Storm Erosion Research Articles

Storm frequency, magnitude, and cumulative storm beach impact along the US east coast

Abstract. This study extracted historical water level data from 12 National Oceanographic and Atmospheric Administration tide gauge stations, spanning the period from the early 20th century to 2022 from central Maine to southern Florida, in order to determine if temporal and spatial trends existed in the frequency and magnitude of storms along the US Atlantic Ocean coast. We used the Storm Erosion Potential Index (SEPI) to identify and quantify storms. We then use the timing and magnitude of those storms to determine the cumulative effect of storm clustering and large-magnitude storms on sandy beaches using the cumulative storm impact index (CSII) empirical model. The results from this study showed (1) no appreciable increase in storm frequency at any of the stations (except for sheltered stations susceptible to storm tide augmentation), (2) statistically significant but modest increases in storm magnitudes over time for 8 of the 12 tidal stations, (3) regional differences in storm magnitudes (SEPI) and cumulative storm impacts (CSII) characteristic of more frequent extratropical storms (temporal clustering) in the north and less frequent tropical storms in the south, and (4) a 4- to 10-year recovery period for regional beach recovery.

Data-driven modelling of coastal storm erosion for real-time forecasting at a wave-dominated embayed beach

Emergency managers have an increasing need for tools to enhance preparedness to extreme coastal storms and support disaster risk reduction measures. With the emergence of Early Warning Systems (EWSs) for coastal storm hazards, a fundamental challenge is the accurate prediction of sandy beach erosion at lead times of days to weeks corresponding to an approaching storm event. This work presents a data-driven modelling approach to predict storm-driven beach erosion (shoreline change) using a large dataset of 276 individual storm events at Narrabeen-Collaroy Beach, SE Australia. Correlation analysis between individual storm characteristics and shoreline response at three locations along the embayment with varying exposure to the prevailing waves indicates that cumulative storm wave energy is the dominant driver of storm erosion at this site. This is followed by the pre-storm beach width, storm wave direction and to a minimal extent, storm wave period and water levels. A multi-linear regression model of storm erosion is developed and found to accurately predict shoreline change due to individual storm events (RMSE = 3.7 m–6.4 m). This work highlights the value of high-frequency shoreline data for storm erosion forecasting and provides a framework for real-time forecasting applications.

Bar and berm dynamics during transition from dissipative to reflective beach profile

Bar and berm morphology characterize the seasonal beach evolution, and determine the protection against storm erosion as well as the touristic use of beaches. Thus, they are of particular interest for coastal management and engineering in the nearshore zone. This study used large-scale wave flume experiments to observe the transition from fully dissipative to fully reflective beach profile at a high level of detail. Starting from a post-storm profile generated under energetic waves, a very low energy wave condition caused dissipation of the outer and the inner bar, shoreline recovery, and berm accretion. Measurements revealed feedback between hydrodynamics and beach profile evolution with an onshore shift of the wave breaking location. As a result, the magnitude and cross-shore evolution of wave asymmetry-related bedload net onshore and suspended net offshore transport changed. The relative magnitudes of the two transport components and the way they shifted relative to each other caused the observed beach recovery. Additionally, a link between bar and berm morphology (surf-swash sand exchange) was observed. The shifting breakpoint enabled sustained, wave asymmetry-related onshore transport in the inner surf zone, feeding the berm accretion which occurred through advective swash zone processes including berm overwash.

Foredune morphology: Regional patterns and surfzone–beach–dune interactions along the New South Wales coast, Australia

AbstractForedunes constitute a fundamental part of the coastal environment, acting as a buffer against storm erosion and natural coastal defence. Foredune size and volume play a pivotal role in determining coastal vulnerability to marine hazards, and the governing processes driving variations in foredune morphology, particularly over regional, or larger spatial scales, are still relatively poorly understood. This paper presents a regional‐scale investigation into foredune morphology along the New South Wales (NSW) coast (SE Australia), comprising not only a state‐wide inventory of foredune morphology for use in management/hazard assessment, but also testing long‐standing hypotheses and conceptual models of the physical processes that are driving, or influencing, foredune size. We applied a remote sensing protocol to 47 foredune environments, covering the length of the NSW coastline, comparing morphology (including dune height, width, shape, and volume) of established and incipient foredunes both along, and between, sandy embayments. Results, at regional spatial scales, show that the largest foredunes (volumes up to 850 m3 m−1) occur along the central coast as large, steep landforms; intermediate‐sized dunes (550–650 m3 m−1) were found on both the Mid‐north and Far North coast, and the smallest foredunes (334–380 m3 m−1) occur on the south coast as wider dunes with steep stoss slopes. Surfzone–beach state was found to be the most significant factor influencing foredune size and volume. The more energetic and more dissipative/high‐energy intermediate surfzone–beach states were correlated with larger foredunes (i.e. greater dune volume, width and crest elevation), whereas the lower‐energy surfzone–beach states generally had smaller dunes. Changes in vegetation cover, shoreline orientation and associated onshore (and alongshore) wind systems were also demonstrated to be important factors in determining foredune morphology. The largest foredunes in NSW generally occur to the north of embayments, with dune size decreasing southward with decreasing exposure to wind/wave energy.

