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Coastal regions are often exposed to marine floods, usually generated by storm surge events. Each year, they are responsible for major losses in terms of lives and economic infrastructures. Numerical models are crucial to understand, assess, and forecast these consequences, especially in the absence of direct observations of these hazardous events. The present study evaluates the impact of the two storms of January 7, 2014, and February 28, 2017, on the coast of El Jadida (Morocco), using Iber software on a high-resolution topo-bathymetric digital elevation model with wind, wave and tide as forcing parameters. It also predicts the effect of the same storms under future sea level rise projections for 2050 and 2100 under the RCP2.6 and RCP 8.5 scenarios. The simulations reveal a total flood area (TFA) of 1.1 and 1.3 km2 for the storms of 2014 and 2017, respectively. The maximum run-up was equal to 6.4 m, as a result of the 2017 storm. The impact of storms similar to the 2017 event will be more dangerous for the coast of El Jadida under future sea level rise. For the RCP 2.6 (optimistic scenario), the TFA will reach 1.7 km2 associated with a run-up of 7.6 m in 2100. For the same period, the TFA and run-up under the RCP 8.5 scenario (pessimistic) are equal to 2 km2 and 7.7 m, respectively. The obtained flood hazard maps show that several economic infrastructures, such as commercial parks, hotels and coffee shops are located in areas at risk of coastal inundation. These results underscore the urgent need for targeted adaptation strategies to mitigate future flooding risks along the coast of El Jadida. [ABSTRACT FROM AUTHOR]

Better understanding the effects of hurricane waves and storm surges on barrier-island breaching is important for both scientific research and coastal hazard mitigations. In this study, the 2D non-hydrostatic Xbeach model has been applied to investigate interactions of hurricane wave, storm surge, and morphological processes in the case study of St. Joseph Peninsula during Category 5 Hurricane Michael. Model validations show a 2.45% average error and the 0.88 skill score between modeled and observed high water marks and bed elevations, respectively. Analysis of spatial distributions of currents and water levels indicates that a narrow area was overtopped at peak storm surge and wave. The gap was then quickly enlarged as the breaching area by wave-surge actions. By investigating foredune and peak dune along the central axis of breaching area, it shows that the foredune erosion on the ocean-side by wave-surge-current directly lead to the breach of the peak dune area in the barrier island. The Froude number shows a strong correlation with quick erosion of the barrier, indicating wave-surge supercritical flow is one of the major factors causing the barrier breaching. Results of cross sections of bed elevations and instantaneous surge-wave profiles at different storm surge stages reveal the evolution of the barrier-island breach. Results from this study provide valuable references for coastal hazard mitigation and resilience communities. Highlights: Application of non-hydrostatic Xbeach model reveals barrier-island breaching process and wave-surge-barrier interactions. Model validations show a 2.45% average error and the 0.88 skill score for high water marks and bed elevations, respectively. The breach started with a narrow gap overtopped in the peak of storm surge and was then quickly enlarged by wave-surge actions. Wave-surge induced supercritical flow is one of the major factors accelerating the barrier breaching. Foredune erosion by wave-surge-current is another factor that accelerates the breach of the barrier island. [ABSTRACT FROM AUTHOR]

A storm surge, classified as an extreme natural disaster, refers to unusual sea level fluctuations induced by severe atmospheric disturbances such as typhoons. Existing reanalysis data, such as ERA5, significantly underestimates the location and maximum wind speed of typhoons. Therefore, this study initially assesses the accuracy of tropical cyclone positions and peak wind speeds in the ERA5 reanalysis dataset. These results are compared against tropical cyclone parameters from the IBTrACS (International Best Track Archive for Climate Stewardship). The position deviation of tropical cyclones in ERA5 is mainly within the range of 10 to 60 km. While the correlation of maximum wind speed is significant, there is still considerable underestimation. A wind field reconstruction model, incorporating tropical cyclone characteristics and a distance correction factor, was employed. This model considers the effects of the surrounding environment during the movement of the tropical cyclone by introducing a decay coefficient. The reconstructed wind field significantly improved the representation of the typhoon eyewall and high-wind-speed regions, showing a closer match with wind speeds observed by the HY-2B scatterometer. Through simulations using the FVCOM (Finite Volume Community Ocean Model) storm surge model, the reconstructed wind field demonstrated higher accuracy in reproducing water level changes at Tanxu, Gaoqiao, and Zhangjiabang stations. During the typhoon's landfall in Shanghai, the area with the greatest water level increase was primarily located in the coastal waters of Pudong New Area, Shanghai, where the highest total water level reached 5.2 m and the storm surge reached 4 m. The methods and results of this study provide robust technical support and a valuable reference for further storm surge forecasting, marine disaster risk assessment, and coastal disaster prevention and mitigation efforts. [ABSTRACT FROM AUTHOR]

Flood risk analyses often focus on a single flooding source, typically storm surge or rainfall‐driven flooding, depending on the predominant threat. However, hurricanes frequently cause compound flooding through significant storm surges accompanied by heavy rainfall. This study employs a hydrodynamic model based on Delft3D‐Flexible Mesh that couples flow, waves, and rainfall‐driven flow to simulate five historical tropical cyclones in Virginia's southeast coastal region. These storms produced varying intensities of storm surge and rainfall in the study area. Model simulations, incorporating rainfall through a rain‐on‐grid approach, account for the dynamic interaction between storm tides, and pluvial flow and enable the definition of flood zones as hydrologic, transitional, and coastal zones. This compound flooding model was validated with water level data from in‐water and overland gauges. The results indicate that the magnitude of the coastal zone correlates strongly with the extent of the surge‐inundated area (SIA) obtained from simulations that only considered storm surges. The extent of the transitional zone correlates strongly with the product of SIA and total rainfall. As an additional measure for flood hazards besides water depth, we calculated flow momentum flux at different flood zones to assess potential damage from hydrodynamic loads on structures, vehicles, and pedestrians. A strong correlation was found between the magnitude of the surge and momentum flux. Furthermore, high rainfall rates and winds can cause a significant increase in momentum flux locally. Understanding flood zones and their flow dynamics helps to identify effective flood risk management strategies that address the dominant flood driver. Plain Language Summary: Flood risk analyses in coastal areas usually study storm surges and rainfall impacts separately. However, hurricanes often cause compound flooding which stems from both sources. This research studies compound flooding using a computational model to simulate five hurricanes that hit coastal Virginia and had a range of surge and rainfall intensities. We identified three flood zones: areas flooded dominantly by rainfall (hydrologic), areas where both surge and rainfall contribute to flooding (transitional), and areas dominated by storm surge (coastal). The extent of the coastal zone correlated strongly with the magnitude of storm surge, and the extent of the transitional zone correlated very strongly with the area inundated by storm surge multiplied by total rainfall. Additionally, we investigated flow momentum, as a measure of flood force on objects. While a large surge causes a large flow momentum, heavy rain and strong winds can create energetic flows in the hydrologic zone too. Analyzing flood zones and flow momentum helps to identify proper flood mitigation measures and quantify their efficiency. For example, flood gates or levees are suitable for coastal zones, and improvements in drainage systems and inland green infrastructure are suitable for hydrologic zones, while a combination suits transitional zones. Key Points: A hydrodynamic model for compound flooding is used to define hydrologic, transitional, and coastal zones at city and neighborhood scalesThe area of the transitional zone correlates very strongly with the inundated area in storm surge simulations multiplied by total rainfallFlow momentum flux, as a measure of flood force, is influenced by surge magnitude, wind, and rainfall intensity [ABSTRACT FROM AUTHOR]

