Weichiang Pang

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 50,000xa0years 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.

Joint Distributions of Hurricane Wind and Storm Surge for the City of Charleston in South Carolina

Major coastal cities, which have large populations and economies, are easily suffered from the losses due to hurricane wind and storm surge hazards. Although current design codes consider the joint occurrence of high wind and surge, information on site specific joint distributions of hurricane wind and storm surge along the U.S. Eastern Coast and Gulf of Mexico is still sparse and limited. In this paper, joint probability distributions of combined hurricane wind and storm surge for the City of Charleston, SC is developed. A stochastic hurricane model was used to simulate 5,000 years of synthetic hurricanes. The simulated hurricanes were inputted into the ADCIRC (Advanced Circulation) surge prediction model to compute the surge heights at selected locations. The calculated peak wind speeds and surge heights were employed to generate the joint probability distributions at each location. These joint distributions developed can be used in a multi-hazard design or risk assessment framework to consider the combined effects of hurricane wind and storm surge hazards.

Wind-wave prediction equations for probabilistic offshore hurricane hazard analysis

The evaluation of natural catastrophe risk to structures often includes consideration of uncertainty in predictions of some measure of the intensity of the hazard caused by the catastrophe. For example, in the well-established method of probabilistic seismic hazard analysis, uncertainty in the intensity measure for the ground motion is considered through so-called ground motion prediction equations, which predict ground motion intensity and uncertainty as a function of earthquake characteristics. An analogous method for evaluating hurricane risk to offshore structures, referred to herein as probabilistic offshore hurricane hazard analysis, has not been studied extensively, and analogous equations do not exist to predict offshore hurricane wind and wave intensity and uncertainty as a function of hurricane characteristics. Such equations, termed here as wind and wave prediction equations (WWPEs), are developed in this paper by comparing wind and wave estimates from parametric models with corresponding measurements during historical hurricanes from 22 offshore buoys maintained as part of the National Data Buoy Center and located near the US Atlantic and Gulf of Mexico coasts. The considered buoys include observations from 27 historical hurricanes spanning from 1999 to 2012. The 27 hurricanes are characterized by their eye position, translation speed, central pressure, radius to maximum winds, maximum wind speed, Holland B parameter and direction. Most of these parameters are provided for historical hurricanes by the National Hurricane Center’s H*Wind program. The exception is the Holland B parameter, which is calculated using a best-fit procedure based on H*Wind’s surface wind reanalyses. The formulation of the WWPEs is based on two parametric models: the Holland model to estimate hurricane winds and Young’s model to estimate hurricane-induced waves. Model predictions are made for the 27 considered historical hurricanes, and bias and uncertainty of these predictions are characterized by comparing predictions with measurements from buoys. The significance of including uncertainty in the WWPEs is evaluated by applying the WWPEs to a 100,000-year stochastic catalog of synthetic hurricanes at three locations near the US Atlantic coast. The limitations of this approach and remaining work are also discussed.

An agent-based framework for modeling the effectiveness of hurricane mitigation incentives

