Coastal Risk Assessment Research Articles

The complexity of the coastal zone: Definition of typologies in Portugal as a contribution to coastal disaster risk reduction and management

Mainland Portugal presents an extensive, diversified and complex coastal zone. This diversity gives rise to diverse geomorphologic features which support different densities of human occupancy and a range of land uses and activities. In this sense, the coastal zone becomes an area of great national strategic value, but has also into a multi-hazard zone. To understand this complexity, two statistical techniques, namely Cluster Analysis and Principal Component Analysis, were used to identify and differentiate, between the different coastal typologies, based on a set of variables that express the territorial complexity and the occurrences and impacts of coastal flooding. The chosen methodology enabled a diverse set of coastal typologies to be identified, which contributes towards differentiating local specific characteristics. The holistic and differentiating nature of the applied methodology and the results obtained allows it to contribute to the definition of mitigation strategies and the implementation of adaptation measures. The results enable us to define a set of typologies associated with Coastal Disaster Risk Reduction and Management which are an important contribution to comprehensive coastal flood risk assessment and management.

Population development as a driver of coastal risk: Current trends and future pathways

AbstractCoastal areas are subject to hazards that can result in severe impacts due to the high concentration of people and assets in exposed locations. While climate-induced sea-level rise will exacerbate these hazards in the course of the 21st century, future dynamics in socioeconomic development will play an important role in driving impacts – as well as adaptation responses – in particular in countries with rapid population growth in low-lying coastal areas. Here, we synthesize the current state of knowledge related to current and future population development in coastal locations and the underlying trends in socioeconomic development affecting coastal impacts at continental to global scales. Currently, 2.15 billion people live in the near-coastal zone and 898 million in the low-elevation coastal zone globally. These numbers could increase to 2.9 billion and 1.2 billion, respectively, depending on the socioeconomic scenario (i.e., Shared Socioeconomic Pathway [SSP]) considered. Nevertheless, although these numbers indicate a rapid increase in exposure of population and assets to coastal hazards, they bear limited information about the actual impacts as they do not include information on the vulnerability of coastal population. Based on these insights, we stress the need to account for dynamics in socioeconomic development in coastal risk assessments, including exposure as well as vulnerability, and additionally exploring potential feedbacks due to adaptation responses and migration decisions. Last, we propose action points for future work that can inform long-term coastal planning for managing coastal risks.

Coastal Flood Risk and Smart Resilience Evaluation under a Changing Climate

Coastal areas are highly vulnerable to flood risks, which are exacerbated by the changing climate. This paper provides a comprehensive review of the literature on coastal flood risk assessment and resilience evaluation and proposes a smart-resilient city framework based on pre-disaster, mid-disaster, and post-disaster evaluations. First, this paper systematically reviews the origin of the resilience concept and the development of flood resilience. Next, it introduces the social-acceptable risk criteria and the flood resilience level for different flood phases. Then, a coastal flood resilience management system for smart cities is proposed, covering 3 phases of flood disasters (before, during, and after). Risk assessment is essential in pre-disaster scenarios because it provides a comprehensive understanding of the potential hazards and vulnerabilities of an area or system. Big data monitoring during disasters is an essential component of effective emergency management and response that can allow for more informed decisions and thus quicker, more effective responses to disasters, ultimately saving lives and minimizing damage. Data-informed loss assessments are crucial in providing a rapid, accurate understanding of post-disaster impact. This understanding, in turn, is instrumental in expediting recovery and reconstruction efforts by aiding decision-making processes and resource allocation. Finally, the impacts of climate change on the 3 phases of flood disasters are summarized. The development of more resilient coastal communities that are better equipped to withstand the impacts of climate change and adapt to changing environmental conditions is crucial. To address coastal compound floods, researchers should focus on understanding trigging factor interactions, assessing economic and social risks, improving monitoring and emergency response systems, and promoting interdisciplinary research with data openness. These strategies will enable comprehensive and holistic management of coastal flood risks in the context of climate change.

