Hurricane Risk Assessment Research Articles

Hurricane risk assessment in a multi-hazard context for Dominica in the Caribbean

Hurricanes can trigger widespread landslides and flooding creating compound hazards and multiple risks for vulnerable populations. An example is the island of Dominica in the Caribbean, where the population lives predominantly along the coast close to sea level and is subject to storm surge, with steep topography rising behind, with a propensity for landslides and flash river flooding. The simultaneous occurrence of the multiple hazards amplifies their impacts and couples with physical and social vulnerabilities to threaten lives, livelihoods, and the environment. Neglecting compound hazards underestimates overall risk. Using a whole island macroscale, (level-I) analysis, susceptibility scenarios for hurricanes, triggered landslides, and floods were developed by incorporating physical process parameters. The susceptibilities were combined with vulnerability indicators to map spatial patterns of hurricane multi-risks in Dominica. The analysis adopted a coupled approach involving the frequency ratio (FR), analytic hierarchy process (AHP), and geographic information system (GIS). Detailed hazard modelling was done at selected sites (level-II), incorporating storm surge estimates, landslide runout simulations, and steady flow analysis for floods. High-resolution terrain data and simulation models, the Rapid Mass Movement Simulation (RAMMS) and the hydrologic engineering center’s river analysis system (HEC-RAS), were employed. Ground validation confirmed reasonable agreement between projected and observed scenarios across different spatial scales. Following the United Nations Office for disaster risk reduction (UNDRR) call for the inclusion of local, traditional, and indigenous knowledge, feedback, and expert opinion to improve understanding of disaster risk, 17 interviews with local experts and 4 participatory workshops with residents were conducted, and findings were incorporated into the analysis, so as to gain insights into risk perceptions. The study’s outcomes encompass projections and quantification of hurricane compound hazards, vulnerabilities, accumulated risks, and an understanding of local priorities. These findings will inform decision-making processes for risk mitigation choices and community actions by providing a new framework for multi-hazard risk assessment that is easy to implement in combining different data forms.

Multi-hazard socio-physical resilience assessment of hurricane-induced hazards on coastal communities

Hurricane-induced hazards can result in significant damage to the built environment cascading into major impacts to the households, social institutions, and local economy. Although quantifying physical impacts of hurricane-induced hazards is essential for risk analysis, it is necessary but not sufficient for community resilience planning. While there have been several studies on hurricane risk and recovery assessment at the building- and community-level, few studies have focused on the nexus of coupled physical and social disruptions, particularly when characterizing recovery in the face of coastal multi-hazards. Therefore, this study presents an integrated approach to quantify the socio-physical disruption following hurricane-induced multi-hazards (e.g., wind, storm surge, wave) by considering the physical damage and functionality of the built environment along with the population dynamics over time. Specifically, high-resolution fragility models of buildings, and power and transportation infrastructures capture the combined impacts of hurricane loading on the built environment. Beyond simulating recovery by tracking infrastructure network performance metrics, such as access to essential facilities, this coupled socio-physical approach affords projection of post-hazard population dislocation and temporal evolution of housing and household recovery constrained by the building and infrastructure recovery. The results reveal the relative importance of multi-hazard consideration in the damage and recovery assessment of communities, along with the role of interdependent socio-physical system modeling when evaluating metrics such as housing recovery or the need for emergency shelter. Furthermore, the methodology presented here provides a foundation for resilience-informed decisions for coastal communities.

Climate change effects on loss assessment and mitigation of residential buildings due to hurricane wind

