Extreme Sea Levels Research Articles

Statistical Analysis of Extreme Sea Levels in the Red Sea

Coastal areas frequently experience high-sea-level events that can cause property damage and loss of life. From a coastal engineering perspective, accurately estimating the probability of extreme sea levels is crucial for designing robust coastal structures. While these estimates are typically based on long-term tide-gauge observations, sea-level hindcasts are also used when available data are limited. This study obtained estimates of extreme sea-level probabilities for the Red Sea, utilizing a 30-year dataset (1993–2022) containing hourly sea level records reconstructed using a surge model (MOG2D), a global tide model (FES2014), and sea-level anomaly data from satellite altimeters. The reconstructed sea-level data were validated against measurements from six tide gauges along the eastern Red Sea and demonstrated good agreement. Spatial maps of extreme sea levels indicated values ranging from approximately 0.4 to 1.8 m. The highest estimates for the 100-year return level were found in the northern Gulf of Suez, with elevated values also observed in the northern and southern Red Sea. Furthermore, we assessed the impact of sea-level rise on extreme sea-level probabilities. Our results revealed that even small increments in the mean sea level could lead to considerable changes in extreme sea-level probabilities across most of the Red Sea. Overall, our findings are valuable for various coastal development projects along the Red Sea coast and for improving coastal vulnerability assessments.

Spatially seamless and temporally continuous assessment on compound flood risk in Hong Kong

Compound flooding results from the simultaneous occurrence of extreme storm surges, sea level rise, and heavy rainfall. These events often lead to impacts significantly more severe than those caused by any individual flood-inducing factor alone. However, the limited and sparse data from tidal gauges hampers precise risk assessment at ungauged sites in coastal cities. Our study addresses this gap by integrating ensemble machine learning with Bayesian inference, offering a comprehensive spatial–temporal analysis of compound flood risk from 1979 to 2022 in Hong Kong. We developed an ensemble machine learning approach within the Bayesian hierarchical modeling framework to achieve spatial–temporal continuity in the estimation of extreme storm surges and mean sea level at sites without tidal gauge stations. Results show a significant yearly increase in maximum storm surge levels by 3 mm and a significant rise in mean sea level of 25 mm per decade in Hong Kong. Our analysis also indicates a significant increase in daily heavy rainfall intensity. Furthermore, in 14.54 % of cases, extreme storm surges coincided with heavy rainfall, while 13.69 % of heavy rainfall events occurred alongside extreme sea level conditions. The copula-based joint analysis reveals significant positive correlations among these extreme events. Our findings further reveal that the return level for a 100-year heavy rainfall event increases dramatically from 126.36 mm in the univariate case to 261.16 mm in the trivariate scenario, underlining the escalated risk associated with compound flooding. Similarly, for storm surge extremes, trivariate analysis reveals elevated risk during compound flood events, with the return level rising from 1.18 m (univariate scenario) to 1.40 m (trivariate scenario) for a 100-year return period. These spatial–temporal maps and comprehensive compound flood risk assessments offer crucial insights for addressing the multi-hazard flood risk in coastal urban areas.

Extending the blended generalized extreme value distribution

AbstractThe generalized extreme value (GEV) distribution is commonly employed to help estimate the likelihood of extreme events in many geophysical and other application areas. The recently proposed blended generalized extreme value (bGEV) distribution modifies the GEV with positive shape parameter to avoid a hard lower bound that complicates fitting and inference. Here, the bGEV is extended to the GEV with negative shape parameter, avoiding a hard upper bound that is unrealistic in many applications. This extended bGEV is shown to improve on the GEV for forecasting heat and sea level extremes based on past data. Software implementing this bGEV and applying it to the example temperature and sea level data is provided.

