Coastal communities worldwide face flooding threats from tides, surge, rain and rivers, yet wave-driven impacts are often inadequately considered in large-scale assessments due to high computational costs. The SFINCS model enables the efficient computation of wave-driven flooding, but until now it required boundary conditions from expensive models like XBeach. Here we overcome this problem and apply and validate a method to efficiently derive nearshore infragravity wave conditions using the fast wave spectral model SnapWave. These nearshore conditions are then used to drive dynamic infragravity waves in SFINCS. The method is validated using a laboratory case of a sandy beach and a field case from Hernani, Philippines, affected by Typhoon Haiyan (2013). This is a first application on a coral-reef lined coast, which is beyond the application area of the methodology to determine infragravity wave transfer. The results indicate that SFINCS can model overland wave heights and flooding extent with reasonable accuracy compared to XBeach, but with a significantly reduced computation time; from over three hours to just 20 s. Overall, the SFINCS-SnapWaveIG methodology demonstrates promise for large-scale applications in coastal hazard studies.

Coastal zones are areas of high economic and ecological value which are under threat of natural hazards such as flooding and erosion. Already 10percent percent of the world’s population lives below 10 meters above mean sea level (McGranahan et al., 2007) and this number is likely to grow in the future. Moreover, coastal zones will experience accelerated sea level rise and will be subject to potentially increasing intensities of extreme water levels, due to extra-tropical and tropical storms, which will aggravate the impact of natural hazards. These increased impacts and costs necessitate an approach to assess flooding and damages a priori for planning and mitigation purposes. It also calls for flood forecasting systems to alert authorities to be able to prepare for an impact and evacuate the population (Roy and Kovordányi, 2012). While a number of forecasting systems are available, these usually focus on a relatively small area and a small number of hazards, mostly total water level, and rarely impacts. This presentation is about upscaling to larger regions, including more physics and more impacts.

Wave-driven flooding is often neglected or included in an approximate way in large-scale flood hazard assessments and early warning systems, despite its significant contribution to coastal flood hazards. This study introduces a method to incorporate incident and infragravity wave processes into a fast compound flood model by extending the SFINCS software with the SnapWave stationary wave energy solver. This extension efficiently translates offshore incident and infragravity wave conditions to the nearshore, allowing for the estimation of incident-wave-induced setup and the resolution of wave runup and overtopping. A quadtree approach is employed to optimize the grid resolution for wave processes in the coastal zone. The approach is validated for Hurricane Florence (2018) along the North and South Carolina coastline of the United States, where observed offshore wave heights reached 10 m. The results illustrate that the impact of the hurricane extended hundreds of kilometers beyond the landfall area due to waves, highlighting its importance as coastal flood driver. In 19% of the coastline analyzed, wave contributions surpassed all other flood drivers combined, with waves contributing to an additional flooded area of 226 km2 and a flood volume of 62 million m3. The study also indicates that simpler parameterized methods for including wave-induced setup can lead to significant discrepancies in modeled water depths. The computational efficiency of the extended SFINCS model allows for the simulation of 1,000 km of coastline with limited computational resources. Hereby the critical role of wave effects in coastal compound flood hazard assessments could be demonstrated.

In the face of climate change, sea level rise, and increased human development on coasts and deltas, better quantification and understanding of coastal sediment transport is essential. Specifically, we need to know more about sediment pathways for system understanding and planning interventions (e.g., nourishments or beneficial reuse of dredged material). Sediment transport models like Delft3D or XBeach adopt an Eulerian (fixed-grid) approach, which is useful for quantifying rates of sediment transport and morphodynamic change. However, tracking the fate of sediment from specific sources using these models is complex and computationally expensive. To better simulate the pathways that sediment takes, we can adopt a Lagrangian (particle-tracking) approach, as is common in oceanographic and water quality studies. To fill these gaps, we present SedTRAILS (Sediment TRAnsport vIsualization and Lagrangian Simulator), a general-use process-based particle tracking model that is fast and easy to use for the coastal community. SedTRAILS provides a suite of analysis tools for better understanding and visualizing sediment pathways. what is special is also how we use it (e.g. querying particle fields to ask management questions, connectivity analysis).