Measurements of dune erosion processes during the RealDune/REFLEX experiments

Nearshore hydro- and morphodynamic data were collected during a field experiment under calm conditions, moderate conditions, and storm conditions with dune erosion in the collision regime. The experiment was conducted on the Sand Engine near Kijkduin, the Netherlands, from October 18, 2021, to January 7, 2022. Two artificial unvegetated dunes were constructed just above the high water line to measure storm erosion and dune impacts from higher water levels and waves. During the experiment, three storms occurred that resulted in significant erosion of both dunes. The collected hydrodynamic data include pressure sensor and velocimeter data along two cross-shore transects. The collected morphodynamic data include bathymetry and topography surveys, optical backscatter sensor data in the inner surf zone, and a continuous cross-shore line-scanning lidar data set of the dune face. This comprehensive data set can be used to (1) study relevant nearshore hydrodynamic and morphodynamic processes that occur during calm conditions, moderate conditions, and storm conditions with dune erosion in the collision regime, and (2) validate existing dune erosion models.

Photogrammetric Investigation of Storm-Induced Erosion Process on Sandy Beach Profile in Medium-Scale Flume

In this study, laboratory experiments were conducted to investigate the influence of changes in storm wave height and water level on beach response in a medium-scale wave flume. A schematic storm was simulated (rising, apex, and waning phases). A non-intrusive photogrammetric method was used to collect high-resolution and synchronous data regarding the free surface water elevation and bed level, from which shoreline location, sandbar position, cross-shore sediment transport rates, and nonlinear wave parameters were derived. The cross-shore sediment transport was in agreement with previous laboratory measurements, including the monotonous exchange from foreshore erosion to shoaling zone accretion in most stages of the storm simulation. The surf zone was the main region supplying sediment for beach morphology modification and sandbar generation. The degree of storm erosion was not completely determined by the largest wave height and water level or the cumulative wave power of the apex phase. The largest gradients of the wave parameter sequence change occurred in the rising phase, and this was the main factor generating efficient beachface erosion. It induced an increase in sandbar size, accompanied by the cross-shore motion of maximum velocity amplitude, more violent disturbances of wave nonlinearity, and increased surf zone erosion, with these factors increasing beach instability and leading to more severe storm erosion. The large wave height and water level resulted in shoreline retreat, with a more significant swash zone erosion under a higher runup. The offshore sediment transport turned toward the onshore direction as the original large sandbar deteriorated under the decreasing wave parameter sequence in the waning phase.

Mangroves as Coastal Protection for Restoring Low-Energy Waterfront Property

Mangroves offer vital ecological advantages including air and water filtration, coastal and estuarine habitat provision, sediment stabilization, and wave energy dissipation. Their intricate root systems play a key role in safeguarding shorelines from tsunamis and erosive storms by dissipating wave energy. Moreover, mangroves shield against boat wakes and wind-waves, thus naturally bolstering shoreline defense. Wave dissipation is a function of forest width, tree diameter, and forest density. Restoration efforts of juvenile mangroves in Florida’s Indian River Lagoon (IRL) aim to reduce wave energy in areas vulnerable to erosion. Physical model testing of wave dissipation through mangroves is limited due to the complexity in representing the mangrove structure, where prop roots are non-uniform in both diameter and location. Previous studies have quantified wave-dissipating effects through the use of scaled and parameterized mangrove structures. This study measures the dissipation effects of live mangroves in a wave flume, forced by conditions representative of the IRL. These measurements are used to validate a parameterized dowel model. Error between wave attenuation factors for the live mangrove and dowel system was on average 2.5%. Validation of the modularized dowel system allowed for further parameterized testing to understand forest structure effects, such as sediment stabilization and wave attenuation. Maximum wave attenuation achieved in this study was 27–35% corresponding to a 40–60% reduction in wave energy depending on the configuration of the system. The wave reduction resulted in a 50–70% decrease in sediment erosion from the berm. The dowel tests indicate a target minimum thickness for mangrove root systems of 0.6 m for shoreline stabilization and restoration in the IRL.

