Abstract/Summary

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. The methods are illustrated by application to a case study flood event (with an estimated return period of 120 to 550 years) of the River Wye and River Lugg (UK) in February 2020. Representation errors are introduced where remote sensing observations capture flood extent at different spatial resolutions in comparison with the model. The sensitivity of the verified skilful scale to the resolution of the observations is investigated. Re-scaling and interpolating observations leads to a small reduction in skill score compared with the observation flood map derived at the model resolution. The domain-averaged skilful scale remains the same with slight location-specific variations in skilful scale evident on the categorical scale map. 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.