Despite investments in disaster resilience, flooding continues to disrupt healthcare systems, both by limiting access and through failures in the surrounding transportation network. Existing models for mitigation planning often overlook critical dynamics, such as traffic rerouting, particularly at the national scales necessary for effective planning. Here we present a scalable method to identify hospitals at risk of emergency response delays and service disruptions caused by flood-induced traffic impacts. Our approach integrates a regional flood model with a gravity-based traffic model to simulate traffic flow from open-source road data. Our findings reveal hidden risks for hospitals located far from flood zones, showing how flood-related road disruptions and traffic rerouting can reduce access to critical healthcare services. In particular, we found 75 (of 2,475) hospitals at risk of patient surges beyond their regular capacity, driven solely by flood-related traffic disruptions. Of these, a third are more than 10 km from the nearest inundation, suggesting these facilities may be unaware and thus under-prepared — risks that have, until now, remained hidden from assessments.

Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model’s ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model’s performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.