Monitoring storm tide and flooding from Hurricane Sandy along the Atlantic coast of the United States, October 2012

McCallum, Brian E.; Wicklein, Shaun M.; Reiser, Robert G.; Busciolano, R.; Morrison, Jonathan; Verdi, Richard J.; Painter, Jaime A.; Frantz, Eric R.; Gotvald, Anthony J.

doi:10.3133/ofr20131043

Cited by 24 publications

(23 citation statements)

References 1 publication

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“…Of those 950 HWM, 650 were classified to be independent (greater than 1000 ft apart from each other), and 257 flagged in CT, RI and MA were not surveyed due to lack of funding. Vertical accuracy was 0.26 ft in all counties except 0.47 ft in NJ-Union, Middlesex and Monmouth counties [3]. 559 HWMs were inside the SLOSH ny3 basin, and 312 had valid data, so excluding those close to the SLOSH boundaries, 284 HWMs were analyzed and 17 outliers (a HWM estimated from a streak on the wall of a steel shipping container, another identified by a mud line inside a small enclosed room under an air-conditioning unit, etc.)…”

Section: Usgs High Water Marks Vs Slosh Maximum Water Levelsmentioning

confidence: 90%

“…This was the second-largest deployment of storm-tide sensors, exceeded only by the number distributed during Hurricane Irene (2011), which made landfall in the same area of the US [3]. 145 water level and 9 wave-height sensors were deployed at 147 locations while 8 rapid deployment gauges (RDGs), and 62 barometric pressure sensors were deployed at additional locations.…”

Section: Usgs Storm Surge Sensors Vs Slosh Water Levelsmentioning

confidence: 99%

“…After Hurricane Sandy made landfall in NJ, its sustained winds increased as an effect of the winter storm approaching from the west. The combination of both Hurricane Sandy and the winter storm, timed with the full-moon high tide on the night of 29 October, worsened the storm-tide flooding along the NJ, NY and CT coastlines and caused significant flooding far inland along the Delaware and Hudson Rivers [3]. Hurricane Sandy caused 147 direct deaths (286 total) and damage of $68 billion dollars.…”

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confidence: 99%

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Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model

Forbes

Rhome

Mattocks

et al. 2014

JMSE

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Numerical simulations of the storm tide that flooded the US Atlantic coastline during Hurricane Sandy (2012) are carried out using the National Weather Service (NWS) Sea Lakes and Overland Surges from Hurricanes (SLOSH) storm surge prediction model to quantify its ability to replicate the height, timing, evolution and extent of the water that was driven ashore by this large, destructive storm. Recent upgrades to the numerical model, including the incorporation of astronomical tides, are described and simulations with and without these upgrades are contrasted to assess their contributions to the increase in forecast accuracy. It is shown, through comprehensive verifications of SLOSH simulation results against peak water surface elevations measured at the National Oceanic and Atmospheric Administration (NOAA) tide gauge stations, by storm surge sensors deployed and hundreds of high water marks collected by the U.S. Geological Survey (USGS), that the SLOSH-simulated water levels at 71% (89%) of the data measurement locations have less than 20% (30%) relative error. The RMS error between observed and modeled peak water levels is 0.47 m. In addition, the model's extreme computational efficiency enables it to OPEN ACCESS J. Mar. Sci. Eng. 2014, 2 438 run large, automated ensembles of predictions in real-time to account for the high variability that can occur in tropical cyclone forecasts, thus furnishing a range of values for the predicted storm surge and inundation threat.

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Section: Usgs High Water Marks Vs Slosh Maximum Water Levelsmentioning

confidence: 90%

Section: Usgs Storm Surge Sensors Vs Slosh Water Levelsmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model

Forbes

Rhome

Mattocks

et al. 2014

JMSE

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“…Due largely to variable along-island morphology, storm impacts can range from beach and dune erosion to inundation and breaching (Leatherman 1985;Lentz and Hapke, 2011;Kratzmann and Hapke, 2012;Wilson et al, 2015;Hapke et al, 2015). Average storm surge elevation values are 0.6 m (MSL) for annual storms and 1.2 m for the 10-year interval storm (Schwab et al, 2000), however, recorded high water marks (which include surge, tide, and wave run-up) on western Fire Island after Hurricane Sandy were as high as 2.9 m (McCallum et al, 2013).…”

