A surge response function approach to coastal hazard assessment. Part 2: Quantification of spatial attributes of response functions

In response to the 2004 and 2005 hurricane seasons, surge risk assessment approaches have been re-evaluated to develop more rapid, reliable methods for predicting the risk associated with extreme hurricanes. Here, the development of dimensionless surge response functions relating surge to hurricane meteorological parameters is presented. Such response functions present an opportunity to maximize surge data usage and to improve statistical estimates of surge probability by providing a means for defining continuous probability density functions. A numerical modeling investigation was carried out for the Texas, USA coastline to develop physical scaling laws relating storm surge response with hurricane parameters including storm size, intensity, and track. It will be shown that these scaling laws successfully estimate the surge response at any arbitrary location for any arbitrary storm track within the study region. Such a prediction methodology has the potential to decrease numerical computation requirements by 75% for hurricane risk assessment studies.     

Implementation/optimization of moving least squares response surfaces for approximation of hurricane/storm surge and wave responses

One of the important recent advances in the field of hurricane/storm modelling has been the development of high-fidelity numerical simulation models for reliable and accurate prediction of wave and surge responses. The computational cost associated with these models has simultaneously created an incentive for researchers to investigate surrogate modelling (i.e. metamodeling) and interpolation/regression methodologies to efficiently approximate hurricane/storm responses exploiting existing databases of high-fidelity simulations. Moving least squares (MLS) response surfaces were recently proposed as such an approximation methodology, providing the ability to efficiently describe different responses of interest (such as surge and wave heights) in a large coastal region that may involve thousands of points for which the hurricane impact needs to be estimated. This paper discusses further implementation details and focuses on optimization characteristics of this surrogate modelling approach. The approximation of different response characteristics is considered, and special attention is given to predicting the storm surge for inland locations, for which the possibility of the location remaining dry needs to be additionally addressed. The optimal selection of the basis functions for the response surface and of the parameters of the MLS character of the approximation is discussed in detail, and the impact of the number of high-fidelity simulations informing the surrogate model is also investigated. Different normalizations of the response as well as choices for the objective function for the optimization problem are considered, and their impact on the accuracy of the resultant (under these choices) surrogate model is examined. Details for implementation of the methodology for efficient coastal risk assessment are reviewed, and the influence in the analysis of the model prediction error introduced through the surrogate modelling is discussed. A case study is provided, utilizing a recently developed database of high-fidelity simulations for the Hawaiian Islands.     

A modelling approach for estimating the frequency of sea level extremes and the impact of climate change in southeast Australia

An efficient approach for evaluating storm tide return levels along the southeastern coastline of Australia under present and future climate conditions is described. Storm surge height probabilities for the present climate are estimated using hydrodynamic model simulations of surges identified in recent tide gauge records. Tides are then accounted for using a joint probability method. Storm tide height return levels obtained in this way are similar to those obtained from the direct analysis of tide gauge records. The impact of climate change on extreme sea levels is explored by adding a variety of estimates of mean sea level rise and by forcing the model with modified wind data. It is shown that climate change has the potential to reduce average recurrence intervals of present climate 1 in 100 year storm tide levels along much of the northern Bass Strait coast to between 1 and 2 years by the year 2070.     

A hurricane surge risk assessment framework using the joint probability method and surge response functions

