When coastal and river floods occur concurrently or in close succession, they can cause a compound flood with significantly higher impacts. While our understanding of compound flooding has improved over the past decade, no studies to date have assessed the spatial correlation of compound flooding. To address this gap, we develop a framework that captures dependence between coastal total water level and river discharge across a set of locations along the US coastline. Using 41 years of observed data from 41 station combinations, we stochastically model 10 000 years of spatially-joint events of extreme sea level and river discharge based on their dependence structure and cooccurrence rate. We define potential compound flooding as events in which both drivers exceed their respective 99th percentile thresholds. Results based on our simulated large event set show that the US West coast shows high spatial correlation of potential compound flooding. Among all three coasts, the West coast has the highest frequency of widespread potential compound flooding, with around 50 % of compound events arising at multiple locations simultaneously. We identify two clusters with mutually high joint occurrence rates of simultaneous compound events on this coast, namely (1) Charleston - Crescent City - North Spit, and (2) Santa Monica - Los Angeles - La Jolla. Widespread compound events are less frequent on the East coast where approximately 30 % of potential compound flooding may affect multiple locations. Moderate spatial dependence is observed in the central region and weaker spatial dependence for the remaining locations on this coast. In contrast, the Gulf coast shows the weakest spatial correlation, where over 82 % of compound events only affect single locations. Our findings highlight the importance of accounting for spatial dependence in compound flood assessments. Our large set of stochastic spatially-joint events can be used as boundary conditions for the hydrologic-hydraulic models to simulate the surface inundation and further assess risks of compound flooding in low-lying coastal and estuarine areas.

Compound flooding results from the interaction of rainfall, river discharge, and coastal surge, posing significant challenges for risk assessment due to its stochastic and multivariate nature. While hydrodynamic models accurately simulate these processes, their high computational cost limits their use in probabilistic analyses requiring large ensembles. Surrogate models provide a faster alternative, but most existing approaches focus on single flood drivers or static peak inundation, reducing their applicability for dynamic compound events. A key gap remains in physics‑guided surrogate models that can both reproduce spatiotemporal compound flood dynamics and assess driver‑specific model skill and dominant flood mechanisms. This study evaluates a hybrid physics‑guided SFINCS–LSG surrogate model, which combines low‑resolution SFINCS simulations with Empirical Orthogonal Function (EOF) decomposition and Sparse Gaussian Process learning to emulate high‑resolution flood dynamics. Two contrasting case studies, Charleston, South Carolina, USA, and Brisbane, Australia, are used to assess model performance under diverse compound flood conditions. The SFINCS–LSG framework achieves 50–150× computational speed‑ups while maintaining good accuracy, with median flood‑depth RMSEs of 0.06 m (Charleston) and 0.36 m (Brisbane), and flood‑extent CSI values of 0.96 (Charleston) and 0.91 (Brisbane). However, the performance varies by flood type, and EOF‑based compression introduces noise that limits identification of dominant driver zones. Overall, the framework shows strong potential for real‑time forecasting and probabilistic risk analysis.

We evaluate the performance of the Super-Fast INundation of CoastS (SFINCS) hydrodynamic model for simulating riverine floods, combined with a fully automated open-source data preprocessing pipeline. To do this, we assessed the simulated extent of 499 historic flood events against the satellite derived flood extents using the Critical Success Index (CSI) as a performance metric. We utilised simulated discharges from the Global Flood Awareness System (GloFAS) hydrological model and found that SFINCS performance improved with upstream basin size, with a global mean CSI of 0.42 for basins with large upstream area (>1000 km2) and a CSI of 0.29 for basins with small upstream area (<50 km2). Our results illustrate the importance of accurate discharge data input to flood hazard simulations. When the (globally simulated) GloFAS data is replaced with observed discharge data for ten events in the US, the CSI improved from 0.39 to 0.67. These results suggest that global hydrological model performance limits the accuracy of the flood hazard simulations. Our findings also showed a significant improvement in the CSI (from 0.37 to 0.57) when changing to a higher-resolution elevation input by contrasting a ∼1 m digital elevation model (DEM; 3DEP) with our default ∼30 m global DEM (FABDEM) in six US events. Sensitivity analysis of bathymetric calculations revealed a systematic underestimation of the default 2-year return period estimated by GloFAS discharge, likely driven by underrepresentation of annual block maxima, which resulted in underestimated channel dimensions. All of these factors resulted in a loss of detail, which impacted model performance, especially in smaller headwater rivers. We recommend to improve the estimation of bathymetry, for instance by employing the “gradually varying solver” method or using data from the SWOT mission. Furthermore, incorporating additional validation data which ideally includes flood depth measurements can largely enhance our understanding of the model performance.

