Storm Research Articles

Dynamic 3D Simulation of Surface Charging on Rotating Asteroids

The surface-charging phenomenon of asteroids, mainly resulting from solar wind plasma and solar radiation, has been extensively studied. However, the influence of the asteroid’s rotation on surface charging is not yet fully understood. In this study, a neural network is established to replace numerical integration, improving the efficiency of dynamic 3D simulations. We simulate rotating asteroids and their surrounding plasma environments under various conditions, including the quiet solar wind and solar storms. Different minerals on the asteroid surface are also considered. Additionally, the effects of orbital motion and obliquity are studied for asteroids with rotation periods comparable to their orbital periods. The results show that under the typical solar wind, the maximum and minimum potentials of asteroids gradually decrease with increasing rotation periods, especially when the solar wind is obliquely incident. For asteroids with rotation periods longer than one week, this decreasing trend becomes extremely slow. During a solar storm, the solar wind plasma changes sharply, and the susceptibility of an asteroid’s surface potential to rotation is greatly pronounced. Surface minerals also play a role; plagioclase is the most sensitive mineral among those explored, while ilmenite appears indifferent to changes in rotation periods. Understanding the surface charging of asteroids under various rotation periods and angles is crucial for further research on solar wind plasma and asteroids’ surface dust motion, providing a reference for the safe landing and exploration of asteroids.

Evaluation of reliability and resilience in wind integrated power systems using 80 meter mast measurements

Renewable energy sources, especially wind turbine generators, are increasingly considered viable alternatives to conventional electric power generation due to their sustainability and positive environmental impact. The rising penetration of wind power highlights the importance of developing widely applicable methods to evaluate the concrete benefits of incorporating wind turbines into traditional power systems. This work proposes an approach for assessing the reliability and resilience of power systems incorporating wind power. Evaluating the impact of wind energy generation on system reliability involves analyzing metrics such as Loss of Load Expectation (LOLE) and Expected Energy Not Supplied (EENS). Additionally, a conceptual framework is developed which is aimed at enhancing the evaluation of power system resilience, when subjected to severe wind events. To evaluate the impact of wind storms on network resilience, a Monte Carlo Optimal Power Flow approach is utilized. The Average Energy Not Supplied (AENS) is used as the resilience metric and is computed for different rated speeds of wind turbines. The proposed methods for evaluating the reliability and resilience of the power systems are tested using the IEEE-RTS 24 bus system. Real-time wind resource data measured at an 80-meters mast at Paradeep location in the Odisha state on the east coast of India are used to evaluate the indices.

Storm surge, seawater flooding, and sea-level rise paradoxically drive fresh surface water expansion

Abstract Coastal storms and sea-level rise (SLR) are expected to increase seawater flooding in low-elevation coastal zones. High sea levels and seawater flooding can drive groundwater table rise via ocean-aquifer connections. These dynamics are often overlooked but can cause groundwater flooding and salinization hazards, increasing freshwater security challenges for coastal communities and driving ecosystem transgressions. Field data and numerical modeling were used to evaluate how heavy rainfall, storm surge, and seawater flooding and infiltration during Hurricane Fiona (September 2022) and projected SLR impact groundwater levels, inland surface waters, and saltwater intrusion on Sable Island National Park Reserve, Canada. During the passage of Hurricane Fiona, precipitation increased groundwater and pond levels before seawater flooded the beach. Seawater flooding and infiltration caused a sharp rise in beach groundwater levels, which in turn caused inland pond levels to rise without coincident direct inputs from precipitation or seawater. Model simulations reveal that seawater infiltration on beaches flooded the subsurface and drove the observed inland groundwater rise and freshwater pond expansion. Simulations of projected SLR show that seawater flooding will only inundate a small area of land along the coast; however, inland groundwater rise and flooding, which is less well-studied, may inundate up to 30 times more land area. Further, groundwater flooding driven by rising sea levels decreases hydraulic gradients and increases saltwater intrusion via freshwater lens (FWL) contraction. Findings demonstrate that seawater flooding from coastal storms and SLR paradoxically cause concurrent fresh surface water expansion but FWL contraction. This study provides new insights into the spatiotemporal dynamics of island freshwater resources and highlights that unseen and often overlooked groundwater-surface water exchanges are critical to consider when evaluating coastal flooding and groundwater salinization hazards and management strategies for low-elevation coastlines.