Substantial reduction in population exposure to sea level changes along the Chinese mainland coast through emission mitigation

Abstract Rising sea level increases the exposure to flooding and related damage in coastal areas with high population density and substantial economic activity. As global temperatures continue to rise due to climate change, sea levels have been consistently increasing and are projected to continue this upward trend. This study assesses the future exposure at provincial and city levels populations coastal mainland China coast to local sea level changes under five greenhouse gas (GHG) emission scenarios from IPCC-AR6, as well as two low-confidence scenarios accounting for the potential impact of uncertain ice sheet processes with low- and high-GHG emissions. We incorporate spatial heterogeneity into regional sea level projections and population projections from 2020 to 2100, extreme sea levels (ESLs) of 10-, 50-, and 100 year return periods (RP), and local coastal protection standards. Our findings indicate that the inundated areas expand continuously within the century with heightened exposure under higher emission scenarios. Although the coastal population is projected to decline, the fraction of the coastal population exposed to flooding increases across all scenarios, with accelerated growth under higher GHG emissions and higher ESLs. Zhejiang and Jiangsu emerge as the provinces most exposed to sea-level rise, whereas Taizhou, Nantong, Wuxi, Panjin, and Huzhou are identified as the top five cities with the highest population exposure to local sea level rise (SLR). Transitioning towards a sustainable scenario (i.e. SSP1-2.6) rather than a fossil fuel-intensive one (i.e. SSP5-8.5) can reduce the local SLR and substantially mitigate these exposures. Compared to the median projections under SSP5-8.5, aligning GHG emissions with SSP1-2.6 could reduce population exposure substantially in all coastal provinces, especially in Jiangsu, where population exposure to 100 year RP coastal floods would be reduced by ∼1.6 M in 2050 and by ∼5.4 M in 2100.

Spatial inhomogeneity analyses of extreme sea levels along Lianyungang coast based on numerical simulation and Monte Carlo model

The prediction of extreme sea levels in coastal areas has been a prominent requirement of coastal engineering design and planning. The research was based on the frequent disasters caused by typhoon storm surges in China and the demand for reasonable improvement of port structures in coastal areas. Delft3D hydrodynamic model and the Monte Carlo model were integrated to predict the spatial distribution of extreme sea levels along the coast of Lianyungang city in Jiangsu province. The Monte Carlo model was adopted to generate stochastic typhoon characteristic sequences as meteorological driving forces. In the hydrodynamic model, meteorological factors were coupled with the astronomical tide to simulate storm surge scenarios. Extreme sea levels were estimated by ranking maximum levels of simulated storm surge events. This programming was feasible compared to the prediction results of frequency analysis methods. The research showed that the predicted 1 in 100-year extreme sea level of Lianyungang station was 3.75 m, while the predicted 1 in 200-year value was 3.85 m. The programming was adopted to predict extreme sea levels of several stations and analyze the spatial distribution characteristics of extreme sea levels in four port areas of Lianyungang Port. Results revealed that extreme sea levels in modeling areas had significant spatial inhomogeneity, which might be related to the concavity of coastlines, islands, and terrains. Estimating spatial extreme sea levels could better cognize the influence of inundation disasters in coastal areas and be conducive to the design of port structures and the safety of port operations.

Predicting storm surge extremes on the Southeast Brazilian Coast: Long-term projections with neural networks

Extreme sea level events, resulting from the confluence of tides and storm surges, pose a significant threat to coastal populations and economies. The escalating risks associated with these events are exacerbated by climate change, manifesting in heightened storm intensity, increased frequency, and rising sea levels. Precise estimation of the probability of extreme storm surges is crucial for effective coastal management and adaptation. However, utilizing historical storm data is challenging due to data scarcity and the imperative to consider potential non-stationarity induced by climate change in predicting such events. This study addresses these challenges by introducing two neural network-based machine learning systems: a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM). Leveraging local and remote atmospheric and oceanic conditions, these systems project storm surges until 2060, incorporating climate projections. Trained and evaluated using sea level data from Imbetiba Port in Macaé, Rio de Janeiro, Brazil, the models utilize dynamic regionalization data from the RegCM4 and WW3 models, forced by HadGEM2-ES and MPI climate models. Both neural network models exhibited similar performance patterns, demonstrating high agreement in predicting storm surge heights with a 100-year return value, based on Imbetiba Port data. Projections utilized peaks-over-threshold (POT) methods, and extremes were calculated using a generalized Pareto distribution (GPD). Long-term projections indicated a 28 % increase (MLP ANN) and a substantial 70 % increase (LSTM RNN) in estimating extreme values, surpassing the observed storm surge of 0.67 m. Projected mean values were 0.86 m for the MLP network and 1.15 m for the LSTM network, providing valuable insights into the potential amplification of extreme sea level events in the studied region.

