Thesis (Ph. D.)--University of Pennsylvania. / On cover: University of Pennsylvania. Bibliography: p. 179-182.

Hurricanes cause enormous loss to life and property worldwide. Predicting the damage caused by hurricane and figuring out what factors are responsible for the damage are important. This study utilizes multiple linear regression models to predict a hurricane – induced Texas Windstorm Insurance Association (TWIA) payout or TWIA payout ratio using independent variables that could affect the hurricane intensity, including distance from the coastline, distance from the hurricane track, distance from the landfall center of Hurricane Ike, proportion in floodplain zone (100 year, 500 year, 100-500 year), building area, proportion in island, number of buildings per parcel, and building age. The methodology of this study includes Pearson’s correlation and multiple linear regressions. First, Pearson’s correlation is used to examine whether there are any significant correlations between the dependent and independent variables. For TWIA payout, three independent variables, distance from the coastline, distance from the landfall center, and building area, are correlated to the TWIA payout at the 0.01 level. Distance from the coastline and distance from the landfall center have negative relations with the TWIA payout. The variable, building area, has a positive relation with the TWIA payout. Moreover, the improvement value is correlated to the TWIA payout at the 0.05 level. For TWIA payout ratio, distance from the coastline is correlated to the TWIA payout ratio at the level of 0.01 and distance from the landfall center is correlated to the TWIA payout ratio at the 0.05 level. These two variables have negative relations to the TWIA payout ratio. Multiple linear regressions are applied to predict the TWIA payout and payout ratio. A regression model with an Adjusted R Square of 0.264 is presented to predict the TWIA payout. This model could explain 26.4 percent of the variability in TWIA payout using the variables, distance from coastline and building area. A regression model with an Adjusted R Square of 0.121 is presented to predict the TWIA payout ratio.

This thesis uses statistical modelling to better understand the relationship between insured losses and hazard footprint characteristics for European windstorms (extra- tropical cyclones). The footprint of a windstorm is defined as the maximum wind gust speed to occur at a set of spatial locations over the duration of the storm. A better understanding of this relationship is required because the most damaging historical windstorms have had footprints with differing characteristics. Some have a large area of relatively low wind gust speeds, while others have a smaller area of higher wind gust speeds. In addition, this insight will help to explain the surprising, sharp decline in European wind related losses in the mid 1990’s. This novel exploration is based on 5730 high resolution model generated historical footprints (1979-2012) representing the whole European domain. Functions of extreme footprint wind gust speeds, known as storm severity measures, are developed to represent footprint characteristics. Exploratory data analysis is used to compare which storm severity measures are most successful at classifying 23 extreme windstorms, known to have caused large insured losses. Summarising the footprint using these scalar severity measures, however, fails to capture different combinations of spatial scale and local intensity characteristics. To overcome this, a novel statistical model for windstorm footprints is developed, initially for pairs of locations using a bivariate Gaussian copula model; subsequently extended to represent the whole European domain using a geostatistical spatial model. Throughout, the distribution of wind gust speeds at each location is modelled using a left-truncated Generalised Extreme Value (GEV) distribution. Synthetic footprints, simulated from the geostatistical model, are then used in a sensitivity study to explore whether the local intensity or spatial dependence structure of a footprint has the most influence on insured loss. This contributes a novel example of sensitivity analysis applied to a stochastic natural hazards model. The area of the footprint exceeding 25ms−1 over land is the most successful storm severity measure at classifying extreme loss windstorms, ranking all 23 within the top 18% of events. Marginally transformed wind gust speeds are identified as being asymptotically independent and second-order stationary, allowing for the spatial dependence to be represented by a geostatistical covariance function. The geostatistical windstorm footprint model is able to quickly (∼3 seconds) simulate synthetic footprints which realistically represent joint losses throughout Europe. The sensitivity study identifies that the left-truncated GEV parameters have a greater influence on insured loss than the geostatistical spatial dependence parameters. The observed decline in wind related losses in the 1990’s can therefore be attributed to a change in the local intensity rather than the spatial structure of footprint wind gust speeds.

