Fishing Pier Design Guidance Part 1
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Wind Speed-Damage Correlation in Hurricane Katrina
JP 1.36 WIND SPEED-DAMAGE CORRELATION IN HURRICANE KATRINA Timothy P. Marshall* Haag Engineering Co. Dallas, Texas 1. INTRODUCTION According to Knabb et al. (2006), Hurricane Katrina Mehta et al. (1983) and Kareem (1984) utilized the was the costliest hurricane disaster in the United States to concept of wind speed-damage correlation after date. The hurricane caused widespread devastation from Hurricanes Frederic and Alicia, respectively. In essence, Florida to Louisiana to Mississippi making a total of three each building acts like an anemometer that records the landfalls before dissipating over the Ohio River Valley. wind speed. A range of failure wind speeds can be The storm damaged or destroyed many properties, determined by analyzing building damage whereas especially near the coasts. undamaged buildings can provide upper bounds to the Since the hurricane, various agencies have conducted wind speeds. In 2006, WSEC developed a wind speed- building damage assessments to estimate the wind fields damage scale entitled the EF-scale, named after the late that occurred during the storm. The National Oceanic Dr. Ted Fujita. The author served on this committee. and Atmospheric Administration (NOAA, 2005a) Wind speed-damage correlation is useful especially conducted aerial and ground surveys and published a when few ground-based wind speed measurements are wind speed map. Likewise, the Federal Emergency available. Such was the case in Hurricane Katrina when Management Agency (FEMA, 2006) conducted a similar most of the automated stations failed before the eye study and produced another wind speed map. Both reached the coast. However, mobile towers were studies used a combination of wind speed-damage deployed by Texas Tech University (TTU) at Slidell, LA correlation, actual wind measurements, as well as and Bay St. -
Atlantic Hurricane Season Outlook for June 2020
Atlantic Hurricane Season Outlook for June 2020 ELEVATED U.S. RISKS FROM HURRICANE SEASON 2020 AS VERY BUSY YEAR BEGINS… Outlook Overview ➢ WeatherTiger’s WeatherTiger's June outlook for the 2020 hurricane season is for a 75-80% chance of an above average year, with a 15-20% chance of near-normal and just a 5% chance of below-normal cumulative activity. The median of our forecast is about 180% of average hurricane season activity, or around 19 named storms, nine hurricanes and five major hurricanes. ➢ This is slightly above the consensus of some other recently issued forecasts, likely due to our model's bullishness towards a La Niña developing by late summer or early fall. However, almost all forecast groups project above normal activity. ➢ Overall, while model skill remains limited at this range, convergent lines of evidence from our objective modeling and seasonal analogs support an active season, with the potential for a risky steering current regime in the peak months. Updated Seasonal Outlook for Tropical Cyclone Activity On a lighter note: hurricane season. The devil you know is back. Unlike a pandemic, at least taking down the shutters after a storm won’t make it return. This doesn’t imply that hurricane season 2020 will be reasonable. WeatherTiger’s updated seasonal forecast and a consensus of other guidance favors an abnormally active year. We expect net activity of about three-quarters more than the average season, with 95% odds of more storms than Dr. Birx has scarves. I’ll get into the forecast rationale and what it may mean for Florida, but first, a quick orientation for new readers: I’m Dr. -
Significant Loss Report
