Characterising performance of environmental models

被引:1123
作者
Bennett, Neil D. [1 ]
Croke, Barry F. W. [1 ]
Guariso, Giorgio [2 ]
Guillaume, Joseph H. A. [1 ]
Hamilton, Serena H. [1 ]
Jakeman, Anthony J. [1 ]
Marsili-Libelli, Stefano [3 ]
Newham, Lachlan T. H. [1 ]
Norton, John P. [1 ]
Perrin, Charles [4 ]
Pierce, Suzanne A. [5 ]
Robson, Barbara [6 ]
Seppelt, Ralf [7 ]
Voinov, Alexey A. [8 ]
Fath, Brian D. [9 ,10 ]
Andreassian, Vazken [4 ]
机构
[1] Australian Natl Univ, Natl Ctr Groundwater Res & Training, Fenner Sch Environm & Soc, Canberra, ACT 0200, Australia
[2] Politecn Milan, Milan, Italy
[3] Univ Florence, Dept Syst & Comp, I-50121 Florence, Italy
[4] IRSTEA, Rennes, France
[5] Univ Texas Austin, Jackson Sch Geosci, Ctr Int Energy & Environm Policy, Austin, TX 78712 USA
[6] CSIRO Land & Water, Adelaide, Australia
[7] UFZ Helmholtz Ctr Environm Res, Dept Computat Landscape Ecol, Leipzig, Germany
[8] Univ Twente, ITC, Enschede, Netherlands
[9] Towson Univ, Dept Biol Sci, Towson, MD USA
[10] Int Inst Appl Syst Anal, Adv Syst Anal Program, Laxenburg, Austria
关键词
Model development; Model evaluation; Performance indicators; Model testing; Sensitivity analysis; RAINFALL-RUNOFF MODELS; SENSITIVITY-ANALYSIS; PARAMETER-ESTIMATION; STANDARDIZED ASSESSMENT; SIMULATION-MODELS; QUALITY-ASSURANCE; ECOLOGICAL MODELS; DISPERSION MODEL; CRITERIA; IDENTIFIABILITY;
D O I
10.1016/j.envsoft.2012.09.011
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1 / 20
页数:20
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