DEM Uncertainty Based Coastal Flood Inundation Modeling Considering Water Quality Impacts

Wastewater Treatment plants (WWTPs) are among the most critical infrastructures in coastal cities and are subject to failure due to extreme flooding events. Failure of these systems could result in combined sewer overflow (CSO), where the stormwater inundation is getting mixed with wastewater. Digital Elevation Model (DEM) as an essential input for quantifying flooding inundation is subject to uncertainties. In this study, the coastal modeling is performed to estimate flood water level, including wave setup, in an ungauged location, by Delft-3D model. Gridded surface subsurface hydrologic analysis (GSSHA) is used to model and map the extent of inundation and stormwater accumulation. Bivariate analysis of annual/extreme water levels in an ungauged location and the rainfall time series at a nearby station is used to find the 100-year joint design values. Selected major historical storms/hurricanes in the last 50 years are used for this assessment. Furthermore, the impacts of flooding on the state of WWTP functionality are analyzed considering three failure scenarios and the resulting quantity and quality of CSOs. For uncertainty analysis, another 2D hydrodynamic model called LISFLOOD-FP is used, which is less computationally intensive than GSSHA. It is used along with a sequential Gaussian simulation (SGS) algorithm to quantify the effect of DEM error uncertainties on the assessment of flood inundation. A probability map is prepared that shows the full extent of flooding, including the less probable area to be flooded. The proposed algorithm is applied to the southern coast of Brooklyn and the Coney Island WWTP in that area. The results show that how the simulation of ungauged water level data as well as the development of an uncertainty-based flood inundation modeling as well as consideration for water quality impacts could significantly improve our ability for better flood preparedness planning in the coastal cities.