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Extreme Storm Surge Hazard Estimatio...

Lopeman, Madeleine.

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Extreme Storm Surge Hazard Estimation and Windstorm Vulnerability Assessment for Quantitative Risk Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Extreme Storm Surge Hazard Estimation and Windstorm Vulnerability Assessment for Quantitative Risk Analysis./
作者:	Lopeman, Madeleine.
面頁冊數:	166 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
Contained By:	Dissertation Abstracts International76-09B(E).
標題:	Civil engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3701301
ISBN:	9781321718485

Extreme Storm Surge Hazard Estimation and Windstorm Vulnerability Assessment for Quantitative Risk Analysis.
Lopeman, Madeleine.

Extreme Storm Surge Hazard Estimation and Windstorm Vulnerability Assessment for Quantitative Risk Analysis. - 166 p.

Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.

Thesis (Ph.D.)--Columbia University, 2015.

Quantification of risk to natural disasters is a valuable endeavor from engineering, policy and (re)insurance perspectives. This work presents two research efforts relating to meteorological risk, specifically with regard to storm surge hazard estimation and wind vulnerability assessment.

ISBN: 9781321718485Subjects--Topical Terms:

860360
Civil engineering.

Extreme Storm Surge Hazard Estimation and Windstorm Vulnerability Assessment for Quantitative Risk Analysis.
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245 1 0 $a Extreme Storm Surge Hazard Estimation and Windstorm Vulnerability Assessment for Quantitative Risk Analysis.
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500 $a Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
500 $a Adviser: George Deodatis.
502 $a Thesis (Ph.D.)--Columbia University, 2015.
520 $a Quantification of risk to natural disasters is a valuable endeavor from engineering, policy and (re)insurance perspectives. This work presents two research efforts relating to meteorological risk, specifically with regard to storm surge hazard estimation and wind vulnerability assessment.
520 $a While many high water level hazard estimation methods have been presented in the literature and used in industry applications, none bases its results on disaggregated tidal gauge data while also capturing the effects of the evolution of storm surge over the duration of a storm. Additionally, the coastal destruction wreaked by Hurricane Sandy in 2012 prompted motivation to estimate the event's return period. To that end, this dissertation first presents the motivation for and development of the clustered separated peaks-over-threshold simulation (CSPS) method, a novel approach to the estimation of high water level return periods at coastal locations. The CSPS uses a Monte Carlo simulation of storm surge activity based on statistics derived from tidal gauge data. The data are separated into three independent components (storm surge, tidal cycle and sea level rise) because different physical processes govern different components of water level. Peak storm surge heights are fit to the generalized Pareto distribution, chosen for its ability to fit a wide tail to limited data, and a clustering algorithm incorporates the evolution of storm surge over surge duration. Confidence intervals on the return period estimates are computed by applying the bootstrapping method to the storm surge data.
520 $a Two case studies demonstrate the application of the CSPS to coastal tidal gauge data. First, the CSPS is applied to tidal gauge data from lower Manhattan. The results suggest that the return period of Hurricane Sandy's peak water level is 103 years (95% confidence interval 38--452 years). That the CSPS estimate is significantly lower than previously published return periods indicates that storm surge hazard in the New York Harbor has, until now, been underestimated. The CSPS is also applied to all tidal gauge stations managed by the National Oceanographic and Atmospheric Administration (NOAA) for which the hourly water level time histories are at least 30 years long. Comparison to NOAA's exceedance probability levels for these stations suggests that the CSPS estimates higher return levels than NOAA, but also that the NOAA values fall within the 95% CI from the CSPS for more than half of the stations tested.
520 $a This dissertation continues with a critical comparison of windstorm vulnerability models. The intent of this research is to provide a compendium of reference curves against which to compare damage curves used in the reinsurance industry. The models tend to represent specific types of construction and use varying characteristic wind speed measurements to represent storm intensity. Wind speed conversion methods are used to harmonize wind speed scales. The different vulnerability models analyzed stem from different datasets and hypotheses, thus rendering them relevant to certain geographies or structural typologies. The resulting collection of comparable windstorm vulnerability models can serve as a reference framework against which damage curves from catastrophe risk models can be evaluated.
590 $a School code: 0054.
650 4 $a Civil engineering. $3 860360
650 4 $a Hydrologic sciences. $3 3168407
650 4 $a Statistics. $3 517247
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710 2 $a Columbia University. $b Civil Engineering and Engineering Mechanics. $3 2093087
773 0 $t Dissertation Abstracts International $g 76-09B(E).
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793 $a English
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