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.

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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.

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Reconstructions of the most severe historic flood events contribute to improved quantification of design discharges corresponding to large return periods. Reducing the uncertainty of design discharges has a great significance in constructing proper flood defences to protect the hinterland from future flooding. However, reconstructions of the peak discharges of such historic flood events are generally associated with large uncertainties, which arise from the accuracy of the historic topography, hydraulic roughness of the river channels and floodplains, and the historic hydrograph shape. This study sets up a one dimensional-two dimensional (1D-2D) coupled hydraulic model, stretching from the upstream of Bonn at Remagen to downstream of Düsseldorf, Germany, with the length of 113 km to reconstruct the maximum discharge of the 1374 flood event (Qmax,1374), which is considered to be the largest flood of the last millennium in the Lower Rhine catchment. An uncertainty analysis was performed by adopting different river bed levels and roughness values in order to estimate the influence of these uncertainties on the reconstructed peak discharge. The upstream discharge wave was varied corresponding to a wide range of peak discharges from 12,000 to 24,000 m3/s. The resulting Qmax,1374 was determined of between 14,400 and 18,500 m3/s, were then used in a flood frequency analysis to determine the design discharges corresponding to different return periods. Compared to the design discharge computed with previous estimations of the 1374 peak discharge, we found a significant reduction of 2,000 m3/s in the design discharge corresponding to a 100,000 year return period, which is the maximum safety standard adopted in the Dutch water policy for some downstream dike sections.@en

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.

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The Delft3D hydrodynamic and wave model is used to hindcast the storm surge and waves that impacted La Rochelle, France, and the surrounding area (Aytré, Châtelaillon-Plage, Yves, Fouras, and Île de Ré) during storm Xynthia. These models are validated against tide and wave measurements. The models then estimate the footprint of flow depth, speed, unit discharge, flow momentum flux, significant wave height, wave energy flux, total water depth (flow depth plus wave height), and total (flow plus wave) force at the locations of damaged buildings for which insurance claims data are available. Correlation of the hydrodynamic and wave results with the claims data generates building damage functions. These damage functions are shown to be sensitive to the topography data used in the simulation, as well as the hydrodynamic or wave forcing parameter chosen for the correlation. The most robust damage functions result from highly accurate topographic data and are correlated with water depth or total (flow plus wave) force.@en

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.

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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.

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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.

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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.

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Sandy beaches occupy more than one-third of the global coastline1 and have high socioeconomic value related to recreation, tourism and ecosystem services2. Beaches are the interface between land and ocean, providing coastal protection from marine storms and cyclones3. However the presence of sandy beaches cannot be taken for granted, as they are under constant change, driven by meteorological4,5, geological6 and anthropogenic factors1,7. A substantial proportion of the world’s sandy coastline is already eroding1,7, a situation that could be exacerbated by climate change8,9. Here, we show that ambient trends in shoreline dynamics, combined with coastal recession driven by sea level rise, could result in the near extinction of almost half of the world’s sandy beaches by the end of the century. Moderate GHG emission mitigation could prevent 40% of shoreline retreat. Projected shoreline dynamics are dominated by sea level rise for the majority of sandy beaches, but in certain regions the erosive trend is counteracted by accretive ambient shoreline changes; for example, in the Amazon, East and Southeast Asia and the north tropical Pacific. A substantial proportion of the threatened sandy shorelines are in densely populated areas, underlining the need for the design and implementation of effective adaptive measures.@en

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.

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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.@en

Climate change impacts threaten the coastal environment and affect Coastal Ecosystem Services (CES) that are vital to human wellbeing. Quantifying the monetary value of climate change driven environmental losses is scarce, especially in coastal areas of developing countries that have low adaptive capacity to climate change impacts. To address this knowledge gap, we present a practical framework to Quantify Climate change driven Environmental Losses (QuantiCEL) that coherently assesses the likely physical impacts of climate change on CES, and pursues the valuation of their losses with primary data collection. The framework is applied to coastal areas in three developing countries, and may serve as a useful guide for practitioners. We quantify potential environmental losses due to relative sea level rise-induced coastal inundation in Indonesia and Bangladesh, and losses due to sea level rise and storm-induced coastline recession in Sri Lanka in the next 100 years. This study illustrates the applicability of the framework in different contexts in the data-scarce developing countries. Our findings suggest that the three case studies will experience the absolute loss value of CES by the end of the 21 st century, with food provision and tourism suffering the highest losses. Moreover, art, amenity, and tourism services are highly affected CES with respect to the percentage loss relative to the present-day value of these CES. The QuantiCEL framework and its application presented in this study could help researchers, policy makers, and coastal zone managers to get better insights into likely climate change driven environmental losses in coastal areas, contributing to the development of much needed environmental risk quantification methods, and sustainable management of coastal wetlands.@en

