Articles | Volume 25, issue 11
Hydrol. Earth Syst. Sci., 25, 5981–5999, 2021
Hydrol. Earth Syst. Sci., 25, 5981–5999, 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.

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Cited articles

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Bates, P. D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., Savage, J., Olcese, G., Neal, J., and Schumann, G.: Combined modelling of US fluvial, pluvial and coastal flood hazard under current and future climates, Water Resour. Res., e2020WR028673,, 2020. 
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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.