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.

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.

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.

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.

An unstructured hydrodynamic model is presented that is able to simulate 2D nearshore hydrodynamics on the wave group scale. A non-stationary wave driver with directional spreading, with physics similar to XBeach (Roelvink et al., 2009) is linked to an improved and extended version of the existing unstructured flow solver Delft3D–FM (Kernkamp et al., 2011; Martyr-Koller et al., 2017). The model equations are discretised on meshes consisting of triangular and rectangular elements. The model allows for coverage of the model domain with locally optimised resolution to accurately resolve the dominant processes, yet with a smaller total number of grid cells. The model also allows a larger explicit time step, compared to structured models with similar functionality. The model reliably reproduces measured datasets of water levels, sea/swell and low frequency wave heights in laboratory and field conditions, and is as such widely deployable in a variety of simple and complex coastal settings to study nearshore hydrodynamics.

A common inference in research studies of observed and projected changes in global ocean wave height and storm surge, is that such changes are potentially important for long-term coastal management. Despite numerous studies of the impacts of anthropogenic climate change on trends in global wind and waves, a clear link to impacts on sandy coastlines, at global scale, is yet to be demonstrated. This study presents a first-pass assessment of the potential link between historical trends in global wave and storm surge values and recession/progradation rates of sandy coastlines since the 1980s. Global datasets of waves, surge and shoreline change rate are used for this purpose. Over the past 30 + years, we show that there have been clear changes in waves and storm surge at global scale. The data, however, does not show an unequivocal linkage between trends in wave and storm surge climate and sandy shoreline recession/progradation. We conclude that these long-term changes in oceanographic parameters may still be too small to have a measurable impact on shoreline recession/progradation and that primary drivers such as ambient imbalances in the coastal sediment budget may be masking any such linkages.

Risk-informed flood risk management requires a comprehensive and quantitative risk assessment, which often demands multiple (thousands of) river and flood model simulations. Performing such a large number of model simulations is a challenge, especially for large, complex river systems (e.g., Mekong) due to the associated computational and resource demands. This article presents an efficient probabilistic modeling approach that combines a simplified 1D hydrodynamic model for the entire Mekong Delta with a detailed 1D/2D coupled model and demonstrates its application at Can Tho city in the Mekong Delta. Probabilistic flood-hazard maps, ranging from 0.5 to 100 year return period events, are obtained for the urban center of Can Tho city under different future scenarios taking into account the impact of climate change forcing (river flow, sea-level rise, storm surge) and land subsidence. Results obtained under present conditions show that more than 12% of the study area is inundated by the present-day 100 year return period of water level. Future projections show that, if the present rate of land subsidence continues, by 2050 (under both RCP 4.5 and RCP 8.5 climate scenarios), the 0.5 and 100 year return period flood extents will increase by around 15- and 8-fold, respectively, relative to the present-day flood extent. However, without land subsidence, the projected increases in the 0.5 and 100 year return period flood extents by 2050 (under RCP 4.5 and RCP 8.5) are limited to between a doubling to tripling of the present-day flood extent. Therefore, adaptation measures that can reduce the rate of land subsidence (e.g., limiting groundwater extraction), would substantially mitigate future flood hazards in the study area. A combination of restricted groundwater extraction and the construction of a new and more efficient urban drainage network would facilitate even further reductions in the flood hazard. The projected 15-fold increase in flood extent projected by 2050 for the twice per year (0.5 year return period) flood event implies that the “do nothing” management approach is not a feasible option for Can Tho.

The Saudi Arabian tourism sector is growing, and its economy has flourished over the last decades. This has resulted in numerous coastal developments close to large economic centers, while many more are proposed or planned. The coastal developments have influenced the behavior of the shoreline in the past. Here we undertake a national assessment on the state of the coast of Saudi Arabia based on recent data sets on historic and future shoreline positions. While at national scale the shoreline is found to be stable over the last three decades, the Red Sea coast shows a regional-mean retreat rate while the Gulf coast shows a regional-mean prograding behavior. Detailed analysis of the temporal evolution of shoreline position at selected locations show that human interventions may have accelerated shoreline retreat along adjacent shorelines, some of which are Marine Protected Areas. Furthermore, reef-fronted coastal sections have a mean accretive shoreline change rate, while the open coast shows a mean retreat rate. Future shoreline projections under RCP 4.5 and RCP 8.5 show that large parts of the shoreline may experience an accelerated retreat or a change in its regime from either stable or sprograding to retreating. Under the high emission RCP 8.5 scenario, the length of coastline projected to retreat more than doubles along the Red Sea coast, and approximately triples along the Gulf coast in 2100. At national scale, the Saudi Arabian coastline is projected to experience regional-mean retreats of ~30 m and of ~130 m by 2050 and 2100 under both RCPs considered in this study. These results indicate that effective adaptation strategies will be required to protect areas of ecological and economic value, and that climate resilience should be a key consideration in planned or proposed coastal interventions.

