Introduction

As demonstrated in many of the most recent works regarding the analysis of climate data on a global and regional scale (Chen et al. 2017; Dieng et al. 2017; Yi et al. 2017; Copernicus Climate Change Service 2018, 2020; Nerem et al. 2018; Dangendorf, et al. 2019, World Meteorological Organization 2019), one of the most evident consequences of global warming is the acceleration in Global Sea Level Rise (GSLR). Future projections provided by the Intergovernmental Panel on Climate Change (IPCC) for the end of the twenty-first century show a global sea level increasing up to 1 m above the reference period (1986–2005) (IPCC, 2014, and related updated data in IPCC 2019). The expected changes in sea level will potentially lead to the permanent submersion of coastal low-lying areas with negative impacts on natural ecosystems and anthropic settings (Nichols 2010). This phenomenon is locally enhanced by Vertical Ground Deformation Movements (VGDMs), which characterize wide coastal areas worldwide. In fact, several areas around the world present natural subsidence trends that have been accelerated since the last century by human causes, such as the increased exploitation of the natural resources (oil and fluid extraction, mining) and the groundwater over-pumping (Brambati et al. 2003; Teatini et al. 2012; Schaeffer et al. 2012; Pappone et al. 2012; IPCC, 2014; Shirzaei and Bürgmann 2018a, b; Wright et al. 2019). In Italy, almost all the alluvial coastal plains, both on the Tyrrhenian and Adriatic coasts, are characterized by subsidence that locally causes severe modifications in Relative Sea Level (RSL) trends (Antonioli and Silenzi 2007; Baldi et al. 2009; Lambeck et al. 2011; Cenni et al. 2013; Aucelli et al. 2017a; Ruberti et al. 2017; Matano et al. 2018; Amato et al. 2020).

In this context, the main international guidelines for the assessment and management of the coastal flooding events were first provided in the framework of the Flood Directive (EC 2007), which forces the Member States to provide inundation maps also accounting for the expected climate change impacts, and then better defined in the EU Adaptation Strategy on Climate Change (European Commission 2013), which was adopted in 2013 by the European Commission with the aim of making Europe more climate-resilient and enhancing the preparedness and capacity of communities to respond to the expected impacts of climate change. In particular, the EU Adaptation Strategy stresses that coastal zones are particularly vulnerable to the impact of sea-level rise, challenging the climate resilience and adaptive capacity of coastal societies. In addition, it is widely recognized that the application of coastal adaptation measures for protecting communities from coastal floods will also have a very high economic benefit. This last aspect has been deeply addressed in a recent study published by Vousdoukas et al. (2020), where a comprehensive analysis of economically efficient protection scenarios along Europe’s coastlines was performed. According to this study, Italy has more than 50% of coasts where benefits of adaptation exceed the costs. Furthermore, in case that no coastal adaptation and risk-reduction measures will be implemented, accelerated sea-level rise in view of global warming could result in unpreceded coastal losses along Europe’s coastlines (Vousdoukas et al. (2018). To support and assist policy-makers and co-ordinators on the national level in identifying, implementing, monitoring and evaluating climate change adaptation strategies, the European Commission issued the “Guidelines on developing adaptation strategies” where the key features of the adaptation policy process are provided. Based on these guideline, the Adaptation Support Tool has been developed as a practical guidance tool by the EC in collaboration with the EEA. This tool provides the steps to be followed for the definition of a national adaptation strategy. In particular, Step 2 “Assessing climate change risks and vulnerabilities” identifies the comprehensive analysis of current and future climate risks as a basis for the definition of adaptation strategies and plans.

In accordance with the most recent international requirements for climate adaptation and risk reduction, our study aims to evaluate the impact of future RSL along the main coastal plains of the Campania Region (southern Italy), which are considered to be particularly prone to sea impacts due to the presence of wide areas characterized by very low topography, reaching a total surface of approximately 30 km2 (Aucelli et al. 2017a, b; Di Paola et al. 2018). Nowadays, these areas are mostly affected by retreat processes mainly due to both human activities, related to the exploitation of the littoral zone and watershed, and natural causes, related to the increasing intensity of marine coastal processes, such as erosion and flooding, associated to the sea-level increase occurred during the last decades in the Tyrrhenian area (Spano et al. 2020). The investigated coastal plains are characterized by wide inhabited areas (such as the coastal sectors of the municipalities of Castel Volturno, Mondragone, Salerno, Castellamare di Stabia) and present a high socio-economic value due to the presence of several touristic infrastructures, wide protected wetland areas, and a number of coastal archaeological sites (such as Paestum and Velia) that can be considered vulnerable to the potential effects of processes linked to marine agents (Mattei et al. 2019, 2020). Coastal processes related to the sea-level rise are therefore expected to cause negative impacts on the local and regional socio-economic settings, as already documented for the Volturno River coastal plain, where recent marine storms coupled with extreme river discharges (dated January 2014, February 2015, October 2015, November 2018; December 2019) have caused floods in the main streets, damages to the beach touristic activities, and displacements of a number of householders, especially along the right side of the Volturno River (Moccia 2018; Frasca 2018; La Repubblica 2019).

