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

Zaherpour, J.; Mount, N.; Gosling, S. N.; Dankers, R.; Eisner, S.; Gerten, Dieter; Liu, X.; Masaki, Y.; Müller Schmied, H.; Tang, Q.; Wada, Y.

doi:10.1016/j.envsoft.2019.01.003

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

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

Authors

Zaherpour, J.
External Organizations;

Mount, N.
External Organizations;

Gosling, S. N.
External Organizations;

Dankers, R.
External Organizations;

Eisner, S.
External Organizations;

/persons/resource/Dieter.Gerten

Gerten, Dieter
Potsdam Institute for Climate Impact Research;

Liu, X.
External Organizations;

Masaki, Y.
External Organizations;

Müller Schmied, H.
External Organizations;

Tang, Q.
External Organizations;

Wada, Y.
External Organizations;

External Ressource

No external resources are shared

Fulltext (public)

22997oa.pdf
(Postprint), 2MB

Supplementary Material (public)

There is no public supplementary material available

Citation

Zaherpour, J., Mount, N., Gosling, S. N., Dankers, R., Eisner, S., Gerten, D., Liu, X., Masaki, Y., Müller Schmied, H., Tang, Q., 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

Cite as: https://publications.pik-potsdam.de/pubman/item/item_22997

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.