Investigation of morphodynamic response to the storm-induced currents and waves in the Bay of Bengal

Storm surges, driven by Tropical Cyclones (TCs), pose a threat to the coastal areas of the Bay of Bengal (BoB), causing sediment transport and beach erosion. This study introduces a coupled hydrodynamic (TELEMAC-2D) -wave (TOMAWAC) -morphodynamic (GAIA) model to investigate morphodynamic changes during storm surges. The cyclonic wind is crucial for precisely predicting storm surges, wind waves, and morphodynamic bed evolution. Thus, this study introduced a modified parametric wind model to enhance wind field representation near and far from the cyclone center. The simulated storm surges and wind waves were validated against in-situ observations for TC Hudhud (2014) and TC Varadah (2016), and the simulated morphodynamic bed evolution was validated with field-measured data collected for TC Nivar (2020). Further, this study evaluates the performance of different bed load transport formulations for predicting morphodynamic bed evolution during TC Nivar (2020) under storm-induced currents and waves. Results indicate that the Engelund-Hansen model is most effective for currents alone, while the Bijker model excels for combined currents and waves along the open coast of BoB. The results also indicate that the incorporation of the effect of waves along with currents has enhanced the predictive capability of the coupled model framework.

Design, optimization, and autonomous construction of Martian infrastructure including slabs using in-situ CO2-based polyethylene

A crucial development in 21st-century space exploration is the establishment of human settlements on Mars. In recent years, Mars exploration rovers have been deployed, and studies investigating human journeys to Mars have been conducted, with the objective of a human-crewed mission to Mars before 2050. This study presents an innovative approach to designing and optimizing Martian habitats by using in-situ CO2-based polyethylene (PE) as a construction material for Martian buildings and greenhouses. It seeks to develop sustainable and resilient habitats on Mars, leveraging local resources to address the environmental challenges specific to the Martian environment. The research targets several key areas: realistic construction techniques, effective utilization of in-situ materials, and building resilience under the harsh Martian conditions. These conditions include radiation, micrometeoroid impacts, low atmospheric pressure, low gravity, wind storms, temperature extremes, Paschen discharge, and marsquakes. Finite element analysis (FEA) of several pressurized spherical structures is used to design and optimize for minimum material usage, inspired by soap bubbles that naturally stick together with separating walls. The proposed habitat design features a central spherical structure surrounded by nine smaller spheres, optimized for material efficiency. It is demonstrated that utilizing two layers of 0.1 m thick PE and a 0.5 m thick CO2 layer in between them offers significant protection against Martian environmental loads such as radiation shielding and thermal insulation. It facilitates light transmission into the building. This study offers a realistic structural system for buildings and greenhouses that can be constructed using in-situ materials and is suitable for an optimal 539-day stay on Mars. The proposed autonomous robotic arm technology can perform all necessary tasks, from producing PE to automatically assembling the Mars Spherical Building (MSB), including in-place slab printing.

