Storm Surge Hazard Research Articles

On the use of synthetic tropical cyclones and hypothetical events for storm surge assessment under climate change

This study presents a new approach to assess storm surge risk from tropical cyclones under climate change by direct calculation of the local flood levels using a limited number of events with an associated probability. The approach is based on the near-worst-case flood scenario, associated with a known tropical cyclone wind intensity probability (return period). We applied the method for the locality of Manzanillo, Colima, Mexico, using synthetic tropical cyclones derived from six different general circulation models for the present and future climates under the Representative Concentration Pathway 8.5. The synthetic events allowed the characterization of the wind intensity for the present and future climates for a given return period, as well as to determine the key tropical cyclones parameters related to storm surge. For Hurricane Patricia (2015), the strongest tropical cyclone to impact the region, we determined that its 95 m/s winds have a return period above 4000 years for the present climate and 198 years in a future climate scenario. Using Hurricane Patricia’s peak wind intensity, we created hypothetical events representing all possible approaches of tropical cyclones (211 events) to Manzanillo. We forced a hydrodynamic model with the hypothetical events over a mesh created with LiDAR-derived topography and then calculated the storm surge to create the near-worst-case flood scenario based on the maximum envelopes of water (MEOWs) and the maximum of MEOWs. Using those results, we created flood risk maps at city block level based on the combination of flood hazard and socioeconomic vulnerability maps. The presented method provides a tool for tropical cyclones storm surge hazard and risk assessment by generating near-worst-case flood maps under projected climates using a limited set of hypothetical events.

Multihazard Hurricane Fragility Model for Wood Structure Homes Considering Hazard Parameters and Building Attributes Interaction

Predicting building damage as a function of hurricane hazards, building attributes, and the interaction between hazard and building attributes is a key to understanding how significant interaction reflect variation hazard intensity effect on damage based on building attribute levels. This paper develops multi-hazard hurricane fragility models for wood structure homes considering interaction between hazard and building attributes. Fragility models are developed for ordered categorical damage states (DS) and binary collapse/no collapse. Exterior physical damage and building attributes from rapid assessment in coastal Mississippi following Hurricane Katrina (2005), high-resolution numerical hindcast hazard intensities from the Simulating WAves Nearshore and ADvanced CIRCulation (SWAN+ADCIRC) models, and base flood elevation values are used as model input. Leave-one-out cross-validation (LOOCV) is used to evaluate model prediction accuracy. Eleven and forty-nine combinations of global damage response variables and main explanatory variables, respectively, were investigated and evaluated. Of these models, one DS and one collapse model met the rejection criteria. These models were refitted considering interaction terms. Maximum 3-second gust wind speed and maximum significant wave height were found to be factors that significantly affect damage. Interaction between maximum significant wave height and number of stories was the significant interaction term for the DS and collapse models. For every 0.3 m (0.98 ft) increase in maximum significant wave height, the estimated odds of being in a higher damage state rather than lower damage state for DS model were found to be 1.95 times greater for one-story buildings rather than two-story buildings. For every 0.3 m (0.98 ft) increase in maximum significant wave height, the estimated odds of collapse were found to be 2.23 times greater for one-story buildings rather than two-story buildings. Model prediction accuracy was 84% and 91% for DS and collapse models, respectively. This paper does not consider the full hazard intensity experienced in Hurricane Katrina; rather, it focuses on single-family homes in a defined study area subjected to wind, wave, and storm surge hazards. Thus, the findings of this paper are not applicable for events with hazards that exceed those experienced in the study area, from which the models were derived.