Windstorms result in significant damage and economic loss and are a major recurring threat in many countries. Estimating surface-level wind speeds resulting from windstorms is a complicated problem, but geostatistical spatial interpolation methods present a potential solution. Maximum sustained and peak gust weather station data from two historic windstorms in Europe were analyzed to predict surface-level wind speed surfaces across a large and topographically varied landscape. Disjunctively sampled maximum sustained wind speeds were adjusted to represent equivalent continuously sampled 10-minute wind speeds and missing peak gust station data were estimated by applying a gust factor to the recorded maximum sustained wind speeds. Wind surfaces were estimated based on anisotropic and isotropic kriging interpolation methodologies. The study found that anisotropic kriging is well-suited for interpolating wind speeds in meso- and macro-scale areas because it accounts for wind direction and trends in wind speeds across a large, heterogeneous surface, and resulted in interpolation surface improvement in most models evaluated. Statistical testing of interpolation error for stations stratified by geographic classification revealed that stations in coastal and/or mountainous locations had significantly higher prediction errors when compared with stations in non-coastal/non-mountainous locations. These results may assist in mitigating losses to structures due to excessive wind events.

Following growing public awareness of the danger from hurricanes and tremendous demands for analysis of loss, many researchers have conducted studies to develop hurricane damage analysis methods. Although researchers have identified the significant indicators, there currently is no comprehensive research for identifying the relationship among the vulnerabilities, natural disasters, and economic losses associated with individual buildings. To address this lack of research, this study will identify vulnerabilities and hurricane indicators, develop metrics to measure the influence of economic losses from hurricanes, and visualize the spatial distribution of vulnerability to evaluate overall hurricane damage. This paper has utilized the Geographic Information System (GIS) to facilitate collecting and managing data, and has combined vulnerability factors to assess the financial losses suffered by Texas coastal counties. A multiple linear regression method has been applied to develop hurricane economic damage predicting models. To reflect the pecuniary loss, insured loss payment was used as the dependent variable to predict the actual financial damage and ratio. Geographical vulnerability indicators, built environment vulnerability indicators, and hurricane indicators were all used as independent variables. Accordingly, the models and findings may possibly provide vital references for government agencies, emergency planners, and insurance companies hoping to predict hurricane damage.

Terpenes and phenols are of importance in conifer defense against insects and pathogens. Knowledge about tree chemical defense is vital for developing practical methods to maintain healthy forests. With the aims of characterizing the defensive chemical induction in Norway spruce Picea abies and demonstrating its ecological function to spruce bark beetle Ips typographus, we measured the terpenoid and phenolic content in the bark of mature Norway spruce trees suffering windstorm, inoculated with Ceratocystis polonica, or treated with methyl jasmonate (MeJA), and investigated the colonization and pheromone emission of I. typographus. All three stressors altered the chemical profile in the bark of Norway spruce. Trees damaged by windstorm had lower proportions of (+)-3-carene and two unidentified stilbenes, and a higher taxifolin glycoside content than trees without apparent windstorm damage; C. polonica inoculation induced extremely strong quantitative terpene accumulation in the wound reaction zone, but only increased the levels of (+)-3-carene, sabinene and terpinolene in the bark near the reaction zone; MeJA treatment generally elicited quantitative terpene accumulation, but the induction differed extensively between individual trees. In addition, logs from MeJA-treated tree showed much stronger wounding response compared to control logs. The chemical profile of Norway spruce affected the colonization and pheromone emission of I. typographus. In response to fungal inoculation, terpene present in the reaction zone inhibited the colonization of I. typographus in a dose-dependent manner. Host defense elicited by MeJA treatment reduced emissions of 2-methyl-3-buten-2-ol and (S)-cis-verbenol, the two key aggregation pheromone components of I. typographus, and altered the ratio between the two components. / QC 20110503

Spruce forest is an important production ecosystem for our civilization. Its development can be affected by three main types of disturbances: forest fire, windstorms and spruce bark beetle attack. Experiences with a massive and long-term attack of spruce bark beetle in the Šumava mountains and a strong windstorm in 2004 in the spruce forest in the High Tatra provoke questions, how natural are such severe disturbances. To understand the current development of forest ecosystems, it is necessary to study past structure of these forests and frequency of forest disturbances. This work is about locality Tatranská Lomnice located in the High Tatra mountains, in the belt of mountain spruce forest. In my work, I used mainly pollen and plant macro-fossils analyses. I compared my data with disturbance information originating from dendroecology for windstorms and from charcoal analysis for fires. My research found that according to the pollen assemblages, most of the disturbances did not have strong influence on the forest composition, and most likely they only caused the forest thinning. The most significant fire occurred around 1420. The most significant wind disturbance probably occurred in 1890. The was not found any effect of spruce bark beetle. In the past millennium disturbances affected spruce forest...