Storm Surge Research Articles

Quantifying sedimentary ’blue carbon’ in relation to canopy cover in the seagrass meadows of Turneffe Atoll, Belize

IntroductionSeagrass sediments are important ‘blue carbon’ reservoirs which store climatically significant quantities of organic carbon (Corg) at the global scale. Seagrass meadows that overly these sediments also provide a range of critical ecosystem services including shoreline stabilization, storm surge protection, and fisheries nursery grounds. However, the controls over accumulation and the sources of organic C to these sediments beds are highly variable and poorly understood with the relative importance of hydrodynamic setting, species composition and canopy density being unclear.MethodsHere we address these questions using the first observation-based estimates of Corg stocks and provenance on Turneffe Atoll, Belize, made via remotely-sensed habitat extent, local Corg data and isotopic data. Sedimentary Corg was highest in sediments underlying the most sheltered meadows and decreased with increasing exposure to wind and wave energy with the seagrass meadows in the central lagoon containing an extensive deposit of mangrove derived organic carbon, stabilized and protected by the overlying seagrass meadow.ResultsThe influence of species composition appeared weak with the ubiquitous species T. testudinum occurring across a wide range of hydrodynamic regimes ranging from the most sheltered to the most energetic and being associated with a wide range of sedimentary organic C concentrations. Importantly from the perspective of remote sensing, org C concentrations were unrelated to canopy density. We hypothesize that this decoupling of organic C concentration from seagrass canopy cover reflects a much longer timescale for carbon storage in the sediments than the lifespan of the seagrass plants themselves and/or a substantial non seagrass derived organic C burden in seagrass sediments. Overall, we conservatively estimate that the top 30cm of sediments underlying the seagrass meadows overlying carbonate sediments on the atoll exterior store 0.58 x 106 Mg Corg, most of which is seagrass-derived, whilst the sediments underlying the meadows within the central lagoon store an additional 1.28 x 106 Mg Corg. When the maximum possible extent of seagrass is considered, this estimate increases to 3.54 x 106 Mg Corg. Substantial Corg stocks extending >1m depth were observed across all sites, and so these inventories are considered conservative.DiscussionA preliminary ‘cost of loss’ for sedimentary Corg in the top 30 cm of Turneffe Atoll’s seagrass meadows, based on a carbon trading value of €60 tCO2 (eq), is estimated at €42 million for the outer atoll, increasing to €136 million when the mangrove-derived sediments of the central atoll are considered and €260 million when turbid areas are assumed to contain seagrass.

Using surrogate modeling to predict storm surge on evolving landscapes under climate change

Using surrogate modeling to predict storm surge on evolving landscapes under climate change

Brief communication: From modelling to reality – flood modelling gaps highlighted by a recent severe storm surge event along the German Baltic Sea coast

Abstract. In October 2023, the Baltic Sea coasts of Germany and Denmark experienced a severe storm surge, predominantly impacting the German state of Schleswig-Holstein and parts of southern Denmark. The surge led to extensive flooding in cities like Flensburg and Schleswig, causing the breaching of at least six (regional) dikes and causing over EUR 200 million in damages in Schleswig-Holstein. By chance, the peak water levels of this storm surge aligned well with those of recent hydrodynamic flood modelling studies of the region. This rare coincidence offers crucial insights for our understanding of flooding impacts, flood management, and modelling. By comparing those studies to the real-world example using extensive media reports, we aim to extract key insights and identify gaps to be tackled in order to improve flood risk modelling in the Baltic Sea region and beyond.

Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake

Abstract. A real-time forecast model of surface hydrodynamics in Lake Ontario (Coastlines-LO) was developed to automatically predict storm surges and surface waves. The system uses a dynamically coupled Delft3D–SWAN model with a structured grid to generate 48 h predictions for the lake that are updated every 6 h. The lake surface is forced with meteorological data from the High Resolution Deterministic Prediction System (HRDPS). The forecast model has been running since May 2021, capturing a wide variety of storm conditions. Good agreement between observations and modelled results is achieved, with root mean squared errors (RMSEs) for water levels and waves under 0.02 and 0.26 m, respectively. During storm events, the magnitude and timing of storm surges are accurately predicted at nine monitoring stations (RMSE <0.05 m), with model accuracy either improving or remaining consistent with decreasing forecast length. Forecast significant wave heights agree with observed data (1 %–12 % relative error for peak wave heights) at four wave buoys in the lake. Coastlines-LO forecasts for storm surge prediction for two consecutive storm events were compared to those from the Great Lakes Coastal Forecasting System (GLCFS) to further evaluate model performance. Both systems achieved comparable results with average RMSEs of 0.02 m. Coastlines-LO is an open-source wrapper code driven by open data and has relatively low computational requirements compared to GLCFS, making this approach suitable for forecasting marine conditions in other coastal regions.

Unveiling the role of storm surges as a driver of flooding on the western Mediterranean: a case study of the Ebro Delta

AbstractStorm surges in the Western Mediterranean are generally low in magnitude, making their contribution to coastal flooding less significant compared to wave overtopping. Nonetheless, low-lying, sheltered coasts such as deltas and wetlands, which are frequent along the Mediterranean basin are particularly vulnerable to storm surges. This study, focusing on the Ebro Delta as representative of this type of coastal environment, investigates the flooding caused by storm surge alone and in conjunction with other non-wave related factors like astronomical tides and sea level rise (SLR), using the LISFLOOD-FP model. The findings highlight the significant flooding potential of storm surges on passive, and unprotected coasts, while also indicate that astronomical tides have a minor effect on flood extent under prevalent microtidal conditions. SLR greatly increases the impact of storm surges, amplifying temporary inundation in the short term and becoming the dominant factor over time. The study underscores the importance of accurately representing surge duration and small topographic features in flood models to ensure robust coastal inundation assessments in low-lying areas.

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.

High Water Level Forecast Under the Effect of the Northeast Monsoon During Spring Tides

One of the manifests of air-sea interactions is the change in sea level due to meteorological forcing through wind stress and atmospheric pressure. When meteorological conditions conducive to water level increase coincide with high tides during spring tides, the sea level may rise higher than expected and pose a flood risk to coastal low-lying areas. In Hong Kong, specifically when the northeast monsoon coincides with the higher spring tides in late autumn and winter, and sometimes even compounded by the storm surge brought by late-season tropical cyclones (TCs), the result may be coastal flooding or sea inundation. Aiming at forecasting such sea level anomalies on the scale of hours and days with local tide gauges using a flexible and computationally efficient method, this study adapts a data-driven method based on empirical orthogonal functions (EOF) regression of non-uniformly lagged regional wind field from ECMWF Reanalysis v5 (ERA5) to capture the effects from synoptic weather evolution patterns, excluding the effect of TCs. Local atmospheric pressure and winds are also included in the predictors of the regression model. Verification results show good performance in general. Hindcast using ECMWF forecasts as input reveals that the reduction of mean absolute error (MAE) by adding the anomaly forecast to the existing predicted astronomical tide was as high as 30% in February on average over the whole range of water levels, as well as that compared against the Delft3D forecast in a strong northeast monsoon case. The EOF method generally outperformed the persistence method in forecasting water level anomaly for a lead time of more than 6 h. The performance was even better particularly for high water levels, making it suitable to serve as a forecast reference tool for providing high water level alerts to relevant emergency response agencies to tackle the risk of coastal inundation in non-TC situations and an estimate of the anomaly contribution from the northeast monsoon under its combined effect with TC. The model is capable of improving water level forecasts up to a week ahead, despite the general decreasing model performance with increasing lead time due to less accurate input from model forecasts at a longer range. Some cases show that the model successfully predicted both positive and negative anomalies with a magnitude similar to observations up to 5 to 7 days in advance.

