This study presents a 3D process-based morphodynamic model that uses orthogonal unstructured grids. It is designed for coastal applications involving complex bathymetry and varying spatial scales. The model extends the Delft3D-FM framework by incorporating full 3D representation of wave, wind, and density-driven effects in the short-wave-averaged, non-linear shallow water equations. The framework includes expressions for wave and roller effects on flow forcing, turbulence, and bed shear stress, and integrates sediment transport and morphodynamic feedback. Multi-fraction sediment transport is supported, and the model tracks stratigraphy through a layered bed composition framework. Features such as infragravity wave dynamics, sediment mass slumping, swash zone slope nudging and morphological acceleration techniques are incorporated to better capture long-term morphological trends as well as storm erosion. The framework supports in-memory model coupling and is fully parallelized, enabling efficient, large-scale simulations. Model verifications presented here include analytical benchmarks and comparisons with laboratory and field observations, demonstrating reliable reproduction of wave-current interaction, sediment transport rates, and bed level changes. The model has the potential to bridge the gap between high-resolution event-scale modelling and long-term morphodynamic prediction, offering a flexible framework to study coastal sedimentary dynamics.

This study quantifies shoreline retreat for multiple sea-level rise (SLR) projections at two contrasting sites along the Spanish Mediterranean coast: the Llobregat delta and Maresme beaches. The Llobregat delta comprises mildly sloped dissipative beaches, while the Maresme coast is characterized by steeper coarse-sediment beaches. Using the probabilistic PCR model, which incorporates both the effects of the long-term wave climate and Sea level rise, we evaluate site-specific responses and compare outcomes with the widely used Bruun rule. The Bruun rule overestimates retreat by up to 70 % at Llobregat compared to PCR projections under the SSP5-8.5 scenario. At the same time, the results of the two approaches converge at Maresme for both SSPs considered, both at 2050 and 2100. Thus, the discrepancies between the two approaches appear to be larger at sites with milder slopes. The PCR model projects an accelerating retreat from mid-century, reflecting strong nonlinear interactions between future hydrodynamic forcing and storm erosion. These findings underscore the potential pitfalls of relying solely on Bruun rule derived projections for local scale coastal adaptation planning. Moreover, they highlight how PCR model derived physics based, probabilistic projections of shoreline retreat could lead to more informed and effective decisions on local scale adaptation along vulnerable coastlines.

Coastal communities around the world are becoming increasingly vulnerable to climate change driven natural hazards. Yet, a global scale coastal vulnerability assessment has not been attempted to date. Here, by employing currently available global datasets together with the widely used Coastal Vulnerability Index (CVI) approach, we assess present-day coastal vulnerability at the global scale. Our country level assessment shows that median coastal vulnerability is highest in Aruba, Benin, Togo, the Democratic Republic of the Congo, Bonaire, Sint Eustatius and Saba, Sri Lanka, Nigeria, French Guiana, Ghana and Liberia. At the IPCC AR6 region scale, Central North America, and Northern South America emerge as the regions with the highest median coastal vulnerability. Results at both country and regional scales indicate that tropical and subtropical regions are more vulnerable to coastal hazards. In countries with High or Very High median CVI, the dominant contributors to present-day coastal vulnerability are geomorphology, mean tidal range, and coastal slope.

European coastal regions host a dense transport network that supports various human activities and well-being. However, global warming is expected to increase coastal flooding risk, whose impact on existing and planned European transport systems remains unknown. Here we present the fully probabilistic assessment of coastal flood risk to Europe’s surface transport infrastructure at different levels of global warming. Under baseline conditions (1980–2020), we find 1,592 km of networks are affected annually, causing expected annual damage of up to €722 million. Roads are projected to be more affected than railways in all countries. Passenger and haulage transport within the low-elevation coastal zone are currently overwhelmingly road dependent, which signals potential for widespread disruptions unless transportation modes change. With 1.5 °C warming, the Europe-wide expected annual damage may reach €1,108 million, and with 4 °C, it is projected to be as high as €1,487 million. Adaptation expenditures will increase with every fraction of global warming in most countries. Transport networks in coastal zones are critical for human activities and are faced with increasing flooding risk. Using a detailed risk analysis in Europe, the authors show that the affected networks and expected annual damage will increase considerably with global warming.