According to the most recent definitions, the risk of climate-related impact results from the interaction of the climate-related hazards with the exposure and the vulnerability of human and natural systems (IPCC 2014). The hazard analysis is, therefore, a preliminary step for to the evaluation of future coastal risk scenarios, which require the identification of the natural and anthropic assets located in the hazard prone zone and, for this reason, potentially exposed to be impacted by sea.

Based on the upgrade of already published data (Aucelli et al. 2017a, 2018; Di Paola et al. 2018) and new analysis of available datasets derived by multi-temporal interferometric processing of satellite Synthetic Aperture Radar (SAR) images, this study evaluates future local sea levels and related inundation risk scenarios for the years 2065 and 2100 along the coastal plains in Campania (Italy). The analyses here proposed allow obtaining: (i) maps of future coastal inundation hazard, intended as the proneness of the investigated plains to be impacted by sea inundation; (ii) maps of coastal exposure and vulnerability; (iii) maps of future coastal inundation risk.

Study area

Campanian coastal plains are the result of the complex interaction among sedimentary inputs, tectonics and eustatism (Patacca et al. 1990; Casciello et al. 2006; Sacchi et al. 2014). The coastal plains along the western flank of the Apennine chain originated during the Pliocene and Early Pleistocene as half-graben structures in extensional tectonic regime induced by the opening of the Tyrrhenian Sea (Patacca et al. 1990). Their sedimentary history has been influenced by a general subsiding tendency that favored the deposition, during the Quaternary, of very thick (thousand meters) sedimentary successions. Depending on the intensity of subsidence rates, sedimentation rates and eustatic sea-level oscillations, these plains were repeatedly invaded and abandoned by the sea (Ippolito et al. 1973; Brancaccio et al. 1991, 1995; Cinque et al. 1995; Cinque 1991).

Volturno, Sele, Sarno and Alento rivers have originated the largest coastal plains in Campania Region (Fig. 1). They are characterized by flat topography and are mostly formed by clayey, silty and sandy marine, alluvial and marsh deposits, including about 500 km long sandy beaches.

Fig. 1
figure 1

Study area. Investigated coastal areas with simplified geological maps of Volturno (a), Sarno (b), Sele (c) and Alento (d) plains. Legend: (1) Dunal ridge deposits (Holocene); (2) Lagoon and marsh deposits (Holocene); (3) Slope waste deposits (Late Pleistocene–Holocene); (4) Fluvial deposits (Late Pleistocene–Holocene); (5) Volcaniclastic deposits (Quaternary); (6) Dunal ridge deposits (Late Pleistocene); (7) Lagoon and marsh deposits (Late Pleistocene); (8) Marine, continental and transitional deposits (Middle–Late Pleistocene); (9) Travertine deposits (Middle Pleistocene–Holocene); (10) Eboli Conglomerates Formation (Early Pleistocene); 11) Pre-Quaternary bedrock

Geomorphological evolution of these coastal plains has been deeply investigated in past studies (Cinque 1991; Cinque et al. 1995; De Pippo et al. 2000; Alberico et al. 2012a; 2012b; Pappone et al. 2011, 2012; Amorosi et al. 2012; Pennetta et al. 2016; Ruberti & Vigliotti 2017; Ruberti et al. 2017; Donadio et al. 2018; Ascione et al. 2020; Amato et al. 2020; Corrado et al. 2020). Their physiography and sedimentary architecture have been influenced in a somewhat different way by the coastal response to Holocene sea-level rise induced by the climatic warming that followed the last glacial maximum (LGM) (Brancaccio et al. 1995).

The Volturno coastal plain (Fig. 1) was largely submerged by the sea since mid-late Pleistocene thanks to tectonic subsidence related to extensional faults bordering the plain. This plain was aggraded greatly after the highly explosive eruption of Campanian Ignimbrite (CI) (40 ky BP; Giaccio et al. 2017) whose deposits covered the entire Campania plain when the sea level was about 50 m below today (Aiello et al. 2021). These volcanoclastic deposits represent the substrate for the uppermost Pleistocene–Holocene sedimentation (Romano et al. 1994; Corrado et al. 2018; Mastrocicco et al. 2019). After the LGM the postglacial sea-level rise caused a rapid flooding of the lower sector of the Volturno valley (Romano et al. 1994; Amorosi et al. 2012; Ruberti et al. 2018b). Since approximately 6.5 ky BP, a coastal progradation phase established, allowing the onset of the present-day alluvial plain characterized by a flat morphology. The plain is bordered seaward by a 35 km long sandy beach-dune system, behind which there is a wide lowland area with elevation comprised between – 2 m and 0 m a.s.l. This area is a remnant of a retrodunal depression, which is characterized by clayey and silty deposits, locally interbedded with peat layers (Amorosi et al. 2012). From the analysis of the present-day topography, it emerges that the extent of the area currently below sea level is equal to 26.2 km2 (corresponding to 14.5% of the investigated area).