Characterizing interactive compound flood drivers in the Pearl River Estuary: A case study of Typhoon Hato (2017)

Tropical cyclone (TC) induced compound floods involve dynamic interactions among astronomical tides, storm surges, precipitation, and associated river pulses. This study employs a one-way coupled WRF, Delft3D, and HEC-RAS model to investigate the impacts of oceanic, pluvial, and fluvial processes on compound flood dynamics during Typhoon Hato (2017) in the Pearl River Estuary (PRE), South China. Total water levels driven by different combinations of flood drivers are modeled and analyzed. Relative contributions of each type of flood driver are quantified and used to categorize flood zones. This study highlights the persistent impacts of storm surges and their nonlinear interactions with other flood drivers. They can modify both the timing and magnitude of maximum water levels, thereby distorting tidal signals and contributing to post-TC landfall water level peaks. Along coastal regions, water levels exhibit three successive peaks, predominantly driven by storm surge, rainfall, and the combined actions of both factors, respectively. In upstream regions and coastal areas sheltered from islands, a singular water level peak arises exclusively from rainfall-runoff processes. Moreover, nonlinear interactions between surge and rainfall-runoff have non-negligible impacts on the relative contribution of individual flood drivers, which underscores the necessity of considering both rainfall and storm surge in modeling compound flood water levels. During the flooding period, peak storm surge and the following peak rainfall resulted in a time-varying distribution of flood zones. Alternating feedback between compounded and ocean-dominant areas manifests in the midsections of the upper PRE. Maximum flooding depth, extent, and duration are mainly influenced by rainfall. Storm surges play a secondary role, causing intense but short-lived flooding in coastal regions. These findings aid in understanding the generation mechanism of compound floods and provide references for hazard mitigation strategies.

The Unequal Impact of Disasters: Assessing the Interplay Between Social Vulnerability, Public Assistance, Flood Insurance, and Migration in the U.S

Extreme weather events, such as hurricanes with intense rainfall and storm surges, are posing increasing challenges to local communities worldwide. These hazards not only result in substantial property damage but also lead to significant population displacement. Federal disaster assistance programs are crucial for providing financial support for disaster response and recovery, but the allocation of these resources often unequal due to the complex interplay of environmental, social, and institutional factors. Relying on datasets collected from diverse sources, this study employs a structural equation model to explore the complex relationships between disaster damage (DD), social vulnerability (SV), public disaster assistance (PDA), the national flood insurance (NFI), and population migration (PM) across counties in the contiguous US. Our findings reveal that communities with lower SV tend to experience higher levels of DD across US counties. SV is negatively associated with PM, PDA, and NFI, both directly and indirectly. Furthermore, PDA is positively linked to PM, whereas DD has a direct negative effect on PM but an indirect positive effect through PDA.

Stalling North Atlantic Tropical Cyclones

Abstract Tropical cyclone (TC) translation speed influences rainfall accumulation, storm surge, and exposure to high winds. These effects are greatest when storms stall. Here, we provide a definition and climatology of slow-moving or stalling TCs in the North Atlantic from 1900 to 2020. A stall is defined as a TC with a track contained in a circular area (“corral”) with a radius of ≤200 km for 72 h. Of the 1274 North Atlantic TCs, 191 storms met this definition (15%). Ten are multistalling storms or those that experienced more than one stall period. Hurricane Ginger in 1971 stalled the most with four separate stalls. Stalling TC locations are clustered in the western Caribbean, the central Gulf Coast, the Bay of Campeche, and near Florida and the Carolinas. Stalling was most common in October TCs (17.3% of October total) and least common in August (8.2%). The estimated annual frequency of stalls significantly increased over the satellite era (1966–2020) by 1.5% yr−1, and the cumulative frequency in the number of stalls compared to all storms also increased. Stalling storms have a significantly higher frequency of major hurricane status than nonstalling storms. Storms are also more likely to stall near the coast (≤200 km). Approximately 40% (n = 77) of the stalling TCs experienced a period of rapid intensification, and five did so within 200 km of a coastal zone. These results will aid emergency managers in regions that experience frequent stalls by providing information they can use to better prepare for the future. Significance Statement The forward movement of a tropical cyclone can influence rainfall, storm surge height, and exposure time to high wind speeds. Storms that slow down or stall can increase total damage by prolonging the exposure time to intense conditions. This study aims to define a stalling storm and then provide a geographic snapshot into our historical experience with these storms. There have been a consistent number of stalling storms over time, with a modest increase starting in the satellite era, likely as a product of increased observational capabilities. Stalls tend to occur in similar places over time and happen more frequently later in the hurricane season (October) when compared to the middle (August). Emergency managers can use this information to identify the likely location and timing for stalls throughout the North Atlantic tropical cyclone season.

