Preprints
https://doi.org/10.5194/hess-2021-60
https://doi.org/10.5194/hess-2021-60
05 Feb 2021
 | 05 Feb 2021
Status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

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

Simon J. Dadson, Eleanor Blyth, Douglas Clark, Helen Davies, Richard Ellis, Huw Lewis, Toby Marthews, and Ponnambalan Rameshwaran

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: closed

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

Status: closed

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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Latest update: 03 Oct 2023
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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.