The Sarno coastal plain (Fig. 1) experienced subsidence of at least 30 m over the last 130,000 years (Barra et al. 1989; Cinque 1991; Cinque & Irollo 2004; Valente et al. 2019; Santo et al. 2019), as indicated by the beach deposits of the Euthyrrenian transgression that are present in the subsoil of the plain at altitudes never higher than -25 m a.s.l. The late stages of coastal progradation that followed the maximum Holocene ingression reached ~ 8 km inland of the present-day coastline (Cinque 1991). Based on age constraints on the sediments buried at shallow depths, the Holocene subsidence postdates the maximum Holocene ingression in the southwestern part of the Sarno Plain (e.g., Albore Livadie et al. 1990; Aucelli et al. 2017b). The current morphology of the Sarno Plain is partly due to the continuation of subsidence until historical times and partly to the lack of significant solid load within the Sarno River waters coming from large karst springs located on the edge of the plain (Cinque et al. 1991). From the analysis of the present-day topography, it emerges that the extent of area currently below sea level is equal to 0.12 km2 (corresponding to 1.4% of the investigated area).

The Sele coastal plain (Fig. 1) is characterized by an elongated beach-dune ridge formed by Gromola-S. Cecilia and Arenosola–Aversana paleoridges originated during the Last Interglacial period (MIS 5, Brancaccio et al. 1987, 1988; Russo and Belluomini 1992). Based on chrono-altimetric data of the MIS 5 palaeo-sea level, the plain experienced a slight uplifting during the last 100–120 ky (Brancaccio et al. 1988; Barra et al. 1998, 1999). The back-barrier domains were filled up with marshy and fluvio-palustrine sediments when the sea-level rise stopped and aeolian sands finally accumulated on the coastal ridges. Remnants of the back-barrier terrace related to these high-stand phases are preserved in the modern landscape at 11–14 m a.s.l., while the coeval shore-face sediments occur up to 13 m a.s.l. and the dunes up to 23 m a.s.l. The present coastline testifies the evolution of the Holocene barrier-lagoon system (Amato et al. 2012). A composite sandy ridge forms a dune system with a mean height of about 3 m a.s.l. currently interrupted by rivers and artificial drainage channels. The large back-ridge depression, having a mean height of about 0.5–1.5 m a.s.l., hosted palustrine and marshy environments (Alberico et al. 2012b), that have been artificially drained and are, today, prone to marine inundation. From the analysis of the present-day topography, it emerges that the extent of area currently below sea level is equal to 1.30 km2 (corresponding to 2.2% of the investigated area).

The infill of the plain is not homogeneous (Amato et al. 2020) because of the presence of unconsolidated deposits with different degrees of compaction. Dunal and coastal sands, back-ridge (lagoonal and palustrine) silty clays, and thick peaty layers are the main facies of this sedimentary sequence (Amato et al. 2020). Such features could promote local subsidence, mainly in sectors where the silty clays and peaty layers show higher thickness, as in the back-barrier sectors of the plain (Amato et al. 2020).

The Alento coastal plain (Fig. 1) is considerably aggraded by Late Quaternary fluvial and transitional deposits. An initial filling dates back to the Last Interglacial period and is partly covered by alluvial fans during the Last Glacial period. The peaking stage of the Holocene ingression transformed the lower Alento valley into a ria, penetrating about 2.5 km inland of the present shoreline (Cinque et al. 1995). During the last millennia, this ria has been gradually filled, and the coastline moved forward thanks to high rates of fluvial supply (Cinque et al. 1995). Nowadays, the coastal plain is characterized by different orders of dune cords at different distances from the current coastline. These dunes consist mainly of clay and silty-clay deposits and have maximum heights between 3.5 m and 5.5 m, and are interspersed in depressed areas with no more than 0.5 m of altitude differences. From the analysis of the present-day topography, it emerges that the extent of the area currently below sea level is equal to 0.03 km2 (corresponding to 0.4% of the investigated area).

Observational data from tide gages and marine buoys allow providing a general characterization of the study area in terms of sea-level oscillations and wave conditions. In particular, data recorded in three tide gage stations sector that can be considered representative of the investigated Tyrrhenian coastal sector (Napoli 40°50′54″N, 14°16′1″E, Salerno 40°40′35″N, 14°16′18″E and Palinuro 40°1′47″N, 15°16′31″E), downloaded from the Permanent Service for Mean Sea Level (PSMSL) website (https://www.psmsl.org/), show increasing rates of 4.5 mm/yr (Salerno), 7.6 mm/yr (Napoli) and 9.1 mm/yr (Palinuro) for the period 2001–2015. Local tide gage series are referred to a period shorter than the recommended 30-year long period suggested by the World Meteorological Organization (WMO 2017) and, for this reason, the estimated rates can be biased. Nevertheless, they are representative of a general increasing trend that is confirmed also by the analysis of the continuous series collected by the Italian Institute of Environmental Protection and Research (ISPRA 2015) for the most recent period (2015–2020). For the characterization of the wave conditions, it is possible to refer to the data acquired recorded by the Ponza buoy (40°52′00″N, 12°57′00″E, 115 m depth), which is included in the Italian Sea Wave Measurement Network and it can be considered representative of the offshore wave conditions of the study area. In this case, data analysis (referred to the period 1989–2010) shows that the study area is frequently affected by moderate wave conditions coming mainly from SSW to NNW sector. A maximum value of Hs (6.90 m) was recorded in December 1999 while the medium annual maximum value is 4.31 m. Furthermore, in a recent study (Buonocore et al. 2020), the results of the analysis carried out by considering an 18-year-long (2002–2019) tide-gage dataset collected on the Island of Ischia (Southern Tyrrhenian Sea; Gulf of Naples) were presented. In this case, the results indicate that RSL rise in Ischia has a magnitude of 3.9 mm/year. Regarding the astronomical tide, the study area experiences a typical semi-diurnal tide with a maximum value of 0.30 m.

