Articles | Volume 25, issue 11
Hydrol. Earth Syst. Sci., 25, 5981–5999, 2021
https://doi.org/10.5194/hess-25-5981-2021
Hydrol. Earth Syst. Sci., 25, 5981–5999, 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 et al.

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.