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Fourteen guidelines for effective model calibration

More sophisticated methods of model calibration and sensitivity analysis are being developed, but they can be confusing and it is not always clear how to use them most effectively. While the best approach is problem dependent, many considerations are common to all problems. The Methods and Guidelines Report and, more recently, the textbook by Hill and Tiedeman (2007) suggests fourteen guidelines that are likely to be useful when calibrating models for groundwater or other problems. Many of the ideas will be familiar to experiences modelers; indeed, the guidelines can be thought of as organized common sense with some new statistical twists. Brief summaries of the guidelines are listed in the following table.

Table 1: Guidelines for effective model calibration.

  1. Apply the principle of parsimony (start very simple; build complexity slowly)

  2. Use a broad range of information to constrain the problem
  3. Maintain a well-posed, comprehensive regression problem
  4. Include many kinds of data as observations in the regression
  5. Use prior information carefully
  6. Assign weights which reflect measurement errors
  7. Encourage convergence by making the model more accurate
  8. Evaluate model fit
  9. Evaluate optimized parameters
  10. Test alternative models
  11. Evaluate potential new data
  12. Evaluate the potential for additional estimated parameters
  13. Use confidence and prediction intervals to indicate parameter and prediction uncertainty
  14. Formally reconsider model calibration from the perspective of the desired predictions

Methods and Guidelines Report

Textbook: Effective Groundwater Model Calibration, with Analysis of Data, Sensitivities, Predictions, and Uncertainty, by Mary C. Hill and Claire R. Tiedeman. Wiley and Sons, publisher

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