Securitization of local adaptation: Signs of riskification in a Dutch region

AbstractClimate change is increasing the frequency of extreme weather events and sea level rise, causing scholars to argue that it has become a security issue and that extraordinary measures are being legitimized by its perceived threat, referred to as securitization. Securitization of climate change has so far been studied predominantly in an international and occasionally national context. Little is known on how security dynamics play out on the local, subnational level. To address this knowledge gap, we investigated if and how adaptation to climate change (CCA) has been securitized for the water domain in a region in the Netherlands that is a CCA frontrunner: Dordrecht. Our case study comprised a detailed document analysis complemented with eight policymaker/expert interviews. In the discourses, actors and tools employed in the governance of CCA in Dordrecht we found signs of securitization in the form of riskification: risk terminology being used in CCA governance; greater involvement of risk and safety actors; and the development of new risk tools like risk and vulnerability analyses. However, securitization remains largely absent: CCA continues to be dealt with as a shared responsibility and as a cross‐sectoral policy issue to be integrated and mainstreamed to become the “new normal.”

Numerical Modeling of Extreme Sea Levels on the Laptev Sea Coast

The present study is devoted to the analysis of extreme sea level oscillations of the Laptev Sea using the ADCIRC model. The numerical modeling is performed on a high-resolution grid and verified for sea level observations from three tide gauges. We have revealed regional characteristics of extreme sea level oscillations for different parts of the Laptev Sea coast. The maximum total sea level range was 544 cm in Ebelyakh Bay, while the minimum was 267 cm in Khatanga Bay, where maximum tidal ranges were obtained. Some areas in Khatanga Bay and Anabar Bay had maximum tidal ranges exceeding 200 cm. The study provided an estimation of the possible magnitude of coastal flooding by calculating the extreme total and residual sea levels for different return periods: 1, 2, 5, 10, 20, 50, and 100 years. The amplitude of extreme surges calculated for the 100-year return period can exceed 300 cm for several sections of the Laptev Sea coast, with the maximum sea level range being about 680 cm for Anabar and Ebelyakh Bays.

Storm surge modelling along European coastlines: The effect of the spatio-temporal resolution of the atmospheric forcing

The spatio-temporal resolution of atmospheric forcing plays a key role in the accuracy of simulated storm surges with hydrodynamic numerical models. Here, we generate five hydrodynamic hindcasts of coastal storm surges along the European Atlantic and the Mediterranean Sea coasts, forced with atmospheric fields of varying temporal (hourly and daily) and spatial (0.25° to 2°) resolution since 1940. Our results, that are validated with insitu tide gauge observations, show that storm surges obtained with daily forcing underestimate the magnitude of coastal extreme sea level events by up to 50% compared to hourly simulations and observations. Nevertheless, low-resolution simulations capture the temporal variability of storm surges, including strong episodes. Furthermore, taking advantage of the consistent set of coastal storm surge hindcasts, we demonstrate that storm surges forced with daily mean atmospheric fields, when bias corrected via quantile mapping, provide accurate values of daily maxima as calculated by a high-resolution hindcast. This transformation paves the way to obtain daily maxima storm surge estimates from low-resolution atmospheric fields, as those typically provided by large-scale and global climate models, at a lower computational cost.