Development of a Bayesian network-based early warning system for storm-driven coastal erosion

Coastal hazards such as flooding and erosion can cause large economic and human losses. Under this threat, early warning systems can be very cost-effective solutions for disaster preparation. The goal of this study was to develop, test, and implement an operational coastal erosion early warning system supported by a particular method of machine learning. Thus, the system combines Bayesian Networks, and state-of-the-art numerical models, such as XBeach and SWAN, to predict storm erosion impacts in urbanized areas. This system was developed in two phases. In the development phase, all information required to apply the machine learning method was generated including the definition of hundreds of oceanic synthetic storms, modeling of the erosion caused by these storms, and characterization of the impact levels according to a newly defined eerosion iimpact index. This adimensional index relates the distance from the edge of the dune/beach scarp to buildings and the height of that scarp. Finally, a Bayesian Network that acted as a surrogate of the previously generated information was built. After the training of the network, the conditional probability tables were created. These tables constituted the ground knowledge to make the predictions in the second phase. This methodology was validated (1) by comparing 6-h predictions obtained with the Bayesian Network and with process-based models, the latest considered as the benchmark, and (2) by assessing the predictive skills of the Bayesian Network through the unbiased iterative k-fold cross-validation procedure. Regarding the first comparison, the analysis considered the entire duration of three large storms whose return periods were 10, 16, and 25 years, and it was observed that the Bayesian Network correctly predicted between 64% and 72% of the impacts during the course of the storms, depending on the area analyzed. Importantly, this method was also able to identify when the hazardous conditions disappeared after predicting potential consequences. Regarding the Regarding the second validation approach, second validation approach, the k-fold cross-validation procedure was applied to the peak of a set of varying storms and it demonstrated that the predictive skills were maximized (63%–72%) when including three nodes as input conditions of the Bayesian Network. In the operational phase, the system was integrated into the architecture of a forecast and early warning system that predicts emergencies in coastal and port zones in Portugal, and the alerts are issued to authorities every day. This study demonstrated that the two-phase approach developed here can provide fast and high-accuracy predictions of erosion impacts. Also, this methodology can be easily implemented on other sandy beaches constituting a powerful tool for disaster management.

Satellite observations of storm erosion and recovery of the Ebro Delta coastline, NE Spain

Storms and extremely energetic events may significantly impact the form and structure of beaches, and so cause erosive processes and coastal damages. Efficient management actions require an up-to-date and accurate knowledge of beach morphological changes, with the shoreline position being a good indicator of such changes. This work proposes the use of the open-source Shoreline Analysis and Extraction Tool (SAET) software for the definition of satellite-derived shorelines (SDSs) from L8 and Sentinel-2 imagery to reveal the shoreline position changes at the beaches of the Ebro Delta, NE Spain. Spatial-temporal models (STMs) of shoreline changes enable a characterisation of how the beaches responded to the storms of 2020. In conjunction with wave data, STMs enable an analysis of the erosive response to storm events, as well as a monitoring of subsequent beach recovery in the short and medium term (<1 year). Results show how Storm Gloria (January 2020, Hs max = 7.62 m) acted as a disruptive event and shifting point in the shoreline trend. As a response to that storm, major erosive processes occurred along the delta that caused an average shoreline retreat of 47 m. A progressive recovery during the spring and summer was mainly associated with periods of low wave energy. Nevertheless, by the end of the year a complete recovery had been achieved for about half of the coast, while the other half showed an average erosion of more than 10 m when compared to the pre-storm situation. Both the erosive and the recovery processes took place unevenly on different sections of the coast, probably dependent on factors such as the orientation of the beach and the pattern of longitudinal sediment transport along the coast.

INFLUENCE ON EVENT-SPECIFIC CALIBRATION DATA IN MODELLING SUBAERIAL STORM EROSION UNDER COMPLEX BATHYMETRY

Key parameters in the process-based model for morphological changes during storm events (i.e., XBeachX, Roelvink et al., 2018) should be adjusted to simulate morpho-hydrodynamics to a specific area of interest, leading to site-, profile-, and event-specific calibration. Although 2-dimensional simulations (area model) eliminate variability in calibrated parameters along with each profile in complex bathymetry, the degree to which datasets having different wave conditions influence model performance is still unclear in the area model at a given parameter space. The main objective of this study is to examine the influence of event-specific calibration data (single storm event and storm clusters) in modelling subaerial storm erosion under complex bathymetry. Using bathymetric field data over four different storm conditions, this study presented guidance for the selection of event-specific data in modeling calibration.

