Reducing Uncertainty in Ecosystem Service Modelling through Weighted Ensembles
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In: Ecosystem Services, Vol. 53, 101398, 02.2022.
Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Reducing Uncertainty in Ecosystem Service Modelling through Weighted Ensembles
AU - Hooftman, Danny
AU - Bullock, James
AU - Jones, Laurence
AU - Eigenbrod, Felix
AU - Barredo, Jose
AU - Forrest, Matthew
AU - Kinderman, George
AU - Thomas, Amy
AU - Willcock, Simon
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
KW - Carbon
KW - Committee averaging
KW - Prediction Error
KW - Accuracy
KW - United Kingdom
KW - Validation
KW - Water supply
KW - Weighted averaging
U2 - 10.1016/j.ecoser.2021.101398
DO - 10.1016/j.ecoser.2021.101398
M3 - Article
VL - 53
JO - Ecosystem Services
JF - Ecosystem Services
SN - 2212-0416
M1 - 101398
ER -