Hindcasting the Typhoon Haiyan Storm Surge in Coastal Eastern Leyte

Hindcasting the Typhoon Haiyan Storm Surge in Coastal Eastern Leyte

Nonlinear Tide‐River‐Surge Interactions and Their Impacts on Compound Flooding During Typhoon Hato in the Pearl River Delta

AbstractIn coastal areas, multiple hydrodynamic processes co‐occur, including the astronomical tide, river discharge, and storm surge. Their propagation and interaction within coastal tidal river result in strong nonlinear behavior in inland areas. This study employed a hydrodynamic model and continuous wavelet transform analysis to investigate the complex tide‐river‐surge interactions and their impacts on compound flooding in the West River of the Pearl River Delta during an extreme typhoon event. Validated model outputs provided insights into the spatial and temporal variations of river discharge, water levels, and currents. Wavelet analysis revealed river discharge modulates tidal properties, causing nonstationary tides and asymmetries, with high flows suppressing tidal ranges but facilitating energy transfers to overtides. During Typhoon Hato, storm surges dominated high‐frequency water level fluctuations that rapidly propagated upstream. Crucially, strong high‐frequency tide‐river‐surge coupling induced significant water level amplifications, with interaction intensities increasing landward. In upstream areas where riverine and coastal drivers converged, flood risks exceeded typical estimates due to this vigorous multi‐driver compounding. Findings highlighted how existing flood mitigation approaches over simplistically superposing individual sources may severely underestimate flood levels by neglecting such nonlinear interactions. A comprehensive accounting of the cumulative, multiplicative effects of tides, discharge, storm surge, and sea level rise is imperative. This quantitative unraveling of key physical drivers offers transferable insights applicable to compound flood risk evaluations and policy guidance for enhancing resilience in other estuary and delta regions. Future work should focus on holistically modeling multivariate extremes and their interactions.

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

Abstract Coastal storms and sea-level rise 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 sea-level rise 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 sea-level rise 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 contraction. Findings demonstrate that seawater flooding from coastal storms and sea-level rise paradoxically cause concurrent fresh surface water expansion but freshwater lens 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.

Assessing the health risks from flooding and chemical contamination: a scoping review

Abstract Flooding can increase the mobilization of hazardous chemicals in the environment. This scoping review provides an overview of the evidence on how flood events effect our health through chemical contamination and describes the exposure pathways to guide prevention and response measures. Flooding includes surface water flooding, coastal flooding, river flooding, storm surge, monsoon rainfall and high tide. We found 110 papers that looked at the association between flooding and chemicals in the environment, with most studies looking at heavy metals, polyhydroxyalkanoates (PAHs), and arsenic. There was a lack of information on rural populations and studies on mobilisation of pesticides. Very few studies mapped or modelled human exposures, including for high-risk populations (e.g., children), and this remains an important evidence gap. Studies considering climate change highlighted the likelihood of chemical contamination and health risks to be exacerbated. Our results highlighted new pathways by which flood events mobilise chemicals and pose risks to human health (i.e., urban runoff, industrial sources). Although found extensively in the environment, gaps were identified in the evidence for pesticides, microplastics and heavy metals. Limited studies reported response measures to address the chemical contamination linked to flooding despite the health risks likely to increase due to poor infrastructure and management measures despite the extent of contaminated land and landfill sites. A dual risk management strategy (i.e., the coordinated management of both flood-related and chemical release risks is required to address flooding events, chemical releases, and health impacts.

