Submitted as: development and technical paper 28 Oct 2020
Submitted as: development and technical paper | 28 Oct 2020
InundatEd: A Large-scale Flood Risk Modeling System on a Big-data – Discrete Global Grid System Framework
- Wilfrid Laurier University, Department of Geography and Environmental Studies, Waterloo, Canada
- Wilfrid Laurier University, Department of Geography and Environmental Studies, Waterloo, Canada
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 the nearest drainage-based solution for Manning's equation, implemented in a big-data discrete global grid systems-based architecture with a web-GIS platform. Specifically, this study aimed to develop, present, and validate InundatEd through binary classification comparisons to known flood extents. The framework is divided into multiple swappable modules including GIS pre-processing; regional regression; inundation model; 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.
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Chiranjib Chaudhuri et al.


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RC1: 'Review', Anonymous Referee #1, 12 Nov 2020
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SC1: 'executive editor comment on gmd-2020-316', Astrid Kerkweg, 14 Nov 2020
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RC2: 'Review', Anonymous Referee #2, 22 Dec 2020
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AC1: 'Comment on gmd-2020-316', Chiranjib Chaudhuri, 22 Jan 2021
Chiranjib Chaudhuri et al.
Chiranjib Chaudhuri et al.
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