Effective IoT-Facilitated Storm Surge Flood Modeling Based on Deep Reinforcement Learning

Storm surge hazard annually costs hundreds of lives and billions of dollars for damages in coastal countries. Simulating the storm surge events and alerting flood areas in advance is essential to guarantee the safety of citizens and urban infrastructure. However, due to the complexity of the urban environment, the accuracy of simulation faces challenges. By leveraging the facility of the Internet-of-Things (IoT) technique, the urban flood simulation procedure under storm surge hazard can be improved. In this article, targeting a coastal city usually under the threat of the storm surge, a deep-reinforcement-learning-based storm surge flood simulation approach is proposed to simulate the flood situation under the specific level of the storm surge. By analyzing real-time urban flood data and weather data collected by the IoT system, the model of the urban flood can be constructed and refined. A case study based on storm surge took place on July 1, 2019 in Shenzhen and was conducted to evaluate the effectiveness of the proposed model. The results demonstrate that the proposed method can effectively simulate the storm surge flood, and the achieved accuracy is up to 97.22%.

Evaluation of tropical cyclone recurrence rate: factors contributing to epistemic uncertainty

In this paper, we identify, assess and characterize epistemic uncertainties associated with the estimation of tropical cyclone storm recurrence rates. To illustrate the potential effects of epistemic uncertainty, we develop a range of storm surge recurrence rates at representative locations along the North Atlantic coast of the USA. We further illustrate how storm rate uncertainty impacts estimates of storm surge hazards. We consider the following factors when evaluating epistemic uncertainty in storm recurrence rate: (1) use of recorded storm track data versus interpolated storm track data, (2) the weighting method used for “counting” historical storms in the vicinity of the representative sites, (3) the kernel size used in the selected weighting method, (4) the intensity parameter used to filter “inconsequential storms” from the historical record, (5) the threshold value associated with the selected intensity filter, (6) the historical time period considered in the evaluation and (7) the impact of coastline geometry definition when partitioning storm populations. We observe that storm rate varies over orders of magnitude when considering these combinations of factors. When considering the effect of epistemic uncertainty in storm recurrence rate on estimated storm hazards, we find that the corresponding range of storm surge elevation spans multiple meters for a given annual exceedance frequency.

Using a WRF-ADCIRC Ensemble and Track Clustering to Investigate Storm Surge Hazards and Inundation Scenarios Associated with Hurricane Irma

AbstractThis article investigates combining a WRF-ADCIRC ensemble with track clustering to evaluate how uncertainties in tropical cyclone–induced storm tide (surge + tide) predictions vary in space and time and to explore whether this method can help elucidate inundation hazard scenarios. The method is demonstrated for simulations of Hurricane Irma (2017) initialized at 1200 UTC 5 September, approximately 5 days before Irma’s Florida landfalls, and 1200 UTC 8 September. Mixture models are used to partition the WRF ensemble tracks from 5 and 8 September into six and five clusters, respectively. Inundation is evaluated in two affected regions: southwest (south and west Florida) and northeast (northeast Florida through South Carolina). For the 5 September simulations, inundation in the southwest region varies significantly across the ensemble, indicating low forecast confidence. However, clustering highlights the areas of inundation risk in south and west Florida associated with different storm tracks. In the northeast region, every cluster has high inundation probabilities along a similar coastal stretch, indicating high confidence at a ~5-day lead time that this area will experience inundation. For the 8 September simulations, track and inundation in both regions vary less across the ensemble, but clustering remains useful for distinguishing among flooding scenarios. These results demonstrate the potential of dynamical TC–surge ensembles to illuminate important aspects of storm surge risk, including highlighting regions of high forecast confidence where preparations can reliably be initiated early. The analysis also shows how clustering can augment probabilistic hazard forecasts by elucidating inundation scenarios and variability across a surge ensemble.