NATIONAL FLOOD INSURANCE PROGRAM Bureau and Statistical Agent W-01049 3019-01 MEMORANDUM TO: Write Your Own (WYO) Principal Coordinators and NFIP Servicing Agent FROM: WYO Clearinghouse DATE: July 18, 2001 SUBJECT: Significant Loss Report Enclosed is a listing of significant flooding events that occurred between February 1978 and October 2000. Only those events that had more than 1500 losses are included on the list. These data were compiled for WYO Companies and others to use to remind their customers of the impact of past flooding events. Please use this information in your marketing efforts as you feel it is appropriate. If you have any questions, please contact your WYO Program Coordinator. Enclosure cc: Vendors, IBHS, FIPNC, WYO Standards Committee, WYO Marketing Committee, ARCHIVEDGovernment Technical Representative APRIL 2018 Suggested Routing: Claims, Marketing, Underwriting 7700 HUBBLE DRIVE • LANHAM, MD 20706 • (301) 731-5300 COMPUTER SCIENCES CORPORATION, under contract to the FEDERAL EMERGENCY MANAGEMENT AGENCY, is the Bureau and Statistical Agent for the National Flood Insurance Program NATIONAL FLOOD INSURANCE PROGRAM SIGNIFICANT FLOOD EVENTS REPORT EVENT YEAR # PD LOSSES AMOUNT PD ($) AVG PD LOSS Massachusetts Flood Feb. 1978 Feb-78 2,195 $20,081,479 $9,149 Louisiana Flood May 1978 May-78 7,284 $43,288,709 $5,943 WV, IN, KY, OH Floods Dec 1978 Dec-78 1,879 $11,934,512 $6,352 PA, CT, MA, NJ, NY, RI Floods Jan-79 8,826 $31,487,015 $3,568 Texas Flood April 1979 Apr-79 1,897 $19,817,668 $10,447 Florida Flood April 1979 Apr-79 -
Hurricane and Tropical Storm
State of New Jersey 2014 Hazard Mitigation Plan Section 5. Risk Assessment 5.8 Hurricane and Tropical Storm 2014 Plan Update Changes The 2014 Plan Update includes tropical storms, hurricanes and storm surge in this hazard profile. In the 2011 HMP, storm surge was included in the flood hazard. The hazard profile has been significantly enhanced to include a detailed hazard description, location, extent, previous occurrences, probability of future occurrence, severity, warning time and secondary impacts. New and updated data and figures from ONJSC are incorporated. New and updated figures from other federal and state agencies are incorporated. Potential change in climate and its impacts on the flood hazard are discussed. The vulnerability assessment now directly follows the hazard profile. An exposure analysis of the population, general building stock, State-owned and leased buildings, critical facilities and infrastructure was conducted using best available SLOSH and storm surge data. Environmental impacts is a new subsection. 5.8.1 Profile Hazard Description A tropical cyclone is a rotating, organized system of clouds and thunderstorms that originates over tropical or sub-tropical waters and has a closed low-level circulation. Tropical depressions, tropical storms, and hurricanes are all considered tropical cyclones. These storms rotate counterclockwise in the northern hemisphere around the center and are accompanied by heavy rain and strong winds (National Oceanic and Atmospheric Administration [NOAA] 2013a). Almost all tropical storms and hurricanes in the Atlantic basin (which includes the Gulf of Mexico and Caribbean Sea) form between June 1 and November 30 (hurricane season). August and September are peak months for hurricane development. -
Hurricane Waves in the Ocean
WAVE-INDUCED SURGES DURING HURRICANE OPAL Chung-Sheng Wu*, Arthur A. Taylor, Jye Chen and Wilson A. Shaffer Meteorological Development Laboratory National Weather Service/NOAA, Silver Spring, Maryland 1. INTRODUCTION Hurricanes storm surges and waves at the coastline Holliday (1977) developed a simple formula relating the have been the cause of damages in the coastal zone. cyclone’s pressure drop to maximum sustained wind for On the U.S. Gulf Coast, for example, Hurricane Opal the Western Pacific. A more general form was (1995) made landfall near the time of low tide and proposed by Holland (1980). The merit of these models resulted in severe flooding by storm surges and waves. is that they are analytical models for the surface wind Storm surge can penetrate miles inland from the coast. profile in a hurricane. A similar formulation was applied Waves ride above the surge levels, causing wave runup to the wave model in the present work. The framework and mean water level set-up. These wave effects are of the hurricane wave model is described below. significant near the landfall area and are affected by the process that hurricane approaches the coastline. 2.1 HURRICANE WIND AND STORM SURGES During 1950-1977, hurricane wave models based on Holland (1980) employed a standard pressure profile for significant wave height and period were developed (e.g. a tropical cyclone and obtained the popular gradient Bretschneider, 1957; Ross, 1976) for marine weather wind profile. Jelesnianski and Taylor (1976) assumed a prediction and offshore oil industry design. Cardone surface wind profile in the pressure equation. -