Hurricane surge events have caused devastating damage in active-hurricane areas all over the world. The ability to predict surge elevations and to use this information for damage estimation is fundamental for saving lives and protecting property. In this study, we developed a framework for evaluating hurricane flood risk and identifying areas that are more prone to them. The approach is based on the joint probability method with optimal sampling (JPM-OS) using surge response functions (SRFs) (JPM-OS-SRF). Derived from a discrete set of high-fidelity storm surge simulations, SRFs are non-dimensional, physics-based empirical equations with an algebraic form, used to rapidly estimate surge as a function of hurricane parameters (i.e., central pressure, radius, forward speed, approach angle and landfall location). The advantage of an SRF-based approach is that a continuum of storm scenarios can be efficiently evaluated and used to estimate continuous probability density functions for surge extremes, producing more statistically stable surge hazard assessments without adding measurably to epistemic uncertainty. SRFs were developed along the coastline and then used to estimate maximum surge elevations with respect to a set of hurricane parameters. Integrating information such as ground elevation, property value and population with the JPM-OS-SRF allows quantification of storm surge-induced hazard impacts over the continuum of storm possibilities, yielding a framework to create the following risk-based products, which can be used to assist in hurricane hazard management and decision making: (1) expected annual loss maps; (2) flood damage versus return period relationships; and (3) affected business (e.g., number of business, number of employees) versus return period relationships. By employing several simplifying assumptions, the framework is demonstrated at three northern Gulf of Mexico study sites exhibiting similar surge hazard exposure. The framework results reveal Gulfport, MS, USA is at relatively more risk of economic loss than Corpus Christi, TX, USA, and Panama City, FL, USA. Note that economic processes are complex and very interrelated to most other human activities. Our intention here is to present a methodology to quantify the flood damage (i.e., infrastructure economic loss, number of businesses affected, number of employees in these affected businesses and sales volume in these affected businesses) but not to discuss the complex interactions of these damages with other economic activities and recovery plans.     

A hurricane surge risk assessment framework using the joint probability method and surge response functions

Hurricane surge events have caused devastating damage in active-hurricane areas all over the world. The ability to predict surge elevations and to use this information for damage estimation is fundamental for saving lives and protecting property. In this study, we developed a framework for evaluating hurricane flood risk and identifying areas that are more prone to them. The approach is based on the joint probability method with optimal sampling (JPM-OS) using surge response functions (SRFs) (JPM-OS-SRF). Derived from a discrete set of high-fidelity storm surge simulations, SRFs are non-dimensional, physics-based empirical equations with an algebraic form, used to rapidly estimate surge as a function of hurricane parameters (i.e., central pressure, radius, forward speed, approach angle and landfall location). The advantage of an SRF-based approach is that a continuum of storm scenarios can be efficiently evaluated and used to estimate continuous probability density functions for surge extremes, producing more statistically stable surge hazard assessments without adding measurably to epistemic uncertainty. SRFs were developed along the coastline and then used to estimate maximum surge elevations with respect to a set of hurricane parameters. Integrating information such as ground elevation, property value and population with the JPM-OS-SRF allows quantification of storm surge-induced hazard impacts over the continuum of storm possibilities, yielding a framework to create the following risk-based products, which can be used to assist in hurricane hazard management and decision making: (1) expected annual loss maps; (2) flood damage versus return period relationships; and (3) affected business (e.g., number of business, number of employees) versus return period relationships. By employing several simplifying assumptions, the framework is demonstrated at three northern Gulf of Mexico study sites exhibiting similar surge hazard exposure. The framework results reveal Gulfport, MS, USA is at relatively more risk of economic loss than Corpus Christi, TX, USA, and Panama City, FL, USA. Note that economic processes are complex and very interrelated to most other human activities. Our intention here is to present a methodology to quantify the flood damage (i.e., infrastructure economic loss, number of businesses affected, number of employees in these affected businesses and sales volume in these affected businesses) but not to discuss the complex interactions of these damages with other economic activities and recovery plans.

     

Evaluation of coastal inundation hazard for present and future climates

Coastal inundation from hurricane storm surges causes catastrophic damage to lives and property, as evidenced by recent hurricanes including Katrina and Wilma in 2005 and Ike in 2008. Changes in hurricane activity and sea level due to a warming climate, together with growing coastal population, are expected to increase the potential for loss of property and lives. Current inundation hazard maps: Base Flood Elevation maps and Maximum of Maximums are computationally expensive to create in order to fully represent the hurricane climatology, and do not account for climate change. This paper evaluates the coastal inundation hazard in Southwest Florida for present and future climates, using a high resolution storm surge modeling system, CH3D-SSMS, and an optimal storm ensemble with multivariate interpolation, while accounting for climate change. Storm surges associated with the optimal storms are simulated with CH3D-SSMS and the results are used to obtain the response to any storm via interpolation, allowing accurate representation of the hurricane climatology and efficient generation of hazard maps. Incorporating the impact of anticipated climate change on hurricane and sea level, the inundation maps for future climate scenarios are made and affected people and property estimated. The future climate scenarios produce little change to coastal inundation, due likely to the reduction in hurricane frequency, except when extreme sea level rise is included. Calculated coastal inundation due to sea level rise without using a coastal surge model is also determined and shown to significantly overestimate the inundation due to neglect of land dissipation.     