Hurricanes are considered as one of the most devastating natural hazards. Their induced risk is expected to significantly increase with the changing climate conditions. Residential buildings are specifically highly vulnerable to hurricane winds. Therefore, accurate assessment of hurricane induced losses under changing climate conditions is crucial to assist in the development of the best mitigation strategies. In this paper, a stochastic hurricane risk assessment framework accounting for several climate scenarios is developed to assess the hurricane-induced losses of residential buildings located in hurricane-prone regions. Specifically, a total of 10,000 years of synthetic storms will be generated for both ‘observed climate’ and ‘future climate’. The future climate models correspond to the worst-case scenario SSP5-8.5 and are simulated based on two global climate models. Then a physics-based wind model is coupled with the synthetic tracks to generate the hazard probabilities. To accurately capture the upper tail effects, the Gaussian kernel density estimation function is proposed in this study. The effects of the variability of climate change models on the wind hazard and its induced losses are quantitatively evaluated. The average annual regional loss is selected as the decision variable. The framework is applied to a one-story single-family wood frame residential building in three different cities, namely Atlantic City, Miami and Galveston. Three mitigation strategies are evaluated, with varying degrees of structural mitigation, by comparing the corresponding losses incurred under different climate change scenarios. It is shown that, due to the disproportionate variation of hurricane intensity and frequency over the coastal areas, the estimated annual losses with the consideration of several mitigation strategies change unevenly from one location to another. Also, the selection of inappropriate probability density function to estimate the wind distribution might underestimate the hurricane-induced loss which is mainly due to its incapability to accurately capture the upper tail ends of the wind distribution.

Hurricane Simulation and Nonstationary Extremal Analysis for a Changing Climate

Abstract Particularly important to hurricane risk assessment for coastal regions is finding accurate approximations of return probabilities of maximum wind speeds. Since extremes in maximum wind speed have a direct relationship with minima in the central pressure, accurate wind speed return estimates rely heavily on proper modeling of the central pressure minima. Using the HURDAT2 database, we show that the central pressure minima of hurricane events can be appropriately modeled by a nonstationary extreme value distribution. We also provide and validate a Poisson distribution with a nonstationary rate parameter to model returns of hurricane events. Using our nonstationary models and numerical simulation techniques from established literature, we perform a simulation study to model returns of maximum wind speeds of hurricane events along the North Atlantic coast. We show that our revised model agrees with current data and results in an expectation of higher maximum wind speeds for all regions along the coast, with the highest maximum wind speeds occurring along the northeast seaboard.

Explainable deep learning powered building risk assessment model for proactive hurricane response.

Climate change and rapid urban development have intensified the impact of hurricanes, especially on the Southeastern Coasts of the United States. Localized and timely risk assessments can facilitate coastal communities' preparedness and response to imminent hurricanes. Existing assessment methods focused on hurricane risks at large spatial scales, which were not specific or could not provide actionable knowledge for residents or property owners. Fragility functions and other widely utilized assessment methods cannot model the complex relationships between building features and hurricane risk levels effectively. Therefore, we develop and test a building-level hurricane risk assessment with deep feedforward neural network (DFNN) models. The input features of DFNN models cover the meta building characteristics, fine-grained meteorological, and hydrological environmental parameters. The assessment outcomes, that is, risk levels, include the probability and intensity of building/property damages induced by wind and surge hazards. We interpret the DFNN models with local interpretable model-agnostic explanations (LIME). We apply the DFNN models to a case building in Cameron County, Louisiana in response to a hypothetical imminent hurricane to illustrate how the building's risk levels can be timely assessed with the updating weather forecast. This research shows the potential of deep-learning models in integrating multi-sourced features and accurately predicting buildings' risks of weather extremes for property owners and households. The AI-powered risk assessment model can help coastal populations form appropriate and updating perceptions of imminent hurricanes and inform actionable knowledge for proactive risk mitigation and long-term climate adaptation.

Florida public hurricane loss model: Software system for insurance loss projection

AbstractA six‐month‐long Atlantic hurricane season impacts Florida residents every year and can result in devastating consequences, including loss of life, property damage, and business interruptions. Hurricane risk assessment and loss prediction are critical to various uses such as determining homeowner insurance premiums, regulating these premiums, conducting scenario analysis, conducting stress tests for companies, disaster management, and evaluating the benefits of disaster mitigation techniques. This article describes the Florida Public Hurricane Loss Model (FPHLM): a large‐scale catastrophe model with massive databases and analytics tools for business and government decision‐making. We will discuss the design and implementation of each component in FPHLM and explain the tools and techniques utilized to tackle challenges in data availability, data analytics, and the interface between the data, analytical techniques, and computing. Results are shown to validate the software system's effectiveness and reliability and illustrate some of the system's use cases.

Hurricane Risk Assessment of Power Distribution Systems: Effects of Wind Direction

AbstractThis paper presents a framework for the risk assessment of urban power distribution systems under hurricane hazard, focusing on the effects that both line orientation and hurricane time his...

