Deterministic and Probabilistic Flood risk assessment

<br>Flood modeling, as an important part of coastal hazard assessment, is highly influenced by topography dataset and specifically ground elevation. Lower resolution Digital Elevation Models (DEMs) are usually used because of their availability and less computational burden. However, inherent errors in these DEMs propagate into flood risk analysis through spatial modeling. This study aims to explore the DEM resolution effects on coastal flood risk assessments. For this purpose, deterministic and probabilistic approaches are employed. Flood inundation modeling is carried out using hydrologically connected bathtub method. Given the high resolution Light Detection And Ranging (LiDAR) DEM, different resolution maps are obtained used resampling techniques and incorporated into an error analysis framework along with USGS national elevation dataset (NED) DEMs. The probabilistic framework is developed by simulating the spatial variability of elevation errors compared to LiDAR DEM through a Monte Carlo based method called sequential Gaussian simulation. The proposed methodology is applied to the lower Manhattan in New York City. By integrating the flood model into the developed framework, this approach results in flood inundation probability at each grid cells. In this study, using the concept of accuracy-efficiency tradeoffs, a framework for selecting a suitable spatial resolution for probabilistic flood risk assessment has been suggested. The results show that by exercising a range of options presented in this paper, a broader insight into mapping resolution can be made for making better flood assessment, evacuation zones, and mitigation plans depending upon the data availability in a region for flood preparedness.<br>