Sandy beaches and dune systems have high recreational and ecological value, and they offer protection against flooding during storms. At the same time, these systems are very vulnerable to storm impacts. Process-based numerical models are presently used to assess the morphological changes of dune and beach systems during storms. However, such models come with high computational costs, hindering their use in real-life applications which demand many simulations and/or involve a large spatial-temporal domain. Here we design a novel meta-model to predict dune erosion volume (DEV) at the Dutch coast, based on artificial neural networks (ANNs), trained with cases from process-based modeling. First, we reduce an initial database of 1/41400 observed sandy profiles along the Dutch coastline to 100 representative typological coastal profiles (TCPs). Next, we synthesize a set of plausible extreme storm events, which reproduces the probability distributions and statistical dependencies of offshore wave and water level records. We choose 100 of these events to simulate the dune response of the 100 TCPs using the process-based model XBeach, resulting in 10 000 cases. Using these cases as training data, we design a two-phase meta-model, comprised of a classifying ANN (which predicts the occurrence (or not) of erosion) and a regression ANN (which gives a DEV prediction). Validation against a benchmark dataset created with XBeach and a sparse set of available dune erosion observations shows high prediction skill with a skill score of 0.82. The meta-model can predict post-storm DEV 103-104 times faster (depending on the duration of the storm) than running XBeach. Hence, this model may be integrated in early warning systems or allow coastal engineers and managers to upscale storm forcing to dune response investigations to large coastal areas with relative ease.

Flood risk reduction strategies play an important role in flood risk management (FRM) and these strategies are being co-designed with the engagement of the stakeholder through multiple consultations and co-designing sessions. Effective participation of stakeholders in interactive work sessions requires fast and accurate modeling systems with a user-friendly interface, which can simulate the impact due to various flood reduction measures selected by the stakeholders and also generate outputs that can be understood by all stakeholders, especially those who are not FRM specialists. Presenting an easy-to-understand tool with easy inputs and outputs for a variety of stakeholders and at the same time providing reliable and accurate results for a range of scenarios and interventions is a challenge. Seven requirements that are essential for a user-friendly flood risk tool were used to develop an instant flood risk modeling tool. This paper presents a web-based hydraulic tool, i.e., instant flood risk model (Inform), to support FRM in the urban center of Can Tho city (Ninh Kieu district), Mekong Delta, Vietnam. Inform was developed based on (i) a simplified 1D model for the entire Mekong Delta; and (ii) flood hazard and damage maps, and estimated flood damage for Ninh Kieu district in Can Tho city obtained directly from the 1D/2D coupled model for Ninh Kieu district. Inform rapidly generates flood levels, flood hazard and damage maps, estimated damages. Pilot testing with experts confirmed that Inform qualifies as a reliable co-design tool for developing FRM strategies as it features an inbuilt input library, comprises flexible options, easy to use, produces quick results and has a user-friendly interface. With the help of an interactive web-based tool such as Inform presented here, it is possible to co-design FRM strategies for Can Tho or any other city that is subject to flood risk.

Dune erosion driven by extreme marine storms can damage local infrastructure or ecosystems and affect the long-term flood safety of the hinterland. These storms typically affect long stretches (∼100 km) of sandy coastlines with variable topo-bathymetries. The large spatial scale makes it computationally challenging for process-based morphological models to be used for predicting dune erosion in early warning systems or probabilistic assessments. To alleviate this, we take a first step to enable efficient estimation of dune erosion using the Dutch coast as a case study, due to the availability of a large topo-bathymetric dataset. Using clustering techniques, we reduce 1,430 elevation profiles in this dataset to a set of typological coastal profiles (TCPs), that can be employed to represent dune erosion dynamics along the whole coast. To do so, we use the topo-bathymetric profiles and historic offshore wave and water level conditions, along with simulations of dune erosion for a number of representative storms to characterize each profile. First, we identify the most important drivers of dune erosion variability at the Dutch coast, which are identified as the pre-storm beach geometry, nearshore slope, tidal level and profile orientation. Then using clustering methods, we produce various sets of TCPs, and we test how well they represent dune morphodynamics by cross-validation on the basis of a benchmark set of dune erosion simulations. We find good prediction skill (0.83) with 100 TCPs, representing a 93% input and associated computational costs reduction. These TCPs can be used in a probabilistic model forced with a range of offshore storm conditions, enabling national scale coastal risk assessments. Additionally, the presented techniques could be used in a global context, utilizing elevation data from diverse sandy coastlines to obtain a first order prediction of dune erosion around the world.

