@article{WangWhiteGrimmetal.2018,
  author    = {Wang, Ming and White, Neil and Grimm, Volker and Hofman, Helen and Doley, David and Thorp, Grant and Cribb, Bronwen and Wherritt, Ella and Han, Liqi and Wilkie, John and Hanan, Jim},
  title     = {Pattern-oriented modelling as a novel way to verify and validate functional-structural plant models},
  series = {Annals of botany},
  volume    = {121},
  journal   = {Annals of botany},
  number    = {5},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {0305-7364},
  doi       = {10.1093/aob/mcx187},
  pages     = {941 -- 959},
  year      = {2018},
  abstract  = {Background and Aims Functional-structural plant (FSP) models have been widely used to understand the complex interactions between plant architecture and underlying developmental mechanisms. However, to obtain evidence that a model captures these mechanisms correctly, a clear distinction must be made between model outputs used for calibration and thus verification, and outputs used for validation. In pattern-oriented modelling (POM), multiple verification patterns are used as filters for rejecting unrealistic model structures and parameter combinations, while a second, independent set of patterns is used for validation. Key Results After calibration, our model simultaneously reproduced multiple observed architectural patterns. The model then successfully predicted, without further calibration, the validation patterns. The model supports the hypothesis that carbon allocation can be modelled as being dependent on current organ biomass and sink strength of each organ type, and also predicted the observed developmental timing of the leaf sink-source transition stage.},
  language  = {en}
}
@article{SchroeterKreibichVogeletal.2014,
  author    = {Schroeter, Kai and Kreibich, Heidi and Vogel, Kristin and Riggelsen, Carsten and Scherbaum, Frank and Merz, Bruno},
  title     = {How useful are complex flood damage models?},
  series = {Water resources research},
  volume    = {50},
  journal   = {Water resources research},
  number    = {4},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1002/2013WR014396},
  pages     = {3378 -- 3395},
  year      = {2014},
  abstract  = {We investigate the usefulness of complex flood damage models for predicting relative damage to residential buildings in a spatial and temporal transfer context. We apply eight different flood damage models to predict relative building damage for five historic flood events in two different regions of Germany. Model complexity is measured in terms of the number of explanatory variables which varies from 1 variable up to 10 variables which are singled out from 28 candidate variables. Model validation is based on empirical damage data, whereas observation uncertainty is taken into consideration. The comparison of model predictive performance shows that additional explanatory variables besides the water depth improve the predictive capability in a spatial and temporal transfer context, i.e., when the models are transferred to different regions and different flood events. Concerning the trade-off between predictive capability and reliability the model structure seem more important than the number of explanatory variables. Among the models considered, the reliability of Bayesian network-based predictions in space-time transfer is larger than for the remaining models, and the uncertainties associated with damage predictions are reflected more completely.},
  language  = {en}
}