The investigated Campanian coastal plains have high socio-economical value due to the presence of a high concentration of infrastructures (highway, roads, railways, etc.), industrial and urban areas. Several touristic activities are mainly located nearby the coastline and provide a high employment ratio, especially during the summer season. Numerous agricultural and livestock farms are located in the inner sector of the plain forming the main economic activities supporting the local economy during the whole year. Finally, these areas are also characterized by high environmental and archaeological values to the presence of protected natural areas and relevant archaeological sites (i.e., Paestum remains).

Methods

To assess the future scenarios of coastal inundation risk, a three-step methodology is here proposed (as shown in the flow chart in Fig. 2):

Fig. 2
figure 2

Coastal inundation risk analysis. The methodology proposed for the evaluation of the coastal inundation risk is composed of three steps of analysis, which are aimed at: evaluating of the proneness of the coastal areas to sea-level impacts (Step 1); evaluating of the exposure level (Step 2); evaluation of the overall risk (Step 3)

Step 1—Proneness analysis: the low topographic areas are identified and classified in four hazard classes of inundation accounting for expected local sea levels, following a modified classification proposed in Aucelli et al. (2017a, 2018) and Di Paola et al. (2018). To this aim, relative sea levels for the years 2065 and 2100 are evaluated as a combination of sea-level projections proposed by IPCC (2014) and local vertical displacement rates estimated by Multi-Temporal Synthetic Aperture Radar Interferometry (MT-InSAR) data analysis (Matano 2019).

Step 2—Exposure analysis: the anthropic and natural elements located in the areas prone to be impacted by sea-level rise as well as the social vulnerability level of the coastal communities are identified and classified in four classes of exposure level (following the methodological approach proposed in Rizzo et al. 2020).

Step 3—Risk assessment: once the hazard and the exposure classes have been identified (Step 1 and Step 2), the inundation risk is calculated by combining these layers and classified the investigated areas in four risk classes (ranging from R1- low risk, to R4 – high risk), as already proposed in several studies (Benassai 2015; Aucelli et al. 2017a, 2018; Di Paola et al. 2018).

In the following subparagraphs, each step and the required data needed for the analysis are described in detail.

Step 1: Proneness Analysis

Vertical Ground Deformation Movements (VGDMs) evaluation

The assessment of VGDMs is carried out by analyzing the MT-InSAR datasets available for the study areas. In details, Italian national and regional remote sensing projects (Regione Campania 2009a, b; EPRS 2015) have produced several processed datasets referred to both ascending and descending orbits, by radar images acquired by C-band sensors on-board ERS-1/2, ENVISAT and RADARSAT satellites (Table 1). These datasets, referred to about two decades (1992–2010) overall, have been implemented with different processing methods, such as Permanent Scatterometers (PS-InSAR) (Ferretti et al. 2001, 2007) and Persistent Scatterometers Pairs (PSP) (Costantini et al. 2009). For those areas common to both acquisition satellite geometries, the availability of coeval datasets from a different point of view allows for the evaluation of the vertical components of the ground deformation, based on the interpolation of Permanent Scatterometer Interferometry (PSI) point data and some trigonometric calculations (Vilardo et al. 2009; Matano 2019). The obtained 50-m spaced grid maps show the distribution of the vertical components of ground deformation velocity in the studied coastal plains related to 1992–2000 (ERS-1/2 dataset), 2003–2007 (RADARSAT dataset) and 2003–2010 (ENVISAT dataset). To obtain a quantitative assessment of the subsidence process, the average rates of Vertical Ground Deformation Movements (expressed in mm/year) were calculated for the whole period 1992–2010 with a weighted average (Matano 2019).

Table 1 Satellite data. Main information related to the PSI data used for the assessment of the VGDM rates

Future Relative Sea Level Rise (RSLR) scenarios

The local sea-level projections are calculated by combing global scenarios with local VGDMs to evaluate the RSLR for the investigated coastal sectors (as already done in Antonioli et al. 2017, 2019). Based on the assumption that the present subsidence trends will be constant in the next decades (Aucelli et al. 2017a), local VGDMs rates are used to obtain the future component of relative variation in sea level. To have two scenarios for short and long term, the analysis is carried out taking into the mean values of global sea-level projections relative to 2046–2065 and 2100 (vs 1986–2005) proposed by the IPCC in the last Assessment Report (AR5, 2014). Furthermore, to represent the most likely scenario, the worst Representative Concentration Pathway scenario (RCP8.5) has been considered (Table 2).