Many low-lying coastal areas are highly vulnerable to compound flooding induced by tropical cyclones (TCs), which often generate storm tides, intense rainfall, and elevated river discharge simultaneously. Despite their significant impacts, the spatiotemporal changes and future hazards of TC-induced compound flooding under climate change remain poorly understood for many coastal regions. This study presents a novel integrated hazard assessment framework to evaluate TC-induced compound flooding across 60 cities in Southeast China, one of the world's most TC-prone regions. Using TC tracks detected from the MRI-AGCM3-2-S climate model combined with simulations using a cascade of hydrodynamic models, we simulate undefended compound flood hazards driven by rainfall, storm tides, and river discharge under the current climate (1960-2014) and future projections under the SSP585 scenario (2015-2099). Our results indicate that, according to simulations using the MRI climate model, more than half of the 60 cities in Southeast China are projected to experience more severe TCinduced compound flooding under future climate scenarios. Shanghai, in particular, is projected to experience the largest increases in compound flood hazard, with the maximum flood volume rising by 83.6%. Our analysis of flood drivers shows that flooding in 6 cities transition from single driver to compound-dominated type. For instance, Suzhou and Nantong are projected to shift from rainfall-dominated flooding to compound-dominated flooding. A detailed case study of Shanghai indicates a significant spatial expansion of areas dominated by compound flood drivers, with the flood-prone area fraction projected to increase from 60.6% to 78.1% under future climate scenario. These findings highlight the growing hazards of TC-induced compound flooding in Southeast China due to climate change. We recommend future research to increase the robustness of the approach by including multiple climate models and collect data on flood defenses to further refine the model outcomes.

Climate risks are increasing globally due to climate change, driven by intensifying climate hazards and changes in socioeconomic conditions that drive exposure and vulnerability. Climate Risk Assessments (CRAs) constitute a tool to understand such risks based on the analysis of geospatial datasets. However, CRA data are often scattered across different platforms, thereby inhibiting their Findability, Accessibility, Interoperability, and Reusability (FAIR). To make CRA data FAIR, we develop Climate Risk STAC, a living metadata catalog of open-access geospatial datasets that is hosted in a collaborative environment for continuous development. Climate Risk STAC (version 1.0) currently includes 214 metadata entries from nine different hazards, five types of exposed elements, and seven vulnerability categories. All data entries can be explored in a user-friendly browser which eases data selection. We encourage contributions of new datasets to maintain a growing, community-led catalog that reflects state-of-the-art CRA concepts and data.

Seeger and Minderhoud (2026) recently published a paper on an isolated geodetic aspect of coastal flood hazards modelling frameworks. While coastal modelling frameworks have improved over the last three decades, multiple uncertainties and biases remain as indicated in the literature. The approach proposed by Seeger and Minderhoud (2026) to correctly reference sea levels to coastal digital elevation models (DEMs) only considers one dimension of this problem. It also overlooks geoid and sea-level errors that are potentially larger than the single sea-level datum correction that they propose. Hence their work is not reducing uncertainties as much as they claim. It is also incorrect that geodetic uncertainties are ignored by coastal scientists and assessments reports of the Intergovernmental Panel on Climate Change (IPCC). At national to local scales, mean and extreme sea levels are routinely tied to vertical datums, including in assessments of flood hazards and adaptation plans and the suggested correction by Seeger and Minderhoud is therefore irrelevant for those studies. Therefore, their strong claims on the importance of this single correction are overstated and widely misleading, calling into question their Artificial-Intelligence-based method used to audit previous studies. Regardless, the urgency of climate action and coastal adaptation is clear.