Wave models are essential for coastal engineering applications, because the (nearshore) wave conditions are required for the design of coastal structures, important drivers of coastal floodings and coastal erosion. Various spectral wave models exist to model the wave propagation, wind growth and energy transfer within a spectrum (Booij, et al. 1996, Günther, et al., 1992). These models have been improved over the last years (e.g. Rogers et al., 2015) and are able to accurately be applied for various applications. As a consequence the wave models became rather slower in terms of computational time than faster. This constricts the use of state-of-the-art wave models in probablistic flooding forecasts or continental scale wave climate downscaling for forcing shoreline modelling. Both these types of applications demand ensemble mode simulations achieved running all simulations in parallel on a super computer. Given that uncertainties in forecasting outcomes are not only a result of model uncertainty but also forcing uncertainty (e.g. hurricane track, storm intensity), this study investigates two alternative approaches to modelling the spectral wave action balance. The first model aims to downscale climate models to the water depths and regions relevant for shoreline modelling. The second alternative model approach aims to provide input to continental scale probabilistic (hurricane-driven) wave forecasts.

The United Nations “Early Warnings for All” initiative aims to protect everyone from water hazards, among other hazards, through life-saving early warning systems by the end of 2027. Flood warning systems on large spatial scales need to be developed that can run within an operational forecast window. The reduced-physics solver SFINCS (Leijnse et al. 2021) was developed, combining all relevant processes to model compound flooding events, with strongly reduced computation times. However, large area models require large input data sets. While global and regional data are becoming more widely available and are continuously updated, setting up models becomes too cumbersome to do manually. Semi- automated and reproducible workflows may help the modeler to select physically realistic model extents and use appropriate data with acceptable quality with an appropriate resolution in order to ensure a good model performance.

Coastal communities worldwide are under threat of flooding due to hazards such as (extra-) tropical storms, spring tides, wind sea and swell waves, high river discharges and heavy rainfall events (Mousavi et al., 2011). In some coastal areas, waves can be the dominant driver of extreme water levels (e.g. Parker et al., 2023), but for regional to continental scales these are often not included in coastal flooding assessments, due to the high computational expense of numerical models (e.g. XBeach; Roelvink et al., 2009). Recent literature has shown that it is possible to model large coastlines in fast reduced-complexity compound flood models (e.g. SFINCS; Leijnse et al., 2021), for instance for thousands of kilometers of Australia in an early warning system (Leijnse et al., 2022). However, to be able to include dynamic wave runup and overtopping in such a system, we need to derive nearshore infragravity (IG) wave conditions in a fast way, without relying on computationally expensive advanced numerical models. Recent advancements in SFINCS and a coupled fast stationary wave spectral model, called SnapWave, have the potential to solve this. In this study we combine many of these advancements and show how to include IG waves as boundary conditions in SFINCS. Afterwards we demonstrate that one can now finally include dynamic IG wave-resolving runup and overtopping processes in large- scale flooding assessments.

This paper presents an efficient, implicit, unstructured-grid wave propagation model, SnapWave (Roelvink, 2025), which provides a simple and fast way to predict nearshore wave conditions at specified locations, for coastline models such as ShorelineS, or wave fields and their forcing of flows, to be used in other models, such as Delft3D-FM, XBeach or SFINCS. We describe the numerical method and verify the correct implementation by comparing against analytical solutions for schematized cases. We then test the model application in four different coastal settings by propagating time series of ERA5 hourly wave conditions to observation points nearshore and through the surf zone. We conclude that the model is robust, easy to set up and fast, and can be applied on open coasts worldwide.

