Recent developments in model calibration suggest that information obtained from calibration is inherently uncertain in nature. Therefore identification of optimum parameter values is often highly nonspecific. A calibration framework using fuzzy logic is presented to deal with such uncertain information. An application of this technique to calibrate the streamflow of a hydrologic submodel embedded within an ecosystem simulation model demonstrates that objective estimates of parameter values and the range of model output associated with a failure to identify a unique solution can be obtained with suitable choices of objective functions. An iterative refinement in parameter estimates through a process of elimination was possible by incorporating multiple objective functions in calibration, thereby reducing the range of parameter values that capture the streamflow response. It is shown that objective function tradeoffs can lead to suboptimal solutions using the process of elimination without an automated procedure for reevaluation. Owing to its computational simplicity and flexibility this framework could be extended into a nonmonotonic system for automated parameter estimation.