Comparison of Methods for Imputing Non-Wetting Storm Surge to Improve Hazard Characterization

Joint probability methods for characterizing storm surge hazards involve the use of a collection of hydrodynamic storm simulations to fit a response surface function describing the relationship between storm surge and storm parameters. However, in areas with a sufficiently low probability of flooding, few storms in the simulated storm suite may produce surge, resulting in a paucity of information for training the response surface fit. Previous approaches have replaced surge elevations for non-wetting storms with a constant value or truncated them from the response surface fitting procedure altogether. The former induces bias in predicted estimates of surge from wetting storms, and the latter can cause the model to be non-identifiable. This study compares these approaches and improves upon current methodology by introducing the concept of “pseudo-surge,” with the intent to describe how close a storm comes to producing surge at a given location. Optimal pseudo-surge values are those which produce the greatest improvement to storm surge predictions when they are used to train a response surface. We identify these values for a storm suite used to characterize surge hazard in coastal Louisiana and compare their performance to the two other methods for adjusting training data. Pseudo-surge shows potential for improving hazard characterization, particularly at locations where less than half of training storms produce surge. We also find that the three methods show only small differences in locations where more than half of training storms wet.

Contribution of relative sea-level rise to bridge failure in the USA during Hurricane Ivan

Highways in the coastal environment are subject to storm surge and wave hazards during extreme events. The magnitude of these hazards, and the subsequent vulnerability of transportation infrastructure, will change over time with relative sea-level rise. These changes, which are often non-linear, lead to an amplification of storm-water levels and wave characteristics. These non-linear changes in storm surge and wave hazards, resulting from over three decades of relative sea-level rise between the time of bridge construction (1968) and the landfall of Hurricane Ivan (2004), contributed to span unseating and displacement along the Interstate 10 twin-span bridges over Escambia Bay, Florida, USA. A direct comparison of simulated hydrodynamics at failed bridge spans shows that 36 years of relative sea-level rise resulted in higher storm surge and larger wave heights and greater wave-induced loads. The increase (∼300 kN) in structural demand due to relative sea-level rise is nearly equal to the structural capacity provided by the minimal connections between the deck girders and bent beams. This finding underscores the importance of incorporating appropriate projections of relative sea-level rise into the design of major infrastructure, along with assessments of hazard risk over the expected service life of an asset.

Application of surrogate models in estimation of storm surge:A comparative assessment

Coastal storm surge hazard assessment has received increased attention due to major hurricane events in the last two decades. Robust hazard assessment requires accurate and efficient storm surge prediction models; however, existing numerical models are either high-fidelity but computationally demanding or low-fidelity but with real-time forecasting application. This dichotomy has prompted the development of surrogate models that leverage available synthetic/historical storm databases to build prediction models intended to better balance efficiency and accuracy. Despite numerous studies that have examined the application of various surrogate modeling methods in coastal response prediction, no study is available that compares all of the frequently used methods for storm surge prediction. Furthermore, in most of these studies, the discussion of the performance of these models is bounded to aggregated error metrics (e.g., RMSE, R). This study aims to provide a comprehensive framework for comparison and assessment of the performance of surrogate models based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR) and Support Vector Regression (SVR) for predicting storm surge. The United States Army Corp of Engineers’ North Atlantic Coast Comprehensive Study (NACCS) database is used for developing the models at representative coastal locations. In this study, the performance of the models is explored by investigating the stability of performance across training sample sizes, identifying systematic trends in errors, assessing performance in predicting large target response quantities, and characterizing the distribution of error. The results indicate that the performance of surrogate models may be improved by use of physically-motivated parameter scaling and that the selection of a surrogate modeling method should be informed by factors such as consistency in performance under a range of target surge elevations. In particular, the results suggest that accuracy of the tested surrogate models may be a function of the target surge elevation. Furthermore, results suggest that model performance should be assessed using factors beyond aggregate error metrics because such measures (particularly when used without modification to focus on risk-significant events) may give incomplete information about performance of surrogate models.

Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of Sundarban Biosphere Reserve, India

The Sundarban Biosphere Reserve (SBR), which is one of the important coastal regions of India, is vulnerable to storm surge hazards. It experiences storm surge flood of varying magnitudes every year causing immense loss to life and property. Thus, an accurate storm surge flood susceptibility assessment in the Reserve is essential for safeguarding the coastal communities. The present study attempts to explore the effectiveness of the conventional frequency ratio, modified frequency ratio and support vector machine (SVM) models in storm surge flood susceptibility analysis. Core areas of the SBR and some areas along rivers experience very high and high susceptibility. Moderate susceptibility was prevalent in the north, north-western and some areas in the south-western parts of the Reserve while low susceptibility was observed in the western part of the Reserve (located away from the coast). The maps were validated using the receiver operator characteristic (ROC), the seed cell area index (SCAI), and the spatially agreed area approach. The area under the success rate (0.8347) and prediction rate (0.8221) curves was highest for the SVM model. Frequency ratio and modified frequency ratio models showed 71.2% spatially agreed area of susceptibility. The spatially agreed area of susceptibility was lower (65.1%) in case of modified frequency ratio and the SVM model while it was highest (83.2%) between the modified frequency ratio and SVM models. Thus, the SVM model was found to be the best fit model for analyzing storm surge flood susceptibility in SBR.

Mangroves protect coastal economic activity from hurricanes

This paper evaluates whether mangroves can mitigate the impact of hurricanes on economic activity. The paper assembles a regionwide panel dataset that measures local economic activity using nightlights, potential hurricane damages using a detailed wind field model, and mangrove protection by mapping the width of mangrove forests on the path to the coast. The results show that hurricanes have negative short-run effects on economic activity, with losses likely concentrated in coastal lowlands that are exposed to both wind and storm surge hazards. In these coastal lowlands, the estimates show that nightlights decrease by up to 24% in areas that are unprotected by mangroves. By comparison, the impact of the hurricanes observed in the sample is fully mitigated in areas protected by mangrove belts of 1 km or more.

Impact of human projects on storm surge in the Yangtze Estuary

The impact of human projects on storm surge hazard in the Yangtze Estuary is studied numerically. The numerical model is based on the shallow water equations, which includes the turbulence effect, bottom friction, and runoff of rivers. The Holland model is used to generate the wind field and pressure field of the typhoon Winnie (9711), which has caused huge hazards to the Yangtze Estuary. A good comparison of typhoon speed and storm surge with measured data indicates the numerical model is acceptable. The water level induced by the typhoon could be up to 4.0 m. The superposition of high tide and storm surge has aggravated the hazard. The maximum setup could reach or exceed 1.5 m. To analyze the impact of human projects, the origin bathymetry and that with several human projects are used to implement the simulation respectively. The numerical results show that the human projects have gentle impact on the effects of the storm surge on coastal regions in terms of tide and setup. The extreme storm surge inundation risk around Pudong International Airport are presented including of relative sea-level rise +0 m, +0.3 m, and 0.7 m.

Extreme storm surge induced coastal inundation in Yangtze Estuary regions

Coastal inundation induced by the storm surge belongs to one of the major natural hazards in Shanghai and other regions around the Yangtze Estuary. The worst scenarios of coupling the extreme storm and the sea level rise are presented to assess the storm surge hazard in the Yangtze Estuary. The numerical model MIKE 21 FM is adopted to implement the numerical study, which includes wind, current, tide and wave. The storm surge induced by typhoon 9711 is simulated, and the measurements of six tide gauges are used to validate the numerical model. To identify the worst case scenario of severe typhoon (9711) and super typhoon (5612), we propose seven typhoon tracks through the Yangtze Estuary and fourteen scenarios of landfall time for each typhoon track, which generates 196 cases. Finally, based on the worst case scenario and the relative sea level rise in the range of 0 m to 0.7 m, the coastal inundation maps of Pudong New District and Changxing Island are provided and discussed.

Further Development of Forecasting Model for Storm Surge Hazard along the Coast of Bangladesh

In this study, numerical prediction of surge associated with a storm is made through finite difference method accurately incorporating the coastal complexities along the coast of Bangladesh. In incorporating the coastal complexities with a considerable accuracy, (1/120) grid resolution is used. To incorporate river dynamics, the fresh water discharge through the Meghna River is taken into account. Simulated results by the study are found to be in good agreement with the available observations and reported data. For better forecasting, estimation of flooding is of importance, which is responsible for most death. Thus, the main objective of this paper is to develop an effective numerical model that will help to reduce the death during storm surges along the coastal area of Bangladesh.