Component-based fragility analysis of transmission towers subjected to hurricane wind load

The fragility model of transmission towers is one of the key elements for the hurricane risk and resilience assessment of regional power utility infrastructure. The current methods for fragility curve assessment of transmission towers rely on either static analysis or dynamic analysis with simplified demand parameters and limit state functions. The details of tower connections, such as joint eccentricities, rotational stiffness and slippage are often overlooked in fragility analyses due to the associated complexity of modeling and large computational effort. The purpose of this study is to model the realistic performance of transmission towers under hurricanes and develop a fragility curve estimation framework balancing between accuracy and efficiency.The correlated multivariate wind load time histories on the tower are simulated, and the load transferred from the cables to the cross-arms of the tower are generated using an effective analytical model. Two types of three dimensional finite element models of transmission towers are developed in the OpenSees platform. One is a complex model with all the joint details, which requires a very large computational effort to conduct nonlinear dynamic analyses, and the other one is a simple model without all the joint details, which is more computationally efficient. A small-scale dynamic probabilistic simulation with the two models is conducted, and a method to replace the complex model with the simple model is developed and validated. Using a high-performance computing cluster, medium-scale nonlinear dynamic time history analyses are carried out to obtain the demands of individual structural members and their correlation. Component-level failures are obtained via large-scale numerical simulations and are linked to global collapse of the tower through element removal and progressive collapse analyses. A case study of two transmission towers under hurricane is carried out. The results indicate that, compared to a traditional tip displacement method, the proposed component-based method with detail driven finite element model and large-scale simulations leads to a more accurate assessment of the transmission tower fragility curve.

Early Season Hurricane Risk Assessment: Climate-Conditioned HITS Simulation of North Atlantic Tropical Storm Tracks

AbstractWe present a hurricane risk assessment model that simulates North Atlantic Ocean tropical cyclone (TC) tracks and intensity, conditioned on the early season large-scale climate state. The model, Cluster-Based Climate-Conditioned Hurricane Intensity and Track Simulator (C3-HITS), extends a previous version of HITS. HITS is a nonparametric, spatial semi-Markov, stochastic model that generates TC tracks by conditionally simulating segments of randomly varying lengths from the TC tracks contained in NOAA’s Best Track Data, version 2, dataset. The distance to neighboring tracks, track direction, TC wind speed, and age are used as conditioning variables. C3-HITS adds conditioning on two early season, large-scale climate covariates to condition the track simulation: the Niño-3.4 index, representing the eastern equatorial Pacific Ocean sea surface temperature (SST) departure from climatology, and main development region, representing tropical North Atlantic SST departure from climatology in the North Atlantic TC main development region. A track clustering procedure is used to identify track families, and a Poisson regression model is used to model the probabilistic number of storms formed in each cluster, conditional on the two climate covariates. The HITS algorithm is then applied to evolve these tracks forward in time. The output of this two-step, climate-conditioned simulator is compared with an unconditional HITS application to illustrate its prognostic efficacy in simulating tracks during the subsequent season. As in the HITS model, each track retains information on velocity and other attributes that can be used for predictive coastal risk modeling for the upcoming TC season.

DEVELOPMENTS IN STORM SURGE ESTIMATION USING SURROGATE MODELING TECHNIQUES

Numerical advances in storm surge prediction over the past couple of decades have produced high-fidelity simulation models that permit a detailed representation of hydrodynamic processes and therefore support high accuracy forecasting. Unfortunately, the computational burden of such numerical models is large, requiring thousands of CPU hours for each simulation, something that limits their applicability for hurricane risk assessment. Use of Kriging-based surrogate modeling techniques has been examined to address the aforementioned challenge Jia et al. [2016], Zhang et al. [2018]. This approach can provide fast predictions using a database of high-fidelity, synthetic storms, with the goal of maintaining the accuracy of the numerical model utilized to produce this database, while offering computational efficiency. This contribution overviews initially recent research developments for the application of Kriging for storm surge predictions. Topics discussed include: enhancement of the initial database for nodes (i.e., geographical locations) that have remained dry in some of the database storms; adaptive selection of storms forming the initial database; use of different surrogate modeling tuning techniques and their impact on the metamodel predictive capabilities for storm surge estimation; implementation for estimation of impact due to near-shore processes (breaking waves), something that requires coupling of different numerical models.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/vL38Kv3kLDM

