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Constructing a geography of heavy-tailed flood distributions: insights from common streamflow dynamics.
  • Hsing-Jui Wang,
  • Ralf Merz,
  • Stefano Basso
Hsing-Jui Wang
Helmholtz-Centre for Environmental Research UFZ

Corresponding Author:[email protected]

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Ralf Merz
Helmholtz Centre for Environmental Research (UFZ)
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Stefano Basso
Norwegian Institute for Water Research (NIVA), Oslo, Norway
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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 proven to be a robust indicator of flood tail propensity across analyses with varying data lengths. Analyzing extensive datasets from Germany, the United Kingdom (UK), Norway, and the United States (US), we uncover distinct patterns: prevalent heavy tails in Germany and the UK, diverse behavior in the US, and predominantly nonheavy tails in Norway. 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.
03 Oct 2023Submitted to ESS Open Archive
05 Oct 2023Published in ESS Open Archive