Abstract
Reliability of column experiment data interpretation by fitting the data to a transport model using the nonlinear least squares method is of concern when the complexity of the experiment increases. Misfits often occur and are accepted with the excuse of system complexity. More often, overfits are published and accepted uncritically. The objective of this work was to improve the reliability of column experiment data interpretation to provide insights for future investigation and select estimates for application prediction. We proposed to achieve this goal by evaluating and comparing various estimates that consider the uncertainties in experimental conditions, sensitivities of model parameters, alternative parameterizations and models. Two examples from the literature were selected for the illustration. Sensitivity analysis showed that the experimentally-controlled parameters (water content, flow rate and pulse volume) are much more sensitive than the transport parameters (dispersivity and mass transfer coefficient) in breakthrough curve fitting. Fixing these sensitive parameters may lead to misfit or biased estimate. Estimating sensitive parameters helps diagnose and remediate the misfit. The dispersion coefficient was parameterized as a function of solute molecular diffusion coefficient, medium dispersivity and average pore water velocity to reduce the number of estimated parameters for multiple tracers. Nonlinear least squares statistics were used to evaluate the goodness-of-fit for the multiple estimates obtained using different fitting schemes (e.g., fixing or estimating parameters), parameterizations and models (e.g., equilibrium or nonequilibrium). The F-test and Akaike��s Information Criteria were used for the discrimination of statistically reasonable estimates. Our results suggest that sensitivity/uncertainty analysis and model evaluation /discrimination are useful to improve the reliability of column experiment data interpretation.
