Preprints
https://doi.org/10.5194/hess-2024-159
https://doi.org/10.5194/hess-2024-159
30 May 2024
 | 30 May 2024
Status: this preprint is currently under review for the journal HESS.

Constructing a geography of heavy-tailed flood distributions: insights from common streamflow dynamics

Hsing-Jui Wang, Ralf Merz, and Stefano Basso

Abstract. Heavy-tailed flood distributions depict the higher occurrence probability of extreme floods. Understanding the spatial distribution of heavy tail floods is essential for effective risk assessment. Conventional methods often encounter data limitations, leading to uncertainty across regions. To address this challenge, we utilize hydrograph recession exponents derived from common streamflow dynamics, which have shown to be a robust indicator of flood tail propensity across analyses with varying data lengths. Analyzing extensive datasets covering the Atlantic Europe, Northern Europe, and the continental United States, we uncover distinct patterns: prevalent heavy tails in the Atlantic Europe, diverse behavior in the continental United States, and predominantly nonheavy tails in Northern Europe. The regional tail behavior has been observed in relation to the interplay between terrain and meteorological characteristics, and we further conducted quantitative analyses to assess the influence of hydroclimatic conditions using Köppen classifications. Notably, temporal variations in catchment storage are a crucial mechanism driving highly nonlinear catchment responses that favor heavy-tailed floods, often intensified by concurrent dry periods and high temperatures. Furthermore, this mechanism is influenced by various flood generation processes, which can be shaped by both hydroclimatic seasonality and catchment scale. These insights deepen our understanding of the interplay between climate, physiographical settings, and flood behavior, while highlighting the utility of hydrograph recession exponents in flood hazard assessment.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Hsing-Jui Wang, Ralf Merz, and Stefano Basso

Status: open (until 25 Jul 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Hsing-Jui Wang, Ralf Merz, and Stefano Basso

Data sets

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

Shuttle Radar Topography Mission (SRTM) A. Jarvis et al. https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/

High-Resolution Present-Day Köppen Climate Map H. E. Beck et al. https://doi.org/10.1038/sdata.2018.214

High-Resolution Map of Derived Potential Evapotranspiration R. J. Zomer et al. https://doi.org/10.1038/s41597-022-01493-1

Global Dams and Reservoirs Dataset: GeoDAR v.1.0 J. Wang et al. https://doi.org/10.5281/zenodo.6163413

Hsing-Jui Wang, Ralf Merz, and Stefano Basso

Viewed

Total article views: 158 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
132 21 5 158 13 3 3
  • HTML: 132
  • PDF: 21
  • XML: 5
  • Total: 158
  • Supplement: 13
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 30 May 2024)
Cumulative views and downloads (calculated since 30 May 2024)

Viewed (geographical distribution)

Total article views: 142 (including HTML, PDF, and XML) Thereof 142 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Jun 2024
Download
Short summary
Extreme floods are more common than expected. Knowing where these floods are likely to occur is key for risk management. Traditional methods struggle with limited data, causing uncertainty. We use common streamflow dynamics to indicate extreme flood propensity. Analyzing data from Atlantic Europe, Northern Europe, and the U.S., we validate this novel approach and unravel intrinsic linkages between regional geographic patterns and extreme flood drivers.