Table 2 Sea-level projections

Coastal Inundation Hazard Analysis

Based on the criteria that the areas potentially prone to be inundated as consequences of RSLR are those areas lying either below sea level or less elevated above it, the coastal inundation hazard analysis is carried out by investigating the topography of the study areas, provided by the Italian Environment Ministry (MATTM 2017), who carried out a LiDAR survey in 2012 producing a DEM with 1 m pixel. To this purpose, the GIS-based “bathtub approach” is used to identify areas below the future sea levels, and four classes (from H1 to H4) hazard classification (Aucelli et al. 2017a) is applied. The hazard zonation is based on different proneness level of the coastal territory and accounts for the potential coastal response. In detail, H1 includes areas prone to be inundated only occasionally, (usually located above 1 m a.s.l.), classes H2 and H3 identify areas that are prone to frequent inundation events and permanent morphological changes, such as beach and dune erosion. Finally, class H4 identifies areas prone to be permanently inundated because below the current mean sea level (Table 3). The inland limit for the inundation analysis has been set at 5 m a.s.l., as proposed in the frame of EUROSION Project (2004).

Table 3 Hazard classes. Inundation hazard classes with the corresponding ranges and the potential coastal response (after Aucelli et al. 2018)

Step 2: Coastal Exposure Analysis

To assess the anthropic assets located in the zones prone to be inundated, an index-based methodology is proposed. The method follows the index approach suggested in previous studies and applied in different coastal areas (Rizzo et al. 2020; Aucelli et al. 2018; Armaroli and Duo 2017; Ferreira et al. 2017; Viavattene et al. 2015), which assumes that the exposure can be represented by a set of socio-economic indicators: land use categories, transport networks, utilities and business settings. Furthermore, the social vulnerability of the municipalities is also taken into account. Sources of exposure data used in this study are reported in Table 4. As for the hazard analysis, the landward limit of the analysis has been set at 5 m a.s.l.

Table 4 Coastal exposure index. Indicators proposed for the evaluation of the exposure index and related sources

The Land Use indicator (Exp_LU) measures the relative exposure of each land use category, after having grouped them in five classes according to an anthropocentric perspective (Ballesteros et al. 2018; Perini et al. 2016). This classification defines as very highly exposed areas the ones occupied by settlements, constructions and human activities, corresponding to the “Artificial surfaces” class (Corine Land Cover—Level 1) while assigns a very low exposure level to the natural areas and the inland water courses, corresponding to the “Water Bodies” class (Corine Land Cover—Level 1). This indicator considers the presence of a given “land use category” and the associated relative importance value but not its physical vulnerability. Nevertheless, natural protected areas (Nature 2000 sites) are considered as highly vulnerable.

The Transport networks indicator (Exp_Tr) is ranked according to the importance of the transport system within its network. The lowest value is attributed to local roads, the highest one, instead, to roads and railways of national or international importance. The potential area of competence of each element of the transport network has been set within a buffer of 150 m along their central line.

The Utilities indicator (Exp_Ut) is evaluated based on the presence of electricity stations and water supply plants, which are considered very critical assets. The potential area of competence of each asset has been set as a buffer of 100 m around them.

The Business indicator (Exp_Bs) considers the presence of bathing establishments located along the coastline because bathing tourism represents one of the main economic activities along these coasts. The potential area of competence has been set as a buffer of 200 m from the coastline.

Transport networks, utilities and business settings are the three indicators used to evaluate the anthropic assets located in the investigated coastal areas.

Detailed information about the classification of each exposure indicator is reported in Table 5.

Table 5 Coastal exposure indicators and their classification.

The Social Vulnerability indicator (Exp_SVu) measures the relative vulnerability of the different communities located along with the coastal sectors. This indicator allows evaluating the socio-economic characteristics of the population exposed to coastal hazards and it is generally obtained from census data. For the Italian case, this indicator is expressed by an index defined and calculated for the whole Italian territory by the National Statistical Institute (ISTAT).

The Italian Social-vulnerability index is calculated at the municipality level and it is constructed through the synthesis of seven indicators that account for the following aspects: family structure (number and age of family members, young single-parent families), living condition, labor market participation, and percentage incidence of households with potential economic and welfare distress. The selected indicators describe, with almost equal weight, the two dimensions of "material" and "social" vulnerability. The operative ISTAT procedure is described in detail at the following link:

http://ottomilacensus.istat.it/fileadmin/download/Indice_di_vulnerabilit%C3%A0_sociale_e_materiale.pdf

The Coastal Exposure is calculated by the geometric mean of the listed indicators, defined as the nth root of the product of n indicators (Eq. 1). The obtained Coastal Exposure Index is converted into a scale from 1 (low exposure) to 4 (very high exposure) (Table 5).

\({\mathbf{E}\mathbf{x}\mathbf{p}\mathbf{o}\mathbf{s}\mathbf{u}\mathbf{r}\mathbf{e}}_{\mathbf{I}\mathbf{n}\mathbf{d}\mathbf{e}\mathbf{x}}=\)[Exp_LU * (Exp_Tr + Exp_Ut + Exp_Bs) * Exp_SVu]1/3 (Eq. 1).