Section: Storm History and Anthropogenic Modificationsmentioning

confidence: 99%

Characterizing storm response and recovery using the beach change envelope: Fire Island, New York

et al. 2018

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Hurricane Sandy at Fire Island, New York presented unique challenges in the quantification of storm impacts using traditional metrics of coastal change, wherein measured changes (shoreline, dune crest, and volume change) did not fully reflect the substantial changes in sediment redistribution following the storm. We used a time series of beach profile data at Fire Island, New York to define a new contour-based morphologic change metric, the Beach Change Envelope (BCE). The BCE quantifies changes to the upper portion of the beach likely to sustain measurable impacts from storm waves and capture a variety of storm and post-storm beach states. We evaluated the ability of the BCE to characterize cycles of beach change by relating it to a conceptual beach recovery regime, and demonstrated that BCE width and BCE height from the profile time series correlate well with established stages of recovery. We also investigated additional applications of this metric to capture impacts from storms and human modification by applying it to several post-storm historical datasets in which impacts varied considerably; Nor'Ida (2009), Hurricane Irene (2011), Hurricane Sandy (2012), and a 2009 community replenishment. In each case, the BCE captured distinctive upper beach morphologic change characteristic of these different beach building and erosional events. Analysis of the beach state at multiple profile locations showed spatial trends in recovery consistent with recent morphologic island evolution, which other studies have linked with sediment availability and the geologic framework. Ultimately we demonstrate a new way of more effectively characterizing beach response and recovery cycles to evaluate change along sandy coasts.Published by Elsevier B.V.

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“…In the aftermath of Hurricane Sandy, high-water marks (HWMs) were surveyed relative to NAVD88 by the U.S. Geological Survey (USGS) in cooperation with the Federal Emergency Management Agency (FEMA) with particular emphasis in New York and New Jersey where the impacts of the storm were the most pronounced [27,28]. HWMs were surveyed to a vertical accuracy of 0.26 ft (0.079 m) at the 95-percent confidence level and within 10 ft (3.048 m) horizontally [27].…”

Section: Propagation Of Flood Waters In a Lidar Elevation Surfacementioning

confidence: 99%

Evaluation of Airborne Lidar Elevation Surfaces for Propagation of Coastal Inundation: The Importance of Hydrologic Connectivity

Poppenga

Worstell

2015

Remote Sensing

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Detailed information about coastal inundation is vital to understanding dynamic and populated areas that are impacted by storm surge and flooding. To understand these natural hazard risks, lidar elevation surfaces are frequently used to model inundation in coastal areas. A single-value surface method is sometimes used to inundate areas in lidar elevation surfaces that are below a specified elevation value. However, such an approach does not take into consideration hydrologic connectivity between elevation grids cells resulting in inland areas that should be hydrologically connected to the ocean, but are not. Because inland areas that should drain to the ocean are hydrologically disconnected by raised features in a lidar elevation surface, simply raising the water level to propagate coastal inundation will lead to inundation uncertainties. We took advantage of this problem to identify hydrologically disconnected inland areas to point out that they should be considered for coastal inundation, and that a lidar-based hydrologic surface should be developed with hydrologic connectivity prior to inundation analysis. The process of achieving hydrologic connectivity with hydrologic-enforcement is not new, however, the application of hydrologically-enforced lidar elevation surfaces for improved coastal inundation mapping as approached in this research is innovative. In this article, we propagated a OPEN ACCESSRemote Sens. 2015, 7 11696 high-resolution lidar elevation surface in coastal Staten Island, New York to demonstrate that inland areas lacking hydrologic connectivity to the ocean could potentially be included in inundation delineations. For inland areas that were hydrologically disconnected, we evaluated if drainage to the ocean was evident, and calculated an area exceeding 11 ha (~0.11 km 2 ) that could be considered in inundation delineations. We also assessed land cover for each inland area to determine the type of physical surfaces that would be potentially impacted if the inland areas were considered as part of a coastal inundation. A visual analysis indicated that developed, medium intensity and palustrine forested wetland land cover types would be impacted for those locations. This article demonstrates that hydrologic connectivity is an important factor to consider when inundating a lidar elevation surface. This information is needed for inundation monitoring and management in sensitive coastal regions.

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Monitoring storm tide and flooding from Hurricane Sandy along the Atlantic coast of the United States, October 2012

Cited by 24 publications

References 1 publication

Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model

Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model

Characterizing storm response and recovery using the beach change envelope: Fire Island, New York

Evaluation of Airborne Lidar Elevation Surfaces for Propagation of Coastal Inundation: The Importance of Hydrologic Connectivity

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