Accurate flood risk assessments and early warning systems are needed to protect and prepare people in coastal areas from storms. In order to provide this information efficiently and on time, computational costs in flood models need to be kept as low as possible. One way to achieve this goal is to apply subgrid corrections to relatively coarse computational grids. Previously, these have been used in full-physics circulation models. In this paper, for the first time, we developed subgrid corrections for the linear inertial equations (LIEs) that account for bed level and friction variations. They were implemented in the Super-Fast INundation of CoastS (SFINCS) model version 2.1.1 Dollerup release. Pre-processed lookup tables that correlate water levels with hydrodynamic quantities make more precise simulations with lower computational costs possible. These subgrid corrections have undergone validation through several conceptual and real-world application scenarios, including rainfall-induced flooding during a hurricane and tidal propagation in an estuary. We demonstrate that the subgrid corrections for linear inertial equations significantly improve model accuracy while utilizing the same resolution without subgrid corrections. In terms of computational efficiency, subgrid corrections increase computational costs by 38 %–128 %. However, this yields a 35–50-time speedup since coarser model resolutions with subgrid corrections can provide the same accuracy as finer resolutions without subgrid corrections. Limitations are also discussed; for example, when grids do not adequately resolve river meanders, fluxes can be overestimated. Our findings show that subgrid corrections are a useful asset for hydrodynamic modelers striving to achieve a balance between accuracy and efficiency.

The 2024 Atlantic hurricane season was active with 5 landfalling hurricanes in the USA: Beryl, Debby, Francine, and major hurricanes Helene and Milton. In the framework of the National Oceanographic Partnership Program “Hurricane Coastal Impacts” project, we forecasted the wave conditions, water levels, flooding, beach and dune erosion, and infra-structural impacts on building and bridges due to the combination of rainfall-induced flooding, surge, waves and tides for all these hurricanes. To this end, we developed and implemented an operational system of coupled numerical models such as SFINCS - which is used innovatively as a surge model and as an overland flood model -, a new fast wave model Hurrywave, the morphodynamical model Xbeach and the damage model FIAT, all developed at Deltares. The models are driven by operational US Navy COAMPS-TC and NOAA GFS forecasts. The presentation will show the model forecast results, validated against in-situ and remote-sensed observations obtained by project partners. The presentation will demonstrate the relative importance of typo-bathy (vertical) accuracy and the presence of vegetation.The system also predicts the uncertainty bands in the forecasts and their evolution over time as the hurricane nears landfall. The system is transferrable to other data-rich and data-poor coasts, such as Mozambique of which an example will be shown.The information that the system provides gives insight to coastal authorities to make decision on anticipatory actions and emergency response. The results of this work are of interest to geomorphological scientists, DRR experts and coastal authorities.

Coastal communities worldwide are under threat of flooding due to multiple hazards (Mousavi et al., 2011). In some coastal areas, waves are the dominant driver of extreme water levels (Parker et al., 2023). However, for regional to continental scales coastal flooding assessments, waves are often not or only crudely accounted for, due to the high computational expense of wave resolving numerical models (e.g., XBeach; Roelvink et al., 2009). Recently, Leijnse et al. (2021) has shown that it is possible to model waves in a fast reduced-complexity compound flood model such as SFINCS. However, boundary conditions for SFINCS are still derived from a computationally expensive numerical model like XBeach or are generated using 1D based (meta) models (e.g., Bertoncelj et al., 2021), that do not (fully) account for alongshore varying 2D effects. To be able to include dynamic wave runup and overtopping in a 2D fast flooding model, we need to derive nearshore infragravity wave conditions also in a fast way. To overcome this challenge, we introduce an integrated model approach, where we couple a fast stationary wave spectral model (SnapWave) to the fast compound flood model SFINCS. Besides incident waves, the SnapWave model can also efficiently estimates nearshore infragravity wave conditions (Leijnse et al. 2024, in review). Together with a nearshore wave generating boundary condition (van Ormondt et al., 2023), our new integrated wave-resolving approach internally drives the flood model SFINCS with waves and can therefore assess the effects of waves on coastal flooding. The performance is validated for several laboratory tests and against XBeach simulations of van Ormondt et al. (2021). References Bertoncelj, V., Leijnse, T., Roelvink, F., Pearson, S., Bricker, J., Tissier, M., and van Dongeren, A.: Efficient and accurate modeling of wave-driven flooding on coral reef-lined coasts: Case Study of Majuro Atoll, Republic of the Marshall Islands, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5418, https://doi.org/10.5194/egusphere-egu21-5418, 2021. Leijnse, van Ormondt, Nederhoff, van Dongeren (2021). Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes. Coastal Engineering, 163, 103796. https://doi.org/10.1016/j.coastaleng.2020.103796 Leijnse, van Ormondt, van Dongeren, Aerts, Muis (2024, in review). Estimating nearshore infragravity wave conditions at large spatial scales. Frontiers in Marine Science. Mousavi, Irish, Frey, Olivera, Edge (2011). Global warming and hurricanes: The potential impact of hurricane intensification and sea level rise on coastal flooding. Climatic Change, 104(3–4), 575–597. https://doi.org/10.1007/s10584-009-9790-0 Parker, Erikson, Thomas, Nederhoff, Barnard, Muis (2023). Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast. Natural Hazards. https://doi.org/10.1007/s11069-023-05939-6 Roelvink, Reniers, van Dongeren, van Thiel de Vries, McCall, Lescinski (2009). Modelling storm impacts on beaches, dunes and barrier islands. Coastal Engineering, 56(11–12), 1133–1152. https://doi.org/10.1016/j.coastaleng.2009.08.006 Van Ormondt, Roelvink, van Dongeren (2021). A Model-Derived Empirical Formulation for Wave Run-Up on Naturally Sloping Beaches. Journal of Marine Science and Engineering, 9(11), 1185. https://doi.org/10.3390/jmse9111185 Van Ormondt, Roelvink, van Dongeren (2023). Wave effects in a rapid compound flood model. 17th International Workshop on Wave Hindcasting and Forecasting.

Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones in coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are thirty times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more area will be flooded, regardless if that flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.

Infragravity waves may contribute significantly to coastal flooding, especially during storm conditions. However, in many national and continental to global assessments of coastal flood risk, their contribution is not accounted for, mostly because of the high computational expense of traditional wave-resolving numerical models. In this study, we present an efficient stationary wave energy solver to estimate the evolution of incident and infragravity waves from offshore to the nearshore for large spatial scales. This solver can be subsequently used to provide nearshore wave boundary conditions for overland flood models. The new wave solver builds upon the stationary wave energy balance for incident wave energy and extends it to include the infragravity wave energy balance. To describe the energy transfer from incident to infragravity waves, an infragravity wave source term is introduced. This term acts as a sink term for incident waves and as a complementary source term for infragravity waves. The source term is simplified using a parameterized infragravity wave shoaling parameter. An empirical relation is derived using observed values of the shoaling parameter from a synthetic dataset of XBeach simulations, covering a wide range of wave conditions and beach profiles. The wave shoaling parameter is related to the local bed slope and relative wave height. As validation, we show for a range of cases from synthetic beach profiles to laboratory tests that infragravity wave transformation can be estimated using this wave solver with reasonable to good accuracy. Additionally, the validity in real-world conditions is verified successfully for DELILAH field case observations at Duck, NC, USA. We demonstrate the wave solver for a large-scale application of the full Outer Banks coastline in the US, covering 450 km of coastline, from deep water up to the coast. For this model, consisting of 4.5 million grid cells, the wave solver can estimate the stationary incident and infragravity wave field in a matter of seconds for the entire domain on a regular laptop PC. This computational efficiency cannot be provided by existing process-based wave-resolving models. Using the presented method, infragravity wave-driven flooding can be incorporated into large-scale coastal compound flood models and risk assessments.

Abstract. Accurate flood risk assessments and early warning systems are needed to protect and prepare people in coastal areas from storms. In order to provide this information efficiently and on time, computational costs need to be kept as low as possible. Reduced-complexity models using linear inertial equations and subgrid approaches have been used previously to achieve this goal. In this paper, for the first time, we developed a subgrid approach for the Linear Inertial Equations (LIE) that account for bed level and friction variations. We implemented this method in the SFINCS model. Pre-processed lookup tables that correlate water levels with hydrodynamic quantities make more precise simulations with lower computational costs possible. These subgrid corrections have undergone validation through a variety of conceptual and real-world application scenarios, including analyses of hurricane hazards and tidal fluctuations. We demonstrate that the subgrid corrections for Linear Inertial Equations significantly improve model accuracy while utilizing the same resolution without subgrid corrections. Moreover, coarser model resolutions with subgrid corrections can provide the same accuracy as finer resolutions without subgrid corrections. Limitations are discussed, for example, when grids do not adequately resolve river meanders, fluxes can be overestimated. Our findings show that subgrid corrections are an invaluable asset for hydrodynamic modelers striving to achieve a balance between accuracy and efficiency.

