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Most flood hazard assessments follow the event-based approach, assuming that the probability of flooding approximates the probability of flood drivers. However, this approach neglects information about the temporal and spatial variability of flood drivers and flood processes such as water propagation inland and its interaction with topography. The response-based approach accounts for these factors by using a large number of flood events that allow the calculation of flood probabilities. Here, we compare differences in flood hazards between the event- and response-based approaches for a case study in Gloucester City (NJ, U.S.). We find that compound events with return periods less than 20 years can produce the 100-year (i.e., 1 % annual exceedance probability) flood depths in large areas of the city. This is caused by the temporal and spatial characteristics of these events, such as prolonged high coastal water levels and rainfall fields with higher rainfall rates over urbanized areas. These event characteristics are not included in extreme value models of the flood drivers and are commonly simplified by using a single design event. However, flood hazards largely depend on them, introducing large discrepancies in resulting flood hazards if neglected. The temporal and spatial variabilities of flood drivers need to be incorporated in flood hazard assessments to produce robust estimates. 

Compound flood risk assessments require probabilistic estimates of flood depths and extents that are derived from compound flood models. It is essential to simulate a wide range of flood driver conditions to capture the full range of variability in resultant flooding. Although recent advancements in computational resources and the development of faster compound flood models allow for more rapid simulations, generating a large enough set of storm events for boundary conditions remains a challenge. In this study, we introduce a statistical framework designed to generate many synthetic but physically plausible compound events, including storm-tide hydrographs and rainfall fields, which can serve as boundary conditions for dynamic compound flood models. We apply the proposed framework to Gloucester City in New Jersey, as a case study. The results demonstrate its effectiveness in producing synthetic events covering the unobserved regions of the parameter space. We use flood model simulations to assess the importance of explicitly accounting for variability in mean sea level (m.s.l.) and tides in generating the boundary conditions. Results highlight that m.s.l. anomalies and tidal conditions alone can lead to differences in flood depths exceeding 1 and 1.2 m, respectively, in parts of Gloucester City. While we use historically observed events, the framework can be applied to model output data including hindcasts or future projections. 

"This dataset contains the resuls of 5000 compound flood simulations generated using SFINCS for the catchments of Gloucester City (NJ). The 5000 compound synthetic events were generated using the approach of Maduwantha et al., 2025. Details about the SIFINCS model and the compound flood data can be found in Santamaria-Aguilar et al., 2025. Part 1 can be found in 10.5281/zenodo.15047845 

This dataset contains the resuls of 5000 compound flood simulations generated using SFINCS for the catchments of Gloucester City (NJ). The 5000 compound synthetic events were generated using the approach of Maduwantha et al., 2025. Details about the SIFINCS model and the compound flood data can be found in Santamaria-Aguilar et al., 2025. Part 2 can be found in 10.5281/zenodo.15065555 

Many climate policies adopt improving equity as a key objective. A key challenge is that policies often conceive of equity in terms of individuals but introduce strategies that focus on spatially coarse administrative areas. For example, the Justice40 Initiative in the United States requires 518 diverse federal programs to prioritize funds for “disadvantaged” census tracts. This strategy is largely untested and contrasts with the federal government’s definition of equity as the “consistent and systematic fair, just and impartial treatment of all individuals (Executive Office of the President, Federal Register, 2021).” How well does the Justice40 approach improve equity in climate adaptation outcomes acrossindividuals? We analyze this question using a case study of a municipality that faces repetitive flooding and struggles to effectively manage these risks due to limited resources and public investment. We find that the way the Federal Emergency Management Agency implements the Justice40 Initiative can be an obstacle to promoting equity in household flood-risk outcomes. For example, in this case study, ensuring the majority of benefits accrue in “Justice40 Communities” does not reduce risk for the most burdened households, does not reduce risk-burden inequality, and produces net costs. In contrast, we design simple funding rules based on household risk burden that cost-effectively target the most burdened households, reduce risk-burden inequality, and accrue large net benefits. Our findings suggest that “disadvantaged community” indicators defined at coarse spatial scales face the risk of poorly capturing many climate risks and can be ineffective for meeting equity promises about climate-related investments. 