Intraseasonal Variations and Extreme Occurrence in the Local Sea Level Along the Western Coast of India Remotely Controlled by a Basin‐Scale Climate Variability

AbstractThe equatorial Kelvin waves, remotely excited by basin‐scale climate modes, and subsequent coastal trapped waves significantly influence the intraseasonal variations, their low‐frequency modulations, and the frequency of extreme sea level events along the western coast of India. This study demonstrates that the frequency of extreme events are linked to the phase of the Indian Ocean Dipole mode. The temporal changes in the occurrence frequency of extremes are simulated in an eddy‐resolving ocean model consistently with observations. However, a non‐eddying model significantly underestimate the occurrence frequency of extreme sea level events, suggesting the importance of coastal trapped wave propagations regulated by the horizontal scale with the Rossby radius of deformation. This result implies that many state‐of‐the‐art climate models with a one‐degree ocean horizontal resolution may underestimate future coastal sea level variability and the frequency of extreme events under global warming and potential modulations of major internal climate modes.

Extreme sea levels and floods: the case study of Klaipėda City, Lithuania

Klaipėda City is located in the Southeast Baltic Sea region, where sea level rise has been observed for decades. The Klaipėda Strait, which separates the Baltic Sea and the Curonian Lagoon, where the port is located, is also more prone to sudden extreme changes in water levels, usually caused by windstorms. Extreme sea level changes pose a threat to port operations, technical structures, city residents, buildings, and infrastructure. Fluctuations in sea levels also affect the water level of the Danė River, which enters the Klaipėda Strait and divides the city into two parts. Therefore, this study aims to determine the past extreme sea level events and their influence on floods in the Danė River within Klaipėda City from 1961 to 2022. For this, the impact of meteorological parameters caused dangerous sea level rises in the Klaipėda Strait, and the following rise of the Danė River was studied. The results show that the annual mean, annual mean maximum, and annual mean minimum water levels in the Klaipėda Strait increased from 1961 to 2022. Also, the number of events where the water level in the Klaipėda Strait was ≥ 100 cm in the Baltic Elevation System was increasing. The increasing frequency of extreme water level events in the Klaipėda Strait puts urban areas at greater risk from Danė River compound floods.

Mean sea level, tidal components and surges in Guanabara Bay (Rio de Janeiro) from 1990 to 2021

AbstractGuanabara Bay, located in the Metropolitan Region of Rio de Janeiro, one of the largest urban coastal areas in the Southern Hemisphere, is subject to intense maritime traffic due to the presence of several ports. These facilities are affected by sea level fluctuations, influenced by atmospheric and astronomical forces, which motivates synoptic and climatological analysis, including meteorological and astronomical tidal phenomena. This study aimed to assess the evolution of tidal components and the relative mean sea level (MSL) between 1990 and 2021, as well as the atmospheric influence on extreme meteorological tide events, in which the MSL exceeded ±2 and ±3 (±29.98 and ±44.97 cm) standard deviations. The results have shown that, albeit small, the main tidal components (, , , , , , and ) indicated positive trends in the increase of amplitude and phase, despite the small quantities. For the relative MSL, an increase of 0.30 cm·year−1 was observed in agreement with estimates from global series from satellite altimeters and climate model predictions. Pressure and wind field analyses led to the identification of seasonal variability and the pattern of evolution of atmospheric systems associated with extreme events. In the case of positive surges, very intense winds from the SW, extending along the S/SE Brazilian coast, are caused by the presence of a high‐pressure centre over the continent and low‐pressure centres over the ocean, leading to water piling up. In turn, negative surges are caused by the presence of an anticyclone over the ocean, generating NE winds parallel to the coast of Rio de Janeiro, inducing Ekman transport and lowering the sea level. Therefore, the extreme sea level events in the Guanabara Bay are not triggered by local forces, but rather depend on the temporal persistence and direction of winds along the S/SE Brazilian coast.