Compound flooding, where the combination or successive occurrence of two or more flood drivers leads to an extreme impact, can greatly exacerbate the adverse consequences associated with flooding in coastal regions. This paper reviews the practices and trends in coastal compound flood research methodologies and applications, as well as synthesizes key findings at regional and global scales. Systematic review is employed to construct a literature database of 271 studies relevant to compound flood hazards in a coastal context. This review explores the types of compound flood events, their mechanistic processes, and synthesizes the definitions and terms exhibited throughout the literature. Considered in the review are six flood drivers (fluvial, pluvial, coastal, groundwater, damming/dam failure, and tsunami) and five precursor events and environmental conditions (soil moisture, snow, temp/heat, fire, and drought). Furthermore, this review summarizes the trends in research methodology, examines the wide range of study applications, and considers the influences of climate change and urban environments. Finally, this review highlights the knowledge gaps in compound flood research and discusses the implications of review findings on future practices. Our five recommendations for future compound flood research are to: 1) adopt consistent definitions, terminology, and approaches; 2) expand the geographic coverage of research; 3) pursue more inter-comparison projects; 4) develop modelling frameworks that better couple dynamic earth systems; and 5) design urban and coastal infrastructure with compound flooding in mind. We hope this review will help to enhance understanding of compound flooding, guide areas for future research focus, and close knowledge gaps.

Modelling of compound flood events, the assessment of their impact, and assessing mitigation and adaptation measures is in increasing demand for local authorities and stakeholders to support their decision making. Additionally, the severity of extreme events driving compound flooding, including storms and heavy rainfall, is projected to increase under climate change. To support local communities in flood risk management, complex modelling systems involving multiple cross-disciplinary models need to be orchestrated in order to effectively and efficiently run a wide range of what-if scenarios or historical events to understand the drivers and impacts of compound floods. The large volume and variety of data needed to configure the necessary models and simulate events strain the reproducibility of modelling frameworks, while the number of events and scenarios demand increasingly powerful computing resources. Here we present a solution to these challenges using automated workflows, leveraging the Common Workflow Language standard. The presented workflows update a base model configuration for a user-specified event or scenario, and automatically reruns multiple defined scenarios. The models are executed in containers and dispatched using the StreamFlow workflow manager designed for hybrid computing infrastructures. This solution offers a single, uniform interface for configuring all models involved in the model train, while also offering a single interface for running the model chain locally or on high performance computing infrastructures. The allows researchers to leverage data and computing resources more efficiently and provide them with a larger and more accurate range of compound flood events to support local authorities and stakeholders in their decision making.

Climate risks are increasing globally due to climate change, driven by intensifying climate hazards (e.g. storms, floods) and changes in socioeconomic conditions that drive exposure and vulnerability. Climate Risk Assessments (CRAs) constitute a tool to understand such risks under current and future conditions, based on the analysis of geospatial datasets. However, CRA data are often scattered across different data platforms, therefore inhibiting their Findability, Accessibility, Interoperability, and Reusability (FAIR). Consequently, selecting appropriate datasets for the CRA at hand can be a daunting and time-consuming task.To make CRA data FAIR, we develop Climate Risk STAC, a living metadata catalog of open-access geospatial datasets that is hosted in a collaborative environment for further development. Climate Risk STAC (version 0.1) includes 214 data entries of 84 global-scale datasets from nine different hazards, five types of exposed elements, and seven vulnerability categories. All data entries can be explored in a user-friendly browser which eases selection of suitable data. We further encourage contributions of new datasets, thereby facilitating a continuously growing, community-led catalog that reflects the current state-of-the-art in CRA concepts and data. Version 0.1 currently focuses on global-scale geospatial data. Due to its flexible and collaborative design, the catalog can easily be extended to accommodate datasets from other domains and at other spatial scales. Climate Risk STAC is available at https://doi.org/10.5281/zenodo.14018438.