Flood risk analysis integrating of Bayesian-based time-varying model and expected annual damage considering non-stationarity and uncertainty in the coastal city

Flood disaster is more serious in coastal cities due to the combined impact of rainfall and tides. Accurate assessment of coastal flood risk is essential for planning effective and targeted adaptation under changing environment. The objective of the study is to propose an integrated framework for future flood risk assessment by Bayesian-based time-varying model and expected annual damage (EAD) in the coastal city. To decrease the uncertainty of non-stationary frequency, Bayesian Model Averaging (BMA) and time-varying parameter distribution (TVPD) models were employed to establish the non-stationary distributions of rainfall and tides, and copula function was adopted to determine the joint and co-occurrence probability. Subsequently, to reflect flooding probability and inundation damage simultaneously, the EAD was applied to quantify flood risk by copula function and hydrodynamic model. The variation and uncertainty of flood risk were also investigated under changing environment. Taking Haidian Island in Hainan Province, China as a case study, the results show that the non-stationary distributions of rainfall and tides can be appropriately derived based on BMA and TVPD models. The joint and co-occurrence probabilities increase significantly under non-stationary scenario with the average rates of 33.22 % and 64.82 %, respectively. Moreover, the EAD will be underestimated by 20.56 %–69.84 % in 2030–2060 year without considering non-stationarity, and the uncertainty of EAD rises with the increase of the design year. The approach and result of our study can help decision makers evaluate the future flood risk in the coastal city, and provide support for sustainable flood management to adapt the climate change.

Field observations and numerical modeling of swash motions at an engineered embayed beach under moderate to energetic conditions

Wave run-up is a key factor for the assessment of coastal flood risk during large swell events. The behavior of swash motions along embayed beaches is strongly influenced by particular features such as rocky headlands, irregular morphology, or tides, that have not received much attention in the past. In the present paper, swash motions along an engineered embayed beach were investigated under moderate to energetic wave conditions through field observations and numerical computations. Using pressure sensors and video-derived run-up data a Boussinesq-type nearshore wave model was found to capture the relevant wave transformation and swash zone processes reasonably well. The tidal water level variations were found to have a significant impact on the overall swash amplitudes and to condition the respective role of short- and long-wave components to the swash motions. In the studied configuration, the tidal level rise was shown to cause a transition from a very dissipative to a reflective or intermediate beach configuration depending on incident wave energy. Moreover, swash amplitudes were observed to vary significantly alongshore with differences of up to a factor of 2.2. Numerical results suggest that both the foreshore slope and the sub-tidal morphology play a key-role in the alongshore swash distribution.

Detection of marine oil spills from radar satellite images for the coastal ecological risk assessment

Coastal ecosystems offer substantial support and space for the sustainable development of human society, and hence the ecological risk evaluation of coastal ecosystems is of great significance. In this article, we propose an innovative framework for evaluating coastal ecological risk by considering oil spill risk information and environmental vulnerability information. Specifically, a deep learning based marine oil spill monitoring method is presented to obtain the oil spill risk information from Sentinel-1 polarimetric synthetic aperture radar (PolSAR) images. The environmental vulnerability information is then obtained from biological sample data and habitat information. Finally, a weighted probability model is introduced to utilize the oil spill risk and environmental vulnerability information, to evaluate the coastal ecological risk. In the experimental part, the proposed oil spill monitoring method shows its reliability in global ocean areas, and the proposed model is adopted to evaluate the ecological risk in Jiaozhou Bay, China. The results show that the ecological situation of more than half of the areas in Jiaozhou Bay is unstable, and the areas with high risk are mainly concentrated in the ports, shipping channels, and those areas with high biodiversity. This study provides some new perspectives on ecological risk assessment for coastal ecosystems, facilitating the planning process and the actions to be taken in response to the accidents that occur in the ocean, especially oil spill accidents.

Forecasts of the Compound Coastal Erosion Risks Based on Time-Variant Assessment: A Case Study on Yunlin Coast, Taiwan

A coastal erosion risk assessment was framed as the basis for the intervention of coastal adaptation strategies under time-variant scenarios. The framework was devised to assess the influence of coastal erosion on coastal defense, the coastal inundation induced by the erosion-induced malfunction of defense, and risks using a downscaling analysis and the mechanism of the compound hazard interaction, which are innovative and practical for the application of coastal management in Taiwan. In addition, the vulnerable socio-economy was also taken into consideration in risk assessment. The adaptive strategy is proposed in terms of the risk origins and time-variance of risk forecasts, and the risk origins were assessed based on the Fuzzy Delphi Method and the analytic hierarchy process instead of subjective consideration. Within the erosion-induced hazard and risk assessments, this study considered erosion rates, decreases in defense elevation due to land subsidence, and population development in time-variant scenario analyses to estimate risk forecasts. Furthermore, a case study of the Yunlin coastal area was undertaken to demonstrate the feasibility of the proposed method. The presented results are informative for coastal hazard reduction and the promotion of the sustainable development of coastal zones.

