Reducing uncertainty in ecosystem service modelling through weighted ensembles

Hooftman, D.A.P., Bullock, J.M., Jones, L., Eigenbrod, F., Barredo, J.I., Forrest, M., Kindermann, G. ORCID: https://orcid.org/0000-0003-4297-1318, Thomas, A., & Willcock, S. (2022). Reducing uncertainty in ecosystem service modelling through weighted ensembles. Ecosystem Services 53 e101398. 10.1016/j.ecoser.2021.101398.

Preview

Text ECOSER-D-21-00429_R2.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (7MB) | Preview

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.

Actions (login required)

View Item