Combination of Lidar Elevations, Bathymetric Data, and Urban Infrastructure in a Sub-Grid Model for Predicting Inundation in New York City during Hurricane Sandy

_______________________________ In this paper, we present the geospatial methods in conjunction with results of a newly developed storm surge and sub-grid inundation model which was applied in New York City during Hurricane Sandy in 2012. Sub-grid modeling takes a novel approach for partial wetting and drying within grid cells, eschewing the conventional hydrodynamic modeling method by nesting a sub-grid containing high-resolution lidar topography and fine scale bathymetry within each computational grid cell. In doing so, the sub-grid modeling method is heavily dependent on building and street configuration provided by the DEM. These prominent ultra-urban infrastructural features are often avoided or otherwise loosely accounted for in typical non-sub-grid modeling approaches, yet inherently accounted for using the sub-grid approach to efficiently simulate street-level inundation. The results of spatial comparisons between the sub-grid model and FEMA’s maximum inundation extents in New York City yielded an unparalleled absolute mean distance difference of 38m and an average of 75% areal spatial match. An in-depth error analysis reveals that the modeled extent contour is well correlated with the FEMA’s extent contour in most areas, except in several distinct areas where differences in special features cause significant de-correlations between the two contours. Examples of these errors were found to be primarily attributed to lack of building representation in the New Jersey region of the model grid, occluded highway underpasses artificially blocking fluid flow, and DEM source differences between the model and FEMA. Accurate representation of these urban infrastructural features is critical in terms of sub-grid modeling, because it uniquely affects the fluid flux through each grid cell side, which ultimately determines the water depth and extent of flooding via distribution of water volume within each grid cell. Incorporation of buildings and highway underpasses allow for the model to improve overall absolute mean distance error metrics from 38m to 32m (a 15.8% improvement) and area comparisons from 75% spatial match to 80% with minimal additional effort. This demonstrates the importance of accurately incorporating detailed topographic features in a DEM for better prediction of urban inundation using sub-grid modeling. Highlights: • Features a sub-grid model specifically designed to model flooding in urban environments. • Hurricane Sandy in New York City is used as an inundation case study. • New method presented for integrating lidar topography, bathymetry, and buildings into a DEM and sub-grid model. • Incorporates highway underpass elevations to minimize vertical occlusion and artificial impediments to storm surge for improved results. • Verified observation data from USGS and FEMA was utilized to characterize spatial results. ________________________________