CART MODEL FOR PREDICTING DUNE EROSION BASED ON STORM INTENSITY AND BEACH MORPHOLOGY

Hurricane Sandy (2012) resulted in historic losses to the beach and dune system in many parts of New Jersey, leaving inland structures susceptible to wave and surge-induced damages. To defend against future threats, the state, in conjunction with the US Army Corps of Engineers, has pursued a statewide system of engineered beaches comprised of a dune paired with a beach nourishment. Currently in the state there is much debate over the degree of protection these projects currently offer, and how this may change in the future due to climate change. Process-based models can be used to try to answer these questions; however, as the number of test cases increase the computational costs may become prohibitive. Here, a data-driven modeling approach is used to assess the variability in the vulnerability of the beach-dune system in New Jersey to a range of storms. Inputs to the model include storm intensity as characterized by the Storm Erosion Index (SEI) (Miller and Livermont 2008) and parameterized pre-storm morphology. The resulting classification tree ensemble has been shown to accurately predict dune volume loss (as a percent) due to historical storms (Lemke and Miller, 2021). Here, the model is applied to further investigate how changes to beach-dune system and sea level rise affect the probability of dune impact.

Unravelling the future changes in rainfall erosivity over India under shared socio-economic pathways

The rainfall erosivity index or R-factor is a climate-driven parameter affecting soil erosion. We studied the spatial distribution of rainfall erosivity over India for the historical (1951 – 2014) and two future periods (near future: 2015–2040; far future: 2041–2070) under two shared socio-economic pathways (SSP245 and SSP585). We used 15-minute rainfall datasets from 70-gauge stations to establish a relationship between rainfall erosivity estimated from 15-minute rainfall data and those estimated from 30 and 60-minute rainfall data. Further, we used 30,203 erosive storm rainfall data from 329 stations in India to estimate the daily rainfall erosivity model parameters. After that, annual rainfall erosivity values of 329 stations, nine bio-climatic variables, and a Gaussian Process Regression (GPR) model were used to prepare rainfall erosivity maps for the historical and two future periods. The bias-corrected Coupled Model Intercomparison Project-6 (CMIP6) dataset of 13 models was used for deriving bioclimatic variables and rainfall erosivity estimation for the historical and future periods. We used the GPR model to find the historical period's mean annual rainfall erosivity value as 5225 MJ.mm.ha−1.h−1.year−1. The result shows a +13 % and +15 % increase in the mean rainfall erosivity over India in the near future under SSP245 and SSP585 projected pathways compared to the historical period. The increase in erosivity values in the far future is even higher at +22 % and +31 % under SSP245, and SSP585 projected pathways, respectively. The northwestern and central India is likely to have a pronounced increase in rainfall erosivity in the far future. The change in rainfall erosivity due to climate change might substantially impact India's land and water resources, which is mainly an agrarian economy. The outcome of the present study could help in better planning and management of land and water resources in the upcoming decades.

Assessing the controlling factors on watershed soil erosion during intense rainstorm events using radar rainfall and process-based modeling

The evaluation of erosion risk in dry areas is challenging because erosion is often an outcome of individual rainstorms and is highly dependent on rainfall spatiotemporal patterns and on local land-use and topography. This study integrates a hybrid erosion model with rainfall data from high-resolution weather radar to simulate soil erosion during 22 high-intensity flash-flood generating rainstorms in a Mediterranean watershed (69 km2). We examine erosion over individual hillslopes and their spatial average over the watershed, representing intra-watershed and watershed-scale erosion, respectively. Our objectives are to: (a) determine how intra-watershed erosion corresponds to various physiographic factors (rainfall, land-use, topography); (b) determine which of these factors contributes to intra-watershed erosion the most; (c) quantify the effect of temporal variations in rainfall intensities on storm-scale erosion in relation to land-use type. We use for the first time a hybrid erosion model (K2-RHEM-DWEPP) based on the watershed-scale KINEROS2 model, that integrates the hillslope-scale Dynamic WEPP (DWEPP) and RHEM models, which were individually developed to represent erosion processes in croplands and rangelands, respectively. Watershed-scale storm erosion is best correlated with spatially-averaged 10-minutes maximum intensities (R2 = 0.58), and the correlation decreases for longer durations (R2 ≤ 0.54). When the spatially-averaged 10-minutes maximum intensity is multiplied by the area that contributes sediment, a better correlation with watershed-scale erosion is observed (R2 = 0.75). Hillslope erosion rates are higher when both rainfall intensities and topographic slopes are high, while land-use has a second-order effect. Higher storms maximal intensities result in higher hillslope erosion rates, especially over croplands.Our conclusions are useful to target locations for conservation practices and to better understand the effects of climate change on soil erosion.

