Articles | Volume 27, issue 24
https://doi.org/10.5194/hess-27-4369-2023
https://doi.org/10.5194/hess-27-4369-2023
Research article
 | 
14 Dec 2023
Research article |  | 14 Dec 2023

Inferring heavy tails of flood distributions through hydrograph recession analysis

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

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Cited articles

Arai, R., Toyoda, Y., and Kazama, S.: Runoff recession features in an analytical probabilistic streamflow model, J. Hydrol., 597, 125745, https://doi.org/10.1016/j.jhydrol.2020.125745, 2020. 
Archfield, S. A., Hirsch, R. M., Viglione, A., and Blöschl, G.: Fragmented patterns of flood change across the United States, Geophys. Res. Lett., 43, 10232–10239, https://doi.org/10.1002/2016GL070590, 2016. 
Bart, R. and Hope, A.: Inter-seasonal variability in baseflow recession rates: The role of aquifer antecedent storage in central California watersheds, J. Hydrol., 519, 205–213, https://doi.org/10.1016/j.jhydrol.2014.07.020, 2014. 
Basso, S., Schirmer, M., and Botter, G.: On the emergence of heavy-tailed streamflow distributions, Adv. Water Resour., 82, 98–105, https://doi.org/10.1016/j.advwatres.2015.04.013, 2015.  
Basso, S., Schirmer, M., and Botter, G.: A physically based analytical model of flood frequency curves, Geophys. Res. Lett., 43, 9070–9076, https://doi.org/10.1002/2016GL069915, 2016. 
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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.