Sea-level rise (SLR) can amplify the episodic erosion from storms and drive chronic erosion on sandy shorelines, threatening many coastal communities. One of the major uncertainties in SLR projections is the potential rapid disintegration of large fractions of the Antarctic ice sheet (AIS). Quantifying this uncertainty is essential to support sound risk management of coastal areas, although it is neglected in many erosion impact assessments. Here, we use the island of Sint Maarten as a case study to evaluate the impact of AIS uncertainty for future coastal recession. We estimate SLR-induced coastal recession using a probabilistic framework and compare and contrast three cases of AIS dynamics within the range of plausible futures. Results indicate that projections of coastal recession are sensitive to local morphological factors and assumptions made on how AIS dynamics are incorporated into SLR projections and that underestimating the potential rapid mass loss from the AIS can lead to ill-informed coastal adaptation decisions.

This study demonstrates how a large-scale satellite-derived dataset can be used to investigate statistically robust trends in shoreline position over a 31-year period from 1987 to 2017, at a regional scale. Regional patterns of shoreline behaviour are important for resolving consistent or, alternatively, dissimilar patterns of past shoreline change. Such patterns are best explored using temporally frequent and spatially extensive datasets. Here we analyse satellite-derived shorelines to identify spatial patterns of hotspots of coastline change on the wave-exposed coast of Victoria in south-east Australia where rates of change exceed 0.5 m yr⁻¹. Analysis of shoreline position changes at a 50 m alongshore interval along 900 km of the 1230 km coastline reveals a number of distinct behaviours related to coastal type (rock vs sand coast), landform, shoreline orientation and/or anthropogenic drivers of change. Overall the results show that statistically significant change in shoreline position has affected only a relatively small proportion of the study region over the last 31 years; that the proportion and rate of progradational and recessional change is similar; and that change is localised but dispersed widely along the Victorian coast. Coasts located at the entrances to large tidal inlets have shown the greatest change. The association of hotspots with embayed sandy beaches and adjacent to headlands points to the importance of geological control on shoreline behaviour. Consistent with other regional scale studies of shoreline change, this study found little regional coherence in shoreline behaviour. Instead change is predominately attributed to local factors such as the geological framework of the coast, localised hydrodynamic conditions and anthropogenic influences. Collectively, these results indicate that there is strong geologic control on shoreline erosion in Victoria due to the high diversity of landforms along the coastline; and that further analysis is required to tease out the seasonal to interannual sensitivities to changes in the historical wave climate and the secondary interaction of sediment supply for headlands and hydrodynamics for tidal inlets.

To guarantee port accessibility, navigation approach channels need to be well maintained. Annual dredging efforts to maintain navigable channels may well exceed tens of millions m³ of sediment per year, which results in high recurrent costs for port operators. Quantification of expected siltation rates with process-based numerical models helps to effectively design and optimise approach channels. The setup of such models requires several assumptions and parameter settings which introduce uncertainty in model output. However, traditional Monte Carlo methods to quantify that uncertainty in model output are often too resource-intensive with current standard computer resources to be feasibly applied in coastal engineering projects such as approach channel design. Here, we use an alternative multifidelity approach to estimate the probability density function of channel siltation, at lower computational costs compared to direct Monte Carlo simulation. The idea behind this method is to map the output uncertainty of a faster, but inaccurate model to a preferred high-detailed model. The key requirement is that the faster, low-fidelity model and the detailed high-fidelity model are correlated, and that his correlation can be modelled with a probabilistic function. Since linearity of the correlation is not a requirement, the coarse-grid model can be very inaccurate but still serve as an adequate predictor of the high-fidelity model. In this study we did observe a highly nonlinear correlation, which in our case is explained by underestimation of channel siltation near the surf zone by the coarse model. In the presented multifidelity approach we adopted a combination of quasi-random Monte Carlo simulation and a non-parametric Gaussian process transfer function to estimate the uncertainty of total siltation and spatial patterns of siltation in a port approach channel. We argue that the multifidelity approach is conceptually straightforward and found that it can be used to significantly decrease the costs of probabilistic analysis; in our case we found a 85% decrease compared to direct Monte Carlo simulation. An additional advantage is that the approach allows for a trade-off between precision and efficiency by varying the number of high-fidelity model runs. Therefore, we conclude that the multifidelity framework is a potential powerful alternative for cases in which direct Monte Carlo simulation is infeasible or undesirable.