Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0

• Published in: Geoscientific Model Development Vol. 14; no. 8; pp. 4865 - 4890
• Main Authors: Uhe, Peter, Mitchell, Daniel, Bates, Paul D, Addor, Nans, Neal, Jeff, Beck, Hylke E
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 05.08.2021; Copernicus Publications
• Subjects: Air temperature; Analysis; Atmospheric models; Automobile drivers; Climate change; Climate models; Climatic changes; Computational efficiency; Computer applications; Datasets; Environmental assessment; Environmental impact; Environmental risk; Estimates; Flood hazards; Flood risk; Flooding; Floodplains; Floods; Floodwater; Frameworks; Geomorphology; Hydrologic processes; Hydrology; Meteorology; Modelling; Performance evaluation; Precipitation; River flow; River networks; Rivers; Runoff; Simulation; Storm damage; Stream flow; Surface runoff; Topography; Uncertainty; Uptake; Weather
• ISSN: 1991-9603; 1991-959X; 1991-962X; 1991-9603; 1991-962X
• DOI: 10.5194/gmd-14-4865-2021

Riverine flood hazard is the consequence of meteorological drivers, primarily precipitation, hydrological processes and the interaction of floodwaters with the floodplain landscape. Modeling this can be particularly challenging because of the multiple steps and differing spatial scales involved in the varying processes. As the climate modeling community increases their focus on the risks associated with climate change, it is important to translate the meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. This is due to the complexity and uncertainties of model cascades and the computational cost of flood inundation modeling. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (â¼90 m) river flooding (fluvial) hazards. Thus, this framework is designed to be an accessible, computationally efficient tool using freely available data to enable greater uptake of this type of modeling.