Two criteria are traditionally used for model parameter estimation and model structure identification: (C-1) fitting observation data; and (C-2) honoring prior information. As we have shown in Chaps. 3 and 7, these two criteria cannot determine a model uniquely by solving either classical inverse problem (CIP) or extended inverse problem (EIP) when observation error and model error exist. Models that cannot be rejected by prior information and observed data are called data-acceptable models. In environmental and water resource (EWR) modeling, because the real system structure is complex and unknown, there may be infinite combinations of model structures and model parameters that can fit the existing data equally well. Different modelers may construct different models for the same system based on the same data. As we explained in Chap. 10, this type of model nonuniqueness is called equifinality by Beven and coworkers.