Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 2.883 IF 2.883
  • IF 5-year value: 3.321 IF 5-year
    3.321
  • CiteScore value: 3.07 CiteScore
    3.07
  • SNIP value: 1.336 SNIP 1.336
  • IPP value: 2.80 IPP 2.80
  • SJR value: 1.024 SJR 1.024
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 81 Scimago H
    index 81
  • h5-index value: 43 h5-index 43
Discussion papers
https://doi.org/10.5194/nhess-2019-415
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-415
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: brief communication 09 Jan 2020

Submitted as: brief communication | 09 Jan 2020

Review status
This preprint is currently under review for the journal NHESS.

Brief communication: The role of using precipitation or river discharge data when assessing global coastal compound flooding

Emanuele Bevacqua1, Michalis I. Vousdoukas2, Theodore G. Shepherd1, and Mathieu Vrac3 Emanuele Bevacqua et al.
  • 1Department of Meteorology, University of Reading, Reading, United Kingdom
  • 2European Commission, Joint Research Centre (JRC), Ispra, Italy
  • 3Laboratoire des Sciences du Climat et de l'Environnement, CNRS/IPSL, Gif-sur-Yvette, France

Abstract. Interacting storm surges and high water-runoff can cause compound flooding (CF) in low-lying coasts and river estuaries. The large-scale CF hazard has been typically studied using proxies such as the concurrence of storm surge extremes either with precipitation or with river discharge extremes. Here the impact of the choice of such proxies is addressed employing state-of-the-art global datasets. Although being proxies of diverse physical mechanisms, we find that the two approaches show similar CF spatial patterns. However, deviations increase with the catchment size and our findings indicate that CF in long rivers (catchment ≳ 5–10 000 Km2) is more accurately analysed using river discharge data. The precipitation-based assessment allows for considering local rainfall-driven CF, and CF in small rivers not resolved by large-scale datasets.

Emanuele Bevacqua et al.

Interactive discussion

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Emanuele Bevacqua et al.

Emanuele Bevacqua et al.

Viewed

Total article views: 314 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
251 61 2 314 3 4
  • HTML: 251
  • PDF: 61
  • XML: 2
  • Total: 314
  • BibTeX: 3
  • EndNote: 4
Views and downloads (calculated since 09 Jan 2020)
Cumulative views and downloads (calculated since 09 Jan 2020)

Viewed (geographical distribution)

Total article views: 229 (including HTML, PDF, and XML) Thereof 226 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 28 Mar 2020
Publications Copernicus
Download
Short summary
Coastal compound flooding (CF), caused by interacting storm surges and high water-runoff, is typically studied based on concurring storm surge extremes with either precipitation or river discharge extremes. Globally, these two approaches show similar CF spatial patterns, but deviations increase with the catchment size. CF in long rivers is more accurately analysed using river discharge data. The precipitation-based analysis allows for considering local rainfall-driven CF, and CF in small rivers.
Coastal compound flooding (CF), caused by interacting storm surges and high water-runoff, is...
Citation