A breakthrough in fast flood simulation
The current status of technological advancement does not allow to generate detailed spatial flood forecasts. This hinders warning-systems, interactive planning tools and detailed forecasts. Our novel method computes flood hazard maps over three orders of magnitude faster than current state-of-the-ar...
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Veröffentlicht in: | Environmental modelling & software : with environment data news 2023-10, Vol.168, p.105787, Article 105787 |
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Sprache: | eng |
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Zusammenfassung: | The current status of technological advancement does not allow to generate detailed spatial flood forecasts. This hinders warning-systems, interactive planning tools and detailed forecasts. Our novel method computes flood hazard maps over three orders of magnitude faster than current state-of-the-art methods. It applies physically-based principles of steady-state flow to evade dynamic aspects of flood simulations. It estimates the relevant information for flood hazard, such as peak flow height, velocity and flood arrival time. Performance indicators show similar or exceeding accuracy compared to traditional flow models depending on the type of and data. In our tests, computation is reduced 1500 times. The method provides new perspective for the field of flood hazards, flood risk reduction through new types of early-warning systems, and user-interactive hazard assessment systems. As climate change is expected to aggravate flood hazard, the presented method can bring efficiency to flood simulation. The method is freely available at www.fastflood.org.
•We have developed a new innovative fast approximate flood simulation method.•A novel method is developed using the fast sweeping numerical algorithm to quickly estimate flow networks, accumulation, and terrain correction.•The fastflood method is over 1500 times faster in the shown cases of flash and fluvial floods, with similar accuracy compared to full simulation.•The platform is freely available as a web-based simulation tool; fastflood.org. |
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ISSN: | 1364-8152 1873-6726 |
DOI: | 10.1016/j.envsoft.2023.105787 |