes exhibited lower vulnerability values for most variables, with the exceptions of wave height and submerged vegetation. This underscores the importance of renaturalising coastal zones through measures such as dune construction and restoration [38].

This paper presents a detailed analysis considering a wide range of factors influencing coastal vulnerability, providing a comprehensive overview of the problem. In this regard, the variables used and the methods employed to obtain them are significant and should be considered. Firstly, it should be noted that most articles analysing the Coastal Vulnerability Index (CVI) include the geomorphology variable, which categorizes vulnerability based on the following: rocky-cliffed coasts, fjords (very low); medium cliffs, indented coasts (low); low cliffs, glacial drift, alluvial plains (moderate); cobble beaches, estuary, lagoon (high); barrier beaches, sand beaches, salt marsh, mud flats, deltas, mangrove, and coral reefs (very high). However, in this paper, we have decided not to employ this variable as we have only studied beaches, of which only two are shingle and the rest are sandy with a median sediment size between 0.163 mm and 0.712 mm. Therefore, the inclusion of this variable would contribute little additional information to the different sections. Secondly, for example, this study utilizes the Linear Regression Rate (LRR) to determine the coastal erosion rate, as opposed to other methods, because the LRR is less sensitive to extreme or outlier values that can distort results. This is crucial in coastal erosion studies, where extreme events such as storms can generate data that do not follow the general trend [39]. By minimizing the impact of outliers, the LRR provides more reliable and representative estimates of the long-term erosion rate. However, some authors employ the LRR over a very extended period; for example, Theocharidis et al. [23] uses the period from 1963 to 2019. Initially, this study also considered using a longer period as orthophoto data for the area becomes available from 1956. However, due to significant anthropogenic activities on the Valencian coast, such as beach nourishments (increasing the width by more than 50 m), port constructions and expansions, and other human interventions, the trend has changed considerably in recent years. Therefore, instead of using the LRR for the entire data period, the LRR for the last 5 years was employed. Potential outliers have also been considered in beach width and dune assessments by considering the average of the last three years rather than a single year. However, when it is known that the value of a shoreline or dune zone in a particular year has been significantly affected by anthropogenic activities or storms, it should not be included in this analysis as it could distort the results.

This study also incorporates variables not considered in other studies, such as relative flood level, marine vegetation, and its depth. The use of relative flood level instead of run-up or absolute flood level is significant because a high absolute flood level does not necessarily imply a problem for wide beaches with large dunes, whereas low levels on very narrow beaches with low elevations can pose problems for structures or elements behind them. For example, although most urban areas in the study area have a seawall protecting buildings from storm events, in some of the most recent major events (2020), the seawalls were insufficient to protect buildings. Therefore, it is more relevant to use relative flood level rather than absolute flood level. However, it is important to emphasize that this paper does not aim to assess the hazard posed to elements behind the beaches (buildings, natural areas, etc.), but rather to determine the relative vulnerability of beaches to erosion, and, therefore, the maximum beach elevation rather than the seawall elevation was used to determine the relative flood level. Regarding the presence of marine vegetation, some authors have used the presence or absence of species such as Posidonia oceanica to determine the Coastal Vulnerability Index [40], but they have not considered the effect of other species that can influence wave energy and sediment retention. Thus, the importance of using, as conducted in this study, all species found in the area that can influence these parameters is highlighted. Furthermore, the depth at which these species are found has been included, as the closer they are to the coast, the greater their influence on sediment retention and wave energy reduction [41,42].

On the other hand, while the results of this study provide valuable information, they also raise questions about the methodology employed to obtain the Coastal Vulnerability Index (CVI). In this study, the square root of the product of all variables divided by the number of variables (Equation (1)) has been used, a formula that has been employed by most authors. However, it should be noted that by multiplying all variables, very large or very small values can have a disproportionate impact on the final result. This has already been studied by Gibb et al. [43] who argued that this formula produces an index that does not accurately represent the ranking of variables and, consequently, the distribution of CVI values should be ordered into vulnerability categories by dividing the data into percentiles. Therefore, this method will always identify sectors with a low to high vulnerability for each study, i.e., the vulnerability assignment for each particular location is clearly relative to the combination of locations considered for each study [44]. This occurs in our study because most sections exhibit high or very high vulnerability for most variables, while only the relative inundation level variable has low or very low vulnerability values, causing the overall vulnerability to decrease (Figure 7). However, if the mean were used to obtain the overall CVI [45], given that outliers have less impact on the outcome, the result would be less sensitive to these extreme values and the overall vulnerability would be much higher, with 66% of sections having high vulnerability, 32% moderate, and the rest very high (2.6%), with no areas of low or very low vulnerability. There was no need to employ any additional systems to discriminate between vulnerability categories. Some authors have used weights for each variable when calculating the overall CVI [46], however, most studies agree that all variables are equally important and, therefore, do not use weighted values. Therefore, the importance of evaluating and considering each variable separately is highlighted, as the method used to obtain the CVI can distort the result.

Finally, it is important to note that the CVI is a relative value that provides a snapshot of vulnerability at a given moment in time, offering coastal managers a tool to target the most vulnerable areas. The CVI should be calculated at least annually to monitor how human interventions or natural factors influence changes in vulnerability over time. For instance, actions such as beach nourishment, urban dune construction, or revegetation can reduce the vulnerability of different coastal segments. It is essential to acknowledge that additional factors contribute to beach erosion, including median sediment size [47], sediment particle mineralogy and morphology [48], and the underlying geological framework [49]. Acquiring sufficiently detailed data on these factors presents significant challenges. Consequently, future research will focus on developing methodologies to obtain more comprehensive data for incorporation into subsequent investigations.

5. Conclusions

This study underscores the utility of the Coastal Vulnerability Index (CVI) to identify the most exposed coastal stretches to prioritize coastal management strategies and investments. The findings highlight the critical role of adequate beach width and slope in mitigating flood risks associated with weather events. Dune ridge systems serve as effective natural barriers, providing significant protection against erosion and inundation. Notably, the transects identified as most vulnerable coincide with urbanized areas where dune ridges have been compromised, emphasizing the detrimental impact of anthropogenic development on coastal resilience. The significance of using relative flood level rather than absolute flood level (run-up) is highlighted. While absolute flood levels can be high in certain areas, if a beach has a significant elevation or a dune of sufficient height to withstand the flood level, its influence may be minimal. Moreover, the results obtained support the initial hypothesis that submerged marine vegetation plays a crucial role in coastal protection. Its absence on many stretches of the Valencian coast increments the vulnerability and poses a significant challenge for coastal management. Therefore, it is crucial to consider not only the presence of Posidonia oceanica but also its depth and the presence of other marine phanerogams, such as Cymodocea nodosa and Caulerpa prolifera, which can influence wave energy and sediment retention.