Analysis of extreme water levels on Feiyun River (Rui’an section) based on peaks-over-threshold (POT) model

ABSTRACT Feiyun River (Rui’an section) is a tidal reach, and the design water levels within different return periods are difficult to determine. The POT analysis methods were used to study the water level maximums of the river section. The study results indicate that skew surges has a measurable impact on extreme water levels and are characterized by a sharp increase in wind speeds at the estuary, followed by a sudden decrease, leading to a significant storm surge event. When the return periods are 50 years, 100 years and 200 years, the corresponding water levels of Feiyun River (Rui’an section) are 5.59 m, 6.04 m and 6.49 m, respectively. The proportion of skew surges in extreme water levels increases with the return period length, and it exceeds 50% when the return period is 200 years. The calculation results based on skew surges are often more reasonable in long return periods.

Geo-spatial based cyclone shelter suitability assessment using analytical hierarchy process (AHP) in the coastal region of Bangladesh

The coastal district of Bangladesh is susceptible to cyclones, which cause significant damage to infrastructure, economy, and social structures every year. The importance of protecting lives and property in these vulnerable areas is a top priority, especially in times of cyclones and storm surges. Therefore, the identification of potentially suitable shelter locations is essential for disaster risk resilience planning and implementation in the coastal regions. In this context, our research focuses on Barguna, which has witnessed severe damage from previous cyclones over the last several decades. We aim to identify and map feasible cyclone shelter suitability by utilizing GIS, AHP, Hotspot Analysis, and Remote Sensing techniques. The use of advanced techniques enables a comprehensive assessment of multiple variables that influence shelter suitability, ensuring the selection of the most strategically significant locations. The goal is to enhance disaster preparedness and resilience in Barguna District, reducing the risk and vulnerability of its coastal communities to cyclones. Seven variables associated with cyclone hazards, such as elevation, slope, distance from roads and rivers, population density, land use, and cover, and proximity to healthcare facilities, are considered to identify the safest and most suitable cyclone shelter areas. The assessment of the appropriateness of shelter locations for the study area was facilitated by the collection of 181 GPS locations regarding existing cyclone shelters. The findings reveal that approximately 15.53 % (1956 ha) of the total land area is considered less suitable, 67.31 % (84763 ha) moderately suitable, 16.70 % (21024 ha) suitable, and 0.29 % (362 ha) highly suitable for optimal cyclone shelter establishment in the study area. This study has a major limitation due to the use of Landsat imagery, which may not always provide fully accurate image classification. To validate our methods, we collected existing cyclone shelter data and performed a pixel-based accuracy assessment, achieving high accuracy and confirming the reliability of our approach. This research addresses a critical gap in cyclone shelter planning, offering valuable insights for residents and decision-makers to mitigate cyclone risks not only in the Barguna district but also in similar coastal regions of Bangladesh. The framework developed in this study can aid non-government organizations and the government in determining the most appropriate locations to construct cyclone shelters to build a safer, more resilient future for the region, capable of withstanding the relentless threats posed by cyclones every year.

