Abstract. Despite the high historical losses attributed to flood events, Canadian
flood mitigation efforts have been hindered by a dearth of current,
accessible flood extent/risk models and maps. Such resources often entail
large datasets and high computational requirements. This study presents a
novel, computationally efficient flood inundation modeling framework
(“InundatEd”) using the height above nearest drainage (HAND)-based solution for
Manning's equation, implemented in a big-data discrete global grid
system (DGGS)-based architecture with a web-GIS (Geographic Information
Systems) platform. Specifically, this study
aimed to develop, present, and validate InundatEd through binary
classification comparisons to recently observed flood events. The framework
is divided into multiple swappable modules including GIS pre-processing;
regional regression; inundation models; and web-GIS visualization. Extent
testing and processing speed results indicate the value of a DGGS-based
architecture alongside a simple conceptual inundation model and a dynamic
user interface.