Flooding is the natural hazard most likely to affect individuals and can be driven by rainfall, river discharge, storm surge, tides, and waves. Compound floods result from their co-occurrence and can generate a larger flood hazard when compared to the synthetic flood hazard generated by the respective flood drivers occurring in isolation from one another. Current state-of-the-art stochastic compound flood risk assessments are based on statistical, hydrodynamic, and impact simulations. However, the stochastic nature of some key variables in the flooding process is often not accounted for as adding stochastic variables exponentially increases the computational costs (i.e., the curse of dimensionality). These simplifications (e.g., a constant flood driver duration or a constant time lag between flood drivers) may lead to a mis-quantification of the flood risk. This study develops a conceptual framework that allows for a better representation of compound flood risk while limiting the increase in the overall computational time. After generating synthetic events from a statistical model fitted to the selected flood drivers, the proposed framework applies a treed Gaussian process (TGP). A TGP uses active learning to explore the uncertainty associated with the response of damages to synthetic events. Thereby, it informs regarding the best choice of hydrodynamic and impact simulations to run to reduce uncertainty in the damages. Once the TGP predicts the damage of all synthetic events within a tolerated uncertainty range, the flood risk is calculated. As a proof of concept, the proposed framework was applied to the case study of Charleston County (South Carolina, USA) and compared with a state-of-the-art stochastic compound flood risk model, which used equidistant sampling with linear scatter interpolation. The proposed framework decreased the overall computational time by a factor of 4 and decreased the root mean square error in damages by a factor of 8. With a reduction in overall computational time and errors, additional stochastic variables such as the drivers' duration and time lag were included in the compound flood risk assessment. Not accounting for these resulted in an underestimation of 11.6 % (USD 25.47 million) in the expected annual damage (EAD). Thus, by accelerating compound flood risk assessments with active learning, the framework presented here allows for more comprehensive assessments as it loosens constraints imposed by the curse of dimensionality.

Recent research has considerably advanced our ability to model extreme surges. Yet, simulating unprecedented events, i.e. events which are more extreme than observed in historical datasets, remains challenging. To some extent, anticipating such events is possible by accounting for the full range of climate variability. Using a 525-year synthetic dataset from a seasonal reforecast archive, this study uncovers potential unprecedented storm surge events for European and Mediterranean coastlines, focussing on their magnitude, spatial extent and seasonality. We identify Germany, the Netherlands and western UK as hotspot regions that could experience unprecedented storm surge levels that are more than half a meter higher than historical events. Spatially unprecedented extreme peak surges, affecting more provinces within a 3 d time window than previously recorded, may impact up to 49% of the provinces, with simultaneous effects in the Mediterranean and the Atlantic regions. Additionally, countries including the Netherlands, Germany and the UK may experience at least one temporally unprecedented surge during the summer months. Understanding these different dimensions of unprecedented events represents a significant advance in our knowledge of coastal flood risk in Europe and supports improved coastal flood risk management decisions, including enhanced flood defence standards, disaster risk management and planning of coastal operations.

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.

Tropical and extratropical cyclones, which can cause coastal flooding, are among the most devastating natural hazards. Understanding coastal flood risk better can help to reduce their potential impacts. Global flood models play a key role in this process. In recent years, global models and methods for flood hazard simulation have improved, but they are still limited in the actionable information that they can provide at local scales. One notable limitation is the insufficient resolution of global models, which cannot accurately capture the complexities of storms and the topography of specific regions. Additionally, most large-scale hazard assessments tend to focus solely on either offshore water level simulations or overland flooding, often relying on static flood modelling approaches. In this study, we introduce the MOSAIC (MOdelling Sea level And Inundation for Cyclones) framework, a flexible Python-based framework designed to dynamically simulate both offshore water levels and coastal flooding. MOSAIC provides a multiscale modelling approach to automatically generate and nest high-resolution local models within a coarser global model. This approach seeks to simulate more accurate water levels, thereby enhancing coastal boundary conditions for dynamic flood modelling. We showcase the potential of MOSAIC using three historical storm events, with the aim of assessing the effects of temporal- and spatial-resolution refinements and bathymetry data. Our findings indicate that the importance of model refinements is linked to the topography of the study area and the storm characteristics. For instance, refining the temporal output resolution has a significant impact on small and rapidly intensifying tropical cyclones but is less critical for extratropical cyclones. Additionally, the refinement of spatial output locations is particularly relevant in regions where water levels exhibit high spatial heterogeneity along the coast. In regions with complex topography, grid refinement and higher-resolution bathymetry play a more significant role. MOSAIC provides an automated approach to provide flood maps at a local scale. Our results confirm the proof of concept that the automated approach of MOSAIC can be used to provide high-resolution flood maps without the need for calibration or other manual steps. As such, MOSAIC provides a bridge between fully global and fully local modelling approaches. In future work, further validation could be carried out to explore the optimal settings for different regions in more detail.

