Reducing uncertainty in ecosystem service modelling through weighted ensembles

A - Papers appearing in refereed journals

Hooftman, D. A. P., Bullock, J. M., Jones, L., Eigenbrod, F., Barredo, J. I., Forrest, M., Kindermann, G., Thomas, A. and Willcock, S. 2022. Reducing uncertainty in ecosystem service modelling through weighted ensembles. Ecosystem Services. 53 (February), p. 101398. https://doi.org/10.1016/j.ecoser.2021.101398

AuthorsHooftman, D. A. P., Bullock, J. M., Jones, L., Eigenbrod, F., Barredo, J. I., Forrest, M., Kindermann, G., Thomas, A. and Willcock, S.
Abstract

Over the last decade many ecosystem service (ES) models have been developed to inform sustainable land and water use planning. However, uncertainty in the predictions of any single model in any specific situation can undermine their utility for decision-making. One solution is creating ensemble predictions, which potentially increase accuracy, but how best to create ES ensembles to reduce uncertainty is unknown and untested. Using ten models for carbon storage and nine for water supply, we tested a series of ensemble approaches against measured validation data in the UK. Ensembles had at minimum a 5–17% higher accuracy than a randomly selected individual model and, in general, ensembles weighted for among model consensus provided better predictions than unweighted ensembles. To support robust decision-making for sustainable development and reducing uncertainty around these decisions, our analysis suggests various ensemble methods should be applied depending on data quality, for example if validation data are available.

KeywordsCarbon; Committee averaging; Prediction error; Accuracy; United Kingdom; Validation; Water supply; Weighted averaging
Year of Publication2022
JournalEcosystem Services
Journal citation53 (February), p. 101398
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ecoser.2021.101398
Web address (URL)https://www.sciencedirect.com/science/article/pii/S221204162100156X?via%3Dihub
Open accessPublished as green open access
FunderNatural Environment Research Council
Funder project or codeEnsemblES project – Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models
Output statusPublished
Publication dates
Online22 Dec 2021
Publication process dates
Accepted08 Dec 2021
ISSN2212-0416
PublisherElsevier

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