Evaluation of surrogate flood models for the use in impact-based flood warning systems at national scale

Abstract

Recent flood events show that gaps in the communication channels from warning services to target groups inhibit mitigation. One approach addressing this issue is impact-based warning. We introduce a library-based surrogate flood model for the use in impact-based warning systems, tested for the main river network of Northern Switzerland. To validate the surrogate model, we compare the impacts to buildings, persons and workplaces with hazard classification, estimated with transient simulations for nine extreme precipitation scenarios. With 78 analyzed model regions, the surrogate approach reaches a Flood Area Index between 0.74 and 0.90 for each scenario (overall 0.84). The Critical Success Index calculated based on exposed persons is 0.77–0.93 (overall 0.89). Our prototype of a library-based flood surrogate model demonstrates the ability of accurately representing a same resolved transient model, bearing the potential to predict flood impacts nationwide in near real-time and the applicability to probabilistic forecasts.