To evaluate the effectiveness of different incentives for mitigating regional hurricane hazards, an agent-based hurricane mitigation framework was developed. This framework, which consists of six essential components, is able to consider stakeholders’ points of view on the selections of hurricane retrofit measures and the dynamic evolutions of building inventories (i.e. constructions and demolitions). Based on the findings of this study, among different levels of property tax reduction, a reduction between 50% and 75% was found to be the most effective one that leads to the highest hurricane cost reduction the study region can get in a 25-year timeframe. Agent-based modeling (ABM), which has a rapid growth in the past few decades, is a relatively new computational method (Gilbert 2007). The ABM is known as a “bottom-up” model. In ABM, the behavior of a complex system is studied without having to define the global behavior of the system. Rather, the behavior of individuals or subcomponents (hereafter referred as “agents”) is defined and the interactions between the agents are modeled to unveil the global behavior (Borshchev and Filippov 2004). Agents’ behaviors are defined using certain rules. Their interactions with each other as well as the environment can affect these rules and in turn influence agents’ behaviors (Macal and North 2010). By modeling agents’ behaviors and interactions individually, the behavior of the entire system can be revealed. Nevertheless, the ABM is seldom used in hurricane related studies. Chen et al. (2006) developed an agent-based hurricane evacuation model for the Florida Keys using survey data. This model was able to estimate not only the minimum clearance time of the evacuation, but also the accommodations needed if the evacuation is interrupted by a landfall hurricane. A dynamic ABM system was developed by Dawson et al. (2011) for flood incident management. This system is able to estimate the vulnerability of individual agents in coastal communities, subject to different flood conditions, defense scenarios, warning times and evacuation strategies. The flood inundation, traffic condition, flood risk, agent’s vulnerability and agent’s behavior were modeled to evaluate the effectiveness of different FIM measures. One of the key challenges in the ABM is the modeling of individual agent (Crooks et al. 2008). Specifically, it is the modeling of agent’s demographic and psychological attributes, and their relations with agent’s behaviors. The psychological attributes usually include risk perception, subjective knowledge, hazard experience, etc. Using data from the 2003 Hurricane Loss Mitigation Baseline Survey (HLMBS) (Peacock 2003b), Peacock et al. conducted three studies in analyzing the influences of Florida residents’ demographic and psychological characteristics on (1) the status of hurricane mitigation (Peacock 2003a), (2) the hurricane risk perception (Peacock et al. 2005), and (3) the household responses to hurricane mitigation incentives (Ge et al. 2011). The influences were modeled using probabilistic models through logistic regressions. The outcomes of their studies are valuable resources for the modeling of agents’ behaviors in hurricane mitigations. In this study, an agent-based hurricane mitigation framework was developed for the purpose of evaluating the effectiveness of different incentives for 12 International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015 2 mitigating the combined effects of hurricane wind and flood hazards. A case study hurricane mitigation domain (Miami, Florida) was constructed to mimic the real configuration of the existing building stock and the attributes of residents in the study domain. The occurrence of the new building constructions, demolitions and hurricane hazards over time was modeled using historical data. Agents’ responses to hazards and incentives for retrofitting their homes, and decision-makings with regard to building retrofits were modeled using the survey data. Finally, the combined wind and flood loss estimations were performed to evaluate the cost-benefit of the hazard mitigation incentives. This paper is organized such that an overview of the agent-based hurricane mitigation framework is presented in Section 1, followed by a case study in Section 2, and preliminary findings and conclusions are given in Section 3. 1. OVERVIEW OF THE AGENT-BASED HURRICANE MITIGATION FRAMEWORK The proposed framework for agent-based regional hurricane mitigation is presented in . This framework, which is composed of six modules, accounts for agents’ responses to incentives and natural hazards in hurricane mitigations through modeling of agents’ behaviors. The six modules are Buildings, Agents, Retrofits, Hazards, Constructions & Demolitions, and Incentives (see ). The behaviors of the six modules are defined individually and the connections between them are defined using a set of rules or functional relationships. These connections are marked as arrows, which indicate directional impacts from one module to the other, in . For instance, hazards can impart damages to buildings and cause losses, and hazards are also able to influence agents’ psychological attributes or behaviors. The Retrofits module, marked as a circle, requires agents’ decision making, which is affected by both hazards and incentives. In this hurricane mitigation framework, the initial locations and characteristics of individual buildings as well as the initial locations and attributes of individual agents were first determined using historical survey data. Then, given selected incentives, simulations were performed at a fixed time interval (e.g. one month) to evaluate the performance of the entire system (i.e. all six modules and the connections). At every time step, the building characteristics and the agent attributes were updated. Agents responded to incentives and hazards, and decided if they wanted to retrofit their buildings. New constructions and demolitions occurred at every time step as well. When affected by a hurricane in a time step, the combined wind and flood losses was computed using a loss estimation framework adapted from the HAZUS-MH methodology. The updates of building characteristics, including demolitions, and agent attributes were simulated after the hurricane. The following subsections are organized to describe this process in detail. 1.1. Building locations and characteristics As mentioned above, a key feature of the agent-based modeling is to model each individual from the bottom. This requires specific modeling of each individual building that exists in the study domain. For simplicity and as an illustrative example, only residential buildings were taken into account in this study. Buildings were modeled as individual dots (dimensionless) on a map (see Figure 2) containing location and characteristic information, which are adequate for regional hurricane mitigations . Figure 2: Comparison between modeled and actual -80.358 -80.357 -80.356 -80.355 -80.354 25.616 25.617 25.618 25.619 25.62 Longitude (deg) L a t it u d e (d eg ) Figure 1: Framework for agent-based regional hurricane mitigation 12 International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015 3 locations of buildings 1.1.1. Locations of buildings The numbers of buildings in each Census block were first determined using the 2010 U.S. Census (U.S. Census Bureau 2010) and the 2011 American Community Survey (ACS) (U.S. Census Bureau 2011) data through a probabilistic method. Then, the buildings were placed along the roads but with a distance to the roads in the Census blocks. A comparison between the modeled building locations and their actual locations on a Google map is presented in Figure 2. It can be clearly seen that the modeled locations (solid circles) agree well with their actual locations. 1.1.2. Characteristics of buildings After the locations of buildings were determined, their characteristics could be assigned. The 20 building characteristics considered in this study include year structure built, household income, building square footage, number of stories, flood zone, base flood elevation, foundation type, foundation height, building replacement value, corresponding wind and flood damage functions, etc. These characteristics were selected because they are essential in hurricane loss estimation and hazard mitigation. It should be noted that these characteristics were assigned to every building using survey data. Among others, the ACS data, which is a typical source available, only provides data at Census block group (i.e. a group of Census blocks) level. Therefore, in order to assign characteristics to each individual building, we used the probability mass functions (PMFs) obtained from the survey data (e.g. ACS data) at the Census block group level. The PMF for a particular characteristic represents the discrete probability of every category in that characteristic. For instance, there are 9 categories for year structure built (2005-2010, 2000-2004, 1990-1999, ..., 1940-1949, 1910-1939). Each of them is associated with a distribution probability corresponding to a Census block group. For each building in that Census block group, the year built was randomly sampled following the PMF. In this framework, similar concept was utilized in the assignment of characteristics. The correlations between characteristics were also considered. Because the assignments heavily rely on the above-mentioned probabilistic method, statistics of the assigned characteristics were checked to ensure the accuracy in terms of capturing the trends of the PMFs used to develop the mitigation framework. PMFs were checked for the entire hurricane mitigation domain and all of them agreed well with the assigned statistics. One example is given in Figure 3. 1.2. Agent attri

