Jon Derek Loftis

Using Lidar Elevation Data to Develop a Topobathymetric Digital Elevation Model for Sub-Grid Inundation Modeling at Langley Research Center

ABSTRACT Loftis, J.D.; Wang, H.V.; DeYoung, R.J., and Ball, W.B., 2016. Using lidar elevation data to develop a topobathymetric digital elevation model for sub-grid inundation modeling at Langley Research Center. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 134–148. Coconut Creek (Florida), ISSN 0749-0208. Technological progression in light detection and ranging permits the production of highly detailed digital elevation models, which are useful in sub-grid hydrodynamic modeling applications. Sub-grid modeling technology is capable of incorporating these high-resolution lidar-derived elevation measurements into the conventional hydrodynamic modeling framework to resolve detailed topographic features for inclusion in a hydrological transport model for runoff simulations. The horizontal resolution and vertical accuracy of the digital elevation model is augmented via inclusion of these lidar elevation values on a nested 5-m sub-grid within each coarse computational grid cell. This aids in resolving ditches and overland drainage infrastructure at Langley Research Center to calculate runoff induced by the heavy precipitation often accompanied with tropical storm systems, such as Hurricane Irene (2011) and Hurricane Isabel (2003). Temporal comparisons of model results with a NASA tide gauge during Hurricane Irene yielded a good R2 correlation of 0.97, and root mean squared error statistic of 0.079 m. A rigorous point-to-point comparison between model results and wrack line observations collected at several sites after Hurricane Irene revealed that when soil infiltration was not accounted for in the model, the mean difference between modeled and observed maximum water levels was approximately 10%. This difference was reduced to 2–5% when infiltration was considered in the model formulation, ultimately resulting in the sub-grid model more accurately predicting the horizontal maximum inundation extents within 1.0–8.5 m of flood sites surveyed. Finally, sea-level rise scenarios using Hurricane Isabel as a base case revealed future storm-induced inundation could extend 0.5–2.5 km inland corresponding to increases in mean sea level of 37.5–150 cm.

Simulation of coastal inundation instigated by storm surge, river discharge, and precipitation in the chesapeake bay using sub-grid modeling with lidar digital elevation models

A storm surge is an aperiodically anomalous rise of sea level accompanied by a tropical or extratropical storm system, wherein water level is the distinction between the observed sea level and the forecasted water level (Blain et al., 1994). Several distinct processes can potentially alter the water level in tidal regions; the pressure effect, the wind effect, the Coriolis effect, the wave effect, and the rainfall effect (Harris, 1963). Coastal inundation initiated via storm surge along the U.S. East Coast is a substantial threat to residential properties, community infrastructure, and human life. Furthermore, prolonged inundation from heavy precipitation and upland drainage during and after the storm has passed can significantly increase coastal flood damage. There are additional implications for inundated coastal habitats, as a major flood event can dramatically alter the regular function of an ecosystem. In order to mitigate human life loss and damage of coastal properties, several numerical models have been developed to provide an early warning system for storm surge and inundation events in various coastal study areas (Blumberg and Mellor, 1987; Flather et al., 1991; Leuttich et al., 1992; Jelesnianski et al., 1992; Westerink et al., 1994; Zhang et al., 2008; Casulli and Stelling, 2011).

Simulation of Coastal Inundation Instigated by Storm Surge and River Discharge in the Chesapeake Bay Using Sub-grid Modeling Coupled with Lidar Data

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