A surge response function approach to coastal hazard assessment – part 1: basic concepts

This paper reviews historical methods for estimating surge hazards and concludes that the class of solutions produced with Joint Probability Method (JPM) solutions provides a much more stable estimate of hazard levels than alternative methods. We proceed to describe changes in our understanding of the winds in hurricanes approaching a coast and the physics of surge generation that have required recent modifications to procedures utilized in earlier JPM studies. Of critical importance to the accuracy of hazard estimates is the ability to maintain a high level of fidelity in the numerical simulations while allowing for a sufficient number of simulations to populate the joint probability matrices for the surges. To accomplish this, it is important to maximize the information content in the sample storm set to be simulated. This paper introduces the fundamentals of a method based on the functional specification of the surge response for this purpose, along with an example of its application in the New Orleans area. A companion paper in this special issue (Irish et al. 2009) provides details of the portion of this new method related to interpolating/extrapolating along spatial dimensions.     

Dynamic analysis of emergency inter-organizational communication network under public health emergency: a case study of COVID-19 in Hubei Province of China

Storm surge induced by hurricane is a major threat to the Gulf Coasts of the United States. A numerical modeling study was conducted to simulate the storm surge during Hurricane Michael, a category 5 hurricane that landed on the Florida Panhandle in 2018. A high-resolution model mesh was used in the ADCIRC hydrodynamic model to simulate storm surge and tides during the hurricane. Two parametric wind models, Holland 1980 model and Holland 2010 model, have been evaluated for their effects on the accuracy of storm surge modeling by comparing simulated and observed maximum water levels along the coast. The wind model parameters are determined by observed hurricane wind and pressure data. Results indicate that both Holland 1980 and Holland 2010 wind models produce reasonable accuracy in predicting maximum water level in Mexico Beach, with errors between 1 and 3.7%. Comparing to the observed peak water level of 4.74 m in Mexico Beach, Holland 1980 wind model with radius of 64-knot wind speed for parameter estimation results in the lowest error of 1%. For a given wind model, the wind profiles are also affected by the wind data used for parameter estimation. Away from hurricane eye wall, using radius of 64-knot wind speed for parameter estimation generally produces weaker wind than those using radius of 34-knot wind speed for parameter estimation. Comparing model simulated storm tides with 17 water marks observed along the coast, Holland 2010 wind model using radius of 34-knot wind speed for parameter estimation leads to the minimum mean absolute error. The results will provide a good reference for researchers to improve storm surge modeling. The validated model can be used to support coastal hazard mitigation planning.

     

The impact of a storm surge on business establishments in the Houston MSA

This paper examines the possible storm surge damage from a major hurricane to hit the Houston Metropolitan Statistical Area (MSA.) Using storm surge analysis on a unique data set compiled from the Texas Workforce Commission (QCEW), the paper estimates the expected industry-level damage for each county in the Houston MSA. The advantages of using GIS to analyze the expected storm surge damage estimation is that it provides an accurate estimation of the number of affected employees and probable wages losses, by industry and county, based on QCEW data. The results indicate that the ‘Basic Chemical Manufacturing’ and ‘Oil and Gas Extraction’ industries incur the highest employee and payroll losses while the ‘Restaurants and Eateries’ has the largest establishment damage if a major hurricane were to hit the Houston MSA.     

