Coastal erosion is controlled by two sets of factors, one related to storm intensity and the other related to a location’s vulnerability. This study investigated the role of each set in controlling dune erosion based on data compiled for eighteen historical events in New Jersey. Here, storm intensity was characterized by the Storm Erosion Index (SEI) and Peak Erosion Intensity (PEI), factors used to describe a storm’s cumulative erosion potential and maximum erosive power, respectively. In this study, a direct relationship between these parameters, beach morphology characteristics, and expected dune response was established through a classification tree ensemble. Of the seven input parameters, PEI was the most important, indicating that peak storm conditions with time scales on the order of hours were the most critical in predicting dune impacts. Results suggested that PEI, alone, was successful in distinguishing between storms most likely to result in no impacts (PEI < 69) and those likely to result in some (PEI > 102), regardless of beach condition. For intensities in between, where no consistent behavior was observed, beach conditions must be considered. Because of the propensity for beach conditions to change over short spatial scales, it is important to predict impacts on a local scale. This study established a model with the computational effectiveness to provide such predictions.

The insurance industry has used parametric solutions to transfer catastrophe risks since the 1990s. Instead of relying on a lengthy process to assess a claim, these products pay the insured a pre-agreed amount if the physical characteristics of the event fulfill pre-defined conditions. Cat-in-a-box or cat-in-a-circle triggers, commonly used tools for tropical cyclone risk transfer, provide a payout to the insured if the track of a hurricane crosses the perimeter of a geographic area defined by a polygon or a circle with a certain intensity. Cat-in-a-grid solutions are novel and more sophisticated. They rely on a set of multiple cat-in-a-box triggers arranged on an orthogonal grid. The consideration of multiple geographic domains instead of a single box or circle is helpful to reduce basis risk, i.e., the difference between the parametric loss estimate and the target loss. In the case study for Miami presented here, for instance, a cat-in-a-grid solution showed 18.5% less basis risk than a typical cat-in-a-box alternative. To organize the different types of triggers within a common framework, we classify the existing alternatives based on whether they use a single geographic domain (like a box or a circle) or multiple domains (like a grid). We discuss their advantages and disadvantages and describe the process required to calibrate any one solution with the help of a catastrophe-risk model. We focus, in particular, on the analysis and construction of cat-in-a-grid triggers, the alternative that we believe offers the greatest potential for global standardization and adoption.

A comprehensive case study of Keansburg Inlet (New Jersey, USA) is presented with the objective of evaluating inlet management alternatives and assessing the influence of an operational flood gate on channel shoaling. The goal of the research is determining the most effective strategy for minimizing the frequency of maintenance dredging. This study compares the effectiveness of (1) traditional structural solutions; (2) modified dredging templates; and (3) assesses the influence of the flood gate operations during conditions representative of a typical year. Alternative analysis is completed using a coupled hydrodynamic–wave model (Delft3D-Flexible Mesh (FM)) with Real Time Control to simulate morphological changes. The model was calibrated and evaluated using collected field data. Water levels are reproduced within 6% of the spring tide range with lag times less than 20 min. The model results and observations suggest sediment transport is dominated by wave action with pronounced variations in dominant wave direction. The results indicate that changes to the operational dredging, or what the authors have termed broadly as “adaptive dredging techniques”, appear to deliver the most promising improvement. Model results suggest that the current operational procedures of the flood gate do not significantly alter the channel infilling rates and patterns during typical (i.e., non-extreme event) conditions.

This paper proposes a novel methodology, based on machine learning and statistical models, for predicting hurricane-related losses to specific assets. Our approach uses three critical storm parameters typically tracked by meteorological agencies: maximum wind speed, minimum sea level pressure, and radius of maximum wind. The system categorizes potential damage events into three insurance-relevant classes: non-payable, partially payable, and fully payable. Three triggers for final payouts were designed: hybrid framework, standalone regression, and standalone non-linear regression. The hybrid framework combines two classification models and a non-linear regression model in an ensemble specifically designed to minimize the absolute differences between predicted and actual payouts (Total Absolute Error or TAE), addressing highly imbalanced and partially compensable events. Although this complex approach may not be suitable for all current contracts due to limited interpretability, it provides an approximate lower bound for the minimization of the absolute error. The standalone non-linear regression model is structurally simpler, yet it likewise offers limited transparency. This hybrid framework is not intended for direct deployment in parametric insurance contracts, but rather serves as a benchmarking and research tool to quantify the achievable reduction in basis risk under highly imbalanced conditions. The standalone linear regression provides an interpretable linear regression model optimized for feature selection and interaction terms, enabling direct deployment in parametric insurance contracts while maintaining transparency. These three approaches allow analysis of the trade-off between model complexity, predictive performance, and interpretability. The three approaches are compared using comprehensive hurricane simulation data from an industry-standard catastrophe model. The methodology is particularly valuable for parametric insurance applications, where rapid assessment and claims settlement are essential.

This dataset quantifies storm intensity of approximately 130 unique historical storms along the New Jersey coastline from 1980 to 2014 for three separate sea level conditions. Namely, (1) as observed in the historical record; (2) detrended to 1997 mean sea level and (3) adjusted to the 2050 and 2100 sea level rise scenarios presented in the International Panel on Climate Change's (IPCC) Sixth Assessment Report (AR6). Projected sea level scenarios are adjusted to include local vertical land movement. Storm intensity is quantified in terms of erosion potential, considering the combination of total water level, wave heights, and storm duration. The observational dataset includes both tropical and extratropical storms and quantifies both the cumulative (duration) and peak (single hour) storm intensity for each storm and sea level rise (SLR) condition. Additionally, hourly time series of wave characteristics and water levels are provided at 13 locations along the New Jersey coast, facilitating hydrodynamic forcing of nearshore models. The dataset provides the means and methods to directly compare historical storms under future SLR conditions.

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.