Florida Hurricanes and Damage Costs

Malmstadt, Jill C.; Scheitlin, Kelsey N.; Elsner, James B.

doi:10.1353/sgo.0.0045

Cited by 54 publications

(24 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, the 1930s were marked by drought and exceptionally warm maximum and minimum temperatures (Rogers 2013;DeGaetano and Allen 2002), as well as heat waves (Kunkel et al 2013). In addition, the Labor Day hurricane of 1935 devastated the Florida Keys with the lowest recorded pressure of any Florida hurricane at the time (Malmstadt et al 2009), and it was one of only three hurricanes to make landfall at category 5 intensity in the United States since 1851.…”

Section: B Station Datamentioning

confidence: 99%

Trends in Daily Temperature and Precipitation Extremes for the Southeastern United States: 1948–2012

2015

View full text Add to dashboard Cite

Spatial and temporal trends in temperature and precipitation extremes were investigated for the period 1948–2012 across the southeastern United States using 27 previously defined indices. Results show that regionwide warming in extreme minimum temperatures and cooling in extreme maximum temperatures occurred. The disproportionate changes in extreme daytime and nighttime temperatures are narrowing diurnal temperature ranges for most locations. The intensity and magnitude of extreme precipitation events increased overall, except for more easterly locations, particularly in South Carolina. These indices further show that warming in minimum temperatures has been pronounced most in summer and least in winter. Fall has become significantly wetter, while spring and summer have become drier, on average. Principal component analysis (PCA) was used to characterize a “geography of extremes” based on temperature and precipitation extreme indices. The PCA based on temperature indices revealed two coherent western and eastern subregions that share common modes of variability in extremes. Precipitation indices resulted in a greater number of smaller, spatially coherent groups exhibiting similar modes of variability. This classification regime illustrates important variations in extremes that exist on subregional scales. These findings have relevance for established climate research institutes, local governments, resource managers, and community planners interested in the variability of extreme events throughout the region.

show abstract

Section: B Station Datamentioning

confidence: 99%

Trends in Daily Temperature and Precipitation Extremes for the Southeastern United States: 1948–2012

2015

View full text Add to dashboard Cite

show abstract

“…Of all the deaths occurring in the United States attributed to Atlantic tropical cyclones from 1963 to 2012, 49% are caused by storm surge and 8% by wind [ Rappaport , ]. TC events also cause economic losses that can reach beyond billions of dollars [ Pielke et al , ; Malmstadt et al , ]. The nearshore environment for these coastal cities provides vast economic activities, including mineral and petroleum extraction, commercial fishing, port traffic, seafood processing, and marine sports fishing [ Adams et al , ].…”

Section: Introductionmentioning

confidence: 99%

The combined risk of extreme tropical cyclone winds and storm surges along the U.S. Gulf of Mexico Coast

2017

View full text Add to dashboard Cite

Tropical cyclones, with their nearshore high wind speeds and deep storm surges, frequently strike the United States Gulf of Mexico coastline influencing millions of people and disrupting offshore economic activities. The combined risk of occurrence of tropical cyclone nearshore wind speeds and storm surges is assessed at 22 coastal cities throughout the United States Gulf of Mexico. The models used are extreme value copulas fitted with margins defined by the generalized Pareto distribution or combinations of Weibull, gamma, lognormal, or normal distributions. The statistical relationships between the nearshore wind speed and storm surge are provided for each coastal city prior to the copula model runs using Spearman's rank correlations. The strongest significant relationship between the nearshore wind speed and storm surge exists at Shell Beach, LA (ρ = 0.67), followed by South Padre Island, TX (ρ = 0.64). The extreme value Archimedean copula models for each city then provide return periods for specific nearshore wind speed and storm surge pairs. Of the 22 cities considered, Bay St. Louis, MS, has the shortest return period for a tropical cyclone with at least a 50 ms−1 nearshore wind speed and a 3 m surge (19.5 years, 17.1‐23.5). The 90% confidence intervals are created by recalculating the return periods for a fixed set of wind speeds and surge levels using 100 samples of the model parameters. The results of this study can be utilized by policy managers and government officials concerned with coastal populations and economic activity in the Gulf of Mexico.

show abstract

“…More than half of the U.S. population lives within fifty miles of a coast, and as coastal populations and infrastructure continue to rise, so does the property damage associated with these events (USGS 2005). Economic losses from individual events can reach billions of dollars, and, collectively, these storms have cost well over $450 billion in the United States since the early 20th century (Pielke et al 2008;Malmstadt, Scheitlin, and Elsner 2009). The active season of 2005, with Katrina, Rita, and Wilma, demonstrated that hurricanes can inflict vast amounts of damage, and the need for hurricane research and preparedness has escalated (USGS 2005).…”

mentioning

confidence: 99%

Combining Surge and Wind Risk from Hurricanes Using a Copula Model: An Example from Galveston, Texas

Trepanier

Needham

Elsner

et al. 2014

The Professional Geographer

View full text Add to dashboard Cite

Consideration of climate-related impacts on coasts is important to ensure readiness for disaster response. Local risk of storm surge and strong winds from hurricanes affecting Galveston, Texas, is quantified using a bivariate copula model fit to observed data. The model uses a two-dimensional Archimedean copula. Parametric uncertainty (5th and 95th percentiles) is quantified using a Monte Carlo procedure. The annual probability of a hurricane producing winds of at least 50 ms −1 and a surge of at least 4 m is 1.7 percent with a 95 percent confidence interval of (1.33, 1.78) percent. The methodology can be extended to include inland flooding and can be applied elsewhere with available information. Key Words: copula, extreme winds, hurricanes, risk, storm surge. , , , , ( ) 50ms-1 , (1.33, 1.78) , : , , , ,Es importante tomar en consideración los impactos relacionados con el clima en las costas para asegurar una pronta respuesta en caso de desastre. El riesgo local por marejadas de tormenta y por vientos fuertes asociados con los huracanes que afectan a Galveston, Texas, se cuantifica utilizando un modelo cópula bivariado ajustado a los datos observados. El modelo utiliza una cópula arquimediana bidimensional. La incertidumbre paramétrica (percentiles 5 • y 95 • ) se cuantifica por medio de un procedimiento de Monte Carlo. La probabilidad anual de que ocurra un huracán que produzca vientos de por lo menos 50 ms −1 , y una marejada de por lo menos 4 m, es de 1.7 por ciento al 95 por ciento de confianza en intervalo (1.33, 1.78 por ciento). La metodología puede extenderse para incluir la inundación tierra adentro y puede aplicarse en otras partes con la información que se tenga a mano. Palabras clave: cópula, vientos extremos, huracanes, riesgo, marejada de tormenta.

show abstract

Florida Hurricanes and Damage Costs

Cited by 54 publications

References 21 publications

Trends in Daily Temperature and Precipitation Extremes for the Southeastern United States: 1948–2012

Trends in Daily Temperature and Precipitation Extremes for the Southeastern United States: 1948–2012

The combined risk of extreme tropical cyclone winds and storm surges along the U.S. Gulf of Mexico Coast

Combining Surge and Wind Risk from Hurricanes Using a Copula Model: An Example from Galveston, Texas

Contact Info

Product

Resources

About