In coastal regions, compound flooding can arise from a combination of different drivers, such as storm surges, high tides, excess river discharge, and rainfall. Compound flood potential is often assessed by quantifying the dependence and joint probabilities of flood drivers using multivariate models. However, most of these studies assume that all extreme events originate from a single population. This assumption may not be valid for regions where flooding can arise from different generation processes, e.g., tropical cyclones (TCs) and extratropical cyclones (ETCs). Here we present a flexible copula-based statistical framework to assess compound flood potential from multiple flood drivers while explicitly accounting for different storm types. The proposed framework is applied to Gloucester City, New Jersey, and St. Petersburg, Florida, as case studies. Our results highlight the importance of characterizing the contributions from TCs and non-TCs separately to avoid potential underestimation of the compound flood potential. In both study regions, TCs modulate the tails of the joint distributions (events with higher return periods), while non-TC events have a strong effect on events with low to moderate joint return periods. We show that relying solely on TCs may be inadequate when estimating compound flood risk in coastal catchments that are also exposed to other storm types. We also assess the impact of non-classified storms that are not linked to either TCs or ETCs in the region (such as locally generated convective rainfall events and remotely forced storm surges). The presented study utilizes historical data and analyzes two populations, but the framework is flexible and can be extended to account for additional storm types (e.g., storms with certain tracks or other characteristics) or can be used with model output data including hindcasts or future projections. 

Abstract. In coastal regions, compound flooding can arise from a combination of different drivers such as storm surges, high tides, excess river discharge, and rainfall. Compound flood potential is often assessed by quantifying the dependence and joint probabilities of the flood drivers using multivariate models. However, most of these studies assume that all extreme events originate from a single population. This assumption may not be valid for regions where flooding can arise from different generation processes, e.g., tropical cyclones (TCs) and extratropical cyclones (ETCs). Here we present a flexible copula-based statistical framework to assess compound flood potential from multiple flood drivers while explicitly accounting for different storm types. The proposed framework is applied to Gloucester City, New Jersey, and St. Petersburg, Florida as case studies. Our results highlight the importance of characterizing the contributions from TCs and non-TCs separately to avoid potential underestimation of the compound flood potential. In both study regions, TCs modulate the tails of the joint distributions (events with higher return periods) while non-TC events have a strong effect on events with low to moderate joint return periods. We show that relying solely on TCs may be inadequate when estimating compound flood risk in coastal catchments that are also exposed to other storm types. We also assess the impact of non-classified storms that are neither linked to TCs or ETCs in the region (such as locally generated convective rainfall events and remotely forced storm surges). The presented study utilizes historical data and analyzes two populations, but the framework is flexible and can be extended to account for additional storm types (e.g., storms with certain tracks or other characteristics) or can be used with model output data including hindcasts or future projections. 

Understanding uncertainties in extreme wind-wave events is essential for offshore/coastal risk and adaptation estimates. Despite this, uncertainties in contemporary extreme wave events have not been assessed, and projections are still limited. Here, we quantify, at global scale, the uncertainties in contemporary extreme wave estimates across an ensemble of widely used global wave reanalyses/hindcasts supported by observations. We find that contemporary uncertainties in 50-year return period wave heights ( $H_s^{50}$ ) reach (on average) ~2.5 m in regions adjacent to coastlines and are primarily driven by atmospheric forcing. Furthermore, we show that uncertainties in contemporary $H_s^{50}$ estimates dominate projected 21st-century changes in $H_s^{50}$ across ~80% of global ocean and coastlines. When translated into broad-scale coastal risk analysis, these uncertainties are comparable to those from storm surges and projected sea level rise. Thus, uncertainties in contemporary extreme wave events need to be combined with those of projections to fully assess potential impacts.