Helen Hooker, Sarah L Dance, David C Mason, John Bevington, Kay Shelton

Flood inundation forecast maps provide an essential tool to disaster management teams for planning and preparation ahead of a flood event in order to mitigate the impacts of flooding on the community. Evaluating the accuracy of forecast flood maps is essential for model development and improving future flood predictions. Conventional, quantitative binary verification measures typically provide a domain averaged score, at grid level, of forecast skill. This score is dependent on the magnitude of the flood and the spatial scale of the flood map. Binary scores have limited physical meaning and do not indicate location specific variations in forecast skill that enable targeted model improvements to be made. A new, scale-selective approach is presented here to evaluate forecast flood inundation maps against remotely observed flood extents. A neighbourhood approach based on the Fraction Skill Score is applied to assess the spatial scale at which the forecast becomes skilful at capturing the observed flood. This skilful scale varies with location and when combined with a contingency map creates a novel categorical scale map, a valuable visual tool for model evaluation and development. The impact of model improvements on forecast flood map accuracy skill scores are often masked by large areas of correctly predicted flooded/unflooded cells. To address this, the accuracy of the flood-edge location is evaluated. The flood-edge location accuracy proves to be more sensitive to variations in forecast skill and spatial scale compared to the accuracy of the entire flood extent. Additionally, the resulting skilful scale of the flood-edge provides a physically meaningful verification measure of the forecast flood-edge discrepancy. Representation errors are introduced where remote sensing observations capture flood extent at different spatial resolutions in comparison with the model. Relative to the spatial scale of the forecast flood maps, the errors introduced in high resolution observations can cause the observed flood extent to be over-estimated with lower resolution observations leading to under-estimation. This has implications for future studies where observations are taken from multiple heterogeneous sources. Overall, our novel emphasis on scale, rather than domain-average score, means that comparisons can be made across different flooding scenarios and forecast systems and between forecasts at different spatial scales.