A temperature tipping point in hypoxic zone size

AbstractTemperature increases will have ubiquitous effects on aquatic food webs, from microbes to consumers, and affect the quality and quantity of carbon flows within and between water layers. A decline in the biological pump moving carbon from surface to lower layers is anticipated. We reviewed 37 years of data on hypoxic zone size and water quality in the northern Gulf of Mexico to determine if air and bottom water temperature increases (0.5°C decade−1) are significantly related to variations in its areal size. There was no significant decline in important river water quality in the 24 years since the national Hypoxia Action Plan was developed to reduce its size. Changes in the ratio of km2 hypoxia per 1000 mt nitrate loading from the Mississippi River from 2000 to 2023 reveals a tipping point in the hypoxic zone size that is driven by ocean warming. Biological factors varying with temperature such as ectotherm growth rates, zooplankton grazing, cell sinking rates, and diatom sequestration of Si or N are apparently more important influences on recent hypoxic zone size than variations in physical factors. This tipping point may be common to similarly warmed and eutrophic coastal waters where warming will likely result in diminished oxygen deficiency (< 2 mgO2 L−1). Hypoxia and food web models assuming a stationary equipoise of nutrient loadings, ratios and food webs will be deficient as coastal waters warm further, coastal storm and hurricane frequency increase and land uses in the watershed are altered with climate change.

Bivariate cubic normal distribution for non-Gaussian problems

Probabilistic models play critical role in various engineering fields. Numerous critical issues exist in probabilistic modeling, especially for non-Gaussian correlated random variables. Traditional parameter-based bivariate distribution models are typically developed for specific types of random variables, which limits their flexibility and applicability. In this study, a flexible bivariate distribution model is proposed, in which the joint cumulative distribution function (JCDF) is derived by expressing the probability as the summation of three basic probabilities corresponding to simple functions. These probabilities are computed using a univariate cubic normal distribution, and thus the proposed model is named as bivariate cubic normal (BCN) distribution. The proposed BCN distribution has been applied in modeling several common bivariate distributions and actual engineering datasets. Results show that the BCN distribution accurately fits the JCDFs of both theoretical distributions and practical datasets, offering significant improvement over existing models. Furthermore, the proposed BCN distribution is utilized in seismic reliability assessment and the calculation of the mean recurrence interval and hazard curve of hurricane wind speed and storm size. Results demonstrate that the BCN distribution excels in modeling and matching capabilities, proving its accuracy and effectiveness in civil engineering applications.

Storm clouds over innovation: Typhoon shocks and corporate R&D activities

This study investigates the economic impact of typhoon disasters on corporate innovation activities, a critical driver of economic growth and resilience. Utilizing a comprehensive dataset of Chinese public companies from 2010 to 2022 and employing a wind field model to construct a city-level typhoon destruction index, we apply difference-in-differences and instrumental variable methods to analyze the effects on corporate innovation. Our findings reveal that typhoon disasters significantly reduce both patent filings and R&D investment intensity, particularly in coastal areas and technology-intensive industries. The adverse effects are transmitted through financial constraints, human capital loss, and infrastructure damage. This research contributes to understanding the microeconomic implications of extreme weather events on innovation-driven economic growth, offering insights for disaster resilience policies in vulnerable regions.

Earth’s Interconnected Climate: Understanding Our Changing World

Have you ever thought about what causes the weather conditions that you experience where you live? Maybe you are thinking, “Sure, when there are storm clouds overhead it rains or snows, and when the sun shines strongly in the summer, we have hot weather”. This is true, but the “big” story is more complicated: the conditions you experience in your area can actually be caused by weather and climate events that occur thousands of kilometers away. These long-distance effects are called teleconnections, and they are a natural part of Earth’s climate system. In this article, we provide examples of teleconnections and describe how scientists study them using powerful computer programs. We also explain how climate change might affect these invisible forces that stretch across the Earth, and how resulting changes in teleconnections could cause extreme weather events, like powerful rainstorms or droughts, in certain areas. The more scientists understand about teleconnections, the better we can protect areas that are vulnerable to the dangerous effects of climate change.

Regime-Dependent Characteristics and Predictability of Cold-Season Precipitation Events in the St. Lawrence River Valley

Abstract The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C. Significance Statement Diverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events.

Measurement of maize stalk shear moduli.

