Preprints
https://doi.org/10.5194/nhess-2022-188
https://doi.org/10.5194/nhess-2022-188
 
14 Jul 2022
14 Jul 2022
Status: this preprint is currently under review for the journal NHESS.

A new skill score for ensemble flood maps: assessing spatial spread-skill with remote sensing observations

Helen Hooker1, Sarah L. Dance1,2,3, David C. Mason4, John Bevington5, and Kay Shelton5 Helen Hooker et al.
  • 1Department of Meteorology, University of Reading, UK
  • 2Department of Mathematics and Statistics, University of Reading, UK
  • 3National Centre for Earth Observation (NCEO), Reading, UK
  • 4Department of Geography and Environmental Science, University of Reading, UK
  • 5Jeremy Benn Associates Limited (JBA Consulting), UK

Abstract. An ensemble of forecast flood inundation maps has the potential to represent the uncertainty in the flood forecast and provide a location specific, probabilistic, likelihood of flooding. This gives valuable information to flood forecasters, flood risk managers and insurers and will ultimately benefit people living in flood prone areas. Spatial verification of the ensemble flood map forecast against remotely observed flooding is important to understand both the skill of the ensemble forecast and the uncertainty represented in the variation or spread of the individual ensemble member flood maps. Previously, a scale-selective approach has been used to evaluate a convective precipitation ensemble forecast. This determines a skilful scale of ensemble performance. By extending this approach through a new application we evaluate the spatial predictability and the spatial spread-skill of an ensemble flood forecast across a domain of interest. The spatial spread-skill method computes an agreement scale at grid level between each unique pair of ensemble flood maps (ensemble spatial spread) and between each ensemble flood map with a SAR-derived flood map (ensemble spatial skill). By comparing these we can determine the spatial spread-skill performance. These methods are applied to an example flood event on the Brahmaputra River in the Assam region of India, August 2017. Both the spatial-skill and spread-skill relationship vary with location and can be related to physical characteristics of the flooding event. Routine validation and mapping of spatial predictability in an operational system would allow better quantification of model systematic biases and uncertainties. This would be particularly useful for ungauged catchments and would enable targeted model improvements to be made across different parts of the forecast chain.

Helen Hooker et al.

Status: open (until 25 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-188', Seth Bryant, 04 Aug 2022 reply

Helen Hooker et al.

Helen Hooker et al.

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Short summary
Ensemble forecasts of flood inundation produce maps indicating the probability of flooding. A new approach is presented to evaluate the spatial performance of an ensemble flood map forecast by comparing against remotely observed flooding extents. This is important for understanding forecast uncertainties and improving flood forecasting systems.
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