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

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.