Maize is the most grown feed crop in the United States. Due to wind storms and other factors, 5% of maize falls over annually. The longitudinal shear modulus of maize stalk tissues is currently unreported and may have a significant influence on stalk failure. To better understand the causes of this phenomenon, maize stalk material properties need to be measured so that they can be used as material constants in computational models that provide detailed analysis of maize stalk failure. This study reports longitudinal shear modulus of maize stalk tissue through repeated torsion testing of dry and fully mature maize stalks. Measurements were focused on the two tissues found in maize stalks: the hard outer rind and the soft inner pith. Uncertainty analysis and comparison of multiple methodologies indicated that all measurements are subject to low error and bias. The results of this study will allow researchers to better understand maize stalk failure modes through computational modeling. This will allow researchers to prevent annual maize loss through later studies. This study also provides a methodology that could be used or adapted in the measurement of tissues from other plants such as sorghum, sugarcane, etc.

Hazard-consistent scenario selection for long-term storm surge risk assessment over extended coastal regions

The recent destructive hurricane seasons and concerns related to the future influence of climate change have increased the relevance of coastal storm hazards and, in particular, of storm surge hazard estimation when discussing the resilience of coastal communities. This hazard is generally represented as surge inundation probabilities over a large number of individual locations in the geographic domain of interest, and is typically assessed utilizing an ensemble of storm scenarios (i.e., storm events) that are representative of the regional climatology. This paper investigates the storm ensemble selection within this setting, with the objective of identifying a small number of storm scenarios that are consistent with some chosen hazard descriptions over a large geographic region. Beyond the storm events, the occurrence rates (i.e., weights) are also updated. Following past works, a two-stage optimization is adopted for the storm selection. The inner-loop identifies the occurrence rates for a given storm subset, formulating the problem as a linear programming optimization for the sum of absolute deviation for the predicted hazard. The outer-loop searches for the best subset with the desired number of storms, adopting a genetic algorithm integer optimization for minimizing the aforementioned deviation. This work extends this implementation to extended coastal regions, with many locations of interest. In this case, it is computationally intractable to consider all the locations in the domain within the linear programming formulation, and for this reason, a subset of representative locations is chosen through cluster analysis. The hazard description for only these locations is used in the storm ensemble selection. For clustering, different strategies using correlation between locations based on geospatial information, surge response, or a combination of both are examined. Additionally, the correlation in the hazard description is better integrated into the storm selection by establishing a modification of the objective function adopted for the outer optimization loop. Applications to different North Atlantic coastal domains are presented as case studies.

Spatial Memory of Notable Hurricane Tracks and Their Geophysical Hazards

Previous research has shown that people use a benchmark hurricane as part of their preparation and evacuation decision-making process. While hurricanes are a common occurrence along the Gulf Coast, research on personal memories of past storms is lacking. Particularly, how well do people remember the track and geophysical hazards (wind speed, storm surge, and total rainfall) of past storms? The accurate or inaccurate recollection and perception of previous storm details can influence personal responses to future storms, such as the decision to evacuate or take other life-saving actions. Survey responses of residents in Alabama and Mississippi were studied to determine if people were accurately able to recall a notable storm’s name when seeing an image of the storm’s track. Those who were able to identify the storm by its track were also asked if they could remember the storm’s maximum reported rainfall, maximum sustained winds, and storm surge at landfall. Results showed that there were statistically significant differences between the levels of accurate recall for different storms, with Hurricanes Katrina and Michael having the most correct responses. Regardless of the storm, most people struggled to remember geophysical hazards. The results of this study are important as they can inform broadcast meteorologists and emergency managers on forecast elements of the storm to better emphasize in future communication in comparison to the actual values from historical benchmark storms.

Storm surge modelling along European coastlines: The effect of the spatio-temporal resolution of the atmospheric forcing

The spatio-temporal resolution of atmospheric forcing plays a key role in the accuracy of simulated storm surges with hydrodynamic numerical models. Here, we generate five hydrodynamic hindcasts of coastal storm surges along the European Atlantic and the Mediterranean Sea coasts, forced with atmospheric fields of varying temporal (hourly and daily) and spatial (0.25° to 2°) resolution since 1940. Our results, that are validated with insitu tide gauge observations, show that storm surges obtained with daily forcing underestimate the magnitude of coastal extreme sea level events by up to 50% compared to hourly simulations and observations. Nevertheless, low-resolution simulations capture the temporal variability of storm surges, including strong episodes. Furthermore, taking advantage of the consistent set of coastal storm surge hindcasts, we demonstrate that storm surges forced with daily mean atmospheric fields, when bias corrected via quantile mapping, provide accurate values of daily maxima as calculated by a high-resolution hindcast. This transformation paves the way to obtain daily maxima storm surge estimates from low-resolution atmospheric fields, as those typically provided by large-scale and global climate models, at a lower computational cost.

