Unlike other optimization algorithms, which find a cost-minimizing point in the parameter space, the Generalized Likelihood Uncertainty Estimation (GLUE) methodology finds a set of possible points in the calibration parameters space. It adopts the idea of equifinality of models, parameters and variables (Beven and Binley 1992). Equifinality originates from the imperfect knowledge of the system under consideration, and many sets of models, variables, and parameters may therefore be considered equal simulators of the system.
In OpenDA, the GLUE analysis is implemented as to consist of (1) random draws of sets of the calibration parameters from the respective distributions, (2) run the model with each parameter set and evaluate the likelihood of each set. User needs to select manually the most probable sets of parameters based on their likelihood. The random draw of the parameters set can be done either from uniform distributions (with user specified ranges) or from a table of the most likely sets of the calibration parameters. For the latter, user needs to prepare such a table manually and can use the results of the previous analysis.