Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models. (April 2019)

• Record Type: Journal Article
• Authors: Zaherpour, Jamal; Mount, Nick; Gosling, Simon N.; Dankers, Rutger; Eisner, Stephanie; Gerten, Dieter; Liu, Xingcai; Masaki, Yoshimitsu; Müller Schmied, Hannes; Tang, Qiuhong; Wada, Yoshihide
• Is Part Of: Environmental modelling & software. Volume 114(2019)
• Page Start: 112
• Page End: 128
• Publication Date: 2019-04
• Subjects: Machine learning -- Model weighting -- Gene expression programming -- Global hydrological models -- Optimisation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.01.003
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: We present the first use of machine learning-based model combination applied to a global hydrological model ensemble. The multi-model combination (MMC) performs in most cases better than any individual input model and the ensemble mean. MMC is not always able to out-perform model combination based on multiple linear regression. The physical interpretation of the MMC solutions is limited by the complexity of their non-linear weighting schemes.