Climate change driven variations in extreme sea level (ESLs) are projected to increase the frequency and severity of damaging coastal flooding in most of the world’s regions over the 21st century. However, to date, detection and emergence of climate change signals in ESLs have not been studied at the global scale. Addressing this knowledge gap is important for informing climate policy, and coastal flood adaptation strategies. Here, we present the first global-scale analysis of detection and emergence of ESL, using a 74-year long (1950–2023) hydrodynamically modelled ESL dataset. We detect a statistically significant increasing trend at 10,136 computational points, collectively spanning 50.5% of the global ice-free coastline. The median increasing trend in ESL magnitude at these computational points is 2.6 [0.8–6.1] mm/yr (values in square brackets denote the 90% confidence interval). Highest increasing trends (median 6–13 mm/yr) are observed along extratropical coastlines, while tropical coastlines show lower increasing trends (median 1–3 mm/yr). At almost all computational points where a detected trend is present, the ESL signal has already emerged. The IPCC AR6 WGI regions with the earliest time of emergence (ToE) are located in the Equatorial Atlantic Ocean, Central Africa, Equatorial Indian Ocean, Western Africa, Northeastern South America, Arabian Sea, and Northern South America (regional-median ToE = between 1979 and 1982), which are also home to many of the world’s socioeconomically vulnerable nations.

Extreme sea level (ESL), a major driver of coastal flooding, is widely used as a benchmark for coastal engineering design, coastal zone management, and risk assessment. With global warming, ESL events are projected to become more frequent and intense along most of the world’s coastline. Tebaldi et al. (2021) estimated the minimum global warming level (GWL) and timing at which the present-day 100-year ESL becomes an annual event along the global ice-free coastline. Comparable information for other return periods—such as 10-, 20-, 50-, 100-, and 200-year events—relevant to broader applications remains unavailable. Here, we present global datasets describing the minimum GWL and timing at which multiple present-day ESL return levels transition to annual occurrence. The datasets are derived using a multimethod framework consistent with Tebaldi et al. (2021), combining multiple global datasets of present-day ESL and projections of relative sea level change, while accounting for uncertainties. The resulting datasets provide a consistent global reference for assessing how the frequency of ESLs changes across return periods and GWLs, supporting applications in large-scale coastal impact assessment, planning, and risk analysis.

Rising future extreme sea levels (ESL) increase coastal flooding, putting people and assets at risk in low-elevation coastal zones. Coastal ecosystems such as mangroves and coral reefs can attenuate waves and storm surge, thereby reducing flood impacts along tropical and subtropical coastlines. Despite earlier studies of global flood risk, the benefits provided by the simultaneous presence of mangroves and coral reefs, under climate change, have not been quantified. By using process-based inundation modelling with and without these coastal ecosystems, and accounting for population and asset changes under future socio-economic conditions, this study estimates how the spatial distribution of these ecosystems reduces coastal flooding for a baseline period (1980–2014), for 2030 and for 2050 under the SSP5-8.5 scenario. When mangroves and reefs are included, our results show a reduction in global expected annual flood damage by USD 1.7 billion (12.9\%) in the baseline period and by USD 2.7 billion (13.9\%) by 2050. The presence of ecosystems reduces the mid-century annual expected flooded area with 7811 \unit{\square\kilo\meter} and reduces the mid-century annual affected assets and population by USD 45.8 billion and 1.6 million people per year, respectively. In relative terms, we find that benefits resulting from the presence of ecosystems are concentrated in countries with high climate vulnerability and low adaptation readiness, implying that ecosystem degradation would raise future flood risk most where adaptive capacity is lowest. Therefore, our results suggest that mangrove preservation/rehabilitation and reef conservation directly support adaptation to climate change.

Cities concentrate population, infrastructure, and economic assets, yet climate risk is still often assessed at regional or national scales. Here we present the first global analysis of climate change emergence and coastal flood exposure across ∼11,000 urban areas, distinguishing four city typologies. Using the policy-relevant CORDEX-CORE ensemble of high-resolution regional climate models, we assess heat extremes, heavy rainfall, droughts, wildfire risk and heat stress. We show that many cities experience stronger changes than their surrounding regions, with the robusteness of this amplification varying across hazards and regions, and that heat extremes have already emerged in most cities. By mid-century, emergence becomes widespread across the analysed hazards, although with greater uncertainty for hydroclimatic indicators, while emissions mitigation delays emergence across regions and city types. We further show that coastal flood exposure is strongly shaped by socio-economic gradients under no-protection assumptions, while accounting for flood protection reduces wealth-dependent disparities. Our results highlight the need to move beyond a megacity-focused perspective and to include small and intermediate cities in global adaptation planning.