Global conservation priorities of coastal habitats towards extreme sea level rise risks

Approximately 70% of the world's largest megacities (>10 million people) are within 60 km of the coast, contributing to 50% of the world's GDP. Sea level rise will intensify coastal flood threats, making cities without sufficient habitat protection even more vulnerable. However, there is currently a lack of thorough evaluation at the global scale about conservation priorities based on the current habitat cover and the high sea level rise in order to adapt coastal flood risks. Here we assessed the disparity among habitat coverage, exposed population and extreme sea level rise to identify priority areas for future coastal conservation. These priority areas are characterized by a large population residing in low-elevation coastal zones (LECZs), high extreme sea levels (ESLs), and insufficient habitat coverages. In many highly populated countries like China and India, the flooding risk has been underestimated due to insufficient adaptation based on natural processes from coastal habitats. These highly populated countries are especially concerning because they have comparatively less vegetation protection capability and are subjected to high ESLs. Globally, Asian and European nations, such as China, Japan, Bangladesh, Viet Nam, the United Kingdom, Belgium, Ireland, the Netherlands, and Germany, have the highest potential for mitigating coastal risks through natural habitat conservation and restoration. We found that the main contributors to variations in the extreme sea level rise risks are not due to exposure headcounts (6%), but due to inadequate habitat cover (42%), which can be improved through targeted conservation and restoration. Our research identified the global priorities for managing climate resilience that most urgently require ecosystem conservation and restoration in order to prepare for the risks of coastal flooding.

Uncovering effects of climate-sensitive health risks on historically marginalized youth in Washington State: creating opportunities for maximum involvement

Abstract Climate-sensitive hazards, including extreme heat, wildfire smoke, flooding, and rising sea levels, can jeopardize the health of all populations. However, certain population groups are more vulnerable to harm from these hazards. While youth–particularly those from historically marginalized groups and communities–are among those at the highest risk, their abilities may be underutilized in local climate adaptation efforts. This exploratory research aimed to identify opportunities for youth involvement in climate and environmental solutions in their historically marginalized communities. Specifically, this study (1) investigated youth risk perceptions and impacts of various climate-sensitive hazards (e.g. extreme heat, wildfire smoke, flooding, and sea level rise); (2) examined current barriers, resource needs, and opportunities for youth to engage in climate and environmental solution work; and (3) explored current methods and spaces where youth and community leaders can support youth-led climate-related initiatives. Through focus group discussions with youth and in-depth interviews with community leaders who are directly with youth, this study identified opportunities to leverage youth experiences, perceptions, and assets to promote a healthy and resilient community in the face of various compounding climate-related threats. Results showed that while youth recognize the changing climate and associated health impacts, they require more financial resources and support from local decision-makers to maintain their engagement and promote community resilience. Engaging youth in climate action and community resilience involves more than just centering youth voices and perspectives–it requires intentional collaboration, capacity-building, organizing, granting decision-making power, and other strategies to produce inclusive, intersectional, and sustainable solutions.

Seasonal amplitude of principal tidal constituents in shallow shelf regions

Understanding the seasonal variation (annual cycle) of tides is crucial for coastal management and the planning of coastal infrastructure, as well as pivotal for navigation safety, prediction of extreme sea levels and so on. Specifically, seasonal variations of M2 amplitudes, larger in summer and smaller in winter, have attracted widespread attention. Nevertheless, investigations into other tidal constituents, such as S2, K1, O1, remain comparatively sparse. Here, seasonal variations of the four principal tidal constituents are studied in the shallow shelf regions of the China Seas and the North Sea, both acknowledged for exhibiting the most pronounced seasonal modulations of M2 amplitude, attributable to vertical stratification. We perform a segmented Harmonic Analysis with moving average filter, to remove semi-annual cycle, at 25 tide gauge data and find a synchronized seasonal modulation of M2 and O1 in China, which can be explained by stratification. Whereas S2 manifest an inverse seasonal modulation, larger in winter and smaller in summer, attributed to the sun's varying proximity to the earth. The K1 tide displays two very different seasonal cycle, also influenced by astronomical modulation. This observed pattern basically persists in the North Sea, while O1 shows a negative seasonal amplitude but still exists high correlation coefficient with M2. Consequently, this study provides a deep insight into 4 principal tidal constituents' seasonal modulation and underlying mechanisms, thereby enhancing our ability to comprehend and predict water level extremes and flooding with greater precision.