Thirty one percent (31%) of the world's coastline consists of sandy beaches and dunes that form a natural defense protecting the hinterland from flooding. A common measure to mitigate erosion along sandy beaches is the implementation of sand nourishments. The design and acceptance of such a mitigating measure require information on the expected evolution at time scales from storms to decades. Process-based morphodynamic models are increasingly applied, together with morphodynamic acceleration techniques, to obtain detailed information on this wide scale of ranges. This study shows that techniques for the acceleration of the morphological evolution can have a significant impact on the simulated evolution and dispersion of sandy interventions. A calibrated Delft3D model of the Sand Engine mega-nourishment is applied to compare different acceleration techniques, focusing on accuracy and computational times. Results show that acceleration techniques using representative (schematized) wave conditions are not capable of accurately reproducing the morphological response in the first two years. The best reproduction of the morphological behavior of the first five years is obtained by the brute force simulations. Applying input filtering and a compression factor provides similar accuracy yet with a factor five gain in computational cost. An attractive method for the medium to long time scales, which further reduces computational costs, is a method that uses representative wave conditions based on gross longshore transports, while showing similar results as the benchmark simulation. Erosional behavior is captured well in all considered techniques with variations in volumes of about 1 million m3 after three decades. The spatio-temporal variability of the predicted alongshore and cross-shore distribution of the morphological evolution however have a strong dependency on the selected acceleration technique. A new technique, called 'brute force merged', which incorporates the full variability of the wave climate, provides the optimal combination of phenomenological accuracy and computational efficiency (a factor of 20 faster than the benchmark brute force technique) at both the short and medium to long time scales. This approach, which combines realistic time series and the mormerge technique, provides an attractive and flexible method to efficiently predict the evolution of complex sandy interventions at time scales from hours to decades. @en

Climate change is globally recognized as one of the key drivers of degradation of coastal wetland ecosystems, causing considerable alteration of services provided by these habitats. Quantifying the physical impacts of climate change on these services is therefore of utmost importance. Yet, practical work in this field is fragmented and scarce in current literature, especially in developing countries which are likely to suffer most from the adverse climate change impacts. Using a coherent scenario-based approach that combines assessment of physical impacts with economic valuation techniques, here we quantify potential climate change driven losses in the value of wetland ecosystems services due to relative sea-level rise (RSLR)-induced inundation in the vulnerable Western coastal area of Bangladesh in 2100. The results show a small inundation area in 2100 under the three IPCC climate scenarios of RCP2.6 (with 0.25 m of RSLR), RCP6.0 (with 1.18 m of RSLR), and RCP8.5 (with 1.77 m of RSLR) for the coastal wetland ecosystems including the Sundarbans mangrove forest, neritic system and aquaculture ponds. In all scenarios, RSLR will drive a loss in the total value of ecosystem services such as provision of raw materials, and food provision, ranging from US$ 0–1 million to US$ 16.5–20 million, respectively. The outcomes of this study reveal that RSLR-induced inundation on its own, is unlikely to be a major threat to the wetland ecosystems in Western coast of Bangladesh. This would suggest that other climate change impacts such as coastal erosion, increase in frequency of cyclone events, and sea temperature rise might be the likely primary drivers of change in the value of wetland ecosystems services in this area.@en

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.

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Understanding long-term sandbar dynamics can be crucial for informed coastal zone management, but is often hampered by data availability. To increase the number of sandbar observations available from bathymetric surveys, this study proposes and evaluates a method to manually extract the sandbar location using freely available satellite imagery for the case study of Anmok beach in South Korea. Validation of the satellite extracted sandbar locations against 9 in-situ measurements shows good agreement with errors well within the pixel resolution of the satellite imagery (i.e. 30 m for Landsat missions). The applicability of the method is constrained to locations where (1) the cross-shore crescentic length scales are larger than the image resolution, (2) frequent wave breaking and clouds are absent and (3) the water clarity is sufficient to enable the manual extraction of the sandbar crest line. Using the additional sandbar observations from the satellite imagery significantly increases the temporal extent and resolution of the dataset for Anmok beach. This allows the study of sandbar characteristics, dynamics and impacts of human interventions to an extent that would not have been possible without the satellite imagery. Within the study period 1990–2017 it is found that the sandbar maintains a persistent crescentic pattern that is only altered during prolonged and very intense storm conditions. The cumulative alongshore migration of the sandbars is investigated and found to be in the order of hundreds of meters over the 27 years study period. Comparing the sandbar characteristics prior and after the construction of Gangneung port shows that both the amplitudes and wavelengths of the sandbar crescents near the port have decreased after its construction.@en

Coastal zones constitute one of the most heavily populated and developed land zones in the world. Despite the utility and economic benefits that coasts provide, there is no reliable global-scale assessment of historical shoreline change trends. Here, via the use of freely available optical satellite images captured since 1984, in conjunction with sophisticated image interrogation and analysis methods, we present a global-scale assessment of the occurrence of sandy beaches and rates of shoreline change therein. Applying pixel-based supervised classification, we found that 31% of the world’s ice-free shoreline are sandy. The application of an automated shoreline detection method to the sandy shorelines thus identified resulted in a global dataset of shoreline change rates for the 33 year period 1984–2016. Analysis of the satellite derived shoreline data indicates that 24% of the world’s sandy beaches are eroding at rates exceeding 0.5 m/yr, while 28% are accreting and 48% are stable. The majority of the sandy shorelines in marine protected areas are eroding, raising cause for serious concern. @en