A global database of coastal flooding impacts resulting from extreme sea levels is developed for the present day and for the years 2050 and 2100. The database consists of three sub-datasets: the extreme sea levels, the coastal areas flooded by these extreme sea levels, and the resulting socioeconomic implications. The extreme sea levels consider the processes of storm surge, tide levels, breaking wave setup and relative sea level rise. The socioeconomic implications are expressed in terms of Expected Annual Population Affected (EAPA) and Expected Annual Damage (EAD), and presented at the global, regional and national scales. The EAPA and EAD are determined both for existing coastal defence levels and assuming two plausible adaptation scenarios, along with socioeconomic development narratives. All the sub-datasets can be visualized with a Digital Twin platform based on a GIS-based mapping host. This publicly available database provides a first-pass assessment, enabling users to extract and identify global and national coastal hotspots under different projections of sea level rise and socioeconomic developments.

Sea levels are increasing at an accelerated rate1,2 and this is expected to increase flooding along coastlines worldwide3. While cultural and natural heritage sites are among the assets most exposed to this threat4,5, a unified global assessment is currently lacking. Here we assess coastal flood exposure of all nearshore UNESCO World Heritage sites worldwide, under different global warming scenarios. We estimate that for a scenario with current climate mitigation policies and action, by the end of this century around one third of World Heritage Sites could be exposed to floods, corresponding to more than 1.1 million hectares of protected and preserved land. Limiting warming to the 1.5°C Paris agreement target would save 89 heritage sites from being exposed. Large countries are projected to face widespread exposure while smaller nations risk losing entire heritage systems. Combining our findings with the ND-GAIN index6 shows that 32 countries with low adaptive capacity are expected to experience high heritage exposure, especially Small Island Developing States. To safeguard these irreplaceable cultural and natural treasures, it is imperative to scale up heritage adaptation efforts and increase support for vulnerable regions.

Approximately 30% of the global ice-free coastline is stated to be sandy (Luijendijk et al., 2018). Sandy beaches hold an important socio-economic value related to tourism and recreation and simultaneously fulfill various ecosystem services like providing food, water and maintaining biodiversity. Sandy systems are among the most dynamic environments in the world and are under enormous pressure from climate change (e.g., sea level rise) and anthropogenic influences (e.g., coastal squeeze from artificial structures), while they are only marginally monitored by costly labor-intensive monitoring campaigns (Turner et al., 2016 and Castelle et al., 2020). Yet, at present, there is no global dataset on sandy beach area available that allows to accurately estimate the potential loss of these aesthetic environments. In this study we will address this gap and present insights in sandy beach areas across the globe, derived from over a million of optical satellite images from the Sentinel-2 constellation.

During most of the 20th century, the global mean sea level (GMSL) has been rising at around 1.8 mm/yr. However, since the early 90s, this rate has increased to beyond 3 mm/yr, and over 2006 – 2018 the rate of rise is 3.7 mm/yr [3.2 – 4.2 mm/yr] (Fox-Kemper et al., 2021). While it appears that the world’s coastlines have remained more or less resilient against the slow sea level rise (SLR) over the 20th century (Stive, 2004), it is almost a certainty that they will respond to the much higher rates of GMSLR (upto ~ 12mm/yr by end of 21st century) projected for the future (Fox-Kemper et al., 2021). In this study, we analyse available long-term beach and dune datasets to investigate detectable trends in coastline indicators, especially since the early 90s when GMSLR rates of over 3 mm/yr have been observed. Here, we subject data from several sites in the US, Europe, Australia and Japan to a detailed analysis, focusing on coastline indicators such as the shoreline, dune crest etc.

Rising sea levels and anthropogenic activities are intensifying pressure on coastal zones. Process-based coastal morphodynamic models are increasingly used to forecast natural and anthropogenic beach morphology changes at various spatio-temporal scales. Such predictions are crucial for the sustainable management of coasts. However, process-based morphodynamic models contain numerous free model parameters, introducing uncertainty in predictions. Systematically exploring the parameter space has remained a challenge due to the high computational demands of these morphodynamic models. Here, for the first time we quantify parameter uncertainty of a state-of-the-art morphodynamic (2DH) coastal area model (Delft3D) by systematically varying key model parameters, utilizing the Dutch national supercomputer: SurfSara. We simulate the initial (14-month) response of the Sand Engine, an innovative mega-nourishment placed along the Holland coast with 1024 strategically chosen parameter sets. The resulting simulations are analysed using Generalised Likelihood Uncertainty Estimation (GLUE) to attain probability distributions of morphological evolution and its sensitivity to parameter settings. The model simulations all show an alongshore redistribution of sediment resembling what is observed. However, even simulations with similar skill reveal substantial differences in predicted morphologies (same order of magnitude as the predictions' 90% confidence interval). Our findings suggest that identifying a single optimal parameter set for coastal numerical models might be unrealistic, even for well-defined cases like large-scale coastal interventions, and that an ensemble modeling approach that quantifies parameter uncertainty is likely better suited for studies relying on morphodynamic predictions. Furthermore, we find that the magnitude of the uncertainty induced by the free model parameters is comparable to that resulting from year-to-year variations in wave climate, underscoring the importance of including both sources in uncertainty assessments.

