Groundwater quality models tend to be complex in their physical, chemical and biological structure containing a large number of parameters. A large amount of data is needed for calibration and verification of these models, and frequently these data contain errors. The Monte Carlo method is applied to evaluate the uncertainty associated with their performance caused by data errors. The uncertainty analysis provides estimates of statistically reliable model outputs of contaminant concentration and arrival time.
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- Performance of Groundwater Quality Models Evaluated with Data Containing Errors
A. G. Bobba
V. P. Singh
- Springer Netherlands