Background Hurricane Katrina
PARTPART 33 IMPACTIMPACT OFOF HURRICANESHURRICANES ONON NEWNEW ORLEANSORLEANS ANDAND THETHE GULFGULF COASTCOAST 19001900--19981998 HURRICANEHURRICANE--CAUSEDCAUSED FLOODINGFLOODING OFOF NEWNEW ORLEANSORLEANS •• SinceSince 1559,1559, 172172 hurricaneshurricanes havehave struckstruck southernsouthern LouisianaLouisiana ((ShallatShallat,, 2000).2000). •• OfOf these,these, 3838 havehave causedcaused floodingflooding inin NewNew thethe OrleansOrleans area,area, usuallyusually viavia LakeLake PonchartrainPonchartrain.. •• SomeSome ofof thethe moremore notablenotable eventsevents havehave included:included: SomeSome ofof thethe moremore notablenotable eventsevents havehave included:included: 1812,1812, 1831,1831, 1860,1860, 1915,1915, 1947,1947, 1965,1965, 1969,1969, andand 20052005.. IsaacIsaac MonroeMonroe ClineCline USWS meteorologist Isaac Monroe Cline pioneered the study of tropical cyclones and hurricanes in the early 20th Century, by recording barometric pressures, storm surges, and wind velocities. •• Cline charted barometric gradients (right) and tracked the eyes of hurricanes as they approached landfall. This shows the event of Sept 29, 1915 hitting the New Orleans area. • Storm or tidal surges are caused by lifting of the oceanic surface by abnormal low atmospheric pressure beneath the eye of a hurricane. The faster the winds, the lower the pressure; and the greater the storm surge. At its peak, Hurricane Katrina caused a surge 53 feet high under its eye as it approached the Louisiana coast, triggering a storm surge advisory of 18 to 28 feet in New Orleans (image from USA Today). StormStorm SurgeSurge •• The surge effect is minimal in the open ocean, because the water falls back on itself •• As the storm makes landfall, water is lifted onto the continent, locally elevating the sea level, much like a tsunami, but with much higher winds Images from USA Today •• Cline showed that it was then northeast quadrant of a cyclonic event that produced the greatest storm surge, in accordance with the drop in barometric pressure. -
Remote Sensing and Statistical Analysis of the Effects of Hurricane María on the Forests of Puerto Rico
Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico Yanlei Feng1*, Robinson I. Negrón Juárez2, Jeffrey Q. Chambers1,2 1University of California, Department of Geography, Berkeley, California, USA 2Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Berkeley, California, USA *corresponding to: [email protected] Received 29 July 2019; Received in revised form 7 March 2020 Remote Sensing of Environment 247 (2020) 111940 https://doi.org/10.1016/j.rse.2020.111940 © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Abstract Widely recognized as one of the worst natural disaster in Puerto Rico’s history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María’s effect on Puerto Rico’s forests as well as the effect of landform and forest characteristics on observed disturbance patterns. We used Google Earth Engine (GEE) to assess the severity of forest disturbance using a disturbance metric based on Landsat 8 satellite data composites with pre and post-hurricane María. Forest structure, tree phenology characteristics, and landforms were obtained from satellite data products, including digital elevation model and global forest canopy height. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. -
Remote Sensing-Based Flood Mapping and Flood Hazard Assessment in Haiti
