Dear Myram,

just starting to vary parameters seems to be a poor choice for calibrating models. This rail and error approach has some serious drawbacks:

-it will much time

-no guarantee on succes

-you will achieve a non-reproducible result

-when using the metric 'acceptable match' your result will be defined by subjectives choices

I suggest you apply a sound metric (simplest example leasts squares)) using some optimisation algorithm (e.g. genetic algorithm, or Levenberg-Marquart), there is software freely available to do this (e.g. PEST). This will result, next to a vector of parameters, also provide information on bias in our model (due to either a wrong proces model, wrong input or systematic errors in the measurements you are using for calibration).

succes

Francois Clemens

Dear Francois,

Thank you so much for your answer and your recommendations, i will try the software you told me, and if i have any other questions i will come back to you.

Best regards,