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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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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 the flood hazard, especially in ungauged areas.
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https://doi.org/10.5194/hess-2020-594
https://doi.org/10.5194/hess-2020-594

  23 Dec 2020

23 Dec 2020

Review status: this preprint is currently under review for the journal HESS.

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

Gang Zhao1, Paul Bates1,2, Jeffrey Neal1,2, and Bo Pang3 Gang Zhao et al.
  • 1School of Geographical Sciences, University of Bristol, Bristol, UK
  • 2Fathom, Engine Shed, Station Approach, Bristol, UK
  • 3College of Water Sciences, Beijing Normal University, Beijing, China

Abstract. Design flood estimation is a fundamental task in hydrology. In this research, we propose a machine learning based approach to estimate design floods globally. This approach mainly involves three stages: (i) estimating at-site flood frequency curve for global gauging stations by the Anderson-Darling test and Bayesian MCMC method; (ii) clustering these stations into subgroups by a K-means model based on twelve globally available catchment descriptors, and (iii) developing a regression model in each subgroup for regional design flood estimation using the same descriptors. A total of 11793 stations globally were selected for model development and three widely used regression models were compared for design flood estimation. The results showed that: (1) the proposed approach achieved the highest accuracy for design flood estimation when using all twelve descriptors for clustering; and the performance of regression was improved by considering more descriptors during the training and validation; (2) a support vector machine regression provide the highest prediction performance among all regression models tested, with root mean square normalised error of 0.708 for 100-year return period flood estimation; (3) 100-year design flood in tropical, arid, temperate, cold and polar climate zones could be reliably estimated with the relative mean relative biases (RBIAS) of −0.199, −0.233, −0.169, 0.179 and −0.091 respectively; (4) This machine learning based approach shows considerable improvement over the index-flood based method introduced by Smith et al. (2015, https://doi.org/10.1002/2014WR015814) for the design flood estimation at global scales; and the average RBIAS in estimation is less than 18 % for 10, 20, 50 and 100-year design floods. We conclude that the proposed approach is a valid method to estimate design floods anywhere on the global river network, improving our prediction of the flood hazard, especially in ungauged areas.

Gang Zhao et al.

 
Status: open (until 17 Feb 2021)
Status: open (until 17 Feb 2021)
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Gang Zhao et al.

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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 the flood hazard, especially in ungauged areas.
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