Is my model too complex? Evaluating model formulation using model reduction

Crout, N.M.J.; Tarsitano, Davide; Wood, Andrew

doi:10.1016/j.envsoft.2008.06.004

Cited by 61 publications

(33 citation statements)

References 26 publications

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“…The theory mentioned above is well described by Johnson and Omland (2004), and have already been applied in few ecosystem modelling studies (e.g. Crout et al, 2009;McDonald and Urban, 2010;Ward et al, 2013). The techniques for model selection have generally shown that more complex models are more vulnerable to over-tuning than simpler models.…”

Section: Model Performance As a Function Of Model Complexitymentioning

confidence: 96%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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Abstract. To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling.We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time-and spacedependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.

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Section: Model Performance As a Function Of Model Complexitymentioning

confidence: 96%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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“…To avoid this dependency, complex model simplification has been proposed as an effective approach to the development of a simple model (e.g., Cox et al, 2005;Crout et al, 2009), as well as Bayesian calibration with data from a wide range of environments (Iizumi et al, 2009;Hashimoto et al, 2011). A simple model is a powerful tool for large-scale applications when the model parameters are determined with consideration of the sources of spatial variability of the features of the modeling target and the appropriate model structure.…”

Section: A Simple Model Prior To a Complex Modelmentioning

confidence: 99%

Development of a simple forest evapotranspiration model using a process-oriented model as a reference to parameterize data from a wide range of environmental conditions

Sawano

Hotta

Tanaka

et al. 2015

Ecological Modelling

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“…This is more likely to occur if models are tested against unchanging datasets because it raises the chances of a hypothetical mechanism being found that explains the variance in the specific dataset. This is known as overfitting, in which overly detailed models make worse predictions than simpler models through being overly tuned to the specifics of the calibration or training datasets (Bishop 2006;Crout et al 2009;Masson and Knutti 2013).…”

Section: The Costs Of Model Complexitymentioning

confidence: 99%

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

Smith

Palmer

Purves

et al. 2014

Bulletin of the American Meteorological Society

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Is my model too complex? Evaluating model formulation using model reduction

Cited by 61 publications

References 26 publications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Development of a simple forest evapotranspiration model using a process-oriented model as a reference to parameterize data from a wide range of environmental conditions

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

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