Preprints
https://doi.org/10.5194/hess-2021-60
https://doi.org/10.5194/hess-2021-60

  05 Feb 2021

05 Feb 2021

Review status: a revised version of this preprint is currently under review for the journal HESS.

A reduced-complexity model of fluvial inundation with a sub-grid representation of floodplain topography evaluated for England, United Kingdom

Simon J. Dadson1,2, Eleanor Blyth1, Douglas Clark1, Helen Davies1, Richard Ellis1, Huw Lewis3, Toby Marthews1, and Ponnambalan Rameshwaran1 Simon J. Dadson et al.
  • 1UK Centre for Ecology and Hydrology, Maclean Building, Crowmarsh Gifford, Wallingford, OX10 8BB, United Kingdom
  • 2School of Geography and the Environment, University of Oxford, OX1 3QY, United Kingdom
  • 3Met Office, FitzRoy Road, Exeter EX1 3PB, United Kingdom

Abstract. Timely predictions of fluvial flooding are important for national and regional planning and real-time flood response. Several new computational techniques have emerged in the past decade for making rapid fluvial flood inundation predictions at time and space scales relevant to early warning, although their efficient use is often constrained by the trade-off between model complexity, topographic fidelity and scale. Here we apply a simplified approach to large-area fluvial flood inundation modelling which combines a 5 solution to the inertial form of the shallow water equations at 1 km horizontal resolution, with two alternative representations of sub-grid floodplain topography. One of these uses a fitted sub-grid probability distribution, the other a quantile-based representation of the floodplain. We evaluate the model's performance when used to simulate the 0.01 Annual Exceedance Probability (AEP; 100-year) flood and compare the results with published benchmark data for England. The quantile-based method accurately predicts flood inundation in 86 % of locations, with a domain-wide hit rate of 95 % and 10 false alarm rate of 10 %. These performance measures compare with a hit rate of 71 %, and false alarm rate of 9 % for the simpler, but faster, distribution-based method. We suggest that these approaches are suitable for rapid, wide-area flood forecasting and climate change impact assessment.

Simon J. Dadson et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on hess-2021-60', Oliver Wing, 05 Mar 2021
    • AC3: 'Reply on CC1', Simon Dadson, 27 Apr 2021
  • RC1: 'Comment on hess-2021-60', Anonymous Referee #1, 05 Mar 2021
    • AC1: 'Reply on RC1', Simon Dadson, 27 Apr 2021
  • RC2: 'Comment on hess-2021-60', Anonymous Referee #2, 08 Mar 2021
    • AC2: 'Reply on RC2', Simon Dadson, 27 Apr 2021

Simon J. Dadson et al.

Simon J. Dadson et al.

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Short summary
Flood prediction helps national and regional planning and real-time flood response. In this study we apply and test a new way to make wide area predictions of flooding which can be combined with weather forecasting and climate models to give faster predictions of flooded areas. By simplifying the detailed floodplain topography we can keep track of the fraction of land flooded for hazard mapping purposes. When tested this approach accurately reproduces benchmark datasets for England.