Assessment and zonation of storm surge hazards in the coastal areas of China

Storm surge is one of the most devastating marine disasters in China, leading to tremendous economic damage and a large number of casualties. Combined storm surge hazard assessment and zonation is an important method for coastal disaster risk reduction and mitigation management. Based on observational data from tide-gauge and hydrological stations in coastal areas, we analyzed storm surge hazard intensity and generated a storm surge hazard map at the county level using the expected values of storm surge and over-warning water levels. The results show that 87, 67, 62, and 40 counties along the coast of China are exposed to the first (highest), second (higher), third (medium), and fourth (low) degree of hazard level, respectively. The areas with the highest risk of storm surge are the coasts of Bohai Bay, Laizhou Bay, and the Yangtze Delta, the coast from the north of Fuzhou to the south of Zhejiang, and the coastal area of Huizhou, Pearl River, and Yangjiang in Guangdong Province. This assessment, which is based on a national storm surge hazards map, can provide decision-making support for the government’s urban planning of coastal cities and site selection in large national projects.

Street-scale storm surge load impact assessment using fine-resolution numerical modelling: a case study from Nemuro, Japan

Due to gradual sea level rise and changes in the climate system, coastal vulnerability to storm surge hazards is expected to increase in some areas. Studies regarding the effect of storm surge inundation on buildings and human lives, especially when it comes to relatively low-threat level events, have been few, however. In this research, storm surge load impact around coastal residential areas was quantitatively assessed, through fine-resolution numerical modelling. Meso- and street-scale simulation results for a storm surge event in Nemuro, Japan, were comprehensively validated against observations and field measurements, and the simulation results showed good accuracy for sea level, significant wave height and inundation area. A fine-resolution, street-scale coastal flood simulation was carried out with individual and grouped buildings, created with a building block model, and the results showed the significant role of buildings by realistically capturing inundation dynamics. Hydrodynamic results showed that coastal flood impact on buildings was insignificant (consistent with surveys). Lastly, the potential flood impact on people in the streets was investigated, using five human instability equations, where the most pessimistic results showed average values between 0.0 and 0.2 (max 0.6–0.7), and slightly below 0.4 for children and the elderly, respectively. These values indicated that threat levels during the Nemuro storm event were low, which corresponded with observations (no fatalities). This study framework could be applied wherever an accurate local storm surge threat estimate was required.

Vertical and lateral distribution of Foraminifera and Ostracoda in the East Frisian Wadden Sea – developing a transfer function for relative sea-level change

In light of rising sea levels and increased storm surge hazards, detailed information on relative sea-level (RSL) histories and local controlling mechanisms is required to support future projections and to better prepare for future coastal-protection challenges. This study contributes to deciphering Holocene RSL changes at the German North Sea coast in high resolution by developing a transfer function for RSL change. Recent associations of Foraminifera and Ostracoda from low intertidal to supratidal settings of the barrier island of Spiekeroog in combination with environmental parameters (granulometry, C/N, TOC, salinity) were investigated and quantified in elevation steps of 15 cm in order to generate a first transfer function (TF) of Holocene RSL change. In a future step, the TF can be applied to the stratigraphic record. Our data show a clear vertical zonation of foraminifer and ostracod taxa between the middle salt marsh and the tidal flat with very few individuals in the sand flat area, suggesting removal by the tidal current or poor preservation. Multivariate statistics identify the elevation, i.e. the inundation frequency, as main driving factor. The smallest vertical error (49 cm) is associated with an entirely new approach of combining Foraminifera and Ostracoda for a TF. Advantages of the TF over classical RSL indicators such as basal and intercalated peat – beside the relatively narrow indicative meaning – include the possible application to a wide range of intertidal facies and that it does not depend on compaction-prone peat.

