Hurricanes are among the most catastrophic types of natural hazards, with the potential to cause serious losses in lives and property. While hurricanes rarely have a huge impact on the New York City area, they do have the potential to cause major damage to the city's transportation infrastructure. This research will deal with two main considerations--fragility curves and exceedance curves of vulnerable points in that infrastructure. The primary objective of this study is to provide a model for predicting future hurricane related storm surge patterns and for estimating possible levels of damage from future events in order to develop planning strategies to mitigate against possible damage. The first step is to describe the frequency of past storm surge events in New York City from 1920 to 2012 and determine a probability distribution for hurricane hazard about the maximum daily and yearly storm surges. The second step is to estimate potential probabilistic models by looking at the empirical data on storm surges in New York City. The last step is to concentrate on the reliability assessment for several infrastructures subjected to hurricane loading and storm surges. No significant studies have been conducted using the available empirical data on storm surge heights in New York City, despite the fact that since an observation station was installed in the Battery, New York in 1920, daily and yearly maximum water levels at that location have been documented by the National Oceanic and Atmospheric Administration (NOAA). Considering the available daily maximum sea water levels from 1920 to 2012 yields a total of 31,148 data points (2,394 days of maximum height data are unfortunately missing); 92 data points of maximum sea water levels are also available. This is the first study to utilize the nearly century's worth of empirical data obtained by the observation station at the Battery. Extensive goodness of fit testing (including the use of various probability papers) is performed on the empirical daily maximum sea water level data. It is concluded that the daily maximum sea water levels at the Battery from 1920 to 2012 follow closely a logistic distribution, with a mean value of 8.10 feet and a coefficient of variation (COV) of 9.63%. The methodology of analyzing the yearly maximum sea water levels is quite similar to that used for the daily sea water levels (and the analysis is performed independently). It is found that the yearly maximum sea water levels at the Battery from 1920 to 2012 follow closely a generalized extreme value (GEV) distribution with a mean value of 10.72 feet and a COV of 10.07%. Then, applying exact and asymptotic Extreme Value Theory, the parent GEV distribution is used to determine the probability distributions for maximum sea water levels over a range of different multi-year periods including 1, 10, 50, 100, 200, and 500 years. Finally, the total volume of flood-vulnerable infrastructure is generated and flood damage probabilities when related to the established probability distributions for sea water levels are considered. The flood vulnerabilities of different parts of the built infrastructure in New York City are studied; specifically, the subway system and the tunnel system. The concept of fragility curves is used to express these vulnerabilities. Conclusions and recommendations are provided for estimating losses probabilistically over different periods, retrofitting and strengthening the infrastructure to reduce future potential losses, and determining repair priorities. This is very useful for cost-benefit analysis.

The magnetosphere-ionosphere contains a number of current systems. These currents vary on a wide range of spatial and temporal scales and physically couple with each other. To study the complicated behaviors of these coupled current systems, the ground-based magnetometer has been a useful tool, but the recorded magnetometer data are always multi-scaled and intermittent due to the nature of these current systems. To distinguish these geomagnetic effects with multiple temporal and frequency scales, the wavelet analysis technique is especially suitable because of its special abilities of presenting information in both temporal and frequency domains. In this dissertation, the geomagnetic disturbances and the ring current variability during storm and quiet times are studied by using wavelet analysis and ground-based magnetic data from multiple stations. The first part of this dis- sertation investigates the strengths of applying the wavelet procedure to geomagnetic data for ring current study during storm and quiet periods. The second part of this dissertation characterizes the geomagnetic effects caused by symmetric and asymmetric components of ring currents during storm and quiet times by applying wavelet analysis to geomagnetic data from multiple stations. The third part of this dissertation studies the spatial variabil- ity of the symmetric ring current by applying the wavelet analysis technique to multiple components of magnetic data from multiple stations. The results show the unique strengths of the wavelet method allow us to quantitatively distinguish the geomagnetic effects on ring current variations from other M-I current systems. The unique strengths of wavelet method also allow us to separate the magnetic effects of the symmetric ring current from those caused by the asymmetric ring current. Quantitative information of the spatial variability of the ring currents is essential for understanding the dynamics of the ring currents, as well as the magnetic storm processes. The techniques developed in this dissertation have potential values as space weather monitoring tools for satellite controls, power grids, com- munication systems, oil pipelines, and other high-tech systems that are vulnerable to the negative impacts of disruptive geomagnetic events.