Mapping joint hurricane wind and surge hazards for Charleston,South Carolina

Combined effects of hurricane wind and surge can pose significant threats to coastal cities. Although current design codes consider the joint occurrence of wind and surge, information on site-specific joint distributions of hurricane wind and surge along the US Coast is still sparse and limited. In this study, joint hazard maps for combined hurricane wind and surge for Charleston, South Carolina (SC), were developed. A stochastic Markov chain hurricane simulation program was utilized to generate 50,000 years of full-track hurricane events. The surface wind speeds and surge heights from individual hurricanes were computed using the Georgiou’s wind field model and the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, respectively. To validate the accuracy of the SLOSH model, the simulated surge levels were compared to the surge levels calculated by another state-of-the-art storm surge model, ADCIRC (Advanced Circulation), and the actual observed water elevations from historical hurricane events. Good agreements were found between the simulated and observed water elevations. The model surface wind speeds were also compared with the design wind speeds in ASCE 7-10 and were found to agree well with the design values. Using the peak wind speeds and maximum surge heights, the joint hazard surfaces and the joint hazard maps for Charleston, SC, were developed. As part of this study, an interactive computer program, which can be used to obtain the joint wind speed and surge height distributions for any location in terms of latitude and longitude in Charleston area, was created. These joint hazard surfaces and hazard maps can be used in a multi-hazard design or risk assessment framework to consider the combined effects of hurricane wind and surge.     

海洋环境因素极值组合及设计标准

由于海洋环境条件的复杂性、多变性及随机性,设计标准的选取是决定工程结构安全度、造价、效益及合理型式的主要因素。传统的设计标准,无法考虑海洋环境条件的随机组合,往往过高估计环境条件设计标准,造成不必要的浪费,甚至使具有开发前景的油田失去开采价值。以实测和后报资料为基础,使用多维联合概率的随机模拟技术,结合不同结构型式的极值响应及不同资料样本的选择方法,提出了海洋工程结构物上的风、浪、流、潮联合荷载及相应的联合概率水平问题,用以作为海洋工程环境荷载设计标准。     

Hydrodynamic Response of Northeastern Gulf of Mexico to Hurricanes

The northeastern Gulf of Mexico in the USA is extremely susceptible to the impacts of tropical cyclones because of its unique geometric and topographic features. Focusing on Hurricanes Ivan (2004) and Katrina (2005), this paper has addressed four scientific questions on this area’s response to hurricanes: (1) How does the shallow, abandoned Mississippi delta contribute to the storm surge? (2) What was the controlling factor that caused the record-high storm surge of Hurricane Katrina? (3) Why are the responses of an estuary to Hurricanes Ivan and Katrina so different from the corresponding surges on the open coast? (4) How would the storm surge differ if Hurricane Katrina had taken a different course? Guided by field observations of winds, waves, water levels, and currents, two state-of-the-art numerical models for storm surges and wind waves have been coupled to hindcast the relevant hydrodynamic conditions, including storm surges, surface waves, and depth-averaged currents. Fairly good agreement between the modeled and measured surge hydrographs was found. The quantitative numerical simulations and simple qualitative analysis have revealed that the record-high storm surge of Hurricane Katrina was caused by the interaction of the surge with the extremely shallow, ancient deltaic lobe of Mississippi River. A hypothetical scenario formed by shifting the path of Hurricane Katrina to the observed path of Hurricane Frederic (1979) resulted in a much smaller surge than that observed in coastal Mississippi and Louisiana. However, this scenario did still result in a high surge near the head of Mobile Bay. One of the important lessons learned from Hurricane Katrina is that the Saffir–Simpson scale should be systematically revised to reflect the topographic and geometric features of a complex, heterogeneous coast, including the possible surge amplification in an estuary or a submerged river delta.     

