Today more than half of the world's population resides in cities (United Nations, 2014). Urban growth leads to changes in natural habitat, loss of open spaces and arable land, alteration of natural hydrological and sediment cycles, as well as an increased contribution to air and water pollution (UNFPA, 2007; Clarke, 2014). Worldwide urbanization processes are particularly intense in coastal zones, considering that over 40 % of the global population live in areas within 100 km of the coastline (IOC/UNESCO, IMO, FAO and UNDP, 2011). This leads to an increased susceptibility of urban settlements to coastal hazards, such as flooding and erosion resulting from the impact of waves, tides, storm surges and sea-level rise (De Sherbinin et al., 2007; Hanson et al., 2011). Low-lying coastal areas are particularly vulnerable to such hazards, especially in delta regions, due to sediment compaction and related subsidence (Ericson et al., 2006; McGranahan et al., 2007). Apart from the visible impacts of temporary coastal inundation (e.g. damage to physical structures), some indirect effects can aggravate the problem, e.g. tourism decline, rise in insurance premiums for house-owners and other business disruptions (Lequeux and Ciavola, 2011; Meyer et al., 2013; Kreibich et al., 2014).

The main aim of this study is to investigate the vulnerability of coastal areas to marine flooding in a way that considers the dynamics of the forcing component (waves, tides and storm surge) and the dynamics of the receptor, i.e. urban areas. This was done by comparing the output of different coastal flooding scenarios to the output of diverse scenarios of urban growth in the coastal zone. This brings a more holistic viewpoint on issues of urbanization in flood-prone coastal areas, which can be beneficial for efficient coastal planning and management.

The study was carried out in the coastal area of the Emilia-Romagna region, Italy. This area is known for intense urbanization along its low-lying setting, as well as for being susceptible to coastal flooding and the related beach erosion, mainly due to storm surges (Perini et al., 2011; Armaroli et al., 2012).

Urban growth scenarios were designed by employing the established cellular automata (CA)-based urban model named SLEUTH (Silva and Clarke, 2002). CA-based models have been recognized as particularly useful in simulating complex systems, such as cities, due to their ability to explicitly simulate spatial and time-related dynamics (Batty and Xie, 1997; Couclelis, 1997; White and Engelen, 2000; Irwin and Geoghegan, 2001; O'Sullivan and Torrens, 2000). Their affinity toward raster data makes them compatible with remote sensing and geographic information system (GIS) technology (Li and Yeh, 2000; Torrens, 2003). Among CA-based models, SLEUTH has recently gained popularity. There were several reasons for choosing SLEUTH in this work: it is available online for free and technical support is provided; it includes quite a robust routine for historical calibration; it has the ability to simulate different future growth scenarios; its output are GIF maps which are quite effective visualization tools; and, finally, it has been successfully applied in many recent studies on urban growth (Rafiee et al., 2009; Wu et al., 2009; Syphard et al., 2011; Al-shalabi et al., 2013; Dezhkam et al., 2013; Akın et al., 2014; Garcia and Loáiciga, 2014 among others). The only application of using SLEUTH to estimate future exposure to marine floods known to us is the one by Garcia and Loáiciga (2014). In their study the flood-damage quantification module was developed by merging flood maps with SLEUTH urbanization predictions in order to calculate the expected annual flood damage (EAFD) for given scenarios of sea-level rise. In general, the subject of growing population exposure to coastal flooding and sea-level rise has seemed to be more in focus lately (e.g. Jongman et al., 2012; Neumann et al., 2015; Stevens et al., 2015).

