02 May 2023
 | 02 May 2023
Status: this preprint is open for discussion.

Inferring heavy tails of flood distributions from common discharge dynamics

Hsing-Jui Wang, Ralf Merz, Soohyun Yang, and Stefano Basso

Abstract. Floods are often disastrous due to underestimation of the magnitude of rare events. Underestimation commonly happens when the occurrence of floods follow a heavy-tailed distribution, but this behavior is not recognized and thus neglected for flood hazard assessment. In fact, 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, even of short length. We test the index in a large set of case studies across Germany encompassing a variety of climatic and physiographic settings. Our findings demonstrate that the new index enables reliable identification of cases with either heavy or nonheavy tailed flood behavior from daily flow records. Additionally, the index suitably estimates the severity of tail heaviness and ranks it across cases, achieving robust results even with short data records. The new index addresses the main limitations of currently used metrics, which lack physical support and require long data records to correctly identify tail behaviors, and provides valuable information on the tail behavior of flood distributions and the related flood hazard in river basins using commonly available discharge data.

Hsing-Jui Wang et al.

Status: open (until 10 Jul 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Hsing-Jui Wang et al.

Data sets

Hydrological dataset Bavarian State Office of Environment

Global Runoff Data Centre (GRDC) Federal Institute for Hydrology

Digital elevation model Shuttle Radar Topography Mission

Hsing-Jui Wang et al.


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Short summary
Accurately assessing heavy-tailed flood behavior with limited data records is challenging and can lead to inaccurate hazard estimates. Our research introduces a new index that uses hydrograph recession to identify heavy-tailed flood behavior, compare severity, and produce reliable results with short data records. This index overcomes the limitations of current metrics, which lack physical meaning and require long records. It thus provides valuable insight into the flood hazard of river basins.