Spatial scale evaluation of forecast flood inundation maps using Synthetic Aperture Radar (SAR) images.

Flood inundation forecast maps provide an essential tool for disaster management teams to aid 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 and can be achieved by comparison with flood maps derived from remote-sensing observations. 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 to evaluate forecast flood inundation maps against Synthetic Aperture Radar (SAR)-derived observed flood extents. We evaluate forecast flood maps out to 10-days lead time for the Rivers Wye and Lugg (UK) during Storm Dennis, February 2020. 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: this provides a physically meaningful verification measure of the forecast flood-edge discrepancy. Representation errors are introduced where remote sensing observations capture the flood extent at different spatial resolutions in comparison with the model. We evaluate the sensitivity of the verification measures to the resolution of the SAR-derived flood map.

An ensemble of forecast flood inundation maps has the potential to represent the uncertainty in the flood forecast and provides 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. We apply a scale selective approach to evaluate the spatial predictability of forecast ensemble flood maps. An ensemble forecast of flooding of the Brahmaputra in the Assam region, August 2017, is evaluated using flood extents derived from Sentinel-1 SAR images. The results are presented on a Spatial Spread-Skill (SSS) map, indicating where the flood map ensemble is over-, under- or well-spread. Overall, 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.