Subtle Coastal Sinking Raises Storm Surge Risks

Subtle Coastal Sinking Raises Storm Surge Risks

Spatially seamless and temporally continuous assessment on compound flood risk in Hong Kong

Compound flooding results from the simultaneous occurrence of extreme storm surges, sea level rise, and heavy rainfall. These events often lead to impacts significantly more severe than those caused by any individual flood-inducing factor alone. However, the limited and sparse data from tidal gauges hampers precise risk assessment at ungauged sites in coastal cities. Our study addresses this gap by integrating ensemble machine learning with Bayesian inference, offering a comprehensive spatial–temporal analysis of compound flood risk from 1979 to 2022 in Hong Kong. We developed an ensemble machine learning approach within the Bayesian hierarchical modeling framework to achieve spatial–temporal continuity in the estimation of extreme storm surges and mean sea level at sites without tidal gauge stations. Results show a significant yearly increase in maximum storm surge levels by 3 mm and a significant rise in mean sea level of 25 mm per decade in Hong Kong. Our analysis also indicates a significant increase in daily heavy rainfall intensity. Furthermore, in 14.54 % of cases, extreme storm surges coincided with heavy rainfall, while 13.69 % of heavy rainfall events occurred alongside extreme sea level conditions. The copula-based joint analysis reveals significant positive correlations among these extreme events. Our findings further reveal that the return level for a 100-year heavy rainfall event increases dramatically from 126.36 mm in the univariate case to 261.16 mm in the trivariate scenario, underlining the escalated risk associated with compound flooding. Similarly, for storm surge extremes, trivariate analysis reveals elevated risk during compound flood events, with the return level rising from 1.18 m (univariate scenario) to 1.40 m (trivariate scenario) for a 100-year return period. These spatial–temporal maps and comprehensive compound flood risk assessments offer crucial insights for addressing the multi-hazard flood risk in coastal urban areas.

Case Study on the Adaptive Assessment of Floods Caused by Climate Change in Coastal Areas of the Republic of Korea

This study aims to assess the adaptability of coastal areas in the Republic of Korea to future climate change-induced flooding. Coastal areas can be susceptible to complex external factors, including rainfall, tide levels, storm surge wave overtopping, etc. The study employs an integrated approach to address this, connecting hydrological and marine engineering technologies. The models utilized in this study encompass XP-SWMM, ADCIRC, SWAN, and FLOW-3D. This study analyzed floods in 2050 and 2100, considering expected rainfall patterns, sea level rising, and an increase in typhoon intensity based on climate change scenarios for six coastal areas in the Republic of Korea. We reviewed the adaptability of flooding to climate change in each region.

A Study on the Pre-Survey and Plan for the Establishment of the Korean Typhoon Impact-Based Forecast

The intensity of typhoons affecting the Korean Peninsula has been rapidly increasing, resulting in significant damage. Notably, this intensification correlates with the rise in Sea Surface Temperature (SST) in the western Pacific Ocean and surrounding sea areas, where typhoons that impact the Korean Peninsula originate and develop. The SST in these regions is increasing at a faster rate than the global average. Typhoon-related meteorological disasters are not isolated events, such as strong winds, heavy rains, or storm surges, but rather multi-hazard occurrences that can affect different areas simultaneously. As a result, preparation and evaluation must address multi-hazard disasters, rather than focusing on individual weather phenomena. This study develops the Typhoon Ready System (TRS) to improve impact-based forecasting in Korea, in response to the growing threat of multi-hazard weather disasters. By providing region-specific pre-disaster information, the TRS enables local governments and individuals to better prepare for and mitigate the impacts of typhoons. The system will be continuously refined in collaboration with the U.S. Weather-Ready Nation (WRN), which possesses advanced impact forecasting capabilities. The findings of this study offer a crucial framework for enhancing Korea’s ability to forecast and respond to the escalating threats posed by typhoons. By utilizing the TRS, it will be possible to assess the risks of various multi-hazard weather disasters specific to each region during the typhoon forecast period, and the relevant data can be efficiently applied at both the individual and local government levels for typhoon prevention efforts. The system will be continuously improved through cooperation with the U.S. WRN, leveraging their advanced impact forecasting systems. It is expected that the TRS will enhance the accuracy of typhoon impact forecasts, which have been responsible for significant damage in Korea.