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

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)

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 ensemble mean (EM). The performance gain offered by MMC suggests that future multi-model applications consider reporting MMCs, alongside the EM and intermodal range, to provide end-users 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.

Original languageEnglish
Pages (from-to)112-128
Number of pages17
JournalEnvironmental Modelling and Software
Volume114
DOIs
Publication statusPublished - Apr 2019
Externally publishedYes

Keywords

  • Gene expression programming
  • Global hydrological models
  • Machine learning
  • Model weighting
  • Optimisation

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