Reducing Uncertainty in Ecosystem Service Modelling through Weighted Ensembles

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  • Danny Hooftman
    Lactuca: Environmental Data Analyses and Modelling
  • James Bullock
    Centre for Ecology and Hydrology, Wallingford
  • Laurence Jones
    Environment Centre Wales
  • Felix Eigenbrod
    University of Southampton
  • Jose Barredo
    Joint Research Centre of the European Commission, Brussels
  • Matthew Forrest
    Senckenberg Biodiversity and Climate Research Centre, Frankfurt
  • George Kinderman
    International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
  • Amy Thomas
    Environment Centre Wales
  • Simon Willcock
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.

Keywords

  • Carbon, Committee averaging, Prediction Error, Accuracy, United Kingdom, Validation, Water supply, Weighted averaging
Original languageEnglish
Article number101398
Number of pages22
JournalEcosystem Services
Volume53
Early online date22 Dec 2021
DOIs
Publication statusPublished - Feb 2022

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