More intense and frequent extreme sea level (ESL) events are projected with high confidence for most of Europe in the IPCC’s latest AR6 (Fox-Kemper, 2021, Ranasinghe et al., 2021). Flooding as a result of ESL events will impact the dense transport infrastructure that is associated with the high degree of urbanization and economic development in European coastal areas. Therefore, high-resolution risk information under present and future climate conditions is essential to inform sustainable risk-based designs and to avoid predictable losses. An object-based, quantitative ESL-driven flood risk assessment for Europe’s coastal transport infrastructure has not been performed to date.

Wave storms present a significant hazard to the coastal environment, particularly affecting the 10% of the population residing in low-lying coastal areas, as well as coastal zone infrastructure and developments. This study utilizes a ~40-year wave hindcast to conduct an analysis of wind-wave storminess along the worldwide coast (Lobeto et al., 2024). The main characteristics of wave storms, such as the associated wave height and direction, as well as the occurrence rate, duration and intensity, are analyzed. Additional climatic wave features including the relative importance of wind seas versus swells during wave storms are also explored. The combination of key storm features has led to a categorization of coastal regions based on their degree of wave storminess.Results indicate Northwestern Europe and Southwestern South America to be the coastal regions experiencing the most severe storms, while the Yellow Sea, along with the South African and Namibian coastlines, are noted for their high frequency of storms. A global holistic analysis of the wave storminess reveals that, for example, the exposed shores of northwestern Europe experience over 10 storms annually, with mean significant wave heights exceeding 6 meters. A general latitudinal pattern in degree of wave storminess is observed, with the main exception of those coasts affected by wave storms generated by tropical cyclones. Accordingly, regions such as Iceland, Ireland, Scotland, Chile, and Australia exhibit the highest storminess levels, contrasting with lower levels observed in Indonesia, Papua-New Guinea, Malaysia, Cambodia, and Myanmar. Lobeto, H, Semedo, A., Lemos, G., Dastgheib, A., Menendez, M., Ranasinghe, R., Bidlot, R. (2024). Global coastal wave storminess. Scientific Reports (in press).

Sandy beaches, which account for about 31percent of the world’s coastline (Luijendijk et al., 2018), have a high socioeconomic value (Cooley et al., 2022). Vousdouskas et al., (2020) projected that on a global scale, 50percent of the sandy beaches are under serious threat of erosion under the high emission RCP8.5 scenario by 2100. The projected shoreline retreat does not automatically translate into complete beach area loss since sandy beaches can translate landward as long as there is enough accommodation space. Only when sandy beaches have been “hardened”, by having artificial structures such as sea walls, or revetments on the back beach, they lose their natural adaptability to future sea level rise. This situation may lead to the complete loss of the sandy beach in front of the structure (Nordstrom 2014, Schonees et al., 2019). To date, the global quantity of “hardened” sandy beaches is still unknown. The global amount of hardened sandy beaches is an important knowledge gap to further disclose the information of sandy beaches that could face severe loss in the presence of chronic shoreline retreat.

Shoreline change due to climate change poses a significant challenge for the future management of beaches globally. Despite its recognized importance, determining the extent of these coastline changes remains a subject of debate, primarily because a universally accepted, reliable, easy-to-use predictive model is lacking. The Bruun rule, despite its uncertainties, is the most commonly employed model – mainly due to its simplicity. Different alternative models have been proposed, among which the Probabilistic Coastline Recession (PCR) model (Ranasinghe et al., 2012) is one that has been used in many parts of the world. This physics-based model evaluates coastline recession by considering the combined impacts of storms and SLR in probabilistic terms while allowing beach recovery between storm events.

