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

Data sets

Global Streamflow Indices and Metadata Archive - Part 1 H. X Do, L. Gudmundsson, M. Leonard, and S. Westra https://doi.org/10.1594/PANGAEA.887477

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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.