Hazard maps of the regional coastal area were issued at the end of 2013 to satisfy the requests of the EU Floods Directive. The 2007/60/EC (European Parliament and Council of the European Communities, 2007) Directive was implemented in Italy with the Decree 49/2010 and requests the member states to collect data, issue hazard maps and prepare disaster risk reduction plans, in order to reduce the negative consequences of river and marine flooding. It gives special attention to human lives and health, historical heritage, economic activities and infrastructure. To evaluate marine flooding hazards, the Geological Service of the Emilia-Romagna region followed a methodology that takes into account three total water level (TWL) scenarios (10, 100 and > 100 year return periods) and high-resolution digital terrain models (DTMs) of the coast that were analysed with the Cost–Distance tool of ArcGIS^® (Perini et al., 2012; Regione Emilia-Romagna, 2012a, b, 2013a; Armaroli et al., 2014). The Cost–Distance tool is a robust tool that is used for different purposes worldwide, ranging from measuring population exposure to pollution (Davies and Duncan, 2009), travel costs (among others, Bernd and Nielsen, 2007), habitat preservation under sea-level rise (Sims et al., 2013) and flooding extent due to sea-level rise (Xingong et al., 2014). It is defined as a tool that “calculates the least accumulative cost–distance for each cell to the nearest source over a cost surface” (i.e. computes the least “costly” path of each cell of a grid from a user-specified source or location; http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//009z00000018000000.htm).

In summary, this study is aimed at reaching several objectives:

to gain a deeper insight into historical urban growth of the coastal area of the Emilia-Romagna region;

The study focused on the coastal zone of the Emilia-Romagna region (Italy), located along the NW Adriatic Sea. Since there is no universal definition of a “coastal zone”, the extent of the area was chosen arbitrarily, by considering the requisites for the SLEUTH model. A large urban centre in the area (the city of Ravenna) was included, because larger urban areas tend to influence the development of smaller ones in their surroundings (Antrop, 2004). The study area is included into a rectangle of approximately 76 km of length and 26 km of width, covering the area around the coastline that stretches from Sacca di Goro (Ferrara province) in the north to the city of Cesenatico (Forlì-Cesena province) in the south (Fig. 1).

In its northern part, the study area is a typical deltaic environment, characterized by reclaimed lowlands, wetlands and brackish lagoons. In ancient times, the natural river system of this area was subject to periodical floods that modified the hydrology and morphology of the Po River floodplain, causing damage to early settlements (Regione Emilia-Romagna, 2010a).

The southern part is extensively urbanized. Shoreward urbanization was driven by the tourism boom that started after World War II, being particularly intense during the 1960s. New settlements were managed mainly by real estate companies, which were buying agricultural land and selling it for further development. This resulted in coastal land occupation by second homes and beach-bathing establishments known as “bagni” (Cencini, 1998). Beach-related tourism in the summer is a very important economic resource for the local community and for the whole region as well. The high degree of urbanization has meant that as of 2005, dunes are present along only 28 % of the 130 km of coastline (Armaroli et al., 2012). Apart from beach-related tourism, land cover change was also driven by the development of oil and chemical industries, located particularly in the vicinity of the Ravenna harbour.

The Emilia-Romagna coast is characterized by very dissipative beaches composed of fine-to-medium sands and with low elevations above mean sea-level (mean height of the backshore is 1.45 m) (Regione Emilia-Romagna, 2010a). The area is microtidal, with mean neap tidal range of 0.3–0.4 m and mean spring tidal range of 0.8–0.9 m (Armaroli et al., 2012). The wave climate is low energetic, with 91 % of significant wave height (Hs) below 1.25 m. As for the storm surges, even the low return period events (e.g. a 1 in 10 year event) can lead to water level elevations of close to 1 m a.m.s.l. (Masina and Ciavola, 2011).

Along with reduced riverine sediment supply (Preciso et al., 2012), dune destruction, disruption of longshore sediment transport by harbours and piers and land subsidence (Teatini et al., 2005), marine storms are one of the major causes of coastal erosion. Intensive storms mainly originate from Bora (NE) and Scirocco (SE) winds (Ciavola et al., 2007). Most storms do not last more than 24 h and the maximum significant wave height is about 2.5 m (Armaroli et al., 2012). A historical review of coastal storms for the 1946–2010 period is discussed in detail in Perini et al. (2011).