PREDICTING COASTAL ROADWAY DAMAGE USING MODIFIED DISPERSION FUNCTIONS

Empirical dispersion functions appear to reasonably predict damage risks for coastal roadways subjected to coastal storm surge and wave hazards. County Road 257 (CR 257) in Brazoria County, Texas had significant damage at various locations during Hurricane Ike in September 2008. Cumulative peak hourly water surface elevation, wave period, and current velocity output from a hindcast ADCIRC+SWAN model was assessed using modified celerity dispersion functions relative to measured distance between road and shoreline. These intensity measures provide a strongly correlated model for predicting likelihood of road damage.

THE DYNAMICS OF STORM SURGE AND MEAN SEA LEVEL VARIABILITY IN THE NORTHEASTERN CASPIAN SEA

Storm surge is a major natural hazard to the construction and operation of infrastructure projects in shallow and gently sloping coastal areas such as the northeastern part of the Caspian Sea. Up-surge events cause significant coastal flooding, while down-surge events disrupt marine transportation by constraining safe navigable water depths. Coastal developments therefore have to achieve a judicious balance of these conflicting constraints. Further, the unpredictability of long-term and intra-annual Mean Sea Level (MSL) variability as well as periodic freeze-up and break-up due to ice formation in this part of the Caspian Sea complicate a robust and unequivocal basis for defining storm surge hazards.

An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging

Abstract. Projections of coastal storm surge hazard are a basic requirement for effective management of coastal risks. A common approach for estimating hazards posed by extreme sea levels is to use a statistical model, which may use a time series of a climate variable as a covariate to modulate the statistical model and account for potentially nonstationary storm surge behavior (e.g., North Atlantic Oscillation index). Previous works using nonstationary statistical approaches to assess coastal flood hazard have demonstrated the importance of accounting for many key modeling uncertainties. However, many assessments have typically relied on a single climate covariate, which may leave out important processes and lead to potential biases in the projected flood hazards. Here, I employ a recently developed approach to integrate stationary and nonstationary statistical models, and characterize the effects of choice of covariate time series on projected flood hazard. Furthermore, I expand upon this approach by developing a nonstationary storm surge statistical model that makes use of multiple covariate time series, namely, global mean temperature, sea level, the North Atlantic Oscillation index and time. Using Norfolk, Virginia, as a case study, I show that a storm surge model that accounts for additional processes raises the projected 100-year storm surge return level by up to 23 cm relative to a stationary model or one that employs a single covariate time series. I find that the total model posterior probability associated with each candidate covariate, as well as a stationary model, is about 20 %. These results shed light on how including a wider range of physical process information and considering nonstationary behavior can better enable modeling efforts to inform coastal risk management.

Assessing storm surge hazard and impact of sea level rise in the Lesser Antilles case study of Martinique

Abstract. In the Lesser Antilles, coastal inundations from hurricane-induced storm surges pose a great threat to lives, properties and ecosystems. Assessing current and future storm surge hazards with sufficient spatial resolution is of primary interest to help coastal planners and decision makers develop mitigation and adaptation measures. Here, we use wave–current numerical models and statistical methods to investigate worst case scenarios and 100-year surge levels for the case study of Martinique under present climate or considering a potential sea level rise. Results confirm that the wave setup plays a major role in the Lesser Antilles, where the narrow island shelf impedes the piling-up of large amounts of wind-driven water on the shoreline during extreme events. The radiation stress gradients thus contribute significantly to the total surge – up to 100 % in some cases. The nonlinear interactions of sea level rise (SLR) with bathymetry and topography are generally found to be relatively small in Martinique but can reach several tens of centimeters in low-lying areas where the inundation extent is strongly enhanced compared to present conditions. These findings further emphasize the importance of waves for developing operational storm surge warning systems in the Lesser Antilles and encourage caution when using static methods to assess the impact of sea level rise on storm surge hazard.