Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 11
Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5981-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-5981-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 11
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: 4,304 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,145 1,098 61 4,304 48 47
  • HTML: 3,145
  • PDF: 1,098
  • XML: 61
  • Total: 4,304
  • BibTeX: 48
  • EndNote: 47
Views and downloads (calculated since 23 Dec 2020)
Cumulative views and downloads (calculated since 23 Dec 2020)

Viewed (geographical distribution)

Total article views: 4,304 (including HTML, PDF, and XML) Thereof 4,045 with geography defined and 259 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 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.
Read more