Because of the high susceptibility of the coastal areas to marine ingression and coastal erosion, different coastal protection structures were built along the shoreline starting from the late 1970s. Approximately 57 % of the coast is currently protected by artificial structures, such as submerged barriers and emerged breakwaters, groynes, etc. (Armaroli et al., 2009). These structures are able to protect the coast but can also generate erosion and interrupt longshore sediment transport. Along with other types of permanent embankments and protections, artificial “winter dunes” are a source of temporary protection that are built at the end of the summer season to avoid coastal flooding and damage (Harley and Ciavola, 2013).

Because of structural interventions, as well as beach nourishment practice, the coast is in a steady state at the present, with mean erosion and accretion rates between -1 and +1 m yr-1 (Armaroli et al., 2012). However, there are hotspots of erosion that show significant recession rates of up to -15 m yr-1, such as the Bellocchio area (Sekovski et al., 2014).

The workflow of this study consists of: (i) preparation of the input layers for the SLEUTH model; (ii) calibration of the SLEUTH model; (iii) prediction of urban growth of the Emilia-Romagna coastal area up to 2050, considering different development scenarios; (iv) development of different coastal flooding hazard maps; and (v) integration of urban growth predictions with flood hazard maps.

According to SLEUTH predictions, urban growth is not likely to change from compact to sprawled in the Emilia-Romagna coastal area in the future. If the urbanization continues to increase, it will probably take place around the existing urban areas in a more compact manner.

It is important to highlight that the compact historic growth, highly dependent on the proximity to road networks, is exclusively related to the studied historic period. Considering the fact that the biggest boom in urbanization took place in the 1950s, and especially in the 1960s (Cencini, 1998), it would be interesting to observe the output of a calibration phase in which the “seed year” starts before the mentioned period. However, quality data needed as input for SLEUTH were not available for the period earlier than 1978, so this remains a recommendation for further research. On the other hand, it seems that the availability of more recent data sets, with shorter time intervals between them, can result in a more accurate agreement between the simulated and observed urbanization (Candau, 2002; Chaudhuri and Clarke, 2014).

The limitation of urban models is that they often do not capture the driving forces behind urbanization (Herold et al., 2003; Jantz et al., 2003). The main drivers of urban growth are population increase and economic development (Rounsevell et al., 2006). The population of the Emilia-Romagna region is expected to increase by almost 13 % up to 2030 (Regione Emilia-Romagna, 2010b) and, therefore, the demand for urbanization is likely to increase accordingly. The demand can be amplified by the economic growth, but economic growth rates are difficult to predict, especially for longer periods. Since the driver of coastal urbanization is highly related to second homes and tourism, it would be crucial to include the expected growth in population well-being and economic activities in this analysis. In addition, sectors such as tourism and trade greatly rely on economic trajectories that go beyond regional boundaries and are not taken into account in CA models (Torrens, 2003).

Some uncertainties arise when applying the SLEUTH model. One is the uncertainty of how much to increase/decrease the coefficient values in order to represent the scenarios as realistically as possible. A second uncertainty is the designation of exclusion levels. Exclusion levels have proven to have a crucial role in previous SLEUTH applications (Akın et al., 2014). It is common that excluded areas are weighted (Dietzel and Clarke, 2007; Akın et al., 2014), so that some areas can be only partially excluded. For this reason the value for the HGS and the SGS was set to 80 %. However, if the demand for urban land increased, the excluded areas could be considered suitable for urban development and hence the weighted exclusion values should be set lower.

When discussing the Cost–Distance tool for calculating scenario-dependant flood extents, there are some details that need to be considered. First of all, the analysis does not include formulas to calculate the run-up, which can be critical when estimating flooded areas (Armaroli et al., 2009). In addition, this methodology does not take into account the morphological evolution of beaches and dunes in front of urban settlements over a short period of time (e.g. related to storm impacts) and thus does not include dune/artificial embankment breaching and overwash/overtopping processes. Furthermore, it does not consider land subsidence that is a critical aspect in the study area. Land subsidence ultimately controls medium-term morphological resilience of dunes and back-barrier environments through complex feedback interactions between biotic and abiotic components (Taramelli et al., 2015). Finally, it offers information neither on flow velocity nor on soil permeability. Without these parameters, the calculations of flood polygon areas can easily be under- or overestimated. Nevertheless, the tool calculates the shortest path that the water covers to move landward, which can be considered a proxy of soil roughness and permeability. The longer the path, the less flood-prone the area, according to the applied methodology. Furthermore, the model excludes from the computation several zones that are not reachable by water; on the contrary, it detects the location of low-lying/open passages that favour the landward water movement. This information is particularly interesting for coastal managers as it can be translated into a list of vulnerable locations which are in need of special attention for risk reduction measures.

