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The importance of multimodel projections to assess uncertainty in projections from simulation models

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dc.contributor.authorValle, Denis
dc.contributor.authorStaudhammer, Christina L.
dc.contributor.authorCropper, Wendell P., Jr.
dc.contributor.authorvan Gardingen, Paul R.
dc.contributor.otherState University System of Florida
dc.contributor.otherUniversity of Florida
dc.contributor.otherEmpresa Brasileira de Pesquisa Agropecuaria (EMBRAPA)
dc.contributor.otherUniversity of Edinburgh
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2018-11-30T20:52:03Z
dc.date.available2018-11-30T20:52:03Z
dc.date.issued2019-01-10
dc.description.abstractSimulation models are increasingly used to gain insights regarding the long-term effect of both direct and indirect anthropogenic impacts on natural resources and to devise and evaluate policies that aim to minimize these effects. If the uncertainty from simulation model projections is not adequately quantified and reported, modeling results might be misleading, with potentially serious implications. A method is described, based on a nested simulation design associated with multimodel projections, that allows the partitioning of the overall uncertainty in model projections into a number of different sources of uncertainty: model stochasticity, starting conditions, parameter uncertainty, and uncertainty that originates from the use of key model assumptions. These sources of uncertainty are likely to be present in most simulation models. Using the forest dynamics model SYMFOR as a case study, it is shown that the uncertainty originated from the use of alternate modeling assumptions, a source of uncertainty seldom reported, can be the greatest source of uncertainty, accounting for 66-97% of the overall variance of the mean after 100 years of stand dynamics simulation. This implicitly reveals the great importance of these multimodel projections even when multiple models from independent research groups are not available. Finally, it is suggested that a weighted multimodel average (in which the weights are estimated from the data) might be substantially more precise than a simple multimodel average (equivalent to equal weights for all models) as models that strongly conflict with the data are given greatly reduced or even zero weights. The method of partitioning modeling uncertainty is likely to be useful for other simulation models, allowing for a better estimate of the uncertainty of model projections and allowing researchers to identify which data need to be collected to reduce this uncertainty.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.citationValle, D., Staudhammer, C. L., Cropper, W. P., Gardingen, P. R. (2009): The importance of multimodel projections to assess uncertainty in projections from simulation models. Ecological Applications, 19(7). DOI: 10.1890/08-1579.1
dc.identifier.doi10.1890/08-1579.1
dc.identifier.orcidhttps://orcid.org/0000-0002-6395-1968
dc.identifier.orcidhttps://orcid.org/0000-0001-7851-7382
dc.identifier.orcidhttps://orcid.org/0000-0002-9830-8876
dc.identifier.urihttp://ir.ua.edu/handle/123456789/5125
dc.languageEnglish
dc.language.isoen_US
dc.publisherWiley
dc.subjectmodel uncertainty
dc.subjectmodeling assumptions
dc.subjectmultimodel
dc.subjectpartitioning of the variance
dc.subjectsimulation model
dc.subjectCLIMATE-CHANGE
dc.subjectRAIN-FOREST
dc.subjectGROWTH
dc.subjectMANAGEMENT
dc.subjectPREDICTIONS
dc.subjectYIELD
dc.subjectDISTRIBUTIONS
dc.subjectPRODUCTIVITY
dc.subjectRECRUITMENT
dc.subjectFORECASTS
dc.subjectEcology
dc.subjectEnvironmental Sciences
dc.subjectEnvironmental Sciences & Ecology
dc.titleThe importance of multimodel projections to assess uncertainty in projections from simulation modelsen_US
dc.typetext
dc.typeArticle

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