Nearshore water-level prediction has a substantial impact on the daily lives of coastal residents, fishing operations, and disaster prevention and mitigation. Compared to the limitations and high costs of traditional empirical forecasts and numerical models for nearshore water-level prediction, data-driven artificial intelligence methods can more efficiently predict water levels. Attention mechanisms have recently shown great potential in natural language processing and video prediction. Convolutional long short-term memory (ConvLSTM) combines the advantages of convolutional neural networks (CNN) and long short-term Memory (LSTM), enabling more effective data feature extraction. Therefore, this study proposes a ConvLSTM nearshore water level prediction model that incorporates an attention mechanism. The ConvLSTM model extracts multiscale information from historical water levels, and the attention mechanism enhances the importance of key features, thereby improving the prediction accuracy and timeliness. The model structure was determined through experiments and relevant previous studies using five years of water level data from the Zhuhai Tide Station and the surrounding wind speed and rainfall data for training and evaluation. The results indicate that this model outperforms the four other baseline models of PCCs, RMSE, and MAE, effectively predicting future water levels at nearshore stations up to 48 h in advance. Compared to the ConvLSTM model, the model with the attention mechanism showed an average improvement of approximately 10% on the test set, with a greater error reduction in short-term forecasts than that in long-term forecasts. During Typhoon Higos, the model reduced the MAE of the best-performing baseline model by approximately 3.2 and 2.4 cm for the 6- and 24-hour forecasts, respectively, decreasing forecast errors by approximately 18% and 11%, effectively enhancing the ability of the model to forecast storm surges. This method provides a new approach for forecasting nearshore tides and storm surges. [ABSTRACT FROM AUTHOR]

Flooding and salinization triggered by storm surges threaten the survival of coastal forests. After a storm surge event, soil salinity can increase by evapotranspiration or decrease by rainfall dilution. Here we used a 1D hydrological model to study the combined effect of evapotranspiration and rainfall on coastal vegetated areas. Our results shed light on tree root uptake and salinity infiltration feedback as a function of soil characteristics. As evaporation increases from 0 to 2.5 mm/day, soil salinity reaches 80 ppt in both sandy and clay loam soils in the first 5 cm of soil depth. Transpiration instead involves the root zone located in the first 40 cm of depth, affecting salinization in a complex way. In sandy loam soils, storm surge events homogeneously salinize the root zone, while in clay loam soils salinization is stratified, partially affecting tree roots. Soil salinity stratification combined with low permeability maintain root uptakes in clay loam soils 4/5‐time higher with respect to sandy loam ones. When cumulative rainfall is larger than potential evapotranspiration ETp (ETp/Rainfall ratios lower than 1), dilution promotes fast recovery to pre‐storm soil salinity conditions, especially in sandy loam soils. Field data collected after two storm surge events support the results obtained. Electrical conductivity (a proxy for salinity) increases when the ratio ETp/Rainfall is around 1.76, while recovery occurs when the ratio is around 0.92. In future climate change scenarios with higher temperatures and storm‐surge frequency, coastal vegetation will be compromised, because of soil salinity values much higher than tolerable thresholds. Key Points: Evapotranspiration and rainfall affect post‐storm surge soil salinity in the root zone of coastal forestsIn clay loam soils, post‐storm surge salinity stratification is beneficial for root uptakeTime to recover to pre‐storm soil salinity values depends on evapotranspiration and rainfall ratios and soil properties [ABSTRACT FROM AUTHOR]

Coastal areas in the Western Canadian Arctic are predicted to experience increases in the frequency and intensity of storm surges as rapid climate change continues. Although storm surges have the potential to cause widespread and persistent vegetation loss, little information is available about the influence of decreasing disturbance intervals (between storms), expected timelines of recovery for vegetation, and how vegetation change alters habitat availability and (or) quality for local wildlife populations. We investigated how Arctic bird diversity is affected by heterogeneous vegetation recovery post-storm. Specifically, we employed field survey protocols from the Program for Regional and International Shorebird Monitoring to investigate how avian community assemblages were affected by heterogeneous re-vegetation 20 years following a record 1999 storm surge. Comparisons of these bird survey data with vegetation and habitat factors showed that the drier, post-storm vegetation barrens were preferred by ground-nesters and species that use open habitats such as Lapland longspur (Calcarius lapponicus) and semipalmated plover (Charadrius semipalmatus), whereas the wetter (usually with surface water) revegetated habitats were frequented by species of ducks (Anas spp.), red-necked phalarope (Phalaropus lobatus), and savannah sparrow (Passerculus sandwichensis). Overall, this research shows that areas that have revegetated after the 1999 storm can be considered as functionally recovered in comparison to our Reference (i.e., unaffected) sites in terms of vegetation and bird communities, but that areas still exist ∼20 years post-storm that do not show evidence of recovery.

In this study, the storm surge processes and characteristics of Tide-Surge Interactions (TSI) induced by the sequential tropical cyclones (TCs) BARIJAT and MANGKHUT (2018) in the Northern South China Sea (NSCS) are investigated using the numerical model. By comparing the impacts of the two TCs, we find that storm surges are significantly influenced by multiple factors. Notably, bays situated on the western side of the cyclone's landfall point exhibit a double peak pattern in storm surge. In addition, TSI exhibits a pronounced impact across bays affected by the two TCs, with amplitude fluctuations ranging from -0.3 to 0.3 meters and contributing approximately -5% to -20% to the peaks of storm surge. Comparative analysis of TSI variations reveals that tides act as the primary determinant, significantly influencing both the magnitude and period of TSI. Dynamic analysis further highlights that variations in TSI are dominated by barotropic pressure gradient and bottom friction stress. Moreover, TSI affects the frequency of storm surges, introducing high-frequency tidal signals to storm surges and reducing the frequency of storm surges. [ABSTRACT FROM AUTHOR]

The increase in natural disaster frequency, intensified by climate change, poses one of the greatest threats to coastal systems and low-lying areas worldwide. It is estimated that the Global Mean Sea Level (GMSL) could rise by approximately 2 m in the twenty-first century, alongside intensifying cyclonic events. Consequently, in Brazil, coastal natural disasters are likely to become more frequent and intense, especially in the southern region. Thus, this study aims to identify, map and discuss coastal natural disasters in municipalities exposed to the open ocean belonging to the coastal zone of Santa Catarina (SC), Brazil, between 1998 and 2020. A review and dating of coastal natural disasters were conducted using four official databases: The Civil Defense of Santa Catarina website, Integrated Disaster Information System (S2ID), Santa Catarina Atlas of Natural Disasters, and the Brazilian Atlas of Natural Disasters. The data were organized into spreadsheets and mapped using QGIS 3.16.0 software. The results and main conclusions indicate: (1) More coastal disasters occurred in the north, central-north, and central sectors of SC between 1998 and 2020; (2) the period between 2010 and 2020 was more impactful; (3) the municipalities with the most records of coastal disasters were Balneário Barra do Sul, Itapoá and Florianópolis (considering only Ilha de Santa Catarina), respectively; (4) the three fastest-growing sectors are the north, central-north, and central; and (5) the seasons of autumn, spring, and winter, respectively, are more impacting for the study area. [ABSTRACT FROM AUTHOR]

Although coastal islands are home to many endemic species, they are also at risk of inundation from storm surge and sea level rise. Three subspecies of mole skink (Plestiodon egregius egregius, P. e. insularis, and the Egmont Key Mole Skink known from a single occurrence) occur on a small number of islands off the Gulf Coast of Florida, USA. We used the most recent sea level rise projections and the latest storm surge simulation data to predict impacts to habitat for insular mole skinks in Florida from 2030 to 2150. Our models predicted that in <100 years (by 2100; intermediate sea level rise scenario; ~1.08–1.15 m sea level rise), >78% of preferred habitat for the Florida Keys Mole Skink, >65% of preferred habitat for the Cedar Key Mole Skink, and >36% of preferred habitat for the Egmont Key Mole Skink will be inundated from sea level rise. Storm surge from tropical cyclones presents a more immediate risk to insular mole skink habitat: our models predicted that between 58% and 75% of Florida Keys Mole Skink habitat is at risk of being submerged under an average maximum of between 0.60 (SD = 0.86) and 0.98 (SD = 0.36) m of storm surge water for a category 1 storm, and the amount of habitat predicted to be impacted increases for higher intensity storms. Our models predicted similar trends for Cedar Key and Egmont Key Mole Skink habitat. Given current sea level rise projections, our models predicted that all three subspecies could be extinct by 2140 due to habitat inundation. There remains uncertainty about how species and ecosystems will respond to sea level rise, thus research to fill these gaps could help mitigate the effects of sea level rise in areas most vulnerable to the effects of climate change. [ABSTRACT FROM AUTHOR]

