Conveying Flood Hazard Risk Through Spatial Modeling: A Case Study for Hurricane Sandy-Affected Communities in Northern New Jersey

Environ Manage. 2016 Oct;58(4):636-44. doi: 10.1007/s00267-016-0731-1. Epub 2016 Jun 24.

Abstract

The accurate forecast from Hurricane Sandy sea surge was the result of integrating the most sophisticated environmental monitoring technology available. This stands in contrast to the limited information and technology that exists at the community level to translate these forecasts into flood hazard levels on the ground at scales that are meaningful to property owners. Appropriately scaled maps with high levels of certainty can be effectively used to convey exposure to flood hazard at the community level. This paper explores the most basic analysis and data required to generate a relatively accurate flood hazard map to convey inundation risk due to sea surge. A Boolean overlay analysis of four input layers: elevation and slope derived from LiDAR data and distances from streams and catch basins derived from aerial photography and field reconnaissance were used to create a spatial model that explained 55 % of the extent and depth of the flood during Hurricane Sandy. When a ponding layer was added to the previous model to account for depressions that would fill and spill over to nearby areas, the new model explained almost 70 % of the extent and depth of the flood. The study concludes that fairly accurate maps can be created with readily available information and that it is possible to infer a great deal about risk of inundation at the property level, from flood hazard maps. The study goes on to conclude that local communities are encouraged to prepare for disasters, but in reality because of the existing Federal emergency management framework there is very little incentive to do so.

Keywords: Flood hazard; GIS modelling; Hackensack river; Hurricane sandy; Storm surge.

MeSH terms

  • Cyclonic Storms*
  • Disaster Planning / methods*
  • Disasters / prevention & control*
  • Floods*
  • Forecasting
  • Models, Theoretical*
  • New Jersey
  • Residence Characteristics
  • Risk Assessment