Spatial and temporal characteristics of dust storm activity in Saudi Arabia has been established using coarse-particle (PM10) dust concentrations recorded by the KACST monitoring network during the 2000-2003 period, backward trajectory analysis and satellite imagery. Thirty three major dust episodes impacted Riyadh city during the 2000-2003 period. The majority of these intense dust episodes are experienced during the March-August period with interannual and monthly variability in both intensity and frequency. Using TOMS images in conjunction with the backward trajectories corresponding to these 33 major episodes, the dust source regions have been identified. The most vulnerable dust source areas within these identified source regions have also been determined by compiling satellite images of dusty days from the true color SeaWiFs and high resolution MODIS archives over a six-year period (2000-2005). In total 45 dust-source areas have been identified in 9 local source regions and 4 external ones. Furthermore, 38 episodes of high fine-particle (PM2.5) and PM10 dust concentrations were observed at Riyadh city and 16 and 6 episodes of elevated PM10 dust concentrations were observed at Dammam and Jeddah cities, respectively, during March-September 2006, corresponding to the dust season in Saudi Arabia. During these episodes, Riyadh city was significantly impacted by dust from the southern Iraqi source areas and the eastern source areas located to the north and to the northeast of the city, respectively. Moreover, Dammam city was also significantly impacted by dust from the southern Iraqi source areas whereas Jeddah city was evenly impacted by dust from northeastern-northwestern sources to the city, with somewhat higher PM10 concentrations from African dust source areas, located to the northwest of the city. Analysis of meteorological maps of surface pressure as well as upper air data associated with high airborne dust concentrations in Saudi Arabia was successfully performed. This analysis revealed seven common types of dust storms, triggered by a clear seasonal distribution of meteorological conditions: (1) frontal, (2) Haboob, (3) jets convergence, (4) jet streak, (5) Shamal, (6) cyclonic, and (7) gap. The majority of dust episodes that impacted Riyadh city during the study period were triggered by Haboob (~ 42 %) and Shamal (~ 37 %). Additional analyses, including elemental, ionic and biological analyses as well as model analyses were used to further characterize the airborne dust in Saudi Arabia.

This thesis develops and presents a practical, rapid and cost effective method of assessing the most efficient strategy to limit sediment and associated contaminants from entering and impacting on Auckland's waterways. Although sediment removal objectives have been defined by Auckland Regional Council's stormwater quality guidelines, the deployment of treatment systems based upon current best management practice does not ensure that these objectives will be achieved. Due to the lack of established methods for the collection and analysis of stormwater related contaminants, an elaborate sampling and analytical protocol was established to validate the results obtained. The research has been undertaken as a series of studies with specific sampling methodology described in detail at the beginning of each study. Current issues and practices relating to the local stormwater industry are also reviewed.The accumulation of metal contaminants in estuarine studies is typically considered in terms of the preferential accumulation of heavy metals in the sediment fine fraction and the accumulation of these contaminants in the bulk sediment. For these data to be considered in the context of stormwater treatment, a third factor, looking at the total mass load distribution of these contaminants within the particle ranges of sediment is introduced. This thesis proposes that a substantial amount of coarser particles are making their way into our receiving environment and that attached to them are a significant proportion of the total contaminant load. Although the contaminants on coarse particles may not be bioavailable in their original state, they undergo physical changes while in transport and once deposited the chemical and biological effects on the receiving environment will ultimately be just as detrimental.A contaminant yield assessment method is developed in which the removal efficiency of every stormwater contaminant can be accessed based on a target sediment removal range. From a cost-benefit perspective, the marginal benefit of higher contaminant removal is also discussed. Finally the framework of a contaminant-based stormwater model, incorporating provenance, nature and transport is developed to access the true nature of stormwater contamination discharged to the receiving environment. The model will generate generic particle and contaminant distributions, which may be used by policy makers to predict treatment efficiencies based on target sediment removal objectives.

This thesis presents a technique for storm surge forecasting. Storm surge is the water that is pushed toward the shore by the force of the winds swirling around the storm. This advancing surge combines with the normal tides to create the hurricane storm tide, which can increase the mean water level by almost 20 feet. Numerical modeling is an important tool used for storm surge forecast. Numerical model ADCIRC (Advanced Circulation model; Luettich et al, 1992) is used in this thesis for simulating hurricanes. A statistical technique, EST (Empirical Statistical Technique) is used to generate life cycle storm surge values from the simulated hurricanes. These two models have been applied to Freeport, TX. The thesis also compares the results with the model SLOSH (Sea, Lake, and Overland Surges from Hurricanes), which is currently used for evacuation and planning. The present approach of classifying hurricanes according to their maximum sustained winds is analyzed. This approach is not found to applicable in all the cases and more research needs to be done. An alternate approach is suggested for hurricane storm surge estimation.

