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Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.
Document ID
20160011402
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Wallach, Daniel
(French National Institute for Agricultural Research (INRA) Paris, France)
Thorburn, Peter
(Commonwealth Scientific and Industrial Research Organization Dutton Park, Queensland, Australia)
Asseng, Senthold
(Florida Univ. Gainesville, FL, United States)
Challinor, Andrew J.
(Leeds Univ. United Kingdom)
Ewert, Frank
(Bonn Univ. Germany)
Jones, James W.
(Florida Univ. Gainesville, FL, United States)
Rotter, Reimund
(Natural Resources Institute Finland Helsinki, Finland)
Ruane, Alexander
(NASA Goddard Inst. for Space Studies New York, NY United States)
Date Acquired
September 21, 2016
Publication Date
August 25, 2016
Publication Information
Publication: Environmental Modelling & Software
Publisher: Elsevier
Volume: 84
ISSN: 1364-8152
Subject Category
Meteorology And Climatology
Report/Patent Number
GSFC-E-DAA-TN35780
Distribution Limits
Public
Copyright
Other
Keywords
model structure uncertainty
prediction error
input uncertainty
crop model
uncertainty
parameter uncertainty

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