Demonstrating the value of beaches for adaptation to future coastal flood risk

Cost-effective coastal flood adaptation requires a realistic valuation of losses, costs and benefits considering the uncertainty of future flood projections and limited resources for adaptation. Here we present an approach to quantify the flood protection benefits of beaches accounting for the dynamic interaction of storm erosion, long-term shoreline evolution and flooding. We apply the method in Narrabeen-Collaroy (Australia) considering uncertainty in different shared socioeconomic pathways, sea-level rise projections, and beach conditions. By 2100, results show that failing to consider erosion can underestimate flood damage by a factor of 2 and maintaining present-day beach width can avoid 785 million AUD worth assets from flood damage. By 2050, the flood protection and recreational benefits of holding the current mean shoreline could be more than 150 times the cost of nourishment. Our results give insight on the benefits of beaches for adaptation and can help accelerate financial instruments for restoration.

An application of the Kentucky state Mesonet system to explore spatial and seasonal characteristics of erosive storm activity

Erosive storms resulting from heavy downpours are a common occurrence across the southeastern United States. We applied the Kentucky Mesonet (mesoscale network) system for a statewide spatial and seasonal analysis of key erosive storm characteristics (total storm precipitation, duration, maximum intensity and erosivity). Results confirmed the key role that low pressure systems, convection and topography play in distributing rainfall and storm characteristics across the state. Shorter, more intensive and more erosive events typically occurred towards the western side of the state across fall and spring, while seasonally summer dominates with shorter duration more intensive and erosive events further east.

Assessing coastline recession for adaptation planning: sea level rise versus storm erosion

The Sixth Assessment report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) states with high confidence that most sandy coasts around the world will experience an increase in coastal erosion over the twenty-first century. An increase in long term coastal erosion (coastline recession) along sandy coasts can translate into massive socio-economic impacts, unless appropriate adaptation measures are implemented in the next few decades. To adequately inform adaptation measures, it is necessary to have a good understanding of the relative importance of the physical processes driving coastline recession, as well as of linkages between consideration (or not) of certain processes and the level of risk tolerance; understandings that are hitherto lacking. Here, we apply the multi-scale Probabilistic Coastline Recession (PCR) model to two end-member sandy coastal types (swell dominated and storm dominated), to investigate where and when coastline recession projections are dominated by the differential contributions from Sea Level Rise (SLR) and storm erosion. Results show that SLR substantially increases the projected end-century recession at both types of coasts and that projected changes in the wave climate have only a marginal impact. An analysis of the Process Dominance Ratio (PDR), introduced here, shows that the dominance of storm erosion over SLR (and vice versa) on total recession by 2100 depends on both the type of the beach and the risk tolerance levels. For moderately risk-averse decisions (i.e. decisions accounting only for high exceedance probability recessions and hence do not account for very high amounts of potential recession—for example, the placement of temporary summer beach cabins), additional erosion due to SLR can be considered as the dominant driver of end-century recession at both types of beaches. However, for more risk-averse decisions that would typically account for higher potential recession (i.e. lower exceedance probability recessions), such as the placement of coastal infrastructure, multi-storey apartment buildings etc., storm erosion becomes the dominant process. The results of this study provide new insights on which physical processes need to be considered when and where in terms of numerical modelling efforts needed for supporting different management decisions, potentially enabling more streamlined and comprehensive assessments of the efficacy of coastal adaptation measures.

The morphodynamic response of a gravel barrier to unimodal and bimodal storm wave conditions

Gravel barrier beaches can offer natural protection to coastlines from adverse storm conditions. Understanding the morphodynamics of gravel barrier beaches is vital for the effective and sustainable management of these systems. Here, we use a synthetic dataset to investigate the morphodynamic response of the gravel barrier beach at Hurst Castle Spit, located on the Southwest coast of the United Kingdom, to both unimodal and bimodal storms. This spit is exposed to wind and swell waves propagating up the English Channel from the Southwest approaches and has suffered repeated storm erosion. The results are analyzed to identify the key drivers that govern the spatio-temporal gravel barrier morphodynamic responses to storms and to explore the morphodynamic states of the barrier. We found that the morphodynamic response of the barrier beach is strongly influenced by the combination of storm wave height and still water level. Further, the presence of swell waves can be a controlling factor in the barrier response.