Step 3: Coastal Inundation Risk Analysis

The inundation risk is defined as the combination of the probability of the hazardous event and its negative consequences (impacts). It is calculated by means of the risk matrix (Table 6) proposed in Aucelli et al. (2017a) and classified in the following four classes:

Table 6 Coastal risk matrix. The risk is evaluated by combining the coastal Hazard level (H) with the exposure level (Ex). The different colors evidence the different risk classes ranging from low in green (R1) to very high in red (R4). From Aucelli et al. (2017a)

R4 (very high risk): permanent loss of the current socio-economical activities; disruption of structures, infrastructures and relevant areas.

R3 (high risk): high damage of the current socio-economical activities; potential loss of structures, infrastructures and relevant areas.

R2 (medium risk): medium damage to the current socio-economical activities.

R1 (low risk): low damage to the current socio-economical activities.

Results

Coastal inundation hazard assessment

The results of the quantitative assessment of the subsidence process referred to the four studied coastal plains are showed in 50-m spaced grid maps (Fig. 3), displaying the average rates of Vertical Ground Deformation Movements (expressed in mm/year) referred to the period 1992–2010. These maps show that the coastal sector of the Volturno and Sele plains are characterized by complex subsidence patterns, resulting in more intense and wider than those characterizing Sarno and Alento plains.

Fig. 3
figure 3

– Local Vertical Ground Deformation Movements maps for the studied coastal plains. Volturno coastal plain (A), Sarno coastal plain (B), Sele coastal plain (C), Alento coastal plain (D)

In detail, the Volturno River mouth is characterized by moderate subsidence values (-2.5 to -7.5 mm/y), while the dune ridge system shows low subsidence (0 to -2.5 mm/y). High subsidence values (up to -10 mm/y) are found in the back-dune depressions, near Lago Patria Lake and Villa Literno town. The central part of the Volturno Plain, in the Cancello Arnone surrounding area, displays subsidence values ranging from -2.5 to -20 mm/y. The Sele River mouth area is characterized by moderate values of subsidence (-2.5 to -7.5 mm/y) with a hot spot developing 5 km inland. In the northern sector, a continuous coastal strip is characterized by subsidence values ranging between  -2.5 and -10 mm/y, while the hilly areas towards the east and the southern sector of the plain are characterized by general condition of stability.

The Sarno coastal plain is characterized by localized moderate subsidence near Castellammare di Stabia ( -0.25 to -1 mm/y) and high subsidence rates (-1 to -5 mm/y) near the river mouth. In the strip between 1 and 2 km inland the coastline, the ground shows stability trend (-0.25 to +0.25 mm/y). Along the inner alluvial plain, strong subsidence affects the area close to the Sarno River around the city of Santa Maria La Carità. The Alento plain shows a moderate pattern of vertical ground deformation. The northern sector shows low subsidence rates (-1.9 to -0.3 mm/y) and the coastal narrow strip among Velia and Marina di Ascea displays stability or fair uplift ( -0.3 to +0.6 mm/y), while 1–2 km inland along the Alento River course, a hot spot of subsidence is evident (up to -3 mm/y).

The area characterized by each hazard class has been evaluated for all the investigated coastal plains considering the RCP8.5 scenario for the years 2065 and 2100. In details, a total surface of approximately 180 km2 (Volturno Plain), 8.5 km2 (Sarno Plain), 60 km2 (Sele Plain), 7 km2 (Alento Plain) has been investigated. The detailed extent of each hazard class is shown in Table 7; values are expressed both as square kilometers and as percentage.

Table 7 Coastal inundation hazard assessment. The surface of each hazard class is indicated as square kilometers and percentage of the total investigated surface by considering sea-level rise scenarios for the years 2065 and 2100 (RCP8.5). For the hazard class rank, refer to Table 3

Considering the 2065 projections (Fig. 4), the extent of H4 class is relevant only for the Volturno plain (38.2 km2 corresponding to 21% of the total investigated area) while, for the others studied coastal plains, it extends less than 10% with the lowest value estimated for the Alento plain (< 1%). Also, H3 and H2 classes extend, respectively, 11% and 13% in the Volturno plain and with a percentage lower than 10% for the other coastal plains. Accounting for H1 class, its surface occupies 94% of the investigated area in the Alento Plain, 92% in the Sarno Plain, 76% in the Sele Plain and 55% in the Volturno Plain. Considering the 2100 projections (Fig. 5), the extent of H4 class in the Volturno Plain is 61.6 km2 and 8.7 km2 in the Sele Plain (34% and 14% of the total investigated area, respectively), while for the others investigated coastal plains, the H4 surface is less than 3%. H3 and H2 classes occupy, respectively, 22.8 km2 (13%) and 19.9 km2 (12%) in the Volturno Plain. Also, in this case, these classes have a surface than 10% in the other coastal plains. Finally, the H1 class surface decreases in all the plains with passing to 86% in the Alento Plain, 84% in the Sarno Plain, 63% in the Sele Plain and 42% in the Volturno Plain.

