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.
