The execution of hydraulic models at large spatial scales has yielded a step change in our understanding of flood risk. Yet their necessary simplification through the use of coarsened terrain data results in an artificially smooth digital elevation model with diminished representation of flood defence structures.
Current approaches in dealing with this, if anything is done at all, involve either employing incomplete inventories of flood defence information or making largely unsubstantiated assumptions about flood defence locations and standards based on socioeconomic data. Here, we introduce a novel solution for application at scale. The geomorphometric characteristics of flood defence structures are sampled, and these are fed into a probabilistic algorithm to identify hydraulically relevant features in the source digital elevation model. The elevation of these features is then preserved during the grid coarsening process. The method was shown to compare favorably to surveyed U.S. levee crest heights. When incorporated into a continental-scale hydrodynamic model based on LISFLOOD-FP and compared to local flood models in Iowa (USA), median correspondence was 69% for high-frequency floods and 80% for low-frequency floods, approaching the error inherent in quantifying extreme flows. However, improvements versus a model with no defenses were muted, and risk-based deviations between the local and continental models were large. When simulating an event on the Po River (Italy), built and tested with higher quality data, the method outperformed both undefended and even engineering-grade models. As such, particularly when employed alongside model components of commensurate quality, the method here generates improved-accuracy simulations of flood inundation.