Fig. 4
figure 4

Inundation hazard maps for the year 2065 (under the RCP8.5 scenario). Volturno coastal plain (A), Sarno coastal plain (B), Sele coastal plain (C), Alento coastal plain (D)

Fig. 5
figure 5

Inundation hazard maps for the year 2100 (under the RCP8.5 scenario). Volturno coastal plain (A), Sarno coastal plain (B), Sele coastal plain (C), Alento coastal plain (D)

Negative rates show subsidence, positive rates uplift

Coastal exposure assessment

The exposure evaluation (Fig. 6) has accounted for the current spatial distribution of the vulnerable assets and social vulnerability. Results are shown in Table 8 both in km2 and as a percentage of the investigated area for each plain.

Fig. 6
figure 6

Exposure maps. Volturno coastal plain (A), Sarno coastal plain (B), Sele coastal plain (C), Alento coastal plain (D)

Table 8 Coastal exposure assessment. The surface of each class is indicated as square kilometers and percentage of the total investigated surface

Results show that Class Ex3 has the highest values almost in all the investigated plains, corresponding to 83% (Alento), 50% (Volturno), 45% (Sele), and 31% (Sarno) of the total surface. Class Ex4 has the highest percentage value in the Sarno Plain (68%), while it occupies 44% of the investigated area in the Volturno Plain and 8.4% in the Sele Plain.

Coastal inundation risk assessment

To assess the spatial distribution of the different risk levels along the investigated coastal plains, the hazard and the exposure classes have been combined following the coastal risk matrix (Table 6). Risk has been classified in four classes and the results are shown as risk maps to each plain (Fig. 7 and Fig. 8). Furthermore, the extent of each class risk is indicated in Table 9, where the results are shown in km2 (a) and as percentage values (b).

Fig. 7
figure 7

Inundation risk maps for the year 2065 (under the RCP8.5 scenario). Volturno coastal plain (A), Sarno coastal plain (B) Sele coastal plain (C), Alento coastal plain (D)

Fig. 8
figure 8

Inundation risk maps for the year 2100 (under the RCP8.5 scenario). Volturno coastal plain (A), Sarno coastal plain (B) Sele coastal plain (C), Alento coastal plain (D)

Table 9 Coastal inundation risk assessment. The surface of each risk class is indicated as square kilometers and percentage of the total investigated surface by considering sea-level rise scenarios for the years 2065 and 2100 (RCP8.5) and the present distribution of exposure classes

Accounting for the R4 class, it has a considerable extension only in the Volturno Plain (48.9 km2 and 73.7 km2, respectively, in 2065 and 2100 scenario) and in the Sele Plain (2.8 km2 and 6.1 km2, respectively, in 2065 and 2100 scenario). In the other investigated coastal plains, R4 class extends for a limited surface (lower than 2% of the total areas). Class R3 has an extension of more than 11% only in the case of Volturno plain. R1 class shows the largest areas in Sele, Volturno and Alento Plains for both considered time-periods, while R2 class occupies the largest surface in Sarno Plains.

In Table 10 R4 and R3 classes areas are shown, referred to 2065 and 2100 scenarios on a municipality basis. The municipalities located in the Volturno Plain show high values of R3 and R4 areas in both scenarios. In particular, the Castel Volturno, Villa Literno and Mondragone municipalities show high values of R4 in 2100 (37.0 km2 – 50.1%) and R4 in 2065 (20.8 km2 – 32.1%). Within the other plains, the only municipality with relevant values of R4 areas is Eboli in the Sele Plain (2.5 km2 – 1.8% in 2065 and 5.5 km2 – 4.0% in 2100).

Table 10 R3 and R4 risk classes evaluation for each municipality located in the study areas, expressed both as square kilometers and percentage of the surface falling within the plain sector

Discussion and conclusion

The analysis of sea-level rise impact along the Campanian coastal plains has provided a detailed mapping of the coastal areas prone to be impacted by sea inundation for the years 2065 and 2100. In addition, risk scenarios suggest that in the next decades, natural areas, including beaches and wetland, human infrastructures, and touristic areas as well as wide portions of agricultural areas can be affected by marine inundation and related impacts.

In detail, the results of the hazard assessment have shown that considering the RCP8.5 scenario, the total surface of H4 class is expected to be 42.5 km2 and 70.8 km2 in the year 2065 and 2100, respectively. Taking into account the increase in H4 surface from 2065 to 2100, the maximum value is calculated for the Volturno coastal plain, where it increases of 23.4 km2 (+ 13%), passing from 38.2 to 61.6 km2, while the lowest value has been estimated for the Sarno coastal plain where it increases only 0.1 km2 (+ 1.3%), passing from 0.1 to 0.2 km2. In the case of the Sele coastal plain, this value increases 4.7 km2 (+ 7.3%) while in the Alento coastal plain, the increasing in H4 surface is 2.3%.

From these results, it is clear that the observed subsidence trends play a consistent role in increasing sea-level rise impact along the investigated coastal stretches, especially in the Volturno coastal plain, where current subsidence rates range from -1 to -25 mm/yr and the H4 class reaches its maximum extend, and in the Northern sector of the Sele coastal plain. Due to the geological setting of these plains, the detected high subsidence can be considered mainly a natural process caused by the compaction of the alluvial sediments infill under the lithostatic load (Ruberti et al. 2017; Aucelli et al. 2017a; 2018; Di Paola et al. 2018; Matano et al. 2018; Amato et al. 2020). Conversely, anthropic influences (e.g., water pumping and urbanization) are considered as an additional factor that may locally increase the natural subsidence processes.