www.dartmouth.edu/~floods/ csdms.colorado.edu Remote Sensing-based Flood Mapping and Flood Hazard Assessment in Haiti “Rebuilding for Resilience: How Science and Engineering Can Inform Haiti's Reconstruction, March 22 - March 23, 2010, University of Miami - Coral Gables, FL Prof. G. Robert Brakenridge Dartmouth Flood Observatory, Dartmouth College, and Visiting Scientist, Community Surface Dynamics Modeling System, University of Colorado Dr. Scott D. Peckham (Presenter) Community Surface Dynamics Modeling System, University of Colorado 1) Floods commonly produce catastrophic damage in Haiti 2) Not all such floods are from tropical cyclones On May 18-25, 2004, a low-pressure system originating from Central America brought exceptionally heavy showers and thunderstorms to Haiti and the Dominican Republic. Rainfall amounts exceeded 500 mm (19.7 inches) across the border areas of Haiti and the Dominican Republic At the town of Jimani, DR, 250 mm (10 inches) of rain fell in just 24 hours. NASA Tropical Rainfall Measuring Mission (TRMM) data. Lethal Major Floods in the Dominican Republic / Haiti are a Near-Annual Event The Dartmouth Flood Observatory data archive dates back to 1985. Between 1986 and early 2004 (prior to Hurricane Jeanne in November), at least, fourteen lethal events impacted the island, including: Year Month Casualties 1986 early June >39 1986 late October 40 1988 early September - Hurricane Gilbert 237 1993 late May 20 1994 early November – Hurricane Gordon >1000 1996 mid November 18 1998 Late August – Hurricane Gustaf >22 1998 late September - Hurricane Georges >400 1999 late October - Hurricane Jose 4 2001 mid-May 15 2002 late May 30 2003 early December - Tropical Storm Odette 8 2003 mid-November 10 2004 late May >2000 NASA’s two MODIS sensors, Aqua and Terra, are an important flood mapping tool: • Visible and near IR spectral bands provide excellent land/water discrimination over wide areas. -
Hurricane & Tropical Storm
5.8 HURRICANE & TROPICAL STORM SECTION 5.8 HURRICANE AND TROPICAL STORM 5.8.1 HAZARD DESCRIPTION A tropical cyclone is a rotating, organized system of clouds and thunderstorms that originates over tropical or sub-tropical waters and has a closed low-level circulation. Tropical depressions, tropical storms, and hurricanes are all considered tropical cyclones. These storms rotate counterclockwise in the northern hemisphere around the center and are accompanied by heavy rain and strong winds (NOAA, 2013). Almost all tropical storms and hurricanes in the Atlantic basin (which includes the Gulf of Mexico and Caribbean Sea) form between June 1 and November 30 (hurricane season). August and September are peak months for hurricane development. The average wind speeds for tropical storms and hurricanes are listed below: . A tropical depression has a maximum sustained wind speeds of 38 miles per hour (mph) or less . A tropical storm has maximum sustained wind speeds of 39 to 73 mph . A hurricane has maximum sustained wind speeds of 74 mph or higher. In the western North Pacific, hurricanes are called typhoons; similar storms in the Indian Ocean and South Pacific Ocean are called cyclones. A major hurricane has maximum sustained wind speeds of 111 mph or higher (NOAA, 2013). Over a two-year period, the United States coastline is struck by an average of three hurricanes, one of which is classified as a major hurricane. Hurricanes, tropical storms, and tropical depressions may pose a threat to life and property. These storms bring heavy rain, storm surge and flooding (NOAA, 2013). The cooler waters off the coast of New Jersey can serve to diminish the energy of storms that have traveled up the eastern seaboard. -
Florida Hurricanes and Tropical Storms
FLORIDA HURRICANES AND TROPICAL STORMS 1871-1995: An Historical Survey Fred Doehring, Iver W. Duedall, and John M. Williams '+wcCopy~~ I~BN 0-912747-08-0 Florida SeaGrant College is supported by award of the Office of Sea Grant, NationalOceanic and Atmospheric Administration, U.S. Department of Commerce,grant number NA 36RG-0070, under provisions of the NationalSea Grant College and Programs Act of 1966. This information is published by the Sea Grant Extension Program which functionsas a coinponentof the Florida Cooperative Extension Service, John T. Woeste, Dean, in conducting Cooperative Extensionwork in Agriculture, Home Economics, and Marine Sciences,State of Florida, U.S. Departmentof Agriculture, U.S. Departmentof Commerce, and Boards of County Commissioners, cooperating.Printed and distributed in furtherance af the Actsof Congressof May 8 andJune 14, 1914.The Florida Sea Grant Collegeis an Equal Opportunity-AffirmativeAction employer authorizedto provide