Improving interpretation of sea-level projections through a machine-learning-based local explanation approach

Abstract. Process-based projections of the sea-level contribution from land ice components are often obtained from simulations using a complex chain of numerical models. Because of their importance in supporting the decision-making process for coastal risk assessment and adaptation, improving the interpretability of these projections is of great interest. To this end, we adopt the local attribution approach developed in the machine learning community known as “SHAP” (SHapley Additive exPlanations). We apply our methodology to a subset of the multi-model ensemble study of the future contribution of the Greenland ice sheet to sea level, taking into account different modelling choices related to (1) numerical implementation, (2) initial conditions, (3) modelling of ice-sheet processes, and (4) environmental forcing. This allows us to quantify the influence of particular modelling decisions, which is directly expressed in terms of sea-level change contribution. This type of diagnosis can be performed on any member of the ensemble, and we show in the Greenland case how the aggregation of the local attribution analyses can help guide future model development as well as scientific interpretation, particularly with regard to spatial model resolution and to retreat parametrisation.

Editorial: Innovations in coastal erosion risk assessment and mitigation

Editorial: Innovations in coastal erosion risk assessment and mitigation

Integrated coastal subsidence analysis using InSAR, LiDAR, and land cover data

Land subsidence is an important cause of relative sea-level rise along the Gulf Coast. There is a lack of effective monitoring of coastal subsidence with high accuracy and high spatial resolution for improving coastal risk assessment and mitigation. This study is the first attempt to integrate satellite interferometric synthetic aperture radar (InSAR) and airborne light detection and ranging (LiDAR) methods to investigate the spatiotemporal pattern of coastal subsidence. The study area is around Eagle Point, Texas, a region known for its fast rate of relative sea-level rise in recent decades. From 2006 to 2011, the line-of-sight velocities were up to −33 mm/year based on ascending ALOS-1 PALSAR-1 images. From 2016 to 2021, the vertical velocities were up to −34 mm/year based on ascending and descending Sentinel-1 images. Additional details of the subsidence pattern were revealed by incorporating the surface difference derived from 1-m airborne LiDAR results. Comparisons of the InSAR-derived velocities from image time series and the LiDAR-derived surface changes from time-lapsed observations were conducted at different spatial levels with linkages to land cover patterns and topography. The results showed that local subsidence rates could vary significantly below the spatial resolution of InSAR results, indicating a valuable role of airborne LiDAR results in extending InSAR results to parcel and building levels and explaining subpixel uncertainties. Also, subsidence appeared to be stronger in vegetated areas than in developed areas and negatively correlated with surface imperviousness. The magnitude of subsidence was not correlated with elevation along selected transect lines. Overall, this study demonstrated the benefits of combining InSAR results with other geospatial datasets to characterize coastal subsidence. In particular, the high vertical accuracy of InSAR results and the high spatial resolution of airborne LiDAR results could be complementary, highlighting the necessity of multi-resolution data fusion to support studies on coastal flood vulnerability, infrastructure reliability, and erosion control.

Spatial assessment of coastal flood risk due to sea level rise in China’s coastal zone through the 21st century

Among the climate change-induced threats to coastal regions, coastal flooding caused by sea level rise (SLR) is considered one of the most serious and presents an intensifying trend over time. The negative impacts and risks associated with coastal flooding are difficult to visualize spatially and cause great inconvenience to policy-makers in understanding the distribution of different risk levels and developing adaptation policies. Our study proposes a framework for coastal flood risk (CFR) based on the hazard, exposure & sensitivity, and adaptive capacity of China’s coastal zone (CCZ) and maps the spatial distribution of CFR by GIS in 2030, 2050, and 2100 under RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP5, respectively. Our results reveal that (1) low-lying coastal areas with densely populated, economically developed, or industrially diverse are faced with serious CFRs, such as the Yellow River Delta, the Yangtze River Delta, the Pearl River Delta, and the coastal areas in Jiangsu. (2) The area of “Very high” CFR level in the CCZ reaches a peak of 44.10×103 km2 in 2100 under RCP8.5-SSP5. And under the higher emission scenario, the areas of five CFR levels would change dramatically in the future. (3) The coastal area of Guangdong is significantly faced with the massive expected population and GDP affected due to CFR among scenarios and years. (4) As threatened by CFR mostly, built-up and farmland are particularly required to guard against the negative impact of coastal flooding, especially in Guangdong and Jiangsu. Results in this study are expected to provide the intuitive information and basis for governments, policy-makers, and local communities in addressing the increased CFR over the CCZ. Besides, our framework of CFR and methodology are flexible and can be adapted for other countries facing the threat of SLR.

