Reducing uncertainty in ecosystem service modelling through weighted ensembles

Hooftman, Danny A.P.; Bullock, James M.; Jones, Laurence; Eigenbrod, Felix; Barredo, José I.; Forrest, Matthew; Kindermann, Georg; Thomas, Amy; Willcock, Simon. 2022 Reducing uncertainty in ecosystem service modelling through weighted ensembles. Ecosystem Services, 53, 101398. 11, pp.

Before downloading, please read NORA policies.
N532479PP.pdf - Accepted Version

Download (1MB) | Preview


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.

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Biodiversity (Science Area 2017-)
Soils and Land Use (Science Area 2017-)
UKCEH Fellows
ISSN: 2212-0416
Additional Keywords: carbon, committee averaging, prediction error, accuracy, United Kingdom, validation, water supply, weighted averaging
NORA Subject Terms: Ecology and Environment
Date made live: 12 Apr 2022 12:01 +0 (UTC)

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...