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Extremely rapid, Lagrangian modeling of 2D flooding: A rivulet-based approach
Estimates of potential flood inundation areas and depths are critical to informing the preparedness, response, and investment decisions of many government agencies and private sector organizations, especially under a changing climate. The standard modeling approaches, however, are often either compu...
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Published in: | Environmental modelling & software : with environment data news 2023-03, Vol.161, p.105630, Article 105630 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Estimates of potential flood inundation areas and depths are critical to informing the preparedness, response, and investment decisions of many government agencies and private sector organizations, especially under a changing climate. The standard modeling approaches, however, are often either computationally intensive or constrained in their accuracy or applicability. A novel, rivulet-based, 2D model of flooding is described in this article that is 10,000 to 10 million times less computationally complex than the full solution of the shallow water equations, yet achieves inundation area hit rates of between 0.8 and 0.9, relative absolute mean errors of 10%–20% across a wide range of flow depths, and comparable accuracy at forecasting empirical high-water marks. This combination of accuracy and efficiency will significantly enhance real-time depth estimates during flood events, support detailed sensitivity analyses, and allow for the generation of large ensembles to enable complex uncertainty analyses.
•Estimates of flood inundation are critical to making many risk-informed decisions.•Standard approaches are computationally complex or of limited applicability.•A novel, Lagrangian, rivulet-based model of 2D flooding is described.•Complexity savings of 10,000 to 10 million times versus solution of SWEs. |
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ISSN: | 1364-8152 1873-6726 |
DOI: | 10.1016/j.envsoft.2023.105630 |