Wang, P.; Royer, E.L.; Jackson, K., and Gutierrez, S., 2024. Impacts of Hurricane Ian along the low-lying southwest Florida coast (USA) in 2022: Lessons learned. Journal of Coastal Research, 40(5), 827–851. Charlotte (North Carolina), ISSN 0749-0208. Hurricane Ian made landfall in the low-lying, densely populated, and developed southwestern Florida coast on 28 September 2022 as a large and slow-moving category 4 hurricane. Various U.S. federal and state agencies collected a large and comprehensive data set, including pre- and poststorm airborne LIDAR topography, in situ water level and wave measurements at numerous locations before, during, and after the storm, and poststorm high-water marks over a large area. This study reports results from a series of poststorm field investigations including ground observations of beach-dune erosion and deposition, catastrophic damage to various infrastructure, and widespread distribution of non-biodegradable materials washed into the estuary and numerous mangrove islands. Hurricane Ian induced large-scale inundation in low-lying southwest Florida, submerging all the barrier islands bordering Charlotte Harbor estuary, all the islands within the estuary, and up to 5 km into the mainland. Dense tree-type vegetation limited the landward penetration of beach-dune erosion and overwash deposition along the barrier islands. Net sand-volume loss from the beach-dune system ranged 10–25 m3/m and was controlled by the deep submergence of the system during the peak of the storm. The extremely high storm surge of up to 5.2 m above mean sea level generated by Hurricane Ian caused severe damage to the built environments over a large area. High storm waves superimposed on the elevated water level, reaching 1.2 m at the seaward edge of vegetated dunes, contributed to the destruction along the barrier islands. Hurricane Ian distributed a tremendous amount of non-biodegradable artificial debris over a large area and into sensitive natural environments, including numerous mangrove islands, barrier-island interior wetlands, and the estuary waterbody. Measures to prevent materials such as single-use plastics, insulation fibers, and household appliances from being washed into sensitive environments should be a significant part of prestorm preparation. [ABSTRACT FROM AUTHOR]

The Río de la Plata Estuary (RdP) is frequently affected by large storm surges that have historically caused social and economic losses. According to recent research, the number and strength of surge events have been increasing over time as a result of climate change. Although process-based models have been widely used for the storm surge prediction, their high computational demand may be a significant disadvantage in some applications, such as rapid or neartime forecasting. Artificial neural network (ANN) becomes an alternative tool to forecast the water level, taking into account meteorological and astronomical forcing as numerical models also do. In this work, an ANN model performance was evaluated to hindcast and forecast water levels in the RdP. Several combinations of lead times and inputs were assessed in order to find the best configuration. The resulting model provides 4-day forecasts for Buenos Aires and Torre Oyarvide stations (located at the upper and intermediate estuary, respectively), using observed water levels, meteorological inputs and predicted astronomical tides. Results also support the ANN model's ability to simulate even extreme events. For instance, for a 12 h-forecast, the RMSE is about 20 cm. Finally, we conclude that the model developed here can effectively complement the empirical and numerical forecasts executed by Naval Hydrographic Service, reducing computational costs and leveraging available datasets. [ABSTRACT FROM AUTHOR]

This study addresses the limitations of and the common challenges faced by one-dimensional river-routing methods in hydrological models, including the National Water Model (NWM), in accurately representing coastal regions. We developed a two-way coupling between the NWM and the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM). The approach demonstrated improvements in modeling coastal river dynamics, particularly during extreme events like Hurricane Matthew. The coupled model successfully captured tidal influences, storm surge effects, and complex river–river interactions that the standalone NWM missed. The approach revealed more accurate representations of peak discharge timing and magnitude as well as water storage and release in coastal floodplains. However, we also identified challenges in reconciling variable representations between hydrological and hydraulic models. This work not only enhances the understanding of coastal–riverine interactions but also provides valuable insights for the development of next-generation hydrological models. The improved modeling capabilities have implications for flood forecasting, coastal management, and climate change adaptation in vulnerable coastal areas. [ABSTRACT FROM AUTHOR]

This study evaluates the storm surge inundation risk in three anthropogenically infilled estuaries—Xichong, Renshan, and Kaozhouyang—located in the Guangdong–Macao–Hong Kong Great Bay Area, China. By integrating GIS spatial analysis with storm surge modeling, we conducted 204 numerical experiments to simulate storm surge inundation under varying typhoon intensities and astronomical tide conditions. Results revealed that coastal terrain plays a crucial role in influencing storm surge levels and inundation extents. Specifically, the pocket-shaped terrain in the Renshan and Kaozhouyang estuaries amplified storm surges, resulting in higher inundation levels compared to the relatively open terrain of Xichong. Furthermore, anthropogenically reclaimed land in these estuaries appear to be particularly vulnerable to storm-induced inundation. Overall, this study underscores the importance of considering coastline morphology and the anthropogenic modifications of coastal terrain in storm surge risk assessments, offering valuable insights for disaster prevention and mitigation strategies. The use of ArcGIS spatial analysis coupled with storm surge modeling, facilitated by high-resolution DEMs, provides a statistical risk assessment of inundation. However, more complex flooding dynamics models need to be developed, particularly when terrestrial bottom friction information, which is heavily modified by human activities, can be accurately incorporated. [ABSTRACT FROM AUTHOR]

The performance of an intermediate-scale modular, permeable, floating breakwater comprised of an array of vertical screens is optimized and tested. A distinctive attribute of this breakwater design is its adaptive capacity to fluctuating water levels owing to its floating configuration, thereby preserving its efficacy during high tide and storm tide scenarios—an advancement over conventional bottom-mounted structures. The initial validation of the concept was tested in a laboratory wave basin in regular waves, which demonstrated promising results for three porous panels. Next, the breakwater's design parameters were optimized using a finite difference computational fluid dynamics software, (FLOW-3D version 2023R2), considering porosity, spacing, and panel count. A scaled prototype, representative of a 1:2 ratio was then deployed during the summer of 2022 along the coast of Castine, ME, within a mesotidal, semi-sheltered system characterized by tidal currents and waves. Notably, the breakwater succeeded in attenuating half of the wave energy for periods shorter than 4 s, evidenced by transmission coefficients below 0.5, making this technology suitable for locally generated waves with shorter periods. During storm events, instantaneous transmission coefficients decreased to as low as 0.25, coinciding with significant wave heights exceeding 0.8 m. Additionally, the efficacy of wave attenuation improved slightly over time as biofoulants adhered to the structure, thereby enhancing drag and mass. [ABSTRACT FROM AUTHOR]

Convergent estuaries have been shortened by dam‐like structures worldwide. Here, we evaluate 31 long‐term water level stations and use a semi‐analytical tide model to investigate how landward‐funneling and a dam influence tidal and storm surge propagation in the greater Charleston Harbor region, South Carolina, where three rivers meet: the Ashley, Cooper, and Wando. Results show that the phase speed and amplification of the principal tidal harmonic (M2) is larger than other long waves such as storm surge (∼1–4 days) and setup‐setdown (∼4–10 days). Further landward, all waves attenuate, but, as they approach the dam on the Cooper River, a frequency dependent response in amplitude and phase progression occurs. A semi‐analytical tidal model shows that funneling and the presence of a dam amplify tidal waves through wave interference from partial and full reflection, respectively. The different phase progressions of the reflected waves interact with the incident wave to increase or decrease the summed overall wave amplitude. Using a friction‐convergence parameter space, we demonstrate that dominant tides in 23 estuaries and the tidal, storm surge, and setup‐setdown waves in the Cooper River can be delineated into three regimes that describe landward amplification or attenuation associated with funneling, a dam, or both. The regime of each tidal constituent is consistent but can change with the duration and height of each storm surge event; dam associated wave interference can attenuate long‐duration events, while the most intense events (short duration, high water) are amplified by dams more than funneling and greatly increase flood exposure. Plain Language Summary: Most ports and cities are located along estuaries and deltas where flood hazards are increasing partially due to human modifications of channel geometry and land use. Dams, salt barrages, and surge barriers are common in estuaries. They modify estuarine geometry, regulate river flow, protect against flooding, and prevent salt intrusion. Many estuaries are naturally convergent, wide near the sea and narrower landward. Dams are barriers which shorten an estuary. Like ocean swell at a seawall, tides reflect off dams and often increase tidal range. Here we investigate how dams and landward changes in estuarine geometry influence tides and storm surges. Using measurements from the greater Charleston Harbor, SC, we find that constructing a dam either increases or decreases water levels, depending primarily on estuary convergence and surge event duration, as well as flow resistance and river flow. The presence of dams results in higher water levels, and thus flood exposure, when convergence is weak and storm surge at the sea has a short duration and high water levels. Further, channelization that extends inland, resulting from dredging of shipping channels, increases the magnitude and seaward extent of dam effects with increasing flood risk. Key Points: Estuarine geomorphic funneling and dams produce partial and full reflections, respectively, with magnitudes that are frequency dependentThree convergence regimes emerge: dominant tides have near peak amplification, overtides attenuate, and long duration surges mildly amplifyDams reflect and amplify long waves—increasing flood exposure—the most in weakly convergent estuaries with low friction [ABSTRACT FROM AUTHOR]

