Characterising Performance of Environmental Models. Bennett, N. D.; Croke, B. F. W.; Guariso, G.; Guillaume, J. H. A.; Hamilton, S. H.; Jakeman, A. J.; Marsili-Libelli, S.; Newham, L. T. H.; Norton, J. P.; Perrin, C.; Pierce, S. A.; Robson, B.; Seppelt, R.; Voinov, A. A.; Fath, B. D.; and Andreassian, V. 40:1–20.
Characterising Performance of Environmental Models [link]Paper  doi  abstract   bibtex   
In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate level of confidence in their performance. This paper reviews techniques available across various fields for characterising the performance of environmental models with focus on numerical, graphical and qualitative methods. General classes of direct value comparison, coupling real and modelled values, preserving data patterns, indirect metrics based on parameter values, and data transformations are discussed. In practice environmental modelling requires the use and implementation of workflows that combine several methods, tailored to the model purpose and dependent upon the data and information available. A five-step procedure for performance evaluation of models is suggested, with the key elements including: (i) (re)assessment of the model's aim, scale and scope; (ii) characterisation of the data for calibration and testing; (iii) visual and other analysis to detect under- or non-modelled behaviour and to gain an overview of overall performance; (iv) selection of basic performance criteria; and (v) consideration of more advanced methods to handle problems such as systematic divergence between modelled and observed values. ⺠Numerical, graphical and qualitative methods for characterising performance of environmental models are reviewed. ⺠A structured, iterative workflow that combines several evaluation methods is suggested. ⺠Selection of methods must be tailored to the model scope and purpose, and quality of data and information available.
@article{bennettCharacterisingPerformanceEnvironmental2013,
  title = {Characterising Performance of Environmental Models},
  author = {Bennett, Neil D. and Croke, Barry F. W. and Guariso, Giorgio and Guillaume, Joseph H. A. and Hamilton, Serena H. and Jakeman, Anthony J. and Marsili-Libelli, Stefano and Newham, Lachlan T. H. and Norton, John P. and Perrin, Charles and Pierce, Suzanne A. and Robson, Barbara and Seppelt, Ralf and Voinov, Alexey A. and Fath, Brian D. and Andreassian, Vazken},
  date = {2013-02},
  journaltitle = {Environmental Modelling \& Software},
  volume = {40},
  pages = {1--20},
  issn = {1364-8152},
  doi = {10.1016/j.envsoft.2012.09.011},
  url = {https://doi.org/10.1016/j.envsoft.2012.09.011},
  abstract = {In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate level of confidence in their performance. This paper reviews techniques available across various fields for characterising the performance of environmental models with focus on numerical, graphical and qualitative methods. General classes of direct value comparison, coupling real and modelled values, preserving data patterns, indirect metrics based on parameter values, and data transformations are discussed. In practice environmental modelling requires the use and implementation of workflows that combine several methods, tailored to the model purpose and dependent upon the data and information available. A five-step procedure for performance evaluation of models is suggested, with the key elements including: (i) (re)assessment of the model's aim, scale and scope; (ii) characterisation of the data for calibration and testing; (iii) visual and other analysis to detect under- or non-modelled behaviour and to gain an overview of overall performance; (iv) selection of basic performance criteria; and (v) consideration of more advanced methods to handle problems such as systematic divergence between modelled and observed values. ⺠Numerical, graphical and qualitative methods for characterising performance of environmental models are reviewed. ⺠A structured, iterative workflow that combines several evaluation methods is suggested. ⺠Selection of methods must be tailored to the model scope and purpose, and quality of data and information available.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-11698435,data-transformation-modelling,environmental-modelling,epistemology,model-assessment,modelling,workflow}
}
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