Geospatial Environments for Hurricane Risk Assessment: Applications to Situational Awareness and Resilience Planning in New Jersey

Mitigation of losses due to coastal hazards has become an increasingly urgent and challenging problem in light of rising seas and the continued escalation of coastal population density. Unfortunately, stakeholders responsible for assuring the safety of these coastal communities are not equipped with the engineering research community’s latest tools for high-fidelity risk assessment and geospatial decision support. In the event of a hurricane or nor’easter, such capabilities are exceptionally vital to project storm impacts on critical infrastructure and other municipal assets and to inform preemptive actions that can save lives and mitigate property damage. In response, a web-based visualization environment was developed using the GeoNode content management system, informed by the needs of municipal stakeholders. Within this secure platform, registered users with roles in planning, emergency management and first response can simulate the impact of hurricanes and nor’easters using the platform’s storm Hazard Projection (SHP) Tool. The SHP Tool integrates fast-to-compute windfield models with surrogate models of high-fidelity storm surge and waves to rapidly simulate user-defined storm scenarios, considering the effects of tides, sea level rise, dune breaches and track uncertainty. In the case of a landfalling hurricane, SHP tool outputs are automatically loaded into the user’s dashboard to visualize the projected wind, storm surge and wave run-up based on the latest track information published by the National Hurricane Center. Under either use case, outputs of the SHP Tool are visualized within a robust collaborative geospatial environment supporting the seamless exploration of centralized libraries of geographic information system (GIS) data from federal, state, county and local authorities, with tools to add user-supplied annotations such as notes or other geospatial mark-ups. This paper will overview the development and deployment of this platform in the State of New Jersey, detailing the cyberinfrastructure design and underlying computational models, as well as the user stories that inspired the platform’s functionalities and interfaces. The study concludes with reflections from the process of piloting this project with stakeholders at the state and municipal level to support more risk-responsive and data-informed decision making.

Revisiting hurricane track model for wind risk assessment

Hurricane wind risk assessment has been significantly improved with the evolvement of synthesis methodologies from the single site probabilistic method to the hurricane track model (including five simulation components of genesis, translation, intensity, decay and boundary-layer wind). As first-step efforts towards advancing the data-driven hurricane track model, widely used by engineering community, to a physics-based framework for more accurate and reliable hurricane risk assessment, a new intensity model integrating important dynamics and thermodynamics inside the storms is developed. Furthermore, an extensive statistical analysis of hurricane trajectories is carried out to obtain an enhanced translation model and a height-resolving analytical wind model is utilized to acquire the vertical profiles of wind speed and direction between ground-surface and gradient levels. The other two simulation components (i.e., genesis and decay) of the hurricane track model are also revisited for the sake of completeness. Ten thousand years of full-track synthetic hurricanes are generated and compared with the HURDAT database at specific mileposts along the US East coast to validate the overall performance of the developed simulation framework (in terms of annual occurrence rate, intensity, translation speed and heading angle). Then, the New Jersey coastline is employed as a case study to compare the simulated hurricane wind speeds with ASCE 7–16 recommendations, to highlight the wind directionality effects on extreme wind speeds, and to investigate the joint distribution of hurricane wind speed and size.

Hurricane Risk Assessment of Petroleum Infrastructure in a Changing Climate

Hurricanes threaten the petroleum industry in the United States and are expected to be influenced by climate change. This study presents an integrated framework for hurricane risk assessment of petroleum infrastructure under changing climatic conditions, calculating risk in terms of monetary loss. Variants of two synthetic probabilistic storms and one historical storm (Hurricane Ike) are simulated using the SWAN+ADCIRC model, representing a range of potential scenarios of impacts of a changing climate on hurricane forward speed and sea-level rise given uncertainties in climate projections. Model outputs inform an infrastructure impact and cascading economic loss analysis that incorporates various sources of uncertainty to estimate five types of losses sustained by petroleum facilities in surge events: land value loss, process-unit damage loss, cost of spill clean-up and repair of aboveground storage tanks, productivity loss, and civil fines. The proposed risk assessment framework is applied as a case study to seven refineries along the Houston Ship Channel (HSC), a densely-industrialized corridor in Texas. The results reveal that either an increase in mean sea level or a decrease in storm forward speed increases the maximum water elevations in the HSC for storms that produce maximum wind setup in Galveston Bay (FEMA 33 and FEMA 36), resulting in larger economic loss estimates. The role of refinery features such as storage capacity and average elevation of the refinery and its critical equipment in the refinery response to hurricane hazards is studied, and the probability distribution of refinery total loss and the loss risk profile in different hurricane scenarios are discussed. Loss estimates are presented, demonstrating the effects of hurricane forward speed and sea level on the losses for the refineries as well as the HSC. Such a framework can enable hurricane risk assessment and loss estimation for petroleum infrastructure to inform future policies and risk mitigation strategies. Potential policy implications for a region like the HSC are highlighted herein as an illustration.