Faced with accelerating sea level rise and changing ocean storm conditions, coastal communities require comprehensive assessments of climate-driven hazard impacts to inform adaptation measures. Previous studies have focused on flooding but rarely on other climate-related coastal hazards, such as subsidence, beach erosion and groundwater. Here, we project societal exposure to multiple hazards along the Southeast Atlantic coast of the United States. Assuming 1 m of sea level rise, more than 70

Tropical-cyclone impacts can have devastating effects on the population, infrastructure, and natural habitats. However, predicting these impacts is difficult due to the inherent uncertainties in the storm track and intensity. In addition, due to computational constraints, both the relevant ocean physics and the uncertainties in meteorological forcing are only partly accounted for. This paper presents a new method, called the Tropical Cyclone Forecasting Framework (TC-FF), to probabilistically forecast compound flooding induced by tropical cyclones, considering uncertainties in track, forward speed, and wind speed and/or intensity. The open-source method accounts for all major relevant physical drivers, including tide, surge, and rainfall, and considers TC uncertainties through Gaussian error distributions and autoregressive techniques. The tool creates temporally and spatially varying wind fields to force a computationally efficient compound-flood model, allowing for the computation of probabilistic wind and flood hazard maps for any oceanic basin in the world as it does not require detailed information on the distribution of historical errors. A comparison of TC-FF and JTWC operational ensembles, both based on DeMaria et al. (2009), revealed minor differences of <10 %, suggesting that TC-FF can be employed as an alternative, for example, in data-scarce environments. The method was applied to Cyclone Idai in Mozambique. The underlying physical model showed reliable skill in terms of tidal propagation, reproducing the storm surge generation during landfall and flooding near the city of Beira (success index of 0.59). The method was successfully applied to forecasting the impact of Idai with different lead times. The case study analyzed needed at least 200 ensemble members to get reliable water levels and flood results 3 d before landfall (<1 % flood probability error and <20 cm sampling errors). Results showed the sensitivity of forecasting, especially with increasing lead times, highlighting the importance of accounting for cyclone variability in decision-making and risk management.

The recent 2022 large scale rainfall events in Australia’s east coast have put in evidence the region’s vulnerability to severe flooding events. In the future, climate change and sea level rise are expected to exacerbate these vulnerabilities. Accurate and reliable flood predictions are required to develop effective emergency management practices as well as appropriate adaptation and mitigation strategies. During events, detailed predictions of flooding can improve early warning systems and enhance the preparedness of relevant governments and communities. To this end, numerical models capable of simulating compound flooding produced by different drivers (e.g., marine, pluvial and riverine) are needed. These models must also be developed at an appropriate level of detail to produce accurate and relevant flood maps.

&lt;p&gt;The ability to anticipate the changes in water levels, waves and current velocities associated with storms is critical to determining the storm damages including morphological changes and coastal structure interaction. Typically, storm surge forecasts are generated using complex modeling systems such as ADCIRC, COAWST or Delft3D which solve the Navier-Stokes equations driven by the wind and wave forcing. These models often take time and effort to set up and needs significant computational resource to produce results at the required resolutions. The advantages are obvious &amp;#8211; all the relevant physics are represented with a high degree of accuracy in these models. However, the accuracy of the models is often overshadowed by the uncertainties in the forecast of the storm itself. To capture the effects of these uncertainties, we need to resort to ensemble simulations, which brings us to the main disadvantage of these systems &amp;#8211; they require significant computational effort to execute even one of the scenarios. Thus, to get timely information about the surge, it is necessary to either reduce the number of members in the ensemble or reduce the resolution at which the model simulates the event, thereby reducing the confidence in the model results. Here we investigate an alternative approach to storm surge predictions &amp;#8211; use a reduced complexity model to compute the surge and compare the results to a full model as well as to data to assess the effectiveness of the models. As a case study, we will compare the two approaches using the forecasts from Hurricane Ida (2021) which impacted Louisiana. We will use the Delft3D FM system as representative of the full physics model and compare the results to that produced by SFINCS, which is a reduced complexity model. Comparisons of water levels at available NOAA tide stations are used to validate the model and quantify errors in the system. Wave statistics are compared against available buoys.&lt;/p&gt;

