Floods are often disastrous due to underestimation of the magnitude of
rare events. When the occurrence of floods follows a heavy-tailed
distribution the chance of extreme events is sizable. However,
identifying heavy-tailed flood behavior is challenging because of
limited data records and the lack of physical support for currently used
indices. We address these issues by deriving a new index of heavy-tailed
flood behavior from a physically-based description of streamflow
dynamics. The proposed index, which is embodied by the hydrograph
recession exponent, enables inferring heavy-tailed flood behavior from
daily flow records. We test the index in a large set of case studies
across Germany. Results show its ability to identify cases with either
heavy- or nonheavy-tailed flood behavior, and to evaluate the tail
heaviness. Remarkably, the results are robust also for decreasing the
lengths of data records. The new index thus allows for assessing flood
hazards from commonly available data.