Simulation of atmospheric states for a storm surge on the west coast of Korea: model comparison between MM5, WRF and COAMPS

High-quality informations on sea level pressure and sea surface wind stress are required to accurately predict storm surges over the Korean Peninsula. The storm surge on 31 March 2007 at Yeonggwang, on the western coast, was an abrupt response to mesocyclone development. In the present study, we attempted to obtain reliable surface winds and sea level pressures. Using an optimal physical parameterization for wind conditions, MM5, WRF and COAMPS were used to simulate the atmospheric states that accompanied the storm surge. The use of MM5, WRF and COAMPS simulations indicated the development of high winds in the strong pressure gradient due to an anticyclone and a mesocyclone in the southern part of the western coast. The response to this situation to the storm surge was sensitive. A low-level warm advection was examined as a possible causal mechanism for the development of a mesocyclone in the generating storm surge. The low-level warm temperature advection was simulated using the three models, but MM5 and WRF tended to underestimate the warm tongue and overestimate the wind speed. The WRF simulation was closer to the observed data than the other simulations in terms of wind speed and the intensity of the mesocyclone. It can be concluded that the magnitude of the storm surge at Yeonggwang was dependent, not only on the development of a mesocyclone but on ocean effects as well.     

On the use of NOAA's storm surge model,SLOSH, in managing coastal hazards — the experience in Puerto Rico

A numerical-dynamic, tropical storm surge model, SLOSH (Sea, Land, and Overland Surges from Hurricanes), was originally developed for real-time forecasting of hurricane storm surges on continental shelves, across inland water bodies and along coastlines and for inland routing of water -either from the sea or from inland water bodies. The model is two-dimensional, covering water bodies and inundated terrain. In the present version available at the University of Puerto Rico a curvilinear, polar coordinate grid scheme is used. The grid cells are approximately 3.2 × 3.2 km in size.The model has been used in a revision of all coastal Flood Insurance Rate Maps (FIRM) for Puerto Rico and the U.S. Virgin Islands, and in hurricane evacuation studies. The FIRM's, since they are based on the 100 year stillwater elevation, are also used by the state Planning Board for regulatory purposes. The hurricane evacuation studies are used by emergency planners and personnel to assign shelters, escape routes, and delimit coastal zones that need to be evacuated during a hurricane threat.Recently, the acquisition of data from hurricane Hugo has allowed the first comparison of model results and observations for Puerto Rico and the other islands.     

Impact of cyclonic wind field on interaction of surge–wave computations using finite-element and finite-difference models

Both finite-element and finite-difference numerical models are applied to simulate storm surges and associated currents generated by tropical cyclones that struck the coast of Andhra Pradesh, located on the east coast of India. During a cyclone, the total water level at any location on the coast is made up of the storm surge, surge–wind wave interaction and the tide. The advanced circulation two-dimensional depth-integrated (ADCIRC-2DDI) model based on finite-element formulation and the two-dimensional finite-difference model of storm surges developed at IIT Delhi, hereafter referred as IITD storm surge model, are used. These models are driven by astronomical tides at the open ocean boundary and cyclonic asymmetric winds over the surface of the computational domain. Comparison of model simulated sea-surface elevations with coarse and finer spatial resolutions suggests that the grid resolution near the coast is very crucial for accurate determination of the surges in addition to the local bathymetry. The model underpredicts surges, and the peak surge location shifts more to the right of the landfall as the spatial resolution of the model becomes coarser. The numerical experiments also demonstrate that the ADCIRC model is robust over the IITD storm surge model for surge computations as the coastline is better represented in the former.     

Improvements in storm surge surrogate modeling for synthetic storm parameterization,node condition classification and implementation to small size databases

Surrogate models are becoming increasingly popular for storm surge predictions. Using existing databases of storm simulations, developed typically during regional flood studies, these models provide fast-to-compute, data-driven approximations quantifying the expected storm surge for any new storm (not included in the training database). This paper considers the development of such a surrogate model for Delaware Bay, using a database of 156 simulations driven by synthetic tropical cyclones and offering predictions for a grid that includes close to 300,000 computational nodes within the geographical domain of interest. Kriging (Gaussian Process regression) is adopted as the surrogate modeling technique, and various relevant advancements are established. The appropriate parameterization of the synthetic storm database is examined. For this, instead of the storm features at landfall, the features when the storm is at closest distance to some representative point of the domain of interest are investigated as an alternative parametrization, and are found to produce a better surrogate. For nodes that remained dry for some of the database storms, imputation of the surge using a weighted k nearest neighbor (kNN) interpolation is considered to fill in the missing data. The use of a secondary, classification surrogate model, combining logistic principal component analysis and Kriging, is examined to address instances for which the imputed surge leads to misclassification of the node condition. Finally, concerns related to overfitting for the surrogate model are discussed, stemming from the small size of the available database. These concerns extend to both the calibration of the surrogate model hyper-parameters, as well as to the validation approaches adopted. During this process, the benefits from the use of principal component analysis as a dimensionality reduction technique, and the appropriate transformation and scaling of the surge output are examined in detail.

