Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5981-2021
https://doi.org/10.5194/hess-25-5981-2021
Research article
 | 
22 Nov 2021
Research article |  | 22 Nov 2021

Design flood estimation for global river networks based on machine learning models

Gang Zhao, Paul Bates, Jeffrey Neal, and Bo Pang

Viewed

Total article views: 4,613 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,398 1,146 69 4,613 59 54
  • HTML: 3,398
  • PDF: 1,146
  • XML: 69
  • Total: 4,613
  • BibTeX: 59
  • EndNote: 54
Views and downloads (calculated since 23 Dec 2020)
Cumulative views and downloads (calculated since 23 Dec 2020)

Viewed (geographical distribution)

Total article views: 4,613 (including HTML, PDF, and XML) Thereof 4,357 with geography defined and 256 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 07 Nov 2024
Download
Short summary
Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based approach to estimate design floods anywhere on the global river network. This approach shows considerable improvement over the index-flood-based method, and the average bias in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. This approach is a valid method to estimate design floods globally, improving our prediction of flood hazard, especially in ungauged areas.