Nature may be seen as a complex array of point data, because of which, environmental modellers should always assess the uncertainty contained in the representativeness of our point-field measurements. The following is an alternative to assess such problematic, through a relatively simple analysis of sensitivity under a Monte Carlo approach.
Next I provide the link to the ppt presented at the IUGG meeting in Australia in 2011. I expect that the material will give you some ideas on practical applications of Monte Carlo based approaches. If useful, you may want to cite it as:
Soria F., Kazama, S. Monte Carlo experiments for uncertainty investigation of glacier melt discharge predictions through surface energy balance analysis. IAHS Publ. 346, 2011.
I hope you find it interesting.
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Problems of calibrating environmental models have long been recognised, and there is an increasing appreciation that model predictions should be associated with some estimate of uncertainty. I think equifinality is much better aproach than just assuming a fixed combination of parameters
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Thanks for the comment Vlady. Equifinality is what took me towards analyzing models with Monte Carlo methods. The small issue now is how to express such qualitative measures into quantitative ones.
ReplyDeletey la otra escuela mas enfocada al optimizong la busqueda, pero tambien con uncertainty y bayesian approach
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