Hurricane Risk Assessment for Residential Buildings in the Southeastern US Coastal Region in Changing Climate Conditions Using Artificial Neural Networks

AbstractHurricanes cause severe damage along US coastal regions, which may be aggravated in changing climate conditions. This paper investigates the impact of climate change on the hurricane risk f...

Hurricane risk assessment of offshore wind turbines

Abstract A barrier to the development of the offshore wind resource along the U.S. Atlantic coast is a lack of quantitative measures of the risk to offshore wind turbines (OWTs) from hurricanes. The research presented in this paper quantifies the risk of failure of OWTs to hurricane-induced wind and waves by developing and implementing a risk assessment framework that is adapted from a well-established framework in performance-based earthquake engineering. Both frameworks involve the convolution of hazard intensity measures (IMs) with engineering demand parameters (EDPs) and damage measures (DMs) to estimate probabilities of damage or failure. The adapted framework in this study is implemented and applied to a hypothetical scenario wherein portions of nine existing Wind Farm Areas (WFAs), spanning the U.S. Atlantic coast, are populated with ∼7000 5 MW OWTs supported by monopiles. The IMs of wind and wave are calculated with a catalog representing 100,000 years of simulated hurricane activity for the Atlantic basin, the EDPs are calculated with 24 1-h time history simulations, and a fragility function for DM is estimated by combining variability observed in over one hundred flexural tests of hollow circular tubes found in the literature. The results of the study are that, for hurricane-induced wind and wave, the mean lifetime (i.e., 20-year) probability of structural failure of the tower or monopile of OWTs installed within the nine WFAs along the U.S. Atlantic coast ranges between 7.3 × 10−10 and 3.4 × 10−4 for a functional yaw control system and between 1.5 × 10−7 and 1.6 × 10−3 for a non-functional yaw control system.

An equivalent boundary model for effects of adjacent spans on wind reliability of wood utility poles in overhead distribution lines

Overhead distribution lines in the United States are primarily supported by wood poles. These utility poles are vulnerable to high wind loads from hurricanes which has resulted in a large number of power outages during past hurricanes, especially in coastal areas. Risk management of distribution systems requires development of distribution line models to reliably assess their performance under hurricanes. However, modeling the entire distribution line is not practically feasible as they are very long and consist of a large number of spans. The present study proposes analytical models to capture boundary effects of adjacent spans on the wind response of the pole of interest via equivalent springs and wind-induced forces. The stiffness of conductors (cables) is derived through a simplified solution that considers the shape of the conductors under lateral static gust wind and gravity loadings; this model is shown to be very accurate when compared to the exact analytical as well as Finite Element solutions. Deterministic and probabilistic studies are performed to assess the impact of boundary conditions on the performance of the poles; results indicate that pole boundaries may have a considerable impact on the estimates of failure probabilities especially when the extent of difference in the properties of adjacent spans is not negligible. Specifically, neglecting boundary effects from neighboring spans may result in underestimation of failure probabilities of stronger poles and overestimation of failure probabilities of weaker poles as load sharing effects among the poles are not considered. The proposed boundary model enables more reliable hurricane risk assessment of power distribution systems to effectively manage the risk of outages.