Accounting for the risk analysis, results have shown that 51.9 km2 are including in the R4 class in 2065. This value will increase up to 80.3 km2 when the scenario for the year 2100 is taken into account. Also in this case, the maximum surface increasing from 2065 to 2100 is expected to occur in the Volturno and Sele coastal plains, with increasing values of + 13.7% and + 5.5%, respectively.

The proposed methodological approach is consistent with several types of research that have been carried out for mapping areas that will be impacted by sea-level rise as a consequence of global warming and local subsidence. In fact, a number of recent international studies focused on the definition of risk management and adaptation strategies tailored accounting for the small-scale criticalities highlighted by high-resolution information obtained by coupling MT-InSAR subsidence maps with expected sea levels. By way of example, an increase of 25% in the potentially flooding surface in the central Singapore zone is expected by 2100 as consequence of local subsidence (Catalao et al. 2020). Local subsidence will exacerbate inundation risk also in the San Francisco Bay, where analysis results show that inundation hazard scenarios based only on the projection of sea-level rise may underestimate the area at flooding risk. In details, considering the lower and upper bounds of likely ranges of various RCP-projected SLR scenarios coupled with local subsidence trends, in 2100, an area of 98 to 218 km2 will be affected by RSLR. The corresponding values for the case of SLR only are 51 to 168 km2 (Shirzaei & Bürgmann 2018a, b). Another example is provided by Le Cozannet et al. (2021), who have analyzed the Guadeloupe area (West Indies, French Antilles). This coastal area is characterized by local subsidence mainly caused by changes in the water content of reclaimed ground and strong urbanization occurring since the 1950’, which is causing high sediment compaction. Also in this case, future chronic flooding events are expected to be exacerbated by land subsidence. In Italy, Antonioli and Silenzi (2007), Lambeck et al. (2011), Antonioli et al. (2017; 2020) proposed a first level of assessment of the impacts of future relative sea levels at a regional scale along the main Italian coastal plains, which are characterized by different geological settings and tectonic movements. Nevertheless, in these studies, the ground deformation processes have been evaluated only for large homogeneous sectors on the basis of geological proxies and constrain. Data with a more accurate and detailed spatial resolution of the local vertical ground movements obtained using Multi-Temporal SAR Interferometry datasets represent a great benefit for the risk assessment allowing to discriminate areas that are affected by different subsidence rates both along the waterfront sectors and in the inland low-lying areas. Similar analysis have been carried out along other Italian coastal sectors. Recently, Anzidei et al. (2021) have evaluated the impact of relative sea-level scenario in 2100 along the Coast of South Eastern Sicily (Italy) using MT-InSAR data, satellite images and high-resolution topography. In this case, a total surface of approximately 10 km2 is expected to be flooded in 2100 under the RCP8.5 scenario, with a marine ingression up to 5 km inland in the Catania coastal area, due to the local complex topographical characteristics.

The results of our study represent a step forward with respect to the findings of previous works published on the Volturno and Sele coastal plains (Aucelli et al. 2017a, b; Di Paola et al. 2018), extending the hazard analysis to all the Campania region coastal plains and defining a uniform risk analysis procedure to be applied in all the investigated coastal plains. This last aspect highlights an important outcome of this work that has enabled the relative ranking of the main Campanian coastal plains in terms of inundation risk. These kinds of studies increase the awareness of the communities that will have to deal with permanent sea ingression, which will force them to find strong adaptation strategies to cope with its potential impacts and to preserve the economic and natural values of the assets potentially impacted. Therefore, this analysis gives a useful instrument to local administrations to evaluate in detail areas to be strictly monitored to avoid loss of goods. In spite of its simplified approach, this work represents a useful instrument for planner authorities and the necessary preliminary step for the identification of the coastal sectors that require to be investigated in more detail by means of higher resolution and dynamic modeling approaches.

In detail, the hazard and risk inundation maps here proposed can represent a tool for increasing efficient territorial management aimed at the reduction of future coastal risk. In fact, up till now, the coastal management plans provided by the regional Authorities are drawn up according to the national regulations, which suggest mapping coastal erosion and flooding scenarios by considering only the impact of storm surges with different return periods without considering the potential contribution of subsidence processes.

The risk assessment procedure applied in this study allows obtaining a coastal zonation that accounts for the dynamic component associated with the local VGD and future SLR projections and therefore it enables decision-makers in tailoring Best Management Practices (BMPs) to local topographical characteristics and identifying the most suitable adaptation measures to cope with future sea levels with a higher resolution spatial scale. The approaches here proposed can be easily exported and applied for the characterization of areas with similar geomorphological features and therefore potentially prone to the impacts of expected sea-level rise.

In conclusion, it is possible to state that the hazard and risk assessments proposed in this study satisfy the EC requirements for addressing the climate adaptation and risk-reduction challenges in coastal areas by improving the knowledge of policymakers and local administrators and raising their awareness about the potential impacts of climate change on the territory and, therefore, promoting the mainstream of coastal adaptation strategies in national policy and regulations.