Timely and accurate prediction of flood inundation extent and potential negative impacts and consequences is fundamental for the sustainable development of a given region and allows decision makers and the local community to understand their exposure and vulnerability. Complex computer models exist for flood risk assessment and while technologically sophisticated, these programs are intended, first of all, for use by a small number of technical and scientific experts and require considerable processing time and extensive inputs. These existing solutions are generally not well suited for flood prediction in near real-time and often exceed the data available for any given community. This research developed standardized methods, adapted into user-friendly tools which accept limited user input, are based on hydrologic principles and processes, widely accepted risk computation methods and leverage open data. The developed flood mapping approaches access, and through a novel data fusion method, create a better quality digital elevation model (DEM) from multiple open source elevation datasets. This fused DEM is combined with other open source data (e.g., IDF curves, river flow data, watershed boundaries, etc.) to generate a flood inundation surface through two methods: (i) a 0D bathtub model and (ii) a hybrid 1D/2D raster cell storage approach. The 0D model ignores flow rates and changes over time, producing a grid of the maximum spatial extent and depth, calculated as the difference between the terrain elevation and the computed water surface. The hybrid model solves 1D kinematic wave approximation of shallow water equations in the channel and treats the floodplain as 2D flooding storage cells. Water depths from the flood grid are combined with local inventory data (e.g., building structural type, occupancy, valuation, height of the first floor, etc.) to compute exposure and damage estimates in either a user friendly MS Office application or a web-based API. The developed methods and user-friendly tools allow non-experts the ability to rapidly generate their own flood inundation scenario on demand and assess risk, thus minimizing the gap between the existing sophisticated tools, designed for scientists and engineers, and community needs in order to support informed emergency response and mitigation planning.
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