Analysis of climate change and climate variability impacts on coastal storms induced by extratropical cyclones: a case study of the August 2015 storm in central Chile

ABSTRACT The projected increase in coastal risk requires a reevaluation of coastal risk reduction strategies. A multi-model approach is proposed to examine the variability of coastal storms influenced by climate change and El Niño Southern Oscillation (ENSO). To this end, the historic coastal storm of August 8 2015, resulting from a local extratropical cyclone (ETC) off the central Chilean coast, was analyzed through the coupling of the WRF atmospheric model, Delft3D FM (D-FLOW and D-WAVE modules), and EOT20 astronomical tide model. The results show that the characteristics of local ETCs are susceptible to regional temperature gradients associated with climate change and ENSO. The coastal storm of August 8 2015, presented a decrease in wave height and counterclockwise rotation of wave direction along the Chilean coast under the climate change scenario. Meanwhile, the ENSO scenarios under cold conditions generated a ETC track’s displacement toward the north, causing both an increase in wave height along the coast of the Antofagasta and Atacama regions and a decrease in wave height in the Valparaíso, O’Higgins, and Maule regions. Findings from this study emphasize the importance of considering dynamic design for coastal structures rather than traditional methods to adapt to changing storm patterns.

Buffalo Bayou Watershed Near Katy Flood Analysis

Buffalo Bayou, originating near Katy, Texas, is a vital watercourse within the Houston metropolitan area, traversing diverse urban, suburban, and rural landscapes. This paper investigates the flood mechanisms impacting the Buffalo Bayou watershed, focusing on significant flood events and various flood mitigation methods. Key historical floods, such as those caused by Tropical Storm Allison (2001), the Memorial Day Flood (2015), the Tax Day Flood (2016), and Hurricane Harvey (2017), underscore the region’s vulnerability to intense rainfall and storm systems. By examining a 20-year period of daily streamflow and climate data, this study employs the bucket model to simulate long-term discharge and predict flood occurrences. The results highlight the critical need for effective flood mitigation strategies, including the use of reservoirs, green infrastructure, and infrastructure improvements. The study aims to enhance flood management practices and community resilience within the Buffalo Bayou watershed, offering insights into the dynamic interplay between natural hydrological processes and human interventions.

Calm Before the Storm?