Selection of hazard-consistent hurricane scenarios for regional combined hurricane wind and flood loss estimation

This paper presents a new methodology for selecting hazard-consistent hurricane scenarios (with similar return periods) for estimating the regional losses due to combined hurricane wind and flood. An in-house stochastic hurricane simulation program was used to simulate 50,000xa0years of full-track synthetic hurricanes. A wind field model along with a boundary layer model was utilized to compute the surface wind speeds. As illustrative examples, the SLOSH (Sea, Lake, and Overland Surges from Hurricanes) model was employed to calculate the corresponding flood elevations for two loss estimation domains (Charleston Peninsula, South Carolina and Miami Beach, Florida) with each of them divided into census blocks. The peak wind speeds and maximum flood elevations at the centroids of respective census blocks were utilized to determine the joint mean recurrence intervals (MRIs) of individual hurricane events. For regional loss estimation purpose, the joint MRIs of hurricanes were weighted by the population of every census block. A hurricane selection procedure was developed to select hazard-consistent hurricane scenarios with a joint MRI of 100xa0years. Three hurricane ensembles, selected based on only wind speeds, only flood elevations, and joint wind speeds and flood elevations, were imported into the HAZUS-MH (Hazards US Multi-Hazards) program to perform combined wind and flood loss estimations. The results indicate that hurricane selection using either only wind speeds or only flood elevations can overestimate the combined losses. The different characteristics of the selected hurricane scenarios for the two loss estimation domains are also discussed.

HURRICANE RISK CONSIDERATIONS FOR OFFSHORE WIND TURBINES ON THE ATLANTIC COAST

The development of renewable energy sources is a critical global need. The Atlantic coast and Gulf of Mexico of the U.S., with large wind resources and proximity to major population centers, are natural places for such development; however, these regions are also at considerable risk from severe hurricanes or tropical cyclones. Current international guidelines for the design of offshore wind turbines (OWTs) do not explicitly consider loading under hurricane conditions, however subsequent editions are anticipated to include language specific to hurricanes. Variability in extreme loads is greater in areas where hurricanes are likely and the design loads and risk profile of offshore structures installed in such areas are expected to be strongly influenced by hurricanes. For many offshore structures, environmental conditions at design recurrence periods and beyond are often estimated through extrapolation of long-term (i.e. multiple decades) wind and wave measurements from buoys, however, for offshore structures located at areas exposed to hurricanes, it is accepted practice to use physics-based models to augment the historical record of Atlantic hurricane activity and generate a stochastic catalog of synthetic hurricanes that provides tens of thousands of realizations for one year of potential hurricane activity. Once a stochastic catalog has been established, appropriate hazard intensity measures (e.g. the one-minute sustained wind speed, the significant wave height, and the peak spectral wave period) can be estimated for each storm at any site using well-known wind and wave parametric models. In this study, we consider several sites along the Atlantic coast and quantify the impact of estimating hazard for design recurrence periods and beyond for three different methods. The first is based on an extrapolation of wind and wave measurements from buoys, and the second and third are based on a stochastic catalog of synthetic hurricanes with wind and wave intensities estimated based on deterministic and probabilistic relationships.Copyright