     

A modeling study of coastal inundation induced by storm surge,sea-level rise,and subsidence in the Gulf of Mexico

The northern coasts of the Gulf of Mexico (GoM) are highly vulnerable to the direct threats of climate change, such as hurricane-induced storm surge, and such risks are exacerbated by land subsidence and global sea-level rise. This paper presents an application of a coastal storm surge model to study the coastal inundation process induced by tide and storm surge, and its response to the effects of land subsidence and sea-level rise in the northern Gulf coast. The unstructured-grid finite-volume coastal ocean model was used to simulate tides and hurricane-induced storm surges in the GoM. Simulated distributions of co-amplitude and co-phase lines for semi-diurnal and diurnal tides are in good agreement with previous modeling studies. The storm surges induced by four historical hurricanes (Rita, Katrina, Ivan, and Dolly) were simulated and compared to observed water levels at National Oceanic and Atmospheric Administration tide stations. Effects of coastal subsidence and future global sea-level rise on coastal inundation in the Louisiana coast were evaluated using a “change of inundation depth” parameter through sensitivity simulations that were based on a projected future subsidence scenario and 1-m global sea-level rise by the end of the century. Model results suggested that hurricane-induced storm surge height and coastal inundation could be exacerbated by future global sea-level rise and subsidence, and that responses of storm surge and coastal inundation to the effects of sea-level rise and subsidence are highly nonlinear and vary on temporal and spatial scales.     

Nearshore propagation of cyclonic waves

Cyclone-generated surface waves are simulated using state-of-art SWAN (Simulating WAves Nearshore) model coupled with hydrodynamic model inputs. A severe cyclonic storm passed over the Arabian Sea during 4–9th November 1982 is selected from UNISYS track records. The cyclone lasted for nearly 6 days and subsided with a land fall at Gujarat coast, west coast of India. In this study, cyclonic wind fields are generated using a well-established relationship suggested by Jelesnianski and Taylor (1973). The associated water level variations due to storm surge and surge generated currents are simulated using POM (Princeton Ocean Model). The outputs are one-way coupled with the wave model SWAN for simulating wave parameters off Gujarat, north-east basin of Arabian Sea. An extensive literature review is carried out on the progress and methodology adopted for storm wave modelling and analysis. The results presented in this paper reveal the severity of the storm event and would be highly useful for assessing the extreme wave event/climate especially for the south coast of Gujarat.     

The impact of extra-tropical transitioning on storm surge and waves in catastrophe risk modelling: application to the Japanese coastline

Catastrophe risk models are used to assess and manage the economic and societal impacts of natural perils such as tropical cyclones. Large ensembles of event simulations are required to generate useful model output. For example, to estimate the risk due to wind-driven storm surge and waves in tropical cyclone risk models, computationally efficient parametric representations of the wind forcing are required to enable the generation of large ensembles. This paper presents new results on the impact of including explicit representations of extra-tropical transitioning in parametric wind models used to force storm surge and wave simulations in a catastrophe risk modelling context. Extra-tropical transitioning is particularly important in modelling risk on the Japanese coastline, as roughly 40 % of typhoons hitting the Japanese mainland are transitioning before landfall. Using both a historical and idealized track set, we compare maximum storm surge and wave footprints along the Japanese coastline for models that include, and do not include, explicit representations of extra-tropical transitioning. We find that the inclusion of extra-tropical transitioning leads to lower storm surge (10–20 %) and waves (5–15 %) on the southern Japanese coast, with significantly higher storm surge and waves along the northern coast (25–50 %). The results of this paper demonstrate that useful risk assessment of coastal flood risk in Japan must consider the extra-tropical transitioning process.