Enhancing a Coastal Territorial Vulnerability Index: Anticipating the Impacts of Coastal Flooding with a Local Scale Approach

The coastal zone of mainland Portugal is characterized by its morpho-sedimentary diversity such as estuaries, lagoons, barrier islands, beaches, dunes and cliffs. The high population density and the multiplicity of land use, occupation and activities, makes it an area of great national strategic value. This transforms the coastal zone into a multi-hazard zone, where the occurrences related to coastal flooding and overtopping stand out. In the present work, a multidimensional methodology called Coastal Territorial Vulnerability Index (CTVI) was developed and applied in three selected areas with a historical record of coastal impacts, to analyze, evaluate and interpret the local vulnerability. The methodology considers four components of coastal territorial vulnerability: morphology, land value, buildings and public areas characteristics. These four components are combined to calculate the CTVI. The results highlight the differences for the analyzed areas, allowing the differentiation of natural and artificial areas. In the natural areas a moderate CTVI predominates, while in the latter, a high and very high CTVI stands out. The results contribute to the development of a comprehensive coastal flood risk assessment and forecasting the impacts.

Spatio-temporal analysis of shoreline changes and erosion risk assessment along Jerba island (Tunisia) based on remote-sensing data and geospatial tools

Jerba, the largest island in North Africa, is among the most exposed islands to the combined effects of climate change and human activities in the Mediterranean. The eastern coast of Jerba island has long been subject to intense tourist development due to its attractive natural potential. The main issue addressed in this study concerns the evolutionary trends of Jerba shoreline and the assessment of coastal erosion risk. This work focuses on the historical study of the shoreline positions based on satellite data processing. The use of geographic information system (GIS) and the Digital Shoreline Analysis System (DSAS) geostatistical calculation tool allowed for the identification of erosion and accretion rates and the description of evolutionary trends across 32 years covering three periods (1989–1999, 1999–2009, and 2009–2021). The results show an overall erosion trend (89% of the coastline). The statistics revealed that the study area, with the exception of the sandy spit, has experienced an intense and alarming level of erosion between 1989 and 1999, with an end point rate (EPR) equal to −4.12 m/year. This net rate continued to be negative in the period between 1999 and 2009, with an EPR of −0.5 m/year. Between 2009 and 2021, the net rate of shoreline change is marked by slight accretion, at an average of +1.4 m/year. The overall long-term trend shows the shoreline is eroding, with an average linear regression rate of −1.38 m/year for the whole period. This trend is due to a combination of the cumulative effects of sea-level rise and anthropogenic actions. Therefore, sustainable management strategies need to be implemented urgently by coastal managers to restore the sectors that are susceptible to further erosion.

Characterizing multivariate coastal flooding events in a semi-arid region: the implications of copula choice, sampling, and infrastructure

Abstract. Multivariate coastal flooding is characterized by multiple flooding pathways (i.e., high offshore water levels, streamflow, energetic waves, precipitation) acting concurrently. This study explores the joint risks caused by the co-occurrence of high marine water levels and precipitation in a highly urbanized semi-arid, tidally dominated region. A novel structural function developed from the multivariate analysis is proposed to consider the implications of flood control infrastructure in multivariate coastal flood risk assessments. Univariate statistics are analyzed for individual sites and events. Conditional and joint probabilities are developed using a range of copulas, sampling methods, and hazard scenarios. The Nelsen, BB1, BB5, and Roch–Alegre were selected based on a Cramér–von Mises test and generally produced robust results across a range of sampling methods. The impacts of sampling are considered using annual maximum, annual coinciding, wet-season monthly maximum, and wet-season monthly coinciding sampling. Although annual maximum sampling is commonly used for characterizing multivariate events, this work suggests annual maximum sampling may substantially underestimate marine water levels for extreme events. Water level and precipitation combinations from wet-season monthly coinciding sampling benefit from a dramatic increase in data pairs and provide a range of physically realistic pairs. Wet-season monthly coinciding sampling may provide a more accurate multivariate flooding risk characterization for long return periods in semi-arid regions. Univariate, conditional, and bivariate results emphasize the importance of proper event definition as this significantly influences the associated event risks.