Background: Embankment dams were built south of the Laizhou bay in China for controlling storm surge disasters, but they are not enough to replace coastal forests in protecting the land. This study was designed to evaluate the effects of embankment dams on natural forests dominated by Tamarix austromongolica and test whether the dam-shrub system is a preferable updated defense. Methods: Coastal forests on two typical flats, one before and one behind embankment dams, were investigated using quadrats and lines. Land bareness, vegetation composition and species co-occurrence were assessed; structures of T. austromongolica populations were evaluated; and spatial patterns of the populations were analyzed using Ripley's K and K1,2 functions. Results: In the area before embankment dams, 84.8% of T. austromongolica were juveniles (basal diameter ≤ 3 cm), and 15.2% were adults (basal diameter > 3 cm); behind the dams, 52.9% were juveniles, and 47.1 were adults. In the area before the dams, the land bareness was 13.7%, four species occurred, and they all were ready to co-occur with T. austromongolica; behind the dams, the land bareness was 0%, and 16 species occurred whereas they somewhat resisted co-occurrence with T. austromongolica. In the area before the dams, the T. austromongolica population was aggregated in heterogeneous patches, and the juveniles tended to co-occur with the adults; behind the dams, they were over-dispersed as nearly uniform distributions, while the juveniles could recruit and were primarily independent of the adults. These results indicate that the T. austromongolica species did not suffer from the unnatural dams, but benefited somehow in population expansion and development. Overall, the T. austromongolica species can adapt to artificial embankment dams to create a synthetic defense against storm surges. [ABSTRACT FROM AUTHOR]

In the context of climate change, coastal flood risk is intensifying globally, particularly in China, where intricate coastlines and frequent tropical cyclones make storm surges a major concern. Despite local government's efforts to initiate coastal monitoring networks and qualitative risk guidelines, there remains a gap in detailed and efficient quantitative assessments for combinations of multiple sea‐level components. To address this, we develop the Tropical Cyclone Storm Surge‐based Flood Risk Assessment under Combined Scenarios (TCSoS‐FRACS). This framework integrates impacts of storm surges, high tides, and sea‐level rise using a hybrid of statistical and dynamic models to balance reliability and efficiency. By combining hazard, exposure, and vulnerability, it incorporates economic and demographic factors for a deeper understanding of risk composition. Applying TCSoS‐FRACS to Hainan Island reveals that the combined effects of storm surges, high tides, and sea‐level rise significantly amplify local coastal flood risk, increasing economic losses to 4.27–5.90 times and affected populations to 4.96–6.23 times. Additionally, transitioning from Fossil‐fueled Development (SSP5‐8.5) to Sustainability (SSP1‐1.9) can reduce the risk increase by approximately half. The equivalence in flood hazard between current high tides and future sea level under a sustainable scenario boosts confidence in climate change adaptation efforts. However, coastal cities with low hazard but high exposure need heightened vigilance in flood defense, as future risk could escalate sharply. Our study provides new insights into coastal flood risk on Hainan Island and other regions with similar profiles, offering a transferable and efficient tool for disaster risk management and aiding in regional sustainable development. Plain Language Summary: Climate change is intensifying coastal flood risk worldwide. We developed TCSoS‐FRACS, a framework that assesses flood risk from storm surges, high tides, and sea‐level rise, using a combination of dynamic and statistical models. This framework evaluates hazard, exposure, and vulnerability to predict affected populations and economic losses across various land uses. When applied to Hainan Island, it shows that the combination of storm surges, high tides, and rising sea levels significantly increases flood risk. However, transitioning from a Fossil‐fueled Development Path to a Sustainability Path can reduce this increased risk almost by half. The study also finds that, on Hainan Island, current high tide hazards may be comparable to future sea‐level rise hazards under a sustainable scenario, boosting confidence in adaptation efforts. Coastal cities with low hazard but high exposure need greater vigilance, as their future flood risk could rise sharply. Our research offers valuable insights and tools for managing coastal flood risk in Hainan Island and similar regions, supporting sustainable development and effective disaster management. Key Points: Design a flood risk framework for TC storm surges combined with high tides and sea‐level rise using hybrid dynamic and statistical modelsEquivalence in flood between current high tides and future sea level under a sustainable scenario boosts confidence in adaptation effortsCoastal cities with low hazard but high exposure need heightened vigilance in flood defense as future risk could dramatically escalate [ABSTRACT FROM AUTHOR]

For Williamstown tide gauge, at the northern‐most point of Port Phillip Bay (PPB), Melbourne, Victoria, tide registers from 1872 to 1966 and marigrams from 1950 to 1966, were digitized to extend sea‐level records back almost 100 years. Despite some vertical datum issues in the early part of the record, the data set is suitable for extreme sea‐level trend analysis after removal of the annual mean sea level. The newly digitized data was combined with the digital record to produce a combined record from 1872 to 2020. Analysis of this record revealed known problems of siltation of the tide gauge stilling well and associated reduction in tidal range at times during 1880–1895 and 1910–1940. A positive trend in tidal amplitude of 0.41 ± 0.01 mm yr−1 was found over 1966–2020, likely due to reduced hydraulic friction at the narrow entrance to PPB. Extreme sea‐level trends were examined over 1872–2020 for storm tides (the combination of storm surge and tide) after removal of the annual mean, and residuals after subtraction of the predicted tides. A non‐stationary Gumbel distribution with a time‐varying location parameter revealed statistically significant declining trends in the residuals of −0.73 ± 0.02 mm yr−1, consistent with the observed poleward movement of storm surge‐producing mid‐latitude weather systems. For storm tides a smaller declining trend of −0.40 ± 0.01 mm yr−1 was found. These trends are approximately an order of magnitude smaller than the current positive rates of mean sea level rise, meaning that storm tide hazard will continue to increase in the future. This information is relevant for future adaptation planning. Plain Language Summary: Sea levels recorded by tide gauges can be studied to understand how sea levels are changing due to mean sea‐level rise (SLR) from ocean warming and melting of land‐based ice as well as due to changes in extreme sea levels (storm tides) due to the combination of astronomical tides and severe‐weather‐induced storm surges. Digitizing historical paper tide gauge records lengthens the digital records and enables more reliable trend analysis. In this study, tide records from 1872 to 1966 were digitized for the Williamstown tide gauge, within the semi‐enclosed Port Phillip Bay, in southeastern Australia, lengthening the existing digital record by almost 100 years. After removing the trend due to SLR, a small negative trend in extreme sea levels was found. Looking at the components separately, a small negative trend in storm surges was found, consistent with the observed poleward movement of the weather systems that cause them. A positive trend in tidal range was found, possibly due to more tidal in and outflow through the narrow entrance to the bay under SLR. The net effect of SLR and storm‐tide trends is an increasing trend in extreme sea‐levels. This information is relevant for coastal managers considering coastal adaptation options. Key Points: Sub‐daily data for the Williamstown, Australia tide gauge has been extended back to 1872 through digitization of paper recordsTrends in extreme sea level events show a small declining trend over the entire record although trends in tidal range are positive over recent decades [ABSTRACT FROM AUTHOR]

