Issue |
E3S Web Conf.
Volume 7, 2016
3rd European Conference on Flood Risk Management (FLOODrisk 2016)
|
|
---|---|---|
Article Number | 11005 | |
Number of page(s) | 5 | |
Section | Risk evaluation and assessment | |
DOI | https://doi.org/10.1051/e3sconf/20160711005 | |
Published online | 20 October 2016 |
Large-scale flood risk assessment using a coupled model chain
1 GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
2 Swedish Meteorological and Hydrological Institute, 60176 Norrköping, Sweden
a Corresponding author: bmerz@gfz-potsdam.de
Large-scale flood risk assessments are needed for a number of purposes, such as national strategic planning or re-insurance purposes. However, large-scale assessments are typically limited to hazard assessment and/or they are pieced together from smaller-scale assessments, leading to spatially inconsistent hazard and risk estimates. We present the coupled model chain RFM (Regional Flood Model) which is able to derive spatially consistent hazard and risk estimates for large scales (several 100,000 km2). It consists of a hydrological model, a coupled 1D–2D hydrodynamic model and a flood loss model. This model chain can be driven by observed meteorology, output from regional climate models or a weather generator. In this application, we demonstrate for river basins in Germany that this approach is able to provide spatially consistent large-scale patterns of hazard and risk. A multi-site, multi-variate weather generator provides 10,000 years of spatially consistent weather at daily resolution which is used as input for the model chain. This approach allows deriving discharge, inundation and damage patterns which respect spatial interactions within and beyond catchment boundaries.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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