A global ensemble of ocean wave climate statistics from contemporary wave reanalysis and hindcasts

There are numerous global ocean wave reanalysis and hindcast products currently being distributed and used across different scientific fields. However, there is not a consistent dataset that can sample across all existing products based on a standardized framework. Here, we present and describe the first coordinated multi-product ensemble of present-day global wave fields available to date. This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, includes general and extreme statistics of significant wave height (Hs), mean wave period (Tm) and mean wave direction (θm) computed across 1980–2014, at different frequency resolutions (monthly, seasonally, and annually). This coordinated global ensemble has been derived from fourteen state-of-the-science global wave products obtained from different atmospheric reanalysis forcing and downscaling methods. This data set has been processed, under a specific framework for consistency and quality, following standard Data Reference Syntax, Directory Structures and Metadata specifications. This new comprehensive dataset provides support to future broad-scale analysis of historical wave climatology and variability as well as coastal risk and vulnerability assessments across offshore and coastal engineering applications.

Generating reliable estimates of tropical-cyclone-induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks

Abstract. Deriving reliable estimates of design water levels and wave conditions resulting from tropical cyclones is a challenging problem of high relevance for, among other things, coastal and offshore engineering projects and risk assessment studies. Tropical cyclone geometry and wind speeds have been recorded for the past few decades only, thus resulting in poorly reliable estimates of the extremes, especially in regions characterized by a low number of past tropical cyclone events. In this paper, this challenge is overcome by using synthetic tropical cyclone tracks and wind fields generated by the open-source tool TCWiSE (Tropical Cyclone Wind Statistical Estimation Tool) to create thousands of realizations representative of 1000 years of tropical cyclone activity for the Bay of Bengal. Each of these realizations is used to force coupled storm surge and wave simulations by means of the processed-based Delft3D Flexible Mesh Suite. It is shown that the use of synthetic tracks provides reliable estimates of the statistics of the first-order hazard (i.e., wind speed) compared to the statistics derived for historical tropical cyclones. Based on estimated wind fields, second-order hazards (i.e., storm surge and waves) are computed that are generated by the first-order hazard of wind. The estimates of the extreme values derived for wind speed, wave height and storm surge are shown to converge within the 1000 years of simulated cyclone tracks. Comparing second-order hazard estimates based on historical and synthetic tracks shows that, for this case study, the use of historical tracks (a deterministic approach) leads to an underestimation of the mean computed storm surge of up to −30 %. Differences between the use of synthetic versus historical tracks are characterized by a large spatial variability along the Bay of Bengal, where regions with a lower probability of occurrence of tropical cyclones show the largest difference in predicted storm surge and wave heights. In addition, the use of historical tracks leads to much larger uncertainty bands in the estimation of both storm surges and wave heights, with confidence intervals being +80 % larger compared to those estimated by using synthetic tracks (probabilistic approach). Based on the same tropical cyclone realizations, the effect that changes in tropical cyclone frequency and intensity, possibly resulting from climate change, may have on modeled storm surge and wave heights was computed. As a proof of concept, an increase in tropical cyclone frequency of +25.6 % and wind intensity of +1.6 %, based on literature values and without accounting for uncertainties in future climate projection, was estimated to possibly result in an increase in storm surge and wave heights of +11 % and +9 %, respectively. This suggests that climate change could increase tropical-cyclone-induced coastal hazards more than just the actual increase in maximum wind speeds.

Future water levels of the Great Lakes under 1.5 °C to 3 °C warmer climates

With the many different interests that are connected to the water levels of the Laurentian Great Lakes, the future of these water levels are of great concern to many people, businesses, and institutions. Projected future lake levels were calculated using data from the North American component of the Coordinated Regional Downscaling Experiment. The final lake level results are presented in relation to a 1.5 °C, 2.0 °C, 2.5 °C, and 3.0 °C change in global mean temperature. The results show that the range of possible values grows as the climate changes, with more extreme values for the lake levels becoming possible with greater changes in the global mean temperature. This increase in the range on both the high and low end may be a more important consideration than any general increase in the average water level for those living around the lakes Because the most severe impacts on the interests around the lake are usually associated with these extreme high or low levels. A greater understanding that the extremes in water levels observed in the past may be exceeded under a changing climate will help in the planning of future developments and activities within the Great Lakes basin with a forward looking coastal risk assessment and help communites build resilience to future extremes.