Storm surges events are investigated using the ECHAR method, which identifies and quantifies the different dynamical structures of a typical storm surge event. In the North Atlantic, analysis of 65 tide gauges revealed that storm surge events display two major and two minor structures, each of them corresponding to specific ocean dynamics. The two major structures refer to a slow‐time Gaussian structure, lasting around 24 days, associated with the impact of the atmospheric pressure and a fast‐time Laplace structure, lasting around 1.4 days, mainly wind‐driven. The absence of the Gaussian structure along the North America coasts is explained by storms of smaller spatial extension, compared to Europe. Concerning the minor structures, a negative surge of around 6 cm just after the peak surge is observed over North America only. Such a sudden drop of the sea level is explained by the turning winds during the storm event, favored by the smaller spatial extension of storms. Finally, high frequency oscillations, with amplitude typically of 3 cm and up to 25 cm, are observed at some tide gauges. These oscillations refer to tide‐surge interactions and they are often maximum at a specific phase of the tide and/or enhanced because of resonant basins. Plain Language Summary: The storm surges refer to the extreme values of the sea level time series, after the removal of tides and the mean sea level. The storm surges are due to extreme weather systems such as storms and are responsible for coastal flooding. Rather than focusing on the maximum values of the sea level during storm events, we consider the full dynamics of the events from 20 days before to 20 days after each maximum. Doing so helps to properly describe the different types of storm surge events and explain the atmospheric and ocean dynamics leading to such extremes. Key Points: The new ECHAR method decomposes storm surge events in two major and two minor structures each of them reflecting specific ocean dynamicsIn North America a large drop of the sea level 1.5 days after the peak surge is explained by turning winds and storms of small extentObserved oscillations with amplitude up to 10 cm are the signature of tide‐surge interaction [ABSTRACT FROM AUTHOR]

Guru Prasath, J.; Sannasiraj, S.A., and Chandramohan, P., 2024. Nearshore morphological changes due to severe cyclonic storm activity along the east coast of India. Journal of Coastal Research, 40(4), 696–713. Charlotte (North Carolina), ISSN 0749-0208. Nearshore waves, wind, and tidal current mobilize seabed sediments; sort, transport, and redistribute the sediments; and modify the nearshore seabed morphology. Waves play a dominant role in morphological changes among winds, waves, and tidal currents. The nearshore wave breaking induces the initiation of sediment movement, and the associated alongshore current transports a large volume of sediments that tend to either accrete toward or erode from the beach face and seabed. Sediment transport rate in the nearshore waters during cyclones is higher than that under normal conditions. The measured changes due to Cyclone Nivar in November 2020 in the nearshore morphology along the Mahabalipuram coast are examined to understand the response of nearshore morphology to cyclonic wave-induced forcing. The beach-profile measurements across 10 transects at 100 m spacing covering about 1000 m along the coast were measured before and after the passage of Cyclone Nivar as a part of the present field study. The evolving beach profiles during Cyclone Nivar were simulated using the storm-induced beach change (SBEACH) model, which is a cross-shore profile evolution model. The changes due to berm erosion and deposition of nearshore sand bars have been compared from field measurements data and model prediction. The calibrated model was then used to simulate the beach profile response at two other locations along the cyclone-affected coastline to examine the shoreline impact. The calibrated model can be used along the coast to understand the poststorm profiles of the beach with reasonable accuracy. [ABSTRACT FROM AUTHOR]

Grid resolution and assimilation window size play significant roles in storm surge models. In the Bohai Sea, Yellow Sea, and East China Sea, the influence of grid resolution and assimilation window size on simulating storm surge levels was investigated during Typhoon 7203. In order to employ a more realistic wind stress drag coefficient that varies with time and space, we corrected the storm surge model using the spatial distribution of the wind stress drag coefficient, which was inverted using the data assimilation method based on the linear expression Cd = (a + b × U10) × 10−3. Initially, two grid resolutions of 5′ × 5′ and 10′ × 10′ were applied to the numerical storm surge model and adjoint assimilation model. It was found that the influence of different grid resolutions on the numerical model is almost negligible. But in the adjoint assimilation model, the root mean square (RMS) errors between the simulated and observed storm surge levels under 5′ × 5′ and 10′ × 10′ grid resolutions were 11.6 cm and 15.6 cm, and the average PCC and WSS values for 10 tidal stations changed from 89% and 92% in E3 to 93% and 96% in E4, respectively. The results indicate that the finer grid resolution can yield a closer consistency between the simulation and observations. Subsequently, the effects of assimilation window sizes of 6 h, 3 h, 2 h, and 1 h on simulated storm surge levels were evaluated in an adjoint assimilation model with a 5′ × 5′ grid resolution. The results show that the average RMS errors were 11.6 cm, 10.6 cm, 9.6 cm, and 9.3 cm under four assimilation window sizes. In particular, the RMS errors for the assimilation window sizes of 1 h and 6 h at RuShan station were 3.9 cm and 10.2 cm, a reduction of 61.76%. The PCC and WSS values from RuShan station in E4 and E7 separately showed significant increases, from 85% to 98% and from 92% to 99%. These results demonstrate that when the assimilation window size is smaller, the simulated storm surge level is closer to the observation. Further, the results show that the simulated storm surge levels are closer to the observation when using the wind stress drag coefficient with a finer grid resolution and smaller temporal resolution. [ABSTRACT FROM AUTHOR]

Geologic reconstructions of overwash events can extend storm records beyond the brief instrumental record. However, the return periods of storms calculated from geologic records alone may underestimate the frequency of events given the preservation bias of geologic records. Here, we compare a geologic reconstruction of storm activity from a salt marsh in New Jersey to two neighboring instrumental records at the Sandy Hook and Battery tide gauges. Eight overwash deposits were identified within the marsh's stratigraphy by their fan‐shaped morphology and coarser mean grain size (3.6 ± 0.7 φ) compared to autochthonous sediments they were embedded in (5.6 ± 0.8 φ). We used an age–depth model based on modern chronohorizons and three radiocarbon dates to provide age constraints for the overwash deposits. Seven of the overwash deposits were attributed to historical storms, including the youngest overwash deposit from Hurricane Sandy in 2012. The four youngest overwash deposits overlap with instrumental records. In contrast, the Sandy Hook and Battery tide gauges recorded eight and 11 extreme water levels above the 10% annual expected probability (AEP) of exceedance level, respectively, between 1932/1920 and the present. The geologic record in northern New Jersey, therefore, has a 36–50% preservation potential of capturing extreme water levels above the 10% AEP level. [ABSTRACT FROM AUTHOR]

Hurricane Michael (2018) made landfall near Mexico Beach, FL, as a Category 5 hurricane, with gauge‐measured water levels over 4 m. Wind and pressure fields were created by blending a parametric near‐field model with a gridded far‐field model. Winds and modeled water levels were well validated across Michael's large impact a‐ea. A detailed analysis of the coastal surge caused by Michael demonstrates that advection contributed significantly to Michael's highest water levels and the timing of the water level across a large portion of the Michael impact area. A momentum balance in a streamwise‐normal coordinate system demonstrates that the advection contributions due to spatial gradients in the flow are identified with streamwise convergence/expansion of the flow field (Bernoulli acceleration) and curvature in the flow field (centrifugal acceleration). These effects are created by the regional geometry and the storm's wind field and are most likely to affect back barrier water levels and along curved coastlines. These findings provide an improved understanding of the role of advection in determining storm surge and, thus, the importance of including it in storm surge models. Plain Language Summary: In 2018, Hurricane Michael hit Mexico Beach, FL, as a Category 5 hurricane, with peak water levels over 4 m. Michael's flooding was studied using computational models that simulated the storm's water levels and currents. These models revealed the influence of the storm's wind patterns and regional geography on water levels and their distribution across the impacted region, particularly emphasizing the flow velocity field surrounding Mexico Beach and the inlet to St. Joseph Bay. Such insights clarify the role of specific flow dynamics and highlight the importance of capturing them accurately in numerical simulations of storm‐induced water surge. Key Points: Advection substantially enhanced Michael's storm surge across a large portion of the Michael impact areaCentrifugal acceleration from flow curvature was the main advection process affecting water levels along the coast during Hurricane MichaelBernoulli acceleration plays an important role back‐barrier in water levels where flow contractions/expansions are present [ABSTRACT FROM AUTHOR]

The Holland wind model remains limitations on the accurate surface wind estimation of tropical cyclone (TC) and the storm surge modelling. This study investigates the influence of TC moving speeds on the attenuation effect of Holland surface wind and proposes a weakening factor to correct Holland surface wind. Then the corrected surface wind fields are combined with ERA5 to produce the improved hybrid surface wind fields to simulate total water levels. The significant linear correlation (R2 = 0.54) between TC moving speeds and the weakening factor shows that the slower the TC moves, the greater attenuation rate the surface wind speeds need. The improved Hybrid wind fields (Hybrid_f) have the best agreement with ground observations, with the Skills and RMSEs in the range of 0.65-0.93 and 2.3-6.9 m/s. The total water levels of storm surge simulated by the Hybrid_f have the best agreement with gauge measurements, with the Skills and RMSEs in the range of 0.72-0.99 and 0.16-0.51 m. In particular, the improved surface wind fields for TCs with slower movement substantially mitigate the severe reduction of water levels. These findings offer valuable insights for optimizing the surface wind fields to improve storm surge modeling and other applications.