research, educational information and other servicesonly to individuals and institutions that function without regardto race,color, sex, age,handicap or nationalorigin. Coverphoto: Hank Brandli & Rob Downey LOANCOPY ONLY Florida Hurricanes and Tropical Storms 1871-1995: An Historical survey Fred Doehring, Iver W. Duedall, and John M. Williams Division of Marine and Environmental Systems, Florida Institute of Technology Melbourne, FL 32901 Technical Paper - 71 June 1994 $5.00 Copies may be obtained from: Florida Sea Grant College Program University of Florida Building 803 P.O. Box 110409 Gainesville, FL 32611-0409 904-392-2801 II Our friend andcolleague, Fred Doehringpictured below, died on January 5, 1993, before this manuscript was completed. Until his death, Fred had spent the last 18 months painstakingly researchingdata for this book. -
Appendix 8 Hazard Analysis
Appendix 8 Hazard Analysis Introduction This appendix includes several components. First is an overall discussion of the Hazard Analysis modeling effort, describing the surge (ADCIRC) and wave (WAM, STWAVE) modeling. That first section is followed by a subsidiary section labeled Appendix 8-1 that defines the many acronyms used in the modeling discussion. This is followed by a major section labeled Appendix 8-2 consisting of the Whitepaper of Donald T. Resio, ERDC-CHL, which forms the basis of the storm statistics and JPM methods discussed in the main body of the report. Owing to its prominence in the discussion, it has, for convenience, been referred to as R2007. Note that R2007, itself, includes several appendices; these are identified as Appendices A-G within Appendix 8-2. Finally, a discussion of the rainfall model is included here as Appendix 8-3. Hazard Analysis The hazard analysis required for the risk assessment was based upon the hurricane modeling conducted by a team of Corps of Engineers, FEMA, NOAA, private sector and academic researchers working toward the definition of a new system for estimating hurricane surges and waves. Following is a discussion of the processes used by this team and the steps taken by the risk team to incorporate the results into the risk analysis. The hurricane hazard definition required as input to the risk analysis involved several steps: 1. Selection of the methodology to be used for estimating surges and waves 2. Determination of hurricane probabilities 3. Production of the ADCIRC grid models for the different HPS configurations. 4. Production of the computer system for development of the large number of hydrographs required by the risk model. -
Looting After a Disaster: a Myth Or Reality?
Volume XXXI • Number 4 March 2007 Disaster Myths...Fourth in a Series Looting After a Disaster: A Myth or Reality? his special article in the Disaster Myths series pres- among those concerned with public safety and response Tents a point-counterpoint on the signifi cance and in disasters. prevalence of looting a� er disasters. Both authors were The fi rst author, E.L. Quarantelli, provides a his- asked to answer, independently, a series of questions, torical overview of looting in disaster research to help including whether looting a� er disasters is a myth, elucidate the myth. The fi ndings of previous disaster what evidence supports that opinion, what previous research are used to support the argument that looting, research has established about looting, and how the in fact, is not prevalent a� er disasters. In the end, there myths (and realities) about looting infl uence disaster is a lack of evidence showing that this behavior is com- planning and response. While the previous articles in monplace. This article can be found on page 2. this series were meant to help dispel disaster myths, As a counterpoint, Kelly Frailing focuses on the this article demonstrates the debate surrounding the events following Hurricane Katrina as evidence that controversial issue of looting and explores it in greater looting is not a myth, but a reality of disasters. This po- depth. Together these positions reveal the arguments sition is also supported by experience during previous and evidence for both sides of the debate. The editors events, such as Hurricane Betsy, and by crime statistics.