EMPOWERING GEO-BASED AI ALGORITHM TO AID COASTAL FLOOD RISK ANALYSIS: A REVIEW AND FRAMEWORK DEVELOPMENT

Abstract. Climate change and current susceptibilities exacerbated the coastal flood loss and damage resulting in livelihoods and property damage. Urban areas in the Low to Lower-Middle Income Countries are expected to be disproportionately impacted by the disaster, given a higher share of citizens living in the Low Elevation Coastal Zone, limited financial resources, and poorly constructed disaster protection. Documentation of historical coastal floods, population, and property affected, could advance the assessment by considering those parameters in risk analysis. Besides, incorporating such geographic features e.g., mangroves as the ecological solution for alternative coastal flood protection in the prediction is also essential. Mangrove is considered fit for the LLMIC primarily situated in the tropical zone. The prediction utilizing spatial Machine Learning (ML) could aid climate-related disaster risk analysis and contribute to risk reduction and policy suggestions to improve disaster resilience. The research aims to archive recent studies on the application of geospatial science empowering Artificial Intelligence, notably ML in coastal flood risk assessment, so-called GIS-based AI. Another aim is to document population, property, and mangrove distribution across the LLMIC. Artificial Neural Networks were mostly utilized for disaster risk assessment in past research. The number of 58 historical coastal flood events and 908 expected coastal flood hotspots for 2006 to 2021 has been documented. Over 1,2 million Km2 falls under vulnerable areas toward coastal flood in LLMIC under different settlement types where Large City (urban areas) dominates it. Mangrove distribution is mainly distributed across tropical regions mostly distributed along the Southeast Asia coast.

Coastal erosion risk assessment and applied mitigation measures at Ezbet Elborg village, Egyptian delta

Coastal areas globally suffer from serious erosion and sedimentation problems. The assessment of coastal erosion risk is fundamental for better coastal management. The main goal of the current study was to enhance coastal management by identifying the coastal erosion risk and suggest possible mitigation measures. The study was developed for the Egyptian Northern Coast. MIKE 21 was used to evaluate the erosion and test the proposed measures. First, a bathymetric survey of the study area was conducted. Then the tidal data, storm surge, and global sea rise data were collected. The offshore wave data, shoreline data were compiled. And then, the different nearshore wave modeling and wave propagation were modeled. The model was calibrated using measured data in 2014 and validated using measured data in 2016. Future shoreline changes for the years 2029, 2039, and 2049 were depicted. A suitable mitigation measure was used and tested for the same years. The groins with a specific size and arrangement were tested. The main goal of the study is to find a suitable solution to protect the coast and investigate its impact on the shoreline in the next 30 years. The proposed mitigation measure minimized the coastal erosion of the study area rigorously.

The proposal of the Coast-RiskBySea: COASTal zones RISK assessment for Built environment bY extreme SEA level, based on the new Copernicus Coastal Zones data

Natural hazards and the long-term effects of climate change could have disastrous consequences for urban settlements, in this context, risk assessment is a key factor in supporting urban designers and planners. The research proposes a simplified model for the assessment of coastal risk on built environment expressed in terms of potential, direct and tangible, economic damages. The Coast-RiskBySea (COASTal zones RISK assessment for Built environment bY extreme SEA level) is developed in GIS and the risk is evaluated based on the IPCC AR5 and AR6 frameworks through an equation that combines exposure, vulnerability and hazard. The first step is the creation of the land use grid derived from the Copernicus Coastal Zones database, after which then damage scenarios are calculated using depth-damage functions that enable to translate climate impacts into economic damages (exposure). Vulnerability is then assessed in relation to coastal elevation through the DTM and, finally, climate projections of Extreme Sea Level (ESL) are introduced (hazard). To test and verify the approach, the model is applied to the metropolitan city of Naples, Italy, the results show multiple areas at medium and high risk for ESL events with significant economic impacts. From the output maps it is possible to identify the higher risk areas and to understand how, with significant mitigation actions, the risks could be significantly reduced. In conclusion, the Coast-RiskBySea, as it has been developed with input data characterised by a homogeneous spatial coverage, it can be replicable in all EU coastal zones that dispose of a DTM with adequate vertical accuracy.