A comprehensive understanding of the processes of storm surge and flooding induced by typhoons is crucial for the development of effective emergency plans and risk management strategies. This study employed the three-dimensional coastal ocean circulation model FVCOM to simulate the storm surge in the Pearl River Estuary (PRE) during Typhoon Hato in 2017. In addition, the storm surge predictions are integrated with the InfoWorks 2D-1D hydrologic network drainage model to study urban flooding in the Inner Harbor district in Macao for the first time. Typhoon's wind and pressure fields were reconstructed based on parametric models, and numerical experiments were conducted to investigate the effects of tide-surge interaction. A thorough comparison between observed and simulated wind and water levels demonstrated a high degree of agreement. The results reveal that nonlinear tide-surge interactions are not uniformly distributed across the PRE. In the Lingdingyang waters, the impact of tide-surge interaction on the total water level ranges from 1% offshore to 30% upstream. In the coastal waters near Macao, this impact varies from 5% to 25%, with a peak impact of 25% observed near Hac Sa Bay in Macao, indicating a significant influence of tide-surge interaction in this area. Predicted city flood levels during HATO are well-validated against field data. The Inner Harbor of Macao is identified as the zone most severely affected by flooding induced by storm surge, with the highest flood depths recorded at its northern and southern ends.

Climate change poses a threat not only to the environmental and physical aspects of the places where people live, but also to their identities and ways of living, as well as those of the communities around them. Conducted as part of the requirements of the Master of Advanced Studies in Marine Biodiversity and Conservation at Scripps Institution of Oceanography (SIO), this capstone project consists of a climate vulnerability assessment of the La Fortaleza and San Juan National Historic Site World Heritage Site in Puerto Rico, based on a literature review of existing scientific data. Its purpose is to inform a broader effort led by SIO’s Human Ecology Lab, which will complement this top-down approach with a bottom-up assessment of climate change threats to the area based on the perspectives of local stakeholders. This project includes two products: the full climate vulnerability assessment presented below and an online summary of its main findings with interactive maps, which can be found at https://arcg.is/1quumK. Ultimately, this project contributes to the ongoing development of a climate vulnerability assessment tool for cultural heritage that can be used independently by stakeholders to meaningfully assess the vulnerability of their own cultural heritage to climate change through a decentralized process that can inspire local climate action, inform decision-making at different levels, and be repeated consistently as the components of vulnerability change over time.  Please see the media created for this project here: https://storymaps.arcgis.com/stories/79ebff9ddfd741d788141b1b10cb0b54

Downscaling is reconstructing data from low to high resolution, capturing local effects and magnitudes. Widely employed methods for downscaling are dynamic and statistical methods with pros and cons. With ample data, machine learning (ML) and deep learning (DL) techniques can be employed to learn mapping from low to high resolution. This article investigates convolutional neural network capabilities for downscaling winds. The speed and direction of the wind are guided by a complex relation among pressure, Coriolis force, friction, and temperature, which leads to highly nonlinear wind patterns and poses a significant challenge for downscaling. The problem can be formulated as a super-resolution technique called a super-resolution convolutional neural network (SRCNN) for data reconstruction. Few variations of SRCNN are studied for wind downscaling. Six years of European Centre for Medium-Range Weather Forecasts (ECMWF) wind datasets along the east coast of India are used in the current study and are downscaled up to four times. Downscaled winds provide better results than traditional interpolation methods. Simulations for an extreme event are conducted with SRCNN downscaled winds and are compared against interpolation methods and original data. The numerical simulation results show that DL-based methods provide results closer to the ground truth than interpolation methods. HIGHLIGHTS The application of machine learning (ML) for downscaling winds is explored.; Three different ML techniques are employed to downscale winds.; The performance of ML methods over traditional interpolation methods is compared by plotting probability density functions and by coupled ADCIRC + SWAN model simulations with winds as atmospheric forcing.; Computed storm surge with ML winds as forcing is superior to interpolation methods.;

Coastal water level information is crucial for understanding flood occurrences and changing risks. Here, we validate the preliminary version (0.9) of NOAA's Coastal Ocean Reanalysis (CORA), which is a 43-year reanalysis (1979-2021) of hourly coastal water levels for the Gulf of Mexico and Atlantic Ocean (i.e., the Gulf and East Coast region, or GEC). CORA-GEC v0.9 was conducted by the Renaissance Computing Institute using the coupled ADCIRC+SWAN coastal circulation and wave model. The model uses an unstructured mesh of nodes with varying spatial resolution that averages 400 m near the coast and is much coarser in the open ocean. Water level variations associated with tides and meteorological forcing are explicitly modeled, while lower-frequency water level variations are included by dynamically assimilating observations from NOAA's National Water Level Observation Network. We compare CORA to water level observations that were either assimilated or not, and find that the reanalysis generally performs better than a state-of-the-art global ocean reanalysis (GLORYS12) in capturing the variability on monthly, seasonal, and interannual timescales as well as the long-term trend. The variability of hourly non-tidal residuals is also shown to be well resolved in CORA when compared to water level observations. Lastly, we present a case study of extreme water levels and coastal inundations around Miami, Florida to demonstrate an application of CORA for studying flood risks. Our assessment suggests that NOAA's CORA-GEC v0.9 provides valuable information on water levels and flooding occurrence from 1979-2021 in areas that are experiencing changes across multiple time scales. CORA potentially can enhance flood risk assessment along parts of the U.S. Coast that do not have historical water level observations. [ABSTRACT FROM AUTHOR]

The risk of high river flow and peak storm surge superimposing in the estuary of small-and medium-sized rivers can be more severe during typhoon landfalls than at other times. In this study, 30 cases of typhoon track ensemble experiments for five rivers in Mikawa Bay, Japan, were evaluated using an atmospheric-riverocean model. The results showed that storm surges coincided with high river flows when typhoons crossed the western side of Mikawa Bay, with the highest potential observed when typhoons passed northwest of the Bay. The average time discrepancy for the five rivers between the storm surge occurrence and increased river flow in the minimum time difference case was 48min at the river mouth. Distinct trends in the time difference between a storm surge and river flow were observed for each river mouth. The order of time differences corresponded to the drainage area scale: Toyo (188min), Umeda (112min), Otowa (114min), Sana (96min), and Yagyu (88min). Time difference trends also varied based on the distance between the typhoon center and each river mouth. For Toyo River, storm surges and high river flows did not substantially overlap, regardless of distance. However, for medium-and smallsized rivers, the time difference increased proportionally with the distance from the typhoon. The Yagyu River showed the highest potential for compound occurrence of a storm surge and river flood because of the synchronization of storm surge peaks and high river flow peaks, with a time difference of 1-2h for small-and medium-sized rivers within 100km2 of the basin. [ABSTRACT FROM AUTHOR]

The sensitivity of hurricane-driven storm surge in the Houston Ship Channel Industrial Corridor to variations in storm characteristics is studied. Hurricane surge from Hurricane Ike from 2008 was simulated using the Delft3D Flexible Mesh model. Use of the HURDAT2 wind parameter database allowed alteration of relevant hurricane characteristics to develop these alternate scenarios. Moving the actual landfall location of Ike toward the southwest at a distance of 50 km yielded a 123% increase at Manchester relative to observations during the storm. Relocation of the landfall location 200 km further southwest caused a 40% decrease in water level at the Galveston Bay entrance relative to observed values, but with a corresponding 50% water level increase at Manchester. Reduction of the storm forward speed to 25% of the actual speed over the nearshore led to a 25% increase in water level in Manchester over recorded values, while a 57% reduction of the storm forward speed over land increased water levels in Manchester by 30%. Incorporating river flow also identified locations where Galena Park's residual floodwater is likely to remain after flooding. The results of this work can be used to guide modeling efforts of storm impacts on areas with narrow waterways. [ABSTRACT FROM AUTHOR]