Challenges, Advances and Opportunities in Regional Sea Level Projections: The Role of Ocean‐Shelf Dynamics

AbstractFuture sea level rise and changes in extreme weather will increase the frequency of flooding and intensify the risks for the millions of people living in low‐lying coastal areas. Concerns about coastal adaptation have been broadened due to societal awareness of the threat from rising seas, leading to a large set of potential adaptation users with diverse needs for adequate sea level projections in coastal areas beyond the current state of the art regional projections. In this paper, we provide an overview of the potential steps for improvement of regional sea level projections along the global coastline, with specific focus on the contribution from ocean dynamics to seasonal‐decadal variability of coastal sea level, and its implications for changes in frequency and magnitude of extreme sea levels. We discuss the key gaps in our knowledge and predictive capability of these dynamics as they relate to sea level variability on seasonal to decadal timescales, and conclude by suggesting ways in which these knowledge gaps could be addressed.

Global application of a regional frequency analysis to extreme sea levels

Abstract. Coastal regions face increasing threats from rising sea levels and extreme weather events, highlighting the urgent need for accurate assessments of coastal flood risk. This study presents a novel approach to estimating global extreme sea level (ESL) exceedance probabilities using a regional frequency analysis (RFA) approach. The research combines observed and modelled hindcast data to produce a high-resolution (∼1 km) dataset of ESL exceedance probabilities, including wave setup, along the entire global coastline (excluding Antarctica). The methodology presented in this paper is an extension of the regional framework of Sweet et al. (2022), with innovations introduced to incorporate wave setup and apply the method globally. Water level records from tide gauges and a global reanalysis of tide and surge levels are integrated with a global ocean wave reanalysis. Subsequently, these data are regionalised, normalised, and aggregated and then fit with a generalised Pareto distribution. The regional distributions are downscaled to the local scale using the tidal range at every location along the global coastline obtained from a global tide model. The results show 8 cm of positive bias at the 1-in-10-year return level when compared to individual tide gauges. The RFA approach offers several advantages over traditional methods, particularly in regions with limited observational data. It overcomes the challenge of short and incomplete observational records by substituting long historical records with a collection of shorter but spatially distributed records. These spatially distributed data not only retain the volume of information but also address the issue of sparse tide gauge coverage in less populated areas and developing nations. The RFA process is illustrated using Cyclone Yasi (2011) as a case study, demonstrating how the approach can improve the characterisation of ESLs in regions prone to tropical cyclone activity. In conclusion, this study provides a valuable resource for quantifying the global coastal flood risk, offering an innovative global methodology that can contribute to preparing for – and mitigating against – coastal flooding.

Vulnerability, Resilience and Adaptation of Lagos Coastal Communities to Flooding

Lagos has been identified as one of the 50 cities most vulnerable to extreme sea levels. The state also ranked 30th among 136 port cities in terms of population exposure to flooding under a past climate scenario (2005) and 15th under a future climate scenario (2070s). The state faces significant environmental strains as a result of its geo-economic status. Some of the problems are wetland loss, pollution, population pressure, restricted access to drinkable water, and flooding. All these factors have contributed to the instability of Lagos ecosystems, but the impact of flooding is particularly significant because Lagos is surrounded by aquatic ecosystems and its low elevation and topography increase its susceptibility to flooding. The method adopted in this review involved the use of PICO (Population, Intervention, Control, and Outcomes) criteria to synthesize the research questions and objectives. Thereafter, PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines was employed for the study selection criteria, search strategies and data extraction methods. A broad search strategy involving databases (Google scholar, Science Direct), pertinent keywords and search filters was used to identify relevant articles and minimize selection bias. After the search, PICO criteria was again used to select the studies to be considered in the review. The selected text were downloaded and the essential scientific information were extracted and analyzed in the study. The impacts of flooding are numerous. Flooding is a key factors that has prevented Africa’s growing urban population from escaping poverty, and it also impedes the achievement of some SDGs. This is because many African cities lack the resources and infrastructures needed to withstand extreme weather conditions. Surviving in flood-prone cities like Lagos is a daunting task because flood affects livelihood, human health, and can even cause death. Specifically, the 2020 flood incidence in Nigeria affected about 40% of the local government areas, and 97% of the states, displacing over 120,000 persons and killing 68 persons aside from properties and farmlands destroyed. Women and children are the most vulnerable. The adoption of advanced flood risk management strategies could help in flood containment and management in the state.