Regarding the predicted urban growth in the study area, some remarkable insights were revealed. First of all, there is no space for further urban development in areas directly facing the coastline. The coastal stretch of the study area is either already urbanized or excluded from development. Therefore, a great majority of future urbanized areas that fall under flooding polygons are located in the hinterlands of existing coastal settlements. This is clear on the given example of Lido Adriano–Lido di Dante (Fig. 6a–f). This urbanized area was chosen for the demonstration of the results since a considerable portion of urban growth areas fall under different scenarios of future flood extent. Two opposite scenarios of urban growth (CGS and SGS) were chosen for the demonstration (HGS was left out for visualization purposes).

If the urban growth increases at the same pace as it has been increasing along the historic period used for calibration, the growth of urban areas will be quite limited (the HGS). The extension of urban areas that are flood-prone to high-frequency events (T10) in current urban settlements (2011) is 12 % of the total inundated area. Indeed, if the urbanization happens in a compact manner, which is most likely, the risk will increase accordingly, and the number of flood-prone zones will rise between 14 and 23 % up to 2050, with respect to the extension of current flood-prone areas.

Among many other concerns, ongoing coastal urbanization will increase the exposure of urban settlements to coastal floods, especially in low-lying areas. Although the level of impact of climate change and sea-level rise on the future flood episodes is quite uncertain, there is a possibility of exacerbation of the problem in the future. Following a precautionary principle, scientists and planners should try to better understand the future dynamics and relations between forcing (coastal floods) and receptor (urban areas). Effective management plans could reduce damage and lead to potential economic savings, involving crucial Civil Protection issues as well (i.e. loss of human lives).

This study is a contribution to a better understanding of coastal hazard and risk, by proposing an approach that combines coastal flooding scenarios with different scenarios of urban growth, obtained using the SLEUTH model. Both methodologies demonstrate advantages in spatial analysis. The SLEUTH model outlines the benefits of applying CA-based models: the ability to capture complex system properties, self-organization of urban clusters emerging from the local interaction between cells and their neighbours and non-linear behaviour in growth patterns. Apart from these “standard” benefits, SLEUTH offers robust historic calibration combined with Monte Carlo averaging. This provides an insight into the historic urban growth of the Emilia-Romagna coastal area. Furthermore, the code is relatively easy to manipulate. The output GIF maps were easily quantifiable, effective when visualizing urban growth scenarios and suitable for further GIS analysis.

The Cost–Distance tool has proven to be a fast and simple method for estimating future flood hazards. It is also quite replicable and exportable if the information needed as input is available (e.g. detailed DTM, forcing parameters).

Once used jointly, these two methodologies can be particularly useful in revealing flood-prone coastal areas that have a high potential for future urbanization. In other words, although exact numbers behind future urban cover projections and flood extent can be debatable, the areas where the damage is expected to be higher can be helpful to decision makers involved in land use planning.

We believe that planners and decision makers should be strongly encouraged to take probabilistic models and scenarios into account – not only ones that consider the dynamics of climate forcing but also spatial dynamic models that project urban growth. Although projecting the future is often quite uncertain, and scenarios are just a simplification of reality, these “what-if” approximations are useful to understand different directions of future development. It is clear that models have their limitations; however, rapid development of remote sensing and geographic information systems helps to supply high-quality data as input for models that can obtain more reliable results. The improvement in computer processing capabilities also reduces the time and complexity of the analysis, so specialized technical knowledge is not always essential for the use of these approaches, and the door is open to researchers, managers and planners. Finally, the visualization property of this approach can have a powerful impact, both in supporting decision-making processes and in raising awareness among the general public of the location of areas that are more vulnerable to coastal hazards.