Estimates of flood inundation from tropical cyclones (TCs) are needed to better understand how exposure varies inland and at the coast. While reduced-complexity flood inundation models have been previously shown to efficiently simulate the drivers of TC flooding across large regions, a lack of detailed validation studies of these models, which are being applied globally, has led to uncertainty about the quality of the predictions of inundation depth and extent and how this translates to exposure. In this study, we complete a comprehensive validation of a reduced-complexity hydrodynamic model (SFINCS) for simulating pluvial, fluvial, and coastal flooding. We hindcast Hurricane Florence (2018) flooding in North and South Carolina, USA using high-resolution meteorologic data and coastal water level output from an ocean recirculation model (ADCIRC). We compare modeled water levels to traditional validation datasets (e.g., water level gages, high-water marks) as well as property-level records of insured damage to draw conclusions about the model’s performance. We demonstrate that SFINCS can accurately simulate coastal and runoff drivers of TC flooding at large scales with minimal computational requirements and limited calibration. We use the validated model to attribute flood extent and building exposure to the individual and compound flood drivers during Hurricane Florence. The results highlight the critical role runoff processes have in TC flood exposure and support the need for broader implementation of models that are capable of realistically representing the compound effects resulting from coastal and runoff processes.

Low-pressure systems and strong winds, when coinciding with high tides can generate severe storm tides, leading to coastal flooding and significant economic losses. Accurate estimates of storm tide frequency and intensity are crucial for flood hazard assessments and risk reduction. However, the limited observational records pose a challenge in estimating high return periods with low uncertainty. In this study, we evaluate the potential of pooling ensembles from the SEAS5 seasonal forecast archive to generate an extensive storm tide data set for robust return period estimates in extra-tropical regions at large spatial scale. Using SEAS5 to force the hydrodynamic model GTSM, we generate 525 synthetic years of storm tides and apply extreme value analysis to estimate 40-year and 500-year return periods. Our findings demonstrate that SEAS5 produces unbiased and independent synthetic mean sea level pressure events across major extra-tropical regions, including Europe, China, Russia, South America and Australia. In Europe, unbiased SEAS5-derived storm tide extremes along the Atlantic coast are particularly well-suited for return period analysis. The results show the benefits of using longer records to improve extreme return periods. SEAS5 not only reduces uncertainties in high return period estimates but also provides more extreme events, enhancing the reliability of extreme value distributions compared to short observational records.

Current large‐scale coastal flood risk assessments are typically based on scenarios considering a range of spatially uniform return periods (RP). These assessments do not account for the spatial variability of real flood events, and only estimate average annual losses. In this study, we address these limitations by developing a novel event‐based probabilistic framework to capture spatial dependence of coastal floods, and use it to investigate the effects of spatial dependence on national flood risk estimates globally. We show that the widely used RP‐based approach estimates lower damages for relatively low return periods while higher damages are estimated for medium‐to‐large return periods. The intersection point where lower damage estimations turn into higher damage estimations varies across countries and is primarily dependent on local flood protection standards. We also provide the first global mapping of differences in risk indicators between these two approaches in terms of expected annual damages (EAD) and 1‐in‐200‐year damages. We show that spatial dependence has minor effects on the EAD but the RP200 damage is estimated higher for 76% of global countries by the RP‐based approach. Accounting for flood protection standards is found to increase these differences. Lastly, we demonstrate the added value of our approach by showing the flood damages of the simulation year with the highest combined annual damages at a subnational scale for each continent. Our study advocates for including spatial dependence in flood risk assessments and our event‐based approach estimates risks from a larger set of theoretically possible events, which can aid in better risk management.

Advances in the field of extreme event attribution allow to estimate how anthropogenic global warming affects the odds of individual climate disasters, such as river floods. Extreme event attribution typically uses precipitation as proxy for flooding. However, hydrological processes and antecedent conditions make the relation between precipitation and floods highly nonlinear. In addition, hydrology acknowledges that changes in floods can be strongly driven by changes in land-cover and by other human interventions in the hydrological system, such as irrigation and construction of dams. These drivers can either amplify, dampen or outweigh the effect of climate change on local flood occurrence. Neglecting these processes and drivers can lead to incorrect flood attribution. Including flooding explicitly, that is, using data and models of hydrology and hydrodynamics that can represent the relevant hydrological processes, will lead to more robust event attribution, and will account for the role of other drivers beyond climate change. Existing attempts are incomplete. We argue that the existing probabilistic framework for extreme event attribution can be extended to explicitly include floods for near-natural cases, where flood occurrence was unlikely to be influenced by land-cover change and human hydrological interventions. However, for the many cases where this assumption is not valid, a multi-driver framework for conditional event attribution needs to be established. Explicit flood attribution will have to grapple with uncertainties from lack of observations and compounding from the many processes involved. Further, it requires collaboration between climatologists and hydrologists, and promises to better address the needs of flood risk management.This article is categorized under:Paleoclimates and Current Trends > Modern Climate ChangePaleoclimates and Current Trends > Detection and Attribution Assessing Impacts of Climate Change > Observed Impacts of Climate Change