Climate change brings intense hurricanes and storm surges to the US Atlantic coast. These disruptive meteorological events, combined with sea level rise (SLR), inundate coastal areas and adversely impact infrastructure and environmental assets. Thus, storm surge projection and associated risk quantification are needed in coastal adaptation planning and emergency management. However, the projections can have large uncertainties depending on the planning time horizon. Excessive uncertainties arise from inadequately quantified ocean-climatic processes that control hurricane formation, storm track, and SLR in time of climate change. For this challenge, we propose an objective-based analytical-statistical approach using the National Oceanic and Atmospheric Administration's (NOAA)'s Sea, Lake, and Overland Surge from Hurricanes (SLOSH) model in scenario analysis of the storm surge impacts. In this approach, synthetic hurricanes (wind profile and track direction) are simulated to yield the likely range of the maximum envelope of water (MEOW), the maximum of the maximum (MOM), local wind speed, and directions. The surge height and time progression at a location are analyzed using a validated SLOSH model for a given adaptation or planning objective with a set of uncertainty tolerance. We further illustrate the approach in three case studies at Mattapoisett (MA), Bridgeport (CT), and Lower Chesapeake Bay along the US Atlantic coast. Simulated MOMs as the worst-case surge scenarios defined the long-term climate risk to the shoreside wastewater plants in Bridgeport and environmental assets in the Lower Chesapeake Bay. The wind-surge probability envelopes in simulated MEOWs provide location-specific estimates of the storm surge probability for local adaptation analysis at four locations in Lower Chesapeake Bay and at Mattapoisett of the southeastern Massachusetts coast. Using the constraints of local bathymetry and topography, the wind-surge probability curves and time progression also provide quantitative probability estimates for emergency response planning, as illustrated in the Mattapoisett case study. [ABSTRACT FROM AUTHOR]

Providing storm surge risk information at multi-day lead times is critical for hurricane evacuation decisions, but predictability of storm surge inundation at these lead times is limited. This study develops a method to parameterize and adjust tropical cyclones derived from global atmospheric model data, for use in storm surge research and prediction. We implement the method to generate storm tide (surge + tide) ensemble forecasts for Hurricane Michael (2018) at five initialization times, using archived operational ECMWF ensemble forecasts and the dynamical storm surge model ADCIRC. The results elucidate the potential for extending hurricane storm surge prediction to several-day lead times, along with the challenges of predicting the details of storm surge inundation even 18 h before landfall. They also indicate that accurately predicting Hurricane Michael's rapid intensification was not needed to predict the storm surge risk. In addition, the analysis illustrates how this approach can help identify situationally and physically realistic scenarios that pose greater storm surge risk. From a practical perspective, the study suggests potential approaches for improving real-time probabilistic storm surge prediction. The method can also be useful for other applications of atmospheric model data in storm surge research, forecasting, and risk analysis, across weather and climate time scales. [ABSTRACT FROM AUTHOR]

Recent environmental change exacerbates living conditions especially in coastal areas due to increases of frequency and intensity of typhoons. A storm surge early warning system (SEWS) can be a useful non-structural measure to protect the public living in coastal areas. The establishment of sea levels for the SEWS has a critical role. Thorough ground surveying is desired to establish sea levels by finding the most vulnerable point. Meanwhile, remote sensing techniques have been addressed for the coastal vulnerability especially with UAV photogrammetry. Therefore, a UAV photogrammetry procedure is proposed for a SEWS to estimate the alarming sea levels in the current study combining historical tide data for storm surge events. The procedure is comprised of five steps: 1) UAV surveying of the target area and producing the DSM data; 2) definition of the vulnerable points with DSM and ground surveying; 3) estimation of the inundation area from the DSM with sea level rising; 4) analysis of the precedent storm surge events; and 5) estimation of sea levels with the inundation area and historical sea levels of the vulnerable points. Wolpo Beach in South Korea was employed, since its coastal area has been damaged from storm surge events. Results indicated that the UAV-based procedure can be a useful method to establish sea levels for the SEWS. [ABSTRACT FROM AUTHOR]

Spatiotemporal variation of storm surges in the Taiwan Strait (TWS) is studied using water level datasets from 14 tidal gauge stations located in the TWS from summer to fall of 2016. The effects of bathymetry on storm surges and tropical cyclone (TC)-induced continental shelf wave (CSW) are explored. By comparing water level response along the east coast of TWS, it is found that storm surges are asymmetric on the north and south sides of the Zhan-Yun Ridge (ZYR), regardless of the different categories and tracks of TCs passing by. Observations indicate that the ZYR could modulate the storm surges and the CSW propagation; ZYR can not only amplify the storm surges that generally peak around the ZYR, but also block the CSW propagation by dramatically dissipating its kinetic energy as revealed by the dispersion relation for the first mode CSW. Moreover, local wind work and the remote forcing, which are induced by TCs and cold fronts, respectively, can also modulate the synoptic variations of water level in the TWS. [ABSTRACT FROM AUTHOR]

The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments, providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions. [ABSTRACT FROM AUTHOR]

Storm surges, a significant coastal hazard, cause substantial damage to both property and lives. Precise and efficient storm surge models are crucial for long-term risk assessment and guiding emergency management decisions. While high-fidelity dynamic models offer accurate predictions, their computational costs are substantial. Hence, recent efforts focus on developing data-driven storm surge surrogate models. This study focuses on the Pearl River Estuary in Guangdong Province. Initially, the dynamic ADvanced CIRCulation (ADCIRC) model was utilized to construct storm surge data for 16 historical typhoons, serving as training, validation, and testing data for data-driven models. Subsequently, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Informer deep learning (DL) models were employed for forecasting of storm surge over the next 1h, 3h, 6h, 12h, and 18h. Finally, Shapley Additive exPlanations (SHAP) values were used for interpretability analysis of the input factors across different models. Results indicated that the proposed DL storm surge prediction model can effectively replicate the dynamic model's simulation results in short-term forecasts, significantly reducing computational costs. This model offers valuable scientific assistance for future coastal storm surge forecasts in the Greater Bay Area. [ABSTRACT FROM AUTHOR]

In probabilistic coastal hazard assessments based on the Joint Probability Method, historical storm data is used to build distribution models of tropical cyclone atmospheric parameters (i.e., central pressure deficit, forward velocity, radius of maximum wind, and heading direction). Recent models have used a range of assumptions regarding the dependence structure between these random variables. This research investigates the performance of a series of joint distribution models based on assumptions of parameter independence, partial-dependence (i.e., dependence between only central pressure deficit and radius of maximum wind), and full dependence (i.e., dependence between each pair of tropical cyclone atmospheric parameters). Full dependence models consider a range of copula models, such as the Gaussian copula and vine copulas that combine linear-circular copulas with Gaussian or Frank copulas. The consideration of linear-circular copulas allows for the characterization of correlation between linear variables (e.g., central pressure deficit) and circular variables (e.g., heading direction). The sensitivity of the results to different joint distribution models is assessed by comparing hazard curves at representative locations in New Orleans, LA (USA). The stability of hazard curves generated using a Gaussian copula considering variation in the selection of the zero-degree convention is also assessed. The tail dependence between large central pressure deficit and large radius of maximum wind associated with various copula models are also compared using estimated conditional probability. It is found that the linear-circular Frank vine copula model improve the stability of hazard curves and maximize tail dependence between large central pressure deficit and large radius of maximum wind. However, the meta-Gaussian copula model exhibits performance within this study region that was generally consistent with the tested vine copulas and have the advantage of being easier to implement. [ABSTRACT FROM AUTHOR]

