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Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models

Zaherpour, Jamal; Mount, Nick; Gosling, Simon; Dankers, Rutger; Eisner, Stephanie; Gerten, Dieter; Liu, Xingcai; Masaki, Yoshimitsu; Müller, Schmied Hannes; Tang, Qiuhong; Wada, Yoshihide

Authors

Jamal Zaherpour

NICK MOUNT nick.mount@nottingham.ac.uk
Associate Professor

Dr SIMON GOSLING SIMON.GOSLING@NOTTINGHAM.AC.UK
Professor of Climate Risks and Environmental Modelling

Rutger Dankers

Stephanie Eisner

Dieter Gerten

Xingcai Liu

Yoshimitsu Masaki

Schmied Hannes Müller

Qiuhong Tang

Yoshihide Wada



Abstract

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the EM. The performance gain offered by MMC suggests that future multimodel applications consider reporting MMCs, alongside the EM and intermodal range, to provide endusers of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.

Journal Article Type Article
Publication Date 2019-04
Journal Environmental Modelling & Software
Print ISSN 1364-8152
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 114
Pages 112-128
APA6 Citation Zaherpour, J., Mount, N., Gosling, S., Dankers, R., Eisner, S., Gerten, D., …Wada, Y. (2019). Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models. Environmental Modelling and Software, 114, 112-128. https://doi.org/10.1016/j.envsoft.2019.01.003
DOI https://doi.org/10.1016/j.envsoft.2019.01.003
Publisher URL https://www.sciencedirect.com/science/article/pii/S1364815217309817
Additional Information This article is maintained by: Elsevier; Article Title: Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models; Journal Title: Environmental Modelling & Software; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.envsoft.2019.01.003; Content Type: article; Copyright: © 2019 Elsevier Ltd. All rights reserved.

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