Atlantic hurricane season poses an annual threat to the billions of dollars of oil and gas hardware pumping hydrocarbons from the depths of the US Gulf of Mexico (GOM). The region’s offshore production amounts to about 1.8 million B/D—roughly 14% of the total crude output in the US, according to the US Energy Information Administration, so any disruption is felt by oil markets across the globe. Prior to the 2024 hurricane season, forecasters pegged this year as primed to be particularly intense. Colorado State University (CSU), known for its storm prognostication, estimated that the basin would experience 23 named storms, with a dozen of those becoming hurricanes (winds of 74 mph or higher), and six of those strengthening to major hurricanes (winds of 111 mph or higher). Similarly, AccuWeather meteorologists expect between 20 and 25 named storms with a possibility of 30 or more. As the season enters its busy period, the basin has only experienced five named storms to date; however, many forecasters are sticking to their initial forecasts and continue to warn of an intense September/October period. “The months of September and October should be very active across the Atlantic,” said AccuWeather lead hurricane expert Alex DaSilva. “We continue to remain very concerned about rapid intensification. Sea-surface temperatures and ocean heat content are near record levels across most of the Atlantic basin. This can act like jet fuel for tropical systems. We also continue to believe that La Niña will emerge sometime in November. When water temperatures near the equator of the eastern Pacific Ocean are lower than the long-term historical averages for an extended period, La Niña is declared. Although this is a phenomenon in the Pacific, it has ramifications for weather patterns over the Atlantic Ocean. Specifically, La Niña can reduce the wind shear across the basin. When wind shear is lower, it makes it easier for tropical systems to take shape and strengthen.” The GOM has experienced one disruption in 2024 courtesy of Hurricane Beryl. Once a Category 5 storm (winds 155 mph and higher)—and the earliest-forming Category 5 storm on record—Beryl weakened as it crossed the southern Yucatán. The storm regained Category 1 strength as it crossed the far western offshore oil patch enroute to landfall near Matagorda Bay, Texas. Shell shut in production at its Perdido spar because of the storm as well as evacuated personnel from it and its Whale facility. Chevron moved all non-essential personnel off its Anchor floating production unit, which was undergoing commissioning at the time. The last major storm to cut a path straight through the heart of the US offshore oil patch was 2021’s Hurricane Ida. Ida tore through the region as a Category 4 storm before slamming into the Louisiana coast near Port Fourchon packing 150 mph winds on 29 August. The storm prompted the shut in of almost 100% of the Gulf’s oil and gas production and caused extensive damage to major hubs in the region, including the Shell-operated West Delta Block 143 complex. The facilities, which serve as the transfer station for production from Shell’s robust Mars corridor, were off line for several months.

The White Hills Gravel of central Victoria: an anomalous Paleogene very coarse-grained fluvial deposit

The White Hills Gravel is the coarsest fluvial gravel within Victoria, southeastern Australia, and outcrops discontinuously across a substantial part (>35 000 km2) of this region. It overlies a major regional unconformity representing >300 Ma, and is dominated by conglomerate (61% of beds), with less abundant sandy lithofacies (36% of beds) and rare mudstones (3% of beds). The sedimentary characteristics of the basal conglomerates (e.g. thick bedding, common boulders, very poor sorting and in places chaotic appearance) indicate deposition by significant paleofloods, including debris floods, within braided streams that had similar flow directions to the modern drainage. Paleohydraulic calculations estimate that the paleofloods had discharges exceeding >7200 m3 s−1, over 25 times the largest recorded floods in the same valleys. These paleofloods were probably caused by exceptionally severe storm systems, consisting of many individual thunderstorms. Deposition of the White Hills Gravel has previously been attributed to the mid-Cretaceous uplift that affected southeastern Australia, but the maximum possible age of the formation (early Paleocene) is too young for this to be a viable explanation. Instead, the White Hills Gravel may have been deposited during a period of increased precipitation at the Paleocene–Eocene Thermal Maximum (ca 56 Ma), when an intensification of the hydrological system generated significant sediment responses within fluvial systems around the world. Modelling indicates that this climatic event had a substantial impact in Victoria, where increases in mean annual precipitation (50–60%) and the most extreme rainfall rates achieved by the strongest storms (60–70%) were some of the largest globally, and frequent and intense Atmospheric Rivers were an important part of the hydrological system. These factors, together with a period of disturbed vegetation cover, resulted in major paleofloods that could have deposited the coarse-grained conglomerates of the White Hills Gravel.

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.

Summary: ASBPA Coastal Summit 2024

The American Shore and Beach Preservation Association (ASBPA), established in 1926, is dedicated to preserving and enhancing coastal areas through the integration of science and public policy. The organization advocates for healthy, sustainable, and resilient coastal systems, emphasizing the importance of federal Coastal Storm Risk Management (CSRM) projects. The ASBPA Coastal Summit 2024, themed “Shaping Coastal Futures: Implementing Science-Based Resilience Policies,” convened 111 coastal practitioners in Washington, D.C. The Summit aims to address pressing challenges and advocate for science-based policies to ensure the sustainability and resilience of coastal systems.