Aftermath of Hurricane Sandy
With sea levels increasing and storms intensifying, preparing for natural disasters has reached a critical mass.
Mantaloking, New Jersey before (left) and after (right) Hurricane Sandy, 2012.
As climate change continues to intensify storms and increase flooding, it is vital for communities and governments to prepare for inevitable storm damage. By creating coastal flooding models and we can create our own storm surge analyses. The chosen study area for this project was Mantoloking, New Jersey-- a low-laying barrier island that suffered major damage from superstorm Sandy in 2012.
More information about Hurricane Sandy can be found on New Jersey's Department of Environmental Protection .
By using NOAA data, a storm path map illustrates Sandy's course as it begins as a tropical depression, strengthens to a Category 2 hurricane, and subsides into a post-tropical cyclone.
Hurricane Sandy started its route as a tropical depression in the Caribbean Sea on 22 October 2012. It gained speed as it ascended northwards, reaching a maximum speed of 105 MPH as a Category 2 hurricane. It continued northward until it made land in New Jersey, United States on 30 October 2012.
For this analysis, we looked at LiDAR cloudpoint data from Mantoloking, New Jersey taken before and shortly after the storm. LiDAR provides incredibly detailed data that allows us to make an exact digital elevation model (DEM). The point cloud is converted into a triangulated irregular network (TIN), which allows us to measure slope throughout the area. By subtracting the post-DEM from the pre-DEM, we can see how the landscape has changed.
LiDAR (left), TIN (middle), volume change (right)
Volume-change analyses let us see areas that have experience erosion and accretion (debris and sediment build-up). Mapping change informs policymakers and first responders on how and where the community has been affected.
In order to prepare for future flooding and other natural disasters, accurate flood map models are essential. The following map compares two differently prepared models and presents data on the type and quantity of structures possibly impacted by flooding.
Because LiDAR provides more precise measurements, a more accurate prediction can be made as to where flooding may occur. This results in a map that includes only the structures that are more likely to be impacted by flooding. Accurate models shape government policy, rescue and recovery measures, and even insurance policies.
How data is collected affects the resulting flood models.
When damage does occur, assessments provide critical information for rescue, recovery, and development plans. The following presents a hypothetical damage assessment performed on a small study in Mantoloking, New Jersey. Structures were identified from satellite imagery and property boundaries. These were then assigned a pre-determined level of storm damage and analyzed for possible patterns between location and amount of damage.
This is a very rudimentary model. All damage assessment was done manually from only satellite imagery. More accurate flooding models consider all environmental factors, such as the area's soil type and features, surrounding vegetation, construction materials, and much more. Additionally, climate change will continue to amplify and alter storms and their impact.
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