Probabilistic assessment of climate-related impacts and risks in ports

Port activities are crucial for sustained, long-term economic growth, serving as the primary nodes for importing and exporting goods within global supply chains. Given their coastal locations, ports are inherently exposed to climate hazards, such as waves and extreme sea levels, requiring large investments in resilient infrastructure. This study introduces an innovative methodology for assessing climate-related impacts and risks in ports, applicable to both existing and new constructions. This approach aims to facilitate climate-informed decision-making and enhance the management of coastal structures and ports under high uncertainty. The methodology’s novelty resides in: (1) the development of a port-specific risk framework capable of estimating impacts from both extreme events and daily conditions; (2) the integration of the latest advancements in nearshore climate hazard modeling; (3) the application of high-resolution tools for accurately simulating wave propagation towards harbor basins and the interaction between waves and structures; (4) the probabilistic determination of failure modes and operational shutdowns susceptible to climate conditions; and (5) the estimation of economic losses resulting from diminished operational capacity, in addition to the degradation of reliability and functionality in port infrastructures. Formulated within the Intergovernmental Panel on Climate Change (IPCC) risk framework and anchored in established Spanish Recommendations for Maritime Works (ROM Program), this methodology has been applied to a complex, state-owned, newly-built outer port in the Mediterranean Sea. Preliminary findings suggest that, over the course of a 50-year lifespan, climate-related risks could lead to cumulative losses nearing 10 million euros for such infrastructure. Nevertheless, in scenarios marked by extreme events, potential losses could escalate to as much as 100 million euros, despite their occurrence being relatively rare (with a probability of only 0.1%). It stresses the significant uncertainties encountered when evaluating climate-related risks for critical infrastructure, including ports, and highlights the critical need for advanced methodologies to accurately understand these risks.

Comparing the Dominant Factors in Coastal Morphology: Inappropriate Infrastructure vs. Climate Change—A Case Study of the Hsinchu Fishery Harbor, Taiwan

The construction of coastal infrastructure alters the natural hydrodynamics, leading to irreversible changes in coastal morphology. Furthermore, there has been an increasing concern about global climate change in recent years, which requires examining how climatic shifts impact the mechanisms that govern oceanic processes, the trends in morphological changes, the extent of the impacts, and the corresponding weightings assigned to coastal infrastructure. This study aims to assess the impacts of climate change on the wave distribution and coastal morphology around the two breakwaters perpendicular to the shoreline of Hsinchu Fishery Harbor in Taiwan at the end of the 21st century. The findings reveal that, by the end of the century, during extreme climatic events, such as typhoons (increasing the maximum wind speed and extreme sea levels), the wave heights around the Hsinchu Fishery Harbor, compared to the present day, may increase by 5.94% to 81.25%. Regarding the potential coastal morphological changes, erosion trends are evident around the harbor, but there is a pronounced tendency toward accretion in the sheltered area. The impact range and weightings of the Hsinchu Fishery Harbor breakwaters under climate change on coastal morphology were then determined based on historical bathymetric data and simulated bathymetric changes using the empirical orthogonal function (EOF) method. Under different-intensity climate change scenarios, the EOF analysis indicates no significant differences in the impact range and weightings on coastal morphological changes. In light of the analysis results, it is evident that structures have a much more substantial impact on coastal morphological change than climate change does.