The wflow_sbm hydrological model, recently released by Deltares, as part of the Wflow.jl (v0.7.3) modelling framework, is being used to better understand and potentially address multiple operational and water resource planning challenges from a catchment scale to national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrological modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrological models. Most wflow_sbm parameters are based on physical characteristics or processes, and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally result in a satisfactory (0.4 ≥ Kling–Gupta efficiency (KGE) < 0.7) to good (KGE ≥ 0.7) performance for discharge a priori (without further tuning). Wflow_sbm includes relevant hydrological processes such as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater, and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resource modelling, the addition of a deep groundwater concept, and computational efficiency improvements through for example distributed computing and graphics processing unit (GPU) acceleration.

Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels, subsidence and changing extreme weather patterns. However, current freely available elevation datasets are not sufficiently accurate to model these risks. We present DeltaDTM, a global coastal Digital Terrain Model (DTM) available in the public domain, with a horizontal spatial resolution of 1 arcsecond (∼30 m) and a vertical mean absolute error (MAE) of 0.45 m overall. DeltaDTM corrects CopernicusDEM with spaceborne lidar from the ICESat-2 and GEDI missions. Specifically, we correct the elevation bias in CopernicusDEM, apply filters to remove non-terrain cells, and fill the gaps using interpolation. Notably, our classification approach produces more accurate results than regression methods recently used by others to correct DEMs, that achieve an overall MAE of 0.72 m at best. We conclude that DeltaDTM will be a valuable resource for coastal flood impact modelling and other applications.

Coastal flood risk is a serious global challenge facing current and future generations. Several disaster risk reduction (DRR) measures have been posited as ways to reduce the deleterious impacts of coastal flooding. On a global scale, however, efforts to model the future effects of DRR measures (beyond structural) are limited. In this paper, we use a global-scale flood risk model to estimate the risk of coastal flooding and to assess and compare the efficacy and economic performance of various DRR measures, namely dykes and coastal levees, dry-proofing of urban assets, zoning restrictions in flood-prone areas, and management of foreshore vegetation. To assess the efficacy of each DRR measure, we determine the extent to which it can limit future flood risk as a percentage of regional GDP to the same proportional value as today (a “relative risk constant” objective). To assess their economic performance, we estimate the economic benefits and costs of implementing each measure. If no DRR measures are implemented to mitigate future coastal flood risk, we estimate expected annual damages to exceed USD 1.3 trillion by 2080, directly affecting an estimated 11.5 million people on an annual basis. Low- and high-end scenarios reveal large ranges of impact uncertainty, especially in lower-income regions. On a global scale, we find the efficacy of dykes and coastal levees in achieving the relative risk constant objective to be 98 %, of dry-proofing to be 49 %, of zoning restrictions to be 11 %, and of foreshore vegetation to be 6 %. In terms of direct costs, the overall figure is largest for dry-proofing (USD 151 billion) and dykes and coastal levees (USD 86 billion), much more than those of zoning restrictions (USD 27 million) and foreshore vegetation (USD 366 million). These two more expensive DRR measures also exhibit the largest potential range of direct costs. While zoning restrictions and foreshore vegetation achieve the highest global benefit–cost ratios (BCRs), they also provide the smallest magnitude of overall benefit. We show that there are large regional patterns in both the efficacy and economic performance of modelled DRR measures that display much potential for flood risk reduction, especially in regions of the world that are projected to experience large amounts of population growth. Over 90 % of sub-national regions in the world can achieve their relative risk constant targets if at least one of the investigated DRR measures is employed. While future research could assess the indirect costs and benefits of these four and other DRR measures, as well as their subsequent hybridization, here we demonstrate to global and regional decision makers the case for investing in DRR now to mitigate future coastal flood risk.