Supercharging hydrodynamic inundation models for instant flood insight
| dc.contributor.author | Fraehr, N. | |
| dc.contributor.author | Wang, Q.J. | |
| dc.contributor.author | Wu, W. | |
| dc.contributor.author | Nathan, R. | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Floods are one of the most frequent and devastating natural disasters for human communities. Currently, food response management globally commonly relies on hydrodynamic models for accurate simulation of complex fow patterns of food events and to provide information on food risks. However, the computational demand of hydrodynamic models means that they cannot be deployed usefully for real-time food inundation forecasting over large domains or for situations where simulations need to be run repeatedly for planning purposes. Here we introduce a new modelling approach that supercharges hydrodynamic models for speed while maintaining high accuracy. We found that spatiotemporal patterns of food inundation simulated using an extremely simplifed (and hence superfast) hydrodynamic model can be mathematically transformed to reproduce the results from a high-resolution model. We exploited the efcacy of this transformation to provide high-resolution and accurate food inundation predictions in a few seconds rather than the many hours required by conventional high-resolution hydrodynamic models, which represents an important practical advancement towards saving lives and protecting assets during food emergencies. | |
| dc.description.statementofresponsibility | Niels Fraehr, Quan J. Wang, Wenyan Wu, Rory Nathan | |
| dc.identifier.citation | nature water, 2023; 1(10):835-843 | |
| dc.identifier.doi | 10.1038/s44221-023-00132-2 | |
| dc.identifier.issn | 2731-6084 | |
| dc.identifier.orcid | Wu, W. [0000-0003-3907-1570] | |
| dc.identifier.uri | https://hdl.handle.net/2440/144312 | |
| dc.language.iso | en | |
| dc.publisher | Springer Science and Business Media LLC | |
| dc.relation.grant | http://purl.org/au-research/grants/arc/DE210100117 | |
| dc.rights | © The Author(s), under exclusive licence to Springer Nature Limited 2023 | |
| dc.source.uri | https://doi.org/10.1038/s44221-023-00132-2 | |
| dc.subject | Hydrology; Natural hazards; Water resources | |
| dc.title | Supercharging hydrodynamic inundation models for instant flood insight | |
| dc.type | Journal article | |
| pubs.publication-status | Published |