Preprints
https://doi.org/10.5194/egusphere-2023-660
https://doi.org/10.5194/egusphere-2023-660
02 May 2023
 | 02 May 2023

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.

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Journal article(s) based on this preprint

14 Dec 2023
Inferring heavy tails of flood distributions through hydrograph recession analysis
Hsing-Jui Wang, Ralf Merz, Soohyun Yang, and Stefano Basso
Hydrol. Earth Syst. Sci., 27, 4369–4384, https://doi.org/10.5194/hess-27-4369-2023,https://doi.org/10.5194/hess-27-4369-2023, 2023
Short summary
Hsing-Jui Wang, Ralf Merz, Soohyun Yang, and Stefano Basso

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-660', Anonymous Referee #1, 03 Jul 2023
    • AC1: 'Reply on RC1', Hsing-Jui Wang, 14 Jul 2023
  • RC2: 'Comment on egusphere-2023-660', Anonymous Referee #2, 19 Jul 2023
    • AC2: 'Reply on RC2', Hsing-Jui Wang, 26 Jul 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-660', Anonymous Referee #1, 03 Jul 2023
    • AC1: 'Reply on RC1', Hsing-Jui Wang, 14 Jul 2023
  • RC2: 'Comment on egusphere-2023-660', Anonymous Referee #2, 19 Jul 2023
    • AC2: 'Reply on RC2', Hsing-Jui Wang, 26 Jul 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (14 Aug 2023) by Thomas Kjeldsen
AR by Hsing-Jui Wang on behalf of the Authors (03 Sep 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (13 Sep 2023) by Thomas Kjeldsen
RR by Anonymous Referee #1 (14 Oct 2023)
ED: Publish subject to minor revisions (review by editor) (16 Oct 2023) by Thomas Kjeldsen
AR by Hsing-Jui Wang on behalf of the Authors (03 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (06 Nov 2023) by Thomas Kjeldsen
AR by Hsing-Jui Wang on behalf of the Authors (06 Nov 2023)  Manuscript 

Journal article(s) based on this preprint

14 Dec 2023
Inferring heavy tails of flood distributions through hydrograph recession analysis
Hsing-Jui Wang, Ralf Merz, Soohyun Yang, and Stefano Basso
Hydrol. Earth Syst. Sci., 27, 4369–4384, https://doi.org/10.5194/hess-27-4369-2023,https://doi.org/10.5194/hess-27-4369-2023, 2023
Short summary
Hsing-Jui Wang, Ralf Merz, Soohyun Yang, and Stefano Basso

Data sets

Hydrological dataset Bavarian State Office of Environment https://www.gkd.bayern.de/de/fluesse/abfluss

Global Runoff Data Centre (GRDC) Federal Institute for Hydrology http://www.bafg.de/GRDC

Digital elevation model Shuttle Radar Topography Mission https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/

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

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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.