In hydrology there exists a great need to adequately choose conceptual models, improve our understanding of the main hydrological processes and their variability, reduce model uncertainty, and improve model predictability. Traditionally, many (even most) data-driven hydrological models and hydraulic designs have considered the hydrological behavior of a basin as steady, representing the basin and its hydro-climatic relationships as stable and as long-term time-invariant. But currently, a new approach based on hydrologic dynamics where watershed response changes are caused by changes in natural and anthropogenic forcings is viewed as more adequate and representative of the real hydrological system. Traditional modeling consists of a single set of parameters representing stable catchment conditions, but experience has shown that one set of calibrated parameters may not yield equally good approximations of all events or for different parts of the observed hydrograph. Nowadays, dynamic identifiability analysis (DYNIA) is becoming more used to help in identification of conceptual model parameters, their time variability and in identification of adequate model structures (simulated processes) with the aim of performing behavioral models. In this study, DYNIA is used to compare the traditional calibration with a fixed model parameter set against a dynamic calibration with seasonal parameters. DYNIA is used to derive the seasonality of model parameters and to improve overall model performance by incorporating the derived parameter seasonality as a dynamic calibration. The Polcura River basin in southern Chile was chosen for this analysis. From the results, it is concluded that DYNIA helps to investigate predominant hydrological processes of the basin and their predominance over the different seasons such as winter (wet), spring (recession) and summer (dry). Moreover, it helpsto detect structural deficiencies, which can later be incorporated into the model parametrization as a seasonal parametrization, resulting in an improvement in model performance and better statistical measurement than that which is achieved using a traditional model with a set of single-fixed model parameters.