Coastal Sediments 2023, pp. 2653-2658 (2023) No AccessFORECASTING HURRICANE IMPACTS ON COASTS USING COASTAL STORM MODELING SYSTEM (COSMOS)AP VAN DONGEREN, JAY VEERAMONY, MAARTEN VAN ORMONDT, KEES NEDERHOFF, and ALLISON PENKOAP VAN DONGERENUnit of Marine and Coastal Management, Deltares, Boussinesqweg 1, Delft, The NetherlandsDepartment Coastal and Urban Risk and Resilience, IHE-Delft, Westvest 7, Delft, The Netherlands, JAY VEERAMONYNaval Research Lab, Stennis Space Center, Mississippi, United States, MAARTEN VAN ORMONDTDeltares USA, 8601 Georgia Avenue #508, Silver Spring, MD 20910, United States, KEES NEDERHOFFDeltares USA, 8601 Georgia Avenue #508, Silver Spring, MD 20910, United States, and ALLISON PENKONaval Research Lab, Stennis Space Center, Mississippi, United Stateshttps://doi.org/10.1142/9789811275135_0242Cited by:0 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: This paper describes the development of a Coastal Storm Modeling System (CoSMoS) that incorporates hydrodynamics and morphodynamics in a computationally efficient way to make real-time forecasts of hydrodynamic and morphodynamic hazards during hurricanes, which includes morphodynamics, wave runup and rainfall-induced flooding. The model chain consists of surge, wave, overland flooding and erosion/deposition models. It has been validated using two previous hurricanes and applied to the recent impact due to Hurricane Ian. The model results for water level, flooding and erosion show good agreement with the observations. The model chain will in coming years be extended to include uncertainties in the impact due to variation in the hurricane's track, forward speed and intensity. Accurate forecasts of flooding due to tides, surge, rain and waves, and the morphodynamic changes of coastlines may potentially help in risk mititgation for coastal communities threatened by large storms and hurricanes. FiguresReferencesRelatedDetails Recommended Coastal Sediments 2023Metrics History PDF download

Coastal communities are susceptible to flooding due to flood drivers such as high tides, surge, waves, rainfall, and river discharges. Recent hurricanes such as Harvey, Florence, and Ian brought devastating impacts from combinations of high rainfall and storm surge, highlighting the need for resilience and adaptation planning to consider compound flood events when evaluating options to reduce future flood risk. Flood risk assessments often focus on a single flood driver (e.g. storm surge) due to the complexity of accounting for compound flood drivers. However, neglecting these compound flood effects can grossly underestimate the total flood risk. A probabilistic compound flood hazard analysis considers all compound events that lead to flooding, estimates their joint probabilities, simulates the flood response, and applies a probabilistic computation technique to translate flood responses and probabilities into probabilistic flood maps (such as the 100-year flood map). Probabilistic flood maps based on compound events can be used to assess risk more accurately for current and future conditions, with and without additional adaptation measures. In this paper we present an example of a probabilistic compound flood hazard analysis for the city of Charleston, South Carolina, considering tide, surge, and rainfall, for both hurricane and non-hurricane events. Charleston is regularly confronted with compound flood events, which are expected to worsen with sea level rise and more frequent tropical storms. Starting with an initial set of over 1,000 synthetic compound events, selection techniques described in the paper led to a final set of 207 compound events. The fast compound flood model SFINCS simulated the flood response for each event and, using numerical integration, compound flood return-period maps were created for Charleston, under current and future sea level rise conditions.