Low‐gradient coastal watersheds are susceptible to flooding caused by various flows such as rainfall‐runoff, astronomical tides, storm surges, and riverine flows. Compound flooding occurs when at least one coastal flood driver occurs simultaneously or in close succession with a pluvial and/or fluvial flood driver, such as during a tropical cyclone event. This study presents a one‐dimensional (1‐D), reduced‐order physics compound inundation model tested over an idealized coastal watershed transect under various forcing conditions (e.g., coastal and pluvial) that varied in magnitude, time, and space. This study aims to evaluate each flooding mechanism and the associated hydrodynamic responses by performing a sensitivity analysis and developing a non‐linear equation that could correlate the flood drivers with the severity of its flood. Compound inundation levels are affected by the magnitude and timing of each flooding mechanism. Results highlight the need to consider momentum exchange during a compound event and the importance of reduced‐physics approaches that can improve the interaction between flood drivers when paired with a moving coupling node approach. The desire is a more holistic compound inundation model that can be a critical tool for decision‐makers, stakeholders, and authorities who provide evacuation planning to save human lives and enhance resilience. Plain Language Summary: Intense rainfall, tides, and hurricane storm surges can flood coastal regions with mild sloping terrain. When multiple flooding events occur at the exact location, the inundation effects can be aggravated by the interaction of these physical processes. This study aims to disentangle the complexities of interactive physics according to fundamental concepts. Thus, identifying regions affected by hydrological and coastal processes can improve flood mapping techniques for coastal communities in future efforts. Results show that each flood event's strength and time of occurrence affect the total inundation levels. Furthermore, the flow velocity from these flood processes is essential when they interact. Our research can serve as a foundation for further studies to enhance tools that accurately predict these flood events, resulting in better planning and preparedness for worldwide coastal communities to reduce property damage and loss of lives. Key Points: A reduced‐physics one‐dimensional flood model was developed to assess compound floods at an idealized coastal watershed transectA moving coupling node approach can capture the momentum exchange between pluvial and coastal flood driversA non‐linear equation correlating storm characteristics with flood severity was developed to examine the driver's interaction [ABSTRACT FROM AUTHOR]

Sagar Island, located in the Indian Sundarbans Delta, is extremely vulnerable to storm surge flooding. Therefore, there is a need for a precise model to assess its susceptibility to storm surges, which is essential for efficient coastal management and reducing the risk of disasters. Traditional modeling methods often lack the capability to consider the intricate relationships between various influencing factors. This study aims to advance the field by developing robust deep learning (DL) models for storm surge susceptibility prediction, specifically incorporation of Bayesian optimization with DL models and implementing Explainable Artificial Intelligence (XAI) methods for model interpretation and data-driven management. Storm surge susceptibility in Sagar Island presents a critical problem requiring advanced predictive modeling. We employ Bayesian Optimization for hyperparameter tuning in Deep Neural Networks (DNN), 1D Convolutional Neural Networks (CNN), and LightGBM models, focusing on 11 variables with low multi-collinearity. Additionally, we applied Explainable AI techniques such as SHapley Additive exPlanations (SHAP) and permutation importance for model interpretability. DNN achieved the highest accuracy of 97.75%, with F1-score at 97.85%. LightGBM and CNN were close competitors with an accuracy of 97.5%. DNN's true positive rate was 95%, compared to CNN's 94% and LightGBM's 93%. In the susceptibility mapping analysis, the CNN detected areas categorized as 'Very Low' and 'Very High' susceptibility, constituting 65.12% of the study area and covering 156.88 km² and 11.38% covering 11.38 km², respectively. Similarly, LightGBM exhibited a comparable pattern, but with a more pronounced representation of 'High' susceptibility zones, spanning 22.61 km², predominantly across the coastal and central regions of Sagar Island. Feature importance assessed through SHAP revealed "Rainfall" as 40% more impactful than "Cyclone Track." While all three models demonstrated robust performance, DNN emerged as marginally superior in most evaluated metrics. Our study provides valuable insights for targeted storm surge management strategies in Sagar Island, combining high predictive accuracy with model interpretability. [ABSTRACT FROM AUTHOR]

The Río de la Plata Estuary (RdP), one of the most populated and developed areas of Southern South America, often experiences positive storm surges (PSS). These episodic rises of sea level due to meteorological forcing drive floods that endanger human lives and cause property damage. In this work, PSS are studied and contextualized in both the dynamics of the adjacent Southwestern Atlantic Continental Shelf (SWACS) and the synoptic dynamics of the atmosphere. The study is based on statistical analyses of tide gauge observations gathered at the upper RdP (period 1934–2020), numerical simulations of sea level in the SWACS and atmospheric reanalysis products. Results reveal hitherto unknown aspects of ocean dynamics in the SWACS forced by the atmosphere in which PSS events impacting the RdP are embedded, showing that: (1) Strong PSS in the RdP can be locally forced by cyclogenesis; nevertheless, most of PSS events affecting the estuary are remotely forced at the southern SWACS and reach the estuary as free propagating coastal waves, producing a surge that can be as strong as the locally forced ones; (2) the spatial pattern of the sea level anomalies in the SWACS during the PSS in the RdP can be described in terms of three distinctive modes (or spatial structures), each one of them related to characteristic regional atmospheric synoptic processes (in particular traveling Rossby waves and cyclogenesis); this provides clues to both better understand the surges and to extend their predictability; and (3) the PSS extension in the SWACS and the RdP depends on the speed at which atmospheric systems pass through the region. These findings provide valuable insights that can aid in anticipating extreme situations several days in advance to the numerical ocean forecast systems. The analysis of the gauge observations at Buenos Aires shows that (1) the number of cases of PSS per year presents large multidecadal and interannual (pseudo-cycle at 5 years) variability; (2) in this frame, it is not reasonable to estimate long-term lineal trends and/or extrapolate tendencies; therefore, our results refute the suggestion that the number of PSS cases in the RdP has been increasing over time, as reported in previous works. [ABSTRACT FROM AUTHOR]

Coastal areas face escalating storm surge disasters due to rising sea levels and urban growth, posing greater risks to lives and property. Comprehensive storm surge risk assessment and sensitivity analysis in coastal areas are crucial for effective disaster prevention and mitigation. This research focuses on Huizhou, China, conducting a comparative study of storm surge risk assessment and sensitivity analysis based on an integrated approach, which includes the storm surge inundation numerical model (FVCOM-SWAN), Geographic Information System (GIS) and Remote Sensing (RS) techniques, and Multiple Criteria Decision-Making (MCDM) methods. Ten flood-related risk indicators are selected from the hazard, exposure and vulnerability, the weights of which are evaluated through a comprehensive comparison among Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight (EW), AHP-EW, and FAHP-EW methods. High-precision risk level maps are generated subsequently utilizing GIS and RS techniques. Sensitivities of the indicators are analyzed using One-At-A-Time (OAT) and Fourier Amplitude Sensitivity Test (FAST) methods. The proposed storm surge risk assessment framework, the MCDM comparative study and the sensitivity analysis can offer insights for better understanding and management of storm surge risks, and contribute to the standardization and application of storm surge risk assessment. [ABSTRACT FROM AUTHOR]

Climate change affects the activity of global and regional tropical cyclones (TCs). Among all TCs, typical super typhoons (STYs) are particularly devastating because they maintain their intensity when landing on the coast and thus cause casualties, economic losses, and environmental damage. Using a 3D tidal model, we reconstructed the typhoon (TY) wind field to simulate the storm surge induced by typical STYs. The TY activity was then analyzed using historical data. Results showed a downtrend of varying degrees in the annual frequency of STYs and TCs in the Western North Pacific (WNP) Basin, with a significant trend change observed for TCs from 1949 to 2021. A large difference in the interannual change in frequency was found between STYs and TCs in the WNP and Eastern China Sea (ECS). Along the coast of EC, the frequency of landfall TCs showed a weak downtrend, and the typical STYs showed reverse micro growth with peak activity in August. Zhejiang, Fujian, and Taiwan were highly vulnerable to the frontal hits of typical STYs. Affected by climate change, the average lifetime maximum intensity (LMI) locations and landfall locations of typical STYs in the ECS basin showed a significant poleward migration trend. In addition, the annual average LMI and accumulated cyclone energy showed an uptrend, indicating the increasing severity of the disaster risk. Affected by the typical STY activity in the ECS, the maximum storm surge area also showed poleward migration, and the coast of North China faced potential growth in high storm surge risks. [ABSTRACT FROM AUTHOR]