Hurricane risk assessment for offshore wind plants

This study describes a framework for estimation of structural damage to offshore wind plants during hurricanes. Related risk assessment is fundamentally dependent on the estimation of hurricane-generated wind speed exceedance probabilities at selected hub heights of wind turbines in the plant and on estimation of associated wind turbine loads. As part of a framework for risk assessment introduced here, synthetic storm tracks are first simulated over the ocean using available historical tropical storm data; then, a hurricane intensity evolution model based on thermodynamic and atmospheric environmental variables is developed for each simulated track as it approaches the chosen wind plant site. Based on this intensity model, a turbulent wind field can be simulated at any location of interest along the hurricane track. The simulated turbulent wind field can then be used to estimate wind speed exceedance probability distributions and, when combined with partially correlated waves, it can also be used in analyzing the response of individual turbines in a wind plant. A framework for the overall risk assessment is presented; individual components that are part of such a framework are described briefly in illustrative applications. Finally, a brief discussion is presented that addresses issues related to the concept of “robustness” checks that are often considered in the context of safe performance-based design. This concept employed in the oil and gas industries provides for a secondary margin for “beyond design-level” external conditions out to more extreme conditions as might accompany hurricanes.

Reliability of Roof Panels in Coastal Areas Considering Effects of Climate Change and Embedded Corrosion of Metal Fasteners

AbstractRoof panels have been recognized as the most vulnerable components in wood construction under hurricane events, which can result in enormous economic losses and human casualties. This paper presents a reliability analysis of roof panels considering effects of climate change on altering patterns of hurricane hazard and embedded corrosion of metal fasteners in hurricane risk assessment. On the one hand, it has been found that the hurricane wind intensity may increase due to the effects of climate change, and the intensified hurricanes will result in more severe damage to coastal constructions. On the other hand, there is corrosion on the shanks of the fasteners that are tightly embedded in the wood roof panels, and the reduced diameter of shanks will reduce the uplift capacity of roof panels. Uncertainties in hurricane wind load and roof panel uplift resistance (the effects of embedded corrosion on the diameter of metal fasteners) are considered. The effectiveness of several retrofitting strategies ...

CyberEye: Development of integrated cyber-infrastructure to support rapid hurricane risk assessment

Recent losses provide sobering evidence of the continued need for more effective tools to support hurricane risk assessment and mitigation that can leverage the latest developments both in the characterization of the hazard and its impacts on built environments. This paper provokes the question of whether open-source virtual environments can indeed provide the appropriate venue to do so, leveraging application programming interfaces and a modular approach to packaging the overall framework that encourages the progressive integration of data, tools and models from the community. This paper presents one opportunity: a virtual environment called CyberEye and the cyberinfrastructure that backs this collaboration space, providing users with a customizable dashboard where they can annotate, mark up and share information with other authorized users through a web-based geospatial environment. The paper focuses on the platform׳s two distinct workflows: Rapid Risk Assessment and Data Intake and Discovery, providing for each working User Scenarios to demonstrate the platform capabilities, relative to the current standard of practice.

Kriging metamodeling for approximation of high-dimensional wave and surge responses in real-time storm/hurricane risk assessment

One of the important recent advances in the field of hurricane/storm modeling has been the development of high-fidelity numerical models that facilitate accurate but also computationally intensive simulations of hurricane responses. For efficient implementation in probabilistic hurricane risk assessment, that typically requires simulation of a large number of hurricane scenarios within the coastal regions of interest, combination with metamodeling approaches has been recently proposed. In this work, kriging is investigated for this purpose, focusing on implementation for real-time assessment, i.e. for evaluating risk during an incoming event (and prior to the hurricane/storm making landfall), and on facilitating the development of efficient standalone tools. An important characteristic of this application is that the model output is very high dimensional, since the hurricane response is calculated in a large coastal region and potentially at different time instances. This makes it impractical to establish a different metamodel for each different output. Considering, though, the potential –spatial and/or temporal- correlation between the different outputs combination with principal component analysis (PCA) is proposed here. This analysis extracts a much smaller number of latent outputs to approximate the initial high-dimensional output. A separate metamodel is then developed for each latent output. Comparisons between kriging with and without PCA and between kriging and moving least squares response surface approximations are discussed in terms of both computational efficiency (speed) and memory requirements. Both these are important considerations when discussing the development of standalone tools that can efficiently run in personal laptops (or even smartphones) of emergency response managers. The impact of the number of latent outputs considered is investigated in this context, and also the optimal selection of basis and correlation functions for the kriging is discussed. Finally for calculating risk the prediction error stemming from the metamodel is explicitly addressed (i.e., the assessment does not rely only on the mean kriging response). The proposed approach is demonstrated for real-time hurricane risk assessment for the Hawaiian Islands, focusing in the region around Oahu.