Long-term (i.e., multi-decadal to century periods) evolution of inlet-interrupted coasts can be assessed in relation to the variation of sediment volume exchange between the inlet-estuary system and its adjacent coastal zone (Ranasinghe et al., 2013; Bamunawala et al., 2020a). The oceanic (e.g., sea-level-rise) and terrestrial processes (e.g., change in fluvial sediment supply) contribute to the long-term evolution of inlet-interrupted coasts. These contributing processes are affected by climate-change-driven impacts and anthropogenic activities. Thus, it is necessary to consider the holistic behaviour of Catchment-Estuary-Coastal (CEC) systems when assessing the evolution of inlet-interrupted coasts under the impacts of projected climate change and increasing pressures due to anthropogenic activities. Such a holistic assessment of the long-term evolution of CEC systems can be achieved via reduced-complexity modelling techniques, which also ably quantify the uncertainties associated with the projections due to their lower simulation times (Bamunawala et al., 2020b; Ranasinghe, 2020). The Generalised Scale- aggregated Model for Inlet-interrupted Coasts (i.e., G-SMIC) presented by Bamunawala et al. (2020b) is one such model that can probabilistically assess the long-term evolution of inlet-interrupted coasts while considering the holistic behaviour of Catchment- Estuary-Coastal systems. However, G-SMIC does not consider the presence of ebb-delta systems when considering the sediment budget at tidal inlets. Here, we present an improved version of G-SMIC by incorporating the ebb-delta dynamics into its computations. The updated model is piloted at two selected case study sites in Vietnam and United Kingdom, and the preliminary results are presented, along with the overall modelling concept.

Around 10 percent of the global population lives in low- lying coastal areas (MacManus, 2021). Several economic activities also develop in these areas. At the same time, they are home to some of the richest ecosystems. Coastal zones are quite susceptible to extreme storms and sea level rise driven by climate change (IPCC, 2021). During the last couple of years, a high number of global datasets have become available, describing different aspects of the earth’s surface such as land-elevation, land-use, waves, water-levels and more. However, for studies focusing in the coastal zone, it is important that this type of information is available directly at coastal locations and in a consistent manner. Previous research (Athanasiou, 2019) has shown the importance of the spatial variability of the input geophysical data when assessing coastal hazards at large spatial scales. In this research we present a Global database of Coastal Characteristics (GCC) with 80 indicators that span the geophysical, hydrometeorological and socioeconomic environment at the coast. These indicators are extracted using the latest freely available global datasets and a newly created global high- resolution transect system. Even though these indicators are derived from global datasets, they can be valuable for coastal screening studies, especially for data-poor locations.

Tidal inlets are a common feature along the world’s coastline. Inlet-adjacent coastlines have for millennia supported communities and livelihoods, and therefore, projected climate change driven variations in catchment-estuary-coast (CEC) system drivers (e.g., sea-level rise (SLR)) are likely to lead to substantial socio-economic impacts. One important SLR-driven process that affects inlet-adjacent shoreline change is basin-infilling (i.e., sediment import to the estuary from the coast to satisfy the SLR-driven increase of estuarine accommodation space). Due to the slow morphological response to hydrodynamic forcing, however, there is a time lag between basin infilling and SLR, which, in numerical models that simulate century-scale evolution of CEC systems, is represented by a basin infilling lag factor (M). To date, an indicative M value has only been derived for small tidal inlet systems (M ~0.5), and due to the lack of M estimates for larger systems, studies have been using M ~0.5 indiscriminately. Here, for the first time, we derive indicative M values for small, medium, and large tidal inlet systems (M ~0.5, ~0.25 and ~0.15 respectively) via analytical considerations. Subsequently, to investigate the consequences of using sub-optimal M values on twenty-first century projections of inlet-adjacent shoreline change, we apply a probabilistic, reduced complexity model (G-SMIC), under four IPCC AR6 climate scenarios, to three CEC systems representing small, medium and large systems. Results show that, in general, shoreline change projections are substantially lower(higher) when M values smaller(larger) than the indicative M for a given system are used. When smaller-than-optimal M values (0.25 and 0.15) are used for the small tidal inlet, both mid- and end-century shoreline retreats are under-estimated by 50–75% (across the four climate scenarios), relative to projections obtained with the optimal M value. For the medium-sized inlet, shoreline retreats for both future periods are over-estimated by ~100% with the larger-than-optimal M value of 0.5, while they are under-estimated by ~40–75% (across climate scenarios) with the smaller-than-optimal M value of 0.15. When the two higher-than-optimal M values (0.25 and 0.5) are used for the large tidal inlet system, shoreline retreat is over-estimated by ~ 65–240% (across climate scenarios) for both future periods. In terms of absolute values, these under/over-estimations increase in time and with the severity of emission scenario.