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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-316
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-316
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 28 Oct 2020

Submitted as: development and technical paper | 28 Oct 2020

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This preprint is currently under review for the journal GMD.

InundatEd: A Large-scale Flood Risk Modeling System on a Big-data – Discrete Global Grid System Framework

Chiranjib Chaudhuri, Annie Gray, and Colin Robertson Chiranjib Chaudhuri et al.
  • 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.

Chiranjib Chaudhuri et al.

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Chiranjib Chaudhuri et al.

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Latest update: 01 Dec 2020
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Short summary
A flood risk estimation model for two study watersheds in Canada and an interactive visualization platform using publicly available hydrometric data are presented. The risk model uses a Height Above Nearest Drainage-based solution for Manning's formula and is implemented. on a big-data-discrete global grid system framework. Overall, the novel data model decreases processing time and provides easy parallelization resulting in performance gains in online flood analytics.
A flood risk estimation model for two study watersheds in Canada and an interactive...
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