Publication Abstracts

Wallach et al. 2016

Wallach, D., P. Thorburn, S. Asseng, A.J. Challinor, F. Ewert, J.W. Jones, R. Rotter, and A.C. Ruane, 2016: Estimating model prediction error: Should you treat predictions as fixed or random? Environ. Model. Softw., 84, 529-539, doi:10.1016/j.envsoft.2016.07.010.

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; MSEPfixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEPuncertain(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 MSEPuncertain(X) can be estimated using a random effects ANOVA. It is argued that MSEPuncertain(X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

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BibTeX Citation

@article{wa05600n,
  author={Wallach, D. and Thorburn, P. and Asseng, S. and Challinor, A. J. and Ewert, F. and Jones, J. W. and Rotter, R. and Ruane, A. C.},
  title={Estimating model prediction error: Should you treat predictions as fixed or random?},
  year={2016},
  journal={Environ. Model. Softw.},
  volume={84},
  pages={529--539},
  doi={10.1016/j.envsoft.2016.07.010},
}

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RIS Citation

TY  - JOUR
ID  - wa05600n
AU  - Wallach, D.
AU  - Thorburn, P.
AU  - Asseng, S.
AU  - Challinor, A. J.
AU  - Ewert, F.
AU  - Jones, J. W.
AU  - Rotter, R.
AU  - Ruane, A. C.
PY  - 2016
TI  - Estimating model prediction error: Should you treat predictions as fixed or random?
JA  - Environ. Model. Softw.
VL  - 84
SP  - 529
EP  - 539
DO  - 10.1016/j.envsoft.2016.07.010
ER  -

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