A spatial-temporal model is developed for modelling the impacts of simulated coastal zone storm surge and flooding using a combined spatial mapping and system dynamics approach. By coupling geographic information systems (GIS) and system dynamics, the interconnecting components of the spatial-temporal model are used with limited historical data to evaluate storm damage. Overlapping cumulative effects layers in GIS (ArcMap) are used for describing the coastal community’s profile, and a system dynamics feedback model (STELLA) is developed to define the interconnecting component relationships of the community. The component-wise changes to the physical environment, community infrastructure, and socioeconomic resources from the storm surge and seal level rise are examined. These changes are used to assess the impacts of the community system as a whole. For the purpose of illustrating this model, the research is applied specifically to the case of Charlottetown, Prince Edward Island, Canada, a vulnerable coastal city subject to considerable impacts from pending sea level rise and more frequent severe storm surge attributed to the changing climate in the coastal zone.

A spatial-temporal model is developed for modelling the impacts of simulated coastal zone storm surge and flooding using a combined spatial mapping and system dynamics approach. By coupling geographic information systems (GIS) and system dynamics, the interconnecting components of the spatial-temporal model are used with limited historical data to evaluate storm damage. Overlapping cumulative effects layers in GIS (ArcMap) are used for describing the coastal community’s profile, and a system dynamics feedback model (STELLA) is developed to define the interconnecting component relationships of the community. The component-wise changes to the physical environment, community infrastructure, and socioeconomic resources from the storm surge and seal level rise are examined. These changes are used to assess the impacts of the community system as a whole. For the purpose of illustrating this model, the research is applied specifically to the case of Charlottetown, Prince Edward Island, Canada, a vulnerable coastal city subject to considerable impacts from pending sea level rise and more frequent severe storm surge attributed to the changing climate in the coastal zone.

Taiwan is a small island state surrounded by the oceans but with large population. With limited land space, it would be worthwhile considering how to stabilize the existing coast or to create stable artificial beaches. Under the onslaught of storm surge and large wave from typhoons, beach erosion would occur accompanying by formation of a submerged bar beyond the surf zone with the sand removed from the beach. After the storm, the bar material maybe transport back by the swell and predominant waves which helps recover the original beach, thus producing a beach profile in dynamic equilibrium. The main purpose of this research is to use the back-propagation neural network¡]BPNN¡^, which trains a sample model and creates a system for the estimation, prediction, decision making and verification of an anticipated event. By the BPNN, we can simulate the key characteristic parameters for the storm beach profile resulting from typhoon action. Source data for training and verification are taken from the experimental results of beach profile change observed in large-scale wave tank¡]LWT¡^conducted by Coastal Engineering Research Center¡]CERC¡^in the USA in the 1960s and that from the Central Research Institute of Electric Power Industry in Japan in the 1980s. Some of the data are used as training pairs and others for verification and prediction of the key parameters of berm erosion and bar formation. Through literature review and simulation on the related parameters for storm beach profile, methodology for the prediction of the beach profile and bar/berm characteristics can be established.

This thesis presents a technique for storm surge forecasting. Storm surge is the water that is pushed toward the shore by the force of the winds swirling around the storm. This advancing surge combines with the normal tides to create the hurricane storm tide, which can increase the mean water level by almost 20 feet. Numerical modeling is an important tool used for storm surge forecast. Numerical model ADCIRC (Advanced Circulation model; Luettich et al, 1992) is used in this thesis for simulating hurricanes. A statistical technique, EST (Empirical Statistical Technique) is used to generate life cycle storm surge values from the simulated hurricanes. These two models have been applied to Freeport, TX. The thesis also compares the results with the model SLOSH (Sea, Lake, and Overland Surges from Hurricanes), which is currently used for evacuation and planning. The present approach of classifying hurricanes according to their maximum sustained winds is analyzed. This approach is not found to applicable in all the cases and more research needs to be done. An alternate approach is suggested for hurricane storm surge estimation.

Thesis (M.S.) in Ecology and Environmental Science--University of Maine, 2004. / Includes vita. Includes bibliographical references (leaves 67-74).