A GIS-based approach for estimating fallow-season cropland soil erosion based on rainfall erosivity

ABSTRACT Rainfall erosivity describes the capability of rainfall to cause soil erosion from a surface during a storm. Currently, the average long-term annual and monthly rainfall erosivity for a location may be obtained from isoerodent maps using a sample of climate stations across the United States (US). However, at the state-scale these measures are sporadic, using rainfall data from an increasingly outdated period, which may not reflect possible changing rainfall erosivity. Focusing on the state of Kentucky, US, we developed rainfall erosivity grids from the state Mesonet system to determine if erosivity has changed spatially and temporally between the original erosivity datasets from the latter twentieth-century and 2011–2020. We further used the Mesonet-derived dataset to develop a GIS-based model to estimate potential soil erosion for croplands during the fallow-season when soil is most prone to erosive storms. Results indicated that monthly erosivity increased across the state between the two periods. Larger increases in erosivity during the latter part of the fallow-season resulted in higher estimated soil erosion from croplands, particularly to the west. These findings may provide guidance in identifying and targeting croplands at greatest potential risk of soil erosion during the fallow-season for additional monitoring, mitigation and management practices.

Quantitative assessment of the shoreline protection performance of geotextile sandbags at an in-situ coastal experimental station

The coast of China is periodically impacted by tropical cyclones and storm surges, and has experienced significant coastal erosion problems. Traditional “hard engineering” coastal protection measures used to protect Chinese sandy coasts from storm erosion are found to be expensive and less environmental and even make sandy beach disappearing. In this study, geotextile system as a more flexible material was developed and qualitatively compared with the traditional coastal protection measures. An in-situ permanent revetment was applied with durable geotextile sandbags on the coast of Chudao in China from October 2018 to October 2020, and it was designed for three different testing segments to optimize the stability and construction cost of geotextile sandbags. The field surveys were carried out to collect the in-situ data on beach profiles, wave dynamics, material durability, and sandbag revetment stability. In analyzing the two-year field data collected, it is found that the testing segment-2 wrapped with sheet of plastic geogrids is the most effective of the three testing segments in terms of their coat, structural stability and material durability, and that both the slope of the seabed and the design thickness of geotextile sandbag are the dominate factors responsible for the failure of sandbag structures.

Neglecting the effect of long- and short-term erosion can lead to spurious coastal flood risk projections and maladaptation

Flooding and erosion are among the most relevant hazards for coastal regions and although they are linked, their inherent complexity has typically led them to be addressed separately, potentially leading to highly uncertain estimates. This paper has three aims: (a) to present a methodology for coupling coastal flood projections with shoreline changes; (b) to quantify the effects of neglecting flooding and erosion coupling on future projections at a case study location; and (c) to analyse the relative importance of the climate-related uncertainty sources. We use a suite of statistical, process-based, and physics-based models to generate and downscale storms, compute water levels affected by storm morphodynamics and long-term profile changes and propagate flooding over topo-bathymetries that are modified to incorporate the impact of sea-level rise, longshore sediment transport and storm-driven erosion. We sample climate uncertainty by considering storm variability (synthetic generation) and ensembles of radiative forcing scenarios, regional climate models, and sea-level rise trajectories. For illustration purposes, we consider a 40-km coastal stretch in the Spanish Mediterranean. We find that that if the effect of erosion is neglected, the mean values of the total water level and flooded area can be either over- or underestimated by up to 18% and 22%, and up to 7% and 85%, respectively, with respect to our coupled results. The factors that most influence total water levels are storm erosion and profile geometry, highlighting the relevance of using real profiles. In the flooded area, longshore transport can play a fundamental enhancing role. We also find that the coupling approach used can contribute more to the projection of flooded areas than the choice of climate models and sea-level rise trajectories even by 2 100 (up to 76% versus 8% and 16%, respectively). We conclude that neglecting erosion effects on coastal flooding can have management implications, especially for urban beaches, leading to poor adaptation planning and maladaptation. • We develop coupled coastal flooding and erosion projections for 2050 and 2 100. • We consider uncertainties in scenarios, climate models, storms, and sea-level rise. • Uncoupling erosion and flooding leads to biases in water levels and flooded areas. • Coupling erosion and flooding can affect flooded areas more than sea-level rise.