Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models - Zaherpour, Jamal; Mount, Nick J.; Gosling, Simon N.; Dankers, Rutger; Eisner, Stephanie; Dieter, Gerten; Liu, Xingcai; Masaki, Yoshimitsu; Müller Schmied, Hannes; Tang, Qiuhong; Wada, Yoshihide (Elsevier, 2019)
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.